diff --git a/shared/package-files/03/030007203a04811b5b5b26ea82c6cd0d1e7b884a27ce1b7aaadbb86402b1619c/arcsinh_ops.h b/shared/package-files/03/030007203a04811b5b5b26ea82c6cd0d1e7b884a27ce1b7aaadbb86402b1619c/arcsinh_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..eb2548f75bb31999b22393f14158cec217ded1f2 --- /dev/null +++ b/shared/package-files/03/030007203a04811b5b5b26ea82c6cd0d1e7b884a27ce1b7aaadbb86402b1619c/arcsinh_ops.h @@ -0,0 +1,50 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API arcsinh { + using schema = at::Tensor (const at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::arcsinh") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "arcsinh(Tensor self) -> Tensor") + static at::Tensor call(const at::Tensor & self); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self); +}; + +struct TORCH_API arcsinh_ { + using schema = at::Tensor & (at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::arcsinh_") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "arcsinh_(Tensor(a!) self) -> Tensor(a!)") + static at::Tensor & call(at::Tensor & self); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self); +}; + +struct TORCH_API arcsinh_out { + using schema = at::Tensor & (const at::Tensor &, at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::arcsinh") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "arcsinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)") + static at::Tensor & call(const at::Tensor & self, at::Tensor & out); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out); +}; + +}} // namespace at::_ops diff --git a/shared/package-files/03/0300e16c77d396a47280154331dad99fa750de58a4adf37d1a71b6eec118167d/isposinf_meta_dispatch.h b/shared/package-files/03/0300e16c77d396a47280154331dad99fa750de58a4adf37d1a71b6eec118167d/isposinf_meta_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..b40d121d4e8baff929717269d4d145fab1f748cf --- /dev/null +++ b/shared/package-files/03/0300e16c77d396a47280154331dad99fa750de58a4adf37d1a71b6eec118167d/isposinf_meta_dispatch.h @@ -0,0 +1,25 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace meta { + +TORCH_API at::Tensor isposinf(const at::Tensor & self); +TORCH_API at::Tensor & isposinf_out(at::Tensor & out, const at::Tensor & self); +TORCH_API at::Tensor & isposinf_outf(const at::Tensor & self, at::Tensor & out); + +} // namespace meta +} // namespace at diff --git a/shared/package-files/03/0309af5e66929870cd20176054218d0fba2d3f23c4af325578d1c2d2d2a815ee/conv_depthwise3d_compositeexplicitautograd_dispatch.h b/shared/package-files/03/0309af5e66929870cd20176054218d0fba2d3f23c4af325578d1c2d2d2a815ee/conv_depthwise3d_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..6a647d1850f7008f6c9090aac95773998bd935de --- /dev/null +++ b/shared/package-files/03/0309af5e66929870cd20176054218d0fba2d3f23c4af325578d1c2d2d2a815ee/conv_depthwise3d_compositeexplicitautograd_dispatch.h @@ -0,0 +1,26 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautograd { + +TORCH_API at::Tensor & conv_depthwise3d_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation); +TORCH_API at::Tensor & conv_depthwise3d_outf(const at::Tensor & self, const at::Tensor & weight, at::IntArrayRef kernel_size, const c10::optional & bias, at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation, at::Tensor & out); +TORCH_API at::Tensor & conv_depthwise3d_symint_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation); +TORCH_API at::Tensor & conv_depthwise3d_symint_outf(const at::Tensor & self, const at::Tensor & weight, c10::SymIntArrayRef kernel_size, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, at::Tensor & out); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/shared/package-files/03/0309d55e361c206c202e3526ddbf065ac68b71d724a9a8b05ec57c437aef0c40/c100-1p b/shared/package-files/03/0309d55e361c206c202e3526ddbf065ac68b71d724a9a8b05ec57c437aef0c40/c100-1p new file mode 100644 index 0000000000000000000000000000000000000000..67fa7a47e415b290dfa1f037f86c957d8d0e5c38 Binary files /dev/null and b/shared/package-files/03/0309d55e361c206c202e3526ddbf065ac68b71d724a9a8b05ec57c437aef0c40/c100-1p differ diff --git a/shared/package-files/03/0309d55e361c206c202e3526ddbf065ac68b71d724a9a8b05ec57c437aef0c40/oc100 b/shared/package-files/03/0309d55e361c206c202e3526ddbf065ac68b71d724a9a8b05ec57c437aef0c40/oc100 new file mode 100644 index 0000000000000000000000000000000000000000..67fa7a47e415b290dfa1f037f86c957d8d0e5c38 Binary files /dev/null and b/shared/package-files/03/0309d55e361c206c202e3526ddbf065ac68b71d724a9a8b05ec57c437aef0c40/oc100 differ diff --git a/shared/package-files/03/0309d55e361c206c202e3526ddbf065ac68b71d724a9a8b05ec57c437aef0c40/oconcept b/shared/package-files/03/0309d55e361c206c202e3526ddbf065ac68b71d724a9a8b05ec57c437aef0c40/oconcept new file mode 100644 index 0000000000000000000000000000000000000000..67fa7a47e415b290dfa1f037f86c957d8d0e5c38 Binary files /dev/null and b/shared/package-files/03/0309d55e361c206c202e3526ddbf065ac68b71d724a9a8b05ec57c437aef0c40/oconcept differ diff --git a/shared/package-files/03/030a6e0a41e1d087b705b1a44c0dcffa204e76a10655e443a9ffba1a0971f57d/apple-uterm-vb b/shared/package-files/03/030a6e0a41e1d087b705b1a44c0dcffa204e76a10655e443a9ffba1a0971f57d/apple-uterm-vb new file mode 100644 index 0000000000000000000000000000000000000000..047855e8a4d277a69afa83e3bda5ce3c1d45edb1 Binary files /dev/null and b/shared/package-files/03/030a6e0a41e1d087b705b1a44c0dcffa204e76a10655e443a9ffba1a0971f57d/apple-uterm-vb differ diff --git a/shared/package-files/03/030ab00fc6f6815d5fe47111b731ba52d388b2114669f508ffd0903952cf9e1a/test_interchange_spec.py b/shared/package-files/03/030ab00fc6f6815d5fe47111b731ba52d388b2114669f508ffd0903952cf9e1a/test_interchange_spec.py new file mode 100644 index 0000000000000000000000000000000000000000..cea694d1c1ee24f5af933f59fcb07e3e38141f9d --- /dev/null +++ b/shared/package-files/03/030ab00fc6f6815d5fe47111b731ba52d388b2114669f508ffd0903952cf9e1a/test_interchange_spec.py @@ -0,0 +1,294 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +import ctypes +import hypothesis as h +import hypothesis.strategies as st + +import pytest +try: + import numpy as np +except ImportError: + np = None +import pyarrow as pa +import pyarrow.tests.strategies as past + + +all_types = st.deferred( + lambda: ( + past.signed_integer_types | + past.unsigned_integer_types | + past.floating_types | + past.bool_type | + past.string_type | + past.large_string_type + ) +) + + +# datetime is tested in test_extra.py +# dictionary is tested in test_categorical() +@pytest.mark.numpy +@h.settings(suppress_health_check=(h.HealthCheck.too_slow,)) +@h.given(past.arrays(all_types, size=3)) +def test_dtypes(arr): + table = pa.table([arr], names=["a"]) + df = table.__dataframe__() + + null_count = df.get_column(0).null_count + assert null_count == arr.null_count + assert isinstance(null_count, int) + assert df.get_column(0).size() == 3 + assert df.get_column(0).offset == 0 + + +@pytest.mark.numpy +@pytest.mark.parametrize( + "uint, uint_bw", + [ + (pa.uint8(), 8), + (pa.uint16(), 16), + (pa.uint32(), 32) + ] +) +@pytest.mark.parametrize( + "int, int_bw", [ + (pa.int8(), 8), + (pa.int16(), 16), + (pa.int32(), 32), + (pa.int64(), 64) + ] +) +@pytest.mark.parametrize( + "float, float_bw, np_float_str", [ + (pa.float16(), 16, "float16"), + (pa.float32(), 32, "float32"), + (pa.float64(), 64, "float64") + ] +) +@pytest.mark.parametrize("unit", ['s', 'ms', 'us', 'ns']) +@pytest.mark.parametrize("tz", ['', 'America/New_York', '+07:30', '-04:30']) +@pytest.mark.parametrize("use_batch", [False, True]) +def test_mixed_dtypes(uint, uint_bw, int, int_bw, + float, float_bw, np_float_str, unit, tz, + use_batch): + from datetime import datetime as dt + arr = [1, 2, 3] + dt_arr = [dt(2007, 7, 13), dt(2007, 7, 14), dt(2007, 7, 15)] + table = pa.table( + { + "a": pa.array(arr, type=uint), + "b": pa.array(arr, type=int), + "c": pa.array(np.array(arr, dtype=np.dtype(np_float_str)), type=float), + "d": [True, False, True], + "e": ["a", "", "c"], + "f": pa.array(dt_arr, type=pa.timestamp(unit, tz=tz)) + } + ) + if use_batch: + table = table.to_batches()[0] + df = table.__dataframe__() + # 0 = DtypeKind.INT, 1 = DtypeKind.UINT, 2 = DtypeKind.FLOAT, + # 20 = DtypeKind.BOOL, 21 = DtypeKind.STRING, 22 = DtypeKind.DATETIME + # see DtypeKind class in column.py + columns = {"a": 1, "b": 0, "c": 2, "d": 20, "e": 21, "f": 22} + + for column, kind in columns.items(): + col = df.get_column_by_name(column) + + assert col.null_count == 0 + assert col.size() == 3 + assert col.offset == 0 + assert col.dtype[0] == kind + + assert df.get_column_by_name("a").dtype[1] == uint_bw + assert df.get_column_by_name("b").dtype[1] == int_bw + assert df.get_column_by_name("c").dtype[1] == float_bw + + +def test_na_float(): + table = pa.table({"a": [1.0, None, 2.0]}) + df = table.__dataframe__() + col = df.get_column_by_name("a") + assert col.null_count == 1 + assert isinstance(col.null_count, int) + + +def test_noncategorical(): + table = pa.table({"a": [1, 2, 3]}) + df = table.__dataframe__() + col = df.get_column_by_name("a") + with pytest.raises(TypeError, match=".*categorical.*"): + col.describe_categorical + + +@pytest.mark.parametrize("use_batch", [False, True]) +def test_categorical(use_batch): + import pyarrow as pa + arr = ["Mon", "Tue", "Mon", "Wed", "Mon", "Thu", "Fri", "Sat", None] + table = pa.table( + {"weekday": pa.array(arr).dictionary_encode()} + ) + if use_batch: + table = table.to_batches()[0] + + col = table.__dataframe__().get_column_by_name("weekday") + categorical = col.describe_categorical + assert isinstance(categorical["is_ordered"], bool) + assert isinstance(categorical["is_dictionary"], bool) + + +@pytest.mark.parametrize("use_batch", [False, True]) +def test_dataframe(use_batch): + n = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) + a = pa.chunked_array([["Flamingo", "Parrot", "Cow"], + ["Horse", "Brittle stars", "Centipede"]]) + table = pa.table([n, a], names=['n_legs', 'animals']) + if use_batch: + table = table.combine_chunks().to_batches()[0] + df = table.__dataframe__() + + assert df.num_columns() == 2 + assert df.num_rows() == 6 + if use_batch: + assert df.num_chunks() == 1 + else: + assert df.num_chunks() == 2 + assert list(df.column_names()) == ['n_legs', 'animals'] + assert list(df.select_columns((1,)).column_names()) == list( + df.select_columns_by_name(("animals",)).column_names() + ) + + +@pytest.mark.parametrize("use_batch", [False, True]) +@pytest.mark.parametrize(["size", "n_chunks"], [(10, 3), (12, 3), (12, 5)]) +def test_df_get_chunks(use_batch, size, n_chunks): + table = pa.table({"x": list(range(size))}) + if use_batch: + table = table.to_batches()[0] + df = table.__dataframe__() + chunks = list(df.get_chunks(n_chunks)) + assert len(chunks) == n_chunks + assert sum(chunk.num_rows() for chunk in chunks) == size + + +@pytest.mark.parametrize("use_batch", [False, True]) +@pytest.mark.parametrize(["size", "n_chunks"], [(10, 3), (12, 3), (12, 5)]) +def test_column_get_chunks(use_batch, size, n_chunks): + table = pa.table({"x": list(range(size))}) + if use_batch: + table = table.to_batches()[0] + df = table.__dataframe__() + chunks = list(df.get_column(0).get_chunks(n_chunks)) + assert len(chunks) == n_chunks + assert sum(chunk.size() for chunk in chunks) == size + + +@pytest.mark.pandas +@pytest.mark.parametrize( + "uint", [pa.uint8(), pa.uint16(), pa.uint32()] +) +@pytest.mark.parametrize( + "int", [pa.int8(), pa.int16(), pa.int32(), pa.int64()] +) +@pytest.mark.parametrize( + "float, np_float_str", [ + (pa.float16(), "float16"), + (pa.float32(), "float32"), + (pa.float64(), "float64") + ] +) +@pytest.mark.parametrize("use_batch", [False, True]) +def test_get_columns(uint, int, float, np_float_str, use_batch): + arr = [[1, 2, 3], [4, 5]] + arr_float = np.array([1, 2, 3, 4, 5], dtype=np.dtype(np_float_str)) + table = pa.table( + { + "a": pa.chunked_array(arr, type=uint), + "b": pa.chunked_array(arr, type=int), + "c": pa.array(arr_float, type=float) + } + ) + if use_batch: + table = table.combine_chunks().to_batches()[0] + df = table.__dataframe__() + for col in df.get_columns(): + assert col.size() == 5 + assert col.num_chunks() == 1 + + # 0 = DtypeKind.INT, 1 = DtypeKind.UINT, 2 = DtypeKind.FLOAT, + # see DtypeKind class in column.py + assert df.get_column(0).dtype[0] == 1 # UINT + assert df.get_column(1).dtype[0] == 0 # INT + assert df.get_column(2).dtype[0] == 2 # FLOAT + + +@pytest.mark.parametrize( + "int", [pa.int8(), pa.int16(), pa.int32(), pa.int64()] +) +@pytest.mark.parametrize("use_batch", [False, True]) +def test_buffer(int, use_batch): + arr = [0, 1, -1] + table = pa.table({"a": pa.array(arr, type=int)}) + if use_batch: + table = table.to_batches()[0] + df = table.__dataframe__() + col = df.get_column(0) + buf = col.get_buffers() + + dataBuf, dataDtype = buf["data"] + + assert dataBuf.bufsize > 0 + assert dataBuf.ptr != 0 + device, _ = dataBuf.__dlpack_device__() + + # 0 = DtypeKind.INT + # see DtypeKind class in column.py + assert dataDtype[0] == 0 + + if device == 1: # CPU-only as we're going to directly read memory here + bitwidth = dataDtype[1] + ctype = { + 8: ctypes.c_int8, + 16: ctypes.c_int16, + 32: ctypes.c_int32, + 64: ctypes.c_int64, + }[bitwidth] + + for idx, truth in enumerate(arr): + val = ctype.from_address(dataBuf.ptr + idx * (bitwidth // 8)).value + assert val == truth, f"Buffer at index {idx} mismatch" + + +@pytest.mark.parametrize( + "indices_type, bitwidth, f_string", [ + (pa.int8(), 8, "c"), + (pa.int16(), 16, "s"), + (pa.int32(), 32, "i"), + (pa.int64(), 64, "l") + ] +) +def test_categorical_dtype(indices_type, bitwidth, f_string): + type = pa.dictionary(indices_type, pa.string()) + arr = pa.array(["a", "b", None, "d"], type) + table = pa.table({'a': arr}) + + df = table.__dataframe__() + col = df.get_column(0) + assert col.dtype[0] == 23 # + assert col.dtype[1] == bitwidth + assert col.dtype[2] == f_string diff --git a/shared/package-files/03/030ece7cf591b8383569b012c413d11d0170a2561e19eb638b9bde5c0603b0e4/minitel1b b/shared/package-files/03/030ece7cf591b8383569b012c413d11d0170a2561e19eb638b9bde5c0603b0e4/minitel1b new file mode 100644 index 0000000000000000000000000000000000000000..e47321d1851eb9b238b007f8a986d1e25ea9d893 Binary files /dev/null and b/shared/package-files/03/030ece7cf591b8383569b012c413d11d0170a2561e19eb638b9bde5c0603b0e4/minitel1b differ diff --git a/shared/package-files/03/031033d205350b1b879352fdd81cf3895b39dc1d25d67951b43266e04559cf0e/ge_meta.h b/shared/package-files/03/031033d205350b1b879352fdd81cf3895b39dc1d25d67951b43266e04559cf0e/ge_meta.h new file mode 100644 index 0000000000000000000000000000000000000000..d9b098bbcd2f9870d963bc0d0f68672d2f01f66d --- /dev/null +++ b/shared/package-files/03/031033d205350b1b879352fdd81cf3895b39dc1d25d67951b43266e04559cf0e/ge_meta.h @@ -0,0 +1,32 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeMetaFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { +namespace meta { + +struct TORCH_API structured_ge_Scalar : public TensorIteratorBase { + + + void meta(const at::Tensor & self, const at::Scalar & other); +}; +struct TORCH_API structured_ge_Tensor : public TensorIteratorBase { + + + void meta(const at::Tensor & self, const at::Tensor & other); +}; + +} // namespace native +} // namespace at diff --git a/shared/package-files/03/0314c417a99dc9b016d650bf0dc8df2a5c6f4ae895ba99ea6c926ba92e71e257/_meta.py b/shared/package-files/03/0314c417a99dc9b016d650bf0dc8df2a5c6f4ae895ba99ea6c926ba92e71e257/_meta.py new file mode 100644 index 0000000000000000000000000000000000000000..b90e5fb7b5be887d24bb42725d59bb056ac126c2 --- /dev/null +++ b/shared/package-files/03/0314c417a99dc9b016d650bf0dc8df2a5c6f4ae895ba99ea6c926ba92e71e257/_meta.py @@ -0,0 +1,279 @@ +from typing import List, Optional, Tuple + +import PIL.Image +import torch +from torchvision import tv_tensors +from torchvision.transforms import _functional_pil as _FP +from torchvision.tv_tensors import BoundingBoxFormat + +from torchvision.utils import _log_api_usage_once + +from ._utils import _get_kernel, _register_kernel_internal, is_pure_tensor + + +def get_dimensions(inpt: torch.Tensor) -> List[int]: + if torch.jit.is_scripting(): + return get_dimensions_image(inpt) + + _log_api_usage_once(get_dimensions) + + kernel = _get_kernel(get_dimensions, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(get_dimensions, torch.Tensor) +@_register_kernel_internal(get_dimensions, tv_tensors.Image, tv_tensor_wrapper=False) +def get_dimensions_image(image: torch.Tensor) -> List[int]: + chw = list(image.shape[-3:]) + ndims = len(chw) + if ndims == 3: + return chw + elif ndims == 2: + chw.insert(0, 1) + return chw + else: + raise TypeError(f"Input tensor should have at least two dimensions, but got {ndims}") + + +_get_dimensions_image_pil = _register_kernel_internal(get_dimensions, PIL.Image.Image)(_FP.get_dimensions) + + +@_register_kernel_internal(get_dimensions, tv_tensors.Video, tv_tensor_wrapper=False) +def get_dimensions_video(video: torch.Tensor) -> List[int]: + return get_dimensions_image(video) + + +def get_num_channels(inpt: torch.Tensor) -> int: + if torch.jit.is_scripting(): + return get_num_channels_image(inpt) + + _log_api_usage_once(get_num_channels) + + kernel = _get_kernel(get_num_channels, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(get_num_channels, torch.Tensor) +@_register_kernel_internal(get_num_channels, tv_tensors.Image, tv_tensor_wrapper=False) +def get_num_channels_image(image: torch.Tensor) -> int: + chw = image.shape[-3:] + ndims = len(chw) + if ndims == 3: + return chw[0] + elif ndims == 2: + return 1 + else: + raise TypeError(f"Input tensor should have at least two dimensions, but got {ndims}") + + +_get_num_channels_image_pil = _register_kernel_internal(get_num_channels, PIL.Image.Image)(_FP.get_image_num_channels) + + +@_register_kernel_internal(get_num_channels, tv_tensors.Video, tv_tensor_wrapper=False) +def get_num_channels_video(video: torch.Tensor) -> int: + return get_num_channels_image(video) + + +# We changed the names to ensure it can be used not only for images but also videos. Thus, we just alias it without +# deprecating the old names. +get_image_num_channels = get_num_channels + + +def get_size(inpt: torch.Tensor) -> List[int]: + if torch.jit.is_scripting(): + return get_size_image(inpt) + + _log_api_usage_once(get_size) + + kernel = _get_kernel(get_size, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(get_size, torch.Tensor) +@_register_kernel_internal(get_size, tv_tensors.Image, tv_tensor_wrapper=False) +def get_size_image(image: torch.Tensor) -> List[int]: + hw = list(image.shape[-2:]) + ndims = len(hw) + if ndims == 2: + return hw + else: + raise TypeError(f"Input tensor should have at least two dimensions, but got {ndims}") + + +@_register_kernel_internal(get_size, PIL.Image.Image) +def _get_size_image_pil(image: PIL.Image.Image) -> List[int]: + width, height = _FP.get_image_size(image) + return [height, width] + + +@_register_kernel_internal(get_size, tv_tensors.Video, tv_tensor_wrapper=False) +def get_size_video(video: torch.Tensor) -> List[int]: + return get_size_image(video) + + +@_register_kernel_internal(get_size, tv_tensors.Mask, tv_tensor_wrapper=False) +def get_size_mask(mask: torch.Tensor) -> List[int]: + return get_size_image(mask) + + +@_register_kernel_internal(get_size, tv_tensors.BoundingBoxes, tv_tensor_wrapper=False) +def get_size_bounding_boxes(bounding_box: tv_tensors.BoundingBoxes) -> List[int]: + return list(bounding_box.canvas_size) + + +def get_num_frames(inpt: torch.Tensor) -> int: + if torch.jit.is_scripting(): + return get_num_frames_video(inpt) + + _log_api_usage_once(get_num_frames) + + kernel = _get_kernel(get_num_frames, type(inpt)) + return kernel(inpt) + + +@_register_kernel_internal(get_num_frames, torch.Tensor) +@_register_kernel_internal(get_num_frames, tv_tensors.Video, tv_tensor_wrapper=False) +def get_num_frames_video(video: torch.Tensor) -> int: + return video.shape[-4] + + +def _xywh_to_xyxy(xywh: torch.Tensor, inplace: bool) -> torch.Tensor: + xyxy = xywh if inplace else xywh.clone() + xyxy[..., 2:] += xyxy[..., :2] + return xyxy + + +def _xyxy_to_xywh(xyxy: torch.Tensor, inplace: bool) -> torch.Tensor: + xywh = xyxy if inplace else xyxy.clone() + xywh[..., 2:] -= xywh[..., :2] + return xywh + + +def _cxcywh_to_xyxy(cxcywh: torch.Tensor, inplace: bool) -> torch.Tensor: + if not inplace: + cxcywh = cxcywh.clone() + + # Trick to do fast division by 2 and ceil, without casting. It produces the same result as + # `torchvision.ops._box_convert._box_cxcywh_to_xyxy`. + half_wh = cxcywh[..., 2:].div(-2, rounding_mode=None if cxcywh.is_floating_point() else "floor").abs_() + # (cx - width / 2) = x1, same for y1 + cxcywh[..., :2].sub_(half_wh) + # (x1 + width) = x2, same for y2 + cxcywh[..., 2:].add_(cxcywh[..., :2]) + + return cxcywh + + +def _xyxy_to_cxcywh(xyxy: torch.Tensor, inplace: bool) -> torch.Tensor: + if not inplace: + xyxy = xyxy.clone() + + # (x2 - x1) = width, same for height + xyxy[..., 2:].sub_(xyxy[..., :2]) + # (x1 * 2 + width) / 2 = x1 + width / 2 = x1 + (x2-x1)/2 = (x1 + x2)/2 = cx, same for cy + xyxy[..., :2].mul_(2).add_(xyxy[..., 2:]).div_(2, rounding_mode=None if xyxy.is_floating_point() else "floor") + + return xyxy + + +def _convert_bounding_box_format( + bounding_boxes: torch.Tensor, old_format: BoundingBoxFormat, new_format: BoundingBoxFormat, inplace: bool = False +) -> torch.Tensor: + + if new_format == old_format: + return bounding_boxes + + # TODO: Add _xywh_to_cxcywh and _cxcywh_to_xywh to improve performance + if old_format == BoundingBoxFormat.XYWH: + bounding_boxes = _xywh_to_xyxy(bounding_boxes, inplace) + elif old_format == BoundingBoxFormat.CXCYWH: + bounding_boxes = _cxcywh_to_xyxy(bounding_boxes, inplace) + + if new_format == BoundingBoxFormat.XYWH: + bounding_boxes = _xyxy_to_xywh(bounding_boxes, inplace) + elif new_format == BoundingBoxFormat.CXCYWH: + bounding_boxes = _xyxy_to_cxcywh(bounding_boxes, inplace) + + return bounding_boxes + + +def convert_bounding_box_format( + inpt: torch.Tensor, + old_format: Optional[BoundingBoxFormat] = None, + new_format: Optional[BoundingBoxFormat] = None, + inplace: bool = False, +) -> torch.Tensor: + """See :func:`~torchvision.transforms.v2.ConvertBoundingBoxFormat` for details.""" + # This being a kernel / functional hybrid, we need an option to pass `old_format` explicitly for pure tensor + # inputs as well as extract it from `tv_tensors.BoundingBoxes` inputs. However, putting a default value on + # `old_format` means we also need to put one on `new_format` to have syntactically correct Python. Here we mimic the + # default error that would be thrown if `new_format` had no default value. + if new_format is None: + raise TypeError("convert_bounding_box_format() missing 1 required argument: 'new_format'") + + if not torch.jit.is_scripting(): + _log_api_usage_once(convert_bounding_box_format) + + if isinstance(old_format, str): + old_format = BoundingBoxFormat[old_format.upper()] + if isinstance(new_format, str): + new_format = BoundingBoxFormat[new_format.upper()] + + if torch.jit.is_scripting() or is_pure_tensor(inpt): + if old_format is None: + raise ValueError("For pure tensor inputs, `old_format` has to be passed.") + return _convert_bounding_box_format(inpt, old_format=old_format, new_format=new_format, inplace=inplace) + elif isinstance(inpt, tv_tensors.BoundingBoxes): + if old_format is not None: + raise ValueError("For bounding box tv_tensor inputs, `old_format` must not be passed.") + output = _convert_bounding_box_format( + inpt.as_subclass(torch.Tensor), old_format=inpt.format, new_format=new_format, inplace=inplace + ) + return tv_tensors.wrap(output, like=inpt, format=new_format) + else: + raise TypeError( + f"Input can either be a plain tensor or a bounding box tv_tensor, but got {type(inpt)} instead." + ) + + +def _clamp_bounding_boxes( + bounding_boxes: torch.Tensor, format: BoundingBoxFormat, canvas_size: Tuple[int, int] +) -> torch.Tensor: + # TODO: Investigate if it makes sense from a performance perspective to have an implementation for every + # BoundingBoxFormat instead of converting back and forth + in_dtype = bounding_boxes.dtype + bounding_boxes = bounding_boxes.clone() if bounding_boxes.is_floating_point() else bounding_boxes.float() + xyxy_boxes = convert_bounding_box_format( + bounding_boxes, old_format=format, new_format=tv_tensors.BoundingBoxFormat.XYXY, inplace=True + ) + xyxy_boxes[..., 0::2].clamp_(min=0, max=canvas_size[1]) + xyxy_boxes[..., 1::2].clamp_(min=0, max=canvas_size[0]) + out_boxes = convert_bounding_box_format( + xyxy_boxes, old_format=BoundingBoxFormat.XYXY, new_format=format, inplace=True + ) + return out_boxes.to(in_dtype) + + +def clamp_bounding_boxes( + inpt: torch.Tensor, + format: Optional[BoundingBoxFormat] = None, + canvas_size: Optional[Tuple[int, int]] = None, +) -> torch.Tensor: + """See :func:`~torchvision.transforms.v2.ClampBoundingBoxes` for details.""" + if not torch.jit.is_scripting(): + _log_api_usage_once(clamp_bounding_boxes) + + if torch.jit.is_scripting() or is_pure_tensor(inpt): + + if format is None or canvas_size is None: + raise ValueError("For pure tensor inputs, `format` and `canvas_size` have to be passed.") + return _clamp_bounding_boxes(inpt, format=format, canvas_size=canvas_size) + elif isinstance(inpt, tv_tensors.BoundingBoxes): + if format is not None or canvas_size is not None: + raise ValueError("For bounding box tv_tensor inputs, `format` and `canvas_size` must not be passed.") + output = _clamp_bounding_boxes(inpt.as_subclass(torch.Tensor), format=inpt.format, canvas_size=inpt.canvas_size) + return tv_tensors.wrap(output, like=inpt) + else: + raise TypeError( + f"Input can either be a plain tensor or a bounding box tv_tensor, but got {type(inpt)} instead." + ) diff --git a/shared/package-files/03/031643f594ecae7d8fd57fab3eb0ca3ef5be1d1bb2d37231980b050bb60c9ebe/ger.h b/shared/package-files/03/031643f594ecae7d8fd57fab3eb0ca3ef5be1d1bb2d37231980b050bb60c9ebe/ger.h new file mode 100644 index 0000000000000000000000000000000000000000..0911a3cfbbd7f77110152a05129093bd48b0c493 --- /dev/null +++ b/shared/package-files/03/031643f594ecae7d8fd57fab3eb0ca3ef5be1d1bb2d37231980b050bb60c9ebe/ger.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::ger(Tensor self, Tensor vec2) -> Tensor +inline at::Tensor ger(const at::Tensor & self, const at::Tensor & vec2) { + return at::_ops::ger::call(self, vec2); +} + +// aten::ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & ger_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & vec2) { + return at::_ops::ger_out::call(self, vec2, out); +} +// aten::ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & ger_outf(const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out) { + return at::_ops::ger_out::call(self, vec2, out); +} + +} diff --git a/shared/package-files/03/03183173734448b7ce55383b69b1a6f5933efe3df2bfe75c3e866c3b8892100a/unique_dim_consecutive_ops.h b/shared/package-files/03/03183173734448b7ce55383b69b1a6f5933efe3df2bfe75c3e866c3b8892100a/unique_dim_consecutive_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..d2b8343b2e7680ae60a861812715f8a291f2a85c --- /dev/null +++ b/shared/package-files/03/03183173734448b7ce55383b69b1a6f5933efe3df2bfe75c3e866c3b8892100a/unique_dim_consecutive_ops.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API unique_dim_consecutive { + using schema = ::std::tuple (const at::Tensor &, int64_t, bool, bool); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::unique_dim_consecutive") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "unique_dim_consecutive(Tensor self, int dim, bool return_inverse=False, bool return_counts=False) -> (Tensor, Tensor, Tensor)") + static ::std::tuple call(const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts); + static ::std::tuple redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts); +}; + +struct TORCH_API unique_dim_consecutive_out { + using schema = ::std::tuple (const at::Tensor &, int64_t, bool, bool, at::Tensor &, at::Tensor &, at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::unique_dim_consecutive") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "unique_dim_consecutive.out(Tensor self, int dim, bool return_inverse=False, bool return_counts=False, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!))") + static ::std::tuple call(const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); + static ::std::tuple redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, bool return_inverse, bool return_counts, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2); +}; + +}} // namespace at::_ops diff --git a/shared/package-files/03/031ace958cbed534d359e432a4329e5eb0c2add1c6a2bd042821387f1cd2406a/AAAA.py b/shared/package-files/03/031ace958cbed534d359e432a4329e5eb0c2add1c6a2bd042821387f1cd2406a/AAAA.py new file mode 100644 index 0000000000000000000000000000000000000000..0cd139e7b5c4b68a8d66e070b231eda9b0e41ca7 --- /dev/null +++ b/shared/package-files/03/031ace958cbed534d359e432a4329e5eb0c2add1c6a2bd042821387f1cd2406a/AAAA.py @@ -0,0 +1,51 @@ +# Copyright (C) Dnspython Contributors, see LICENSE for text of ISC license + +# Copyright (C) 2003-2007, 2009-2011 Nominum, Inc. +# +# Permission to use, copy, modify, and distribute this software and its +# documentation for any purpose with or without fee is hereby granted, +# provided that the above copyright notice and this permission notice +# appear in all copies. +# +# THE SOFTWARE IS PROVIDED "AS IS" AND NOMINUM DISCLAIMS ALL WARRANTIES +# WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF +# MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL NOMINUM BE LIABLE FOR +# ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES +# WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN +# ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT +# OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. + +import dns.exception +import dns.immutable +import dns.ipv6 +import dns.rdata +import dns.tokenizer + + +@dns.immutable.immutable +class AAAA(dns.rdata.Rdata): + """AAAA record.""" + + __slots__ = ["address"] + + def __init__(self, rdclass, rdtype, address): + super().__init__(rdclass, rdtype) + self.address = self._as_ipv6_address(address) + + def to_text(self, origin=None, relativize=True, **kw): + return self.address + + @classmethod + def from_text( + cls, rdclass, rdtype, tok, origin=None, relativize=True, relativize_to=None + ): + address = tok.get_identifier() + return cls(rdclass, rdtype, address) + + def _to_wire(self, file, compress=None, origin=None, canonicalize=False): + file.write(dns.ipv6.inet_aton(self.address)) + + @classmethod + def from_wire_parser(cls, rdclass, rdtype, parser, origin=None): + address = parser.get_remaining() + return cls(rdclass, rdtype, address) diff --git a/shared/package-files/03/031b2634b8ae567c2c371e6ad2a5b5e554eee6a6f9951c5e35d2c7d4b233156a/softplus_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/03/031b2634b8ae567c2c371e6ad2a5b5e554eee6a6f9951c5e35d2c7d4b233156a/softplus_compositeexplicitautogradnonfunctional_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..235553d184921f0fc2b6a761e6c803b0194c2c7d --- /dev/null +++ b/shared/package-files/03/031b2634b8ae567c2c371e6ad2a5b5e554eee6a6f9951c5e35d2c7d4b233156a/softplus_compositeexplicitautogradnonfunctional_dispatch.h @@ -0,0 +1,23 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautogradnonfunctional { + +TORCH_API at::Tensor softplus(const at::Tensor & self, const at::Scalar & beta=1, const at::Scalar & threshold=20); + +} // namespace compositeexplicitautogradnonfunctional +} // namespace at diff --git a/shared/package-files/03/031c14693c4b798295b344469fa56ed3fba46991e605949b39babb3dca9370e4/new_empty_native.h b/shared/package-files/03/031c14693c4b798295b344469fa56ed3fba46991e605949b39babb3dca9370e4/new_empty_native.h new file mode 100644 index 0000000000000000000000000000000000000000..448142a4dfae0486de6071cf83dbe3c071c7864c --- /dev/null +++ b/shared/package-files/03/031c14693c4b798295b344469fa56ed3fba46991e605949b39babb3dca9370e4/new_empty_native.h @@ -0,0 +1,22 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor new_empty_symint(const at::Tensor & self, c10::SymIntArrayRef size, c10::optional dtype={}, c10::optional layout={}, c10::optional device={}, c10::optional pin_memory={}); +TORCH_API at::Tensor & new_empty_out_symint(const at::Tensor & self, c10::SymIntArrayRef size, at::Tensor & out); +} // namespace native +} // namespace at diff --git a/shared/package-files/03/0320c34b6f70b510cecbcd728603b2eee19abb8258434e68fc347e8acba1cded/_dedup_tensors.py b/shared/package-files/03/0320c34b6f70b510cecbcd728603b2eee19abb8258434e68fc347e8acba1cded/_dedup_tensors.py new file mode 100644 index 0000000000000000000000000000000000000000..7419e8cbb97b031396dfb3b411df0bc1c5f5c474 --- /dev/null +++ b/shared/package-files/03/0320c34b6f70b510cecbcd728603b2eee19abb8258434e68fc347e8acba1cded/_dedup_tensors.py @@ -0,0 +1,59 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates +import dataclasses +import logging +from typing import Dict, List + +from torch.distributed.checkpoint.metadata import MetadataIndex +from torch.distributed.checkpoint.planner import SavePlan + +__all__ = ["dedup_tensors"] + + +def init_logger() -> logging.Logger: + logger = logging.getLogger(__name__) + level = logging.INFO + logger.setLevel(level) + console = logging.StreamHandler() + formatter = logging.Formatter( + "%(asctime)s %(filename)s:%(lineno)s %(levelname)s p:%(processName)s t:%(threadName)s: %(message)s" + ) + console.setFormatter(formatter) + console.setLevel(level) + logger.addHandler(console) + logger.propagate = False + return logger + + +logger = init_logger() + + +# TODO add docstring for dedup_tensors +def dedup_tensors(all_plans: List[SavePlan]) -> List[SavePlan]: + all_plans = list(all_plans) + key_to_plan: Dict[MetadataIndex, List[int]] = {} + for plan_idx, plan in enumerate(all_plans): + for write_item in plan.items: + key_to_plan.setdefault(write_item.index, []).append(plan_idx) + + replicated_items = {k: v for k, v in key_to_plan.items() if len(v) > 1} + + # Remove duplicates by always keeping the first entry. + # Compute the per-rank remove set. + plan_to_keys: Dict[int, List[MetadataIndex]] = {} + for key, plans in replicated_items.items(): + for plan_idx in plans[1:]: + plan_to_keys.setdefault(plan_idx, []).append(key) + if len(plan_to_keys) > 0: + logger.info("Duplicate keys to remove: %s", plan_to_keys) + + for plan_idx, keys in plan_to_keys.items(): + key_set = set(keys) + # rewrite items and remove elements + new_items = [ + write_item + for write_item in all_plans[plan_idx].items + if write_item.index not in key_set + ] + all_plans[plan_idx] = dataclasses.replace(all_plans[plan_idx], items=new_items) + + return all_plans diff --git a/shared/package-files/03/0321bb9206a7c95d23571f9a34111416882a44b1d0aaa167f315376527c9a1a3/cache.py b/shared/package-files/03/0321bb9206a7c95d23571f9a34111416882a44b1d0aaa167f315376527c9a1a3/cache.py new file mode 100644 index 0000000000000000000000000000000000000000..ec11600a5e40ad446a6e5dde8820d46ea915b06a --- /dev/null +++ b/shared/package-files/03/0321bb9206a7c95d23571f9a34111416882a44b1d0aaa167f315376527c9a1a3/cache.py @@ -0,0 +1,210 @@ +""" Caching facility for SymPy """ +from importlib import import_module +from typing import Callable + +class _cache(list): + """ List of cached functions """ + + def print_cache(self): + """print cache info""" + + for item in self: + name = item.__name__ + myfunc = item + while hasattr(myfunc, '__wrapped__'): + if hasattr(myfunc, 'cache_info'): + info = myfunc.cache_info() + break + else: + myfunc = myfunc.__wrapped__ + else: + info = None + + print(name, info) + + def clear_cache(self): + """clear cache content""" + for item in self: + myfunc = item + while hasattr(myfunc, '__wrapped__'): + if hasattr(myfunc, 'cache_clear'): + myfunc.cache_clear() + break + else: + myfunc = myfunc.__wrapped__ + + +# global cache registry: +CACHE = _cache() +# make clear and print methods available +print_cache = CACHE.print_cache +clear_cache = CACHE.clear_cache + +from functools import lru_cache, wraps + +def __cacheit(maxsize): + """caching decorator. + + important: the result of cached function must be *immutable* + + + Examples + ======== + + >>> from sympy import cacheit + >>> @cacheit + ... def f(a, b): + ... return a+b + + >>> @cacheit + ... def f(a, b): # noqa: F811 + ... return [a, b] # <-- WRONG, returns mutable object + + to force cacheit to check returned results mutability and consistency, + set environment variable SYMPY_USE_CACHE to 'debug' + """ + def func_wrapper(func): + cfunc = lru_cache(maxsize, typed=True)(func) + + @wraps(func) + def wrapper(*args, **kwargs): + try: + retval = cfunc(*args, **kwargs) + except TypeError as e: + if not e.args or not e.args[0].startswith('unhashable type:'): + raise + retval = func(*args, **kwargs) + return retval + + wrapper.cache_info = cfunc.cache_info + wrapper.cache_clear = cfunc.cache_clear + + CACHE.append(wrapper) + return wrapper + + return func_wrapper +######################################## + + +def __cacheit_nocache(func): + return func + + +def __cacheit_debug(maxsize): + """cacheit + code to check cache consistency""" + def func_wrapper(func): + cfunc = __cacheit(maxsize)(func) + + @wraps(func) + def wrapper(*args, **kw_args): + # always call function itself and compare it with cached version + r1 = func(*args, **kw_args) + r2 = cfunc(*args, **kw_args) + + # try to see if the result is immutable + # + # this works because: + # + # hash([1,2,3]) -> raise TypeError + # hash({'a':1, 'b':2}) -> raise TypeError + # hash((1,[2,3])) -> raise TypeError + # + # hash((1,2,3)) -> just computes the hash + hash(r1), hash(r2) + + # also see if returned values are the same + if r1 != r2: + raise RuntimeError("Returned values are not the same") + return r1 + return wrapper + return func_wrapper + + +def _getenv(key, default=None): + from os import getenv + return getenv(key, default) + +# SYMPY_USE_CACHE=yes/no/debug +USE_CACHE = _getenv('SYMPY_USE_CACHE', 'yes').lower() +# SYMPY_CACHE_SIZE=some_integer/None +# special cases : +# SYMPY_CACHE_SIZE=0 -> No caching +# SYMPY_CACHE_SIZE=None -> Unbounded caching +scs = _getenv('SYMPY_CACHE_SIZE', '1000') +if scs.lower() == 'none': + SYMPY_CACHE_SIZE = None +else: + try: + SYMPY_CACHE_SIZE = int(scs) + except ValueError: + raise RuntimeError( + 'SYMPY_CACHE_SIZE must be a valid integer or None. ' + \ + 'Got: %s' % SYMPY_CACHE_SIZE) + +if USE_CACHE == 'no': + cacheit = __cacheit_nocache +elif USE_CACHE == 'yes': + cacheit = __cacheit(SYMPY_CACHE_SIZE) +elif USE_CACHE == 'debug': + cacheit = __cacheit_debug(SYMPY_CACHE_SIZE) # a lot slower +else: + raise RuntimeError( + 'unrecognized value for SYMPY_USE_CACHE: %s' % USE_CACHE) + + +def cached_property(func): + '''Decorator to cache property method''' + attrname = '__' + func.__name__ + _cached_property_sentinel = object() + def propfunc(self): + val = getattr(self, attrname, _cached_property_sentinel) + if val is _cached_property_sentinel: + val = func(self) + setattr(self, attrname, val) + return val + return property(propfunc) + + +def lazy_function(module : str, name : str) -> Callable: + """Create a lazy proxy for a function in a module. + + The module containing the function is not imported until the function is used. + + """ + func = None + + def _get_function(): + nonlocal func + if func is None: + func = getattr(import_module(module), name) + return func + + # The metaclass is needed so that help() shows the docstring + class LazyFunctionMeta(type): + @property + def __doc__(self): + docstring = _get_function().__doc__ + docstring += f"\n\nNote: this is a {self.__class__.__name__} wrapper of '{module}.{name}'" + return docstring + + class LazyFunction(metaclass=LazyFunctionMeta): + def __call__(self, *args, **kwargs): + # inline get of function for performance gh-23832 + nonlocal func + if func is None: + func = getattr(import_module(module), name) + return func(*args, **kwargs) + + @property + def __doc__(self): + docstring = _get_function().__doc__ + docstring += f"\n\nNote: this is a {self.__class__.__name__} wrapper of '{module}.{name}'" + return docstring + + def __str__(self): + return _get_function().__str__() + + def __repr__(self): + return f"<{__class__.__name__} object at 0x{id(self):x}>: wrapping '{module}.{name}'" + + return LazyFunction() diff --git a/shared/package-files/03/0324c87cf5dbfe64812b08fbac549ebf9444c0f074499c549145bba37ae2c259/_softmax_backward_data_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/03/0324c87cf5dbfe64812b08fbac549ebf9444c0f074499c549145bba37ae2c259/_softmax_backward_data_compositeexplicitautogradnonfunctional_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..7e712e737214b47605d0bf81c8810e019652274a --- /dev/null +++ b/shared/package-files/03/0324c87cf5dbfe64812b08fbac549ebf9444c0f074499c549145bba37ae2c259/_softmax_backward_data_compositeexplicitautogradnonfunctional_dispatch.h @@ -0,0 +1,23 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautogradnonfunctional { + +TORCH_API at::Tensor _softmax_backward_data(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype); + +} // namespace compositeexplicitautogradnonfunctional +} // namespace at diff --git a/shared/package-files/03/03251d9f4fc8b1aeb609b087ab0098ee1192b6e790b73100407a717f58a54506/convolution_ops.h b/shared/package-files/03/03251d9f4fc8b1aeb609b087ab0098ee1192b6e790b73100407a717f58a54506/convolution_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..2eb449374c1a72489069e661abee8367540d207d --- /dev/null +++ b/shared/package-files/03/03251d9f4fc8b1aeb609b087ab0098ee1192b6e790b73100407a717f58a54506/convolution_ops.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API convolution { + using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const c10::optional &, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymIntArrayRef, bool, c10::SymIntArrayRef, c10::SymInt); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::convolution") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "convolution(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups) -> Tensor") + static at::Tensor call(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups); +}; + +struct TORCH_API convolution_out { + using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const c10::optional &, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymIntArrayRef, bool, c10::SymIntArrayRef, c10::SymInt, at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::convolution") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "convolution.out(Tensor input, Tensor weight, Tensor? bias, SymInt[] stride, SymInt[] padding, SymInt[] dilation, bool transposed, SymInt[] output_padding, SymInt groups, *, Tensor(a!) out) -> Tensor(a!)") + static at::Tensor & call(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, at::Tensor & out); + static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef dilation, bool transposed, c10::SymIntArrayRef output_padding, c10::SymInt groups, at::Tensor & out); +}; + +}} // namespace at::_ops diff --git a/shared/package-files/03/0325c1dc539efa12931bc6f28e6465ed7a6ef45ff407dc466c873823aa0cc11e/native_batch_norm_backward.h b/shared/package-files/03/0325c1dc539efa12931bc6f28e6465ed7a6ef45ff407dc466c873823aa0cc11e/native_batch_norm_backward.h new file mode 100644 index 0000000000000000000000000000000000000000..e91f96306178ca2f8b678331b4a60f9fa9b6387c --- /dev/null +++ b/shared/package-files/03/0325c1dc539efa12931bc6f28e6465ed7a6ef45ff407dc466c873823aa0cc11e/native_batch_norm_backward.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::native_batch_norm_backward(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask) -> (Tensor, Tensor, Tensor) +inline ::std::tuple native_batch_norm_backward(const at::Tensor & grad_out, const at::Tensor & input, const c10::optional & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_invstd, bool train, double eps, ::std::array output_mask) { + return at::_ops::native_batch_norm_backward::call(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask); +} + +// aten::native_batch_norm_backward.out(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) +inline ::std::tuple native_batch_norm_backward_out(at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, const at::Tensor & grad_out, const at::Tensor & input, const c10::optional & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_invstd, bool train, double eps, ::std::array output_mask) { + return at::_ops::native_batch_norm_backward_out::call(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask, out0, out1, out2); +} +// aten::native_batch_norm_backward.out(Tensor grad_out, Tensor input, Tensor? weight, Tensor? running_mean, Tensor? running_var, Tensor? save_mean, Tensor? save_invstd, bool train, float eps, bool[3] output_mask, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2) -> (Tensor(a!), Tensor(b!), Tensor(c!)) +inline ::std::tuple native_batch_norm_backward_outf(const at::Tensor & grad_out, const at::Tensor & input, const c10::optional & weight, const c10::optional & running_mean, const c10::optional & running_var, const c10::optional & save_mean, const c10::optional & save_invstd, bool train, double eps, ::std::array output_mask, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2) { + return at::_ops::native_batch_norm_backward_out::call(grad_out, input, weight, running_mean, running_var, save_mean, save_invstd, train, eps, output_mask, out0, out1, out2); +} + +} diff --git a/shared/package-files/03/032863b05f89846c07759a3f0473d99c0a90839a7a238cab43e180cdb2208821/__init__.pyi b/shared/package-files/03/032863b05f89846c07759a3f0473d99c0a90839a7a238cab43e180cdb2208821/__init__.pyi new file mode 100644 index 0000000000000000000000000000000000000000..2abd726d3e38e2f4965c3153fdf9bb8d76a25a6a --- /dev/null +++ b/shared/package-files/03/032863b05f89846c07759a3f0473d99c0a90839a7a238cab43e180cdb2208821/__init__.pyi @@ -0,0 +1,7 @@ +from typing import Any, Tuple + +from waitress.server import create_server as create_server + +def serve(app: Any, **kw: Any) -> None: ... +def serve_paste(app: Any, global_conf: Any, **kw: Any) -> int: ... +def profile(cmd: Any, globals: Any, locals: Any, sort_order: Tuple[str, ...], callers: bool) -> None: ... diff --git a/shared/package-files/03/033099246f5f08faa159aa6b542aa2189859b6a4a50934c89abb4f3b07b3b801/katz.py b/shared/package-files/03/033099246f5f08faa159aa6b542aa2189859b6a4a50934c89abb4f3b07b3b801/katz.py new file mode 100644 index 0000000000000000000000000000000000000000..f4294007b14d4bb837d11abee62dfdea4bea5d72 --- /dev/null +++ b/shared/package-files/03/033099246f5f08faa159aa6b542aa2189859b6a4a50934c89abb4f3b07b3b801/katz.py @@ -0,0 +1,334 @@ +"""Katz centrality.""" +import math + +import networkx as nx +from networkx.utils import not_implemented_for + +__all__ = ["katz_centrality", "katz_centrality_numpy"] + + +@nx._dispatch +@not_implemented_for("multigraph") +def katz_centrality( + G, + alpha=0.1, + beta=1.0, + max_iter=1000, + tol=1.0e-6, + nstart=None, + normalized=True, + weight=None, +): + r"""Compute the Katz centrality for the nodes of the graph G. + + Katz centrality computes the centrality for a node based on the centrality + of its neighbors. It is a generalization of the eigenvector centrality. The + Katz centrality for node $i$ is + + .. math:: + + x_i = \alpha \sum_{j} A_{ij} x_j + \beta, + + where $A$ is the adjacency matrix of graph G with eigenvalues $\lambda$. + + The parameter $\beta$ controls the initial centrality and + + .. math:: + + \alpha < \frac{1}{\lambda_{\max}}. + + Katz centrality computes the relative influence of a node within a + network by measuring the number of the immediate neighbors (first + degree nodes) and also all other nodes in the network that connect + to the node under consideration through these immediate neighbors. + + Extra weight can be provided to immediate neighbors through the + parameter $\beta$. Connections made with distant neighbors + are, however, penalized by an attenuation factor $\alpha$ which + should be strictly less than the inverse largest eigenvalue of the + adjacency matrix in order for the Katz centrality to be computed + correctly. More information is provided in [1]_. + + Parameters + ---------- + G : graph + A NetworkX graph. + + alpha : float + Attenuation factor + + beta : scalar or dictionary, optional (default=1.0) + Weight attributed to the immediate neighborhood. If not a scalar, the + dictionary must have an value for every node. + + max_iter : integer, optional (default=1000) + Maximum number of iterations in power method. + + tol : float, optional (default=1.0e-6) + Error tolerance used to check convergence in power method iteration. + + nstart : dictionary, optional + Starting value of Katz iteration for each node. + + normalized : bool, optional (default=True) + If True normalize the resulting values. + + weight : None or string, optional (default=None) + If None, all edge weights are considered equal. + Otherwise holds the name of the edge attribute used as weight. + In this measure the weight is interpreted as the connection strength. + + Returns + ------- + nodes : dictionary + Dictionary of nodes with Katz centrality as the value. + + Raises + ------ + NetworkXError + If the parameter `beta` is not a scalar but lacks a value for at least + one node + + PowerIterationFailedConvergence + If the algorithm fails to converge to the specified tolerance + within the specified number of iterations of the power iteration + method. + + Examples + -------- + >>> import math + >>> G = nx.path_graph(4) + >>> phi = (1 + math.sqrt(5)) / 2.0 # largest eigenvalue of adj matrix + >>> centrality = nx.katz_centrality(G, 1 / phi - 0.01) + >>> for n, c in sorted(centrality.items()): + ... print(f"{n} {c:.2f}") + 0 0.37 + 1 0.60 + 2 0.60 + 3 0.37 + + See Also + -------- + katz_centrality_numpy + eigenvector_centrality + eigenvector_centrality_numpy + pagerank + hits + + Notes + ----- + Katz centrality was introduced by [2]_. + + This algorithm it uses the power method to find the eigenvector + corresponding to the largest eigenvalue of the adjacency matrix of ``G``. + The parameter ``alpha`` should be strictly less than the inverse of largest + eigenvalue of the adjacency matrix for the algorithm to converge. + You can use ``max(nx.adjacency_spectrum(G))`` to get $\lambda_{\max}$ the largest + eigenvalue of the adjacency matrix. + The iteration will stop after ``max_iter`` iterations or an error tolerance of + ``number_of_nodes(G) * tol`` has been reached. + + When $\alpha = 1/\lambda_{\max}$ and $\beta=0$, Katz centrality is the same + as eigenvector centrality. + + For directed graphs this finds "left" eigenvectors which corresponds + to the in-edges in the graph. For out-edges Katz centrality + first reverse the graph with ``G.reverse()``. + + References + ---------- + .. [1] Mark E. J. Newman: + Networks: An Introduction. + Oxford University Press, USA, 2010, p. 720. + .. [2] Leo Katz: + A New Status Index Derived from Sociometric Index. + Psychometrika 18(1):39–43, 1953 + https://link.springer.com/content/pdf/10.1007/BF02289026.pdf + """ + if len(G) == 0: + return {} + + nnodes = G.number_of_nodes() + + if nstart is None: + # choose starting vector with entries of 0 + x = {n: 0 for n in G} + else: + x = nstart + + try: + b = dict.fromkeys(G, float(beta)) + except (TypeError, ValueError, AttributeError) as err: + b = beta + if set(beta) != set(G): + raise nx.NetworkXError( + "beta dictionary " "must have a value for every node" + ) from err + + # make up to max_iter iterations + for _ in range(max_iter): + xlast = x + x = dict.fromkeys(xlast, 0) + # do the multiplication y^T = Alpha * x^T A + Beta + for n in x: + for nbr in G[n]: + x[nbr] += xlast[n] * G[n][nbr].get(weight, 1) + for n in x: + x[n] = alpha * x[n] + b[n] + + # check convergence + error = sum(abs(x[n] - xlast[n]) for n in x) + if error < nnodes * tol: + if normalized: + # normalize vector + try: + s = 1.0 / math.hypot(*x.values()) + # this should never be zero? + except ZeroDivisionError: + s = 1.0 + else: + s = 1 + for n in x: + x[n] *= s + return x + raise nx.PowerIterationFailedConvergence(max_iter) + + +@not_implemented_for("multigraph") +def katz_centrality_numpy(G, alpha=0.1, beta=1.0, normalized=True, weight=None): + r"""Compute the Katz centrality for the graph G. + + Katz centrality computes the centrality for a node based on the centrality + of its neighbors. It is a generalization of the eigenvector centrality. The + Katz centrality for node $i$ is + + .. math:: + + x_i = \alpha \sum_{j} A_{ij} x_j + \beta, + + where $A$ is the adjacency matrix of graph G with eigenvalues $\lambda$. + + The parameter $\beta$ controls the initial centrality and + + .. math:: + + \alpha < \frac{1}{\lambda_{\max}}. + + Katz centrality computes the relative influence of a node within a + network by measuring the number of the immediate neighbors (first + degree nodes) and also all other nodes in the network that connect + to the node under consideration through these immediate neighbors. + + Extra weight can be provided to immediate neighbors through the + parameter $\beta$. Connections made with distant neighbors + are, however, penalized by an attenuation factor $\alpha$ which + should be strictly less than the inverse largest eigenvalue of the + adjacency matrix in order for the Katz centrality to be computed + correctly. More information is provided in [1]_. + + Parameters + ---------- + G : graph + A NetworkX graph + + alpha : float + Attenuation factor + + beta : scalar or dictionary, optional (default=1.0) + Weight attributed to the immediate neighborhood. If not a scalar the + dictionary must have an value for every node. + + normalized : bool + If True normalize the resulting values. + + weight : None or string, optional + If None, all edge weights are considered equal. + Otherwise holds the name of the edge attribute used as weight. + In this measure the weight is interpreted as the connection strength. + + Returns + ------- + nodes : dictionary + Dictionary of nodes with Katz centrality as the value. + + Raises + ------ + NetworkXError + If the parameter `beta` is not a scalar but lacks a value for at least + one node + + Examples + -------- + >>> import math + >>> G = nx.path_graph(4) + >>> phi = (1 + math.sqrt(5)) / 2.0 # largest eigenvalue of adj matrix + >>> centrality = nx.katz_centrality_numpy(G, 1 / phi) + >>> for n, c in sorted(centrality.items()): + ... print(f"{n} {c:.2f}") + 0 0.37 + 1 0.60 + 2 0.60 + 3 0.37 + + See Also + -------- + katz_centrality + eigenvector_centrality_numpy + eigenvector_centrality + pagerank + hits + + Notes + ----- + Katz centrality was introduced by [2]_. + + This algorithm uses a direct linear solver to solve the above equation. + The parameter ``alpha`` should be strictly less than the inverse of largest + eigenvalue of the adjacency matrix for there to be a solution. + You can use ``max(nx.adjacency_spectrum(G))`` to get $\lambda_{\max}$ the largest + eigenvalue of the adjacency matrix. + + When $\alpha = 1/\lambda_{\max}$ and $\beta=0$, Katz centrality is the same + as eigenvector centrality. + + For directed graphs this finds "left" eigenvectors which corresponds + to the in-edges in the graph. For out-edges Katz centrality + first reverse the graph with ``G.reverse()``. + + References + ---------- + .. [1] Mark E. J. Newman: + Networks: An Introduction. + Oxford University Press, USA, 2010, p. 173. + .. [2] Leo Katz: + A New Status Index Derived from Sociometric Index. + Psychometrika 18(1):39–43, 1953 + https://link.springer.com/content/pdf/10.1007/BF02289026.pdf + """ + import numpy as np + + if len(G) == 0: + return {} + try: + nodelist = beta.keys() + if set(nodelist) != set(G): + raise nx.NetworkXError( + "beta dictionary " "must have a value for every node" + ) + b = np.array(list(beta.values()), dtype=float) + except AttributeError: + nodelist = list(G) + try: + b = np.ones((len(nodelist), 1)) * beta + except (TypeError, ValueError, AttributeError) as err: + raise nx.NetworkXError("beta must be a number") from err + + A = nx.adjacency_matrix(G, nodelist=nodelist, weight=weight).todense().T + n = A.shape[0] + centrality = np.linalg.solve(np.eye(n, n) - (alpha * A), b) + if normalized: + norm = np.sign(sum(centrality)) * np.linalg.norm(centrality) + else: + norm = 1.0 + centrality = dict(zip(nodelist, map(float, centrality / norm))) + return centrality diff --git a/shared/package-files/03/0333d6dcb876478b139be454522e0e7776926daf73a0b712ff31c1c73b6fe332/process.h b/shared/package-files/03/0333d6dcb876478b139be454522e0e7776926daf73a0b712ff31c1c73b6fe332/process.h new file mode 100644 index 0000000000000000000000000000000000000000..d4d2ae124f427b8143e7fb9d69c6b97e914befd5 --- /dev/null +++ b/shared/package-files/03/0333d6dcb876478b139be454522e0e7776926daf73a0b712ff31c1c73b6fe332/process.h @@ -0,0 +1,46 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +#pragma once + +#include +#include +#include + +#include "arrow/status.h" +#include "arrow/testing/visibility.h" + +namespace arrow::util { + +class ARROW_TESTING_EXPORT Process { + public: + Process(); + ~Process(); + + Status SetExecutable(const std::string& path); + void SetArgs(const std::vector& args); + void SetEnv(const std::string& name, const std::string& value); + void IgnoreStderr(); + Status Execute(); + bool IsRunning(); + uint64_t pid(); + + private: + class Impl; + std::unique_ptr impl_; +}; +} // namespace arrow::util diff --git a/shared/package-files/03/0334201b81a04b5e76fdcaa61abfcecf63085ec09a97ec5fb22b3b7c0ee7994d/xmlrpc.py b/shared/package-files/03/0334201b81a04b5e76fdcaa61abfcecf63085ec09a97ec5fb22b3b7c0ee7994d/xmlrpc.py new file mode 100644 index 0000000000000000000000000000000000000000..4a7d55d0e50cb8b892caa021695522e5ddd54a17 --- /dev/null +++ b/shared/package-files/03/0334201b81a04b5e76fdcaa61abfcecf63085ec09a97ec5fb22b3b7c0ee7994d/xmlrpc.py @@ -0,0 +1,60 @@ +"""xmlrpclib.Transport implementation +""" + +import logging +import urllib.parse +import xmlrpc.client +from typing import TYPE_CHECKING, Tuple + +from pip._internal.exceptions import NetworkConnectionError +from pip._internal.network.session import PipSession +from pip._internal.network.utils import raise_for_status + +if TYPE_CHECKING: + from xmlrpc.client import _HostType, _Marshallable + +logger = logging.getLogger(__name__) + + +class PipXmlrpcTransport(xmlrpc.client.Transport): + """Provide a `xmlrpclib.Transport` implementation via a `PipSession` + object. + """ + + def __init__( + self, index_url: str, session: PipSession, use_datetime: bool = False + ) -> None: + super().__init__(use_datetime) + index_parts = urllib.parse.urlparse(index_url) + self._scheme = index_parts.scheme + self._session = session + + def request( + self, + host: "_HostType", + handler: str, + request_body: bytes, + verbose: bool = False, + ) -> Tuple["_Marshallable", ...]: + assert isinstance(host, str) + parts = (self._scheme, host, handler, None, None, None) + url = urllib.parse.urlunparse(parts) + try: + headers = {"Content-Type": "text/xml"} + response = self._session.post( + url, + data=request_body, + headers=headers, + stream=True, + ) + raise_for_status(response) + self.verbose = verbose + return self.parse_response(response.raw) + except NetworkConnectionError as exc: + assert exc.response + logger.critical( + "HTTP error %s while getting %s", + exc.response.status_code, + url, + ) + raise diff --git a/shared/package-files/03/03357aaf07136b86f8b5c7a7a0a87de68e9517ecfbc3be65f3564e761862eb21/test_trophic.py b/shared/package-files/03/03357aaf07136b86f8b5c7a7a0a87de68e9517ecfbc3be65f3564e761862eb21/test_trophic.py new file mode 100644 index 0000000000000000000000000000000000000000..f1d6813160eed6da3cd1fd0b254b7352bd1bd4ad --- /dev/null +++ b/shared/package-files/03/03357aaf07136b86f8b5c7a7a0a87de68e9517ecfbc3be65f3564e761862eb21/test_trophic.py @@ -0,0 +1,302 @@ +"""Test trophic levels, trophic differences and trophic coherence +""" +import pytest + +np = pytest.importorskip("numpy") +pytest.importorskip("scipy") + +import networkx as nx + + +def test_trophic_levels(): + """Trivial example""" + G = nx.DiGraph() + G.add_edge("a", "b") + G.add_edge("b", "c") + + d = nx.trophic_levels(G) + assert d == {"a": 1, "b": 2, "c": 3} + + +def test_trophic_levels_levine(): + """Example from Figure 5 in Stephen Levine (1980) J. theor. Biol. 83, + 195-207 + """ + S = nx.DiGraph() + S.add_edge(1, 2, weight=1.0) + S.add_edge(1, 3, weight=0.2) + S.add_edge(1, 4, weight=0.8) + S.add_edge(2, 3, weight=0.2) + S.add_edge(2, 5, weight=0.3) + S.add_edge(4, 3, weight=0.6) + S.add_edge(4, 5, weight=0.7) + S.add_edge(5, 4, weight=0.2) + + # save copy for later, test intermediate implementation details first + S2 = S.copy() + + # drop nodes of in-degree zero + z = [nid for nid, d in S.in_degree if d == 0] + for nid in z: + S.remove_node(nid) + + # find adjacency matrix + q = nx.linalg.graphmatrix.adjacency_matrix(S).T + + # fmt: off + expected_q = np.array([ + [0, 0, 0., 0], + [0.2, 0, 0.6, 0], + [0, 0, 0, 0.2], + [0.3, 0, 0.7, 0] + ]) + # fmt: on + assert np.array_equal(q.todense(), expected_q) + + # must be square, size of number of nodes + assert len(q.shape) == 2 + assert q.shape[0] == q.shape[1] + assert q.shape[0] == len(S) + + nn = q.shape[0] + + i = np.eye(nn) + n = np.linalg.inv(i - q) + y = np.asarray(n) @ np.ones(nn) + + expected_y = np.array([1, 2.07906977, 1.46511628, 2.3255814]) + assert np.allclose(y, expected_y) + + expected_d = {1: 1, 2: 2, 3: 3.07906977, 4: 2.46511628, 5: 3.3255814} + + d = nx.trophic_levels(S2) + + for nid, level in d.items(): + expected_level = expected_d[nid] + assert expected_level == pytest.approx(level, abs=1e-7) + + +def test_trophic_levels_simple(): + matrix_a = np.array([[0, 0], [1, 0]]) + G = nx.from_numpy_array(matrix_a, create_using=nx.DiGraph) + d = nx.trophic_levels(G) + assert d[0] == pytest.approx(2, abs=1e-7) + assert d[1] == pytest.approx(1, abs=1e-7) + + +def test_trophic_levels_more_complex(): + # fmt: off + matrix = np.array([ + [0, 1, 0, 0], + [0, 0, 1, 0], + [0, 0, 0, 1], + [0, 0, 0, 0] + ]) + # fmt: on + G = nx.from_numpy_array(matrix, create_using=nx.DiGraph) + d = nx.trophic_levels(G) + expected_result = [1, 2, 3, 4] + for ind in range(4): + assert d[ind] == pytest.approx(expected_result[ind], abs=1e-7) + + # fmt: off + matrix = np.array([ + [0, 1, 1, 0], + [0, 0, 1, 1], + [0, 0, 0, 1], + [0, 0, 0, 0] + ]) + # fmt: on + G = nx.from_numpy_array(matrix, create_using=nx.DiGraph) + d = nx.trophic_levels(G) + + expected_result = [1, 2, 2.5, 3.25] + print("Calculated result: ", d) + print("Expected Result: ", expected_result) + + for ind in range(4): + assert d[ind] == pytest.approx(expected_result[ind], abs=1e-7) + + +def test_trophic_levels_even_more_complex(): + # fmt: off + # Another, bigger matrix + matrix = np.array([ + [0, 0, 0, 0, 0], + [0, 1, 0, 1, 0], + [1, 0, 0, 0, 0], + [0, 1, 0, 0, 0], + [0, 0, 0, 1, 0] + ]) + # Generated this linear system using pen and paper: + K = np.array([ + [1, 0, -1, 0, 0], + [0, 0.5, 0, -0.5, 0], + [0, 0, 1, 0, 0], + [0, -0.5, 0, 1, -0.5], + [0, 0, 0, 0, 1], + ]) + # fmt: on + result_1 = np.ravel(np.linalg.inv(K) @ np.ones(5)) + G = nx.from_numpy_array(matrix, create_using=nx.DiGraph) + result_2 = nx.trophic_levels(G) + + for ind in range(5): + assert result_1[ind] == pytest.approx(result_2[ind], abs=1e-7) + + +def test_trophic_levels_singular_matrix(): + """Should raise an error with graphs with only non-basal nodes""" + matrix = np.identity(4) + G = nx.from_numpy_array(matrix, create_using=nx.DiGraph) + with pytest.raises(nx.NetworkXError) as e: + nx.trophic_levels(G) + msg = ( + "Trophic levels are only defined for graphs where every node " + + "has a path from a basal node (basal nodes are nodes with no " + + "incoming edges)." + ) + assert msg in str(e.value) + + +def test_trophic_levels_singular_with_basal(): + """Should fail to compute if there are any parts of the graph which are not + reachable from any basal node (with in-degree zero). + """ + G = nx.DiGraph() + # a has in-degree zero + G.add_edge("a", "b") + + # b is one level above a, c and d + G.add_edge("c", "b") + G.add_edge("d", "b") + + # c and d form a loop, neither are reachable from a + G.add_edge("c", "d") + G.add_edge("d", "c") + + with pytest.raises(nx.NetworkXError) as e: + nx.trophic_levels(G) + msg = ( + "Trophic levels are only defined for graphs where every node " + + "has a path from a basal node (basal nodes are nodes with no " + + "incoming edges)." + ) + assert msg in str(e.value) + + # if self-loops are allowed, smaller example: + G = nx.DiGraph() + G.add_edge("a", "b") # a has in-degree zero + G.add_edge("c", "b") # b is one level above a and c + G.add_edge("c", "c") # c has a self-loop + with pytest.raises(nx.NetworkXError) as e: + nx.trophic_levels(G) + msg = ( + "Trophic levels are only defined for graphs where every node " + + "has a path from a basal node (basal nodes are nodes with no " + + "incoming edges)." + ) + assert msg in str(e.value) + + +def test_trophic_differences(): + matrix_a = np.array([[0, 1], [0, 0]]) + G = nx.from_numpy_array(matrix_a, create_using=nx.DiGraph) + diffs = nx.trophic_differences(G) + assert diffs[(0, 1)] == pytest.approx(1, abs=1e-7) + + # fmt: off + matrix_b = np.array([ + [0, 1, 1, 0], + [0, 0, 1, 1], + [0, 0, 0, 1], + [0, 0, 0, 0] + ]) + # fmt: on + G = nx.from_numpy_array(matrix_b, create_using=nx.DiGraph) + diffs = nx.trophic_differences(G) + + assert diffs[(0, 1)] == pytest.approx(1, abs=1e-7) + assert diffs[(0, 2)] == pytest.approx(1.5, abs=1e-7) + assert diffs[(1, 2)] == pytest.approx(0.5, abs=1e-7) + assert diffs[(1, 3)] == pytest.approx(1.25, abs=1e-7) + assert diffs[(2, 3)] == pytest.approx(0.75, abs=1e-7) + + +def test_trophic_incoherence_parameter_no_cannibalism(): + matrix_a = np.array([[0, 1], [0, 0]]) + G = nx.from_numpy_array(matrix_a, create_using=nx.DiGraph) + q = nx.trophic_incoherence_parameter(G, cannibalism=False) + assert q == pytest.approx(0, abs=1e-7) + + # fmt: off + matrix_b = np.array([ + [0, 1, 1, 0], + [0, 0, 1, 1], + [0, 0, 0, 1], + [0, 0, 0, 0] + ]) + # fmt: on + G = nx.from_numpy_array(matrix_b, create_using=nx.DiGraph) + q = nx.trophic_incoherence_parameter(G, cannibalism=False) + assert q == pytest.approx(np.std([1, 1.5, 0.5, 0.75, 1.25]), abs=1e-7) + + # fmt: off + matrix_c = np.array([ + [0, 1, 1, 0], + [0, 1, 1, 1], + [0, 0, 0, 1], + [0, 0, 0, 1] + ]) + # fmt: on + G = nx.from_numpy_array(matrix_c, create_using=nx.DiGraph) + q = nx.trophic_incoherence_parameter(G, cannibalism=False) + # Ignore the -link + assert q == pytest.approx(np.std([1, 1.5, 0.5, 0.75, 1.25]), abs=1e-7) + + # no self-loops case + # fmt: off + matrix_d = np.array([ + [0, 1, 1, 0], + [0, 0, 1, 1], + [0, 0, 0, 1], + [0, 0, 0, 0] + ]) + # fmt: on + G = nx.from_numpy_array(matrix_d, create_using=nx.DiGraph) + q = nx.trophic_incoherence_parameter(G, cannibalism=False) + # Ignore the -link + assert q == pytest.approx(np.std([1, 1.5, 0.5, 0.75, 1.25]), abs=1e-7) + + +def test_trophic_incoherence_parameter_cannibalism(): + matrix_a = np.array([[0, 1], [0, 0]]) + G = nx.from_numpy_array(matrix_a, create_using=nx.DiGraph) + q = nx.trophic_incoherence_parameter(G, cannibalism=True) + assert q == pytest.approx(0, abs=1e-7) + + # fmt: off + matrix_b = np.array([ + [0, 0, 0, 0, 0], + [0, 1, 0, 1, 0], + [1, 0, 0, 0, 0], + [0, 1, 0, 0, 0], + [0, 0, 0, 1, 0] + ]) + # fmt: on + G = nx.from_numpy_array(matrix_b, create_using=nx.DiGraph) + q = nx.trophic_incoherence_parameter(G, cannibalism=True) + assert q == pytest.approx(2, abs=1e-7) + + # fmt: off + matrix_c = np.array([ + [0, 1, 1, 0], + [0, 0, 1, 1], + [0, 0, 0, 1], + [0, 0, 0, 0] + ]) + # fmt: on + G = nx.from_numpy_array(matrix_c, create_using=nx.DiGraph) + q = nx.trophic_incoherence_parameter(G, cannibalism=True) + # Ignore the -link + assert q == pytest.approx(np.std([1, 1.5, 0.5, 0.75, 1.25]), abs=1e-7) diff --git a/shared/package-files/03/0336f7c5cf56be0c41bd044d0b99bcdd2269aabef788bac6489549e6ce3fccd2/linux-ubuntu18.04-broadwell b/shared/package-files/03/0336f7c5cf56be0c41bd044d0b99bcdd2269aabef788bac6489549e6ce3fccd2/linux-ubuntu18.04-broadwell new file mode 100644 index 0000000000000000000000000000000000000000..bbe287ebf14956e40272be4e06f20d26f5a00cab --- /dev/null +++ b/shared/package-files/03/0336f7c5cf56be0c41bd044d0b99bcdd2269aabef788bac6489549e6ce3fccd2/linux-ubuntu18.04-broadwell @@ -0,0 +1,26 @@ +processor : 0 +vendor_id : GenuineIntel +cpu family : 6 +model : 61 +model name : Intel(R) Core(TM) i5-5200U CPU @ 2.20GHz +stepping : 4 +microcode : 0x2d +cpu MHz : 1944.124 +cache size : 3072 KB +physical id : 0 +siblings : 4 +core id : 0 +cpu cores : 2 +apicid : 0 +initial apicid : 0 +fpu : yes +fpu_exception : yes +cpuid level : 20 +wp : yes +flags : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap intel_pt xsaveopt dtherm ida arat pln pts md_clear flush_l1d +bugs : cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds +bogomips : 4389.80 +clflush size : 64 +cache_alignment : 64 +address sizes : 39 bits physical, 48 bits virtual +power management: diff --git a/shared/package-files/03/03396305249fef000efc6f56f29920fa8dc87651f331da7926e9613067283913/_optimize.py b/shared/package-files/03/03396305249fef000efc6f56f29920fa8dc87651f331da7926e9613067283913/_optimize.py new file mode 100644 index 0000000000000000000000000000000000000000..4c0214daad371c51c5b860e4773e2bb01faf123d --- /dev/null +++ b/shared/package-files/03/03396305249fef000efc6f56f29920fa8dc87651f331da7926e9613067283913/_optimize.py @@ -0,0 +1,4131 @@ +#__docformat__ = "restructuredtext en" +# ******NOTICE*************** +# optimize.py module by Travis E. Oliphant +# +# You may copy and use this module as you see fit with no +# guarantee implied provided you keep this notice in all copies. +# *****END NOTICE************ + +# A collection of optimization algorithms. Version 0.5 +# CHANGES +# Added fminbound (July 2001) +# Added brute (Aug. 2002) +# Finished line search satisfying strong Wolfe conditions (Mar. 2004) +# Updated strong Wolfe conditions line search to use +# cubic-interpolation (Mar. 2004) + + +# Minimization routines + +__all__ = ['fmin', 'fmin_powell', 'fmin_bfgs', 'fmin_ncg', 'fmin_cg', + 'fminbound', 'brent', 'golden', 'bracket', 'rosen', 'rosen_der', + 'rosen_hess', 'rosen_hess_prod', 'brute', 'approx_fprime', + 'line_search', 'check_grad', 'OptimizeResult', 'show_options', + 'OptimizeWarning'] + +__docformat__ = "restructuredtext en" + +import math +import warnings +import sys +import inspect +from numpy import eye, argmin, zeros, shape, asarray, sqrt +import numpy as np +from scipy.linalg import cholesky, issymmetric, LinAlgError +from scipy.sparse.linalg import LinearOperator +from ._linesearch import (line_search_wolfe1, line_search_wolfe2, + line_search_wolfe2 as line_search, + LineSearchWarning) +from ._numdiff import approx_derivative +from scipy._lib._util import getfullargspec_no_self as _getfullargspec +from scipy._lib._util import (MapWrapper, check_random_state, _RichResult, + _call_callback_maybe_halt, _transition_to_rng) +from scipy.optimize._differentiable_functions import ScalarFunction, FD_METHODS +from scipy._lib._array_api import array_namespace +from scipy._lib import array_api_extra as xpx + + +# standard status messages of optimizers +_status_message = {'success': 'Optimization terminated successfully.', + 'maxfev': 'Maximum number of function evaluations has ' + 'been exceeded.', + 'maxiter': 'Maximum number of iterations has been ' + 'exceeded.', + 'pr_loss': 'Desired error not necessarily achieved due ' + 'to precision loss.', + 'nan': 'NaN result encountered.', + 'out_of_bounds': 'The result is outside of the provided ' + 'bounds.'} + + +class MemoizeJac: + """Decorator that caches the return values of a function returning ``(fun, grad)`` + each time it is called.""" + + def __init__(self, fun): + self.fun = fun + self.jac = None + self._value = None + self.x = None + + def _compute_if_needed(self, x, *args): + if not np.all(x == self.x) or self._value is None or self.jac is None: + self.x = np.asarray(x).copy() + fg = self.fun(x, *args) + self.jac = fg[1] + self._value = fg[0] + + def __call__(self, x, *args): + """ returns the function value """ + self._compute_if_needed(x, *args) + return self._value + + def derivative(self, x, *args): + self._compute_if_needed(x, *args) + return self.jac + + +def _wrap_callback(callback, method=None): + """Wrap a user-provided callback so that attributes can be attached.""" + if callback is None or method in {'tnc', 'slsqp', 'cobyla', 'cobyqa'}: + return callback # don't wrap + + sig = inspect.signature(callback) + + if set(sig.parameters) == {'intermediate_result'}: + def wrapped_callback(res): + return callback(intermediate_result=res) + elif method == 'trust-constr': + def wrapped_callback(res): + return callback(np.copy(res.x), res) + elif method == 'differential_evolution': + def wrapped_callback(res): + return callback(np.copy(res.x), res.convergence) + else: + def wrapped_callback(res): + return callback(np.copy(res.x)) + + wrapped_callback.stop_iteration = False + return wrapped_callback + + +class OptimizeResult(_RichResult): + """ + Represents the optimization result. + + Attributes + ---------- + x : ndarray + The solution of the optimization. + success : bool + Whether or not the optimizer exited successfully. + status : int + Termination status of the optimizer. Its value depends on the + underlying solver. Refer to `message` for details. + message : str + Description of the cause of the termination. + fun : float + Value of objective function at `x`. + jac, hess : ndarray + Values of objective function's Jacobian and its Hessian at `x` (if + available). The Hessian may be an approximation, see the documentation + of the function in question. + hess_inv : object + Inverse of the objective function's Hessian; may be an approximation. + Not available for all solvers. The type of this attribute may be + either np.ndarray or scipy.sparse.linalg.LinearOperator. + nfev, njev, nhev : int + Number of evaluations of the objective functions and of its + Jacobian and Hessian. + nit : int + Number of iterations performed by the optimizer. + maxcv : float + The maximum constraint violation. + + Notes + ----- + Depending on the specific solver being used, `OptimizeResult` may + not have all attributes listed here, and they may have additional + attributes not listed here. Since this class is essentially a + subclass of dict with attribute accessors, one can see which + attributes are available using the `OptimizeResult.keys` method. + + """ + pass + + +class OptimizeWarning(UserWarning): + pass + +def _check_positive_definite(Hk): + def is_pos_def(A): + if issymmetric(A): + try: + cholesky(A) + return True + except LinAlgError: + return False + else: + return False + if Hk is not None: + if not is_pos_def(Hk): + raise ValueError("'hess_inv0' matrix isn't positive definite.") + + +def _check_unknown_options(unknown_options): + if unknown_options: + msg = ", ".join(map(str, unknown_options.keys())) + # Stack level 4: this is called from _minimize_*, which is + # called from another function in SciPy. Level 4 is the first + # level in user code. + warnings.warn(f"Unknown solver options: {msg}", OptimizeWarning, stacklevel=4) + + +def is_finite_scalar(x): + """Test whether `x` is either a finite scalar or a finite array scalar. + + """ + return np.size(x) == 1 and np.isfinite(x) + + +_epsilon = sqrt(np.finfo(float).eps) + + +def vecnorm(x, ord=2): + if ord == np.inf: + return np.amax(np.abs(x)) + elif ord == -np.inf: + return np.amin(np.abs(x)) + else: + return np.sum(np.abs(x)**ord, axis=0)**(1.0 / ord) + + +def _prepare_scalar_function(fun, x0, jac=None, args=(), bounds=None, + epsilon=None, finite_diff_rel_step=None, + hess=None): + """ + Creates a ScalarFunction object for use with scalar minimizers + (BFGS/LBFGSB/SLSQP/TNC/CG/etc). + + Parameters + ---------- + fun : callable + The objective function to be minimized. + + ``fun(x, *args) -> float`` + + where ``x`` is an 1-D array with shape (n,) and ``args`` + is a tuple of the fixed parameters needed to completely + specify the function. + x0 : ndarray, shape (n,) + Initial guess. Array of real elements of size (n,), + where 'n' is the number of independent variables. + jac : {callable, '2-point', '3-point', 'cs', None}, optional + Method for computing the gradient vector. If it is a callable, it + should be a function that returns the gradient vector: + + ``jac(x, *args) -> array_like, shape (n,)`` + + If one of `{'2-point', '3-point', 'cs'}` is selected then the gradient + is calculated with a relative step for finite differences. If `None`, + then two-point finite differences with an absolute step is used. + args : tuple, optional + Extra arguments passed to the objective function and its + derivatives (`fun`, `jac` functions). + bounds : sequence, optional + Bounds on variables. 'new-style' bounds are required. + eps : float or ndarray + If ``jac is None`` the absolute step size used for numerical + approximation of the jacobian via forward differences. + finite_diff_rel_step : None or array_like, optional + If ``jac in ['2-point', '3-point', 'cs']`` the relative step size to + use for numerical approximation of the jacobian. The absolute step + size is computed as ``h = rel_step * sign(x0) * max(1, abs(x0))``, + possibly adjusted to fit into the bounds. For ``jac='3-point'`` + the sign of `h` is ignored. If None (default) then step is selected + automatically. + hess : {callable, '2-point', '3-point', 'cs', None} + Computes the Hessian matrix. If it is callable, it should return the + Hessian matrix: + + ``hess(x, *args) -> {LinearOperator, spmatrix, array}, (n, n)`` + + Alternatively, the keywords {'2-point', '3-point', 'cs'} select a + finite difference scheme for numerical estimation. + Whenever the gradient is estimated via finite-differences, the Hessian + cannot be estimated with options {'2-point', '3-point', 'cs'} and needs + to be estimated using one of the quasi-Newton strategies. + + Returns + ------- + sf : ScalarFunction + """ + if callable(jac): + grad = jac + elif jac in FD_METHODS: + # epsilon is set to None so that ScalarFunction is made to use + # rel_step + epsilon = None + grad = jac + else: + # default (jac is None) is to do 2-point finite differences with + # absolute step size. ScalarFunction has to be provided an + # epsilon value that is not None to use absolute steps. This is + # normally the case from most _minimize* methods. + grad = '2-point' + epsilon = epsilon + + if hess is None: + # ScalarFunction requires something for hess, so we give a dummy + # implementation here if nothing is provided, return a value of None + # so that downstream minimisers halt. The results of `fun.hess` + # should not be used. + def hess(x, *args): + return None + + if bounds is None: + bounds = (-np.inf, np.inf) + + # ScalarFunction caches. Reuse of fun(x) during grad + # calculation reduces overall function evaluations. + sf = ScalarFunction(fun, x0, args, grad, hess, + finite_diff_rel_step, bounds, epsilon=epsilon) + + return sf + + +def _clip_x_for_func(func, bounds): + # ensures that x values sent to func are clipped to bounds + + # this is used as a mitigation for gh11403, slsqp/tnc sometimes + # suggest a move that is outside the limits by 1 or 2 ULP. This + # unclean fix makes sure x is strictly within bounds. + def eval(x): + x = _check_clip_x(x, bounds) + return func(x) + + return eval + + +def _check_clip_x(x, bounds): + if (x < bounds[0]).any() or (x > bounds[1]).any(): + warnings.warn("Values in x were outside bounds during a " + "minimize step, clipping to bounds", + RuntimeWarning, stacklevel=3) + x = np.clip(x, bounds[0], bounds[1]) + return x + + return x + + +def rosen(x): + """ + The Rosenbrock function. + + The function computed is:: + + sum(100.0*(x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0) + + Parameters + ---------- + x : array_like + 1-D array of points at which the Rosenbrock function is to be computed. + + Returns + ------- + f : float + The value of the Rosenbrock function. + + See Also + -------- + rosen_der, rosen_hess, rosen_hess_prod + + Examples + -------- + >>> import numpy as np + >>> from scipy.optimize import rosen + >>> X = 0.1 * np.arange(10) + >>> rosen(X) + 76.56 + + For higher-dimensional input ``rosen`` broadcasts. + In the following example, we use this to plot a 2D landscape. + Note that ``rosen_hess`` does not broadcast in this manner. + + >>> import matplotlib.pyplot as plt + >>> from mpl_toolkits.mplot3d import Axes3D + >>> x = np.linspace(-1, 1, 50) + >>> X, Y = np.meshgrid(x, x) + >>> ax = plt.subplot(111, projection='3d') + >>> ax.plot_surface(X, Y, rosen([X, Y])) + >>> plt.show() + """ + xp = array_namespace(x) + x = xp.asarray(x) + if xp.isdtype(x.dtype, 'integral'): + x = xp.astype(x, xp.asarray(1.).dtype) + r = xp.sum(100.0 * (x[1:] - x[:-1]**2.0)**2.0 + (1 - x[:-1])**2.0, + axis=0, dtype=x.dtype) + return r + + +def rosen_der(x): + """ + The derivative (i.e. gradient) of the Rosenbrock function. + + Parameters + ---------- + x : array_like + 1-D array of points at which the derivative is to be computed. + + Returns + ------- + rosen_der : (N,) ndarray + The gradient of the Rosenbrock function at `x`. + + See Also + -------- + rosen, rosen_hess, rosen_hess_prod + + Examples + -------- + >>> import numpy as np + >>> from scipy.optimize import rosen_der + >>> X = 0.1 * np.arange(9) + >>> rosen_der(X) + array([ -2. , 10.6, 15.6, 13.4, 6.4, -3. , -12.4, -19.4, 62. ]) + + """ + xp = array_namespace(x) + x = xp.asarray(x) + if xp.isdtype(x.dtype, 'integral'): + x = xp.astype(x, xp.asarray(1.).dtype) + xm = x[1:-1] + xm_m1 = x[:-2] + xm_p1 = x[2:] + der = xp.zeros_like(x) + der[1:-1] = (200 * (xm - xm_m1**2) - + 400 * (xm_p1 - xm**2) * xm - 2 * (1 - xm)) + der[0] = -400 * x[0] * (x[1] - x[0]**2) - 2 * (1 - x[0]) + der[-1] = 200 * (x[-1] - x[-2]**2) + return der + + +def rosen_hess(x): + """ + The Hessian matrix of the Rosenbrock function. + + Parameters + ---------- + x : array_like + 1-D array of points at which the Hessian matrix is to be computed. + + Returns + ------- + rosen_hess : ndarray + The Hessian matrix of the Rosenbrock function at `x`. + + See Also + -------- + rosen, rosen_der, rosen_hess_prod + + Examples + -------- + >>> import numpy as np + >>> from scipy.optimize import rosen_hess + >>> X = 0.1 * np.arange(4) + >>> rosen_hess(X) + array([[-38., 0., 0., 0.], + [ 0., 134., -40., 0.], + [ 0., -40., 130., -80.], + [ 0., 0., -80., 200.]]) + + """ + xp = array_namespace(x) + x = xpx.atleast_nd(x, ndim=1, xp=xp) + if xp.isdtype(x.dtype, 'integral'): + x = xp.astype(x, xp.asarray(1.).dtype) + H = (xpx.create_diagonal(-400 * x[:-1], offset=1, xp=xp) + - xpx.create_diagonal(400 * x[:-1], offset=-1, xp=xp)) + diagonal = xp.zeros(x.shape[0], dtype=x.dtype) + diagonal[0] = 1200 * x[0]**2 - 400 * x[1] + 2 + diagonal[-1] = 200 + diagonal[1:-1] = 202 + 1200 * x[1:-1]**2 - 400 * x[2:] + return H + xpx.create_diagonal(diagonal, xp=xp) + + +def rosen_hess_prod(x, p): + """ + Product of the Hessian matrix of the Rosenbrock function with a vector. + + Parameters + ---------- + x : array_like + 1-D array of points at which the Hessian matrix is to be computed. + p : array_like + 1-D array, the vector to be multiplied by the Hessian matrix. + + Returns + ------- + rosen_hess_prod : ndarray + The Hessian matrix of the Rosenbrock function at `x` multiplied + by the vector `p`. + + See Also + -------- + rosen, rosen_der, rosen_hess + + Examples + -------- + >>> import numpy as np + >>> from scipy.optimize import rosen_hess_prod + >>> X = 0.1 * np.arange(9) + >>> p = 0.5 * np.arange(9) + >>> rosen_hess_prod(X, p) + array([ -0., 27., -10., -95., -192., -265., -278., -195., -180.]) + + """ + xp = array_namespace(x, p) + x = xpx.atleast_nd(x, ndim=1, xp=xp) + if xp.isdtype(x.dtype, 'integral'): + x = xp.astype(x, xp.asarray(1.).dtype) + p = xp.asarray(p, dtype=x.dtype) + Hp = xp.zeros(x.shape[0], dtype=x.dtype) + Hp[0] = (1200 * x[0]**2 - 400 * x[1] + 2) * p[0] - 400 * x[0] * p[1] + Hp[1:-1] = (-400 * x[:-2] * p[:-2] + + (202 + 1200 * x[1:-1]**2 - 400 * x[2:]) * p[1:-1] - + 400 * x[1:-1] * p[2:]) + Hp[-1] = -400 * x[-2] * p[-2] + 200*p[-1] + return Hp + + +def _wrap_scalar_function(function, args): + # wraps a minimizer function to count number of evaluations + # and to easily provide an args kwd. + ncalls = [0] + if function is None: + return ncalls, None + + def function_wrapper(x, *wrapper_args): + ncalls[0] += 1 + # A copy of x is sent to the user function (gh13740) + fx = function(np.copy(x), *(wrapper_args + args)) + # Ideally, we'd like to a have a true scalar returned from f(x). For + # backwards-compatibility, also allow np.array([1.3]), np.array([[1.3]]) etc. + if not np.isscalar(fx): + try: + fx = np.asarray(fx).item() + except (TypeError, ValueError) as e: + raise ValueError("The user-provided objective function " + "must return a scalar value.") from e + return fx + + return ncalls, function_wrapper + + +class _MaxFuncCallError(RuntimeError): + pass + + +def _wrap_scalar_function_maxfun_validation(function, args, maxfun): + # wraps a minimizer function to count number of evaluations + # and to easily provide an args kwd. + ncalls = [0] + if function is None: + return ncalls, None + + def function_wrapper(x, *wrapper_args): + if ncalls[0] >= maxfun: + raise _MaxFuncCallError("Too many function calls") + ncalls[0] += 1 + # A copy of x is sent to the user function (gh13740) + fx = function(np.copy(x), *(wrapper_args + args)) + # Ideally, we'd like to a have a true scalar returned from f(x). For + # backwards-compatibility, also allow np.array([1.3]), + # np.array([[1.3]]) etc. + if not np.isscalar(fx): + try: + fx = np.asarray(fx).item() + except (TypeError, ValueError) as e: + raise ValueError("The user-provided objective function " + "must return a scalar value.") from e + return fx + + return ncalls, function_wrapper + + +def fmin(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, maxfun=None, + full_output=0, disp=1, retall=0, callback=None, initial_simplex=None): + """ + Minimize a function using the downhill simplex algorithm. + + This algorithm only uses function values, not derivatives or second + derivatives. + + Parameters + ---------- + func : callable func(x,*args) + The objective function to be minimized. + x0 : ndarray + Initial guess. + args : tuple, optional + Extra arguments passed to func, i.e., ``f(x,*args)``. + xtol : float, optional + Absolute error in xopt between iterations that is acceptable for + convergence. + ftol : number, optional + Absolute error in func(xopt) between iterations that is acceptable for + convergence. + maxiter : int, optional + Maximum number of iterations to perform. + maxfun : number, optional + Maximum number of function evaluations to make. + full_output : bool, optional + Set to True if fopt and warnflag outputs are desired. + disp : bool, optional + Set to True to print convergence messages. + retall : bool, optional + Set to True to return list of solutions at each iteration. + callback : callable, optional + Called after each iteration, as callback(xk), where xk is the + current parameter vector. + initial_simplex : array_like of shape (N + 1, N), optional + Initial simplex. If given, overrides `x0`. + ``initial_simplex[j,:]`` should contain the coordinates of + the jth vertex of the ``N+1`` vertices in the simplex, where + ``N`` is the dimension. + + Returns + ------- + xopt : ndarray + Parameter that minimizes function. + fopt : float + Value of function at minimum: ``fopt = func(xopt)``. + iter : int + Number of iterations performed. + funcalls : int + Number of function calls made. + warnflag : int + 1 : Maximum number of function evaluations made. + 2 : Maximum number of iterations reached. + allvecs : list + Solution at each iteration. + + See also + -------- + minimize: Interface to minimization algorithms for multivariate + functions. See the 'Nelder-Mead' `method` in particular. + + Notes + ----- + Uses a Nelder-Mead simplex algorithm to find the minimum of function of + one or more variables. + + This algorithm has a long history of successful use in applications. + But it will usually be slower than an algorithm that uses first or + second derivative information. In practice, it can have poor + performance in high-dimensional problems and is not robust to + minimizing complicated functions. Additionally, there currently is no + complete theory describing when the algorithm will successfully + converge to the minimum, or how fast it will if it does. Both the ftol and + xtol criteria must be met for convergence. + + Examples + -------- + >>> def f(x): + ... return x**2 + + >>> from scipy import optimize + + >>> minimum = optimize.fmin(f, 1) + Optimization terminated successfully. + Current function value: 0.000000 + Iterations: 17 + Function evaluations: 34 + >>> minimum[0] + -8.8817841970012523e-16 + + References + ---------- + .. [1] Nelder, J.A. and Mead, R. (1965), "A simplex method for function + minimization", The Computer Journal, 7, pp. 308-313 + + .. [2] Wright, M.H. (1996), "Direct Search Methods: Once Scorned, Now + Respectable", in Numerical Analysis 1995, Proceedings of the + 1995 Dundee Biennial Conference in Numerical Analysis, D.F. + Griffiths and G.A. Watson (Eds.), Addison Wesley Longman, + Harlow, UK, pp. 191-208. + + """ + opts = {'xatol': xtol, + 'fatol': ftol, + 'maxiter': maxiter, + 'maxfev': maxfun, + 'disp': disp, + 'return_all': retall, + 'initial_simplex': initial_simplex} + + callback = _wrap_callback(callback) + res = _minimize_neldermead(func, x0, args, callback=callback, **opts) + if full_output: + retlist = res['x'], res['fun'], res['nit'], res['nfev'], res['status'] + if retall: + retlist += (res['allvecs'], ) + return retlist + else: + if retall: + return res['x'], res['allvecs'] + else: + return res['x'] + + +def _minimize_neldermead(func, x0, args=(), callback=None, + maxiter=None, maxfev=None, disp=False, + return_all=False, initial_simplex=None, + xatol=1e-4, fatol=1e-4, adaptive=False, bounds=None, + **unknown_options): + """ + Minimization of scalar function of one or more variables using the + Nelder-Mead algorithm. + + Options + ------- + disp : bool + Set to True to print convergence messages. + maxiter, maxfev : int + Maximum allowed number of iterations and function evaluations. + Will default to ``N*200``, where ``N`` is the number of + variables, if neither `maxiter` or `maxfev` is set. If both + `maxiter` and `maxfev` are set, minimization will stop at the + first reached. + return_all : bool, optional + Set to True to return a list of the best solution at each of the + iterations. + initial_simplex : array_like of shape (N + 1, N) + Initial simplex. If given, overrides `x0`. + ``initial_simplex[j,:]`` should contain the coordinates of + the jth vertex of the ``N+1`` vertices in the simplex, where + ``N`` is the dimension. + xatol : float, optional + Absolute error in xopt between iterations that is acceptable for + convergence. + fatol : number, optional + Absolute error in func(xopt) between iterations that is acceptable for + convergence. + adaptive : bool, optional + Adapt algorithm parameters to dimensionality of problem. Useful for + high-dimensional minimization [1]_. + bounds : sequence or `Bounds`, optional + Bounds on variables. There are two ways to specify the bounds: + + 1. Instance of `Bounds` class. + 2. Sequence of ``(min, max)`` pairs for each element in `x`. None + is used to specify no bound. + + Note that this just clips all vertices in simplex based on + the bounds. + + References + ---------- + .. [1] Gao, F. and Han, L. + Implementing the Nelder-Mead simplex algorithm with adaptive + parameters. 2012. Computational Optimization and Applications. + 51:1, pp. 259-277 + + """ + _check_unknown_options(unknown_options) + maxfun = maxfev + retall = return_all + + x0 = np.atleast_1d(x0).flatten() + dtype = x0.dtype if np.issubdtype(x0.dtype, np.inexact) else np.float64 + x0 = np.asarray(x0, dtype=dtype) + + if adaptive: + dim = float(len(x0)) + rho = 1 + chi = 1 + 2/dim + psi = 0.75 - 1/(2*dim) + sigma = 1 - 1/dim + else: + rho = 1 + chi = 2 + psi = 0.5 + sigma = 0.5 + + nonzdelt = 0.05 + zdelt = 0.00025 + + if bounds is not None: + lower_bound, upper_bound = bounds.lb, bounds.ub + # check bounds + if (lower_bound > upper_bound).any(): + raise ValueError("Nelder Mead - one of the lower bounds " + "is greater than an upper bound.") + if np.any(lower_bound > x0) or np.any(x0 > upper_bound): + warnings.warn("Initial guess is not within the specified bounds", + OptimizeWarning, stacklevel=3) + + if bounds is not None: + x0 = np.clip(x0, lower_bound, upper_bound) + + if initial_simplex is None: + N = len(x0) + + sim = np.empty((N + 1, N), dtype=x0.dtype) + sim[0] = x0 + for k in range(N): + y = np.array(x0, copy=True) + if y[k] != 0: + y[k] = (1 + nonzdelt)*y[k] + else: + y[k] = zdelt + sim[k + 1] = y + else: + sim = np.atleast_2d(initial_simplex).copy() + dtype = sim.dtype if np.issubdtype(sim.dtype, np.inexact) else np.float64 + sim = np.asarray(sim, dtype=dtype) + if sim.ndim != 2 or sim.shape[0] != sim.shape[1] + 1: + raise ValueError("`initial_simplex` should be an array of shape (N+1,N)") + if len(x0) != sim.shape[1]: + raise ValueError("Size of `initial_simplex` is not consistent with `x0`") + N = sim.shape[1] + + if retall: + allvecs = [sim[0]] + + # If neither are set, then set both to default + if maxiter is None and maxfun is None: + maxiter = N * 200 + maxfun = N * 200 + elif maxiter is None: + # Convert remaining Nones, to np.inf, unless the other is np.inf, in + # which case use the default to avoid unbounded iteration + if maxfun == np.inf: + maxiter = N * 200 + else: + maxiter = np.inf + elif maxfun is None: + if maxiter == np.inf: + maxfun = N * 200 + else: + maxfun = np.inf + + if bounds is not None: + # The default simplex construction may make all entries (for a given + # parameter) greater than an upper bound if x0 is very close to the + # upper bound. If one simply clips the simplex to the bounds this could + # make the simplex entries degenerate. If that occurs reflect into the + # interior. + msk = sim > upper_bound + # reflect into the interior + sim = np.where(msk, 2*upper_bound - sim, sim) + # but make sure the reflection is no less than the lower_bound + sim = np.clip(sim, lower_bound, upper_bound) + + one2np1 = list(range(1, N + 1)) + fsim = np.full((N + 1,), np.inf, dtype=float) + + fcalls, func = _wrap_scalar_function_maxfun_validation(func, args, maxfun) + + try: + for k in range(N + 1): + fsim[k] = func(sim[k]) + except _MaxFuncCallError: + pass + finally: + ind = np.argsort(fsim) + sim = np.take(sim, ind, 0) + fsim = np.take(fsim, ind, 0) + + ind = np.argsort(fsim) + fsim = np.take(fsim, ind, 0) + # sort so sim[0,:] has the lowest function value + sim = np.take(sim, ind, 0) + + iterations = 1 + + while (fcalls[0] < maxfun and iterations < maxiter): + try: + if (np.max(np.ravel(np.abs(sim[1:] - sim[0]))) <= xatol and + np.max(np.abs(fsim[0] - fsim[1:])) <= fatol): + break + + xbar = np.add.reduce(sim[:-1], 0) / N + xr = (1 + rho) * xbar - rho * sim[-1] + if bounds is not None: + xr = np.clip(xr, lower_bound, upper_bound) + fxr = func(xr) + doshrink = 0 + + if fxr < fsim[0]: + xe = (1 + rho * chi) * xbar - rho * chi * sim[-1] + if bounds is not None: + xe = np.clip(xe, lower_bound, upper_bound) + fxe = func(xe) + + if fxe < fxr: + sim[-1] = xe + fsim[-1] = fxe + else: + sim[-1] = xr + fsim[-1] = fxr + else: # fsim[0] <= fxr + if fxr < fsim[-2]: + sim[-1] = xr + fsim[-1] = fxr + else: # fxr >= fsim[-2] + # Perform contraction + if fxr < fsim[-1]: + xc = (1 + psi * rho) * xbar - psi * rho * sim[-1] + if bounds is not None: + xc = np.clip(xc, lower_bound, upper_bound) + fxc = func(xc) + + if fxc <= fxr: + sim[-1] = xc + fsim[-1] = fxc + else: + doshrink = 1 + else: + # Perform an inside contraction + xcc = (1 - psi) * xbar + psi * sim[-1] + if bounds is not None: + xcc = np.clip(xcc, lower_bound, upper_bound) + fxcc = func(xcc) + + if fxcc < fsim[-1]: + sim[-1] = xcc + fsim[-1] = fxcc + else: + doshrink = 1 + + if doshrink: + for j in one2np1: + sim[j] = sim[0] + sigma * (sim[j] - sim[0]) + if bounds is not None: + sim[j] = np.clip( + sim[j], lower_bound, upper_bound) + fsim[j] = func(sim[j]) + iterations += 1 + except _MaxFuncCallError: + pass + finally: + ind = np.argsort(fsim) + sim = np.take(sim, ind, 0) + fsim = np.take(fsim, ind, 0) + if retall: + allvecs.append(sim[0]) + intermediate_result = OptimizeResult(x=sim[0], fun=fsim[0]) + if _call_callback_maybe_halt(callback, intermediate_result): + break + + x = sim[0] + fval = np.min(fsim) + warnflag = 0 + + if fcalls[0] >= maxfun: + warnflag = 1 + msg = _status_message['maxfev'] + if disp: + warnings.warn(msg, RuntimeWarning, stacklevel=3) + elif iterations >= maxiter: + warnflag = 2 + msg = _status_message['maxiter'] + if disp: + warnings.warn(msg, RuntimeWarning, stacklevel=3) + else: + msg = _status_message['success'] + if disp: + print(msg) + print(f" Current function value: {fval:f}") + print(" Iterations: %d" % iterations) + print(" Function evaluations: %d" % fcalls[0]) + + result = OptimizeResult(fun=fval, nit=iterations, nfev=fcalls[0], + status=warnflag, success=(warnflag == 0), + message=msg, x=x, final_simplex=(sim, fsim)) + if retall: + result['allvecs'] = allvecs + return result + + +def approx_fprime(xk, f, epsilon=_epsilon, *args): + """Finite difference approximation of the derivatives of a + scalar or vector-valued function. + + If a function maps from :math:`R^n` to :math:`R^m`, its derivatives form + an m-by-n matrix + called the Jacobian, where an element :math:`(i, j)` is a partial + derivative of f[i] with respect to ``xk[j]``. + + Parameters + ---------- + xk : array_like + The coordinate vector at which to determine the gradient of `f`. + f : callable + Function of which to estimate the derivatives of. Has the signature + ``f(xk, *args)`` where `xk` is the argument in the form of a 1-D array + and `args` is a tuple of any additional fixed parameters needed to + completely specify the function. The argument `xk` passed to this + function is an ndarray of shape (n,) (never a scalar even if n=1). + It must return a 1-D array_like of shape (m,) or a scalar. + + Suppose the callable has signature ``f0(x, *my_args, **my_kwargs)``, where + ``my_args`` and ``my_kwargs`` are required positional and keyword arguments. + Rather than passing ``f0`` as the callable, wrap it to accept + only ``x``; e.g., pass ``fun=lambda x: f0(x, *my_args, **my_kwargs)`` as the + callable, where ``my_args`` (tuple) and ``my_kwargs`` (dict) have been + gathered before invoking this function. + + .. versionchanged:: 1.9.0 + `f` is now able to return a 1-D array-like, with the :math:`(m, n)` + Jacobian being estimated. + + epsilon : {float, array_like}, optional + Increment to `xk` to use for determining the function gradient. + If a scalar, uses the same finite difference delta for all partial + derivatives. If an array, should contain one value per element of + `xk`. Defaults to ``sqrt(np.finfo(float).eps)``, which is approximately + 1.49e-08. + \\*args : args, optional + Any other arguments that are to be passed to `f`. + + Returns + ------- + jac : ndarray + The partial derivatives of `f` to `xk`. + + See Also + -------- + check_grad : Check correctness of gradient function against approx_fprime. + + Notes + ----- + The function gradient is determined by the forward finite difference + formula:: + + f(xk[i] + epsilon[i]) - f(xk[i]) + f'[i] = --------------------------------- + epsilon[i] + + Examples + -------- + >>> import numpy as np + >>> from scipy import optimize + >>> def func(x, c0, c1): + ... "Coordinate vector `x` should be an array of size two." + ... return c0 * x[0]**2 + c1*x[1]**2 + + >>> x = np.ones(2) + >>> c0, c1 = (1, 200) + >>> eps = np.sqrt(np.finfo(float).eps) + >>> optimize.approx_fprime(x, func, [eps, np.sqrt(200) * eps], c0, c1) + array([ 2. , 400.00004208]) + + """ + xk = np.asarray(xk, float) + f0 = f(xk, *args) + + return approx_derivative(f, xk, method='2-point', abs_step=epsilon, + args=args, f0=f0) + + +@_transition_to_rng("seed", position_num=6) +def check_grad(func, grad, x0, *args, epsilon=_epsilon, + direction='all', rng=None): + r"""Check the correctness of a gradient function by comparing it against a + (forward) finite-difference approximation of the gradient. + + Parameters + ---------- + func : callable ``func(x0, *args)`` + Function whose derivative is to be checked. + grad : callable ``grad(x0, *args)`` + Jacobian of `func`. + x0 : ndarray + Points to check `grad` against forward difference approximation of grad + using `func`. + args : \\*args, optional + Extra arguments passed to `func` and `grad`. + epsilon : float, optional + Step size used for the finite difference approximation. It defaults to + ``sqrt(np.finfo(float).eps)``, which is approximately 1.49e-08. + direction : str, optional + If set to ``'random'``, then gradients along a random vector + are used to check `grad` against forward difference approximation + using `func`. By default it is ``'all'``, in which case, all + the one hot direction vectors are considered to check `grad`. + If `func` is a vector valued function then only ``'all'`` can be used. + rng : `numpy.random.Generator`, optional + Pseudorandom number generator state. When `rng` is None, a new + `numpy.random.Generator` is created using entropy from the + operating system. Types other than `numpy.random.Generator` are + passed to `numpy.random.default_rng` to instantiate a ``Generator``. + + The random numbers generated affect the random vector along which gradients + are computed to check ``grad``. Note that `rng` is only used when `direction` + argument is set to `'random'`. + + Returns + ------- + err : float + The square root of the sum of squares (i.e., the 2-norm) of the + difference between ``grad(x0, *args)`` and the finite difference + approximation of `grad` using func at the points `x0`. + + See Also + -------- + approx_fprime + + Examples + -------- + >>> import numpy as np + >>> def func(x): + ... return x[0]**2 - 0.5 * x[1]**3 + >>> def grad(x): + ... return [2 * x[0], -1.5 * x[1]**2] + >>> from scipy.optimize import check_grad + >>> check_grad(func, grad, [1.5, -1.5]) + 2.9802322387695312e-08 # may vary + >>> rng = np.random.default_rng() + >>> check_grad(func, grad, [1.5, -1.5], + ... direction='random', seed=rng) + 2.9802322387695312e-08 + + """ + step = epsilon + x0 = np.asarray(x0) + + def g(w, func, x0, v, *args): + return func(x0 + w*v, *args) + + if direction == 'random': + _grad = np.asanyarray(grad(x0, *args)) + if _grad.ndim > 1: + raise ValueError("'random' can only be used with scalar valued" + " func") + rng_gen = check_random_state(rng) + v = rng_gen.standard_normal(size=(x0.shape)) + _args = (func, x0, v) + args + _func = g + vars = np.zeros((1,)) + analytical_grad = np.dot(_grad, v) + elif direction == 'all': + _args = args + _func = func + vars = x0 + analytical_grad = grad(x0, *args) + else: + raise ValueError(f"{direction} is not a valid string for " + "``direction`` argument") + + return np.sqrt(np.sum(np.abs( + (analytical_grad - approx_fprime(vars, _func, step, *_args))**2 + ))) + + +def approx_fhess_p(x0, p, fprime, epsilon, *args): + # calculate fprime(x0) first, as this may be cached by ScalarFunction + f1 = fprime(*((x0,) + args)) + f2 = fprime(*((x0 + epsilon*p,) + args)) + return (f2 - f1) / epsilon + + +class _LineSearchError(RuntimeError): + pass + + +def _line_search_wolfe12(f, fprime, xk, pk, gfk, old_fval, old_old_fval, + **kwargs): + """ + Same as line_search_wolfe1, but fall back to line_search_wolfe2 if + suitable step length is not found, and raise an exception if a + suitable step length is not found. + + Raises + ------ + _LineSearchError + If no suitable step size is found + + """ + + extra_condition = kwargs.pop('extra_condition', None) + + ret = line_search_wolfe1(f, fprime, xk, pk, gfk, + old_fval, old_old_fval, + **kwargs) + + if ret[0] is not None and extra_condition is not None: + xp1 = xk + ret[0] * pk + if not extra_condition(ret[0], xp1, ret[3], ret[5]): + # Reject step if extra_condition fails + ret = (None,) + + if ret[0] is None: + # line search failed: try different one. + with warnings.catch_warnings(): + warnings.simplefilter('ignore', LineSearchWarning) + kwargs2 = {} + for key in ('c1', 'c2', 'amax'): + if key in kwargs: + kwargs2[key] = kwargs[key] + ret = line_search_wolfe2(f, fprime, xk, pk, gfk, + old_fval, old_old_fval, + extra_condition=extra_condition, + **kwargs2) + + if ret[0] is None: + raise _LineSearchError() + + return ret + + +def fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-5, norm=np.inf, + epsilon=_epsilon, maxiter=None, full_output=0, disp=1, + retall=0, callback=None, xrtol=0, c1=1e-4, c2=0.9, + hess_inv0=None): + """ + Minimize a function using the BFGS algorithm. + + Parameters + ---------- + f : callable ``f(x,*args)`` + Objective function to be minimized. + x0 : ndarray + Initial guess, shape (n,) + fprime : callable ``f'(x,*args)``, optional + Gradient of f. + args : tuple, optional + Extra arguments passed to f and fprime. + gtol : float, optional + Terminate successfully if gradient norm is less than `gtol` + norm : float, optional + Order of norm (Inf is max, -Inf is min) + epsilon : int or ndarray, optional + If `fprime` is approximated, use this value for the step size. + callback : callable, optional + An optional user-supplied function to call after each + iteration. Called as ``callback(xk)``, where ``xk`` is the + current parameter vector. + maxiter : int, optional + Maximum number of iterations to perform. + full_output : bool, optional + If True, return ``fopt``, ``func_calls``, ``grad_calls``, and + ``warnflag`` in addition to ``xopt``. + disp : bool, optional + Print convergence message if True. + retall : bool, optional + Return a list of results at each iteration if True. + xrtol : float, default: 0 + Relative tolerance for `x`. Terminate successfully if step + size is less than ``xk * xrtol`` where ``xk`` is the current + parameter vector. + c1 : float, default: 1e-4 + Parameter for Armijo condition rule. + c2 : float, default: 0.9 + Parameter for curvature condition rule. + hess_inv0 : None or ndarray, optional`` + Initial inverse hessian estimate, shape (n, n). If None (default) then + the identity matrix is used. + + Returns + ------- + xopt : ndarray + Parameters which minimize f, i.e., ``f(xopt) == fopt``. + fopt : float + Minimum value. + gopt : ndarray + Value of gradient at minimum, f'(xopt), which should be near 0. + Bopt : ndarray + Value of 1/f''(xopt), i.e., the inverse Hessian matrix. + func_calls : int + Number of function_calls made. + grad_calls : int + Number of gradient calls made. + warnflag : integer + 1 : Maximum number of iterations exceeded. + 2 : Gradient and/or function calls not changing. + 3 : NaN result encountered. + allvecs : list + The value of `xopt` at each iteration. Only returned if `retall` is + True. + + Notes + ----- + Optimize the function, `f`, whose gradient is given by `fprime` + using the quasi-Newton method of Broyden, Fletcher, Goldfarb, + and Shanno (BFGS). + + Parameters `c1` and `c2` must satisfy ``0 < c1 < c2 < 1``. + + See Also + -------- + minimize: Interface to minimization algorithms for multivariate + functions. See ``method='BFGS'`` in particular. + + References + ---------- + Wright, and Nocedal 'Numerical Optimization', 1999, p. 198. + + Examples + -------- + >>> import numpy as np + >>> from scipy.optimize import fmin_bfgs + >>> def quadratic_cost(x, Q): + ... return x @ Q @ x + ... + >>> x0 = np.array([-3, -4]) + >>> cost_weight = np.diag([1., 10.]) + >>> # Note that a trailing comma is necessary for a tuple with single element + >>> fmin_bfgs(quadratic_cost, x0, args=(cost_weight,)) + Optimization terminated successfully. + Current function value: 0.000000 + Iterations: 7 # may vary + Function evaluations: 24 # may vary + Gradient evaluations: 8 # may vary + array([ 2.85169950e-06, -4.61820139e-07]) + + >>> def quadratic_cost_grad(x, Q): + ... return 2 * Q @ x + ... + >>> fmin_bfgs(quadratic_cost, x0, quadratic_cost_grad, args=(cost_weight,)) + Optimization terminated successfully. + Current function value: 0.000000 + Iterations: 7 + Function evaluations: 8 + Gradient evaluations: 8 + array([ 2.85916637e-06, -4.54371951e-07]) + + """ + opts = {'gtol': gtol, + 'norm': norm, + 'eps': epsilon, + 'disp': disp, + 'maxiter': maxiter, + 'return_all': retall, + 'xrtol': xrtol, + 'c1': c1, + 'c2': c2, + 'hess_inv0': hess_inv0} + + callback = _wrap_callback(callback) + res = _minimize_bfgs(f, x0, args, fprime, callback=callback, **opts) + + if full_output: + retlist = (res['x'], res['fun'], res['jac'], res['hess_inv'], + res['nfev'], res['njev'], res['status']) + if retall: + retlist += (res['allvecs'], ) + return retlist + else: + if retall: + return res['x'], res['allvecs'] + else: + return res['x'] + + +def _minimize_bfgs(fun, x0, args=(), jac=None, callback=None, + gtol=1e-5, norm=np.inf, eps=_epsilon, maxiter=None, + disp=False, return_all=False, finite_diff_rel_step=None, + xrtol=0, c1=1e-4, c2=0.9, + hess_inv0=None, **unknown_options): + """ + Minimization of scalar function of one or more variables using the + BFGS algorithm. + + Options + ------- + disp : bool + Set to True to print convergence messages. + maxiter : int + Maximum number of iterations to perform. + gtol : float + Terminate successfully if gradient norm is less than `gtol`. + norm : float + Order of norm (Inf is max, -Inf is min). + eps : float or ndarray + If `jac is None` the absolute step size used for numerical + approximation of the jacobian via forward differences. + return_all : bool, optional + Set to True to return a list of the best solution at each of the + iterations. + finite_diff_rel_step : None or array_like, optional + If ``jac in ['2-point', '3-point', 'cs']`` the relative step size to + use for numerical approximation of the jacobian. The absolute step + size is computed as ``h = rel_step * sign(x) * max(1, abs(x))``, + possibly adjusted to fit into the bounds. For ``jac='3-point'`` + the sign of `h` is ignored. If None (default) then step is selected + automatically. + xrtol : float, default: 0 + Relative tolerance for `x`. Terminate successfully if step size is + less than ``xk * xrtol`` where ``xk`` is the current parameter vector. + c1 : float, default: 1e-4 + Parameter for Armijo condition rule. + c2 : float, default: 0.9 + Parameter for curvature condition rule. + hess_inv0 : None or ndarray, optional + Initial inverse hessian estimate, shape (n, n). If None (default) then + the identity matrix is used. + + Notes + ----- + Parameters `c1` and `c2` must satisfy ``0 < c1 < c2 < 1``. + + If minimization doesn't complete successfully, with an error message of + ``Desired error not necessarily achieved due to precision loss``, then + consider setting `gtol` to a higher value. This precision loss typically + occurs when the (finite difference) numerical differentiation cannot provide + sufficient precision to satisfy the `gtol` termination criterion. + This can happen when working in single precision and a callable jac is not + provided. For single precision problems a `gtol` of 1e-3 seems to work. + """ + _check_unknown_options(unknown_options) + _check_positive_definite(hess_inv0) + retall = return_all + + x0 = asarray(x0).flatten() + if x0.ndim == 0: + x0.shape = (1,) + if maxiter is None: + maxiter = len(x0) * 200 + + sf = _prepare_scalar_function(fun, x0, jac, args=args, epsilon=eps, + finite_diff_rel_step=finite_diff_rel_step) + + f = sf.fun + myfprime = sf.grad + + old_fval = f(x0) + gfk = myfprime(x0) + + k = 0 + N = len(x0) + I = np.eye(N, dtype=int) + Hk = I if hess_inv0 is None else hess_inv0 + + # Sets the initial step guess to dx ~ 1 + old_old_fval = old_fval + np.linalg.norm(gfk) / 2 + + xk = x0 + if retall: + allvecs = [x0] + warnflag = 0 + gnorm = vecnorm(gfk, ord=norm) + while (gnorm > gtol) and (k < maxiter): + pk = -np.dot(Hk, gfk) + try: + alpha_k, fc, gc, old_fval, old_old_fval, gfkp1 = \ + _line_search_wolfe12(f, myfprime, xk, pk, gfk, + old_fval, old_old_fval, amin=1e-100, + amax=1e100, c1=c1, c2=c2) + except _LineSearchError: + # Line search failed to find a better solution. + warnflag = 2 + break + + sk = alpha_k * pk + xkp1 = xk + sk + + if retall: + allvecs.append(xkp1) + xk = xkp1 + if gfkp1 is None: + gfkp1 = myfprime(xkp1) + + yk = gfkp1 - gfk + gfk = gfkp1 + k += 1 + intermediate_result = OptimizeResult(x=xk, fun=old_fval) + if _call_callback_maybe_halt(callback, intermediate_result): + break + gnorm = vecnorm(gfk, ord=norm) + if (gnorm <= gtol): + break + + # See Chapter 5 in P.E. Frandsen, K. Jonasson, H.B. Nielsen, + # O. Tingleff: "Unconstrained Optimization", IMM, DTU. 1999. + # These notes are available here: + # http://www2.imm.dtu.dk/documents/ftp/publlec.html + if (alpha_k*vecnorm(pk) <= xrtol*(xrtol + vecnorm(xk))): + break + + if not np.isfinite(old_fval): + # We correctly found +-Inf as optimal value, or something went + # wrong. + warnflag = 2 + break + + rhok_inv = np.dot(yk, sk) + # this was handled in numeric, let it remains for more safety + # Cryptic comment above is preserved for posterity. Future reader: + # consider change to condition below proposed in gh-1261/gh-17345. + if rhok_inv == 0.: + rhok = 1000.0 + if disp: + msg = "Divide-by-zero encountered: rhok assumed large" + _print_success_message_or_warn(True, msg) + else: + rhok = 1. / rhok_inv + + A1 = I - sk[:, np.newaxis] * yk[np.newaxis, :] * rhok + A2 = I - yk[:, np.newaxis] * sk[np.newaxis, :] * rhok + Hk = np.dot(A1, np.dot(Hk, A2)) + (rhok * sk[:, np.newaxis] * + sk[np.newaxis, :]) + + fval = old_fval + + if warnflag == 2: + msg = _status_message['pr_loss'] + elif k >= maxiter: + warnflag = 1 + msg = _status_message['maxiter'] + elif np.isnan(gnorm) or np.isnan(fval) or np.isnan(xk).any(): + warnflag = 3 + msg = _status_message['nan'] + else: + msg = _status_message['success'] + + if disp: + _print_success_message_or_warn(warnflag, msg) + print(f" Current function value: {fval:f}") + print(" Iterations: %d" % k) + print(" Function evaluations: %d" % sf.nfev) + print(" Gradient evaluations: %d" % sf.ngev) + + result = OptimizeResult(fun=fval, jac=gfk, hess_inv=Hk, nfev=sf.nfev, + njev=sf.ngev, status=warnflag, + success=(warnflag == 0), message=msg, x=xk, + nit=k) + if retall: + result['allvecs'] = allvecs + return result + + +def _print_success_message_or_warn(warnflag, message, warntype=None): + if not warnflag: + print(message) + else: + warnings.warn(message, warntype or OptimizeWarning, stacklevel=3) + + +def fmin_cg(f, x0, fprime=None, args=(), gtol=1e-5, norm=np.inf, + epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0, + callback=None, c1=1e-4, c2=0.4): + """ + Minimize a function using a nonlinear conjugate gradient algorithm. + + Parameters + ---------- + f : callable, ``f(x, *args)`` + Objective function to be minimized. Here `x` must be a 1-D array of + the variables that are to be changed in the search for a minimum, and + `args` are the other (fixed) parameters of `f`. + x0 : ndarray + A user-supplied initial estimate of `xopt`, the optimal value of `x`. + It must be a 1-D array of values. + fprime : callable, ``fprime(x, *args)``, optional + A function that returns the gradient of `f` at `x`. Here `x` and `args` + are as described above for `f`. The returned value must be a 1-D array. + Defaults to None, in which case the gradient is approximated + numerically (see `epsilon`, below). + args : tuple, optional + Parameter values passed to `f` and `fprime`. Must be supplied whenever + additional fixed parameters are needed to completely specify the + functions `f` and `fprime`. + gtol : float, optional + Stop when the norm of the gradient is less than `gtol`. + norm : float, optional + Order to use for the norm of the gradient + (``-np.inf`` is min, ``np.inf`` is max). + epsilon : float or ndarray, optional + Step size(s) to use when `fprime` is approximated numerically. Can be a + scalar or a 1-D array. Defaults to ``sqrt(eps)``, with eps the + floating point machine precision. Usually ``sqrt(eps)`` is about + 1.5e-8. + maxiter : int, optional + Maximum number of iterations to perform. Default is ``200 * len(x0)``. + full_output : bool, optional + If True, return `fopt`, `func_calls`, `grad_calls`, and `warnflag` in + addition to `xopt`. See the Returns section below for additional + information on optional return values. + disp : bool, optional + If True, return a convergence message, followed by `xopt`. + retall : bool, optional + If True, add to the returned values the results of each iteration. + callback : callable, optional + An optional user-supplied function, called after each iteration. + Called as ``callback(xk)``, where ``xk`` is the current value of `x0`. + c1 : float, default: 1e-4 + Parameter for Armijo condition rule. + c2 : float, default: 0.4 + Parameter for curvature condition rule. + + Returns + ------- + xopt : ndarray + Parameters which minimize f, i.e., ``f(xopt) == fopt``. + fopt : float, optional + Minimum value found, f(xopt). Only returned if `full_output` is True. + func_calls : int, optional + The number of function_calls made. Only returned if `full_output` + is True. + grad_calls : int, optional + The number of gradient calls made. Only returned if `full_output` is + True. + warnflag : int, optional + Integer value with warning status, only returned if `full_output` is + True. + + 0 : Success. + + 1 : The maximum number of iterations was exceeded. + + 2 : Gradient and/or function calls were not changing. May indicate + that precision was lost, i.e., the routine did not converge. + + 3 : NaN result encountered. + + allvecs : list of ndarray, optional + List of arrays, containing the results at each iteration. + Only returned if `retall` is True. + + See Also + -------- + minimize : common interface to all `scipy.optimize` algorithms for + unconstrained and constrained minimization of multivariate + functions. It provides an alternative way to call + ``fmin_cg``, by specifying ``method='CG'``. + + Notes + ----- + This conjugate gradient algorithm is based on that of Polak and Ribiere + [1]_. + + Conjugate gradient methods tend to work better when: + + 1. `f` has a unique global minimizing point, and no local minima or + other stationary points, + 2. `f` is, at least locally, reasonably well approximated by a + quadratic function of the variables, + 3. `f` is continuous and has a continuous gradient, + 4. `fprime` is not too large, e.g., has a norm less than 1000, + 5. The initial guess, `x0`, is reasonably close to `f` 's global + minimizing point, `xopt`. + + Parameters `c1` and `c2` must satisfy ``0 < c1 < c2 < 1``. + + References + ---------- + .. [1] Wright & Nocedal, "Numerical Optimization", 1999, pp. 120-122. + + Examples + -------- + Example 1: seek the minimum value of the expression + ``a*u**2 + b*u*v + c*v**2 + d*u + e*v + f`` for given values + of the parameters and an initial guess ``(u, v) = (0, 0)``. + + >>> import numpy as np + >>> args = (2, 3, 7, 8, 9, 10) # parameter values + >>> def f(x, *args): + ... u, v = x + ... a, b, c, d, e, f = args + ... return a*u**2 + b*u*v + c*v**2 + d*u + e*v + f + >>> def gradf(x, *args): + ... u, v = x + ... a, b, c, d, e, f = args + ... gu = 2*a*u + b*v + d # u-component of the gradient + ... gv = b*u + 2*c*v + e # v-component of the gradient + ... return np.asarray((gu, gv)) + >>> x0 = np.asarray((0, 0)) # Initial guess. + >>> from scipy import optimize + >>> res1 = optimize.fmin_cg(f, x0, fprime=gradf, args=args) + Optimization terminated successfully. + Current function value: 1.617021 + Iterations: 4 + Function evaluations: 8 + Gradient evaluations: 8 + >>> res1 + array([-1.80851064, -0.25531915]) + + Example 2: solve the same problem using the `minimize` function. + (This `myopts` dictionary shows all of the available options, + although in practice only non-default values would be needed. + The returned value will be a dictionary.) + + >>> opts = {'maxiter' : None, # default value. + ... 'disp' : True, # non-default value. + ... 'gtol' : 1e-5, # default value. + ... 'norm' : np.inf, # default value. + ... 'eps' : 1.4901161193847656e-08} # default value. + >>> res2 = optimize.minimize(f, x0, jac=gradf, args=args, + ... method='CG', options=opts) + Optimization terminated successfully. + Current function value: 1.617021 + Iterations: 4 + Function evaluations: 8 + Gradient evaluations: 8 + >>> res2.x # minimum found + array([-1.80851064, -0.25531915]) + + """ + opts = {'gtol': gtol, + 'norm': norm, + 'eps': epsilon, + 'disp': disp, + 'maxiter': maxiter, + 'return_all': retall} + + callback = _wrap_callback(callback) + res = _minimize_cg(f, x0, args, fprime, callback=callback, c1=c1, c2=c2, + **opts) + + if full_output: + retlist = res['x'], res['fun'], res['nfev'], res['njev'], res['status'] + if retall: + retlist += (res['allvecs'], ) + return retlist + else: + if retall: + return res['x'], res['allvecs'] + else: + return res['x'] + + +def _minimize_cg(fun, x0, args=(), jac=None, callback=None, + gtol=1e-5, norm=np.inf, eps=_epsilon, maxiter=None, + disp=False, return_all=False, finite_diff_rel_step=None, + c1=1e-4, c2=0.4, **unknown_options): + """ + Minimization of scalar function of one or more variables using the + conjugate gradient algorithm. + + Options + ------- + disp : bool + Set to True to print convergence messages. + maxiter : int + Maximum number of iterations to perform. + gtol : float + Gradient norm must be less than `gtol` before successful + termination. + norm : float + Order of norm (Inf is max, -Inf is min). + eps : float or ndarray + If `jac is None` the absolute step size used for numerical + approximation of the jacobian via forward differences. + return_all : bool, optional + Set to True to return a list of the best solution at each of the + iterations. + finite_diff_rel_step : None or array_like, optional + If ``jac in ['2-point', '3-point', 'cs']`` the relative step size to + use for numerical approximation of the jacobian. The absolute step + size is computed as ``h = rel_step * sign(x) * max(1, abs(x))``, + possibly adjusted to fit into the bounds. For ``jac='3-point'`` + the sign of `h` is ignored. If None (default) then step is selected + automatically. + c1 : float, default: 1e-4 + Parameter for Armijo condition rule. + c2 : float, default: 0.4 + Parameter for curvature condition rule. + + Notes + ----- + Parameters `c1` and `c2` must satisfy ``0 < c1 < c2 < 1``. + """ + _check_unknown_options(unknown_options) + + retall = return_all + + x0 = asarray(x0).flatten() + if maxiter is None: + maxiter = len(x0) * 200 + + sf = _prepare_scalar_function(fun, x0, jac=jac, args=args, epsilon=eps, + finite_diff_rel_step=finite_diff_rel_step) + + f = sf.fun + myfprime = sf.grad + + old_fval = f(x0) + gfk = myfprime(x0) + + k = 0 + xk = x0 + # Sets the initial step guess to dx ~ 1 + old_old_fval = old_fval + np.linalg.norm(gfk) / 2 + + if retall: + allvecs = [xk] + warnflag = 0 + pk = -gfk + gnorm = vecnorm(gfk, ord=norm) + + sigma_3 = 0.01 + + while (gnorm > gtol) and (k < maxiter): + deltak = np.dot(gfk, gfk) + + cached_step = [None] + + def polak_ribiere_powell_step(alpha, gfkp1=None): + xkp1 = xk + alpha * pk + if gfkp1 is None: + gfkp1 = myfprime(xkp1) + yk = gfkp1 - gfk + beta_k = max(0, np.dot(yk, gfkp1) / deltak) + pkp1 = -gfkp1 + beta_k * pk + gnorm = vecnorm(gfkp1, ord=norm) + return (alpha, xkp1, pkp1, gfkp1, gnorm) + + def descent_condition(alpha, xkp1, fp1, gfkp1): + # Polak-Ribiere+ needs an explicit check of a sufficient + # descent condition, which is not guaranteed by strong Wolfe. + # + # See Gilbert & Nocedal, "Global convergence properties of + # conjugate gradient methods for optimization", + # SIAM J. Optimization 2, 21 (1992). + cached_step[:] = polak_ribiere_powell_step(alpha, gfkp1) + alpha, xk, pk, gfk, gnorm = cached_step + + # Accept step if it leads to convergence. + if gnorm <= gtol: + return True + + # Accept step if sufficient descent condition applies. + return np.dot(pk, gfk) <= -sigma_3 * np.dot(gfk, gfk) + + try: + alpha_k, fc, gc, old_fval, old_old_fval, gfkp1 = \ + _line_search_wolfe12(f, myfprime, xk, pk, gfk, old_fval, + old_old_fval, c1=c1, c2=c2, amin=1e-100, + amax=1e100, extra_condition=descent_condition) + except _LineSearchError: + # Line search failed to find a better solution. + warnflag = 2 + break + + # Reuse already computed results if possible + if alpha_k == cached_step[0]: + alpha_k, xk, pk, gfk, gnorm = cached_step + else: + alpha_k, xk, pk, gfk, gnorm = polak_ribiere_powell_step(alpha_k, gfkp1) + + if retall: + allvecs.append(xk) + k += 1 + intermediate_result = OptimizeResult(x=xk, fun=old_fval) + if _call_callback_maybe_halt(callback, intermediate_result): + break + + fval = old_fval + if warnflag == 2: + msg = _status_message['pr_loss'] + elif k >= maxiter: + warnflag = 1 + msg = _status_message['maxiter'] + elif np.isnan(gnorm) or np.isnan(fval) or np.isnan(xk).any(): + warnflag = 3 + msg = _status_message['nan'] + else: + msg = _status_message['success'] + + if disp: + _print_success_message_or_warn(warnflag, msg) + print(f" Current function value: {fval:f}") + print(" Iterations: %d" % k) + print(" Function evaluations: %d" % sf.nfev) + print(" Gradient evaluations: %d" % sf.ngev) + + result = OptimizeResult(fun=fval, jac=gfk, nfev=sf.nfev, + njev=sf.ngev, status=warnflag, + success=(warnflag == 0), message=msg, x=xk, + nit=k) + if retall: + result['allvecs'] = allvecs + return result + + +def fmin_ncg(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, + epsilon=_epsilon, maxiter=None, full_output=0, disp=1, retall=0, + callback=None, c1=1e-4, c2=0.9): + """ + Unconstrained minimization of a function using the Newton-CG method. + + Parameters + ---------- + f : callable ``f(x, *args)`` + Objective function to be minimized. + x0 : ndarray + Initial guess. + fprime : callable ``f'(x, *args)`` + Gradient of f. + fhess_p : callable ``fhess_p(x, p, *args)``, optional + Function which computes the Hessian of f times an + arbitrary vector, p. + fhess : callable ``fhess(x, *args)``, optional + Function to compute the Hessian matrix of f. + args : tuple, optional + Extra arguments passed to f, fprime, fhess_p, and fhess + (the same set of extra arguments is supplied to all of + these functions). + epsilon : float or ndarray, optional + If fhess is approximated, use this value for the step size. + callback : callable, optional + An optional user-supplied function which is called after + each iteration. Called as callback(xk), where xk is the + current parameter vector. + avextol : float, optional + Convergence is assumed when the average relative error in + the minimizer falls below this amount. + maxiter : int, optional + Maximum number of iterations to perform. + full_output : bool, optional + If True, return the optional outputs. + disp : bool, optional + If True, print convergence message. + retall : bool, optional + If True, return a list of results at each iteration. + c1 : float, default: 1e-4 + Parameter for Armijo condition rule. + c2 : float, default: 0.9 + Parameter for curvature condition rule + + Returns + ------- + xopt : ndarray + Parameters which minimize f, i.e., ``f(xopt) == fopt``. + fopt : float + Value of the function at xopt, i.e., ``fopt = f(xopt)``. + fcalls : int + Number of function calls made. + gcalls : int + Number of gradient calls made. + hcalls : int + Number of Hessian calls made. + warnflag : int + Warnings generated by the algorithm. + 1 : Maximum number of iterations exceeded. + 2 : Line search failure (precision loss). + 3 : NaN result encountered. + allvecs : list + The result at each iteration, if retall is True (see below). + + See also + -------- + minimize: Interface to minimization algorithms for multivariate + functions. See the 'Newton-CG' `method` in particular. + + Notes + ----- + Only one of `fhess_p` or `fhess` need to be given. If `fhess` + is provided, then `fhess_p` will be ignored. If neither `fhess` + nor `fhess_p` is provided, then the hessian product will be + approximated using finite differences on `fprime`. `fhess_p` + must compute the hessian times an arbitrary vector. If it is not + given, finite-differences on `fprime` are used to compute + it. + + Newton-CG methods are also called truncated Newton methods. This + function differs from scipy.optimize.fmin_tnc because + + 1. scipy.optimize.fmin_ncg is written purely in Python using NumPy + and scipy while scipy.optimize.fmin_tnc calls a C function. + 2. scipy.optimize.fmin_ncg is only for unconstrained minimization + while scipy.optimize.fmin_tnc is for unconstrained minimization + or box constrained minimization. (Box constraints give + lower and upper bounds for each variable separately.) + + Parameters `c1` and `c2` must satisfy ``0 < c1 < c2 < 1``. + + References + ---------- + Wright & Nocedal, 'Numerical Optimization', 1999, p. 140. + + """ + opts = {'xtol': avextol, + 'eps': epsilon, + 'maxiter': maxiter, + 'disp': disp, + 'return_all': retall} + + callback = _wrap_callback(callback) + res = _minimize_newtoncg(f, x0, args, fprime, fhess, fhess_p, + callback=callback, c1=c1, c2=c2, **opts) + + if full_output: + retlist = (res['x'], res['fun'], res['nfev'], res['njev'], + res['nhev'], res['status']) + if retall: + retlist += (res['allvecs'], ) + return retlist + else: + if retall: + return res['x'], res['allvecs'] + else: + return res['x'] + + +def _minimize_newtoncg(fun, x0, args=(), jac=None, hess=None, hessp=None, + callback=None, xtol=1e-5, eps=_epsilon, maxiter=None, + disp=False, return_all=False, c1=1e-4, c2=0.9, + **unknown_options): + """ + Minimization of scalar function of one or more variables using the + Newton-CG algorithm. + + Note that the `jac` parameter (Jacobian) is required. + + Options + ------- + disp : bool + Set to True to print convergence messages. + xtol : float + Average relative error in solution `xopt` acceptable for + convergence. + maxiter : int + Maximum number of iterations to perform. + eps : float or ndarray + If `hessp` is approximated, use this value for the step size. + return_all : bool, optional + Set to True to return a list of the best solution at each of the + iterations. + c1 : float, default: 1e-4 + Parameter for Armijo condition rule. + c2 : float, default: 0.9 + Parameter for curvature condition rule. + + Notes + ----- + Parameters `c1` and `c2` must satisfy ``0 < c1 < c2 < 1``. + """ + _check_unknown_options(unknown_options) + if jac is None: + raise ValueError('Jacobian is required for Newton-CG method') + fhess_p = hessp + fhess = hess + avextol = xtol + epsilon = eps + retall = return_all + + x0 = asarray(x0).flatten() + # TODO: add hessp (callable or FD) to ScalarFunction? + sf = _prepare_scalar_function( + fun, x0, jac, args=args, epsilon=eps, hess=hess + ) + f = sf.fun + fprime = sf.grad + _h = sf.hess(x0) + + # Logic for hess/hessp + # - If a callable(hess) is provided, then use that + # - If hess is a FD_METHOD, or the output from hess(x) is a LinearOperator + # then create a hessp function using those. + # - If hess is None but you have callable(hessp) then use the hessp. + # - If hess and hessp are None then approximate hessp using the grad/jac. + + if (hess in FD_METHODS or isinstance(_h, LinearOperator)): + fhess = None + + def _hessp(x, p, *args): + return sf.hess(x).dot(p) + + fhess_p = _hessp + + def terminate(warnflag, msg): + if disp: + _print_success_message_or_warn(warnflag, msg) + print(f" Current function value: {old_fval:f}") + print(" Iterations: %d" % k) + print(" Function evaluations: %d" % sf.nfev) + print(" Gradient evaluations: %d" % sf.ngev) + print(" Hessian evaluations: %d" % hcalls) + fval = old_fval + result = OptimizeResult(fun=fval, jac=gfk, nfev=sf.nfev, + njev=sf.ngev, nhev=hcalls, status=warnflag, + success=(warnflag == 0), message=msg, x=xk, + nit=k) + if retall: + result['allvecs'] = allvecs + return result + + hcalls = 0 + if maxiter is None: + maxiter = len(x0)*200 + cg_maxiter = 20*len(x0) + + xtol = len(x0) * avextol + # Make sure we enter the while loop. + update_l1norm = np.finfo(float).max + xk = np.copy(x0) + if retall: + allvecs = [xk] + k = 0 + gfk = None + old_fval = f(x0) + old_old_fval = None + float64eps = np.finfo(np.float64).eps + while update_l1norm > xtol: + if k >= maxiter: + msg = "Warning: " + _status_message['maxiter'] + return terminate(1, msg) + # Compute a search direction pk by applying the CG method to + # del2 f(xk) p = - grad f(xk) starting from 0. + b = -fprime(xk) + maggrad = np.linalg.norm(b, ord=1) + eta = min(0.5, math.sqrt(maggrad)) + termcond = eta * maggrad + xsupi = zeros(len(x0), dtype=x0.dtype) + ri = -b + psupi = -ri + i = 0 + dri0 = np.dot(ri, ri) + + if fhess is not None: # you want to compute hessian once. + A = sf.hess(xk) + hcalls += 1 + + for k2 in range(cg_maxiter): + if np.add.reduce(np.abs(ri)) <= termcond: + break + if fhess is None: + if fhess_p is None: + Ap = approx_fhess_p(xk, psupi, fprime, epsilon) + else: + Ap = fhess_p(xk, psupi, *args) + hcalls += 1 + else: + # hess was supplied as a callable or hessian update strategy, so + # A is a dense numpy array or sparse matrix + Ap = A.dot(psupi) + # check curvature + Ap = asarray(Ap).squeeze() # get rid of matrices... + curv = np.dot(psupi, Ap) + if 0 <= curv <= 3 * float64eps: + break + elif curv < 0: + if (i > 0): + break + else: + # fall back to steepest descent direction + xsupi = dri0 / (-curv) * b + break + alphai = dri0 / curv + xsupi += alphai * psupi + ri += alphai * Ap + dri1 = np.dot(ri, ri) + betai = dri1 / dri0 + psupi = -ri + betai * psupi + i += 1 + dri0 = dri1 # update np.dot(ri,ri) for next time. + else: + # curvature keeps increasing, bail out + msg = ("Warning: CG iterations didn't converge. The Hessian is not " + "positive definite.") + return terminate(3, msg) + + pk = xsupi # search direction is solution to system. + gfk = -b # gradient at xk + + try: + alphak, fc, gc, old_fval, old_old_fval, gfkp1 = \ + _line_search_wolfe12(f, fprime, xk, pk, gfk, + old_fval, old_old_fval, c1=c1, c2=c2) + except _LineSearchError: + # Line search failed to find a better solution. + msg = "Warning: " + _status_message['pr_loss'] + return terminate(2, msg) + + update = alphak * pk + xk += update # upcast if necessary + if retall: + allvecs.append(xk) + k += 1 + intermediate_result = OptimizeResult(x=xk, fun=old_fval) + if _call_callback_maybe_halt(callback, intermediate_result): + return terminate(5, "") + update_l1norm = np.linalg.norm(update, ord=1) + + else: + if np.isnan(old_fval) or np.isnan(update_l1norm): + return terminate(3, _status_message['nan']) + + msg = _status_message['success'] + return terminate(0, msg) + + +def fminbound(func, x1, x2, args=(), xtol=1e-5, maxfun=500, + full_output=0, disp=1): + """Bounded minimization for scalar functions. + + Parameters + ---------- + func : callable f(x,*args) + Objective function to be minimized (must accept and return scalars). + x1, x2 : float or array scalar + Finite optimization bounds. + args : tuple, optional + Extra arguments passed to function. + xtol : float, optional + The convergence tolerance. + maxfun : int, optional + Maximum number of function evaluations allowed. + full_output : bool, optional + If True, return optional outputs. + disp: int, optional + If non-zero, print messages. + + ``0`` : no message printing. + + ``1`` : non-convergence notification messages only. + + ``2`` : print a message on convergence too. + + ``3`` : print iteration results. + + Returns + ------- + xopt : ndarray + Parameters (over given interval) which minimize the + objective function. + fval : number + (Optional output) The function value evaluated at the minimizer. + ierr : int + (Optional output) An error flag (0 if converged, 1 if maximum number of + function calls reached). + numfunc : int + (Optional output) The number of function calls made. + + See also + -------- + minimize_scalar: Interface to minimization algorithms for scalar + univariate functions. See the 'Bounded' `method` in particular. + + Notes + ----- + Finds a local minimizer of the scalar function `func` in the + interval x1 < xopt < x2 using Brent's method. (See `brent` + for auto-bracketing.) + + References + ---------- + .. [1] Forsythe, G.E., M. A. Malcolm, and C. B. Moler. "Computer Methods + for Mathematical Computations." Prentice-Hall Series in Automatic + Computation 259 (1977). + .. [2] Brent, Richard P. Algorithms for Minimization Without Derivatives. + Courier Corporation, 2013. + + Examples + -------- + `fminbound` finds the minimizer of the function in the given range. + The following examples illustrate this. + + >>> from scipy import optimize + >>> def f(x): + ... return (x-1)**2 + >>> minimizer = optimize.fminbound(f, -4, 4) + >>> minimizer + 1.0 + >>> minimum = f(minimizer) + >>> minimum + 0.0 + >>> res = optimize.fminbound(f, 3, 4, full_output=True) + >>> minimizer, fval, ierr, numfunc = res + >>> minimizer + 3.000005960860986 + >>> minimum = f(minimizer) + >>> minimum, fval + (4.000023843479476, 4.000023843479476) + """ + options = {'xatol': xtol, + 'maxiter': maxfun, + 'disp': disp} + + res = _minimize_scalar_bounded(func, (x1, x2), args, **options) + if full_output: + return res['x'], res['fun'], res['status'], res['nfev'] + else: + return res['x'] + + +def _minimize_scalar_bounded(func, bounds, args=(), + xatol=1e-5, maxiter=500, disp=0, + **unknown_options): + """ + Options + ------- + maxiter : int + Maximum number of iterations to perform. + disp: int, optional + If non-zero, print messages. + + ``0`` : no message printing. + + ``1`` : non-convergence notification messages only. + + ``2`` : print a message on convergence too. + + ``3`` : print iteration results. + + xatol : float + Absolute error in solution `xopt` acceptable for convergence. + + """ + _check_unknown_options(unknown_options) + maxfun = maxiter + # Test bounds are of correct form + if len(bounds) != 2: + raise ValueError('bounds must have two elements.') + x1, x2 = bounds + + if not (is_finite_scalar(x1) and is_finite_scalar(x2)): + raise ValueError("Optimization bounds must be finite scalars.") + + if x1 > x2: + raise ValueError("The lower bound exceeds the upper bound.") + + flag = 0 + header = ' Func-count x f(x) Procedure' + step = ' initial' + + sqrt_eps = sqrt(2.2e-16) + golden_mean = 0.5 * (3.0 - sqrt(5.0)) + a, b = x1, x2 + fulc = a + golden_mean * (b - a) + nfc, xf = fulc, fulc + rat = e = 0.0 + x = xf + fx = func(x, *args) + num = 1 + fmin_data = (1, xf, fx) + fu = np.inf + + ffulc = fnfc = fx + xm = 0.5 * (a + b) + tol1 = sqrt_eps * np.abs(xf) + xatol / 3.0 + tol2 = 2.0 * tol1 + + if disp > 2: + print(" ") + print(header) + print("%5.0f %12.6g %12.6g %s" % (fmin_data + (step,))) + + while (np.abs(xf - xm) > (tol2 - 0.5 * (b - a))): + golden = 1 + # Check for parabolic fit + if np.abs(e) > tol1: + golden = 0 + r = (xf - nfc) * (fx - ffulc) + q = (xf - fulc) * (fx - fnfc) + p = (xf - fulc) * q - (xf - nfc) * r + q = 2.0 * (q - r) + if q > 0.0: + p = -p + q = np.abs(q) + r = e + e = rat + + # Check for acceptability of parabola + if ((np.abs(p) < np.abs(0.5*q*r)) and (p > q*(a - xf)) and + (p < q * (b - xf))): + rat = (p + 0.0) / q + x = xf + rat + step = ' parabolic' + + if ((x - a) < tol2) or ((b - x) < tol2): + si = np.sign(xm - xf) + ((xm - xf) == 0) + rat = tol1 * si + else: # do a golden-section step + golden = 1 + + if golden: # do a golden-section step + if xf >= xm: + e = a - xf + else: + e = b - xf + rat = golden_mean*e + step = ' golden' + + si = np.sign(rat) + (rat == 0) + x = xf + si * np.maximum(np.abs(rat), tol1) + fu = func(x, *args) + num += 1 + fmin_data = (num, x, fu) + if disp > 2: + print("%5.0f %12.6g %12.6g %s" % (fmin_data + (step,))) + + if fu <= fx: + if x >= xf: + a = xf + else: + b = xf + fulc, ffulc = nfc, fnfc + nfc, fnfc = xf, fx + xf, fx = x, fu + else: + if x < xf: + a = x + else: + b = x + if (fu <= fnfc) or (nfc == xf): + fulc, ffulc = nfc, fnfc + nfc, fnfc = x, fu + elif (fu <= ffulc) or (fulc == xf) or (fulc == nfc): + fulc, ffulc = x, fu + + xm = 0.5 * (a + b) + tol1 = sqrt_eps * np.abs(xf) + xatol / 3.0 + tol2 = 2.0 * tol1 + + if num >= maxfun: + flag = 1 + break + + if np.isnan(xf) or np.isnan(fx) or np.isnan(fu): + flag = 2 + + fval = fx + if disp > 0: + _endprint(x, flag, fval, maxfun, xatol, disp) + + result = OptimizeResult(fun=fval, status=flag, success=(flag == 0), + message={0: 'Solution found.', + 1: 'Maximum number of function calls ' + 'reached.', + 2: _status_message['nan']}.get(flag, ''), + x=xf, nfev=num, nit=num) + + return result + + +class Brent: + #need to rethink design of __init__ + def __init__(self, func, args=(), tol=1.48e-8, maxiter=500, + full_output=0, disp=0): + self.func = func + self.args = args + self.tol = tol + self.maxiter = maxiter + self._mintol = 1.0e-11 + self._cg = 0.3819660 + self.xmin = None + self.fval = None + self.iter = 0 + self.funcalls = 0 + self.disp = disp + + # need to rethink design of set_bracket (new options, etc.) + def set_bracket(self, brack=None): + self.brack = brack + + def get_bracket_info(self): + #set up + func = self.func + args = self.args + brack = self.brack + ### BEGIN core bracket_info code ### + ### carefully DOCUMENT any CHANGES in core ## + if brack is None: + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, args=args) + elif len(brack) == 2: + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + xb=brack[1], args=args) + elif len(brack) == 3: + xa, xb, xc = brack + if (xa > xc): # swap so xa < xc can be assumed + xc, xa = xa, xc + if not ((xa < xb) and (xb < xc)): + raise ValueError( + "Bracketing values (xa, xb, xc) do not" + " fulfill this requirement: (xa < xb) and (xb < xc)" + ) + fa = func(*((xa,) + args)) + fb = func(*((xb,) + args)) + fc = func(*((xc,) + args)) + if not ((fb < fa) and (fb < fc)): + raise ValueError( + "Bracketing values (xa, xb, xc) do not fulfill" + " this requirement: (f(xb) < f(xa)) and (f(xb) < f(xc))" + ) + + funcalls = 3 + else: + raise ValueError("Bracketing interval must be " + "length 2 or 3 sequence.") + ### END core bracket_info code ### + + return xa, xb, xc, fa, fb, fc, funcalls + + def optimize(self): + # set up for optimization + func = self.func + xa, xb, xc, fa, fb, fc, funcalls = self.get_bracket_info() + _mintol = self._mintol + _cg = self._cg + ################################# + #BEGIN CORE ALGORITHM + ################################# + x = w = v = xb + fw = fv = fx = fb + if (xa < xc): + a = xa + b = xc + else: + a = xc + b = xa + deltax = 0.0 + iter = 0 + + if self.disp > 2: + print(" ") + print(f"{'Func-count':^12} {'x':^12} {'f(x)': ^12}") + print(f"{funcalls:^12g} {x:^12.6g} {fx:^12.6g}") + + while (iter < self.maxiter): + tol1 = self.tol * np.abs(x) + _mintol + tol2 = 2.0 * tol1 + xmid = 0.5 * (a + b) + # check for convergence + if np.abs(x - xmid) < (tol2 - 0.5 * (b - a)): + break + # XXX In the first iteration, rat is only bound in the true case + # of this conditional. This used to cause an UnboundLocalError + # (gh-4140). It should be set before the if (but to what?). + if (np.abs(deltax) <= tol1): + if (x >= xmid): + deltax = a - x # do a golden section step + else: + deltax = b - x + rat = _cg * deltax + else: # do a parabolic step + tmp1 = (x - w) * (fx - fv) + tmp2 = (x - v) * (fx - fw) + p = (x - v) * tmp2 - (x - w) * tmp1 + tmp2 = 2.0 * (tmp2 - tmp1) + if (tmp2 > 0.0): + p = -p + tmp2 = np.abs(tmp2) + dx_temp = deltax + deltax = rat + # check parabolic fit + if ((p > tmp2 * (a - x)) and (p < tmp2 * (b - x)) and + (np.abs(p) < np.abs(0.5 * tmp2 * dx_temp))): + rat = p * 1.0 / tmp2 # if parabolic step is useful. + u = x + rat + if ((u - a) < tol2 or (b - u) < tol2): + if xmid - x >= 0: + rat = tol1 + else: + rat = -tol1 + else: + if (x >= xmid): + deltax = a - x # if it's not do a golden section step + else: + deltax = b - x + rat = _cg * deltax + + if (np.abs(rat) < tol1): # update by at least tol1 + if rat >= 0: + u = x + tol1 + else: + u = x - tol1 + else: + u = x + rat + fu = func(*((u,) + self.args)) # calculate new output value + funcalls += 1 + + if (fu > fx): # if it's bigger than current + if (u < x): + a = u + else: + b = u + if (fu <= fw) or (w == x): + v = w + w = u + fv = fw + fw = fu + elif (fu <= fv) or (v == x) or (v == w): + v = u + fv = fu + else: + if (u >= x): + a = x + else: + b = x + v = w + w = x + x = u + fv = fw + fw = fx + fx = fu + + if self.disp > 2: + print(f"{funcalls:^12g} {x:^12.6g} {fx:^12.6g}") + + iter += 1 + ################################# + #END CORE ALGORITHM + ################################# + + self.xmin = x + self.fval = fx + self.iter = iter + self.funcalls = funcalls + + def get_result(self, full_output=False): + if full_output: + return self.xmin, self.fval, self.iter, self.funcalls + else: + return self.xmin + + +def brent(func, args=(), brack=None, tol=1.48e-8, full_output=0, maxiter=500): + """ + Given a function of one variable and a possible bracket, return + a local minimizer of the function isolated to a fractional precision + of tol. + + Parameters + ---------- + func : callable f(x,*args) + Objective function. + args : tuple, optional + Additional arguments (if present). + brack : tuple, optional + Either a triple ``(xa, xb, xc)`` satisfying ``xa < xb < xc`` and + ``func(xb) < func(xa) and func(xb) < func(xc)``, or a pair + ``(xa, xb)`` to be used as initial points for a downhill bracket search + (see `scipy.optimize.bracket`). + The minimizer ``x`` will not necessarily satisfy ``xa <= x <= xb``. + tol : float, optional + Relative error in solution `xopt` acceptable for convergence. + full_output : bool, optional + If True, return all output args (xmin, fval, iter, + funcalls). + maxiter : int, optional + Maximum number of iterations in solution. + + Returns + ------- + xmin : ndarray + Optimum point. + fval : float + (Optional output) Optimum function value. + iter : int + (Optional output) Number of iterations. + funcalls : int + (Optional output) Number of objective function evaluations made. + + See also + -------- + minimize_scalar: Interface to minimization algorithms for scalar + univariate functions. See the 'Brent' `method` in particular. + + Notes + ----- + Uses inverse parabolic interpolation when possible to speed up + convergence of golden section method. + + Does not ensure that the minimum lies in the range specified by + `brack`. See `scipy.optimize.fminbound`. + + Examples + -------- + We illustrate the behaviour of the function when `brack` is of + size 2 and 3 respectively. In the case where `brack` is of the + form ``(xa, xb)``, we can see for the given values, the output does + not necessarily lie in the range ``(xa, xb)``. + + >>> def f(x): + ... return (x-1)**2 + + >>> from scipy import optimize + + >>> minimizer = optimize.brent(f, brack=(1, 2)) + >>> minimizer + 1 + >>> res = optimize.brent(f, brack=(-1, 0.5, 2), full_output=True) + >>> xmin, fval, iter, funcalls = res + >>> f(xmin), fval + (0.0, 0.0) + + """ + options = {'xtol': tol, + 'maxiter': maxiter} + res = _minimize_scalar_brent(func, brack, args, **options) + if full_output: + return res['x'], res['fun'], res['nit'], res['nfev'] + else: + return res['x'] + + +def _minimize_scalar_brent(func, brack=None, args=(), xtol=1.48e-8, + maxiter=500, disp=0, + **unknown_options): + """ + Options + ------- + maxiter : int + Maximum number of iterations to perform. + xtol : float + Relative error in solution `xopt` acceptable for convergence. + disp : int, optional + If non-zero, print messages. + + ``0`` : no message printing. + + ``1`` : non-convergence notification messages only. + + ``2`` : print a message on convergence too. + + ``3`` : print iteration results. + + Notes + ----- + Uses inverse parabolic interpolation when possible to speed up + convergence of golden section method. + + """ + _check_unknown_options(unknown_options) + tol = xtol + if tol < 0: + raise ValueError(f'tolerance should be >= 0, got {tol!r}') + + brent = Brent(func=func, args=args, tol=tol, + full_output=True, maxiter=maxiter, disp=disp) + brent.set_bracket(brack) + brent.optimize() + x, fval, nit, nfev = brent.get_result(full_output=True) + + success = nit < maxiter and not (np.isnan(x) or np.isnan(fval)) + + if success: + message = ("\nOptimization terminated successfully;\n" + "The returned value satisfies the termination criteria\n" + f"(using xtol = {xtol} )") + else: + if nit >= maxiter: + message = "\nMaximum number of iterations exceeded" + if np.isnan(x) or np.isnan(fval): + message = f"{_status_message['nan']}" + + if disp: + _print_success_message_or_warn(not success, message) + + return OptimizeResult(fun=fval, x=x, nit=nit, nfev=nfev, + success=success, message=message) + + +def golden(func, args=(), brack=None, tol=_epsilon, + full_output=0, maxiter=5000): + """ + Return the minimizer of a function of one variable using the golden section + method. + + Given a function of one variable and a possible bracketing interval, + return a minimizer of the function isolated to a fractional precision of + tol. + + Parameters + ---------- + func : callable func(x,*args) + Objective function to minimize. + args : tuple, optional + Additional arguments (if present), passed to func. + brack : tuple, optional + Either a triple ``(xa, xb, xc)`` where ``xa < xb < xc`` and + ``func(xb) < func(xa) and func(xb) < func(xc)``, or a pair (xa, xb) + to be used as initial points for a downhill bracket search (see + `scipy.optimize.bracket`). + The minimizer ``x`` will not necessarily satisfy ``xa <= x <= xb``. + tol : float, optional + x tolerance stop criterion + full_output : bool, optional + If True, return optional outputs. + maxiter : int + Maximum number of iterations to perform. + + Returns + ------- + xmin : ndarray + Optimum point. + fval : float + (Optional output) Optimum function value. + funcalls : int + (Optional output) Number of objective function evaluations made. + + See also + -------- + minimize_scalar: Interface to minimization algorithms for scalar + univariate functions. See the 'Golden' `method` in particular. + + Notes + ----- + Uses analog of bisection method to decrease the bracketed + interval. + + Examples + -------- + We illustrate the behaviour of the function when `brack` is of + size 2 and 3, respectively. In the case where `brack` is of the + form (xa,xb), we can see for the given values, the output need + not necessarily lie in the range ``(xa, xb)``. + + >>> def f(x): + ... return (x-1)**2 + + >>> from scipy import optimize + + >>> minimizer = optimize.golden(f, brack=(1, 2)) + >>> minimizer + 1 + >>> res = optimize.golden(f, brack=(-1, 0.5, 2), full_output=True) + >>> xmin, fval, funcalls = res + >>> f(xmin), fval + (9.925165290385052e-18, 9.925165290385052e-18) + + """ + options = {'xtol': tol, 'maxiter': maxiter} + res = _minimize_scalar_golden(func, brack, args, **options) + if full_output: + return res['x'], res['fun'], res['nfev'] + else: + return res['x'] + + +def _minimize_scalar_golden(func, brack=None, args=(), + xtol=_epsilon, maxiter=5000, disp=0, + **unknown_options): + """ + Options + ------- + xtol : float + Relative error in solution `xopt` acceptable for convergence. + maxiter : int + Maximum number of iterations to perform. + disp: int, optional + If non-zero, print messages. + + ``0`` : no message printing. + + ``1`` : non-convergence notification messages only. + + ``2`` : print a message on convergence too. + + ``3`` : print iteration results. + """ + _check_unknown_options(unknown_options) + tol = xtol + if brack is None: + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, args=args) + elif len(brack) == 2: + xa, xb, xc, fa, fb, fc, funcalls = bracket(func, xa=brack[0], + xb=brack[1], args=args) + elif len(brack) == 3: + xa, xb, xc = brack + if (xa > xc): # swap so xa < xc can be assumed + xc, xa = xa, xc + if not ((xa < xb) and (xb < xc)): + raise ValueError( + "Bracketing values (xa, xb, xc) do not" + " fulfill this requirement: (xa < xb) and (xb < xc)" + ) + fa = func(*((xa,) + args)) + fb = func(*((xb,) + args)) + fc = func(*((xc,) + args)) + if not ((fb < fa) and (fb < fc)): + raise ValueError( + "Bracketing values (xa, xb, xc) do not fulfill" + " this requirement: (f(xb) < f(xa)) and (f(xb) < f(xc))" + ) + funcalls = 3 + else: + raise ValueError("Bracketing interval must be length 2 or 3 sequence.") + + _gR = 0.61803399 # golden ratio conjugate: 2.0/(1.0+sqrt(5.0)) + _gC = 1.0 - _gR + x3 = xc + x0 = xa + if (np.abs(xc - xb) > np.abs(xb - xa)): + x1 = xb + x2 = xb + _gC * (xc - xb) + else: + x2 = xb + x1 = xb - _gC * (xb - xa) + f1 = func(*((x1,) + args)) + f2 = func(*((x2,) + args)) + funcalls += 2 + nit = 0 + + if disp > 2: + print(" ") + print(f"{'Func-count':^12} {'x':^12} {'f(x)': ^12}") + + for i in range(maxiter): + if np.abs(x3 - x0) <= tol * (np.abs(x1) + np.abs(x2)): + break + if (f2 < f1): + x0 = x1 + x1 = x2 + x2 = _gR * x1 + _gC * x3 + f1 = f2 + f2 = func(*((x2,) + args)) + else: + x3 = x2 + x2 = x1 + x1 = _gR * x2 + _gC * x0 + f2 = f1 + f1 = func(*((x1,) + args)) + funcalls += 1 + if disp > 2: + if (f1 < f2): + xmin, fval = x1, f1 + else: + xmin, fval = x2, f2 + print(f"{funcalls:^12g} {xmin:^12.6g} {fval:^12.6g}") + + nit += 1 + # end of iteration loop + + if (f1 < f2): + xmin = x1 + fval = f1 + else: + xmin = x2 + fval = f2 + + success = nit < maxiter and not (np.isnan(fval) or np.isnan(xmin)) + + if success: + message = ("\nOptimization terminated successfully;\n" + "The returned value satisfies the termination criteria\n" + f"(using xtol = {xtol} )") + else: + if nit >= maxiter: + message = "\nMaximum number of iterations exceeded" + if np.isnan(xmin) or np.isnan(fval): + message = f"{_status_message['nan']}" + + if disp: + _print_success_message_or_warn(not success, message) + + return OptimizeResult(fun=fval, nfev=funcalls, x=xmin, nit=nit, + success=success, message=message) + + +def bracket(func, xa=0.0, xb=1.0, args=(), grow_limit=110.0, maxiter=1000): + """ + Bracket the minimum of a function. + + Given a function and distinct initial points, search in the + downhill direction (as defined by the initial points) and return + three points that bracket the minimum of the function. + + Parameters + ---------- + func : callable f(x,*args) + Objective function to minimize. + xa, xb : float, optional + Initial points. Defaults `xa` to 0.0, and `xb` to 1.0. + A local minimum need not be contained within this interval. + args : tuple, optional + Additional arguments (if present), passed to `func`. + grow_limit : float, optional + Maximum grow limit. Defaults to 110.0 + maxiter : int, optional + Maximum number of iterations to perform. Defaults to 1000. + + Returns + ------- + xa, xb, xc : float + Final points of the bracket. + fa, fb, fc : float + Objective function values at the bracket points. + funcalls : int + Number of function evaluations made. + + Raises + ------ + BracketError + If no valid bracket is found before the algorithm terminates. + See notes for conditions of a valid bracket. + + Notes + ----- + The algorithm attempts to find three strictly ordered points (i.e. + :math:`x_a < x_b < x_c` or :math:`x_c < x_b < x_a`) satisfying + :math:`f(x_b) ≤ f(x_a)` and :math:`f(x_b) ≤ f(x_c)`, where one of the + inequalities must be satisfied strictly and all :math:`x_i` must be + finite. + + Examples + -------- + This function can find a downward convex region of a function: + + >>> import numpy as np + >>> import matplotlib.pyplot as plt + >>> from scipy.optimize import bracket + >>> def f(x): + ... return 10*x**2 + 3*x + 5 + >>> x = np.linspace(-2, 2) + >>> y = f(x) + >>> init_xa, init_xb = 0.1, 1 + >>> xa, xb, xc, fa, fb, fc, funcalls = bracket(f, xa=init_xa, xb=init_xb) + >>> plt.axvline(x=init_xa, color="k", linestyle="--") + >>> plt.axvline(x=init_xb, color="k", linestyle="--") + >>> plt.plot(x, y, "-k") + >>> plt.plot(xa, fa, "bx") + >>> plt.plot(xb, fb, "rx") + >>> plt.plot(xc, fc, "bx") + >>> plt.show() + + Note that both initial points were to the right of the minimum, and the + third point was found in the "downhill" direction: the direction + in which the function appeared to be decreasing (to the left). + The final points are strictly ordered, and the function value + at the middle point is less than the function values at the endpoints; + it follows that a minimum must lie within the bracket. + + """ + _gold = 1.618034 # golden ratio: (1.0+sqrt(5.0))/2.0 + _verysmall_num = 1e-21 + # convert to numpy floats if not already + xa, xb = np.asarray([xa, xb]) + fa = func(*(xa,) + args) + fb = func(*(xb,) + args) + if (fa < fb): # Switch so fa > fb + xa, xb = xb, xa + fa, fb = fb, fa + xc = xb + _gold * (xb - xa) + fc = func(*((xc,) + args)) + funcalls = 3 + iter = 0 + while (fc < fb): + tmp1 = (xb - xa) * (fb - fc) + tmp2 = (xb - xc) * (fb - fa) + val = tmp2 - tmp1 + if np.abs(val) < _verysmall_num: + denom = 2.0 * _verysmall_num + else: + denom = 2.0 * val + w = xb - ((xb - xc) * tmp2 - (xb - xa) * tmp1) / denom + wlim = xb + grow_limit * (xc - xb) + msg = ("No valid bracket was found before the iteration limit was " + "reached. Consider trying different initial points or " + "increasing `maxiter`.") + if iter > maxiter: + raise RuntimeError(msg) + iter += 1 + if (w - xc) * (xb - w) > 0.0: + fw = func(*((w,) + args)) + funcalls += 1 + if (fw < fc): + xa = xb + xb = w + fa = fb + fb = fw + break + elif (fw > fb): + xc = w + fc = fw + break + w = xc + _gold * (xc - xb) + fw = func(*((w,) + args)) + funcalls += 1 + elif (w - wlim)*(wlim - xc) >= 0.0: + w = wlim + fw = func(*((w,) + args)) + funcalls += 1 + elif (w - wlim)*(xc - w) > 0.0: + fw = func(*((w,) + args)) + funcalls += 1 + if (fw < fc): + xb = xc + xc = w + w = xc + _gold * (xc - xb) + fb = fc + fc = fw + fw = func(*((w,) + args)) + funcalls += 1 + else: + w = xc + _gold * (xc - xb) + fw = func(*((w,) + args)) + funcalls += 1 + xa = xb + xb = xc + xc = w + fa = fb + fb = fc + fc = fw + + # three conditions for a valid bracket + cond1 = (fb < fc and fb <= fa) or (fb < fa and fb <= fc) + cond2 = (xa < xb < xc or xc < xb < xa) + cond3 = np.isfinite(xa) and np.isfinite(xb) and np.isfinite(xc) + msg = ("The algorithm terminated without finding a valid bracket. " + "Consider trying different initial points.") + if not (cond1 and cond2 and cond3): + e = BracketError(msg) + e.data = (xa, xb, xc, fa, fb, fc, funcalls) + raise e + + return xa, xb, xc, fa, fb, fc, funcalls + + +class BracketError(RuntimeError): + pass + + +def _recover_from_bracket_error(solver, fun, bracket, args, **options): + # `bracket` was originally written without checking whether the resulting + # bracket is valid. `brent` and `golden` built on top of it without + # checking the returned bracket for validity, and their output can be + # incorrect without warning/error if the original bracket is invalid. + # gh-14858 noticed the problem, and the following is the desired + # behavior: + # - `scipy.optimize.bracket`, `scipy.optimize.brent`, and + # `scipy.optimize.golden` should raise an error if the bracket is + # invalid, as opposed to silently returning garbage + # - `scipy.optimize.minimize_scalar` should return with `success=False` + # and other information + # The changes that would be required to achieve this the traditional + # way (`return`ing all the required information from bracket all the way + # up to `minimizer_scalar`) are extensive and invasive. (See a6aa40d.) + # We can achieve the same thing by raising the error in `bracket`, but + # storing the information needed by `minimize_scalar` in the error object, + # and intercepting it here. + try: + res = solver(fun, bracket, args, **options) + except BracketError as e: + msg = str(e) + xa, xb, xc, fa, fb, fc, funcalls = e.data + xs, fs = [xa, xb, xc], [fa, fb, fc] + if np.any(np.isnan([xs, fs])): + x, fun = np.nan, np.nan + else: + imin = np.argmin(fs) + x, fun = xs[imin], fs[imin] + return OptimizeResult(fun=fun, nfev=funcalls, x=x, + nit=0, success=False, message=msg) + return res + + +def _line_for_search(x0, alpha, lower_bound, upper_bound): + """ + Given a parameter vector ``x0`` with length ``n`` and a direction + vector ``alpha`` with length ``n``, and lower and upper bounds on + each of the ``n`` parameters, what are the bounds on a scalar + ``l`` such that ``lower_bound <= x0 + alpha * l <= upper_bound``. + + + Parameters + ---------- + x0 : np.array. + The vector representing the current location. + Note ``np.shape(x0) == (n,)``. + alpha : np.array. + The vector representing the direction. + Note ``np.shape(alpha) == (n,)``. + lower_bound : np.array. + The lower bounds for each parameter in ``x0``. If the ``i``th + parameter in ``x0`` is unbounded below, then ``lower_bound[i]`` + should be ``-np.inf``. + Note ``np.shape(lower_bound) == (n,)``. + upper_bound : np.array. + The upper bounds for each parameter in ``x0``. If the ``i``th + parameter in ``x0`` is unbounded above, then ``upper_bound[i]`` + should be ``np.inf``. + Note ``np.shape(upper_bound) == (n,)``. + + Returns + ------- + res : tuple ``(lmin, lmax)`` + The bounds for ``l`` such that + ``lower_bound[i] <= x0[i] + alpha[i] * l <= upper_bound[i]`` + for all ``i``. + + """ + # get nonzero indices of alpha so we don't get any zero division errors. + # alpha will not be all zero, since it is called from _linesearch_powell + # where we have a check for this. + nonzero, = alpha.nonzero() + lower_bound, upper_bound = lower_bound[nonzero], upper_bound[nonzero] + x0, alpha = x0[nonzero], alpha[nonzero] + low = (lower_bound - x0) / alpha + high = (upper_bound - x0) / alpha + + # positive and negative indices + pos = alpha > 0 + + lmin_pos = np.where(pos, low, 0) + lmin_neg = np.where(pos, 0, high) + lmax_pos = np.where(pos, high, 0) + lmax_neg = np.where(pos, 0, low) + + lmin = np.max(lmin_pos + lmin_neg) + lmax = np.min(lmax_pos + lmax_neg) + + # if x0 is outside the bounds, then it is possible that there is + # no way to get back in the bounds for the parameters being updated + # with the current direction alpha. + # when this happens, lmax < lmin. + # If this is the case, then we can just return (0, 0) + return (lmin, lmax) if lmax >= lmin else (0, 0) + + +def _linesearch_powell(func, p, xi, tol=1e-3, + lower_bound=None, upper_bound=None, fval=None): + """Line-search algorithm using fminbound. + + Find the minimum of the function ``func(x0 + alpha*direc)``. + + lower_bound : np.array. + The lower bounds for each parameter in ``x0``. If the ``i``th + parameter in ``x0`` is unbounded below, then ``lower_bound[i]`` + should be ``-np.inf``. + Note ``np.shape(lower_bound) == (n,)``. + upper_bound : np.array. + The upper bounds for each parameter in ``x0``. If the ``i``th + parameter in ``x0`` is unbounded above, then ``upper_bound[i]`` + should be ``np.inf``. + Note ``np.shape(upper_bound) == (n,)``. + fval : number. + ``fval`` is equal to ``func(p)``, the idea is just to avoid + recomputing it so we can limit the ``fevals``. + + """ + def myfunc(alpha): + return func(p + alpha*xi) + + # if xi is zero, then don't optimize + if not np.any(xi): + return ((fval, p, xi) if fval is not None else (func(p), p, xi)) + elif lower_bound is None and upper_bound is None: + # non-bounded minimization + res = _recover_from_bracket_error(_minimize_scalar_brent, + myfunc, None, tuple(), xtol=tol) + alpha_min, fret = res.x, res.fun + xi = alpha_min * xi + return fret, p + xi, xi + else: + bound = _line_for_search(p, xi, lower_bound, upper_bound) + if np.isneginf(bound[0]) and np.isposinf(bound[1]): + # equivalent to unbounded + return _linesearch_powell(func, p, xi, fval=fval, tol=tol) + elif not np.isneginf(bound[0]) and not np.isposinf(bound[1]): + # we can use a bounded scalar minimization + res = _minimize_scalar_bounded(myfunc, bound, xatol=tol / 100) + xi = res.x * xi + return res.fun, p + xi, xi + else: + # only bounded on one side. use the tangent function to convert + # the infinity bound to a finite bound. The new bounded region + # is a subregion of the region bounded by -np.pi/2 and np.pi/2. + bound = np.arctan(bound[0]), np.arctan(bound[1]) + res = _minimize_scalar_bounded( + lambda x: myfunc(np.tan(x)), + bound, + xatol=tol / 100) + xi = np.tan(res.x) * xi + return res.fun, p + xi, xi + + +def fmin_powell(func, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=None, + maxfun=None, full_output=0, disp=1, retall=0, callback=None, + direc=None): + """ + Minimize a function using modified Powell's method. + + This method only uses function values, not derivatives. + + Parameters + ---------- + func : callable f(x,*args) + Objective function to be minimized. + x0 : ndarray + Initial guess. + args : tuple, optional + Extra arguments passed to func. + xtol : float, optional + Line-search error tolerance. + ftol : float, optional + Relative error in ``func(xopt)`` acceptable for convergence. + maxiter : int, optional + Maximum number of iterations to perform. + maxfun : int, optional + Maximum number of function evaluations to make. + full_output : bool, optional + If True, ``fopt``, ``xi``, ``direc``, ``iter``, ``funcalls``, and + ``warnflag`` are returned. + disp : bool, optional + If True, print convergence messages. + retall : bool, optional + If True, return a list of the solution at each iteration. + callback : callable, optional + An optional user-supplied function, called after each + iteration. Called as ``callback(xk)``, where ``xk`` is the + current parameter vector. + direc : ndarray, optional + Initial fitting step and parameter order set as an (N, N) array, where N + is the number of fitting parameters in `x0`. Defaults to step size 1.0 + fitting all parameters simultaneously (``np.eye((N, N))``). To + prevent initial consideration of values in a step or to change initial + step size, set to 0 or desired step size in the Jth position in the Mth + block, where J is the position in `x0` and M is the desired evaluation + step, with steps being evaluated in index order. Step size and ordering + will change freely as minimization proceeds. + + Returns + ------- + xopt : ndarray + Parameter which minimizes `func`. + fopt : number + Value of function at minimum: ``fopt = func(xopt)``. + direc : ndarray + Current direction set. + iter : int + Number of iterations. + funcalls : int + Number of function calls made. + warnflag : int + Integer warning flag: + 1 : Maximum number of function evaluations. + 2 : Maximum number of iterations. + 3 : NaN result encountered. + 4 : The result is out of the provided bounds. + allvecs : list + List of solutions at each iteration. + + See also + -------- + minimize: Interface to unconstrained minimization algorithms for + multivariate functions. See the 'Powell' method in particular. + + Notes + ----- + Uses a modification of Powell's method to find the minimum of + a function of N variables. Powell's method is a conjugate + direction method. + + The algorithm has two loops. The outer loop merely iterates over the inner + loop. The inner loop minimizes over each current direction in the direction + set. At the end of the inner loop, if certain conditions are met, the + direction that gave the largest decrease is dropped and replaced with the + difference between the current estimated x and the estimated x from the + beginning of the inner-loop. + + The technical conditions for replacing the direction of greatest + increase amount to checking that + + 1. No further gain can be made along the direction of greatest increase + from that iteration. + 2. The direction of greatest increase accounted for a large sufficient + fraction of the decrease in the function value from that iteration of + the inner loop. + + References + ---------- + Powell M.J.D. (1964) An efficient method for finding the minimum of a + function of several variables without calculating derivatives, + Computer Journal, 7 (2):155-162. + + Press W., Teukolsky S.A., Vetterling W.T., and Flannery B.P.: + Numerical Recipes (any edition), Cambridge University Press + + Examples + -------- + >>> def f(x): + ... return x**2 + + >>> from scipy import optimize + + >>> minimum = optimize.fmin_powell(f, -1) + Optimization terminated successfully. + Current function value: 0.000000 + Iterations: 2 + Function evaluations: 16 + >>> minimum + array(0.0) + + """ + opts = {'xtol': xtol, + 'ftol': ftol, + 'maxiter': maxiter, + 'maxfev': maxfun, + 'disp': disp, + 'direc': direc, + 'return_all': retall} + + callback = _wrap_callback(callback) + res = _minimize_powell(func, x0, args, callback=callback, **opts) + + if full_output: + retlist = (res['x'], res['fun'], res['direc'], res['nit'], + res['nfev'], res['status']) + if retall: + retlist += (res['allvecs'], ) + return retlist + else: + if retall: + return res['x'], res['allvecs'] + else: + return res['x'] + + +def _minimize_powell(func, x0, args=(), callback=None, bounds=None, + xtol=1e-4, ftol=1e-4, maxiter=None, maxfev=None, + disp=False, direc=None, return_all=False, + **unknown_options): + """ + Minimization of scalar function of one or more variables using the + modified Powell algorithm. + + Parameters + ---------- + fun : callable + The objective function to be minimized:: + + fun(x, *args) -> float + + where ``x`` is a 1-D array with shape (n,) and ``args`` + is a tuple of the fixed parameters needed to completely + specify the function. + x0 : ndarray, shape (n,) + Initial guess. Array of real elements of size (n,), + where ``n`` is the number of independent variables. + args : tuple, optional + Extra arguments passed to the objective function and its + derivatives (`fun`, `jac` and `hess` functions). + method : str or callable, optional + The present documentation is specific to ``method='powell'``, but other + options are available. See documentation for `scipy.optimize.minimize`. + bounds : sequence or `Bounds`, optional + Bounds on decision variables. There are two ways to specify the bounds: + + 1. Instance of `Bounds` class. + 2. Sequence of ``(min, max)`` pairs for each element in `x`. None + is used to specify no bound. + + If bounds are not provided, then an unbounded line search will be used. + If bounds are provided and the initial guess is within the bounds, then + every function evaluation throughout the minimization procedure will be + within the bounds. If bounds are provided, the initial guess is outside + the bounds, and `direc` is full rank (or left to default), then some + function evaluations during the first iteration may be outside the + bounds, but every function evaluation after the first iteration will be + within the bounds. If `direc` is not full rank, then some parameters + may not be optimized and the solution is not guaranteed to be within + the bounds. + + options : dict, optional + A dictionary of solver options. All methods accept the following + generic options: + + maxiter : int + Maximum number of iterations to perform. Depending on the + method each iteration may use several function evaluations. + disp : bool + Set to True to print convergence messages. + + See method-specific options for ``method='powell'`` below. + callback : callable, optional + Called after each iteration. The signature is:: + + callback(xk) + + where ``xk`` is the current parameter vector. + + Returns + ------- + res : OptimizeResult + The optimization result represented as a ``OptimizeResult`` object. + Important attributes are: ``x`` the solution array, ``success`` a + Boolean flag indicating if the optimizer exited successfully and + ``message`` which describes the cause of the termination. See + `OptimizeResult` for a description of other attributes. + + Options + ------- + disp : bool + Set to True to print convergence messages. + xtol : float + Relative error in solution `xopt` acceptable for convergence. + ftol : float + Relative error in ``fun(xopt)`` acceptable for convergence. + maxiter, maxfev : int + Maximum allowed number of iterations and function evaluations. + Will default to ``N*1000``, where ``N`` is the number of + variables, if neither `maxiter` or `maxfev` is set. If both + `maxiter` and `maxfev` are set, minimization will stop at the + first reached. + direc : ndarray + Initial set of direction vectors for the Powell method. + return_all : bool, optional + Set to True to return a list of the best solution at each of the + iterations. + """ + _check_unknown_options(unknown_options) + maxfun = maxfev + retall = return_all + + x = asarray(x0).flatten() + if retall: + allvecs = [x] + N = len(x) + # If neither are set, then set both to default + if maxiter is None and maxfun is None: + maxiter = N * 1000 + maxfun = N * 1000 + elif maxiter is None: + # Convert remaining Nones, to np.inf, unless the other is np.inf, in + # which case use the default to avoid unbounded iteration + if maxfun == np.inf: + maxiter = N * 1000 + else: + maxiter = np.inf + elif maxfun is None: + if maxiter == np.inf: + maxfun = N * 1000 + else: + maxfun = np.inf + + # we need to use a mutable object here that we can update in the + # wrapper function + fcalls, func = _wrap_scalar_function_maxfun_validation(func, args, maxfun) + + if direc is None: + direc = eye(N, dtype=float) + else: + direc = asarray(direc, dtype=float) + if np.linalg.matrix_rank(direc) != direc.shape[0]: + warnings.warn("direc input is not full rank, some parameters may " + "not be optimized", + OptimizeWarning, stacklevel=3) + + if bounds is None: + # don't make these arrays of all +/- inf. because + # _linesearch_powell will do an unnecessary check of all the elements. + # just keep them None, _linesearch_powell will not have to check + # all the elements. + lower_bound, upper_bound = None, None + else: + # bounds is standardized in _minimize.py. + lower_bound, upper_bound = bounds.lb, bounds.ub + if np.any(lower_bound > x0) or np.any(x0 > upper_bound): + warnings.warn("Initial guess is not within the specified bounds", + OptimizeWarning, stacklevel=3) + + fval = func(x) + x1 = x.copy() + iter = 0 + while True: + try: + fx = fval + bigind = 0 + delta = 0.0 + for i in range(N): + direc1 = direc[i] + fx2 = fval + fval, x, direc1 = _linesearch_powell(func, x, direc1, + tol=xtol * 100, + lower_bound=lower_bound, + upper_bound=upper_bound, + fval=fval) + if (fx2 - fval) > delta: + delta = fx2 - fval + bigind = i + iter += 1 + if retall: + allvecs.append(x) + intermediate_result = OptimizeResult(x=x, fun=fval) + if _call_callback_maybe_halt(callback, intermediate_result): + break + bnd = ftol * (np.abs(fx) + np.abs(fval)) + 1e-20 + if 2.0 * (fx - fval) <= bnd: + break + if fcalls[0] >= maxfun: + break + if iter >= maxiter: + break + if np.isnan(fx) and np.isnan(fval): + # Ended up in a nan-region: bail out + break + + # Construct the extrapolated point + direc1 = x - x1 + x1 = x.copy() + # make sure that we don't go outside the bounds when extrapolating + if lower_bound is None and upper_bound is None: + lmax = 1 + else: + _, lmax = _line_for_search(x, direc1, lower_bound, upper_bound) + x2 = x + min(lmax, 1) * direc1 + fx2 = func(x2) + + if (fx > fx2): + t = 2.0*(fx + fx2 - 2.0*fval) + temp = (fx - fval - delta) + t *= temp*temp + temp = fx - fx2 + t -= delta*temp*temp + if t < 0.0: + fval, x, direc1 = _linesearch_powell( + func, x, direc1, + tol=xtol * 100, + lower_bound=lower_bound, + upper_bound=upper_bound, + fval=fval + ) + if np.any(direc1): + direc[bigind] = direc[-1] + direc[-1] = direc1 + except _MaxFuncCallError: + break + + warnflag = 0 + msg = _status_message['success'] + # out of bounds is more urgent than exceeding function evals or iters, + # but I don't want to cause inconsistencies by changing the + # established warning flags for maxfev and maxiter, so the out of bounds + # warning flag becomes 3, but is checked for first. + if bounds and (np.any(lower_bound > x) or np.any(x > upper_bound)): + warnflag = 4 + msg = _status_message['out_of_bounds'] + elif fcalls[0] >= maxfun: + warnflag = 1 + msg = _status_message['maxfev'] + elif iter >= maxiter: + warnflag = 2 + msg = _status_message['maxiter'] + elif np.isnan(fval) or np.isnan(x).any(): + warnflag = 3 + msg = _status_message['nan'] + + if disp: + _print_success_message_or_warn(warnflag, msg, RuntimeWarning) + print(f" Current function value: {fval:f}") + print(" Iterations: %d" % iter) + print(" Function evaluations: %d" % fcalls[0]) + result = OptimizeResult(fun=fval, direc=direc, nit=iter, nfev=fcalls[0], + status=warnflag, success=(warnflag == 0), + message=msg, x=x) + if retall: + result['allvecs'] = allvecs + return result + + +def _endprint(x, flag, fval, maxfun, xtol, disp): + if flag == 0: + if disp > 1: + print("\nOptimization terminated successfully;\n" + "The returned value satisfies the termination criteria\n" + "(using xtol = ", xtol, ")") + return + + if flag == 1: + msg = ("\nMaximum number of function evaluations exceeded --- " + "increase maxfun argument.\n") + elif flag == 2: + msg = f"\n{_status_message['nan']}" + + _print_success_message_or_warn(flag, msg) + return + + +def brute(func, ranges, args=(), Ns=20, full_output=0, finish=fmin, + disp=False, workers=1): + """Minimize a function over a given range by brute force. + + Uses the "brute force" method, i.e., computes the function's value + at each point of a multidimensional grid of points, to find the global + minimum of the function. + + The function is evaluated everywhere in the range with the datatype of the + first call to the function, as enforced by the ``vectorize`` NumPy + function. The value and type of the function evaluation returned when + ``full_output=True`` are affected in addition by the ``finish`` argument + (see Notes). + + The brute force approach is inefficient because the number of grid points + increases exponentially - the number of grid points to evaluate is + ``Ns ** len(x)``. Consequently, even with coarse grid spacing, even + moderately sized problems can take a long time to run, and/or run into + memory limitations. + + Parameters + ---------- + func : callable + The objective function to be minimized. Must be in the + form ``f(x, *args)``, where ``x`` is the argument in + the form of a 1-D array and ``args`` is a tuple of any + additional fixed parameters needed to completely specify + the function. + ranges : tuple + Each component of the `ranges` tuple must be either a + "slice object" or a range tuple of the form ``(low, high)``. + The program uses these to create the grid of points on which + the objective function will be computed. See `Note 2` for + more detail. + args : tuple, optional + Any additional fixed parameters needed to completely specify + the function. + Ns : int, optional + Number of grid points along the axes, if not otherwise + specified. See `Note2`. + full_output : bool, optional + If True, return the evaluation grid and the objective function's + values on it. + finish : callable, optional + An optimization function that is called with the result of brute force + minimization as initial guess. `finish` should take `func` and + the initial guess as positional arguments, and take `args` as + keyword arguments. It may additionally take `full_output` + and/or `disp` as keyword arguments. Use None if no "polishing" + function is to be used. See Notes for more details. + disp : bool, optional + Set to True to print convergence messages from the `finish` callable. + workers : int or map-like callable, optional + If `workers` is an int the grid is subdivided into `workers` + sections and evaluated in parallel (uses + `multiprocessing.Pool `). + Supply `-1` to use all cores available to the Process. + Alternatively supply a map-like callable, such as + `multiprocessing.Pool.map` for evaluating the grid in parallel. + This evaluation is carried out as ``workers(func, iterable)``. + Requires that `func` be pickleable. + + .. versionadded:: 1.3.0 + + Returns + ------- + x0 : ndarray + A 1-D array containing the coordinates of a point at which the + objective function had its minimum value. (See `Note 1` for + which point is returned.) + fval : float + Function value at the point `x0`. (Returned when `full_output` is + True.) + grid : tuple + Representation of the evaluation grid. It has the same + length as `x0`. (Returned when `full_output` is True.) + Jout : ndarray + Function values at each point of the evaluation + grid, i.e., ``Jout = func(*grid)``. (Returned + when `full_output` is True.) + + See Also + -------- + basinhopping, differential_evolution + + Notes + ----- + *Note 1*: The program finds the gridpoint at which the lowest value + of the objective function occurs. If `finish` is None, that is the + point returned. When the global minimum occurs within (or not very far + outside) the grid's boundaries, and the grid is fine enough, that + point will be in the neighborhood of the global minimum. + + However, users often employ some other optimization program to + "polish" the gridpoint values, i.e., to seek a more precise + (local) minimum near `brute's` best gridpoint. + The `brute` function's `finish` option provides a convenient way to do + that. Any polishing program used must take `brute's` output as its + initial guess as a positional argument, and take `brute's` input values + for `args` as keyword arguments, otherwise an error will be raised. + It may additionally take `full_output` and/or `disp` as keyword arguments. + + `brute` assumes that the `finish` function returns either an + `OptimizeResult` object or a tuple in the form: + ``(xmin, Jmin, ... , statuscode)``, where ``xmin`` is the minimizing + value of the argument, ``Jmin`` is the minimum value of the objective + function, "..." may be some other returned values (which are not used + by `brute`), and ``statuscode`` is the status code of the `finish` program. + + Note that when `finish` is not None, the values returned are those + of the `finish` program, *not* the gridpoint ones. Consequently, + while `brute` confines its search to the input grid points, + the `finish` program's results usually will not coincide with any + gridpoint, and may fall outside the grid's boundary. Thus, if a + minimum only needs to be found over the provided grid points, make + sure to pass in ``finish=None``. + + *Note 2*: The grid of points is a `numpy.mgrid` object. + For `brute` the `ranges` and `Ns` inputs have the following effect. + Each component of the `ranges` tuple can be either a slice object or a + two-tuple giving a range of values, such as (0, 5). If the component is a + slice object, `brute` uses it directly. If the component is a two-tuple + range, `brute` internally converts it to a slice object that interpolates + `Ns` points from its low-value to its high-value, inclusive. + + Examples + -------- + We illustrate the use of `brute` to seek the global minimum of a function + of two variables that is given as the sum of a positive-definite + quadratic and two deep "Gaussian-shaped" craters. Specifically, define + the objective function `f` as the sum of three other functions, + ``f = f1 + f2 + f3``. We suppose each of these has a signature + ``(z, *params)``, where ``z = (x, y)``, and ``params`` and the functions + are as defined below. + + >>> import numpy as np + >>> params = (2, 3, 7, 8, 9, 10, 44, -1, 2, 26, 1, -2, 0.5) + >>> def f1(z, *params): + ... x, y = z + ... a, b, c, d, e, f, g, h, i, j, k, l, scale = params + ... return (a * x**2 + b * x * y + c * y**2 + d*x + e*y + f) + + >>> def f2(z, *params): + ... x, y = z + ... a, b, c, d, e, f, g, h, i, j, k, l, scale = params + ... return (-g*np.exp(-((x-h)**2 + (y-i)**2) / scale)) + + >>> def f3(z, *params): + ... x, y = z + ... a, b, c, d, e, f, g, h, i, j, k, l, scale = params + ... return (-j*np.exp(-((x-k)**2 + (y-l)**2) / scale)) + + >>> def f(z, *params): + ... return f1(z, *params) + f2(z, *params) + f3(z, *params) + + Thus, the objective function may have local minima near the minimum + of each of the three functions of which it is composed. To + use `fmin` to polish its gridpoint result, we may then continue as + follows: + + >>> rranges = (slice(-4, 4, 0.25), slice(-4, 4, 0.25)) + >>> from scipy import optimize + >>> resbrute = optimize.brute(f, rranges, args=params, full_output=True, + ... finish=optimize.fmin) + >>> resbrute[0] # global minimum + array([-1.05665192, 1.80834843]) + >>> resbrute[1] # function value at global minimum + -3.4085818767 + + Note that if `finish` had been set to None, we would have gotten the + gridpoint [-1.0 1.75] where the rounded function value is -2.892. + + """ + N = len(ranges) + if N > 40: + raise ValueError("Brute Force not possible with more " + "than 40 variables.") + lrange = list(ranges) + for k in range(N): + if not isinstance(lrange[k], slice): + if len(lrange[k]) < 3: + lrange[k] = tuple(lrange[k]) + (complex(Ns),) + lrange[k] = slice(*lrange[k]) + if (N == 1): + lrange = lrange[0] + + grid = np.mgrid[lrange] + + # obtain an array of parameters that is iterable by a map-like callable + inpt_shape = grid.shape + if (N > 1): + grid = np.reshape(grid, (inpt_shape[0], np.prod(inpt_shape[1:]))).T + + if not np.iterable(args): + args = (args,) + + wrapped_func = _Brute_Wrapper(func, args) + + # iterate over input arrays, possibly in parallel + with MapWrapper(pool=workers) as mapper: + Jout = np.array(list(mapper(wrapped_func, grid))) + if (N == 1): + grid = (grid,) + Jout = np.squeeze(Jout) + elif (N > 1): + Jout = np.reshape(Jout, inpt_shape[1:]) + grid = np.reshape(grid.T, inpt_shape) + + Nshape = shape(Jout) + + indx = argmin(Jout.ravel(), axis=-1) + Nindx = np.empty(N, int) + xmin = np.empty(N, float) + for k in range(N - 1, -1, -1): + thisN = Nshape[k] + Nindx[k] = indx % Nshape[k] + indx = indx // thisN + for k in range(N): + xmin[k] = grid[k][tuple(Nindx)] + + Jmin = Jout[tuple(Nindx)] + if (N == 1): + grid = grid[0] + xmin = xmin[0] + + if callable(finish): + # set up kwargs for `finish` function + finish_args = _getfullargspec(finish).args + finish_kwargs = dict() + if 'full_output' in finish_args: + finish_kwargs['full_output'] = 1 + if 'disp' in finish_args: + finish_kwargs['disp'] = disp + elif 'options' in finish_args: + # pass 'disp' as `options` + # (e.g., if `finish` is `minimize`) + finish_kwargs['options'] = {'disp': disp} + + # run minimizer + res = finish(func, xmin, args=args, **finish_kwargs) + + if isinstance(res, OptimizeResult): + xmin = res.x + Jmin = res.fun + success = res.success + else: + xmin = res[0] + Jmin = res[1] + success = res[-1] == 0 + if not success: + if disp: + warnings.warn("Either final optimization did not succeed or `finish` " + "does not return `statuscode` as its last argument.", + RuntimeWarning, stacklevel=2) + + if full_output: + return xmin, Jmin, grid, Jout + else: + return xmin + + +class _Brute_Wrapper: + """ + Object to wrap user cost function for optimize.brute, allowing picklability + """ + + def __init__(self, f, args): + self.f = f + self.args = [] if args is None else args + + def __call__(self, x): + # flatten needed for one dimensional case. + return self.f(np.asarray(x).flatten(), *self.args) + + +def show_options(solver=None, method=None, disp=True): + """ + Show documentation for additional options of optimization solvers. + + These are method-specific options that can be supplied through the + ``options`` dict. + + Parameters + ---------- + solver : str + Type of optimization solver. One of 'minimize', 'minimize_scalar', + 'root', 'root_scalar', 'linprog', or 'quadratic_assignment'. + method : str, optional + If not given, shows all methods of the specified solver. Otherwise, + show only the options for the specified method. Valid values + corresponds to methods' names of respective solver (e.g., 'BFGS' for + 'minimize'). + disp : bool, optional + Whether to print the result rather than returning it. + + Returns + ------- + text + Either None (for disp=True) or the text string (disp=False) + + Notes + ----- + The solver-specific methods are: + + `scipy.optimize.minimize` + + - :ref:`Nelder-Mead ` + - :ref:`Powell ` + - :ref:`CG ` + - :ref:`BFGS ` + - :ref:`Newton-CG ` + - :ref:`L-BFGS-B ` + - :ref:`TNC ` + - :ref:`COBYLA ` + - :ref:`COBYQA ` + - :ref:`SLSQP ` + - :ref:`dogleg ` + - :ref:`trust-ncg ` + + `scipy.optimize.root` + + - :ref:`hybr ` + - :ref:`lm ` + - :ref:`broyden1 ` + - :ref:`broyden2 ` + - :ref:`anderson ` + - :ref:`linearmixing ` + - :ref:`diagbroyden ` + - :ref:`excitingmixing ` + - :ref:`krylov ` + - :ref:`df-sane ` + + `scipy.optimize.minimize_scalar` + + - :ref:`brent ` + - :ref:`golden ` + - :ref:`bounded ` + + `scipy.optimize.root_scalar` + + - :ref:`bisect ` + - :ref:`brentq ` + - :ref:`brenth ` + - :ref:`ridder ` + - :ref:`toms748 ` + - :ref:`newton ` + - :ref:`secant ` + - :ref:`halley ` + + `scipy.optimize.linprog` + + - :ref:`simplex ` + - :ref:`interior-point ` + - :ref:`revised simplex ` + - :ref:`highs ` + - :ref:`highs-ds ` + - :ref:`highs-ipm ` + + `scipy.optimize.quadratic_assignment` + + - :ref:`faq ` + - :ref:`2opt ` + + Examples + -------- + We can print documentations of a solver in stdout: + + >>> from scipy.optimize import show_options + >>> show_options(solver="minimize") + ... + + Specifying a method is possible: + + >>> show_options(solver="minimize", method="Nelder-Mead") + ... + + We can also get the documentations as a string: + + >>> show_options(solver="minimize", method="Nelder-Mead", disp=False) + Minimization of scalar function of one or more variables using the ... + + """ + import textwrap + + doc_routines = { + 'minimize': ( + ('bfgs', 'scipy.optimize._optimize._minimize_bfgs'), + ('cg', 'scipy.optimize._optimize._minimize_cg'), + ('cobyla', 'scipy.optimize._cobyla_py._minimize_cobyla'), + ('cobyqa', 'scipy.optimize._cobyqa_py._minimize_cobyqa'), + ('dogleg', 'scipy.optimize._trustregion_dogleg._minimize_dogleg'), + ('l-bfgs-b', 'scipy.optimize._lbfgsb_py._minimize_lbfgsb'), + ('nelder-mead', 'scipy.optimize._optimize._minimize_neldermead'), + ('newton-cg', 'scipy.optimize._optimize._minimize_newtoncg'), + ('powell', 'scipy.optimize._optimize._minimize_powell'), + ('slsqp', 'scipy.optimize._slsqp_py._minimize_slsqp'), + ('tnc', 'scipy.optimize._tnc._minimize_tnc'), + ('trust-ncg', + 'scipy.optimize._trustregion_ncg._minimize_trust_ncg'), + ('trust-constr', + 'scipy.optimize._trustregion_constr.' + '_minimize_trustregion_constr'), + ('trust-exact', + 'scipy.optimize._trustregion_exact._minimize_trustregion_exact'), + ('trust-krylov', + 'scipy.optimize._trustregion_krylov._minimize_trust_krylov'), + ), + 'root': ( + ('hybr', 'scipy.optimize._minpack_py._root_hybr'), + ('lm', 'scipy.optimize._root._root_leastsq'), + ('broyden1', 'scipy.optimize._root._root_broyden1_doc'), + ('broyden2', 'scipy.optimize._root._root_broyden2_doc'), + ('anderson', 'scipy.optimize._root._root_anderson_doc'), + ('diagbroyden', 'scipy.optimize._root._root_diagbroyden_doc'), + ('excitingmixing', 'scipy.optimize._root._root_excitingmixing_doc'), + ('linearmixing', 'scipy.optimize._root._root_linearmixing_doc'), + ('krylov', 'scipy.optimize._root._root_krylov_doc'), + ('df-sane', 'scipy.optimize._spectral._root_df_sane'), + ), + 'root_scalar': ( + ('bisect', 'scipy.optimize._root_scalar._root_scalar_bisect_doc'), + ('brentq', 'scipy.optimize._root_scalar._root_scalar_brentq_doc'), + ('brenth', 'scipy.optimize._root_scalar._root_scalar_brenth_doc'), + ('ridder', 'scipy.optimize._root_scalar._root_scalar_ridder_doc'), + ('toms748', 'scipy.optimize._root_scalar._root_scalar_toms748_doc'), + ('secant', 'scipy.optimize._root_scalar._root_scalar_secant_doc'), + ('newton', 'scipy.optimize._root_scalar._root_scalar_newton_doc'), + ('halley', 'scipy.optimize._root_scalar._root_scalar_halley_doc'), + ), + 'linprog': ( + ('simplex', 'scipy.optimize._linprog._linprog_simplex_doc'), + ('interior-point', 'scipy.optimize._linprog._linprog_ip_doc'), + ('revised simplex', 'scipy.optimize._linprog._linprog_rs_doc'), + ('highs-ipm', 'scipy.optimize._linprog._linprog_highs_ipm_doc'), + ('highs-ds', 'scipy.optimize._linprog._linprog_highs_ds_doc'), + ('highs', 'scipy.optimize._linprog._linprog_highs_doc'), + ), + 'quadratic_assignment': ( + ('faq', 'scipy.optimize._qap._quadratic_assignment_faq'), + ('2opt', 'scipy.optimize._qap._quadratic_assignment_2opt'), + ), + 'minimize_scalar': ( + ('brent', 'scipy.optimize._optimize._minimize_scalar_brent'), + ('bounded', 'scipy.optimize._optimize._minimize_scalar_bounded'), + ('golden', 'scipy.optimize._optimize._minimize_scalar_golden'), + ), + } + + if solver is None: + text = ["\n\n\n========\n", "minimize\n", "========\n"] + text.append(show_options('minimize', disp=False)) + text.extend(["\n\n===============\n", "minimize_scalar\n", + "===============\n"]) + text.append(show_options('minimize_scalar', disp=False)) + text.extend(["\n\n\n====\n", "root\n", + "====\n"]) + text.append(show_options('root', disp=False)) + text.extend(['\n\n\n=======\n', 'linprog\n', + '=======\n']) + text.append(show_options('linprog', disp=False)) + text = "".join(text) + else: + solver = solver.lower() + if solver not in doc_routines: + raise ValueError(f'Unknown solver {solver!r}') + + if method is None: + text = [] + for name, _ in doc_routines[solver]: + text.extend(["\n\n" + name, "\n" + "="*len(name) + "\n\n"]) + text.append(show_options(solver, name, disp=False)) + text = "".join(text) + else: + method = method.lower() + methods = dict(doc_routines[solver]) + if method not in methods: + raise ValueError(f"Unknown method {method!r}") + name = methods[method] + + # Import function object + parts = name.split('.') + mod_name = ".".join(parts[:-1]) + __import__(mod_name) + obj = getattr(sys.modules[mod_name], parts[-1]) + + # Get doc + doc = obj.__doc__ + if doc is not None: + text = textwrap.dedent(doc).strip() + else: + text = "" + + if disp: + print(text) + return + else: + return text diff --git a/shared/package-files/03/033fa0a1a0e2fdb48f6c7bdb2ad38311971f28276f8b0dd72da94faad2e2db8a/MiscUtils.h b/shared/package-files/03/033fa0a1a0e2fdb48f6c7bdb2ad38311971f28276f8b0dd72da94faad2e2db8a/MiscUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..e616a7d1fcfb8254528dccc4e6b9d0658ffe1a3c --- /dev/null +++ b/shared/package-files/03/033fa0a1a0e2fdb48f6c7bdb2ad38311971f28276f8b0dd72da94faad2e2db8a/MiscUtils.h @@ -0,0 +1,32 @@ +#pragma once +#include +#include +#include +#include + +namespace at { +namespace native { + +static inline int cuda_int_cast(int64_t value, const char* varname) { + auto result = static_cast(value); + TORCH_CHECK(static_cast(result) == value, + "cuda_int_cast: The value of ", varname, "(", (long long)value, + ") is too large to fit into a int (", sizeof(int), " bytes)"); + return result; +} + +// Creates an array of size elements of type T, backed by pinned memory +// wrapped in a Storage +template +static inline Storage pin_memory(int64_t size) { + auto* allocator = cuda::getPinnedMemoryAllocator(); + int64_t adjusted_size = size * sizeof(T); + return Storage( + Storage::use_byte_size_t(), + adjusted_size, + allocator, + /*resizable=*/false); +} + +} // namespace native +} // namespace at diff --git a/shared/package-files/03/0340993c4015c58ae9cad8d750640909375dc06f0aaef5156ed05edc0d4caa02/_m_e_t_a.py b/shared/package-files/03/0340993c4015c58ae9cad8d750640909375dc06f0aaef5156ed05edc0d4caa02/_m_e_t_a.py new file mode 100644 index 0000000000000000000000000000000000000000..c5cea1b993e8e15968065f9a50b27784fcf4eb8c --- /dev/null +++ b/shared/package-files/03/0340993c4015c58ae9cad8d750640909375dc06f0aaef5156ed05edc0d4caa02/_m_e_t_a.py @@ -0,0 +1,112 @@ +from fontTools.misc import sstruct +from fontTools.misc.textTools import bytesjoin, strjoin, readHex +from fontTools.ttLib import TTLibError +from . import DefaultTable + +# Apple's documentation of 'meta': +# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6meta.html + +META_HEADER_FORMAT = """ + > # big endian + version: L + flags: L + dataOffset: L + numDataMaps: L +""" + + +DATA_MAP_FORMAT = """ + > # big endian + tag: 4s + dataOffset: L + dataLength: L +""" + + +class table__m_e_t_a(DefaultTable.DefaultTable): + """Metadata table + + The ``meta`` table contains various metadata values for the font. Each + category of metadata in the table is identified by a four-character tag. + + See also https://learn.microsoft.com/en-us/typography/opentype/spec/meta + """ + + def __init__(self, tag=None): + DefaultTable.DefaultTable.__init__(self, tag) + self.data = {} + + def decompile(self, data, ttFont): + headerSize = sstruct.calcsize(META_HEADER_FORMAT) + header = sstruct.unpack(META_HEADER_FORMAT, data[0:headerSize]) + if header["version"] != 1: + raise TTLibError("unsupported 'meta' version %d" % header["version"]) + dataMapSize = sstruct.calcsize(DATA_MAP_FORMAT) + for i in range(header["numDataMaps"]): + dataMapOffset = headerSize + i * dataMapSize + dataMap = sstruct.unpack( + DATA_MAP_FORMAT, data[dataMapOffset : dataMapOffset + dataMapSize] + ) + tag = dataMap["tag"] + offset = dataMap["dataOffset"] + self.data[tag] = data[offset : offset + dataMap["dataLength"]] + if tag in ["dlng", "slng"]: + self.data[tag] = self.data[tag].decode("utf-8") + + def compile(self, ttFont): + keys = sorted(self.data.keys()) + headerSize = sstruct.calcsize(META_HEADER_FORMAT) + dataOffset = headerSize + len(keys) * sstruct.calcsize(DATA_MAP_FORMAT) + header = sstruct.pack( + META_HEADER_FORMAT, + { + "version": 1, + "flags": 0, + "dataOffset": dataOffset, + "numDataMaps": len(keys), + }, + ) + dataMaps = [] + dataBlocks = [] + for tag in keys: + if tag in ["dlng", "slng"]: + data = self.data[tag].encode("utf-8") + else: + data = self.data[tag] + dataMaps.append( + sstruct.pack( + DATA_MAP_FORMAT, + {"tag": tag, "dataOffset": dataOffset, "dataLength": len(data)}, + ) + ) + dataBlocks.append(data) + dataOffset += len(data) + return bytesjoin([header] + dataMaps + dataBlocks) + + def toXML(self, writer, ttFont): + for tag in sorted(self.data.keys()): + if tag in ["dlng", "slng"]: + writer.begintag("text", tag=tag) + writer.newline() + writer.write(self.data[tag]) + writer.newline() + writer.endtag("text") + writer.newline() + else: + writer.begintag("hexdata", tag=tag) + writer.newline() + data = self.data[tag] + if min(data) >= 0x20 and max(data) <= 0x7E: + writer.comment("ascii: " + data.decode("ascii")) + writer.newline() + writer.dumphex(data) + writer.endtag("hexdata") + writer.newline() + + def fromXML(self, name, attrs, content, ttFont): + if name == "hexdata": + self.data[attrs["tag"]] = readHex(content) + elif name == "text" and attrs["tag"] in ["dlng", "slng"]: + self.data[attrs["tag"]] = strjoin(content).strip() + else: + raise TTLibError("can't handle '%s' element" % name) diff --git a/shared/package-files/03/03416510aea384836f168c4978ca1fd9167c4c41562e4369ccb335d08b2e046a/_fixed.py b/shared/package-files/03/03416510aea384836f168c4978ca1fd9167c4c41562e4369ccb335d08b2e046a/_fixed.py new file mode 100644 index 0000000000000000000000000000000000000000..5fd50e7e2b2a62abc02263d39b7dd105f377ed91 --- /dev/null +++ b/shared/package-files/03/03416510aea384836f168c4978ca1fd9167c4c41562e4369ccb335d08b2e046a/_fixed.py @@ -0,0 +1,26 @@ +import numpy as np + +from ._masker import Masker + + +class Fixed(Masker): + """This leaves the input unchanged during masking, and is used for things like scoring labels. + + Sometimes there are inputs to the model that we do not want to explain, but rather we want to + consider them fixed. The primary example of this is when we explain the loss of the model using + the labels. These "true" labels are inputs to the function we are explaining, but we don't want + to attribute credit to them, instead we want to consider them fixed and assign all the credit to + the model's input features. This is where the Fixed masker can help, since we can apply it to the + label inputs. + """ + + def __init__(self): + self.shape = (None, 0) + self.clustering = np.zeros((0, 4)) + + def __call__(self, mask, x): + return ([x],) + + def mask_shapes(self, x): + """The shape of the masks we expect.""" + return [(0,)] diff --git a/shared/package-files/03/034494ddbb8e506853f8d23fe8b43aa7bd1f152214de22c5760cefceb291e921/data_x_x2_x3.csv b/shared/package-files/03/034494ddbb8e506853f8d23fe8b43aa7bd1f152214de22c5760cefceb291e921/data_x_x2_x3.csv new file mode 100644 index 0000000000000000000000000000000000000000..521da1453204a97f4189ecd3c82e99970786f208 --- /dev/null +++ b/shared/package-files/03/034494ddbb8e506853f8d23fe8b43aa7bd1f152214de22c5760cefceb291e921/data_x_x2_x3.csv @@ -0,0 +1,11 @@ + 0 0 0 + 1 1 1 + 2 4 8 + 3 9 27 + 4 16 64 + 5 25 125 + 6 36 216 + 7 49 343 + 8 64 512 + 9 81 729 +10 100 1000 diff --git a/shared/package-files/03/0346d975de368cfa7d65a971d91b47f3c5b4dbd7cbd3913cf4fb416e5bc39141/mgterm b/shared/package-files/03/0346d975de368cfa7d65a971d91b47f3c5b4dbd7cbd3913cf4fb416e5bc39141/mgterm new file mode 100644 index 0000000000000000000000000000000000000000..3902af02cf98be788dfff897b457816e331f9fd8 Binary files /dev/null and b/shared/package-files/03/0346d975de368cfa7d65a971d91b47f3c5b4dbd7cbd3913cf4fb416e5bc39141/mgterm differ diff --git a/shared/package-files/03/034812a748714b21037ee80956263709e59a945faf2bbd3561fb73ae3490cd0e/calculus.py b/shared/package-files/03/034812a748714b21037ee80956263709e59a945faf2bbd3561fb73ae3490cd0e/calculus.py new file mode 100644 index 0000000000000000000000000000000000000000..24256f121d6c07e5ce954f2a5f5024f156f64016 --- /dev/null +++ b/shared/package-files/03/034812a748714b21037ee80956263709e59a945faf2bbd3561fb73ae3490cd0e/calculus.py @@ -0,0 +1,6 @@ +class CalculusMethods(object): + pass + +def defun(f): + setattr(CalculusMethods, f.__name__, f) + return f diff --git a/shared/package-files/03/034870dc0a84357793169d5171bff86b212c2d82ee315173f6ced4ae095ab4b4/test_ivp.py b/shared/package-files/03/034870dc0a84357793169d5171bff86b212c2d82ee315173f6ced4ae095ab4b4/test_ivp.py new file mode 100644 index 0000000000000000000000000000000000000000..cd318b9a165051293ac13b9b0e63be2df322963b --- /dev/null +++ b/shared/package-files/03/034870dc0a84357793169d5171bff86b212c2d82ee315173f6ced4ae095ab4b4/test_ivp.py @@ -0,0 +1,1287 @@ +from itertools import product +from numpy.testing import (assert_, assert_allclose, assert_array_less, + assert_equal, assert_no_warnings, suppress_warnings) +import pytest +from pytest import raises as assert_raises +import numpy as np +from scipy.optimize._numdiff import group_columns +from scipy.integrate import solve_ivp, RK23, RK45, DOP853, Radau, BDF, LSODA +from scipy.integrate import OdeSolution +from scipy.integrate._ivp.common import num_jac, select_initial_step +from scipy.integrate._ivp.base import ConstantDenseOutput +from scipy.sparse import coo_matrix, csc_matrix + + +def fun_zero(t, y): + return np.zeros_like(y) + + +def fun_linear(t, y): + return np.array([-y[0] - 5 * y[1], y[0] + y[1]]) + + +def jac_linear(): + return np.array([[-1, -5], [1, 1]]) + + +def sol_linear(t): + return np.vstack((-5 * np.sin(2 * t), + 2 * np.cos(2 * t) + np.sin(2 * t))) + + +def fun_rational(t, y): + return np.array([y[1] / t, + y[1] * (y[0] + 2 * y[1] - 1) / (t * (y[0] - 1))]) + + +def fun_rational_vectorized(t, y): + return np.vstack((y[1] / t, + y[1] * (y[0] + 2 * y[1] - 1) / (t * (y[0] - 1)))) + + +def jac_rational(t, y): + return np.array([ + [0, 1 / t], + [-2 * y[1] ** 2 / (t * (y[0] - 1) ** 2), + (y[0] + 4 * y[1] - 1) / (t * (y[0] - 1))] + ]) + + +def jac_rational_sparse(t, y): + return csc_matrix([ + [0, 1 / t], + [-2 * y[1] ** 2 / (t * (y[0] - 1) ** 2), + (y[0] + 4 * y[1] - 1) / (t * (y[0] - 1))] + ]) + + +def sol_rational(t): + return np.asarray((t / (t + 10), 10 * t / (t + 10) ** 2)) + + +def fun_medazko(t, y): + n = y.shape[0] // 2 + k = 100 + c = 4 + + phi = 2 if t <= 5 else 0 + y = np.hstack((phi, 0, y, y[-2])) + + d = 1 / n + j = np.arange(n) + 1 + alpha = 2 * (j * d - 1) ** 3 / c ** 2 + beta = (j * d - 1) ** 4 / c ** 2 + + j_2_p1 = 2 * j + 2 + j_2_m3 = 2 * j - 2 + j_2_m1 = 2 * j + j_2 = 2 * j + 1 + + f = np.empty(2 * n) + f[::2] = (alpha * (y[j_2_p1] - y[j_2_m3]) / (2 * d) + + beta * (y[j_2_m3] - 2 * y[j_2_m1] + y[j_2_p1]) / d ** 2 - + k * y[j_2_m1] * y[j_2]) + f[1::2] = -k * y[j_2] * y[j_2_m1] + + return f + + +def medazko_sparsity(n): + cols = [] + rows = [] + + i = np.arange(n) * 2 + + cols.append(i[1:]) + rows.append(i[1:] - 2) + + cols.append(i) + rows.append(i) + + cols.append(i) + rows.append(i + 1) + + cols.append(i[:-1]) + rows.append(i[:-1] + 2) + + i = np.arange(n) * 2 + 1 + + cols.append(i) + rows.append(i) + + cols.append(i) + rows.append(i - 1) + + cols = np.hstack(cols) + rows = np.hstack(rows) + + return coo_matrix((np.ones_like(cols), (cols, rows))) + + +def fun_complex(t, y): + return -y + + +def jac_complex(t, y): + return -np.eye(y.shape[0]) + + +def jac_complex_sparse(t, y): + return csc_matrix(jac_complex(t, y)) + + +def sol_complex(t): + y = (0.5 + 1j) * np.exp(-t) + return y.reshape((1, -1)) + + +def fun_event_dense_output_LSODA(t, y): + return y * (t - 2) + + +def jac_event_dense_output_LSODA(t, y): + return t - 2 + + +def sol_event_dense_output_LSODA(t): + return np.exp(t ** 2 / 2 - 2 * t + np.log(0.05) - 6) + + +def compute_error(y, y_true, rtol, atol): + e = (y - y_true) / (atol + rtol * np.abs(y_true)) + return np.linalg.norm(e, axis=0) / np.sqrt(e.shape[0]) + +def test_duplicate_timestamps(): + def upward_cannon(t, y): + return [y[1], -9.80665] + + def hit_ground(t, y): + return y[0] + + hit_ground.terminal = True + hit_ground.direction = -1 + + sol = solve_ivp(upward_cannon, [0, np.inf], [0, 0.01], + max_step=0.05 * 0.001 / 9.80665, + events=hit_ground, dense_output=True) + assert_allclose(sol.sol(0.01), np.asarray([-0.00039033, -0.08806632]), + rtol=1e-5, atol=1e-8) + assert_allclose(sol.t_events, np.asarray([[0.00203943]]), rtol=1e-5, atol=1e-8) + assert_allclose(sol.y_events, [np.asarray([[ 0.0, -0.01 ]])], atol=1e-9) + assert sol.success + assert_equal(sol.status, 1) + +@pytest.mark.thread_unsafe +def test_integration(): + rtol = 1e-3 + atol = 1e-6 + y0 = [1/3, 2/9] + + for vectorized, method, t_span, jac in product( + [False, True], + ['RK23', 'RK45', 'DOP853', 'Radau', 'BDF', 'LSODA'], + [[5, 9], [5, 1]], + [None, jac_rational, jac_rational_sparse]): + + if vectorized: + fun = fun_rational_vectorized + else: + fun = fun_rational + + with suppress_warnings() as sup: + sup.filter(UserWarning, + "The following arguments have no effect for a chosen " + "solver: `jac`") + res = solve_ivp(fun, t_span, y0, rtol=rtol, + atol=atol, method=method, dense_output=True, + jac=jac, vectorized=vectorized) + assert_equal(res.t[0], t_span[0]) + assert_(res.t_events is None) + assert_(res.y_events is None) + assert_(res.success) + assert_equal(res.status, 0) + + if method == 'DOP853': + # DOP853 spends more functions evaluation because it doesn't + # have enough time to develop big enough step size. + assert_(res.nfev < 50) + else: + assert_(res.nfev < 40) + + if method in ['RK23', 'RK45', 'DOP853', 'LSODA']: + assert_equal(res.njev, 0) + assert_equal(res.nlu, 0) + else: + assert_(0 < res.njev < 3) + assert_(0 < res.nlu < 10) + + y_true = sol_rational(res.t) + e = compute_error(res.y, y_true, rtol, atol) + assert_(np.all(e < 5)) + + tc = np.linspace(*t_span) + yc_true = sol_rational(tc) + yc = res.sol(tc) + + e = compute_error(yc, yc_true, rtol, atol) + assert_(np.all(e < 5)) + + tc = (t_span[0] + t_span[-1]) / 2 + yc_true = sol_rational(tc) + yc = res.sol(tc) + + e = compute_error(yc, yc_true, rtol, atol) + assert_(np.all(e < 5)) + + assert_allclose(res.sol(res.t), res.y, rtol=1e-15, atol=1e-15) + + +@pytest.mark.thread_unsafe +def test_integration_complex(): + rtol = 1e-3 + atol = 1e-6 + y0 = [0.5 + 1j] + t_span = [0, 1] + tc = np.linspace(t_span[0], t_span[1]) + for method, jac in product(['RK23', 'RK45', 'DOP853', 'BDF'], + [None, jac_complex, jac_complex_sparse]): + with suppress_warnings() as sup: + sup.filter(UserWarning, + "The following arguments have no effect for a chosen " + "solver: `jac`") + res = solve_ivp(fun_complex, t_span, y0, method=method, + dense_output=True, rtol=rtol, atol=atol, jac=jac) + + assert_equal(res.t[0], t_span[0]) + assert_(res.t_events is None) + assert_(res.y_events is None) + assert_(res.success) + assert_equal(res.status, 0) + + if method == 'DOP853': + assert res.nfev < 35 + else: + assert res.nfev < 25 + + if method == 'BDF': + assert_equal(res.njev, 1) + assert res.nlu < 6 + else: + assert res.njev == 0 + assert res.nlu == 0 + + y_true = sol_complex(res.t) + e = compute_error(res.y, y_true, rtol, atol) + assert np.all(e < 5) + + yc_true = sol_complex(tc) + yc = res.sol(tc) + e = compute_error(yc, yc_true, rtol, atol) + + assert np.all(e < 5) + + +@pytest.mark.fail_slow(5) +def test_integration_sparse_difference(): + n = 200 + t_span = [0, 20] + y0 = np.zeros(2 * n) + y0[1::2] = 1 + sparsity = medazko_sparsity(n) + + for method in ['BDF', 'Radau']: + res = solve_ivp(fun_medazko, t_span, y0, method=method, + jac_sparsity=sparsity) + + assert_equal(res.t[0], t_span[0]) + assert_(res.t_events is None) + assert_(res.y_events is None) + assert_(res.success) + assert_equal(res.status, 0) + + assert_allclose(res.y[78, -1], 0.233994e-3, rtol=1e-2) + assert_allclose(res.y[79, -1], 0, atol=1e-3) + assert_allclose(res.y[148, -1], 0.359561e-3, rtol=1e-2) + assert_allclose(res.y[149, -1], 0, atol=1e-3) + assert_allclose(res.y[198, -1], 0.117374129e-3, rtol=1e-2) + assert_allclose(res.y[199, -1], 0.6190807e-5, atol=1e-3) + assert_allclose(res.y[238, -1], 0, atol=1e-3) + assert_allclose(res.y[239, -1], 0.9999997, rtol=1e-2) + + +def test_integration_const_jac(): + rtol = 1e-3 + atol = 1e-6 + y0 = [0, 2] + t_span = [0, 2] + J = jac_linear() + J_sparse = csc_matrix(J) + + for method, jac in product(['Radau', 'BDF'], [J, J_sparse]): + res = solve_ivp(fun_linear, t_span, y0, rtol=rtol, atol=atol, + method=method, dense_output=True, jac=jac) + assert_equal(res.t[0], t_span[0]) + assert_(res.t_events is None) + assert_(res.y_events is None) + assert_(res.success) + assert_equal(res.status, 0) + + assert_(res.nfev < 100) + assert_equal(res.njev, 0) + assert_(0 < res.nlu < 15) + + y_true = sol_linear(res.t) + e = compute_error(res.y, y_true, rtol, atol) + assert_(np.all(e < 10)) + + tc = np.linspace(*t_span) + yc_true = sol_linear(tc) + yc = res.sol(tc) + + e = compute_error(yc, yc_true, rtol, atol) + assert_(np.all(e < 15)) + + assert_allclose(res.sol(res.t), res.y, rtol=1e-14, atol=1e-14) + + +@pytest.mark.slow +@pytest.mark.parametrize('method', ['Radau', 'BDF', 'LSODA']) +def test_integration_stiff(method, num_parallel_threads): + rtol = 1e-6 + atol = 1e-6 + y0 = [1e4, 0, 0] + tspan = [0, 1e8] + + if method == 'LSODA' and num_parallel_threads > 1: + pytest.skip(reason='LSODA does not allow for concurrent calls') + + def fun_robertson(t, state): + x, y, z = state + return [ + -0.04 * x + 1e4 * y * z, + 0.04 * x - 1e4 * y * z - 3e7 * y * y, + 3e7 * y * y, + ] + + res = solve_ivp(fun_robertson, tspan, y0, rtol=rtol, + atol=atol, method=method) + + # If the stiff mode is not activated correctly, these numbers will be much bigger + assert res.nfev < 5000 + assert res.njev < 200 + + +def test_events(num_parallel_threads): + def event_rational_1(t, y): + return y[0] - y[1] ** 0.7 + + def event_rational_2(t, y): + return y[1] ** 0.6 - y[0] + + def event_rational_3(t, y): + return t - 7.4 + + event_rational_3.terminal = True + + for method in ['RK23', 'RK45', 'DOP853', 'Radau', 'BDF', 'LSODA']: + if method == 'LSODA' and num_parallel_threads > 1: + continue + + res = solve_ivp(fun_rational, [5, 8], [1/3, 2/9], method=method, + events=(event_rational_1, event_rational_2)) + assert_equal(res.status, 0) + assert_equal(res.t_events[0].size, 1) + assert_equal(res.t_events[1].size, 1) + assert_(5.3 < res.t_events[0][0] < 5.7) + assert_(7.3 < res.t_events[1][0] < 7.7) + + assert_equal(res.y_events[0].shape, (1, 2)) + assert_equal(res.y_events[1].shape, (1, 2)) + assert np.isclose( + event_rational_1(res.t_events[0][0], res.y_events[0][0]), 0) + assert np.isclose( + event_rational_2(res.t_events[1][0], res.y_events[1][0]), 0) + + event_rational_1.direction = 1 + event_rational_2.direction = 1 + res = solve_ivp(fun_rational, [5, 8], [1 / 3, 2 / 9], method=method, + events=(event_rational_1, event_rational_2)) + assert_equal(res.status, 0) + assert_equal(res.t_events[0].size, 1) + assert_equal(res.t_events[1].size, 0) + assert_(5.3 < res.t_events[0][0] < 5.7) + assert_equal(res.y_events[0].shape, (1, 2)) + assert_equal(res.y_events[1].shape, (0,)) + assert np.isclose( + event_rational_1(res.t_events[0][0], res.y_events[0][0]), 0) + + event_rational_1.direction = -1 + event_rational_2.direction = -1 + res = solve_ivp(fun_rational, [5, 8], [1 / 3, 2 / 9], method=method, + events=(event_rational_1, event_rational_2)) + assert_equal(res.status, 0) + assert_equal(res.t_events[0].size, 0) + assert_equal(res.t_events[1].size, 1) + assert_(7.3 < res.t_events[1][0] < 7.7) + assert_equal(res.y_events[0].shape, (0,)) + assert_equal(res.y_events[1].shape, (1, 2)) + assert np.isclose( + event_rational_2(res.t_events[1][0], res.y_events[1][0]), 0) + + event_rational_1.direction = 0 + event_rational_2.direction = 0 + + res = solve_ivp(fun_rational, [5, 8], [1 / 3, 2 / 9], method=method, + events=(event_rational_1, event_rational_2, + event_rational_3), dense_output=True) + assert_equal(res.status, 1) + assert_equal(res.t_events[0].size, 1) + assert_equal(res.t_events[1].size, 0) + assert_equal(res.t_events[2].size, 1) + assert_(5.3 < res.t_events[0][0] < 5.7) + assert_(7.3 < res.t_events[2][0] < 7.5) + assert_equal(res.y_events[0].shape, (1, 2)) + assert_equal(res.y_events[1].shape, (0,)) + assert_equal(res.y_events[2].shape, (1, 2)) + assert np.isclose( + event_rational_1(res.t_events[0][0], res.y_events[0][0]), 0) + assert np.isclose( + event_rational_3(res.t_events[2][0], res.y_events[2][0]), 0) + + res = solve_ivp(fun_rational, [5, 8], [1 / 3, 2 / 9], method=method, + events=event_rational_1, dense_output=True) + assert_equal(res.status, 0) + assert_equal(res.t_events[0].size, 1) + assert_(5.3 < res.t_events[0][0] < 5.7) + + assert_equal(res.y_events[0].shape, (1, 2)) + assert np.isclose( + event_rational_1(res.t_events[0][0], res.y_events[0][0]), 0) + + # Also test that termination by event doesn't break interpolants. + tc = np.linspace(res.t[0], res.t[-1]) + yc_true = sol_rational(tc) + yc = res.sol(tc) + e = compute_error(yc, yc_true, 1e-3, 1e-6) + assert_(np.all(e < 5)) + + # Test that the y_event matches solution + assert np.allclose(sol_rational(res.t_events[0][0]), res.y_events[0][0], + rtol=1e-3, atol=1e-6) + + # Test in backward direction. + event_rational_1.direction = 0 + event_rational_2.direction = 0 + for method in ['RK23', 'RK45', 'DOP853', 'Radau', 'BDF', 'LSODA']: + if method == 'LSODA' and num_parallel_threads > 1: + continue + + res = solve_ivp(fun_rational, [8, 5], [4/9, 20/81], method=method, + events=(event_rational_1, event_rational_2)) + assert_equal(res.status, 0) + assert_equal(res.t_events[0].size, 1) + assert_equal(res.t_events[1].size, 1) + assert_(5.3 < res.t_events[0][0] < 5.7) + assert_(7.3 < res.t_events[1][0] < 7.7) + + assert_equal(res.y_events[0].shape, (1, 2)) + assert_equal(res.y_events[1].shape, (1, 2)) + assert np.isclose( + event_rational_1(res.t_events[0][0], res.y_events[0][0]), 0) + assert np.isclose( + event_rational_2(res.t_events[1][0], res.y_events[1][0]), 0) + + event_rational_1.direction = -1 + event_rational_2.direction = -1 + res = solve_ivp(fun_rational, [8, 5], [4/9, 20/81], method=method, + events=(event_rational_1, event_rational_2)) + assert_equal(res.status, 0) + assert_equal(res.t_events[0].size, 1) + assert_equal(res.t_events[1].size, 0) + assert_(5.3 < res.t_events[0][0] < 5.7) + + assert_equal(res.y_events[0].shape, (1, 2)) + assert_equal(res.y_events[1].shape, (0,)) + assert np.isclose( + event_rational_1(res.t_events[0][0], res.y_events[0][0]), 0) + + event_rational_1.direction = 1 + event_rational_2.direction = 1 + res = solve_ivp(fun_rational, [8, 5], [4/9, 20/81], method=method, + events=(event_rational_1, event_rational_2)) + assert_equal(res.status, 0) + assert_equal(res.t_events[0].size, 0) + assert_equal(res.t_events[1].size, 1) + assert_(7.3 < res.t_events[1][0] < 7.7) + + assert_equal(res.y_events[0].shape, (0,)) + assert_equal(res.y_events[1].shape, (1, 2)) + assert np.isclose( + event_rational_2(res.t_events[1][0], res.y_events[1][0]), 0) + + event_rational_1.direction = 0 + event_rational_2.direction = 0 + + res = solve_ivp(fun_rational, [8, 5], [4/9, 20/81], method=method, + events=(event_rational_1, event_rational_2, + event_rational_3), dense_output=True) + assert_equal(res.status, 1) + assert_equal(res.t_events[0].size, 0) + assert_equal(res.t_events[1].size, 1) + assert_equal(res.t_events[2].size, 1) + assert_(7.3 < res.t_events[1][0] < 7.7) + assert_(7.3 < res.t_events[2][0] < 7.5) + + assert_equal(res.y_events[0].shape, (0,)) + assert_equal(res.y_events[1].shape, (1, 2)) + assert_equal(res.y_events[2].shape, (1, 2)) + assert np.isclose( + event_rational_2(res.t_events[1][0], res.y_events[1][0]), 0) + assert np.isclose( + event_rational_3(res.t_events[2][0], res.y_events[2][0]), 0) + + # Also test that termination by event doesn't break interpolants. + tc = np.linspace(res.t[-1], res.t[0]) + yc_true = sol_rational(tc) + yc = res.sol(tc) + e = compute_error(yc, yc_true, 1e-3, 1e-6) + assert_(np.all(e < 5)) + + assert np.allclose(sol_rational(res.t_events[1][0]), res.y_events[1][0], + rtol=1e-3, atol=1e-6) + assert np.allclose(sol_rational(res.t_events[2][0]), res.y_events[2][0], + rtol=1e-3, atol=1e-6) + + +def _get_harmonic_oscillator(): + def f(t, y): + return [y[1], -y[0]] + + def event(t, y): + return y[0] + + return f, event + + +@pytest.mark.parametrize('n_events', [3, 4]) +def test_event_terminal_integer(n_events): + f, event = _get_harmonic_oscillator() + event.terminal = n_events + res = solve_ivp(f, (0, 100), [1, 0], events=event) + assert len(res.t_events[0]) == n_events + assert len(res.y_events[0]) == n_events + assert_allclose(res.y_events[0][:, 0], 0, atol=1e-14) + + +def test_event_terminal_iv(): + f, event = _get_harmonic_oscillator() + args = (f, (0, 100), [1, 0]) + + event.terminal = None + res = solve_ivp(*args, events=event) + event.terminal = 0 + ref = solve_ivp(*args, events=event) + assert_allclose(res.t_events, ref.t_events) + + message = "The `terminal` attribute..." + event.terminal = -1 + with pytest.raises(ValueError, match=message): + solve_ivp(*args, events=event) + event.terminal = 3.5 + with pytest.raises(ValueError, match=message): + solve_ivp(*args, events=event) + + +def test_max_step(num_parallel_threads): + rtol = 1e-3 + atol = 1e-6 + y0 = [1/3, 2/9] + for method in [RK23, RK45, DOP853, Radau, BDF, LSODA]: + if method is LSODA and num_parallel_threads > 1: + continue + for t_span in ([5, 9], [5, 1]): + res = solve_ivp(fun_rational, t_span, y0, rtol=rtol, + max_step=0.5, atol=atol, method=method, + dense_output=True) + assert_equal(res.t[0], t_span[0]) + assert_equal(res.t[-1], t_span[-1]) + assert_(np.all(np.abs(np.diff(res.t)) <= 0.5 + 1e-15)) + assert_(res.t_events is None) + assert_(res.success) + assert_equal(res.status, 0) + + y_true = sol_rational(res.t) + e = compute_error(res.y, y_true, rtol, atol) + assert_(np.all(e < 5)) + + tc = np.linspace(*t_span) + yc_true = sol_rational(tc) + yc = res.sol(tc) + + e = compute_error(yc, yc_true, rtol, atol) + assert_(np.all(e < 5)) + + assert_allclose(res.sol(res.t), res.y, rtol=1e-15, atol=1e-15) + + assert_raises(ValueError, method, fun_rational, t_span[0], y0, + t_span[1], max_step=-1) + + if method is not LSODA: + solver = method(fun_rational, t_span[0], y0, t_span[1], + rtol=rtol, atol=atol, max_step=1e-20) + message = solver.step() + message = solver.step() # First step succeeds but second step fails. + assert_equal(solver.status, 'failed') + assert_("step size is less" in message) + assert_raises(RuntimeError, solver.step) + + +def test_first_step(num_parallel_threads): + rtol = 1e-3 + atol = 1e-6 + y0 = [1/3, 2/9] + first_step = 0.1 + for method in [RK23, RK45, DOP853, Radau, BDF, LSODA]: + if method is LSODA and num_parallel_threads > 1: + continue + for t_span in ([5, 9], [5, 1]): + res = solve_ivp(fun_rational, t_span, y0, rtol=rtol, + max_step=0.5, atol=atol, method=method, + dense_output=True, first_step=first_step) + + assert_equal(res.t[0], t_span[0]) + assert_equal(res.t[-1], t_span[-1]) + assert_allclose(first_step, np.abs(res.t[1] - 5)) + assert_(res.t_events is None) + assert_(res.success) + assert_equal(res.status, 0) + + y_true = sol_rational(res.t) + e = compute_error(res.y, y_true, rtol, atol) + assert_(np.all(e < 5)) + + tc = np.linspace(*t_span) + yc_true = sol_rational(tc) + yc = res.sol(tc) + + e = compute_error(yc, yc_true, rtol, atol) + assert_(np.all(e < 5)) + + assert_allclose(res.sol(res.t), res.y, rtol=1e-15, atol=1e-15) + + assert_raises(ValueError, method, fun_rational, t_span[0], y0, + t_span[1], first_step=-1) + assert_raises(ValueError, method, fun_rational, t_span[0], y0, + t_span[1], first_step=5) + + +def test_t_eval(): + rtol = 1e-3 + atol = 1e-6 + y0 = [1/3, 2/9] + for t_span in ([5, 9], [5, 1]): + t_eval = np.linspace(t_span[0], t_span[1], 10) + res = solve_ivp(fun_rational, t_span, y0, rtol=rtol, atol=atol, + t_eval=t_eval) + assert_equal(res.t, t_eval) + assert_(res.t_events is None) + assert_(res.success) + assert_equal(res.status, 0) + + y_true = sol_rational(res.t) + e = compute_error(res.y, y_true, rtol, atol) + assert_(np.all(e < 5)) + + t_eval = [5, 5.01, 7, 8, 8.01, 9] + res = solve_ivp(fun_rational, [5, 9], y0, rtol=rtol, atol=atol, + t_eval=t_eval) + assert_equal(res.t, t_eval) + assert_(res.t_events is None) + assert_(res.success) + assert_equal(res.status, 0) + + y_true = sol_rational(res.t) + e = compute_error(res.y, y_true, rtol, atol) + assert_(np.all(e < 5)) + + t_eval = [5, 4.99, 3, 1.5, 1.1, 1.01, 1] + res = solve_ivp(fun_rational, [5, 1], y0, rtol=rtol, atol=atol, + t_eval=t_eval) + assert_equal(res.t, t_eval) + assert_(res.t_events is None) + assert_(res.success) + assert_equal(res.status, 0) + + t_eval = [5.01, 7, 8, 8.01] + res = solve_ivp(fun_rational, [5, 9], y0, rtol=rtol, atol=atol, + t_eval=t_eval) + assert_equal(res.t, t_eval) + assert_(res.t_events is None) + assert_(res.success) + assert_equal(res.status, 0) + + y_true = sol_rational(res.t) + e = compute_error(res.y, y_true, rtol, atol) + assert_(np.all(e < 5)) + + t_eval = [4.99, 3, 1.5, 1.1, 1.01] + res = solve_ivp(fun_rational, [5, 1], y0, rtol=rtol, atol=atol, + t_eval=t_eval) + assert_equal(res.t, t_eval) + assert_(res.t_events is None) + assert_(res.success) + assert_equal(res.status, 0) + + t_eval = [4, 6] + assert_raises(ValueError, solve_ivp, fun_rational, [5, 9], y0, + rtol=rtol, atol=atol, t_eval=t_eval) + + +def test_t_eval_dense_output(): + rtol = 1e-3 + atol = 1e-6 + y0 = [1/3, 2/9] + t_span = [5, 9] + t_eval = np.linspace(t_span[0], t_span[1], 10) + res = solve_ivp(fun_rational, t_span, y0, rtol=rtol, atol=atol, + t_eval=t_eval) + res_d = solve_ivp(fun_rational, t_span, y0, rtol=rtol, atol=atol, + t_eval=t_eval, dense_output=True) + assert_equal(res.t, t_eval) + assert_(res.t_events is None) + assert_(res.success) + assert_equal(res.status, 0) + + assert_equal(res.t, res_d.t) + assert_equal(res.y, res_d.y) + assert_(res_d.t_events is None) + assert_(res_d.success) + assert_equal(res_d.status, 0) + + # if t and y are equal only test values for one case + y_true = sol_rational(res.t) + e = compute_error(res.y, y_true, rtol, atol) + assert_(np.all(e < 5)) + + +@pytest.mark.thread_unsafe +def test_t_eval_early_event(): + def early_event(t, y): + return t - 7 + + early_event.terminal = True + + rtol = 1e-3 + atol = 1e-6 + y0 = [1/3, 2/9] + t_span = [5, 9] + t_eval = np.linspace(7.5, 9, 16) + for method in ['RK23', 'RK45', 'DOP853', 'Radau', 'BDF', 'LSODA']: + with suppress_warnings() as sup: + sup.filter(UserWarning, + "The following arguments have no effect for a chosen " + "solver: `jac`") + res = solve_ivp(fun_rational, t_span, y0, rtol=rtol, atol=atol, + method=method, t_eval=t_eval, events=early_event, + jac=jac_rational) + assert res.success + assert res.message == 'A termination event occurred.' + assert res.status == 1 + assert not res.t and not res.y + assert len(res.t_events) == 1 + assert res.t_events[0].size == 1 + assert res.t_events[0][0] == 7 + + +def test_event_dense_output_LSODA(num_parallel_threads): + if num_parallel_threads > 1: + pytest.skip('LSODA does not allow for concurrent execution') + + def event_lsoda(t, y): + return y[0] - 2.02e-5 + + rtol = 1e-3 + atol = 1e-6 + y0 = [0.05] + t_span = [-2, 2] + first_step = 1e-3 + res = solve_ivp( + fun_event_dense_output_LSODA, + t_span, + y0, + method="LSODA", + dense_output=True, + events=event_lsoda, + first_step=first_step, + max_step=1, + rtol=rtol, + atol=atol, + jac=jac_event_dense_output_LSODA, + ) + + assert_equal(res.t[0], t_span[0]) + assert_equal(res.t[-1], t_span[-1]) + assert_allclose(first_step, np.abs(res.t[1] - t_span[0])) + assert res.success + assert_equal(res.status, 0) + + y_true = sol_event_dense_output_LSODA(res.t) + e = compute_error(res.y, y_true, rtol, atol) + assert_array_less(e, 5) + + tc = np.linspace(*t_span) + yc_true = sol_event_dense_output_LSODA(tc) + yc = res.sol(tc) + e = compute_error(yc, yc_true, rtol, atol) + assert_array_less(e, 5) + + assert_allclose(res.sol(res.t), res.y, rtol=1e-15, atol=1e-15) + + +def test_no_integration(): + for method in ['RK23', 'RK45', 'DOP853', 'Radau', 'BDF', 'LSODA']: + sol = solve_ivp(lambda t, y: -y, [4, 4], [2, 3], + method=method, dense_output=True) + assert_equal(sol.sol(4), [2, 3]) + assert_equal(sol.sol([4, 5, 6]), [[2, 2, 2], [3, 3, 3]]) + + +def test_no_integration_class(): + for method in [RK23, RK45, DOP853, Radau, BDF, LSODA]: + solver = method(lambda t, y: -y, 0.0, [10.0, 0.0], 0.0) + solver.step() + assert_equal(solver.status, 'finished') + sol = solver.dense_output() + assert_equal(sol(0.0), [10.0, 0.0]) + assert_equal(sol([0, 1, 2]), [[10, 10, 10], [0, 0, 0]]) + + solver = method(lambda t, y: -y, 0.0, [], np.inf) + solver.step() + assert_equal(solver.status, 'finished') + sol = solver.dense_output() + assert_equal(sol(100.0), []) + assert_equal(sol([0, 1, 2]), np.empty((0, 3))) + + +def test_empty(): + def fun(t, y): + return np.zeros((0,)) + + y0 = np.zeros((0,)) + + for method in ['RK23', 'RK45', 'DOP853', 'Radau', 'BDF', 'LSODA']: + sol = assert_no_warnings(solve_ivp, fun, [0, 10], y0, + method=method, dense_output=True) + assert_equal(sol.sol(10), np.zeros((0,))) + assert_equal(sol.sol([1, 2, 3]), np.zeros((0, 3))) + + for method in ['RK23', 'RK45', 'DOP853', 'Radau', 'BDF', 'LSODA']: + sol = assert_no_warnings(solve_ivp, fun, [0, np.inf], y0, + method=method, dense_output=True) + assert_equal(sol.sol(10), np.zeros((0,))) + assert_equal(sol.sol([1, 2, 3]), np.zeros((0, 3))) + + +def test_ConstantDenseOutput(): + sol = ConstantDenseOutput(0, 1, np.array([1, 2])) + assert_allclose(sol(1.5), [1, 2]) + assert_allclose(sol([1, 1.5, 2]), [[1, 1, 1], [2, 2, 2]]) + + sol = ConstantDenseOutput(0, 1, np.array([])) + assert_allclose(sol(1.5), np.empty(0)) + assert_allclose(sol([1, 1.5, 2]), np.empty((0, 3))) + + +def test_classes(): + y0 = [1 / 3, 2 / 9] + for cls in [RK23, RK45, DOP853, Radau, BDF, LSODA]: + solver = cls(fun_rational, 5, y0, np.inf) + assert_equal(solver.n, 2) + assert_equal(solver.status, 'running') + assert_equal(solver.t_bound, np.inf) + assert_equal(solver.direction, 1) + assert_equal(solver.t, 5) + assert_equal(solver.y, y0) + assert_(solver.step_size is None) + if cls is not LSODA: + assert_(solver.nfev > 0) + assert_(solver.njev >= 0) + assert_equal(solver.nlu, 0) + else: + assert_equal(solver.nfev, 0) + assert_equal(solver.njev, 0) + assert_equal(solver.nlu, 0) + + assert_raises(RuntimeError, solver.dense_output) + + message = solver.step() + assert_equal(solver.status, 'running') + assert_equal(message, None) + assert_equal(solver.n, 2) + assert_equal(solver.t_bound, np.inf) + assert_equal(solver.direction, 1) + assert_(solver.t > 5) + assert_(not np.all(np.equal(solver.y, y0))) + assert_(solver.step_size > 0) + assert_(solver.nfev > 0) + assert_(solver.njev >= 0) + assert_(solver.nlu >= 0) + sol = solver.dense_output() + assert_allclose(sol(5), y0, rtol=1e-15, atol=0) + + +def test_OdeSolution(): + ts = np.array([0, 2, 5], dtype=float) + s1 = ConstantDenseOutput(ts[0], ts[1], np.array([-1])) + s2 = ConstantDenseOutput(ts[1], ts[2], np.array([1])) + + sol = OdeSolution(ts, [s1, s2]) + + assert_equal(sol(-1), [-1]) + assert_equal(sol(1), [-1]) + assert_equal(sol(2), [-1]) + assert_equal(sol(3), [1]) + assert_equal(sol(5), [1]) + assert_equal(sol(6), [1]) + + assert_equal(sol([0, 6, -2, 1.5, 4.5, 2.5, 5, 5.5, 2]), + np.array([[-1, 1, -1, -1, 1, 1, 1, 1, -1]])) + + ts = np.array([10, 4, -3]) + s1 = ConstantDenseOutput(ts[0], ts[1], np.array([-1])) + s2 = ConstantDenseOutput(ts[1], ts[2], np.array([1])) + + sol = OdeSolution(ts, [s1, s2]) + assert_equal(sol(11), [-1]) + assert_equal(sol(10), [-1]) + assert_equal(sol(5), [-1]) + assert_equal(sol(4), [-1]) + assert_equal(sol(0), [1]) + assert_equal(sol(-3), [1]) + assert_equal(sol(-4), [1]) + + assert_equal(sol([12, -5, 10, -3, 6, 1, 4]), + np.array([[-1, 1, -1, 1, -1, 1, -1]])) + + ts = np.array([1, 1]) + s = ConstantDenseOutput(1, 1, np.array([10])) + sol = OdeSolution(ts, [s]) + assert_equal(sol(0), [10]) + assert_equal(sol(1), [10]) + assert_equal(sol(2), [10]) + + assert_equal(sol([2, 1, 0]), np.array([[10, 10, 10]])) + + +def test_num_jac(): + def fun(t, y): + return np.vstack([ + -0.04 * y[0] + 1e4 * y[1] * y[2], + 0.04 * y[0] - 1e4 * y[1] * y[2] - 3e7 * y[1] ** 2, + 3e7 * y[1] ** 2 + ]) + + def jac(t, y): + return np.array([ + [-0.04, 1e4 * y[2], 1e4 * y[1]], + [0.04, -1e4 * y[2] - 6e7 * y[1], -1e4 * y[1]], + [0, 6e7 * y[1], 0] + ]) + + t = 1 + y = np.array([1, 0, 0]) + J_true = jac(t, y) + threshold = 1e-5 + f = fun(t, y).ravel() + + J_num, factor = num_jac(fun, t, y, f, threshold, None) + assert_allclose(J_num, J_true, rtol=1e-5, atol=1e-5) + + J_num, factor = num_jac(fun, t, y, f, threshold, factor) + assert_allclose(J_num, J_true, rtol=1e-5, atol=1e-5) + + +def test_num_jac_sparse(): + def fun(t, y): + e = y[1:]**3 - y[:-1]**2 + z = np.zeros(y.shape[1]) + return np.vstack((z, 3 * e)) + np.vstack((2 * e, z)) + + def structure(n): + A = np.zeros((n, n), dtype=int) + A[0, 0] = 1 + A[0, 1] = 1 + for i in range(1, n - 1): + A[i, i - 1: i + 2] = 1 + A[-1, -1] = 1 + A[-1, -2] = 1 + + return A + + np.random.seed(0) + n = 20 + y = np.random.randn(n) + A = structure(n) + groups = group_columns(A) + + f = fun(0, y[:, None]).ravel() + + # Compare dense and sparse results, assuming that dense implementation + # is correct (as it is straightforward). + J_num_sparse, factor_sparse = num_jac(fun, 0, y.ravel(), f, 1e-8, None, + sparsity=(A, groups)) + J_num_dense, factor_dense = num_jac(fun, 0, y.ravel(), f, 1e-8, None) + assert_allclose(J_num_dense, J_num_sparse.toarray(), + rtol=1e-12, atol=1e-14) + assert_allclose(factor_dense, factor_sparse, rtol=1e-12, atol=1e-14) + + # Take small factors to trigger their recomputing inside. + factor = np.random.uniform(0, 1e-12, size=n) + J_num_sparse, factor_sparse = num_jac(fun, 0, y.ravel(), f, 1e-8, factor, + sparsity=(A, groups)) + J_num_dense, factor_dense = num_jac(fun, 0, y.ravel(), f, 1e-8, factor) + + assert_allclose(J_num_dense, J_num_sparse.toarray(), + rtol=1e-12, atol=1e-14) + assert_allclose(factor_dense, factor_sparse, rtol=1e-12, atol=1e-14) + + +def test_args(): + + # sys3 is actually two decoupled systems. (x, y) form a + # linear oscillator, while z is a nonlinear first order + # system with equilibria at z=0 and z=1. If k > 0, z=1 + # is stable and z=0 is unstable. + + def sys3(t, w, omega, k, zfinal): + x, y, z = w + return [-omega*y, omega*x, k*z*(1 - z)] + + def sys3_jac(t, w, omega, k, zfinal): + x, y, z = w + J = np.array([[0, -omega, 0], + [omega, 0, 0], + [0, 0, k*(1 - 2*z)]]) + return J + + def sys3_x0decreasing(t, w, omega, k, zfinal): + x, y, z = w + return x + + def sys3_y0increasing(t, w, omega, k, zfinal): + x, y, z = w + return y + + def sys3_zfinal(t, w, omega, k, zfinal): + x, y, z = w + return z - zfinal + + # Set the event flags for the event functions. + sys3_x0decreasing.direction = -1 + sys3_y0increasing.direction = 1 + sys3_zfinal.terminal = True + + omega = 2 + k = 4 + + tfinal = 5 + zfinal = 0.99 + # Find z0 such that when z(0) = z0, z(tfinal) = zfinal. + # The condition z(tfinal) = zfinal is the terminal event. + z0 = np.exp(-k*tfinal)/((1 - zfinal)/zfinal + np.exp(-k*tfinal)) + + w0 = [0, -1, z0] + + # Provide the jac argument and use the Radau method to ensure that the use + # of the Jacobian function is exercised. + # If event handling is working, the solution will stop at tfinal, not tend. + tend = 2*tfinal + sol = solve_ivp(sys3, [0, tend], w0, + events=[sys3_x0decreasing, sys3_y0increasing, sys3_zfinal], + dense_output=True, args=(omega, k, zfinal), + method='Radau', jac=sys3_jac, + rtol=1e-10, atol=1e-13) + + # Check that we got the expected events at the expected times. + x0events_t = sol.t_events[0] + y0events_t = sol.t_events[1] + zfinalevents_t = sol.t_events[2] + assert_allclose(x0events_t, [0.5*np.pi, 1.5*np.pi]) + assert_allclose(y0events_t, [0.25*np.pi, 1.25*np.pi]) + assert_allclose(zfinalevents_t, [tfinal]) + + # Check that the solution agrees with the known exact solution. + t = np.linspace(0, zfinalevents_t[0], 250) + w = sol.sol(t) + assert_allclose(w[0], np.sin(omega*t), rtol=1e-9, atol=1e-12) + assert_allclose(w[1], -np.cos(omega*t), rtol=1e-9, atol=1e-12) + assert_allclose(w[2], 1/(((1 - z0)/z0)*np.exp(-k*t) + 1), + rtol=1e-9, atol=1e-12) + + # Check that the state variables have the expected values at the events. + x0events = sol.sol(x0events_t) + y0events = sol.sol(y0events_t) + zfinalevents = sol.sol(zfinalevents_t) + assert_allclose(x0events[0], np.zeros_like(x0events[0]), atol=5e-14) + assert_allclose(x0events[1], np.ones_like(x0events[1])) + assert_allclose(y0events[0], np.ones_like(y0events[0])) + assert_allclose(y0events[1], np.zeros_like(y0events[1]), atol=5e-14) + assert_allclose(zfinalevents[2], [zfinal]) + + +@pytest.mark.thread_unsafe +def test_array_rtol(): + # solve_ivp had a bug with array_like `rtol`; see gh-15482 + # check that it's fixed + def f(t, y): + return y[0], y[1] + + # no warning (or error) when `rtol` is array_like + sol = solve_ivp(f, (0, 1), [1., 1.], rtol=[1e-1, 1e-1]) + err1 = np.abs(np.linalg.norm(sol.y[:, -1] - np.exp(1))) + + # warning when an element of `rtol` is too small + with pytest.warns(UserWarning, match="At least one element..."): + sol = solve_ivp(f, (0, 1), [1., 1.], rtol=[1e-1, 1e-16]) + err2 = np.abs(np.linalg.norm(sol.y[:, -1] - np.exp(1))) + + # tighter rtol improves the error + assert err2 < err1 + + +@pytest.mark.parametrize('method', ['RK23', 'RK45', 'DOP853', 'Radau', 'BDF', 'LSODA']) +def test_integration_zero_rhs(method, num_parallel_threads): + if method == 'LSODA' and num_parallel_threads > 1: + pytest.skip(reason='LSODA does not allow for concurrent execution') + + result = solve_ivp(fun_zero, [0, 10], np.ones(3), method=method) + assert_(result.success) + assert_equal(result.status, 0) + assert_allclose(result.y, 1.0, rtol=1e-15) + + +def test_args_single_value(): + def fun_with_arg(t, y, a): + return a*y + + message = "Supplied 'args' cannot be unpacked." + with pytest.raises(TypeError, match=message): + solve_ivp(fun_with_arg, (0, 0.1), [1], args=-1) + + sol = solve_ivp(fun_with_arg, (0, 0.1), [1], args=(-1,)) + assert_allclose(sol.y[0, -1], np.exp(-0.1)) + + +@pytest.mark.parametrize("f0_fill", [np.nan, np.inf]) +def test_initial_state_finiteness(f0_fill): + # regression test for gh-17846 + msg = "All components of the initial state `y0` must be finite." + with pytest.raises(ValueError, match=msg): + solve_ivp(fun_zero, [0, 10], np.full(3, f0_fill)) + + +@pytest.mark.parametrize('method', ['RK23', 'RK45', 'DOP853', 'Radau', 'BDF']) +def test_zero_interval(method): + # Case where upper and lower limits of integration are the same + # Result of integration should match initial state. + # f[y(t)] = 2y(t) + def f(t, y): + return 2 * y + res = solve_ivp(f, (0.0, 0.0), np.array([1.0]), method=method) + assert res.success + assert_allclose(res.y[0, -1], 1.0) + + +@pytest.mark.parametrize('method', ['RK23', 'RK45', 'DOP853', 'Radau', 'BDF']) +def test_tbound_respected_small_interval(method): + """Regression test for gh-17341""" + SMALL = 1e-4 + + # f[y(t)] = 2y(t) on t in [0,SMALL] + # undefined otherwise + def f(t, y): + if t > SMALL: + raise ValueError("Function was evaluated outside interval") + return 2 * y + res = solve_ivp(f, (0.0, SMALL), np.array([1]), method=method) + assert res.success + + +@pytest.mark.parametrize('method', ['RK23', 'RK45', 'DOP853', 'Radau', 'BDF']) +def test_tbound_respected_larger_interval(method): + """Regression test for gh-8848""" + def V(r): + return -11/r + 10 * r / (0.05 + r**2) + + def func(t, p): + if t < -17 or t > 2: + raise ValueError("Function was evaluated outside interval") + P = p[0] + Q = p[1] + r = np.exp(t) + dPdr = r * Q + dQdr = -2.0 * r * ((-0.2 - V(r)) * P + 1 / r * Q) + return np.array([dPdr, dQdr]) + + result = solve_ivp(func, + (-17, 2), + y0=np.array([1, -11]), + max_step=0.03, + vectorized=False, + t_eval=None, + atol=1e-8, + rtol=1e-5) + assert result.success + + +@pytest.mark.parametrize('method', ['RK23', 'RK45', 'DOP853', 'Radau', 'BDF']) +def test_tbound_respected_oscillator(method): + "Regression test for gh-9198" + def reactions_func(t, y): + if (t > 205): + raise ValueError("Called outside interval") + yprime = np.array([1.73307544e-02, + 6.49376470e-06, + 0.00000000e+00, + 0.00000000e+00]) + return yprime + + def run_sim2(t_end, n_timepoints=10, shortest_delay_line=10000000): + init_state = np.array([134.08298555, 138.82348612, 100., 0.]) + t0 = 100.0 + t1 = 200.0 + return solve_ivp(reactions_func, + (t0, t1), + init_state.copy(), + dense_output=True, + max_step=t1 - t0) + result = run_sim2(1000, 100, 100) + assert result.success + + +def test_inital_maxstep(): + """Verify that select_inital_step respects max_step""" + rtol = 1e-3 + atol = 1e-6 + y0 = np.array([1/3, 2/9]) + for (t0, t_bound) in ((5, 9), (5, 1)): + for method_order in [RK23.error_estimator_order, + RK45.error_estimator_order, + DOP853.error_estimator_order, + 3, #RADAU + 1 #BDF + ]: + step_no_max = select_initial_step(fun_rational, t0, y0, t_bound, + np.inf, + fun_rational(t0,y0), + np.sign(t_bound - t0), + method_order, + rtol, atol) + max_step = step_no_max/2 + step_with_max = select_initial_step(fun_rational, t0, y0, t_bound, + max_step, + fun_rational(t0, y0), + np.sign(t_bound - t0), + method_order, + rtol, atol) + assert_equal(max_step, step_with_max) diff --git a/shared/package-files/03/034b10af10c15474b243ef2bbb72f5d613177f7abd815b81f71fd88076f847d3/__init__.py b/shared/package-files/03/034b10af10c15474b243ef2bbb72f5d613177f7abd815b81f71fd88076f847d3/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e3dcc689930da95198c251ce806637d6413c8b1e --- /dev/null +++ b/shared/package-files/03/034b10af10c15474b243ef2bbb72f5d613177f7abd815b81f71fd88076f847d3/__init__.py @@ -0,0 +1,5 @@ +""" +Benchmarks for validation. + +This package is *not* public API. +""" diff --git a/shared/package-files/03/034b861abd5808226ebe75c2ac69282fc3f7c48135e403636e17c53d7a7f9e6a/_cudnn_init_dropout_state.h b/shared/package-files/03/034b861abd5808226ebe75c2ac69282fc3f7c48135e403636e17c53d7a7f9e6a/_cudnn_init_dropout_state.h new file mode 100644 index 0000000000000000000000000000000000000000..b9d9ddab43d52331fa38527b2b8f1fe3abb2a3c7 --- /dev/null +++ b/shared/package-files/03/034b861abd5808226ebe75c2ac69282fc3f7c48135e403636e17c53d7a7f9e6a/_cudnn_init_dropout_state.h @@ -0,0 +1,43 @@ +#pragma once + +// @generated by torchgen/gen.py from Function.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + + +#include + +namespace at { + + +// aten::_cudnn_init_dropout_state(float dropout, bool train, int dropout_seed, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor +inline at::Tensor _cudnn_init_dropout_state(double dropout, bool train, int64_t dropout_seed, at::TensorOptions options) { + return at::_ops::_cudnn_init_dropout_state::call(dropout, train, dropout_seed, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt()); +} +// aten::_cudnn_init_dropout_state(float dropout, bool train, int dropout_seed, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor +inline at::Tensor _cudnn_init_dropout_state(double dropout, bool train, int64_t dropout_seed, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) { + return at::_ops::_cudnn_init_dropout_state::call(dropout, train, dropout_seed, dtype, layout, device, pin_memory); +} + +// aten::_cudnn_init_dropout_state.out(float dropout, bool train, int dropout_seed, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & _cudnn_init_dropout_state_out(at::Tensor & out, double dropout, bool train, int64_t dropout_seed) { + return at::_ops::_cudnn_init_dropout_state_out::call(dropout, train, dropout_seed, out); +} +// aten::_cudnn_init_dropout_state.out(float dropout, bool train, int dropout_seed, *, Tensor(a!) out) -> Tensor(a!) +inline at::Tensor & _cudnn_init_dropout_state_outf(double dropout, bool train, int64_t dropout_seed, at::Tensor & out) { + return at::_ops::_cudnn_init_dropout_state_out::call(dropout, train, dropout_seed, out); +} + +} diff --git a/shared/package-files/03/034e916d6a62449e09e22e7f4a60e7f23f2659d3dd1a2a0a6521f540d623d9f0/is_floating_point_compositeimplicitautograd_dispatch.h b/shared/package-files/03/034e916d6a62449e09e22e7f4a60e7f23f2659d3dd1a2a0a6521f540d623d9f0/is_floating_point_compositeimplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..d039f3aed74723577c5334cb6ac7f4816b23cc86 --- /dev/null +++ b/shared/package-files/03/034e916d6a62449e09e22e7f4a60e7f23f2659d3dd1a2a0a6521f540d623d9f0/is_floating_point_compositeimplicitautograd_dispatch.h @@ -0,0 +1,23 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeimplicitautograd { + +TORCH_API bool is_floating_point(const at::Tensor & self); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/shared/package-files/03/0351667ed3afd3310ebd353526824d6f6f34d641ef0a785552c6893b7f95fdf3/heapq.py b/shared/package-files/03/0351667ed3afd3310ebd353526824d6f6f34d641ef0a785552c6893b7f95fdf3/heapq.py new file mode 100644 index 0000000000000000000000000000000000000000..fabefd87f8bf8c804e8eb3155c1aacbf05dd02bd --- /dev/null +++ b/shared/package-files/03/0351667ed3afd3310ebd353526824d6f6f34d641ef0a785552c6893b7f95fdf3/heapq.py @@ -0,0 +1,601 @@ +"""Heap queue algorithm (a.k.a. priority queue). + +Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for +all k, counting elements from 0. For the sake of comparison, +non-existing elements are considered to be infinite. The interesting +property of a heap is that a[0] is always its smallest element. + +Usage: + +heap = [] # creates an empty heap +heappush(heap, item) # pushes a new item on the heap +item = heappop(heap) # pops the smallest item from the heap +item = heap[0] # smallest item on the heap without popping it +heapify(x) # transforms list into a heap, in-place, in linear time +item = heapreplace(heap, item) # pops and returns smallest item, and adds + # new item; the heap size is unchanged + +Our API differs from textbook heap algorithms as follows: + +- We use 0-based indexing. This makes the relationship between the + index for a node and the indexes for its children slightly less + obvious, but is more suitable since Python uses 0-based indexing. + +- Our heappop() method returns the smallest item, not the largest. + +These two make it possible to view the heap as a regular Python list +without surprises: heap[0] is the smallest item, and heap.sort() +maintains the heap invariant! +""" + +# Original code by Kevin O'Connor, augmented by Tim Peters and Raymond Hettinger + +__about__ = """Heap queues + +[explanation by François Pinard] + +Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for +all k, counting elements from 0. For the sake of comparison, +non-existing elements are considered to be infinite. The interesting +property of a heap is that a[0] is always its smallest element. + +The strange invariant above is meant to be an efficient memory +representation for a tournament. The numbers below are `k', not a[k]: + + 0 + + 1 2 + + 3 4 5 6 + + 7 8 9 10 11 12 13 14 + + 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 + + +In the tree above, each cell `k' is topping `2*k+1' and `2*k+2'. In +a usual binary tournament we see in sports, each cell is the winner +over the two cells it tops, and we can trace the winner down the tree +to see all opponents s/he had. However, in many computer applications +of such tournaments, we do not need to trace the history of a winner. +To be more memory efficient, when a winner is promoted, we try to +replace it by something else at a lower level, and the rule becomes +that a cell and the two cells it tops contain three different items, +but the top cell "wins" over the two topped cells. + +If this heap invariant is protected at all time, index 0 is clearly +the overall winner. The simplest algorithmic way to remove it and +find the "next" winner is to move some loser (let's say cell 30 in the +diagram above) into the 0 position, and then percolate this new 0 down +the tree, exchanging values, until the invariant is re-established. +This is clearly logarithmic on the total number of items in the tree. +By iterating over all items, you get an O(n ln n) sort. + +A nice feature of this sort is that you can efficiently insert new +items while the sort is going on, provided that the inserted items are +not "better" than the last 0'th element you extracted. This is +especially useful in simulation contexts, where the tree holds all +incoming events, and the "win" condition means the smallest scheduled +time. When an event schedule other events for execution, they are +scheduled into the future, so they can easily go into the heap. So, a +heap is a good structure for implementing schedulers (this is what I +used for my MIDI sequencer :-). + +Various structures for implementing schedulers have been extensively +studied, and heaps are good for this, as they are reasonably speedy, +the speed is almost constant, and the worst case is not much different +than the average case. However, there are other representations which +are more efficient overall, yet the worst cases might be terrible. + +Heaps are also very useful in big disk sorts. You most probably all +know that a big sort implies producing "runs" (which are pre-sorted +sequences, which size is usually related to the amount of CPU memory), +followed by a merging passes for these runs, which merging is often +very cleverly organised[1]. It is very important that the initial +sort produces the longest runs possible. Tournaments are a good way +to that. If, using all the memory available to hold a tournament, you +replace and percolate items that happen to fit the current run, you'll +produce runs which are twice the size of the memory for random input, +and much better for input fuzzily ordered. + +Moreover, if you output the 0'th item on disk and get an input which +may not fit in the current tournament (because the value "wins" over +the last output value), it cannot fit in the heap, so the size of the +heap decreases. The freed memory could be cleverly reused immediately +for progressively building a second heap, which grows at exactly the +same rate the first heap is melting. When the first heap completely +vanishes, you switch heaps and start a new run. Clever and quite +effective! + +In a word, heaps are useful memory structures to know. I use them in +a few applications, and I think it is good to keep a `heap' module +around. :-) + +-------------------- +[1] The disk balancing algorithms which are current, nowadays, are +more annoying than clever, and this is a consequence of the seeking +capabilities of the disks. On devices which cannot seek, like big +tape drives, the story was quite different, and one had to be very +clever to ensure (far in advance) that each tape movement will be the +most effective possible (that is, will best participate at +"progressing" the merge). Some tapes were even able to read +backwards, and this was also used to avoid the rewinding time. +Believe me, real good tape sorts were quite spectacular to watch! +From all times, sorting has always been a Great Art! :-) +""" + +__all__ = ['heappush', 'heappop', 'heapify', 'heapreplace', 'merge', + 'nlargest', 'nsmallest', 'heappushpop'] + +def heappush(heap, item): + """Push item onto heap, maintaining the heap invariant.""" + heap.append(item) + _siftdown(heap, 0, len(heap)-1) + +def heappop(heap): + """Pop the smallest item off the heap, maintaining the heap invariant.""" + lastelt = heap.pop() # raises appropriate IndexError if heap is empty + if heap: + returnitem = heap[0] + heap[0] = lastelt + _siftup(heap, 0) + return returnitem + return lastelt + +def heapreplace(heap, item): + """Pop and return the current smallest value, and add the new item. + + This is more efficient than heappop() followed by heappush(), and can be + more appropriate when using a fixed-size heap. Note that the value + returned may be larger than item! That constrains reasonable uses of + this routine unless written as part of a conditional replacement: + + if item > heap[0]: + item = heapreplace(heap, item) + """ + returnitem = heap[0] # raises appropriate IndexError if heap is empty + heap[0] = item + _siftup(heap, 0) + return returnitem + +def heappushpop(heap, item): + """Fast version of a heappush followed by a heappop.""" + if heap and heap[0] < item: + item, heap[0] = heap[0], item + _siftup(heap, 0) + return item + +def heapify(x): + """Transform list into a heap, in-place, in O(len(x)) time.""" + n = len(x) + # Transform bottom-up. The largest index there's any point to looking at + # is the largest with a child index in-range, so must have 2*i + 1 < n, + # or i < (n-1)/2. If n is even = 2*j, this is (2*j-1)/2 = j-1/2 so + # j-1 is the largest, which is n//2 - 1. If n is odd = 2*j+1, this is + # (2*j+1-1)/2 = j so j-1 is the largest, and that's again n//2-1. + for i in reversed(range(n//2)): + _siftup(x, i) + +def _heappop_max(heap): + """Maxheap version of a heappop.""" + lastelt = heap.pop() # raises appropriate IndexError if heap is empty + if heap: + returnitem = heap[0] + heap[0] = lastelt + _siftup_max(heap, 0) + return returnitem + return lastelt + +def _heapreplace_max(heap, item): + """Maxheap version of a heappop followed by a heappush.""" + returnitem = heap[0] # raises appropriate IndexError if heap is empty + heap[0] = item + _siftup_max(heap, 0) + return returnitem + +def _heapify_max(x): + """Transform list into a maxheap, in-place, in O(len(x)) time.""" + n = len(x) + for i in reversed(range(n//2)): + _siftup_max(x, i) + +# 'heap' is a heap at all indices >= startpos, except possibly for pos. pos +# is the index of a leaf with a possibly out-of-order value. Restore the +# heap invariant. +def _siftdown(heap, startpos, pos): + newitem = heap[pos] + # Follow the path to the root, moving parents down until finding a place + # newitem fits. + while pos > startpos: + parentpos = (pos - 1) >> 1 + parent = heap[parentpos] + if newitem < parent: + heap[pos] = parent + pos = parentpos + continue + break + heap[pos] = newitem + +# The child indices of heap index pos are already heaps, and we want to make +# a heap at index pos too. We do this by bubbling the smaller child of +# pos up (and so on with that child's children, etc) until hitting a leaf, +# then using _siftdown to move the oddball originally at index pos into place. +# +# We *could* break out of the loop as soon as we find a pos where newitem <= +# both its children, but turns out that's not a good idea, and despite that +# many books write the algorithm that way. During a heap pop, the last array +# element is sifted in, and that tends to be large, so that comparing it +# against values starting from the root usually doesn't pay (= usually doesn't +# get us out of the loop early). See Knuth, Volume 3, where this is +# explained and quantified in an exercise. +# +# Cutting the # of comparisons is important, since these routines have no +# way to extract "the priority" from an array element, so that intelligence +# is likely to be hiding in custom comparison methods, or in array elements +# storing (priority, record) tuples. Comparisons are thus potentially +# expensive. +# +# On random arrays of length 1000, making this change cut the number of +# comparisons made by heapify() a little, and those made by exhaustive +# heappop() a lot, in accord with theory. Here are typical results from 3 +# runs (3 just to demonstrate how small the variance is): +# +# Compares needed by heapify Compares needed by 1000 heappops +# -------------------------- -------------------------------- +# 1837 cut to 1663 14996 cut to 8680 +# 1855 cut to 1659 14966 cut to 8678 +# 1847 cut to 1660 15024 cut to 8703 +# +# Building the heap by using heappush() 1000 times instead required +# 2198, 2148, and 2219 compares: heapify() is more efficient, when +# you can use it. +# +# The total compares needed by list.sort() on the same lists were 8627, +# 8627, and 8632 (this should be compared to the sum of heapify() and +# heappop() compares): list.sort() is (unsurprisingly!) more efficient +# for sorting. + +def _siftup(heap, pos): + endpos = len(heap) + startpos = pos + newitem = heap[pos] + # Bubble up the smaller child until hitting a leaf. + childpos = 2*pos + 1 # leftmost child position + while childpos < endpos: + # Set childpos to index of smaller child. + rightpos = childpos + 1 + if rightpos < endpos and not heap[childpos] < heap[rightpos]: + childpos = rightpos + # Move the smaller child up. + heap[pos] = heap[childpos] + pos = childpos + childpos = 2*pos + 1 + # The leaf at pos is empty now. Put newitem there, and bubble it up + # to its final resting place (by sifting its parents down). + heap[pos] = newitem + _siftdown(heap, startpos, pos) + +def _siftdown_max(heap, startpos, pos): + 'Maxheap variant of _siftdown' + newitem = heap[pos] + # Follow the path to the root, moving parents down until finding a place + # newitem fits. + while pos > startpos: + parentpos = (pos - 1) >> 1 + parent = heap[parentpos] + if parent < newitem: + heap[pos] = parent + pos = parentpos + continue + break + heap[pos] = newitem + +def _siftup_max(heap, pos): + 'Maxheap variant of _siftup' + endpos = len(heap) + startpos = pos + newitem = heap[pos] + # Bubble up the larger child until hitting a leaf. + childpos = 2*pos + 1 # leftmost child position + while childpos < endpos: + # Set childpos to index of larger child. + rightpos = childpos + 1 + if rightpos < endpos and not heap[rightpos] < heap[childpos]: + childpos = rightpos + # Move the larger child up. + heap[pos] = heap[childpos] + pos = childpos + childpos = 2*pos + 1 + # The leaf at pos is empty now. Put newitem there, and bubble it up + # to its final resting place (by sifting its parents down). + heap[pos] = newitem + _siftdown_max(heap, startpos, pos) + +def merge(*iterables, key=None, reverse=False): + '''Merge multiple sorted inputs into a single sorted output. + + Similar to sorted(itertools.chain(*iterables)) but returns a generator, + does not pull the data into memory all at once, and assumes that each of + the input streams is already sorted (smallest to largest). + + >>> list(merge([1,3,5,7], [0,2,4,8], [5,10,15,20], [], [25])) + [0, 1, 2, 3, 4, 5, 5, 7, 8, 10, 15, 20, 25] + + If *key* is not None, applies a key function to each element to determine + its sort order. + + >>> list(merge(['dog', 'horse'], ['cat', 'fish', 'kangaroo'], key=len)) + ['dog', 'cat', 'fish', 'horse', 'kangaroo'] + + ''' + + h = [] + h_append = h.append + + if reverse: + _heapify = _heapify_max + _heappop = _heappop_max + _heapreplace = _heapreplace_max + direction = -1 + else: + _heapify = heapify + _heappop = heappop + _heapreplace = heapreplace + direction = 1 + + if key is None: + for order, it in enumerate(map(iter, iterables)): + try: + next = it.__next__ + h_append([next(), order * direction, next]) + except StopIteration: + pass + _heapify(h) + while len(h) > 1: + try: + while True: + value, order, next = s = h[0] + yield value + s[0] = next() # raises StopIteration when exhausted + _heapreplace(h, s) # restore heap condition + except StopIteration: + _heappop(h) # remove empty iterator + if h: + # fast case when only a single iterator remains + value, order, next = h[0] + yield value + yield from next.__self__ + return + + for order, it in enumerate(map(iter, iterables)): + try: + next = it.__next__ + value = next() + h_append([key(value), order * direction, value, next]) + except StopIteration: + pass + _heapify(h) + while len(h) > 1: + try: + while True: + key_value, order, value, next = s = h[0] + yield value + value = next() + s[0] = key(value) + s[2] = value + _heapreplace(h, s) + except StopIteration: + _heappop(h) + if h: + key_value, order, value, next = h[0] + yield value + yield from next.__self__ + + +# Algorithm notes for nlargest() and nsmallest() +# ============================================== +# +# Make a single pass over the data while keeping the k most extreme values +# in a heap. Memory consumption is limited to keeping k values in a list. +# +# Measured performance for random inputs: +# +# number of comparisons +# n inputs k-extreme values (average of 5 trials) % more than min() +# ------------- ---------------- --------------------- ----------------- +# 1,000 100 3,317 231.7% +# 10,000 100 14,046 40.5% +# 100,000 100 105,749 5.7% +# 1,000,000 100 1,007,751 0.8% +# 10,000,000 100 10,009,401 0.1% +# +# Theoretical number of comparisons for k smallest of n random inputs: +# +# Step Comparisons Action +# ---- -------------------------- --------------------------- +# 1 1.66 * k heapify the first k-inputs +# 2 n - k compare remaining elements to top of heap +# 3 k * (1 + lg2(k)) * ln(n/k) replace the topmost value on the heap +# 4 k * lg2(k) - (k/2) final sort of the k most extreme values +# +# Combining and simplifying for a rough estimate gives: +# +# comparisons = n + k * (log(k, 2) * log(n/k) + log(k, 2) + log(n/k)) +# +# Computing the number of comparisons for step 3: +# ----------------------------------------------- +# * For the i-th new value from the iterable, the probability of being in the +# k most extreme values is k/i. For example, the probability of the 101st +# value seen being in the 100 most extreme values is 100/101. +# * If the value is a new extreme value, the cost of inserting it into the +# heap is 1 + log(k, 2). +# * The probability times the cost gives: +# (k/i) * (1 + log(k, 2)) +# * Summing across the remaining n-k elements gives: +# sum((k/i) * (1 + log(k, 2)) for i in range(k+1, n+1)) +# * This reduces to: +# (H(n) - H(k)) * k * (1 + log(k, 2)) +# * Where H(n) is the n-th harmonic number estimated by: +# gamma = 0.5772156649 +# H(n) = log(n, e) + gamma + 1 / (2 * n) +# http://en.wikipedia.org/wiki/Harmonic_series_(mathematics)#Rate_of_divergence +# * Substituting the H(n) formula: +# comparisons = k * (1 + log(k, 2)) * (log(n/k, e) + (1/n - 1/k) / 2) +# +# Worst-case for step 3: +# ---------------------- +# In the worst case, the input data is reversed sorted so that every new element +# must be inserted in the heap: +# +# comparisons = 1.66 * k + log(k, 2) * (n - k) +# +# Alternative Algorithms +# ---------------------- +# Other algorithms were not used because they: +# 1) Took much more auxiliary memory, +# 2) Made multiple passes over the data. +# 3) Made more comparisons in common cases (small k, large n, semi-random input). +# See the more detailed comparison of approach at: +# http://code.activestate.com/recipes/577573-compare-algorithms-for-heapqsmallest + +def nsmallest(n, iterable, key=None): + """Find the n smallest elements in a dataset. + + Equivalent to: sorted(iterable, key=key)[:n] + """ + + # Short-cut for n==1 is to use min() + if n == 1: + it = iter(iterable) + sentinel = object() + result = min(it, default=sentinel, key=key) + return [] if result is sentinel else [result] + + # When n>=size, it's faster to use sorted() + try: + size = len(iterable) + except (TypeError, AttributeError): + pass + else: + if n >= size: + return sorted(iterable, key=key)[:n] + + # When key is none, use simpler decoration + if key is None: + it = iter(iterable) + # put the range(n) first so that zip() doesn't + # consume one too many elements from the iterator + result = [(elem, i) for i, elem in zip(range(n), it)] + if not result: + return result + _heapify_max(result) + top = result[0][0] + order = n + _heapreplace = _heapreplace_max + for elem in it: + if elem < top: + _heapreplace(result, (elem, order)) + top, _order = result[0] + order += 1 + result.sort() + return [elem for (elem, order) in result] + + # General case, slowest method + it = iter(iterable) + result = [(key(elem), i, elem) for i, elem in zip(range(n), it)] + if not result: + return result + _heapify_max(result) + top = result[0][0] + order = n + _heapreplace = _heapreplace_max + for elem in it: + k = key(elem) + if k < top: + _heapreplace(result, (k, order, elem)) + top, _order, _elem = result[0] + order += 1 + result.sort() + return [elem for (k, order, elem) in result] + +def nlargest(n, iterable, key=None): + """Find the n largest elements in a dataset. + + Equivalent to: sorted(iterable, key=key, reverse=True)[:n] + """ + + # Short-cut for n==1 is to use max() + if n == 1: + it = iter(iterable) + sentinel = object() + result = max(it, default=sentinel, key=key) + return [] if result is sentinel else [result] + + # When n>=size, it's faster to use sorted() + try: + size = len(iterable) + except (TypeError, AttributeError): + pass + else: + if n >= size: + return sorted(iterable, key=key, reverse=True)[:n] + + # When key is none, use simpler decoration + if key is None: + it = iter(iterable) + result = [(elem, i) for i, elem in zip(range(0, -n, -1), it)] + if not result: + return result + heapify(result) + top = result[0][0] + order = -n + _heapreplace = heapreplace + for elem in it: + if top < elem: + _heapreplace(result, (elem, order)) + top, _order = result[0] + order -= 1 + result.sort(reverse=True) + return [elem for (elem, order) in result] + + # General case, slowest method + it = iter(iterable) + result = [(key(elem), i, elem) for i, elem in zip(range(0, -n, -1), it)] + if not result: + return result + heapify(result) + top = result[0][0] + order = -n + _heapreplace = heapreplace + for elem in it: + k = key(elem) + if top < k: + _heapreplace(result, (k, order, elem)) + top, _order, _elem = result[0] + order -= 1 + result.sort(reverse=True) + return [elem for (k, order, elem) in result] + +# If available, use C implementation +try: + from _heapq import * +except ImportError: + pass +try: + from _heapq import _heapreplace_max +except ImportError: + pass +try: + from _heapq import _heapify_max +except ImportError: + pass +try: + from _heapq import _heappop_max +except ImportError: + pass + + +if __name__ == "__main__": + + import doctest # pragma: no cover + print(doctest.testmod()) # pragma: no cover diff --git a/shared/package-files/03/0353721b70c5f97f936acb7c0eafc7c6878e2bdf770b0bb3a7e00e7ab1199451/distributions.py b/shared/package-files/03/0353721b70c5f97f936acb7c0eafc7c6878e2bdf770b0bb3a7e00e7ab1199451/distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..305906a35a5320f864eb276cbc9d11c186534908 --- /dev/null +++ b/shared/package-files/03/0353721b70c5f97f936acb7c0eafc7c6878e2bdf770b0bb3a7e00e7ab1199451/distributions.py @@ -0,0 +1,680 @@ +""" +Algorithmic implementations for generating different types +of random distributions. +""" + +import numpy as np + +from numba.core.extending import register_jitable +from numba.np.random._constants import (wi_double, ki_double, + ziggurat_nor_r, fi_double, + wi_float, ki_float, + ziggurat_nor_inv_r_f, + ziggurat_nor_r_f, fi_float, + we_double, ke_double, + ziggurat_exp_r, fe_double, + we_float, ke_float, + ziggurat_exp_r_f, fe_float, + INT64_MAX, ziggurat_nor_inv_r) +from numba.cpython._binomial import make_binomial_btpe +from numba.np.random.generator_core import (next_double, next_float, + next_uint32, next_uint64) +from numba import float32, int64 +from numba.np.numpy_support import numpy_version +# All of the following implementations are direct translations from: +# https://github.com/numpy/numpy/blob/7cfef93c77599bd387ecc6a15d186c5a46024dac/numpy/random/src/distributions/distributions.c + + +@register_jitable +def np_log1p(x): + return np.log1p(x) + + +@register_jitable +def np_log1pf(x): + return np.log1p(float32(x)) + + +@register_jitable +def random_rayleigh(bitgen, mode): + return mode * np.sqrt(2.0 * random_standard_exponential(bitgen)) + + +@register_jitable +def np_expm1(x): + return np.expm1(x) + + +@register_jitable +def random_standard_normal(bitgen): + while 1: + r = next_uint64(bitgen) + idx = r & 0xff + r >>= 8 + sign = r & 0x1 + rabs = (r >> 1) & 0x000fffffffffffff + x = rabs * wi_double[idx] + if (sign & 0x1): + x = -x + if rabs < ki_double[idx]: + return x + if idx == 0: + while 1: + xx = -ziggurat_nor_inv_r * np.log1p(-next_double(bitgen)) + yy = -np.log1p(-next_double(bitgen)) + if (yy + yy > xx * xx): + if ((rabs >> 8) & 0x1): + return -(ziggurat_nor_r + xx) + else: + return ziggurat_nor_r + xx + else: + if (((fi_double[idx - 1] - fi_double[idx]) * + next_double(bitgen) + fi_double[idx]) < + np.exp(-0.5 * x * x)): + return x + + +@register_jitable +def random_standard_normal_f(bitgen): + while 1: + r = next_uint32(bitgen) + idx = r & 0xff + sign = (r >> 8) & 0x1 + rabs = (r >> 9) & 0x0007fffff + x = float32(float32(rabs) * wi_float[idx]) + if (sign & 0x1): + x = -x + if (rabs < ki_float[idx]): + return x + if (idx == 0): + while 1: + xx = float32(-ziggurat_nor_inv_r_f * + np_log1pf(-next_float(bitgen))) + yy = float32(-np_log1pf(-next_float(bitgen))) + if (float32(yy + yy) > float32(xx * xx)): + if ((rabs >> 8) & 0x1): + return -float32(ziggurat_nor_r_f + xx) + else: + return float32(ziggurat_nor_r_f + xx) + else: + if (((fi_float[idx - 1] - fi_float[idx]) * next_float(bitgen) + + fi_float[idx]) < float32(np.exp(-float32(0.5) * x * x))): + return x + + +@register_jitable +def random_standard_exponential(bitgen): + while 1: + ri = next_uint64(bitgen) + ri >>= 3 + idx = ri & 0xFF + ri >>= 8 + x = ri * we_double[idx] + if (ri < ke_double[idx]): + return x + else: + if idx == 0: + return ziggurat_exp_r - np_log1p(-next_double(bitgen)) + elif ((fe_double[idx - 1] - fe_double[idx]) * next_double(bitgen) + + fe_double[idx] < np.exp(-x)): + return x + + +@register_jitable +def random_standard_exponential_f(bitgen): + while 1: + ri = next_uint32(bitgen) + ri >>= 1 + idx = ri & 0xFF + ri >>= 8 + x = float32(float32(ri) * we_float[idx]) + if (ri < ke_float[idx]): + return x + else: + if (idx == 0): + return float32(ziggurat_exp_r_f - + float32(np_log1pf(-next_float(bitgen)))) + elif ((fe_float[idx - 1] - fe_float[idx]) * next_float(bitgen) + + fe_float[idx] < float32(np.exp(float32(-x)))): + return x + + +@register_jitable +def random_standard_exponential_inv(bitgen): + return -np_log1p(-next_double(bitgen)) + + +@register_jitable +def random_standard_exponential_inv_f(bitgen): + return -np.log(float32(1.0) - next_float(bitgen)) + + +@register_jitable +def random_standard_gamma(bitgen, shape): + if (shape == 1.0): + return random_standard_exponential(bitgen) + elif (shape == 0.0): + return 0.0 + elif (shape < 1.0): + while 1: + U = next_double(bitgen) + V = random_standard_exponential(bitgen) + if (U <= 1.0 - shape): + X = pow(U, 1. / shape) + if (X <= V): + return X + else: + Y = -np.log((1 - U) / shape) + X = pow(1.0 - shape + shape * Y, 1. / shape) + if (X <= (V + Y)): + return X + else: + b = shape - 1. / 3. + c = 1. / np.sqrt(9 * b) + while 1: + while 1: + X = random_standard_normal(bitgen) + V = 1.0 + c * X + if (V > 0.0): + break + + V = V * V * V + U = next_double(bitgen) + if (U < 1.0 - 0.0331 * (X * X) * (X * X)): + return (b * V) + + if (np.log(U) < 0.5 * X * X + b * (1. - V + np.log(V))): + return (b * V) + + +@register_jitable +def random_standard_gamma_f(bitgen, shape): + f32_one = float32(1.0) + shape = float32(shape) + if (shape == f32_one): + return random_standard_exponential_f(bitgen) + elif (shape == float32(0.0)): + return float32(0.0) + elif (shape < f32_one): + while 1: + U = next_float(bitgen) + V = random_standard_exponential_f(bitgen) + if (U <= f32_one - shape): + X = float32(pow(U, float32(f32_one / shape))) + if (X <= V): + return X + else: + Y = float32(-np.log(float32((f32_one - U) / shape))) + X = float32(pow(f32_one - shape + float32(shape * Y), + float32(f32_one / shape))) + if (X <= (V + Y)): + return X + else: + b = shape - f32_one / float32(3.0) + c = float32(f32_one / float32(np.sqrt(float32(9.0) * b))) + while 1: + while 1: + X = float32(random_standard_normal_f(bitgen)) + V = float32(f32_one + c * X) + if (V > float32(0.0)): + break + + V = float32(V * V * V) + U = next_float(bitgen) + if (U < f32_one - float32(0.0331) * (X * X) * (X * X)): + return float32(b * V) + + if (np.log(U) < float32(0.5) * X * X + b * + (f32_one - V + np.log(V))): + return float32(b * V) + + +@register_jitable +def random_normal(bitgen, loc, scale): + scaled_normal = scale * random_standard_normal(bitgen) + return loc + scaled_normal + + +@register_jitable +def random_normal_f(bitgen, loc, scale): + scaled_normal = float32(scale * random_standard_normal_f(bitgen)) + return float32(loc + scaled_normal) + + +@register_jitable +def random_exponential(bitgen, scale): + return scale * random_standard_exponential(bitgen) + + +@register_jitable +def random_uniform(bitgen, lower, range): + scaled_uniform = range * next_double(bitgen) + return lower + scaled_uniform + + +@register_jitable +def random_gamma(bitgen, shape, scale): + return scale * random_standard_gamma(bitgen, shape) + + +@register_jitable +def random_gamma_f(bitgen, shape, scale): + return float32(scale * random_standard_gamma_f(bitgen, shape)) + + +@register_jitable +def random_beta(bitgen, a, b): + if a <= 1.0 and b <= 1.0: + while 1: + U = next_double(bitgen) + V = next_double(bitgen) + X = pow(U, 1.0 / a) + Y = pow(V, 1.0 / b) + XpY = X + Y + if XpY <= 1.0 and XpY > 0.0: + if (X + Y > 0): + return X / XpY + else: + logX = np.log(U) / a + logY = np.log(V) / b + logM = min(logX, logY) + logX -= logM + logY -= logM + + return np.exp(logX - np.log(np.exp(logX) + np.exp(logY))) + else: + Ga = random_standard_gamma(bitgen, a) + Gb = random_standard_gamma(bitgen, b) + return Ga / (Ga + Gb) + + +@register_jitable +def random_chisquare(bitgen, df): + return 2.0 * random_standard_gamma(bitgen, df / 2.0) + + +@register_jitable +def random_f(bitgen, dfnum, dfden): + return ((random_chisquare(bitgen, dfnum) * dfden) / + (random_chisquare(bitgen, dfden) * dfnum)) + + +@register_jitable +def random_standard_cauchy(bitgen): + return random_standard_normal(bitgen) / random_standard_normal(bitgen) + + +@register_jitable +def random_pareto(bitgen, a): + return np_expm1(random_standard_exponential(bitgen) / a) + + +@register_jitable +def random_weibull(bitgen, a): + if (a == 0.0): + return 0.0 + return pow(random_standard_exponential(bitgen), 1. / a) + + +@register_jitable +def random_power(bitgen, a): + return pow(-np_expm1(-random_standard_exponential(bitgen)), 1. / a) + + +@register_jitable +def random_laplace(bitgen, loc, scale): + U = next_double(bitgen) + while U <= 0: + U = next_double(bitgen) + if (U >= 0.5): + U = loc - scale * np.log(2.0 - U - U) + elif (U > 0.0): + U = loc + scale * np.log(U + U) + return U + + +@register_jitable +def random_logistic(bitgen, loc, scale): + U = next_double(bitgen) + while U <= 0.0: + U = next_double(bitgen) + return loc + scale * np.log(U / (1.0 - U)) + + +@register_jitable +def random_lognormal(bitgen, mean, sigma): + return np.exp(random_normal(bitgen, mean, sigma)) + + +@register_jitable +def random_standard_t(bitgen, df): + num = random_standard_normal(bitgen) + denom = random_standard_gamma(bitgen, df / 2) + return np.sqrt(df / 2) * num / np.sqrt(denom) + + +@register_jitable +def random_wald(bitgen, mean, scale): + mu_2l = mean / (2 * scale) + Y = random_standard_normal(bitgen) + Y = mean * Y * Y + X = mean + mu_2l * (Y - np.sqrt(4 * scale * Y + Y * Y)) + U = next_double(bitgen) + if (U <= mean / (mean + X)): + return X + else: + return mean * mean / X + + +@register_jitable +def random_geometric_search(bitgen, p): + X = 1 + sum = prod = p + q = 1.0 - p + U = next_double(bitgen) + while (U > sum): + prod *= q + sum += prod + X = X + 1 + return X + + +@register_jitable +def random_geometric_inversion(bitgen, p): + return np.ceil(-random_standard_exponential(bitgen) / np.log1p(-p)) + + +@register_jitable +def random_geometric(bitgen, p): + if (p >= 0.333333333333333333333333): + return random_geometric_search(bitgen, p) + else: + return random_geometric_inversion(bitgen, p) + + +if numpy_version < (2, 1): + @register_jitable + def random_zipf(bitgen, a): + am1 = a - 1.0 + b = pow(2.0, am1) + while 1: + U = 1.0 - next_double(bitgen) + V = next_double(bitgen) + X = np.floor(pow(U, -1.0 / am1)) + if (X > INT64_MAX or X < 1.0): + continue + T = pow(1.0 + 1.0 / X, am1) + if (V * X * (T - 1.0) / (b - 1.0) <= T / b): + return X +else: + @register_jitable + def random_zipf(bitgen, a): + am1 = a - 1.0 + b = pow(2.0, am1) + Umin = pow(INT64_MAX, -am1) + while 1: + U01 = next_double(bitgen) + U = U01 * Umin + (1 - U01) + V = next_double(bitgen) + X = np.floor(pow(U, -1.0 / am1)) + if (X > INT64_MAX or X < 1.0): + continue + + T = pow(1.0 + 1.0 / X, am1) + if (V * X * (T - 1.0) / (b - 1.0) <= T / b): + return X + + +@register_jitable +def random_triangular(bitgen, left, mode, + right): + base = right - left + leftbase = mode - left + ratio = leftbase / base + leftprod = leftbase * base + rightprod = (right - mode) * base + + U = next_double(bitgen) + if (U <= ratio): + return left + np.sqrt(U * leftprod) + else: + return right - np.sqrt((1.0 - U) * rightprod) + + +@register_jitable +def random_loggam(x): + a = [8.333333333333333e-02, -2.777777777777778e-03, + 7.936507936507937e-04, -5.952380952380952e-04, + 8.417508417508418e-04, -1.917526917526918e-03, + 6.410256410256410e-03, -2.955065359477124e-02, + 1.796443723688307e-01, -1.39243221690590e+00] + + if ((x == 1.0) or (x == 2.0)): + return 0.0 + elif (x < 7.0): + n = int(7 - x) + else: + n = 0 + + x0 = x + n + x2 = (1.0 / x0) * (1.0 / x0) + # /* log(2 * M_PI) */ + lg2pi = 1.8378770664093453e+00 + gl0 = a[9] + + for k in range(0, 9): + gl0 *= x2 + gl0 += a[8 - k] + + gl = gl0 / x0 + 0.5 * lg2pi + (x0 - 0.5) * np.log(x0) - x0 + if (x < 7.0): + for k in range(1, n + 1): + gl = gl - np.log(x0 - 1.0) + x0 = x0 - 1.0 + + return gl + + +@register_jitable +def random_poisson_mult(bitgen, lam): + enlam = np.exp(-lam) + X = 0 + prod = 1.0 + while (1): + U = next_double(bitgen) + prod *= U + if (prod > enlam): + X += 1 + else: + return X + + +@register_jitable +def random_poisson_ptrs(bitgen, lam): + + slam = np.sqrt(lam) + loglam = np.log(lam) + b = 0.931 + 2.53 * slam + a = -0.059 + 0.02483 * b + invalpha = 1.1239 + 1.1328 / (b - 3.4) + vr = 0.9277 - 3.6224 / (b - 2) + + while (1): + U = next_double(bitgen) - 0.5 + V = next_double(bitgen) + us = 0.5 - np.fabs(U) + k = int((2 * a / us + b) * U + lam + 0.43) + if ((us >= 0.07) and (V <= vr)): + return k + + if ((k < 0) or ((us < 0.013) and (V > us))): + continue + + # /* log(V) == log(0.0) ok here */ + # /* if U==0.0 so that us==0.0, log is ok since always returns */ + if ((np.log(V) + np.log(invalpha) - np.log(a / (us * us) + b)) <= + (-lam + k * loglam - random_loggam(k + 1))): + return k + + +@register_jitable +def random_poisson(bitgen, lam): + if (lam >= 10): + return random_poisson_ptrs(bitgen, lam) + elif (lam == 0): + return 0 + else: + return random_poisson_mult(bitgen, lam) + + +@register_jitable +def random_negative_binomial(bitgen, n, p): + Y = random_gamma(bitgen, n, (1 - p) / p) + return random_poisson(bitgen, Y) + + +@register_jitable +def random_noncentral_chisquare(bitgen, df, nonc): + if np.isnan(nonc): + return np.nan + + if nonc == 0: + return random_chisquare(bitgen, df) + + if 1 < df: + Chi2 = random_chisquare(bitgen, df - 1) + n = random_standard_normal(bitgen) + np.sqrt(nonc) + return Chi2 + n * n + else: + i = random_poisson(bitgen, nonc / 2.0) + return random_chisquare(bitgen, df + 2 * i) + + +@register_jitable +def random_noncentral_f(bitgen, dfnum, dfden, nonc): + t = random_noncentral_chisquare(bitgen, dfnum, nonc) * dfden + return t / (random_chisquare(bitgen, dfden) * dfnum) + + +@register_jitable +def random_logseries(bitgen, p): + r = np_log1p(-p) + + while 1: + V = next_double(bitgen) + if (V >= p): + return 1 + U = next_double(bitgen) + q = -np.expm1(r * U) + if (V <= q * q): + result = int64(np.floor(1 + np.log(V) / np.log(q))) + if result < 1 or V == 0.0: + continue + else: + return result + if (V >= q): + return 1 + else: + return 2 + + +if numpy_version < (2, 5): + @register_jitable + def _binomial_btpe_squeeze(A, xm, m, n, y, r, q): + # Generator path's case-52 Stirling squeeze, pre-2.5 form: all terms + # added, constant 13680. Deliberately matches NumPy < 2.5's + # random_binomial_btpe. Do not fix here. + x1 = y + 1 + f1 = m + 1 + z = n + 1 - m + w = n - y + 1 + x2 = x1 * x1 + f2 = f1 * f1 + z2 = z * z + w2 = w * w + return A > (xm * np.log(f1 / x1) + (n - m + 0.5) * np.log(z / w) + + (y - m) * np.log(w * r / (x1 * q)) + + (13680. - (462. - (132. - (99. - 140. / f2) / f2) / f2) + / f2) / f1 / 166320. + + (13680. - (462. - (132. - (99. - 140. / z2) / z2) / z2) + / z2) / z / 166320. + + (13680. - (462. - (132. - (99. - 140. / x2) / x2) / x2) + / x2) / x1 / 166320. + + (13680. - (462. - (132. - (99. - 140. / w2) / w2) / w2) + / w2) / w / 166320.) +else: + @register_jitable + def _binomial_btpe_squeeze(A, xm, m, n, y, r, q): + # Generator path's case-52 Stirling squeeze, NumPy >= 2.5 form. Keep in + # sync with NumPy's random_binomial_btpe, which fixed the Stirling + # constant (13680 -> 13860) and subtracts the x1 and w terms. + # https://github.com/numpy/numpy/pull/31238 + x1 = y + 1 + f1 = m + 1 + z = n + 1 - m + w = n - y + 1 + x2 = x1 * x1 + f2 = f1 * f1 + z2 = z * z + w2 = w * w + return A > (xm * np.log(f1 / x1) + (n - m + 0.5) * np.log(z / w) + + (y - m) * np.log(w * r / (x1 * q)) + + (13860. - (462. - (132. - (99. - 140. / f2) / f2) / f2) + / f2) / f1 / 166320. + + (13860. - (462. - (132. - (99. - 140. / z2) / z2) / z2) + / z2) / z / 166320. - + (13860. - (462. - (132. - (99. - 140. / x2) / x2) / x2) + / x2) / x1 / 166320. - + (13860. - (462. - (132. - (99. - 140. / w2) / w2) / w2) + / w2) / w / 166320.) + + +_binomial_btpe_impl = make_binomial_btpe(next_double, _binomial_btpe_squeeze) + + +@register_jitable +def random_binomial_btpe(bitgen, n, p): + r = min(p, 1.0 - p) + y = _binomial_btpe_impl(bitgen, n, r) + return n - y if p > 0.5 else y + + +@register_jitable +def random_binomial_inversion(bitgen, n, p): + q = 1.0 - p + qn = np.exp(n * np.log(q)) + _np = n * p + bound = min(n, _np + 10.0 * np.sqrt(_np * q + 1)) + + X = 0 + px = qn + U = next_double(bitgen) + while (U > px): + X = X + 1 + if (X > bound): + X = 0 + px = qn + U = next_double(bitgen) + else: + U -= px + px = ((n - X + 1) * p * px) / (X * q) + + return X + + +@register_jitable +def random_binomial(bitgen, n, p): + if ((n == 0) or (p == 0.0)): + return 0 + + if (p <= 0.5): + if (p * n <= 30.0): + return random_binomial_inversion(bitgen, n, p) + else: + return random_binomial_btpe(bitgen, n, p) + else: + q = 1.0 - p + if (q * n <= 30.0): + return n - random_binomial_inversion(bitgen, n, q) + else: + return n - random_binomial_btpe(bitgen, n, q) diff --git a/shared/package-files/03/035415e61eb105ad5c4c87a38ec73535f8c6d0ca8c6f1a85a9789c0b638b36b3/idl.py b/shared/package-files/03/035415e61eb105ad5c4c87a38ec73535f8c6d0ca8c6f1a85a9789c0b638b36b3/idl.py new file mode 100644 index 0000000000000000000000000000000000000000..01cc8903f95ecd328eb3e6fffa6a90382a550d91 --- /dev/null +++ b/shared/package-files/03/035415e61eb105ad5c4c87a38ec73535f8c6d0ca8c6f1a85a9789c0b638b36b3/idl.py @@ -0,0 +1,17 @@ +# This file is not meant for public use and will be removed in SciPy v2.0.0. +# Use the `scipy.io` namespace for importing the functions +# included below. + +from scipy._lib.deprecation import _sub_module_deprecation + +__all__ = ["readsav"] # noqa: F822 + + +def __dir__(): + return __all__ + + +def __getattr__(name): + return _sub_module_deprecation(sub_package="io", module="idl", + private_modules=["_idl"], all=__all__, + attribute=name) diff --git a/shared/package-files/03/0356e6f80e58762b36899255236fabdda14a52e7d6478d8d67c62db6ba7c9b38/testutils.py b/shared/package-files/03/0356e6f80e58762b36899255236fabdda14a52e7d6478d8d67c62db6ba7c9b38/testutils.py new file mode 100644 index 0000000000000000000000000000000000000000..765b9a157c06b428f35e57666d4df962910d0de3 --- /dev/null +++ b/shared/package-files/03/0356e6f80e58762b36899255236fabdda14a52e7d6478d8d67c62db6ba7c9b38/testutils.py @@ -0,0 +1,9 @@ +def return_slice_of_data(arr, start_idx, end_idx): + return arr[start_idx:end_idx] + + +def print_filename_and_raise(arr): + from joblib._memmapping_reducer import _get_backing_memmap + + print(_get_backing_memmap(arr).filename) + raise ValueError diff --git a/shared/package-files/03/03587701f3e110c6c6e4f2dd0ebd606e7c2dfc395a40e06016ec7942c2290a93/test_backend_tk.py b/shared/package-files/03/03587701f3e110c6c6e4f2dd0ebd606e7c2dfc395a40e06016ec7942c2290a93/test_backend_tk.py new file mode 100644 index 0000000000000000000000000000000000000000..1210c8c9993ed11f606fb9888520df2bab4d4da7 --- /dev/null +++ b/shared/package-files/03/03587701f3e110c6c6e4f2dd0ebd606e7c2dfc395a40e06016ec7942c2290a93/test_backend_tk.py @@ -0,0 +1,280 @@ +import functools +import importlib +import os +import platform +import subprocess +import sys + +import pytest + +from matplotlib import _c_internal_utils +from matplotlib.testing import subprocess_run_helper + + +_test_timeout = 60 # A reasonably safe value for slower architectures. + + +def _isolated_tk_test(success_count, func=None): + """ + A decorator to run *func* in a subprocess and assert that it prints + "success" *success_count* times and nothing on stderr. + + TkAgg tests seem to have interactions between tests, so isolate each test + in a subprocess. See GH#18261 + """ + + if func is None: + return functools.partial(_isolated_tk_test, success_count) + + if "MPL_TEST_ESCAPE_HATCH" in os.environ: + # set in subprocess_run_helper() below + return func + + @pytest.mark.skipif( + not importlib.util.find_spec('tkinter'), + reason="missing tkinter" + ) + @pytest.mark.skipif( + sys.platform == "linux" and not _c_internal_utils.xdisplay_is_valid(), + reason="$DISPLAY is unset" + ) + @pytest.mark.xfail( # https://github.com/actions/setup-python/issues/649 + ('TF_BUILD' in os.environ or 'GITHUB_ACTION' in os.environ) and + sys.platform == 'darwin' and sys.version_info[:2] < (3, 11), + reason='Tk version mismatch on Azure macOS CI' + ) + @functools.wraps(func) + def test_func(): + # even if the package exists, may not actually be importable this can + # be the case on some CI systems. + pytest.importorskip('tkinter') + try: + proc = subprocess_run_helper( + func, timeout=_test_timeout, extra_env=dict( + MPLBACKEND="TkAgg", MPL_TEST_ESCAPE_HATCH="1")) + except subprocess.TimeoutExpired: + pytest.fail("Subprocess timed out") + except subprocess.CalledProcessError as e: + pytest.fail("Subprocess failed to test intended behavior\n" + + str(e.stderr)) + else: + # macOS may actually emit irrelevant errors about Accelerated + # OpenGL vs. software OpenGL, or some permission error on Azure, so + # suppress them. + # Asserting stderr first (and printing it on failure) should be + # more helpful for debugging that printing a failed success count. + ignored_lines = ["OpenGL", "CFMessagePort: bootstrap_register", + "/usr/include/servers/bootstrap_defs.h"] + assert not [line for line in proc.stderr.splitlines() + if all(msg not in line for msg in ignored_lines)] + assert proc.stdout.count("success") == success_count + + return test_func + + +@_isolated_tk_test(success_count=6) # len(bad_boxes) +def test_blit(): + import matplotlib.pyplot as plt + import numpy as np + import matplotlib.backends.backend_tkagg # noqa + from matplotlib.backends import _backend_tk, _tkagg + + fig, ax = plt.subplots() + photoimage = fig.canvas._tkphoto + data = np.ones((4, 4, 4), dtype=np.uint8) + # Test out of bounds blitting. + bad_boxes = ((-1, 2, 0, 2), + (2, 0, 0, 2), + (1, 6, 0, 2), + (0, 2, -1, 2), + (0, 2, 2, 0), + (0, 2, 1, 6)) + for bad_box in bad_boxes: + try: + _tkagg.blit( + photoimage.tk.interpaddr(), str(photoimage), data, + _tkagg.TK_PHOTO_COMPOSITE_OVERLAY, (0, 1, 2, 3), bad_box) + except ValueError: + print("success") + + # Test blitting to a destroyed canvas. + plt.close(fig) + _backend_tk.blit(photoimage, data, (0, 1, 2, 3)) + + +@_isolated_tk_test(success_count=1) +def test_figuremanager_preserves_host_mainloop(): + import tkinter + import matplotlib.pyplot as plt + success = [] + + def do_plot(): + plt.figure() + plt.plot([1, 2], [3, 5]) + plt.close() + root.after(0, legitimate_quit) + + def legitimate_quit(): + root.quit() + success.append(True) + + root = tkinter.Tk() + root.after(0, do_plot) + root.mainloop() + + if success: + print("success") + + +@pytest.mark.skipif(platform.python_implementation() != 'CPython', + reason='PyPy does not support Tkinter threading: ' + 'https://foss.heptapod.net/pypy/pypy/-/issues/1929') +@pytest.mark.flaky(reruns=3) +@_isolated_tk_test(success_count=1) +def test_figuremanager_cleans_own_mainloop(): + import tkinter + import time + import matplotlib.pyplot as plt + import threading + from matplotlib.cbook import _get_running_interactive_framework + + root = tkinter.Tk() + plt.plot([1, 2, 3], [1, 2, 5]) + + def target(): + while not 'tk' == _get_running_interactive_framework(): + time.sleep(.01) + plt.close() + if show_finished_event.wait(): + print('success') + + show_finished_event = threading.Event() + thread = threading.Thread(target=target, daemon=True) + thread.start() + plt.show(block=True) # Testing if this function hangs. + show_finished_event.set() + thread.join() + + +@pytest.mark.flaky(reruns=3) +@_isolated_tk_test(success_count=0) +def test_never_update(): + import tkinter + del tkinter.Misc.update + del tkinter.Misc.update_idletasks + + import matplotlib.pyplot as plt + fig = plt.figure() + plt.show(block=False) + + plt.draw() # Test FigureCanvasTkAgg. + fig.canvas.toolbar.configure_subplots() # Test NavigationToolbar2Tk. + # Test FigureCanvasTk filter_destroy callback + fig.canvas.get_tk_widget().after(100, plt.close, fig) + + # Check for update() or update_idletasks() in the event queue, functionally + # equivalent to tkinter.Misc.update. + plt.show(block=True) + + # Note that exceptions would be printed to stderr; _isolated_tk_test + # checks them. + + +@_isolated_tk_test(success_count=2) +def test_missing_back_button(): + import matplotlib.pyplot as plt + from matplotlib.backends.backend_tkagg import NavigationToolbar2Tk + + class Toolbar(NavigationToolbar2Tk): + # Only display the buttons we need. + toolitems = [t for t in NavigationToolbar2Tk.toolitems if + t[0] in ('Home', 'Pan', 'Zoom')] + + fig = plt.figure() + print("success") + Toolbar(fig.canvas, fig.canvas.manager.window) # This should not raise. + print("success") + + +@_isolated_tk_test(success_count=2) +def test_save_figure_return(): + import matplotlib.pyplot as plt + from unittest import mock + fig = plt.figure() + prop = "tkinter.filedialog.asksaveasfilename" + with mock.patch(prop, return_value="foobar.png"): + fname = fig.canvas.manager.toolbar.save_figure() + os.remove("foobar.png") + assert fname == "foobar.png" + print("success") + with mock.patch(prop, return_value=""): + fname = fig.canvas.manager.toolbar.save_figure() + assert fname is None + print("success") + + +@_isolated_tk_test(success_count=1) +def test_canvas_focus(): + import tkinter as tk + import matplotlib.pyplot as plt + success = [] + + def check_focus(): + tkcanvas = fig.canvas.get_tk_widget() + # Give the plot window time to appear + if not tkcanvas.winfo_viewable(): + tkcanvas.wait_visibility() + # Make sure the canvas has the focus, so that it's able to receive + # keyboard events. + if tkcanvas.focus_lastfor() == tkcanvas: + success.append(True) + plt.close() + root.destroy() + + root = tk.Tk() + fig = plt.figure() + plt.plot([1, 2, 3]) + root.after(0, plt.show) + root.after(100, check_focus) + root.mainloop() + + if success: + print("success") + + +@_isolated_tk_test(success_count=2) +def test_embedding(): + import tkinter as tk + from matplotlib.backends.backend_tkagg import ( + FigureCanvasTkAgg, NavigationToolbar2Tk) + from matplotlib.backend_bases import key_press_handler + from matplotlib.figure import Figure + + root = tk.Tk() + + def test_figure(master): + fig = Figure() + ax = fig.add_subplot() + ax.plot([1, 2, 3]) + + canvas = FigureCanvasTkAgg(fig, master=master) + canvas.draw() + canvas.mpl_connect("key_press_event", key_press_handler) + canvas.get_tk_widget().pack(expand=True, fill="both") + + toolbar = NavigationToolbar2Tk(canvas, master, pack_toolbar=False) + toolbar.pack(expand=True, fill="x") + + canvas.get_tk_widget().forget() + toolbar.forget() + + test_figure(root) + print("success") + + # Test with a dark button color. Doesn't actually check whether the icon + # color becomes lighter, just that the code doesn't break. + + root.tk_setPalette(background="sky blue", selectColor="midnight blue", + foreground="white") + test_figure(root) + print("success") diff --git a/shared/package-files/03/035a2ecc772fbc43c641b0fa2dcecd147f0e44d61a88d4f22fc0d17a0c5cb40d/native_functions.py b/shared/package-files/03/035a2ecc772fbc43c641b0fa2dcecd147f0e44d61a88d4f22fc0d17a0c5cb40d/native_functions.py new file mode 100644 index 0000000000000000000000000000000000000000..57a9217550d9c9afbbe7f1ab544771381b1359eb --- /dev/null +++ b/shared/package-files/03/035a2ecc772fbc43c641b0fa2dcecd147f0e44d61a88d4f22fc0d17a0c5cb40d/native_functions.py @@ -0,0 +1,64 @@ +from typing import List, Optional, Union + +import torchgen.api.meta as meta +import torchgen.api.structured as structured +from torchgen.api.types import kernel_signature + +from torchgen.context import with_native_function_and_index +from torchgen.model import BackendIndex, NativeFunction, NativeFunctionsGroup +from torchgen.utils import mapMaybe + + +@with_native_function_and_index +def gen_unstructured(f: NativeFunction, backend_index: BackendIndex) -> Optional[str]: + sig = kernel_signature(f, backend_index) + metadata = backend_index.get_kernel(f) + if metadata is None: + return None + if "legacy::" in metadata.kernel: + return None + else: + prefix = "static" if backend_index.external else "TORCH_API" + return f"{prefix} {sig.decl(name=metadata.kernel)};" + + +@with_native_function_and_index +def gen_structured(g: NativeFunctionsGroup, backend_index: BackendIndex) -> List[str]: + meta_name = meta.name(g) + out_args = structured.impl_arguments(g) + metadata = backend_index.get_kernel(g) + if metadata is None: + return [] + prefix = "" if backend_index.external else "TORCH_API " + return [ + f"""\ +struct {prefix}structured_{metadata.kernel} : public at::meta::structured_{meta_name} {{ +void impl({', '.join(a.decl() for a in out_args)}); +}}; +""" + ] + + +# Generates NativeFunctions.h, a list of forward declarations of all +# actual kernel definitions we keep in aten/src/ATen/native/ +@with_native_function_and_index +def compute_native_function_declaration( + g: Union[NativeFunctionsGroup, NativeFunction], backend_index: BackendIndex +) -> List[str]: + metadata = backend_index.get_kernel(g) + if isinstance(g, NativeFunctionsGroup): + if metadata is not None and metadata.structured: + if backend_index.external: + # Structured hasn't been tested with external backends yet. + raise AssertionError( + "Structured external backend functions are not implemented yet." + ) + else: + return gen_structured(g, backend_index) + else: + return list( + mapMaybe(lambda f: gen_unstructured(f, backend_index), g.functions()) + ) + else: + x = gen_unstructured(g, backend_index) + return [] if x is None else [x] diff --git a/shared/package-files/03/035d95e258d6c6f5da48527bde61d214ad9834efa4f1ac7a31ee321164ec238b/test_sparsifiers.py b/shared/package-files/03/035d95e258d6c6f5da48527bde61d214ad9834efa4f1ac7a31ee321164ec238b/test_sparsifiers.py new file mode 100644 index 0000000000000000000000000000000000000000..78cabceed0102bf2ffe01d8675102c1ae85efac2 --- /dev/null +++ b/shared/package-files/03/035d95e258d6c6f5da48527bde61d214ad9834efa4f1ac7a31ee321164ec238b/test_sparsifiers.py @@ -0,0 +1,137 @@ +"""Unit tests for the sparsifier computation functions.""" +import pytest + +import networkx as nx +from networkx.utils import py_random_state + +_seed = 2 + + +def _test_spanner(G, spanner, stretch, weight=None): + """Test whether a spanner is valid. + + This function tests whether the given spanner is a subgraph of the + given graph G with the same node set. It also tests for all shortest + paths whether they adhere to the given stretch. + + Parameters + ---------- + G : NetworkX graph + The original graph for which the spanner was constructed. + + spanner : NetworkX graph + The spanner to be tested. + + stretch : float + The proclaimed stretch of the spanner. + + weight : object + The edge attribute to use as distance. + """ + # check node set + assert set(G.nodes()) == set(spanner.nodes()) + + # check edge set and weights + for u, v in spanner.edges(): + assert G.has_edge(u, v) + if weight: + assert spanner[u][v][weight] == G[u][v][weight] + + # check connectivity and stretch + original_length = dict(nx.shortest_path_length(G, weight=weight)) + spanner_length = dict(nx.shortest_path_length(spanner, weight=weight)) + for u in G.nodes(): + for v in G.nodes(): + if u in original_length and v in original_length[u]: + assert spanner_length[u][v] <= stretch * original_length[u][v] + + +@py_random_state(1) +def _assign_random_weights(G, seed=None): + """Assigns random weights to the edges of a graph. + + Parameters + ---------- + + G : NetworkX graph + The original graph for which the spanner was constructed. + + seed : integer, random_state, or None (default) + Indicator of random number generation state. + See :ref:`Randomness`. + """ + for u, v in G.edges(): + G[u][v]["weight"] = seed.random() + + +def test_spanner_trivial(): + """Test a trivial spanner with stretch 1.""" + G = nx.complete_graph(20) + spanner = nx.spanner(G, 1, seed=_seed) + + for u, v in G.edges: + assert spanner.has_edge(u, v) + + +def test_spanner_unweighted_complete_graph(): + """Test spanner construction on a complete unweighted graph.""" + G = nx.complete_graph(20) + + spanner = nx.spanner(G, 4, seed=_seed) + _test_spanner(G, spanner, 4) + + spanner = nx.spanner(G, 10, seed=_seed) + _test_spanner(G, spanner, 10) + + +def test_spanner_weighted_complete_graph(): + """Test spanner construction on a complete weighted graph.""" + G = nx.complete_graph(20) + _assign_random_weights(G, seed=_seed) + + spanner = nx.spanner(G, 4, weight="weight", seed=_seed) + _test_spanner(G, spanner, 4, weight="weight") + + spanner = nx.spanner(G, 10, weight="weight", seed=_seed) + _test_spanner(G, spanner, 10, weight="weight") + + +def test_spanner_unweighted_gnp_graph(): + """Test spanner construction on an unweighted gnp graph.""" + G = nx.gnp_random_graph(20, 0.4, seed=_seed) + + spanner = nx.spanner(G, 4, seed=_seed) + _test_spanner(G, spanner, 4) + + spanner = nx.spanner(G, 10, seed=_seed) + _test_spanner(G, spanner, 10) + + +def test_spanner_weighted_gnp_graph(): + """Test spanner construction on an weighted gnp graph.""" + G = nx.gnp_random_graph(20, 0.4, seed=_seed) + _assign_random_weights(G, seed=_seed) + + spanner = nx.spanner(G, 4, weight="weight", seed=_seed) + _test_spanner(G, spanner, 4, weight="weight") + + spanner = nx.spanner(G, 10, weight="weight", seed=_seed) + _test_spanner(G, spanner, 10, weight="weight") + + +def test_spanner_unweighted_disconnected_graph(): + """Test spanner construction on a disconnected graph.""" + G = nx.disjoint_union(nx.complete_graph(10), nx.complete_graph(10)) + + spanner = nx.spanner(G, 4, seed=_seed) + _test_spanner(G, spanner, 4) + + spanner = nx.spanner(G, 10, seed=_seed) + _test_spanner(G, spanner, 10) + + +def test_spanner_invalid_stretch(): + """Check whether an invalid stretch is caught.""" + with pytest.raises(ValueError): + G = nx.empty_graph() + nx.spanner(G, 0) diff --git a/shared/package-files/03/03606ac3664f19fc2472f2e633fdbad766d6f1b4e2414f21c1234692ff91dc55/multilabel_margin_loss_backward_native.h b/shared/package-files/03/03606ac3664f19fc2472f2e633fdbad766d6f1b4e2414f21c1234692ff91dc55/multilabel_margin_loss_backward_native.h new file mode 100644 index 0000000000000000000000000000000000000000..ae7cd05e94e3df0d80cf58b6f16fc7038ae27f71 --- /dev/null +++ b/shared/package-files/03/03606ac3664f19fc2472f2e633fdbad766d6f1b4e2414f21c1234692ff91dc55/multilabel_margin_loss_backward_native.h @@ -0,0 +1,24 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor multilabel_margin_loss_backward_cpu(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target); +TORCH_API at::Tensor & multilabel_margin_loss_backward_cpu_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target, at::Tensor & grad_input); +TORCH_API at::Tensor multilabel_margin_loss_backward_cuda(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target); +TORCH_API at::Tensor & multilabel_margin_loss_backward_cuda_out(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, int64_t reduction, const at::Tensor & is_target, at::Tensor & grad_input); +} // namespace native +} // namespace at diff --git a/shared/package-files/03/036238ba9b78a8da259571a9c33aea83b334a0a1c9859c750543080678d1228c/dispatcher.py b/shared/package-files/03/036238ba9b78a8da259571a9c33aea83b334a0a1c9859c750543080678d1228c/dispatcher.py new file mode 100644 index 0000000000000000000000000000000000000000..89471d678e1c330138a91ec6a41a324d29a037d7 --- /dev/null +++ b/shared/package-files/03/036238ba9b78a8da259571a9c33aea83b334a0a1c9859c750543080678d1228c/dispatcher.py @@ -0,0 +1,413 @@ +from __future__ import annotations + +from warnings import warn +import inspect +from .conflict import ordering, ambiguities, super_signature, AmbiguityWarning +from .utils import expand_tuples +import itertools as itl + + +class MDNotImplementedError(NotImplementedError): + """ A NotImplementedError for multiple dispatch """ + + +### Functions for on_ambiguity + +def ambiguity_warn(dispatcher, ambiguities): + """ Raise warning when ambiguity is detected + + Parameters + ---------- + dispatcher : Dispatcher + The dispatcher on which the ambiguity was detected + ambiguities : set + Set of type signature pairs that are ambiguous within this dispatcher + + See Also: + Dispatcher.add + warning_text + """ + warn(warning_text(dispatcher.name, ambiguities), AmbiguityWarning) + + +class RaiseNotImplementedError: + """Raise ``NotImplementedError`` when called.""" + + def __init__(self, dispatcher): + self.dispatcher = dispatcher + + def __call__(self, *args, **kwargs): + types = tuple(type(a) for a in args) + raise NotImplementedError( + "Ambiguous signature for %s: <%s>" % ( + self.dispatcher.name, str_signature(types) + )) + +def ambiguity_register_error_ignore_dup(dispatcher, ambiguities): + """ + If super signature for ambiguous types is duplicate types, ignore it. + Else, register instance of ``RaiseNotImplementedError`` for ambiguous types. + + Parameters + ---------- + dispatcher : Dispatcher + The dispatcher on which the ambiguity was detected + ambiguities : set + Set of type signature pairs that are ambiguous within this dispatcher + + See Also: + Dispatcher.add + ambiguity_warn + """ + for amb in ambiguities: + signature = tuple(super_signature(amb)) + if len(set(signature)) == 1: + continue + dispatcher.add( + signature, RaiseNotImplementedError(dispatcher), + on_ambiguity=ambiguity_register_error_ignore_dup + ) + +### + + +_unresolved_dispatchers: set[Dispatcher] = set() +_resolve = [True] + + +def halt_ordering(): + _resolve[0] = False + + +def restart_ordering(on_ambiguity=ambiguity_warn): + _resolve[0] = True + while _unresolved_dispatchers: + dispatcher = _unresolved_dispatchers.pop() + dispatcher.reorder(on_ambiguity=on_ambiguity) + + +class Dispatcher: + """ Dispatch methods based on type signature + + Use ``dispatch`` to add implementations + + Examples + -------- + + >>> from sympy.multipledispatch import dispatch + >>> @dispatch(int) + ... def f(x): + ... return x + 1 + + >>> @dispatch(float) + ... def f(x): # noqa: F811 + ... return x - 1 + + >>> f(3) + 4 + >>> f(3.0) + 2.0 + """ + __slots__ = '__name__', 'name', 'funcs', 'ordering', '_cache', 'doc' + + def __init__(self, name, doc=None): + self.name = self.__name__ = name + self.funcs = {} + self._cache = {} + self.ordering = [] + self.doc = doc + + def register(self, *types, **kwargs): + """ Register dispatcher with new implementation + + >>> from sympy.multipledispatch.dispatcher import Dispatcher + >>> f = Dispatcher('f') + >>> @f.register(int) + ... def inc(x): + ... return x + 1 + + >>> @f.register(float) + ... def dec(x): + ... return x - 1 + + >>> @f.register(list) + ... @f.register(tuple) + ... def reverse(x): + ... return x[::-1] + + >>> f(1) + 2 + + >>> f(1.0) + 0.0 + + >>> f([1, 2, 3]) + [3, 2, 1] + """ + def _(func): + self.add(types, func, **kwargs) + return func + return _ + + @classmethod + def get_func_params(cls, func): + if hasattr(inspect, "signature"): + sig = inspect.signature(func) + return sig.parameters.values() + + @classmethod + def get_func_annotations(cls, func): + """ Get annotations of function positional parameters + """ + params = cls.get_func_params(func) + if params: + Parameter = inspect.Parameter + + params = (param for param in params + if param.kind in + (Parameter.POSITIONAL_ONLY, + Parameter.POSITIONAL_OR_KEYWORD)) + + annotations = tuple( + param.annotation + for param in params) + + if not any(ann is Parameter.empty for ann in annotations): + return annotations + + def add(self, signature, func, on_ambiguity=ambiguity_warn): + """ Add new types/method pair to dispatcher + + >>> from sympy.multipledispatch import Dispatcher + >>> D = Dispatcher('add') + >>> D.add((int, int), lambda x, y: x + y) + >>> D.add((float, float), lambda x, y: x + y) + + >>> D(1, 2) + 3 + >>> D(1, 2.0) + Traceback (most recent call last): + ... + NotImplementedError: Could not find signature for add: + + When ``add`` detects a warning it calls the ``on_ambiguity`` callback + with a dispatcher/itself, and a set of ambiguous type signature pairs + as inputs. See ``ambiguity_warn`` for an example. + """ + # Handle annotations + if not signature: + annotations = self.get_func_annotations(func) + if annotations: + signature = annotations + + # Handle union types + if any(isinstance(typ, tuple) for typ in signature): + for typs in expand_tuples(signature): + self.add(typs, func, on_ambiguity) + return + + for typ in signature: + if not isinstance(typ, type): + str_sig = ', '.join(c.__name__ if isinstance(c, type) + else str(c) for c in signature) + raise TypeError("Tried to dispatch on non-type: %s\n" + "In signature: <%s>\n" + "In function: %s" % + (typ, str_sig, self.name)) + + self.funcs[signature] = func + self.reorder(on_ambiguity=on_ambiguity) + self._cache.clear() + + def reorder(self, on_ambiguity=ambiguity_warn): + if _resolve[0]: + self.ordering = ordering(self.funcs) + amb = ambiguities(self.funcs) + if amb: + on_ambiguity(self, amb) + else: + _unresolved_dispatchers.add(self) + + def __call__(self, *args, **kwargs): + types = tuple([type(arg) for arg in args]) + try: + func = self._cache[types] + except KeyError: + func = self.dispatch(*types) + if not func: + raise NotImplementedError( + 'Could not find signature for %s: <%s>' % + (self.name, str_signature(types))) + self._cache[types] = func + try: + return func(*args, **kwargs) + + except MDNotImplementedError: + funcs = self.dispatch_iter(*types) + next(funcs) # burn first + for func in funcs: + try: + return func(*args, **kwargs) + except MDNotImplementedError: + pass + raise NotImplementedError("Matching functions for " + "%s: <%s> found, but none completed successfully" + % (self.name, str_signature(types))) + + def __str__(self): + return "" % self.name + __repr__ = __str__ + + def dispatch(self, *types): + """ Deterimine appropriate implementation for this type signature + + This method is internal. Users should call this object as a function. + Implementation resolution occurs within the ``__call__`` method. + + >>> from sympy.multipledispatch import dispatch + >>> @dispatch(int) + ... def inc(x): + ... return x + 1 + + >>> implementation = inc.dispatch(int) + >>> implementation(3) + 4 + + >>> print(inc.dispatch(float)) + None + + See Also: + ``sympy.multipledispatch.conflict`` - module to determine resolution order + """ + + if types in self.funcs: + return self.funcs[types] + + try: + return next(self.dispatch_iter(*types)) + except StopIteration: + return None + + def dispatch_iter(self, *types): + n = len(types) + for signature in self.ordering: + if len(signature) == n and all(map(issubclass, types, signature)): + result = self.funcs[signature] + yield result + + def resolve(self, types): + """ Deterimine appropriate implementation for this type signature + + .. deprecated:: 0.4.4 + Use ``dispatch(*types)`` instead + """ + warn("resolve() is deprecated, use dispatch(*types)", + DeprecationWarning) + + return self.dispatch(*types) + + def __getstate__(self): + return {'name': self.name, + 'funcs': self.funcs} + + def __setstate__(self, d): + self.name = d['name'] + self.funcs = d['funcs'] + self.ordering = ordering(self.funcs) + self._cache = {} + + @property + def __doc__(self): + docs = ["Multiply dispatched method: %s" % self.name] + + if self.doc: + docs.append(self.doc) + + other = [] + for sig in self.ordering[::-1]: + func = self.funcs[sig] + if func.__doc__: + s = 'Inputs: <%s>\n' % str_signature(sig) + s += '-' * len(s) + '\n' + s += func.__doc__.strip() + docs.append(s) + else: + other.append(str_signature(sig)) + + if other: + docs.append('Other signatures:\n ' + '\n '.join(other)) + + return '\n\n'.join(docs) + + def _help(self, *args): + return self.dispatch(*map(type, args)).__doc__ + + def help(self, *args, **kwargs): + """ Print docstring for the function corresponding to inputs """ + print(self._help(*args)) + + def _source(self, *args): + func = self.dispatch(*map(type, args)) + if not func: + raise TypeError("No function found") + return source(func) + + def source(self, *args, **kwargs): + """ Print source code for the function corresponding to inputs """ + print(self._source(*args)) + + +def source(func): + s = 'File: %s\n\n' % inspect.getsourcefile(func) + s = s + inspect.getsource(func) + return s + + +class MethodDispatcher(Dispatcher): + """ Dispatch methods based on type signature + + See Also: + Dispatcher + """ + + @classmethod + def get_func_params(cls, func): + if hasattr(inspect, "signature"): + sig = inspect.signature(func) + return itl.islice(sig.parameters.values(), 1, None) + + def __get__(self, instance, owner): + self.obj = instance + self.cls = owner + return self + + def __call__(self, *args, **kwargs): + types = tuple([type(arg) for arg in args]) + func = self.dispatch(*types) + if not func: + raise NotImplementedError('Could not find signature for %s: <%s>' % + (self.name, str_signature(types))) + return func(self.obj, *args, **kwargs) + + +def str_signature(sig): + """ String representation of type signature + + >>> from sympy.multipledispatch.dispatcher import str_signature + >>> str_signature((int, float)) + 'int, float' + """ + return ', '.join(cls.__name__ for cls in sig) + + +def warning_text(name, amb): + """ The text for ambiguity warnings """ + text = "\nAmbiguities exist in dispatched function %s\n\n" % (name) + text += "The following signatures may result in ambiguous behavior:\n" + for pair in amb: + text += "\t" + \ + ', '.join('[' + str_signature(s) + ']' for s in pair) + "\n" + text += "\n\nConsider making the following additions:\n\n" + text += '\n\n'.join(['@dispatch(' + str_signature(super_signature(s)) + + ')\ndef %s(...)' % name for s in amb]) + return text diff --git a/shared/package-files/03/0367001befa2ee94d5a2520af7a9b136e093b4bbe16cb551a89e0c01f5940444/test_coloring.py b/shared/package-files/03/0367001befa2ee94d5a2520af7a9b136e093b4bbe16cb551a89e0c01f5940444/test_coloring.py new file mode 100644 index 0000000000000000000000000000000000000000..a2a4e39589ea981445f6e9e222087714ef88e141 --- /dev/null +++ b/shared/package-files/03/0367001befa2ee94d5a2520af7a9b136e093b4bbe16cb551a89e0c01f5940444/test_coloring.py @@ -0,0 +1,865 @@ +"""Greedy coloring test suite. + +""" + +import itertools + +import pytest + +import networkx as nx + +is_coloring = nx.algorithms.coloring.equitable_coloring.is_coloring +is_equitable = nx.algorithms.coloring.equitable_coloring.is_equitable + + +ALL_STRATEGIES = [ + "largest_first", + "random_sequential", + "smallest_last", + "independent_set", + "connected_sequential_bfs", + "connected_sequential_dfs", + "connected_sequential", + "saturation_largest_first", + "DSATUR", +] + +# List of strategies where interchange=True results in an error +INTERCHANGE_INVALID = ["independent_set", "saturation_largest_first", "DSATUR"] + + +class TestColoring: + def test_basic_cases(self): + def check_basic_case(graph_func, n_nodes, strategy, interchange): + graph = graph_func() + coloring = nx.coloring.greedy_color( + graph, strategy=strategy, interchange=interchange + ) + assert verify_length(coloring, n_nodes) + assert verify_coloring(graph, coloring) + + for graph_func, n_nodes in BASIC_TEST_CASES.items(): + for interchange in [True, False]: + for strategy in ALL_STRATEGIES: + check_basic_case(graph_func, n_nodes, strategy, False) + if strategy not in INTERCHANGE_INVALID: + check_basic_case(graph_func, n_nodes, strategy, True) + + def test_special_cases(self): + def check_special_case(strategy, graph_func, interchange, colors): + graph = graph_func() + coloring = nx.coloring.greedy_color( + graph, strategy=strategy, interchange=interchange + ) + if not hasattr(colors, "__len__"): + colors = [colors] + assert any(verify_length(coloring, n_colors) for n_colors in colors) + assert verify_coloring(graph, coloring) + + for strategy, arglist in SPECIAL_TEST_CASES.items(): + for args in arglist: + check_special_case(strategy, args[0], args[1], args[2]) + + def test_interchange_invalid(self): + graph = one_node_graph() + for strategy in INTERCHANGE_INVALID: + pytest.raises( + nx.NetworkXPointlessConcept, + nx.coloring.greedy_color, + graph, + strategy=strategy, + interchange=True, + ) + + def test_bad_inputs(self): + graph = one_node_graph() + pytest.raises( + nx.NetworkXError, + nx.coloring.greedy_color, + graph, + strategy="invalid strategy", + ) + + def test_strategy_as_function(self): + graph = lf_shc() + colors_1 = nx.coloring.greedy_color(graph, "largest_first") + colors_2 = nx.coloring.greedy_color(graph, nx.coloring.strategy_largest_first) + assert colors_1 == colors_2 + + def test_seed_argument(self): + graph = lf_shc() + rs = nx.coloring.strategy_random_sequential + c1 = nx.coloring.greedy_color(graph, lambda g, c: rs(g, c, seed=1)) + for u, v in graph.edges: + assert c1[u] != c1[v] + + def test_is_coloring(self): + G = nx.Graph() + G.add_edges_from([(0, 1), (1, 2)]) + coloring = {0: 0, 1: 1, 2: 0} + assert is_coloring(G, coloring) + + coloring[0] = 1 + assert not is_coloring(G, coloring) + assert not is_equitable(G, coloring) + + def test_is_equitable(self): + G = nx.Graph() + G.add_edges_from([(0, 1), (1, 2)]) + coloring = {0: 0, 1: 1, 2: 0} + assert is_equitable(G, coloring) + + G.add_edges_from([(2, 3), (2, 4), (2, 5)]) + coloring[3] = 1 + coloring[4] = 1 + coloring[5] = 1 + assert is_coloring(G, coloring) + assert not is_equitable(G, coloring) + + def test_num_colors(self): + G = nx.Graph() + G.add_edges_from([(0, 1), (0, 2), (0, 3)]) + pytest.raises(nx.NetworkXAlgorithmError, nx.coloring.equitable_color, G, 2) + + def test_equitable_color(self): + G = nx.fast_gnp_random_graph(n=10, p=0.2, seed=42) + coloring = nx.coloring.equitable_color(G, max_degree(G) + 1) + assert is_equitable(G, coloring) + + def test_equitable_color_empty(self): + G = nx.empty_graph() + coloring = nx.coloring.equitable_color(G, max_degree(G) + 1) + assert is_equitable(G, coloring) + + def test_equitable_color_large(self): + G = nx.fast_gnp_random_graph(100, 0.1, seed=42) + coloring = nx.coloring.equitable_color(G, max_degree(G) + 1) + assert is_equitable(G, coloring, num_colors=max_degree(G) + 1) + + def test_case_V_plus_not_in_A_cal(self): + # Hand crafted case to avoid the easy case. + L = { + 0: [2, 5], + 1: [3, 4], + 2: [0, 8], + 3: [1, 7], + 4: [1, 6], + 5: [0, 6], + 6: [4, 5], + 7: [3], + 8: [2], + } + + F = { + # Color 0 + 0: 0, + 1: 0, + # Color 1 + 2: 1, + 3: 1, + 4: 1, + 5: 1, + # Color 2 + 6: 2, + 7: 2, + 8: 2, + } + + C = nx.algorithms.coloring.equitable_coloring.make_C_from_F(F) + N = nx.algorithms.coloring.equitable_coloring.make_N_from_L_C(L, C) + H = nx.algorithms.coloring.equitable_coloring.make_H_from_C_N(C, N) + + nx.algorithms.coloring.equitable_coloring.procedure_P( + V_minus=0, V_plus=1, N=N, H=H, F=F, C=C, L=L + ) + check_state(L=L, N=N, H=H, F=F, C=C) + + def test_cast_no_solo(self): + L = { + 0: [8, 9], + 1: [10, 11], + 2: [8], + 3: [9], + 4: [10, 11], + 5: [8], + 6: [9], + 7: [10, 11], + 8: [0, 2, 5], + 9: [0, 3, 6], + 10: [1, 4, 7], + 11: [1, 4, 7], + } + + F = {0: 0, 1: 0, 2: 2, 3: 2, 4: 2, 5: 3, 6: 3, 7: 3, 8: 1, 9: 1, 10: 1, 11: 1} + + C = nx.algorithms.coloring.equitable_coloring.make_C_from_F(F) + N = nx.algorithms.coloring.equitable_coloring.make_N_from_L_C(L, C) + H = nx.algorithms.coloring.equitable_coloring.make_H_from_C_N(C, N) + + nx.algorithms.coloring.equitable_coloring.procedure_P( + V_minus=0, V_plus=1, N=N, H=H, F=F, C=C, L=L + ) + check_state(L=L, N=N, H=H, F=F, C=C) + + def test_hard_prob(self): + # Tests for two levels of recursion. + num_colors, s = 5, 5 + + G = nx.Graph() + G.add_edges_from( + [ + (0, 10), + (0, 11), + (0, 12), + (0, 23), + (10, 4), + (10, 9), + (10, 20), + (11, 4), + (11, 8), + (11, 16), + (12, 9), + (12, 22), + (12, 23), + (23, 7), + (1, 17), + (1, 18), + (1, 19), + (1, 24), + (17, 5), + (17, 13), + (17, 22), + (18, 5), + (19, 5), + (19, 6), + (19, 8), + (24, 7), + (24, 16), + (2, 4), + (2, 13), + (2, 14), + (2, 15), + (4, 6), + (13, 5), + (13, 21), + (14, 6), + (14, 15), + (15, 6), + (15, 21), + (3, 16), + (3, 20), + (3, 21), + (3, 22), + (16, 8), + (20, 8), + (21, 9), + (22, 7), + ] + ) + F = {node: node // s for node in range(num_colors * s)} + F[s - 1] = num_colors - 1 + + params = make_params_from_graph(G=G, F=F) + + nx.algorithms.coloring.equitable_coloring.procedure_P( + V_minus=0, V_plus=num_colors - 1, **params + ) + check_state(**params) + + def test_hardest_prob(self): + # Tests for two levels of recursion. + num_colors, s = 10, 4 + + G = nx.Graph() + G.add_edges_from( + [ + (0, 19), + (0, 24), + (0, 29), + (0, 30), + (0, 35), + (19, 3), + (19, 7), + (19, 9), + (19, 15), + (19, 21), + (19, 24), + (19, 30), + (19, 38), + (24, 5), + (24, 11), + (24, 13), + (24, 20), + (24, 30), + (24, 37), + (24, 38), + (29, 6), + (29, 10), + (29, 13), + (29, 15), + (29, 16), + (29, 17), + (29, 20), + (29, 26), + (30, 6), + (30, 10), + (30, 15), + (30, 22), + (30, 23), + (30, 39), + (35, 6), + (35, 9), + (35, 14), + (35, 18), + (35, 22), + (35, 23), + (35, 25), + (35, 27), + (1, 20), + (1, 26), + (1, 31), + (1, 34), + (1, 38), + (20, 4), + (20, 8), + (20, 14), + (20, 18), + (20, 28), + (20, 33), + (26, 7), + (26, 10), + (26, 14), + (26, 18), + (26, 21), + (26, 32), + (26, 39), + (31, 5), + (31, 8), + (31, 13), + (31, 16), + (31, 17), + (31, 21), + (31, 25), + (31, 27), + (34, 7), + (34, 8), + (34, 13), + (34, 18), + (34, 22), + (34, 23), + (34, 25), + (34, 27), + (38, 4), + (38, 9), + (38, 12), + (38, 14), + (38, 21), + (38, 27), + (2, 3), + (2, 18), + (2, 21), + (2, 28), + (2, 32), + (2, 33), + (2, 36), + (2, 37), + (2, 39), + (3, 5), + (3, 9), + (3, 13), + (3, 22), + (3, 23), + (3, 25), + (3, 27), + (18, 6), + (18, 11), + (18, 15), + (18, 39), + (21, 4), + (21, 10), + (21, 14), + (21, 36), + (28, 6), + (28, 10), + (28, 14), + (28, 16), + (28, 17), + (28, 25), + (28, 27), + (32, 5), + (32, 10), + (32, 12), + (32, 16), + (32, 17), + (32, 22), + (32, 23), + (33, 7), + (33, 10), + (33, 12), + (33, 16), + (33, 17), + (33, 25), + (33, 27), + (36, 5), + (36, 8), + (36, 15), + (36, 16), + (36, 17), + (36, 25), + (36, 27), + (37, 5), + (37, 11), + (37, 15), + (37, 16), + (37, 17), + (37, 22), + (37, 23), + (39, 7), + (39, 8), + (39, 15), + (39, 22), + (39, 23), + ] + ) + F = {node: node // s for node in range(num_colors * s)} + F[s - 1] = num_colors - 1 # V- = 0, V+ = num_colors - 1 + + params = make_params_from_graph(G=G, F=F) + + nx.algorithms.coloring.equitable_coloring.procedure_P( + V_minus=0, V_plus=num_colors - 1, **params + ) + check_state(**params) + + def test_strategy_saturation_largest_first(self): + def color_remaining_nodes( + G, + colored_nodes, + full_color_assignment=None, + nodes_to_add_between_calls=1, + ): + color_assignments = [] + aux_colored_nodes = colored_nodes.copy() + + node_iterator = nx.algorithms.coloring.greedy_coloring.strategy_saturation_largest_first( + G, aux_colored_nodes + ) + + for u in node_iterator: + # Set to keep track of colors of neighbours + neighbour_colors = { + aux_colored_nodes[v] for v in G[u] if v in aux_colored_nodes + } + # Find the first unused color. + for color in itertools.count(): + if color not in neighbour_colors: + break + aux_colored_nodes[u] = color + color_assignments.append((u, color)) + + # Color nodes between iterations + for i in range(nodes_to_add_between_calls - 1): + if not len(color_assignments) + len(colored_nodes) >= len( + full_color_assignment + ): + full_color_assignment_node, color = full_color_assignment[ + len(color_assignments) + len(colored_nodes) + ] + + # Assign the new color to the current node. + aux_colored_nodes[full_color_assignment_node] = color + color_assignments.append((full_color_assignment_node, color)) + + return color_assignments, aux_colored_nodes + + for G, _, _ in SPECIAL_TEST_CASES["saturation_largest_first"]: + G = G() + + # Check that function still works when nodes are colored between iterations + for nodes_to_add_between_calls in range(1, 5): + # Get a full color assignment, (including the order in which nodes were colored) + colored_nodes = {} + full_color_assignment, full_colored_nodes = color_remaining_nodes( + G, colored_nodes + ) + + # For each node in the color assignment, add it to colored_nodes and re-run the function + for ind, (node, color) in enumerate(full_color_assignment): + colored_nodes[node] = color + + ( + partial_color_assignment, + partial_colored_nodes, + ) = color_remaining_nodes( + G, + colored_nodes, + full_color_assignment=full_color_assignment, + nodes_to_add_between_calls=nodes_to_add_between_calls, + ) + + # Check that the color assignment and order of remaining nodes are the same + assert full_color_assignment[ind + 1 :] == partial_color_assignment + assert full_colored_nodes == partial_colored_nodes + + +# ############################ Utility functions ############################ +def verify_coloring(graph, coloring): + for node in graph.nodes(): + if node not in coloring: + return False + + color = coloring[node] + for neighbor in graph.neighbors(node): + if coloring[neighbor] == color: + return False + + return True + + +def verify_length(coloring, expected): + coloring = dict_to_sets(coloring) + return len(coloring) == expected + + +def dict_to_sets(colors): + if len(colors) == 0: + return [] + + k = max(colors.values()) + 1 + sets = [set() for _ in range(k)] + + for node, color in colors.items(): + sets[color].add(node) + + return sets + + +# ############################ Graph Generation ############################ + + +def empty_graph(): + return nx.Graph() + + +def one_node_graph(): + graph = nx.Graph() + graph.add_nodes_from([1]) + return graph + + +def two_node_graph(): + graph = nx.Graph() + graph.add_nodes_from([1, 2]) + graph.add_edges_from([(1, 2)]) + return graph + + +def three_node_clique(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3]) + graph.add_edges_from([(1, 2), (1, 3), (2, 3)]) + return graph + + +def disconnected(): + graph = nx.Graph() + graph.add_edges_from([(1, 2), (2, 3), (4, 5), (5, 6)]) + return graph + + +def rs_shc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4]) + graph.add_edges_from([(1, 2), (2, 3), (3, 4)]) + return graph + + +def slf_shc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7]) + graph.add_edges_from( + [(1, 2), (1, 5), (1, 6), (2, 3), (2, 7), (3, 4), (3, 7), (4, 5), (4, 6), (5, 6)] + ) + return graph + + +def slf_hc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7, 8]) + graph.add_edges_from( + [ + (1, 2), + (1, 3), + (1, 4), + (1, 5), + (2, 3), + (2, 4), + (2, 6), + (5, 7), + (5, 8), + (6, 7), + (6, 8), + (7, 8), + ] + ) + return graph + + +def lf_shc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4, 5, 6]) + graph.add_edges_from([(6, 1), (1, 4), (4, 3), (3, 2), (2, 5)]) + return graph + + +def lf_hc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7]) + graph.add_edges_from( + [ + (1, 7), + (1, 6), + (1, 3), + (1, 4), + (7, 2), + (2, 6), + (2, 3), + (2, 5), + (5, 3), + (5, 4), + (4, 3), + ] + ) + return graph + + +def sl_shc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4, 5, 6]) + graph.add_edges_from( + [(1, 2), (1, 3), (2, 3), (1, 4), (2, 5), (3, 6), (4, 5), (4, 6), (5, 6)] + ) + return graph + + +def sl_hc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7, 8]) + graph.add_edges_from( + [ + (1, 2), + (1, 3), + (1, 5), + (1, 7), + (2, 3), + (2, 4), + (2, 8), + (8, 4), + (8, 6), + (8, 7), + (7, 5), + (7, 6), + (3, 4), + (4, 6), + (6, 5), + (5, 3), + ] + ) + return graph + + +def gis_shc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4]) + graph.add_edges_from([(1, 2), (2, 3), (3, 4)]) + return graph + + +def gis_hc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4, 5, 6]) + graph.add_edges_from([(1, 5), (2, 5), (3, 6), (4, 6), (5, 6)]) + return graph + + +def cs_shc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4, 5]) + graph.add_edges_from([(1, 2), (1, 5), (2, 3), (2, 4), (2, 5), (3, 4), (4, 5)]) + return graph + + +def rsi_shc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4, 5, 6]) + graph.add_edges_from( + [(1, 2), (1, 5), (1, 6), (2, 3), (3, 4), (4, 5), (4, 6), (5, 6)] + ) + return graph + + +def lfi_shc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7]) + graph.add_edges_from( + [(1, 2), (1, 5), (1, 6), (2, 3), (2, 7), (3, 4), (3, 7), (4, 5), (4, 6), (5, 6)] + ) + return graph + + +def lfi_hc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7, 8, 9]) + graph.add_edges_from( + [ + (1, 2), + (1, 5), + (1, 6), + (1, 7), + (2, 3), + (2, 8), + (2, 9), + (3, 4), + (3, 8), + (3, 9), + (4, 5), + (4, 6), + (4, 7), + (5, 6), + ] + ) + return graph + + +def sli_shc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7]) + graph.add_edges_from( + [ + (1, 2), + (1, 3), + (1, 5), + (1, 7), + (2, 3), + (2, 6), + (3, 4), + (4, 5), + (4, 6), + (5, 7), + (6, 7), + ] + ) + return graph + + +def sli_hc(): + graph = nx.Graph() + graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7, 8, 9]) + graph.add_edges_from( + [ + (1, 2), + (1, 3), + (1, 4), + (1, 5), + (2, 3), + (2, 7), + (2, 8), + (2, 9), + (3, 6), + (3, 7), + (3, 9), + (4, 5), + (4, 6), + (4, 8), + (4, 9), + (5, 6), + (5, 7), + (5, 8), + (6, 7), + (6, 9), + (7, 8), + (8, 9), + ] + ) + return graph + + +# -------------------------------------------------------------------------- +# Basic tests for all strategies +# For each basic graph function, specify the number of expected colors. +BASIC_TEST_CASES = { + empty_graph: 0, + one_node_graph: 1, + two_node_graph: 2, + disconnected: 2, + three_node_clique: 3, +} + + +# -------------------------------------------------------------------------- +# Special test cases. Each strategy has a list of tuples of the form +# (graph function, interchange, valid # of colors) +SPECIAL_TEST_CASES = { + "random_sequential": [ + (rs_shc, False, (2, 3)), + (rs_shc, True, 2), + (rsi_shc, True, (3, 4)), + ], + "saturation_largest_first": [(slf_shc, False, (3, 4)), (slf_hc, False, 4)], + "largest_first": [ + (lf_shc, False, (2, 3)), + (lf_hc, False, 4), + (lf_shc, True, 2), + (lf_hc, True, 3), + (lfi_shc, True, (3, 4)), + (lfi_hc, True, 4), + ], + "smallest_last": [ + (sl_shc, False, (3, 4)), + (sl_hc, False, 5), + (sl_shc, True, 3), + (sl_hc, True, 4), + (sli_shc, True, (3, 4)), + (sli_hc, True, 5), + ], + "independent_set": [(gis_shc, False, (2, 3)), (gis_hc, False, 3)], + "connected_sequential": [(cs_shc, False, (3, 4)), (cs_shc, True, 3)], + "connected_sequential_dfs": [(cs_shc, False, (3, 4))], +} + + +# -------------------------------------------------------------------------- +# Helper functions to test +# (graph function, interchange, valid # of colors) + + +def check_state(L, N, H, F, C): + s = len(C[0]) + num_colors = len(C.keys()) + + assert all(u in L[v] for u in L for v in L[u]) + assert all(F[u] != F[v] for u in L for v in L[u]) + assert all(len(L[u]) < num_colors for u in L) + assert all(len(C[x]) == s for x in C) + assert all(H[(c1, c2)] >= 0 for c1 in C for c2 in C) + assert all(N[(u, F[u])] == 0 for u in F) + + +def max_degree(G): + """Get the maximum degree of any node in G.""" + return max(G.degree(node) for node in G.nodes) if len(G.nodes) > 0 else 0 + + +def make_params_from_graph(G, F): + """Returns {N, L, H, C} from the given graph.""" + num_nodes = len(G) + L = {u: [] for u in range(num_nodes)} + for u, v in G.edges: + L[u].append(v) + L[v].append(u) + + C = nx.algorithms.coloring.equitable_coloring.make_C_from_F(F) + N = nx.algorithms.coloring.equitable_coloring.make_N_from_L_C(L, C) + H = nx.algorithms.coloring.equitable_coloring.make_H_from_C_N(C, N) + + return {"N": N, "F": F, "C": C, "H": H, "L": L} diff --git a/shared/package-files/03/036de769e968aa7fb534a5400013ac26d1d4f55cfc95fbeb0080ae2e4fbba813/chrono.h b/shared/package-files/03/036de769e968aa7fb534a5400013ac26d1d4f55cfc95fbeb0080ae2e4fbba813/chrono.h new file mode 100644 index 0000000000000000000000000000000000000000..167ea0e3d1057c54a469c8ecd656d49eec495fe7 --- /dev/null +++ b/shared/package-files/03/036de769e968aa7fb534a5400013ac26d1d4f55cfc95fbeb0080ae2e4fbba813/chrono.h @@ -0,0 +1,225 @@ +/* + pybind11/chrono.h: Transparent conversion between std::chrono and python's datetime + + Copyright (c) 2016 Trent Houliston and + Wenzel Jakob + + All rights reserved. Use of this source code is governed by a + BSD-style license that can be found in the LICENSE file. +*/ + +#pragma once + +#include "pybind11.h" + +#include +#include +#include +#include +#include + +PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE) +PYBIND11_NAMESPACE_BEGIN(detail) + +template +class duration_caster { +public: + using rep = typename type::rep; + using period = typename type::period; + + // signed 25 bits required by the standard. + using days = std::chrono::duration>; + + bool load(handle src, bool) { + using namespace std::chrono; + + // Lazy initialise the PyDateTime import + if (!PyDateTimeAPI) { + PyDateTime_IMPORT; + } + + if (!src) { + return false; + } + // If invoked with datetime.delta object + if (PyDelta_Check(src.ptr())) { + value = type(duration_cast>( + days(PyDateTime_DELTA_GET_DAYS(src.ptr())) + + seconds(PyDateTime_DELTA_GET_SECONDS(src.ptr())) + + microseconds(PyDateTime_DELTA_GET_MICROSECONDS(src.ptr())))); + return true; + } + // If invoked with a float we assume it is seconds and convert + if (PyFloat_Check(src.ptr())) { + value = type(duration_cast>( + duration(PyFloat_AsDouble(src.ptr())))); + return true; + } + return false; + } + + // If this is a duration just return it back + static const std::chrono::duration & + get_duration(const std::chrono::duration &src) { + return src; + } + + // If this is a time_point get the time_since_epoch + template + static std::chrono::duration + get_duration(const std::chrono::time_point> &src) { + return src.time_since_epoch(); + } + + static handle cast(const type &src, return_value_policy /* policy */, handle /* parent */) { + using namespace std::chrono; + + // Use overloaded function to get our duration from our source + // Works out if it is a duration or time_point and get the duration + auto d = get_duration(src); + + // Lazy initialise the PyDateTime import + if (!PyDateTimeAPI) { + PyDateTime_IMPORT; + } + + // Declare these special duration types so the conversions happen with the correct + // primitive types (int) + using dd_t = duration>; + using ss_t = duration>; + using us_t = duration; + + auto dd = duration_cast(d); + auto subd = d - dd; + auto ss = duration_cast(subd); + auto us = duration_cast(subd - ss); + return PyDelta_FromDSU(dd.count(), ss.count(), us.count()); + } + + PYBIND11_TYPE_CASTER(type, const_name("datetime.timedelta")); +}; + +inline std::tm *localtime_thread_safe(const std::time_t *time, std::tm *buf) { +#if (defined(__STDC_LIB_EXT1__) && defined(__STDC_WANT_LIB_EXT1__)) || defined(_MSC_VER) + if (localtime_s(buf, time)) + return nullptr; + return buf; +#else + static std::mutex mtx; + std::lock_guard lock(mtx); + std::tm *tm_ptr = std::localtime(time); + if (tm_ptr != nullptr) { + *buf = *tm_ptr; + } + return tm_ptr; +#endif +} + +// This is for casting times on the system clock into datetime.datetime instances +template +class type_caster> { +public: + using type = std::chrono::time_point; + bool load(handle src, bool) { + using namespace std::chrono; + + // Lazy initialise the PyDateTime import + if (!PyDateTimeAPI) { + PyDateTime_IMPORT; + } + + if (!src) { + return false; + } + + std::tm cal; + microseconds msecs; + + if (PyDateTime_Check(src.ptr())) { + cal.tm_sec = PyDateTime_DATE_GET_SECOND(src.ptr()); + cal.tm_min = PyDateTime_DATE_GET_MINUTE(src.ptr()); + cal.tm_hour = PyDateTime_DATE_GET_HOUR(src.ptr()); + cal.tm_mday = PyDateTime_GET_DAY(src.ptr()); + cal.tm_mon = PyDateTime_GET_MONTH(src.ptr()) - 1; + cal.tm_year = PyDateTime_GET_YEAR(src.ptr()) - 1900; + cal.tm_isdst = -1; + msecs = microseconds(PyDateTime_DATE_GET_MICROSECOND(src.ptr())); + } else if (PyDate_Check(src.ptr())) { + cal.tm_sec = 0; + cal.tm_min = 0; + cal.tm_hour = 0; + cal.tm_mday = PyDateTime_GET_DAY(src.ptr()); + cal.tm_mon = PyDateTime_GET_MONTH(src.ptr()) - 1; + cal.tm_year = PyDateTime_GET_YEAR(src.ptr()) - 1900; + cal.tm_isdst = -1; + msecs = microseconds(0); + } else if (PyTime_Check(src.ptr())) { + cal.tm_sec = PyDateTime_TIME_GET_SECOND(src.ptr()); + cal.tm_min = PyDateTime_TIME_GET_MINUTE(src.ptr()); + cal.tm_hour = PyDateTime_TIME_GET_HOUR(src.ptr()); + cal.tm_mday = 1; // This date (day, month, year) = (1, 0, 70) + cal.tm_mon = 0; // represents 1-Jan-1970, which is the first + cal.tm_year = 70; // earliest available date for Python's datetime + cal.tm_isdst = -1; + msecs = microseconds(PyDateTime_TIME_GET_MICROSECOND(src.ptr())); + } else { + return false; + } + + value = time_point_cast(system_clock::from_time_t(std::mktime(&cal)) + msecs); + return true; + } + + static handle cast(const std::chrono::time_point &src, + return_value_policy /* policy */, + handle /* parent */) { + using namespace std::chrono; + + // Lazy initialise the PyDateTime import + if (!PyDateTimeAPI) { + PyDateTime_IMPORT; + } + + // Get out microseconds, and make sure they are positive, to avoid bug in eastern + // hemisphere time zones (cfr. https://github.com/pybind/pybind11/issues/2417) + using us_t = duration; + auto us = duration_cast(src.time_since_epoch() % seconds(1)); + if (us.count() < 0) { + us += seconds(1); + } + + // Subtract microseconds BEFORE `system_clock::to_time_t`, because: + // > If std::time_t has lower precision, it is implementation-defined whether the value is + // rounded or truncated. (https://en.cppreference.com/w/cpp/chrono/system_clock/to_time_t) + std::time_t tt + = system_clock::to_time_t(time_point_cast(src - us)); + + std::tm localtime; + std::tm *localtime_ptr = localtime_thread_safe(&tt, &localtime); + if (!localtime_ptr) { + throw cast_error("Unable to represent system_clock in local time"); + } + return PyDateTime_FromDateAndTime(localtime.tm_year + 1900, + localtime.tm_mon + 1, + localtime.tm_mday, + localtime.tm_hour, + localtime.tm_min, + localtime.tm_sec, + us.count()); + } + PYBIND11_TYPE_CASTER(type, const_name("datetime.datetime")); +}; + +// Other clocks that are not the system clock are not measured as datetime.datetime objects +// since they are not measured on calendar time. So instead we just make them timedeltas +// Or if they have passed us a time as a float we convert that +template +class type_caster> + : public duration_caster> {}; + +template +class type_caster> + : public duration_caster> {}; + +PYBIND11_NAMESPACE_END(detail) +PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE) diff --git a/shared/package-files/03/036e6516c4ec6bd5d4ead3eea8578e587d7880e8c8e3484f7c9eb6e8119e08e6/test_ir.py b/shared/package-files/03/036e6516c4ec6bd5d4ead3eea8578e587d7880e8c8e3484f7c9eb6e8119e08e6/test_ir.py new file mode 100644 index 0000000000000000000000000000000000000000..29f22b8e11610d479568e8866f9d5513ae065155 --- /dev/null +++ b/shared/package-files/03/036e6516c4ec6bd5d4ead3eea8578e587d7880e8c8e3484f7c9eb6e8119e08e6/test_ir.py @@ -0,0 +1,3194 @@ +""" +IR Construction Tests +""" + +import copy +import itertools +import pickle +import re +import textwrap +import unittest + +from . import TestCase +from llvmlite import ir +from llvmlite import binding as llvm +from llvmlite import ir_layer_typed_pointers_enabled + + +int1 = ir.IntType(1) +int8 = ir.IntType(8) +int16 = ir.IntType(16) +int32 = ir.IntType(32) +int64 = ir.IntType(64) +hlf = ir.HalfType() +flt = ir.FloatType() +dbl = ir.DoubleType() + + +class TestBase(TestCase): + """ + Utilities for IR tests. + """ + + def assertInText(self, pattern, text): + """ + Assert *pattern* is in *text*, ignoring any whitespace differences + (including newlines). + """ + + def escape(c): + if not c.isalnum() and not c.isspace(): + return '\\' + c + return c + + pattern = ''.join(map(escape, pattern)) + regex = re.sub(r'\s+', r'\\s*', pattern) + self.assertRegex(text, regex) + + def assert_ir_line(self, line, mod): + lines = [line.strip() for line in str(mod).splitlines()] + self.assertIn(line, lines) + + def assert_valid_ir(self, mod): + llvm.parse_assembly(str(mod)) + + def assert_pickle_correctly(self, irobject): + """Assert that the IR object pickles and unpickles correctly. + The IR string is equal and that their type is equal + """ + newobject = pickle.loads(pickle.dumps(irobject, protocol=-1)) + self.assertIs(irobject.__class__, newobject.__class__) + self.assertEqual(str(irobject), str(newobject)) + return newobject + + def module(self): + return ir.Module() + + def function(self, module=None, name='my_func'): + module = module or self.module() + fnty = ir.FunctionType(int32, (int32, int32, dbl, + ir.PointerType(int32))) + return ir.Function(module, fnty, name) + + def block(self, func=None, name=''): + func = func or self.function() + return func.append_basic_block(name) + + def descr(self, thing): + buf = [] + thing.descr(buf) + return "".join(buf) + + def _normalize_asm(self, asm): + asm = textwrap.dedent(asm) + # Normalize indent + asm = asm.replace("\n ", "\n ") + return asm + + def check_descr_regex(self, descr, asm): + expected = self._normalize_asm(asm) + self.assertRegex(descr, expected) + + def check_descr(self, descr, asm): + expected = self._normalize_asm(asm) + self.assertEqual(descr, expected) + + def check_block(self, block, asm): + self.check_descr(self.descr(block), asm) + + def check_block_regex(self, block, asm): + self.check_descr_regex(self.descr(block), asm) + + def check_module_body(self, module, asm): + expected = self._normalize_asm(asm) + actual = module._stringify_body() + self.assertEqual(actual.strip(), expected.strip()) + + def check_metadata(self, module, asm): + """ + Check module metadata against *asm*. + """ + expected = self._normalize_asm(asm) + actual = module._stringify_metadata() + self.assertEqual(actual.strip(), expected.strip()) + + def check_func_body(self, func, asm): + expected = self._normalize_asm(asm) + actual = self.descr(func) + actual = actual.partition('{')[2].rpartition('}')[0] + self.assertEqual(actual.strip(), expected.strip()) + + +class TestFunction(TestBase): + + proto = \ + """i32 @"my_func"(i32 %".1", i32 %".2", double %".3", ptr %".4")""" \ + if not ir_layer_typed_pointers_enabled else \ + """i32 @"my_func"(i32 %".1", i32 %".2", double %".3", i32* %".4")""" + + def test_declare(self): + # A simple declaration + func = self.function() + asm = self.descr(func).strip() + self.assertEqual(asm.strip(), "declare %s" % self.proto) + + def test_declare_attributes(self): + # Now with function attributes + func = self.function() + func.attributes.add("optsize") + func.attributes.add("alwaysinline") + func.attributes.add("convergent") + func.attributes.alignstack = 16 + tp_pers = ir.FunctionType(int8, (), var_arg=True) + pers = ir.Function(self.module(), tp_pers, '__gxx_personality_v0') + func.attributes.personality = pers + asm = self.descr(func).strip() + if not ir_layer_typed_pointers_enabled: + self.assertEqual(asm, + ("declare %s alwaysinline convergent optsize " + "alignstack(16) " + "personality ptr @\"__gxx_personality_v0\"") % + self.proto) + else: + self.assertEqual(asm, + ("declare %s alwaysinline convergent optsize " + "alignstack(16) personality " + "i8 (...)* @\"__gxx_personality_v0\"") % + self.proto) + # Check pickling + self.assert_pickle_correctly(func) + + def test_function_attributes(self): + # Now with parameter attributes + func = self.function() + func.args[0].add_attribute("zeroext") + func.args[1].attributes.dereferenceable = 5 + func.args[1].attributes.dereferenceable_or_null = 10 + func.args[3].attributes.align = 4 + func.args[3].add_attribute("nonnull") + func.return_value.add_attribute("noalias") + asm = self.descr(func).strip() + if not ir_layer_typed_pointers_enabled: + self.assertEqual(asm, + """declare noalias i32 @"my_func"(i32 zeroext %".1", i32 dereferenceable(5) dereferenceable_or_null(10) %".2", double %".3", ptr nonnull align 4 %".4")""" # noqa E501 + ) + else: + self.assertEqual(asm, + """declare noalias i32 @"my_func"(i32 zeroext %".1", i32 dereferenceable(5) dereferenceable_or_null(10) %".2", double %".3", i32* nonnull align 4 %".4")""" # noqa E501 + ) + # Check pickling + self.assert_pickle_correctly(func) + + def test_function_metadata(self): + # Now with function metadata + module = self.module() + func = self.function(module) + func.set_metadata('dbg', module.add_metadata([])) + asm = self.descr(func).strip() + self.assertEqual(asm, + f'declare {self.proto} !dbg !0' + ) + # Check pickling + self.assert_pickle_correctly(func) + + def test_function_section(self): + # Test function with section + func = self.function() + func.section = "a_section" + asm = self.descr(func).strip() + self.assertEqual(asm, + f'declare {self.proto} section "a_section"' + ) + # Check pickling + self.assert_pickle_correctly(func) + + def test_function_section_meta(self): + # Test function with section and metadata + module = self.module() + func = self.function(module) + func.section = "a_section" + func.set_metadata('dbg', module.add_metadata([])) + asm = self.descr(func).strip() + self.assertEqual(asm, + f'declare {self.proto} section "a_section" !dbg !0' + ) + # Check pickling + self.assert_pickle_correctly(func) + + def test_function_attr_meta(self): + # Test function with attributes and metadata + module = self.module() + func = self.function(module) + func.attributes.add("alwaysinline") + func.set_metadata('dbg', module.add_metadata([])) + asm = self.descr(func).strip() + self.assertEqual(asm, + f'declare {self.proto} alwaysinline !dbg !0' + ) + # Check pickling + self.assert_pickle_correctly(func) + + def test_function_attr_section(self): + # Test function with attributes and section + func = self.function() + func.attributes.add("optsize") + func.section = "a_section" + asm = self.descr(func).strip() + self.assertEqual(asm, + f'declare {self.proto} optsize section "a_section"') + # Check pickling + self.assert_pickle_correctly(func) + + def test_function_attr_section_meta(self): + # Test function with attributes, section and metadata + module = self.module() + func = self.function(module) + func.attributes.add("alwaysinline") + func.section = "a_section" + func.set_metadata('dbg', module.add_metadata([])) + asm = self.descr(func).strip() + self.assertEqual(asm, + f'declare {self.proto} alwaysinline section "a_section" !dbg !0' # noqa E501 + ) + # Check pickling + self.assert_pickle_correctly(func) + + def test_define(self): + # A simple definition + func = self.function() + func.attributes.add("alwaysinline") + block = func.append_basic_block('my_block') + builder = ir.IRBuilder(block) + builder.ret_void() + asm = self.descr(func) + self.check_descr(asm, """\ + define {proto} alwaysinline + {{ + my_block: + ret void + }} + """.format(proto=self.proto)) + + def test_declare_intrinsics(self): + module = self.module() + pint8 = int8.as_pointer() + + powi = module.declare_intrinsic('llvm.powi', [dbl]) + memset = module.declare_intrinsic('llvm.memset', [pint8, int32]) + memcpy = module.declare_intrinsic('llvm.memcpy', [pint8, pint8, int32]) + assume = module.declare_intrinsic('llvm.assume') + self.check_descr(self.descr(powi).strip(), """\ + declare double @"llvm.powi.f64"(double %".1", i32 %".2")""") + if not ir_layer_typed_pointers_enabled: + self.check_descr(self.descr(memset).strip(), """\ + declare void @"llvm.memset.p0.i32"(ptr %".1", i8 %".2", i32 %".3", i1 %".4")""") # noqa E501 + self.check_descr(self.descr(memcpy).strip(), """\ + declare void @"llvm.memcpy.p0.p0.i32"(ptr %".1", ptr %".2", i32 %".3", i1 %".4")""") # noqa E501 + else: + self.check_descr(self.descr(memset).strip(), """\ + declare void @"llvm.memset.p0i8.i32"(i8* %".1", i8 %".2", i32 %".3", i1 %".4")""") # noqa E501 + self.check_descr(self.descr(memcpy).strip(), """\ + declare void @"llvm.memcpy.p0i8.p0i8.i32"(i8* %".1", i8* %".2", i32 %".3", i1 %".4")""") # noqa E501 + self.check_descr(self.descr(assume).strip(), """\ + declare void @"llvm.assume"(i1 %".1")""") + + def test_redeclare_intrinsic(self): + module = self.module() + powi = module.declare_intrinsic('llvm.powi', [dbl]) + powi2 = module.declare_intrinsic('llvm.powi', [dbl]) + self.assertIs(powi, powi2) + + def test_pickling(self): + fn = self.function() + self.assert_pickle_correctly(fn) + + def test_alwaysinline_noinline_disallowed(self): + module = self.module() + func = self.function(module) + func.attributes.add('alwaysinline') + + msg = "Can't have alwaysinline and noinline" + with self.assertRaisesRegex(ValueError, msg): + func.attributes.add('noinline') + + def test_noinline_alwaysinline_disallowed(self): + module = self.module() + func = self.function(module) + func.attributes.add('noinline') + + msg = "Can't have alwaysinline and noinline" + with self.assertRaisesRegex(ValueError, msg): + func.attributes.add('alwaysinline') + + +class TestIR(TestBase): + + def test_unnamed_metadata(self): + # An unnamed metadata node + mod = self.module() + mod.add_metadata([int32(123), int8(42)]) + self.assert_ir_line("!0 = !{ i32 123, i8 42 }", mod) + self.assert_valid_ir(mod) + + def test_unnamed_metadata_2(self): + # Several unnamed metadata nodes + mod = self.module() + # First node has a literal metadata string + m0 = mod.add_metadata([int32(123), "kernel"]) + # Second node refers to the first one + m1 = mod.add_metadata([int64(456), m0]) + # Third node is the same as the second one + m2 = mod.add_metadata([int64(456), m0]) + self.assertIs(m2, m1) + # Fourth node refers to the first three + mod.add_metadata([m0, m1, m2]) + self.assert_ir_line('!0 = !{ i32 123, !"kernel" }', mod) + self.assert_ir_line('!1 = !{ i64 456, !0 }', mod) + self.assert_ir_line('!2 = !{ !0, !1, !1 }', mod) + + def test_unnamed_metadata_3(self): + # Passing nested metadata as a sequence + mod = self.module() + mod.add_metadata([int32(123), [int32(456)], [int32(789)], [int32(456)]]) + self.assert_ir_line('!0 = !{ i32 456 }', mod) + self.assert_ir_line('!1 = !{ i32 789 }', mod) + self.assert_ir_line('!2 = !{ i32 123, !0, !1, !0 }', mod) + + def test_metadata_string(self): + # Escaping contents of a metadata string + mod = self.module() + mod.add_metadata(["\"\\$"]) + self.assert_ir_line('!0 = !{ !"\\22\\5c$" }', mod) + + def test_named_metadata(self): + # Add a named metadata node and add metadata values to it + mod = self.module() + m0 = mod.add_metadata([int32(123)]) + m1 = mod.add_metadata([int64(456)]) + nmd = mod.add_named_metadata("foo") + nmd.add(m0) + nmd.add(m1) + nmd.add(m0) + self.assert_ir_line("!foo = !{ !0, !1, !0 }", mod) + self.assert_valid_ir(mod) + # Check get_named_metadata() + self.assertIs(nmd, mod.get_named_metadata("foo")) + with self.assertRaises(KeyError): + mod.get_named_metadata("bar") + + def test_named_metadata_2(self): + # Add and set named metadata through a single add_named_metadata() call + mod = self.module() + m0 = mod.add_metadata([int32(123)]) + mod.add_named_metadata("foo", m0) + mod.add_named_metadata("foo", [int64(456)]) + mod.add_named_metadata("foo", ["kernel"]) + mod.add_named_metadata("bar", []) + self.assert_ir_line("!foo = !{ !0, !1, !2 }", mod) + self.assert_ir_line("!0 = !{ i32 123 }", mod) + self.assert_ir_line("!1 = !{ i64 456 }", mod) + self.assert_ir_line('!2 = !{ !"kernel" }', mod) + self.assert_ir_line("!bar = !{ !3 }", mod) + self.assert_ir_line('!3 = !{ }', mod) + self.assert_valid_ir(mod) + + def test_metadata_null(self): + # A null metadata (typed) value + mod = self.module() + mod.add_metadata([int32.as_pointer()(None)]) + if not ir_layer_typed_pointers_enabled: + self.assert_ir_line("!0 = !{ ptr null }", mod) + else: + self.assert_ir_line("!0 = !{ i32* null }", mod) + self.assert_valid_ir(mod) + # A null metadata (untyped) value + mod = self.module() + mod.add_metadata([None, int32(123)]) + self.assert_ir_line("!0 = !{ null, i32 123 }", mod) + self.assert_valid_ir(mod) + + def test_debug_info(self): + # Add real world-looking debug information to a module + # (with various value types) + mod = self.module() + di_file = mod.add_debug_info("DIFile", { + "filename": "foo", + "directory": "bar", + }) + di_func_type = mod.add_debug_info("DISubroutineType", { + # None as `null` + "types": mod.add_metadata([None]), + }) + di_compileunit = mod.add_debug_info("DICompileUnit", { + "language": ir.DIToken("DW_LANG_Python"), + "file": di_file, + "producer": "ARTIQ", + "runtimeVersion": 0, + "isOptimized": True, + }, is_distinct=True) + mod.add_debug_info("DISubprogram", { + "name": "my_func", + "file": di_file, + "line": 11, + "type": di_func_type, + "isLocal": False, + "unit": di_compileunit, + }, is_distinct=True) + + # Check output + strmod = str(mod) + self.assert_ir_line('!0 = !DIFile(directory: "bar", filename: "foo")', + strmod) + self.assert_ir_line('!1 = !{ null }', strmod) + self.assert_ir_line('!2 = !DISubroutineType(types: !1)', strmod) + # self.assert_ir_line('!4 = !{ !3 }', strmod) + self.assert_ir_line('!3 = distinct !DICompileUnit(file: !0, ' + 'isOptimized: true, language: DW_LANG_Python, ' + 'producer: "ARTIQ", runtimeVersion: 0)', + strmod) + self.assert_ir_line('!4 = distinct !DISubprogram(file: !0, isLocal: ' + 'false, line: 11, name: "my_func", type: !2, unit: ' + '!3)', + strmod) + self.assert_valid_ir(mod) + + def test_debug_info_2(self): + # Identical debug info nodes should be merged + mod = self.module() + di1 = mod.add_debug_info("DIFile", + {"filename": "foo", + "directory": "bar", + }) + di2 = mod.add_debug_info("DIFile", + {"filename": "foo", + "directory": "bar", + }) + di3 = mod.add_debug_info("DIFile", + {"filename": "bar", + "directory": "foo", + }) + di4 = mod.add_debug_info("DIFile", + {"filename": "foo", + "directory": "bar", + }, is_distinct=True) + self.assertIs(di1, di2) + self.assertEqual(len({di1, di2, di3, di4}), 3) + # Check output + strmod = str(mod) + self.assert_ir_line('!0 = !DIFile(directory: "bar", filename: "foo")', + strmod) + self.assert_ir_line('!1 = !DIFile(directory: "foo", filename: "bar")', + strmod) + self.assert_ir_line('!2 = distinct !DIFile(directory: "bar", filename: ' + '"foo")', strmod) + self.assert_valid_ir(mod) + + def test_debug_info_3(self): + # Identical DIBasicType metadata nodes should be merged + mod = self.module() + di1 = mod.add_debug_info("DIBasicType", { + "name": "i8", + "size": 8, + "encoding": ir.DIToken("DW_ATE_unsigned") + }) + di2 = mod.add_debug_info("DIBasicType", { + "name": "i8", + "size": 8, + "encoding": ir.DIToken("DW_ATE_unsigned") + }) + di3 = mod.add_debug_info("DIBasicType", { + "name": "i32", + "size": 32, + "encoding": ir.DIToken("DW_ATE_unsigned") + }) + di4 = mod.add_debug_info("DIBasicType", { + "name": "i8", + "size": 8, + "encoding": ir.DIToken("DW_ATE_unsigned") + }) + di5 = mod.add_debug_info("DIBasicType", { + "name": "i8", + "size": 8, + "encoding": ir.DIToken("DW_ATE_unsigned") + }) + di6 = mod.add_debug_info("DIBasicType", { + "name": "i32", + "size": 32, + "encoding": ir.DIToken("DW_ATE_unsigned") + }) + di7 = mod.add_debug_info("DIBasicType", { + "name": "i64", + "size": 64, + "encoding": ir.DIToken("DW_ATE_signed") + }) + di8 = mod.add_debug_info("DIBasicType", { + "name": "i64", + "size": 64, + "encoding": ir.DIToken("DW_ATE_signed") + }) + di9 = mod.add_debug_info("DIBasicType", { + "name": "i64", + "size": 64, + "encoding": ir.DIToken("DW_ATE_signed") + }) + self.assertIs(di1, di2) + self.assertIs(di1, di4) + self.assertIs(di1, di5) + self.assertIs(di3, di6) + self.assertIs(di7, di8) + self.assertIs(di7, di9) + self.assertEqual(len({di1, di2, di3, di4, di5, di6, di7, di8, di9}), 3) + # Check output + strmod = str(mod) + self.assert_ir_line('!0 = !DIBasicType(encoding: DW_ATE_unsigned, ' + 'name: "i8", size: 8)', strmod) + self.assert_ir_line('!1 = !DIBasicType(encoding: DW_ATE_unsigned, ' + 'name: "i32", size: 32)', strmod) + self.assert_ir_line('!2 = !DIBasicType(encoding: DW_ATE_signed, ' + 'name: "i64", size: 64)', strmod) + self.assert_valid_ir(mod) + + def test_debug_info_4(self): + # Typed constant as output value of debug info fields + mod = self.module() + # Create base types first + di_base_i32 = mod.add_debug_info("DIBasicType", { + "name": "int32", + "size": 32, + "encoding": ir.DIToken("DW_ATE_signed") + }) + di_base_i8 = mod.add_debug_info("DIBasicType", { + "name": "int8", + "size": 8, + "encoding": ir.DIToken("DW_ATE_signed") + }) + # Create debug info nodes with typed constants + mod.add_debug_info("DIDerivedType", { + "tag": ir.DIToken("DW_TAG_member"), + "name": "field_i32", + "baseType": di_base_i32, + "size": 32, + "extraData": ir.IntType(32)(1) + }) + mod.add_debug_info("DIDerivedType", { + "tag": ir.DIToken("DW_TAG_member"), + "name": "field_i8", + "baseType": di_base_i8, + "size": 8, + "extraData": ir.IntType(8)(2) + }) + # Check output + strmod = str(mod) + self.assert_ir_line('!0 = !DIBasicType(encoding: DW_ATE_signed, ' + 'name: "int32", size: 32)', strmod) + self.assert_ir_line('!1 = !DIBasicType(encoding: DW_ATE_signed, ' + 'name: "int8", size: 8)', strmod) + self.assert_ir_line( + '!2 = !DIDerivedType(baseType: !0, extraData: i32 1, ' + 'name: "field_i32", size: 32, tag: DW_TAG_member)', strmod) + self.assert_ir_line( + '!3 = !DIDerivedType(baseType: !1, extraData: i8 2, ' + 'name: "field_i8", size: 8, tag: DW_TAG_member)', strmod) + self.assert_valid_ir(mod) + + def test_debug_info_gvar(self): + # This test defines a module with a global variable named 'gvar'. + # When the module is compiled and linked with a main function, gdb can + # be used to interpret and print the the value of 'gvar'. + mod = self.module() + + gvar = ir.GlobalVariable(mod, ir.FloatType(), 'gvar') + gvar.initializer = ir.Constant(ir.FloatType(), 42) + + di_float = mod.add_debug_info("DIBasicType", { + "name": "float", + "size": 32, + "encoding": ir.DIToken("DW_ATE_float") + }) + di_gvar = mod.add_debug_info("DIGlobalVariableExpression", { + "expr": mod.add_debug_info("DIExpression", {}), + "var": mod.add_debug_info("DIGlobalVariable", { + "name": gvar.name, + "type": di_float, + "isDefinition": True + }, is_distinct=True) + }) + gvar.set_metadata('dbg', di_gvar) + + # Check output + strmod = str(mod) + self.assert_ir_line('!0 = !DIBasicType(encoding: DW_ATE_float, ' + 'name: "float", size: 32)', strmod) + self.assert_ir_line('!1 = !DIExpression()', strmod) + self.assert_ir_line('!2 = distinct !DIGlobalVariable(isDefinition: ' + 'true, name: "gvar", type: !0)', strmod) + self.assert_ir_line('!3 = !DIGlobalVariableExpression(expr: !1, ' + 'var: !2)', strmod) + self.assert_ir_line('@"gvar" = global float 0x4045000000000000, ' + '!dbg !3', strmod) + + # The remaining debug info is not part of the automated test, but + # can be used to produce an object file that can be loaded into a + # debugger to print the value of gvar. This can be done by printing the + # module then compiling it with clang and inspecting with gdb: + # + # clang test_debug_info_gvar.ll -c + # printf "file test_debug_info_gvar.o \n p gvar" | gdb + # + # Which should result in the output: + # + # (gdb) $1 = 42 + + dver = [ir.IntType(32)(2), 'Dwarf Version', ir.IntType(32)(4)] + diver = [ir.IntType(32)(2), 'Debug Info Version', ir.IntType(32)(3)] + dver = mod.add_metadata(dver) + diver = mod.add_metadata(diver) + flags = mod.add_named_metadata('llvm.module.flags') + flags.add(dver) + flags.add(diver) + + di_file = mod.add_debug_info("DIFile", { + "filename": "foo", + "directory": "bar", + }) + di_cu = mod.add_debug_info("DICompileUnit", { + "language": ir.DIToken("DW_LANG_Python"), + "file": di_file, + 'emissionKind': ir.DIToken('FullDebug'), + "globals": mod.add_metadata([di_gvar]) + }, is_distinct=True) + mod.add_named_metadata('llvm.dbg.cu', di_cu) + + def test_debug_info_unicode_string(self): + mod = self.module() + mod.add_debug_info("DILocalVariable", {"name": "a∆"}) + # Check output + strmod = str(mod) + # The unicode character is utf8 encoded with \XX format, where XX is hex + name = ''.join(map(lambda x: f"\\{x:02x}", "∆".encode())) + self.assert_ir_line(f'!0 = !DILocalVariable(name: "a{name}")', strmod) + + def test_inline_assembly(self): + mod = self.module() + foo = ir.Function(mod, ir.FunctionType(ir.VoidType(), []), 'foo') + builder = ir.IRBuilder(foo.append_basic_block('')) + asmty = ir.FunctionType(int32, [int32]) + asm = ir.InlineAsm(asmty, "mov $1, $2", "=r,r", side_effect=True) + builder.call(asm, [int32(123)]) + builder.ret_void() + pat = 'call i32 asm sideeffect "mov $1, $2", "=r,r"( i32 123 )' + self.assertInText(pat, str(mod)) + self.assert_valid_ir(mod) + + def test_builder_asm(self): + mod = self.module() + foo = ir.Function(mod, ir.FunctionType(ir.VoidType(), []), 'foo') + builder = ir.IRBuilder(foo.append_basic_block('')) + asmty = ir.FunctionType(int32, [int32]) + builder.asm(asmty, "mov $1, $2", "=r,r", [int32(123)], side_effect=True) + builder.ret_void() + pat = 'call i32 asm sideeffect "mov $1, $2", "=r,r" ( i32 123 )' + self.assertInText(pat, str(mod)) + self.assert_valid_ir(mod) + + def test_builder_load_reg(self): + mod = self.module() + foo = ir.Function(mod, ir.FunctionType(ir.VoidType(), []), 'foo') + builder = ir.IRBuilder(foo.append_basic_block('')) + builder.load_reg(ir.IntType(64), "rax") + builder.ret_void() + pat = 'call i64 asm "", "={rax}"' + self.assertInText(pat, str(mod)) + self.assert_valid_ir(mod) + + def test_builder_store_reg(self): + mod = self.module() + foo = ir.Function(mod, ir.FunctionType(ir.VoidType(), []), 'foo') + builder = ir.IRBuilder(foo.append_basic_block('')) + builder.store_reg(int64(123), ir.IntType(64), "rax") + builder.ret_void() + pat = 'call void asm sideeffect "", "{rax}" ( i64 123 )' + self.assertInText(pat, str(mod)) + self.assert_valid_ir(mod) + + +class TestGlobalValues(TestBase): + + def test_globals_access(self): + mod = self.module() + foo = ir.Function(mod, ir.FunctionType(ir.VoidType(), []), 'foo') + ir.Function(mod, ir.FunctionType(ir.VoidType(), []), 'bar') + globdouble = ir.GlobalVariable(mod, ir.DoubleType(), 'globdouble') + self.assertEqual(mod.get_global('foo'), foo) + self.assertEqual(mod.get_global('globdouble'), globdouble) + with self.assertRaises(KeyError): + mod.get_global('kkk') + # Globals should have a useful repr() + if not ir_layer_typed_pointers_enabled: + self.assertEqual(repr(globdouble), + "") + else: + self.assertEqual( + repr(globdouble), + "") + + def test_functions_global_values_access(self): + """ + Accessing functions and global values through Module.functions + and Module.global_values. + """ + mod = self.module() + fty = ir.FunctionType(ir.VoidType(), []) + foo = ir.Function(mod, fty, 'foo') + bar = ir.Function(mod, fty, 'bar') + globdouble = ir.GlobalVariable(mod, ir.DoubleType(), 'globdouble') + self.assertEqual(set(mod.functions), set((foo, bar))) + self.assertEqual(set(mod.global_values), set((foo, bar, globdouble))) + + def test_global_variables_ir(self): + """ + IR serialization of global variables. + """ + mod = self.module() + # the following have side effects and write to self.module() + a = ir.GlobalVariable(mod, int8, 'a') # noqa F841 + b = ir.GlobalVariable(mod, int8, 'b', addrspace=42) # noqa F841 + # Initialized global variable doesn't default to "external" + c = ir.GlobalVariable(mod, int32, 'c') + c.initializer = int32(123) + d = ir.GlobalVariable(mod, int32, 'd') + d.global_constant = True + # Non-external linkage implies default "undef" initializer + e = ir.GlobalVariable(mod, int32, 'e') + e.linkage = "internal" + f = ir.GlobalVariable(mod, int32, 'f', addrspace=456) + f.unnamed_addr = True + g = ir.GlobalVariable(mod, int32, 'g') + g.linkage = "internal" + g.initializer = int32(123) + g.align = 16 + h = ir.GlobalVariable(mod, int32, 'h') + h.linkage = "internal" + h.initializer = int32(123) + h.section = "h_section" + i = ir.GlobalVariable(mod, int32, 'i') + i.linkage = "internal" + i.initializer = int32(456) + i.align = 8 + i.section = "i_section" + self.check_module_body(mod, """\ + @"a" = external global i8 + @"b" = external addrspace(42) global i8 + @"c" = global i32 123 + @"d" = external constant i32 + @"e" = internal global i32 undef + @"f" = external unnamed_addr addrspace(456) global i32 + @"g" = internal global i32 123, align 16 + @"h" = internal global i32 123, section "h_section" + @"i" = internal global i32 456, section "i_section", align 8 + """) + + def test_pickle(self): + mod = self.module() + self.assert_pickle_correctly(mod) + + +class TestBlock(TestBase): + + def test_attributes(self): + func = self.function() + block = ir.Block(parent=func, name='start') + self.assertIs(block.parent, func) + self.assertFalse(block.is_terminated) + + def test_descr(self): + block = self.block(name='my_block') + self.assertEqual(self.descr(block), "my_block:\n") + block.instructions.extend(['a', 'b']) + self.assertEqual(self.descr(block), "my_block:\n a\n b\n") + + def test_replace(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + c = builder.add(a, b, 'c') + d = builder.sub(a, b, 'd') + builder.mul(d, b, 'e') + f = ir.Instruction(block, a.type, 'sdiv', (c, b), 'f') + self.check_block(block, """\ + my_block: + %"c" = add i32 %".1", %".2" + %"d" = sub i32 %".1", %".2" + %"e" = mul i32 %"d", %".2" + """) + block.replace(d, f) + self.check_block(block, """\ + my_block: + %"c" = add i32 %".1", %".2" + %"f" = sdiv i32 %"c", %".2" + %"e" = mul i32 %"f", %".2" + """) + + def test_repr(self): + """ + Blocks should have a useful repr() + """ + func = self.function() + block = ir.Block(parent=func, name='start') + self.assertEqual(repr(block), "") + + +class TestBuildInstructions(TestBase): + """ + Test IR generation of LLVM instructions through the IRBuilder class. + """ + + maxDiff = 4000 + + def test_simple(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + inst = builder.add(a, b, 'res') + self.check_block(block, """\ + my_block: + %"res" = add i32 %".1", %".2" + """) + # Instructions should have a useful repr() + self.assertEqual(repr(inst), + ", " + ")>") + + def test_binops(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b, ff = builder.function.args[:3] + builder.add(a, b, 'c') + builder.fadd(a, b, 'd') + builder.sub(a, b, 'e') + builder.fsub(a, b, 'f') + builder.mul(a, b, 'g') + builder.fmul(a, b, 'h') + builder.udiv(a, b, 'i') + builder.sdiv(a, b, 'j') + builder.fdiv(a, b, 'k') + builder.urem(a, b, 'l') + builder.srem(a, b, 'm') + builder.frem(a, b, 'n') + builder.or_(a, b, 'o') + builder.and_(a, b, 'p') + builder.xor(a, b, 'q') + builder.shl(a, b, 'r') + builder.ashr(a, b, 's') + builder.lshr(a, b, 't') + with self.assertRaises(ValueError) as cm: + builder.add(a, ff) + self.assertEqual(str(cm.exception), + "Operands must be the same type, got (i32, double)") + self.assertFalse(block.is_terminated) + self.check_block(block, """\ + my_block: + %"c" = add i32 %".1", %".2" + %"d" = fadd i32 %".1", %".2" + %"e" = sub i32 %".1", %".2" + %"f" = fsub i32 %".1", %".2" + %"g" = mul i32 %".1", %".2" + %"h" = fmul i32 %".1", %".2" + %"i" = udiv i32 %".1", %".2" + %"j" = sdiv i32 %".1", %".2" + %"k" = fdiv i32 %".1", %".2" + %"l" = urem i32 %".1", %".2" + %"m" = srem i32 %".1", %".2" + %"n" = frem i32 %".1", %".2" + %"o" = or i32 %".1", %".2" + %"p" = and i32 %".1", %".2" + %"q" = xor i32 %".1", %".2" + %"r" = shl i32 %".1", %".2" + %"s" = ashr i32 %".1", %".2" + %"t" = lshr i32 %".1", %".2" + """) + + def test_binop_flags(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + # As tuple + builder.add(a, b, 'c', flags=('nuw',)) + # and as list + builder.sub(a, b, 'd', flags=['nuw', 'nsw']) + self.check_block(block, """\ + my_block: + %"c" = add nuw i32 %".1", %".2" + %"d" = sub nuw nsw i32 %".1", %".2" + """) + + def test_binop_fastmath_flags(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + # As tuple + builder.fadd(a, b, 'c', flags=('fast',)) + # and as list + builder.fsub(a, b, 'd', flags=['ninf', 'nsz']) + self.check_block(block, """\ + my_block: + %"c" = fadd fast i32 %".1", %".2" + %"d" = fsub ninf nsz i32 %".1", %".2" + """) + + def test_binops_with_overflow(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + builder.sadd_with_overflow(a, b, 'c') + builder.smul_with_overflow(a, b, 'd') + builder.ssub_with_overflow(a, b, 'e') + builder.uadd_with_overflow(a, b, 'f') + builder.umul_with_overflow(a, b, 'g') + builder.usub_with_overflow(a, b, 'h') + self.check_block(block, """\ +my_block: + %"c" = call {i32, i1} @"llvm.sadd.with.overflow.i32"(i32 %".1", i32 %".2") + %"d" = call {i32, i1} @"llvm.smul.with.overflow.i32"(i32 %".1", i32 %".2") + %"e" = call {i32, i1} @"llvm.ssub.with.overflow.i32"(i32 %".1", i32 %".2") + %"f" = call {i32, i1} @"llvm.uadd.with.overflow.i32"(i32 %".1", i32 %".2") + %"g" = call {i32, i1} @"llvm.umul.with.overflow.i32"(i32 %".1", i32 %".2") + %"h" = call {i32, i1} @"llvm.usub.with.overflow.i32"(i32 %".1", i32 %".2") + """) + + def test_unary_ops(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b, c = builder.function.args[:3] + builder.neg(a, 'd') + builder.not_(b, 'e') + builder.fneg(c, 'f') + self.assertFalse(block.is_terminated) + self.check_block(block, """\ + my_block: + %"d" = sub i32 0, %".1" + %"e" = xor i32 %".2", -1 + %"f" = fneg double %".3" + """) + + def test_replace_operand(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + undef1 = ir.Constant(ir.IntType(32), ir.Undefined) + undef2 = ir.Constant(ir.IntType(32), ir.Undefined) + c = builder.add(undef1, undef2, 'c') + self.check_block(block, """\ + my_block: + %"c" = add i32 undef, undef + """) + c.replace_usage(undef1, a) + c.replace_usage(undef2, b) + self.check_block(block, """\ + my_block: + %"c" = add i32 %".1", %".2" + """) + + def test_integer_comparisons(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + builder.icmp_unsigned('==', a, b, 'c') + builder.icmp_unsigned('!=', a, b, 'd') + builder.icmp_unsigned('<', a, b, 'e') + builder.icmp_unsigned('<=', a, b, 'f') + builder.icmp_unsigned('>', a, b, 'g') + builder.icmp_unsigned('>=', a, b, 'h') + builder.icmp_signed('==', a, b, 'i') + builder.icmp_signed('!=', a, b, 'j') + builder.icmp_signed('<', a, b, 'k') + builder.icmp_signed('<=', a, b, 'l') + builder.icmp_signed('>', a, b, 'm') + builder.icmp_signed('>=', a, b, 'n') + with self.assertRaises(ValueError): + builder.icmp_signed('uno', a, b, 'zz') + with self.assertRaises(ValueError): + builder.icmp_signed('foo', a, b, 'zz') + self.assertFalse(block.is_terminated) + self.check_block(block, """\ + my_block: + %"c" = icmp eq i32 %".1", %".2" + %"d" = icmp ne i32 %".1", %".2" + %"e" = icmp ult i32 %".1", %".2" + %"f" = icmp ule i32 %".1", %".2" + %"g" = icmp ugt i32 %".1", %".2" + %"h" = icmp uge i32 %".1", %".2" + %"i" = icmp eq i32 %".1", %".2" + %"j" = icmp ne i32 %".1", %".2" + %"k" = icmp slt i32 %".1", %".2" + %"l" = icmp sle i32 %".1", %".2" + %"m" = icmp sgt i32 %".1", %".2" + %"n" = icmp sge i32 %".1", %".2" + """) + + def test_float_comparisons(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + builder.fcmp_ordered('==', a, b, 'c') + builder.fcmp_ordered('!=', a, b, 'd') + builder.fcmp_ordered('<', a, b, 'e') + builder.fcmp_ordered('<=', a, b, 'f') + builder.fcmp_ordered('>', a, b, 'g') + builder.fcmp_ordered('>=', a, b, 'h') + builder.fcmp_unordered('==', a, b, 'i') + builder.fcmp_unordered('!=', a, b, 'j') + builder.fcmp_unordered('<', a, b, 'k') + builder.fcmp_unordered('<=', a, b, 'l') + builder.fcmp_unordered('>', a, b, 'm') + builder.fcmp_unordered('>=', a, b, 'n') + # fcmp_ordered and fcmp_unordered are the same for these cases + builder.fcmp_ordered('ord', a, b, 'u') + builder.fcmp_ordered('uno', a, b, 'v') + builder.fcmp_unordered('ord', a, b, 'w') + builder.fcmp_unordered('uno', a, b, 'x') + builder.fcmp_unordered('olt', a, b, 'y', + flags=['nnan', 'ninf', 'nsz', 'arcp', 'fast']) + self.assertFalse(block.is_terminated) + self.check_block(block, """\ + my_block: + %"c" = fcmp oeq i32 %".1", %".2" + %"d" = fcmp one i32 %".1", %".2" + %"e" = fcmp olt i32 %".1", %".2" + %"f" = fcmp ole i32 %".1", %".2" + %"g" = fcmp ogt i32 %".1", %".2" + %"h" = fcmp oge i32 %".1", %".2" + %"i" = fcmp ueq i32 %".1", %".2" + %"j" = fcmp une i32 %".1", %".2" + %"k" = fcmp ult i32 %".1", %".2" + %"l" = fcmp ule i32 %".1", %".2" + %"m" = fcmp ugt i32 %".1", %".2" + %"n" = fcmp uge i32 %".1", %".2" + %"u" = fcmp ord i32 %".1", %".2" + %"v" = fcmp uno i32 %".1", %".2" + %"w" = fcmp ord i32 %".1", %".2" + %"x" = fcmp uno i32 %".1", %".2" + %"y" = fcmp nnan ninf nsz arcp fast olt i32 %".1", %".2" + """) + + def test_misc_ops(self): + block = self.block(name='my_block') + t = ir.Constant(int1, True) + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + builder.select(t, a, b, 'c', flags=('arcp', 'nnan')) + self.assertFalse(block.is_terminated) + builder.unreachable() + self.assertTrue(block.is_terminated) + self.check_block(block, """\ + my_block: + %"c" = select arcp nnan i1 true, i32 %".1", i32 %".2" + unreachable + """) + + def test_phi(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + bb2 = builder.function.append_basic_block('b2') + bb3 = builder.function.append_basic_block('b3') + phi = builder.phi(int32, 'my_phi', flags=('fast',)) + phi.add_incoming(a, bb2) + phi.add_incoming(b, bb3) + self.assertFalse(block.is_terminated) + self.check_block(block, """\ + my_block: + %"my_phi" = phi fast i32 [%".1", %"b2"], [%".2", %"b3"] + """) + + def test_mem_ops(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b, z = builder.function.args[:3] + c = builder.alloca(int32, name='c') + d = builder.alloca(int32, size=42, name='d') # noqa F841 + e = builder.alloca(dbl, size=a, name='e') + e.align = 8 + self.assertEqual(e.type, ir.PointerType(dbl)) + ee = builder.store(z, e) + self.assertEqual(ee.type, ir.VoidType()) + f = builder.store(b, c) + self.assertEqual(f.type, ir.VoidType()) + g = builder.load(c, 'g') + self.assertEqual(g.type, int32) + # With alignment + h = builder.store(b, c, align=1) + self.assertEqual(h.type, ir.VoidType()) + i = builder.load(c, 'i', align=1) + self.assertEqual(i.type, int32) + # Atomics + j = builder.store_atomic(b, c, ordering="seq_cst", align=4) + self.assertEqual(j.type, ir.VoidType()) + k = builder.load_atomic(c, ordering="seq_cst", align=4, name='k') + self.assertEqual(k.type, int32) + if not ir_layer_typed_pointers_enabled: + ptr = ir.Constant(ir.PointerType(), None) + else: + ptr = ir.Constant(ir.PointerType(int32), None) + builder.store(ir.Constant(int32, 5), ptr) + # Not pointer types + with self.assertRaises(TypeError): + builder.store(b, a) + with self.assertRaises(TypeError): + builder.load(b) + # Mismatching pointer type + with self.assertRaises(TypeError) as cm: + builder.store(b, e) + + if not ir_layer_typed_pointers_enabled: + self.assertEqual(str(cm.exception), + "cannot store i32 to ptr: mismatching types") + self.check_block(block, """\ + my_block: + %"c" = alloca i32 + %"d" = alloca i32, i32 42 + %"e" = alloca double, i32 %".1", align 8 + store double %".3", ptr %"e" + store i32 %".2", ptr %"c" + %"g" = load i32, ptr %"c" + store i32 %".2", ptr %"c", align 1 + %"i" = load i32, ptr %"c", align 1 + store atomic i32 %".2", ptr %"c" seq_cst, align 4 + %"k" = load atomic i32, ptr %"c" seq_cst, align 4 + store i32 5, ptr null + """) + else: + self.assertEqual(str(cm.exception), + "cannot store i32 to double*: mismatching types") + self.check_block(block, """\ + my_block: + %"c" = alloca i32 + %"d" = alloca i32, i32 42 + %"e" = alloca double, i32 %".1", align 8 + store double %".3", double* %"e" + store i32 %".2", i32* %"c" + %"g" = load i32, i32* %"c" + store i32 %".2", i32* %"c", align 1 + %"i" = load i32, i32* %"c", align 1 + store atomic i32 %".2", i32* %"c" seq_cst, align 4 + %"k" = load atomic i32, i32* %"c" seq_cst, align 4 + store i32 5, i32* null + """) + + def test_gep(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + c = builder.alloca(ir.PointerType(int32), name='c') + d = builder.gep(c, [ir.Constant(int32, 5), a], name='d') + self.assertEqual(d.type, ir.PointerType(int32)) + if not ir_layer_typed_pointers_enabled: + self.check_block(block, """\ + my_block: + %"c" = alloca ptr + %"d" = getelementptr ptr, ptr %"c", i32 5, i32 %".1" + """) + else: + self.check_block(block, """\ + my_block: + %"c" = alloca i32* + %"d" = getelementptr i32*, i32** %"c", i32 5, i32 %".1" + """) + # XXX test with more complex types + + def test_gep_castinstr(self): + # similar to: + # numba::runtime::nrtdynmod.py_define_nrt_meminfo_data() + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + int8ptr = int8.as_pointer() + ls = ir.LiteralStructType([int64, int8ptr, int8ptr, int8ptr, int64]) + d = builder.bitcast(a, ls.as_pointer(), name='d') + e = builder.gep(d, [ir.Constant(int32, x) for x in [0, 3]], name='e') + self.assertEqual(e.type, ir.PointerType(int8ptr)) + if not ir_layer_typed_pointers_enabled: + self.check_block(block, """\ + my_block: + %"d" = bitcast i32 %".1" to ptr + %"e" = getelementptr {i64, ptr, ptr, ptr, i64}, ptr %"d", i32 0, i32 3 + """) # noqa E501 + else: + self.check_block(block, """\ + my_block: + %"d" = bitcast i32 %".1" to {i64, i8*, i8*, i8*, i64}* + %"e" = getelementptr {i64, i8*, i8*, i8*, i64}, {i64, i8*, i8*, i8*, i64}* %"d", i32 0, i32 3 + """) # noqa E501 + + def test_gep_castinstr_addrspace(self): + # similar to: + # numba::runtime::nrtdynmod.py_define_nrt_meminfo_data() + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + addrspace = 4 + int8ptr = int8.as_pointer() + ls = ir.LiteralStructType([int64, int8ptr, int8ptr, int8ptr, int64]) + d = builder.bitcast(a, ls.as_pointer(addrspace=addrspace), name='d') + e = builder.gep(d, [ir.Constant(int32, x) for x in [0, 3]], name='e') + self.assertEqual(e.type.addrspace, addrspace) + self.assertEqual(e.type, ir.PointerType(int8ptr, addrspace=addrspace)) + if not ir_layer_typed_pointers_enabled: + self.check_block(block, """\ + my_block: + %"d" = bitcast i32 %".1" to ptr addrspace(4) + %"e" = getelementptr {i64, ptr, ptr, ptr, i64}, ptr addrspace(4) %"d", i32 0, i32 3 + """) # noqa E501 + else: + self.check_block(block, """\ + my_block: + %"d" = bitcast i32 %".1" to {i64, i8*, i8*, i8*, i64} addrspace(4)* + %"e" = getelementptr {i64, i8*, i8*, i8*, i64}, {i64, i8*, i8*, i8*, i64} addrspace(4)* %"d", i32 0, i32 3 + """) # noqa E501 + + def test_gep_addrspace(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + addrspace = 4 + c = builder.alloca(ir.PointerType(int32, addrspace=addrspace), name='c') + if not ir_layer_typed_pointers_enabled: + self.assertEqual(str(c.type), 'ptr') + else: + self.assertEqual(str(c.type), 'i32 addrspace(4)**') + self.assertEqual(c.type.pointee.addrspace, addrspace) + d = builder.gep(c, [ir.Constant(int32, 5), a], name='d') + self.assertEqual(d.type.addrspace, addrspace) + e = builder.gep(d, [ir.Constant(int32, 10)], name='e') + self.assertEqual(e.type.addrspace, addrspace) + if not ir_layer_typed_pointers_enabled: + self.check_block(block, """\ + my_block: + %"c" = alloca ptr addrspace(4) + %"d" = getelementptr ptr addrspace(4), ptr %"c", i32 5, i32 %".1" + %"e" = getelementptr i32, ptr addrspace(4) %"d", i32 10 + """) # noqa E501 + else: + self.check_block(block, """\ + my_block: + %"c" = alloca i32 addrspace(4)* + %"d" = getelementptr i32 addrspace(4)*, i32 addrspace(4)** %"c", i32 5, i32 %".1" + %"e" = getelementptr i32, i32 addrspace(4)* %"d", i32 10 + """) # noqa E501 + + def test_extract_insert_value(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + tp_inner = ir.LiteralStructType([int32, int1]) + tp_outer = ir.LiteralStructType([int8, tp_inner]) + c_inner = ir.Constant(tp_inner, (ir.Constant(int32, 4), + ir.Constant(int1, True))) + # Flat structure + c = builder.extract_value(c_inner, 0, name='c') # noqa F841 + d = builder.insert_value(c_inner, a, 0, name='d') # noqa F841 + e = builder.insert_value(d, ir.Constant(int1, False), 1, name='e') # noqa F841 E501 + self.assertEqual(d.type, tp_inner) + self.assertEqual(e.type, tp_inner) + # Nested structure + p_outer = builder.alloca(tp_outer, name='ptr') + j = builder.load(p_outer, name='j') + k = builder.extract_value(j, 0, name='k') + l = builder.extract_value(j, 1, name='l') + m = builder.extract_value(j, (1, 0), name='m') + n = builder.extract_value(j, (1, 1), name='n') + o = builder.insert_value(j, l, 1, name='o') + p = builder.insert_value(j, a, (1, 0), name='p') + self.assertEqual(k.type, int8) + self.assertEqual(l.type, tp_inner) + self.assertEqual(m.type, int32) + self.assertEqual(n.type, int1) + self.assertEqual(o.type, tp_outer) + self.assertEqual(p.type, tp_outer) + + with self.assertRaises(TypeError): + # Not an aggregate + builder.extract_value(p_outer, 0) + with self.assertRaises(TypeError): + # Indexing too deep + builder.extract_value(c_inner, (0, 0)) + with self.assertRaises(TypeError): + # Index out of structure bounds + builder.extract_value(c_inner, 5) + with self.assertRaises(TypeError): + # Not an aggregate + builder.insert_value(a, b, 0) + with self.assertRaises(TypeError): + # Replacement value has the wrong type + builder.insert_value(c_inner, a, 1) + + if not ir_layer_typed_pointers_enabled: + self.check_block(block, """\ + my_block: + %"c" = extractvalue {i32, i1} {i32 4, i1 true}, 0 + %"d" = insertvalue {i32, i1} {i32 4, i1 true}, i32 %".1", 0 + %"e" = insertvalue {i32, i1} %"d", i1 false, 1 + %"ptr" = alloca {i8, {i32, i1}} + %"j" = load {i8, {i32, i1}}, ptr %"ptr" + %"k" = extractvalue {i8, {i32, i1}} %"j", 0 + %"l" = extractvalue {i8, {i32, i1}} %"j", 1 + %"m" = extractvalue {i8, {i32, i1}} %"j", 1, 0 + %"n" = extractvalue {i8, {i32, i1}} %"j", 1, 1 + %"o" = insertvalue {i8, {i32, i1}} %"j", {i32, i1} %"l", 1 + %"p" = insertvalue {i8, {i32, i1}} %"j", i32 %".1", 1, 0 + """) + else: + self.check_block(block, """\ + my_block: + %"c" = extractvalue {i32, i1} {i32 4, i1 true}, 0 + %"d" = insertvalue {i32, i1} {i32 4, i1 true}, i32 %".1", 0 + %"e" = insertvalue {i32, i1} %"d", i1 false, 1 + %"ptr" = alloca {i8, {i32, i1}} + %"j" = load {i8, {i32, i1}}, {i8, {i32, i1}}* %"ptr" + %"k" = extractvalue {i8, {i32, i1}} %"j", 0 + %"l" = extractvalue {i8, {i32, i1}} %"j", 1 + %"m" = extractvalue {i8, {i32, i1}} %"j", 1, 0 + %"n" = extractvalue {i8, {i32, i1}} %"j", 1, 1 + %"o" = insertvalue {i8, {i32, i1}} %"j", {i32, i1} %"l", 1 + %"p" = insertvalue {i8, {i32, i1}} %"j", i32 %".1", 1, 0 + """) + + def test_cast_ops(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b, fa, ptr = builder.function.args[:4] + c = builder.trunc(a, int8, name='c') + d = builder.zext(c, int32, name='d') # noqa F841 + e = builder.sext(c, int32, name='e') # noqa F841 + fb = builder.fptrunc(fa, flt, 'fb') + fc = builder.fpext(fb, dbl, 'fc') # noqa F841 + g = builder.fptoui(fa, int32, 'g') + h = builder.fptosi(fa, int8, 'h') + fd = builder.uitofp(g, flt, 'fd') # noqa F841 + fe = builder.sitofp(h, dbl, 'fe') # noqa F841 + i = builder.ptrtoint(ptr, int32, 'i') + j = builder.inttoptr(i, ir.PointerType(int8), 'j') # noqa F841 + k = builder.bitcast(a, flt, "k") # noqa F841 + self.assertFalse(block.is_terminated) + if not ir_layer_typed_pointers_enabled: + self.check_block(block, """\ + my_block: + %"c" = trunc i32 %".1" to i8 + %"d" = zext i8 %"c" to i32 + %"e" = sext i8 %"c" to i32 + %"fb" = fptrunc double %".3" to float + %"fc" = fpext float %"fb" to double + %"g" = fptoui double %".3" to i32 + %"h" = fptosi double %".3" to i8 + %"fd" = uitofp i32 %"g" to float + %"fe" = sitofp i8 %"h" to double + %"i" = ptrtoint ptr %".4" to i32 + %"j" = inttoptr i32 %"i" to ptr + %"k" = bitcast i32 %".1" to float + """) + else: + self.check_block(block, """\ + my_block: + %"c" = trunc i32 %".1" to i8 + %"d" = zext i8 %"c" to i32 + %"e" = sext i8 %"c" to i32 + %"fb" = fptrunc double %".3" to float + %"fc" = fpext float %"fb" to double + %"g" = fptoui double %".3" to i32 + %"h" = fptosi double %".3" to i8 + %"fd" = uitofp i32 %"g" to float + %"fe" = sitofp i8 %"h" to double + %"i" = ptrtoint i32* %".4" to i32 + %"j" = inttoptr i32 %"i" to i8* + %"k" = bitcast i32 %".1" to float + """) + + def test_atomicrmw(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + c = builder.alloca(int32, name='c') + d = builder.atomic_rmw('add', c, a, 'monotonic', 'd') + self.assertEqual(d.type, int32) + if not ir_layer_typed_pointers_enabled: + self.check_block(block, """\ + my_block: + %"c" = alloca i32 + %"d" = atomicrmw add ptr %"c", i32 %".1" monotonic + """) + else: + self.check_block(block, """\ + my_block: + %"c" = alloca i32 + %"d" = atomicrmw add i32* %"c", i32 %".1" monotonic + """) + + def test_branch(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + bb_target = builder.function.append_basic_block(name='target') + builder.branch(bb_target) + self.assertTrue(block.is_terminated) + self.check_block(block, """\ + my_block: + br label %"target" + """) + + def test_cbranch(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + bb_true = builder.function.append_basic_block(name='b_true') + bb_false = builder.function.append_basic_block(name='b_false') + builder.cbranch(ir.Constant(int1, False), bb_true, bb_false) + self.assertTrue(block.is_terminated) + self.check_block(block, """\ + my_block: + br i1 false, label %"b_true", label %"b_false" + """) + + def test_cbranch_weights(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + bb_true = builder.function.append_basic_block(name='b_true') + bb_false = builder.function.append_basic_block(name='b_false') + br = builder.cbranch(ir.Constant(int1, False), bb_true, bb_false) + br.set_weights([5, 42]) + self.assertTrue(block.is_terminated) + self.check_block(block, """\ + my_block: + br i1 false, label %"b_true", label %"b_false", !prof !0 + """) + self.check_metadata(builder.module, """\ + !0 = !{ !"branch_weights", i32 5, i32 42 } + """) + + def test_branch_indirect(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + bb_1 = builder.function.append_basic_block(name='b_1') + bb_2 = builder.function.append_basic_block(name='b_2') + indirectbr = builder.branch_indirect( + ir.BlockAddress(builder.function, bb_1)) + indirectbr.add_destination(bb_1) + indirectbr.add_destination(bb_2) + self.assertTrue(block.is_terminated) + if not ir_layer_typed_pointers_enabled: + self.check_block(block, """\ + my_block: + indirectbr ptr blockaddress(@"my_func", %"b_1"), [label %"b_1", label %"b_2"] + """) # noqa E501 + else: + self.check_block(block, """\ + my_block: + indirectbr i8* blockaddress(@"my_func", %"b_1"), [label %"b_1", label %"b_2"] + """) # noqa E501 + + def test_returns(self): + def check(block, expected_ir): + self.assertTrue(block.is_terminated) + self.check_block(block, expected_ir) + + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + builder.ret_void() + check(block, """\ + my_block: + ret void + """) + + block = self.block(name='other_block') + builder = ir.IRBuilder(block) + builder.ret(int32(5)) + check(block, """\ + other_block: + ret i32 5 + """) + + # With metadata + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + inst = builder.ret_void() + inst.set_metadata("dbg", block.module.add_metadata(())) + check(block, """\ + my_block: + ret void, !dbg !0 + """) + + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + inst = builder.ret(int32(6)) + inst.set_metadata("dbg", block.module.add_metadata(())) + check(block, """\ + my_block: + ret i32 6, !dbg !0 + """) + + def test_switch(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + bb_onzero = builder.function.append_basic_block(name='onzero') + bb_onone = builder.function.append_basic_block(name='onone') + bb_ontwo = builder.function.append_basic_block(name='ontwo') + bb_else = builder.function.append_basic_block(name='otherwise') + sw = builder.switch(a, bb_else) + sw.add_case(ir.Constant(int32, 0), bb_onzero) + sw.add_case(ir.Constant(int32, 1), bb_onone) + # A plain Python value gets converted into the right IR constant + sw.add_case(2, bb_ontwo) + self.assertTrue(block.is_terminated) + self.check_block(block, """\ + my_block: + switch i32 %".1", label %"otherwise" [i32 0, label %"onzero" i32 1, label %"onone" i32 2, label %"ontwo"] + """) # noqa E501 + + def test_call(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + tp_f = ir.FunctionType(flt, (int32, int32)) + tp_g = ir.FunctionType(dbl, (int32,), var_arg=True) + tp_h = ir.FunctionType(hlf, (int32, int32)) + f = ir.Function(builder.function.module, tp_f, 'f') + g = ir.Function(builder.function.module, tp_g, 'g') + h = ir.Function(builder.function.module, tp_h, 'h') + builder.call(f, (a, b), 'res_f') + builder.call(g, (b, a), 'res_g') + builder.call(h, (a, b), 'res_h') + builder.call(f, (a, b), 'res_f_fast', cconv='fastcc') + res_f_readonly = builder.call(f, (a, b), 'res_f_readonly') + res_f_readonly.attributes.add('readonly') + builder.call(f, (a, b), 'res_fast', fastmath='fast') + builder.call(f, (a, b), 'res_nnan_ninf', fastmath=('nnan', 'ninf')) + builder.call(f, (a, b), 'res_noinline', attrs='noinline') + builder.call(f, (a, b), 'res_alwaysinline', attrs='alwaysinline') + builder.call(f, (a, b), 'res_noinline_ro', attrs=('noinline', + 'readonly')) + builder.call(f, (a, b), 'res_convergent', attrs='convergent') + self.check_block(block, """\ + my_block: + %"res_f" = call float @"f"(i32 %".1", i32 %".2") + %"res_g" = call double (i32, ...) @"g"(i32 %".2", i32 %".1") + %"res_h" = call half @"h"(i32 %".1", i32 %".2") + %"res_f_fast" = call fastcc float @"f"(i32 %".1", i32 %".2") + %"res_f_readonly" = call float @"f"(i32 %".1", i32 %".2") readonly + %"res_fast" = call fast float @"f"(i32 %".1", i32 %".2") + %"res_nnan_ninf" = call ninf nnan float @"f"(i32 %".1", i32 %".2") + %"res_noinline" = call float @"f"(i32 %".1", i32 %".2") noinline + %"res_alwaysinline" = call float @"f"(i32 %".1", i32 %".2") alwaysinline + %"res_noinline_ro" = call float @"f"(i32 %".1", i32 %".2") noinline readonly + %"res_convergent" = call float @"f"(i32 %".1", i32 %".2") convergent + """) # noqa E501 + + def test_call_metadata(self): + """ + Function calls with metadata arguments. + """ + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + dbg_declare_ty = ir.FunctionType(ir.VoidType(), [ir.MetaDataType()] * 3) + dbg_declare = ir.Function( + builder.module, + dbg_declare_ty, + 'llvm.dbg.declare') + a = builder.alloca(int32, name="a") + b = builder.module.add_metadata(()) + builder.call(dbg_declare, (a, b, b)) + if not ir_layer_typed_pointers_enabled: + self.check_block(block, """\ + my_block: + %"a" = alloca i32 + call void @"llvm.dbg.declare"(metadata ptr %"a", metadata !0, metadata !0) + """) # noqa E501 + else: + self.check_block(block, """\ + my_block: + %"a" = alloca i32 + call void @"llvm.dbg.declare"(metadata i32* %"a", metadata !0, metadata !0) + """) # noqa E501 + + def test_call_attributes(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + fun_ty = ir.FunctionType( + ir.VoidType(), (int32.as_pointer(), int32, int32.as_pointer())) + fun = ir.Function(builder.function.module, fun_ty, 'fun') + fun.args[0].add_attribute('sret') + retval = builder.alloca(int32, name='retval') + other = builder.alloca(int32, name='other') + builder.call( + fun, + (retval, ir.Constant(int32, 42), other), + arg_attrs={ + 0: ('sret', 'noalias'), + 2: 'noalias' + } + ) + if not ir_layer_typed_pointers_enabled: + self.check_block_regex(block, """\ + my_block: + %"retval" = alloca i32 + %"other" = alloca i32 + call void @"fun"\\(ptr noalias sret(\\(i32\\))? %"retval", i32 42, ptr noalias %"other"\\) + """) # noqa E501 + else: + self.check_block_regex(block, """\ + my_block: + %"retval" = alloca i32 + %"other" = alloca i32 + call void @"fun"\\(i32\\* noalias sret(\\(i32\\))? %"retval", i32 42, i32\\* noalias %"other"\\) + """) # noqa E501 + + def test_call_tail(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + fun_ty = ir.FunctionType(ir.VoidType(), ()) + fun = ir.Function(builder.function.module, fun_ty, 'my_fun') + + builder.call(fun, ()) + builder.call(fun, (), tail=False) + builder.call(fun, (), tail=True) + builder.call(fun, (), tail='tail') + builder.call(fun, (), tail='notail') + builder.call(fun, (), tail='musttail') + builder.call(fun, (), tail=[]) # This is a falsy value + builder.call(fun, (), tail='not a marker') # This is a truthy value + + self.check_block(block, """\ + my_block: + call void @"my_fun"() + call void @"my_fun"() + tail call void @"my_fun"() + tail call void @"my_fun"() + notail call void @"my_fun"() + musttail call void @"my_fun"() + call void @"my_fun"() + tail call void @"my_fun"() + """) # noqa E501 + + def test_invalid_call_attributes(self): + block = self.block() + builder = ir.IRBuilder(block) + fun_ty = ir.FunctionType(ir.VoidType(), ()) + fun = ir.Function(builder.function.module, fun_ty, 'fun') + with self.assertRaises(ValueError): + # The function has no arguments, so this should fail. + builder.call(fun, (), arg_attrs={0: 'sret'}) + + def test_invoke(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + tp_f = ir.FunctionType(flt, (int32, int32)) + f = ir.Function(builder.function.module, tp_f, 'f') + bb_normal = builder.function.append_basic_block(name='normal') + bb_unwind = builder.function.append_basic_block(name='unwind') + builder.invoke(f, (a, b), bb_normal, bb_unwind, 'res_f') + self.check_block(block, """\ + my_block: + %"res_f" = invoke float @"f"(i32 %".1", i32 %".2") + to label %"normal" unwind label %"unwind" + """) + + def test_invoke_attributes(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + fun_ty = ir.FunctionType( + ir.VoidType(), (int32.as_pointer(), int32, int32.as_pointer())) + fun = ir.Function(builder.function.module, fun_ty, 'fun') + fun.calling_convention = "fastcc" + fun.args[0].add_attribute('sret') + retval = builder.alloca(int32, name='retval') + other = builder.alloca(int32, name='other') + bb_normal = builder.function.append_basic_block(name='normal') + bb_unwind = builder.function.append_basic_block(name='unwind') + builder.invoke( + fun, + (retval, ir.Constant(int32, 42), other), + bb_normal, + bb_unwind, + cconv='fastcc', + fastmath='fast', + attrs='noinline', + arg_attrs={ + 0: ('sret', 'noalias'), + 2: 'noalias' + } + ) + if not ir_layer_typed_pointers_enabled: + self.check_block_regex(block, """\ + my_block: + %"retval" = alloca i32 + %"other" = alloca i32 + invoke fast fastcc void @"fun"\\(ptr noalias sret(\\(i32\\))? %"retval", i32 42, ptr noalias %"other"\\) noinline + to label %"normal" unwind label %"unwind" + """) # noqa E501 + else: + self.check_block_regex(block, """\ + my_block: + %"retval" = alloca i32 + %"other" = alloca i32 + invoke fast fastcc void @"fun"\\(i32\\* noalias sret(\\(i32\\))? %"retval", i32 42, i32\\* noalias %"other"\\) noinline + to label %"normal" unwind label %"unwind" + """) # noqa E501 + + def test_landingpad(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + lp = builder.landingpad(ir.LiteralStructType([int32, + int8.as_pointer()]), 'lp') + int_typeinfo = ir.GlobalVariable(builder.function.module, + int8.as_pointer(), "_ZTIi") + int_typeinfo.global_constant = True + lp.add_clause(ir.CatchClause(int_typeinfo)) + lp.add_clause(ir.FilterClause(ir.Constant(ir.ArrayType( + int_typeinfo.type, 1), [int_typeinfo]))) + builder.resume(lp) + if not ir_layer_typed_pointers_enabled: + self.check_block(block, """\ + my_block: + %"lp" = landingpad {i32, ptr} + catch ptr @"_ZTIi" + filter [1 x ptr] [ptr @"_ZTIi"] + resume {i32, ptr} %"lp" + """) + else: + self.check_block(block, """\ + my_block: + %"lp" = landingpad {i32, i8*} + catch i8** @"_ZTIi" + filter [1 x i8**] [i8** @"_ZTIi"] + resume {i32, i8*} %"lp" + """) + + def test_assume(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + c = builder.icmp_signed('>', a, b, name='c') + builder.assume(c) + self.check_block(block, """\ + my_block: + %"c" = icmp sgt i32 %".1", %".2" + call void @"llvm.assume"(i1 %"c") + """) + + def test_vector_ops(self): + block = self.block(name='insert_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + a.name = 'a' + b.name = 'b' + + vecty = ir.VectorType(a.type, 2) + vec = ir.Constant(vecty, ir.Undefined) + idxty = ir.IntType(32) + vec = builder.insert_element(vec, a, idxty(0), name='vec1') + vec = builder.insert_element(vec, b, idxty(1), name='vec2') + + self.check_block(block, """\ +insert_block: + %"vec1" = insertelement <2 x i32> , i32 %"a", i32 0 + %"vec2" = insertelement <2 x i32> %"vec1", i32 %"b", i32 1 + """) + + block = builder.append_basic_block("shuffle_block") + builder.branch(block) + builder.position_at_end(block) + + mask = ir.Constant(vecty, [1, 0]) + builder.shuffle_vector(vec, vec, mask, name='shuf') + + self.check_block(block, """\ + shuffle_block: + %"shuf" = shufflevector <2 x i32> %"vec2", <2 x i32> %"vec2", <2 x i32> + """) # noqa E501 + + block = builder.append_basic_block("add_block") + builder.branch(block) + builder.position_at_end(block) + + builder.add(vec, vec, name='sum') + + self.check_block(block, """\ + add_block: + %"sum" = add <2 x i32> %"vec2", %"vec2" + """) + + block = builder.append_basic_block("extract_block") + builder.branch(block) + builder.position_at_end(block) + + c = builder.extract_element(vec, idxty(0), name='ex1') + d = builder.extract_element(vec, idxty(1), name='ex2') + + self.check_block(block, """\ + extract_block: + %"ex1" = extractelement <2 x i32> %"vec2", i32 0 + %"ex2" = extractelement <2 x i32> %"vec2", i32 1 + """) + + builder.ret(builder.add(c, d)) + self.assert_valid_ir(builder.module) + + def test_bitreverse(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(int64, 5) + c = builder.bitreverse(a, name='c') + builder.ret(c) + self.check_block(block, """\ + my_block: + %"c" = call i64 @"llvm.bitreverse.i64"(i64 5) + ret i64 %"c" + """) + + def test_bitreverse_wrongtype(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(flt, 5) + + with self.assertRaises(TypeError) as raises: + builder.bitreverse(a, name='c') + self.assertIn( + "expected an integer type, got float", + str(raises.exception)) + + def test_fence(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + with self.assertRaises(ValueError) as raises: + builder.fence("monotonic", None) + self.assertIn( + "Invalid fence ordering \"monotonic\"!", + str(raises.exception)) + with self.assertRaises(ValueError) as raises: + builder.fence(None, "monotonic") + self.assertIn( + "Invalid fence ordering \"None\"!", + str(raises.exception)) + builder.fence("acquire", None) + builder.fence("release", "singlethread") + builder.fence("acq_rel", "singlethread") + builder.fence("seq_cst") + builder.ret_void() + self.check_block(block, """\ + my_block: + fence acquire + fence syncscope("singlethread") release + fence syncscope("singlethread") acq_rel + fence seq_cst + ret void + """) + + def test_comment(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + with self.assertRaises(AssertionError): + builder.comment("so\nmany lines") + builder.comment("my comment") + builder.ret_void() + self.check_block(block, """\ + my_block: + ; my comment + ret void + """) + + def test_bswap(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(int32, 5) + c = builder.bswap(a, name='c') + builder.ret(c) + self.check_block(block, """\ + my_block: + %"c" = call i32 @"llvm.bswap.i32"(i32 5) + ret i32 %"c" + """) + + def test_ctpop(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(int16, 5) + c = builder.ctpop(a, name='c') + builder.ret(c) + self.check_block(block, """\ + my_block: + %"c" = call i16 @"llvm.ctpop.i16"(i16 5) + ret i16 %"c" + """) + + def test_ctlz(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(int16, 5) + b = ir.Constant(int1, 1) + c = builder.ctlz(a, b, name='c') + builder.ret(c) + self.check_block(block, """\ + my_block: + %"c" = call i16 @"llvm.ctlz.i16"(i16 5, i1 1) + ret i16 %"c" + """) + + def test_convert_to_fp16_f32(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(flt, 5.0) + b = builder.convert_to_fp16(a, name='b') + builder.ret(b) + self.check_block(block, """\ + my_block: + %"b" = call i16 @"llvm.convert.to.fp16.f32"(float 0x4014000000000000) + ret i16 %"b" + """) # noqa E501 + + def test_convert_to_fp16_f32_wrongtype(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(int16, 5) + + with self.assertRaises(TypeError) as raises: + builder.convert_to_fp16(a, name='b') + self.assertIn( + "expected a float type, got i16", + str(raises.exception)) + + def test_convert_from_fp16_f32(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(int16, 5) + b = builder.convert_from_fp16(a, name='b', to=flt) + builder.ret(b) + self.check_block(block, """\ + my_block: + %"b" = call float @"llvm.convert.from.fp16.f32"(i16 5) + ret float %"b" + """) + + def test_convert_from_fp16_f32_notype(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(flt, 5.5) + + with self.assertRaises(TypeError) as raises: + builder.convert_from_fp16(a, name='b') + self.assertIn( + "expected a float return type", + str(raises.exception)) + + def test_convert_from_fp16_f32_wrongtype(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(flt, 5.5) + + with self.assertRaises(TypeError) as raises: + builder.convert_from_fp16(a, name='b', to=flt) + self.assertIn( + "expected an i16 type, got float", + str(raises.exception)) + + def test_convert_from_fp16_f32_wrongtype2(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(flt, 5.5) + + with self.assertRaises(TypeError) as raises: + builder.convert_from_fp16(a, name='b', to=int16) + self.assertIn( + "expected a float type, got i16", + str(raises.exception)) + + def test_cttz(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(int64, 5) + b = ir.Constant(int1, 1) + c = builder.cttz(a, b, name='c') + builder.ret(c) + self.check_block(block, """\ + my_block: + %"c" = call i64 @"llvm.cttz.i64"(i64 5, i1 1) + ret i64 %"c" + """) + + def test_cttz_wrongflag(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(int64, 5) + b = ir.Constant(int32, 3) + + with self.assertRaises(TypeError) as raises: + builder.cttz(a, b, name='c') + self.assertIn( + "expected an i1 type, got i32", + str(raises.exception)) + + def test_cttz_wrongtype(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(flt, 5) + b = ir.Constant(int1, 1) + + with self.assertRaises(TypeError) as raises: + builder.cttz(a, b, name='c') + self.assertIn( + "expected an integer type, got float", + str(raises.exception)) + + def test_fma(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(flt, 5) + b = ir.Constant(flt, 1) + c = ir.Constant(flt, 2) + fma = builder.fma(a, b, c, name='fma') + builder.ret(fma) + self.check_block(block, """\ + my_block: + %"fma" = call float @"llvm.fma.f32"(float 0x4014000000000000, float 0x3ff0000000000000, float 0x4000000000000000) + ret float %"fma" + """) # noqa E501 + + def test_fma_wrongtype(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(int32, 5) + b = ir.Constant(int32, 1) + c = ir.Constant(int32, 2) + + with self.assertRaises(TypeError) as raises: + builder.fma(a, b, c, name='fma') + self.assertIn( + "expected an floating point type, got i32", + str(raises.exception)) + + def test_fma_mixedtypes(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a = ir.Constant(flt, 5) + b = ir.Constant(dbl, 1) + c = ir.Constant(flt, 2) + + with self.assertRaises(TypeError) as raises: + builder.fma(a, b, c, name='fma') + self.assertIn( + "expected types to be the same, got float, double, float", + str(raises.exception)) + + def test_arg_attributes(self): + def gen_code(attr_name): + fnty = ir.FunctionType(ir.IntType(32), [ir.IntType(32).as_pointer(), + ir.IntType(32)]) + module = ir.Module() + + func = ir.Function(module, fnty, name="sum") + + bb_entry = func.append_basic_block() + bb_loop = func.append_basic_block() + bb_exit = func.append_basic_block() + + builder = ir.IRBuilder() + builder.position_at_end(bb_entry) + + builder.branch(bb_loop) + builder.position_at_end(bb_loop) + + index = builder.phi(ir.IntType(32)) + index.add_incoming(ir.Constant(index.type, 0), bb_entry) + accum = builder.phi(ir.IntType(32)) + accum.add_incoming(ir.Constant(accum.type, 0), bb_entry) + + func.args[0].add_attribute(attr_name) + ptr = builder.gep(func.args[0], [index]) + value = builder.load(ptr) + + added = builder.add(accum, value) + accum.add_incoming(added, bb_loop) + + indexp1 = builder.add(index, ir.Constant(index.type, 1)) + index.add_incoming(indexp1, bb_loop) + + cond = builder.icmp_unsigned('<', indexp1, func.args[1]) + builder.cbranch(cond, bb_loop, bb_exit) + + builder.position_at_end(bb_exit) + builder.ret(added) + + return str(module) + + for attr_name in ( + 'byref', + 'byval', + 'captures(none)', + 'elementtype', + 'immarg', + 'inalloca', + 'inreg', + 'nest', + 'noalias', + 'nofree', + 'nonnull', + 'noundef', + 'preallocated', + 'returned', + 'signext', + 'swiftasync', + 'swifterror', + 'swiftself', + 'zeroext', + ): + # If this parses, we emitted the right byval attribute format + llvm.parse_assembly(gen_code(attr_name)) + # sret doesn't fit this pattern and is tested in test_call_attributes + + +class TestBuilderMisc(TestBase): + """ + Test various other features of the IRBuilder class. + """ + + def test_attributes(self): + block = self.block(name='start') + builder = ir.IRBuilder(block) + self.assertIs(builder.function, block.parent) + self.assertIsInstance(builder.function, ir.Function) + self.assertIs(builder.module, block.parent.module) + self.assertIsInstance(builder.module, ir.Module) + + def test_goto_block(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + a, b = builder.function.args[:2] + builder.add(a, b, 'c') + bb_new = builder.append_basic_block(name='foo') + with builder.goto_block(bb_new): + builder.fadd(a, b, 'd') + with builder.goto_entry_block(): + builder.sub(a, b, 'e') + builder.fsub(a, b, 'f') + builder.branch(bb_new) + builder.mul(a, b, 'g') + with builder.goto_block(bb_new): + builder.fmul(a, b, 'h') + self.check_block(block, """\ + my_block: + %"c" = add i32 %".1", %".2" + %"e" = sub i32 %".1", %".2" + %"g" = mul i32 %".1", %".2" + """) + self.check_block(bb_new, """\ + foo: + %"d" = fadd i32 %".1", %".2" + %"f" = fsub i32 %".1", %".2" + %"h" = fmul i32 %".1", %".2" + br label %"foo" + """) + + def test_if_then(self): + block = self.block(name='one') + builder = ir.IRBuilder(block) + z = ir.Constant(int1, 0) + a = builder.add(z, z, 'a') + with builder.if_then(a) as bbend: + builder.add(z, z, 'b') + # Block will be terminated implicitly + self.assertIs(builder.block, bbend) + c = builder.add(z, z, 'c') + with builder.if_then(c): + builder.add(z, z, 'd') + builder.branch(block) + # No implicit termination + self.check_func_body(builder.function, """\ + one: + %"a" = add i1 0, 0 + br i1 %"a", label %"one.if", label %"one.endif" + one.if: + %"b" = add i1 0, 0 + br label %"one.endif" + one.endif: + %"c" = add i1 0, 0 + br i1 %"c", label %"one.endif.if", label %"one.endif.endif" + one.endif.if: + %"d" = add i1 0, 0 + br label %"one" + one.endif.endif: + """) + + def test_if_then_nested(self): + # Implicit termination in a nested if/then + block = self.block(name='one') + builder = ir.IRBuilder(block) + z = ir.Constant(int1, 0) + a = builder.add(z, z, 'a') + with builder.if_then(a): + b = builder.add(z, z, 'b') + with builder.if_then(b): + builder.add(z, z, 'c') + builder.ret_void() + self.check_func_body(builder.function, """\ + one: + %"a" = add i1 0, 0 + br i1 %"a", label %"one.if", label %"one.endif" + one.if: + %"b" = add i1 0, 0 + br i1 %"b", label %"one.if.if", label %"one.if.endif" + one.endif: + ret void + one.if.if: + %"c" = add i1 0, 0 + br label %"one.if.endif" + one.if.endif: + br label %"one.endif" + """) + + def test_if_then_long_label(self): + full_label = 'Long' * 20 + block = self.block(name=full_label) + builder = ir.IRBuilder(block) + z = ir.Constant(int1, 0) + a = builder.add(z, z, 'a') + with builder.if_then(a): + b = builder.add(z, z, 'b') + with builder.if_then(b): + builder.add(z, z, 'c') + builder.ret_void() + self.check_func_body(builder.function, """\ + {full_label}: + %"a" = add i1 0, 0 + br i1 %"a", label %"{label}.if", label %"{label}.endif" + {label}.if: + %"b" = add i1 0, 0 + br i1 %"b", label %"{label}.if.if", label %"{label}.if.endif" + {label}.endif: + ret void + {label}.if.if: + %"c" = add i1 0, 0 + br label %"{label}.if.endif" + {label}.if.endif: + br label %"{label}.endif" + """.format(full_label=full_label, label=full_label[:25] + '..')) + + def test_if_then_likely(self): + def check(likely): + block = self.block(name='one') + builder = ir.IRBuilder(block) + z = ir.Constant(int1, 0) + with builder.if_then(z, likely=likely): + pass + self.check_block(block, """\ + one: + br i1 0, label %"one.if", label %"one.endif", !prof !0 + """) + return builder + builder = check(True) + self.check_metadata(builder.module, """\ + !0 = !{ !"branch_weights", i32 99, i32 1 } + """) + builder = check(False) + self.check_metadata(builder.module, """\ + !0 = !{ !"branch_weights", i32 1, i32 99 } + """) + + def test_if_else(self): + block = self.block(name='one') + builder = ir.IRBuilder(block) + z = ir.Constant(int1, 0) + a = builder.add(z, z, 'a') + with builder.if_else(a) as (then, otherwise): + with then: + builder.add(z, z, 'b') + with otherwise: + builder.add(z, z, 'c') + # Each block will be terminated implicitly + with builder.if_else(a) as (then, otherwise): + with then: + builder.branch(block) + with otherwise: + builder.ret_void() + # No implicit termination + self.check_func_body(builder.function, """\ + one: + %"a" = add i1 0, 0 + br i1 %"a", label %"one.if", label %"one.else" + one.if: + %"b" = add i1 0, 0 + br label %"one.endif" + one.else: + %"c" = add i1 0, 0 + br label %"one.endif" + one.endif: + br i1 %"a", label %"one.endif.if", label %"one.endif.else" + one.endif.if: + br label %"one" + one.endif.else: + ret void + one.endif.endif: + """) + + def test_if_else_likely(self): + def check(likely): + block = self.block(name='one') + builder = ir.IRBuilder(block) + z = ir.Constant(int1, 0) + with builder.if_else(z, likely=likely) as (then, otherwise): + with then: + builder.branch(block) + with otherwise: + builder.ret_void() + self.check_func_body(builder.function, """\ + one: + br i1 0, label %"one.if", label %"one.else", !prof !0 + one.if: + br label %"one" + one.else: + ret void + one.endif: + """) + return builder + builder = check(True) + self.check_metadata(builder.module, """\ + !0 = !{ !"branch_weights", i32 99, i32 1 } + """) + builder = check(False) + self.check_metadata(builder.module, """\ + !0 = !{ !"branch_weights", i32 1, i32 99 } + """) + + def test_positioning(self): + """ + Test IRBuilder.position_{before,after,at_start,at_end}. + """ + func = self.function() + builder = ir.IRBuilder() + z = ir.Constant(int32, 0) + bb_one = func.append_basic_block(name='one') + bb_two = func.append_basic_block(name='two') + bb_three = func.append_basic_block(name='three') + # .at_start(empty block) + builder.position_at_start(bb_one) + builder.add(z, z, 'a') + # .at_end(empty block) + builder.position_at_end(bb_two) + builder.add(z, z, 'm') + builder.add(z, z, 'n') + # .at_start(block) + builder.position_at_start(bb_two) + o = builder.add(z, z, 'o') + builder.add(z, z, 'p') + # .at_end(block) + builder.position_at_end(bb_one) + b = builder.add(z, z, 'b') + # .after(instr) + builder.position_after(o) + builder.add(z, z, 'q') + # .before(instr) + builder.position_before(b) + builder.add(z, z, 'c') + self.check_block(bb_one, """\ + one: + %"a" = add i32 0, 0 + %"c" = add i32 0, 0 + %"b" = add i32 0, 0 + """) + self.check_block(bb_two, """\ + two: + %"o" = add i32 0, 0 + %"q" = add i32 0, 0 + %"p" = add i32 0, 0 + %"m" = add i32 0, 0 + %"n" = add i32 0, 0 + """) + self.check_block(bb_three, """\ + three: + """) + + def test_instruction_removal(self): + func = self.function() + builder = ir.IRBuilder() + blk = func.append_basic_block(name='entry') + builder.position_at_end(blk) + k = ir.Constant(int32, 1234) + a = builder.add(k, k, 'a') + retvoid = builder.ret_void() + self.assertTrue(blk.is_terminated) + builder.remove(retvoid) + self.assertFalse(blk.is_terminated) + b = builder.mul(a, a, 'b') + c = builder.add(b, b, 'c') + builder.remove(c) + builder.ret_void() + self.assertTrue(blk.is_terminated) + self.check_block(blk, """\ + entry: + %"a" = add i32 1234, 1234 + %"b" = mul i32 %"a", %"a" + ret void + """) + + def test_metadata(self): + block = self.block(name='my_block') + builder = ir.IRBuilder(block) + builder.debug_metadata = builder.module.add_metadata([]) + builder.alloca(ir.PointerType(int32), name='c') + if not ir_layer_typed_pointers_enabled: + self.check_block(block, """\ + my_block: + %"c" = alloca ptr, !dbg !0 + """) + else: + self.check_block(block, """\ + my_block: + %"c" = alloca i32*, !dbg !0 + """) + + +class TestTypes(TestBase): + + def has_logical_equality(self, ty): + while isinstance(ty, ir.PointerType): + ty = ty.pointee + return not isinstance(ty, ir.LabelType) + + def assorted_types(self): + """ + A bunch of mutually unequal types + """ + # Avoid polluting the namespace + context = ir.Context() + types = [ + ir.LabelType(), ir.VoidType(), + ir.FunctionType(int1, (int8, int8)), ir.FunctionType(int1, (int8,)), + ir.FunctionType(int1, (int8,), var_arg=True), + ir.FunctionType(int8, (int8,)), + int1, int8, int32, flt, dbl, + ir.ArrayType(flt, 5), ir.ArrayType(dbl, 5), ir.ArrayType(dbl, 4), + ir.LiteralStructType((int1, int8)), ir.LiteralStructType((int8, + int1)), + context.get_identified_type("MyType1"), + context.get_identified_type("MyType2"), + ] + types += [ir.PointerType(tp) for tp in types + if not isinstance(tp, (ir.VoidType, ir.LabelType))] + + return types + + def test_pickling(self): + types = self.assorted_types() + for ty in types: + newty = self.assert_pickle_correctly(ty) + if self.has_logical_equality(ty): + self.assertEqual(newty, ty) + + def test_comparisons(self): + types = self.assorted_types() + for a, b in itertools.product(types, types): + if a is not b: + self.assertFalse(a == b, (a, b)) + self.assertTrue(a != b, (a, b)) + # We assume copy.copy() works fine here... + for tp in types: + other = copy.copy(tp) + if self.has_logical_equality(tp): + self.assertTrue(tp == other, (tp, other)) + self.assertFalse(tp != other, (tp, other)) + else: + self.assertFalse(tp == other, (tp, other)) + self.assertTrue(tp != other, (tp, other)) + + def test_ptr_comparisons(self): + # Create instances of: + # * Opaque pointers. + # * Typed pointers of i1's. + # * Typed pointers of i8's. + # The choice of types for the typed pointers are not consequential - + # they just need to differ. Each pointer class has two instances, one + # in address space 0, another in address space 1. + ptrs = { + 'op_a0': ir.PointerType(), + 'op_a1': ir.PointerType(addrspace=1), + 'tp_i1_a0': ir.PointerType(int1), + 'tp_i1_a1': ir.PointerType(int1, addrspace=1), + 'tp_i8_a0': ir.PointerType(int8), + 'tp_i8_a1': ir.PointerType(int8, addrspace=1), + } + + def assert_eq(ptr1, ptr2): + self.assertTrue(ptr1 == ptr2, (ptr1, ptr2)) + self.assertTrue(ptr2 == ptr1, (ptr2, ptr1)) + + self.assertFalse(ptr1 != ptr2, (ptr1, ptr2)) + self.assertFalse(ptr2 != ptr1, (ptr2, ptr1)) + + def assert_ne(ptr1, ptr2): + self.assertFalse(ptr1 == ptr2, (ptr1, ptr2)) + self.assertFalse(ptr2 == ptr1, (ptr2, ptr1)) + + self.assertTrue(ptr1 != ptr2, (ptr1, ptr2)) + self.assertTrue(ptr2 != ptr1, (ptr2, ptr1)) + + for ptr in ptrs.values(): + # Compare the pointers against any non-pointer type. + for other in self.assorted_types(): + if not isinstance(other, ir.PointerType): + assert_ne(ptr, other) + # Compare the pointers against themselves. + assert_eq(ptr, ptr) + + # Compare the pointers against each other. + # Opaque pointers are always equal, unless their address space differs. + # Typed pointers always differ, unless their pointee type and address + # space match. + assert_ne(ptrs['op_a0'], ptrs['op_a1']) + assert_eq(ptrs['op_a0'], ptrs['tp_i1_a0']) + assert_ne(ptrs['op_a0'], ptrs['tp_i1_a1']) + assert_eq(ptrs['op_a0'], ptrs['tp_i8_a0']) + assert_ne(ptrs['op_a0'], ptrs['tp_i8_a1']) + assert_ne(ptrs['op_a1'], ptrs['tp_i1_a0']) + assert_eq(ptrs['op_a1'], ptrs['tp_i1_a1']) + assert_ne(ptrs['op_a1'], ptrs['tp_i8_a0']) + assert_eq(ptrs['op_a1'], ptrs['tp_i8_a1']) + assert_ne(ptrs['tp_i1_a0'], ptrs['tp_i1_a1']) + assert_ne(ptrs['tp_i1_a0'], ptrs['tp_i8_a0']) + assert_ne(ptrs['tp_i1_a0'], ptrs['tp_i8_a1']) + assert_ne(ptrs['tp_i1_a1'], ptrs['tp_i8_a0']) + assert_ne(ptrs['tp_i1_a1'], ptrs['tp_i8_a1']) + assert_ne(ptrs['tp_i8_a0'], ptrs['tp_i8_a1']) + + def test_pointers(self): + # Basic opaque pointers. + ptr = ir.PointerType() + ptr2 = ir.PointerType(addrspace=2) + + self.assertTrue(ptr.is_opaque) + self.assertTrue(ptr2.is_opaque) + + self.assertEqual(str(ptr), "ptr") + self.assertEqual(str(ptr2), "ptr addrspace(2)") + + # Pointers of opaque pointers (necessarily opaque pointers). + ptr_ptr = ptr.as_pointer() + ptr2_ptr = ptr2.as_pointer() + ptr2_ptr3 = ptr2.as_pointer(addrspace=3) + + self.assertTrue(ptr_ptr.is_opaque) + self.assertTrue(ptr2_ptr.is_opaque) + self.assertTrue(ptr2_ptr3.is_opaque) + + self.assertEqual(str(ptr_ptr), "ptr") + self.assertEqual(str(ptr2_ptr), "ptr") + self.assertEqual(str(ptr2_ptr3), "ptr addrspace(3)") + + # Basic typed pointers. + tptr = ir.IntType(32).as_pointer() + tptr2 = ir.IntType(32).as_pointer(addrspace=2) + + self.assertTrue(not tptr.is_opaque) + self.assertTrue(not tptr2.is_opaque) + + if ir_layer_typed_pointers_enabled: + self.assertEqual(str(tptr), "i32*") + self.assertEqual(str(tptr2), "i32 addrspace(2)*") + else: + self.assertEqual(str(tptr), "ptr") + self.assertEqual(str(tptr2), "ptr addrspace(2)") + + # Pointers of typed pointers (necessarily typed pointers). + tptr_ptr = tptr.as_pointer() + tptr2_ptr = tptr2.as_pointer() + tptr2_ptr3 = tptr2.as_pointer(addrspace=3) + + self.assertTrue(not tptr_ptr.is_opaque) + self.assertTrue(not tptr2_ptr.is_opaque) + self.assertTrue(not tptr2_ptr3.is_opaque) + + if ir_layer_typed_pointers_enabled: + self.assertEqual(str(tptr_ptr), "i32**") + self.assertEqual(str(tptr2_ptr), "i32 addrspace(2)**") + self.assertEqual(str(tptr2_ptr3), "i32 addrspace(2)* addrspace(3)*") + else: + self.assertEqual(str(tptr_ptr), "ptr") + self.assertEqual(str(tptr2_ptr), "ptr") + self.assertEqual(str(tptr2_ptr3), "ptr addrspace(3)") + + def test_ptr_intrinsic_name(self): + self.assertEqual(ir.PointerType().intrinsic_name, 'p0') + self.assertEqual(ir.PointerType(addrspace=1).intrinsic_name, 'p1') + if not ir_layer_typed_pointers_enabled: + self.assertEqual(ir.PointerType(int1).intrinsic_name, 'p0') + self.assertEqual(ir.PointerType(int1, 1).intrinsic_name, 'p1') + else: + self.assertEqual(ir.PointerType(int1).intrinsic_name, 'p0i1') + self.assertEqual(ir.PointerType(int1, 1).intrinsic_name, 'p1i1') + + def test_str(self): + """ + Test the string representation of types. + """ + self.assertEqual(str(int1), 'i1') + self.assertEqual(str(ir.IntType(29)), 'i29') + self.assertEqual(str(flt), 'float') + self.assertEqual(str(dbl), 'double') + self.assertEqual(str(ir.VoidType()), 'void') + self.assertEqual(str(ir.FunctionType(int1, ())), 'i1 ()') + self.assertEqual(str(ir.FunctionType(int1, (flt,))), 'i1 (float)') + self.assertEqual(str(ir.FunctionType(int1, (flt, dbl))), + 'i1 (float, double)') + self.assertEqual(str(ir.FunctionType(int1, (), var_arg=True)), + 'i1 (...)') + self.assertEqual(str(ir.FunctionType(int1, (flt,), var_arg=True)), + 'i1 (float, ...)') + self.assertEqual(str(ir.FunctionType(int1, (flt, dbl), var_arg=True)), + 'i1 (float, double, ...)') + if not ir_layer_typed_pointers_enabled: + self.assertEqual(str(ir.PointerType(int32)), 'ptr') + self.assertEqual(str(ir.PointerType(ir.PointerType(int32))), 'ptr') + else: + self.assertEqual(str(ir.PointerType(int32)), 'i32*') + self.assertEqual(str(ir.PointerType(ir.PointerType(int32))), + 'i32**') + self.assertEqual(str(ir.ArrayType(int1, 5)), '[5 x i1]') + if not ir_layer_typed_pointers_enabled: + self.assertEqual(str(ir.ArrayType(ir.PointerType(int1), 5)), + '[5 x ptr]') + self.assertEqual(str(ir.PointerType(ir.ArrayType(int1, 5))), 'ptr') + else: + self.assertEqual(str(ir.ArrayType(ir.PointerType(int1), 5)), + '[5 x i1*]') + self.assertEqual(str(ir.PointerType(ir.ArrayType(int1, 5))), + '[5 x i1]*') + self.assertEqual(str(ir.LiteralStructType((int1,))), '{i1}') + self.assertEqual(str(ir.LiteralStructType((int1, flt))), '{i1, float}') + if not ir_layer_typed_pointers_enabled: + self.assertEqual(str(ir.LiteralStructType(( + ir.PointerType(int1), ir.LiteralStructType((int32, int8))))), + '{ptr, {i32, i8}}') + else: + self.assertEqual(str(ir.LiteralStructType(( + ir.PointerType(int1), ir.LiteralStructType((int32, int8))))), + '{i1*, {i32, i8}}') + self.assertEqual(str(ir.LiteralStructType((int1,), packed=True)), + '<{i1}>') + self.assertEqual(str(ir.LiteralStructType((int1, flt), packed=True)), + '<{i1, float}>') + + # Avoid polluting the namespace + context = ir.Context() + mytype = context.get_identified_type("MyType") + self.assertEqual(str(mytype), "%\"MyType\"") + mytype1 = context.get_identified_type("MyType\\") + self.assertEqual(str(mytype1), "%\"MyType\\5c\"") + mytype2 = context.get_identified_type("MyType\"") + self.assertEqual(str(mytype2), "%\"MyType\\22\"") + + def test_hash(self): + for typ in filter(self.has_logical_equality, self.assorted_types()): + self.assertEqual(hash(typ), hash(copy.copy(typ))) + + def test_gep(self): + def check_constant(tp, i, expected): + actual = tp.gep(ir.Constant(int32, i)) + self.assertEqual(actual, expected) + + def check_index_type(tp): + index = ir.Constant(dbl, 1.0) + with self.assertRaises(TypeError): + tp.gep(index) + + tp = ir.PointerType(dbl) + for i in range(5): + check_constant(tp, i, dbl) + check_index_type(tp) + + tp = ir.ArrayType(int1, 3) + for i in range(3): + check_constant(tp, i, int1) + check_index_type(tp) + + tp = ir.LiteralStructType((dbl, ir.LiteralStructType((int1, int8)))) + check_constant(tp, 0, dbl) + check_constant(tp, 1, ir.LiteralStructType((int1, int8))) + with self.assertRaises(IndexError): + tp.gep(ir.Constant(int32, 2)) + check_index_type(tp) + + context = ir.Context() + tp = ir.IdentifiedStructType(context, "MyType") + tp.set_body(dbl, ir.LiteralStructType((int1, int8))) + check_constant(tp, 0, dbl) + check_constant(tp, 1, ir.LiteralStructType((int1, int8))) + with self.assertRaises(IndexError): + tp.gep(ir.Constant(int32, 2)) + check_index_type(tp) + + def test_abi_size(self): + td = llvm.create_target_data("e-m:e-i64:64-f80:128-n8:16:32:64-S128") + + def check(tp, expected): + self.assertEqual(tp.get_abi_size(td), expected) + check(int8, 1) + check(int32, 4) + check(int64, 8) + check(ir.ArrayType(int8, 5), 5) + check(ir.ArrayType(int32, 5), 20) + check(ir.LiteralStructType((dbl, flt, flt)), 16) + + def test_abi_alignment(self): + td = llvm.create_target_data("e-m:e-i64:64-f80:128-n8:16:32:64-S128") + + def check(tp, expected): + self.assertIn(tp.get_abi_alignment(td), expected) + check(int8, (1, 2, 4)) + check(int32, (4,)) + check(int64, (8,)) + check(ir.ArrayType(int8, 5), (1, 2, 4)) + check(ir.ArrayType(int32, 5), (4,)) + check(ir.LiteralStructType((dbl, flt, flt)), (8,)) + + def test_identified_struct(self): + context = ir.Context() + mytype = context.get_identified_type("MyType") + module = ir.Module(context=context) + self.assertTrue(mytype.is_opaque) + self.assert_valid_ir(module) + oldstr = str(module) + mytype.set_body(ir.IntType(32), ir.IntType(64), ir.FloatType()) + self.assertFalse(mytype.is_opaque) + self.assert_valid_ir(module) + self.assertNotEqual(oldstr, str(module)) + + def test_identified_struct_packed(self): + td = llvm.create_target_data("e-m:e-i64:64-f80:128-n8:16:32:64-S128") + context = ir.Context() + mytype = context.get_identified_type("MyType", True) + module = ir.Module(context=context) + self.assertTrue(mytype.is_opaque) + self.assert_valid_ir(module) + oldstr = str(module) + mytype.set_body(ir.IntType(16), ir.IntType(64), ir.FloatType()) + self.assertEqual(mytype.get_element_offset(td, 1, context), 2) + self.assertFalse(mytype.is_opaque) + self.assert_valid_ir(module) + self.assertNotEqual(oldstr, str(module)) + + def test_target_data_non_default_context(self): + context = ir.Context() + mytype = context.get_identified_type("MyType") + mytype.elements = [ir.IntType(32)] + td = llvm.create_target_data("e-m:e-i64:64-f80:128-n8:16:32:64-S128") + self.assertEqual(mytype.get_abi_size(td, context=context), 4) + + def test_vector(self): + vecty = ir.VectorType(ir.IntType(32), 8) + self.assertEqual(str(vecty), "<8 x i32>") + + +def c32(i): + return ir.Constant(int32, i) + + +class TestConstant(TestBase): + + def test_integers(self): + c = ir.Constant(int32, 42) + self.assertEqual(str(c), 'i32 42') + c = ir.Constant(int1, 1) + self.assertEqual(str(c), 'i1 1') + c = ir.Constant(int1, 0) + self.assertEqual(str(c), 'i1 0') + c = ir.Constant(int1, True) + self.assertEqual(str(c), 'i1 true') + c = ir.Constant(int1, False) + self.assertEqual(str(c), 'i1 false') + c = ir.Constant(int1, ir.Undefined) + self.assertEqual(str(c), 'i1 undef') + c = ir.Constant(int1, None) + self.assertEqual(str(c), 'i1 0') + + def test_reals(self): + # XXX Test NaNs and infs + c = ir.Constant(flt, 1.5) + self.assertEqual(str(c), 'float 0x3ff8000000000000') + c = ir.Constant(flt, -1.5) + self.assertEqual(str(c), 'float 0xbff8000000000000') + c = ir.Constant(dbl, 1.5) + self.assertEqual(str(c), 'double 0x3ff8000000000000') + c = ir.Constant(dbl, -1.5) + self.assertEqual(str(c), 'double 0xbff8000000000000') + c = ir.Constant(dbl, ir.Undefined) + self.assertEqual(str(c), 'double undef') + c = ir.Constant(dbl, None) + self.assertEqual(str(c), 'double 0.0') + + def test_arrays(self): + c = ir.Constant(ir.ArrayType(int32, 3), (c32(5), c32(6), c32(4))) + self.assertEqual(str(c), '[3 x i32] [i32 5, i32 6, i32 4]') + c = ir.Constant(ir.ArrayType(int32, 2), (c32(5), c32(ir.Undefined))) + self.assertEqual(str(c), '[2 x i32] [i32 5, i32 undef]') + + c = ir.Constant.literal_array((c32(5), c32(6), c32(ir.Undefined))) + self.assertEqual(str(c), '[3 x i32] [i32 5, i32 6, i32 undef]') + with self.assertRaises(TypeError) as raises: + ir.Constant.literal_array((c32(5), ir.Constant(flt, 1.5))) + self.assertEqual(str(raises.exception), + "all elements must have the same type") + + c = ir.Constant(ir.ArrayType(int32, 2), ir.Undefined) + self.assertEqual(str(c), '[2 x i32] undef') + c = ir.Constant(ir.ArrayType(int32, 2), None) + self.assertEqual(str(c), '[2 x i32] zeroinitializer') + # Raw array syntax + c = ir.Constant(ir.ArrayType(int8, 11), bytearray(b"foobar_123\x80")) + self.assertEqual(str(c), r'[11 x i8] c"foobar_123\80"') + c = ir.Constant(ir.ArrayType(int8, 4), bytearray(b"\x00\x01\x04\xff")) + self.assertEqual(str(c), r'[4 x i8] c"\00\01\04\ff"') + # Recursive instantiation of inner constants + c = ir.Constant(ir.ArrayType(int32, 3), (5, ir.Undefined, 6)) + self.assertEqual(str(c), '[3 x i32] [i32 5, i32 undef, i32 6]') + # Invalid number of args + with self.assertRaises(ValueError): + ir.Constant(ir.ArrayType(int32, 3), (5, 6)) + + def test_vector(self): + vecty = ir.VectorType(ir.IntType(32), 8) + vals = [1, 2, 4, 3, 8, 6, 9, 7] + vec = ir.Constant(vecty, vals) + vec_repr = "<8 x i32> <{}>".format( + ', '.join(map('i32 {}'.format, vals))) + self.assertEqual(str(vec), vec_repr) + + def test_non_nullable_int(self): + constant = ir.Constant(ir.IntType(32), None).constant + self.assertEqual(constant, 0) + + def test_structs(self): + st1 = ir.LiteralStructType((flt, int1)) + st2 = ir.LiteralStructType((int32, st1)) + c = ir.Constant(st1, (ir.Constant(ir.FloatType(), 1.5), + ir.Constant(int1, True))) + self.assertEqual(str(c), + '{float, i1} {float 0x3ff8000000000000, i1 true}') + c = ir.Constant.literal_struct((ir.Constant(ir.FloatType(), 1.5), + ir.Constant(int1, True))) + self.assertEqual(c.type, st1) + self.assertEqual(str(c), + '{float, i1} {float 0x3ff8000000000000, i1 true}') + c = ir.Constant.literal_struct((ir.Constant(ir.FloatType(), 1.5), + ir.Constant(int1, ir.Undefined))) + self.assertEqual(c.type, st1) + self.assertEqual(str(c), + '{float, i1} {float 0x3ff8000000000000, i1 undef}') + c = ir.Constant(st1, ir.Undefined) + self.assertEqual(str(c), '{float, i1} undef') + c = ir.Constant(st1, None) + self.assertEqual(str(c), '{float, i1} zeroinitializer') + # Recursive instantiation of inner constants + c1 = ir.Constant(st1, (1.5, True)) + self.assertEqual(str(c1), + '{float, i1} {float 0x3ff8000000000000, i1 true}') + c2 = ir.Constant(st2, (42, c1)) + self.assertEqual(str(c2), ('{i32, {float, i1}} {i32 42, {float, i1} ' + '{float 0x3ff8000000000000, i1 true}}')) + c3 = ir.Constant(st2, (42, (1.5, True))) + self.assertEqual(str(c3), str(c2)) + # Invalid number of args + with self.assertRaises(ValueError): + ir.Constant(st2, (4, 5, 6)) + + def test_undefined_literal_struct_pickling(self): + i8 = ir.IntType(8) + st = ir.Constant(ir.LiteralStructType([i8, i8]), ir.Undefined) + self.assert_pickle_correctly(st) + + def test_type_instantiaton(self): + """ + Instantiating a type should create a constant. + """ + c = int8(42) + self.assertIsInstance(c, ir.Constant) + self.assertEqual(str(c), 'i8 42') + c = int1(True) + self.assertIsInstance(c, ir.Constant) + self.assertEqual(str(c), 'i1 true') + # Arrays + at = ir.ArrayType(int32, 3) + c = at([c32(4), c32(5), c32(6)]) + self.assertEqual(str(c), '[3 x i32] [i32 4, i32 5, i32 6]') + c = at([4, 5, 6]) + self.assertEqual(str(c), '[3 x i32] [i32 4, i32 5, i32 6]') + c = at(None) + self.assertEqual(str(c), '[3 x i32] zeroinitializer') + with self.assertRaises(ValueError): + at([4, 5, 6, 7]) + # Structs + st1 = ir.LiteralStructType((flt, int1)) + st2 = ir.LiteralStructType((int32, st1)) + c = st1((1.5, True)) + self.assertEqual(str(c), ('{float, i1} {float 0x3ff8000000000000, i1 ' + 'true}')) + c = st2((42, (1.5, True))) + self.assertEqual(str(c), ('{i32, {float, i1}} {i32 42, {float, i1} ' + '{float 0x3ff8000000000000, i1 true}}')) + + def test_repr(self): + """ + Constants should have a useful repr(). + """ + c = int32(42) + self.assertEqual(repr(c), "") + + def test_encoding_problem(self): + c = ir.Constant(ir.ArrayType(ir.IntType(8), 256), + bytearray(range(256))) + m = self.module() + gv = ir.GlobalVariable(m, c.type, "myconstant") + gv.global_constant = True + gv.initializer = c + # With utf-8, the following will cause: + # UnicodeDecodeError: 'utf-8' codec can't decode byte 0xe0 in position + # 136: invalid continuation byte + parsed = llvm.parse_assembly(str(m)) + # Make sure the encoding does not modify the IR + reparsed = llvm.parse_assembly(str(parsed)) + self.assertEqual(str(parsed), str(reparsed)) + + def test_gep(self): + m = self.module() + tp = ir.LiteralStructType((flt, int1)) + gv = ir.GlobalVariable(m, tp, "myconstant") + c = gv.gep([ir.Constant(int32, x) for x in (0, 1)]) + if not ir_layer_typed_pointers_enabled: + self.assertEqual(str(c), + 'getelementptr ({float, i1}, ptr @"myconstant", i32 0, i32 1)') # noqa E501 + else: + self.assertEqual(str(c), + 'getelementptr ({float, i1}, {float, i1}* @"myconstant", i32 0, i32 1)') # noqa E501 + self.assertEqual(c.type, ir.PointerType(int1)) + + const = ir.Constant(tp, None) + with self.assertRaises(TypeError): + const.gep([ir.Constant(int32, 0)]) + + const_ptr = ir.Constant(tp.as_pointer(), None) + c2 = const_ptr.gep([ir.Constant(int32, 0)]) + if not ir_layer_typed_pointers_enabled: + self.assertEqual(str(c2), + 'getelementptr ({float, i1}, ptr null, i32 0)') # noqa E501 + else: + self.assertEqual(str(c2), + 'getelementptr ({float, i1}, {float, i1}* null, i32 0)') # noqa E501 + self.assertEqual(c.type, ir.PointerType(int1)) + + def test_gep_addrspace_globalvar(self): + m = self.module() + tp = ir.LiteralStructType((flt, int1)) + addrspace = 4 + + gv = ir.GlobalVariable(m, tp, "myconstant", addrspace=addrspace) + self.assertEqual(gv.addrspace, addrspace) + c = gv.gep([ir.Constant(int32, x) for x in (0, 1)]) + self.assertEqual(c.type.addrspace, addrspace) + if not ir_layer_typed_pointers_enabled: + self.assertEqual(str(c), + ('getelementptr ({float, i1}, ptr ' + 'addrspace(4) @"myconstant", i32 0, i32 1)')) + else: + self.assertEqual(str(c), + ('getelementptr ({float, i1}, {float, i1} ' + 'addrspace(4)* @"myconstant", i32 0, i32 1)')) + self.assertEqual(c.type, ir.PointerType(int1, addrspace=addrspace)) + + def test_trunc(self): + c = ir.Constant(int64, 1).trunc(int32) + self.assertEqual(str(c), 'trunc (i64 1 to i32)') + + def test_zext(self): + c = ir.Constant(int32, 1).zext(int64) + self.assertEqual(str(c), 'zext (i32 1 to i64)') + + def test_sext(self): + c = ir.Constant(int32, -1).sext(int64) + self.assertEqual(str(c), 'sext (i32 -1 to i64)') + + def test_fptrunc(self): + c = ir.Constant(flt, 1).fptrunc(hlf) + self.assertEqual(str(c), 'fptrunc (float 0x3ff0000000000000 to half)') + + def test_fpext(self): + c = ir.Constant(flt, 1).fpext(dbl) + self.assertEqual(str(c), 'fpext (float 0x3ff0000000000000 to double)') + + def test_bitcast(self): + m = self.module() + gv = ir.GlobalVariable(m, int32, "myconstant") + c = gv.bitcast(int64.as_pointer()) + if not ir_layer_typed_pointers_enabled: + self.assertEqual(str(c), 'bitcast (ptr @"myconstant" to ptr)') + else: + self.assertEqual(str(c), 'bitcast (i32* @"myconstant" to i64*)') + + def test_fptoui(self): + c = ir.Constant(flt, 1).fptoui(int32) + self.assertEqual(str(c), 'fptoui (float 0x3ff0000000000000 to i32)') + + def test_uitofp(self): + c = ir.Constant(int32, 1).uitofp(flt) + self.assertEqual(str(c), 'uitofp (i32 1 to float)') + + def test_fptosi(self): + c = ir.Constant(flt, 1).fptosi(int32) + self.assertEqual(str(c), 'fptosi (float 0x3ff0000000000000 to i32)') + + def test_sitofp(self): + c = ir.Constant(int32, 1).sitofp(flt) + self.assertEqual(str(c), 'sitofp (i32 1 to float)') + + def test_ptrtoint_1(self): + ptr = ir.Constant(int64.as_pointer(), None) + one = ir.Constant(int32, 1) + c = ptr.ptrtoint(int32) + + self.assertRaises(TypeError, one.ptrtoint, int64) + self.assertRaises(TypeError, ptr.ptrtoint, flt) + if not ir_layer_typed_pointers_enabled: + self.assertEqual(str(c), 'ptrtoint (ptr null to i32)') + else: + self.assertEqual(str(c), 'ptrtoint (i64* null to i32)') + + def test_ptrtoint_2(self): + m = self.module() + gv = ir.GlobalVariable(m, int32, "myconstant") + c = gv.ptrtoint(int64) + if not ir_layer_typed_pointers_enabled: + self.assertEqual(str(c), 'ptrtoint (ptr @"myconstant" to i64)') + + self.assertRaisesRegex( + TypeError, + r"can only ptrtoint\(\) to integer type, not 'ptr'", + gv.ptrtoint, + int64.as_pointer()) + else: + self.assertEqual(str(c), 'ptrtoint (i32* @"myconstant" to i64)') + + self.assertRaisesRegex( + TypeError, + r"can only ptrtoint\(\) to integer type, not 'i64\*'", + gv.ptrtoint, + int64.as_pointer()) + + c2 = ir.Constant(int32, 0) + self.assertRaisesRegex( + TypeError, + r"can only call ptrtoint\(\) on pointer type, not 'i32'", + c2.ptrtoint, + int64) + + def test_inttoptr(self): + one = ir.Constant(int32, 1) + pi = ir.Constant(flt, 3.14) + c = one.inttoptr(int64.as_pointer()) + + self.assertRaises(TypeError, one.inttoptr, int64) + self.assertRaises(TypeError, pi.inttoptr, int64.as_pointer()) + if not ir_layer_typed_pointers_enabled: + self.assertEqual(str(c), 'inttoptr (i32 1 to ptr)') + else: + self.assertEqual(str(c), 'inttoptr (i32 1 to i64*)') + + def test_neg(self): + one = ir.Constant(int32, 1) + self.assertEqual(str(one.neg()), 'sub (i32 0, i32 1)') + + def test_not(self): + one = ir.Constant(int32, 1) + self.assertEqual(str(one.not_()), 'xor (i32 1, i32 -1)') + + def test_fneg(self): + one = ir.Constant(flt, 1) + self.assertEqual(str(one.fneg()), 'fneg (float 0x3ff0000000000000)') + + def test_int_binops(self): + one = ir.Constant(int32, 1) + two = ir.Constant(int32, 2) + + oracle = {one.shl: 'shl', one.lshr: 'lshr', one.ashr: 'ashr', + one.add: 'add', one.sub: 'sub', one.mul: 'mul', + one.udiv: 'udiv', one.sdiv: 'sdiv', one.urem: 'urem', + one.srem: 'srem', one.or_: 'or', one.and_: 'and', + one.xor: 'xor'} + for fn, irop in oracle.items(): + self.assertEqual(str(fn(two)), irop + ' (i32 1, i32 2)') + + # unsigned integer compare + oracle = {'==': 'eq', '!=': 'ne', '>': + 'ugt', '>=': 'uge', '<': 'ult', '<=': 'ule'} + for cop, cond in oracle.items(): + actual = str(one.icmp_unsigned(cop, two)) + expected = 'icmp ' + cond + ' (i32 1, i32 2)' + self.assertEqual(actual, expected) + + # signed integer compare + oracle = {'==': 'eq', '!=': 'ne', + '>': 'sgt', '>=': 'sge', '<': 'slt', '<=': 'sle'} + for cop, cond in oracle.items(): + actual = str(one.icmp_signed(cop, two)) + expected = 'icmp ' + cond + ' (i32 1, i32 2)' + self.assertEqual(actual, expected) + + def test_flt_binops(self): + one = ir.Constant(flt, 1) + two = ir.Constant(flt, 2) + + oracle = {one.fadd: 'fadd', one.fsub: 'fsub', one.fmul: 'fmul', + one.fdiv: 'fdiv', one.frem: 'frem'} + for fn, irop in oracle.items(): + actual = str(fn(two)) + expected = irop + (' (float 0x3ff0000000000000,' + ' float 0x4000000000000000)') + self.assertEqual(actual, expected) + + # ordered float compare + oracle = {'==': 'oeq', '!=': 'one', '>': 'ogt', '>=': 'oge', + '<': 'olt', '<=': 'ole'} + for cop, cond in oracle.items(): + actual = str(one.fcmp_ordered(cop, two)) + expected = 'fcmp ' + cond + (' (float 0x3ff0000000000000,' + ' float 0x4000000000000000)') + self.assertEqual(actual, expected) + + # unordered float compare + oracle = {'==': 'ueq', '!=': 'une', '>': 'ugt', '>=': 'uge', + '<': 'ult', '<=': 'ule'} + for cop, cond in oracle.items(): + actual = str(one.fcmp_unordered(cop, two)) + expected = 'fcmp ' + cond + (' (float 0x3ff0000000000000,' + ' float 0x4000000000000000)') + self.assertEqual(actual, expected) + + +class TestTransforms(TestBase): + def test_call_transform(self): + mod = ir.Module() + foo = ir.Function(mod, ir.FunctionType(ir.VoidType(), ()), "foo") + bar = ir.Function(mod, ir.FunctionType(ir.VoidType(), ()), "bar") + builder = ir.IRBuilder() + builder.position_at_end(foo.append_basic_block()) + call = builder.call(foo, ()) + self.assertEqual(call.callee, foo) + modified = ir.replace_all_calls(mod, foo, bar) + self.assertIn(call, modified) + self.assertNotEqual(call.callee, foo) + self.assertEqual(call.callee, bar) + + +class TestSingleton(TestBase): + def test_undefined(self): + self.assertIs(ir.Undefined, ir.values._Undefined()) + self.assertIs(ir.Undefined, copy.copy(ir.Undefined)) + self.assertIs(ir.Undefined, copy.deepcopy(ir.Undefined)) + self.assert_pickle_correctly(ir.Undefined) + + +if __name__ == '__main__': + unittest.main() diff --git a/shared/package-files/03/036e7f6b5cab4cbe799d002c1293962ac9e2db5e7eef96b8ac0a0527b83b42bd/tty.pyi b/shared/package-files/03/036e7f6b5cab4cbe799d002c1293962ac9e2db5e7eef96b8ac0a0527b83b42bd/tty.pyi new file mode 100644 index 0000000000000000000000000000000000000000..c0dc418e9933c26f1cb749f61984fdc91c316906 --- /dev/null +++ b/shared/package-files/03/036e7f6b5cab4cbe799d002c1293962ac9e2db5e7eef96b8ac0a0527b83b42bd/tty.pyi @@ -0,0 +1,15 @@ +from typing import IO, Union + +_FD = Union[int, IO[str]] + +# XXX: Undocumented integer constants +IFLAG: int +OFLAG: int +CFLAG: int +LFLAG: int +ISPEED: int +OSPEED: int +CC: int + +def setraw(fd: _FD, when: int = ...) -> None: ... +def setcbreak(fd: _FD, when: int = ...) -> None: ... diff --git a/shared/package-files/03/036f750b7d3fb76088d5eacf0aa3755b40e8023df3a8d03c7d384f223e1192ed/test_stoer_wagner.py b/shared/package-files/03/036f750b7d3fb76088d5eacf0aa3755b40e8023df3a8d03c7d384f223e1192ed/test_stoer_wagner.py new file mode 100644 index 0000000000000000000000000000000000000000..2b9e2bab41eb29067166b6faa331e022d4074ce3 --- /dev/null +++ b/shared/package-files/03/036f750b7d3fb76088d5eacf0aa3755b40e8023df3a8d03c7d384f223e1192ed/test_stoer_wagner.py @@ -0,0 +1,102 @@ +from itertools import chain + +import pytest + +import networkx as nx + + +def _check_partition(G, cut_value, partition, weight): + assert isinstance(partition, tuple) + assert len(partition) == 2 + assert isinstance(partition[0], list) + assert isinstance(partition[1], list) + assert len(partition[0]) > 0 + assert len(partition[1]) > 0 + assert sum(map(len, partition)) == len(G) + assert set(chain.from_iterable(partition)) == set(G) + partition = tuple(map(set, partition)) + w = 0 + for u, v, e in G.edges(data=True): + if (u in partition[0]) == (v in partition[1]): + w += e.get(weight, 1) + assert w == cut_value + + +def _test_stoer_wagner(G, answer, weight="weight"): + cut_value, partition = nx.stoer_wagner(G, weight, heap=nx.utils.PairingHeap) + assert cut_value == answer + _check_partition(G, cut_value, partition, weight) + cut_value, partition = nx.stoer_wagner(G, weight, heap=nx.utils.BinaryHeap) + assert cut_value == answer + _check_partition(G, cut_value, partition, weight) + + +def test_graph1(): + G = nx.Graph() + G.add_edge("x", "a", weight=3) + G.add_edge("x", "b", weight=1) + G.add_edge("a", "c", weight=3) + G.add_edge("b", "c", weight=5) + G.add_edge("b", "d", weight=4) + G.add_edge("d", "e", weight=2) + G.add_edge("c", "y", weight=2) + G.add_edge("e", "y", weight=3) + _test_stoer_wagner(G, 4) + + +def test_graph2(): + G = nx.Graph() + G.add_edge("x", "a") + G.add_edge("x", "b") + G.add_edge("a", "c") + G.add_edge("b", "c") + G.add_edge("b", "d") + G.add_edge("d", "e") + G.add_edge("c", "y") + G.add_edge("e", "y") + _test_stoer_wagner(G, 2) + + +def test_graph3(): + # Source: + # Stoer, M. and Wagner, F. (1997). "A simple min-cut algorithm". Journal of + # the ACM 44 (4), 585-591. + G = nx.Graph() + G.add_edge(1, 2, weight=2) + G.add_edge(1, 5, weight=3) + G.add_edge(2, 3, weight=3) + G.add_edge(2, 5, weight=2) + G.add_edge(2, 6, weight=2) + G.add_edge(3, 4, weight=4) + G.add_edge(3, 7, weight=2) + G.add_edge(4, 7, weight=2) + G.add_edge(4, 8, weight=2) + G.add_edge(5, 6, weight=3) + G.add_edge(6, 7, weight=1) + G.add_edge(7, 8, weight=3) + _test_stoer_wagner(G, 4) + + +def test_weight_name(): + G = nx.Graph() + G.add_edge(1, 2, weight=1, cost=8) + G.add_edge(1, 3, cost=2) + G.add_edge(2, 3, cost=4) + _test_stoer_wagner(G, 6, weight="cost") + + +def test_exceptions(): + G = nx.Graph() + pytest.raises(nx.NetworkXError, nx.stoer_wagner, G) + G.add_node(1) + pytest.raises(nx.NetworkXError, nx.stoer_wagner, G) + G.add_node(2) + pytest.raises(nx.NetworkXError, nx.stoer_wagner, G) + G.add_edge(1, 2, weight=-2) + pytest.raises(nx.NetworkXError, nx.stoer_wagner, G) + G = nx.DiGraph() + pytest.raises(nx.NetworkXNotImplemented, nx.stoer_wagner, G) + G = nx.MultiGraph() + pytest.raises(nx.NetworkXNotImplemented, nx.stoer_wagner, G) + G = nx.MultiDiGraph() + pytest.raises(nx.NetworkXNotImplemented, nx.stoer_wagner, G) diff --git a/shared/package-files/03/03702ffd45cc191e7523c4e60504c92105322db2d4c14868d201ca0c4408229d/DcxImagePlugin.py b/shared/package-files/03/03702ffd45cc191e7523c4e60504c92105322db2d4c14868d201ca0c4408229d/DcxImagePlugin.py new file mode 100644 index 0000000000000000000000000000000000000000..f7344df44a7cfac3b37c909e3811cd2d9b158bc2 --- /dev/null +++ b/shared/package-files/03/03702ffd45cc191e7523c4e60504c92105322db2d4c14868d201ca0c4408229d/DcxImagePlugin.py @@ -0,0 +1,80 @@ +# +# The Python Imaging Library. +# $Id$ +# +# DCX file handling +# +# DCX is a container file format defined by Intel, commonly used +# for fax applications. Each DCX file consists of a directory +# (a list of file offsets) followed by a set of (usually 1-bit) +# PCX files. +# +# History: +# 1995-09-09 fl Created +# 1996-03-20 fl Properly derived from PcxImageFile. +# 1998-07-15 fl Renamed offset attribute to avoid name clash +# 2002-07-30 fl Fixed file handling +# +# Copyright (c) 1997-98 by Secret Labs AB. +# Copyright (c) 1995-96 by Fredrik Lundh. +# +# See the README file for information on usage and redistribution. +# +from __future__ import annotations + +from . import Image +from ._binary import i32le as i32 +from .PcxImagePlugin import PcxImageFile + +MAGIC = 0x3ADE68B1 # QUIZ: what's this value, then? + + +def _accept(prefix): + return len(prefix) >= 4 and i32(prefix) == MAGIC + + +## +# Image plugin for the Intel DCX format. + + +class DcxImageFile(PcxImageFile): + format = "DCX" + format_description = "Intel DCX" + _close_exclusive_fp_after_loading = False + + def _open(self): + # Header + s = self.fp.read(4) + if not _accept(s): + msg = "not a DCX file" + raise SyntaxError(msg) + + # Component directory + self._offset = [] + for i in range(1024): + offset = i32(self.fp.read(4)) + if not offset: + break + self._offset.append(offset) + + self._fp = self.fp + self.frame = None + self.n_frames = len(self._offset) + self.is_animated = self.n_frames > 1 + self.seek(0) + + def seek(self, frame): + if not self._seek_check(frame): + return + self.frame = frame + self.fp = self._fp + self.fp.seek(self._offset[frame]) + PcxImageFile._open(self) + + def tell(self): + return self.frame + + +Image.register_open(DcxImageFile.format, DcxImageFile, _accept) + +Image.register_extension(DcxImageFile.format, ".dcx") diff --git a/shared/package-files/03/037226f15369a8cde459111b8320b657536e864b6cd5d1197c24eb39dec4ec78/qu2cu.py b/shared/package-files/03/037226f15369a8cde459111b8320b657536e864b6cd5d1197c24eb39dec4ec78/qu2cu.py new file mode 100644 index 0000000000000000000000000000000000000000..b3ca651c3839d95a0b40c07b871f8005fa388ae0 --- /dev/null +++ b/shared/package-files/03/037226f15369a8cde459111b8320b657536e864b6cd5d1197c24eb39dec4ec78/qu2cu.py @@ -0,0 +1,433 @@ +# cython: language_level=3 +# distutils: define_macros=CYTHON_TRACE_NOGIL=1 + +# Copyright 2023 Google Inc. All Rights Reserved. +# Copyright 2023 Behdad Esfahbod. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +try: + import cython +except (AttributeError, ImportError): + # if cython not installed, use mock module with no-op decorators and types + from fontTools.misc import cython +COMPILED = cython.compiled + +from fontTools.misc.bezierTools import splitCubicAtTC +from collections import namedtuple +import math +from typing import ( + List, + Sequence, + Tuple, + Union, +) + +__all__ = ["quadratic_to_curves"] + + +# Copied from cu2qu +@cython.cfunc +@cython.returns(cython.int) +@cython.locals( + tolerance=cython.double, + p0=cython.complex, + p1=cython.complex, + p2=cython.complex, + p3=cython.complex, +) +@cython.locals(mid=cython.complex, deriv3=cython.complex) +def cubic_farthest_fit_inside(p0, p1, p2, p3, tolerance): + """Check if a cubic Bezier lies within a given distance of the origin. + + "Origin" means *the* origin (0,0), not the start of the curve. Note that no + checks are made on the start and end positions of the curve; this function + only checks the inside of the curve. + + Args: + p0 (complex): Start point of curve. + p1 (complex): First handle of curve. + p2 (complex): Second handle of curve. + p3 (complex): End point of curve. + tolerance (double): Distance from origin. + + Returns: + bool: True if the cubic Bezier ``p`` entirely lies within a distance + ``tolerance`` of the origin, False otherwise. + """ + # First check p2 then p1, as p2 has higher error early on. + if abs(p2) <= tolerance and abs(p1) <= tolerance: + return True + + # Split. + mid = (p0 + 3 * (p1 + p2) + p3) * 0.125 + if abs(mid) > tolerance: + return False + deriv3 = (p3 + p2 - p1 - p0) * 0.125 + return cubic_farthest_fit_inside( + p0, (p0 + p1) * 0.5, mid - deriv3, mid, tolerance + ) and cubic_farthest_fit_inside(mid, mid + deriv3, (p2 + p3) * 0.5, p3, tolerance) + + +@cython.locals( + p0=cython.complex, + p1=cython.complex, + p2=cython.complex, + p1_2_3=cython.complex, +) +def elevate_quadratic(p0, p1, p2): + """Given a quadratic bezier curve, return its degree-elevated cubic.""" + + # https://pomax.github.io/bezierinfo/#reordering + p1_2_3 = p1 * (2 / 3) + return ( + p0, + (p0 * (1 / 3) + p1_2_3), + (p2 * (1 / 3) + p1_2_3), + p2, + ) + + +@cython.cfunc +@cython.locals( + start=cython.int, + n=cython.int, + k=cython.int, + prod_ratio=cython.double, + sum_ratio=cython.double, + ratio=cython.double, + t=cython.double, + p0=cython.complex, + p1=cython.complex, + p2=cython.complex, + p3=cython.complex, +) +def merge_curves(curves, start, n): + """Give a cubic-Bezier spline, reconstruct one cubic-Bezier + that has the same endpoints and tangents and approxmates + the spline.""" + + # Reconstruct the t values of the cut segments + prod_ratio = 1.0 + sum_ratio = 1.0 + ts = [1] + for k in range(1, n): + ck = curves[start + k] + c_before = curves[start + k - 1] + + # |t_(k+1) - t_k| / |t_k - t_(k - 1)| = ratio + assert ck[0] == c_before[3] + ratio = abs(ck[1] - ck[0]) / abs(c_before[3] - c_before[2]) + + prod_ratio *= ratio + sum_ratio += prod_ratio + ts.append(sum_ratio) + + # (t(n) - t(n - 1)) / (t_(1) - t(0)) = prod_ratio + + ts = [t / sum_ratio for t in ts[:-1]] + + p0 = curves[start][0] + p1 = curves[start][1] + p2 = curves[start + n - 1][2] + p3 = curves[start + n - 1][3] + + # Build the curve by scaling the control-points. + p1 = p0 + (p1 - p0) / (ts[0] if ts else 1) + p2 = p3 + (p2 - p3) / ((1 - ts[-1]) if ts else 1) + + curve = (p0, p1, p2, p3) + + return curve, ts + + +@cython.locals( + count=cython.int, + num_offcurves=cython.int, + i=cython.int, + off1=cython.complex, + off2=cython.complex, + on=cython.complex, +) +def add_implicit_on_curves(p): + q = list(p) + count = 0 + num_offcurves = len(p) - 2 + for i in range(1, num_offcurves): + off1 = p[i] + off2 = p[i + 1] + on = off1 + (off2 - off1) * 0.5 + q.insert(i + 1 + count, on) + count += 1 + return q + + +Point = Union[Tuple[float, float], complex] + + +def _raise_incompatible_point(point, previous_point): + raise ValueError( + f"Quadratic splines must connect end-to-start; got {previous_point!r} then {point!r}" + ) + + +def _validate_spline_length(spline): + if len(spline) < 3: + raise ValueError("Quadratic splines must contain at least 3 points") + + +def _validate_positive_tolerance(max_err): + if max_err <= 0: + raise ValueError("max_err must be greater than zero") + + +@cython.locals( + cost=cython.int, + is_complex=cython.int, +) +def quadratic_to_curves( + quads: List[Sequence[Point]], + max_err: float = 0.5, + all_cubic: bool = False, +) -> List[Tuple[Point, ...]]: + """Converts a connecting list of quadratic splines to a list of quadratic + and cubic curves. + + A quadratic spline is specified as a sequence of points. Either each point is + a 2-tuple of X,Y coordinates, or each point is a complex number with + real/imaginary components representing X,Y coordinates. + + The first and last points are on-curve points and the rest are off-curve + points, with an implied on-curve point in the middle between every two + consequtive off-curve points. + + Returns: + The output is a list of tuples of points. Points are represented + in the same format as the input, either as 2-tuples or complex numbers. + If ``quads`` is empty, returns an empty list. + + Each tuple is either of length three, for a quadratic curve, or four, + for a cubic curve. Each curve's last point is the same as the next + curve's first point. + + Args: + quads: quadratic splines + + max_err: absolute error tolerance; defaults to 0.5 + + all_cubic: if True, only cubic curves are generated; defaults to False + + Raises: + ValueError: if an input spline has fewer than 3 points, or if adjacent + splines do not connect end-to-start. + """ + if not quads: + return [] + _validate_positive_tolerance(max_err) + for spline in quads: + _validate_spline_length(spline) + + is_complex = type(quads[0][0]) is complex + if not is_complex: + quads = [[complex(x, y) for (x, y) in p] for p in quads] + + q = [quads[0][0]] + costs = [1] + cost = 1 + for p in quads: + if q[-1] != p[0]: + _raise_incompatible_point(p[0], q[-1]) + for i in range(len(p) - 2): + cost += 1 + costs.append(cost) + costs.append(cost) + qq = add_implicit_on_curves(p)[1:] + costs.pop() + q.extend(qq) + cost += 1 + costs.append(cost) + + curves = spline_to_curves(q, costs, max_err, all_cubic) + + if not is_complex: + curves = [tuple((c.real, c.imag) for c in curve) for curve in curves] + return curves + + +Solution = namedtuple("Solution", ["num_points", "error", "start_index", "is_cubic"]) + + +@cython.locals( + i=cython.int, + j=cython.int, + k=cython.int, + start=cython.int, + i_sol_count=cython.int, + j_sol_count=cython.int, + this_sol_count=cython.int, + tolerance=cython.double, + err=cython.double, + error=cython.double, + i_sol_error=cython.double, + j_sol_error=cython.double, + all_cubic=cython.int, + is_cubic=cython.int, + count=cython.int, + p0=cython.complex, + p1=cython.complex, + p2=cython.complex, + p3=cython.complex, + v=cython.complex, + u=cython.complex, +) +def spline_to_curves(q, costs, tolerance=0.5, all_cubic=False): + """ + q: quadratic spline with alternating on-curve / off-curve points. + + costs: cumulative list of encoding cost of q in terms of number of + points that need to be encoded. Implied on-curve points do not + contribute to the cost. If all points need to be encoded, then + costs will be range(1, len(q)+1). + """ + + assert len(q) >= 3, "quadratic spline requires at least 3 points" + + # Elevate quadratic segments to cubic + elevated_quadratics = [ + elevate_quadratic(*q[i : i + 3]) for i in range(0, len(q) - 2, 2) + ] + + # Find sharp corners; they have to be oncurves for sure. + forced = set() + for i in range(1, len(elevated_quadratics)): + p0 = elevated_quadratics[i - 1][2] + p1 = elevated_quadratics[i][0] + p2 = elevated_quadratics[i][1] + if abs(p1 - p0) + abs(p2 - p1) > tolerance + abs(p2 - p0): + forced.add(i) + + # Dynamic-Programming to find the solution with fewest number of + # cubic curves, and within those the one with smallest error. + sols = [Solution(0, 0, 0, False)] + impossible = Solution(len(elevated_quadratics) * 3 + 1, 0, 1, False) + start = 0 + for i in range(1, len(elevated_quadratics) + 1): + best_sol = impossible + for j in range(start, i): + j_sol_count, j_sol_error = sols[j].num_points, sols[j].error + + if not all_cubic: + # Solution with quadratics between j:i + this_count = costs[2 * i - 1] - costs[2 * j] + 1 + i_sol_count = j_sol_count + this_count + i_sol_error = j_sol_error + i_sol = Solution(i_sol_count, i_sol_error, i - j, False) + if i_sol < best_sol: + best_sol = i_sol + + if this_count <= 3: + # Can't get any better than this in the path below + continue + + # Fit elevated_quadratics[j:i] into one cubic + try: + curve, ts = merge_curves(elevated_quadratics, j, i - j) + except ZeroDivisionError: + continue + + # Now reconstruct the segments from the fitted curve + reconstructed_iter = splitCubicAtTC(*curve, *ts) + reconstructed = [] + + # Knot errors + error = 0 + for k, reconst in enumerate(reconstructed_iter): + orig = elevated_quadratics[j + k] + err = abs(reconst[3] - orig[3]) + error = max(error, err) + if error > tolerance: + break + reconstructed.append(reconst) + if error > tolerance: + # Not feasible + continue + + # Interior errors + for k, reconst in enumerate(reconstructed): + orig = elevated_quadratics[j + k] + p0, p1, p2, p3 = tuple(v - u for v, u in zip(reconst, orig)) + + if not cubic_farthest_fit_inside(p0, p1, p2, p3, tolerance): + error = tolerance + 1 + break + if error > tolerance: + # Not feasible + continue + + # Save best solution + i_sol_count = j_sol_count + 3 + i_sol_error = max(j_sol_error, error) + i_sol = Solution(i_sol_count, i_sol_error, i - j, True) + if i_sol < best_sol: + best_sol = i_sol + + if i_sol_count == 3: + # Can't get any better than this + break + + sols.append(best_sol) + if i in forced: + start = i + + # Reconstruct solution + splits = [] + cubic = [] + i = len(sols) - 1 + while i: + count, is_cubic = sols[i].start_index, sols[i].is_cubic + splits.append(i) + cubic.append(is_cubic) + i -= count + curves = [] + j = 0 + for i, is_cubic in reversed(list(zip(splits, cubic))): + if is_cubic: + curves.append(merge_curves(elevated_quadratics, j, i - j)[0]) + else: + for k in range(j, i): + curves.append(q[k * 2 : k * 2 + 3]) + j = i + + return curves + + +def main(): + from fontTools.cu2qu.benchmark import generate_curve + from fontTools.cu2qu import curve_to_quadratic + + tolerance = 0.05 + reconstruct_tolerance = tolerance * 1 + curve = generate_curve() + quadratics = curve_to_quadratic(curve, tolerance) + print( + "cu2qu tolerance %g. qu2cu tolerance %g." % (tolerance, reconstruct_tolerance) + ) + print("One random cubic turned into %d quadratics." % len(quadratics)) + curves = quadratic_to_curves([quadratics], reconstruct_tolerance) + print("Those quadratics turned back into %d cubics. " % len(curves)) + print("Original curve:", curve) + print("Reconstructed curve(s):", curves) + + +if __name__ == "__main__": + main() diff --git a/shared/package-files/03/0373b9975b64166175984aa269bd3c781711ea501d8aa289dae3daa3751cbe59/coset_table.py b/shared/package-files/03/0373b9975b64166175984aa269bd3c781711ea501d8aa289dae3daa3751cbe59/coset_table.py new file mode 100644 index 0000000000000000000000000000000000000000..06cc427c87c6860892f34735112f2f833d3f7f6d --- /dev/null +++ b/shared/package-files/03/0373b9975b64166175984aa269bd3c781711ea501d8aa289dae3daa3751cbe59/coset_table.py @@ -0,0 +1,1255 @@ +from sympy.combinatorics.free_groups import free_group +from sympy.printing.defaults import DefaultPrinting + +from itertools import chain, product +from bisect import bisect_left + + +############################################################################### +# COSET TABLE # +############################################################################### + +class CosetTable(DefaultPrinting): + # coset_table: Mathematically a coset table + # represented using a list of lists + # alpha: Mathematically a coset (precisely, a live coset) + # represented by an integer between i with 1 <= i <= n + # alpha in c + # x: Mathematically an element of "A" (set of generators and + # their inverses), represented using "FpGroupElement" + # fp_grp: Finitely Presented Group with < X|R > as presentation. + # H: subgroup of fp_grp. + # NOTE: We start with H as being only a list of words in generators + # of "fp_grp". Since `.subgroup` method has not been implemented. + + r""" + + Properties + ========== + + [1] `0 \in \Omega` and `\tau(1) = \epsilon` + [2] `\alpha^x = \beta \Leftrightarrow \beta^{x^{-1}} = \alpha` + [3] If `\alpha^x = \beta`, then `H \tau(\alpha)x = H \tau(\beta)` + [4] `\forall \alpha \in \Omega, 1^{\tau(\alpha)} = \alpha` + + References + ========== + + .. [1] Holt, D., Eick, B., O'Brien, E. + "Handbook of Computational Group Theory" + + .. [2] John J. Cannon; Lucien A. Dimino; George Havas; Jane M. Watson + Mathematics of Computation, Vol. 27, No. 123. (Jul., 1973), pp. 463-490. + "Implementation and Analysis of the Todd-Coxeter Algorithm" + + """ + # default limit for the number of cosets allowed in a + # coset enumeration. + coset_table_max_limit = 4096000 + # limit for the current instance + coset_table_limit = None + # maximum size of deduction stack above or equal to + # which it is emptied + max_stack_size = 100 + + def __init__(self, fp_grp, subgroup, max_cosets=None): + if not max_cosets: + max_cosets = CosetTable.coset_table_max_limit + self.fp_group = fp_grp + self.subgroup = subgroup + self.coset_table_limit = max_cosets + # "p" is setup independent of Omega and n + self.p = [0] + # a list of the form `[gen_1, gen_1^{-1}, ... , gen_k, gen_k^{-1}]` + self.A = list(chain.from_iterable((gen, gen**-1) \ + for gen in self.fp_group.generators)) + #P[alpha, x] Only defined when alpha^x is defined. + self.P = [[None]*len(self.A)] + # the mathematical coset table which is a list of lists + self.table = [[None]*len(self.A)] + self.A_dict = {x: self.A.index(x) for x in self.A} + self.A_dict_inv = {} + for x, index in self.A_dict.items(): + if index % 2 == 0: + self.A_dict_inv[x] = self.A_dict[x] + 1 + else: + self.A_dict_inv[x] = self.A_dict[x] - 1 + # used in the coset-table based method of coset enumeration. Each of + # the element is called a "deduction" which is the form (alpha, x) whenever + # a value is assigned to alpha^x during a definition or "deduction process" + self.deduction_stack = [] + # Attributes for modified methods. + H = self.subgroup + self._grp = free_group(', ' .join(["a_%d" % i for i in range(len(H))]))[0] + self.P = [[None]*len(self.A)] + self.p_p = {} + + @property + def omega(self): + """Set of live cosets. """ + return [coset for coset in range(len(self.p)) if self.p[coset] == coset] + + def copy(self): + """ + Return a shallow copy of Coset Table instance ``self``. + + """ + self_copy = self.__class__(self.fp_group, self.subgroup) + self_copy.table = [list(perm_rep) for perm_rep in self.table] + self_copy.p = list(self.p) + self_copy.deduction_stack = list(self.deduction_stack) + return self_copy + + def __str__(self): + return "Coset Table on %s with %s as subgroup generators" \ + % (self.fp_group, self.subgroup) + + __repr__ = __str__ + + @property + def n(self): + """The number `n` represents the length of the sublist containing the + live cosets. + + """ + if not self.table: + return 0 + return max(self.omega) + 1 + + # Pg. 152 [1] + def is_complete(self): + r""" + The coset table is called complete if it has no undefined entries + on the live cosets; that is, `\alpha^x` is defined for all + `\alpha \in \Omega` and `x \in A`. + + """ + return not any(None in self.table[coset] for coset in self.omega) + + # Pg. 153 [1] + def define(self, alpha, x, modified=False): + r""" + This routine is used in the relator-based strategy of Todd-Coxeter + algorithm if some `\alpha^x` is undefined. We check whether there is + space available for defining a new coset. If there is enough space + then we remedy this by adjoining a new coset `\beta` to `\Omega` + (i.e to set of live cosets) and put that equal to `\alpha^x`, then + make an assignment satisfying Property[1]. If there is not enough space + then we halt the Coset Table creation. The maximum amount of space that + can be used by Coset Table can be manipulated using the class variable + ``CosetTable.coset_table_max_limit``. + + See Also + ======== + + define_c + + """ + A = self.A + table = self.table + len_table = len(table) + if len_table >= self.coset_table_limit: + # abort the further generation of cosets + raise ValueError("the coset enumeration has defined more than " + "%s cosets. Try with a greater value max number of cosets " + % self.coset_table_limit) + table.append([None]*len(A)) + self.P.append([None]*len(self.A)) + # beta is the new coset generated + beta = len_table + self.p.append(beta) + table[alpha][self.A_dict[x]] = beta + table[beta][self.A_dict_inv[x]] = alpha + # P[alpha][x] = epsilon, P[beta][x**-1] = epsilon + if modified: + self.P[alpha][self.A_dict[x]] = self._grp.identity + self.P[beta][self.A_dict_inv[x]] = self._grp.identity + self.p_p[beta] = self._grp.identity + + def define_c(self, alpha, x): + r""" + A variation of ``define`` routine, described on Pg. 165 [1], used in + the coset table-based strategy of Todd-Coxeter algorithm. It differs + from ``define`` routine in that for each definition it also adds the + tuple `(\alpha, x)` to the deduction stack. + + See Also + ======== + + define + + """ + A = self.A + table = self.table + len_table = len(table) + if len_table >= self.coset_table_limit: + # abort the further generation of cosets + raise ValueError("the coset enumeration has defined more than " + "%s cosets. Try with a greater value max number of cosets " + % self.coset_table_limit) + table.append([None]*len(A)) + # beta is the new coset generated + beta = len_table + self.p.append(beta) + table[alpha][self.A_dict[x]] = beta + table[beta][self.A_dict_inv[x]] = alpha + # append to deduction stack + self.deduction_stack.append((alpha, x)) + + def scan_c(self, alpha, word): + """ + A variation of ``scan`` routine, described on pg. 165 of [1], which + puts at tuple, whenever a deduction occurs, to deduction stack. + + See Also + ======== + + scan, scan_check, scan_and_fill, scan_and_fill_c + + """ + # alpha is an integer representing a "coset" + # since scanning can be in two cases + # 1. for alpha=0 and w in Y (i.e generating set of H) + # 2. alpha in Omega (set of live cosets), w in R (relators) + A_dict = self.A_dict + A_dict_inv = self.A_dict_inv + table = self.table + f = alpha + i = 0 + r = len(word) + b = alpha + j = r - 1 + # list of union of generators and their inverses + while i <= j and table[f][A_dict[word[i]]] is not None: + f = table[f][A_dict[word[i]]] + i += 1 + if i > j: + if f != b: + self.coincidence_c(f, b) + return + while j >= i and table[b][A_dict_inv[word[j]]] is not None: + b = table[b][A_dict_inv[word[j]]] + j -= 1 + if j < i: + # we have an incorrect completed scan with coincidence f ~ b + # run the "coincidence" routine + self.coincidence_c(f, b) + elif j == i: + # deduction process + table[f][A_dict[word[i]]] = b + table[b][A_dict_inv[word[i]]] = f + self.deduction_stack.append((f, word[i])) + # otherwise scan is incomplete and yields no information + + # alpha, beta coincide, i.e. alpha, beta represent the pair of cosets where + # coincidence occurs + def coincidence_c(self, alpha, beta): + """ + A variation of ``coincidence`` routine used in the coset-table based + method of coset enumeration. The only difference being on addition of + a new coset in coset table(i.e new coset introduction), then it is + appended to ``deduction_stack``. + + See Also + ======== + + coincidence + + """ + A_dict = self.A_dict + A_dict_inv = self.A_dict_inv + table = self.table + # behaves as a queue + q = [] + self.merge(alpha, beta, q) + while len(q) > 0: + gamma = q.pop(0) + for x in A_dict: + delta = table[gamma][A_dict[x]] + if delta is not None: + table[delta][A_dict_inv[x]] = None + # only line of difference from ``coincidence`` routine + self.deduction_stack.append((delta, x**-1)) + mu = self.rep(gamma) + nu = self.rep(delta) + if table[mu][A_dict[x]] is not None: + self.merge(nu, table[mu][A_dict[x]], q) + elif table[nu][A_dict_inv[x]] is not None: + self.merge(mu, table[nu][A_dict_inv[x]], q) + else: + table[mu][A_dict[x]] = nu + table[nu][A_dict_inv[x]] = mu + + def scan(self, alpha, word, y=None, fill=False, modified=False): + r""" + ``scan`` performs a scanning process on the input ``word``. + It first locates the largest prefix ``s`` of ``word`` for which + `\alpha^s` is defined (i.e is not ``None``), ``s`` may be empty. Let + ``word=sv``, let ``t`` be the longest suffix of ``v`` for which + `\alpha^{t^{-1}}` is defined, and let ``v=ut``. Then three + possibilities are there: + + 1. If ``t=v``, then we say that the scan completes, and if, in addition + `\alpha^s = \alpha^{t^{-1}}`, then we say that the scan completes + correctly. + + 2. It can also happen that scan does not complete, but `|u|=1`; that + is, the word ``u`` consists of a single generator `x \in A`. In that + case, if `\alpha^s = \beta` and `\alpha^{t^{-1}} = \gamma`, then we can + set `\beta^x = \gamma` and `\gamma^{x^{-1}} = \beta`. These assignments + are known as deductions and enable the scan to complete correctly. + + 3. See ``coicidence`` routine for explanation of third condition. + + Notes + ===== + + The code for the procedure of scanning `\alpha \in \Omega` + under `w \in A*` is defined on pg. 155 [1] + + See Also + ======== + + scan_c, scan_check, scan_and_fill, scan_and_fill_c + + Scan and Fill + ============= + + Performed when the default argument fill=True. + + Modified Scan + ============= + + Performed when the default argument modified=True + + """ + # alpha is an integer representing a "coset" + # since scanning can be in two cases + # 1. for alpha=0 and w in Y (i.e generating set of H) + # 2. alpha in Omega (set of live cosets), w in R (relators) + A_dict = self.A_dict + A_dict_inv = self.A_dict_inv + table = self.table + f = alpha + i = 0 + r = len(word) + b = alpha + j = r - 1 + b_p = y + if modified: + f_p = self._grp.identity + flag = 0 + while fill or flag == 0: + flag = 1 + while i <= j and table[f][A_dict[word[i]]] is not None: + if modified: + f_p = f_p*self.P[f][A_dict[word[i]]] + f = table[f][A_dict[word[i]]] + i += 1 + if i > j: + if f != b: + if modified: + self.modified_coincidence(f, b, f_p**-1*y) + else: + self.coincidence(f, b) + return + while j >= i and table[b][A_dict_inv[word[j]]] is not None: + if modified: + b_p = b_p*self.P[b][self.A_dict_inv[word[j]]] + b = table[b][A_dict_inv[word[j]]] + j -= 1 + if j < i: + # we have an incorrect completed scan with coincidence f ~ b + # run the "coincidence" routine + if modified: + self.modified_coincidence(f, b, f_p**-1*b_p) + else: + self.coincidence(f, b) + elif j == i: + # deduction process + table[f][A_dict[word[i]]] = b + table[b][A_dict_inv[word[i]]] = f + if modified: + self.P[f][self.A_dict[word[i]]] = f_p**-1*b_p + self.P[b][self.A_dict_inv[word[i]]] = b_p**-1*f_p + return + elif fill: + self.define(f, word[i], modified=modified) + # otherwise scan is incomplete and yields no information + + # used in the low-index subgroups algorithm + def scan_check(self, alpha, word): + r""" + Another version of ``scan`` routine, described on, it checks whether + `\alpha` scans correctly under `word`, it is a straightforward + modification of ``scan``. ``scan_check`` returns ``False`` (rather than + calling ``coincidence``) if the scan completes incorrectly; otherwise + it returns ``True``. + + See Also + ======== + + scan, scan_c, scan_and_fill, scan_and_fill_c + + """ + # alpha is an integer representing a "coset" + # since scanning can be in two cases + # 1. for alpha=0 and w in Y (i.e generating set of H) + # 2. alpha in Omega (set of live cosets), w in R (relators) + A_dict = self.A_dict + A_dict_inv = self.A_dict_inv + table = self.table + f = alpha + i = 0 + r = len(word) + b = alpha + j = r - 1 + while i <= j and table[f][A_dict[word[i]]] is not None: + f = table[f][A_dict[word[i]]] + i += 1 + if i > j: + return f == b + while j >= i and table[b][A_dict_inv[word[j]]] is not None: + b = table[b][A_dict_inv[word[j]]] + j -= 1 + if j < i: + # we have an incorrect completed scan with coincidence f ~ b + # return False, instead of calling coincidence routine + return False + elif j == i: + # deduction process + table[f][A_dict[word[i]]] = b + table[b][A_dict_inv[word[i]]] = f + return True + + def merge(self, k, lamda, q, w=None, modified=False): + """ + Merge two classes with representatives ``k`` and ``lamda``, described + on Pg. 157 [1] (for pseudocode), start by putting ``p[k] = lamda``. + It is more efficient to choose the new representative from the larger + of the two classes being merged, i.e larger among ``k`` and ``lamda``. + procedure ``merge`` performs the merging operation, adds the deleted + class representative to the queue ``q``. + + Parameters + ========== + + 'k', 'lamda' being the two class representatives to be merged. + + Notes + ===== + + Pg. 86-87 [1] contains a description of this method. + + See Also + ======== + + coincidence, rep + + """ + p = self.p + rep = self.rep + phi = rep(k, modified=modified) + psi = rep(lamda, modified=modified) + if phi != psi: + mu = min(phi, psi) + v = max(phi, psi) + p[v] = mu + if modified: + if v == phi: + self.p_p[phi] = self.p_p[k]**-1*w*self.p_p[lamda] + else: + self.p_p[psi] = self.p_p[lamda]**-1*w**-1*self.p_p[k] + q.append(v) + + def rep(self, k, modified=False): + r""" + Parameters + ========== + + `k \in [0 \ldots n-1]`, as for ``self`` only array ``p`` is used + + Returns + ======= + + Representative of the class containing ``k``. + + Returns the representative of `\sim` class containing ``k``, it also + makes some modification to array ``p`` of ``self`` to ease further + computations, described on Pg. 157 [1]. + + The information on classes under `\sim` is stored in array `p` of + ``self`` argument, which will always satisfy the property: + + `p[\alpha] \sim \alpha` and `p[\alpha]=\alpha \iff \alpha=rep(\alpha)` + `\forall \in [0 \ldots n-1]`. + + So, for `\alpha \in [0 \ldots n-1]`, we find `rep(self, \alpha)` by + continually replacing `\alpha` by `p[\alpha]` until it becomes + constant (i.e satisfies `p[\alpha] = \alpha`):w + + To increase the efficiency of later ``rep`` calculations, whenever we + find `rep(self, \alpha)=\beta`, we set + `p[\gamma] = \beta \forall \gamma \in p-chain` from `\alpha` to `\beta` + + Notes + ===== + + ``rep`` routine is also described on Pg. 85-87 [1] in Atkinson's + algorithm, this results from the fact that ``coincidence`` routine + introduces functionality similar to that introduced by the + ``minimal_block`` routine on Pg. 85-87 [1]. + + See Also + ======== + + coincidence, merge + + """ + p = self.p + lamda = k + rho = p[lamda] + if modified: + s = p[:] + while rho != lamda: + if modified: + s[rho] = lamda + lamda = rho + rho = p[lamda] + if modified: + rho = s[lamda] + while rho != k: + mu = rho + rho = s[mu] + p[rho] = lamda + self.p_p[rho] = self.p_p[rho]*self.p_p[mu] + else: + mu = k + rho = p[mu] + while rho != lamda: + p[mu] = lamda + mu = rho + rho = p[mu] + return lamda + + # alpha, beta coincide, i.e. alpha, beta represent the pair of cosets + # where coincidence occurs + def coincidence(self, alpha, beta, w=None, modified=False): + r""" + The third situation described in ``scan`` routine is handled by this + routine, described on Pg. 156-161 [1]. + + The unfortunate situation when the scan completes but not correctly, + then ``coincidence`` routine is run. i.e when for some `i` with + `1 \le i \le r+1`, we have `w=st` with `s = x_1 x_2 \dots x_{i-1}`, + `t = x_i x_{i+1} \dots x_r`, and `\beta = \alpha^s` and + `\gamma = \alpha^{t-1}` are defined but unequal. This means that + `\beta` and `\gamma` represent the same coset of `H` in `G`. Described + on Pg. 156 [1]. ``rep`` + + See Also + ======== + + scan + + """ + A_dict = self.A_dict + A_dict_inv = self.A_dict_inv + table = self.table + # behaves as a queue + q = [] + if modified: + self.modified_merge(alpha, beta, w, q) + else: + self.merge(alpha, beta, q) + while len(q) > 0: + gamma = q.pop(0) + for x in A_dict: + delta = table[gamma][A_dict[x]] + if delta is not None: + table[delta][A_dict_inv[x]] = None + mu = self.rep(gamma, modified=modified) + nu = self.rep(delta, modified=modified) + if table[mu][A_dict[x]] is not None: + if modified: + v = self.p_p[delta]**-1*self.P[gamma][self.A_dict[x]]**-1 + v = v*self.p_p[gamma]*self.P[mu][self.A_dict[x]] + self.modified_merge(nu, table[mu][self.A_dict[x]], v, q) + else: + self.merge(nu, table[mu][A_dict[x]], q) + elif table[nu][A_dict_inv[x]] is not None: + if modified: + v = self.p_p[gamma]**-1*self.P[gamma][self.A_dict[x]] + v = v*self.p_p[delta]*self.P[mu][self.A_dict_inv[x]] + self.modified_merge(mu, table[nu][self.A_dict_inv[x]], v, q) + else: + self.merge(mu, table[nu][A_dict_inv[x]], q) + else: + table[mu][A_dict[x]] = nu + table[nu][A_dict_inv[x]] = mu + if modified: + v = self.p_p[gamma]**-1*self.P[gamma][self.A_dict[x]]*self.p_p[delta] + self.P[mu][self.A_dict[x]] = v + self.P[nu][self.A_dict_inv[x]] = v**-1 + + # method used in the HLT strategy + def scan_and_fill(self, alpha, word): + """ + A modified version of ``scan`` routine used in the relator-based + method of coset enumeration, described on pg. 162-163 [1], which + follows the idea that whenever the procedure is called and the scan + is incomplete then it makes new definitions to enable the scan to + complete; i.e it fills in the gaps in the scan of the relator or + subgroup generator. + + """ + self.scan(alpha, word, fill=True) + + def scan_and_fill_c(self, alpha, word): + """ + A modified version of ``scan`` routine, described on Pg. 165 second + para. [1], with modification similar to that of ``scan_anf_fill`` the + only difference being it calls the coincidence procedure used in the + coset-table based method i.e. the routine ``coincidence_c`` is used. + + See Also + ======== + + scan, scan_and_fill + + """ + A_dict = self.A_dict + A_dict_inv = self.A_dict_inv + table = self.table + r = len(word) + f = alpha + i = 0 + b = alpha + j = r - 1 + # loop until it has filled the alpha row in the table. + while True: + # do the forward scanning + while i <= j and table[f][A_dict[word[i]]] is not None: + f = table[f][A_dict[word[i]]] + i += 1 + if i > j: + if f != b: + self.coincidence_c(f, b) + return + # forward scan was incomplete, scan backwards + while j >= i and table[b][A_dict_inv[word[j]]] is not None: + b = table[b][A_dict_inv[word[j]]] + j -= 1 + if j < i: + self.coincidence_c(f, b) + elif j == i: + table[f][A_dict[word[i]]] = b + table[b][A_dict_inv[word[i]]] = f + self.deduction_stack.append((f, word[i])) + else: + self.define_c(f, word[i]) + + # method used in the HLT strategy + def look_ahead(self): + """ + When combined with the HLT method this is known as HLT+Lookahead + method of coset enumeration, described on pg. 164 [1]. Whenever + ``define`` aborts due to lack of space available this procedure is + executed. This routine helps in recovering space resulting from + "coincidence" of cosets. + + """ + R = self.fp_group.relators + p = self.p + # complete scan all relators under all cosets(obviously live) + # without making new definitions + for beta in self.omega: + for w in R: + self.scan(beta, w) + if p[beta] < beta: + break + + # Pg. 166 + def process_deductions(self, R_c_x, R_c_x_inv): + """ + Processes the deductions that have been pushed onto ``deduction_stack``, + described on Pg. 166 [1] and is used in coset-table based enumeration. + + See Also + ======== + + deduction_stack + + """ + p = self.p + table = self.table + while len(self.deduction_stack) > 0: + if len(self.deduction_stack) >= CosetTable.max_stack_size: + self.look_ahead() + del self.deduction_stack[:] + continue + else: + alpha, x = self.deduction_stack.pop() + if p[alpha] == alpha: + for w in R_c_x: + self.scan_c(alpha, w) + if p[alpha] < alpha: + break + beta = table[alpha][self.A_dict[x]] + if beta is not None and p[beta] == beta: + for w in R_c_x_inv: + self.scan_c(beta, w) + if p[beta] < beta: + break + + def process_deductions_check(self, R_c_x, R_c_x_inv): + """ + A variation of ``process_deductions``, this calls ``scan_check`` + wherever ``process_deductions`` calls ``scan``, described on Pg. [1]. + + See Also + ======== + + process_deductions + + """ + table = self.table + while len(self.deduction_stack) > 0: + alpha, x = self.deduction_stack.pop() + for w in R_c_x: + if not self.scan_check(alpha, w): + return False + beta = table[alpha][self.A_dict[x]] + if beta is not None: + for w in R_c_x_inv: + if not self.scan_check(beta, w): + return False + return True + + def switch(self, beta, gamma): + r"""Switch the elements `\beta, \gamma \in \Omega` of ``self``, used + by the ``standardize`` procedure, described on Pg. 167 [1]. + + See Also + ======== + + standardize + + """ + A = self.A + A_dict = self.A_dict + table = self.table + for x in A: + z = table[gamma][A_dict[x]] + table[gamma][A_dict[x]] = table[beta][A_dict[x]] + table[beta][A_dict[x]] = z + for alpha in range(len(self.p)): + if self.p[alpha] == alpha: + if table[alpha][A_dict[x]] == beta: + table[alpha][A_dict[x]] = gamma + elif table[alpha][A_dict[x]] == gamma: + table[alpha][A_dict[x]] = beta + + def standardize(self): + r""" + A coset table is standardized if when running through the cosets and + within each coset through the generator images (ignoring generator + inverses), the cosets appear in order of the integers + `0, 1, \dots, n`. "Standardize" reorders the elements of `\Omega` + such that, if we scan the coset table first by elements of `\Omega` + and then by elements of A, then the cosets occur in ascending order. + ``standardize()`` is used at the end of an enumeration to permute the + cosets so that they occur in some sort of standard order. + + Notes + ===== + + procedure is described on pg. 167-168 [1], it also makes use of the + ``switch`` routine to replace by smaller integer value. + + Examples + ======== + + >>> from sympy.combinatorics import free_group + >>> from sympy.combinatorics.fp_groups import FpGroup, coset_enumeration_r + >>> F, x, y = free_group("x, y") + + # Example 5.3 from [1] + >>> f = FpGroup(F, [x**2*y**2, x**3*y**5]) + >>> C = coset_enumeration_r(f, []) + >>> C.compress() + >>> C.table + [[1, 3, 1, 3], [2, 0, 2, 0], [3, 1, 3, 1], [0, 2, 0, 2]] + >>> C.standardize() + >>> C.table + [[1, 2, 1, 2], [3, 0, 3, 0], [0, 3, 0, 3], [2, 1, 2, 1]] + + """ + A = self.A + A_dict = self.A_dict + gamma = 1 + for alpha, x in product(range(self.n), A): + beta = self.table[alpha][A_dict[x]] + if beta >= gamma: + if beta > gamma: + self.switch(gamma, beta) + gamma += 1 + if gamma == self.n: + return + + # Compression of a Coset Table + def compress(self): + """Removes the non-live cosets from the coset table, described on + pg. 167 [1]. + + """ + gamma = -1 + A = self.A + A_dict = self.A_dict + A_dict_inv = self.A_dict_inv + table = self.table + chi = tuple([i for i in range(len(self.p)) if self.p[i] != i]) + for alpha in self.omega: + gamma += 1 + if gamma != alpha: + # replace alpha by gamma in coset table + for x in A: + beta = table[alpha][A_dict[x]] + table[gamma][A_dict[x]] = beta + table[beta][A_dict_inv[x]] == gamma + # all the cosets in the table are live cosets + self.p = list(range(gamma + 1)) + # delete the useless columns + del table[len(self.p):] + # re-define values + for row in table: + for j in range(len(self.A)): + row[j] -= bisect_left(chi, row[j]) + + def conjugates(self, R): + R_c = list(chain.from_iterable((rel.cyclic_conjugates(), \ + (rel**-1).cyclic_conjugates()) for rel in R)) + R_set = set() + for conjugate in R_c: + R_set = R_set.union(conjugate) + R_c_list = [] + for x in self.A: + r = {word for word in R_set if word[0] == x} + R_c_list.append(r) + R_set.difference_update(r) + return R_c_list + + def coset_representative(self, coset): + ''' + Compute the coset representative of a given coset. + + Examples + ======== + + >>> from sympy.combinatorics import free_group + >>> from sympy.combinatorics.fp_groups import FpGroup, coset_enumeration_r + >>> F, x, y = free_group("x, y") + >>> f = FpGroup(F, [x**3, y**3, x**-1*y**-1*x*y]) + >>> C = coset_enumeration_r(f, [x]) + >>> C.compress() + >>> C.table + [[0, 0, 1, 2], [1, 1, 2, 0], [2, 2, 0, 1]] + >>> C.coset_representative(0) + + >>> C.coset_representative(1) + y + >>> C.coset_representative(2) + y**-1 + + ''' + for x in self.A: + gamma = self.table[coset][self.A_dict[x]] + if coset == 0: + return self.fp_group.identity + if gamma < coset: + return self.coset_representative(gamma)*x**-1 + + ############################## + # Modified Methods # + ############################## + + def modified_define(self, alpha, x): + r""" + Define a function p_p from from [1..n] to A* as + an additional component of the modified coset table. + + Parameters + ========== + + \alpha \in \Omega + x \in A* + + See Also + ======== + + define + + """ + self.define(alpha, x, modified=True) + + def modified_scan(self, alpha, w, y, fill=False): + r""" + Parameters + ========== + \alpha \in \Omega + w \in A* + y \in (YUY^-1) + fill -- `modified_scan_and_fill` when set to True. + + See Also + ======== + + scan + """ + self.scan(alpha, w, y=y, fill=fill, modified=True) + + def modified_scan_and_fill(self, alpha, w, y): + self.modified_scan(alpha, w, y, fill=True) + + def modified_merge(self, k, lamda, w, q): + r""" + Parameters + ========== + + 'k', 'lamda' -- the two class representatives to be merged. + q -- queue of length l of elements to be deleted from `\Omega` *. + w -- Word in (YUY^-1) + + See Also + ======== + + merge + """ + self.merge(k, lamda, q, w=w, modified=True) + + def modified_rep(self, k): + r""" + Parameters + ========== + + `k \in [0 \ldots n-1]` + + See Also + ======== + + rep + """ + self.rep(k, modified=True) + + def modified_coincidence(self, alpha, beta, w): + r""" + Parameters + ========== + + A coincident pair `\alpha, \beta \in \Omega, w \in Y \cup Y^{-1}` + + See Also + ======== + + coincidence + + """ + self.coincidence(alpha, beta, w=w, modified=True) + +############################################################################### +# COSET ENUMERATION # +############################################################################### + +# relator-based method +def coset_enumeration_r(fp_grp, Y, max_cosets=None, draft=None, + incomplete=False, modified=False): + """ + This is easier of the two implemented methods of coset enumeration. + and is often called the HLT method, after Hazelgrove, Leech, Trotter + The idea is that we make use of ``scan_and_fill`` makes new definitions + whenever the scan is incomplete to enable the scan to complete; this way + we fill in the gaps in the scan of the relator or subgroup generator, + that's why the name relator-based method. + + An instance of `CosetTable` for `fp_grp` can be passed as the keyword + argument `draft` in which case the coset enumeration will start with + that instance and attempt to complete it. + + When `incomplete` is `True` and the function is unable to complete for + some reason, the partially complete table will be returned. + + # TODO: complete the docstring + + See Also + ======== + + scan_and_fill, + + Examples + ======== + + >>> from sympy.combinatorics.free_groups import free_group + >>> from sympy.combinatorics.fp_groups import FpGroup, coset_enumeration_r + >>> F, x, y = free_group("x, y") + + # Example 5.1 from [1] + >>> f = FpGroup(F, [x**3, y**3, x**-1*y**-1*x*y]) + >>> C = coset_enumeration_r(f, [x]) + >>> for i in range(len(C.p)): + ... if C.p[i] == i: + ... print(C.table[i]) + [0, 0, 1, 2] + [1, 1, 2, 0] + [2, 2, 0, 1] + >>> C.p + [0, 1, 2, 1, 1] + + # Example from exercises Q2 [1] + >>> f = FpGroup(F, [x**2*y**2, y**-1*x*y*x**-3]) + >>> C = coset_enumeration_r(f, []) + >>> C.compress(); C.standardize() + >>> C.table + [[1, 2, 3, 4], + [5, 0, 6, 7], + [0, 5, 7, 6], + [7, 6, 5, 0], + [6, 7, 0, 5], + [2, 1, 4, 3], + [3, 4, 2, 1], + [4, 3, 1, 2]] + + # Example 5.2 + >>> f = FpGroup(F, [x**2, y**3, (x*y)**3]) + >>> Y = [x*y] + >>> C = coset_enumeration_r(f, Y) + >>> for i in range(len(C.p)): + ... if C.p[i] == i: + ... print(C.table[i]) + [1, 1, 2, 1] + [0, 0, 0, 2] + [3, 3, 1, 0] + [2, 2, 3, 3] + + # Example 5.3 + >>> f = FpGroup(F, [x**2*y**2, x**3*y**5]) + >>> Y = [] + >>> C = coset_enumeration_r(f, Y) + >>> for i in range(len(C.p)): + ... if C.p[i] == i: + ... print(C.table[i]) + [1, 3, 1, 3] + [2, 0, 2, 0] + [3, 1, 3, 1] + [0, 2, 0, 2] + + # Example 5.4 + >>> F, a, b, c, d, e = free_group("a, b, c, d, e") + >>> f = FpGroup(F, [a*b*c**-1, b*c*d**-1, c*d*e**-1, d*e*a**-1, e*a*b**-1]) + >>> Y = [a] + >>> C = coset_enumeration_r(f, Y) + >>> for i in range(len(C.p)): + ... if C.p[i] == i: + ... print(C.table[i]) + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] + + # example of "compress" method + >>> C.compress() + >>> C.table + [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]] + + # Exercises Pg. 161, Q2. + >>> F, x, y = free_group("x, y") + >>> f = FpGroup(F, [x**2*y**2, y**-1*x*y*x**-3]) + >>> Y = [] + >>> C = coset_enumeration_r(f, Y) + >>> C.compress() + >>> C.standardize() + >>> C.table + [[1, 2, 3, 4], + [5, 0, 6, 7], + [0, 5, 7, 6], + [7, 6, 5, 0], + [6, 7, 0, 5], + [2, 1, 4, 3], + [3, 4, 2, 1], + [4, 3, 1, 2]] + + # John J. Cannon; Lucien A. Dimino; George Havas; Jane M. Watson + # Mathematics of Computation, Vol. 27, No. 123. (Jul., 1973), pp. 463-490 + # from 1973chwd.pdf + # Table 1. Ex. 1 + >>> F, r, s, t = free_group("r, s, t") + >>> E1 = FpGroup(F, [t**-1*r*t*r**-2, r**-1*s*r*s**-2, s**-1*t*s*t**-2]) + >>> C = coset_enumeration_r(E1, [r]) + >>> for i in range(len(C.p)): + ... if C.p[i] == i: + ... print(C.table[i]) + [0, 0, 0, 0, 0, 0] + + Ex. 2 + >>> F, a, b = free_group("a, b") + >>> Cox = FpGroup(F, [a**6, b**6, (a*b)**2, (a**2*b**2)**2, (a**3*b**3)**5]) + >>> C = coset_enumeration_r(Cox, [a]) + >>> index = 0 + >>> for i in range(len(C.p)): + ... if C.p[i] == i: + ... index += 1 + >>> index + 500 + + # Ex. 3 + >>> F, a, b = free_group("a, b") + >>> B_2_4 = FpGroup(F, [a**4, b**4, (a*b)**4, (a**-1*b)**4, (a**2*b)**4, \ + (a*b**2)**4, (a**2*b**2)**4, (a**-1*b*a*b)**4, (a*b**-1*a*b)**4]) + >>> C = coset_enumeration_r(B_2_4, [a]) + >>> index = 0 + >>> for i in range(len(C.p)): + ... if C.p[i] == i: + ... index += 1 + >>> index + 1024 + + References + ========== + + .. [1] Holt, D., Eick, B., O'Brien, E. + "Handbook of computational group theory" + + """ + # 1. Initialize a coset table C for < X|R > + C = CosetTable(fp_grp, Y, max_cosets=max_cosets) + # Define coset table methods. + if modified: + _scan_and_fill = C.modified_scan_and_fill + _define = C.modified_define + else: + _scan_and_fill = C.scan_and_fill + _define = C.define + if draft: + C.table = draft.table[:] + C.p = draft.p[:] + R = fp_grp.relators + A_dict = C.A_dict + p = C.p + for i in range(len(Y)): + if modified: + _scan_and_fill(0, Y[i], C._grp.generators[i]) + else: + _scan_and_fill(0, Y[i]) + alpha = 0 + while alpha < C.n: + if p[alpha] == alpha: + try: + for w in R: + if modified: + _scan_and_fill(alpha, w, C._grp.identity) + else: + _scan_and_fill(alpha, w) + # if alpha was eliminated during the scan then break + if p[alpha] < alpha: + break + if p[alpha] == alpha: + for x in A_dict: + if C.table[alpha][A_dict[x]] is None: + _define(alpha, x) + except ValueError as e: + if incomplete: + return C + raise e + alpha += 1 + return C + +def modified_coset_enumeration_r(fp_grp, Y, max_cosets=None, draft=None, + incomplete=False): + r""" + Introduce a new set of symbols y \in Y that correspond to the + generators of the subgroup. Store the elements of Y as a + word P[\alpha, x] and compute the coset table similar to that of + the regular coset enumeration methods. + + Examples + ======== + + >>> from sympy.combinatorics.free_groups import free_group + >>> from sympy.combinatorics.fp_groups import FpGroup + >>> from sympy.combinatorics.coset_table import modified_coset_enumeration_r + >>> F, x, y = free_group("x, y") + >>> f = FpGroup(F, [x**3, y**3, x**-1*y**-1*x*y]) + >>> C = modified_coset_enumeration_r(f, [x]) + >>> C.table + [[0, 0, 1, 2], [1, 1, 2, 0], [2, 2, 0, 1], [None, 1, None, None], [1, 3, None, None]] + + See Also + ======== + + coset_enumertation_r + + References + ========== + + .. [1] Holt, D., Eick, B., O'Brien, E., + "Handbook of Computational Group Theory", + Section 5.3.2 + """ + return coset_enumeration_r(fp_grp, Y, max_cosets=max_cosets, draft=draft, + incomplete=incomplete, modified=True) + +# Pg. 166 +# coset-table based method +def coset_enumeration_c(fp_grp, Y, max_cosets=None, draft=None, + incomplete=False): + """ + >>> from sympy.combinatorics.free_groups import free_group + >>> from sympy.combinatorics.fp_groups import FpGroup, coset_enumeration_c + >>> F, x, y = free_group("x, y") + >>> f = FpGroup(F, [x**3, y**3, x**-1*y**-1*x*y]) + >>> C = coset_enumeration_c(f, [x]) + >>> C.table + [[0, 0, 1, 2], [1, 1, 2, 0], [2, 2, 0, 1]] + + """ + # Initialize a coset table C for < X|R > + X = fp_grp.generators + R = fp_grp.relators + C = CosetTable(fp_grp, Y, max_cosets=max_cosets) + if draft: + C.table = draft.table[:] + C.p = draft.p[:] + C.deduction_stack = draft.deduction_stack + for alpha, x in product(range(len(C.table)), X): + if C.table[alpha][C.A_dict[x]] is not None: + C.deduction_stack.append((alpha, x)) + A = C.A + # replace all the elements by cyclic reductions + R_cyc_red = [rel.identity_cyclic_reduction() for rel in R] + R_c = list(chain.from_iterable((rel.cyclic_conjugates(), (rel**-1).cyclic_conjugates()) \ + for rel in R_cyc_red)) + R_set = set() + for conjugate in R_c: + R_set = R_set.union(conjugate) + # a list of subsets of R_c whose words start with "x". + R_c_list = [] + for x in C.A: + r = {word for word in R_set if word[0] == x} + R_c_list.append(r) + R_set.difference_update(r) + for w in Y: + C.scan_and_fill_c(0, w) + for x in A: + C.process_deductions(R_c_list[C.A_dict[x]], R_c_list[C.A_dict_inv[x]]) + alpha = 0 + while alpha < len(C.table): + if C.p[alpha] == alpha: + try: + for x in C.A: + if C.p[alpha] != alpha: + break + if C.table[alpha][C.A_dict[x]] is None: + C.define_c(alpha, x) + C.process_deductions(R_c_list[C.A_dict[x]], R_c_list[C.A_dict_inv[x]]) + except ValueError as e: + if incomplete: + return C + raise e + alpha += 1 + return C diff --git a/shared/package-files/03/03765a463f3cbb422b96108da76298fc0f2dc6b6fbcb6a4a3e0115bc8edeedb5/test_datetime.py b/shared/package-files/03/03765a463f3cbb422b96108da76298fc0f2dc6b6fbcb6a4a3e0115bc8edeedb5/test_datetime.py new file mode 100644 index 0000000000000000000000000000000000000000..b89fd97cb91e690552c39e4641dfdbc1b354d955 --- /dev/null +++ b/shared/package-files/03/03765a463f3cbb422b96108da76298fc0f2dc6b6fbcb6a4a3e0115bc8edeedb5/test_datetime.py @@ -0,0 +1,461 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +import datetime +import io +import warnings + +try: + import numpy as np +except ImportError: + np = None +import pytest + +import pyarrow as pa +from pyarrow.tests.parquet.common import _check_roundtrip + +try: + import pyarrow.parquet as pq + from pyarrow.tests.parquet.common import _read_table, _write_table +except ImportError: + pq = None + + +try: + import pandas as pd + import pandas.testing as tm + + from pyarrow.tests.parquet.common import _roundtrip_pandas_dataframe +except ImportError: + pd = tm = None + + +# Marks all of the tests in this module +# Ignore these with pytest ... -m 'not parquet' +pytestmark = pytest.mark.parquet + + +@pytest.mark.pandas +def test_pandas_parquet_datetime_tz(): + # Pandas v2 defaults to [ns], but Arrow defaults to [us] time units + # so we need to cast the pandas dtype. Pandas v1 will always silently + # coerce to [ns] due to lack of non-[ns] support. + s = pd.Series([datetime.datetime(2017, 9, 6)], dtype='datetime64[us]') + s = s.dt.tz_localize('utc') + s.index = s + + # Both a column and an index to hit both use cases + df = pd.DataFrame({'tz_aware': s, + 'tz_eastern': s.dt.tz_convert('US/Eastern')}, + index=s) + + f = io.BytesIO() + + arrow_table = pa.Table.from_pandas(df) + + _write_table(arrow_table, f) + f.seek(0) + + table_read = pq.read_pandas(f) + + df_read = table_read.to_pandas() + tm.assert_frame_equal(df, df_read) + + +@pytest.mark.pandas +def test_datetime_timezone_tzinfo(): + value = datetime.datetime(2018, 1, 1, 1, 23, 45, + tzinfo=datetime.timezone.utc) + df = pd.DataFrame({'foo': [value]}) + + _roundtrip_pandas_dataframe(df, write_kwargs={}) + + +@pytest.mark.pandas +def test_coerce_timestamps(tempdir): + from collections import OrderedDict + + # ARROW-622 + arrays = OrderedDict() + fields = [pa.field('datetime64', + pa.list_(pa.timestamp('ms')))] + arrays['datetime64'] = [ + np.array(['2007-07-13T01:23:34.123456789', + None, + '2010-08-13T05:46:57.437699912'], + dtype='datetime64[ms]'), + None, + None, + np.array(['2007-07-13T02', + None, + '2010-08-13T05:46:57.437699912'], + dtype='datetime64[ms]'), + ] + + df = pd.DataFrame(arrays) + schema = pa.schema(fields) + + filename = tempdir / 'pandas_roundtrip.parquet' + arrow_table = pa.Table.from_pandas(df, schema=schema) + + _write_table(arrow_table, filename, version='2.6', coerce_timestamps='us') + table_read = _read_table(filename) + df_read = table_read.to_pandas() + + df_expected = df.copy() + for i, x in enumerate(df_expected['datetime64']): + if isinstance(x, np.ndarray): + df_expected.loc[i, 'datetime64'] = x.astype('M8[us]') + + tm.assert_frame_equal(df_expected, df_read) + + with pytest.raises(ValueError): + _write_table(arrow_table, filename, version='2.6', + coerce_timestamps='unknown') + + +@pytest.mark.pandas +def test_coerce_timestamps_truncated(tempdir): + """ + ARROW-2555: Test that we can truncate timestamps when coercing if + explicitly allowed. + """ + dt_us = datetime.datetime(year=2017, month=1, day=1, hour=1, minute=1, + second=1, microsecond=1) + dt_ms = datetime.datetime(year=2017, month=1, day=1, hour=1, minute=1, + second=1) + + fields_us = [pa.field('datetime64', pa.timestamp('us'))] + arrays_us = {'datetime64': [dt_us, dt_ms]} + + df_us = pd.DataFrame(arrays_us) + schema_us = pa.schema(fields_us) + + filename = tempdir / 'pandas_truncated.parquet' + table_us = pa.Table.from_pandas(df_us, schema=schema_us) + + _write_table(table_us, filename, version='2.6', coerce_timestamps='ms', + allow_truncated_timestamps=True) + table_ms = _read_table(filename) + df_ms = table_ms.to_pandas() + + arrays_expected = {'datetime64': [dt_ms, dt_ms]} + df_expected = pd.DataFrame(arrays_expected, dtype='datetime64[ms]') + tm.assert_frame_equal(df_expected, df_ms) + + +@pytest.mark.pandas +def test_date_time_types(tempdir): + t1 = pa.date32() + data1 = np.array([17259, 17260, 17261], dtype='int32') + a1 = pa.array(data1, type=t1) + + t2 = pa.date64() + data2 = data1.astype('int64') * 86400000 + a2 = pa.array(data2, type=t2) + + t3 = pa.timestamp('us') + start = pd.Timestamp('2001-01-01').value / 1000 + data3 = np.array([start, start + 1, start + 2], dtype='int64') + a3 = pa.array(data3, type=t3) + + t4 = pa.time32('ms') + data4 = np.arange(3, dtype='i4') + a4 = pa.array(data4, type=t4) + + t5 = pa.time64('us') + a5 = pa.array(data4.astype('int64'), type=t5) + + t6 = pa.time32('s') + a6 = pa.array(data4, type=t6) + + ex_t6 = pa.time32('ms') + ex_a6 = pa.array(data4 * 1000, type=ex_t6) + + t7 = pa.timestamp('ns') + start = pd.Timestamp('2001-01-01').value + data7 = np.array([start, start + 1000, start + 2000], + dtype='int64') + a7 = pa.array(data7, type=t7) + + table = pa.Table.from_arrays([a1, a2, a3, a4, a5, a6, a7], + ['date32', 'date64', 'timestamp[us]', + 'time32[s]', 'time64[us]', + 'time32_from64[s]', + 'timestamp[ns]']) + + # date64 as date32 + # time32[s] to time32[ms] + expected = pa.Table.from_arrays([a1, a1, a3, a4, a5, ex_a6, a7], + ['date32', 'date64', 'timestamp[us]', + 'time32[s]', 'time64[us]', + 'time32_from64[s]', + 'timestamp[ns]']) + + _check_roundtrip(table, expected=expected, version='2.6') + + t0 = pa.timestamp('ms') + data0 = np.arange(4, dtype='int64') + a0 = pa.array(data0, type=t0) + + t1 = pa.timestamp('us') + data1 = np.arange(4, dtype='int64') + a1 = pa.array(data1, type=t1) + + t2 = pa.timestamp('ns') + data2 = np.arange(4, dtype='int64') + a2 = pa.array(data2, type=t2) + + table = pa.Table.from_arrays([a0, a1, a2], + ['ts[ms]', 'ts[us]', 'ts[ns]']) + expected = pa.Table.from_arrays([a0, a1, a2], + ['ts[ms]', 'ts[us]', 'ts[ns]']) + + # int64 for all timestamps supported by default + filename = tempdir / 'int64_timestamps.parquet' + _write_table(table, filename, version='2.6') + parquet_schema = pq.ParquetFile(filename).schema + for i in range(3): + assert parquet_schema.column(i).physical_type == 'INT64' + read_table = _read_table(filename) + assert read_table.equals(expected) + + t0_ns = pa.timestamp('ns') + data0_ns = np.array(data0 * 1000000, dtype='int64') + a0_ns = pa.array(data0_ns, type=t0_ns) + + t1_ns = pa.timestamp('ns') + data1_ns = np.array(data1 * 1000, dtype='int64') + a1_ns = pa.array(data1_ns, type=t1_ns) + + expected = pa.Table.from_arrays([a0_ns, a1_ns, a2], + ['ts[ms]', 'ts[us]', 'ts[ns]']) + + # int96 nanosecond timestamps produced upon request + filename = tempdir / 'explicit_int96_timestamps.parquet' + _write_table(table, filename, version='2.6', + use_deprecated_int96_timestamps=True) + parquet_schema = pq.ParquetFile(filename).schema + for i in range(3): + assert parquet_schema.column(i).physical_type == 'INT96' + read_table = _read_table(filename) + assert read_table.equals(expected) + + # int96 nanosecond timestamps implied by flavor 'spark' + filename = tempdir / 'spark_int96_timestamps.parquet' + _write_table(table, filename, version='2.6', + flavor='spark') + parquet_schema = pq.ParquetFile(filename).schema + for i in range(3): + assert parquet_schema.column(i).physical_type == 'INT96' + read_table = _read_table(filename) + assert read_table.equals(expected) + + +@pytest.mark.pandas +@pytest.mark.parametrize('unit', ['s', 'ms', 'us', 'ns']) +def test_coerce_int96_timestamp_unit(unit): + i_s = pd.Timestamp('2010-01-01').value / 1000000000 # := 1262304000 + + d_s = np.arange(i_s, i_s + 10, 1, dtype='int64') + d_ms = d_s * 1000 + d_us = d_ms * 1000 + d_ns = d_us * 1000 + + a_s = pa.array(d_s, type=pa.timestamp('s')) + a_ms = pa.array(d_ms, type=pa.timestamp('ms')) + a_us = pa.array(d_us, type=pa.timestamp('us')) + a_ns = pa.array(d_ns, type=pa.timestamp('ns')) + + arrays = {"s": a_s, "ms": a_ms, "us": a_us, "ns": a_ns} + names = ['ts_s', 'ts_ms', 'ts_us', 'ts_ns'] + table = pa.Table.from_arrays([a_s, a_ms, a_us, a_ns], names) + + # For either Parquet version, coercing to nanoseconds is allowed + # if Int96 storage is used + expected = pa.Table.from_arrays([arrays.get(unit)]*4, names) + read_table_kwargs = {"coerce_int96_timestamp_unit": unit} + _check_roundtrip(table, expected, + read_table_kwargs=read_table_kwargs, + use_deprecated_int96_timestamps=True) + _check_roundtrip(table, expected, version='2.6', + read_table_kwargs=read_table_kwargs, + use_deprecated_int96_timestamps=True) + + +@pytest.mark.pandas +@pytest.mark.parametrize('pq_reader_method', ['ParquetFile', 'read_table']) +def test_coerce_int96_timestamp_overflow(pq_reader_method, tempdir): + + def get_table(pq_reader_method, filename, **kwargs): + if pq_reader_method == "ParquetFile": + return pq.ParquetFile(filename, **kwargs).read() + elif pq_reader_method == "read_table": + return pq.read_table(filename, **kwargs) + + # Recreating the initial JIRA issue referenced in ARROW-12096 + oob_dts = [ + datetime.datetime(1000, 1, 1), + datetime.datetime(2000, 1, 1), + datetime.datetime(3000, 1, 1) + ] + df = pd.DataFrame({"a": oob_dts}) + table = pa.table(df) + + filename = tempdir / "test_round_trip_overflow.parquet" + pq.write_table(table, filename, use_deprecated_int96_timestamps=True, + version="1.0") + + # with the default resolution of ns, we get wrong values for INT96 + # that are out of bounds for nanosecond range + tab_error = get_table(pq_reader_method, filename) + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", + "Discarding nonzero nanoseconds in conversion", + UserWarning) + assert tab_error["a"].to_pylist() != oob_dts + + # avoid this overflow by specifying the resolution to use for INT96 values + tab_correct = get_table( + pq_reader_method, filename, coerce_int96_timestamp_unit="s" + ) + df_correct = tab_correct.to_pandas(timestamp_as_object=True) + df["a"] = df["a"].astype(object) + tm.assert_frame_equal(df, df_correct) + + +@pytest.mark.parametrize('unit', ['ms', 'us', 'ns']) +def test_timestamp_restore_timezone(unit): + # ARROW-5888, restore timezone from serialized metadata + ty = pa.timestamp(unit, tz='America/New_York') + arr = pa.array([1, 2, 3], type=ty) + t = pa.table([arr], names=['f0']) + _check_roundtrip(t) + + +def test_timestamp_restore_timezone_nanosecond(): + # ARROW-9634, also restore timezone for nanosecond data that get stored + # as microseconds in the parquet file for Parquet ver 2.4 and less + ty = pa.timestamp('ns', tz='America/New_York') + arr = pa.array([1000, 2000, 3000], type=ty) + table = pa.table([arr], names=['f0']) + ty_us = pa.timestamp('us', tz='America/New_York') + expected = pa.table([arr.cast(ty_us)], names=['f0']) + _check_roundtrip(table, expected=expected, version='2.4') + + +@pytest.mark.pandas +def test_list_of_datetime_time_roundtrip(): + # ARROW-4135 + times = pd.to_datetime(['09:00', '09:30', '10:00', '10:30', '11:00', + '11:30', '12:00'], format="%H:%M") + df = pd.DataFrame({'time': [times.time]}) + _roundtrip_pandas_dataframe(df, write_kwargs={}) + + +@pytest.mark.pandas +def test_parquet_version_timestamp_differences(): + i_s = pd.Timestamp('2010-01-01').value / 1000000000 # := 1262304000 + + d_s = np.arange(i_s, i_s + 10, 1, dtype='int64') + d_ms = d_s * 1000 + d_us = d_ms * 1000 + d_ns = d_us * 1000 + + a_s = pa.array(d_s, type=pa.timestamp('s')) + a_ms = pa.array(d_ms, type=pa.timestamp('ms')) + a_us = pa.array(d_us, type=pa.timestamp('us')) + a_ns = pa.array(d_ns, type=pa.timestamp('ns')) + + all_versions = ['1.0', '2.4', '2.6'] + + names = ['ts:s', 'ts:ms', 'ts:us', 'ts:ns'] + table = pa.Table.from_arrays([a_s, a_ms, a_us, a_ns], names) + + # Using Parquet version 1.0 and 2.4, seconds should be coerced to milliseconds + # and nanoseconds should be coerced to microseconds by default + expected = pa.Table.from_arrays([a_ms, a_ms, a_us, a_us], names) + _check_roundtrip(table, expected, version='1.0') + _check_roundtrip(table, expected, version='2.4') + + # Using Parquet version 2.6, seconds should be coerced to milliseconds + # and nanoseconds should be retained by default + expected = pa.Table.from_arrays([a_ms, a_ms, a_us, a_ns], names) + _check_roundtrip(table, expected, version='2.6') + + # For either Parquet version coercing to milliseconds or microseconds + # is allowed + expected = pa.Table.from_arrays([a_ms, a_ms, a_ms, a_ms], names) + for ver in all_versions: + _check_roundtrip(table, expected, coerce_timestamps='ms', version=ver) + + expected = pa.Table.from_arrays([a_us, a_us, a_us, a_us], names) + for ver in all_versions: + _check_roundtrip(table, expected, version=ver, coerce_timestamps='us') + + # TODO: after pyarrow allows coerce_timestamps='ns', tests like the + # following should pass ... + + # Using Parquet version 1.0, coercing to nanoseconds is not allowed + # expected = None + # with pytest.raises(NotImplementedError): + # _roundtrip_table(table, coerce_timestamps='ns') + + # Using Parquet version 2.0, coercing to nanoseconds is allowed + # expected = pa.Table.from_arrays([a_ns, a_ns, a_ns, a_ns], names) + # _check_roundtrip(table, expected, version='2.6', coerce_timestamps='ns') + + # For either Parquet version, coercing to nanoseconds is allowed + # if Int96 storage is used + expected = pa.Table.from_arrays([a_ns, a_ns, a_ns, a_ns], names) + for ver in all_versions: + _check_roundtrip(table, expected, version=ver, + use_deprecated_int96_timestamps=True) + + +@pytest.mark.pandas +def test_noncoerced_nanoseconds_written_without_exception(tempdir): + # ARROW-1957: the Parquet version 2.0 writer preserves Arrow + # nanosecond timestamps by default + n = 9 + df = pd.DataFrame({'x': range(n)}, + index=pd.date_range('2017-01-01', freq='ns', periods=n)) + tb = pa.Table.from_pandas(df) + + filename = tempdir / 'written.parquet' + try: + pq.write_table(tb, filename, version='2.6') + except Exception: + pass + assert filename.exists() + + recovered_table = pq.read_table(filename) + assert tb.equals(recovered_table) + + # Loss of data through coercion (without explicit override) still an error + filename = tempdir / 'not_written.parquet' + with pytest.raises(ValueError): + pq.write_table(tb, filename, coerce_timestamps='ms', version='2.6') + + +def test_duration_type(): + # ARROW-6780 + arrays = [pa.array([0, 1, 2, 3], type=pa.duration(unit)) + for unit in ["s", "ms", "us", "ns"]] + table = pa.Table.from_arrays(arrays, ["d[s]", "d[ms]", "d[us]", "d[ns]"]) + + _check_roundtrip(table) diff --git a/shared/package-files/03/03773f1ab93379c10f167f03686255ee8a199b740778c45a0f6d26a0922fc13c/z39-a b/shared/package-files/03/03773f1ab93379c10f167f03686255ee8a199b740778c45a0f6d26a0922fc13c/z39-a new file mode 100644 index 0000000000000000000000000000000000000000..d9344010eb000703a29b696bd7087e74d62868aa Binary files /dev/null and b/shared/package-files/03/03773f1ab93379c10f167f03686255ee8a199b740778c45a0f6d26a0922fc13c/z39-a differ diff --git a/shared/package-files/03/03773f1ab93379c10f167f03686255ee8a199b740778c45a0f6d26a0922fc13c/z39a b/shared/package-files/03/03773f1ab93379c10f167f03686255ee8a199b740778c45a0f6d26a0922fc13c/z39a new file mode 100644 index 0000000000000000000000000000000000000000..d9344010eb000703a29b696bd7087e74d62868aa Binary files /dev/null and b/shared/package-files/03/03773f1ab93379c10f167f03686255ee8a199b740778c45a0f6d26a0922fc13c/z39a differ diff --git a/shared/package-files/03/03773f1ab93379c10f167f03686255ee8a199b740778c45a0f6d26a0922fc13c/zenith39-a b/shared/package-files/03/03773f1ab93379c10f167f03686255ee8a199b740778c45a0f6d26a0922fc13c/zenith39-a new file mode 100644 index 0000000000000000000000000000000000000000..d9344010eb000703a29b696bd7087e74d62868aa Binary files /dev/null and b/shared/package-files/03/03773f1ab93379c10f167f03686255ee8a199b740778c45a0f6d26a0922fc13c/zenith39-a differ diff --git a/shared/package-files/03/03773f1ab93379c10f167f03686255ee8a199b740778c45a0f6d26a0922fc13c/zenith39-ansi b/shared/package-files/03/03773f1ab93379c10f167f03686255ee8a199b740778c45a0f6d26a0922fc13c/zenith39-ansi new file mode 100644 index 0000000000000000000000000000000000000000..d9344010eb000703a29b696bd7087e74d62868aa Binary files /dev/null and b/shared/package-files/03/03773f1ab93379c10f167f03686255ee8a199b740778c45a0f6d26a0922fc13c/zenith39-ansi differ diff --git a/shared/package-files/03/037bd92819b1722ef0df938ef65c607d4aab44a813afe37f754cdad0cd395b11/_tight_layout.py b/shared/package-files/03/037bd92819b1722ef0df938ef65c607d4aab44a813afe37f754cdad0cd395b11/_tight_layout.py new file mode 100644 index 0000000000000000000000000000000000000000..548da79fff04320193a7170d5cf0f9b9fa743c0a --- /dev/null +++ b/shared/package-files/03/037bd92819b1722ef0df938ef65c607d4aab44a813afe37f754cdad0cd395b11/_tight_layout.py @@ -0,0 +1,301 @@ +""" +Routines to adjust subplot params so that subplots are +nicely fit in the figure. In doing so, only axis labels, tick labels, Axes +titles and offsetboxes that are anchored to Axes are currently considered. + +Internally, this module assumes that the margins (left margin, etc.) which are +differences between ``Axes.get_tightbbox`` and ``Axes.bbox`` are independent of +Axes position. This may fail if ``Axes.adjustable`` is ``datalim`` as well as +such cases as when left or right margin are affected by xlabel. +""" + +import numpy as np + +import matplotlib as mpl +from matplotlib import _api, artist as martist +from matplotlib.font_manager import FontProperties +from matplotlib.transforms import Bbox + + +def _auto_adjust_subplotpars( + fig, renderer, shape, span_pairs, subplot_list, + ax_bbox_list=None, pad=1.08, h_pad=None, w_pad=None, rect=None): + """ + Return a dict of subplot parameters to adjust spacing between subplots + or ``None`` if resulting Axes would have zero height or width. + + Note that this function ignores geometry information of subplot itself, but + uses what is given by the *shape* and *subplot_list* parameters. Also, the + results could be incorrect if some subplots have ``adjustable=datalim``. + + Parameters + ---------- + shape : tuple[int, int] + Number of rows and columns of the grid. + span_pairs : list[tuple[slice, slice]] + List of rowspans and colspans occupied by each subplot. + subplot_list : list of subplots + List of subplots that will be used to calculate optimal subplot_params. + pad : float + Padding between the figure edge and the edges of subplots, as a + fraction of the font size. + h_pad, w_pad : float + Padding (height/width) between edges of adjacent subplots, as a + fraction of the font size. Defaults to *pad*. + rect : tuple + (left, bottom, right, top), default: None. + """ + rows, cols = shape + + font_size_inch = (FontProperties( + size=mpl.rcParams["font.size"]).get_size_in_points() / 72) + pad_inch = pad * font_size_inch + vpad_inch = h_pad * font_size_inch if h_pad is not None else pad_inch + hpad_inch = w_pad * font_size_inch if w_pad is not None else pad_inch + + if len(span_pairs) != len(subplot_list) or len(subplot_list) == 0: + raise ValueError + + if rect is None: + margin_left = margin_bottom = margin_right = margin_top = None + else: + margin_left, margin_bottom, _right, _top = rect + margin_right = 1 - _right if _right else None + margin_top = 1 - _top if _top else None + + vspaces = np.zeros((rows + 1, cols)) + hspaces = np.zeros((rows, cols + 1)) + + if ax_bbox_list is None: + ax_bbox_list = [ + Bbox.union([ax.get_position(original=True) for ax in subplots]) + for subplots in subplot_list] + + for subplots, ax_bbox, (rowspan, colspan) in zip( + subplot_list, ax_bbox_list, span_pairs): + if all(not ax.get_visible() for ax in subplots): + continue + + bb = [] + for ax in subplots: + if ax.get_visible(): + bb += [martist._get_tightbbox_for_layout_only(ax, renderer)] + + tight_bbox_raw = Bbox.union(bb) + tight_bbox = fig.transFigure.inverted().transform_bbox(tight_bbox_raw) + + hspaces[rowspan, colspan.start] += ax_bbox.xmin - tight_bbox.xmin # l + hspaces[rowspan, colspan.stop] += tight_bbox.xmax - ax_bbox.xmax # r + vspaces[rowspan.start, colspan] += tight_bbox.ymax - ax_bbox.ymax # t + vspaces[rowspan.stop, colspan] += ax_bbox.ymin - tight_bbox.ymin # b + + fig_width_inch, fig_height_inch = fig.get_size_inches() + + # margins can be negative for Axes with aspect applied, so use max(, 0) to + # make them nonnegative. + if not margin_left: + margin_left = max(hspaces[:, 0].max(), 0) + pad_inch/fig_width_inch + suplabel = fig._supylabel + if suplabel and suplabel.get_in_layout(): + rel_width = fig.transFigure.inverted().transform_bbox( + suplabel.get_window_extent(renderer)).width + margin_left += rel_width + pad_inch/fig_width_inch + if not margin_right: + margin_right = max(hspaces[:, -1].max(), 0) + pad_inch/fig_width_inch + if not margin_top: + margin_top = max(vspaces[0, :].max(), 0) + pad_inch/fig_height_inch + if fig._suptitle and fig._suptitle.get_in_layout(): + rel_height = fig.transFigure.inverted().transform_bbox( + fig._suptitle.get_window_extent(renderer)).height + margin_top += rel_height + pad_inch/fig_height_inch + if not margin_bottom: + margin_bottom = max(vspaces[-1, :].max(), 0) + pad_inch/fig_height_inch + suplabel = fig._supxlabel + if suplabel and suplabel.get_in_layout(): + rel_height = fig.transFigure.inverted().transform_bbox( + suplabel.get_window_extent(renderer)).height + margin_bottom += rel_height + pad_inch/fig_height_inch + + if margin_left + margin_right >= 1: + _api.warn_external('Tight layout not applied. The left and right ' + 'margins cannot be made large enough to ' + 'accommodate all Axes decorations.') + return None + if margin_bottom + margin_top >= 1: + _api.warn_external('Tight layout not applied. The bottom and top ' + 'margins cannot be made large enough to ' + 'accommodate all Axes decorations.') + return None + + kwargs = dict(left=margin_left, + right=1 - margin_right, + bottom=margin_bottom, + top=1 - margin_top) + + if cols > 1: + hspace = hspaces[:, 1:-1].max() + hpad_inch / fig_width_inch + # axes widths: + h_axes = (1 - margin_right - margin_left - hspace * (cols - 1)) / cols + if h_axes < 0: + _api.warn_external('Tight layout not applied. tight_layout ' + 'cannot make Axes width small enough to ' + 'accommodate all Axes decorations') + return None + else: + kwargs["wspace"] = hspace / h_axes + if rows > 1: + vspace = vspaces[1:-1, :].max() + vpad_inch / fig_height_inch + v_axes = (1 - margin_top - margin_bottom - vspace * (rows - 1)) / rows + if v_axes < 0: + _api.warn_external('Tight layout not applied. tight_layout ' + 'cannot make Axes height small enough to ' + 'accommodate all Axes decorations.') + return None + else: + kwargs["hspace"] = vspace / v_axes + + return kwargs + + +def get_subplotspec_list(axes_list, grid_spec=None): + """ + Return a list of subplotspec from the given list of Axes. + + For an instance of Axes that does not support subplotspec, None is inserted + in the list. + + If grid_spec is given, None is inserted for those not from the given + grid_spec. + """ + subplotspec_list = [] + for ax in axes_list: + axes_or_locator = ax.get_axes_locator() + if axes_or_locator is None: + axes_or_locator = ax + + if hasattr(axes_or_locator, "get_subplotspec"): + subplotspec = axes_or_locator.get_subplotspec() + if subplotspec is not None: + subplotspec = subplotspec.get_topmost_subplotspec() + gs = subplotspec.get_gridspec() + if grid_spec is not None: + if gs != grid_spec: + subplotspec = None + elif gs.locally_modified_subplot_params(): + subplotspec = None + else: + subplotspec = None + + subplotspec_list.append(subplotspec) + + return subplotspec_list + + +def get_tight_layout_figure(fig, axes_list, subplotspec_list, renderer, + pad=1.08, h_pad=None, w_pad=None, rect=None): + """ + Return subplot parameters for tight-layouted-figure with specified padding. + + Parameters + ---------- + fig : Figure + axes_list : list of Axes + subplotspec_list : list of `.SubplotSpec` + The subplotspecs of each Axes. + renderer : renderer + pad : float + Padding between the figure edge and the edges of subplots, as a + fraction of the font size. + h_pad, w_pad : float + Padding (height/width) between edges of adjacent subplots. Defaults to + *pad*. + rect : tuple (left, bottom, right, top), default: None. + rectangle in normalized figure coordinates + that the whole subplots area (including labels) will fit into. + Defaults to using the entire figure. + + Returns + ------- + subplotspec or None + subplotspec kwargs to be passed to `.Figure.subplots_adjust` or + None if tight_layout could not be accomplished. + """ + + # Multiple Axes can share same subplotspec (e.g., if using axes_grid1); + # we need to group them together. + ss_to_subplots = {ss: [] for ss in subplotspec_list} + for ax, ss in zip(axes_list, subplotspec_list): + ss_to_subplots[ss].append(ax) + if ss_to_subplots.pop(None, None): + _api.warn_external( + "This figure includes Axes that are not compatible with " + "tight_layout, so results might be incorrect.") + if not ss_to_subplots: + return {} + subplot_list = list(ss_to_subplots.values()) + ax_bbox_list = [ss.get_position(fig) for ss in ss_to_subplots] + + max_nrows = max(ss.get_gridspec().nrows for ss in ss_to_subplots) + max_ncols = max(ss.get_gridspec().ncols for ss in ss_to_subplots) + + span_pairs = [] + for ss in ss_to_subplots: + # The intent here is to support Axes from different gridspecs where + # one's nrows (or ncols) is a multiple of the other (e.g. 2 and 4), + # but this doesn't actually work because the computed wspace, in + # relative-axes-height, corresponds to different physical spacings for + # the 2-row grid and the 4-row grid. Still, this code is left, mostly + # for backcompat. + rows, cols = ss.get_gridspec().get_geometry() + div_row, mod_row = divmod(max_nrows, rows) + div_col, mod_col = divmod(max_ncols, cols) + if mod_row != 0: + _api.warn_external('tight_layout not applied: number of rows ' + 'in subplot specifications must be ' + 'multiples of one another.') + return {} + if mod_col != 0: + _api.warn_external('tight_layout not applied: number of ' + 'columns in subplot specifications must be ' + 'multiples of one another.') + return {} + span_pairs.append(( + slice(ss.rowspan.start * div_row, ss.rowspan.stop * div_row), + slice(ss.colspan.start * div_col, ss.colspan.stop * div_col))) + + kwargs = _auto_adjust_subplotpars(fig, renderer, + shape=(max_nrows, max_ncols), + span_pairs=span_pairs, + subplot_list=subplot_list, + ax_bbox_list=ax_bbox_list, + pad=pad, h_pad=h_pad, w_pad=w_pad) + + # kwargs can be none if tight_layout fails... + if rect is not None and kwargs is not None: + # if rect is given, the whole subplots area (including + # labels) will fit into the rect instead of the + # figure. Note that the rect argument of + # *auto_adjust_subplotpars* specify the area that will be + # covered by the total area of axes.bbox. Thus we call + # auto_adjust_subplotpars twice, where the second run + # with adjusted rect parameters. + + left, bottom, right, top = rect + if left is not None: + left += kwargs["left"] + if bottom is not None: + bottom += kwargs["bottom"] + if right is not None: + right -= (1 - kwargs["right"]) + if top is not None: + top -= (1 - kwargs["top"]) + + kwargs = _auto_adjust_subplotpars(fig, renderer, + shape=(max_nrows, max_ncols), + span_pairs=span_pairs, + subplot_list=subplot_list, + ax_bbox_list=ax_bbox_list, + pad=pad, h_pad=h_pad, w_pad=w_pad, + rect=(left, bottom, right, top)) + + return kwargs diff --git a/shared/package-files/03/037d7a74e0286c0aa933a0c75fcbd8927f942f56d1f7ae085575062d35151694/special_hermite_polynomial_he_compositeexplicitautograd_dispatch.h b/shared/package-files/03/037d7a74e0286c0aa933a0c75fcbd8927f942f56d1f7ae085575062d35151694/special_hermite_polynomial_he_compositeexplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..e90ea164a1ca94cd75aac0caef7281bd3bc54958 --- /dev/null +++ b/shared/package-files/03/037d7a74e0286c0aa933a0c75fcbd8927f942f56d1f7ae085575062d35151694/special_hermite_polynomial_he_compositeexplicitautograd_dispatch.h @@ -0,0 +1,28 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeexplicitautograd { + +TORCH_API at::Tensor special_hermite_polynomial_he(const at::Scalar & x, const at::Tensor & n); +TORCH_API at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Scalar & x, const at::Tensor & n); +TORCH_API at::Tensor & special_hermite_polynomial_he_outf(const at::Scalar & x, const at::Tensor & n, at::Tensor & out); +TORCH_API at::Tensor special_hermite_polynomial_he(const at::Tensor & x, const at::Scalar & n); +TORCH_API at::Tensor & special_hermite_polynomial_he_out(at::Tensor & out, const at::Tensor & x, const at::Scalar & n); +TORCH_API at::Tensor & special_hermite_polynomial_he_outf(const at::Tensor & x, const at::Scalar & n, at::Tensor & out); + +} // namespace compositeexplicitautograd +} // namespace at diff --git a/shared/package-files/03/0380882c501df0c4551b51e85cfa78e622bd44b956c95ef76b512dc04f13be7f/version.py b/shared/package-files/03/0380882c501df0c4551b51e85cfa78e622bd44b956c95ef76b512dc04f13be7f/version.py new file mode 100644 index 0000000000000000000000000000000000000000..70369b9d663414c24f1e042c5b30a8f8c7bbd2b2 --- /dev/null +++ b/shared/package-files/03/0380882c501df0c4551b51e85cfa78e622bd44b956c95ef76b512dc04f13be7f/version.py @@ -0,0 +1,9 @@ +""" +This module exists only to simplify retrieving the version number of chardet +from within setup.py and from chardet subpackages. + +:author: Dan Blanchard (dan.blanchard@gmail.com) +""" + +__version__ = "4.0.0" +VERSION = __version__.split('.') diff --git a/shared/package-files/03/038106225eedc6b53f426908d4dd16f617ddf2dabccb1eb3ab7da861769d74b4/drop_block.py b/shared/package-files/03/038106225eedc6b53f426908d4dd16f617ddf2dabccb1eb3ab7da861769d74b4/drop_block.py new file mode 100644 index 0000000000000000000000000000000000000000..e65496ea29ad1ee6e04bc19280a00a397a4f1040 --- /dev/null +++ b/shared/package-files/03/038106225eedc6b53f426908d4dd16f617ddf2dabccb1eb3ab7da861769d74b4/drop_block.py @@ -0,0 +1,155 @@ +import torch +import torch.fx +import torch.nn.functional as F +from torch import nn, Tensor + +from ..utils import _log_api_usage_once + + +def drop_block2d( + input: Tensor, p: float, block_size: int, inplace: bool = False, eps: float = 1e-06, training: bool = True +) -> Tensor: + """ + Implements DropBlock2d from `"DropBlock: A regularization method for convolutional networks" + `. + + Args: + input (Tensor[N, C, H, W]): The input tensor or 4-dimensions with the first one + being its batch i.e. a batch with ``N`` rows. + p (float): Probability of an element to be dropped. + block_size (int): Size of the block to drop. + inplace (bool): If set to ``True``, will do this operation in-place. Default: ``False``. + eps (float): A value added to the denominator for numerical stability. Default: 1e-6. + training (bool): apply dropblock if is ``True``. Default: ``True``. + + Returns: + Tensor[N, C, H, W]: The randomly zeroed tensor after dropblock. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(drop_block2d) + if p < 0.0 or p > 1.0: + raise ValueError(f"drop probability has to be between 0 and 1, but got {p}.") + if input.ndim != 4: + raise ValueError(f"input should be 4 dimensional. Got {input.ndim} dimensions.") + if not training or p == 0.0: + return input + + N, C, H, W = input.size() + block_size = min(block_size, W, H) + # compute the gamma of Bernoulli distribution + gamma = (p * H * W) / ((block_size**2) * ((H - block_size + 1) * (W - block_size + 1))) + noise = torch.empty((N, C, H - block_size + 1, W - block_size + 1), dtype=input.dtype, device=input.device) + noise.bernoulli_(gamma) + + noise = F.pad(noise, [block_size // 2] * 4, value=0) + noise = F.max_pool2d(noise, stride=(1, 1), kernel_size=(block_size, block_size), padding=block_size // 2) + noise = 1 - noise + normalize_scale = noise.numel() / (eps + noise.sum()) + if inplace: + input.mul_(noise).mul_(normalize_scale) + else: + input = input * noise * normalize_scale + return input + + +def drop_block3d( + input: Tensor, p: float, block_size: int, inplace: bool = False, eps: float = 1e-06, training: bool = True +) -> Tensor: + """ + Implements DropBlock3d from `"DropBlock: A regularization method for convolutional networks" + `. + + Args: + input (Tensor[N, C, D, H, W]): The input tensor or 5-dimensions with the first one + being its batch i.e. a batch with ``N`` rows. + p (float): Probability of an element to be dropped. + block_size (int): Size of the block to drop. + inplace (bool): If set to ``True``, will do this operation in-place. Default: ``False``. + eps (float): A value added to the denominator for numerical stability. Default: 1e-6. + training (bool): apply dropblock if is ``True``. Default: ``True``. + + Returns: + Tensor[N, C, D, H, W]: The randomly zeroed tensor after dropblock. + """ + if not torch.jit.is_scripting() and not torch.jit.is_tracing(): + _log_api_usage_once(drop_block3d) + if p < 0.0 or p > 1.0: + raise ValueError(f"drop probability has to be between 0 and 1, but got {p}.") + if input.ndim != 5: + raise ValueError(f"input should be 5 dimensional. Got {input.ndim} dimensions.") + if not training or p == 0.0: + return input + + N, C, D, H, W = input.size() + block_size = min(block_size, D, H, W) + # compute the gamma of Bernoulli distribution + gamma = (p * D * H * W) / ((block_size**3) * ((D - block_size + 1) * (H - block_size + 1) * (W - block_size + 1))) + noise = torch.empty( + (N, C, D - block_size + 1, H - block_size + 1, W - block_size + 1), dtype=input.dtype, device=input.device + ) + noise.bernoulli_(gamma) + + noise = F.pad(noise, [block_size // 2] * 6, value=0) + noise = F.max_pool3d( + noise, stride=(1, 1, 1), kernel_size=(block_size, block_size, block_size), padding=block_size // 2 + ) + noise = 1 - noise + normalize_scale = noise.numel() / (eps + noise.sum()) + if inplace: + input.mul_(noise).mul_(normalize_scale) + else: + input = input * noise * normalize_scale + return input + + +torch.fx.wrap("drop_block2d") + + +class DropBlock2d(nn.Module): + """ + See :func:`drop_block2d`. + """ + + def __init__(self, p: float, block_size: int, inplace: bool = False, eps: float = 1e-06) -> None: + super().__init__() + + self.p = p + self.block_size = block_size + self.inplace = inplace + self.eps = eps + + def forward(self, input: Tensor) -> Tensor: + """ + Args: + input (Tensor): Input feature map on which some areas will be randomly + dropped. + Returns: + Tensor: The tensor after DropBlock layer. + """ + return drop_block2d(input, self.p, self.block_size, self.inplace, self.eps, self.training) + + def __repr__(self) -> str: + s = f"{self.__class__.__name__}(p={self.p}, block_size={self.block_size}, inplace={self.inplace})" + return s + + +torch.fx.wrap("drop_block3d") + + +class DropBlock3d(DropBlock2d): + """ + See :func:`drop_block3d`. + """ + + def __init__(self, p: float, block_size: int, inplace: bool = False, eps: float = 1e-06) -> None: + super().__init__(p, block_size, inplace, eps) + + def forward(self, input: Tensor) -> Tensor: + """ + Args: + input (Tensor): Input feature map on which some areas will be randomly + dropped. + Returns: + Tensor: The tensor after DropBlock layer. + """ + return drop_block3d(input, self.p, self.block_size, self.inplace, self.eps, self.training) diff --git a/shared/package-files/03/0381f7870bc8799f969fb8abccd2c6754dd45f01af697b6f37f2bc3b1d5e9516/test_lock.py b/shared/package-files/03/0381f7870bc8799f969fb8abccd2c6754dd45f01af697b6f37f2bc3b1d5e9516/test_lock.py new file mode 100644 index 0000000000000000000000000000000000000000..b114c1fc32b1188ed8fd56d285e373c9f2f7cece --- /dev/null +++ b/shared/package-files/03/0381f7870bc8799f969fb8abccd2c6754dd45f01af697b6f37f2bc3b1d5e9516/test_lock.py @@ -0,0 +1,197 @@ +import etcd +try: + import mock +except ImportError: + from unittest import mock +from etcd.tests.unit import TestClientApiBase + + +class TestClientLock(TestClientApiBase): + + def recursive_read(self): + nodes = [ + {"key": "/_locks/test_lock/1", "value": "2qwwwq", + "modifiedIndex":33,"createdIndex":33}, + {"key": "/_locks/test_lock/34", "value": self.locker.uuid, + "modifiedIndex":34,"createdIndex":34}, + ] + d = { + "action": "get", + "node": {"dir": True, + "nodes": [{"key":"/_locks/test_lock", "dir": True, + "nodes": nodes}]} + } + self._mock_api(200, d) + + def setUp(self): + super(TestClientLock, self).setUp() + self.locker = etcd.Lock(self.client, 'test_lock') + + def test_initialization(self): + """ + Verify the lock gets initialized correctly + """ + self.assertEquals(self.locker.name, u'test_lock') + self.assertEquals(self.locker.path, u'/_locks/test_lock') + self.assertEquals(self.locker.is_taken, False) + + def test_acquire(self): + """ + Acquiring a precedingly inexistent lock works. + """ + l = etcd.Lock(self.client, 'test_lock') + l._find_lock = mock.MagicMock(spec=l._find_lock, return_value=False) + l._acquired = mock.MagicMock(spec=l._acquired, return_value=True) + # Mock the write + d = { + u'action': u'set', + u'node': { + u'modifiedIndex': 190, + u'key': u'/_locks/test_lock/1', + u'value': l.uuid + } + } + self._mock_api(200, d) + self.assertEquals(l.acquire(), True) + self.assertEquals(l._sequence, '1') + + def test_is_acquired(self): + """ + Test is_acquired + """ + self.locker._sequence = '1' + d = { + u'action': u'get', + u'node': { + u'modifiedIndex': 190, + u'key': u'/_locks/test_lock/1', + u'value': self.locker.uuid + } + } + self._mock_api(200, d) + self.locker.is_taken = True + self.assertEquals(self.locker.is_acquired, True) + + def test_is_not_acquired(self): + """ + Test is_acquired failures + """ + self.locker._sequence = '2' + self.locker.is_taken = False + self.assertEquals(self.locker.is_acquired, False) + self.locker.is_taken = True + self._mock_exception(etcd.EtcdKeyNotFound, self.locker.lock_key) + self.assertEquals(self.locker.is_acquired, False) + self.assertEquals(self.locker.is_taken, False) + + def test_acquired(self): + """ + Test the acquiring primitives + """ + self.locker._sequence = '4' + retval = ('/_locks/test_lock/4', None) + self.locker._get_locker = mock.MagicMock( + spec=self.locker._get_locker, return_value=retval) + self.assertTrue(self.locker._acquired()) + self.assertTrue(self.locker.is_taken) + retval = ('/_locks/test_lock/1', '/_locks/test_lock/4') + self.locker._get_locker = mock.MagicMock(return_value=retval) + self.assertFalse(self.locker._acquired(blocking=False)) + self.assertFalse(self.locker.is_taken) + d = { + u'action': u'delete', + u'node': { + u'modifiedIndex': 190, + u'key': u'/_locks/test_lock/1', + u'value': self.locker.uuid + } + } + self._mock_api(200, d) + returns = [('/_locks/test_lock/1', '/_locks/test_lock/4'), ('/_locks/test_lock/4', None)] + + def side_effect(): + return returns.pop() + + self.locker._get_locker = mock.MagicMock( + spec=self.locker._get_locker, side_effect=side_effect) + self.assertTrue(self.locker._acquired()) + + def test_acquired_no_timeout(self): + self.locker._sequence = 4 + returns = [ + ('/_locks/test_lock/4', None), + ('/_locks/test_lock/1', etcd.EtcdResult(node={"key": '/_locks/test_lock/4', "modifiedIndex": 1})) + ] + + def side_effect(): + return returns.pop() + + d = { + u'action': u'get', + u'node': { + u'modifiedIndex': 190, + u'key': u'/_locks/test_lock/4', + u'value': self.locker.uuid + } + } + self._mock_api(200, d) + + self.locker._get_locker = mock.create_autospec( + self.locker._get_locker, side_effect=side_effect) + self.assertTrue(self.locker._acquired()) + + def test_lock_key(self): + """ + Test responses from the lock_key property + """ + with self.assertRaises(ValueError): + self.locker.lock_key + self.locker._sequence = '5' + self.assertEquals(u'/_locks/test_lock/5',self.locker.lock_key) + + def test_set_sequence(self): + self.locker._set_sequence('/_locks/test_lock/10') + self.assertEquals('10', self.locker._sequence) + + def test_find_lock(self): + d = { + u'action': u'get', + u'node': { + u'modifiedIndex': 190, + u'key': u'/_locks/test_lock/1', + u'value': self.locker.uuid + } + } + self._mock_api(200, d) + self.locker._sequence = '1' + self.assertTrue(self.locker._find_lock()) + # Now let's pretend the lock is not there + self._mock_exception(etcd.EtcdKeyNotFound, self.locker.lock_key) + self.assertFalse(self.locker._find_lock()) + self.locker._sequence = None + self.recursive_read() + self.assertTrue(self.locker._find_lock()) + self.assertEquals(self.locker._sequence, '34') + + def test_get_locker(self): + self.recursive_read() + self.assertEquals((u'/_locks/test_lock/1', etcd.EtcdResult(node={'newKey': False, '_children': [], 'createdIndex': 33, 'modifiedIndex': 33, 'value': u'2qwwwq', 'expiration': None, 'key': u'/_locks/test_lock/1', 'ttl': None, 'action': None, 'dir': False})), + self.locker._get_locker()) + with self.assertRaises(etcd.EtcdLockExpired): + self.locker._sequence = '35' + self.locker._get_locker() + + def test_release(self): + d = { + u'action': u'delete', + u'node': { + u'modifiedIndex': 190, + u'key': u'/_locks/test_lock/1', + u'value': self.locker.uuid + } + } + self._mock_api(200, d) + self.locker._sequence = 1 + self.locker.is_taken = True + self.locker.release() + self.assertFalse(self.locker.is_taken) diff --git a/shared/package-files/03/03827180c09f7d3933f7e5d3d504f5b60d93958651473d60ee220c9aace8bea0/TestOrcFile.test1.orc b/shared/package-files/03/03827180c09f7d3933f7e5d3d504f5b60d93958651473d60ee220c9aace8bea0/TestOrcFile.test1.orc new file mode 100644 index 0000000000000000000000000000000000000000..4fb0beff868971efb653739fe6ae47a37e4a1c66 Binary files /dev/null and b/shared/package-files/03/03827180c09f7d3933f7e5d3d504f5b60d93958651473d60ee220c9aace8bea0/TestOrcFile.test1.orc differ diff --git a/shared/package-files/03/0389ab41f3df073ae69f1126e000e2a6c3234411a4d3e788b7766fb03904d08e/_dynfunc.cpython-310-x86_64-linux-gnu.so b/shared/package-files/03/0389ab41f3df073ae69f1126e000e2a6c3234411a4d3e788b7766fb03904d08e/_dynfunc.cpython-310-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..61c4932cf1db5143e7ca459dddb01f2564a9c830 Binary files /dev/null and b/shared/package-files/03/0389ab41f3df073ae69f1126e000e2a6c3234411a4d3e788b7766fb03904d08e/_dynfunc.cpython-310-x86_64-linux-gnu.so differ diff --git a/shared/package-files/03/038ced9cc955a3ea75cfbeee66f27eb098b2136d7f42bba3693c68e4fd6094e6/backend_wx.py b/shared/package-files/03/038ced9cc955a3ea75cfbeee66f27eb098b2136d7f42bba3693c68e4fd6094e6/backend_wx.py new file mode 100644 index 0000000000000000000000000000000000000000..027000c1d78cc5ab0f8cde2156889b199d596b33 --- /dev/null +++ b/shared/package-files/03/038ced9cc955a3ea75cfbeee66f27eb098b2136d7f42bba3693c68e4fd6094e6/backend_wx.py @@ -0,0 +1,1381 @@ +""" +A wxPython backend for matplotlib. + +Originally contributed by Jeremy O'Donoghue (jeremy@o-donoghue.com) and John +Hunter (jdhunter@ace.bsd.uchicago.edu). + +Copyright (C) Jeremy O'Donoghue & John Hunter, 2003-4. +""" + +import functools +import logging +import math +import pathlib +import sys +import weakref + +import numpy as np +import PIL.Image + +import matplotlib as mpl +from matplotlib.backend_bases import ( + _Backend, FigureCanvasBase, FigureManagerBase, + GraphicsContextBase, MouseButton, NavigationToolbar2, RendererBase, + TimerBase, ToolContainerBase, cursors, + CloseEvent, KeyEvent, LocationEvent, MouseEvent, ResizeEvent) + +from matplotlib import _api, cbook, backend_tools, _c_internal_utils +from matplotlib._pylab_helpers import Gcf +from matplotlib.path import Path +from matplotlib.transforms import Affine2D + +import wx +import wx.svg # noqa: F401 + +_log = logging.getLogger(__name__) + +# the True dots per inch on the screen; should be display dependent; see +# http://groups.google.com/d/msg/comp.lang.postscript/-/omHAc9FEuAsJ?hl=en +# for some info about screen dpi +PIXELS_PER_INCH = 75 + + +# lru_cache holds a reference to the App and prevents it from being gc'ed. +@functools.lru_cache(1) +def _create_wxapp(): + wxapp = wx.App(False) + wxapp.SetExitOnFrameDelete(True) + cbook._setup_new_guiapp() + # Set per-process DPI awareness. This is a NoOp except in MSW + _c_internal_utils.Win32_SetProcessDpiAwareness_max() + return wxapp + + +class TimerWx(TimerBase): + """Subclass of `.TimerBase` using wx.Timer events.""" + + def __init__(self, *args, **kwargs): + self._timer = wx.Timer() + self._timer.Notify = self._on_timer + super().__init__(*args, **kwargs) + + def _timer_start(self): + self._timer.Start(self._interval, self._single) + + def _timer_stop(self): + self._timer.Stop() + + def _timer_set_interval(self): + if self._timer.IsRunning(): + self._timer_start() # Restart with new interval. + + +@_api.deprecated( + "2.0", name="wx", obj_type="backend", removal="the future", + alternative="wxagg", + addendum="See the Matplotlib usage FAQ for more info on backends.") +class RendererWx(RendererBase): + """ + The renderer handles all the drawing primitives using a graphics + context instance that controls the colors/styles. It acts as the + 'renderer' instance used by many classes in the hierarchy. + """ + # In wxPython, drawing is performed on a wxDC instance, which will + # generally be mapped to the client area of the window displaying + # the plot. Under wxPython, the wxDC instance has a wx.Pen which + # describes the colour and weight of any lines drawn, and a wxBrush + # which describes the fill colour of any closed polygon. + + # Font styles, families and weight. + fontweights = { + 100: wx.FONTWEIGHT_LIGHT, + 200: wx.FONTWEIGHT_LIGHT, + 300: wx.FONTWEIGHT_LIGHT, + 400: wx.FONTWEIGHT_NORMAL, + 500: wx.FONTWEIGHT_NORMAL, + 600: wx.FONTWEIGHT_NORMAL, + 700: wx.FONTWEIGHT_BOLD, + 800: wx.FONTWEIGHT_BOLD, + 900: wx.FONTWEIGHT_BOLD, + 'ultralight': wx.FONTWEIGHT_LIGHT, + 'light': wx.FONTWEIGHT_LIGHT, + 'normal': wx.FONTWEIGHT_NORMAL, + 'medium': wx.FONTWEIGHT_NORMAL, + 'semibold': wx.FONTWEIGHT_NORMAL, + 'bold': wx.FONTWEIGHT_BOLD, + 'heavy': wx.FONTWEIGHT_BOLD, + 'ultrabold': wx.FONTWEIGHT_BOLD, + 'black': wx.FONTWEIGHT_BOLD, + } + fontangles = { + 'italic': wx.FONTSTYLE_ITALIC, + 'normal': wx.FONTSTYLE_NORMAL, + 'oblique': wx.FONTSTYLE_SLANT, + } + + # wxPython allows for portable font styles, choosing them appropriately for + # the target platform. Map some standard font names to the portable styles. + # QUESTION: Is it wise to agree to standard fontnames across all backends? + fontnames = { + 'Sans': wx.FONTFAMILY_SWISS, + 'Roman': wx.FONTFAMILY_ROMAN, + 'Script': wx.FONTFAMILY_SCRIPT, + 'Decorative': wx.FONTFAMILY_DECORATIVE, + 'Modern': wx.FONTFAMILY_MODERN, + 'Courier': wx.FONTFAMILY_MODERN, + 'courier': wx.FONTFAMILY_MODERN, + } + + def __init__(self, bitmap, dpi): + """Initialise a wxWindows renderer instance.""" + super().__init__() + _log.debug("%s - __init__()", type(self)) + self.width = bitmap.GetWidth() + self.height = bitmap.GetHeight() + self.bitmap = bitmap + self.fontd = {} + self.dpi = dpi + self.gc = None + + def flipy(self): + # docstring inherited + return True + + def get_text_width_height_descent(self, s, prop, ismath): + # docstring inherited + + if ismath: + s = cbook.strip_math(s) + + if self.gc is None: + gc = self.new_gc() + else: + gc = self.gc + gfx_ctx = gc.gfx_ctx + font = self.get_wx_font(s, prop) + gfx_ctx.SetFont(font, wx.BLACK) + w, h, descent, leading = gfx_ctx.GetFullTextExtent(s) + + return w, h, descent + + def get_canvas_width_height(self): + # docstring inherited + return self.width, self.height + + def handle_clip_rectangle(self, gc): + new_bounds = gc.get_clip_rectangle() + if new_bounds is not None: + new_bounds = new_bounds.bounds + gfx_ctx = gc.gfx_ctx + if gfx_ctx._lastcliprect != new_bounds: + gfx_ctx._lastcliprect = new_bounds + if new_bounds is None: + gfx_ctx.ResetClip() + else: + gfx_ctx.Clip(new_bounds[0], + self.height - new_bounds[1] - new_bounds[3], + new_bounds[2], new_bounds[3]) + + @staticmethod + def convert_path(gfx_ctx, path, transform): + wxpath = gfx_ctx.CreatePath() + for points, code in path.iter_segments(transform): + if code == Path.MOVETO: + wxpath.MoveToPoint(*points) + elif code == Path.LINETO: + wxpath.AddLineToPoint(*points) + elif code == Path.CURVE3: + wxpath.AddQuadCurveToPoint(*points) + elif code == Path.CURVE4: + wxpath.AddCurveToPoint(*points) + elif code == Path.CLOSEPOLY: + wxpath.CloseSubpath() + return wxpath + + def draw_path(self, gc, path, transform, rgbFace=None): + # docstring inherited + gc.select() + self.handle_clip_rectangle(gc) + gfx_ctx = gc.gfx_ctx + transform = transform + \ + Affine2D().scale(1.0, -1.0).translate(0.0, self.height) + wxpath = self.convert_path(gfx_ctx, path, transform) + if rgbFace is not None: + gfx_ctx.SetBrush(wx.Brush(gc.get_wxcolour(rgbFace))) + gfx_ctx.DrawPath(wxpath) + else: + gfx_ctx.StrokePath(wxpath) + gc.unselect() + + def draw_image(self, gc, x, y, im): + bbox = gc.get_clip_rectangle() + if bbox is not None: + l, b, w, h = bbox.bounds + else: + l = 0 + b = 0 + w = self.width + h = self.height + rows, cols = im.shape[:2] + bitmap = wx.Bitmap.FromBufferRGBA(cols, rows, im.tobytes()) + gc.select() + gc.gfx_ctx.DrawBitmap(bitmap, int(l), int(self.height - b), + int(w), int(-h)) + gc.unselect() + + def draw_text(self, gc, x, y, s, prop, angle, ismath=False, mtext=None): + # docstring inherited + + if ismath: + s = cbook.strip_math(s) + _log.debug("%s - draw_text()", type(self)) + gc.select() + self.handle_clip_rectangle(gc) + gfx_ctx = gc.gfx_ctx + + font = self.get_wx_font(s, prop) + color = gc.get_wxcolour(gc.get_rgb()) + gfx_ctx.SetFont(font, color) + + w, h, d = self.get_text_width_height_descent(s, prop, ismath) + x = int(x) + y = int(y - h) + + if angle == 0.0: + gfx_ctx.DrawText(s, x, y) + else: + rads = math.radians(angle) + xo = h * math.sin(rads) + yo = h * math.cos(rads) + gfx_ctx.DrawRotatedText(s, x - xo, y - yo, rads) + + gc.unselect() + + def new_gc(self): + # docstring inherited + _log.debug("%s - new_gc()", type(self)) + self.gc = GraphicsContextWx(self.bitmap, self) + self.gc.select() + self.gc.unselect() + return self.gc + + def get_wx_font(self, s, prop): + """Return a wx font. Cache font instances for efficiency.""" + _log.debug("%s - get_wx_font()", type(self)) + key = hash(prop) + font = self.fontd.get(key) + if font is not None: + return font + size = self.points_to_pixels(prop.get_size_in_points()) + # Font colour is determined by the active wx.Pen + # TODO: It may be wise to cache font information + self.fontd[key] = font = wx.Font( # Cache the font and gc. + pointSize=round(size), + family=self.fontnames.get(prop.get_name(), wx.ROMAN), + style=self.fontangles[prop.get_style()], + weight=self.fontweights[prop.get_weight()]) + return font + + def points_to_pixels(self, points): + # docstring inherited + return points * (PIXELS_PER_INCH / 72.0 * self.dpi / 72.0) + + +class GraphicsContextWx(GraphicsContextBase): + """ + The graphics context provides the color, line styles, etc. + + This class stores a reference to a wxMemoryDC, and a + wxGraphicsContext that draws to it. Creating a wxGraphicsContext + seems to be fairly heavy, so these objects are cached based on the + bitmap object that is passed in. + + The base GraphicsContext stores colors as an RGB tuple on the unit + interval, e.g., (0.5, 0.0, 1.0). wxPython uses an int interval, but + since wxPython colour management is rather simple, I have not chosen + to implement a separate colour manager class. + """ + _capd = {'butt': wx.CAP_BUTT, + 'projecting': wx.CAP_PROJECTING, + 'round': wx.CAP_ROUND} + + _joind = {'bevel': wx.JOIN_BEVEL, + 'miter': wx.JOIN_MITER, + 'round': wx.JOIN_ROUND} + + _cache = weakref.WeakKeyDictionary() + + def __init__(self, bitmap, renderer): + super().__init__() + # assert self.Ok(), "wxMemoryDC not OK to use" + _log.debug("%s - __init__(): %s", type(self), bitmap) + + dc, gfx_ctx = self._cache.get(bitmap, (None, None)) + if dc is None: + dc = wx.MemoryDC(bitmap) + gfx_ctx = wx.GraphicsContext.Create(dc) + gfx_ctx._lastcliprect = None + self._cache[bitmap] = dc, gfx_ctx + + self.bitmap = bitmap + self.dc = dc + self.gfx_ctx = gfx_ctx + self._pen = wx.Pen('BLACK', 1, wx.SOLID) + gfx_ctx.SetPen(self._pen) + self.renderer = renderer + + def select(self): + """Select the current bitmap into this wxDC instance.""" + if sys.platform == 'win32': + self.dc.SelectObject(self.bitmap) + self.IsSelected = True + + def unselect(self): + """Select a Null bitmap into this wxDC instance.""" + if sys.platform == 'win32': + self.dc.SelectObject(wx.NullBitmap) + self.IsSelected = False + + def set_foreground(self, fg, isRGBA=None): + # docstring inherited + # Implementation note: wxPython has a separate concept of pen and + # brush - the brush fills any outline trace left by the pen. + # Here we set both to the same colour - if a figure is not to be + # filled, the renderer will set the brush to be transparent + # Same goes for text foreground... + _log.debug("%s - set_foreground()", type(self)) + self.select() + super().set_foreground(fg, isRGBA) + + self._pen.SetColour(self.get_wxcolour(self.get_rgb())) + self.gfx_ctx.SetPen(self._pen) + self.unselect() + + def set_linewidth(self, w): + # docstring inherited + w = float(w) + _log.debug("%s - set_linewidth()", type(self)) + self.select() + if 0 < w < 1: + w = 1 + super().set_linewidth(w) + lw = int(self.renderer.points_to_pixels(self._linewidth)) + if lw == 0: + lw = 1 + self._pen.SetWidth(lw) + self.gfx_ctx.SetPen(self._pen) + self.unselect() + + def set_capstyle(self, cs): + # docstring inherited + _log.debug("%s - set_capstyle()", type(self)) + self.select() + super().set_capstyle(cs) + self._pen.SetCap(GraphicsContextWx._capd[self._capstyle]) + self.gfx_ctx.SetPen(self._pen) + self.unselect() + + def set_joinstyle(self, js): + # docstring inherited + _log.debug("%s - set_joinstyle()", type(self)) + self.select() + super().set_joinstyle(js) + self._pen.SetJoin(GraphicsContextWx._joind[self._joinstyle]) + self.gfx_ctx.SetPen(self._pen) + self.unselect() + + def get_wxcolour(self, color): + """Convert an RGB(A) color to a wx.Colour.""" + _log.debug("%s - get_wx_color()", type(self)) + return wx.Colour(*[int(255 * x) for x in color]) + + +class _FigureCanvasWxBase(FigureCanvasBase, wx.Panel): + """ + The FigureCanvas contains the figure and does event handling. + + In the wxPython backend, it is derived from wxPanel, and (usually) lives + inside a frame instantiated by a FigureManagerWx. The parent window + probably implements a wx.Sizer to control the displayed control size - but + we give a hint as to our preferred minimum size. + """ + + required_interactive_framework = "wx" + _timer_cls = TimerWx + manager_class = _api.classproperty(lambda cls: FigureManagerWx) + + keyvald = { + wx.WXK_CONTROL: 'control', + wx.WXK_SHIFT: 'shift', + wx.WXK_ALT: 'alt', + wx.WXK_CAPITAL: 'caps_lock', + wx.WXK_LEFT: 'left', + wx.WXK_UP: 'up', + wx.WXK_RIGHT: 'right', + wx.WXK_DOWN: 'down', + wx.WXK_ESCAPE: 'escape', + wx.WXK_F1: 'f1', + wx.WXK_F2: 'f2', + wx.WXK_F3: 'f3', + wx.WXK_F4: 'f4', + wx.WXK_F5: 'f5', + wx.WXK_F6: 'f6', + wx.WXK_F7: 'f7', + wx.WXK_F8: 'f8', + wx.WXK_F9: 'f9', + wx.WXK_F10: 'f10', + wx.WXK_F11: 'f11', + wx.WXK_F12: 'f12', + wx.WXK_SCROLL: 'scroll_lock', + wx.WXK_PAUSE: 'break', + wx.WXK_BACK: 'backspace', + wx.WXK_RETURN: 'enter', + wx.WXK_INSERT: 'insert', + wx.WXK_DELETE: 'delete', + wx.WXK_HOME: 'home', + wx.WXK_END: 'end', + wx.WXK_PAGEUP: 'pageup', + wx.WXK_PAGEDOWN: 'pagedown', + wx.WXK_NUMPAD0: '0', + wx.WXK_NUMPAD1: '1', + wx.WXK_NUMPAD2: '2', + wx.WXK_NUMPAD3: '3', + wx.WXK_NUMPAD4: '4', + wx.WXK_NUMPAD5: '5', + wx.WXK_NUMPAD6: '6', + wx.WXK_NUMPAD7: '7', + wx.WXK_NUMPAD8: '8', + wx.WXK_NUMPAD9: '9', + wx.WXK_NUMPAD_ADD: '+', + wx.WXK_NUMPAD_SUBTRACT: '-', + wx.WXK_NUMPAD_MULTIPLY: '*', + wx.WXK_NUMPAD_DIVIDE: '/', + wx.WXK_NUMPAD_DECIMAL: 'dec', + wx.WXK_NUMPAD_ENTER: 'enter', + wx.WXK_NUMPAD_UP: 'up', + wx.WXK_NUMPAD_RIGHT: 'right', + wx.WXK_NUMPAD_DOWN: 'down', + wx.WXK_NUMPAD_LEFT: 'left', + wx.WXK_NUMPAD_PAGEUP: 'pageup', + wx.WXK_NUMPAD_PAGEDOWN: 'pagedown', + wx.WXK_NUMPAD_HOME: 'home', + wx.WXK_NUMPAD_END: 'end', + wx.WXK_NUMPAD_INSERT: 'insert', + wx.WXK_NUMPAD_DELETE: 'delete', + } + + def __init__(self, parent, id, figure=None): + """ + Initialize a FigureWx instance. + + - Initialize the FigureCanvasBase and wxPanel parents. + - Set event handlers for resize, paint, and keyboard and mouse + interaction. + """ + + FigureCanvasBase.__init__(self, figure) + size = wx.Size(*map(math.ceil, self.figure.bbox.size)) + if wx.Platform != '__WXMSW__': + size = parent.FromDIP(size) + # Set preferred window size hint - helps the sizer, if one is connected + wx.Panel.__init__(self, parent, id, size=size) + self.bitmap = None + self._isDrawn = False + self._rubberband_rect = None + self._rubberband_pen_black = wx.Pen('BLACK', 1, wx.PENSTYLE_SHORT_DASH) + self._rubberband_pen_white = wx.Pen('WHITE', 1, wx.PENSTYLE_SOLID) + + self.Bind(wx.EVT_SIZE, self._on_size) + self.Bind(wx.EVT_PAINT, self._on_paint) + self.Bind(wx.EVT_CHAR_HOOK, self._on_key_down) + self.Bind(wx.EVT_KEY_UP, self._on_key_up) + self.Bind(wx.EVT_LEFT_DOWN, self._on_mouse_button) + self.Bind(wx.EVT_LEFT_DCLICK, self._on_mouse_button) + self.Bind(wx.EVT_LEFT_UP, self._on_mouse_button) + self.Bind(wx.EVT_MIDDLE_DOWN, self._on_mouse_button) + self.Bind(wx.EVT_MIDDLE_DCLICK, self._on_mouse_button) + self.Bind(wx.EVT_MIDDLE_UP, self._on_mouse_button) + self.Bind(wx.EVT_RIGHT_DOWN, self._on_mouse_button) + self.Bind(wx.EVT_RIGHT_DCLICK, self._on_mouse_button) + self.Bind(wx.EVT_RIGHT_UP, self._on_mouse_button) + self.Bind(wx.EVT_MOUSE_AUX1_DOWN, self._on_mouse_button) + self.Bind(wx.EVT_MOUSE_AUX1_UP, self._on_mouse_button) + self.Bind(wx.EVT_MOUSE_AUX2_DOWN, self._on_mouse_button) + self.Bind(wx.EVT_MOUSE_AUX2_UP, self._on_mouse_button) + self.Bind(wx.EVT_MOUSE_AUX1_DCLICK, self._on_mouse_button) + self.Bind(wx.EVT_MOUSE_AUX2_DCLICK, self._on_mouse_button) + self.Bind(wx.EVT_MOUSEWHEEL, self._on_mouse_wheel) + self.Bind(wx.EVT_MOTION, self._on_motion) + self.Bind(wx.EVT_ENTER_WINDOW, self._on_enter) + self.Bind(wx.EVT_LEAVE_WINDOW, self._on_leave) + + self.Bind(wx.EVT_MOUSE_CAPTURE_CHANGED, self._on_capture_lost) + self.Bind(wx.EVT_MOUSE_CAPTURE_LOST, self._on_capture_lost) + + self.SetBackgroundStyle(wx.BG_STYLE_PAINT) # Reduce flicker. + self.SetBackgroundColour(wx.WHITE) + + if wx.Platform == '__WXMAC__': + # Initial scaling. Other platforms handle this automatically + dpiScale = self.GetDPIScaleFactor() + self.SetInitialSize(self.GetSize()*(1/dpiScale)) + self._set_device_pixel_ratio(dpiScale) + + def Copy_to_Clipboard(self, event=None): + """Copy bitmap of canvas to system clipboard.""" + bmp_obj = wx.BitmapDataObject() + bmp_obj.SetBitmap(self.bitmap) + + if not wx.TheClipboard.IsOpened(): + open_success = wx.TheClipboard.Open() + if open_success: + wx.TheClipboard.SetData(bmp_obj) + wx.TheClipboard.Flush() + wx.TheClipboard.Close() + + def _update_device_pixel_ratio(self, *args, **kwargs): + # We need to be careful in cases with mixed resolution displays if + # device_pixel_ratio changes. + if self._set_device_pixel_ratio(self.GetDPIScaleFactor()): + self.draw() + + def draw_idle(self): + # docstring inherited + _log.debug("%s - draw_idle()", type(self)) + self._isDrawn = False # Force redraw + # Triggering a paint event is all that is needed to defer drawing + # until later. The platform will send the event when it thinks it is + # a good time (usually as soon as there are no other events pending). + self.Refresh(eraseBackground=False) + + def flush_events(self): + # docstring inherited + wx.Yield() + + def start_event_loop(self, timeout=0): + # docstring inherited + if hasattr(self, '_event_loop'): + raise RuntimeError("Event loop already running") + timer = wx.Timer(self, id=wx.ID_ANY) + if timeout > 0: + timer.Start(int(timeout * 1000), oneShot=True) + self.Bind(wx.EVT_TIMER, self.stop_event_loop, id=timer.GetId()) + # Event loop handler for start/stop event loop + self._event_loop = wx.GUIEventLoop() + self._event_loop.Run() + timer.Stop() + + def stop_event_loop(self, event=None): + # docstring inherited + if hasattr(self, '_event_loop'): + if self._event_loop.IsRunning(): + self._event_loop.Exit() + del self._event_loop + + def _get_imagesave_wildcards(self): + """Return the wildcard string for the filesave dialog.""" + default_filetype = self.get_default_filetype() + filetypes = self.get_supported_filetypes_grouped() + sorted_filetypes = sorted(filetypes.items()) + wildcards = [] + extensions = [] + filter_index = 0 + for i, (name, exts) in enumerate(sorted_filetypes): + ext_list = ';'.join(['*.%s' % ext for ext in exts]) + extensions.append(exts[0]) + wildcard = f'{name} ({ext_list})|{ext_list}' + if default_filetype in exts: + filter_index = i + wildcards.append(wildcard) + wildcards = '|'.join(wildcards) + return wildcards, extensions, filter_index + + def gui_repaint(self, drawDC=None): + """ + Update the displayed image on the GUI canvas, using the supplied + wx.PaintDC device context. + """ + _log.debug("%s - gui_repaint()", type(self)) + # The "if self" check avoids a "wrapped C/C++ object has been deleted" + # RuntimeError if doing things after window is closed. + if not (self and self.IsShownOnScreen()): + return + if not drawDC: # not called from OnPaint use a ClientDC + drawDC = wx.ClientDC(self) + # For 'WX' backend on Windows, the bitmap cannot be in use by another + # DC (see GraphicsContextWx._cache). + bmp = (self.bitmap.ConvertToImage().ConvertToBitmap() + if wx.Platform == '__WXMSW__' + and isinstance(self.figure.canvas.get_renderer(), RendererWx) + else self.bitmap) + drawDC.DrawBitmap(bmp, 0, 0) + if self._rubberband_rect is not None: + # Some versions of wx+python don't support numpy.float64 here. + x0, y0, x1, y1 = map(round, self._rubberband_rect) + rect = [(x0, y0, x1, y0), (x1, y0, x1, y1), + (x0, y0, x0, y1), (x0, y1, x1, y1)] + drawDC.DrawLineList(rect, self._rubberband_pen_white) + drawDC.DrawLineList(rect, self._rubberband_pen_black) + + filetypes = { + **FigureCanvasBase.filetypes, + 'bmp': 'Windows bitmap', + 'jpeg': 'JPEG', + 'jpg': 'JPEG', + 'pcx': 'PCX', + 'png': 'Portable Network Graphics', + 'tif': 'Tagged Image Format File', + 'tiff': 'Tagged Image Format File', + 'xpm': 'X pixmap', + } + + def _on_paint(self, event): + """Called when wxPaintEvt is generated.""" + _log.debug("%s - _on_paint()", type(self)) + drawDC = wx.PaintDC(self) + if not self._isDrawn: + self.draw(drawDC=drawDC) + else: + self.gui_repaint(drawDC=drawDC) + drawDC.Destroy() + + def _on_size(self, event): + """ + Called when wxEventSize is generated. + + In this application we attempt to resize to fit the window, so it + is better to take the performance hit and redraw the whole window. + """ + self._update_device_pixel_ratio() + _log.debug("%s - _on_size()", type(self)) + sz = self.GetParent().GetSizer() + if sz: + si = sz.GetItem(self) + if sz and si and not si.Proportion and not si.Flag & wx.EXPAND: + # managed by a sizer, but with a fixed size + size = self.GetMinSize() + else: + # variable size + size = self.GetClientSize() + # Do not allow size to become smaller than MinSize + size.IncTo(self.GetMinSize()) + if getattr(self, "_width", None): + if size == (self._width, self._height): + # no change in size + return + self._width, self._height = size + self._isDrawn = False + + if self._width <= 1 or self._height <= 1: + return # Empty figure + + # Create a new, correctly sized bitmap + dpival = self.figure.dpi + if not wx.Platform == '__WXMSW__': + scale = self.GetDPIScaleFactor() + dpival /= scale + winch = self._width / dpival + hinch = self._height / dpival + self.figure.set_size_inches(winch, hinch, forward=False) + + # Rendering will happen on the associated paint event + # so no need to do anything here except to make sure + # the whole background is repainted. + self.Refresh(eraseBackground=False) + ResizeEvent("resize_event", self)._process() + self.draw_idle() + + @staticmethod + def _mpl_buttons(): + state = wx.GetMouseState() + # NOTE: Alternatively, we could use event.LeftIsDown() / etc. but this + # fails to report multiclick drags on macOS (other OSes have not been + # verified). + mod_table = [ + (MouseButton.LEFT, state.LeftIsDown()), + (MouseButton.RIGHT, state.RightIsDown()), + (MouseButton.MIDDLE, state.MiddleIsDown()), + (MouseButton.BACK, state.Aux1IsDown()), + (MouseButton.FORWARD, state.Aux2IsDown()), + ] + # State *after* press/release. + return {button for button, flag in mod_table if flag} + + @staticmethod + def _mpl_modifiers(event=None, *, exclude=None): + mod_table = [ + ("ctrl", wx.MOD_CONTROL, wx.WXK_CONTROL), + ("alt", wx.MOD_ALT, wx.WXK_ALT), + ("shift", wx.MOD_SHIFT, wx.WXK_SHIFT), + ] + if event is not None: + modifiers = event.GetModifiers() + return [name for name, mod, key in mod_table + if modifiers & mod and exclude != key] + else: + return [name for name, mod, key in mod_table + if wx.GetKeyState(key)] + + def _get_key(self, event): + keyval = event.KeyCode + if keyval in self.keyvald: + key = self.keyvald[keyval] + elif keyval < 256: + key = chr(keyval) + # wx always returns an uppercase, so make it lowercase if the shift + # key is not depressed (NOTE: this will not handle Caps Lock) + if not event.ShiftDown(): + key = key.lower() + else: + return None + mods = self._mpl_modifiers(event, exclude=keyval) + if "shift" in mods and key.isupper(): + mods.remove("shift") + return "+".join([*mods, key]) + + def _mpl_coords(self, pos=None): + """ + Convert a wx position, defaulting to the current cursor position, to + Matplotlib coordinates. + """ + if pos is None: + pos = wx.GetMouseState() + x, y = self.ScreenToClient(pos.X, pos.Y) + else: + x, y = pos.X, pos.Y + # flip y so y=0 is bottom of canvas + if not wx.Platform == '__WXMSW__': + scale = self.GetDPIScaleFactor() + return x*scale, self.figure.bbox.height - y*scale + else: + return x, self.figure.bbox.height - y + + def _on_key_down(self, event): + """Capture key press.""" + KeyEvent("key_press_event", self, + self._get_key(event), *self._mpl_coords(), + guiEvent=event)._process() + if self: + event.Skip() + + def _on_key_up(self, event): + """Release key.""" + KeyEvent("key_release_event", self, + self._get_key(event), *self._mpl_coords(), + guiEvent=event)._process() + if self: + event.Skip() + + def set_cursor(self, cursor): + # docstring inherited + cursor = wx.Cursor(_api.check_getitem({ + cursors.MOVE: wx.CURSOR_HAND, + cursors.HAND: wx.CURSOR_HAND, + cursors.POINTER: wx.CURSOR_ARROW, + cursors.SELECT_REGION: wx.CURSOR_CROSS, + cursors.WAIT: wx.CURSOR_WAIT, + cursors.RESIZE_HORIZONTAL: wx.CURSOR_SIZEWE, + cursors.RESIZE_VERTICAL: wx.CURSOR_SIZENS, + }, cursor=cursor)) + self.SetCursor(cursor) + self.Refresh() + + def _set_capture(self, capture=True): + """Control wx mouse capture.""" + if self.HasCapture(): + self.ReleaseMouse() + if capture: + self.CaptureMouse() + + def _on_capture_lost(self, event): + """Capture changed or lost""" + self._set_capture(False) + + def _on_mouse_button(self, event): + """Start measuring on an axis.""" + event.Skip() + self._set_capture(event.ButtonDown() or event.ButtonDClick()) + x, y = self._mpl_coords(event) + button_map = { + wx.MOUSE_BTN_LEFT: MouseButton.LEFT, + wx.MOUSE_BTN_MIDDLE: MouseButton.MIDDLE, + wx.MOUSE_BTN_RIGHT: MouseButton.RIGHT, + wx.MOUSE_BTN_AUX1: MouseButton.BACK, + wx.MOUSE_BTN_AUX2: MouseButton.FORWARD, + } + button = event.GetButton() + button = button_map.get(button, button) + modifiers = self._mpl_modifiers(event) + if event.ButtonDown(): + MouseEvent("button_press_event", self, x, y, button, + modifiers=modifiers, guiEvent=event)._process() + elif event.ButtonDClick(): + MouseEvent("button_press_event", self, x, y, button, dblclick=True, + modifiers=modifiers, guiEvent=event)._process() + elif event.ButtonUp(): + MouseEvent("button_release_event", self, x, y, button, + modifiers=modifiers, guiEvent=event)._process() + + def _on_mouse_wheel(self, event): + """Translate mouse wheel events into matplotlib events""" + x, y = self._mpl_coords(event) + # Convert delta/rotation/rate into a floating point step size + step = event.LinesPerAction * event.WheelRotation / event.WheelDelta + # Done handling event + event.Skip() + # Mac gives two events for every wheel event; skip every second one. + if wx.Platform == '__WXMAC__': + if not hasattr(self, '_skipwheelevent'): + self._skipwheelevent = True + elif self._skipwheelevent: + self._skipwheelevent = False + return # Return without processing event + else: + self._skipwheelevent = True + MouseEvent("scroll_event", self, x, y, step=step, + modifiers=self._mpl_modifiers(event), + guiEvent=event)._process() + + def _on_motion(self, event): + """Start measuring on an axis.""" + event.Skip() + MouseEvent("motion_notify_event", self, + *self._mpl_coords(event), + buttons=self._mpl_buttons(), + modifiers=self._mpl_modifiers(event), + guiEvent=event)._process() + + def _on_enter(self, event): + """Mouse has entered the window.""" + event.Skip() + LocationEvent("figure_enter_event", self, + *self._mpl_coords(event), + modifiers=self._mpl_modifiers(), + guiEvent=event)._process() + + def _on_leave(self, event): + """Mouse has left the window.""" + event.Skip() + LocationEvent("figure_leave_event", self, + *self._mpl_coords(event), + modifiers=self._mpl_modifiers(), + guiEvent=event)._process() + + +class FigureCanvasWx(_FigureCanvasWxBase): + # Rendering to a Wx canvas using the deprecated Wx renderer. + + def draw(self, drawDC=None): + """ + Render the figure using RendererWx instance renderer, or using a + previously defined renderer if none is specified. + """ + _log.debug("%s - draw()", type(self)) + self.renderer = RendererWx(self.bitmap, self.figure.dpi) + self.figure.draw(self.renderer) + self._isDrawn = True + self.gui_repaint(drawDC=drawDC) + + def _print_image(self, filetype, filename): + bitmap = wx.Bitmap(math.ceil(self.figure.bbox.width), + math.ceil(self.figure.bbox.height)) + self.figure.draw(RendererWx(bitmap, self.figure.dpi)) + saved_obj = (bitmap.ConvertToImage() + if cbook.is_writable_file_like(filename) + else bitmap) + if not saved_obj.SaveFile(filename, filetype): + raise RuntimeError(f'Could not save figure to {filename}') + # draw() is required here since bits of state about the last renderer + # are strewn about the artist draw methods. Do not remove the draw + # without first verifying that these have been cleaned up. The artist + # contains() methods will fail otherwise. + if self._isDrawn: + self.draw() + # The "if self" check avoids a "wrapped C/C++ object has been deleted" + # RuntimeError if doing things after window is closed. + if self: + self.Refresh() + + print_bmp = functools.partialmethod( + _print_image, wx.BITMAP_TYPE_BMP) + print_jpeg = print_jpg = functools.partialmethod( + _print_image, wx.BITMAP_TYPE_JPEG) + print_pcx = functools.partialmethod( + _print_image, wx.BITMAP_TYPE_PCX) + print_png = functools.partialmethod( + _print_image, wx.BITMAP_TYPE_PNG) + print_tiff = print_tif = functools.partialmethod( + _print_image, wx.BITMAP_TYPE_TIF) + print_xpm = functools.partialmethod( + _print_image, wx.BITMAP_TYPE_XPM) + + +class FigureFrameWx(wx.Frame): + def __init__(self, num, fig, *, canvas_class): + # On non-Windows platform, explicitly set the position - fix + # positioning bug on some Linux platforms + if wx.Platform == '__WXMSW__': + pos = wx.DefaultPosition + else: + pos = wx.Point(20, 20) + super().__init__(parent=None, id=-1, pos=pos) + # Frame will be sized later by the Fit method + _log.debug("%s - __init__()", type(self)) + _set_frame_icon(self) + + self.canvas = canvas_class(self, -1, fig) + # Auto-attaches itself to self.canvas.manager + manager = FigureManagerWx(self.canvas, num, self) + + toolbar = self.canvas.manager.toolbar + if toolbar is not None: + self.SetToolBar(toolbar) + + # On Windows, canvas sizing must occur after toolbar addition; + # otherwise the toolbar further resizes the canvas. + w, h = map(math.ceil, fig.bbox.size) + self.canvas.SetInitialSize(self.FromDIP(wx.Size(w, h))) + self.canvas.SetMinSize(self.FromDIP(wx.Size(2, 2))) + self.canvas.SetFocus() + + self.Fit() + + self.Bind(wx.EVT_CLOSE, self._on_close) + + def _on_close(self, event): + _log.debug("%s - on_close()", type(self)) + CloseEvent("close_event", self.canvas)._process() + self.canvas.stop_event_loop() + # set FigureManagerWx.frame to None to prevent repeated attempts to + # close this frame from FigureManagerWx.destroy() + self.canvas.manager.frame = None + # remove figure manager from Gcf.figs + Gcf.destroy(self.canvas.manager) + try: # See issue 2941338. + self.canvas.mpl_disconnect(self.canvas.toolbar._id_drag) + except AttributeError: # If there's no toolbar. + pass + # Carry on with close event propagation, frame & children destruction + event.Skip() + + +class FigureManagerWx(FigureManagerBase): + """ + Container/controller for the FigureCanvas and GUI frame. + + It is instantiated by Gcf whenever a new figure is created. Gcf is + responsible for managing multiple instances of FigureManagerWx. + + Attributes + ---------- + canvas : `FigureCanvas` + a FigureCanvasWx(wx.Panel) instance + window : wxFrame + a wxFrame instance - wxpython.org/Phoenix/docs/html/Frame.html + """ + + def __init__(self, canvas, num, frame): + _log.debug("%s - __init__()", type(self)) + self.frame = self.window = frame + super().__init__(canvas, num) + + @classmethod + def create_with_canvas(cls, canvas_class, figure, num): + # docstring inherited + wxapp = wx.GetApp() or _create_wxapp() + frame = FigureFrameWx(num, figure, canvas_class=canvas_class) + manager = figure.canvas.manager + if mpl.is_interactive(): + manager.frame.Show() + figure.canvas.draw_idle() + return manager + + @classmethod + def start_main_loop(cls): + if not wx.App.IsMainLoopRunning(): + wxapp = wx.GetApp() + if wxapp is not None: + wxapp.MainLoop() + + def show(self): + # docstring inherited + self.frame.Show() + self.canvas.draw() + if mpl.rcParams['figure.raise_window']: + self.frame.Raise() + + def destroy(self, *args): + # docstring inherited + _log.debug("%s - destroy()", type(self)) + frame = self.frame + if frame: # Else, may have been already deleted, e.g. when closing. + # As this can be called from non-GUI thread from plt.close use + # wx.CallAfter to ensure thread safety. + wx.CallAfter(frame.Close) + + def full_screen_toggle(self): + # docstring inherited + self.frame.ShowFullScreen(not self.frame.IsFullScreen()) + + def get_window_title(self): + # docstring inherited + return self.window.GetTitle() + + def set_window_title(self, title): + # docstring inherited + self.window.SetTitle(title) + + def resize(self, width, height): + # docstring inherited + # Directly using SetClientSize doesn't handle the toolbar on Windows. + self.window.SetSize(self.window.ClientToWindowSize(wx.Size( + math.ceil(width), math.ceil(height)))) + + +def _load_bitmap(filename): + """ + Load a wx.Bitmap from a file in the "images" directory of the Matplotlib + data. + """ + return wx.Bitmap(str(cbook._get_data_path('images', filename))) + + +def _set_frame_icon(frame): + bundle = wx.IconBundle() + for image in ('matplotlib.png', 'matplotlib_large.png'): + icon = wx.Icon(_load_bitmap(image)) + if not icon.IsOk(): + return + bundle.AddIcon(icon) + frame.SetIcons(bundle) + + +class NavigationToolbar2Wx(NavigationToolbar2, wx.ToolBar): + def __init__(self, canvas, coordinates=True, *, style=wx.TB_BOTTOM): + wx.ToolBar.__init__(self, canvas.GetParent(), -1, style=style) + if wx.Platform == '__WXMAC__': + self.SetToolBitmapSize(self.GetToolBitmapSize()*self.GetDPIScaleFactor()) + + self.wx_ids = {} + for text, tooltip_text, image_file, callback in self.toolitems: + if text is None: + self.AddSeparator() + continue + self.wx_ids[text] = ( + self.AddTool( + -1, + bitmap=self._icon(f"{image_file}.svg"), + bmpDisabled=wx.NullBitmap, + label=text, shortHelp=tooltip_text, + kind=(wx.ITEM_CHECK if text in ["Pan", "Zoom"] + else wx.ITEM_NORMAL)) + .Id) + self.Bind(wx.EVT_TOOL, getattr(self, callback), + id=self.wx_ids[text]) + + self._coordinates = coordinates + if self._coordinates: + self.AddStretchableSpace() + self._label_text = wx.StaticText(self, style=wx.ALIGN_RIGHT) + self.AddControl(self._label_text) + + self.Realize() + + NavigationToolbar2.__init__(self, canvas) + + @staticmethod + def _icon(name): + """ + Construct a `wx.Bitmap` suitable for use as icon from an image file + *name*, including the extension and relative to Matplotlib's "images" + data directory. + """ + try: + dark = wx.SystemSettings.GetAppearance().IsDark() + except AttributeError: # wxpython < 4.1 + # copied from wx's IsUsingDarkBackground / GetLuminance. + bg = wx.SystemSettings.GetColour(wx.SYS_COLOUR_WINDOW) + fg = wx.SystemSettings.GetColour(wx.SYS_COLOUR_WINDOWTEXT) + # See wx.Colour.GetLuminance. + bg_lum = (.299 * bg.red + .587 * bg.green + .114 * bg.blue) / 255 + fg_lum = (.299 * fg.red + .587 * fg.green + .114 * fg.blue) / 255 + dark = fg_lum - bg_lum > .2 + + path = cbook._get_data_path('images', name) + if path.suffix == '.svg': + svg = path.read_bytes() + if dark: + svg = svg.replace(b'fill:black;', b'fill:white;') + toolbarIconSize = wx.ArtProvider().GetDIPSizeHint(wx.ART_TOOLBAR) + return wx.BitmapBundle.FromSVG(svg, toolbarIconSize) + else: + pilimg = PIL.Image.open(path) + # ensure RGBA as wx BitMap expects RGBA format + image = np.array(pilimg.convert("RGBA")) + if dark: + fg = wx.SystemSettings.GetColour(wx.SYS_COLOUR_WINDOWTEXT) + black_mask = (image[..., :3] == 0).all(axis=-1) + image[black_mask, :3] = (fg.Red(), fg.Green(), fg.Blue()) + return wx.Bitmap.FromBufferRGBA( + image.shape[1], image.shape[0], image.tobytes()) + + def _update_buttons_checked(self): + if "Pan" in self.wx_ids: + self.ToggleTool(self.wx_ids["Pan"], self.mode.name == "PAN") + if "Zoom" in self.wx_ids: + self.ToggleTool(self.wx_ids["Zoom"], self.mode.name == "ZOOM") + + def zoom(self, *args): + super().zoom(*args) + self._update_buttons_checked() + + def pan(self, *args): + super().pan(*args) + self._update_buttons_checked() + + def save_figure(self, *args): + # Fetch the required filename and file type. + filetypes, exts, filter_index = self.canvas._get_imagesave_wildcards() + default_file = self.canvas.get_default_filename() + dialog = wx.FileDialog( + self.canvas.GetParent(), "Save to file", + mpl.rcParams["savefig.directory"], default_file, filetypes, + wx.FD_SAVE | wx.FD_OVERWRITE_PROMPT) + dialog.SetFilterIndex(filter_index) + if dialog.ShowModal() == wx.ID_OK: + path = pathlib.Path(dialog.GetPath()) + _log.debug('%s - Save file path: %s', type(self), path) + fmt = exts[dialog.GetFilterIndex()] + ext = path.suffix[1:] + if ext in self.canvas.get_supported_filetypes() and fmt != ext: + # looks like they forgot to set the image type drop + # down, going with the extension. + _log.warning('extension %s did not match the selected ' + 'image type %s; going with %s', + ext, fmt, ext) + fmt = ext + # Save dir for next time, unless empty str (which means use cwd). + if mpl.rcParams["savefig.directory"]: + mpl.rcParams["savefig.directory"] = str(path.parent) + try: + self.canvas.figure.savefig(path, format=fmt) + return path + except Exception as e: + dialog = wx.MessageDialog( + parent=self.canvas.GetParent(), message=str(e), + caption='Matplotlib error') + dialog.ShowModal() + dialog.Destroy() + + def draw_rubberband(self, event, x0, y0, x1, y1): + height = self.canvas.figure.bbox.height + sf = 1 if wx.Platform == '__WXMSW__' else self.canvas.GetDPIScaleFactor() + self.canvas._rubberband_rect = (x0/sf, (height - y0)/sf, + x1/sf, (height - y1)/sf) + self.canvas.Refresh() + + def remove_rubberband(self): + self.canvas._rubberband_rect = None + self.canvas.Refresh() + + def set_message(self, s): + if self._coordinates: + self._label_text.SetLabel(s) + + def set_history_buttons(self): + can_backward = self._nav_stack._pos > 0 + can_forward = self._nav_stack._pos < len(self._nav_stack) - 1 + if 'Back' in self.wx_ids: + self.EnableTool(self.wx_ids['Back'], can_backward) + if 'Forward' in self.wx_ids: + self.EnableTool(self.wx_ids['Forward'], can_forward) + + +# tools for matplotlib.backend_managers.ToolManager: + +class ToolbarWx(ToolContainerBase, wx.ToolBar): + _icon_extension = '.svg' + + def __init__(self, toolmanager, parent=None, style=wx.TB_BOTTOM): + if parent is None: + parent = toolmanager.canvas.GetParent() + ToolContainerBase.__init__(self, toolmanager) + wx.ToolBar.__init__(self, parent, -1, style=style) + self._space = self.AddStretchableSpace() + self._label_text = wx.StaticText(self, style=wx.ALIGN_RIGHT) + self.AddControl(self._label_text) + self._toolitems = {} + self._groups = {} # Mapping of groups to the separator after them. + + def _get_tool_pos(self, tool): + """ + Find the position (index) of a wx.ToolBarToolBase in a ToolBar. + + ``ToolBar.GetToolPos`` is not useful because wx assigns the same Id to + all Separators and StretchableSpaces. + """ + pos, = (pos for pos in range(self.ToolsCount) + if self.GetToolByPos(pos) == tool) + return pos + + def add_toolitem(self, name, group, position, image_file, description, + toggle): + # Find or create the separator that follows this group. + if group not in self._groups: + self._groups[group] = self.InsertSeparator( + self._get_tool_pos(self._space)) + sep = self._groups[group] + # List all separators. + seps = [t for t in map(self.GetToolByPos, range(self.ToolsCount)) + if t.IsSeparator() and not t.IsStretchableSpace()] + # Find where to insert the tool. + if position >= 0: + # Find the start of the group by looking for the separator + # preceding this one; then move forward from it. + start = (0 if sep == seps[0] + else self._get_tool_pos(seps[seps.index(sep) - 1]) + 1) + else: + # Move backwards from this separator. + start = self._get_tool_pos(sep) + 1 + idx = start + position + if image_file: + bmp = NavigationToolbar2Wx._icon(image_file) + kind = wx.ITEM_NORMAL if not toggle else wx.ITEM_CHECK + tool = self.InsertTool(idx, -1, name, bmp, wx.NullBitmap, kind, + description or "") + else: + size = (self.GetTextExtent(name)[0] + 10, -1) + if toggle: + control = wx.ToggleButton(self, -1, name, size=size) + else: + control = wx.Button(self, -1, name, size=size) + tool = self.InsertControl(idx, control, label=name) + self.Realize() + + def handler(event): + self.trigger_tool(name) + + if image_file: + self.Bind(wx.EVT_TOOL, handler, tool) + else: + control.Bind(wx.EVT_LEFT_DOWN, handler) + + self._toolitems.setdefault(name, []) + self._toolitems[name].append((tool, handler)) + + def toggle_toolitem(self, name, toggled): + if name not in self._toolitems: + return + for tool, handler in self._toolitems[name]: + if not tool.IsControl(): + self.ToggleTool(tool.Id, toggled) + else: + tool.GetControl().SetValue(toggled) + self.Refresh() + + def remove_toolitem(self, name): + for tool, handler in self._toolitems.pop(name, []): + self.DeleteTool(tool.Id) + + def set_message(self, s): + self._label_text.SetLabel(s) + + +@backend_tools._register_tool_class(_FigureCanvasWxBase) +class ConfigureSubplotsWx(backend_tools.ConfigureSubplotsBase): + def trigger(self, *args): + NavigationToolbar2Wx.configure_subplots(self) + + +@backend_tools._register_tool_class(_FigureCanvasWxBase) +class SaveFigureWx(backend_tools.SaveFigureBase): + def trigger(self, *args): + NavigationToolbar2Wx.save_figure( + self._make_classic_style_pseudo_toolbar()) + + +@backend_tools._register_tool_class(_FigureCanvasWxBase) +class RubberbandWx(backend_tools.RubberbandBase): + def draw_rubberband(self, x0, y0, x1, y1): + NavigationToolbar2Wx.draw_rubberband( + self._make_classic_style_pseudo_toolbar(), None, x0, y0, x1, y1) + + def remove_rubberband(self): + NavigationToolbar2Wx.remove_rubberband( + self._make_classic_style_pseudo_toolbar()) + + +class _HelpDialog(wx.Dialog): + _instance = None # a reference to an open dialog singleton + headers = [("Action", "Shortcuts", "Description")] + widths = [100, 140, 300] + + def __init__(self, parent, help_entries): + super().__init__(parent, title="Help", + style=wx.DEFAULT_DIALOG_STYLE | wx.RESIZE_BORDER) + + sizer = wx.BoxSizer(wx.VERTICAL) + grid_sizer = wx.FlexGridSizer(0, 3, 8, 6) + # create and add the entries + bold = self.GetFont().MakeBold() + for r, row in enumerate(self.headers + help_entries): + for (col, width) in zip(row, self.widths): + label = wx.StaticText(self, label=col) + if r == 0: + label.SetFont(bold) + label.Wrap(width) + grid_sizer.Add(label, 0, 0, 0) + # finalize layout, create button + sizer.Add(grid_sizer, 0, wx.ALL, 6) + ok = wx.Button(self, wx.ID_OK) + sizer.Add(ok, 0, wx.ALIGN_CENTER_HORIZONTAL | wx.ALL, 8) + self.SetSizer(sizer) + sizer.Fit(self) + self.Layout() + self.Bind(wx.EVT_CLOSE, self._on_close) + ok.Bind(wx.EVT_BUTTON, self._on_close) + + def _on_close(self, event): + _HelpDialog._instance = None # remove global reference + self.DestroyLater() + event.Skip() + + @classmethod + def show(cls, parent, help_entries): + # if no dialog is shown, create one; otherwise just re-raise it + if cls._instance: + cls._instance.Raise() + return + cls._instance = cls(parent, help_entries) + cls._instance.Show() + + +@backend_tools._register_tool_class(_FigureCanvasWxBase) +class HelpWx(backend_tools.ToolHelpBase): + def trigger(self, *args): + _HelpDialog.show(self.figure.canvas.GetTopLevelParent(), + self._get_help_entries()) + + +@backend_tools._register_tool_class(_FigureCanvasWxBase) +class ToolCopyToClipboardWx(backend_tools.ToolCopyToClipboardBase): + def trigger(self, *args, **kwargs): + if not self.canvas._isDrawn: + self.canvas.draw() + if not self.canvas.bitmap.IsOk() or not wx.TheClipboard.Open(): + return + try: + wx.TheClipboard.SetData(wx.BitmapDataObject(self.canvas.bitmap)) + finally: + wx.TheClipboard.Close() + + +FigureManagerWx._toolbar2_class = NavigationToolbar2Wx +FigureManagerWx._toolmanager_toolbar_class = ToolbarWx + + +@_Backend.export +class _BackendWx(_Backend): + FigureCanvas = FigureCanvasWx + FigureManager = FigureManagerWx + mainloop = FigureManagerWx.start_main_loop diff --git a/shared/package-files/03/0392294f02103cfec11ec63a14ce85d10afb7e9c614df79b7f81af56230e4a08/test_stl.py b/shared/package-files/03/0392294f02103cfec11ec63a14ce85d10afb7e9c614df79b7f81af56230e4a08/test_stl.py new file mode 100644 index 0000000000000000000000000000000000000000..1524d02e6e3d3a971dd8cb88850af1075b9b0c0c --- /dev/null +++ b/shared/package-files/03/0392294f02103cfec11ec63a14ce85d10afb7e9c614df79b7f81af56230e4a08/test_stl.py @@ -0,0 +1,203 @@ +from statsmodels.compat.pandas import MONTH_END + +import numpy as np +from numpy.testing import assert_allclose +import pandas as pd +import pytest + +import statsmodels.datasets +from statsmodels.tsa.ar_model import AutoReg +from statsmodels.tsa.arima.model import ARIMA +from statsmodels.tsa.base.prediction import PredictionResults +from statsmodels.tsa.deterministic import Fourier +from statsmodels.tsa.exponential_smoothing.ets import ETSModel +from statsmodels.tsa.forecasting.stl import STLForecast +from statsmodels.tsa.seasonal import STL, DecomposeResult +from statsmodels.tsa.statespace.exponential_smoothing import ( + ExponentialSmoothing, +) + + +@pytest.fixture(scope="module") +def data(request): + rs = np.random.RandomState(987654321) + err = rs.standard_normal(500) + index = pd.date_range("1980-1-1", freq=MONTH_END, periods=500) + fourier = Fourier(12, 1) + terms = fourier.in_sample(index) + det = np.squeeze(np.asarray(terms @ np.array([[2], [1]]))) + for i in range(1, 500): + err[i] += 0.9 * err[i - 1] + det[i] + return pd.Series(err, index=index) + + +def test_smoke(data): + stlf = STLForecast(data, ARIMA, model_kwargs={"order": (2, 0, 0)}) + res = stlf.fit(fit_kwargs={}) + res.forecast(37) + assert isinstance(res.summary().as_text(), str) + assert isinstance(res.stl, STL) + assert isinstance(res.result, DecomposeResult) + assert isinstance(res.model, ARIMA) + assert hasattr(res.model_result, "forecast") + assert res.result.nobs == 500 + + +@pytest.mark.thread_unsafe(reason="Uses matplotlib") +@pytest.mark.matplotlib +def test_sharex(data, close_figures): + stlf = STLForecast(data, ARIMA, model_kwargs={"order": (2, 0, 0)}) + res = stlf.fit(fit_kwargs={}) + plt = res.result.plot() + grouper_view = plt.axes[0].get_shared_x_axes() + sibs = grouper_view.get_siblings(plt.axes[1]) + assert len(sibs) == 4 + + +MODELS = [ + (ARIMA, {"order": (2, 0, 0), "trend": "c"}), + (ExponentialSmoothing, {"trend": True}), + (AutoReg, {"lags": 2}), + (ETSModel, {}), +] +MODELS = MODELS[-1:] +IDS = [str(c[0]).split(".")[-1][:-2] for c in MODELS] + + +@pytest.mark.parametrize("config", MODELS, ids=IDS) +@pytest.mark.parametrize("horizon", [1, 7, 23]) +def test_equivalence_forecast(data, config, horizon, close_figures): + model, kwargs = config + + stl = STL(data) + stl_fit = stl.fit() + resids = data - stl_fit.seasonal + mod = model(resids, **kwargs) + fit_kwarg = {} + if model is ETSModel: + fit_kwarg["disp"] = False + res = mod.fit(**fit_kwarg) + stlf = STLForecast(data, model, model_kwargs=kwargs).fit(fit_kwargs=fit_kwarg) + + seasonal = np.asarray(stl_fit.seasonal)[-12:] + seasonal = np.tile(seasonal, 1 + horizon // 12) + fcast = res.forecast(horizon) + seasonal[:horizon] + actual = stlf.forecast(horizon) + assert_allclose(actual, fcast, rtol=1e-4) + if not hasattr(res, "get_prediction"): + return + pred = stlf.get_prediction(data.shape[0], data.shape[0] + horizon - 1) + assert isinstance(pred, PredictionResults) + assert_allclose(pred.predicted_mean, fcast, rtol=1e-4) + + half = data.shape[0] // 2 + stlf.get_prediction(half, data.shape[0] + horizon - 1) + stlf.get_prediction(half, data.shape[0] + horizon - 1, dynamic=True) + stlf.get_prediction(half, data.shape[0] + horizon - 1, dynamic=half // 2) + if hasattr(data, "index"): + loc = data.index[half + half // 2] + a = stlf.get_prediction( + half, data.shape[0] + horizon - 1, dynamic=loc.strftime("%Y-%m-%d") + ) + b = stlf.get_prediction( + half, data.shape[0] + horizon - 1, dynamic=loc.to_pydatetime() + ) + c = stlf.get_prediction(half, data.shape[0] + horizon - 1, dynamic=loc) + assert_allclose(a.predicted_mean, b.predicted_mean, rtol=1e-4) + assert_allclose(a.predicted_mean, c.predicted_mean, rtol=1e-4) + + +def test_exceptions(data): + class BadModel: + def __init__(self, *args, **kwargs): + pass + + with pytest.raises(AttributeError, match="model must expose"): + STLForecast(data, BadModel) + + class NoForecast(BadModel): + def fit(self, *args, **kwargs): + return BadModel() + + with pytest.raises(AttributeError, match="The model's result"): + STLForecast(data, NoForecast).fit() + + class BadResult: + def forecast(self, *args, **kwargs): + pass + + class FakeModel(BadModel): + def fit(self, *args, **kwargs): + return BadResult() + + with pytest.raises(AttributeError, match="The model result does not"): + STLForecast(data, FakeModel).fit().summary() + + class BadResultSummary(BadResult): + def summary(self, *args, **kwargs): + return object() + + class FakeModelSummary(BadModel): + def fit(self, *args, **kwargs): + return BadResultSummary() + + with pytest.raises(TypeError, match="The model result's summary"): + STLForecast(data, FakeModelSummary).fit().summary() + + +@pytest.fixture +def sunspots(): + df = statsmodels.datasets.sunspots.load_pandas().data + df.index = np.arange(df.shape[0]) + return df.iloc[:, 0] + + +def test_get_prediction(sunspots): + # GH7309 + stlf_model = STLForecast( + sunspots, model=ARIMA, model_kwargs={"order": (2, 2, 0)}, period=11 + ) + stlf_res = stlf_model.fit() + pred = stlf_res.get_prediction() + assert pred.predicted_mean.shape == (309,) + assert pred.var_pred_mean.shape == (309,) + + +@pytest.mark.parametrize("not_implemented", [True, False]) +def test_no_var_pred(sunspots, not_implemented, close_figures): + class DummyPred: + def __init__(self, predicted_mean, row_labels): + self.predicted_mean = predicted_mean + self.row_labels = row_labels + + def f(): + raise NotImplementedError + + if not_implemented: + self.forecast = property(f) + + class DummyRes: + def __init__(self, res): + self._res = res + + def forecast(self, *args, **kwargs): + return self._res.forecast(*args, **kwargs) + + def get_prediction(self, *args, **kwargs): + pred = self._res.get_prediction(*args, **kwargs) + + return DummyPred(pred.predicted_mean, pred.row_labels) + + class DummyMod: + def __init__(self, y): + self._mod = ARIMA(y) + + def fit(self, *args, **kwargs): + res = self._mod.fit(*args, **kwargs) + return DummyRes(res) + + stl_mod = STLForecast(sunspots, model=DummyMod, period=11) + stl_res = stl_mod.fit() + with pytest.warns(UserWarning, match="The variance of"): + pred = stl_res.get_prediction() + assert np.all(np.isnan(pred.var_pred_mean)) diff --git a/shared/package-files/03/0393257c3519e0ed7e8cc4d1b7458f7a6eeaa9c5b436bb2596cd6195cf02e3e4/General b/shared/package-files/03/0393257c3519e0ed7e8cc4d1b7458f7a6eeaa9c5b436bb2596cd6195cf02e3e4/General new file mode 100644 index 0000000000000000000000000000000000000000..68176daa4976b015fb79026f3053e74e4a7457ab Binary files /dev/null and b/shared/package-files/03/0393257c3519e0ed7e8cc4d1b7458f7a6eeaa9c5b436bb2596cd6195cf02e3e4/General differ diff --git a/shared/package-files/03/0393257c3519e0ed7e8cc4d1b7458f7a6eeaa9c5b436bb2596cd6195cf02e3e4/Mexico_City b/shared/package-files/03/0393257c3519e0ed7e8cc4d1b7458f7a6eeaa9c5b436bb2596cd6195cf02e3e4/Mexico_City new file mode 100644 index 0000000000000000000000000000000000000000..68176daa4976b015fb79026f3053e74e4a7457ab Binary files /dev/null and b/shared/package-files/03/0393257c3519e0ed7e8cc4d1b7458f7a6eeaa9c5b436bb2596cd6195cf02e3e4/Mexico_City differ diff --git a/shared/package-files/03/0393e8bccce3469db3b25175bf0dd37534238f8634748109fff1c3055fecbc63/memmap.pyi b/shared/package-files/03/0393e8bccce3469db3b25175bf0dd37534238f8634748109fff1c3055fecbc63/memmap.pyi new file mode 100644 index 0000000000000000000000000000000000000000..53278ff1122b126c90ac45ecbd1b0b26b09a037b --- /dev/null +++ b/shared/package-files/03/0393e8bccce3469db3b25175bf0dd37534238f8634748109fff1c3055fecbc63/memmap.pyi @@ -0,0 +1,25 @@ +import sys +from typing import Any + +import numpy as np + +if sys.version_info >= (3, 11): + from typing import assert_type +else: + from typing_extensions import assert_type + +memmap_obj: np.memmap[Any, np.dtype[np.str_]] + +assert_type(np.memmap.__array_priority__, float) +assert_type(memmap_obj.__array_priority__, float) +assert_type(memmap_obj.filename, str | None) +assert_type(memmap_obj.offset, int) +assert_type(memmap_obj.mode, str) +assert_type(memmap_obj.flush(), None) + +assert_type(np.memmap("file.txt", offset=5), np.memmap[Any, np.dtype[np.uint8]]) +assert_type(np.memmap(b"file.txt", dtype=np.float64, shape=(10, 3)), np.memmap[Any, np.dtype[np.float64]]) +with open("file.txt", "rb") as f: + assert_type(np.memmap(f, dtype=float, order="K"), np.memmap[Any, np.dtype[Any]]) + +assert_type(memmap_obj.__array_finalize__(object()), None) diff --git a/shared/package-files/03/0394331ddddca4306a0c4afc3ba47a8eac0b2a2aa42e575ee958df81d417a394/wheelfile.py b/shared/package-files/03/0394331ddddca4306a0c4afc3ba47a8eac0b2a2aa42e575ee958df81d417a394/wheelfile.py new file mode 100644 index 0000000000000000000000000000000000000000..0cefbdbd0bd892951c6b7889851fa1a4c6c6b861 --- /dev/null +++ b/shared/package-files/03/0394331ddddca4306a0c4afc3ba47a8eac0b2a2aa42e575ee958df81d417a394/wheelfile.py @@ -0,0 +1,196 @@ +from __future__ import annotations + +import csv +import hashlib +import os.path +import re +import stat +import time +from io import StringIO, TextIOWrapper +from zipfile import ZIP_DEFLATED, ZipFile, ZipInfo + +from wheel.cli import WheelError +from wheel.util import log, urlsafe_b64decode, urlsafe_b64encode + +# Non-greedy matching of an optional build number may be too clever (more +# invalid wheel filenames will match). Separate regex for .dist-info? +WHEEL_INFO_RE = re.compile( + r"""^(?P(?P[^\s-]+?)-(?P[^\s-]+?))(-(?P\d[^\s-]*))? + -(?P[^\s-]+?)-(?P[^\s-]+?)-(?P\S+)\.whl$""", + re.VERBOSE, +) +MINIMUM_TIMESTAMP = 315532800 # 1980-01-01 00:00:00 UTC + + +def get_zipinfo_datetime(timestamp=None): + # Some applications need reproducible .whl files, but they can't do this without + # forcing the timestamp of the individual ZipInfo objects. See issue #143. + timestamp = int(os.environ.get("SOURCE_DATE_EPOCH", timestamp or time.time())) + timestamp = max(timestamp, MINIMUM_TIMESTAMP) + return time.gmtime(timestamp)[0:6] + + +class WheelFile(ZipFile): + """A ZipFile derivative class that also reads SHA-256 hashes from + .dist-info/RECORD and checks any read files against those. + """ + + _default_algorithm = hashlib.sha256 + + def __init__(self, file, mode="r", compression=ZIP_DEFLATED): + basename = os.path.basename(file) + self.parsed_filename = WHEEL_INFO_RE.match(basename) + if not basename.endswith(".whl") or self.parsed_filename is None: + raise WheelError(f"Bad wheel filename {basename!r}") + + ZipFile.__init__(self, file, mode, compression=compression, allowZip64=True) + + self.dist_info_path = "{}.dist-info".format( + self.parsed_filename.group("namever") + ) + self.record_path = self.dist_info_path + "/RECORD" + self._file_hashes = {} + self._file_sizes = {} + if mode == "r": + # Ignore RECORD and any embedded wheel signatures + self._file_hashes[self.record_path] = None, None + self._file_hashes[self.record_path + ".jws"] = None, None + self._file_hashes[self.record_path + ".p7s"] = None, None + + # Fill in the expected hashes by reading them from RECORD + try: + record = self.open(self.record_path) + except KeyError: + raise WheelError(f"Missing {self.record_path} file") from None + + with record: + for line in csv.reader( + TextIOWrapper(record, newline="", encoding="utf-8") + ): + path, hash_sum, size = line + if not hash_sum: + continue + + algorithm, hash_sum = hash_sum.split("=") + try: + hashlib.new(algorithm) + except ValueError: + raise WheelError( + f"Unsupported hash algorithm: {algorithm}" + ) from None + + if algorithm.lower() in {"md5", "sha1"}: + raise WheelError( + "Weak hash algorithm ({}) is not permitted by PEP " + "427".format(algorithm) + ) + + self._file_hashes[path] = ( + algorithm, + urlsafe_b64decode(hash_sum.encode("ascii")), + ) + + def open(self, name_or_info, mode="r", pwd=None): + def _update_crc(newdata): + eof = ef._eof + update_crc_orig(newdata) + running_hash.update(newdata) + if eof and running_hash.digest() != expected_hash: + raise WheelError(f"Hash mismatch for file '{ef_name}'") + + ef_name = ( + name_or_info.filename if isinstance(name_or_info, ZipInfo) else name_or_info + ) + if ( + mode == "r" + and not ef_name.endswith("/") + and ef_name not in self._file_hashes + ): + raise WheelError(f"No hash found for file '{ef_name}'") + + ef = ZipFile.open(self, name_or_info, mode, pwd) + if mode == "r" and not ef_name.endswith("/"): + algorithm, expected_hash = self._file_hashes[ef_name] + if expected_hash is not None: + # Monkey patch the _update_crc method to also check for the hash from + # RECORD + running_hash = hashlib.new(algorithm) + update_crc_orig, ef._update_crc = ef._update_crc, _update_crc + + return ef + + def write_files(self, base_dir): + log.info(f"creating '{self.filename}' and adding '{base_dir}' to it") + deferred = [] + for root, dirnames, filenames in os.walk(base_dir): + # Sort the directory names so that `os.walk` will walk them in a + # defined order on the next iteration. + dirnames.sort() + for name in sorted(filenames): + path = os.path.normpath(os.path.join(root, name)) + if os.path.isfile(path): + arcname = os.path.relpath(path, base_dir).replace(os.path.sep, "/") + if arcname == self.record_path: + pass + elif root.endswith(".dist-info"): + deferred.append((path, arcname)) + else: + self.write(path, arcname) + + deferred.sort() + for path, arcname in deferred: + self.write(path, arcname) + + def write(self, filename, arcname=None, compress_type=None): + with open(filename, "rb") as f: + st = os.fstat(f.fileno()) + data = f.read() + + zinfo = ZipInfo( + arcname or filename, date_time=get_zipinfo_datetime(st.st_mtime) + ) + zinfo.external_attr = (stat.S_IMODE(st.st_mode) | stat.S_IFMT(st.st_mode)) << 16 + zinfo.compress_type = compress_type or self.compression + self.writestr(zinfo, data, compress_type) + + def writestr(self, zinfo_or_arcname, data, compress_type=None): + if isinstance(zinfo_or_arcname, str): + zinfo_or_arcname = ZipInfo( + zinfo_or_arcname, date_time=get_zipinfo_datetime() + ) + zinfo_or_arcname.compress_type = self.compression + zinfo_or_arcname.external_attr = (0o664 | stat.S_IFREG) << 16 + + if isinstance(data, str): + data = data.encode("utf-8") + + ZipFile.writestr(self, zinfo_or_arcname, data, compress_type) + fname = ( + zinfo_or_arcname.filename + if isinstance(zinfo_or_arcname, ZipInfo) + else zinfo_or_arcname + ) + log.info(f"adding '{fname}'") + if fname != self.record_path: + hash_ = self._default_algorithm(data) + self._file_hashes[fname] = ( + hash_.name, + urlsafe_b64encode(hash_.digest()).decode("ascii"), + ) + self._file_sizes[fname] = len(data) + + def close(self): + # Write RECORD + if self.fp is not None and self.mode == "w" and self._file_hashes: + data = StringIO() + writer = csv.writer(data, delimiter=",", quotechar='"', lineterminator="\n") + writer.writerows( + ( + (fname, algorithm + "=" + hash_, self._file_sizes[fname]) + for fname, (algorithm, hash_) in self._file_hashes.items() + ) + ) + writer.writerow((format(self.record_path), "", "")) + self.writestr(self.record_path, data.getvalue()) + + ZipFile.close(self) diff --git a/shared/package-files/03/03950ad7681549157802d8c38005c59ed3c5df5d9f328636701bf24c9953984f/test_check.py b/shared/package-files/03/03950ad7681549157802d8c38005c59ed3c5df5d9f328636701bf24c9953984f/test_check.py new file mode 100644 index 0000000000000000000000000000000000000000..e534aca1d47637ce8058740bfd4385719c505dab --- /dev/null +++ b/shared/package-files/03/03950ad7681549157802d8c38005c59ed3c5df5d9f328636701bf24c9953984f/test_check.py @@ -0,0 +1,163 @@ +"""Tests for distutils.command.check.""" +import os +import textwrap +import unittest +from test.support import run_unittest + +from distutils.command.check import check, HAS_DOCUTILS +from distutils.tests import support +from distutils.errors import DistutilsSetupError + +try: + import pygments +except ImportError: + pygments = None + + +HERE = os.path.dirname(__file__) + + +class CheckTestCase(support.LoggingSilencer, + support.TempdirManager, + unittest.TestCase): + + def _run(self, metadata=None, cwd=None, **options): + if metadata is None: + metadata = {} + if cwd is not None: + old_dir = os.getcwd() + os.chdir(cwd) + pkg_info, dist = self.create_dist(**metadata) + cmd = check(dist) + cmd.initialize_options() + for name, value in options.items(): + setattr(cmd, name, value) + cmd.ensure_finalized() + cmd.run() + if cwd is not None: + os.chdir(old_dir) + return cmd + + def test_check_metadata(self): + # let's run the command with no metadata at all + # by default, check is checking the metadata + # should have some warnings + cmd = self._run() + self.assertEqual(cmd._warnings, 2) + + # now let's add the required fields + # and run it again, to make sure we don't get + # any warning anymore + metadata = {'url': 'xxx', 'author': 'xxx', + 'author_email': 'xxx', + 'name': 'xxx', 'version': 'xxx'} + cmd = self._run(metadata) + self.assertEqual(cmd._warnings, 0) + + # now with the strict mode, we should + # get an error if there are missing metadata + self.assertRaises(DistutilsSetupError, self._run, {}, **{'strict': 1}) + + # and of course, no error when all metadata are present + cmd = self._run(metadata, strict=1) + self.assertEqual(cmd._warnings, 0) + + # now a test with non-ASCII characters + metadata = {'url': 'xxx', 'author': '\u00c9ric', + 'author_email': 'xxx', 'name': 'xxx', + 'version': 'xxx', + 'description': 'Something about esszet \u00df', + 'long_description': 'More things about esszet \u00df'} + cmd = self._run(metadata) + self.assertEqual(cmd._warnings, 0) + + @unittest.skipUnless(HAS_DOCUTILS, "won't test without docutils") + def test_check_document(self): + pkg_info, dist = self.create_dist() + cmd = check(dist) + + # let's see if it detects broken rest + broken_rest = 'title\n===\n\ntest' + msgs = cmd._check_rst_data(broken_rest) + self.assertEqual(len(msgs), 1) + + # and non-broken rest + rest = 'title\n=====\n\ntest' + msgs = cmd._check_rst_data(rest) + self.assertEqual(len(msgs), 0) + + @unittest.skipUnless(HAS_DOCUTILS, "won't test without docutils") + def test_check_restructuredtext(self): + # let's see if it detects broken rest in long_description + broken_rest = 'title\n===\n\ntest' + pkg_info, dist = self.create_dist(long_description=broken_rest) + cmd = check(dist) + cmd.check_restructuredtext() + self.assertEqual(cmd._warnings, 1) + + # let's see if we have an error with strict=1 + metadata = {'url': 'xxx', 'author': 'xxx', + 'author_email': 'xxx', + 'name': 'xxx', 'version': 'xxx', + 'long_description': broken_rest} + self.assertRaises(DistutilsSetupError, self._run, metadata, + **{'strict': 1, 'restructuredtext': 1}) + + # and non-broken rest, including a non-ASCII character to test #12114 + metadata['long_description'] = 'title\n=====\n\ntest \u00df' + cmd = self._run(metadata, strict=1, restructuredtext=1) + self.assertEqual(cmd._warnings, 0) + + # check that includes work to test #31292 + metadata['long_description'] = 'title\n=====\n\n.. include:: includetest.rst' + cmd = self._run(metadata, cwd=HERE, strict=1, restructuredtext=1) + self.assertEqual(cmd._warnings, 0) + + @unittest.skipUnless(HAS_DOCUTILS, "won't test without docutils") + def test_check_restructuredtext_with_syntax_highlight(self): + # Don't fail if there is a `code` or `code-block` directive + + example_rst_docs = [] + example_rst_docs.append(textwrap.dedent("""\ + Here's some code: + + .. code:: python + + def foo(): + pass + """)) + example_rst_docs.append(textwrap.dedent("""\ + Here's some code: + + .. code-block:: python + + def foo(): + pass + """)) + + for rest_with_code in example_rst_docs: + pkg_info, dist = self.create_dist(long_description=rest_with_code) + cmd = check(dist) + cmd.check_restructuredtext() + msgs = cmd._check_rst_data(rest_with_code) + if pygments is not None: + self.assertEqual(len(msgs), 0) + else: + self.assertEqual(len(msgs), 1) + self.assertEqual( + str(msgs[0][1]), + 'Cannot analyze code. Pygments package not found.' + ) + + def test_check_all(self): + + metadata = {'url': 'xxx', 'author': 'xxx'} + self.assertRaises(DistutilsSetupError, self._run, + {}, **{'strict': 1, + 'restructuredtext': 1}) + +def test_suite(): + return unittest.makeSuite(CheckTestCase) + +if __name__ == "__main__": + run_unittest(test_suite()) diff --git a/shared/package-files/03/039555990bf2543e833e97cc4aa09c2c43f94342b0704c0cffce226f1d4a1699/min_meta.h b/shared/package-files/03/039555990bf2543e833e97cc4aa09c2c43f94342b0704c0cffce226f1d4a1699/min_meta.h new file mode 100644 index 0000000000000000000000000000000000000000..1cc78609dd56eb8fbaa9da543c0757f1634c5d53 --- /dev/null +++ b/shared/package-files/03/039555990bf2543e833e97cc4aa09c2c43f94342b0704c0cffce226f1d4a1699/min_meta.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeMetaFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace at { +namespace meta { + +struct TORCH_API structured_min_dim : public at::impl::MetaBase { + + template + struct TORCH_API precompute_out { + + precompute_out set_dim(int64_t value) { + static_assert(DIM == false, "dim already set"); + precompute_out ret; +ret.dim = value; +return ret; + } + + int64_t dim; + }; + using meta_return_ty = precompute_out ; + meta_return_ty meta(const at::Tensor & self, int64_t dim, bool keepdim); +}; + +} // namespace native +} // namespace at diff --git a/shared/package-files/03/0396845a4788f43f1826cdf59764d500aaf74708d1d7c6893fd3eddd738d3017/cff.py b/shared/package-files/03/0396845a4788f43f1826cdf59764d500aaf74708d1d7c6893fd3eddd738d3017/cff.py new file mode 100644 index 0000000000000000000000000000000000000000..4c36c96a8dda3b7d45975470e6cf87d66ce1b053 --- /dev/null +++ b/shared/package-files/03/0396845a4788f43f1826cdf59764d500aaf74708d1d7c6893fd3eddd738d3017/cff.py @@ -0,0 +1,630 @@ +from collections import namedtuple +from fontTools.cffLib import ( + maxStackLimit, + TopDictIndex, + buildOrder, + topDictOperators, + topDictOperators2, + privateDictOperators, + privateDictOperators2, + FDArrayIndex, + FontDict, + VarStoreData, +) +from io import BytesIO +from fontTools.cffLib.specializer import specializeCommands, commandsToProgram +from fontTools.ttLib import newTable +from fontTools import varLib +from fontTools.varLib.models import allEqual +from fontTools.misc.loggingTools import deprecateFunction +from fontTools.misc.roundTools import roundFunc +from fontTools.misc.psCharStrings import T2CharString, T2OutlineExtractor +from fontTools.pens.t2CharStringPen import T2CharStringPen +from functools import partial + +from .errors import ( + VarLibCFFDictMergeError, + VarLibCFFPointTypeMergeError, + VarLibCFFHintTypeMergeError, + VarLibMergeError, +) + +# Backwards compatibility +MergeDictError = VarLibCFFDictMergeError +MergeTypeError = VarLibCFFPointTypeMergeError + + +def addCFFVarStore(varFont, varModel, varDataList, masterSupports): + fvarTable = varFont["fvar"] + axisKeys = [axis.axisTag for axis in fvarTable.axes] + varTupleList = varLib.builder.buildVarRegionList(masterSupports, axisKeys) + varStoreCFFV = varLib.builder.buildVarStore(varTupleList, varDataList) + + topDict = varFont["CFF2"].cff.topDictIndex[0] + topDict.VarStore = VarStoreData(otVarStore=varStoreCFFV) + if topDict.FDArray[0].vstore is None: + fdArray = topDict.FDArray + for fontDict in fdArray: + if hasattr(fontDict, "Private"): + fontDict.Private.vstore = topDict.VarStore + + +@deprecateFunction("Use fontTools.cffLib.CFFToCFF2.convertCFFToCFF2 instead.") +def convertCFFtoCFF2(varFont): + from fontTools.cffLib.CFFToCFF2 import convertCFFToCFF2 + + return convertCFFToCFF2(varFont) + + +def conv_to_int(num): + if isinstance(num, float) and num.is_integer(): + return int(num) + return num + + +pd_blend_fields = ( + "BlueValues", + "OtherBlues", + "FamilyBlues", + "FamilyOtherBlues", + "BlueScale", + "BlueShift", + "BlueFuzz", + "StdHW", + "StdVW", + "StemSnapH", + "StemSnapV", +) + + +def get_private(regionFDArrays, fd_index, ri, fd_map): + region_fdArray = regionFDArrays[ri] + region_fd_map = fd_map[fd_index] + if ri in region_fd_map: + region_fdIndex = region_fd_map[ri] + private = region_fdArray[region_fdIndex].Private + else: + private = None + return private + + +def merge_PrivateDicts(top_dicts, vsindex_dict, var_model, fd_map): + """ + I step through the FontDicts in the FDArray of the varfont TopDict. + For each varfont FontDict: + + * step through each key in FontDict.Private. + * For each key, step through each relevant source font Private dict, and + build a list of values to blend. + + The 'relevant' source fonts are selected by first getting the right + submodel using ``vsindex_dict[vsindex]``. The indices of the + ``subModel.locations`` are mapped to source font list indices by + assuming the latter order is the same as the order of the + ``var_model.locations``. I can then get the index of each subModel + location in the list of ``var_model.locations``. + """ + + topDict = top_dicts[0] + region_top_dicts = top_dicts[1:] + if hasattr(region_top_dicts[0], "FDArray"): + regionFDArrays = [fdTopDict.FDArray for fdTopDict in region_top_dicts] + else: + regionFDArrays = [[fdTopDict] for fdTopDict in region_top_dicts] + for fd_index, font_dict in enumerate(topDict.FDArray): + private_dict = font_dict.Private + vsindex = getattr(private_dict, "vsindex", 0) + # At the moment, no PrivateDict has a vsindex key, but let's support + # how it should work. See comment at end of + # merge_charstrings() - still need to optimize use of vsindex. + sub_model, _ = vsindex_dict[vsindex] + master_indices = [] + for loc in sub_model.locations[1:]: + i = var_model.locations.index(loc) - 1 + master_indices.append(i) + pds = [private_dict] + last_pd = private_dict + for ri in master_indices: + pd = get_private(regionFDArrays, fd_index, ri, fd_map) + # If the region font doesn't have this FontDict, just reference + # the last one used. + if pd is None: + pd = last_pd + else: + last_pd = pd + pds.append(pd) + num_masters = len(pds) + for key, value in private_dict.rawDict.items(): + dataList = [] + if key not in pd_blend_fields: + continue + if isinstance(value, list): + try: + values = [pd.rawDict[key] for pd in pds] + except KeyError: + print( + "Warning: {key} in default font Private dict is " + "missing from another font, and was " + "discarded.".format(key=key) + ) + continue + try: + values = zip(*values) + except IndexError: + raise VarLibCFFDictMergeError(key, value, values) + """ + Row 0 contains the first value from each master. + Convert each row from absolute values to relative + values from the previous row. + e.g for three masters, a list of values was: + master 0 OtherBlues = [-217,-205] + master 1 OtherBlues = [-234,-222] + master 1 OtherBlues = [-188,-176] + The call to zip() converts this to: + [(-217, -234, -188), (-205, -222, -176)] + and is converted finally to: + OtherBlues = [[-217, 17.0, 46.0], [-205, 0.0, 0.0]] + """ + prev_val_list = [0] * num_masters + any_points_differ = False + for val_list in values: + rel_list = [ + (val - prev_val_list[i]) for (i, val) in enumerate(val_list) + ] + if (not any_points_differ) and not allEqual(rel_list): + any_points_differ = True + prev_val_list = val_list + deltas = sub_model.getDeltas(rel_list) + # For PrivateDict BlueValues, the default font + # values are absolute, not relative to the prior value. + deltas[0] = val_list[0] + dataList.append(deltas) + # If there are no blend values,then + # we can collapse the blend lists. + if not any_points_differ: + dataList = [data[0] for data in dataList] + else: + values = [pd.rawDict[key] for pd in pds] + if not allEqual(values): + dataList = sub_model.getDeltas(values) + else: + dataList = values[0] + + # Convert numbers with no decimal part to an int + if isinstance(dataList, list): + for i, item in enumerate(dataList): + if isinstance(item, list): + for j, jtem in enumerate(item): + dataList[i][j] = conv_to_int(jtem) + else: + dataList[i] = conv_to_int(item) + else: + dataList = conv_to_int(dataList) + + private_dict.rawDict[key] = dataList + + +def _cff_or_cff2(font): + if "CFF " in font: + return font["CFF "] + return font["CFF2"] + + +def getfd_map(varFont, fonts_list): + """Since a subset source font may have fewer FontDicts in their + FDArray than the default font, we have to match up the FontDicts in + the different fonts . We do this with the FDSelect array, and by + assuming that the same glyph will reference matching FontDicts in + each source font. We return a mapping from fdIndex in the default + font to a dictionary which maps each master list index of each + region font to the equivalent fdIndex in the region font.""" + fd_map = {} + default_font = fonts_list[0] + region_fonts = fonts_list[1:] + num_regions = len(region_fonts) + topDict = _cff_or_cff2(default_font).cff.topDictIndex[0] + if not hasattr(topDict, "FDSelect"): + # All glyphs reference only one FontDict. + # Map the FD index for regions to index 0. + fd_map[0] = {ri: 0 for ri in range(num_regions)} + return fd_map + + gname_mapping = {} + default_fdSelect = topDict.FDSelect + glyphOrder = default_font.getGlyphOrder() + for gid, fdIndex in enumerate(default_fdSelect): + gname_mapping[glyphOrder[gid]] = fdIndex + if fdIndex not in fd_map: + fd_map[fdIndex] = {} + for ri, region_font in enumerate(region_fonts): + region_glyphOrder = region_font.getGlyphOrder() + region_topDict = _cff_or_cff2(region_font).cff.topDictIndex[0] + if not hasattr(region_topDict, "FDSelect"): + # All the glyphs share the same FontDict. Pick any glyph. + default_fdIndex = gname_mapping[region_glyphOrder[0]] + fd_map[default_fdIndex][ri] = 0 + else: + region_fdSelect = region_topDict.FDSelect + for gid, fdIndex in enumerate(region_fdSelect): + default_fdIndex = gname_mapping[region_glyphOrder[gid]] + region_map = fd_map[default_fdIndex] + if ri not in region_map: + region_map[ri] = fdIndex + return fd_map + + +CVarData = namedtuple("CVarData", "varDataList masterSupports vsindex_dict") + + +def merge_region_fonts(varFont, model, ordered_fonts_list, glyphOrder): + topDict = varFont["CFF2"].cff.topDictIndex[0] + top_dicts = [topDict] + [ + _cff_or_cff2(ttFont).cff.topDictIndex[0] for ttFont in ordered_fonts_list[1:] + ] + num_masters = len(model.mapping) + cvData = merge_charstrings(glyphOrder, num_masters, top_dicts, model) + fd_map = getfd_map(varFont, ordered_fonts_list) + merge_PrivateDicts(top_dicts, cvData.vsindex_dict, model, fd_map) + addCFFVarStore(varFont, model, cvData.varDataList, cvData.masterSupports) + + +def _get_cs(charstrings, glyphName, filterEmpty=False): + if glyphName not in charstrings: + return None + cs = charstrings[glyphName] + + if filterEmpty: + cs.decompile() + if cs.program == []: # CFF2 empty charstring + return None + elif ( + len(cs.program) <= 2 + and cs.program[-1] == "endchar" + and (len(cs.program) == 1 or type(cs.program[0]) in (int, float)) + ): # CFF1 empty charstring + return None + + return cs + + +def _add_new_vsindex( + model, key, masterSupports, vsindex_dict, vsindex_by_key, varDataList +): + varTupleIndexes = [] + for support in model.supports[1:]: + if support not in masterSupports: + masterSupports.append(support) + varTupleIndexes.append(masterSupports.index(support)) + var_data = varLib.builder.buildVarData(varTupleIndexes, None, False) + vsindex = len(vsindex_dict) + vsindex_by_key[key] = vsindex + vsindex_dict[vsindex] = (model, [key]) + varDataList.append(var_data) + return vsindex + + +def merge_charstrings(glyphOrder, num_masters, top_dicts, masterModel): + vsindex_dict = {} + vsindex_by_key = {} + varDataList = [] + masterSupports = [] + default_charstrings = top_dicts[0].CharStrings + for gid, gname in enumerate(glyphOrder): + # interpret empty non-default masters as missing glyphs from a sparse master + all_cs = [ + _get_cs(td.CharStrings, gname, i != 0) for i, td in enumerate(top_dicts) + ] + model, model_cs = masterModel.getSubModel(all_cs) + # create the first pass CFF2 charstring, from + # the default charstring. + default_charstring = model_cs[0] + var_pen = CFF2CharStringMergePen([], gname, num_masters, 0) + # We need to override outlineExtractor because these + # charstrings do have widths in the 'program'; we need to drop these + # values rather than post assertion error for them. + default_charstring.outlineExtractor = MergeOutlineExtractor + default_charstring.draw(var_pen) + + # Add the coordinates from all the other regions to the + # blend lists in the CFF2 charstring. + region_cs = model_cs[1:] + for region_idx, region_charstring in enumerate(region_cs, start=1): + var_pen.restart(region_idx) + region_charstring.outlineExtractor = MergeOutlineExtractor + region_charstring.draw(var_pen) + + # Collapse each coordinate list to a blend operator and its args. + new_cs = var_pen.getCharString( + private=default_charstring.private, + globalSubrs=default_charstring.globalSubrs, + var_model=model, + optimize=True, + ) + default_charstrings[gname] = new_cs + + if not region_cs: + continue + + if (not var_pen.seen_moveto) or ("blend" not in new_cs.program): + # If this is not a marking glyph, or if there are no blend + # arguments, then we can use vsindex 0. No need to + # check if we need a new vsindex. + continue + + # If the charstring required a new model, create + # a VarData table to go with, and set vsindex. + key = tuple(v is not None for v in all_cs) + try: + vsindex = vsindex_by_key[key] + except KeyError: + vsindex = _add_new_vsindex( + model, key, masterSupports, vsindex_dict, vsindex_by_key, varDataList + ) + # We do not need to check for an existing new_cs.private.vsindex, + # as we know it doesn't exist yet. + if vsindex != 0: + new_cs.program[:0] = [vsindex, "vsindex"] + + # If there is no variation in any of the charstrings, then vsindex_dict + # never gets built. This could still be needed if there is variation + # in the PrivatDict, so we will build the default data for vsindex = 0. + if not vsindex_dict: + key = (True,) * num_masters + _add_new_vsindex( + masterModel, key, masterSupports, vsindex_dict, vsindex_by_key, varDataList + ) + cvData = CVarData( + varDataList=varDataList, + masterSupports=masterSupports, + vsindex_dict=vsindex_dict, + ) + # XXX To do: optimize use of vsindex between the PrivateDicts and + # charstrings + return cvData + + +class CFFToCFF2OutlineExtractor(T2OutlineExtractor): + """This class is used to remove the initial width from the CFF + charstring without trying to add the width to self.nominalWidthX, + which is None.""" + + def popallWidth(self, evenOdd=0): + args = self.popall() + if not self.gotWidth: + if evenOdd ^ (len(args) % 2): + args = args[1:] + self.width = self.defaultWidthX + self.gotWidth = 1 + return args + + +class MergeOutlineExtractor(CFFToCFF2OutlineExtractor): + """Used to extract the charstring commands - including hints - from a + CFF charstring in order to merge it as another set of region data + into a CFF2 variable font charstring.""" + + def __init__( + self, + pen, + localSubrs, + globalSubrs, + nominalWidthX, + defaultWidthX, + private=None, + blender=None, + ): + super().__init__( + pen, localSubrs, globalSubrs, nominalWidthX, defaultWidthX, private, blender + ) + + def countHints(self): + args = self.popallWidth() + self.hintCount = self.hintCount + len(args) // 2 + return args + + def _hint_op(self, type, args): + self.pen.add_hint(type, args) + + def op_hstem(self, index): + args = self.countHints() + self._hint_op("hstem", args) + + def op_vstem(self, index): + args = self.countHints() + self._hint_op("vstem", args) + + def op_hstemhm(self, index): + args = self.countHints() + self._hint_op("hstemhm", args) + + def op_vstemhm(self, index): + args = self.countHints() + self._hint_op("vstemhm", args) + + def _get_hintmask(self, index): + if not self.hintMaskBytes: + args = self.countHints() + if args: + self._hint_op("vstemhm", args) + self.hintMaskBytes = (self.hintCount + 7) // 8 + hintMaskBytes, index = self.callingStack[-1].getBytes(index, self.hintMaskBytes) + return index, hintMaskBytes + + def op_hintmask(self, index): + index, hintMaskBytes = self._get_hintmask(index) + self.pen.add_hintmask("hintmask", [hintMaskBytes]) + return hintMaskBytes, index + + def op_cntrmask(self, index): + index, hintMaskBytes = self._get_hintmask(index) + self.pen.add_hintmask("cntrmask", [hintMaskBytes]) + return hintMaskBytes, index + + +class CFF2CharStringMergePen(T2CharStringPen): + """Pen to merge Type 2 CharStrings.""" + + def __init__( + self, default_commands, glyphName, num_masters, master_idx, roundTolerance=0.01 + ): + # For roundTolerance see https://github.com/fonttools/fonttools/issues/2838 + super().__init__( + width=None, glyphSet=None, CFF2=True, roundTolerance=roundTolerance + ) + self.pt_index = 0 + self._commands = default_commands + self.m_index = master_idx + self.num_masters = num_masters + self.prev_move_idx = 0 + self.seen_moveto = False + self.glyphName = glyphName + self.round = roundFunc(roundTolerance, round=round) + + def add_point(self, point_type, pt_coords): + if self.m_index == 0: + self._commands.append([point_type, [pt_coords]]) + else: + cmd = self._commands[self.pt_index] + if cmd[0] != point_type: + raise VarLibCFFPointTypeMergeError( + point_type, self.pt_index, len(cmd[1]), cmd[0], self.glyphName + ) + cmd[1].append(pt_coords) + self.pt_index += 1 + + def add_hint(self, hint_type, args): + if self.m_index == 0: + self._commands.append([hint_type, [args]]) + else: + cmd = self._commands[self.pt_index] + if cmd[0] != hint_type: + raise VarLibCFFHintTypeMergeError( + hint_type, self.pt_index, len(cmd[1]), cmd[0], self.glyphName + ) + cmd[1].append(args) + self.pt_index += 1 + + def add_hintmask(self, hint_type, abs_args): + # For hintmask, fonttools.cffLib.specializer.py expects + # each of these to be represented by two sequential commands: + # first holding only the operator name, with an empty arg list, + # second with an empty string as the op name, and the mask arg list. + if self.m_index == 0: + self._commands.append([hint_type, []]) + self._commands.append(["", [abs_args]]) + else: + cmd = self._commands[self.pt_index] + if cmd[0] != hint_type: + raise VarLibCFFHintTypeMergeError( + hint_type, self.pt_index, len(cmd[1]), cmd[0], self.glyphName + ) + self.pt_index += 1 + cmd = self._commands[self.pt_index] + cmd[1].append(abs_args) + self.pt_index += 1 + + def _moveTo(self, pt): + if not self.seen_moveto: + self.seen_moveto = True + pt_coords = self._p(pt) + self.add_point("rmoveto", pt_coords) + # I set prev_move_idx here because add_point() + # can change self.pt_index. + self.prev_move_idx = self.pt_index - 1 + + def _lineTo(self, pt): + pt_coords = self._p(pt) + self.add_point("rlineto", pt_coords) + + def _curveToOne(self, pt1, pt2, pt3): + _p = self._p + pt_coords = _p(pt1) + _p(pt2) + _p(pt3) + self.add_point("rrcurveto", pt_coords) + + def _closePath(self): + pass + + def _endPath(self): + pass + + def restart(self, region_idx): + self.pt_index = 0 + self.m_index = region_idx + self._p0 = (0, 0) + + def getCommands(self): + return self._commands + + def reorder_blend_args(self, commands, get_delta_func): + """ + We first re-order the master coordinate values. + For a moveto to lineto, the args are now arranged as:: + + [ [master_0 x,y], [master_1 x,y], [master_2 x,y] ] + + We re-arrange this to:: + + [ [master_0 x, master_1 x, master_2 x], + [master_0 y, master_1 y, master_2 y] + ] + + If the master values are all the same, we collapse the list to + as single value instead of a list. + + We then convert this to:: + + [ [master_0 x] + [x delta tuple] + [numBlends=1] + [master_0 y] + [y delta tuple] + [numBlends=1] + ] + """ + for cmd in commands: + # arg[i] is the set of arguments for this operator from master i. + args = cmd[1] + m_args = zip(*args) + # m_args[n] is now all num_master args for the i'th argument + # for this operation. + cmd[1] = list(m_args) + lastOp = None + for cmd in commands: + op = cmd[0] + # masks are represented by two cmd's: first has only op names, + # second has only args. + if lastOp in ["hintmask", "cntrmask"]: + coord = list(cmd[1]) + if not allEqual(coord): + raise VarLibMergeError( + "Hintmask values cannot differ between source fonts." + ) + cmd[1] = [coord[0][0]] + else: + coords = cmd[1] + new_coords = [] + for coord in coords: + if allEqual(coord): + new_coords.append(coord[0]) + else: + # convert to deltas + deltas = get_delta_func(coord)[1:] + coord = [coord[0]] + deltas + coord.append(1) + new_coords.append(coord) + cmd[1] = new_coords + lastOp = op + return commands + + def getCharString( + self, private=None, globalSubrs=None, var_model=None, optimize=True + ): + commands = self._commands + commands = self.reorder_blend_args( + commands, partial(var_model.getDeltas, round=self.round) + ) + if optimize: + commands = specializeCommands( + commands, generalizeFirst=False, maxstack=maxStackLimit + ) + program = commandsToProgram(commands) + charString = T2CharString( + program=program, private=private, globalSubrs=globalSubrs + ) + return charString diff --git a/shared/package-files/03/0399662496ee162127c175b91e84b16f998b9042b94fdb794f458931c467f7d8/rrelu_compositeimplicitautograd_dispatch.h b/shared/package-files/03/0399662496ee162127c175b91e84b16f998b9042b94fdb794f458931c467f7d8/rrelu_compositeimplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..1f4c6cd0a813945c3398ed15de5587bea285bff0 --- /dev/null +++ b/shared/package-files/03/0399662496ee162127c175b91e84b16f998b9042b94fdb794f458931c467f7d8/rrelu_compositeimplicitautograd_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeimplicitautograd { + +TORCH_API at::Tensor rrelu(const at::Tensor & self, const at::Scalar & lower=0.125, const at::Scalar & upper=0.3333333333333333, bool training=false, c10::optional generator=c10::nullopt); +TORCH_API at::Tensor & rrelu_(at::Tensor & self, const at::Scalar & lower=0.125, const at::Scalar & upper=0.3333333333333333, bool training=false, c10::optional generator=c10::nullopt); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/shared/package-files/03/039c2663fc6e6f0f8a8fc6ee0f8590f49b918a310a759992471004ea7cfca2b6/native_group_norm_cpu_dispatch.h b/shared/package-files/03/039c2663fc6e6f0f8a8fc6ee0f8590f49b918a310a759992471004ea7cfca2b6/native_group_norm_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..21effc0f3bb48d27f0463e9a51a6320b0c514875 --- /dev/null +++ b/shared/package-files/03/039c2663fc6e6f0f8a8fc6ee0f8590f49b918a310a759992471004ea7cfca2b6/native_group_norm_cpu_dispatch.h @@ -0,0 +1,24 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cpu { + +TORCH_API ::std::tuple native_group_norm(const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, int64_t N, int64_t C, int64_t HxW, int64_t group, double eps); +TORCH_API ::std::tuple native_group_norm_symint(const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, c10::SymInt N, c10::SymInt C, c10::SymInt HxW, int64_t group, double eps); + +} // namespace cpu +} // namespace at diff --git a/shared/package-files/03/03a21ba55958f4820fa1228c15147d0cc44e7705d44837361ec012b9e3929eaa/Ushuaia b/shared/package-files/03/03a21ba55958f4820fa1228c15147d0cc44e7705d44837361ec012b9e3929eaa/Ushuaia new file mode 100644 index 0000000000000000000000000000000000000000..e74ce049c793c96e855ebb15779887a48622bf26 Binary files /dev/null and b/shared/package-files/03/03a21ba55958f4820fa1228c15147d0cc44e7705d44837361ec012b9e3929eaa/Ushuaia differ diff --git a/shared/package-files/03/03a6c3a6c55a8f0ead89a7ad2daa767f33cd6de515bed35d7e106fc82021ba67/replication_pad1d_backward_cpu_dispatch.h b/shared/package-files/03/03a6c3a6c55a8f0ead89a7ad2daa767f33cd6de515bed35d7e106fc82021ba67/replication_pad1d_backward_cpu_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..a5395ed964a9709a66d32f4523c5a761fdf8cb75 --- /dev/null +++ b/shared/package-files/03/03a6c3a6c55a8f0ead89a7ad2daa767f33cd6de515bed35d7e106fc82021ba67/replication_pad1d_backward_cpu_dispatch.h @@ -0,0 +1,28 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cpu { + +TORCH_API at::Tensor replication_pad1d_backward(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding); +TORCH_API at::Tensor replication_pad1d_backward_symint(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding); +TORCH_API at::Tensor & replication_pad1d_backward_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding); +TORCH_API at::Tensor & replication_pad1d_backward_outf(const at::Tensor & grad_output, const at::Tensor & self, at::IntArrayRef padding, at::Tensor & grad_input); +TORCH_API at::Tensor & replication_pad1d_backward_symint_out(at::Tensor & grad_input, const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding); +TORCH_API at::Tensor & replication_pad1d_backward_symint_outf(const at::Tensor & grad_output, const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & grad_input); + +} // namespace cpu +} // namespace at diff --git a/shared/package-files/03/03a735aae3d1ded9dbfc8da135eca568cf9aa2a5e2dbd9825673e2e34bd5c8b7/MPSGuardImpl.h b/shared/package-files/03/03a735aae3d1ded9dbfc8da135eca568cf9aa2a5e2dbd9825673e2e34bd5c8b7/MPSGuardImpl.h new file mode 100644 index 0000000000000000000000000000000000000000..fe43fcf40fd34a38c9633b0c7a01e5668bfc9aba --- /dev/null +++ b/shared/package-files/03/03a735aae3d1ded9dbfc8da135eca568cf9aa2a5e2dbd9825673e2e34bd5c8b7/MPSGuardImpl.h @@ -0,0 +1,174 @@ +// Copyright © 2022 Apple Inc. + +#pragma once +#include +#include +#include +#include +#include +#include + +#ifdef __OBJC__ +#include +#include +#include +#endif + +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at::mps { + +typedef MPSEvent* mpsEvent_t; + +// TODO: Move the MPSGuardImpl to inherit from NoOpDeviceGuardImpl +// https://github.com/pytorch/pytorch/issues/77170 +struct TORCH_API MPSGuardImpl final : public c10::impl::DeviceGuardImplInterface { + static constexpr c10::DeviceType static_type = c10::DeviceType::MPS; + + // constructor + MPSGuardImpl() {} + explicit MPSGuardImpl(c10::DeviceType t) { + TORCH_INTERNAL_ASSERT(t == c10::DeviceType::MPS); + } + + // returns the type + c10::DeviceType type() const override { + return c10::DeviceType::MPS; + } + + Device exchangeDevice(Device d) const override { + return Device(c10::DeviceType::MPS, 0); + } + + Device getDevice() const override { + return Device(c10::DeviceType::MPS, 0); + } + + c10::optional uncheckedGetDevice() const noexcept { + return Device(c10::DeviceType::MPS, 0); + } + + void setDevice(Device d) const override { + TORCH_INTERNAL_ASSERT(d.is_mps()); + } + + void uncheckedSetDevice(Device d) const noexcept override { + // TODO: Currently setting only device 0 + } + + Stream getStream(Device d) const noexcept override { + return Stream(Stream::DEFAULT, Device(c10::DeviceType::MPS, 0)); + } + + Stream getDefaultStream(Device d) const override { + return Stream(Stream::DEFAULT, Device(c10::DeviceType::MPS, 0)); + } + + // NB: These do NOT set the current device + Stream exchangeStream(Stream s) const noexcept override { + return Stream(Stream::DEFAULT, Device(c10::DeviceType::MPS, 0)); + } + DeviceIndex deviceCount() const noexcept override { + if (at::hasMPS()) { + //TODO: extend it for multi-device case + return 1; + } else { + return 0; + } + } + + // Event-related functions + void createEvent( + mpsEvent_t* event, + const EventFlag flag) const; + + void destroyEvent( + void* event, + const DeviceIndex device_index) const noexcept override; + + void record( + void** event, + const Stream& stream, + const DeviceIndex device_index, + const EventFlag flag) const override; + + void block( + void* event, + const Stream& stream) const override; + + bool queryEvent(void* event) const override; + +}; + +/// A variant of OptionalDeviceGuard that is specialized for MPS. +struct OptionalMPSGuard { + explicit OptionalMPSGuard() : guard_() {} + + explicit OptionalMPSGuard(c10::optional device_opt) + : guard_(device_opt) {} + + /// Set the current MPS device to the passed device index, if it is not + /// nullopt + explicit OptionalMPSGuard(c10::optional device_index_opt) + : guard_(device_index_opt) {} + + // Copy is not allowed + OptionalMPSGuard(const OptionalMPSGuard&) = delete; + OptionalMPSGuard& operator=(const OptionalMPSGuard&) = delete; + OptionalMPSGuard(OptionalMPSGuard&& other) = delete; + OptionalMPSGuard& operator=(OptionalMPSGuard&& other) = delete; + + /// Sets the MPS device to the given device, initializing the guard if it + /// is not already initialized. Errors if the given device is not a MPS + /// device. + void set_device(Device device) { + guard_.set_device(device); + } + + /// Sets the MPS device to the given device, initializing the guard if it is + /// not already initialized. Errors if the given device is not a MPS device. + void reset_device(Device device) { + guard_.reset_device(device); + } + + /// Sets the MPS device to the given device index, initializing the guard if + /// it is not already initialized. + void set_index(DeviceIndex device_index) { + guard_.set_index(device_index); + } + + /// Returns the device that was set immediately prior to initialization of the + /// guard, or nullopt if the guard is uninitialized. + c10::optional original_device() const { + return guard_.original_device(); + } + + /// Returns the most recent device that was set using this device guard, + /// either from construction, or via set_device, if the guard is initialized, + /// or nullopt if the guard is uninitialized. + c10::optional current_device() const { + return guard_.current_device(); + } + + /// Restore the original MPS device, resetting this guard to uninitialized + /// state. + void reset() { + guard_.reset(); + } + + private: + c10::impl::InlineOptionalDeviceGuard guard_; +}; + + +C10_REGISTER_GUARD_IMPL(MPS, MPSGuardImpl); + +} // namespace at::mps diff --git a/shared/package-files/03/03a8c15b1c5a5419774fef60f60405c8e70b1f791fe32c9e73ad7215173efe52/cookiejar.py b/shared/package-files/03/03a8c15b1c5a5419774fef60f60405c8e70b1f791fe32c9e73ad7215173efe52/cookiejar.py new file mode 100644 index 0000000000000000000000000000000000000000..0380d9f1096c0800b1cb34d3300cdf83db2027fb --- /dev/null +++ b/shared/package-files/03/03a8c15b1c5a5419774fef60f60405c8e70b1f791fe32c9e73ad7215173efe52/cookiejar.py @@ -0,0 +1,2125 @@ +r"""HTTP cookie handling for web clients. + +This module has (now fairly distant) origins in Gisle Aas' Perl module +HTTP::Cookies, from the libwww-perl library. + +Docstrings, comments and debug strings in this code refer to the +attributes of the HTTP cookie system as cookie-attributes, to distinguish +them clearly from Python attributes. + +Class diagram (note that BSDDBCookieJar and the MSIE* classes are not +distributed with the Python standard library, but are available from +http://wwwsearch.sf.net/): + + CookieJar____ + / \ \ + FileCookieJar \ \ + / | \ \ \ + MozillaCookieJar | LWPCookieJar \ \ + | | \ + | ---MSIEBase | \ + | / | | \ + | / MSIEDBCookieJar BSDDBCookieJar + |/ + MSIECookieJar + +""" + +__all__ = ['Cookie', 'CookieJar', 'CookiePolicy', 'DefaultCookiePolicy', + 'FileCookieJar', 'LWPCookieJar', 'LoadError', 'MozillaCookieJar'] + +import os +import copy +import datetime +import re +import time +import urllib.parse, urllib.request +import threading as _threading +import http.client # only for the default HTTP port +from calendar import timegm + +debug = False # set to True to enable debugging via the logging module +logger = None + +def _debug(*args): + if not debug: + return + global logger + if not logger: + import logging + logger = logging.getLogger("http.cookiejar") + return logger.debug(*args) + +HTTPONLY_ATTR = "HTTPOnly" +HTTPONLY_PREFIX = "#HttpOnly_" +DEFAULT_HTTP_PORT = str(http.client.HTTP_PORT) +NETSCAPE_MAGIC_RGX = re.compile("#( Netscape)? HTTP Cookie File") +MISSING_FILENAME_TEXT = ("a filename was not supplied (nor was the CookieJar " + "instance initialised with one)") +NETSCAPE_HEADER_TEXT = """\ +# Netscape HTTP Cookie File +# http://curl.haxx.se/rfc/cookie_spec.html +# This is a generated file! Do not edit. + +""" + +def _warn_unhandled_exception(): + # There are a few catch-all except: statements in this module, for + # catching input that's bad in unexpected ways. Warn if any + # exceptions are caught there. + import io, warnings, traceback + f = io.StringIO() + traceback.print_exc(None, f) + msg = f.getvalue() + warnings.warn("http.cookiejar bug!\n%s" % msg, stacklevel=2) + + +# Date/time conversion +# ----------------------------------------------------------------------------- + +EPOCH_YEAR = 1970 +def _timegm(tt): + year, month, mday, hour, min, sec = tt[:6] + if ((year >= EPOCH_YEAR) and (1 <= month <= 12) and (1 <= mday <= 31) and + (0 <= hour <= 24) and (0 <= min <= 59) and (0 <= sec <= 61)): + return timegm(tt) + else: + return None + +DAYS = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"] +MONTHS = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", + "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"] +MONTHS_LOWER = [] +for month in MONTHS: MONTHS_LOWER.append(month.lower()) + +def time2isoz(t=None): + """Return a string representing time in seconds since epoch, t. + + If the function is called without an argument, it will use the current + time. + + The format of the returned string is like "YYYY-MM-DD hh:mm:ssZ", + representing Universal Time (UTC, aka GMT). An example of this format is: + + 1994-11-24 08:49:37Z + + """ + if t is None: + dt = datetime.datetime.utcnow() + else: + dt = datetime.datetime.utcfromtimestamp(t) + return "%04d-%02d-%02d %02d:%02d:%02dZ" % ( + dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second) + +def time2netscape(t=None): + """Return a string representing time in seconds since epoch, t. + + If the function is called without an argument, it will use the current + time. + + The format of the returned string is like this: + + Wed, DD-Mon-YYYY HH:MM:SS GMT + + """ + if t is None: + dt = datetime.datetime.utcnow() + else: + dt = datetime.datetime.utcfromtimestamp(t) + return "%s, %02d-%s-%04d %02d:%02d:%02d GMT" % ( + DAYS[dt.weekday()], dt.day, MONTHS[dt.month-1], + dt.year, dt.hour, dt.minute, dt.second) + + +UTC_ZONES = {"GMT": None, "UTC": None, "UT": None, "Z": None} + +TIMEZONE_RE = re.compile(r"^([-+])?(\d\d?):?(\d\d)?$", re.ASCII) +def offset_from_tz_string(tz): + offset = None + if tz in UTC_ZONES: + offset = 0 + else: + m = TIMEZONE_RE.search(tz) + if m: + offset = 3600 * int(m.group(2)) + if m.group(3): + offset = offset + 60 * int(m.group(3)) + if m.group(1) == '-': + offset = -offset + return offset + +def _str2time(day, mon, yr, hr, min, sec, tz): + yr = int(yr) + if yr > datetime.MAXYEAR: + return None + + # translate month name to number + # month numbers start with 1 (January) + try: + mon = MONTHS_LOWER.index(mon.lower())+1 + except ValueError: + # maybe it's already a number + try: + imon = int(mon) + except ValueError: + return None + if 1 <= imon <= 12: + mon = imon + else: + return None + + # make sure clock elements are defined + if hr is None: hr = 0 + if min is None: min = 0 + if sec is None: sec = 0 + + day = int(day) + hr = int(hr) + min = int(min) + sec = int(sec) + + if yr < 1000: + # find "obvious" year + cur_yr = time.localtime(time.time())[0] + m = cur_yr % 100 + tmp = yr + yr = yr + cur_yr - m + m = m - tmp + if abs(m) > 50: + if m > 0: yr = yr + 100 + else: yr = yr - 100 + + # convert UTC time tuple to seconds since epoch (not timezone-adjusted) + t = _timegm((yr, mon, day, hr, min, sec, tz)) + + if t is not None: + # adjust time using timezone string, to get absolute time since epoch + if tz is None: + tz = "UTC" + tz = tz.upper() + offset = offset_from_tz_string(tz) + if offset is None: + return None + t = t - offset + + return t + +STRICT_DATE_RE = re.compile( + r"^[SMTWF][a-z][a-z], (\d\d) ([JFMASOND][a-z][a-z]) " + r"(\d\d\d\d) (\d\d):(\d\d):(\d\d) GMT$", re.ASCII) +WEEKDAY_RE = re.compile( + r"^(?:Sun|Mon|Tue|Wed|Thu|Fri|Sat)[a-z]*,?\s*", re.I | re.ASCII) +LOOSE_HTTP_DATE_RE = re.compile( + r"""^ + (\d\d?) # day + (?:\s+|[-\/]) + (\w+) # month + (?:\s+|[-\/]) + (\d+) # year + (?: + (?:\s+|:) # separator before clock + (\d\d?):(\d\d) # hour:min + (?::(\d\d))? # optional seconds + )? # optional clock + \s* + (?: + ([-+]?\d{2,4}|(?![APap][Mm]\b)[A-Za-z]+) # timezone + \s* + )? + (?: + \(\w+\) # ASCII representation of timezone in parens. + \s* + )?$""", re.X | re.ASCII) +def http2time(text): + """Returns time in seconds since epoch of time represented by a string. + + Return value is an integer. + + None is returned if the format of str is unrecognized, the time is outside + the representable range, or the timezone string is not recognized. If the + string contains no timezone, UTC is assumed. + + The timezone in the string may be numerical (like "-0800" or "+0100") or a + string timezone (like "UTC", "GMT", "BST" or "EST"). Currently, only the + timezone strings equivalent to UTC (zero offset) are known to the function. + + The function loosely parses the following formats: + + Wed, 09 Feb 1994 22:23:32 GMT -- HTTP format + Tuesday, 08-Feb-94 14:15:29 GMT -- old rfc850 HTTP format + Tuesday, 08-Feb-1994 14:15:29 GMT -- broken rfc850 HTTP format + 09 Feb 1994 22:23:32 GMT -- HTTP format (no weekday) + 08-Feb-94 14:15:29 GMT -- rfc850 format (no weekday) + 08-Feb-1994 14:15:29 GMT -- broken rfc850 format (no weekday) + + The parser ignores leading and trailing whitespace. The time may be + absent. + + If the year is given with only 2 digits, the function will select the + century that makes the year closest to the current date. + + """ + # fast exit for strictly conforming string + m = STRICT_DATE_RE.search(text) + if m: + g = m.groups() + mon = MONTHS_LOWER.index(g[1].lower()) + 1 + tt = (int(g[2]), mon, int(g[0]), + int(g[3]), int(g[4]), float(g[5])) + return _timegm(tt) + + # No, we need some messy parsing... + + # clean up + text = text.lstrip() + text = WEEKDAY_RE.sub("", text, 1) # Useless weekday + + # tz is time zone specifier string + day, mon, yr, hr, min, sec, tz = [None]*7 + + # loose regexp parse + m = LOOSE_HTTP_DATE_RE.search(text) + if m is not None: + day, mon, yr, hr, min, sec, tz = m.groups() + else: + return None # bad format + + return _str2time(day, mon, yr, hr, min, sec, tz) + +ISO_DATE_RE = re.compile( + r"""^ + (\d{4}) # year + [-\/]? + (\d\d?) # numerical month + [-\/]? + (\d\d?) # day + (?: + (?:\s+|[-:Tt]) # separator before clock + (\d\d?):?(\d\d) # hour:min + (?::?(\d\d(?:\.\d*)?))? # optional seconds (and fractional) + )? # optional clock + \s* + (?: + ([-+]?\d\d?:?(:?\d\d)? + |Z|z) # timezone (Z is "zero meridian", i.e. GMT) + \s* + )?$""", re.X | re. ASCII) +def iso2time(text): + """ + As for http2time, but parses the ISO 8601 formats: + + 1994-02-03 14:15:29 -0100 -- ISO 8601 format + 1994-02-03 14:15:29 -- zone is optional + 1994-02-03 -- only date + 1994-02-03T14:15:29 -- Use T as separator + 19940203T141529Z -- ISO 8601 compact format + 19940203 -- only date + + """ + # clean up + text = text.lstrip() + + # tz is time zone specifier string + day, mon, yr, hr, min, sec, tz = [None]*7 + + # loose regexp parse + m = ISO_DATE_RE.search(text) + if m is not None: + # XXX there's an extra bit of the timezone I'm ignoring here: is + # this the right thing to do? + yr, mon, day, hr, min, sec, tz, _ = m.groups() + else: + return None # bad format + + return _str2time(day, mon, yr, hr, min, sec, tz) + + +# Header parsing +# ----------------------------------------------------------------------------- + +def unmatched(match): + """Return unmatched part of re.Match object.""" + start, end = match.span(0) + return match.string[:start]+match.string[end:] + +HEADER_TOKEN_RE = re.compile(r"^\s*([^=\s;,]+)") +HEADER_QUOTED_VALUE_RE = re.compile(r"^\s*=\s*\"([^\"\\]*(?:\\.[^\"\\]*)*)\"") +HEADER_VALUE_RE = re.compile(r"^\s*=\s*([^\s;,]*)") +HEADER_ESCAPE_RE = re.compile(r"\\(.)") +def split_header_words(header_values): + r"""Parse header values into a list of lists containing key,value pairs. + + The function knows how to deal with ",", ";" and "=" as well as quoted + values after "=". A list of space separated tokens are parsed as if they + were separated by ";". + + If the header_values passed as argument contains multiple values, then they + are treated as if they were a single value separated by comma ",". + + This means that this function is useful for parsing header fields that + follow this syntax (BNF as from the HTTP/1.1 specification, but we relax + the requirement for tokens). + + headers = #header + header = (token | parameter) *( [";"] (token | parameter)) + + token = 1* + separators = "(" | ")" | "<" | ">" | "@" + | "," | ";" | ":" | "\" | <"> + | "/" | "[" | "]" | "?" | "=" + | "{" | "}" | SP | HT + + quoted-string = ( <"> *(qdtext | quoted-pair ) <"> ) + qdtext = > + quoted-pair = "\" CHAR + + parameter = attribute "=" value + attribute = token + value = token | quoted-string + + Each header is represented by a list of key/value pairs. The value for a + simple token (not part of a parameter) is None. Syntactically incorrect + headers will not necessarily be parsed as you would want. + + This is easier to describe with some examples: + + >>> split_header_words(['foo="bar"; port="80,81"; discard, bar=baz']) + [[('foo', 'bar'), ('port', '80,81'), ('discard', None)], [('bar', 'baz')]] + >>> split_header_words(['text/html; charset="iso-8859-1"']) + [[('text/html', None), ('charset', 'iso-8859-1')]] + >>> split_header_words([r'Basic realm="\"foo\bar\""']) + [[('Basic', None), ('realm', '"foobar"')]] + + """ + assert not isinstance(header_values, str) + result = [] + for text in header_values: + orig_text = text + pairs = [] + while text: + m = HEADER_TOKEN_RE.search(text) + if m: + text = unmatched(m) + name = m.group(1) + m = HEADER_QUOTED_VALUE_RE.search(text) + if m: # quoted value + text = unmatched(m) + value = m.group(1) + value = HEADER_ESCAPE_RE.sub(r"\1", value) + else: + m = HEADER_VALUE_RE.search(text) + if m: # unquoted value + text = unmatched(m) + value = m.group(1) + value = value.rstrip() + else: + # no value, a lone token + value = None + pairs.append((name, value)) + elif text.lstrip().startswith(","): + # concatenated headers, as per RFC 2616 section 4.2 + text = text.lstrip()[1:] + if pairs: result.append(pairs) + pairs = [] + else: + # skip junk + non_junk, nr_junk_chars = re.subn(r"^[=\s;]*", "", text) + assert nr_junk_chars > 0, ( + "split_header_words bug: '%s', '%s', %s" % + (orig_text, text, pairs)) + text = non_junk + if pairs: result.append(pairs) + return result + +HEADER_JOIN_ESCAPE_RE = re.compile(r"([\"\\])") +def join_header_words(lists): + """Do the inverse (almost) of the conversion done by split_header_words. + + Takes a list of lists of (key, value) pairs and produces a single header + value. Attribute values are quoted if needed. + + >>> join_header_words([[("text/plain", None), ("charset", "iso-8859-1")]]) + 'text/plain; charset="iso-8859-1"' + >>> join_header_words([[("text/plain", None)], [("charset", "iso-8859-1")]]) + 'text/plain, charset="iso-8859-1"' + + """ + headers = [] + for pairs in lists: + attr = [] + for k, v in pairs: + if v is not None: + if not re.search(r"^\w+$", v): + v = HEADER_JOIN_ESCAPE_RE.sub(r"\\\1", v) # escape " and \ + v = '"%s"' % v + k = "%s=%s" % (k, v) + attr.append(k) + if attr: headers.append("; ".join(attr)) + return ", ".join(headers) + +def strip_quotes(text): + if text.startswith('"'): + text = text[1:] + if text.endswith('"'): + text = text[:-1] + return text + +def parse_ns_headers(ns_headers): + """Ad-hoc parser for Netscape protocol cookie-attributes. + + The old Netscape cookie format for Set-Cookie can for instance contain + an unquoted "," in the expires field, so we have to use this ad-hoc + parser instead of split_header_words. + + XXX This may not make the best possible effort to parse all the crap + that Netscape Cookie headers contain. Ronald Tschalar's HTTPClient + parser is probably better, so could do worse than following that if + this ever gives any trouble. + + Currently, this is also used for parsing RFC 2109 cookies. + + """ + known_attrs = ("expires", "domain", "path", "secure", + # RFC 2109 attrs (may turn up in Netscape cookies, too) + "version", "port", "max-age") + + result = [] + for ns_header in ns_headers: + pairs = [] + version_set = False + + # XXX: The following does not strictly adhere to RFCs in that empty + # names and values are legal (the former will only appear once and will + # be overwritten if multiple occurrences are present). This is + # mostly to deal with backwards compatibility. + for ii, param in enumerate(ns_header.split(';')): + param = param.strip() + + key, sep, val = param.partition('=') + key = key.strip() + + if not key: + if ii == 0: + break + else: + continue + + # allow for a distinction between present and empty and missing + # altogether + val = val.strip() if sep else None + + if ii != 0: + lc = key.lower() + if lc in known_attrs: + key = lc + + if key == "version": + # This is an RFC 2109 cookie. + if val is not None: + val = strip_quotes(val) + version_set = True + elif key == "expires": + # convert expires date to seconds since epoch + if val is not None: + val = http2time(strip_quotes(val)) # None if invalid + pairs.append((key, val)) + + if pairs: + if not version_set: + pairs.append(("version", "0")) + result.append(pairs) + + return result + + +IPV4_RE = re.compile(r"\.\d+$", re.ASCII) +def is_HDN(text): + """Return True if text is a host domain name.""" + # XXX + # This may well be wrong. Which RFC is HDN defined in, if any (for + # the purposes of RFC 2965)? + # For the current implementation, what about IPv6? Remember to look + # at other uses of IPV4_RE also, if change this. + if IPV4_RE.search(text): + return False + if text == "": + return False + if text[0] == "." or text[-1] == ".": + return False + return True + +def domain_match(A, B): + """Return True if domain A domain-matches domain B, according to RFC 2965. + + A and B may be host domain names or IP addresses. + + RFC 2965, section 1: + + Host names can be specified either as an IP address or a HDN string. + Sometimes we compare one host name with another. (Such comparisons SHALL + be case-insensitive.) Host A's name domain-matches host B's if + + * their host name strings string-compare equal; or + + * A is a HDN string and has the form NB, where N is a non-empty + name string, B has the form .B', and B' is a HDN string. (So, + x.y.com domain-matches .Y.com but not Y.com.) + + Note that domain-match is not a commutative operation: a.b.c.com + domain-matches .c.com, but not the reverse. + + """ + # Note that, if A or B are IP addresses, the only relevant part of the + # definition of the domain-match algorithm is the direct string-compare. + A = A.lower() + B = B.lower() + if A == B: + return True + if not is_HDN(A): + return False + i = A.rfind(B) + if i == -1 or i == 0: + # A does not have form NB, or N is the empty string + return False + if not B.startswith("."): + return False + if not is_HDN(B[1:]): + return False + return True + +def liberal_is_HDN(text): + """Return True if text is a sort-of-like a host domain name. + + For accepting/blocking domains. + + """ + if IPV4_RE.search(text): + return False + return True + +def user_domain_match(A, B): + """For blocking/accepting domains. + + A and B may be host domain names or IP addresses. + + """ + A = A.lower() + B = B.lower() + if not (liberal_is_HDN(A) and liberal_is_HDN(B)): + if A == B: + # equal IP addresses + return True + return False + initial_dot = B.startswith(".") + if initial_dot and A.endswith(B): + return True + if not initial_dot and A == B: + return True + return False + +cut_port_re = re.compile(r":\d+$", re.ASCII) +def request_host(request): + """Return request-host, as defined by RFC 2965. + + Variation from RFC: returned value is lowercased, for convenient + comparison. + + """ + url = request.get_full_url() + host = urllib.parse.urlparse(url)[1] + if host == "": + host = request.get_header("Host", "") + + # remove port, if present + host = cut_port_re.sub("", host, 1) + return host.lower() + +def eff_request_host(request): + """Return a tuple (request-host, effective request-host name). + + As defined by RFC 2965, except both are lowercased. + + """ + erhn = req_host = request_host(request) + if req_host.find(".") == -1 and not IPV4_RE.search(req_host): + erhn = req_host + ".local" + return req_host, erhn + +def request_path(request): + """Path component of request-URI, as defined by RFC 2965.""" + url = request.get_full_url() + parts = urllib.parse.urlsplit(url) + path = escape_path(parts.path) + if not path.startswith("/"): + # fix bad RFC 2396 absoluteURI + path = "/" + path + return path + +def request_port(request): + host = request.host + i = host.find(':') + if i >= 0: + port = host[i+1:] + try: + int(port) + except ValueError: + _debug("nonnumeric port: '%s'", port) + return None + else: + port = DEFAULT_HTTP_PORT + return port + +# Characters in addition to A-Z, a-z, 0-9, '_', '.', and '-' that don't +# need to be escaped to form a valid HTTP URL (RFCs 2396 and 1738). +HTTP_PATH_SAFE = "%/;:@&=+$,!~*'()" +ESCAPED_CHAR_RE = re.compile(r"%([0-9a-fA-F][0-9a-fA-F])") +def uppercase_escaped_char(match): + return "%%%s" % match.group(1).upper() +def escape_path(path): + """Escape any invalid characters in HTTP URL, and uppercase all escapes.""" + # There's no knowing what character encoding was used to create URLs + # containing %-escapes, but since we have to pick one to escape invalid + # path characters, we pick UTF-8, as recommended in the HTML 4.0 + # specification: + # http://www.w3.org/TR/REC-html40/appendix/notes.html#h-B.2.1 + # And here, kind of: draft-fielding-uri-rfc2396bis-03 + # (And in draft IRI specification: draft-duerst-iri-05) + # (And here, for new URI schemes: RFC 2718) + path = urllib.parse.quote(path, HTTP_PATH_SAFE) + path = ESCAPED_CHAR_RE.sub(uppercase_escaped_char, path) + return path + +def reach(h): + """Return reach of host h, as defined by RFC 2965, section 1. + + The reach R of a host name H is defined as follows: + + * If + + - H is the host domain name of a host; and, + + - H has the form A.B; and + + - A has no embedded (that is, interior) dots; and + + - B has at least one embedded dot, or B is the string "local". + then the reach of H is .B. + + * Otherwise, the reach of H is H. + + >>> reach("www.acme.com") + '.acme.com' + >>> reach("acme.com") + 'acme.com' + >>> reach("acme.local") + '.local' + + """ + i = h.find(".") + if i >= 0: + #a = h[:i] # this line is only here to show what a is + b = h[i+1:] + i = b.find(".") + if is_HDN(h) and (i >= 0 or b == "local"): + return "."+b + return h + +def is_third_party(request): + """ + + RFC 2965, section 3.3.6: + + An unverifiable transaction is to a third-party host if its request- + host U does not domain-match the reach R of the request-host O in the + origin transaction. + + """ + req_host = request_host(request) + if not domain_match(req_host, reach(request.origin_req_host)): + return True + else: + return False + + +class Cookie: + """HTTP Cookie. + + This class represents both Netscape and RFC 2965 cookies. + + This is deliberately a very simple class. It just holds attributes. It's + possible to construct Cookie instances that don't comply with the cookie + standards. CookieJar.make_cookies is the factory function for Cookie + objects -- it deals with cookie parsing, supplying defaults, and + normalising to the representation used in this class. CookiePolicy is + responsible for checking them to see whether they should be accepted from + and returned to the server. + + Note that the port may be present in the headers, but unspecified ("Port" + rather than"Port=80", for example); if this is the case, port is None. + + """ + + def __init__(self, version, name, value, + port, port_specified, + domain, domain_specified, domain_initial_dot, + path, path_specified, + secure, + expires, + discard, + comment, + comment_url, + rest, + rfc2109=False, + ): + + if version is not None: version = int(version) + if expires is not None: expires = int(float(expires)) + if port is None and port_specified is True: + raise ValueError("if port is None, port_specified must be false") + + self.version = version + self.name = name + self.value = value + self.port = port + self.port_specified = port_specified + # normalise case, as per RFC 2965 section 3.3.3 + self.domain = domain.lower() + self.domain_specified = domain_specified + # Sigh. We need to know whether the domain given in the + # cookie-attribute had an initial dot, in order to follow RFC 2965 + # (as clarified in draft errata). Needed for the returned $Domain + # value. + self.domain_initial_dot = domain_initial_dot + self.path = path + self.path_specified = path_specified + self.secure = secure + self.expires = expires + self.discard = discard + self.comment = comment + self.comment_url = comment_url + self.rfc2109 = rfc2109 + + self._rest = copy.copy(rest) + + def has_nonstandard_attr(self, name): + return name in self._rest + def get_nonstandard_attr(self, name, default=None): + return self._rest.get(name, default) + def set_nonstandard_attr(self, name, value): + self._rest[name] = value + + def is_expired(self, now=None): + if now is None: now = time.time() + if (self.expires is not None) and (self.expires <= now): + return True + return False + + def __str__(self): + if self.port is None: p = "" + else: p = ":"+self.port + limit = self.domain + p + self.path + if self.value is not None: + namevalue = "%s=%s" % (self.name, self.value) + else: + namevalue = self.name + return "" % (namevalue, limit) + + def __repr__(self): + args = [] + for name in ("version", "name", "value", + "port", "port_specified", + "domain", "domain_specified", "domain_initial_dot", + "path", "path_specified", + "secure", "expires", "discard", "comment", "comment_url", + ): + attr = getattr(self, name) + args.append("%s=%s" % (name, repr(attr))) + args.append("rest=%s" % repr(self._rest)) + args.append("rfc2109=%s" % repr(self.rfc2109)) + return "%s(%s)" % (self.__class__.__name__, ", ".join(args)) + + +class CookiePolicy: + """Defines which cookies get accepted from and returned to server. + + May also modify cookies, though this is probably a bad idea. + + The subclass DefaultCookiePolicy defines the standard rules for Netscape + and RFC 2965 cookies -- override that if you want a customized policy. + + """ + def set_ok(self, cookie, request): + """Return true if (and only if) cookie should be accepted from server. + + Currently, pre-expired cookies never get this far -- the CookieJar + class deletes such cookies itself. + + """ + raise NotImplementedError() + + def return_ok(self, cookie, request): + """Return true if (and only if) cookie should be returned to server.""" + raise NotImplementedError() + + def domain_return_ok(self, domain, request): + """Return false if cookies should not be returned, given cookie domain. + """ + return True + + def path_return_ok(self, path, request): + """Return false if cookies should not be returned, given cookie path. + """ + return True + + +class DefaultCookiePolicy(CookiePolicy): + """Implements the standard rules for accepting and returning cookies.""" + + DomainStrictNoDots = 1 + DomainStrictNonDomain = 2 + DomainRFC2965Match = 4 + + DomainLiberal = 0 + DomainStrict = DomainStrictNoDots|DomainStrictNonDomain + + def __init__(self, + blocked_domains=None, allowed_domains=None, + netscape=True, rfc2965=False, + rfc2109_as_netscape=None, + hide_cookie2=False, + strict_domain=False, + strict_rfc2965_unverifiable=True, + strict_ns_unverifiable=False, + strict_ns_domain=DomainLiberal, + strict_ns_set_initial_dollar=False, + strict_ns_set_path=False, + secure_protocols=("https", "wss") + ): + """Constructor arguments should be passed as keyword arguments only.""" + self.netscape = netscape + self.rfc2965 = rfc2965 + self.rfc2109_as_netscape = rfc2109_as_netscape + self.hide_cookie2 = hide_cookie2 + self.strict_domain = strict_domain + self.strict_rfc2965_unverifiable = strict_rfc2965_unverifiable + self.strict_ns_unverifiable = strict_ns_unverifiable + self.strict_ns_domain = strict_ns_domain + self.strict_ns_set_initial_dollar = strict_ns_set_initial_dollar + self.strict_ns_set_path = strict_ns_set_path + self.secure_protocols = secure_protocols + + if blocked_domains is not None: + self._blocked_domains = tuple(blocked_domains) + else: + self._blocked_domains = () + + if allowed_domains is not None: + allowed_domains = tuple(allowed_domains) + self._allowed_domains = allowed_domains + + def blocked_domains(self): + """Return the sequence of blocked domains (as a tuple).""" + return self._blocked_domains + def set_blocked_domains(self, blocked_domains): + """Set the sequence of blocked domains.""" + self._blocked_domains = tuple(blocked_domains) + + def is_blocked(self, domain): + for blocked_domain in self._blocked_domains: + if user_domain_match(domain, blocked_domain): + return True + return False + + def allowed_domains(self): + """Return None, or the sequence of allowed domains (as a tuple).""" + return self._allowed_domains + def set_allowed_domains(self, allowed_domains): + """Set the sequence of allowed domains, or None.""" + if allowed_domains is not None: + allowed_domains = tuple(allowed_domains) + self._allowed_domains = allowed_domains + + def is_not_allowed(self, domain): + if self._allowed_domains is None: + return False + for allowed_domain in self._allowed_domains: + if user_domain_match(domain, allowed_domain): + return False + return True + + def set_ok(self, cookie, request): + """ + If you override .set_ok(), be sure to call this method. If it returns + false, so should your subclass (assuming your subclass wants to be more + strict about which cookies to accept). + + """ + _debug(" - checking cookie %s=%s", cookie.name, cookie.value) + + assert cookie.name is not None + + for n in "version", "verifiability", "name", "path", "domain", "port": + fn_name = "set_ok_"+n + fn = getattr(self, fn_name) + if not fn(cookie, request): + return False + + return True + + def set_ok_version(self, cookie, request): + if cookie.version is None: + # Version is always set to 0 by parse_ns_headers if it's a Netscape + # cookie, so this must be an invalid RFC 2965 cookie. + _debug(" Set-Cookie2 without version attribute (%s=%s)", + cookie.name, cookie.value) + return False + if cookie.version > 0 and not self.rfc2965: + _debug(" RFC 2965 cookies are switched off") + return False + elif cookie.version == 0 and not self.netscape: + _debug(" Netscape cookies are switched off") + return False + return True + + def set_ok_verifiability(self, cookie, request): + if request.unverifiable and is_third_party(request): + if cookie.version > 0 and self.strict_rfc2965_unverifiable: + _debug(" third-party RFC 2965 cookie during " + "unverifiable transaction") + return False + elif cookie.version == 0 and self.strict_ns_unverifiable: + _debug(" third-party Netscape cookie during " + "unverifiable transaction") + return False + return True + + def set_ok_name(self, cookie, request): + # Try and stop servers setting V0 cookies designed to hack other + # servers that know both V0 and V1 protocols. + if (cookie.version == 0 and self.strict_ns_set_initial_dollar and + cookie.name.startswith("$")): + _debug(" illegal name (starts with '$'): '%s'", cookie.name) + return False + return True + + def set_ok_path(self, cookie, request): + if cookie.path_specified: + req_path = request_path(request) + if ((cookie.version > 0 or + (cookie.version == 0 and self.strict_ns_set_path)) and + not self.path_return_ok(cookie.path, request)): + _debug(" path attribute %s is not a prefix of request " + "path %s", cookie.path, req_path) + return False + return True + + def set_ok_domain(self, cookie, request): + if self.is_blocked(cookie.domain): + _debug(" domain %s is in user block-list", cookie.domain) + return False + if self.is_not_allowed(cookie.domain): + _debug(" domain %s is not in user allow-list", cookie.domain) + return False + if cookie.domain_specified: + req_host, erhn = eff_request_host(request) + domain = cookie.domain + if self.strict_domain and (domain.count(".") >= 2): + # XXX This should probably be compared with the Konqueror + # (kcookiejar.cpp) and Mozilla implementations, but it's a + # losing battle. + i = domain.rfind(".") + j = domain.rfind(".", 0, i) + if j == 0: # domain like .foo.bar + tld = domain[i+1:] + sld = domain[j+1:i] + if sld.lower() in ("co", "ac", "com", "edu", "org", "net", + "gov", "mil", "int", "aero", "biz", "cat", "coop", + "info", "jobs", "mobi", "museum", "name", "pro", + "travel", "eu") and len(tld) == 2: + # domain like .co.uk + _debug(" country-code second level domain %s", domain) + return False + if domain.startswith("."): + undotted_domain = domain[1:] + else: + undotted_domain = domain + embedded_dots = (undotted_domain.find(".") >= 0) + if not embedded_dots and domain != ".local": + _debug(" non-local domain %s contains no embedded dot", + domain) + return False + if cookie.version == 0: + if (not erhn.endswith(domain) and + (not erhn.startswith(".") and + not ("."+erhn).endswith(domain))): + _debug(" effective request-host %s (even with added " + "initial dot) does not end with %s", + erhn, domain) + return False + if (cookie.version > 0 or + (self.strict_ns_domain & self.DomainRFC2965Match)): + if not domain_match(erhn, domain): + _debug(" effective request-host %s does not domain-match " + "%s", erhn, domain) + return False + if (cookie.version > 0 or + (self.strict_ns_domain & self.DomainStrictNoDots)): + host_prefix = req_host[:-len(domain)] + if (host_prefix.find(".") >= 0 and + not IPV4_RE.search(req_host)): + _debug(" host prefix %s for domain %s contains a dot", + host_prefix, domain) + return False + return True + + def set_ok_port(self, cookie, request): + if cookie.port_specified: + req_port = request_port(request) + if req_port is None: + req_port = "80" + else: + req_port = str(req_port) + for p in cookie.port.split(","): + try: + int(p) + except ValueError: + _debug(" bad port %s (not numeric)", p) + return False + if p == req_port: + break + else: + _debug(" request port (%s) not found in %s", + req_port, cookie.port) + return False + return True + + def return_ok(self, cookie, request): + """ + If you override .return_ok(), be sure to call this method. If it + returns false, so should your subclass (assuming your subclass wants to + be more strict about which cookies to return). + + """ + # Path has already been checked by .path_return_ok(), and domain + # blocking done by .domain_return_ok(). + _debug(" - checking cookie %s=%s", cookie.name, cookie.value) + + for n in "version", "verifiability", "secure", "expires", "port", "domain": + fn_name = "return_ok_"+n + fn = getattr(self, fn_name) + if not fn(cookie, request): + return False + return True + + def return_ok_version(self, cookie, request): + if cookie.version > 0 and not self.rfc2965: + _debug(" RFC 2965 cookies are switched off") + return False + elif cookie.version == 0 and not self.netscape: + _debug(" Netscape cookies are switched off") + return False + return True + + def return_ok_verifiability(self, cookie, request): + if request.unverifiable and is_third_party(request): + if cookie.version > 0 and self.strict_rfc2965_unverifiable: + _debug(" third-party RFC 2965 cookie during unverifiable " + "transaction") + return False + elif cookie.version == 0 and self.strict_ns_unverifiable: + _debug(" third-party Netscape cookie during unverifiable " + "transaction") + return False + return True + + def return_ok_secure(self, cookie, request): + if cookie.secure and request.type not in self.secure_protocols: + _debug(" secure cookie with non-secure request") + return False + return True + + def return_ok_expires(self, cookie, request): + if cookie.is_expired(self._now): + _debug(" cookie expired") + return False + return True + + def return_ok_port(self, cookie, request): + if cookie.port: + req_port = request_port(request) + if req_port is None: + req_port = "80" + for p in cookie.port.split(","): + if p == req_port: + break + else: + _debug(" request port %s does not match cookie port %s", + req_port, cookie.port) + return False + return True + + def return_ok_domain(self, cookie, request): + req_host, erhn = eff_request_host(request) + domain = cookie.domain + + if domain and not domain.startswith("."): + dotdomain = "." + domain + else: + dotdomain = domain + + # strict check of non-domain cookies: Mozilla does this, MSIE5 doesn't + if (cookie.version == 0 and + (self.strict_ns_domain & self.DomainStrictNonDomain) and + not cookie.domain_specified and domain != erhn): + _debug(" cookie with unspecified domain does not string-compare " + "equal to request domain") + return False + + if cookie.version > 0 and not domain_match(erhn, domain): + _debug(" effective request-host name %s does not domain-match " + "RFC 2965 cookie domain %s", erhn, domain) + return False + if cookie.version == 0 and not ("."+erhn).endswith(dotdomain): + _debug(" request-host %s does not match Netscape cookie domain " + "%s", req_host, domain) + return False + return True + + def domain_return_ok(self, domain, request): + # Liberal check of. This is here as an optimization to avoid + # having to load lots of MSIE cookie files unless necessary. + req_host, erhn = eff_request_host(request) + if not req_host.startswith("."): + req_host = "."+req_host + if not erhn.startswith("."): + erhn = "."+erhn + if domain and not domain.startswith("."): + dotdomain = "." + domain + else: + dotdomain = domain + if not (req_host.endswith(dotdomain) or erhn.endswith(dotdomain)): + #_debug(" request domain %s does not match cookie domain %s", + # req_host, domain) + return False + + if self.is_blocked(domain): + _debug(" domain %s is in user block-list", domain) + return False + if self.is_not_allowed(domain): + _debug(" domain %s is not in user allow-list", domain) + return False + + return True + + def path_return_ok(self, path, request): + _debug("- checking cookie path=%s", path) + req_path = request_path(request) + pathlen = len(path) + if req_path == path: + return True + elif (req_path.startswith(path) and + (path.endswith("/") or req_path[pathlen:pathlen+1] == "/")): + return True + + _debug(" %s does not path-match %s", req_path, path) + return False + +def vals_sorted_by_key(adict): + keys = sorted(adict.keys()) + return map(adict.get, keys) + +def deepvalues(mapping): + """Iterates over nested mapping, depth-first, in sorted order by key.""" + values = vals_sorted_by_key(mapping) + for obj in values: + mapping = False + try: + obj.items + except AttributeError: + pass + else: + mapping = True + yield from deepvalues(obj) + if not mapping: + yield obj + + +# Used as second parameter to dict.get() method, to distinguish absent +# dict key from one with a None value. +class Absent: pass + +class CookieJar: + """Collection of HTTP cookies. + + You may not need to know about this class: try + urllib.request.build_opener(HTTPCookieProcessor).open(url). + """ + + non_word_re = re.compile(r"\W") + quote_re = re.compile(r"([\"\\])") + strict_domain_re = re.compile(r"\.?[^.]*") + domain_re = re.compile(r"[^.]*") + dots_re = re.compile(r"^\.+") + + magic_re = re.compile(r"^\#LWP-Cookies-(\d+\.\d+)", re.ASCII) + + def __init__(self, policy=None): + if policy is None: + policy = DefaultCookiePolicy() + self._policy = policy + + self._cookies_lock = _threading.RLock() + self._cookies = {} + + def set_policy(self, policy): + self._policy = policy + + def _cookies_for_domain(self, domain, request): + cookies = [] + if not self._policy.domain_return_ok(domain, request): + return [] + _debug("Checking %s for cookies to return", domain) + cookies_by_path = self._cookies[domain] + for path in cookies_by_path.keys(): + if not self._policy.path_return_ok(path, request): + continue + cookies_by_name = cookies_by_path[path] + for cookie in cookies_by_name.values(): + if not self._policy.return_ok(cookie, request): + _debug(" not returning cookie") + continue + _debug(" it's a match") + cookies.append(cookie) + return cookies + + def _cookies_for_request(self, request): + """Return a list of cookies to be returned to server.""" + cookies = [] + for domain in self._cookies.keys(): + cookies.extend(self._cookies_for_domain(domain, request)) + return cookies + + def _cookie_attrs(self, cookies): + """Return a list of cookie-attributes to be returned to server. + + like ['foo="bar"; $Path="/"', ...] + + The $Version attribute is also added when appropriate (currently only + once per request). + + """ + # add cookies in order of most specific (ie. longest) path first + cookies.sort(key=lambda a: len(a.path), reverse=True) + + version_set = False + + attrs = [] + for cookie in cookies: + # set version of Cookie header + # XXX + # What should it be if multiple matching Set-Cookie headers have + # different versions themselves? + # Answer: there is no answer; was supposed to be settled by + # RFC 2965 errata, but that may never appear... + version = cookie.version + if not version_set: + version_set = True + if version > 0: + attrs.append("$Version=%s" % version) + + # quote cookie value if necessary + # (not for Netscape protocol, which already has any quotes + # intact, due to the poorly-specified Netscape Cookie: syntax) + if ((cookie.value is not None) and + self.non_word_re.search(cookie.value) and version > 0): + value = self.quote_re.sub(r"\\\1", cookie.value) + else: + value = cookie.value + + # add cookie-attributes to be returned in Cookie header + if cookie.value is None: + attrs.append(cookie.name) + else: + attrs.append("%s=%s" % (cookie.name, value)) + if version > 0: + if cookie.path_specified: + attrs.append('$Path="%s"' % cookie.path) + if cookie.domain.startswith("."): + domain = cookie.domain + if (not cookie.domain_initial_dot and + domain.startswith(".")): + domain = domain[1:] + attrs.append('$Domain="%s"' % domain) + if cookie.port is not None: + p = "$Port" + if cookie.port_specified: + p = p + ('="%s"' % cookie.port) + attrs.append(p) + + return attrs + + def add_cookie_header(self, request): + """Add correct Cookie: header to request (urllib.request.Request object). + + The Cookie2 header is also added unless policy.hide_cookie2 is true. + + """ + _debug("add_cookie_header") + self._cookies_lock.acquire() + try: + + self._policy._now = self._now = int(time.time()) + + cookies = self._cookies_for_request(request) + + attrs = self._cookie_attrs(cookies) + if attrs: + if not request.has_header("Cookie"): + request.add_unredirected_header( + "Cookie", "; ".join(attrs)) + + # if necessary, advertise that we know RFC 2965 + if (self._policy.rfc2965 and not self._policy.hide_cookie2 and + not request.has_header("Cookie2")): + for cookie in cookies: + if cookie.version != 1: + request.add_unredirected_header("Cookie2", '$Version="1"') + break + + finally: + self._cookies_lock.release() + + self.clear_expired_cookies() + + def _normalized_cookie_tuples(self, attrs_set): + """Return list of tuples containing normalised cookie information. + + attrs_set is the list of lists of key,value pairs extracted from + the Set-Cookie or Set-Cookie2 headers. + + Tuples are name, value, standard, rest, where name and value are the + cookie name and value, standard is a dictionary containing the standard + cookie-attributes (discard, secure, version, expires or max-age, + domain, path and port) and rest is a dictionary containing the rest of + the cookie-attributes. + + """ + cookie_tuples = [] + + boolean_attrs = "discard", "secure" + value_attrs = ("version", + "expires", "max-age", + "domain", "path", "port", + "comment", "commenturl") + + for cookie_attrs in attrs_set: + name, value = cookie_attrs[0] + + # Build dictionary of standard cookie-attributes (standard) and + # dictionary of other cookie-attributes (rest). + + # Note: expiry time is normalised to seconds since epoch. V0 + # cookies should have the Expires cookie-attribute, and V1 cookies + # should have Max-Age, but since V1 includes RFC 2109 cookies (and + # since V0 cookies may be a mish-mash of Netscape and RFC 2109), we + # accept either (but prefer Max-Age). + max_age_set = False + + bad_cookie = False + + standard = {} + rest = {} + for k, v in cookie_attrs[1:]: + lc = k.lower() + # don't lose case distinction for unknown fields + if lc in value_attrs or lc in boolean_attrs: + k = lc + if k in boolean_attrs and v is None: + # boolean cookie-attribute is present, but has no value + # (like "discard", rather than "port=80") + v = True + if k in standard: + # only first value is significant + continue + if k == "domain": + if v is None: + _debug(" missing value for domain attribute") + bad_cookie = True + break + # RFC 2965 section 3.3.3 + v = v.lower() + if k == "expires": + if max_age_set: + # Prefer max-age to expires (like Mozilla) + continue + if v is None: + _debug(" missing or invalid value for expires " + "attribute: treating as session cookie") + continue + if k == "max-age": + max_age_set = True + try: + v = int(v) + except ValueError: + _debug(" missing or invalid (non-numeric) value for " + "max-age attribute") + bad_cookie = True + break + # convert RFC 2965 Max-Age to seconds since epoch + # XXX Strictly you're supposed to follow RFC 2616 + # age-calculation rules. Remember that zero Max-Age + # is a request to discard (old and new) cookie, though. + k = "expires" + v = self._now + v + if (k in value_attrs) or (k in boolean_attrs): + if (v is None and + k not in ("port", "comment", "commenturl")): + _debug(" missing value for %s attribute" % k) + bad_cookie = True + break + standard[k] = v + else: + rest[k] = v + + if bad_cookie: + continue + + cookie_tuples.append((name, value, standard, rest)) + + return cookie_tuples + + def _cookie_from_cookie_tuple(self, tup, request): + # standard is dict of standard cookie-attributes, rest is dict of the + # rest of them + name, value, standard, rest = tup + + domain = standard.get("domain", Absent) + path = standard.get("path", Absent) + port = standard.get("port", Absent) + expires = standard.get("expires", Absent) + + # set the easy defaults + version = standard.get("version", None) + if version is not None: + try: + version = int(version) + except ValueError: + return None # invalid version, ignore cookie + secure = standard.get("secure", False) + # (discard is also set if expires is Absent) + discard = standard.get("discard", False) + comment = standard.get("comment", None) + comment_url = standard.get("commenturl", None) + + # set default path + if path is not Absent and path != "": + path_specified = True + path = escape_path(path) + else: + path_specified = False + path = request_path(request) + i = path.rfind("/") + if i != -1: + if version == 0: + # Netscape spec parts company from reality here + path = path[:i] + else: + path = path[:i+1] + if len(path) == 0: path = "/" + + # set default domain + domain_specified = domain is not Absent + # but first we have to remember whether it starts with a dot + domain_initial_dot = False + if domain_specified: + domain_initial_dot = bool(domain.startswith(".")) + if domain is Absent: + req_host, erhn = eff_request_host(request) + domain = erhn + elif not domain.startswith("."): + domain = "."+domain + + # set default port + port_specified = False + if port is not Absent: + if port is None: + # Port attr present, but has no value: default to request port. + # Cookie should then only be sent back on that port. + port = request_port(request) + else: + port_specified = True + port = re.sub(r"\s+", "", port) + else: + # No port attr present. Cookie can be sent back on any port. + port = None + + # set default expires and discard + if expires is Absent: + expires = None + discard = True + elif expires <= self._now: + # Expiry date in past is request to delete cookie. This can't be + # in DefaultCookiePolicy, because can't delete cookies there. + try: + self.clear(domain, path, name) + except KeyError: + pass + _debug("Expiring cookie, domain='%s', path='%s', name='%s'", + domain, path, name) + return None + + return Cookie(version, + name, value, + port, port_specified, + domain, domain_specified, domain_initial_dot, + path, path_specified, + secure, + expires, + discard, + comment, + comment_url, + rest) + + def _cookies_from_attrs_set(self, attrs_set, request): + cookie_tuples = self._normalized_cookie_tuples(attrs_set) + + cookies = [] + for tup in cookie_tuples: + cookie = self._cookie_from_cookie_tuple(tup, request) + if cookie: cookies.append(cookie) + return cookies + + def _process_rfc2109_cookies(self, cookies): + rfc2109_as_ns = getattr(self._policy, 'rfc2109_as_netscape', None) + if rfc2109_as_ns is None: + rfc2109_as_ns = not self._policy.rfc2965 + for cookie in cookies: + if cookie.version == 1: + cookie.rfc2109 = True + if rfc2109_as_ns: + # treat 2109 cookies as Netscape cookies rather than + # as RFC2965 cookies + cookie.version = 0 + + def make_cookies(self, response, request): + """Return sequence of Cookie objects extracted from response object.""" + # get cookie-attributes for RFC 2965 and Netscape protocols + headers = response.info() + rfc2965_hdrs = headers.get_all("Set-Cookie2", []) + ns_hdrs = headers.get_all("Set-Cookie", []) + self._policy._now = self._now = int(time.time()) + + rfc2965 = self._policy.rfc2965 + netscape = self._policy.netscape + + if ((not rfc2965_hdrs and not ns_hdrs) or + (not ns_hdrs and not rfc2965) or + (not rfc2965_hdrs and not netscape) or + (not netscape and not rfc2965)): + return [] # no relevant cookie headers: quick exit + + try: + cookies = self._cookies_from_attrs_set( + split_header_words(rfc2965_hdrs), request) + except Exception: + _warn_unhandled_exception() + cookies = [] + + if ns_hdrs and netscape: + try: + # RFC 2109 and Netscape cookies + ns_cookies = self._cookies_from_attrs_set( + parse_ns_headers(ns_hdrs), request) + except Exception: + _warn_unhandled_exception() + ns_cookies = [] + self._process_rfc2109_cookies(ns_cookies) + + # Look for Netscape cookies (from Set-Cookie headers) that match + # corresponding RFC 2965 cookies (from Set-Cookie2 headers). + # For each match, keep the RFC 2965 cookie and ignore the Netscape + # cookie (RFC 2965 section 9.1). Actually, RFC 2109 cookies are + # bundled in with the Netscape cookies for this purpose, which is + # reasonable behaviour. + if rfc2965: + lookup = {} + for cookie in cookies: + lookup[(cookie.domain, cookie.path, cookie.name)] = None + + def no_matching_rfc2965(ns_cookie, lookup=lookup): + key = ns_cookie.domain, ns_cookie.path, ns_cookie.name + return key not in lookup + ns_cookies = filter(no_matching_rfc2965, ns_cookies) + + if ns_cookies: + cookies.extend(ns_cookies) + + return cookies + + def set_cookie_if_ok(self, cookie, request): + """Set a cookie if policy says it's OK to do so.""" + self._cookies_lock.acquire() + try: + self._policy._now = self._now = int(time.time()) + + if self._policy.set_ok(cookie, request): + self.set_cookie(cookie) + + + finally: + self._cookies_lock.release() + + def set_cookie(self, cookie): + """Set a cookie, without checking whether or not it should be set.""" + c = self._cookies + self._cookies_lock.acquire() + try: + if cookie.domain not in c: c[cookie.domain] = {} + c2 = c[cookie.domain] + if cookie.path not in c2: c2[cookie.path] = {} + c3 = c2[cookie.path] + c3[cookie.name] = cookie + finally: + self._cookies_lock.release() + + def extract_cookies(self, response, request): + """Extract cookies from response, where allowable given the request.""" + _debug("extract_cookies: %s", response.info()) + self._cookies_lock.acquire() + try: + for cookie in self.make_cookies(response, request): + if self._policy.set_ok(cookie, request): + _debug(" setting cookie: %s", cookie) + self.set_cookie(cookie) + finally: + self._cookies_lock.release() + + def clear(self, domain=None, path=None, name=None): + """Clear some cookies. + + Invoking this method without arguments will clear all cookies. If + given a single argument, only cookies belonging to that domain will be + removed. If given two arguments, cookies belonging to the specified + path within that domain are removed. If given three arguments, then + the cookie with the specified name, path and domain is removed. + + Raises KeyError if no matching cookie exists. + + """ + if name is not None: + if (domain is None) or (path is None): + raise ValueError( + "domain and path must be given to remove a cookie by name") + del self._cookies[domain][path][name] + elif path is not None: + if domain is None: + raise ValueError( + "domain must be given to remove cookies by path") + del self._cookies[domain][path] + elif domain is not None: + del self._cookies[domain] + else: + self._cookies = {} + + def clear_session_cookies(self): + """Discard all session cookies. + + Note that the .save() method won't save session cookies anyway, unless + you ask otherwise by passing a true ignore_discard argument. + + """ + self._cookies_lock.acquire() + try: + for cookie in self: + if cookie.discard: + self.clear(cookie.domain, cookie.path, cookie.name) + finally: + self._cookies_lock.release() + + def clear_expired_cookies(self): + """Discard all expired cookies. + + You probably don't need to call this method: expired cookies are never + sent back to the server (provided you're using DefaultCookiePolicy), + this method is called by CookieJar itself every so often, and the + .save() method won't save expired cookies anyway (unless you ask + otherwise by passing a true ignore_expires argument). + + """ + self._cookies_lock.acquire() + try: + now = time.time() + for cookie in self: + if cookie.is_expired(now): + self.clear(cookie.domain, cookie.path, cookie.name) + finally: + self._cookies_lock.release() + + def __iter__(self): + return deepvalues(self._cookies) + + def __len__(self): + """Return number of contained cookies.""" + i = 0 + for cookie in self: i = i + 1 + return i + + def __repr__(self): + r = [] + for cookie in self: r.append(repr(cookie)) + return "<%s[%s]>" % (self.__class__.__name__, ", ".join(r)) + + def __str__(self): + r = [] + for cookie in self: r.append(str(cookie)) + return "<%s[%s]>" % (self.__class__.__name__, ", ".join(r)) + + +# derives from OSError for backwards-compatibility with Python 2.4.0 +class LoadError(OSError): pass + +class FileCookieJar(CookieJar): + """CookieJar that can be loaded from and saved to a file.""" + + def __init__(self, filename=None, delayload=False, policy=None): + """ + Cookies are NOT loaded from the named file until either the .load() or + .revert() method is called. + + """ + CookieJar.__init__(self, policy) + if filename is not None: + filename = os.fspath(filename) + self.filename = filename + self.delayload = bool(delayload) + + def save(self, filename=None, ignore_discard=False, ignore_expires=False): + """Save cookies to a file.""" + raise NotImplementedError() + + def load(self, filename=None, ignore_discard=False, ignore_expires=False): + """Load cookies from a file.""" + if filename is None: + if self.filename is not None: filename = self.filename + else: raise ValueError(MISSING_FILENAME_TEXT) + + with open(filename) as f: + self._really_load(f, filename, ignore_discard, ignore_expires) + + def revert(self, filename=None, + ignore_discard=False, ignore_expires=False): + """Clear all cookies and reload cookies from a saved file. + + Raises LoadError (or OSError) if reversion is not successful; the + object's state will not be altered if this happens. + + """ + if filename is None: + if self.filename is not None: filename = self.filename + else: raise ValueError(MISSING_FILENAME_TEXT) + + self._cookies_lock.acquire() + try: + + old_state = copy.deepcopy(self._cookies) + self._cookies = {} + try: + self.load(filename, ignore_discard, ignore_expires) + except OSError: + self._cookies = old_state + raise + + finally: + self._cookies_lock.release() + + +def lwp_cookie_str(cookie): + """Return string representation of Cookie in the LWP cookie file format. + + Actually, the format is extended a bit -- see module docstring. + + """ + h = [(cookie.name, cookie.value), + ("path", cookie.path), + ("domain", cookie.domain)] + if cookie.port is not None: h.append(("port", cookie.port)) + if cookie.path_specified: h.append(("path_spec", None)) + if cookie.port_specified: h.append(("port_spec", None)) + if cookie.domain_initial_dot: h.append(("domain_dot", None)) + if cookie.secure: h.append(("secure", None)) + if cookie.expires: h.append(("expires", + time2isoz(float(cookie.expires)))) + if cookie.discard: h.append(("discard", None)) + if cookie.comment: h.append(("comment", cookie.comment)) + if cookie.comment_url: h.append(("commenturl", cookie.comment_url)) + + keys = sorted(cookie._rest.keys()) + for k in keys: + h.append((k, str(cookie._rest[k]))) + + h.append(("version", str(cookie.version))) + + return join_header_words([h]) + +class LWPCookieJar(FileCookieJar): + """ + The LWPCookieJar saves a sequence of "Set-Cookie3" lines. + "Set-Cookie3" is the format used by the libwww-perl library, not known + to be compatible with any browser, but which is easy to read and + doesn't lose information about RFC 2965 cookies. + + Additional methods + + as_lwp_str(ignore_discard=True, ignore_expired=True) + + """ + + def as_lwp_str(self, ignore_discard=True, ignore_expires=True): + """Return cookies as a string of "\\n"-separated "Set-Cookie3" headers. + + ignore_discard and ignore_expires: see docstring for FileCookieJar.save + + """ + now = time.time() + r = [] + for cookie in self: + if not ignore_discard and cookie.discard: + continue + if not ignore_expires and cookie.is_expired(now): + continue + r.append("Set-Cookie3: %s" % lwp_cookie_str(cookie)) + return "\n".join(r+[""]) + + def save(self, filename=None, ignore_discard=False, ignore_expires=False): + if filename is None: + if self.filename is not None: filename = self.filename + else: raise ValueError(MISSING_FILENAME_TEXT) + + with open(filename, "w") as f: + # There really isn't an LWP Cookies 2.0 format, but this indicates + # that there is extra information in here (domain_dot and + # port_spec) while still being compatible with libwww-perl, I hope. + f.write("#LWP-Cookies-2.0\n") + f.write(self.as_lwp_str(ignore_discard, ignore_expires)) + + def _really_load(self, f, filename, ignore_discard, ignore_expires): + magic = f.readline() + if not self.magic_re.search(magic): + msg = ("%r does not look like a Set-Cookie3 (LWP) format " + "file" % filename) + raise LoadError(msg) + + now = time.time() + + header = "Set-Cookie3:" + boolean_attrs = ("port_spec", "path_spec", "domain_dot", + "secure", "discard") + value_attrs = ("version", + "port", "path", "domain", + "expires", + "comment", "commenturl") + + try: + while 1: + line = f.readline() + if line == "": break + if not line.startswith(header): + continue + line = line[len(header):].strip() + + for data in split_header_words([line]): + name, value = data[0] + standard = {} + rest = {} + for k in boolean_attrs: + standard[k] = False + for k, v in data[1:]: + if k is not None: + lc = k.lower() + else: + lc = None + # don't lose case distinction for unknown fields + if (lc in value_attrs) or (lc in boolean_attrs): + k = lc + if k in boolean_attrs: + if v is None: v = True + standard[k] = v + elif k in value_attrs: + standard[k] = v + else: + rest[k] = v + + h = standard.get + expires = h("expires") + discard = h("discard") + if expires is not None: + expires = iso2time(expires) + if expires is None: + discard = True + domain = h("domain") + domain_specified = domain.startswith(".") + c = Cookie(h("version"), name, value, + h("port"), h("port_spec"), + domain, domain_specified, h("domain_dot"), + h("path"), h("path_spec"), + h("secure"), + expires, + discard, + h("comment"), + h("commenturl"), + rest) + if not ignore_discard and c.discard: + continue + if not ignore_expires and c.is_expired(now): + continue + self.set_cookie(c) + except OSError: + raise + except Exception: + _warn_unhandled_exception() + raise LoadError("invalid Set-Cookie3 format file %r: %r" % + (filename, line)) + + +class MozillaCookieJar(FileCookieJar): + """ + + WARNING: you may want to backup your browser's cookies file if you use + this class to save cookies. I *think* it works, but there have been + bugs in the past! + + This class differs from CookieJar only in the format it uses to save and + load cookies to and from a file. This class uses the Mozilla/Netscape + `cookies.txt' format. curl and lynx use this file format, too. + + Don't expect cookies saved while the browser is running to be noticed by + the browser (in fact, Mozilla on unix will overwrite your saved cookies if + you change them on disk while it's running; on Windows, you probably can't + save at all while the browser is running). + + Note that the Mozilla/Netscape format will downgrade RFC2965 cookies to + Netscape cookies on saving. + + In particular, the cookie version and port number information is lost, + together with information about whether or not Path, Port and Discard were + specified by the Set-Cookie2 (or Set-Cookie) header, and whether or not the + domain as set in the HTTP header started with a dot (yes, I'm aware some + domains in Netscape files start with a dot and some don't -- trust me, you + really don't want to know any more about this). + + Note that though Mozilla and Netscape use the same format, they use + slightly different headers. The class saves cookies using the Netscape + header by default (Mozilla can cope with that). + + """ + + def _really_load(self, f, filename, ignore_discard, ignore_expires): + now = time.time() + + if not NETSCAPE_MAGIC_RGX.match(f.readline()): + raise LoadError( + "%r does not look like a Netscape format cookies file" % + filename) + + try: + while 1: + line = f.readline() + rest = {} + + if line == "": break + + # httponly is a cookie flag as defined in rfc6265 + # when encoded in a netscape cookie file, + # the line is prepended with "#HttpOnly_" + if line.startswith(HTTPONLY_PREFIX): + rest[HTTPONLY_ATTR] = "" + line = line[len(HTTPONLY_PREFIX):] + + # last field may be absent, so keep any trailing tab + if line.endswith("\n"): line = line[:-1] + + # skip comments and blank lines XXX what is $ for? + if (line.strip().startswith(("#", "$")) or + line.strip() == ""): + continue + + domain, domain_specified, path, secure, expires, name, value = \ + line.split("\t") + secure = (secure == "TRUE") + domain_specified = (domain_specified == "TRUE") + if name == "": + # cookies.txt regards 'Set-Cookie: foo' as a cookie + # with no name, whereas http.cookiejar regards it as a + # cookie with no value. + name = value + value = None + + initial_dot = domain.startswith(".") + assert domain_specified == initial_dot + + discard = False + if expires == "": + expires = None + discard = True + + # assume path_specified is false + c = Cookie(0, name, value, + None, False, + domain, domain_specified, initial_dot, + path, False, + secure, + expires, + discard, + None, + None, + rest) + if not ignore_discard and c.discard: + continue + if not ignore_expires and c.is_expired(now): + continue + self.set_cookie(c) + + except OSError: + raise + except Exception: + _warn_unhandled_exception() + raise LoadError("invalid Netscape format cookies file %r: %r" % + (filename, line)) + + def save(self, filename=None, ignore_discard=False, ignore_expires=False): + if filename is None: + if self.filename is not None: filename = self.filename + else: raise ValueError(MISSING_FILENAME_TEXT) + + with open(filename, "w") as f: + f.write(NETSCAPE_HEADER_TEXT) + now = time.time() + for cookie in self: + domain = cookie.domain + if not ignore_discard and cookie.discard: + continue + if not ignore_expires and cookie.is_expired(now): + continue + if cookie.secure: secure = "TRUE" + else: secure = "FALSE" + if domain.startswith("."): initial_dot = "TRUE" + else: initial_dot = "FALSE" + if cookie.expires is not None: + expires = str(cookie.expires) + else: + expires = "" + if cookie.value is None: + # cookies.txt regards 'Set-Cookie: foo' as a cookie + # with no name, whereas http.cookiejar regards it as a + # cookie with no value. + name = "" + value = cookie.name + else: + name = cookie.name + value = cookie.value + if cookie.has_nonstandard_attr(HTTPONLY_ATTR): + domain = HTTPONLY_PREFIX + domain + f.write( + "\t".join([domain, initial_dot, cookie.path, + secure, expires, name, value])+ + "\n") diff --git a/shared/package-files/03/03a8ef7d3dcd393be8ec04d51893def5da1cc992a99d30909ba1c01ba5c6928e/out_types.h b/shared/package-files/03/03a8ef7d3dcd393be8ec04d51893def5da1cc992a99d30909ba1c01ba5c6928e/out_types.h new file mode 100644 index 0000000000000000000000000000000000000000..1cab00bc270f2e3dff532cc715327c030698c190 --- /dev/null +++ b/shared/package-files/03/03a8ef7d3dcd393be8ec04d51893def5da1cc992a99d30909ba1c01ba5c6928e/out_types.h @@ -0,0 +1,17 @@ +#pragma once + +#include + +namespace torch { +namespace utils { + +TORCH_API void check_out_type_matches( + const at::Tensor& result, + c10::optional scalarType, + bool scalarType_is_none, + c10::optional layout, + c10::optional device, + bool device_is_none); + +} +} // namespace torch diff --git a/shared/package-files/03/03b2203d9f5b6ea1f8727de0ac27aa2282283456dd48decd6f5fff30245a9f3c/python_api.py b/shared/package-files/03/03b2203d9f5b6ea1f8727de0ac27aa2282283456dd48decd6f5fff30245a9f3c/python_api.py new file mode 100644 index 0000000000000000000000000000000000000000..ab3c27d831e385b1f29f79ca9868da1d35f02f6c --- /dev/null +++ b/shared/package-files/03/03b2203d9f5b6ea1f8727de0ac27aa2282283456dd48decd6f5fff30245a9f3c/python_api.py @@ -0,0 +1,126 @@ +# Copyright (C) 2012 Anaconda, Inc +# SPDX-License-Identifier: BSD-3-Clause +from logging import getLogger + +from ..base.constants import SEARCH_PATH +from ..base.context import context +from ..common.compat import encode_arguments +from ..common.io import CaptureTarget, argv, captured +from ..exceptions import conda_exception_handler +from ..gateways.logging import initialize_std_loggers +from .conda_argparse import do_call +from .main import generate_parser + +log = getLogger(__name__) + + +class Commands: + CLEAN = "clean" + CONFIG = "config" + CREATE = "create" + INFO = "info" + INSTALL = "install" + LIST = "list" + REMOVE = "remove" + SEARCH = "search" + UPDATE = "update" + RUN = "run" + NOTICES = "notices" + + +STRING = CaptureTarget.STRING +STDOUT = CaptureTarget.STDOUT + + +# Note, a deviated copy of this code appears in tests/test_create.py +def run_command(command, *arguments, **kwargs): + """Runs a conda command in-process with a given set of command-line interface arguments. + + Differences from the command-line interface: + Always uses --yes flag, thus does not ask for confirmation. + + Args: + command: one of the Commands. + *arguments: instructions you would normally pass to the conda command on the command line + see below for examples. Be very careful to delimit arguments exactly as you + want them to be delivered. No 'combine then split at spaces' or other + information destroying processing gets performed on the arguments. + **kwargs: special instructions for programmatic overrides + + Keyword Args: + use_exception_handler: defaults to False. False will let the code calling + `run_command` handle all exceptions. True won't raise when an exception + has occurred, and instead give a non-zero return code + search_path: an optional non-standard search path for configuration information + that overrides the default SEARCH_PATH + stdout: Define capture behavior for stream sys.stdout. Defaults to STRING. + STRING captures as a string. None leaves stream untouched. + Otherwise redirect to file-like object stdout. + stderr: Define capture behavior for stream sys.stderr. Defaults to STRING. + STRING captures as a string. None leaves stream untouched. + STDOUT redirects to stdout target and returns None as stderr value. + Otherwise redirect to file-like object stderr. + + Returns: + a tuple of stdout, stderr, and return_code. + stdout, stderr are either strings, None or the corresponding file-like function argument. + + Examples: + >>> run_command(Commands.CREATE, "-n", "newenv", "python=3", "flask", \ + use_exception_handler=True) + >>> run_command(Commands.CREATE, "-n", "newenv", "python=3", "flask") + >>> run_command(Commands.CREATE, ["-n", "newenv", "python=3", "flask"], search_path=()) + """ + initialize_std_loggers() + use_exception_handler = kwargs.pop("use_exception_handler", False) + configuration_search_path = kwargs.pop("search_path", SEARCH_PATH) + stdout = kwargs.pop("stdout", STRING) + stderr = kwargs.pop("stderr", STRING) + p = generate_parser() + + if arguments and isinstance(arguments[0], list): + arguments = arguments[0] + + arguments = list(arguments) + arguments.insert(0, command) + + args = p.parse_args(arguments) + args.yes = True # always skip user confirmation, force setting context.always_yes + context.__init__( + search_path=configuration_search_path, + argparse_args=args, + ) + + from subprocess import list2cmdline + + log.debug("executing command >>> conda %s", list2cmdline(arguments)) + + is_run = arguments[0] == "run" + if is_run: + cap_args = (None, None) + else: + cap_args = (stdout, stderr) + try: + with argv(["python_api"] + encode_arguments(arguments)), captured( + *cap_args + ) as c: + if use_exception_handler: + result = conda_exception_handler(do_call, args, p) + else: + result = do_call(args, p) + if is_run: + stdout = result.stdout + stderr = result.stderr + result = result.rc + else: + stdout = c.stdout + stderr = c.stderr + except Exception as e: + log.debug("\n stdout: %s\n stderr: %s", stdout, stderr) + e.stdout, e.stderr = stdout, stderr + raise e + return_code = result or 0 + log.debug( + "\n stdout: %s\n stderr: %s\n return_code: %s", stdout, stderr, return_code + ) + return stdout, stderr, return_code diff --git a/shared/package-files/03/03b28bceeeb601af394fa2dabdc5f626dd6909b16cd2b466177730b971ddb414/_thnn_fused_gru_cell_ops.h b/shared/package-files/03/03b28bceeeb601af394fa2dabdc5f626dd6909b16cd2b466177730b971ddb414/_thnn_fused_gru_cell_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..ac4b543e47d103f09d3efd519813263194cab430 --- /dev/null +++ b/shared/package-files/03/03b28bceeeb601af394fa2dabdc5f626dd6909b16cd2b466177730b971ddb414/_thnn_fused_gru_cell_ops.h @@ -0,0 +1,39 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API _thnn_fused_gru_cell { + using schema = ::std::tuple (const at::Tensor &, const at::Tensor &, const at::Tensor &, const c10::optional &, const c10::optional &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_thnn_fused_gru_cell") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_thnn_fused_gru_cell(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None) -> (Tensor, Tensor)") + static ::std::tuple call(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional & input_bias, const c10::optional & hidden_bias); + static ::std::tuple redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional & input_bias, const c10::optional & hidden_bias); +}; + +struct TORCH_API _thnn_fused_gru_cell_out { + using schema = ::std::tuple (const at::Tensor &, const at::Tensor &, const at::Tensor &, const c10::optional &, const c10::optional &, at::Tensor &, at::Tensor &); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_thnn_fused_gru_cell") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_thnn_fused_gru_cell.out(Tensor input_gates, Tensor hidden_gates, Tensor hx, Tensor? input_bias=None, Tensor? hidden_bias=None, *, Tensor(a!) out0, Tensor(b!) out1) -> (Tensor(a!), Tensor(b!))") + static ::std::tuple call(const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional & input_bias, const c10::optional & hidden_bias, at::Tensor & out0, at::Tensor & out1); + static ::std::tuple redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input_gates, const at::Tensor & hidden_gates, const at::Tensor & hx, const c10::optional & input_bias, const c10::optional & hidden_bias, at::Tensor & out0, at::Tensor & out1); +}; + +}} // namespace at::_ops diff --git a/shared/package-files/03/03b52e868612a399d1aa5c0d172526eecca072d1b497aa6f08a60e2b8af972dd/copy_sparse_to_sparse_native.h b/shared/package-files/03/03b52e868612a399d1aa5c0d172526eecca072d1b497aa6f08a60e2b8af972dd/copy_sparse_to_sparse_native.h new file mode 100644 index 0000000000000000000000000000000000000000..19b1fa3459df6213c415764683f9fd8c3fd76ee8 --- /dev/null +++ b/shared/package-files/03/03b52e868612a399d1aa5c0d172526eecca072d1b497aa6f08a60e2b8af972dd/copy_sparse_to_sparse_native.h @@ -0,0 +1,23 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor copy_sparse_to_sparse(const at::Tensor & self, const at::Tensor & src, bool non_blocking=false); +TORCH_API at::Tensor & copy_sparse_to_sparse_out(const at::Tensor & self, const at::Tensor & src, bool non_blocking, at::Tensor & out); +TORCH_API at::Tensor & copy_sparse_(at::Tensor & self, const at::Tensor & src, bool non_blocking=false); +} // namespace native +} // namespace at diff --git a/shared/package-files/03/03b628fb3a3b2b902c2fd053dd017676be3733c1c34c7ed787cd330452abbd7e/moment_helpers.py b/shared/package-files/03/03b628fb3a3b2b902c2fd053dd017676be3733c1c34c7ed787cd330452abbd7e/moment_helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..04ad7184e909353a63b5bdb03a683ab10d03695a --- /dev/null +++ b/shared/package-files/03/03b628fb3a3b2b902c2fd053dd017676be3733c1c34c7ed787cd330452abbd7e/moment_helpers.py @@ -0,0 +1,468 @@ +""" +Helper functions for conversion between moments + +Contains: + +* conversion between central and non-central moments, skew, kurtosis and + cumulants +* cov2corr : convert covariance matrix to correlation matrix + + +Author: Josef Perktold +License: BSD-3 + +""" + +from typing import NamedTuple + +import numpy as np +from scipy.special import comb + +from statsmodels.tools.validation import bool_like + + +def _convert_to_multidim(x): + if any([isinstance(x, list), isinstance(x, tuple)]): + return np.array(x) + elif isinstance(x, np.ndarray): + return x + else: + # something strange was passed and the function probably + # will fall, maybe insert an exception? + return x + + +def _convert_from_multidim(x, totype=list): + if len(x.shape) < 2: + return totype(x) + return x.T + + +def mc2mnc(mc): + """ + Convert central to non-central moments, uses recursive formula + + Optionally adjusts first moment to return mean. + + Parameters + ---------- + mc : array_like + Central moments, with the first element equal to the mean. + + Returns + ------- + mnc : ndarray or list + Non-central moments. + """ + x = _convert_to_multidim(mc) + + def _local_counts(mc): + mean = mc[0] + mc = [1, *list(mc)] # add zero moment = 1 + mc[1] = 0 # define central mean as zero for formula + mnc = [1, mean] # zero and first raw moments + for nn, _ in enumerate(mc[2:]): + n = nn + 2 + mnc.append(0) + for k in range(n + 1): + mnc[n] += comb(n, k, exact=True) * mc[k] * mean ** (n - k) + return mnc[1:] + + res = np.apply_along_axis(_local_counts, 0, x) + # for backward compatibility convert 1-dim output to list/tuple + return _convert_from_multidim(res) + + +def mnc2mc(mnc, wmean=True): + """ + Convert non-central to central moments, uses recursive formula + + Optionally adjusts first moment to return mean. + + Parameters + ---------- + mnc : array_like + Non-central moments, with the first element equal to the mean. + wmean : bool, optional + If True (default), the first returned moment is the mean instead + of zero. + + Returns + ------- + mu : ndarray or list + Central moments. + """ + X = _convert_to_multidim(mnc) + + def _local_counts(mnc): + mean = mnc[0] + mnc = [1, *list(mnc)] # add zero moment = 1 + mu = [] + for n, _ in enumerate(mnc): + mu.append(0) + for k in range(n + 1): + sgn_comb = (-1) ** (n - k) * comb(n, k, exact=True) + mu[n] += sgn_comb * mnc[k] * mean ** (n - k) + if wmean: + mu[1] = mean + return mu[1:] + + res = np.apply_along_axis(_local_counts, 0, X) + # for backward compatibility convert 1-dim output to list/tuple + return _convert_from_multidim(res) + + +def cum2mc(kappa): + """ + Convert cumulants to central moments + + Recursive formula produces as many central moments as cumulants. + + Parameters + ---------- + kappa : array_like + Cumulants. + + Returns + ------- + mc : ndarray or list + Central moments. + + References + ---------- + Kenneth Lange: Numerical Analysis for Statisticians, page 40 + """ + X = _convert_to_multidim(kappa) + + def _local_counts(kappa): + mc = [1, 0.0] # _kappa[0]] # insert 0-moment and mean + kappa0 = kappa[0] + kappa = [1, *list(kappa)] + for nn, _ in enumerate(kappa[2:]): + n = nn + 2 + mc.append(0) + for k in range(n - 1): + mc[n] += comb(n - 1, k, exact=True) * kappa[n - k] * mc[k] + mc[1] = kappa0 # insert mean as first moments by convention + return mc[1:] + + res = np.apply_along_axis(_local_counts, 0, X) + # for backward compatibility convert 1-dim output to list/tuple + return _convert_from_multidim(res) + + +def mnc2cum(mnc): + """ + Convert non-central moments to cumulants + + Recursive formula produces as many cumulants as moments. + + Parameters + ---------- + mnc : array_like + Non-central moments. + + Returns + ------- + kappa : ndarray or list + Cumulants. + + References + ---------- + https://en.wikipedia.org/wiki/Cumulant#Cumulants_and_moments + """ + X = _convert_to_multidim(mnc) + + def _local_counts(mnc): + mnc = [1, *list(mnc)] + kappa = [1] + for nn, m in enumerate(mnc[1:]): + n = nn + 1 + kappa.append(m) + for k in range(1, n): + num_ways = comb(n - 1, k - 1, exact=True) + kappa[n] -= num_ways * kappa[k] * mnc[n - k] + return kappa[1:] + + res = np.apply_along_axis(_local_counts, 0, X) + # for backward compatibility convert 1-dim output to list/tuple + return _convert_from_multidim(res) + + +def mc2cum(mc): + """ + Convert central moments to cumulants + + Just chained because there is still a test case for it. + + Parameters + ---------- + mc : array_like + Central moments. + + Returns + ------- + kappa : ndarray or list + Cumulants. + """ + first_step = mc2mnc(mc) + if isinstance(first_step, np.ndarray): + first_step = first_step.T + return mnc2cum(first_step) + # return np.apply_along_axis(lambda x: mnc2cum(mc2mnc(x)), 0, mc) + + +def mvsk2mc(args): + """ + Convert mean, variance, skew, kurtosis to central moments + + Parameters + ---------- + args : array_like + Sequence containing the mean, variance, skew, and kurtosis, in + that order. + + Returns + ------- + mc : tuple or ndarray + Central moments (mean, variance, 3rd and 4th central moments). + """ + X = _convert_to_multidim(args) + + def _local_counts(args): + mu, sig2, sk, kur = args + cnt = [None] * 4 + cnt[0] = mu + cnt[1] = sig2 + cnt[2] = sk * sig2**1.5 + cnt[3] = (kur + 3.0) * sig2**2.0 + return tuple(cnt) + + res = np.apply_along_axis(_local_counts, 0, X) + # for backward compatibility convert 1-dim output to list/tuple + return _convert_from_multidim(res, tuple) + + +def mvsk2mnc(args): + """ + Convert mean, variance, skew, kurtosis to non-central moments + + Parameters + ---------- + args : array_like + Sequence containing the mean, variance, skew, and kurtosis, in + that order. + + Returns + ------- + mnc : tuple or ndarray + Non-central moments. + """ + X = _convert_to_multidim(args) + + def _local_counts(args): + mc, mc2, skew, kurt = args + mnc = mc + mnc2 = mc2 + mc * mc + mc3 = skew * (mc2**1.5) # 3rd central moment + mnc3 = mc3 + 3 * mc * mc2 + mc**3 # 3rd non-central moment + mc4 = (kurt + 3.0) * (mc2**2.0) # 4th central moment + mnc4 = mc4 + 4 * mc * mc3 + 6 * mc * mc * mc2 + mc**4 + return (mnc, mnc2, mnc3, mnc4) + + res = np.apply_along_axis(_local_counts, 0, X) + # for backward compatibility convert 1-dim output to list/tuple + return _convert_from_multidim(res, tuple) + + +def mc2mvsk(args): + """ + Convert central moments to mean, variance, skew, kurtosis + + Parameters + ---------- + args : array_like + Sequence containing the central moments (mean, variance, 3rd and + 4th central moments), in that order. + + Returns + ------- + res : tuple or ndarray + Mean, variance, skew, and kurtosis. + """ + X = _convert_to_multidim(args) + + def _local_counts(args): + mc, mc2, mc3, mc4 = args + skew = np.divide(mc3, mc2**1.5) + kurt = np.divide(mc4, mc2**2.0) - 3.0 + return (mc, mc2, skew, kurt) + + res = np.apply_along_axis(_local_counts, 0, X) + # for backward compatibility convert 1-dim output to list/tuple + return _convert_from_multidim(res, tuple) + + +def mnc2mvsk(args): + """ + Convert non-central moments to mean, variance, skew, kurtosis + + Parameters + ---------- + args : array_like + Sequence containing the non-central moments, in order. + + Returns + ------- + res : tuple or ndarray + Mean, variance, skew, and kurtosis. + """ + X = _convert_to_multidim(args) + + def _local_counts(args): + # convert four non-central moments to central moments + mnc, mnc2, mnc3, mnc4 = args + mc = mnc + mc2 = mnc2 - mnc * mnc + mc3 = mnc3 - (3 * mc * mc2 + mc**3) # 3rd central moment + mc4 = mnc4 - (4 * mc * mc3 + 6 * mc * mc * mc2 + mc**4) + return mc2mvsk((mc, mc2, mc3, mc4)) + + res = np.apply_along_axis(_local_counts, 0, X) + # for backward compatibility convert 1-dim output to list/tuple + return _convert_from_multidim(res, tuple) + + +# def mnc2mc(args): +# """convert four non-central moments to central moments +# """ +# mnc, mnc2, mnc3, mnc4 = args +# mc = mnc +# mc2 = mnc2 - mnc*mnc +# mc3 = mnc3 - (3*mc*mc2+mc**3) # 3rd central moment +# mc4 = mnc4 - (4*mc*mc3+6*mc*mc*mc2+mc**4) +# return mc, mc2, mc + +# TODO: no return, did it get lost in cut-paste? + + +class Cov2CorrResult(NamedTuple): + """ + Result of :func:`cov2corr` when the standard deviations are returned. + + Parameters + ---------- + corr : ndarray + Correlation matrix. + std : ndarray + Standard deviation taken from the diagonal of the covariance + matrix. + """ + + corr: np.ndarray + std: np.ndarray + + +def cov2corr(cov, return_std=False, *, result_object: bool | None = None): + """ + Convert covariance matrix to correlation matrix + + Parameters + ---------- + cov : array_like, 2d + Covariance matrix, see Notes. + return_std : bool, optional + If this is true then the standard deviation is also returned. + By default only the correlation matrix is returned. + result_object : bool, optional + Flag controlling whether a ``Cov2CorrResult`` NamedTuple is + returned. When ``return_std=True`` a ``Cov2CorrResult`` is always + returned; it holds the same two elements as the legacy tuple, so it + unpacks and indexes identically. When ``return_std=False`` a bare + correlation matrix is returned unless ``result_object=True``, which + yields a ``Cov2CorrResult`` carrying the standard deviations too. + + Returns + ------- + Cov2CorrResult or ndarray + When ``return_std=True`` (or ``result_object=True``), a NamedTuple + with fields: + + corr : ndarray (subclass) + Correlation matrix. + std : ndarray + Standard deviation from the diagonal of cov. + + ``Cov2CorrResult`` has the same length and contents as the plain + ``(corr, std_)`` tuple it replaces, so it unpacks and indexes + identically. See + :class:`~statsmodels.stats.moment_helpers.Cov2CorrResult`. + + When ``return_std=False`` and ``result_object`` is not True, a bare + correlation matrix is returned instead. + + Notes + ----- + This function does not convert subclasses of ndarrays. This requires that + division is defined elementwise. np.ma.array and np.matrix are allowed. + """ + result_object = bool_like(result_object, "result_object", optional=True) + cov = np.asanyarray(cov) + std_ = np.sqrt(np.diag(cov)) + corr = cov / np.outer(std_, std_) + # Cov2CorrResult has exactly the same length and contents as the legacy + # (corr, std_) tuple, so it unpacks and indexes identically and is always + # used when return_std is True. When return_std is False a bare + # correlation matrix is returned, as before; pass result_object=True to + # always get a Cov2CorrResult. The standard deviations are computed + # either way, so nothing needs to be None-filled. + if result_object or return_std: + return Cov2CorrResult(corr, std_) + return corr + + +def corr2cov(corr, std): + """ + Convert correlation matrix to covariance matrix given standard deviation + + Parameters + ---------- + corr : array_like, 2d + Correlation matrix, see Notes. + std : array_like, 1d + Standard deviation. + + Returns + ------- + cov : ndarray (subclass) + Covariance matrix. + + Notes + ----- + This function does not convert subclasses of ndarrays. This requires + that multiplication is defined elementwise. np.ma.array are allowed, but + not matrices. + """ + corr = np.asanyarray(corr) + std_ = np.asanyarray(std) + cov = corr * np.outer(std_, std_) + return cov + + +def se_cov(cov): + """ + Get standard deviation from covariance matrix + + Just a shorthand function np.sqrt(np.diag(cov)). + + Parameters + ---------- + cov : array_like, square + Covariance matrix. + + Returns + ------- + std : ndarray + Standard deviation from diagonal of cov. + """ + return np.sqrt(np.diag(cov)) diff --git a/shared/package-files/03/03b6335889cd1f449242b944e305c0c36bdb9c2370f77ce4caed7de869bcaeda/descriptor_pb2.pyi b/shared/package-files/03/03b6335889cd1f449242b944e305c0c36bdb9c2370f77ce4caed7de869bcaeda/descriptor_pb2.pyi new file mode 100644 index 0000000000000000000000000000000000000000..67b3ad55c1b022c566ac15fa6b54993de467ed64 --- /dev/null +++ b/shared/package-files/03/03b6335889cd1f449242b944e305c0c36bdb9c2370f77ce4caed7de869bcaeda/descriptor_pb2.pyi @@ -0,0 +1,734 @@ +""" +@generated by mypy-protobuf. Do not edit manually! +isort:skip_file +""" +from google.protobuf.descriptor import ( + Descriptor as google___protobuf___descriptor___Descriptor, + EnumDescriptor as google___protobuf___descriptor___EnumDescriptor, + FileDescriptor as google___protobuf___descriptor___FileDescriptor, +) + +from google.protobuf.internal.containers import ( + RepeatedCompositeFieldContainer as google___protobuf___internal___containers___RepeatedCompositeFieldContainer, + RepeatedScalarFieldContainer as google___protobuf___internal___containers___RepeatedScalarFieldContainer, +) + +from google.protobuf.internal.enum_type_wrapper import ( + _EnumTypeWrapper as google___protobuf___internal___enum_type_wrapper____EnumTypeWrapper, +) + +from google.protobuf.message import ( + Message as google___protobuf___message___Message, +) + +from typing import ( + Iterable as typing___Iterable, + NewType as typing___NewType, + Optional as typing___Optional, + Text as typing___Text, + cast as typing___cast, +) + +from typing_extensions import ( + Literal as typing_extensions___Literal, +) + + +builtin___bool = bool +builtin___bytes = bytes +builtin___float = float +builtin___int = int + + +DESCRIPTOR: google___protobuf___descriptor___FileDescriptor = ... + +class FileDescriptorSet(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + + @property + def file(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___FileDescriptorProto]: ... + + def __init__(self, + *, + file : typing___Optional[typing___Iterable[type___FileDescriptorProto]] = None, + ) -> None: ... + def ClearField(self, field_name: typing_extensions___Literal[u"file",b"file"]) -> None: ... +type___FileDescriptorSet = FileDescriptorSet + +class FileDescriptorProto(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + name: typing___Text = ... + package: typing___Text = ... + dependency: google___protobuf___internal___containers___RepeatedScalarFieldContainer[typing___Text] = ... + public_dependency: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int] = ... + weak_dependency: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int] = ... + syntax: typing___Text = ... + + @property + def message_type(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___DescriptorProto]: ... + + @property + def enum_type(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___EnumDescriptorProto]: ... + + @property + def service(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___ServiceDescriptorProto]: ... + + @property + def extension(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___FieldDescriptorProto]: ... + + @property + def options(self) -> type___FileOptions: ... + + @property + def source_code_info(self) -> type___SourceCodeInfo: ... + + def __init__(self, + *, + name : typing___Optional[typing___Text] = None, + package : typing___Optional[typing___Text] = None, + dependency : typing___Optional[typing___Iterable[typing___Text]] = None, + public_dependency : typing___Optional[typing___Iterable[builtin___int]] = None, + weak_dependency : typing___Optional[typing___Iterable[builtin___int]] = None, + message_type : typing___Optional[typing___Iterable[type___DescriptorProto]] = None, + enum_type : typing___Optional[typing___Iterable[type___EnumDescriptorProto]] = None, + service : typing___Optional[typing___Iterable[type___ServiceDescriptorProto]] = None, + extension : typing___Optional[typing___Iterable[type___FieldDescriptorProto]] = None, + options : typing___Optional[type___FileOptions] = None, + source_code_info : typing___Optional[type___SourceCodeInfo] = None, + syntax : typing___Optional[typing___Text] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"name",b"name",u"options",b"options",u"package",b"package",u"source_code_info",b"source_code_info",u"syntax",b"syntax"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"dependency",b"dependency",u"enum_type",b"enum_type",u"extension",b"extension",u"message_type",b"message_type",u"name",b"name",u"options",b"options",u"package",b"package",u"public_dependency",b"public_dependency",u"service",b"service",u"source_code_info",b"source_code_info",u"syntax",b"syntax",u"weak_dependency",b"weak_dependency"]) -> None: ... +type___FileDescriptorProto = FileDescriptorProto + +class DescriptorProto(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + class ExtensionRange(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + start: builtin___int = ... + end: builtin___int = ... + + @property + def options(self) -> type___ExtensionRangeOptions: ... + + def __init__(self, + *, + start : typing___Optional[builtin___int] = None, + end : typing___Optional[builtin___int] = None, + options : typing___Optional[type___ExtensionRangeOptions] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"end",b"end",u"options",b"options",u"start",b"start"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"end",b"end",u"options",b"options",u"start",b"start"]) -> None: ... + type___ExtensionRange = ExtensionRange + + class ReservedRange(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + start: builtin___int = ... + end: builtin___int = ... + + def __init__(self, + *, + start : typing___Optional[builtin___int] = None, + end : typing___Optional[builtin___int] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"end",b"end",u"start",b"start"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"end",b"end",u"start",b"start"]) -> None: ... + type___ReservedRange = ReservedRange + + name: typing___Text = ... + reserved_name: google___protobuf___internal___containers___RepeatedScalarFieldContainer[typing___Text] = ... + + @property + def field(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___FieldDescriptorProto]: ... + + @property + def extension(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___FieldDescriptorProto]: ... + + @property + def nested_type(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___DescriptorProto]: ... + + @property + def enum_type(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___EnumDescriptorProto]: ... + + @property + def extension_range(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___DescriptorProto.ExtensionRange]: ... + + @property + def oneof_decl(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___OneofDescriptorProto]: ... + + @property + def options(self) -> type___MessageOptions: ... + + @property + def reserved_range(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___DescriptorProto.ReservedRange]: ... + + def __init__(self, + *, + name : typing___Optional[typing___Text] = None, + field : typing___Optional[typing___Iterable[type___FieldDescriptorProto]] = None, + extension : typing___Optional[typing___Iterable[type___FieldDescriptorProto]] = None, + nested_type : typing___Optional[typing___Iterable[type___DescriptorProto]] = None, + enum_type : typing___Optional[typing___Iterable[type___EnumDescriptorProto]] = None, + extension_range : typing___Optional[typing___Iterable[type___DescriptorProto.ExtensionRange]] = None, + oneof_decl : typing___Optional[typing___Iterable[type___OneofDescriptorProto]] = None, + options : typing___Optional[type___MessageOptions] = None, + reserved_range : typing___Optional[typing___Iterable[type___DescriptorProto.ReservedRange]] = None, + reserved_name : typing___Optional[typing___Iterable[typing___Text]] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"name",b"name",u"options",b"options"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"enum_type",b"enum_type",u"extension",b"extension",u"extension_range",b"extension_range",u"field",b"field",u"name",b"name",u"nested_type",b"nested_type",u"oneof_decl",b"oneof_decl",u"options",b"options",u"reserved_name",b"reserved_name",u"reserved_range",b"reserved_range"]) -> None: ... +type___DescriptorProto = DescriptorProto + +class ExtensionRangeOptions(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + + @property + def uninterpreted_option(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___UninterpretedOption]: ... + + def __init__(self, + *, + uninterpreted_option : typing___Optional[typing___Iterable[type___UninterpretedOption]] = None, + ) -> None: ... + def ClearField(self, field_name: typing_extensions___Literal[u"uninterpreted_option",b"uninterpreted_option"]) -> None: ... +type___ExtensionRangeOptions = ExtensionRangeOptions + +class FieldDescriptorProto(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + TypeValue = typing___NewType('TypeValue', builtin___int) + type___TypeValue = TypeValue + Type: _Type + class _Type(google___protobuf___internal___enum_type_wrapper____EnumTypeWrapper[FieldDescriptorProto.TypeValue]): + DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ... + TYPE_DOUBLE = typing___cast(FieldDescriptorProto.TypeValue, 1) + TYPE_FLOAT = typing___cast(FieldDescriptorProto.TypeValue, 2) + TYPE_INT64 = typing___cast(FieldDescriptorProto.TypeValue, 3) + TYPE_UINT64 = typing___cast(FieldDescriptorProto.TypeValue, 4) + TYPE_INT32 = typing___cast(FieldDescriptorProto.TypeValue, 5) + TYPE_FIXED64 = typing___cast(FieldDescriptorProto.TypeValue, 6) + TYPE_FIXED32 = typing___cast(FieldDescriptorProto.TypeValue, 7) + TYPE_BOOL = typing___cast(FieldDescriptorProto.TypeValue, 8) + TYPE_STRING = typing___cast(FieldDescriptorProto.TypeValue, 9) + TYPE_GROUP = typing___cast(FieldDescriptorProto.TypeValue, 10) + TYPE_MESSAGE = typing___cast(FieldDescriptorProto.TypeValue, 11) + TYPE_BYTES = typing___cast(FieldDescriptorProto.TypeValue, 12) + TYPE_UINT32 = typing___cast(FieldDescriptorProto.TypeValue, 13) + TYPE_ENUM = typing___cast(FieldDescriptorProto.TypeValue, 14) + TYPE_SFIXED32 = typing___cast(FieldDescriptorProto.TypeValue, 15) + TYPE_SFIXED64 = typing___cast(FieldDescriptorProto.TypeValue, 16) + TYPE_SINT32 = typing___cast(FieldDescriptorProto.TypeValue, 17) + TYPE_SINT64 = typing___cast(FieldDescriptorProto.TypeValue, 18) + TYPE_DOUBLE = typing___cast(FieldDescriptorProto.TypeValue, 1) + TYPE_FLOAT = typing___cast(FieldDescriptorProto.TypeValue, 2) + TYPE_INT64 = typing___cast(FieldDescriptorProto.TypeValue, 3) + TYPE_UINT64 = typing___cast(FieldDescriptorProto.TypeValue, 4) + TYPE_INT32 = typing___cast(FieldDescriptorProto.TypeValue, 5) + TYPE_FIXED64 = typing___cast(FieldDescriptorProto.TypeValue, 6) + TYPE_FIXED32 = typing___cast(FieldDescriptorProto.TypeValue, 7) + TYPE_BOOL = typing___cast(FieldDescriptorProto.TypeValue, 8) + TYPE_STRING = typing___cast(FieldDescriptorProto.TypeValue, 9) + TYPE_GROUP = typing___cast(FieldDescriptorProto.TypeValue, 10) + TYPE_MESSAGE = typing___cast(FieldDescriptorProto.TypeValue, 11) + TYPE_BYTES = typing___cast(FieldDescriptorProto.TypeValue, 12) + TYPE_UINT32 = typing___cast(FieldDescriptorProto.TypeValue, 13) + TYPE_ENUM = typing___cast(FieldDescriptorProto.TypeValue, 14) + TYPE_SFIXED32 = typing___cast(FieldDescriptorProto.TypeValue, 15) + TYPE_SFIXED64 = typing___cast(FieldDescriptorProto.TypeValue, 16) + TYPE_SINT32 = typing___cast(FieldDescriptorProto.TypeValue, 17) + TYPE_SINT64 = typing___cast(FieldDescriptorProto.TypeValue, 18) + + LabelValue = typing___NewType('LabelValue', builtin___int) + type___LabelValue = LabelValue + Label: _Label + class _Label(google___protobuf___internal___enum_type_wrapper____EnumTypeWrapper[FieldDescriptorProto.LabelValue]): + DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ... + LABEL_OPTIONAL = typing___cast(FieldDescriptorProto.LabelValue, 1) + LABEL_REQUIRED = typing___cast(FieldDescriptorProto.LabelValue, 2) + LABEL_REPEATED = typing___cast(FieldDescriptorProto.LabelValue, 3) + LABEL_OPTIONAL = typing___cast(FieldDescriptorProto.LabelValue, 1) + LABEL_REQUIRED = typing___cast(FieldDescriptorProto.LabelValue, 2) + LABEL_REPEATED = typing___cast(FieldDescriptorProto.LabelValue, 3) + + name: typing___Text = ... + number: builtin___int = ... + label: type___FieldDescriptorProto.LabelValue = ... + type: type___FieldDescriptorProto.TypeValue = ... + type_name: typing___Text = ... + extendee: typing___Text = ... + default_value: typing___Text = ... + oneof_index: builtin___int = ... + json_name: typing___Text = ... + proto3_optional: builtin___bool = ... + + @property + def options(self) -> type___FieldOptions: ... + + def __init__(self, + *, + name : typing___Optional[typing___Text] = None, + number : typing___Optional[builtin___int] = None, + label : typing___Optional[type___FieldDescriptorProto.LabelValue] = None, + type : typing___Optional[type___FieldDescriptorProto.TypeValue] = None, + type_name : typing___Optional[typing___Text] = None, + extendee : typing___Optional[typing___Text] = None, + default_value : typing___Optional[typing___Text] = None, + oneof_index : typing___Optional[builtin___int] = None, + json_name : typing___Optional[typing___Text] = None, + options : typing___Optional[type___FieldOptions] = None, + proto3_optional : typing___Optional[builtin___bool] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"default_value",b"default_value",u"extendee",b"extendee",u"json_name",b"json_name",u"label",b"label",u"name",b"name",u"number",b"number",u"oneof_index",b"oneof_index",u"options",b"options",u"proto3_optional",b"proto3_optional",u"type",b"type",u"type_name",b"type_name"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"default_value",b"default_value",u"extendee",b"extendee",u"json_name",b"json_name",u"label",b"label",u"name",b"name",u"number",b"number",u"oneof_index",b"oneof_index",u"options",b"options",u"proto3_optional",b"proto3_optional",u"type",b"type",u"type_name",b"type_name"]) -> None: ... +type___FieldDescriptorProto = FieldDescriptorProto + +class OneofDescriptorProto(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + name: typing___Text = ... + + @property + def options(self) -> type___OneofOptions: ... + + def __init__(self, + *, + name : typing___Optional[typing___Text] = None, + options : typing___Optional[type___OneofOptions] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"name",b"name",u"options",b"options"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"name",b"name",u"options",b"options"]) -> None: ... +type___OneofDescriptorProto = OneofDescriptorProto + +class EnumDescriptorProto(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + class EnumReservedRange(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + start: builtin___int = ... + end: builtin___int = ... + + def __init__(self, + *, + start : typing___Optional[builtin___int] = None, + end : typing___Optional[builtin___int] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"end",b"end",u"start",b"start"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"end",b"end",u"start",b"start"]) -> None: ... + type___EnumReservedRange = EnumReservedRange + + name: typing___Text = ... + reserved_name: google___protobuf___internal___containers___RepeatedScalarFieldContainer[typing___Text] = ... + + @property + def value(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___EnumValueDescriptorProto]: ... + + @property + def options(self) -> type___EnumOptions: ... + + @property + def reserved_range(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___EnumDescriptorProto.EnumReservedRange]: ... + + def __init__(self, + *, + name : typing___Optional[typing___Text] = None, + value : typing___Optional[typing___Iterable[type___EnumValueDescriptorProto]] = None, + options : typing___Optional[type___EnumOptions] = None, + reserved_range : typing___Optional[typing___Iterable[type___EnumDescriptorProto.EnumReservedRange]] = None, + reserved_name : typing___Optional[typing___Iterable[typing___Text]] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"name",b"name",u"options",b"options"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"name",b"name",u"options",b"options",u"reserved_name",b"reserved_name",u"reserved_range",b"reserved_range",u"value",b"value"]) -> None: ... +type___EnumDescriptorProto = EnumDescriptorProto + +class EnumValueDescriptorProto(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + name: typing___Text = ... + number: builtin___int = ... + + @property + def options(self) -> type___EnumValueOptions: ... + + def __init__(self, + *, + name : typing___Optional[typing___Text] = None, + number : typing___Optional[builtin___int] = None, + options : typing___Optional[type___EnumValueOptions] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"name",b"name",u"number",b"number",u"options",b"options"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"name",b"name",u"number",b"number",u"options",b"options"]) -> None: ... +type___EnumValueDescriptorProto = EnumValueDescriptorProto + +class ServiceDescriptorProto(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + name: typing___Text = ... + + @property + def method(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___MethodDescriptorProto]: ... + + @property + def options(self) -> type___ServiceOptions: ... + + def __init__(self, + *, + name : typing___Optional[typing___Text] = None, + method : typing___Optional[typing___Iterable[type___MethodDescriptorProto]] = None, + options : typing___Optional[type___ServiceOptions] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"name",b"name",u"options",b"options"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"method",b"method",u"name",b"name",u"options",b"options"]) -> None: ... +type___ServiceDescriptorProto = ServiceDescriptorProto + +class MethodDescriptorProto(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + name: typing___Text = ... + input_type: typing___Text = ... + output_type: typing___Text = ... + client_streaming: builtin___bool = ... + server_streaming: builtin___bool = ... + + @property + def options(self) -> type___MethodOptions: ... + + def __init__(self, + *, + name : typing___Optional[typing___Text] = None, + input_type : typing___Optional[typing___Text] = None, + output_type : typing___Optional[typing___Text] = None, + options : typing___Optional[type___MethodOptions] = None, + client_streaming : typing___Optional[builtin___bool] = None, + server_streaming : typing___Optional[builtin___bool] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"client_streaming",b"client_streaming",u"input_type",b"input_type",u"name",b"name",u"options",b"options",u"output_type",b"output_type",u"server_streaming",b"server_streaming"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"client_streaming",b"client_streaming",u"input_type",b"input_type",u"name",b"name",u"options",b"options",u"output_type",b"output_type",u"server_streaming",b"server_streaming"]) -> None: ... +type___MethodDescriptorProto = MethodDescriptorProto + +class FileOptions(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + OptimizeModeValue = typing___NewType('OptimizeModeValue', builtin___int) + type___OptimizeModeValue = OptimizeModeValue + OptimizeMode: _OptimizeMode + class _OptimizeMode(google___protobuf___internal___enum_type_wrapper____EnumTypeWrapper[FileOptions.OptimizeModeValue]): + DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ... + SPEED = typing___cast(FileOptions.OptimizeModeValue, 1) + CODE_SIZE = typing___cast(FileOptions.OptimizeModeValue, 2) + LITE_RUNTIME = typing___cast(FileOptions.OptimizeModeValue, 3) + SPEED = typing___cast(FileOptions.OptimizeModeValue, 1) + CODE_SIZE = typing___cast(FileOptions.OptimizeModeValue, 2) + LITE_RUNTIME = typing___cast(FileOptions.OptimizeModeValue, 3) + + java_package: typing___Text = ... + java_outer_classname: typing___Text = ... + java_multiple_files: builtin___bool = ... + java_generate_equals_and_hash: builtin___bool = ... + java_string_check_utf8: builtin___bool = ... + optimize_for: type___FileOptions.OptimizeModeValue = ... + go_package: typing___Text = ... + cc_generic_services: builtin___bool = ... + java_generic_services: builtin___bool = ... + py_generic_services: builtin___bool = ... + php_generic_services: builtin___bool = ... + deprecated: builtin___bool = ... + cc_enable_arenas: builtin___bool = ... + objc_class_prefix: typing___Text = ... + csharp_namespace: typing___Text = ... + swift_prefix: typing___Text = ... + php_class_prefix: typing___Text = ... + php_namespace: typing___Text = ... + php_metadata_namespace: typing___Text = ... + ruby_package: typing___Text = ... + + @property + def uninterpreted_option(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___UninterpretedOption]: ... + + def __init__(self, + *, + java_package : typing___Optional[typing___Text] = None, + java_outer_classname : typing___Optional[typing___Text] = None, + java_multiple_files : typing___Optional[builtin___bool] = None, + java_generate_equals_and_hash : typing___Optional[builtin___bool] = None, + java_string_check_utf8 : typing___Optional[builtin___bool] = None, + optimize_for : typing___Optional[type___FileOptions.OptimizeModeValue] = None, + go_package : typing___Optional[typing___Text] = None, + cc_generic_services : typing___Optional[builtin___bool] = None, + java_generic_services : typing___Optional[builtin___bool] = None, + py_generic_services : typing___Optional[builtin___bool] = None, + php_generic_services : typing___Optional[builtin___bool] = None, + deprecated : typing___Optional[builtin___bool] = None, + cc_enable_arenas : typing___Optional[builtin___bool] = None, + objc_class_prefix : typing___Optional[typing___Text] = None, + csharp_namespace : typing___Optional[typing___Text] = None, + swift_prefix : typing___Optional[typing___Text] = None, + php_class_prefix : typing___Optional[typing___Text] = None, + php_namespace : typing___Optional[typing___Text] = None, + php_metadata_namespace : typing___Optional[typing___Text] = None, + ruby_package : typing___Optional[typing___Text] = None, + uninterpreted_option : typing___Optional[typing___Iterable[type___UninterpretedOption]] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"cc_enable_arenas",b"cc_enable_arenas",u"cc_generic_services",b"cc_generic_services",u"csharp_namespace",b"csharp_namespace",u"deprecated",b"deprecated",u"go_package",b"go_package",u"java_generate_equals_and_hash",b"java_generate_equals_and_hash",u"java_generic_services",b"java_generic_services",u"java_multiple_files",b"java_multiple_files",u"java_outer_classname",b"java_outer_classname",u"java_package",b"java_package",u"java_string_check_utf8",b"java_string_check_utf8",u"objc_class_prefix",b"objc_class_prefix",u"optimize_for",b"optimize_for",u"php_class_prefix",b"php_class_prefix",u"php_generic_services",b"php_generic_services",u"php_metadata_namespace",b"php_metadata_namespace",u"php_namespace",b"php_namespace",u"py_generic_services",b"py_generic_services",u"ruby_package",b"ruby_package",u"swift_prefix",b"swift_prefix"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"cc_enable_arenas",b"cc_enable_arenas",u"cc_generic_services",b"cc_generic_services",u"csharp_namespace",b"csharp_namespace",u"deprecated",b"deprecated",u"go_package",b"go_package",u"java_generate_equals_and_hash",b"java_generate_equals_and_hash",u"java_generic_services",b"java_generic_services",u"java_multiple_files",b"java_multiple_files",u"java_outer_classname",b"java_outer_classname",u"java_package",b"java_package",u"java_string_check_utf8",b"java_string_check_utf8",u"objc_class_prefix",b"objc_class_prefix",u"optimize_for",b"optimize_for",u"php_class_prefix",b"php_class_prefix",u"php_generic_services",b"php_generic_services",u"php_metadata_namespace",b"php_metadata_namespace",u"php_namespace",b"php_namespace",u"py_generic_services",b"py_generic_services",u"ruby_package",b"ruby_package",u"swift_prefix",b"swift_prefix",u"uninterpreted_option",b"uninterpreted_option"]) -> None: ... +type___FileOptions = FileOptions + +class MessageOptions(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + message_set_wire_format: builtin___bool = ... + no_standard_descriptor_accessor: builtin___bool = ... + deprecated: builtin___bool = ... + map_entry: builtin___bool = ... + + @property + def uninterpreted_option(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___UninterpretedOption]: ... + + def __init__(self, + *, + message_set_wire_format : typing___Optional[builtin___bool] = None, + no_standard_descriptor_accessor : typing___Optional[builtin___bool] = None, + deprecated : typing___Optional[builtin___bool] = None, + map_entry : typing___Optional[builtin___bool] = None, + uninterpreted_option : typing___Optional[typing___Iterable[type___UninterpretedOption]] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"deprecated",b"deprecated",u"map_entry",b"map_entry",u"message_set_wire_format",b"message_set_wire_format",u"no_standard_descriptor_accessor",b"no_standard_descriptor_accessor"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"deprecated",b"deprecated",u"map_entry",b"map_entry",u"message_set_wire_format",b"message_set_wire_format",u"no_standard_descriptor_accessor",b"no_standard_descriptor_accessor",u"uninterpreted_option",b"uninterpreted_option"]) -> None: ... +type___MessageOptions = MessageOptions + +class FieldOptions(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + CTypeValue = typing___NewType('CTypeValue', builtin___int) + type___CTypeValue = CTypeValue + CType: _CType + class _CType(google___protobuf___internal___enum_type_wrapper____EnumTypeWrapper[FieldOptions.CTypeValue]): + DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ... + STRING = typing___cast(FieldOptions.CTypeValue, 0) + CORD = typing___cast(FieldOptions.CTypeValue, 1) + STRING_PIECE = typing___cast(FieldOptions.CTypeValue, 2) + STRING = typing___cast(FieldOptions.CTypeValue, 0) + CORD = typing___cast(FieldOptions.CTypeValue, 1) + STRING_PIECE = typing___cast(FieldOptions.CTypeValue, 2) + + JSTypeValue = typing___NewType('JSTypeValue', builtin___int) + type___JSTypeValue = JSTypeValue + JSType: _JSType + class _JSType(google___protobuf___internal___enum_type_wrapper____EnumTypeWrapper[FieldOptions.JSTypeValue]): + DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ... + JS_NORMAL = typing___cast(FieldOptions.JSTypeValue, 0) + JS_STRING = typing___cast(FieldOptions.JSTypeValue, 1) + JS_NUMBER = typing___cast(FieldOptions.JSTypeValue, 2) + JS_NORMAL = typing___cast(FieldOptions.JSTypeValue, 0) + JS_STRING = typing___cast(FieldOptions.JSTypeValue, 1) + JS_NUMBER = typing___cast(FieldOptions.JSTypeValue, 2) + + ctype: type___FieldOptions.CTypeValue = ... + packed: builtin___bool = ... + jstype: type___FieldOptions.JSTypeValue = ... + lazy: builtin___bool = ... + deprecated: builtin___bool = ... + weak: builtin___bool = ... + + @property + def uninterpreted_option(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___UninterpretedOption]: ... + + def __init__(self, + *, + ctype : typing___Optional[type___FieldOptions.CTypeValue] = None, + packed : typing___Optional[builtin___bool] = None, + jstype : typing___Optional[type___FieldOptions.JSTypeValue] = None, + lazy : typing___Optional[builtin___bool] = None, + deprecated : typing___Optional[builtin___bool] = None, + weak : typing___Optional[builtin___bool] = None, + uninterpreted_option : typing___Optional[typing___Iterable[type___UninterpretedOption]] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"ctype",b"ctype",u"deprecated",b"deprecated",u"jstype",b"jstype",u"lazy",b"lazy",u"packed",b"packed",u"weak",b"weak"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"ctype",b"ctype",u"deprecated",b"deprecated",u"jstype",b"jstype",u"lazy",b"lazy",u"packed",b"packed",u"uninterpreted_option",b"uninterpreted_option",u"weak",b"weak"]) -> None: ... +type___FieldOptions = FieldOptions + +class OneofOptions(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + + @property + def uninterpreted_option(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___UninterpretedOption]: ... + + def __init__(self, + *, + uninterpreted_option : typing___Optional[typing___Iterable[type___UninterpretedOption]] = None, + ) -> None: ... + def ClearField(self, field_name: typing_extensions___Literal[u"uninterpreted_option",b"uninterpreted_option"]) -> None: ... +type___OneofOptions = OneofOptions + +class EnumOptions(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + allow_alias: builtin___bool = ... + deprecated: builtin___bool = ... + + @property + def uninterpreted_option(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___UninterpretedOption]: ... + + def __init__(self, + *, + allow_alias : typing___Optional[builtin___bool] = None, + deprecated : typing___Optional[builtin___bool] = None, + uninterpreted_option : typing___Optional[typing___Iterable[type___UninterpretedOption]] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"allow_alias",b"allow_alias",u"deprecated",b"deprecated"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"allow_alias",b"allow_alias",u"deprecated",b"deprecated",u"uninterpreted_option",b"uninterpreted_option"]) -> None: ... +type___EnumOptions = EnumOptions + +class EnumValueOptions(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + deprecated: builtin___bool = ... + + @property + def uninterpreted_option(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___UninterpretedOption]: ... + + def __init__(self, + *, + deprecated : typing___Optional[builtin___bool] = None, + uninterpreted_option : typing___Optional[typing___Iterable[type___UninterpretedOption]] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"deprecated",b"deprecated"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"deprecated",b"deprecated",u"uninterpreted_option",b"uninterpreted_option"]) -> None: ... +type___EnumValueOptions = EnumValueOptions + +class ServiceOptions(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + deprecated: builtin___bool = ... + + @property + def uninterpreted_option(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___UninterpretedOption]: ... + + def __init__(self, + *, + deprecated : typing___Optional[builtin___bool] = None, + uninterpreted_option : typing___Optional[typing___Iterable[type___UninterpretedOption]] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"deprecated",b"deprecated"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"deprecated",b"deprecated",u"uninterpreted_option",b"uninterpreted_option"]) -> None: ... +type___ServiceOptions = ServiceOptions + +class MethodOptions(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + IdempotencyLevelValue = typing___NewType('IdempotencyLevelValue', builtin___int) + type___IdempotencyLevelValue = IdempotencyLevelValue + IdempotencyLevel: _IdempotencyLevel + class _IdempotencyLevel(google___protobuf___internal___enum_type_wrapper____EnumTypeWrapper[MethodOptions.IdempotencyLevelValue]): + DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ... + IDEMPOTENCY_UNKNOWN = typing___cast(MethodOptions.IdempotencyLevelValue, 0) + NO_SIDE_EFFECTS = typing___cast(MethodOptions.IdempotencyLevelValue, 1) + IDEMPOTENT = typing___cast(MethodOptions.IdempotencyLevelValue, 2) + IDEMPOTENCY_UNKNOWN = typing___cast(MethodOptions.IdempotencyLevelValue, 0) + NO_SIDE_EFFECTS = typing___cast(MethodOptions.IdempotencyLevelValue, 1) + IDEMPOTENT = typing___cast(MethodOptions.IdempotencyLevelValue, 2) + + deprecated: builtin___bool = ... + idempotency_level: type___MethodOptions.IdempotencyLevelValue = ... + + @property + def uninterpreted_option(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___UninterpretedOption]: ... + + def __init__(self, + *, + deprecated : typing___Optional[builtin___bool] = None, + idempotency_level : typing___Optional[type___MethodOptions.IdempotencyLevelValue] = None, + uninterpreted_option : typing___Optional[typing___Iterable[type___UninterpretedOption]] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"deprecated",b"deprecated",u"idempotency_level",b"idempotency_level"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"deprecated",b"deprecated",u"idempotency_level",b"idempotency_level",u"uninterpreted_option",b"uninterpreted_option"]) -> None: ... +type___MethodOptions = MethodOptions + +class UninterpretedOption(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + class NamePart(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + name_part: typing___Text = ... + is_extension: builtin___bool = ... + + def __init__(self, + *, + name_part : typing___Optional[typing___Text] = None, + is_extension : typing___Optional[builtin___bool] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"is_extension",b"is_extension",u"name_part",b"name_part"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"is_extension",b"is_extension",u"name_part",b"name_part"]) -> None: ... + type___NamePart = NamePart + + identifier_value: typing___Text = ... + positive_int_value: builtin___int = ... + negative_int_value: builtin___int = ... + double_value: builtin___float = ... + string_value: builtin___bytes = ... + aggregate_value: typing___Text = ... + + @property + def name(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___UninterpretedOption.NamePart]: ... + + def __init__(self, + *, + name : typing___Optional[typing___Iterable[type___UninterpretedOption.NamePart]] = None, + identifier_value : typing___Optional[typing___Text] = None, + positive_int_value : typing___Optional[builtin___int] = None, + negative_int_value : typing___Optional[builtin___int] = None, + double_value : typing___Optional[builtin___float] = None, + string_value : typing___Optional[builtin___bytes] = None, + aggregate_value : typing___Optional[typing___Text] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"aggregate_value",b"aggregate_value",u"double_value",b"double_value",u"identifier_value",b"identifier_value",u"negative_int_value",b"negative_int_value",u"positive_int_value",b"positive_int_value",u"string_value",b"string_value"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"aggregate_value",b"aggregate_value",u"double_value",b"double_value",u"identifier_value",b"identifier_value",u"name",b"name",u"negative_int_value",b"negative_int_value",u"positive_int_value",b"positive_int_value",u"string_value",b"string_value"]) -> None: ... +type___UninterpretedOption = UninterpretedOption + +class SourceCodeInfo(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + class Location(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + path: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int] = ... + span: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int] = ... + leading_comments: typing___Text = ... + trailing_comments: typing___Text = ... + leading_detached_comments: google___protobuf___internal___containers___RepeatedScalarFieldContainer[typing___Text] = ... + + def __init__(self, + *, + path : typing___Optional[typing___Iterable[builtin___int]] = None, + span : typing___Optional[typing___Iterable[builtin___int]] = None, + leading_comments : typing___Optional[typing___Text] = None, + trailing_comments : typing___Optional[typing___Text] = None, + leading_detached_comments : typing___Optional[typing___Iterable[typing___Text]] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"leading_comments",b"leading_comments",u"trailing_comments",b"trailing_comments"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"leading_comments",b"leading_comments",u"leading_detached_comments",b"leading_detached_comments",u"path",b"path",u"span",b"span",u"trailing_comments",b"trailing_comments"]) -> None: ... + type___Location = Location + + + @property + def location(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___SourceCodeInfo.Location]: ... + + def __init__(self, + *, + location : typing___Optional[typing___Iterable[type___SourceCodeInfo.Location]] = None, + ) -> None: ... + def ClearField(self, field_name: typing_extensions___Literal[u"location",b"location"]) -> None: ... +type___SourceCodeInfo = SourceCodeInfo + +class GeneratedCodeInfo(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + class Annotation(google___protobuf___message___Message): + DESCRIPTOR: google___protobuf___descriptor___Descriptor = ... + path: google___protobuf___internal___containers___RepeatedScalarFieldContainer[builtin___int] = ... + source_file: typing___Text = ... + begin: builtin___int = ... + end: builtin___int = ... + + def __init__(self, + *, + path : typing___Optional[typing___Iterable[builtin___int]] = None, + source_file : typing___Optional[typing___Text] = None, + begin : typing___Optional[builtin___int] = None, + end : typing___Optional[builtin___int] = None, + ) -> None: ... + def HasField(self, field_name: typing_extensions___Literal[u"begin",b"begin",u"end",b"end",u"source_file",b"source_file"]) -> builtin___bool: ... + def ClearField(self, field_name: typing_extensions___Literal[u"begin",b"begin",u"end",b"end",u"path",b"path",u"source_file",b"source_file"]) -> None: ... + type___Annotation = Annotation + + + @property + def annotation(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[type___GeneratedCodeInfo.Annotation]: ... + + def __init__(self, + *, + annotation : typing___Optional[typing___Iterable[type___GeneratedCodeInfo.Annotation]] = None, + ) -> None: ... + def ClearField(self, field_name: typing_extensions___Literal[u"annotation",b"annotation"]) -> None: ... +type___GeneratedCodeInfo = GeneratedCodeInfo diff --git a/shared/package-files/03/03b81a4bafac7b1a2052fd1a631f07f4461b459c6f68d5f236d832e28bda65f3/CachedTensorUtils.h b/shared/package-files/03/03b81a4bafac7b1a2052fd1a631f07f4461b459c6f68d5f236d832e28bda65f3/CachedTensorUtils.h new file mode 100644 index 0000000000000000000000000000000000000000..3413341e666b549226911a35dd84460376267f26 --- /dev/null +++ b/shared/package-files/03/03b81a4bafac7b1a2052fd1a631f07f4461b459c6f68d5f236d832e28bda65f3/CachedTensorUtils.h @@ -0,0 +1,24 @@ +#pragma once + +#include + +namespace at::caching { + +// Some systems (just cudagraphs currently) will persist a static tensor output +// whose TensorImpl does not change across iterations. For these tensors caching +// dtype conversions is invalid. Additionally, there will be an extra reference +// count to these cached tensors that would prevent buffer inplacing and other +// checks on tensor uniqueness. If we are not using these systems the enabled +// flag will be false and we will avoid the hash lookup. + +TORCH_API bool is_cached_tensor(const at::Tensor& t); +TORCH_API void add_cached_tensor(const at::Tensor& t); +TORCH_API void remove_cached_tensor(const at::Tensor& t); +TORCH_API void set_cached_tensors_enabled(bool enable); + +// For gradient buffer stealing we will adjust the use count of tensors +// which are persisted by cudagraphs, just as we need to adjust reference +// count of tensors with hooks. +TORCH_API size_t adjusted_use_count(const at::Tensor& t); + +} // namespace at::caching diff --git a/shared/package-files/03/03b8e6da8f906901fd492a621711ad6646d55342c9644ebd8df3efed1733383e/test_afm.py b/shared/package-files/03/03b8e6da8f906901fd492a621711ad6646d55342c9644ebd8df3efed1733383e/test_afm.py new file mode 100644 index 0000000000000000000000000000000000000000..e5c6a83937cd68e3ae20a14d8babb9a99bd5a4f5 --- /dev/null +++ b/shared/package-files/03/03b8e6da8f906901fd492a621711ad6646d55342c9644ebd8df3efed1733383e/test_afm.py @@ -0,0 +1,137 @@ +from io import BytesIO +import pytest +import logging + +from matplotlib import _afm +from matplotlib import font_manager as fm + + +# See note in afm.py re: use of comma as decimal separator in the +# UnderlineThickness field and re: use of non-ASCII characters in the Notice +# field. +AFM_TEST_DATA = b"""StartFontMetrics 2.0 +Comment Comments are ignored. +Comment Creation Date:Mon Nov 13 12:34:11 GMT 2017 +FontName MyFont-Bold +EncodingScheme FontSpecific +FullName My Font Bold +FamilyName Test Fonts +Weight Bold +ItalicAngle 0.0 +IsFixedPitch false +UnderlinePosition -100 +UnderlineThickness 56,789 +Version 001.000 +Notice Copyright \xa9 2017 No one. +FontBBox 0 -321 1234 369 +StartCharMetrics 3 +C 0 ; WX 250 ; N space ; B 0 0 0 0 ; +C 42 ; WX 1141 ; N foo ; B 40 60 800 360 ; +C 99 ; WX 583 ; N bar ; B 40 -10 543 210 ; +EndCharMetrics +EndFontMetrics +""" + + +def test_nonascii_str(): + # This tests that we also decode bytes as utf-8 properly. + # Else, font files with non ascii characters fail to load. + inp_str = "привет" + byte_str = inp_str.encode("utf8") + + ret = _afm._to_str(byte_str) + assert ret == inp_str + + +def test_parse_header(): + fh = BytesIO(AFM_TEST_DATA) + header = _afm._parse_header(fh) + assert header == { + b'StartFontMetrics': 2.0, + b'FontName': 'MyFont-Bold', + b'EncodingScheme': 'FontSpecific', + b'FullName': 'My Font Bold', + b'FamilyName': 'Test Fonts', + b'Weight': 'Bold', + b'ItalicAngle': 0.0, + b'IsFixedPitch': False, + b'UnderlinePosition': -100, + b'UnderlineThickness': 56.789, + b'Version': '001.000', + b'Notice': b'Copyright \xa9 2017 No one.', + b'FontBBox': [0, -321, 1234, 369], + b'StartCharMetrics': 3, + } + + +def test_parse_char_metrics(): + fh = BytesIO(AFM_TEST_DATA) + _afm._parse_header(fh) # position + metrics = _afm._parse_char_metrics(fh) + assert metrics == ( + {0: (250.0, 'space', [0, 0, 0, 0]), + 42: (1141.0, 'foo', [40, 60, 800, 360]), + 99: (583.0, 'bar', [40, -10, 543, 210]), + }, + {'space': (250.0, 'space', [0, 0, 0, 0]), + 'foo': (1141.0, 'foo', [40, 60, 800, 360]), + 'bar': (583.0, 'bar', [40, -10, 543, 210]), + }) + + +def test_get_familyname_guessed(): + fh = BytesIO(AFM_TEST_DATA) + font = _afm.AFM(fh) + del font._header[b'FamilyName'] # remove FamilyName, so we have to guess + assert font.get_familyname() == 'My Font' + + +def test_font_manager_weight_normalization(): + font = _afm.AFM(BytesIO( + AFM_TEST_DATA.replace(b"Weight Bold\n", b"Weight Custom\n"))) + assert fm.afmFontProperty("", font).weight == "normal" + + +@pytest.mark.parametrize( + "afm_data", + [ + b"""nope +really nope""", + b"""StartFontMetrics 2.0 +Comment Comments are ignored. +Comment Creation Date:Mon Nov 13 12:34:11 GMT 2017 +FontName MyFont-Bold +EncodingScheme FontSpecific""", + ], +) +def test_bad_afm(afm_data): + fh = BytesIO(afm_data) + with pytest.raises(RuntimeError): + _afm._parse_header(fh) + + +@pytest.mark.parametrize( + "afm_data", + [ + b"""StartFontMetrics 2.0 +Comment Comments are ignored. +Comment Creation Date:Mon Nov 13 12:34:11 GMT 2017 +Aardvark bob +FontName MyFont-Bold +EncodingScheme FontSpecific +StartCharMetrics 3""", + b"""StartFontMetrics 2.0 +Comment Comments are ignored. +Comment Creation Date:Mon Nov 13 12:34:11 GMT 2017 +ItalicAngle zero degrees +FontName MyFont-Bold +EncodingScheme FontSpecific +StartCharMetrics 3""", + ], +) +def test_malformed_header(afm_data, caplog): + fh = BytesIO(afm_data) + with caplog.at_level(logging.ERROR): + _afm._parse_header(fh) + + assert len(caplog.records) == 1 diff --git a/shared/package-files/03/03ba5eeb031f9c58d3c50b27ae22d5f3f25d6dbb8192b377567c8afa54be4c10/_version.py b/shared/package-files/03/03ba5eeb031f9c58d3c50b27ae22d5f3f25d6dbb8192b377567c8afa54be4c10/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..773afd396bfcaf4f0d3f3e283c8817feca2884d1 --- /dev/null +++ b/shared/package-files/03/03ba5eeb031f9c58d3c50b27ae22d5f3f25d6dbb8192b377567c8afa54be4c10/_version.py @@ -0,0 +1,5 @@ +# coding: utf-8 +# file generated by setuptools_scm +# don't change, don't track in version control +version = '3.0.1' +version_tuple = (3, 0, 1) diff --git a/shared/package-files/03/03ba62f78d719aca3a4b77402383facfebc747da7d17836f3eadb85c655b8545/test_inputsplitter.py b/shared/package-files/03/03ba62f78d719aca3a4b77402383facfebc747da7d17836f3eadb85c655b8545/test_inputsplitter.py new file mode 100644 index 0000000000000000000000000000000000000000..c7b9bcef256144d22a5d13b723eb85803702ad77 --- /dev/null +++ b/shared/package-files/03/03ba62f78d719aca3a4b77402383facfebc747da7d17836f3eadb85c655b8545/test_inputsplitter.py @@ -0,0 +1,643 @@ +# -*- coding: utf-8 -*- +"""Tests for the inputsplitter module.""" + + +# Copyright (c) IPython Development Team. +# Distributed under the terms of the Modified BSD License. + +import unittest +import pytest +import sys + +with pytest.warns(DeprecationWarning, match="inputsplitter"): + from IPython.core import inputsplitter as isp +from IPython.core.inputtransformer import InputTransformer +from IPython.core.tests.test_inputtransformer import syntax, syntax_ml +from IPython.testing import tools as tt + +#----------------------------------------------------------------------------- +# Semi-complete examples (also used as tests) +#----------------------------------------------------------------------------- + +# Note: at the bottom, there's a slightly more complete version of this that +# can be useful during development of code here. + +def mini_interactive_loop(input_func): + """Minimal example of the logic of an interactive interpreter loop. + + This serves as an example, and it is used by the test system with a fake + raw_input that simulates interactive input.""" + + from IPython.core.inputsplitter import InputSplitter + + isp = InputSplitter() + # In practice, this input loop would be wrapped in an outside loop to read + # input indefinitely, until some exit/quit command was issued. Here we + # only illustrate the basic inner loop. + while isp.push_accepts_more(): + indent = ' '*isp.get_indent_spaces() + prompt = '>>> ' + indent + line = indent + input_func(prompt) + isp.push(line) + + # Here we just return input so we can use it in a test suite, but a real + # interpreter would instead send it for execution somewhere. + src = isp.source_reset() + #print 'Input source was:\n', src # dbg + return src + +#----------------------------------------------------------------------------- +# Test utilities, just for local use +#----------------------------------------------------------------------------- + + +def pseudo_input(lines): + """Return a function that acts like raw_input but feeds the input list.""" + ilines = iter(lines) + def raw_in(prompt): + try: + return next(ilines) + except StopIteration: + return '' + return raw_in + +#----------------------------------------------------------------------------- +# Tests +#----------------------------------------------------------------------------- +def test_spaces(): + tests = [('', 0), + (' ', 1), + ('\n', 0), + (' \n', 1), + ('x', 0), + (' x', 1), + (' x',2), + (' x',4), + # Note: tabs are counted as a single whitespace! + ('\tx', 1), + ('\t x', 2), + ] + with pytest.warns(PendingDeprecationWarning): + tt.check_pairs(isp.num_ini_spaces, tests) + + +def test_remove_comments(): + tests = [('text', 'text'), + ('text # comment', 'text '), + ('text # comment\n', 'text \n'), + ('text # comment \n', 'text \n'), + ('line # c \nline\n','line \nline\n'), + ('line # c \nline#c2 \nline\nline #c\n\n', + 'line \nline\nline\nline \n\n'), + ] + tt.check_pairs(isp.remove_comments, tests) + + +def test_get_input_encoding(): + encoding = isp.get_input_encoding() + assert isinstance(encoding, str) + # simple-minded check that at least encoding a simple string works with the + # encoding we got. + assert "test".encode(encoding) == b"test" + + +class NoInputEncodingTestCase(unittest.TestCase): + def setUp(self): + self.old_stdin = sys.stdin + class X: pass + fake_stdin = X() + sys.stdin = fake_stdin + + def test(self): + # Verify that if sys.stdin has no 'encoding' attribute we do the right + # thing + enc = isp.get_input_encoding() + self.assertEqual(enc, 'ascii') + + def tearDown(self): + sys.stdin = self.old_stdin + + +class InputSplitterTestCase(unittest.TestCase): + def setUp(self): + self.isp = isp.InputSplitter() + + def test_reset(self): + isp = self.isp + isp.push('x=1') + isp.reset() + self.assertEqual(isp._buffer, []) + self.assertEqual(isp.get_indent_spaces(), 0) + self.assertEqual(isp.source, '') + self.assertEqual(isp.code, None) + self.assertEqual(isp._is_complete, False) + + def test_source(self): + self.isp._store('1') + self.isp._store('2') + self.assertEqual(self.isp.source, '1\n2\n') + self.assertEqual(len(self.isp._buffer)>0, True) + self.assertEqual(self.isp.source_reset(), '1\n2\n') + self.assertEqual(self.isp._buffer, []) + self.assertEqual(self.isp.source, '') + + def test_indent(self): + isp = self.isp # shorthand + isp.push('x=1') + self.assertEqual(isp.get_indent_spaces(), 0) + isp.push('if 1:\n x=1') + self.assertEqual(isp.get_indent_spaces(), 4) + isp.push('y=2\n') + self.assertEqual(isp.get_indent_spaces(), 0) + + def test_indent2(self): + isp = self.isp + isp.push('if 1:') + self.assertEqual(isp.get_indent_spaces(), 4) + isp.push(' x=1') + self.assertEqual(isp.get_indent_spaces(), 4) + # Blank lines shouldn't change the indent level + isp.push(' '*2) + self.assertEqual(isp.get_indent_spaces(), 4) + + def test_indent3(self): + isp = self.isp + # When a multiline statement contains parens or multiline strings, we + # shouldn't get confused. + isp.push("if 1:") + isp.push(" x = (1+\n 2)") + self.assertEqual(isp.get_indent_spaces(), 4) + + def test_indent4(self): + isp = self.isp + # whitespace after ':' should not screw up indent level + isp.push('if 1: \n x=1') + self.assertEqual(isp.get_indent_spaces(), 4) + isp.push('y=2\n') + self.assertEqual(isp.get_indent_spaces(), 0) + isp.push('if 1:\t\n x=1') + self.assertEqual(isp.get_indent_spaces(), 4) + isp.push('y=2\n') + self.assertEqual(isp.get_indent_spaces(), 0) + + def test_dedent_pass(self): + isp = self.isp # shorthand + # should NOT cause dedent + isp.push('if 1:\n passes = 5') + self.assertEqual(isp.get_indent_spaces(), 4) + isp.push('if 1:\n pass') + self.assertEqual(isp.get_indent_spaces(), 0) + isp.push('if 1:\n pass ') + self.assertEqual(isp.get_indent_spaces(), 0) + + def test_dedent_break(self): + isp = self.isp # shorthand + # should NOT cause dedent + isp.push('while 1:\n breaks = 5') + self.assertEqual(isp.get_indent_spaces(), 4) + isp.push('while 1:\n break') + self.assertEqual(isp.get_indent_spaces(), 0) + isp.push('while 1:\n break ') + self.assertEqual(isp.get_indent_spaces(), 0) + + def test_dedent_continue(self): + isp = self.isp # shorthand + # should NOT cause dedent + isp.push('while 1:\n continues = 5') + self.assertEqual(isp.get_indent_spaces(), 4) + isp.push('while 1:\n continue') + self.assertEqual(isp.get_indent_spaces(), 0) + isp.push('while 1:\n continue ') + self.assertEqual(isp.get_indent_spaces(), 0) + + def test_dedent_raise(self): + isp = self.isp # shorthand + # should NOT cause dedent + isp.push('if 1:\n raised = 4') + self.assertEqual(isp.get_indent_spaces(), 4) + isp.push('if 1:\n raise TypeError()') + self.assertEqual(isp.get_indent_spaces(), 0) + isp.push('if 1:\n raise') + self.assertEqual(isp.get_indent_spaces(), 0) + isp.push('if 1:\n raise ') + self.assertEqual(isp.get_indent_spaces(), 0) + + def test_dedent_return(self): + isp = self.isp # shorthand + # should NOT cause dedent + isp.push('if 1:\n returning = 4') + self.assertEqual(isp.get_indent_spaces(), 4) + isp.push('if 1:\n return 5 + 493') + self.assertEqual(isp.get_indent_spaces(), 0) + isp.push('if 1:\n return') + self.assertEqual(isp.get_indent_spaces(), 0) + isp.push('if 1:\n return ') + self.assertEqual(isp.get_indent_spaces(), 0) + isp.push('if 1:\n return(0)') + self.assertEqual(isp.get_indent_spaces(), 0) + + def test_push(self): + isp = self.isp + self.assertEqual(isp.push('x=1'), True) + + def test_push2(self): + isp = self.isp + self.assertEqual(isp.push('if 1:'), False) + for line in [' x=1', '# a comment', ' y=2']: + print(line) + self.assertEqual(isp.push(line), True) + + def test_push3(self): + isp = self.isp + isp.push('if True:') + isp.push(' a = 1') + self.assertEqual(isp.push('b = [1,'), False) + + def test_push_accepts_more(self): + isp = self.isp + isp.push('x=1') + self.assertEqual(isp.push_accepts_more(), False) + + def test_push_accepts_more2(self): + isp = self.isp + isp.push('if 1:') + self.assertEqual(isp.push_accepts_more(), True) + isp.push(' x=1') + self.assertEqual(isp.push_accepts_more(), True) + isp.push('') + self.assertEqual(isp.push_accepts_more(), False) + + def test_push_accepts_more3(self): + isp = self.isp + isp.push("x = (2+\n3)") + self.assertEqual(isp.push_accepts_more(), False) + + def test_push_accepts_more4(self): + isp = self.isp + # When a multiline statement contains parens or multiline strings, we + # shouldn't get confused. + # FIXME: we should be able to better handle de-dents in statements like + # multiline strings and multiline expressions (continued with \ or + # parens). Right now we aren't handling the indentation tracking quite + # correctly with this, though in practice it may not be too much of a + # problem. We'll need to see. + isp.push("if 1:") + isp.push(" x = (2+") + isp.push(" 3)") + self.assertEqual(isp.push_accepts_more(), True) + isp.push(" y = 3") + self.assertEqual(isp.push_accepts_more(), True) + isp.push('') + self.assertEqual(isp.push_accepts_more(), False) + + def test_push_accepts_more5(self): + isp = self.isp + isp.push('try:') + isp.push(' a = 5') + isp.push('except:') + isp.push(' raise') + # We want to be able to add an else: block at this point, so it should + # wait for a blank line. + self.assertEqual(isp.push_accepts_more(), True) + + def test_continuation(self): + isp = self.isp + isp.push("import os, \\") + self.assertEqual(isp.push_accepts_more(), True) + isp.push("sys") + self.assertEqual(isp.push_accepts_more(), False) + + def test_syntax_error(self): + isp = self.isp + # Syntax errors immediately produce a 'ready' block, so the invalid + # Python can be sent to the kernel for evaluation with possible ipython + # special-syntax conversion. + isp.push('run foo') + self.assertEqual(isp.push_accepts_more(), False) + + def test_unicode(self): + self.isp.push(u"Pérez") + self.isp.push(u'\xc3\xa9') + self.isp.push(u"u'\xc3\xa9'") + + @pytest.mark.xfail( + reason="Bug in python 3.9.8 – bpo 45738", + condition=sys.version_info in [(3, 11, 0, "alpha", 2)], + raises=SystemError, + strict=True, + ) + def test_line_continuation(self): + """ Test issue #2108.""" + isp = self.isp + # A blank line after a line continuation should not accept more + isp.push("1 \\\n\n") + self.assertEqual(isp.push_accepts_more(), False) + # Whitespace after a \ is a SyntaxError. The only way to test that + # here is to test that push doesn't accept more (as with + # test_syntax_error() above). + isp.push(r"1 \ ") + self.assertEqual(isp.push_accepts_more(), False) + # Even if the line is continuable (c.f. the regular Python + # interpreter) + isp.push(r"(1 \ ") + self.assertEqual(isp.push_accepts_more(), False) + + def test_check_complete(self): + isp = self.isp + self.assertEqual(isp.check_complete("a = 1"), ('complete', None)) + self.assertEqual(isp.check_complete("for a in range(5):"), ('incomplete', 4)) + self.assertEqual(isp.check_complete("raise = 2"), ('invalid', None)) + self.assertEqual(isp.check_complete("a = [1,\n2,"), ('incomplete', 0)) + self.assertEqual(isp.check_complete("def a():\n x=1\n global x"), ('invalid', None)) + +class InteractiveLoopTestCase(unittest.TestCase): + """Tests for an interactive loop like a python shell. + """ + def check_ns(self, lines, ns): + """Validate that the given input lines produce the resulting namespace. + + Note: the input lines are given exactly as they would be typed in an + auto-indenting environment, as mini_interactive_loop above already does + auto-indenting and prepends spaces to the input. + """ + src = mini_interactive_loop(pseudo_input(lines)) + test_ns = {} + exec(src, test_ns) + # We can't check that the provided ns is identical to the test_ns, + # because Python fills test_ns with extra keys (copyright, etc). But + # we can check that the given dict is *contained* in test_ns + for k,v in ns.items(): + self.assertEqual(test_ns[k], v) + + def test_simple(self): + self.check_ns(['x=1'], dict(x=1)) + + def test_simple2(self): + self.check_ns(['if 1:', 'x=2'], dict(x=2)) + + def test_xy(self): + self.check_ns(['x=1; y=2'], dict(x=1, y=2)) + + def test_abc(self): + self.check_ns(['if 1:','a=1','b=2','c=3'], dict(a=1, b=2, c=3)) + + def test_multi(self): + self.check_ns(['x =(1+','1+','2)'], dict(x=4)) + + +class IPythonInputTestCase(InputSplitterTestCase): + """By just creating a new class whose .isp is a different instance, we + re-run the same test battery on the new input splitter. + + In addition, this runs the tests over the syntax and syntax_ml dicts that + were tested by individual functions, as part of the OO interface. + + It also makes some checks on the raw buffer storage. + """ + + def setUp(self): + self.isp = isp.IPythonInputSplitter() + + def test_syntax(self): + """Call all single-line syntax tests from the main object""" + isp = self.isp + for example in syntax.values(): + for raw, out_t in example: + if raw.startswith(' '): + continue + + isp.push(raw+'\n') + out_raw = isp.source_raw + out = isp.source_reset() + self.assertEqual(out.rstrip(), out_t, + tt.pair_fail_msg.format("inputsplitter",raw, out_t, out)) + self.assertEqual(out_raw.rstrip(), raw.rstrip()) + + def test_syntax_multiline(self): + isp = self.isp + for example in syntax_ml.values(): + for line_pairs in example: + out_t_parts = [] + raw_parts = [] + for lraw, out_t_part in line_pairs: + if out_t_part is not None: + out_t_parts.append(out_t_part) + + if lraw is not None: + isp.push(lraw) + raw_parts.append(lraw) + + out_raw = isp.source_raw + out = isp.source_reset() + out_t = '\n'.join(out_t_parts).rstrip() + raw = '\n'.join(raw_parts).rstrip() + self.assertEqual(out.rstrip(), out_t) + self.assertEqual(out_raw.rstrip(), raw) + + def test_syntax_multiline_cell(self): + isp = self.isp + for example in syntax_ml.values(): + + out_t_parts = [] + for line_pairs in example: + raw = '\n'.join(r for r, _ in line_pairs if r is not None) + out_t = '\n'.join(t for _,t in line_pairs if t is not None) + out = isp.transform_cell(raw) + # Match ignoring trailing whitespace + self.assertEqual(out.rstrip(), out_t.rstrip()) + + def test_cellmagic_preempt(self): + isp = self.isp + for raw, name, line, cell in [ + ("%%cellm a\nIn[1]:", u'cellm', u'a', u'In[1]:'), + ("%%cellm \nline\n>>> hi", u'cellm', u'', u'line\n>>> hi'), + (">>> %%cellm \nline\n>>> hi", u'cellm', u'', u'line\nhi'), + ("%%cellm \n>>> hi", u'cellm', u'', u'>>> hi'), + ("%%cellm \nline1\nline2", u'cellm', u'', u'line1\nline2'), + ("%%cellm \nline1\\\\\nline2", u'cellm', u'', u'line1\\\\\nline2'), + ]: + expected = "get_ipython().run_cell_magic(%r, %r, %r)" % ( + name, line, cell + ) + out = isp.transform_cell(raw) + self.assertEqual(out.rstrip(), expected.rstrip()) + + def test_multiline_passthrough(self): + isp = self.isp + class CommentTransformer(InputTransformer): + def __init__(self): + self._lines = [] + + def push(self, line): + self._lines.append(line + '#') + + def reset(self): + text = '\n'.join(self._lines) + self._lines = [] + return text + + isp.physical_line_transforms.insert(0, CommentTransformer()) + + for raw, expected in [ + ("a=5", "a=5#"), + ("%ls foo", "get_ipython().run_line_magic(%r, %r)" % (u'ls', u'foo#')), + ("!ls foo\n%ls bar", "get_ipython().system(%r)\nget_ipython().run_line_magic(%r, %r)" % ( + u'ls foo#', u'ls', u'bar#' + )), + ("1\n2\n3\n%ls foo\n4\n5", "1#\n2#\n3#\nget_ipython().run_line_magic(%r, %r)\n4#\n5#" % (u'ls', u'foo#')), + ]: + out = isp.transform_cell(raw) + self.assertEqual(out.rstrip(), expected.rstrip()) + +#----------------------------------------------------------------------------- +# Main - use as a script, mostly for developer experiments +#----------------------------------------------------------------------------- + +if __name__ == '__main__': + # A simple demo for interactive experimentation. This code will not get + # picked up by any test suite. + from IPython.core.inputsplitter import IPythonInputSplitter + + # configure here the syntax to use, prompt and whether to autoindent + #isp, start_prompt = InputSplitter(), '>>> ' + isp, start_prompt = IPythonInputSplitter(), 'In> ' + + autoindent = True + #autoindent = False + + try: + while True: + prompt = start_prompt + while isp.push_accepts_more(): + indent = ' '*isp.get_indent_spaces() + if autoindent: + line = indent + input(prompt+indent) + else: + line = input(prompt) + isp.push(line) + prompt = '... ' + + # Here we just return input so we can use it in a test suite, but a + # real interpreter would instead send it for execution somewhere. + #src = isp.source; raise EOFError # dbg + raw = isp.source_raw + src = isp.source_reset() + print('Input source was:\n', src) + print('Raw source was:\n', raw) + except EOFError: + print('Bye') + +# Tests for cell magics support + +def test_last_blank(): + assert isp.last_blank("") is False + assert isp.last_blank("abc") is False + assert isp.last_blank("abc\n") is False + assert isp.last_blank("abc\na") is False + + assert isp.last_blank("\n") is True + assert isp.last_blank("\n ") is True + assert isp.last_blank("abc\n ") is True + assert isp.last_blank("abc\n\n") is True + assert isp.last_blank("abc\nd\n\n") is True + assert isp.last_blank("abc\nd\ne\n\n") is True + assert isp.last_blank("abc \n \n \n\n") is True + + +def test_last_two_blanks(): + assert isp.last_two_blanks("") is False + assert isp.last_two_blanks("abc") is False + assert isp.last_two_blanks("abc\n") is False + assert isp.last_two_blanks("abc\n\na") is False + assert isp.last_two_blanks("abc\n \n") is False + assert isp.last_two_blanks("abc\n\n") is False + + assert isp.last_two_blanks("\n\n") is True + assert isp.last_two_blanks("\n\n ") is True + assert isp.last_two_blanks("\n \n") is True + assert isp.last_two_blanks("abc\n\n ") is True + assert isp.last_two_blanks("abc\n\n\n") is True + assert isp.last_two_blanks("abc\n\n \n") is True + assert isp.last_two_blanks("abc\n\n \n ") is True + assert isp.last_two_blanks("abc\n\n \n \n") is True + assert isp.last_two_blanks("abc\nd\n\n\n") is True + assert isp.last_two_blanks("abc\nd\ne\nf\n\n\n") is True + + +class CellMagicsCommon(object): + + def test_whole_cell(self): + src = "%%cellm line\nbody\n" + out = self.sp.transform_cell(src) + ref = "get_ipython().run_cell_magic('cellm', 'line', 'body')\n" + assert out == ref + + def test_cellmagic_help(self): + self.sp.push('%%cellm?') + assert self.sp.push_accepts_more() is False + + def tearDown(self): + self.sp.reset() + + +class CellModeCellMagics(CellMagicsCommon, unittest.TestCase): + sp = isp.IPythonInputSplitter(line_input_checker=False) + + def test_incremental(self): + sp = self.sp + sp.push("%%cellm firstline\n") + assert sp.push_accepts_more() is True # 1 + sp.push("line2\n") + assert sp.push_accepts_more() is True # 2 + sp.push("\n") + # This should accept a blank line and carry on until the cell is reset + assert sp.push_accepts_more() is True # 3 + + def test_no_strip_coding(self): + src = '\n'.join([ + '%%writefile foo.py', + '# coding: utf-8', + 'print(u"üñîçø∂é")', + ]) + out = self.sp.transform_cell(src) + assert "# coding: utf-8" in out + + +class LineModeCellMagics(CellMagicsCommon, unittest.TestCase): + sp = isp.IPythonInputSplitter(line_input_checker=True) + + def test_incremental(self): + sp = self.sp + sp.push("%%cellm line2\n") + assert sp.push_accepts_more() is True # 1 + sp.push("\n") + # In this case, a blank line should end the cell magic + assert sp.push_accepts_more() is False # 2 + + +indentation_samples = [ + ('a = 1', 0), + ('for a in b:', 4), + ('def f():', 4), + ('def f(): #comment', 4), + ('a = ":#not a comment"', 0), + ('def f():\n a = 1', 4), + ('def f():\n return 1', 0), + ('for a in b:\n' + ' if a < 0:' + ' continue', 3), + ('a = {', 4), + ('a = {\n' + ' 1,', 5), + ('b = """123', 0), + ('', 0), + ('def f():\n pass', 0), + ('class Bar:\n def f():\n pass', 4), + ('class Bar:\n def f():\n raise', 4), +] + +def test_find_next_indent(): + for code, exp in indentation_samples: + res = isp.find_next_indent(code) + msg = "{!r} != {!r} (expected)\n Code: {!r}".format(res, exp, code) + assert res == exp, msg diff --git a/shared/package-files/03/03bbc83773c226e33528c0c1b3ad066ff585d651c9b2b92b965739fd1315fa71/models.py b/shared/package-files/03/03bbc83773c226e33528c0c1b3ad066ff585d651c9b2b92b965739fd1315fa71/models.py new file mode 100644 index 0000000000000000000000000000000000000000..bcea76acc21287934efabb5d04d7f67fb923f5c4 --- /dev/null +++ b/shared/package-files/03/03bbc83773c226e33528c0c1b3ad066ff585d651c9b2b92b965739fd1315fa71/models.py @@ -0,0 +1,357 @@ +import warnings +from encodings.aliases import aliases +from hashlib import sha256 +from json import dumps +from typing import Optional, List, Tuple, Set +from collections import Counter +from re import sub, compile as re_compile + +from charset_normalizer.constant import TOO_BIG_SEQUENCE +from charset_normalizer.md import mess_ratio +from charset_normalizer.utils import iana_name, is_multi_byte_encoding, unicode_range + + +class CharsetMatch: + def __init__( + self, + payload: bytes, + guessed_encoding: str, + mean_mess_ratio: float, + has_sig_or_bom: bool, + languages: "CoherenceMatches", + decoded_payload: Optional[str] = None + ): + self._payload = payload # type: bytes + + self._encoding = guessed_encoding # type: str + self._mean_mess_ratio = mean_mess_ratio # type: float + self._languages = languages # type: CoherenceMatches + self._has_sig_or_bom = has_sig_or_bom # type: bool + self._unicode_ranges = None # type: Optional[List[str]] + + self._leaves = [] # type: List[CharsetMatch] + self._mean_coherence_ratio = 0. # type: float + + self._output_payload = None # type: Optional[bytes] + self._output_encoding = None # type: Optional[str] + + self._string = decoded_payload # type: Optional[str] + + def __eq__(self, other) -> bool: + if not isinstance(other, CharsetMatch): + raise TypeError('__eq__ cannot be invoked on {} and {}.'.format(str(other.__class__), str(self.__class__))) + return self.encoding == other.encoding and self.fingerprint == other.fingerprint + + def __lt__(self, other) -> bool: + """ + Implemented to make sorted available upon CharsetMatches items. + """ + if not isinstance(other, CharsetMatch): + raise ValueError + + chaos_difference = abs(self.chaos - other.chaos) # type: float + + # Bellow 1% difference --> Use Coherence + if chaos_difference < 0.01: + return self.coherence > other.coherence + + return self.chaos < other.chaos + + @property + def chaos_secondary_pass(self) -> float: + """ + Check once again chaos in decoded text, except this time, with full content. + Use with caution, this can be very slow. + Notice: Will be removed in 3.0 + """ + warnings.warn("chaos_secondary_pass is deprecated and will be removed in 3.0", DeprecationWarning) + return mess_ratio( + str(self), + 1. + ) + + @property + def coherence_non_latin(self) -> float: + """ + Coherence ratio on the first non-latin language detected if ANY. + Notice: Will be removed in 3.0 + """ + warnings.warn("coherence_non_latin is deprecated and will be removed in 3.0", DeprecationWarning) + return 0. + + @property + def w_counter(self) -> Counter: + """ + Word counter instance on decoded text. + Notice: Will be removed in 3.0 + """ + warnings.warn("w_counter is deprecated and will be removed in 3.0", DeprecationWarning) + not_printable_pattern = re_compile(r'[0-9\W\n\r\t]+') + string_printable_only = sub(not_printable_pattern, ' ', str(self).lower()) + + return Counter(string_printable_only.split()) + + def __str__(self) -> str: + # Lazy Str Loading + if self._string is None: + self._string = str(self._payload, self._encoding, "strict") + return self._string + + def __repr__(self) -> str: + return "".format(self.encoding, self.fingerprint) + + def add_submatch(self, other: "CharsetMatch") -> None: + if not isinstance(other, CharsetMatch) or other == self: + raise ValueError("Unable to add instance <{}> as a submatch of a CharsetMatch".format(other.__class__)) + + other._string = None # Unload RAM usage; dirty trick. + self._leaves.append(other) + + @property + def encoding(self) -> str: + return self._encoding + + @property + def encoding_aliases(self) -> List[str]: + """ + Encoding name are known by many name, using this could help when searching for IBM855 when it's listed as CP855. + """ + also_known_as = [] # type: List[str] + for u, p in aliases.items(): + if self.encoding == u: + also_known_as.append(p) + elif self.encoding == p: + also_known_as.append(u) + return also_known_as + + @property + def bom(self) -> bool: + return self._has_sig_or_bom + + @property + def byte_order_mark(self) -> bool: + return self._has_sig_or_bom + + @property + def languages(self) -> List[str]: + """ + Return the complete list of possible languages found in decoded sequence. + Usually not really useful. Returned list may be empty even if 'language' property return something != 'Unknown'. + """ + return [e[0] for e in self._languages] + + @property + def language(self) -> str: + """ + Most probable language found in decoded sequence. If none were detected or inferred, the property will return + "Unknown". + """ + if not self._languages: + # Trying to infer the language based on the given encoding + # Its either English or we should not pronounce ourselves in certain cases. + if "ascii" in self.could_be_from_charset: + return "English" + + # doing it there to avoid circular import + from charset_normalizer.cd import mb_encoding_languages, encoding_languages + + languages = mb_encoding_languages(self.encoding) if is_multi_byte_encoding(self.encoding) else encoding_languages(self.encoding) + + if len(languages) == 0 or "Latin Based" in languages: + return "Unknown" + + return languages[0] + + return self._languages[0][0] + + @property + def chaos(self) -> float: + return self._mean_mess_ratio + + @property + def coherence(self) -> float: + if not self._languages: + return 0. + return self._languages[0][1] + + @property + def percent_chaos(self) -> float: + return round(self.chaos * 100, ndigits=3) + + @property + def percent_coherence(self) -> float: + return round(self.coherence * 100, ndigits=3) + + @property + def raw(self) -> bytes: + """ + Original untouched bytes. + """ + return self._payload + + @property + def submatch(self) -> List["CharsetMatch"]: + return self._leaves + + @property + def has_submatch(self) -> bool: + return len(self._leaves) > 0 + + @property + def alphabets(self) -> List[str]: + if self._unicode_ranges is not None: + return self._unicode_ranges + detected_ranges = set() # type: Set[str] + for character in str(self): + detected_range = unicode_range(character) # type: Optional[str] + if detected_range: + detected_ranges.add( + unicode_range(character) + ) + self._unicode_ranges = sorted(list(detected_ranges)) + return self._unicode_ranges + + @property + def could_be_from_charset(self) -> List[str]: + """ + The complete list of encoding that output the exact SAME str result and therefore could be the originating + encoding. + This list does include the encoding available in property 'encoding'. + """ + return [self._encoding] + [m.encoding for m in self._leaves] + + def first(self) -> "CharsetMatch": + """ + Kept for BC reasons. Will be removed in 3.0. + """ + return self + + def best(self) -> "CharsetMatch": + """ + Kept for BC reasons. Will be removed in 3.0. + """ + return self + + def output(self, encoding: str = "utf_8") -> bytes: + """ + Method to get re-encoded bytes payload using given target encoding. Default to UTF-8. + Any errors will be simply ignored by the encoder NOT replaced. + """ + if self._output_encoding is None or self._output_encoding != encoding: + self._output_encoding = encoding + self._output_payload = str(self).encode(encoding, "replace") + + return self._output_payload # type: ignore + + @property + def fingerprint(self) -> str: + """ + Retrieve the unique SHA256 computed using the transformed (re-encoded) payload. Not the original one. + """ + return sha256(self.output()).hexdigest() + + +class CharsetMatches: + """ + Container with every CharsetMatch items ordered by default from most probable to the less one. + Act like a list(iterable) but does not implements all related methods. + """ + def __init__(self, results: List[CharsetMatch] = None): + self._results = sorted(results) if results else [] # type: List[CharsetMatch] + + def __iter__(self): + for result in self._results: + yield result + + def __getitem__(self, item) -> CharsetMatch: + """ + Retrieve a single item either by its position or encoding name (alias may be used here). + Raise KeyError upon invalid index or encoding not present in results. + """ + if isinstance(item, int): + return self._results[item] + if isinstance(item, str): + item = iana_name(item, False) + for result in self._results: + if item in result.could_be_from_charset: + return result + raise KeyError + + def __len__(self) -> int: + return len(self._results) + + def append(self, item: CharsetMatch) -> None: + """ + Insert a single match. Will be inserted accordingly to preserve sort. + Can be inserted as a submatch. + """ + if not isinstance(item, CharsetMatch): + raise ValueError("Cannot append instance '{}' to CharsetMatches".format(str(item.__class__))) + # We should disable the submatch factoring when the input file is too heavy (conserve RAM usage) + if len(item.raw) <= TOO_BIG_SEQUENCE: + for match in self._results: + if match.fingerprint == item.fingerprint and match.chaos == item.chaos: + match.add_submatch(item) + return + self._results.append(item) + self._results = sorted(self._results) + + def best(self) -> Optional["CharsetMatch"]: + """ + Simply return the first match. Strict equivalent to matches[0]. + """ + if not self._results: + return None + return self._results[0] + + def first(self) -> Optional["CharsetMatch"]: + """ + Redundant method, call the method best(). Kept for BC reasons. + """ + return self.best() + + +CoherenceMatch = Tuple[str, float] +CoherenceMatches = List[CoherenceMatch] + + +class CliDetectionResult: + + def __init__(self, path: str, encoding: str, encoding_aliases: List[str], alternative_encodings: List[str], language: str, alphabets: List[str], has_sig_or_bom: bool, chaos: float, coherence: float, unicode_path: Optional[str], is_preferred: bool): + self.path = path # type: str + self.unicode_path = unicode_path # type: Optional[str] + self.encoding = encoding # type: str + self.encoding_aliases = encoding_aliases # type: List[str] + self.alternative_encodings = alternative_encodings # type: List[str] + self.language = language # type: str + self.alphabets = alphabets # type: List[str] + self.has_sig_or_bom = has_sig_or_bom # type: bool + self.chaos = chaos # type: float + self.coherence = coherence # type: float + self.is_preferred = is_preferred # type: bool + + @property + def __dict__(self): + return { + 'path': self.path, + 'encoding': self.encoding, + 'encoding_aliases': self.encoding_aliases, + 'alternative_encodings': self.alternative_encodings, + 'language': self.language, + 'alphabets': self.alphabets, + 'has_sig_or_bom': self.has_sig_or_bom, + 'chaos': self.chaos, + 'coherence': self.coherence, + 'unicode_path': self.unicode_path, + 'is_preferred': self.is_preferred + } + + def to_json(self) -> str: + return dumps( + self.__dict__, + ensure_ascii=True, + indent=4 + ) + + +CharsetNormalizerMatch = CharsetMatch diff --git a/shared/package-files/03/03c068ec5b60b02c28f4bc3faa9c655ff76c91737a0a4005ec39b51eb23204b7/_cast_Byte_compositeimplicitautograd_dispatch.h b/shared/package-files/03/03c068ec5b60b02c28f4bc3faa9c655ff76c91737a0a4005ec39b51eb23204b7/_cast_Byte_compositeimplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..d62d445dae7cdce7672dde8d761dbe1aeee0cd47 --- /dev/null +++ b/shared/package-files/03/03c068ec5b60b02c28f4bc3faa9c655ff76c91737a0a4005ec39b51eb23204b7/_cast_Byte_compositeimplicitautograd_dispatch.h @@ -0,0 +1,23 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeimplicitautograd { + +TORCH_API at::Tensor _cast_Byte(const at::Tensor & self, bool non_blocking=false); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/shared/package-files/03/03c0dee7b6a65f61a5652ac363055808cc4715bf00b181ab1f493f6c2c6e26eb/deprecation.pyi b/shared/package-files/03/03c0dee7b6a65f61a5652ac363055808cc4715bf00b181ab1f493f6c2c6e26eb/deprecation.pyi new file mode 100644 index 0000000000000000000000000000000000000000..e050290662d925fd68bd534881512e86db13a4bb --- /dev/null +++ b/shared/package-files/03/03c0dee7b6a65f61a5652ac363055808cc4715bf00b181ab1f493f6c2c6e26eb/deprecation.pyi @@ -0,0 +1,75 @@ +from collections.abc import Callable +import contextlib +from typing import Any, Literal, ParamSpec, TypedDict, TypeVar, overload +from typing_extensions import ( + Unpack, # < Py 3.11 +) + +_P = ParamSpec("_P") +_R = TypeVar("_R") +_T = TypeVar("_T") + +class MatplotlibDeprecationWarning(DeprecationWarning): ... + +class DeprecationKwargs(TypedDict, total=False): + message: str + alternative: str + pending: bool + obj_type: str + addendum: str + removal: str | Literal[False] + +class NamedDeprecationKwargs(DeprecationKwargs, total=False): + name: str + +def warn_deprecated(since: str, **kwargs: Unpack[NamedDeprecationKwargs]) -> None: ... +def deprecated( + since: str, **kwargs: Unpack[NamedDeprecationKwargs] +) -> Callable[[_T], _T]: ... + +class deprecate_privatize_attribute(Any): + def __init__(self, since: str, **kwargs: Unpack[NamedDeprecationKwargs]): ... + def __set_name__(self, owner: type[object], name: str) -> None: ... + +DECORATORS: dict[Callable, Callable] = ... + +@overload +def rename_parameter( + since: str, old: str, new: str, func: None = ... +) -> Callable[[Callable[_P, _R]], Callable[_P, _R]]: ... +@overload +def rename_parameter( + since: str, old: str, new: str, func: Callable[_P, _R] +) -> Callable[_P, _R]: ... + +class _deprecated_parameter_class: ... + +_deprecated_parameter: _deprecated_parameter_class + +@overload +def delete_parameter( + since: str, name: str, func: None = ..., **kwargs: Unpack[DeprecationKwargs] +) -> Callable[[Callable[_P, _R]], Callable[_P, _R]]: ... +@overload +def delete_parameter( + since: str, name: str, func: Callable[_P, _R], **kwargs: Unpack[DeprecationKwargs] +) -> Callable[_P, _R]: ... +@overload +def make_keyword_only( + since: str, name: str, func: None = ... +) -> Callable[[Callable[_P, _R]], Callable[_P, _R]]: ... +@overload +def make_keyword_only( + since: str, name: str, func: Callable[_P, _R] +) -> Callable[_P, _R]: ... +def deprecate_method_override( + method: Callable[_P, _R], + obj: object | type, + *, + allow_empty: bool = ..., + since: str, + **kwargs: Unpack[NamedDeprecationKwargs] +) -> Callable[_P, _R]: ... +def suppress_matplotlib_deprecation_warning() -> ( + contextlib.AbstractContextManager[None] +): ... diff --git a/shared/package-files/03/03c1903c4d5b977a141e9b83f4d0f9dc3579e7b20f638e39f4534b5bab155c62/__init__.py b/shared/package-files/03/03c1903c4d5b977a141e9b83f4d0f9dc3579e7b20f638e39f4534b5bab155c62/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6ea0a1f7178b6ca03776d58c17411a8ff483f8b2 --- /dev/null +++ b/shared/package-files/03/03c1903c4d5b977a141e9b83f4d0f9dc3579e7b20f638e39f4534b5bab155c62/__init__.py @@ -0,0 +1,23 @@ +from .alexnet import * +from .convnext import * +from .densenet import * +from .efficientnet import * +from .googlenet import * +from .inception import * +from .mnasnet import * +from .mobilenet import * +from .regnet import * +from .resnet import * +from .shufflenetv2 import * +from .squeezenet import * +from .vgg import * +from .vision_transformer import * +from .swin_transformer import * +from .maxvit import * +from . import detection, optical_flow, quantization, segmentation, video + +# The Weights and WeightsEnum are developer-facing utils that we make public for +# downstream libs like torchgeo https://github.com/pytorch/vision/issues/7094 +# TODO: we could / should document them publicly, but it's not clear where, as +# they're not intended for end users. +from ._api import get_model, get_model_builder, get_model_weights, get_weight, list_models, Weights, WeightsEnum diff --git a/shared/package-files/03/03c4ea8ef291e21e1668bb68bc647d84350ba56b38ad5fb5271fd0e19cf90dec/common.py b/shared/package-files/03/03c4ea8ef291e21e1668bb68bc647d84350ba56b38ad5fb5271fd0e19cf90dec/common.py new file mode 100644 index 0000000000000000000000000000000000000000..b4d153df54059ca2a82f336e19afb4297eb218a2 --- /dev/null +++ b/shared/package-files/03/03c4ea8ef291e21e1668bb68bc647d84350ba56b38ad5fb5271fd0e19cf90dec/common.py @@ -0,0 +1,7 @@ +from pandas.core.groupby.base import transformation_kernels + +# There is no Series.cumcount or DataFrame.cumcount +series_transform_kernels = [ + x for x in sorted(transformation_kernels) if x != "cumcount" +] +frame_transform_kernels = [x for x in sorted(transformation_kernels) if x != "cumcount"] diff --git a/shared/package-files/03/03c717173a7893ea7a279ce43b19437c3be4a053310d6da1a531555dc690455a/test_numpy_version.py b/shared/package-files/03/03c717173a7893ea7a279ce43b19437c3be4a053310d6da1a531555dc690455a/test_numpy_version.py new file mode 100644 index 0000000000000000000000000000000000000000..61643426c8d757c8367dc7e8d19f6d4c106314a3 --- /dev/null +++ b/shared/package-files/03/03c717173a7893ea7a279ce43b19437c3be4a053310d6da1a531555dc690455a/test_numpy_version.py @@ -0,0 +1,41 @@ +""" +Check the numpy version is valid. + +Note that a development version is marked by the presence of 'dev0' or '+' +in the version string, all else is treated as a release. The version string +itself is set from the output of ``git describe`` which relies on tags. + +Examples +-------- + +Valid Development: 1.22.0.dev0 1.22.0.dev0+5-g7999db4df2 1.22.0+5-g7999db4df2 +Valid Release: 1.21.0.rc1, 1.21.0.b1, 1.21.0 +Invalid: 1.22.0.dev, 1.22.0.dev0-5-g7999db4dfB, 1.21.0.d1, 1.21.a + +Note that a release is determined by the version string, which in turn +is controlled by the result of the ``git describe`` command. +""" +import re + +import numpy as np +from numpy.testing import assert_ + + +def test_valid_numpy_version(): + # Verify that the numpy version is a valid one (no .post suffix or other + # nonsense). See gh-6431 for an issue caused by an invalid version. + version_pattern = r"^[0-9]+\.[0-9]+\.[0-9]+(a[0-9]|b[0-9]|rc[0-9])?" + dev_suffix = r"(\.dev[0-9]+(\+git[0-9]+\.[0-9a-f]+)?)?" + res = re.match(version_pattern + dev_suffix + '$', np.__version__) + + assert_(res is not None, np.__version__) + + +def test_short_version(): + # Check numpy.short_version actually exists + if np.version.release: + assert_(np.__version__ == np.version.short_version, + "short_version mismatch in release version") + else: + assert_(np.__version__.split("+")[0] == np.version.short_version, + "short_version mismatch in development version") diff --git a/shared/package-files/03/03c7701716ca735adf8deb29d8594e4fffd05d63d4d1a1013956479a4c8816c0/client.pyi b/shared/package-files/03/03c7701716ca735adf8deb29d8594e4fffd05d63d4d1a1013956479a4c8816c0/client.pyi new file mode 100644 index 0000000000000000000000000000000000000000..ece73fedeb5151a0ee92d4d463f40dfda0b88ee8 --- /dev/null +++ b/shared/package-files/03/03c7701716ca735adf8deb29d8594e4fffd05d63d4d1a1013956479a4c8816c0/client.pyi @@ -0,0 +1,132 @@ +from io import BytesIO +from types import TracebackType +from typing import Any, Dict, List, Optional, Pattern, Tuple, Type, Union + +from django.contrib.auth.models import AbstractUser +from django.contrib.sessions.backends.base import SessionBase +from django.core.handlers.base import BaseHandler +from django.http.cookie import SimpleCookie +from django.http.request import HttpRequest +from django.http.response import HttpResponse, HttpResponseBase + +from django.core.handlers.wsgi import WSGIRequest +from json import JSONEncoder + +BOUNDARY: str = ... +MULTIPART_CONTENT: str = ... +CONTENT_TYPE_RE: Pattern = ... +JSON_CONTENT_TYPE_RE: Pattern = ... + +class RedirectCycleError(Exception): + last_response: HttpResponseBase = ... + redirect_chain: List[Tuple[str, int]] = ... + def __init__(self, message: str, last_response: HttpResponseBase) -> None: ... + +class FakePayload: + read_started: bool = ... + def __init__(self, content: Optional[Union[bytes, str]] = ...) -> None: ... + def __len__(self) -> int: ... + def read(self, num_bytes: int = ...) -> bytes: ... + def write(self, content: Union[bytes, str]) -> None: ... + +class ClientHandler(BaseHandler): + enforce_csrf_checks: bool = ... + def __init__(self, enforce_csrf_checks: bool = ..., *args: Any, **kwargs: Any) -> None: ... + def __call__(self, environ: Dict[str, Any]) -> HttpResponseBase: ... + +def encode_multipart(boundary: str, data: Dict[str, Any]) -> bytes: ... +def encode_file(boundary: str, key: str, file: Any) -> List[bytes]: ... + +class RequestFactory: + json_encoder: Type[JSONEncoder] + defaults: Dict[str, str] + cookies: SimpleCookie + errors: BytesIO + def __init__(self, *, json_encoder: Type[JSONEncoder] = ..., **defaults: Any) -> None: ... + def request(self, **request: Any) -> WSGIRequest: ... + def get(self, path: str, data: Any = ..., secure: bool = ..., **extra: Any) -> WSGIRequest: ... + def post( + self, path: str, data: Any = ..., content_type: str = ..., secure: bool = ..., **extra: Any + ) -> WSGIRequest: ... + def head(self, path: str, data: Any = ..., secure: bool = ..., **extra: Any) -> WSGIRequest: ... + def trace(self, path: str, secure: bool = ..., **extra: Any) -> WSGIRequest: ... + def options( + self, + path: str, + data: Union[Dict[str, str], str] = ..., + content_type: str = ..., + follow: bool = ..., + secure: bool = ..., + **extra: Any + ) -> WSGIRequest: ... + def put( + self, path: str, data: Any = ..., content_type: str = ..., secure: bool = ..., **extra: Any + ) -> WSGIRequest: ... + def patch( + self, path: str, data: Any = ..., content_type: str = ..., secure: bool = ..., **extra: Any + ) -> WSGIRequest: ... + def delete( + self, path: str, data: Any = ..., content_type: str = ..., secure: bool = ..., **extra: Any + ) -> WSGIRequest: ... + def generic( + self, + method: str, + path: str, + data: Any = ..., + content_type: Optional[str] = ..., + secure: bool = ..., + **extra: Any + ) -> WSGIRequest: ... + +class Client(RequestFactory): + handler: ClientHandler + raise_request_exception: bool + exc_info: Optional[Tuple[Type[BaseException], BaseException, TracebackType]] + def __init__( + self, + enforce_csrf_checks: bool = ..., + raise_request_exception: bool = ..., + *, + json_encoder: Type[JSONEncoder] = ..., + **defaults: Any + ) -> None: ... + # Silence type warnings, since this class overrides arguments and return types in an unsafe manner. + def request(self, **request: Any) -> HttpResponse: ... # type: ignore + def get( # type: ignore + self, path: str, data: Any = ..., follow: bool = ..., secure: bool = ..., **extra: Any + ) -> HttpResponse: ... # type: ignore + def post( # type: ignore + self, path: str, data: Any = ..., content_type: str = ..., follow: bool = ..., secure: bool = ..., **extra: Any + ) -> HttpResponse: ... # type: ignore + def head( # type: ignore + self, path: str, data: Any = ..., follow: bool = ..., secure: bool = ..., **extra: Any + ) -> HttpResponse: ... # type: ignore + def trace( # type: ignore + self, path: str, follow: bool = ..., secure: bool = ..., **extra: Any + ) -> HttpResponse: ... # type: ignore + def options( # type: ignore + self, + path: str, + data: Union[Dict[str, str], str] = ..., + content_type: str = ..., + follow: bool = ..., + secure: bool = ..., + **extra: Any + ) -> HttpResponse: ... # type: ignore + def put( # type: ignore + self, path: str, data: Any = ..., content_type: str = ..., follow: bool = ..., secure: bool = ..., **extra: Any + ) -> HttpResponse: ... # type: ignore + def patch( # type: ignore + self, path: str, data: Any = ..., content_type: str = ..., follow: bool = ..., secure: bool = ..., **extra: Any + ) -> HttpResponse: ... # type: ignore + def delete( # type: ignore + self, path: str, data: Any = ..., content_type: str = ..., follow: bool = ..., secure: bool = ..., **extra: Any + ) -> HttpResponse: ... # type: ignore + def store_exc_info(self, **kwargs: Any) -> None: ... + @property + def session(self) -> SessionBase: ... + def login(self, **credentials: Any) -> bool: ... + def force_login(self, user: AbstractUser, backend: Optional[str] = ...) -> None: ... + def logout(self) -> None: ... + +def conditional_content_removal(request: HttpRequest, response: HttpResponseBase) -> HttpResponse: ... diff --git a/shared/package-files/03/03c85e9ba93c29365681f618f731478242a42a8a24e92cdda242ff1a8b088d86/terminet b/shared/package-files/03/03c85e9ba93c29365681f618f731478242a42a8a24e92cdda242ff1a8b088d86/terminet new file mode 100644 index 0000000000000000000000000000000000000000..87c639c630fb257f00cec14b838ba0f6749171ed Binary files /dev/null and b/shared/package-files/03/03c85e9ba93c29365681f618f731478242a42a8a24e92cdda242ff1a8b088d86/terminet differ diff --git a/shared/package-files/03/03c85e9ba93c29365681f618f731478242a42a8a24e92cdda242ff1a8b088d86/terminet1200 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b/shared/package-files/03/03ce914ed017281a46bacce5d2f24925707eaef29ce4120a90cac501463c5ae9/dot_cuda_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..5b1afbaf3fad2ce3d9412807d2725e44663d2471 --- /dev/null +++ b/shared/package-files/03/03ce914ed017281a46bacce5d2f24925707eaef29ce4120a90cac501463c5ae9/dot_cuda_dispatch.h @@ -0,0 +1,23 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace cuda { + +TORCH_API at::Tensor dot(const at::Tensor & self, const at::Tensor & tensor); + +} // namespace cuda +} // namespace at diff --git a/shared/package-files/03/03cf0b8d84a4aea3ad4f977a12c5f6354d0229afa7a1470f73b5926e75ad3eb2/_nested_view_from_buffer_native.h b/shared/package-files/03/03cf0b8d84a4aea3ad4f977a12c5f6354d0229afa7a1470f73b5926e75ad3eb2/_nested_view_from_buffer_native.h new file mode 100644 index 0000000000000000000000000000000000000000..4ea015c41d59b1f2d4899fa25cad366c5343baed --- /dev/null +++ b/shared/package-files/03/03cf0b8d84a4aea3ad4f977a12c5f6354d0229afa7a1470f73b5926e75ad3eb2/_nested_view_from_buffer_native.h @@ -0,0 +1,21 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor _nested_view_from_buffer(const at::Tensor & self, const at::Tensor & nested_size, const at::Tensor & nested_strides, const at::Tensor & offsets); +} // namespace native +} // namespace at diff --git a/shared/package-files/03/03cf0e1ee334460de230b1e32a05eafddda36427554b2b5442cfbd5b429c1724/Petersburg b/shared/package-files/03/03cf0e1ee334460de230b1e32a05eafddda36427554b2b5442cfbd5b429c1724/Petersburg new file mode 100644 index 0000000000000000000000000000000000000000..3082de00c2e0bfe89d4ac5e093d9e5921aa63610 Binary files /dev/null and b/shared/package-files/03/03cf0e1ee334460de230b1e32a05eafddda36427554b2b5442cfbd5b429c1724/Petersburg differ diff --git a/shared/package-files/03/03d0ac89b0be29dbf5cbe40af82b27fe4fdfb8c1cc8a48fb254abbacc7bb6e93/projection.py b/shared/package-files/03/03d0ac89b0be29dbf5cbe40af82b27fe4fdfb8c1cc8a48fb254abbacc7bb6e93/projection.py new file mode 100644 index 0000000000000000000000000000000000000000..93f2c29180e284104e9fd03689fc273d89904119 --- /dev/null +++ b/shared/package-files/03/03d0ac89b0be29dbf5cbe40af82b27fe4fdfb8c1cc8a48fb254abbacc7bb6e93/projection.py @@ -0,0 +1,523 @@ +"""One-mode (unipartite) projections of bipartite graphs.""" +import networkx as nx +from networkx.exception import NetworkXAlgorithmError +from networkx.utils import not_implemented_for + +__all__ = [ + "projected_graph", + "weighted_projected_graph", + "collaboration_weighted_projected_graph", + "overlap_weighted_projected_graph", + "generic_weighted_projected_graph", +] + + +def projected_graph(B, nodes, multigraph=False): + r"""Returns the projection of B onto one of its node sets. + + Returns the graph G that is the projection of the bipartite graph B + onto the specified nodes. They retain their attributes and are connected + in G if they have a common neighbor in B. + + Parameters + ---------- + B : NetworkX graph + The input graph should be bipartite. + + nodes : list or iterable + Nodes to project onto (the "bottom" nodes). + + multigraph: bool (default=False) + If True return a multigraph where the multiple edges represent multiple + shared neighbors. They edge key in the multigraph is assigned to the + label of the neighbor. + + Returns + ------- + Graph : NetworkX graph or multigraph + A graph that is the projection onto the given nodes. + + Examples + -------- + >>> from networkx.algorithms import bipartite + >>> B = nx.path_graph(4) + >>> G = bipartite.projected_graph(B, [1, 3]) + >>> list(G) + [1, 3] + >>> list(G.edges()) + [(1, 3)] + + If nodes `a`, and `b` are connected through both nodes 1 and 2 then + building a multigraph results in two edges in the projection onto + [`a`, `b`]: + + >>> B = nx.Graph() + >>> B.add_edges_from([("a", 1), ("b", 1), ("a", 2), ("b", 2)]) + >>> G = bipartite.projected_graph(B, ["a", "b"], multigraph=True) + >>> print([sorted((u, v)) for u, v in G.edges()]) + [['a', 'b'], ['a', 'b']] + + Notes + ----- + No attempt is made to verify that the input graph B is bipartite. + Returns a simple graph that is the projection of the bipartite graph B + onto the set of nodes given in list nodes. If multigraph=True then + a multigraph is returned with an edge for every shared neighbor. + + Directed graphs are allowed as input. The output will also then + be a directed graph with edges if there is a directed path between + the nodes. + + The graph and node properties are (shallow) copied to the projected graph. + + See :mod:`bipartite documentation ` + for further details on how bipartite graphs are handled in NetworkX. + + See Also + -------- + is_bipartite, + is_bipartite_node_set, + sets, + weighted_projected_graph, + collaboration_weighted_projected_graph, + overlap_weighted_projected_graph, + generic_weighted_projected_graph + """ + if B.is_multigraph(): + raise nx.NetworkXError("not defined for multigraphs") + if B.is_directed(): + directed = True + if multigraph: + G = nx.MultiDiGraph() + else: + G = nx.DiGraph() + else: + directed = False + if multigraph: + G = nx.MultiGraph() + else: + G = nx.Graph() + G.graph.update(B.graph) + G.add_nodes_from((n, B.nodes[n]) for n in nodes) + for u in nodes: + nbrs2 = {v for nbr in B[u] for v in B[nbr] if v != u} + if multigraph: + for n in nbrs2: + if directed: + links = set(B[u]) & set(B.pred[n]) + else: + links = set(B[u]) & set(B[n]) + for l in links: + if not G.has_edge(u, n, l): + G.add_edge(u, n, key=l) + else: + G.add_edges_from((u, n) for n in nbrs2) + return G + + +@not_implemented_for("multigraph") +def weighted_projected_graph(B, nodes, ratio=False): + r"""Returns a weighted projection of B onto one of its node sets. + + The weighted projected graph is the projection of the bipartite + network B onto the specified nodes with weights representing the + number of shared neighbors or the ratio between actual shared + neighbors and possible shared neighbors if ``ratio is True`` [1]_. + The nodes retain their attributes and are connected in the resulting + graph if they have an edge to a common node in the original graph. + + Parameters + ---------- + B : NetworkX graph + The input graph should be bipartite. + + nodes : list or iterable + Distinct nodes to project onto (the "bottom" nodes). + + ratio: Bool (default=False) + If True, edge weight is the ratio between actual shared neighbors + and maximum possible shared neighbors (i.e., the size of the other + node set). If False, edges weight is the number of shared neighbors. + + Returns + ------- + Graph : NetworkX graph + A graph that is the projection onto the given nodes. + + Examples + -------- + >>> from networkx.algorithms import bipartite + >>> B = nx.path_graph(4) + >>> G = bipartite.weighted_projected_graph(B, [1, 3]) + >>> list(G) + [1, 3] + >>> list(G.edges(data=True)) + [(1, 3, {'weight': 1})] + >>> G = bipartite.weighted_projected_graph(B, [1, 3], ratio=True) + >>> list(G.edges(data=True)) + [(1, 3, {'weight': 0.5})] + + Notes + ----- + No attempt is made to verify that the input graph B is bipartite, or that + the input nodes are distinct. However, if the length of the input nodes is + greater than or equal to the nodes in the graph B, an exception is raised. + If the nodes are not distinct but don't raise this error, the output weights + will be incorrect. + The graph and node properties are (shallow) copied to the projected graph. + + See :mod:`bipartite documentation ` + for further details on how bipartite graphs are handled in NetworkX. + + See Also + -------- + is_bipartite, + is_bipartite_node_set, + sets, + collaboration_weighted_projected_graph, + overlap_weighted_projected_graph, + generic_weighted_projected_graph + projected_graph + + References + ---------- + .. [1] Borgatti, S.P. and Halgin, D. In press. "Analyzing Affiliation + Networks". In Carrington, P. and Scott, J. (eds) The Sage Handbook + of Social Network Analysis. Sage Publications. + """ + if B.is_directed(): + pred = B.pred + G = nx.DiGraph() + else: + pred = B.adj + G = nx.Graph() + G.graph.update(B.graph) + G.add_nodes_from((n, B.nodes[n]) for n in nodes) + n_top = len(B) - len(nodes) + + if n_top < 1: + raise NetworkXAlgorithmError( + f"the size of the nodes to project onto ({len(nodes)}) is >= the graph size ({len(B)}).\n" + "They are either not a valid bipartite partition or contain duplicates" + ) + + for u in nodes: + unbrs = set(B[u]) + nbrs2 = {n for nbr in unbrs for n in B[nbr]} - {u} + for v in nbrs2: + vnbrs = set(pred[v]) + common = unbrs & vnbrs + if not ratio: + weight = len(common) + else: + weight = len(common) / n_top + G.add_edge(u, v, weight=weight) + return G + + +@not_implemented_for("multigraph") +def collaboration_weighted_projected_graph(B, nodes): + r"""Newman's weighted projection of B onto one of its node sets. + + The collaboration weighted projection is the projection of the + bipartite network B onto the specified nodes with weights assigned + using Newman's collaboration model [1]_: + + .. math:: + + w_{u, v} = \sum_k \frac{\delta_{u}^{k} \delta_{v}^{k}}{d_k - 1} + + where `u` and `v` are nodes from the bottom bipartite node set, + and `k` is a node of the top node set. + The value `d_k` is the degree of node `k` in the bipartite + network and `\delta_{u}^{k}` is 1 if node `u` is + linked to node `k` in the original bipartite graph or 0 otherwise. + + The nodes retain their attributes and are connected in the resulting + graph if have an edge to a common node in the original bipartite + graph. + + Parameters + ---------- + B : NetworkX graph + The input graph should be bipartite. + + nodes : list or iterable + Nodes to project onto (the "bottom" nodes). + + Returns + ------- + Graph : NetworkX graph + A graph that is the projection onto the given nodes. + + Examples + -------- + >>> from networkx.algorithms import bipartite + >>> B = nx.path_graph(5) + >>> B.add_edge(1, 5) + >>> G = bipartite.collaboration_weighted_projected_graph(B, [0, 2, 4, 5]) + >>> list(G) + [0, 2, 4, 5] + >>> for edge in sorted(G.edges(data=True)): + ... print(edge) + ... + (0, 2, {'weight': 0.5}) + (0, 5, {'weight': 0.5}) + (2, 4, {'weight': 1.0}) + (2, 5, {'weight': 0.5}) + + Notes + ----- + No attempt is made to verify that the input graph B is bipartite. + The graph and node properties are (shallow) copied to the projected graph. + + See :mod:`bipartite documentation ` + for further details on how bipartite graphs are handled in NetworkX. + + See Also + -------- + is_bipartite, + is_bipartite_node_set, + sets, + weighted_projected_graph, + overlap_weighted_projected_graph, + generic_weighted_projected_graph, + projected_graph + + References + ---------- + .. [1] Scientific collaboration networks: II. + Shortest paths, weighted networks, and centrality, + M. E. J. Newman, Phys. Rev. E 64, 016132 (2001). + """ + if B.is_directed(): + pred = B.pred + G = nx.DiGraph() + else: + pred = B.adj + G = nx.Graph() + G.graph.update(B.graph) + G.add_nodes_from((n, B.nodes[n]) for n in nodes) + for u in nodes: + unbrs = set(B[u]) + nbrs2 = {n for nbr in unbrs for n in B[nbr] if n != u} + for v in nbrs2: + vnbrs = set(pred[v]) + common_degree = (len(B[n]) for n in unbrs & vnbrs) + weight = sum(1.0 / (deg - 1) for deg in common_degree if deg > 1) + G.add_edge(u, v, weight=weight) + return G + + +@not_implemented_for("multigraph") +def overlap_weighted_projected_graph(B, nodes, jaccard=True): + r"""Overlap weighted projection of B onto one of its node sets. + + The overlap weighted projection is the projection of the bipartite + network B onto the specified nodes with weights representing + the Jaccard index between the neighborhoods of the two nodes in the + original bipartite network [1]_: + + .. math:: + + w_{v, u} = \frac{|N(u) \cap N(v)|}{|N(u) \cup N(v)|} + + or if the parameter 'jaccard' is False, the fraction of common + neighbors by minimum of both nodes degree in the original + bipartite graph [1]_: + + .. math:: + + w_{v, u} = \frac{|N(u) \cap N(v)|}{min(|N(u)|, |N(v)|)} + + The nodes retain their attributes and are connected in the resulting + graph if have an edge to a common node in the original bipartite graph. + + Parameters + ---------- + B : NetworkX graph + The input graph should be bipartite. + + nodes : list or iterable + Nodes to project onto (the "bottom" nodes). + + jaccard: Bool (default=True) + + Returns + ------- + Graph : NetworkX graph + A graph that is the projection onto the given nodes. + + Examples + -------- + >>> from networkx.algorithms import bipartite + >>> B = nx.path_graph(5) + >>> nodes = [0, 2, 4] + >>> G = bipartite.overlap_weighted_projected_graph(B, nodes) + >>> list(G) + [0, 2, 4] + >>> list(G.edges(data=True)) + [(0, 2, {'weight': 0.5}), (2, 4, {'weight': 0.5})] + >>> G = bipartite.overlap_weighted_projected_graph(B, nodes, jaccard=False) + >>> list(G.edges(data=True)) + [(0, 2, {'weight': 1.0}), (2, 4, {'weight': 1.0})] + + Notes + ----- + No attempt is made to verify that the input graph B is bipartite. + The graph and node properties are (shallow) copied to the projected graph. + + See :mod:`bipartite documentation ` + for further details on how bipartite graphs are handled in NetworkX. + + See Also + -------- + is_bipartite, + is_bipartite_node_set, + sets, + weighted_projected_graph, + collaboration_weighted_projected_graph, + generic_weighted_projected_graph, + projected_graph + + References + ---------- + .. [1] Borgatti, S.P. and Halgin, D. In press. Analyzing Affiliation + Networks. In Carrington, P. and Scott, J. (eds) The Sage Handbook + of Social Network Analysis. Sage Publications. + + """ + if B.is_directed(): + pred = B.pred + G = nx.DiGraph() + else: + pred = B.adj + G = nx.Graph() + G.graph.update(B.graph) + G.add_nodes_from((n, B.nodes[n]) for n in nodes) + for u in nodes: + unbrs = set(B[u]) + nbrs2 = {n for nbr in unbrs for n in B[nbr]} - {u} + for v in nbrs2: + vnbrs = set(pred[v]) + if jaccard: + wt = len(unbrs & vnbrs) / len(unbrs | vnbrs) + else: + wt = len(unbrs & vnbrs) / min(len(unbrs), len(vnbrs)) + G.add_edge(u, v, weight=wt) + return G + + +@not_implemented_for("multigraph") +def generic_weighted_projected_graph(B, nodes, weight_function=None): + r"""Weighted projection of B with a user-specified weight function. + + The bipartite network B is projected on to the specified nodes + with weights computed by a user-specified function. This function + must accept as a parameter the neighborhood sets of two nodes and + return an integer or a float. + + The nodes retain their attributes and are connected in the resulting graph + if they have an edge to a common node in the original graph. + + Parameters + ---------- + B : NetworkX graph + The input graph should be bipartite. + + nodes : list or iterable + Nodes to project onto (the "bottom" nodes). + + weight_function : function + This function must accept as parameters the same input graph + that this function, and two nodes; and return an integer or a float. + The default function computes the number of shared neighbors. + + Returns + ------- + Graph : NetworkX graph + A graph that is the projection onto the given nodes. + + Examples + -------- + >>> from networkx.algorithms import bipartite + >>> # Define some custom weight functions + >>> def jaccard(G, u, v): + ... unbrs = set(G[u]) + ... vnbrs = set(G[v]) + ... return float(len(unbrs & vnbrs)) / len(unbrs | vnbrs) + ... + >>> def my_weight(G, u, v, weight="weight"): + ... w = 0 + ... for nbr in set(G[u]) & set(G[v]): + ... w += G[u][nbr].get(weight, 1) + G[v][nbr].get(weight, 1) + ... return w + ... + >>> # A complete bipartite graph with 4 nodes and 4 edges + >>> B = nx.complete_bipartite_graph(2, 2) + >>> # Add some arbitrary weight to the edges + >>> for i, (u, v) in enumerate(B.edges()): + ... B.edges[u, v]["weight"] = i + 1 + ... + >>> for edge in B.edges(data=True): + ... print(edge) + ... + (0, 2, {'weight': 1}) + (0, 3, {'weight': 2}) + (1, 2, {'weight': 3}) + (1, 3, {'weight': 4}) + >>> # By default, the weight is the number of shared neighbors + >>> G = bipartite.generic_weighted_projected_graph(B, [0, 1]) + >>> print(list(G.edges(data=True))) + [(0, 1, {'weight': 2})] + >>> # To specify a custom weight function use the weight_function parameter + >>> G = bipartite.generic_weighted_projected_graph( + ... B, [0, 1], weight_function=jaccard + ... ) + >>> print(list(G.edges(data=True))) + [(0, 1, {'weight': 1.0})] + >>> G = bipartite.generic_weighted_projected_graph( + ... B, [0, 1], weight_function=my_weight + ... ) + >>> print(list(G.edges(data=True))) + [(0, 1, {'weight': 10})] + + Notes + ----- + No attempt is made to verify that the input graph B is bipartite. + The graph and node properties are (shallow) copied to the projected graph. + + See :mod:`bipartite documentation ` + for further details on how bipartite graphs are handled in NetworkX. + + See Also + -------- + is_bipartite, + is_bipartite_node_set, + sets, + weighted_projected_graph, + collaboration_weighted_projected_graph, + overlap_weighted_projected_graph, + projected_graph + + """ + if B.is_directed(): + pred = B.pred + G = nx.DiGraph() + else: + pred = B.adj + G = nx.Graph() + if weight_function is None: + + def weight_function(G, u, v): + # Notice that we use set(pred[v]) for handling the directed case. + return len(set(G[u]) & set(pred[v])) + + G.graph.update(B.graph) + G.add_nodes_from((n, B.nodes[n]) for n in nodes) + for u in nodes: + nbrs2 = {n for nbr in set(B[u]) for n in B[nbr]} - {u} + for v in nbrs2: + weight = weight_function(B, u, v) + G.add_edge(u, v, weight=weight) + return G diff --git a/shared/package-files/03/03d0b53d66c17368fd00abe7bfb5243c26b08454c419899e50b5b4bf06ccbd74/exceptions.py b/shared/package-files/03/03d0b53d66c17368fd00abe7bfb5243c26b08454c419899e50b5b4bf06ccbd74/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..cb360f02614304b306714b309cbc9b0a0f2ff770 --- /dev/null +++ b/shared/package-files/03/03d0b53d66c17368fd00abe7bfb5243c26b08454c419899e50b5b4bf06ccbd74/exceptions.py @@ -0,0 +1,35 @@ +"""Exception classes used by Pexpect""" + +import traceback +import sys + +class ExceptionPexpect(Exception): + '''Base class for all exceptions raised by this module. + ''' + + def __init__(self, value): + super(ExceptionPexpect, self).__init__(value) + self.value = value + + def __str__(self): + return str(self.value) + + def get_trace(self): + '''This returns an abbreviated stack trace with lines that only concern + the caller. In other words, the stack trace inside the Pexpect module + is not included. ''' + + tblist = traceback.extract_tb(sys.exc_info()[2]) + tblist = [item for item in tblist if ('pexpect/__init__' not in item[0]) + and ('pexpect/expect' not in item[0])] + tblist = traceback.format_list(tblist) + return ''.join(tblist) + + +class EOF(ExceptionPexpect): + '''Raised when EOF is read from a child. + This usually means the child has exited.''' + + +class TIMEOUT(ExceptionPexpect): + '''Raised when a read time exceeds the timeout. ''' diff --git a/shared/package-files/03/03d270c39b1ce1a78fb8dedd122b72f91015d17605668a70f3b19667c3e5d4bf/_cudnn_ctc_loss_native.h b/shared/package-files/03/03d270c39b1ce1a78fb8dedd122b72f91015d17605668a70f3b19667c3e5d4bf/_cudnn_ctc_loss_native.h new file mode 100644 index 0000000000000000000000000000000000000000..89b1aaa425c9a0129aadd77bc11baf157b0c9274 --- /dev/null +++ b/shared/package-files/03/03d270c39b1ce1a78fb8dedd122b72f91015d17605668a70f3b19667c3e5d4bf/_cudnn_ctc_loss_native.h @@ -0,0 +1,23 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API ::std::tuple _cudnn_ctc_loss_out(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity, at::Tensor & out0, at::Tensor & out1); +TORCH_API ::std::tuple _cudnn_ctc_loss(const at::Tensor & log_probs, const at::Tensor & targets, at::IntArrayRef input_lengths, at::IntArrayRef target_lengths, int64_t blank, bool deterministic, bool zero_infinity); +TORCH_API ::std::tuple _cudnn_ctc_loss_tensor(const at::Tensor & log_probs, const at::Tensor & targets, const at::Tensor & input_lengths, const at::Tensor & target_lengths, int64_t blank, bool deterministic, bool zero_infinity); +} // namespace native +} // namespace at diff --git a/shared/package-files/03/03d2729814121d9b50686910aa630e08a75b0e063dec930a9d88c811654c74c8/test_intwidth.py b/shared/package-files/03/03d2729814121d9b50686910aa630e08a75b0e063dec930a9d88c811654c74c8/test_intwidth.py new file mode 100644 index 0000000000000000000000000000000000000000..76c0effebf14364f0001db6736da2b68766f0dfe --- /dev/null +++ b/shared/package-files/03/03d2729814121d9b50686910aa630e08a75b0e063dec930a9d88c811654c74c8/test_intwidth.py @@ -0,0 +1,90 @@ +import unittest + +import math +import sys + +from numba import jit +from numba.core import utils +from numba.tests.support import TestCase, tag + + +max_uint64 = 18446744073709551615 + +def usecase_uint64_global(): + return max_uint64 + +def usecase_uint64_constant(): + return 18446744073709551615 + +def usecase_uint64_func(): + return max(18446744073709551614, 18446744073709551615) + +def usecase_int64_pos(): + return 9223372036854775807 + +def usecase_int64_neg(): + return -9223372036854775808 + +def usecase_int64_func(): + return (max(9223372036854775807, -9223372036854775808) + + min(9223372036854775807, -9223372036854775808)) + + +class IntWidthTest(TestCase): + + def check_nullary_func(self, pyfunc, **kwargs): + cfunc = jit(**kwargs)(pyfunc) + self.assertPreciseEqual(cfunc(), pyfunc()) + + def test_global_uint64(self, nopython=False): + pyfunc = usecase_uint64_global + self.check_nullary_func(pyfunc, nopython=nopython) + + def test_global_uint64_npm(self): + self.test_global_uint64(nopython=True) + + def test_constant_uint64(self, nopython=False): + pyfunc = usecase_uint64_constant + self.check_nullary_func(pyfunc, nopython=nopython) + + def test_constant_uint64_npm(self): + self.test_constant_uint64(nopython=True) + + def test_constant_uint64_function_call(self, nopython=False): + pyfunc = usecase_uint64_func + self.check_nullary_func(pyfunc, nopython=nopython) + + def test_constant_uint64_function_call_npm(self): + self.test_constant_uint64_function_call(nopython=True) + + def test_bit_length(self): + f = utils.bit_length + self.assertEqual(f(0x7f), 7) + self.assertEqual(f(-0x7f), 7) + self.assertEqual(f(0x80), 8) + self.assertEqual(f(-0x80), 7) + self.assertEqual(f(0xff), 8) + self.assertEqual(f(-0xff), 8) + self.assertEqual(f(0x100), 9) + self.assertEqual(f(-0x100), 8) + self.assertEqual(f(-0x101), 9) + self.assertEqual(f(0x7fffffff), 31) + self.assertEqual(f(-0x7fffffff), 31) + self.assertEqual(f(-0x80000000), 31) + self.assertEqual(f(0x80000000), 32) + self.assertEqual(f(0xffffffff), 32) + self.assertEqual(f(0xffffffffffffffff), 64) + self.assertEqual(f(0x10000000000000000), 65) + + def test_constant_int64(self, nopython=False): + self.check_nullary_func(usecase_int64_pos, nopython=nopython) + self.check_nullary_func(usecase_int64_neg, nopython=nopython) + self.check_nullary_func(usecase_int64_func, nopython=nopython) + + def test_constant_int64_npm(self): + self.test_constant_int64(nopython=True) + + +if __name__ == '__main__': + unittest.main() + diff --git a/shared/package-files/03/03d4dffcd7dc97b7c9a1f576f92932e25d9974be90607d80bc75e82142d82e91/test_comprehension.py b/shared/package-files/03/03d4dffcd7dc97b7c9a1f576f92932e25d9974be90607d80bc75e82142d82e91/test_comprehension.py new file mode 100644 index 0000000000000000000000000000000000000000..1e8e293e4d5744cd5b5da566bebaea90b812f31d --- /dev/null +++ b/shared/package-files/03/03d4dffcd7dc97b7c9a1f576f92932e25d9974be90607d80bc75e82142d82e91/test_comprehension.py @@ -0,0 +1,529 @@ +import unittest +from numba.tests.support import TestCase + +import sys +import operator +import numpy as np +import numpy + +from numba import jit, njit, typed +from numba.core import types, utils +from numba.core.errors import TypingError, LoweringError +from numba.core.types.functions import _header_lead +from numba.np.numpy_support import numpy_version +from numba.tests.support import tag, _32bit, captured_stdout + + +# deliberately imported twice for different use cases + + +PARALLEL_SUPPORTED = not _32bit + +def comp_list(n): + l = [i for i in range(n)] + s = 0 + for i in l: + s += i + return s + + +class TestListComprehension(TestCase): + + def test_comp_list(self): + pyfunc = comp_list + cfunc = njit((types.intp,))(pyfunc) + self.assertEqual(cfunc(5), pyfunc(5)) + self.assertEqual(cfunc(0), pyfunc(0)) + self.assertEqual(cfunc(-1), pyfunc(-1)) + + def test_bulk_use_cases(self): + """ Tests the large number of use cases defined below """ + + # jitted function used in some tests + @jit(nopython=True) + def fib3(n): + if n < 2: + return n + return fib3(n - 1) + fib3(n - 2) + + def list1(x): + """ Test basic list comprehension """ + return [i for i in range(1, len(x) - 1)] + + def list2(x): + """ Test conditional list comprehension """ + return [y for y in x if y < 2] + + def list3(x): + """ Test ternary list comprehension """ + return [y if y < 2 else -1 for y in x] + + def list4(x): + """ Test list comprehension to np.array ctor """ + return np.array([1, 2, 3]) + + # expected fail, unsupported type in sequence + def list5(x): + """ Test nested list comprehension to np.array ctor """ + return np.array([np.array([z for z in x]) for y in x]) + + def list6(x): + """ Test use of inner function in list comprehension """ + def inner(x): + return x + 1 + return [inner(z) for z in x] + + def list7(x): + """ Test use of closure in list comprehension """ + y = 3 + + def inner(x): + return x + y + return [inner(z) for z in x] + + def list8(x): + """ Test use of list comprehension as arg to inner function """ + l = [z + 1 for z in x] + + def inner(x): + return x[0] + 1 + q = inner(l) + return q + + def list9(x): + """ Test use of list comprehension access in closure """ + l = [z + 1 for z in x] + + def inner(x): + return x[0] + l[1] + return inner(x) + + def list10(x): + """ Test use of list comprehension access in closure and as arg """ + l = [z + 1 for z in x] + + def inner(x): + return [y + l[0] for y in x] + return inner(l) + + def list11(x): + """ Test scalar array construction in list comprehension """ + l = [np.array(z) for z in x] + return l + + def list12(x): + """ Test scalar type conversion construction in list comprehension """ + l = [np.float64(z) for z in x] + return l + + def list13(x): + """ Test use of explicit numpy scalar ctor reference in list comprehension """ + l = [numpy.float64(z) for z in x] + return l + + def list14(x): + """ Test use of python scalar ctor reference in list comprehension """ + l = [float(z) for z in x] + return l + + def list15(x): + """ Test use of python scalar ctor reference in list comprehension followed by np array construction from the list""" + l = [float(z) for z in x] + return np.array(l) + + def list16(x): + """ Test type unification from np array ctors consuming list comprehension """ + l1 = [float(z) for z in x] + l2 = [z for z in x] + ze = np.array(l1) + oe = np.array(l2) + return ze + oe + + def list17(x): + """ Test complex list comprehension including math calls """ + return [(a, b, c) + for a in x for b in x for c in x if np.sqrt(a**2 + b**2) == c] + + _OUTER_SCOPE_VAR = 9 + + def list18(x): + """ Test loop list with outer scope var as conditional""" + z = [] + for i in x: + if i < _OUTER_SCOPE_VAR: + z.append(i) + return z + + _OUTER_SCOPE_VAR = 9 + + def list19(x): + """ Test list comprehension with outer scope as conditional""" + return [i for i in x if i < _OUTER_SCOPE_VAR] + + def list20(x): + """ Test return empty list """ + return [i for i in x if i == -1000] + + def list21(x): + """ Test call a jitted function in a list comprehension """ + return [fib3(i) for i in x] + + def list22(x): + """ Test create two lists comprehensions and a third walking the first two """ + a = [y - 1 for y in x] + b = [y + 1 for y in x] + return [x for x in a for y in b if x == y] + + def list23(x): + """ Test operation on comprehension generated list """ + z = [y for y in x] + z.append(1) + return z + + def list24(x): + """ Test type promotion """ + z = [float(y) if y > 3 else y for y in x] + return z + + def list25(x): + # See issue #6260. Old style inline_closure_call uses get_ir_of_code + # for the closure->IR transform, without SSA there's multiply + # defined labels, the unary negation is self referent and DCE runs + # eliminating the duplicated labels. + included = np.array([1, 2, 6, 8]) + not_included = [i for i in range(10) if i not in list(included)] + return not_included + + # functions to test that are expected to pass + f = [list1, list2, list3, list4, + list6, list7, list8, list9, list10, list11, + list12, list13, list14, list15, + list16, list17, list18, list19, list20, + list21, list22, list23, list24, list25] + + var = [1, 2, 3, 4, 5] + for ref in f: + try: + cfunc = jit(nopython=True)(ref) + self.assertEqual(cfunc(var), ref(var)) + except ValueError: # likely np array returned + try: + np.testing.assert_allclose(cfunc(var), ref(var)) + except Exception: + raise + + # test functions that are expected to fail + with self.assertRaises(TypingError) as raises: + cfunc = jit(nopython=True)(list5) + cfunc(var) + # TODO: we can't really assert the error message for the above + # Also, test_nested_array is a similar case (but without list) that works. + + if sys.maxsize > 2 ** 32: + bits = 64 + else: + bits = 32 + + def test_objmode_inlining(self): + def objmode_func(y): + z = object() + inlined = [x for x in y] + return inlined + + cfunc = jit(forceobj=True)(objmode_func) + t = [1, 2, 3] + expected = objmode_func(t) + got = cfunc(t) + self.assertPreciseEqual(expected, got) + + +class TestArrayComprehension(unittest.TestCase): + + _numba_parallel_test_ = False + + def check(self, pyfunc, *args, **kwargs): + """A generic check function that run both pyfunc, and jitted pyfunc, + and compare results.""" + run_parallel = kwargs.get('run_parallel', False) + assert_allocate_list = kwargs.get('assert_allocate_list', False) + assert_dtype = kwargs.get('assert_dtype', False) + cfunc = jit(nopython=True,parallel=run_parallel)(pyfunc) + pyres = pyfunc(*args) + cres = cfunc(*args) + np.testing.assert_array_equal(pyres, cres) + if assert_dtype: + self.assertEqual(cres[1].dtype, assert_dtype) + if assert_allocate_list: + self.assertIn('allocate list', cfunc.inspect_llvm(cfunc.signatures[0])) + else: + self.assertNotIn('allocate list', cfunc.inspect_llvm(cfunc.signatures[0])) + if run_parallel: + self.assertIn('@do_scheduling', cfunc.inspect_llvm(cfunc.signatures[0])) + + def test_comp_with_array_1(self): + def comp_with_array_1(n): + m = n * 2 + l = np.array([i + m for i in range(n)]) + return l + + self.check(comp_with_array_1, 5) + if PARALLEL_SUPPORTED: + self.check(comp_with_array_1, 5, run_parallel=True) + + def test_comp_with_array_2(self): + def comp_with_array_2(n, threshold): + A = np.arange(-n, n) + return np.array([ x * x if x < threshold else x * 2 for x in A ]) + + self.check(comp_with_array_2, 5, 0) + + def test_comp_with_array_noinline(self): + def comp_with_array_noinline(n): + m = n * 2 + l = np.array([i + m for i in range(n)]) + return l + + import numba.core.inline_closurecall as ic + try: + ic.enable_inline_arraycall = False + self.check(comp_with_array_noinline, 5, assert_allocate_list=True) + finally: + ic.enable_inline_arraycall = True + + def test_comp_with_array_noinline_issue_6053(self): + def comp_with_array_noinline(n): + lst = [0] + for i in range(n): + lst.append(i) + l = np.array(lst) + return l + + self.check(comp_with_array_noinline, 5, assert_allocate_list=True) + + def test_comp_nest_with_array(self): + def comp_nest_with_array(n): + l = np.array([[i * j for j in range(n)] for i in range(n)]) + return l + + self.check(comp_nest_with_array, 5) + if PARALLEL_SUPPORTED: + self.check(comp_nest_with_array, 5, run_parallel=True) + + def test_comp_nest_with_array_3(self): + def comp_nest_with_array_3(n): + l = np.array([[[i * j * k for k in range(n)] for j in range(n)] for i in range(n)]) + return l + + self.check(comp_nest_with_array_3, 5) + if PARALLEL_SUPPORTED: + self.check(comp_nest_with_array_3, 5, run_parallel=True) + + def test_comp_nest_with_array_noinline(self): + def comp_nest_with_array_noinline(n): + l = np.array([[i * j for j in range(n)] for i in range(n)]) + return l + + import numba.core.inline_closurecall as ic + try: + ic.enable_inline_arraycall = False + self.check(comp_nest_with_array_noinline, 5, + assert_allocate_list=True) + finally: + ic.enable_inline_arraycall = True + + def test_comp_with_array_range(self): + def comp_with_array_range(m, n): + l = np.array([i for i in range(m, n)]) + return l + + self.check(comp_with_array_range, 5, 10) + + def test_comp_with_array_range_and_step(self): + def comp_with_array_range_and_step(m, n): + l = np.array([i for i in range(m, n, 2)]) + return l + + self.check(comp_with_array_range_and_step, 5, 10) + + def test_comp_with_array_conditional(self): + def comp_with_array_conditional(n): + l = np.array([i for i in range(n) if i % 2 == 1]) + return l + # arraycall inline would not happen when conditional is present + self.check(comp_with_array_conditional, 10, assert_allocate_list=True) + + def test_comp_nest_with_array_conditional(self): + def comp_nest_with_array_conditional(n): + l = np.array([[i * j for j in range(n)] for i in range(n) if i % 2 == 1]) + return l + self.check(comp_nest_with_array_conditional, 5, + assert_allocate_list=True) + + @unittest.skipUnless(numpy_version < (1, 24), + 'Setting an array element with a sequence is removed ' + 'in NumPy 1.24') + def test_comp_nest_with_dependency(self): + def comp_nest_with_dependency(n): + l = np.array([[i * j for j in range(i+1)] for i in range(n)]) + return l + # test is expected to fail + with self.assertRaises(TypingError) as raises: + self.check(comp_nest_with_dependency, 5) + self.assertIn(_header_lead, str(raises.exception)) + self.assertIn('array(undefined,', str(raises.exception)) + + def test_comp_unsupported_iter(self): + def comp_unsupported_iter(): + val = zip([1, 2, 3], [4, 5, 6]) + return np.array([a for a, b in val]) + with self.assertRaises(TypingError) as raises: + self.check(comp_unsupported_iter) + self.assertIn(_header_lead, str(raises.exception)) + self.assertIn('Unsupported iterator found in array comprehension', + str(raises.exception)) + + def test_no_array_comp(self): + def no_array_comp1(n): + l = [1,2,3,4] + a = np.array(l) + return a + # const 1D array is actually inlined + self.check(no_array_comp1, 10, assert_allocate_list=False) + def no_array_comp2(n): + l = [1,2,3,4] + a = np.array(l) + l.append(5) + return a + self.check(no_array_comp2, 10, assert_allocate_list=True) + + def test_nested_array(self): + def nested_array(n): + l = np.array([ np.array([x for x in range(n)]) for y in range(n)]) + return l + + self.check(nested_array, 10) + + def test_nested_array_with_const(self): + def nested_array(n): + l = np.array([ np.array([x for x in range(3)]) for y in range(4)]) + return l + + self.check(nested_array, 0) + + def test_array_comp_with_iter(self): + def array_comp(a): + l = np.array([ x * x for x in a ]) + return l + # with list iterator + l = [1,2,3,4,5] + self.check(array_comp, l) + # with array iterator + self.check(array_comp, np.array(l)) + # with tuple iterator (issue #7394) + self.check(array_comp, tuple(l)) + # with typed.List iterator (issue #6550) + self.check(array_comp, typed.List(l)) + + def test_array_comp_with_dtype(self): + def array_comp(n): + l = np.array([i for i in range(n)], dtype=np.complex64) + return l + + self.check(array_comp, 10, assert_dtype=np.complex64) + + def test_array_comp_inferred_dtype(self): + def array_comp(n): + l = np.array([i * 1j for i in range(n)]) + return l + + self.check(array_comp, 10) + + def test_array_comp_inferred_dtype_nested(self): + def array_comp(n): + l = np.array([[i * j for j in range(n)] for i in range(n)]) + return l + + self.check(array_comp, 10) + + def test_array_comp_inferred_dtype_nested_sum(self): + def array_comp(n): + l = np.array([[i * j for j in range(n)] for i in range(n)]) + # checks that operations on the inferred array + return l + + self.check(array_comp, 10) + + def test_array_comp_inferred_dtype_outside_setitem(self): + def array_comp(n, v): + arr = np.array([i for i in range(n)]) + # the following should not change the dtype + arr[0] = v + return arr + + # float to int cast is valid + v = 1.2 + self.check(array_comp, 10, v, assert_dtype=np.intp) + # complex to int cast is invalid + with self.assertRaises(TypingError) as raises: + cfunc = jit(nopython=True)(array_comp) + cfunc(10, 2.3j) + self.assertIn( + _header_lead + " Function({})".format(operator.setitem), + str(raises.exception), + ) + self.assertIn( + "(array({}, 1d, C), Literal[int](0), complex128)".format(types.intp), + str(raises.exception), + ) + + def test_array_comp_shuffle_sideeffect(self): + nelem = 100 + + @jit(nopython=True) + def foo(): + numbers = np.array([i for i in range(nelem)]) + np.random.shuffle(numbers) + print(numbers) + + with captured_stdout() as gotbuf: + foo() + got = gotbuf.getvalue().strip() + + with captured_stdout() as expectbuf: + print(np.array([i for i in range(nelem)])) + expect = expectbuf.getvalue().strip() + + # For a large enough array, the chances of shuffle to not move any + # element is tiny enough. + self.assertNotEqual(got, expect) + self.assertRegex(got, r'\[(\s*\d+)+\]') + + def test_empty_list_not_removed(self): + # see issue #3724 + def f(x): + t = [] + myList = np.array([1]) + a = np.random.choice(myList, 1) + t.append(x + a) + return a + self.check(f, 5, assert_allocate_list=True) + + def test_reuse_of_array_var(self): + """ Test issue 3742 """ + # redefinition of z breaks array comp as there's multiple defn + def foo(n): + # doesn't matter where this is in the code, it's just to ensure a + # `make_function` opcode exists + [i for i in range(1)] + z = np.empty(n) + for i in range(n): + z = np.zeros(n) + z[i] = i # write is required to trip the bug + + return z + + self.check(foo, 10, assert_allocate_list=True) + + +if __name__ == '__main__': + unittest.main() diff --git a/shared/package-files/03/03d52c6f89accff4a54f830c8c3a324afa09a7ed36b9c735999d7621f98b2aac/refbug.py b/shared/package-files/03/03d52c6f89accff4a54f830c8c3a324afa09a7ed36b9c735999d7621f98b2aac/refbug.py new file mode 100644 index 0000000000000000000000000000000000000000..92e2eead347aa6607547a0b1e516f5021b8414c8 --- /dev/null +++ b/shared/package-files/03/03d52c6f89accff4a54f830c8c3a324afa09a7ed36b9c735999d7621f98b2aac/refbug.py @@ -0,0 +1,46 @@ +"""Minimal script to reproduce our nasty reference counting bug. + +The problem is related to https://github.com/ipython/ipython/issues/141 + +The original fix for that appeared to work, but John D. Hunter found a +matplotlib example which, when run twice in a row, would break. The problem +were references held by open figures to internals of Tkinter. + +This code reproduces the problem that John saw, without matplotlib. + +This script is meant to be called by other parts of the test suite that call it +via %run as if it were executed interactively by the user. As of 2011-05-29, +test_run.py calls it. +""" + +#----------------------------------------------------------------------------- +# Module imports +#----------------------------------------------------------------------------- + +from IPython import get_ipython + +#----------------------------------------------------------------------------- +# Globals +#----------------------------------------------------------------------------- + +# This needs to be here because nose and other test runners will import +# this module. Importing this module has potential side effects that we +# want to prevent. +if __name__ == '__main__': + + ip = get_ipython() + + if not '_refbug_cache' in ip.user_ns: + ip.user_ns['_refbug_cache'] = [] + + + aglobal = 'Hello' + def f(): + return aglobal + + cache = ip.user_ns['_refbug_cache'] + cache.append(f) + + def call_f(): + for func in cache: + print('lowercased:',func().lower()) diff --git a/shared/package-files/03/03d713112751dfb4f2b997a28888df0c9848fb405a8b372efd8187654ea1a935/code.pyi b/shared/package-files/03/03d713112751dfb4f2b997a28888df0c9848fb405a8b372efd8187654ea1a935/code.pyi new file mode 100644 index 0000000000000000000000000000000000000000..16c2b129dd1edbb46bf829fd0b42198c674dd5c7 --- /dev/null +++ b/shared/package-files/03/03d713112751dfb4f2b997a28888df0c9848fb405a8b372efd8187654ea1a935/code.pyi @@ -0,0 +1,36 @@ +import sys +from types import CodeType +from typing import Any, Callable, Mapping, Optional + +class InteractiveInterpreter: + def __init__(self, locals: Optional[Mapping[str, Any]] = ...) -> None: ... + def runsource(self, source: str, filename: str = ..., symbol: str = ...) -> bool: ... + def runcode(self, code: CodeType) -> None: ... + def showsyntaxerror(self, filename: Optional[str] = ...) -> None: ... + def showtraceback(self) -> None: ... + def write(self, data: str) -> None: ... + +class InteractiveConsole(InteractiveInterpreter): + def __init__(self, locals: Optional[Mapping[str, Any]] = ..., filename: str = ...) -> None: ... + if sys.version_info >= (3, 6): + def interact(self, banner: Optional[str] = ..., exitmsg: Optional[str] = ...) -> None: ... + else: + def interact(self, banner: Optional[str] = ...) -> None: ... + def push(self, line: str) -> bool: ... + def resetbuffer(self) -> None: ... + def raw_input(self, prompt: str = ...) -> str: ... + +if sys.version_info >= (3, 6): + def interact( + banner: Optional[str] = ..., + readfunc: Optional[Callable[[str], str]] = ..., + local: Optional[Mapping[str, Any]] = ..., + exitmsg: Optional[str] = ..., + ) -> None: ... + +else: + def interact( + banner: Optional[str] = ..., readfunc: Optional[Callable[[str], str]] = ..., local: Optional[Mapping[str, Any]] = ... + ) -> None: ... + +def compile_command(source: str, filename: str = ..., symbol: str = ...) -> Optional[CodeType]: ... diff --git a/shared/package-files/03/03da10b561c69c902dd6b3a42675726804783acc96f3426247a5ce646fe6f0ba/conv_transpose1d_ops.h b/shared/package-files/03/03da10b561c69c902dd6b3a42675726804783acc96f3426247a5ce646fe6f0ba/conv_transpose1d_ops.h new file mode 100644 index 0000000000000000000000000000000000000000..547a5c1c6e16b0b1ab55e39242b9fec591cee45b --- /dev/null +++ b/shared/package-files/03/03da10b561c69c902dd6b3a42675726804783acc96f3426247a5ce646fe6f0ba/conv_transpose1d_ops.h @@ -0,0 +1,28 @@ +#pragma once + +// @generated by torchgen/gen.py from Operator.h + +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { +namespace _ops { + + +struct TORCH_API conv_transpose1d { + using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const c10::optional &, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymIntArrayRef, c10::SymInt, c10::SymIntArrayRef); + using ptr_schema = schema*; + // See Note [static constexpr char* members for windows NVCC] + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::conv_transpose1d") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "") + STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "conv_transpose1d(Tensor input, Tensor weight, Tensor? bias=None, SymInt[1] stride=1, SymInt[1] padding=0, SymInt[1] output_padding=0, SymInt groups=1, SymInt[1] dilation=1) -> Tensor") + static at::Tensor call(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymInt groups, c10::SymIntArrayRef dilation); + static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias, c10::SymIntArrayRef stride, c10::SymIntArrayRef padding, c10::SymIntArrayRef output_padding, c10::SymInt groups, c10::SymIntArrayRef dilation); +}; + +}} // namespace at::_ops diff --git a/shared/package-files/03/03db5ee3bdda44ef76d220d5bb22b2a5e548429fae7992ba12223ea8b4b024d8/ctypeslib.pyi b/shared/package-files/03/03db5ee3bdda44ef76d220d5bb22b2a5e548429fae7992ba12223ea8b4b024d8/ctypeslib.pyi new file mode 100644 index 0000000000000000000000000000000000000000..3edf98e143cf17e8a14fe585be75f4735b1fa6e7 --- /dev/null +++ b/shared/package-files/03/03db5ee3bdda44ef76d220d5bb22b2a5e548429fae7992ba12223ea8b4b024d8/ctypeslib.pyi @@ -0,0 +1,251 @@ +# NOTE: Numpy's mypy plugin is used for importing the correct +# platform-specific `ctypes._SimpleCData[int]` sub-type +from ctypes import c_int64 as _c_intp + +import os +import sys +import ctypes +from collections.abc import Iterable, Sequence +from typing import ( + Literal as L, + Any, + Union, + TypeVar, + Generic, + overload, + ClassVar, +) + +from numpy import ( + ndarray, + dtype, + generic, + bool_, + byte, + short, + intc, + int_, + longlong, + ubyte, + ushort, + uintc, + uint, + ulonglong, + single, + double, + longdouble, + void, +) +from numpy.core._internal import _ctypes +from numpy.core.multiarray import flagsobj +from numpy._typing import ( + # Arrays + NDArray, + _ArrayLike, + + # Shapes + _ShapeLike, + + # DTypes + DTypeLike, + _DTypeLike, + _VoidDTypeLike, + _BoolCodes, + _UByteCodes, + _UShortCodes, + _UIntCCodes, + _UIntCodes, + _ULongLongCodes, + _ByteCodes, + _ShortCodes, + _IntCCodes, + _IntCodes, + _LongLongCodes, + _SingleCodes, + _DoubleCodes, + _LongDoubleCodes, +) + +# TODO: Add a proper `_Shape` bound once we've got variadic typevars +_DType = TypeVar("_DType", bound=dtype[Any]) +_DTypeOptional = TypeVar("_DTypeOptional", bound=None | dtype[Any]) +_SCT = TypeVar("_SCT", bound=generic) + +_FlagsKind = L[ + 'C_CONTIGUOUS', 'CONTIGUOUS', 'C', + 'F_CONTIGUOUS', 'FORTRAN', 'F', + 'ALIGNED', 'A', + 'WRITEABLE', 'W', + 'OWNDATA', 'O', + 'WRITEBACKIFCOPY', 'X', +] + +# TODO: Add a shape typevar once we have variadic typevars (PEP 646) +class _ndptr(ctypes.c_void_p, Generic[_DTypeOptional]): + # In practice these 4 classvars are defined in the dynamic class + # returned by `ndpointer` + _dtype_: ClassVar[_DTypeOptional] + _shape_: ClassVar[None] + _ndim_: ClassVar[None | int] + _flags_: ClassVar[None | list[_FlagsKind]] + + @overload + @classmethod + def from_param(cls: type[_ndptr[None]], obj: ndarray[Any, Any]) -> _ctypes[Any]: ... + @overload + @classmethod + def from_param(cls: type[_ndptr[_DType]], obj: ndarray[Any, _DType]) -> _ctypes[Any]: ... + +class _concrete_ndptr(_ndptr[_DType]): + _dtype_: ClassVar[_DType] + _shape_: ClassVar[tuple[int, ...]] + @property + def contents(self) -> ndarray[Any, _DType]: ... + +def load_library( + libname: str | bytes | os.PathLike[str] | os.PathLike[bytes], + loader_path: str | bytes | os.PathLike[str] | os.PathLike[bytes], +) -> ctypes.CDLL: ... + +__all__: list[str] + +c_intp = _c_intp + +@overload +def ndpointer( + dtype: None = ..., + ndim: int = ..., + shape: None | _ShapeLike = ..., + flags: None | _FlagsKind | Iterable[_FlagsKind] | int | flagsobj = ..., +) -> type[_ndptr[None]]: ... +@overload +def ndpointer( + dtype: _DTypeLike[_SCT], + ndim: int = ..., + *, + shape: _ShapeLike, + flags: None | _FlagsKind | Iterable[_FlagsKind] | int | flagsobj = ..., +) -> type[_concrete_ndptr[dtype[_SCT]]]: ... +@overload +def ndpointer( + dtype: DTypeLike, + ndim: int = ..., + *, + shape: _ShapeLike, + flags: None | _FlagsKind | Iterable[_FlagsKind] | int | flagsobj = ..., +) -> type[_concrete_ndptr[dtype[Any]]]: ... +@overload +def ndpointer( + dtype: _DTypeLike[_SCT], + ndim: int = ..., + shape: None = ..., + flags: None | _FlagsKind | Iterable[_FlagsKind] | int | flagsobj = ..., +) -> type[_ndptr[dtype[_SCT]]]: ... +@overload +def ndpointer( + dtype: DTypeLike, + ndim: int = ..., + shape: None = ..., + flags: None | _FlagsKind | Iterable[_FlagsKind] | int | flagsobj = ..., +) -> type[_ndptr[dtype[Any]]]: ... + +@overload +def as_ctypes_type(dtype: _BoolCodes | _DTypeLike[bool_] | type[ctypes.c_bool]) -> type[ctypes.c_bool]: ... +@overload +def as_ctypes_type(dtype: _ByteCodes | _DTypeLike[byte] | type[ctypes.c_byte]) -> type[ctypes.c_byte]: ... +@overload +def as_ctypes_type(dtype: _ShortCodes | _DTypeLike[short] | type[ctypes.c_short]) -> type[ctypes.c_short]: ... +@overload +def as_ctypes_type(dtype: _IntCCodes | _DTypeLike[intc] | type[ctypes.c_int]) -> type[ctypes.c_int]: ... +@overload +def as_ctypes_type(dtype: _IntCodes | _DTypeLike[int_] | type[int | ctypes.c_long]) -> type[ctypes.c_long]: ... +@overload +def as_ctypes_type(dtype: _LongLongCodes | _DTypeLike[longlong] | type[ctypes.c_longlong]) -> type[ctypes.c_longlong]: ... +@overload +def as_ctypes_type(dtype: _UByteCodes | _DTypeLike[ubyte] | type[ctypes.c_ubyte]) -> type[ctypes.c_ubyte]: ... +@overload +def as_ctypes_type(dtype: _UShortCodes | _DTypeLike[ushort] | type[ctypes.c_ushort]) -> type[ctypes.c_ushort]: ... +@overload +def as_ctypes_type(dtype: _UIntCCodes | _DTypeLike[uintc] | type[ctypes.c_uint]) -> type[ctypes.c_uint]: ... +@overload +def as_ctypes_type(dtype: _UIntCodes | _DTypeLike[uint] | type[ctypes.c_ulong]) -> type[ctypes.c_ulong]: ... +@overload +def as_ctypes_type(dtype: _ULongLongCodes | _DTypeLike[ulonglong] | type[ctypes.c_ulonglong]) -> type[ctypes.c_ulonglong]: ... +@overload +def as_ctypes_type(dtype: _SingleCodes | _DTypeLike[single] | type[ctypes.c_float]) -> type[ctypes.c_float]: ... +@overload +def as_ctypes_type(dtype: _DoubleCodes | _DTypeLike[double] | type[float | ctypes.c_double]) -> type[ctypes.c_double]: ... +@overload +def as_ctypes_type(dtype: _LongDoubleCodes | _DTypeLike[longdouble] | type[ctypes.c_longdouble]) -> type[ctypes.c_longdouble]: ... +@overload +def as_ctypes_type(dtype: _VoidDTypeLike) -> type[Any]: ... # `ctypes.Union` or `ctypes.Structure` +@overload +def as_ctypes_type(dtype: str) -> type[Any]: ... + +@overload +def as_array(obj: ctypes._PointerLike, shape: Sequence[int]) -> NDArray[Any]: ... +@overload +def as_array(obj: _ArrayLike[_SCT], shape: None | _ShapeLike = ...) -> NDArray[_SCT]: ... +@overload +def as_array(obj: object, shape: None | _ShapeLike = ...) -> NDArray[Any]: ... + +@overload +def as_ctypes(obj: bool_) -> ctypes.c_bool: ... +@overload +def as_ctypes(obj: byte) -> ctypes.c_byte: ... +@overload +def as_ctypes(obj: short) -> ctypes.c_short: ... +@overload +def as_ctypes(obj: intc) -> ctypes.c_int: ... +@overload +def as_ctypes(obj: int_) -> ctypes.c_long: ... +@overload +def as_ctypes(obj: longlong) -> ctypes.c_longlong: ... +@overload +def as_ctypes(obj: ubyte) -> ctypes.c_ubyte: ... +@overload +def as_ctypes(obj: ushort) -> ctypes.c_ushort: ... +@overload +def as_ctypes(obj: uintc) -> ctypes.c_uint: ... +@overload +def as_ctypes(obj: uint) -> ctypes.c_ulong: ... +@overload +def as_ctypes(obj: ulonglong) -> ctypes.c_ulonglong: ... +@overload +def as_ctypes(obj: single) -> ctypes.c_float: ... +@overload +def as_ctypes(obj: double) -> ctypes.c_double: ... +@overload +def as_ctypes(obj: longdouble) -> ctypes.c_longdouble: ... +@overload +def as_ctypes(obj: void) -> Any: ... # `ctypes.Union` or `ctypes.Structure` +@overload +def as_ctypes(obj: NDArray[bool_]) -> ctypes.Array[ctypes.c_bool]: ... +@overload +def as_ctypes(obj: NDArray[byte]) -> ctypes.Array[ctypes.c_byte]: ... +@overload +def as_ctypes(obj: NDArray[short]) -> ctypes.Array[ctypes.c_short]: ... +@overload +def as_ctypes(obj: NDArray[intc]) -> ctypes.Array[ctypes.c_int]: ... +@overload +def as_ctypes(obj: NDArray[int_]) -> ctypes.Array[ctypes.c_long]: ... +@overload +def as_ctypes(obj: NDArray[longlong]) -> ctypes.Array[ctypes.c_longlong]: ... +@overload +def as_ctypes(obj: NDArray[ubyte]) -> ctypes.Array[ctypes.c_ubyte]: ... +@overload +def as_ctypes(obj: NDArray[ushort]) -> ctypes.Array[ctypes.c_ushort]: ... +@overload +def as_ctypes(obj: NDArray[uintc]) -> ctypes.Array[ctypes.c_uint]: ... +@overload +def as_ctypes(obj: NDArray[uint]) -> ctypes.Array[ctypes.c_ulong]: ... +@overload +def as_ctypes(obj: NDArray[ulonglong]) -> ctypes.Array[ctypes.c_ulonglong]: ... +@overload +def as_ctypes(obj: NDArray[single]) -> ctypes.Array[ctypes.c_float]: ... +@overload +def as_ctypes(obj: NDArray[double]) -> ctypes.Array[ctypes.c_double]: ... +@overload +def as_ctypes(obj: NDArray[longdouble]) -> ctypes.Array[ctypes.c_longdouble]: ... +@overload +def as_ctypes(obj: NDArray[void]) -> ctypes.Array[Any]: ... # `ctypes.Union` or `ctypes.Structure` diff --git a/shared/package-files/03/03db7507ae6516b6112d780325283798c0c07bf872a483dc4cc54e7406086d4d/test_strings.py b/shared/package-files/03/03db7507ae6516b6112d780325283798c0c07bf872a483dc4cc54e7406086d4d/test_strings.py new file mode 100644 index 0000000000000000000000000000000000000000..42f775e857b806b314329b7b69cb4973e0d1ea57 --- /dev/null +++ b/shared/package-files/03/03db7507ae6516b6112d780325283798c0c07bf872a483dc4cc54e7406086d4d/test_strings.py @@ -0,0 +1,99 @@ +import pytest + +import operator +import numpy as np + +from numpy.testing import assert_array_equal + + +COMPARISONS = [ + (operator.eq, np.equal, "=="), + (operator.ne, np.not_equal, "!="), + (operator.lt, np.less, "<"), + (operator.le, np.less_equal, "<="), + (operator.gt, np.greater, ">"), + (operator.ge, np.greater_equal, ">="), +] + + +@pytest.mark.parametrize(["op", "ufunc", "sym"], COMPARISONS) +def test_mixed_string_comparison_ufuncs_fail(op, ufunc, sym): + arr_string = np.array(["a", "b"], dtype="S") + arr_unicode = np.array(["a", "c"], dtype="U") + + with pytest.raises(TypeError, match="did not contain a loop"): + ufunc(arr_string, arr_unicode) + + with pytest.raises(TypeError, match="did not contain a loop"): + ufunc(arr_unicode, arr_string) + +@pytest.mark.parametrize(["op", "ufunc", "sym"], COMPARISONS) +def test_mixed_string_comparisons_ufuncs_with_cast(op, ufunc, sym): + arr_string = np.array(["a", "b"], dtype="S") + arr_unicode = np.array(["a", "c"], dtype="U") + + # While there is no loop, manual casting is acceptable: + res1 = ufunc(arr_string, arr_unicode, signature="UU->?", casting="unsafe") + res2 = ufunc(arr_string, arr_unicode, signature="SS->?", casting="unsafe") + + expected = op(arr_string.astype('U'), arr_unicode) + assert_array_equal(res1, expected) + assert_array_equal(res2, expected) + + +@pytest.mark.parametrize(["op", "ufunc", "sym"], COMPARISONS) +@pytest.mark.parametrize("dtypes", [ + ("S2", "S2"), ("S2", "S10"), + ("U1"), (">U1", ">U1"), + ("U10")]) +@pytest.mark.parametrize("aligned", [True, False]) +def test_string_comparisons(op, ufunc, sym, dtypes, aligned): + # ensure native byte-order for the first view to stay within unicode range + native_dt = np.dtype(dtypes[0]).newbyteorder("=") + arr = np.arange(2**15).view(native_dt).astype(dtypes[0]) + if not aligned: + # Make `arr` unaligned: + new = np.zeros(arr.nbytes + 1, dtype=np.uint8)[1:].view(dtypes[0]) + new[...] = arr + arr = new + + arr2 = arr.astype(dtypes[1], copy=True) + np.random.shuffle(arr2) + arr[0] = arr2[0] # make sure one matches + + expected = [op(d1, d2) for d1, d2 in zip(arr.tolist(), arr2.tolist())] + assert_array_equal(op(arr, arr2), expected) + assert_array_equal(ufunc(arr, arr2), expected) + assert_array_equal(np.compare_chararrays(arr, arr2, sym, False), expected) + + expected = [op(d2, d1) for d1, d2 in zip(arr.tolist(), arr2.tolist())] + assert_array_equal(op(arr2, arr), expected) + assert_array_equal(ufunc(arr2, arr), expected) + assert_array_equal(np.compare_chararrays(arr2, arr, sym, False), expected) + + +@pytest.mark.parametrize(["op", "ufunc", "sym"], COMPARISONS) +@pytest.mark.parametrize("dtypes", [ + ("S2", "S2"), ("S2", "S10"), ("U10")]) +def test_string_comparisons_empty(op, ufunc, sym, dtypes): + arr = np.empty((1, 0, 1, 5), dtype=dtypes[0]) + arr2 = np.empty((100, 1, 0, 1), dtype=dtypes[1]) + + expected = np.empty(np.broadcast_shapes(arr.shape, arr2.shape), dtype=bool) + assert_array_equal(op(arr, arr2), expected) + assert_array_equal(ufunc(arr, arr2), expected) + assert_array_equal(np.compare_chararrays(arr, arr2, sym, False), expected) + + +@pytest.mark.parametrize("str_dt", ["S", "U"]) +@pytest.mark.parametrize("float_dt", np.typecodes["AllFloat"]) +def test_float_to_string_cast(str_dt, float_dt): + float_dt = np.dtype(float_dt) + fi = np.finfo(float_dt) + arr = np.array([np.nan, np.inf, -np.inf, fi.max, fi.min], dtype=float_dt) + expected = ["nan", "inf", "-inf", repr(fi.max), repr(fi.min)] + if float_dt.kind == 'c': + expected = [f"({r}+0j)" for r in expected] + + res = arr.astype(str_dt) + assert_array_equal(res, np.array(expected, dtype=str_dt)) diff --git a/shared/package-files/03/03dbaf94f9a13bc241ccd70453f1a68b1d90fea6095b53576605cfc2d379028d/popen_spawn_win32.py b/shared/package-files/03/03dbaf94f9a13bc241ccd70453f1a68b1d90fea6095b53576605cfc2d379028d/popen_spawn_win32.py new file mode 100644 index 0000000000000000000000000000000000000000..9c4098d0fa4f1e6e3ec94ecc8e596dd3857d741f --- /dev/null +++ b/shared/package-files/03/03dbaf94f9a13bc241ccd70453f1a68b1d90fea6095b53576605cfc2d379028d/popen_spawn_win32.py @@ -0,0 +1,131 @@ +import os +import msvcrt +import signal +import sys +import _winapi + +from .context import reduction, get_spawning_popen, set_spawning_popen +from . import spawn +from . import util + +__all__ = ['Popen'] + +# +# +# + +TERMINATE = 0x10000 +WINEXE = (sys.platform == 'win32' and getattr(sys, 'frozen', False)) +WINSERVICE = sys.executable.lower().endswith("pythonservice.exe") + + +def _path_eq(p1, p2): + return p1 == p2 or os.path.normcase(p1) == os.path.normcase(p2) + +WINENV = not _path_eq(sys.executable, sys._base_executable) + + +def _close_handles(*handles): + for handle in handles: + _winapi.CloseHandle(handle) + + +# +# We define a Popen class similar to the one from subprocess, but +# whose constructor takes a process object as its argument. +# + +class Popen(object): + ''' + Start a subprocess to run the code of a process object + ''' + method = 'spawn' + + def __init__(self, process_obj): + prep_data = spawn.get_preparation_data(process_obj._name) + + # read end of pipe will be duplicated by the child process + # -- see spawn_main() in spawn.py. + # + # bpo-33929: Previously, the read end of pipe was "stolen" by the child + # process, but it leaked a handle if the child process had been + # terminated before it could steal the handle from the parent process. + rhandle, whandle = _winapi.CreatePipe(None, 0) + wfd = msvcrt.open_osfhandle(whandle, 0) + cmd = spawn.get_command_line(parent_pid=os.getpid(), + pipe_handle=rhandle) + cmd = ' '.join('"%s"' % x for x in cmd) + + python_exe = spawn.get_executable() + + # bpo-35797: When running in a venv, we bypass the redirect + # executor and launch our base Python. + if WINENV and _path_eq(python_exe, sys.executable): + python_exe = sys._base_executable + env = os.environ.copy() + env["__PYVENV_LAUNCHER__"] = sys.executable + else: + env = None + + with open(wfd, 'wb', closefd=True) as to_child: + # start process + try: + hp, ht, pid, tid = _winapi.CreateProcess( + python_exe, cmd, + None, None, False, 0, env, None, None) + _winapi.CloseHandle(ht) + except: + _winapi.CloseHandle(rhandle) + raise + + # set attributes of self + self.pid = pid + self.returncode = None + self._handle = hp + self.sentinel = int(hp) + self.finalizer = util.Finalize(self, _close_handles, + (self.sentinel, int(rhandle))) + + # send information to child + set_spawning_popen(self) + try: + reduction.dump(prep_data, to_child) + reduction.dump(process_obj, to_child) + finally: + set_spawning_popen(None) + + def duplicate_for_child(self, handle): + assert self is get_spawning_popen() + return reduction.duplicate(handle, self.sentinel) + + def wait(self, timeout=None): + if self.returncode is None: + if timeout is None: + msecs = _winapi.INFINITE + else: + msecs = max(0, int(timeout * 1000 + 0.5)) + + res = _winapi.WaitForSingleObject(int(self._handle), msecs) + if res == _winapi.WAIT_OBJECT_0: + code = _winapi.GetExitCodeProcess(self._handle) + if code == TERMINATE: + code = -signal.SIGTERM + self.returncode = code + + return self.returncode + + def poll(self): + return self.wait(timeout=0) + + def terminate(self): + if self.returncode is None: + try: + _winapi.TerminateProcess(int(self._handle), TERMINATE) + except OSError: + if self.wait(timeout=1.0) is None: + raise + + kill = terminate + + def close(self): + self.finalizer() diff --git a/shared/package-files/03/03e094a672cb6ccee706b108ee70837cfd05c2ad42797e7bece7688a40d21c5a/tek4112-nd b/shared/package-files/03/03e094a672cb6ccee706b108ee70837cfd05c2ad42797e7bece7688a40d21c5a/tek4112-nd new file mode 100644 index 0000000000000000000000000000000000000000..efc60419627ad1087a8756405928e3e44fc11c80 Binary files /dev/null and b/shared/package-files/03/03e094a672cb6ccee706b108ee70837cfd05c2ad42797e7bece7688a40d21c5a/tek4112-nd differ diff --git a/shared/package-files/03/03e0a058b8c50bd93f3a3caa2456aaee40c076c4aa998113e1a665fde1f6535d/plane.py b/shared/package-files/03/03e0a058b8c50bd93f3a3caa2456aaee40c076c4aa998113e1a665fde1f6535d/plane.py new file mode 100644 index 0000000000000000000000000000000000000000..aecb320068815e5425574c7a6cf5c01659fa2b10 --- /dev/null +++ b/shared/package-files/03/03e0a058b8c50bd93f3a3caa2456aaee40c076c4aa998113e1a665fde1f6535d/plane.py @@ -0,0 +1,884 @@ +"""Geometrical Planes. + +Contains +======== +Plane + +""" + +from sympy.core import Dummy, Rational, S, Symbol +from sympy.core.symbol import _symbol +from sympy.functions.elementary.trigonometric import cos, sin, acos, asin, sqrt +from .entity import GeometryEntity +from .line import (Line, Ray, Segment, Line3D, LinearEntity, LinearEntity3D, + Ray3D, Segment3D) +from .point import Point, Point3D +from sympy.matrices import Matrix +from sympy.polys.polytools import cancel +from sympy.solvers import solve, linsolve +from sympy.utilities.iterables import uniq, is_sequence +from sympy.utilities.misc import filldedent, func_name, Undecidable + +from mpmath.libmp.libmpf import prec_to_dps + +import random + + +x, y, z, t = [Dummy('plane_dummy') for i in range(4)] + + +class Plane(GeometryEntity): + """ + A plane is a flat, two-dimensional surface. A plane is the two-dimensional + analogue of a point (zero-dimensions), a line (one-dimension) and a solid + (three-dimensions). A plane can generally be constructed by two types of + inputs. They are three non-collinear points and a point and the plane's + normal vector. + + Attributes + ========== + + p1 + normal_vector + + Examples + ======== + + >>> from sympy import Plane, Point3D + >>> Plane(Point3D(1, 1, 1), Point3D(2, 3, 4), Point3D(2, 2, 2)) + Plane(Point3D(1, 1, 1), (-1, 2, -1)) + >>> Plane((1, 1, 1), (2, 3, 4), (2, 2, 2)) + Plane(Point3D(1, 1, 1), (-1, 2, -1)) + >>> Plane(Point3D(1, 1, 1), normal_vector=(1,4,7)) + Plane(Point3D(1, 1, 1), (1, 4, 7)) + + """ + def __new__(cls, p1, a=None, b=None, **kwargs): + p1 = Point3D(p1, dim=3) + if a and b: + p2 = Point(a, dim=3) + p3 = Point(b, dim=3) + if Point3D.are_collinear(p1, p2, p3): + raise ValueError('Enter three non-collinear points') + a = p1.direction_ratio(p2) + b = p1.direction_ratio(p3) + normal_vector = tuple(Matrix(a).cross(Matrix(b))) + else: + a = kwargs.pop('normal_vector', a) + evaluate = kwargs.get('evaluate', True) + if is_sequence(a) and len(a) == 3: + normal_vector = Point3D(a).args if evaluate else a + else: + raise ValueError(filldedent(''' + Either provide 3 3D points or a point with a + normal vector expressed as a sequence of length 3''')) + if all(coord.is_zero for coord in normal_vector): + raise ValueError('Normal vector cannot be zero vector') + return GeometryEntity.__new__(cls, p1, normal_vector, **kwargs) + + def __contains__(self, o): + k = self.equation(x, y, z) + if isinstance(o, (LinearEntity, LinearEntity3D)): + d = Point3D(o.arbitrary_point(t)) + e = k.subs([(x, d.x), (y, d.y), (z, d.z)]) + return e.equals(0) + try: + o = Point(o, dim=3, strict=True) + d = k.xreplace(dict(zip((x, y, z), o.args))) + return d.equals(0) + except TypeError: + return False + + def _eval_evalf(self, prec=15, **options): + pt, tup = self.args + dps = prec_to_dps(prec) + pt = pt.evalf(n=dps, **options) + tup = tuple([i.evalf(n=dps, **options) for i in tup]) + return self.func(pt, normal_vector=tup, evaluate=False) + + def angle_between(self, o): + """Angle between the plane and other geometric entity. + + Parameters + ========== + + LinearEntity3D, Plane. + + Returns + ======= + + angle : angle in radians + + Notes + ===== + + This method accepts only 3D entities as it's parameter, but if you want + to calculate the angle between a 2D entity and a plane you should + first convert to a 3D entity by projecting onto a desired plane and + then proceed to calculate the angle. + + Examples + ======== + + >>> from sympy import Point3D, Line3D, Plane + >>> a = Plane(Point3D(1, 2, 2), normal_vector=(1, 2, 3)) + >>> b = Line3D(Point3D(1, 3, 4), Point3D(2, 2, 2)) + >>> a.angle_between(b) + -asin(sqrt(21)/6) + + """ + if isinstance(o, LinearEntity3D): + a = Matrix(self.normal_vector) + b = Matrix(o.direction_ratio) + c = a.dot(b) + d = sqrt(sum([i**2 for i in self.normal_vector])) + e = sqrt(sum([i**2 for i in o.direction_ratio])) + return asin(c/(d*e)) + if isinstance(o, Plane): + a = Matrix(self.normal_vector) + b = Matrix(o.normal_vector) + c = a.dot(b) + d = sqrt(sum([i**2 for i in self.normal_vector])) + e = sqrt(sum([i**2 for i in o.normal_vector])) + return acos(c/(d*e)) + + + def arbitrary_point(self, u=None, v=None): + """ Returns an arbitrary point on the Plane. If given two + parameters, the point ranges over the entire plane. If given 1 + or no parameters, returns a point with one parameter which, + when varying from 0 to 2*pi, moves the point in a circle of + radius 1 about p1 of the Plane. + + Examples + ======== + + >>> from sympy import Plane, Ray + >>> from sympy.abc import u, v, t, r + >>> p = Plane((1, 1, 1), normal_vector=(1, 0, 0)) + >>> p.arbitrary_point(u, v) + Point3D(1, u + 1, v + 1) + >>> p.arbitrary_point(t) + Point3D(1, cos(t) + 1, sin(t) + 1) + + While arbitrary values of u and v can move the point anywhere in + the plane, the single-parameter point can be used to construct a + ray whose arbitrary point can be located at angle t and radius + r from p.p1: + + >>> Ray(p.p1, _).arbitrary_point(r) + Point3D(1, r*cos(t) + 1, r*sin(t) + 1) + + Returns + ======= + + Point3D + + """ + circle = v is None + if circle: + u = _symbol(u or 't', real=True) + else: + u = _symbol(u or 'u', real=True) + v = _symbol(v or 'v', real=True) + x, y, z = self.normal_vector + a, b, c = self.p1.args + # x1, y1, z1 is a nonzero vector parallel to the plane + if x.is_zero and y.is_zero: + x1, y1, z1 = S.One, S.Zero, S.Zero + else: + x1, y1, z1 = -y, x, S.Zero + # x2, y2, z2 is also parallel to the plane, and orthogonal to x1, y1, z1 + x2, y2, z2 = tuple(Matrix((x, y, z)).cross(Matrix((x1, y1, z1)))) + if circle: + x1, y1, z1 = (w/sqrt(x1**2 + y1**2 + z1**2) for w in (x1, y1, z1)) + x2, y2, z2 = (w/sqrt(x2**2 + y2**2 + z2**2) for w in (x2, y2, z2)) + p = Point3D(a + x1*cos(u) + x2*sin(u), \ + b + y1*cos(u) + y2*sin(u), \ + c + z1*cos(u) + z2*sin(u)) + else: + p = Point3D(a + x1*u + x2*v, b + y1*u + y2*v, c + z1*u + z2*v) + return p + + + @staticmethod + def are_concurrent(*planes): + """Is a sequence of Planes concurrent? + + Two or more Planes are concurrent if their intersections + are a common line. + + Parameters + ========== + + planes: list + + Returns + ======= + + Boolean + + Examples + ======== + + >>> from sympy import Plane, Point3D + >>> a = Plane(Point3D(5, 0, 0), normal_vector=(1, -1, 1)) + >>> b = Plane(Point3D(0, -2, 0), normal_vector=(3, 1, 1)) + >>> c = Plane(Point3D(0, -1, 0), normal_vector=(5, -1, 9)) + >>> Plane.are_concurrent(a, b) + True + >>> Plane.are_concurrent(a, b, c) + False + + """ + planes = list(uniq(planes)) + for i in planes: + if not isinstance(i, Plane): + raise ValueError('All objects should be Planes but got %s' % i.func) + if len(planes) < 2: + return False + planes = list(planes) + first = planes.pop(0) + sol = first.intersection(planes[0]) + if sol == []: + return False + else: + line = sol[0] + for i in planes[1:]: + l = first.intersection(i) + if not l or l[0] not in line: + return False + return True + + + def distance(self, o): + """Distance between the plane and another geometric entity. + + Parameters + ========== + + Point3D, LinearEntity3D, Plane. + + Returns + ======= + + distance + + Notes + ===== + + This method accepts only 3D entities as it's parameter, but if you want + to calculate the distance between a 2D entity and a plane you should + first convert to a 3D entity by projecting onto a desired plane and + then proceed to calculate the distance. + + Examples + ======== + + >>> from sympy import Point3D, Line3D, Plane + >>> a = Plane(Point3D(1, 1, 1), normal_vector=(1, 1, 1)) + >>> b = Point3D(1, 2, 3) + >>> a.distance(b) + sqrt(3) + >>> c = Line3D(Point3D(2, 3, 1), Point3D(1, 2, 2)) + >>> a.distance(c) + 0 + + """ + if self.intersection(o) != []: + return S.Zero + + if isinstance(o, (Segment3D, Ray3D)): + a, b = o.p1, o.p2 + pi, = self.intersection(Line3D(a, b)) + if pi in o: + return self.distance(pi) + elif a in Segment3D(pi, b): + return self.distance(a) + else: + assert isinstance(o, Segment3D) is True + return self.distance(b) + + # following code handles `Point3D`, `LinearEntity3D`, `Plane` + a = o if isinstance(o, Point3D) else o.p1 + n = Point3D(self.normal_vector).unit + d = (a - self.p1).dot(n) + return abs(d) + + + def equals(self, o): + """ + Returns True if self and o are the same mathematical entities. + + Examples + ======== + + >>> from sympy import Plane, Point3D + >>> a = Plane(Point3D(1, 2, 3), normal_vector=(1, 1, 1)) + >>> b = Plane(Point3D(1, 2, 3), normal_vector=(2, 2, 2)) + >>> c = Plane(Point3D(1, 2, 3), normal_vector=(-1, 4, 6)) + >>> a.equals(a) + True + >>> a.equals(b) + True + >>> a.equals(c) + False + """ + if isinstance(o, Plane): + a = self.equation() + b = o.equation() + return cancel(a/b).is_constant() + else: + return False + + + def equation(self, x=None, y=None, z=None): + """The equation of the Plane. + + Examples + ======== + + >>> from sympy import Point3D, Plane + >>> a = Plane(Point3D(1, 1, 2), Point3D(2, 4, 7), Point3D(3, 5, 1)) + >>> a.equation() + -23*x + 11*y - 2*z + 16 + >>> a = Plane(Point3D(1, 4, 2), normal_vector=(6, 6, 6)) + >>> a.equation() + 6*x + 6*y + 6*z - 42 + + """ + x, y, z = [i if i else Symbol(j, real=True) for i, j in zip((x, y, z), 'xyz')] + a = Point3D(x, y, z) + b = self.p1.direction_ratio(a) + c = self.normal_vector + return (sum(i*j for i, j in zip(b, c))) + + + def intersection(self, o): + """ The intersection with other geometrical entity. + + Parameters + ========== + + Point, Point3D, LinearEntity, LinearEntity3D, Plane + + Returns + ======= + + List + + Examples + ======== + + >>> from sympy import Point3D, Line3D, Plane + >>> a = Plane(Point3D(1, 2, 3), normal_vector=(1, 1, 1)) + >>> b = Point3D(1, 2, 3) + >>> a.intersection(b) + [Point3D(1, 2, 3)] + >>> c = Line3D(Point3D(1, 4, 7), Point3D(2, 2, 2)) + >>> a.intersection(c) + [Point3D(2, 2, 2)] + >>> d = Plane(Point3D(6, 0, 0), normal_vector=(2, -5, 3)) + >>> e = Plane(Point3D(2, 0, 0), normal_vector=(3, 4, -3)) + >>> d.intersection(e) + [Line3D(Point3D(78/23, -24/23, 0), Point3D(147/23, 321/23, 23))] + + """ + if not isinstance(o, GeometryEntity): + o = Point(o, dim=3) + if isinstance(o, Point): + if o in self: + return [o] + else: + return [] + if isinstance(o, (LinearEntity, LinearEntity3D)): + # recast to 3D + p1, p2 = o.p1, o.p2 + if isinstance(o, Segment): + o = Segment3D(p1, p2) + elif isinstance(o, Ray): + o = Ray3D(p1, p2) + elif isinstance(o, Line): + o = Line3D(p1, p2) + else: + raise ValueError('unhandled linear entity: %s' % o.func) + if o in self: + return [o] + else: + a = Point3D(o.arbitrary_point(t)) + p1, n = self.p1, Point3D(self.normal_vector) + + # TODO: Replace solve with solveset, when this line is tested + c = solve((a - p1).dot(n), t) + if not c: + return [] + else: + c = [i for i in c if i.is_real is not False] + if len(c) > 1: + c = [i for i in c if i.is_real] + if len(c) != 1: + raise Undecidable("not sure which point is real") + p = a.subs(t, c[0]) + if p not in o: + return [] # e.g. a segment might not intersect a plane + return [p] + if isinstance(o, Plane): + if self.equals(o): + return [self] + if self.is_parallel(o): + return [] + else: + x, y, z = map(Dummy, 'xyz') + a, b = Matrix([self.normal_vector]), Matrix([o.normal_vector]) + c = list(a.cross(b)) + d = self.equation(x, y, z) + e = o.equation(x, y, z) + result = list(linsolve([d, e], x, y, z))[0] + for i in (x, y, z): result = result.subs(i, 0) + return [Line3D(Point3D(result), direction_ratio=c)] + + + def is_coplanar(self, o): + """ Returns True if `o` is coplanar with self, else False. + + Examples + ======== + + >>> from sympy import Plane + >>> o = (0, 0, 0) + >>> p = Plane(o, (1, 1, 1)) + >>> p2 = Plane(o, (2, 2, 2)) + >>> p == p2 + False + >>> p.is_coplanar(p2) + True + """ + if isinstance(o, Plane): + return not cancel(self.equation(x, y, z)/o.equation(x, y, z)).has(x, y, z) + if isinstance(o, Point3D): + return o in self + elif isinstance(o, LinearEntity3D): + return all(i in self for i in self) + elif isinstance(o, GeometryEntity): # XXX should only be handling 2D objects now + return all(i == 0 for i in self.normal_vector[:2]) + + + def is_parallel(self, l): + """Is the given geometric entity parallel to the plane? + + Parameters + ========== + + LinearEntity3D or Plane + + Returns + ======= + + Boolean + + Examples + ======== + + >>> from sympy import Plane, Point3D + >>> a = Plane(Point3D(1,4,6), normal_vector=(2, 4, 6)) + >>> b = Plane(Point3D(3,1,3), normal_vector=(4, 8, 12)) + >>> a.is_parallel(b) + True + + """ + if isinstance(l, LinearEntity3D): + a = l.direction_ratio + b = self.normal_vector + c = sum([i*j for i, j in zip(a, b)]) + if c == 0: + return True + else: + return False + elif isinstance(l, Plane): + a = Matrix(l.normal_vector) + b = Matrix(self.normal_vector) + if a.cross(b).is_zero_matrix: + return True + else: + return False + + + def is_perpendicular(self, l): + """Is the given geometric entity perpendicualar to the given plane? + + Parameters + ========== + + LinearEntity3D or Plane + + Returns + ======= + + Boolean + + Examples + ======== + + >>> from sympy import Plane, Point3D + >>> a = Plane(Point3D(1,4,6), normal_vector=(2, 4, 6)) + >>> b = Plane(Point3D(2, 2, 2), normal_vector=(-1, 2, -1)) + >>> a.is_perpendicular(b) + True + + """ + if isinstance(l, LinearEntity3D): + a = Matrix(l.direction_ratio) + b = Matrix(self.normal_vector) + if a.cross(b).is_zero_matrix: + return True + else: + return False + elif isinstance(l, Plane): + a = Matrix(l.normal_vector) + b = Matrix(self.normal_vector) + if a.dot(b) == 0: + return True + else: + return False + else: + return False + + @property + def normal_vector(self): + """Normal vector of the given plane. + + Examples + ======== + + >>> from sympy import Point3D, Plane + >>> a = Plane(Point3D(1, 1, 1), Point3D(2, 3, 4), Point3D(2, 2, 2)) + >>> a.normal_vector + (-1, 2, -1) + >>> a = Plane(Point3D(1, 1, 1), normal_vector=(1, 4, 7)) + >>> a.normal_vector + (1, 4, 7) + + """ + return self.args[1] + + @property + def p1(self): + """The only defining point of the plane. Others can be obtained from the + arbitrary_point method. + + See Also + ======== + + sympy.geometry.point.Point3D + + Examples + ======== + + >>> from sympy import Point3D, Plane + >>> a = Plane(Point3D(1, 1, 1), Point3D(2, 3, 4), Point3D(2, 2, 2)) + >>> a.p1 + Point3D(1, 1, 1) + + """ + return self.args[0] + + def parallel_plane(self, pt): + """ + Plane parallel to the given plane and passing through the point pt. + + Parameters + ========== + + pt: Point3D + + Returns + ======= + + Plane + + Examples + ======== + + >>> from sympy import Plane, Point3D + >>> a = Plane(Point3D(1, 4, 6), normal_vector=(2, 4, 6)) + >>> a.parallel_plane(Point3D(2, 3, 5)) + Plane(Point3D(2, 3, 5), (2, 4, 6)) + + """ + a = self.normal_vector + return Plane(pt, normal_vector=a) + + def perpendicular_line(self, pt): + """A line perpendicular to the given plane. + + Parameters + ========== + + pt: Point3D + + Returns + ======= + + Line3D + + Examples + ======== + + >>> from sympy import Plane, Point3D + >>> a = Plane(Point3D(1,4,6), normal_vector=(2, 4, 6)) + >>> a.perpendicular_line(Point3D(9, 8, 7)) + Line3D(Point3D(9, 8, 7), Point3D(11, 12, 13)) + + """ + a = self.normal_vector + return Line3D(pt, direction_ratio=a) + + def perpendicular_plane(self, *pts): + """ + Return a perpendicular passing through the given points. If the + direction ratio between the points is the same as the Plane's normal + vector then, to select from the infinite number of possible planes, + a third point will be chosen on the z-axis (or the y-axis + if the normal vector is already parallel to the z-axis). If less than + two points are given they will be supplied as follows: if no point is + given then pt1 will be self.p1; if a second point is not given it will + be a point through pt1 on a line parallel to the z-axis (if the normal + is not already the z-axis, otherwise on the line parallel to the + y-axis). + + Parameters + ========== + + pts: 0, 1 or 2 Point3D + + Returns + ======= + + Plane + + Examples + ======== + + >>> from sympy import Plane, Point3D + >>> a, b = Point3D(0, 0, 0), Point3D(0, 1, 0) + >>> Z = (0, 0, 1) + >>> p = Plane(a, normal_vector=Z) + >>> p.perpendicular_plane(a, b) + Plane(Point3D(0, 0, 0), (1, 0, 0)) + """ + if len(pts) > 2: + raise ValueError('No more than 2 pts should be provided.') + + pts = list(pts) + if len(pts) == 0: + pts.append(self.p1) + if len(pts) == 1: + x, y, z = self.normal_vector + if x == y == 0: + dir = (0, 1, 0) + else: + dir = (0, 0, 1) + pts.append(pts[0] + Point3D(*dir)) + + p1, p2 = [Point(i, dim=3) for i in pts] + l = Line3D(p1, p2) + n = Line3D(p1, direction_ratio=self.normal_vector) + if l in n: # XXX should an error be raised instead? + # there are infinitely many perpendicular planes; + x, y, z = self.normal_vector + if x == y == 0: + # the z axis is the normal so pick a pt on the y-axis + p3 = Point3D(0, 1, 0) # case 1 + else: + # else pick a pt on the z axis + p3 = Point3D(0, 0, 1) # case 2 + # in case that point is already given, move it a bit + if p3 in l: + p3 *= 2 # case 3 + else: + p3 = p1 + Point3D(*self.normal_vector) # case 4 + return Plane(p1, p2, p3) + + def projection_line(self, line): + """Project the given line onto the plane through the normal plane + containing the line. + + Parameters + ========== + + LinearEntity or LinearEntity3D + + Returns + ======= + + Point3D, Line3D, Ray3D or Segment3D + + Notes + ===== + + For the interaction between 2D and 3D lines(segments, rays), you should + convert the line to 3D by using this method. For example for finding the + intersection between a 2D and a 3D line, convert the 2D line to a 3D line + by projecting it on a required plane and then proceed to find the + intersection between those lines. + + Examples + ======== + + >>> from sympy import Plane, Line, Line3D, Point3D + >>> a = Plane(Point3D(1, 1, 1), normal_vector=(1, 1, 1)) + >>> b = Line(Point3D(1, 1), Point3D(2, 2)) + >>> a.projection_line(b) + Line3D(Point3D(4/3, 4/3, 1/3), Point3D(5/3, 5/3, -1/3)) + >>> c = Line3D(Point3D(1, 1, 1), Point3D(2, 2, 2)) + >>> a.projection_line(c) + Point3D(1, 1, 1) + + """ + if not isinstance(line, (LinearEntity, LinearEntity3D)): + raise NotImplementedError('Enter a linear entity only') + a, b = self.projection(line.p1), self.projection(line.p2) + if a == b: + # projection does not imply intersection so for + # this case (line parallel to plane's normal) we + # return the projection point + return a + if isinstance(line, (Line, Line3D)): + return Line3D(a, b) + if isinstance(line, (Ray, Ray3D)): + return Ray3D(a, b) + if isinstance(line, (Segment, Segment3D)): + return Segment3D(a, b) + + def projection(self, pt): + """Project the given point onto the plane along the plane normal. + + Parameters + ========== + + Point or Point3D + + Returns + ======= + + Point3D + + Examples + ======== + + >>> from sympy import Plane, Point3D + >>> A = Plane(Point3D(1, 1, 2), normal_vector=(1, 1, 1)) + + The projection is along the normal vector direction, not the z + axis, so (1, 1) does not project to (1, 1, 2) on the plane A: + + >>> b = Point3D(1, 1) + >>> A.projection(b) + Point3D(5/3, 5/3, 2/3) + >>> _ in A + True + + But the point (1, 1, 2) projects to (1, 1) on the XY-plane: + + >>> XY = Plane((0, 0, 0), (0, 0, 1)) + >>> XY.projection((1, 1, 2)) + Point3D(1, 1, 0) + """ + rv = Point(pt, dim=3) + if rv in self: + return rv + return self.intersection(Line3D(rv, rv + Point3D(self.normal_vector)))[0] + + def random_point(self, seed=None): + """ Returns a random point on the Plane. + + Returns + ======= + + Point3D + + Examples + ======== + + >>> from sympy import Plane + >>> p = Plane((1, 0, 0), normal_vector=(0, 1, 0)) + >>> r = p.random_point(seed=42) # seed value is optional + >>> r.n(3) + Point3D(2.29, 0, -1.35) + + The random point can be moved to lie on the circle of radius + 1 centered on p1: + + >>> c = p.p1 + (r - p.p1).unit + >>> c.distance(p.p1).equals(1) + True + """ + if seed is not None: + rng = random.Random(seed) + else: + rng = random + params = { + x: 2*Rational(rng.gauss(0, 1)) - 1, + y: 2*Rational(rng.gauss(0, 1)) - 1} + return self.arbitrary_point(x, y).subs(params) + + def parameter_value(self, other, u, v=None): + """Return the parameter(s) corresponding to the given point. + + Examples + ======== + + >>> from sympy import pi, Plane + >>> from sympy.abc import t, u, v + >>> p = Plane((2, 0, 0), (0, 0, 1), (0, 1, 0)) + + By default, the parameter value returned defines a point + that is a distance of 1 from the Plane's p1 value and + in line with the given point: + + >>> on_circle = p.arbitrary_point(t).subs(t, pi/4) + >>> on_circle.distance(p.p1) + 1 + >>> p.parameter_value(on_circle, t) + {t: pi/4} + + Moving the point twice as far from p1 does not change + the parameter value: + + >>> off_circle = p.p1 + (on_circle - p.p1)*2 + >>> off_circle.distance(p.p1) + 2 + >>> p.parameter_value(off_circle, t) + {t: pi/4} + + If the 2-value parameter is desired, supply the two + parameter symbols and a replacement dictionary will + be returned: + + >>> p.parameter_value(on_circle, u, v) + {u: sqrt(10)/10, v: sqrt(10)/30} + >>> p.parameter_value(off_circle, u, v) + {u: sqrt(10)/5, v: sqrt(10)/15} + """ + if not isinstance(other, GeometryEntity): + other = Point(other, dim=self.ambient_dimension) + if not isinstance(other, Point): + raise ValueError("other must be a point") + if other == self.p1: + return other + if isinstance(u, Symbol) and v is None: + delta = self.arbitrary_point(u) - self.p1 + eq = delta - (other - self.p1).unit + sol = solve(eq, u, dict=True) + elif isinstance(u, Symbol) and isinstance(v, Symbol): + pt = self.arbitrary_point(u, v) + sol = solve(pt - other, (u, v), dict=True) + else: + raise ValueError('expecting 1 or 2 symbols') + if not sol: + raise ValueError("Given point is not on %s" % func_name(self)) + return sol[0] # {t: tval} or {u: uval, v: vval} + + @property + def ambient_dimension(self): + return self.p1.ambient_dimension diff --git a/shared/package-files/03/03e19b08795b0df54f181d7474d1a71a0e03b6b2ab9516d1b2961fb834d498f3/test_get_value.py b/shared/package-files/03/03e19b08795b0df54f181d7474d1a71a0e03b6b2ab9516d1b2961fb834d498f3/test_get_value.py new file mode 100644 index 0000000000000000000000000000000000000000..65a1c64a1578ad0cadd9ed6470ab60a2087ffec5 --- /dev/null +++ b/shared/package-files/03/03e19b08795b0df54f181d7474d1a71a0e03b6b2ab9516d1b2961fb834d498f3/test_get_value.py @@ -0,0 +1,22 @@ +import pytest + +from pandas import ( + DataFrame, + MultiIndex, +) + + +class TestGetValue: + def test_get_set_value_no_partial_indexing(self): + # partial w/ MultiIndex raise exception + index = MultiIndex.from_tuples([(0, 1), (0, 2), (1, 1), (1, 2)]) + df = DataFrame(index=index, columns=range(4)) + with pytest.raises(KeyError, match=r"^0$"): + df._get_value(0, 1) + + def test_get_value(self, float_frame): + for idx in float_frame.index: + for col in float_frame.columns: + result = float_frame._get_value(idx, col) + expected = float_frame[col][idx] + assert result == expected diff --git a/shared/package-files/03/03e585aabd3c5fa66502bb6a031f7afdc20cb924ac8e18ea1cd6daf5a0434d79/full_like_native.h b/shared/package-files/03/03e585aabd3c5fa66502bb6a031f7afdc20cb924ac8e18ea1cd6daf5a0434d79/full_like_native.h new file mode 100644 index 0000000000000000000000000000000000000000..a37accf4c783dd7314208ee702259bfac0fe16a9 --- /dev/null +++ b/shared/package-files/03/03e585aabd3c5fa66502bb6a031f7afdc20cb924ac8e18ea1cd6daf5a0434d79/full_like_native.h @@ -0,0 +1,22 @@ +#pragma once + +// @generated by torchgen/gen.py from NativeFunction.h + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + + +namespace at { +namespace native { +TORCH_API at::Tensor full_like(const at::Tensor & self, const at::Scalar & fill_value, c10::optional dtype={}, c10::optional layout={}, c10::optional device={}, c10::optional pin_memory={}, c10::optional memory_format=c10::nullopt); +TORCH_API at::Tensor & full_like_out(const at::Tensor & self, const at::Scalar & fill_value, c10::optional memory_format, at::Tensor & out); +} // namespace native +} // namespace at diff --git a/shared/package-files/03/03e76e341f49b3b725a7b2fd013f43f4aaf121073fedebadb8a6571b5ee35705/tek4025-ex b/shared/package-files/03/03e76e341f49b3b725a7b2fd013f43f4aaf121073fedebadb8a6571b5ee35705/tek4025-ex new file mode 100644 index 0000000000000000000000000000000000000000..5ef6ea6f72b8d3f5543d82d679d27715a2352123 Binary files /dev/null and b/shared/package-files/03/03e76e341f49b3b725a7b2fd013f43f4aaf121073fedebadb8a6571b5ee35705/tek4025-ex differ diff --git a/shared/package-files/03/03e76e341f49b3b725a7b2fd013f43f4aaf121073fedebadb8a6571b5ee35705/tek4027-ex b/shared/package-files/03/03e76e341f49b3b725a7b2fd013f43f4aaf121073fedebadb8a6571b5ee35705/tek4027-ex new file mode 100644 index 0000000000000000000000000000000000000000..5ef6ea6f72b8d3f5543d82d679d27715a2352123 Binary files /dev/null and b/shared/package-files/03/03e76e341f49b3b725a7b2fd013f43f4aaf121073fedebadb8a6571b5ee35705/tek4027-ex differ diff --git a/shared/package-files/03/03e9a060b12fcede67d74c8c0e8a3764f3707942e9a79d9a540321a53adb39ed/lgmres.py b/shared/package-files/03/03e9a060b12fcede67d74c8c0e8a3764f3707942e9a79d9a540321a53adb39ed/lgmres.py new file mode 100644 index 0000000000000000000000000000000000000000..ce368e81a07f282d091cd9bc8281c98e720a206b --- /dev/null +++ b/shared/package-files/03/03e9a060b12fcede67d74c8c0e8a3764f3707942e9a79d9a540321a53adb39ed/lgmres.py @@ -0,0 +1,230 @@ +# Copyright (C) 2009, Pauli Virtanen +# Distributed under the same license as SciPy. + +import numpy as np +from numpy.linalg import LinAlgError +from scipy.linalg import get_blas_funcs +from .iterative import _get_atol_rtol +from .utils import make_system + +from ._gcrotmk import _fgmres + +__all__ = ['lgmres'] + + +def lgmres(A, b, x0=None, *, rtol=1e-5, atol=0., maxiter=1000, M=None, callback=None, + inner_m=30, outer_k=3, outer_v=None, store_outer_Av=True, + prepend_outer_v=False): + """ + Solve a matrix equation using the LGMRES algorithm. + + The LGMRES algorithm [1]_ [2]_ is designed to avoid some problems + in the convergence in restarted GMRES, and often converges in fewer + iterations. + + Parameters + ---------- + A : {sparse array, ndarray, LinearOperator} + The real or complex N-by-N matrix of the linear system. + Alternatively, ``A`` can be a linear operator which can + produce ``Ax`` using, e.g., + ``scipy.sparse.linalg.LinearOperator``. + b : ndarray + Right hand side of the linear system. Has shape (N,) or (N,1). + x0 : ndarray + Starting guess for the solution. + rtol, atol : float, optional + Parameters for the convergence test. For convergence, + ``norm(b - A @ x) <= max(rtol*norm(b), atol)`` should be satisfied. + The default is ``rtol=1e-5``, the default for ``atol`` is ``0.0``. + maxiter : int, optional + Maximum number of iterations. Iteration will stop after maxiter + steps even if the specified tolerance has not been achieved. + M : {sparse array, ndarray, LinearOperator}, optional + Preconditioner for A. The preconditioner should approximate the + inverse of A. Effective preconditioning dramatically improves the + rate of convergence, which implies that fewer iterations are needed + to reach a given error tolerance. + callback : function, optional + User-supplied function to call after each iteration. It is called + as callback(xk), where xk is the current solution vector. + inner_m : int, optional + Number of inner GMRES iterations per each outer iteration. + outer_k : int, optional + Number of vectors to carry between inner GMRES iterations. + According to [1]_, good values are in the range of 1...3. + However, note that if you want to use the additional vectors to + accelerate solving multiple similar problems, larger values may + be beneficial. + outer_v : list of tuples, optional + List containing tuples ``(v, Av)`` of vectors and corresponding + matrix-vector products, used to augment the Krylov subspace, and + carried between inner GMRES iterations. The element ``Av`` can + be `None` if the matrix-vector product should be re-evaluated. + This parameter is modified in-place by `lgmres`, and can be used + to pass "guess" vectors in and out of the algorithm when solving + similar problems. + store_outer_Av : bool, optional + Whether LGMRES should store also A@v in addition to vectors `v` + in the `outer_v` list. Default is True. + prepend_outer_v : bool, optional + Whether to put outer_v augmentation vectors before Krylov iterates. + In standard LGMRES, prepend_outer_v=False. + + Returns + ------- + x : ndarray + The converged solution. + info : int + Provides convergence information: + + - 0 : successful exit + - >0 : convergence to tolerance not achieved, number of iterations + - <0 : illegal input or breakdown + + Notes + ----- + The LGMRES algorithm [1]_ [2]_ is designed to avoid the + slowing of convergence in restarted GMRES, due to alternating + residual vectors. Typically, it often outperforms GMRES(m) of + comparable memory requirements by some measure, or at least is not + much worse. + + Another advantage in this algorithm is that you can supply it with + 'guess' vectors in the `outer_v` argument that augment the Krylov + subspace. If the solution lies close to the span of these vectors, + the algorithm converges faster. This can be useful if several very + similar matrices need to be inverted one after another, such as in + Newton-Krylov iteration where the Jacobian matrix often changes + little in the nonlinear steps. + + References + ---------- + .. [1] A.H. Baker and E.R. Jessup and T. Manteuffel, "A Technique for + Accelerating the Convergence of Restarted GMRES", SIAM J. Matrix + Anal. Appl. 26, 962 (2005). + .. [2] A.H. Baker, "On Improving the Performance of the Linear Solver + restarted GMRES", PhD thesis, University of Colorado (2003). + + Examples + -------- + >>> import numpy as np + >>> from scipy.sparse import csc_array + >>> from scipy.sparse.linalg import lgmres + >>> A = csc_array([[3, 2, 0], [1, -1, 0], [0, 5, 1]], dtype=float) + >>> b = np.array([2, 4, -1], dtype=float) + >>> x, exitCode = lgmres(A, b, atol=1e-5) + >>> print(exitCode) # 0 indicates successful convergence + 0 + >>> np.allclose(A.dot(x), b) + True + """ + A,M,x,b,postprocess = make_system(A,M,x0,b) + + if not np.isfinite(b).all(): + raise ValueError("RHS must contain only finite numbers") + + matvec = A.matvec + psolve = M.matvec + + if outer_v is None: + outer_v = [] + + axpy, dot, scal = None, None, None + nrm2 = get_blas_funcs('nrm2', [b]) + + b_norm = nrm2(b) + + # we call this to get the right atol/rtol and raise errors as necessary + atol, rtol = _get_atol_rtol('lgmres', b_norm, atol, rtol) + + if b_norm == 0: + x = b + return (postprocess(x), 0) + + ptol_max_factor = 1.0 + + for k_outer in range(maxiter): + r_outer = matvec(x) - b + + # -- callback + if callback is not None: + callback(x) + + # -- determine input type routines + if axpy is None: + if np.iscomplexobj(r_outer) and not np.iscomplexobj(x): + x = x.astype(r_outer.dtype) + axpy, dot, scal, nrm2 = get_blas_funcs(['axpy', 'dot', 'scal', 'nrm2'], + (x, r_outer)) + + # -- check stopping condition + r_norm = nrm2(r_outer) + if r_norm <= max(atol, rtol * b_norm): + break + + # -- inner LGMRES iteration + v0 = -psolve(r_outer) + inner_res_0 = nrm2(v0) + + if inner_res_0 == 0: + rnorm = nrm2(r_outer) + raise RuntimeError("Preconditioner returned a zero vector; " + f"|v| ~ {rnorm:.1g}, |M v| = 0") + + v0 = scal(1.0/inner_res_0, v0) + + ptol = min(ptol_max_factor, max(atol, rtol*b_norm)/r_norm) + + try: + Q, R, B, vs, zs, y, pres = _fgmres(matvec, + v0, + inner_m, + lpsolve=psolve, + atol=ptol, + outer_v=outer_v, + prepend_outer_v=prepend_outer_v) + y *= inner_res_0 + if not np.isfinite(y).all(): + # Overflow etc. in computation. There's no way to + # recover from this, so we have to bail out. + raise LinAlgError() + except LinAlgError: + # Floating point over/underflow, non-finite result from + # matmul etc. -- report failure. + return postprocess(x), k_outer + 1 + + # Inner loop tolerance control + if pres > ptol: + ptol_max_factor = min(1.0, 1.5 * ptol_max_factor) + else: + ptol_max_factor = max(1e-16, 0.25 * ptol_max_factor) + + # -- GMRES terminated: eval solution + dx = zs[0]*y[0] + for w, yc in zip(zs[1:], y[1:]): + dx = axpy(w, dx, dx.shape[0], yc) # dx += w*yc + + # -- Store LGMRES augmentation vectors + nx = nrm2(dx) + if nx > 0: + if store_outer_Av: + q = Q.dot(R.dot(y)) + ax = vs[0]*q[0] + for v, qc in zip(vs[1:], q[1:]): + ax = axpy(v, ax, ax.shape[0], qc) + outer_v.append((dx/nx, ax/nx)) + else: + outer_v.append((dx/nx, None)) + + # -- Retain only a finite number of augmentation vectors + while len(outer_v) > outer_k: + del outer_v[0] + + # -- Apply step + x += dx + else: + # didn't converge ... + return postprocess(x), maxiter + + return postprocess(x), 0 diff --git a/shared/package-files/03/03ea4ddd5d7686722a95b15802b61b754fbdf7646027e7eab1a29b334caf60fc/test_assert_produces_warning.py b/shared/package-files/03/03ea4ddd5d7686722a95b15802b61b754fbdf7646027e7eab1a29b334caf60fc/test_assert_produces_warning.py new file mode 100644 index 0000000000000000000000000000000000000000..5c27a3ee79d4a82bce83eec56ab9d88e10dc06cd --- /dev/null +++ b/shared/package-files/03/03ea4ddd5d7686722a95b15802b61b754fbdf7646027e7eab1a29b334caf60fc/test_assert_produces_warning.py @@ -0,0 +1,241 @@ +"""" +Test module for testing ``pandas._testing.assert_produces_warning``. +""" +import warnings + +import pytest + +from pandas.errors import ( + DtypeWarning, + PerformanceWarning, +) + +import pandas._testing as tm + + +@pytest.fixture( + params=[ + RuntimeWarning, + ResourceWarning, + UserWarning, + FutureWarning, + DeprecationWarning, + PerformanceWarning, + DtypeWarning, + ], +) +def category(request): + """ + Return unique warning. + + Useful for testing behavior of tm.assert_produces_warning with various categories. + """ + return request.param + + +@pytest.fixture( + params=[ + (RuntimeWarning, UserWarning), + (UserWarning, FutureWarning), + (FutureWarning, RuntimeWarning), + (DeprecationWarning, PerformanceWarning), + (PerformanceWarning, FutureWarning), + (DtypeWarning, DeprecationWarning), + (ResourceWarning, DeprecationWarning), + (FutureWarning, DeprecationWarning), + ], + ids=lambda x: type(x).__name__, +) +def pair_different_warnings(request): + """ + Return pair or different warnings. + + Useful for testing how several different warnings are handled + in tm.assert_produces_warning. + """ + return request.param + + +def f(): + warnings.warn("f1", FutureWarning) + warnings.warn("f2", RuntimeWarning) + + +@pytest.mark.filterwarnings("ignore:f1:FutureWarning") +def test_assert_produces_warning_honors_filter(): + # Raise by default. + msg = r"Caused unexpected warning\(s\)" + with pytest.raises(AssertionError, match=msg): + with tm.assert_produces_warning(RuntimeWarning): + f() + + with tm.assert_produces_warning(RuntimeWarning, raise_on_extra_warnings=False): + f() + + +@pytest.mark.parametrize( + "message, match", + [ + ("", None), + ("", ""), + ("Warning message", r".*"), + ("Warning message", "War"), + ("Warning message", r"[Ww]arning"), + ("Warning message", "age"), + ("Warning message", r"age$"), + ("Message 12-234 with numbers", r"\d{2}-\d{3}"), + ("Message 12-234 with numbers", r"^Mes.*\d{2}-\d{3}"), + ("Message 12-234 with numbers", r"\d{2}-\d{3}\s\S+"), + ("Message, which we do not match", None), + ], +) +def test_catch_warning_category_and_match(category, message, match): + with tm.assert_produces_warning(category, match=match): + warnings.warn(message, category) + + +def test_fail_to_match_runtime_warning(): + category = RuntimeWarning + match = "Did not see this warning" + unmatched = ( + r"Did not see warning 'RuntimeWarning' matching 'Did not see this warning'. " + r"The emitted warning messages are " + r"\[RuntimeWarning\('This is not a match.'\), " + r"RuntimeWarning\('Another unmatched warning.'\)\]" + ) + with pytest.raises(AssertionError, match=unmatched): + with tm.assert_produces_warning(category, match=match): + warnings.warn("This is not a match.", category) + warnings.warn("Another unmatched warning.", category) + + +def test_fail_to_match_future_warning(): + category = FutureWarning + match = "Warning" + unmatched = ( + r"Did not see warning 'FutureWarning' matching 'Warning'. " + r"The emitted warning messages are " + r"\[FutureWarning\('This is not a match.'\), " + r"FutureWarning\('Another unmatched warning.'\)\]" + ) + with pytest.raises(AssertionError, match=unmatched): + with tm.assert_produces_warning(category, match=match): + warnings.warn("This is not a match.", category) + warnings.warn("Another unmatched warning.", category) + + +def test_fail_to_match_resource_warning(): + category = ResourceWarning + match = r"\d+" + unmatched = ( + r"Did not see warning 'ResourceWarning' matching '\\d\+'. " + r"The emitted warning messages are " + r"\[ResourceWarning\('This is not a match.'\), " + r"ResourceWarning\('Another unmatched warning.'\)\]" + ) + with pytest.raises(AssertionError, match=unmatched): + with tm.assert_produces_warning(category, match=match): + warnings.warn("This is not a match.", category) + warnings.warn("Another unmatched warning.", category) + + +def test_fail_to_catch_actual_warning(pair_different_warnings): + expected_category, actual_category = pair_different_warnings + match = "Did not see expected warning of class" + with pytest.raises(AssertionError, match=match): + with tm.assert_produces_warning(expected_category): + warnings.warn("warning message", actual_category) + + +def test_ignore_extra_warning(pair_different_warnings): + expected_category, extra_category = pair_different_warnings + with tm.assert_produces_warning(expected_category, raise_on_extra_warnings=False): + warnings.warn("Expected warning", expected_category) + warnings.warn("Unexpected warning OK", extra_category) + + +def test_raise_on_extra_warning(pair_different_warnings): + expected_category, extra_category = pair_different_warnings + match = r"Caused unexpected warning\(s\)" + with pytest.raises(AssertionError, match=match): + with tm.assert_produces_warning(expected_category): + warnings.warn("Expected warning", expected_category) + warnings.warn("Unexpected warning NOT OK", extra_category) + + +def test_same_category_different_messages_first_match(): + category = UserWarning + with tm.assert_produces_warning(category, match=r"^Match this"): + warnings.warn("Match this", category) + warnings.warn("Do not match that", category) + warnings.warn("Do not match that either", category) + + +def test_same_category_different_messages_last_match(): + category = DeprecationWarning + with tm.assert_produces_warning(category, match=r"^Match this"): + warnings.warn("Do not match that", category) + warnings.warn("Do not match that either", category) + warnings.warn("Match this", category) + + +def test_match_multiple_warnings(): + # https://github.com/pandas-dev/pandas/issues/47829 + category = (FutureWarning, UserWarning) + with tm.assert_produces_warning(category, match=r"^Match this"): + warnings.warn("Match this", FutureWarning) + warnings.warn("Match this too", UserWarning) + + +def test_right_category_wrong_match_raises(pair_different_warnings): + target_category, other_category = pair_different_warnings + with pytest.raises(AssertionError, match="Did not see warning.*matching"): + with tm.assert_produces_warning(target_category, match=r"^Match this"): + warnings.warn("Do not match it", target_category) + warnings.warn("Match this", other_category) + + +@pytest.mark.parametrize("false_or_none", [False, None]) +class TestFalseOrNoneExpectedWarning: + def test_raise_on_warning(self, false_or_none): + msg = r"Caused unexpected warning\(s\)" + with pytest.raises(AssertionError, match=msg): + with tm.assert_produces_warning(false_or_none): + f() + + def test_no_raise_without_warning(self, false_or_none): + with tm.assert_produces_warning(false_or_none): + pass + + def test_no_raise_with_false_raise_on_extra(self, false_or_none): + with tm.assert_produces_warning(false_or_none, raise_on_extra_warnings=False): + f() + + +def test_raises_during_exception(): + msg = "Did not see expected warning of class 'UserWarning'" + with pytest.raises(AssertionError, match=msg): + with tm.assert_produces_warning(UserWarning): + raise ValueError + + with pytest.raises(AssertionError, match=msg): + with tm.assert_produces_warning(UserWarning): + warnings.warn("FutureWarning", FutureWarning) + raise IndexError + + msg = "Caused unexpected warning" + with pytest.raises(AssertionError, match=msg): + with tm.assert_produces_warning(None): + warnings.warn("FutureWarning", FutureWarning) + raise SystemError + + +def test_passes_during_exception(): + with pytest.raises(SyntaxError, match="Error"): + with tm.assert_produces_warning(None): + raise SyntaxError("Error") + + with pytest.raises(ValueError, match="Error"): + with tm.assert_produces_warning(FutureWarning, match="FutureWarning"): + warnings.warn("FutureWarning", FutureWarning) + raise ValueError("Error") diff --git a/shared/package-files/03/03ec3616f8d77bdf7fd4e6b7f3f0fbada0c69972de9a9b2e8fab001544f115d7/to_dense_compositeimplicitautograd_dispatch.h b/shared/package-files/03/03ec3616f8d77bdf7fd4e6b7f3f0fbada0c69972de9a9b2e8fab001544f115d7/to_dense_compositeimplicitautograd_dispatch.h new file mode 100644 index 0000000000000000000000000000000000000000..2a8b6c520c112cb72250a4083478d31a9fe6b574 --- /dev/null +++ b/shared/package-files/03/03ec3616f8d77bdf7fd4e6b7f3f0fbada0c69972de9a9b2e8fab001544f115d7/to_dense_compositeimplicitautograd_dispatch.h @@ -0,0 +1,23 @@ +#pragma once +// @generated by torchgen/gen.py from DispatchKeyFunction.h + +// NB: The implementing C++ file is RegisterDispatchKey.cpp + +// The only #includes we need are for custom classes that have defaults in the C++ API +#include +#include +#include + +// Forward declarations of any types needed in the operator signatures. +// We can't directly include these classes because it will cause circular include dependencies. +// This file is included by TensorBody.h, which defines the Tensor class. +#include + +namespace at { + +namespace compositeimplicitautograd { + +TORCH_API at::Tensor to_dense(const at::Tensor & self, c10::optional dtype=c10::nullopt, c10::optional masked_grad=c10::nullopt); + +} // namespace compositeimplicitautograd +} // namespace at diff --git a/shared/package-files/03/03f49eb9dd0846bda03d8973e8931065adec06144bdceee056a848bcc451bc4d/__init__.py b/shared/package-files/03/03f49eb9dd0846bda03d8973e8931065adec06144bdceee056a848bcc451bc4d/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..968c4caa0d4562b71285f414bfb70f43d0b35111 --- /dev/null +++ b/shared/package-files/03/03f49eb9dd0846bda03d8973e8931065adec06144bdceee056a848bcc451bc4d/__init__.py @@ -0,0 +1,20 @@ +"""This module contains functions which operate on discrete sequences. + +Transforms - ``fft``, ``ifft``, ``ntt``, ``intt``, ``fwht``, ``ifwht``, + ``mobius_transform``, ``inverse_mobius_transform`` + +Convolutions - ``convolution``, ``convolution_fft``, ``convolution_ntt``, + ``convolution_fwht``, ``convolution_subset``, + ``covering_product``, ``intersecting_product`` +""" + +from .transforms import (fft, ifft, ntt, intt, fwht, ifwht, + mobius_transform, inverse_mobius_transform) +from .convolutions import convolution, covering_product, intersecting_product + +__all__ = [ + 'fft', 'ifft', 'ntt', 'intt', 'fwht', 'ifwht', 'mobius_transform', + 'inverse_mobius_transform', + + 'convolution', 'covering_product', 'intersecting_product', +] diff --git a/shared/package-files/03/03f5c72689a0d3a6a63baf3d01623e6e09f7a7bf92e440b33b0c8c6b50ffd654/dt-100w 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b/shared/package-files/03/03f8ee09465cc618849cbe4ccf4a228cfa61291449fba42118cdea3aa8608b04/distribution.py new file mode 100644 index 0000000000000000000000000000000000000000..9278ca6eeaeb68e03869f6a93d7aa358e0e027e4 --- /dev/null +++ b/shared/package-files/03/03f8ee09465cc618849cbe4ccf4a228cfa61291449fba42118cdea3aa8608b04/distribution.py @@ -0,0 +1,162 @@ +import io +from email.parser import Parser + +def _must_decode(value): + if type(value) is bytes: + try: + return value.decode('utf-8') + except UnicodeDecodeError: + return value.decode('latin1') + return value + +must_decode = _must_decode # deprecated compatibility alias FBO twine. + + +def parse(fp): + return Parser().parse(fp) +def get(msg, header): + return _collapse_leading_ws(header, msg.get(header)) +def get_all(msg, header): + return [_collapse_leading_ws(header, x) for x in msg.get_all(header)] + +def _collapse_leading_ws(header, txt): + """ + ``Description`` header must preserve newlines; all others need not + """ + if header.lower() == 'description': # preserve newlines + return '\n'.join([x[8:] if x.startswith(' ' * 8) else x + for x in txt.strip().splitlines()]) + else: + return ' '.join([x.strip() for x in txt.splitlines()]) + + +HEADER_ATTRS_1_0 = ( # PEP 241 + ('Metadata-Version', 'metadata_version', False), + ('Name', 'name', False), + ('Version', 'version', False), + ('Platform', 'platforms', True), + ('Supported-Platform', 'supported_platforms', True), + ('Summary', 'summary', False), + ('Description', 'description', False), + ('Keywords', 'keywords', False), + ('Home-Page', 'home_page', False), + ('Author', 'author', False), + ('Author-email', 'author_email', False), + ('License', 'license', False), +) + +HEADER_ATTRS_1_1 = HEADER_ATTRS_1_0 + ( # PEP 314 + ('Classifier', 'classifiers', True), + ('Download-URL', 'download_url', False), + ('Requires', 'requires', True), + ('Provides', 'provides', True), + ('Obsoletes', 'obsoletes', True), +) + +HEADER_ATTRS_1_2 = HEADER_ATTRS_1_1 + ( # PEP 345 + ('Maintainer', 'maintainer', False), + ('Maintainer-email', 'maintainer_email', False), + ('Requires-Python', 'requires_python', False), + ('Requires-External', 'requires_external', True), + ('Requires-Dist', 'requires_dist', True), + ('Provides-Dist', 'provides_dist', True), + ('Obsoletes-Dist', 'obsoletes_dist', True), + ('Project-URL', 'project_urls', True), +) + +HEADER_ATTRS_2_0 = HEADER_ATTRS_1_2 #XXX PEP 426? + +HEADER_ATTRS_2_1 = HEADER_ATTRS_1_2 + ( # PEP 566 + ('Provides-Extra', 'provides_extras', True), + ('Description-Content-Type', 'description_content_type', False) +) + +HEADER_ATTRS_2_2 = HEADER_ATTRS_2_1 + ( # PEP 643 + ('Dynamic', 'dynamic', True), +) + +HEADER_ATTRS = { + '1.0': HEADER_ATTRS_1_0, + '1.1': HEADER_ATTRS_1_1, + '1.2': HEADER_ATTRS_1_2, + '2.0': HEADER_ATTRS_2_0, + '2.1': HEADER_ATTRS_2_1, + '2.2': HEADER_ATTRS_2_2, +} + +class Distribution(object): + metadata_version = None + # version 1.0 + name = None + version = None + platforms = () + supported_platforms = () + summary = None + description = None + keywords = None + home_page = None + download_url = None + author = None + author_email = None + license = None + # version 1.1 + classifiers = () + requires = () + provides = () + obsoletes = () + # version 1.2 + maintainer = None + maintainer_email = None + requires_python = None + requires_external = () + requires_dist = () + provides_dist = () + obsoletes_dist = () + project_urls = () + # version 2.1 + provides_extras = () + description_content_type = None + # version 2.2 + dynamic = () + + def extractMetadata(self): + data = self.read() + self.parse(data) + + def read(self): + raise NotImplementedError + + def _getHeaderAttrs(self): + return HEADER_ATTRS.get(self.metadata_version, []) + + def parse(self, data): + fp = io.StringIO(_must_decode(data)) + msg = parse(fp) + + if 'Metadata-Version' in msg and self.metadata_version is None: + value = get(msg, 'Metadata-Version') + metadata_version = self.metadata_version = value + + for header_name, attr_name, multiple in self._getHeaderAttrs(): + + if attr_name == 'metadata_version': + continue + + if header_name in msg: + if multiple: + values = get_all(msg, header_name) + setattr(self, attr_name, values) + else: + value = get(msg, header_name) + if value != 'UNKNOWN': + setattr(self, attr_name, value) + + body = msg.get_payload() + if body: + setattr(self, 'description', body) + + def __iter__(self): + for header_name, attr_name, multiple in self._getHeaderAttrs(): + yield attr_name + + iterkeys = __iter__ diff --git a/shared/package-files/03/03fc575a47d2d6c402c4fe0e35ecde119be52845f06754cf1b6ac208576604d3/array_float32_1d.sav b/shared/package-files/03/03fc575a47d2d6c402c4fe0e35ecde119be52845f06754cf1b6ac208576604d3/array_float32_1d.sav new file mode 100644 index 0000000000000000000000000000000000000000..619a1259670a361ac76ffa86c481a813dbaec07a Binary files /dev/null and b/shared/package-files/03/03fc575a47d2d6c402c4fe0e35ecde119be52845f06754cf1b6ac208576604d3/array_float32_1d.sav differ diff --git a/shared/package-files/03/03ff657678445c30725992fbede4eaf897722e717f2802746f2ee38f353bd06e/markup.py b/shared/package-files/03/03ff657678445c30725992fbede4eaf897722e717f2802746f2ee38f353bd06e/markup.py new file mode 100644 index 0000000000000000000000000000000000000000..727ba84ad86e5eb13fd0828e3a1a212ebeebf2d6 --- /dev/null +++ b/shared/package-files/03/03ff657678445c30725992fbede4eaf897722e717f2802746f2ee38f353bd06e/markup.py @@ -0,0 +1,1503 @@ +""" + pygments.lexers.markup + ~~~~~~~~~~~~~~~~~~~~~~ + + Lexers for non-HTML markup languages. + + :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" + +import re + +from pygments.lexers.html import XmlLexer +from pygments.lexers.javascript import JavascriptLexer +from pygments.lexers.css import CssLexer +from pygments.lexers.lilypond import LilyPondLexer +from pygments.lexers.data import JsonLexer + +from pygments.lexer import RegexLexer, DelegatingLexer, include, bygroups, \ + using, this, do_insertions, default, words +from pygments.token import Text, Comment, Operator, Keyword, Name, String, \ + Number, Punctuation, Generic, Other, Whitespace +from pygments.util import get_bool_opt, ClassNotFound + +__all__ = ['BBCodeLexer', 'MoinWikiLexer', 'RstLexer', 'TexLexer', 'GroffLexer', + 'MozPreprocHashLexer', 'MozPreprocPercentLexer', + 'MozPreprocXulLexer', 'MozPreprocJavascriptLexer', + 'MozPreprocCssLexer', 'MarkdownLexer', 'TiddlyWiki5Lexer', 'WikitextLexer'] + + +class BBCodeLexer(RegexLexer): + """ + A lexer that highlights BBCode(-like) syntax. + + .. versionadded:: 0.6 + """ + + name = 'BBCode' + aliases = ['bbcode'] + mimetypes = ['text/x-bbcode'] + + tokens = { + 'root': [ + (r'[^[]+', Text), + # tag/end tag begin + (r'\[/?\w+', Keyword, 'tag'), + # stray bracket + (r'\[', Text), + ], + 'tag': [ + (r'\s+', Text), + # attribute with value + (r'(\w+)(=)("?[^\s"\]]+"?)', + bygroups(Name.Attribute, Operator, String)), + # tag argument (a la [color=green]) + (r'(=)("?[^\s"\]]+"?)', + bygroups(Operator, String)), + # tag end + (r'\]', Keyword, '#pop'), + ], + } + + +class MoinWikiLexer(RegexLexer): + """ + For MoinMoin (and Trac) Wiki markup. + + .. versionadded:: 0.7 + """ + + name = 'MoinMoin/Trac Wiki markup' + aliases = ['trac-wiki', 'moin'] + filenames = [] + mimetypes = ['text/x-trac-wiki'] + flags = re.MULTILINE | re.IGNORECASE + + tokens = { + 'root': [ + (r'^#.*$', Comment), + (r'(!)(\S+)', bygroups(Keyword, Text)), # Ignore-next + # Titles + (r'^(=+)([^=]+)(=+)(\s*#.+)?$', + bygroups(Generic.Heading, using(this), Generic.Heading, String)), + # Literal code blocks, with optional shebang + (r'(\{\{\{)(\n#!.+)?', bygroups(Name.Builtin, Name.Namespace), 'codeblock'), + (r'(\'\'\'?|\|\||`|__|~~|\^|,,|::)', Comment), # Formatting + # Lists + (r'^( +)([.*-])( )', bygroups(Text, Name.Builtin, Text)), + (r'^( +)([a-z]{1,5}\.)( )', bygroups(Text, Name.Builtin, Text)), + # Other Formatting + (r'\[\[\w+.*?\]\]', Keyword), # Macro + (r'(\[[^\s\]]+)(\s+[^\]]+?)?(\])', + bygroups(Keyword, String, Keyword)), # Link + (r'^----+$', Keyword), # Horizontal rules + (r'[^\n\'\[{!_~^,|]+', Text), + (r'\n', Text), + (r'.', Text), + ], + 'codeblock': [ + (r'\}\}\}', Name.Builtin, '#pop'), + # these blocks are allowed to be nested in Trac, but not MoinMoin + (r'\{\{\{', Text, '#push'), + (r'[^{}]+', Comment.Preproc), # slurp boring text + (r'.', Comment.Preproc), # allow loose { or } + ], + } + + +class RstLexer(RegexLexer): + """ + For reStructuredText markup. + + .. versionadded:: 0.7 + + Additional options accepted: + + `handlecodeblocks` + Highlight the contents of ``.. sourcecode:: language``, + ``.. code:: language`` and ``.. code-block:: language`` + directives with a lexer for the given language (default: + ``True``). + + .. versionadded:: 0.8 + """ + name = 'reStructuredText' + url = 'https://docutils.sourceforge.io/rst.html' + aliases = ['restructuredtext', 'rst', 'rest'] + filenames = ['*.rst', '*.rest'] + mimetypes = ["text/x-rst", "text/prs.fallenstein.rst"] + flags = re.MULTILINE + + def _handle_sourcecode(self, match): + from pygments.lexers import get_lexer_by_name + + # section header + yield match.start(1), Punctuation, match.group(1) + yield match.start(2), Text, match.group(2) + yield match.start(3), Operator.Word, match.group(3) + yield match.start(4), Punctuation, match.group(4) + yield match.start(5), Text, match.group(5) + yield match.start(6), Keyword, match.group(6) + yield match.start(7), Text, match.group(7) + + # lookup lexer if wanted and existing + lexer = None + if self.handlecodeblocks: + try: + lexer = get_lexer_by_name(match.group(6).strip()) + except ClassNotFound: + pass + indention = match.group(8) + indention_size = len(indention) + code = (indention + match.group(9) + match.group(10) + match.group(11)) + + # no lexer for this language. handle it like it was a code block + if lexer is None: + yield match.start(8), String, code + return + + # highlight the lines with the lexer. + ins = [] + codelines = code.splitlines(True) + code = '' + for line in codelines: + if len(line) > indention_size: + ins.append((len(code), [(0, Text, line[:indention_size])])) + code += line[indention_size:] + else: + code += line + yield from do_insertions(ins, lexer.get_tokens_unprocessed(code)) + + # from docutils.parsers.rst.states + closers = '\'")]}>\u2019\u201d\xbb!?' + unicode_delimiters = '\u2010\u2011\u2012\u2013\u2014\u00a0' + end_string_suffix = (r'((?=$)|(?=[-/:.,; \n\x00%s%s]))' + % (re.escape(unicode_delimiters), + re.escape(closers))) + + tokens = { + 'root': [ + # Heading with overline + (r'^(=+|-+|`+|:+|\.+|\'+|"+|~+|\^+|_+|\*+|\++|#+)([ \t]*\n)' + r'(.+)(\n)(\1)(\n)', + bygroups(Generic.Heading, Text, Generic.Heading, + Text, Generic.Heading, Text)), + # Plain heading + (r'^(\S.*)(\n)(={3,}|-{3,}|`{3,}|:{3,}|\.{3,}|\'{3,}|"{3,}|' + r'~{3,}|\^{3,}|_{3,}|\*{3,}|\+{3,}|#{3,})(\n)', + bygroups(Generic.Heading, Text, Generic.Heading, Text)), + # Bulleted lists + (r'^(\s*)([-*+])( .+\n(?:\1 .+\n)*)', + bygroups(Text, Number, using(this, state='inline'))), + # Numbered lists + (r'^(\s*)([0-9#ivxlcmIVXLCM]+\.)( .+\n(?:\1 .+\n)*)', + bygroups(Text, Number, using(this, state='inline'))), + (r'^(\s*)(\(?[0-9#ivxlcmIVXLCM]+\))( .+\n(?:\1 .+\n)*)', + bygroups(Text, Number, using(this, state='inline'))), + # Numbered, but keep words at BOL from becoming lists + (r'^(\s*)([A-Z]+\.)( .+\n(?:\1 .+\n)+)', + bygroups(Text, Number, using(this, state='inline'))), + (r'^(\s*)(\(?[A-Za-z]+\))( .+\n(?:\1 .+\n)+)', + bygroups(Text, Number, using(this, state='inline'))), + # Line blocks + (r'^(\s*)(\|)( .+\n(?:\| .+\n)*)', + bygroups(Text, Operator, using(this, state='inline'))), + # Sourcecode directives + (r'^( *\.\.)(\s*)((?:source)?code(?:-block)?)(::)([ \t]*)([^\n]+)' + r'(\n[ \t]*\n)([ \t]+)(.*)(\n)((?:(?:\8.*)?\n)+)', + _handle_sourcecode), + # A directive + (r'^( *\.\.)(\s*)([\w:-]+?)(::)(?:([ \t]*)(.*))', + bygroups(Punctuation, Text, Operator.Word, Punctuation, Text, + using(this, state='inline'))), + # A reference target + (r'^( *\.\.)(\s*)(_(?:[^:\\]|\\.)+:)(.*?)$', + bygroups(Punctuation, Text, Name.Tag, using(this, state='inline'))), + # A footnote/citation target + (r'^( *\.\.)(\s*)(\[.+\])(.*?)$', + bygroups(Punctuation, Text, Name.Tag, using(this, state='inline'))), + # A substitution def + (r'^( *\.\.)(\s*)(\|.+\|)(\s*)([\w:-]+?)(::)(?:([ \t]*)(.*))', + bygroups(Punctuation, Text, Name.Tag, Text, Operator.Word, + Punctuation, Text, using(this, state='inline'))), + # Comments + (r'^ *\.\..*(\n( +.*\n|\n)+)?', Comment.Preproc), + # Field list marker + (r'^( *)(:(?:\\\\|\\:|[^:\n])+:(?=\s))([ \t]*)', + bygroups(Text, Name.Class, Text)), + # Definition list + (r'^(\S.*(?)(`__?)', # reference with inline target + bygroups(String, String.Interpol, String)), + (r'`.+?`__?', String), # reference + (r'(`.+?`)(:[a-zA-Z0-9:-]+?:)?', + bygroups(Name.Variable, Name.Attribute)), # role + (r'(:[a-zA-Z0-9:-]+?:)(`.+?`)', + bygroups(Name.Attribute, Name.Variable)), # role (content first) + (r'\*\*.+?\*\*', Generic.Strong), # Strong emphasis + (r'\*.+?\*', Generic.Emph), # Emphasis + (r'\[.*?\]_', String), # Footnote or citation + (r'<.+?>', Name.Tag), # Hyperlink + (r'[^\\\n\[*`:]+', Text), + (r'.', Text), + ], + 'literal': [ + (r'[^`]+', String), + (r'``' + end_string_suffix, String, '#pop'), + (r'`', String), + ] + } + + def __init__(self, **options): + self.handlecodeblocks = get_bool_opt(options, 'handlecodeblocks', True) + RegexLexer.__init__(self, **options) + + def analyse_text(text): + if text[:2] == '..' and text[2:3] != '.': + return 0.3 + p1 = text.find("\n") + p2 = text.find("\n", p1 + 1) + if (p2 > -1 and # has two lines + p1 * 2 + 1 == p2 and # they are the same length + text[p1+1] in '-=' and # the next line both starts and ends with + text[p1+1] == text[p2-1]): # ...a sufficiently high header + return 0.5 + + +class TexLexer(RegexLexer): + """ + Lexer for the TeX and LaTeX typesetting languages. + """ + + name = 'TeX' + aliases = ['tex', 'latex'] + filenames = ['*.tex', '*.aux', '*.toc'] + mimetypes = ['text/x-tex', 'text/x-latex'] + + tokens = { + 'general': [ + (r'%.*?\n', Comment), + (r'[{}]', Name.Builtin), + (r'[&_^]', Name.Builtin), + ], + 'root': [ + (r'\\\[', String.Backtick, 'displaymath'), + (r'\\\(', String, 'inlinemath'), + (r'\$\$', String.Backtick, 'displaymath'), + (r'\$', String, 'inlinemath'), + (r'\\([a-zA-Z]+|.)', Keyword, 'command'), + (r'\\$', Keyword), + include('general'), + (r'[^\\$%&_^{}]+', Text), + ], + 'math': [ + (r'\\([a-zA-Z]+|.)', Name.Variable), + include('general'), + (r'[0-9]+', Number), + (r'[-=!+*/()\[\]]', Operator), + (r'[^=!+*/()\[\]\\$%&_^{}0-9-]+', Name.Builtin), + ], + 'inlinemath': [ + (r'\\\)', String, '#pop'), + (r'\$', String, '#pop'), + include('math'), + ], + 'displaymath': [ + (r'\\\]', String, '#pop'), + (r'\$\$', String, '#pop'), + (r'\$', Name.Builtin), + include('math'), + ], + 'command': [ + (r'\[.*?\]', Name.Attribute), + (r'\*', Keyword), + default('#pop'), + ], + } + + def analyse_text(text): + for start in ("\\documentclass", "\\input", "\\documentstyle", + "\\relax"): + if text[:len(start)] == start: + return True + + +class GroffLexer(RegexLexer): + """ + Lexer for the (g)roff typesetting language, supporting groff + extensions. Mainly useful for highlighting manpage sources. + + .. versionadded:: 0.6 + """ + + name = 'Groff' + aliases = ['groff', 'nroff', 'man'] + filenames = ['*.[1-9]', '*.man', '*.1p', '*.3pm'] + mimetypes = ['application/x-troff', 'text/troff'] + + tokens = { + 'root': [ + (r'(\.)(\w+)', bygroups(Text, Keyword), 'request'), + (r'\.', Punctuation, 'request'), + # Regular characters, slurp till we find a backslash or newline + (r'[^\\\n]+', Text, 'textline'), + default('textline'), + ], + 'textline': [ + include('escapes'), + (r'[^\\\n]+', Text), + (r'\n', Text, '#pop'), + ], + 'escapes': [ + # groff has many ways to write escapes. + (r'\\"[^\n]*', Comment), + (r'\\[fn]\w', String.Escape), + (r'\\\(.{2}', String.Escape), + (r'\\.\[.*\]', String.Escape), + (r'\\.', String.Escape), + (r'\\\n', Text, 'request'), + ], + 'request': [ + (r'\n', Text, '#pop'), + include('escapes'), + (r'"[^\n"]+"', String.Double), + (r'\d+', Number), + (r'\S+', String), + (r'\s+', Text), + ], + } + + def analyse_text(text): + if text[:1] != '.': + return False + if text[:3] == '.\\"': + return True + if text[:4] == '.TH ': + return True + if text[1:3].isalnum() and text[3].isspace(): + return 0.9 + + +class MozPreprocHashLexer(RegexLexer): + """ + Lexer for Mozilla Preprocessor files (with '#' as the marker). + + Other data is left untouched. + + .. versionadded:: 2.0 + """ + name = 'mozhashpreproc' + aliases = [name] + filenames = [] + mimetypes = [] + + tokens = { + 'root': [ + (r'^#', Comment.Preproc, ('expr', 'exprstart')), + (r'.+', Other), + ], + 'exprstart': [ + (r'(literal)(.*)', bygroups(Comment.Preproc, Text), '#pop:2'), + (words(( + 'define', 'undef', 'if', 'ifdef', 'ifndef', 'else', 'elif', + 'elifdef', 'elifndef', 'endif', 'expand', 'filter', 'unfilter', + 'include', 'includesubst', 'error')), + Comment.Preproc, '#pop'), + ], + 'expr': [ + (words(('!', '!=', '==', '&&', '||')), Operator), + (r'(defined)(\()', bygroups(Keyword, Punctuation)), + (r'\)', Punctuation), + (r'[0-9]+', Number.Decimal), + (r'__\w+?__', Name.Variable), + (r'@\w+?@', Name.Class), + (r'\w+', Name), + (r'\n', Text, '#pop'), + (r'\s+', Text), + (r'\S', Punctuation), + ], + } + + +class MozPreprocPercentLexer(MozPreprocHashLexer): + """ + Lexer for Mozilla Preprocessor files (with '%' as the marker). + + Other data is left untouched. + + .. versionadded:: 2.0 + """ + name = 'mozpercentpreproc' + aliases = [name] + filenames = [] + mimetypes = [] + + tokens = { + 'root': [ + (r'^%', Comment.Preproc, ('expr', 'exprstart')), + (r'.+', Other), + ], + } + + +class MozPreprocXulLexer(DelegatingLexer): + """ + Subclass of the `MozPreprocHashLexer` that highlights unlexed data with the + `XmlLexer`. + + .. versionadded:: 2.0 + """ + name = "XUL+mozpreproc" + aliases = ['xul+mozpreproc'] + filenames = ['*.xul.in'] + mimetypes = [] + + def __init__(self, **options): + super().__init__(XmlLexer, MozPreprocHashLexer, **options) + + +class MozPreprocJavascriptLexer(DelegatingLexer): + """ + Subclass of the `MozPreprocHashLexer` that highlights unlexed data with the + `JavascriptLexer`. + + .. versionadded:: 2.0 + """ + name = "Javascript+mozpreproc" + aliases = ['javascript+mozpreproc'] + filenames = ['*.js.in'] + mimetypes = [] + + def __init__(self, **options): + super().__init__(JavascriptLexer, MozPreprocHashLexer, **options) + + +class MozPreprocCssLexer(DelegatingLexer): + """ + Subclass of the `MozPreprocHashLexer` that highlights unlexed data with the + `CssLexer`. + + .. versionadded:: 2.0 + """ + name = "CSS+mozpreproc" + aliases = ['css+mozpreproc'] + filenames = ['*.css.in'] + mimetypes = [] + + def __init__(self, **options): + super().__init__(CssLexer, MozPreprocPercentLexer, **options) + + +class MarkdownLexer(RegexLexer): + """ + For Markdown markup. + + .. versionadded:: 2.2 + """ + name = 'Markdown' + url = 'https://daringfireball.net/projects/markdown/' + aliases = ['markdown', 'md'] + filenames = ['*.md', '*.markdown'] + mimetypes = ["text/x-markdown"] + flags = re.MULTILINE + + def _handle_codeblock(self, match): + """ + match args: 1:backticks, 2:lang_name, 3:newline, 4:code, 5:backticks + """ + from pygments.lexers import get_lexer_by_name + + # section header + yield match.start(1), String.Backtick, match.group(1) + yield match.start(2), String.Backtick, match.group(2) + yield match.start(3), Text , match.group(3) + + # lookup lexer if wanted and existing + lexer = None + if self.handlecodeblocks: + try: + lexer = get_lexer_by_name( match.group(2).strip() ) + except ClassNotFound: + pass + code = match.group(4) + + # no lexer for this language. handle it like it was a code block + if lexer is None: + yield match.start(4), String, code + else: + yield from do_insertions([], lexer.get_tokens_unprocessed(code)) + + yield match.start(5), String.Backtick, match.group(5) + + tokens = { + 'root': [ + # heading with '#' prefix (atx-style) + (r'(^#[^#].+)(\n)', bygroups(Generic.Heading, Text)), + # subheading with '#' prefix (atx-style) + (r'(^#{2,6}[^#].+)(\n)', bygroups(Generic.Subheading, Text)), + # heading with '=' underlines (Setext-style) + (r'^(.+)(\n)(=+)(\n)', bygroups(Generic.Heading, Text, Generic.Heading, Text)), + # subheading with '-' underlines (Setext-style) + (r'^(.+)(\n)(-+)(\n)', bygroups(Generic.Subheading, Text, Generic.Subheading, Text)), + # task list + (r'^(\s*)([*-] )(\[[ xX]\])( .+\n)', + bygroups(Whitespace, Keyword, Keyword, using(this, state='inline'))), + # bulleted list + (r'^(\s*)([*-])(\s)(.+\n)', + bygroups(Whitespace, Keyword, Whitespace, using(this, state='inline'))), + # numbered list + (r'^(\s*)([0-9]+\.)( .+\n)', + bygroups(Whitespace, Keyword, using(this, state='inline'))), + # quote + (r'^(\s*>\s)(.+\n)', bygroups(Keyword, Generic.Emph)), + # code block fenced by 3 backticks + (r'^(\s*```\n[\w\W]*?^\s*```$\n)', String.Backtick), + # code block with language + (r'^(\s*```)(\w+)(\n)([\w\W]*?)(^\s*```$\n)', _handle_codeblock), + + include('inline'), + ], + 'inline': [ + # escape + (r'\\.', Text), + # inline code + (r'([^`]?)(`[^`\n]+`)', bygroups(Text, String.Backtick)), + # warning: the following rules eat outer tags. + # eg. **foo _bar_ baz** => foo and baz are not recognized as bold + # bold fenced by '**' + (r'([^\*]?)(\*\*[^* \n][^*\n]*\*\*)', bygroups(Text, Generic.Strong)), + # bold fenced by '__' + (r'([^_]?)(__[^_ \n][^_\n]*__)', bygroups(Text, Generic.Strong)), + # italics fenced by '*' + (r'([^\*]?)(\*[^* \n][^*\n]*\*)', bygroups(Text, Generic.Emph)), + # italics fenced by '_' + (r'([^_]?)(_[^_ \n][^_\n]*_)', bygroups(Text, Generic.Emph)), + # strikethrough + (r'([^~]?)(~~[^~ \n][^~\n]*~~)', bygroups(Text, Generic.Deleted)), + # mentions and topics (twitter and github stuff) + (r'[@#][\w/:]+', Name.Entity), + # (image?) links eg: ![Image of Yaktocat](https://octodex.github.com/images/yaktocat.png) + (r'(!?\[)([^]]+)(\])(\()([^)]+)(\))', + bygroups(Text, Name.Tag, Text, Text, Name.Attribute, Text)), + # reference-style links, e.g.: + # [an example][id] + # [id]: http://example.com/ + (r'(\[)([^]]+)(\])(\[)([^]]*)(\])', + bygroups(Text, Name.Tag, Text, Text, Name.Label, Text)), + (r'^(\s*\[)([^]]*)(\]:\s*)(.+)', + bygroups(Text, Name.Label, Text, Name.Attribute)), + + # general text, must come last! + (r'[^\\\s]+', Text), + (r'.', Text), + ], + } + + def __init__(self, **options): + self.handlecodeblocks = get_bool_opt(options, 'handlecodeblocks', True) + RegexLexer.__init__(self, **options) + + +class TiddlyWiki5Lexer(RegexLexer): + """ + For TiddlyWiki5 markup. + + .. versionadded:: 2.7 + """ + name = 'tiddler' + url = 'https://tiddlywiki.com/#TiddlerFiles' + aliases = ['tid'] + filenames = ['*.tid'] + mimetypes = ["text/vnd.tiddlywiki"] + flags = re.MULTILINE + + def _handle_codeblock(self, match): + """ + match args: 1:backticks, 2:lang_name, 3:newline, 4:code, 5:backticks + """ + from pygments.lexers import get_lexer_by_name + + # section header + yield match.start(1), String, match.group(1) + yield match.start(2), String, match.group(2) + yield match.start(3), Text, match.group(3) + + # lookup lexer if wanted and existing + lexer = None + if self.handlecodeblocks: + try: + lexer = get_lexer_by_name(match.group(2).strip()) + except ClassNotFound: + pass + code = match.group(4) + + # no lexer for this language. handle it like it was a code block + if lexer is None: + yield match.start(4), String, code + return + + yield from do_insertions([], lexer.get_tokens_unprocessed(code)) + + yield match.start(5), String, match.group(5) + + def _handle_cssblock(self, match): + """ + match args: 1:style tag 2:newline, 3:code, 4:closing style tag + """ + from pygments.lexers import get_lexer_by_name + + # section header + yield match.start(1), String, match.group(1) + yield match.start(2), String, match.group(2) + + lexer = None + if self.handlecodeblocks: + try: + lexer = get_lexer_by_name('css') + except ClassNotFound: + pass + code = match.group(3) + + # no lexer for this language. handle it like it was a code block + if lexer is None: + yield match.start(3), String, code + return + + yield from do_insertions([], lexer.get_tokens_unprocessed(code)) + + yield match.start(4), String, match.group(4) + + tokens = { + 'root': [ + # title in metadata section + (r'^(title)(:\s)(.+\n)', bygroups(Keyword, Text, Generic.Heading)), + # headings + (r'^(!)([^!].+\n)', bygroups(Generic.Heading, Text)), + (r'^(!{2,6})(.+\n)', bygroups(Generic.Subheading, Text)), + # bulleted or numbered lists or single-line block quotes + # (can be mixed) + (r'^(\s*)([*#>]+)(\s*)(.+\n)', + bygroups(Text, Keyword, Text, using(this, state='inline'))), + # multi-line block quotes + (r'^(<<<.*\n)([\w\W]*?)(^<<<.*$)', bygroups(String, Text, String)), + # table header + (r'^(\|.*?\|h)$', bygroups(Generic.Strong)), + # table footer or caption + (r'^(\|.*?\|[cf])$', bygroups(Generic.Emph)), + # table class + (r'^(\|.*?\|k)$', bygroups(Name.Tag)), + # definitions + (r'^(;.*)$', bygroups(Generic.Strong)), + # text block + (r'^(```\n)([\w\W]*?)(^```$)', bygroups(String, Text, String)), + # code block with language + (r'^(```)(\w+)(\n)([\w\W]*?)(^```$)', _handle_codeblock), + # CSS style block + (r'^($)', _handle_cssblock), + + include('keywords'), + include('inline'), + ], + 'keywords': [ + (words(( + '\\define', '\\end', 'caption', 'created', 'modified', 'tags', + 'title', 'type'), prefix=r'^', suffix=r'\b'), + Keyword), + ], + 'inline': [ + # escape + (r'\\.', Text), + # created or modified date + (r'\d{17}', Number.Integer), + # italics + (r'(\s)(//[^/]+//)((?=\W|\n))', + bygroups(Text, Generic.Emph, Text)), + # superscript + (r'(\s)(\^\^[^\^]+\^\^)', bygroups(Text, Generic.Emph)), + # subscript + (r'(\s)(,,[^,]+,,)', bygroups(Text, Generic.Emph)), + # underscore + (r'(\s)(__[^_]+__)', bygroups(Text, Generic.Strong)), + # bold + (r"(\s)(''[^']+'')((?=\W|\n))", + bygroups(Text, Generic.Strong, Text)), + # strikethrough + (r'(\s)(~~[^~]+~~)((?=\W|\n))', + bygroups(Text, Generic.Deleted, Text)), + # TiddlyWiki variables + (r'<<[^>]+>>', Name.Tag), + (r'\$\$[^$]+\$\$', Name.Tag), + (r'\$\([^)]+\)\$', Name.Tag), + # TiddlyWiki style or class + (r'^@@.*$', Name.Tag), + # HTML tags + (r']+>', Name.Tag), + # inline code + (r'`[^`]+`', String.Backtick), + # HTML escaped symbols + (r'&\S*?;', String.Regex), + # Wiki links + (r'(\[{2})([^]\|]+)(\]{2})', bygroups(Text, Name.Tag, Text)), + # External links + (r'(\[{2})([^]\|]+)(\|)([^]\|]+)(\]{2})', + bygroups(Text, Name.Tag, Text, Name.Attribute, Text)), + # Transclusion + (r'(\{{2})([^}]+)(\}{2})', bygroups(Text, Name.Tag, Text)), + # URLs + (r'(\b.?.?tps?://[^\s"]+)', bygroups(Name.Attribute)), + + # general text, must come last! + (r'[\w]+', Text), + (r'.', Text) + ], + } + + def __init__(self, **options): + self.handlecodeblocks = get_bool_opt(options, 'handlecodeblocks', True) + RegexLexer.__init__(self, **options) + + +class WikitextLexer(RegexLexer): + """ + For MediaWiki Wikitext. + + Parsing Wikitext is tricky, and results vary between different MediaWiki + installations, so we only highlight common syntaxes (built-in or from + popular extensions), and also assume templates produce no unbalanced + syntaxes. + + .. versionadded:: 2.15 + """ + name = 'Wikitext' + url = 'https://www.mediawiki.org/wiki/Wikitext' + aliases = ['wikitext', 'mediawiki'] + filenames = [] + mimetypes = ['text/x-wiki'] + flags = re.MULTILINE + + def nowiki_tag_rules(tag_name): + return [ + (r'(?i)()'.format(tag_name), bygroups(Punctuation, + Name.Tag, Whitespace, Punctuation), '#pop'), + include('entity'), + include('text'), + ] + + def plaintext_tag_rules(tag_name): + return [ + (r'(?si)(.*?)()'.format(tag_name), bygroups(Text, + Punctuation, Name.Tag, Whitespace, Punctuation), '#pop'), + ] + + def delegate_tag_rules(tag_name, lexer): + return [ + (r'(?i)()'.format(tag_name), bygroups(Punctuation, + Name.Tag, Whitespace, Punctuation), '#pop'), + (r'(?si).+?(?=)'.format(tag_name), using(lexer)), + ] + + def text_rules(token): + return [ + (r'\w+', token), + (r'[^\S\n]+', token), + (r'(?s).', token), + ] + + def handle_syntaxhighlight(self, match, ctx): + from pygments.lexers import get_lexer_by_name + + attr_content = match.group() + start = 0 + index = 0 + while True: + index = attr_content.find('>', start) + # Exclude comment end (-->) + if attr_content[index-2:index] != '--': + break + start = index + 1 + + if index == -1: + # No tag end + yield from self.get_tokens_unprocessed(attr_content, stack=['root', 'attr']) + return + attr = attr_content[:index] + yield from self.get_tokens_unprocessed(attr, stack=['root', 'attr']) + yield match.start(3) + index, Punctuation, '>' + + lexer = None + content = attr_content[index+1:] + lang_match = re.findall(r'\blang=("|\'|)(\w+)(\1)', attr) + + if len(lang_match) >= 1: + # Pick the last match in case of multiple matches + lang = lang_match[-1][1] + try: + lexer = get_lexer_by_name(lang) + except ClassNotFound: + pass + + if lexer is None: + yield match.start() + index + 1, Text, content + else: + yield from lexer.get_tokens_unprocessed(content) + + def handle_score(self, match, ctx): + attr_content = match.group() + start = 0 + index = 0 + while True: + index = attr_content.find('>', start) + # Exclude comment end (-->) + if attr_content[index-2:index] != '--': + break + start = index + 1 + + if index == -1: + # No tag end + yield from self.get_tokens_unprocessed(attr_content, stack=['root', 'attr']) + return + attr = attr_content[:index] + content = attr_content[index+1:] + yield from self.get_tokens_unprocessed(attr, stack=['root', 'attr']) + yield match.start(3) + index, Punctuation, '>' + + lang_match = re.findall(r'\blang=("|\'|)(\w+)(\1)', attr) + # Pick the last match in case of multiple matches + lang = lang_match[-1][1] if len(lang_match) >= 1 else 'lilypond' + + if lang == 'lilypond': # Case sensitive + yield from LilyPondLexer().get_tokens_unprocessed(content) + else: # ABC + # FIXME: Use ABC lexer in the future + yield match.start() + index + 1, Text, content + + # a-z removed to prevent linter from complaining, REMEMBER to use (?i) + title_char = r' %!"$&\'()*,\-./0-9:;=?@A-Z\\\^_`~+\u0080-\uFFFF' + nbsp_char = r'(?:\t| |&\#0*160;|&\#[Xx]0*[Aa]0;|[ \xA0\u1680\u2000-\u200A\u202F\u205F\u3000])' + link_address = r'(?:[0-9.]+|\[[0-9a-f:.]+\]|[^\x00-\x20"<>\[\]\x7F\xA0\u1680\u2000-\u200A\u202F\u205F\u3000\uFFFD])' + link_char_class = r'[^\x00-\x20"<>\[\]\x7F\xA0\u1680\u2000-\u200A\u202F\u205F\u3000\uFFFD]' + double_slashes_i = { + '__FORCETOC__', '__NOCONTENTCONVERT__', '__NOCC__', '__NOEDITSECTION__', '__NOGALLERY__', + '__NOTITLECONVERT__', '__NOTC__', '__NOTOC__', '__TOC__', + } + double_slashes = { + '__EXPECTUNUSEDCATEGORY__', '__HIDDENCAT__', '__INDEX__', '__NEWSECTIONLINK__', + '__NOINDEX__', '__NONEWSECTIONLINK__', '__STATICREDIRECT__', '__NOGLOBAL__', + '__DISAMBIG__', '__EXPECTED_UNCONNECTED_PAGE__', + } + protocols = { + 'bitcoin:', 'ftp://', 'ftps://', 'geo:', 'git://', 'gopher://', 'http://', 'https://', + 'irc://', 'ircs://', 'magnet:', 'mailto:', 'mms://', 'news:', 'nntp://', 'redis://', + 'sftp://', 'sip:', 'sips:', 'sms:', 'ssh://', 'svn://', 'tel:', 'telnet://', 'urn:', + 'worldwind://', 'xmpp:', '//', + } + non_relative_protocols = protocols - {'//'} + html_tags = { + 'abbr', 'b', 'bdi', 'bdo', 'big', 'blockquote', 'br', 'caption', 'center', 'cite', 'code', + 'data', 'dd', 'del', 'dfn', 'div', 'dl', 'dt', 'em', 'font', 'h1', 'h2', 'h3', 'h4', 'h5', + 'h6', 'hr', 'i', 'ins', 'kbd', 'li', 'link', 'mark', 'meta', 'ol', 'p', 'q', 'rb', 'rp', + 'rt', 'rtc', 'ruby', 's', 'samp', 'small', 'span', 'strike', 'strong', 'sub', 'sup', + 'table', 'td', 'th', 'time', 'tr', 'tt', 'u', 'ul', 'var', 'wbr', + } + parser_tags = { + 'graph', 'charinsert', 'rss', 'chem', 'categorytree', 'nowiki', 'inputbox', 'math', + 'hiero', 'score', 'pre', 'ref', 'translate', 'imagemap', 'templatestyles', 'languages', + 'noinclude', 'mapframe', 'section', 'poem', 'syntaxhighlight', 'includeonly', 'tvar', + 'onlyinclude', 'templatedata', 'langconvert', 'timeline', 'dynamicpagelist', 'gallery', + 'maplink', 'ce', 'references', + } + variant_langs = { + # ZhConverter.php + 'zh', 'zh-hans', 'zh-hant', 'zh-cn', 'zh-hk', 'zh-mo', 'zh-my', 'zh-sg', 'zh-tw', + # UnConverter.php + 'uz', 'uz-latn', 'uz-cyrl', + # TlyConverter.php + 'tly', 'tly-cyrl', + # TgConverter.php + 'tg', 'tg-latn', + # SrConverter.php + 'sr', 'sr-ec', 'sr-el', + # ShiConverter.php + 'shi', 'shi-tfng', 'shi-latn', + # ShConverter.php + 'sh-latn', 'sh-cyrl', + # KuConverter.php + 'ku', 'ku-arab', 'ku-latn', + # KkConverter.php + 'kk', 'kk-cyrl', 'kk-latn', 'kk-arab', 'kk-kz', 'kk-tr', 'kk-cn', + # IuConverter.php + 'iu', 'ike-cans', 'ike-latn', + # GanConverter.php + 'gan', 'gan-hans', 'gan-hant', + # EnConverter.php + 'en', 'en-x-piglatin', + # CrhConverter.php + 'crh', 'crh-cyrl', 'crh-latn', + # BanConverter.php + 'ban', 'ban-bali', 'ban-x-dharma', 'ban-x-palmleaf', 'ban-x-pku', + } + magic_vars_i = { + 'ARTICLEPATH', 'INT', 'PAGEID', 'SCRIPTPATH', 'SERVER', 'SERVERNAME', 'STYLEPATH', + } + magic_vars = { + '!', '=', 'BASEPAGENAME', 'BASEPAGENAMEE', 'CASCADINGSOURCES', 'CONTENTLANGUAGE', + 'CONTENTLANG', 'CURRENTDAY', 'CURRENTDAY2', 'CURRENTDAYNAME', 'CURRENTDOW', 'CURRENTHOUR', + 'CURRENTMONTH', 'CURRENTMONTH2', 'CURRENTMONTH1', 'CURRENTMONTHABBREV', 'CURRENTMONTHNAME', + 'CURRENTMONTHNAMEGEN', 'CURRENTTIME', 'CURRENTTIMESTAMP', 'CURRENTVERSION', 'CURRENTWEEK', + 'CURRENTYEAR', 'DIRECTIONMARK', 'DIRMARK', 'FULLPAGENAME', 'FULLPAGENAMEE', 'LOCALDAY', + 'LOCALDAY2', 'LOCALDAYNAME', 'LOCALDOW', 'LOCALHOUR', 'LOCALMONTH', 'LOCALMONTH2', + 'LOCALMONTH1', 'LOCALMONTHABBREV', 'LOCALMONTHNAME', 'LOCALMONTHNAMEGEN', 'LOCALTIME', + 'LOCALTIMESTAMP', 'LOCALWEEK', 'LOCALYEAR', 'NAMESPACE', 'NAMESPACEE', 'NAMESPACENUMBER', + 'NUMBEROFACTIVEUSERS', 'NUMBEROFADMINS', 'NUMBEROFARTICLES', 'NUMBEROFEDITS', + 'NUMBEROFFILES', 'NUMBEROFPAGES', 'NUMBEROFUSERS', 'PAGELANGUAGE', 'PAGENAME', 'PAGENAMEE', + 'REVISIONDAY', 'REVISIONDAY2', 'REVISIONID', 'REVISIONMONTH', 'REVISIONMONTH1', + 'REVISIONSIZE', 'REVISIONTIMESTAMP', 'REVISIONUSER', 'REVISIONYEAR', 'ROOTPAGENAME', + 'ROOTPAGENAMEE', 'SITENAME', 'SUBJECTPAGENAME', 'ARTICLEPAGENAME', 'SUBJECTPAGENAMEE', + 'ARTICLEPAGENAMEE', 'SUBJECTSPACE', 'ARTICLESPACE', 'SUBJECTSPACEE', 'ARTICLESPACEE', + 'SUBPAGENAME', 'SUBPAGENAMEE', 'TALKPAGENAME', 'TALKPAGENAMEE', 'TALKSPACE', 'TALKSPACEE', + } + parser_functions_i = { + 'ANCHORENCODE', 'BIDI', 'CANONICALURL', 'CANONICALURLE', 'FILEPATH', 'FORMATNUM', + 'FULLURL', 'FULLURLE', 'GENDER', 'GRAMMAR', 'INT', r'\#LANGUAGE', 'LC', 'LCFIRST', 'LOCALURL', + 'LOCALURLE', 'NS', 'NSE', 'PADLEFT', 'PADRIGHT', 'PAGEID', 'PLURAL', 'UC', 'UCFIRST', + 'URLENCODE', + } + parser_functions = { + 'BASEPAGENAME', 'BASEPAGENAMEE', 'CASCADINGSOURCES', 'DEFAULTSORT', 'DEFAULTSORTKEY', + 'DEFAULTCATEGORYSORT', 'FULLPAGENAME', 'FULLPAGENAMEE', 'NAMESPACE', 'NAMESPACEE', + 'NAMESPACENUMBER', 'NUMBERINGROUP', 'NUMINGROUP', 'NUMBEROFACTIVEUSERS', 'NUMBEROFADMINS', + 'NUMBEROFARTICLES', 'NUMBEROFEDITS', 'NUMBEROFFILES', 'NUMBEROFPAGES', 'NUMBEROFUSERS', + 'PAGENAME', 'PAGENAMEE', 'PAGESINCATEGORY', 'PAGESINCAT', 'PAGESIZE', 'PROTECTIONEXPIRY', + 'PROTECTIONLEVEL', 'REVISIONDAY', 'REVISIONDAY2', 'REVISIONID', 'REVISIONMONTH', + 'REVISIONMONTH1', 'REVISIONTIMESTAMP', 'REVISIONUSER', 'REVISIONYEAR', 'ROOTPAGENAME', + 'ROOTPAGENAMEE', 'SUBJECTPAGENAME', 'ARTICLEPAGENAME', 'SUBJECTPAGENAMEE', + 'ARTICLEPAGENAMEE', 'SUBJECTSPACE', 'ARTICLESPACE', 'SUBJECTSPACEE', 'ARTICLESPACEE', + 'SUBPAGENAME', 'SUBPAGENAMEE', 'TALKPAGENAME', 'TALKPAGENAMEE', 'TALKSPACE', 'TALKSPACEE', + 'INT', 'DISPLAYTITLE', 'PAGESINNAMESPACE', 'PAGESINNS', + } + + tokens = { + 'root': [ + # Redirects + (r"""(?xi) + (\A\s*?)(\#REDIRECT:?) # may contain a colon + (\s+)(\[\[) (?=[^\]\n]* \]\]$) + """, + bygroups(Whitespace, Keyword, Whitespace, Punctuation), 'redirect-inner'), + # Subheadings + (r'^(={2,6})(.+?)(\1)(\s*$\n)', + bygroups(Generic.Subheading, Generic.Subheading, Generic.Subheading, Whitespace)), + # Headings + (r'^(=.+?=)(\s*$\n)', + bygroups(Generic.Heading, Whitespace)), + # Double-slashed magic words + (words(double_slashes_i, prefix=r'(?i)'), Name.Function.Magic), + (words(double_slashes), Name.Function.Magic), + # Raw URLs + (r'(?i)\b(?:{}){}{}*'.format('|'.join(protocols), + link_address, link_char_class), Name.Label), + # Magic links + (r'\b(?:RFC|PMID){}+[0-9]+\b'.format(nbsp_char), + Name.Function.Magic), + (r"""(?x) + \bISBN {nbsp_char} + (?: 97[89] {nbsp_dash}? )? + (?: [0-9] {nbsp_dash}? ){{9}} # escape format() + [0-9Xx]\b + """.format(nbsp_char=nbsp_char, nbsp_dash=f'(?:-|{nbsp_char})'), Name.Function.Magic), + include('list'), + include('inline'), + include('text'), + ], + 'redirect-inner': [ + (r'(\]\])(\s*?\n)', bygroups(Punctuation, Whitespace), '#pop'), + (r'(\#)([^#]*?)', bygroups(Punctuation, Name.Label)), + (r'(?i)[{}]+'.format(title_char), Name.Tag), + ], + 'list': [ + # Description lists + (r'^;', Keyword, 'dt'), + # Ordered lists, unordered lists and indents + (r'^[#:*]+', Keyword), + # Horizontal rules + (r'^-{4,}', Keyword), + ], + 'inline': [ + # Signatures + (r'~{3,5}', Keyword), + # Entities + include('entity'), + # Bold & italic + (r"('')(''')(?!')", bygroups(Generic.Emph, + Generic.Strong), 'inline-italic-bold'), + (r"'''(?!')", Generic.Strong, 'inline-bold'), + (r"''(?!')", Generic.Emph, 'inline-italic'), + # Comments & parameters & templates + include('replaceable'), + # Media links + ( + r"""(?xi) + (\[\[) + (File|Image) (:) + ([{}]*) + (?: (\#) ([{}]*?) )? + """.format(title_char, f'{title_char}#'), + bygroups(Punctuation, Name.Namespace, Punctuation, + Name.Tag, Punctuation, Name.Label), + 'medialink-inner' + ), + # Wikilinks + ( + r"""(?xi) + (\[\[)(?!{}) # Should not contain URLs + (?: ([{}]*) (:))? + ([{}]*?) + (?: (\#) ([{}]*?) )? + (\]\]) + """.format('|'.join(protocols), title_char.replace('/', ''), + title_char, f'{title_char}#'), + bygroups(Punctuation, Name.Namespace, Punctuation, + Name.Tag, Punctuation, Name.Label, Punctuation) + ), + ( + r"""(?xi) + (\[\[)(?!{}) + (?: ([{}]*) (:))? + ([{}]*?) + (?: (\#) ([{}]*?) )? + (\|) + """.format('|'.join(protocols), title_char.replace('/', ''), + title_char, f'{title_char}#'), + bygroups(Punctuation, Name.Namespace, Punctuation, + Name.Tag, Punctuation, Name.Label, Punctuation), + 'wikilink-inner' + ), + # External links + ( + r"""(?xi) + (\[) + ((?:{}) {} {}*) + (\s*) + """.format('|'.join(protocols), link_address, link_char_class), + bygroups(Punctuation, Name.Label, Whitespace), + 'extlink-inner' + ), + # Tables + (r'^(:*)(\s*?)(\{\|)([^\n]*)$', bygroups(Keyword, + Whitespace, Punctuation, using(this, state=['root', 'attr'])), 'table'), + # HTML tags + (r'(?i)(<)({})\b'.format('|'.join(html_tags)), + bygroups(Punctuation, Name.Tag), 'tag-inner-ordinary'), + (r'(?i)()'.format('|'.join(html_tags)), + bygroups(Punctuation, Name.Tag, Whitespace, Punctuation)), + # + (r'(?i)(<)(nowiki)\b', bygroups(Punctuation, + Name.Tag), ('tag-nowiki', 'tag-inner')), + # 
+            (r'(?i)(<)(pre)\b', bygroups(Punctuation,
+             Name.Tag), ('tag-pre', 'tag-inner')),
+            # 
+            (r'(?i)(<)(categorytree)\b', bygroups(
+                Punctuation, Name.Tag), ('tag-categorytree', 'tag-inner')),
+            # 
+            (r'(?i)(<)(hiero)\b', bygroups(Punctuation,
+             Name.Tag), ('tag-hiero', 'tag-inner')),
+            # 
+            (r'(?i)(<)(math)\b', bygroups(Punctuation,
+             Name.Tag), ('tag-math', 'tag-inner')),
+            # 
+            (r'(?i)(<)(chem)\b', bygroups(Punctuation,
+             Name.Tag), ('tag-chem', 'tag-inner')),
+            # 
+            (r'(?i)(<)(ce)\b', bygroups(Punctuation,
+             Name.Tag), ('tag-ce', 'tag-inner')),
+            # 
+            (r'(?i)(<)(charinsert)\b', bygroups(
+                Punctuation, Name.Tag), ('tag-charinsert', 'tag-inner')),
+            # 
+            (r'(?i)(<)(templatedata)\b', bygroups(
+                Punctuation, Name.Tag), ('tag-templatedata', 'tag-inner')),
+            # 
+            (r'(?i)(<)(gallery)\b', bygroups(
+                Punctuation, Name.Tag), ('tag-gallery', 'tag-inner')),
+            # 
+            (r'(?i)(<)(gallery)\b', bygroups(
+                Punctuation, Name.Tag), ('tag-graph', 'tag-inner')),
+            # 
+            (r'(?i)(<)(dynamicpagelist)\b', bygroups(
+                Punctuation, Name.Tag), ('tag-dynamicpagelist', 'tag-inner')),
+            # 
+            (r'(?i)(<)(inputbox)\b', bygroups(
+                Punctuation, Name.Tag), ('tag-inputbox', 'tag-inner')),
+            # 
+            (r'(?i)(<)(rss)\b', bygroups(
+                Punctuation, Name.Tag), ('tag-rss', 'tag-inner')),
+            # 
+            (r'(?i)(<)(imagemap)\b', bygroups(
+                Punctuation, Name.Tag), ('tag-imagemap', 'tag-inner')),
+            # 
+            (r'(?i)()',
+             bygroups(Punctuation, Name.Tag, Whitespace, Punctuation)),
+            (r'(?si)(<)(syntaxhighlight)\b([^>]*?(?.*?)(?=)',
+             bygroups(Punctuation, Name.Tag, handle_syntaxhighlight)),
+            # : Fallback case for self-closing tags
+            (r'(?i)(<)(syntaxhighlight)\b(\s*?)((?:[^>]|-->)*?)(/\s*?(?)*?)(/\s*?(?)*?)(/\s*?(?|\Z)', Comment.Multiline),
+            # Parameters
+            (
+                r"""(?x)
+                (\{{3})
+                    ([^|]*?)
+                    (?=\}{3}|\|)
+                """,
+                bygroups(Punctuation, Name.Variable),
+                'parameter-inner',
+            ),
+            # Magic variables
+            (r'(?i)(\{\{)(\s*)(%s)(\s*)(\}\})' % '|'.join(magic_vars_i),
+             bygroups(Punctuation, Whitespace, Name.Function, Whitespace, Punctuation)),
+            (r'(\{\{)(\s*)(%s)(\s*)(\}\})' % '|'.join(magic_vars),
+                bygroups(Punctuation, Whitespace, Name.Function, Whitespace, Punctuation)),
+            # Parser functions & templates
+            (r'\{\{', Punctuation, 'template-begin-space'),
+            #  legacy syntax
+            (r'(?i)(<)(tvar)\b(\|)([^>]*?)(>)', bygroups(Punctuation,
+             Name.Tag, Punctuation, String, Punctuation)),
+            (r'', Punctuation, '#pop'),
+            # 
+            (r'(?i)(<)(tvar)\b', bygroups(Punctuation, Name.Tag), 'tag-inner-ordinary'),
+            (r'(?i)()',
+             bygroups(Punctuation, Name.Tag, Whitespace, Punctuation)),
+        ],
+        'parameter-inner': [
+            (r'\}{3}', Punctuation, '#pop'),
+            (r'\|', Punctuation),
+            include('inline'),
+            include('text'),
+        ],
+        'template-begin-space': [
+            # Templates allow line breaks at the beginning, and due to how MediaWiki handles
+            # comments, an extra state is required to handle things like {{\n\n name}}
+            (r'|\Z)', Comment.Multiline),
+            (r'\s+', Whitespace),
+            # Parser functions
+            (
+                r'(?i)(\#[%s]*?|%s)(:)' % (title_char,
+                                           '|'.join(parser_functions_i)),
+                bygroups(Name.Function, Punctuation), ('#pop', 'template-inner')
+            ),
+            (
+                r'(%s)(:)' % ('|'.join(parser_functions)),
+                bygroups(Name.Function, Punctuation), ('#pop', 'template-inner')
+            ),
+            # Templates
+            (
+                r'(?i)([%s]*?)(:)' % title_char,
+                bygroups(Name.Namespace, Punctuation), ('#pop', 'template-name')
+            ),
+            default(('#pop', 'template-name'),),
+        ],
+        'template-name': [
+            (r'(\s*?)(\|)', bygroups(Text, Punctuation), ('#pop', 'template-inner')),
+            (r'\}\}', Punctuation, '#pop'),
+            (r'\n', Text, '#pop'),
+            include('replaceable'),
+            *text_rules(Name.Tag),
+        ],
+        'template-inner': [
+            (r'\}\}', Punctuation, '#pop'),
+            (r'\|', Punctuation),
+            (
+                r"""(?x)
+                    (?<=\|)
+                    ( (?: (?! \{\{ | \}\} )[^=\|<])*? ) # Exclude templates and tags
+                    (=)
+                """,
+                bygroups(Name.Label, Operator)
+            ),
+            include('inline'),
+            include('text'),
+        ],
+        'table': [
+            # Use [ \t\n\r\0\x0B] instead of \s to follow PHP trim() behavior
+            # Endings
+            (r'^([ \t\n\r\0\x0B]*?)(\|\})',
+             bygroups(Whitespace, Punctuation), '#pop'),
+            # Table rows
+            (r'^([ \t\n\r\0\x0B]*?)(\|-+)(.*)$', bygroups(Whitespace, Punctuation,
+             using(this, state=['root', 'attr']))),
+            # Captions
+            (
+                r"""(?x)
+                ^([ \t\n\r\0\x0B]*?)(\|\+)
+                # Exclude links, template and tags
+                (?: ( (?: (?! \[\[ | \{\{ )[^|\n<] )*? )(\|) )?
+                (.*?)$
+                """,
+                bygroups(Whitespace, Punctuation, using(this, state=[
+                         'root', 'attr']), Punctuation, Generic.Heading),
+            ),
+            # Table data
+            (
+                r"""(?x)
+                ( ^(?:[ \t\n\r\0\x0B]*?)\| | \|\| )
+                (?: ( (?: (?! \[\[ | \{\{ )[^|\n<] )*? )(\|)(?!\|) )?
+                """,
+                bygroups(Punctuation, using(this, state=[
+                         'root', 'attr']), Punctuation),
+            ),
+            # Table headers
+            (
+                r"""(?x)
+                ( ^(?:[ \t\n\r\0\x0B]*?)!  )
+                (?: ( (?: (?! \[\[ | \{\{ )[^|\n<] )*? )(\|)(?!\|) )?
+                """,
+                bygroups(Punctuation, using(this, state=[
+                         'root', 'attr']), Punctuation),
+                'table-header',
+            ),
+            include('list'),
+            include('inline'),
+            include('text'),
+        ],
+        'table-header': [
+            # Requires another state for || handling inside headers
+            (r'\n', Text, '#pop'),
+            (
+                r"""(?x)
+                (!!|\|\|)
+                (?:
+                    ( (?: (?! \[\[ | \{\{ )[^|\n<] )*? )
+                    (\|)(?!\|)
+                )?
+                """,
+                bygroups(Punctuation, using(this, state=[
+                         'root', 'attr']), Punctuation)
+            ),
+            *text_rules(Generic.Subheading),
+        ],
+        'entity': [
+            (r'&\S*?;', Name.Entity),
+        ],
+        'dt': [
+            (r'\n', Text, '#pop'),
+            include('inline'),
+            (r':', Keyword, '#pop'),
+            include('text'),
+        ],
+        'extlink-inner': [
+            (r'\]', Punctuation, '#pop'),
+            include('inline'),
+            include('text'),
+        ],
+        'nowiki-ish': [
+            include('entity'),
+            include('text'),
+        ],
+        'attr': [
+            include('replaceable'),
+            (r'\s+', Whitespace),
+            (r'(=)(\s*)(")', bygroups(Operator, Whitespace, String.Double), 'attr-val-2'),
+            (r"(=)(\s*)(')", bygroups(Operator, Whitespace, String.Single), 'attr-val-1'),
+            (r'(=)(\s*)', bygroups(Operator, Whitespace), 'attr-val-0'),
+            (r'[\w:-]+', Name.Attribute),
+
+        ],
+        'attr-val-0': [
+            (r'\s', Whitespace, '#pop'),
+            include('replaceable'),
+            *text_rules(String),
+        ],
+        'attr-val-1': [
+            (r"'", String.Single, '#pop'),
+            include('replaceable'),
+            *text_rules(String.Single),
+        ],
+        'attr-val-2': [
+            (r'"', String.Double, '#pop'),
+            include('replaceable'),
+            *text_rules(String.Double),
+        ],
+        'tag-inner-ordinary': [
+            (r'/?\s*>', Punctuation, '#pop'),
+            include('tag-attr'),
+        ],
+        'tag-inner': [
+            # Return to root state for self-closing tags
+            (r'/\s*>', Punctuation, '#pop:2'),
+            (r'\s*>', Punctuation, '#pop'),
+            include('tag-attr'),
+        ],
+        # There states below are just like their non-tag variants, the key difference is
+        # they forcibly quit when encountering tag closing markup
+        'tag-attr': [
+            include('replaceable'),
+            (r'\s+', Whitespace),
+            (r'(=)(\s*)(")', bygroups(Operator,
+             Whitespace, String.Double), 'tag-attr-val-2'),
+            (r"(=)(\s*)(')", bygroups(Operator,
+             Whitespace, String.Single), 'tag-attr-val-1'),
+            (r'(=)(\s*)', bygroups(Operator, Whitespace), 'tag-attr-val-0'),
+            (r'[\w:-]+', Name.Attribute),
+
+        ],
+        'tag-attr-val-0': [
+            (r'\s', Whitespace, '#pop'),
+            (r'/?>', Punctuation, '#pop:2'),
+            include('replaceable'),
+            *text_rules(String),
+        ],
+        'tag-attr-val-1': [
+            (r"'", String.Single, '#pop'),
+            (r'/?>', Punctuation, '#pop:2'),
+            include('replaceable'),
+            *text_rules(String.Single),
+        ],
+        'tag-attr-val-2': [
+            (r'"', String.Double, '#pop'),
+            (r'/?>', Punctuation, '#pop:2'),
+            include('replaceable'),
+            *text_rules(String.Double),
+        ],
+        'tag-nowiki': nowiki_tag_rules('nowiki'),
+        'tag-pre': nowiki_tag_rules('pre'),
+        'tag-categorytree': plaintext_tag_rules('categorytree'),
+        'tag-dynamicpagelist': plaintext_tag_rules('dynamicpagelist'),
+        'tag-hiero': plaintext_tag_rules('hiero'),
+        'tag-inputbox': plaintext_tag_rules('inputbox'),
+        'tag-imagemap': plaintext_tag_rules('imagemap'),
+        'tag-charinsert': plaintext_tag_rules('charinsert'),
+        'tag-timeline': plaintext_tag_rules('timeline'),
+        'tag-gallery': plaintext_tag_rules('gallery'),
+        'tag-graph': plaintext_tag_rules('graph'),
+        'tag-rss': plaintext_tag_rules('rss'),
+        'tag-math': delegate_tag_rules('math', TexLexer),
+        'tag-chem': delegate_tag_rules('chem', TexLexer),
+        'tag-ce': delegate_tag_rules('ce', TexLexer),
+        'tag-templatedata': delegate_tag_rules('templatedata', JsonLexer),
+        'text-italic': text_rules(Generic.Emph),
+        'text-bold': text_rules(Generic.Strong),
+        'text': text_rules(Text),
+    }
diff --git a/shared/package-files/04/040476f5502f67243a6e7e9c3681099944125a3179ba27babe347ee519b182b3/_partial_dependence.py b/shared/package-files/04/040476f5502f67243a6e7e9c3681099944125a3179ba27babe347ee519b182b3/_partial_dependence.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad352c45cc03bd6018617c4ccaa6247fd68718b5
--- /dev/null
+++ b/shared/package-files/04/040476f5502f67243a6e7e9c3681099944125a3179ba27babe347ee519b182b3/_partial_dependence.py
@@ -0,0 +1,775 @@
+"""Partial dependence plots for regression and classification models."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from collections.abc import Iterable
+
+import numpy as np
+from scipy import sparse
+from scipy.stats.mstats import mquantiles
+
+from ..base import is_classifier, is_regressor
+from ..ensemble import RandomForestRegressor
+from ..ensemble._gb import BaseGradientBoosting
+from ..ensemble._hist_gradient_boosting.gradient_boosting import (
+    BaseHistGradientBoosting,
+)
+from ..tree import DecisionTreeRegressor
+from ..utils import Bunch, _safe_indexing, check_array
+from ..utils._indexing import _determine_key_type, _get_column_indices, _safe_assign
+from ..utils._optional_dependencies import check_matplotlib_support  # noqa: F401
+from ..utils._param_validation import (
+    HasMethods,
+    Integral,
+    Interval,
+    StrOptions,
+    validate_params,
+)
+from ..utils._response import _get_response_values
+from ..utils.extmath import cartesian
+from ..utils.validation import _check_sample_weight, check_is_fitted
+from ._pd_utils import _check_feature_names, _get_feature_index
+
+__all__ = [
+    "partial_dependence",
+]
+
+
+def _grid_from_X(X, percentiles, is_categorical, grid_resolution, custom_values):
+    """Generate a grid of points based on the percentiles of X.
+
+    The grid is a cartesian product between the columns of ``values``. The
+    ith column of ``values`` consists in ``grid_resolution`` equally-spaced
+    points between the percentiles of the jth column of X.
+
+    If ``grid_resolution`` is bigger than the number of unique values in the
+    j-th column of X or if the feature is a categorical feature (by inspecting
+    `is_categorical`) , then those unique values will be used instead.
+
+    Parameters
+    ----------
+    X : array-like of shape (n_samples, n_target_features)
+        The data.
+
+    percentiles : tuple of float
+        The percentiles which are used to construct the extreme values of
+        the grid. Must be in [0, 1].
+
+    is_categorical : list of bool
+        For each feature, tells whether it is categorical or not. If a feature
+        is categorical, then the values used will be the unique ones
+        (i.e. categories) instead of the percentiles.
+
+    grid_resolution : int
+        The number of equally spaced points to be placed on the grid for each
+        feature.
+
+    custom_values: dict
+        Mapping from column index of X to an array-like of values where
+        the partial dependence should be calculated for that feature
+
+    Returns
+    -------
+    grid : ndarray of shape (n_points, n_target_features)
+        A value for each feature at each point in the grid. ``n_points`` is
+        always ``<= grid_resolution ** X.shape[1]``.
+
+    values : list of 1d ndarrays
+        The values with which the grid has been created. The size of each
+        array ``values[j]`` is either ``grid_resolution``, the number of
+        unique values in ``X[:, j]``, if j is not in ``custom_range``.
+        If j is in ``custom_range``, then it is the length of ``custom_range[j]``.
+    """
+    if not isinstance(percentiles, Iterable) or len(percentiles) != 2:
+        raise ValueError("'percentiles' must be a sequence of 2 elements.")
+    if not all(0 <= x <= 1 for x in percentiles):
+        raise ValueError("'percentiles' values must be in [0, 1].")
+    if percentiles[0] >= percentiles[1]:
+        raise ValueError("percentiles[0] must be strictly less than percentiles[1].")
+
+    if grid_resolution <= 1:
+        raise ValueError("'grid_resolution' must be strictly greater than 1.")
+
+    def _convert_custom_values(values):
+        # Convert custom types such that object types are always used for string arrays
+        dtype = object if any(isinstance(v, str) for v in values) else None
+        return np.asarray(values, dtype=dtype)
+
+    custom_values = {k: _convert_custom_values(v) for k, v in custom_values.items()}
+    if any(v.ndim != 1 for v in custom_values.values()):
+        error_string = ", ".join(
+            f"Feature {k}: {v.ndim} dimensions"
+            for k, v in custom_values.items()
+            if v.ndim != 1
+        )
+
+        raise ValueError(
+            "The custom grid for some features is not a one-dimensional array. "
+            f"{error_string}"
+        )
+
+    values = []
+    # TODO: we should handle missing values (i.e. `np.nan`) specifically and store them
+    # in a different Bunch attribute.
+    for feature, is_cat in enumerate(is_categorical):
+        if feature in custom_values:
+            # Use values in the custom range
+            axis = custom_values[feature]
+        else:
+            try:
+                uniques = np.unique(_safe_indexing(X, feature, axis=1))
+            except TypeError as exc:
+                # `np.unique` will fail in the presence of `np.nan` and `str` categories
+                # due to sorting. Temporary, we reraise an error explaining the problem.
+                raise ValueError(
+                    f"The column #{feature} contains mixed data types. Finding unique "
+                    "categories fail due to sorting. It usually means that the column "
+                    "contains `np.nan` values together with `str` categories. Such use "
+                    "case is not yet supported in scikit-learn."
+                ) from exc
+
+            if is_cat or uniques.shape[0] < grid_resolution:
+                # Use the unique values either because:
+                # - feature has low resolution use unique values
+                # - feature is categorical
+                axis = uniques
+            else:
+                # create axis based on percentiles and grid resolution
+                emp_percentiles = mquantiles(
+                    _safe_indexing(X, feature, axis=1), prob=percentiles, axis=0
+                )
+                if np.allclose(emp_percentiles[0], emp_percentiles[1]):
+                    raise ValueError(
+                        "percentiles are too close to each other, "
+                        "unable to build the grid. Please choose percentiles "
+                        "that are further apart."
+                    )
+                axis = np.linspace(
+                    emp_percentiles[0],
+                    emp_percentiles[1],
+                    num=grid_resolution,
+                    endpoint=True,
+                )
+        values.append(axis)
+
+    return cartesian(values), values
+
+
+def _partial_dependence_recursion(est, grid, features):
+    """Calculate partial dependence via the recursion method.
+
+    The recursion method is in particular enabled for tree-based estimators.
+
+    For each `grid` value, a weighted tree traversal is performed: if a split node
+    involves an input feature of interest, the corresponding left or right branch
+    is followed; otherwise both branches are followed, each branch being weighted
+    by the fraction of training samples that entered that branch. Finally, the
+    partial dependence is given by a weighted average of all the visited leaves
+    values.
+
+    This method is more efficient in terms of speed than the `'brute'` method
+    (:func:`~sklearn.inspection._partial_dependence._partial_dependence_brute`).
+    However, here, the partial dependence computation is done explicitly with the
+    `X` used during training of `est`.
+
+    Parameters
+    ----------
+    est : BaseEstimator
+        A fitted estimator object implementing :term:`predict` or
+        :term:`decision_function`. Multioutput-multiclass classifiers are not
+        supported. Note that `'recursion'` is only supported for some tree-based
+        estimators (namely
+        :class:`~sklearn.ensemble.GradientBoostingClassifier`,
+        :class:`~sklearn.ensemble.GradientBoostingRegressor`,
+        :class:`~sklearn.ensemble.HistGradientBoostingClassifier`,
+        :class:`~sklearn.ensemble.HistGradientBoostingRegressor`,
+        :class:`~sklearn.tree.DecisionTreeRegressor`,
+        :class:`~sklearn.ensemble.RandomForestRegressor`,
+        ).
+
+    grid : array-like of shape (n_points, n_target_features)
+        The grid of feature values for which the partial dependence is calculated.
+        Note that `n_points` is the number of points in the grid and `n_target_features`
+        is the number of features you are doing partial dependence at.
+
+    features : array-like of {int, str}
+        The feature (e.g. `[0]`) or pair of interacting features
+        (e.g. `[(0, 1)]`) for which the partial dependency should be computed.
+
+    Returns
+    -------
+    averaged_predictions : array-like of shape (n_targets, n_points)
+        The averaged predictions for the given `grid` of features values.
+        Note that `n_targets` is the number of targets (e.g. 1 for binary
+        classification, `n_tasks` for multi-output regression, and `n_classes` for
+        multiclass classification) and `n_points` is the number of points in the `grid`.
+    """
+    averaged_predictions = est._compute_partial_dependence_recursion(grid, features)
+    if averaged_predictions.ndim == 1:
+        # reshape to (1, n_points) for consistency with
+        # _partial_dependence_brute
+        averaged_predictions = averaged_predictions.reshape(1, -1)
+
+    return averaged_predictions
+
+
+def _partial_dependence_brute(
+    est, grid, features, X, response_method, sample_weight=None
+):
+    """Calculate partial dependence via the brute force method.
+
+    The brute method explicitly averages the predictions of an estimator over a
+    grid of feature values.
+
+    For each `grid` value, all the samples from `X` have their variables of
+    interest replaced by that specific `grid` value. The predictions are then made
+    and averaged across the samples.
+
+    This method is slower than the `'recursion'`
+    (:func:`~sklearn.inspection._partial_dependence._partial_dependence_recursion`)
+    version for estimators with this second option. However, with the `'brute'`
+    force method, the average will be done with the given `X` and not the `X`
+    used during training, as it is done in the `'recursion'` version. Therefore
+    the average can always accept `sample_weight` (even when the estimator was
+    fitted without).
+
+    Parameters
+    ----------
+    est : BaseEstimator
+        A fitted estimator object implementing :term:`predict`,
+        :term:`predict_proba`, or :term:`decision_function`.
+        Multioutput-multiclass classifiers are not supported.
+
+    grid : array-like of shape (n_points, n_target_features)
+        The grid of feature values for which the partial dependence is calculated.
+        Note that `n_points` is the number of points in the grid and `n_target_features`
+        is the number of features you are doing partial dependence at.
+
+    features : array-like of {int, str}
+        The feature (e.g. `[0]`) or pair of interacting features
+        (e.g. `[(0, 1)]`) for which the partial dependency should be computed.
+
+    X : array-like of shape (n_samples, n_features)
+        `X` is used to generate values for the complement features. That is, for
+        each value in `grid`, the method will average the prediction of each
+        sample from `X` having that grid value for `features`.
+
+    response_method : {'auto', 'predict_proba', 'decision_function'}, \
+            default='auto'
+        Specifies whether to use :term:`predict_proba` or
+        :term:`decision_function` as the target response. For regressors
+        this parameter is ignored and the response is always the output of
+        :term:`predict`. By default, :term:`predict_proba` is tried first
+        and we revert to :term:`decision_function` if it doesn't exist.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights are used to calculate weighted means when averaging the
+        model output. If `None`, then samples are equally weighted. Note that
+        `sample_weight` does not change the individual predictions.
+
+    Returns
+    -------
+    averaged_predictions : array-like of shape (n_targets, n_points)
+        The averaged predictions for the given `grid` of features values.
+        Note that `n_targets` is the number of targets (e.g. 1 for binary
+        classification, `n_tasks` for multi-output regression, and `n_classes` for
+        multiclass classification) and `n_points` is the number of points in the `grid`.
+
+    predictions : array-like
+        The predictions for the given `grid` of features values over the samples
+        from `X`. For non-multioutput regression and binary classification the
+        shape is `(n_instances, n_points)` and for multi-output regression and
+        multiclass classification the shape is `(n_targets, n_instances, n_points)`,
+        where `n_targets` is the number of targets (`n_tasks` for multi-output
+        regression, and `n_classes` for multiclass classification), `n_instances`
+        is the number of instances in `X`, and `n_points` is the number of points
+        in the `grid`.
+    """
+    predictions = []
+    averaged_predictions = []
+
+    if response_method == "auto":
+        response_method = (
+            "predict" if is_regressor(est) else ["predict_proba", "decision_function"]
+        )
+
+    X_eval = X.copy()
+    for new_values in grid:
+        for i, variable in enumerate(features):
+            _safe_assign(X_eval, new_values[i], column_indexer=variable)
+
+        # Note: predictions is of shape
+        # (n_points,) for non-multioutput regressors
+        # (n_points, n_tasks) for multioutput regressors
+        # (n_points, 1) for the regressors in cross_decomposition (I think)
+        # (n_points, 1) for binary classification (positive class already selected)
+        # (n_points, n_classes) for multiclass classification
+        pred, _ = _get_response_values(est, X_eval, response_method=response_method)
+
+        predictions.append(pred)
+        # average over samples
+        averaged_predictions.append(np.average(pred, axis=0, weights=sample_weight))
+
+    n_samples = X.shape[0]
+
+    # reshape to (n_targets, n_instances, n_points) where n_targets is:
+    # - 1 for non-multioutput regression and binary classification (shape is
+    #   already correct in those cases)
+    # - n_tasks for multi-output regression
+    # - n_classes for multiclass classification.
+    predictions = np.array(predictions).T
+    if is_regressor(est) and predictions.ndim == 2:
+        # non-multioutput regression, shape is (n_instances, n_points,)
+        predictions = predictions.reshape(n_samples, -1)
+    elif is_classifier(est) and predictions.shape[0] == 2:
+        # Binary classification, shape is (2, n_instances, n_points).
+        # we output the effect of **positive** class
+        predictions = predictions[1]
+        predictions = predictions.reshape(n_samples, -1)
+
+    # reshape averaged_predictions to (n_targets, n_points) where n_targets is:
+    # - 1 for non-multioutput regression and binary classification (shape is
+    #   already correct in those cases)
+    # - n_tasks for multi-output regression
+    # - n_classes for multiclass classification.
+    averaged_predictions = np.array(averaged_predictions).T
+    if averaged_predictions.ndim == 1:
+        # reshape to (1, n_points) for consistency with
+        # _partial_dependence_recursion
+        averaged_predictions = averaged_predictions.reshape(1, -1)
+
+    return averaged_predictions, predictions
+
+
+@validate_params(
+    {
+        "estimator": [
+            HasMethods(["fit", "predict"]),
+            HasMethods(["fit", "predict_proba"]),
+            HasMethods(["fit", "decision_function"]),
+        ],
+        "X": ["array-like", "sparse matrix"],
+        "features": ["array-like", Integral, str],
+        "sample_weight": ["array-like", None],
+        "categorical_features": ["array-like", None],
+        "feature_names": ["array-like", None],
+        "response_method": [StrOptions({"auto", "predict_proba", "decision_function"})],
+        "percentiles": [tuple],
+        "grid_resolution": [Interval(Integral, 1, None, closed="left")],
+        "method": [StrOptions({"auto", "recursion", "brute"})],
+        "kind": [StrOptions({"average", "individual", "both"})],
+        "custom_values": [dict, None],
+    },
+    prefer_skip_nested_validation=True,
+)
+def partial_dependence(
+    estimator,
+    X,
+    features,
+    *,
+    sample_weight=None,
+    categorical_features=None,
+    feature_names=None,
+    response_method="auto",
+    percentiles=(0.05, 0.95),
+    grid_resolution=100,
+    custom_values=None,
+    method="auto",
+    kind="average",
+):
+    """Partial dependence of ``features``.
+
+    Partial dependence of a feature (or a set of features) corresponds to
+    the average response of an estimator for each possible value of the
+    feature.
+
+    Read more in
+    :ref:`sphx_glr_auto_examples_inspection_plot_partial_dependence.py`
+    and the :ref:`User Guide `.
+
+    .. warning::
+
+        For :class:`~sklearn.ensemble.GradientBoostingClassifier` and
+        :class:`~sklearn.ensemble.GradientBoostingRegressor`, the
+        `'recursion'` method (used by default) will not account for the `init`
+        predictor of the boosting process. In practice, this will produce
+        the same values as `'brute'` up to a constant offset in the target
+        response, provided that `init` is a constant estimator (which is the
+        default). However, if `init` is not a constant estimator, the
+        partial dependence values are incorrect for `'recursion'` because the
+        offset will be sample-dependent. It is preferable to use the `'brute'`
+        method. Note that this only applies to
+        :class:`~sklearn.ensemble.GradientBoostingClassifier` and
+        :class:`~sklearn.ensemble.GradientBoostingRegressor`, not to
+        :class:`~sklearn.ensemble.HistGradientBoostingClassifier` and
+        :class:`~sklearn.ensemble.HistGradientBoostingRegressor`.
+
+    Parameters
+    ----------
+    estimator : BaseEstimator
+        A fitted estimator object implementing :term:`predict`,
+        :term:`predict_proba`, or :term:`decision_function`.
+        Multioutput-multiclass classifiers are not supported.
+
+    X : {array-like, sparse matrix or dataframe} of shape (n_samples, n_features)
+        ``X`` is used to generate a grid of values for the target
+        ``features`` (where the partial dependence will be evaluated), and
+        also to generate values for the complement features when the
+        `method` is 'brute'.
+
+    features : array-like of {int, str, bool} or int or str
+        The feature (e.g. `[0]`) or pair of interacting features
+        (e.g. `[(0, 1)]`) for which the partial dependency should be computed.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights are used to calculate weighted means when averaging the
+        model output. If `None`, then samples are equally weighted. If
+        `sample_weight` is not `None`, then `method` will be set to `'brute'`.
+        Note that `sample_weight` is ignored for `kind='individual'`.
+
+        .. versionadded:: 1.3
+
+    categorical_features : array-like of shape (n_features,) or shape \
+            (n_categorical_features,), dtype={bool, int, str}, default=None
+        Indicates the categorical features.
+
+        - `None`: no feature will be considered categorical;
+        - boolean array-like: boolean mask of shape `(n_features,)`
+            indicating which features are categorical. Thus, this array has
+            the same shape has `X.shape[1]`;
+        - integer or string array-like: integer indices or strings
+            indicating categorical features.
+
+        .. versionadded:: 1.2
+
+    feature_names : array-like of shape (n_features,), dtype=str, default=None
+        Name of each feature; `feature_names[i]` holds the name of the feature
+        with index `i`.
+        By default, the name of the feature corresponds to their numerical
+        index for NumPy array and their column name for pandas dataframe.
+
+        .. versionadded:: 1.2
+
+    response_method : {'auto', 'predict_proba', 'decision_function'}, \
+            default='auto'
+        Specifies whether to use :term:`predict_proba` or
+        :term:`decision_function` as the target response. For regressors
+        this parameter is ignored and the response is always the output of
+        :term:`predict`. By default, :term:`predict_proba` is tried first
+        and we revert to :term:`decision_function` if it doesn't exist. If
+        ``method`` is 'recursion', the response is always the output of
+        :term:`decision_function`.
+
+    percentiles : tuple of float, default=(0.05, 0.95)
+        The lower and upper percentile used to create the extreme values
+        for the grid. Must be in [0, 1].
+        This parameter is overridden by `custom_values` if that parameter is set.
+
+    grid_resolution : int, default=100
+        The number of equally spaced points on the grid, for each target
+        feature.
+        This parameter is overridden by `custom_values` if that parameter is set.
+
+    custom_values : dict
+        A dictionary mapping the index of an element of `features` to an array
+        of values where the partial dependence should be calculated
+        for that feature. Setting a range of values for a feature overrides
+        `grid_resolution` and `percentiles`.
+
+        See :ref:`how to use partial_dependence
+        ` for an example of how this parameter can
+        be used.
+
+        .. versionadded:: 1.7
+
+    method : {'auto', 'recursion', 'brute'}, default='auto'
+        The method used to calculate the averaged predictions:
+
+        - `'recursion'` is only supported for some tree-based estimators
+          (namely
+          :class:`~sklearn.ensemble.GradientBoostingClassifier`,
+          :class:`~sklearn.ensemble.GradientBoostingRegressor`,
+          :class:`~sklearn.ensemble.HistGradientBoostingClassifier`,
+          :class:`~sklearn.ensemble.HistGradientBoostingRegressor`,
+          :class:`~sklearn.tree.DecisionTreeRegressor`,
+          :class:`~sklearn.ensemble.RandomForestRegressor`,
+          ) when `kind='average'`.
+          This is more efficient in terms of speed.
+          With this method, the target response of a
+          classifier is always the decision function, not the predicted
+          probabilities. Since the `'recursion'` method implicitly computes
+          the average of the Individual Conditional Expectation (ICE) by
+          design, it is not compatible with ICE and thus `kind` must be
+          `'average'`.
+
+        - `'brute'` is supported for any estimator, but is more
+          computationally intensive.
+
+        - `'auto'`: the `'recursion'` is used for estimators that support it,
+          and `'brute'` is used otherwise. If `sample_weight` is not `None`,
+          then `'brute'` is used regardless of the estimator.
+
+        Please see :ref:`this note ` for
+        differences between the `'brute'` and `'recursion'` method.
+
+    kind : {'average', 'individual', 'both'}, default='average'
+        Whether to return the partial dependence averaged across all the
+        samples in the dataset or one value per sample or both.
+        See Returns below.
+
+        Note that the fast `method='recursion'` option is only available for
+        `kind='average'` and `sample_weights=None`. Computing individual
+        dependencies and doing weighted averages requires using the slower
+        `method='brute'`.
+
+        .. versionadded:: 0.24
+
+    Returns
+    -------
+    predictions : :class:`~sklearn.utils.Bunch`
+        Dictionary-like object, with the following attributes.
+
+        individual : ndarray of shape (n_outputs, n_instances, \
+                len(values[0]), len(values[1]), ...)
+            The predictions for all the points in the grid for all
+            samples in X. This is also known as Individual
+            Conditional Expectation (ICE).
+            Only available when `kind='individual'` or `kind='both'`.
+
+        average : ndarray of shape (n_outputs, len(values[0]), \
+                len(values[1]), ...)
+            The predictions for all the points in the grid, averaged
+            over all samples in X (or over the training data if
+            `method` is 'recursion').
+            Only available when `kind='average'` or `kind='both'`.
+
+        grid_values : seq of 1d ndarrays
+            The values with which the grid has been created. The generated
+            grid is a cartesian product of the arrays in `grid_values` where
+            `len(grid_values) == len(features)`. The size of each array
+            `grid_values[j]` is either `grid_resolution`, or the number of
+            unique values in `X[:, j]`, whichever is smaller.
+
+            .. versionadded:: 1.3
+
+        `n_outputs` corresponds to the number of classes in a multi-class
+        setting, or to the number of tasks for multi-output regression.
+        For classical regression and binary classification `n_outputs==1`.
+        `n_values_feature_j` corresponds to the size `grid_values[j]`.
+
+    See Also
+    --------
+    PartialDependenceDisplay.from_estimator : Plot Partial Dependence.
+    PartialDependenceDisplay : Partial Dependence visualization.
+
+    Examples
+    --------
+    >>> X = [[0, 0, 2], [1, 0, 0]]
+    >>> y = [0, 1]
+    >>> from sklearn.ensemble import GradientBoostingClassifier
+    >>> gb = GradientBoostingClassifier(random_state=0).fit(X, y)
+    >>> partial_dependence(gb, features=[0], X=X, percentiles=(0, 1),
+    ...                    grid_resolution=2) # doctest: +SKIP
+    (array([[-4.52,  4.52]]), [array([ 0.,  1.])])
+    """
+    check_is_fitted(estimator)
+
+    if not (is_classifier(estimator) or is_regressor(estimator)):
+        raise ValueError("'estimator' must be a fitted regressor or classifier.")
+
+    if is_classifier(estimator) and isinstance(estimator.classes_[0], np.ndarray):
+        raise ValueError("Multiclass-multioutput estimators are not supported")
+
+    # Use check_array only on lists and other non-array-likes / sparse. Do not
+    # convert DataFrame into a NumPy array.
+    if not (hasattr(X, "__array__") or sparse.issparse(X)):
+        X = check_array(X, ensure_all_finite="allow-nan", dtype=object)
+
+    if is_regressor(estimator) and response_method != "auto":
+        raise ValueError(
+            "The response_method parameter is ignored for regressors and "
+            "must be 'auto'."
+        )
+
+    if kind != "average":
+        if method == "recursion":
+            raise ValueError(
+                "The 'recursion' method only applies when 'kind' is set to 'average'"
+            )
+        method = "brute"
+
+    if method == "recursion" and sample_weight is not None:
+        raise ValueError(
+            "The 'recursion' method can only be applied when sample_weight is None."
+        )
+
+    if method == "auto":
+        if sample_weight is not None:
+            method = "brute"
+        elif isinstance(estimator, BaseGradientBoosting) and estimator.init is None:
+            method = "recursion"
+        elif isinstance(
+            estimator,
+            (BaseHistGradientBoosting, DecisionTreeRegressor, RandomForestRegressor),
+        ):
+            method = "recursion"
+        else:
+            method = "brute"
+
+    if method == "recursion":
+        if not isinstance(
+            estimator,
+            (
+                BaseGradientBoosting,
+                BaseHistGradientBoosting,
+                DecisionTreeRegressor,
+                RandomForestRegressor,
+            ),
+        ):
+            supported_classes_recursion = (
+                "GradientBoostingClassifier",
+                "GradientBoostingRegressor",
+                "HistGradientBoostingClassifier",
+                "HistGradientBoostingRegressor",
+                "HistGradientBoostingRegressor",
+                "DecisionTreeRegressor",
+                "RandomForestRegressor",
+            )
+            raise ValueError(
+                "Only the following estimators support the 'recursion' "
+                "method: {}. Try using method='brute'.".format(
+                    ", ".join(supported_classes_recursion)
+                )
+            )
+        if response_method == "auto":
+            response_method = "decision_function"
+
+        if response_method != "decision_function":
+            raise ValueError(
+                "With the 'recursion' method, the response_method must be "
+                "'decision_function'. Got {}.".format(response_method)
+            )
+
+    if sample_weight is not None:
+        sample_weight = _check_sample_weight(sample_weight, X)
+
+    if _determine_key_type(features, accept_slice=False) == "int":
+        # _get_column_indices() supports negative indexing. Here, we limit
+        # the indexing to be positive. The upper bound will be checked
+        # by _get_column_indices()
+        if np.any(np.less(features, 0)):
+            raise ValueError("all features must be in [0, {}]".format(X.shape[1] - 1))
+
+    features_indices = np.asarray(
+        _get_column_indices(X, features), dtype=np.intp, order="C"
+    ).ravel()
+
+    feature_names = _check_feature_names(X, feature_names)
+
+    n_features = X.shape[1]
+    if categorical_features is None:
+        is_categorical = [False] * len(features_indices)
+    else:
+        categorical_features = np.asarray(categorical_features)
+        if categorical_features.size == 0:
+            raise ValueError(
+                "Passing an empty list (`[]`) to `categorical_features` is not "
+                "supported. Use `None` instead to indicate that there are no "
+                "categorical features."
+            )
+        if categorical_features.dtype.kind == "b":
+            # categorical features provided as a list of boolean
+            if categorical_features.size != n_features:
+                raise ValueError(
+                    "When `categorical_features` is a boolean array-like, "
+                    "the array should be of shape (n_features,). Got "
+                    f"{categorical_features.size} elements while `X` contains "
+                    f"{n_features} features."
+                )
+            is_categorical = [categorical_features[idx] for idx in features_indices]
+        elif categorical_features.dtype.kind in ("i", "O", "U"):
+            # categorical features provided as a list of indices or feature names
+            categorical_features_idx = [
+                _get_feature_index(cat, feature_names=feature_names)
+                for cat in categorical_features
+            ]
+            is_categorical = [
+                idx in categorical_features_idx for idx in features_indices
+            ]
+        else:
+            raise ValueError(
+                "Expected `categorical_features` to be an array-like of boolean,"
+                f" integer, or string. Got {categorical_features.dtype} instead."
+            )
+
+    custom_values = custom_values or {}
+    if isinstance(features, (str, int)):
+        features = [features]
+
+    for feature_idx, feature, is_cat in zip(features_indices, features, is_categorical):
+        if is_cat:
+            continue
+
+        if _safe_indexing(X, feature_idx, axis=1).dtype.kind in "iu":
+            # TODO(1.9): raise a ValueError instead.
+            warnings.warn(
+                f"The column {feature!r} contains integer data. Partial "
+                "dependence plots are not supported for integer data: this "
+                "can lead to implicit rounding with NumPy arrays or even errors "
+                "with newer pandas versions. Please convert numerical features"
+                "to floating point dtypes ahead of time to avoid problems. "
+                "This will raise ValueError in scikit-learn 1.9.",
+                FutureWarning,
+            )
+            # Do not warn again for other features to avoid spamming the caller.
+            break
+
+    X_subset = _safe_indexing(X, features_indices, axis=1)
+
+    custom_values_for_X_subset = {
+        index: custom_values.get(feature)
+        for index, feature in enumerate(features)
+        if feature in custom_values
+    }
+
+    grid, values = _grid_from_X(
+        X_subset,
+        percentiles,
+        is_categorical,
+        grid_resolution,
+        custom_values_for_X_subset,
+    )
+
+    if method == "brute":
+        averaged_predictions, predictions = _partial_dependence_brute(
+            estimator, grid, features_indices, X, response_method, sample_weight
+        )
+
+        # reshape predictions to
+        # (n_outputs, n_instances, n_values_feature_0, n_values_feature_1, ...)
+        predictions = predictions.reshape(
+            -1, X.shape[0], *[val.shape[0] for val in values]
+        )
+    else:
+        averaged_predictions = _partial_dependence_recursion(
+            estimator, grid, features_indices
+        )
+
+    # reshape averaged_predictions to
+    # (n_outputs, n_values_feature_0, n_values_feature_1, ...)
+    averaged_predictions = averaged_predictions.reshape(
+        -1, *[val.shape[0] for val in values]
+    )
+    pdp_results = Bunch(grid_values=values)
+
+    if kind == "average":
+        pdp_results["average"] = averaged_predictions
+    elif kind == "individual":
+        pdp_results["individual"] = predictions
+    else:  # kind='both'
+        pdp_results["average"] = averaged_predictions
+        pdp_results["individual"] = predictions
+
+    return pdp_results
diff --git a/shared/package-files/04/04052634c6d146f2665eca68b385d3ab1a46b8f706fa6f3fe76f99ccfd51b015/buffers.pyi b/shared/package-files/04/04052634c6d146f2665eca68b385d3ab1a46b8f706fa6f3fe76f99ccfd51b015/buffers.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..fc5147e00d35e44311ea0c3711a5f208f7ac4845
--- /dev/null
+++ b/shared/package-files/04/04052634c6d146f2665eca68b385d3ab1a46b8f706fa6f3fe76f99ccfd51b015/buffers.pyi
@@ -0,0 +1,57 @@
+from io import BufferedIOBase, BufferedRandom, BytesIO
+from typing import Any, Callable, Optional
+
+COPY_BYTES: int
+STRBUF_LIMIT: int
+
+class FileBasedBuffer:
+    remain: int = ...
+    file: BytesIO = ...
+    def __init__(self, file: BytesIO, from_buffer: Optional[BytesIO] = ...) -> None: ...
+    def __len__(self) -> int: ...
+    def __nonzero__(self) -> bool: ...
+    __bool__: Callable[[], bool] = ...
+    def append(self, s: Any) -> None: ...
+    def get(self, numbytes: int = ..., skip: bool = ...) -> bytes: ...
+    def skip(self, numbytes: int, allow_prune: int = ...) -> None: ...
+    def newfile(self) -> Any: ...
+    def prune(self) -> None: ...
+    def getfile(self) -> Any: ...
+    def close(self) -> None: ...
+
+class TempfileBasedBuffer(FileBasedBuffer):
+    def __init__(self, from_buffer: Optional[BytesIO] = ...) -> None: ...
+    def newfile(self) -> BufferedRandom: ...
+
+class BytesIOBasedBuffer(FileBasedBuffer):
+    file: BytesIO = ...
+    def __init__(self, from_buffer: Optional[BytesIO] = ...) -> None: ...
+    def newfile(self) -> BytesIO: ...
+
+class ReadOnlyFileBasedBuffer(FileBasedBuffer):
+    file: BytesIO = ...
+    block_size: int = ...
+    def __init__(self, file: BytesIO, block_size: int = ...) -> None: ...
+    remain: int = ...
+    def prepare(self, size: Optional[int] = ...) -> int: ...
+    def get(self, numbytes: int = ..., skip: bool = ...) -> bytes: ...
+    def __iter__(self) -> ReadOnlyFileBasedBuffer: ...
+    def next(self) -> Optional[bytes]: ...
+    __next__: Callable[[], Optional[bytes]] = ...
+    def append(self, s: Any) -> None: ...
+
+class OverflowableBuffer:
+    overflowed: bool = ...
+    buf: Optional[BufferedIOBase] = ...
+    strbuf: bytes = ...
+    overflow: int = ...
+    def __init__(self, overflow: int) -> None: ...
+    def __len__(self) -> int: ...
+    def __nonzero__(self) -> bool: ...
+    __bool__: Callable[[], bool] = ...
+    def append(self, s: bytes) -> None: ...
+    def get(self, numbytes: int = ..., skip: bool = ...) -> bytes: ...
+    def skip(self, numbytes: int, allow_prune: bool = ...) -> None: ...
+    def prune(self) -> None: ...
+    def getfile(self) -> BytesIO: ...
+    def close(self) -> None: ...
diff --git a/shared/package-files/04/04087cc25b2e35867104a220f4a6be8f9a4ad6c597270a6ffc05e312d9af31d2/_empty_per_channel_affine_quantized_cpu_dispatch.h b/shared/package-files/04/04087cc25b2e35867104a220f4a6be8f9a4ad6c597270a6ffc05e312d9af31d2/_empty_per_channel_affine_quantized_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..c967991684c98f0956dffb1e08d204a4fa28a0fd
--- /dev/null
+++ b/shared/package-files/04/04087cc25b2e35867104a220f4a6be8f9a4ad6c597270a6ffc05e312d9af31d2/_empty_per_channel_affine_quantized_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor _empty_per_channel_affine_quantized(at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::TensorOptions options={}, c10::optional memory_format=MemoryFormat::Contiguous);
+TORCH_API at::Tensor _empty_per_channel_affine_quantized(at::IntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format);
+TORCH_API at::Tensor _empty_per_channel_affine_quantized_symint(c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, at::TensorOptions options={}, c10::optional memory_format=MemoryFormat::Contiguous);
+TORCH_API at::Tensor _empty_per_channel_affine_quantized_symint(c10::SymIntArrayRef size, const at::Tensor & scales, const at::Tensor & zero_points, int64_t axis, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional memory_format);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/04/0408d12f67299322f9e249d3a7a570005800c87f16dbd48e90c59419aca22fff/_foreach_reciprocal_ops.h b/shared/package-files/04/0408d12f67299322f9e249d3a7a570005800c87f16dbd48e90c59419aca22fff/_foreach_reciprocal_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..946bec271173fa5f4d2f0150300ac2ea24823d90
--- /dev/null
+++ b/shared/package-files/04/0408d12f67299322f9e249d3a7a570005800c87f16dbd48e90c59419aca22fff/_foreach_reciprocal_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _foreach_reciprocal {
+  using schema = ::std::vector (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_reciprocal")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_reciprocal(Tensor[] self) -> Tensor[]")
+  static ::std::vector call(at::TensorList self);
+  static ::std::vector redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self);
+};
+
+struct TORCH_API _foreach_reciprocal_ {
+  using schema = void (at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_reciprocal_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_reciprocal_(Tensor(a!)[] self) -> ()")
+  static void call(at::TensorList self);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self);
+};
+
+struct TORCH_API _foreach_reciprocal_out {
+  using schema = void (at::TensorList, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_reciprocal")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_reciprocal.out(Tensor[] self, *, Tensor(a!)[] out) -> ()")
+  static void call(at::TensorList self, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, at::TensorList out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/04/040cefc7b11c9af82c5b0e73a6d2ce5effa9ea0a26c1a0ea3000a70fd0db4f0f/macrodata_jmulti_cst_granger_causality_realgdp.txt b/shared/package-files/04/040cefc7b11c9af82c5b0e73a6d2ce5effa9ea0a26c1a0ea3000a70fd0db4f0f/macrodata_jmulti_cst_granger_causality_realgdp.txt
new file mode 100644
index 0000000000000000000000000000000000000000..bcee77de04152a0877c8b18d9521142939617c47
--- /dev/null
+++ b/shared/package-files/04/040cefc7b11c9af82c5b0e73a6d2ce5effa9ea0a26c1a0ea3000a70fd0db4f0f/macrodata_jmulti_cst_granger_causality_realgdp.txt
@@ -0,0 +1,13 @@
+*** Sat, 3 Sep 2016 22:35:30 ***
+TEST FOR GRANGER-CAUSALITY:
+H0: "realgdp" do not Granger-cause "realcons, realinv"
+
+Test statistic l = 3.3656
+pval-F( l; 8, 546) = 0.0009 
+
+TEST FOR INSTANTANEOUS CAUSALITY:
+H0: No instantaneous causality between "realgdp" and "realcons, realinv"
+
+Test statistic: c = 86.5484
+pval-Chi( c; 2) = 0.0000 
+
diff --git a/shared/package-files/04/040dcb9fb1c9b1d84c83800dd057366dad3bc0bbc50ba97995e268bdb491f236/_backward.h b/shared/package-files/04/040dcb9fb1c9b1d84c83800dd057366dad3bc0bbc50ba97995e268bdb491f236/_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..07012a5b9d7026c4f7b2cb0e8568c20b640bce42
--- /dev/null
+++ b/shared/package-files/04/040dcb9fb1c9b1d84c83800dd057366dad3bc0bbc50ba97995e268bdb491f236/_backward.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+
+}
diff --git a/shared/package-files/04/041037a86775c6d3054fac7c70ec1b9c893f2df3a0be645f8d4baf1b6d560115/sparse_coo_tensor.h b/shared/package-files/04/041037a86775c6d3054fac7c70ec1b9c893f2df3a0be645f8d4baf1b6d560115/sparse_coo_tensor.h
new file mode 100644
index 0000000000000000000000000000000000000000..b4328d0982ea0aec036adf2187952f1202bd6784
--- /dev/null
+++ b/shared/package-files/04/041037a86775c6d3054fac7c70ec1b9c893f2df3a0be645f8d4baf1b6d560115/sparse_coo_tensor.h
@@ -0,0 +1,61 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::sparse_coo_tensor.size(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
+inline at::Tensor sparse_coo_tensor(at::IntArrayRef size, at::TensorOptions options) {
+    return at::_ops::sparse_coo_tensor_size::call(size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+// aten::sparse_coo_tensor.size(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=False) -> Tensor
+inline at::Tensor sparse_coo_tensor(at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) {
+    return at::_ops::sparse_coo_tensor_size::call(size, dtype, layout, device, pin_memory);
+}
+
+// aten::sparse_coo_tensor.indices(Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor
+inline at::Tensor sparse_coo_tensor(const at::Tensor & indices, const at::Tensor & values, at::TensorOptions options={}, c10::optional is_coalesced=c10::nullopt) {
+    return at::_ops::sparse_coo_tensor_indices::call(indices, values, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced);
+}
+// aten::sparse_coo_tensor.indices(Tensor indices, Tensor values, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor
+inline at::Tensor sparse_coo_tensor(const at::Tensor & indices, const at::Tensor & values, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional is_coalesced) {
+    return at::_ops::sparse_coo_tensor_indices::call(indices, values, dtype, layout, device, pin_memory, is_coalesced);
+}
+
+// aten::sparse_coo_tensor.indices_size(Tensor indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor
+inline at::Tensor sparse_coo_tensor(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, at::TensorOptions options={}, c10::optional is_coalesced=c10::nullopt) {
+    return at::_ops::sparse_coo_tensor_indices_size::call(indices, values, size, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt(), is_coalesced);
+}
+// aten::sparse_coo_tensor.indices_size(Tensor indices, Tensor values, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, bool? is_coalesced=None) -> Tensor
+inline at::Tensor sparse_coo_tensor(const at::Tensor & indices, const at::Tensor & values, at::IntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory, c10::optional is_coalesced) {
+    return at::_ops::sparse_coo_tensor_indices_size::call(indices, values, size, dtype, layout, device, pin_memory, is_coalesced);
+}
+
+// aten::sparse_coo_tensor.size_out(int[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sparse_coo_tensor_out(at::Tensor & out, at::IntArrayRef size) {
+    return at::_ops::sparse_coo_tensor_size_out::call(size, out);
+}
+// aten::sparse_coo_tensor.size_out(int[] size, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & sparse_coo_tensor_outf(at::IntArrayRef size, at::Tensor & out) {
+    return at::_ops::sparse_coo_tensor_size_out::call(size, out);
+}
+
+}
diff --git a/shared/package-files/04/04109b325b900885b3869bec629911ca56d3d9d64ac2bc213911184d9ad83966/reflection_pad2d.h b/shared/package-files/04/04109b325b900885b3869bec629911ca56d3d9d64ac2bc213911184d9ad83966/reflection_pad2d.h
new file mode 100644
index 0000000000000000000000000000000000000000..efda45d7d74fc3c17ef35d567c92e170705528f6
--- /dev/null
+++ b/shared/package-files/04/04109b325b900885b3869bec629911ca56d3d9d64ac2bc213911184d9ad83966/reflection_pad2d.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & reflection_pad2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
+    return at::_ops::reflection_pad2d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
+}
+namespace symint {
+  template ::value>>
+  at::Tensor & reflection_pad2d_out(at::Tensor & out, const at::Tensor & self, at::IntArrayRef padding) {
+    return at::_ops::reflection_pad2d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
+  }
+}
+
+// aten::reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & reflection_pad2d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
+    return at::_ops::reflection_pad2d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
+}
+namespace symint {
+  template ::value>>
+  at::Tensor & reflection_pad2d_outf(const at::Tensor & self, at::IntArrayRef padding, at::Tensor & out) {
+    return at::_ops::reflection_pad2d_out::call(self, c10::fromIntArrayRefSlow(padding), out);
+  }
+}
+
+// aten::reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & reflection_pad2d_symint_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
+    return at::_ops::reflection_pad2d_out::call(self, padding, out);
+}
+namespace symint {
+  template ::value>>
+  at::Tensor & reflection_pad2d_out(at::Tensor & out, const at::Tensor & self, c10::SymIntArrayRef padding) {
+    return at::_ops::reflection_pad2d_out::call(self, padding, out);
+  }
+}
+
+// aten::reflection_pad2d.out(Tensor self, SymInt[4] padding, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & reflection_pad2d_symint_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
+    return at::_ops::reflection_pad2d_out::call(self, padding, out);
+}
+namespace symint {
+  template ::value>>
+  at::Tensor & reflection_pad2d_outf(const at::Tensor & self, c10::SymIntArrayRef padding, at::Tensor & out) {
+    return at::_ops::reflection_pad2d_out::call(self, padding, out);
+  }
+}
+
+// aten::reflection_pad2d(Tensor self, SymInt[4] padding) -> Tensor
+inline at::Tensor reflection_pad2d(const at::Tensor & self, at::IntArrayRef padding) {
+    return at::_ops::reflection_pad2d::call(self, c10::fromIntArrayRefSlow(padding));
+}
+namespace symint {
+  template ::value>>
+  at::Tensor reflection_pad2d(const at::Tensor & self, at::IntArrayRef padding) {
+    return at::_ops::reflection_pad2d::call(self, c10::fromIntArrayRefSlow(padding));
+  }
+}
+
+// aten::reflection_pad2d(Tensor self, SymInt[4] padding) -> Tensor
+inline at::Tensor reflection_pad2d_symint(const at::Tensor & self, c10::SymIntArrayRef padding) {
+    return at::_ops::reflection_pad2d::call(self, padding);
+}
+namespace symint {
+  template ::value>>
+  at::Tensor reflection_pad2d(const at::Tensor & self, c10::SymIntArrayRef padding) {
+    return at::_ops::reflection_pad2d::call(self, padding);
+  }
+}
+
+}
diff --git a/shared/package-files/04/0411490cc19035bd314d178d71b7a0d12a6eeeb08e2e39593be9422e6a75da92/macrodata_jmulti_cs_granger_causality_realcons.txt b/shared/package-files/04/0411490cc19035bd314d178d71b7a0d12a6eeeb08e2e39593be9422e6a75da92/macrodata_jmulti_cs_granger_causality_realcons.txt
new file mode 100644
index 0000000000000000000000000000000000000000..1abb4fb3accea1ae7416e149acedbe67950b4cb8
--- /dev/null
+++ b/shared/package-files/04/0411490cc19035bd314d178d71b7a0d12a6eeeb08e2e39593be9422e6a75da92/macrodata_jmulti_cs_granger_causality_realcons.txt
@@ -0,0 +1,13 @@
+*** Sat, 3 Sep 2016 22:26:39 ***
+TEST FOR GRANGER-CAUSALITY:
+H0: "realcons" do not Granger-cause "realgdp, realinv"
+
+Test statistic l = 10.9780
+pval-F( l; 8, 549) = 0.0000 
+
+TEST FOR INSTANTANEOUS CAUSALITY:
+H0: No instantaneous causality between "realcons" and "realgdp, realinv"
+
+Test statistic: c = 72.2185
+pval-Chi( c; 2) = 0.0000 
+
diff --git a/shared/package-files/04/04139d49b6dd2467002b721538a1054ff5e2adae0a73eeaa4cce328430af5332/_foreach_zero_cpu_dispatch.h b/shared/package-files/04/04139d49b6dd2467002b721538a1054ff5e2adae0a73eeaa4cce328430af5332/_foreach_zero_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..06146dfd77c3a3abcb98080f7a58a8de85ce2d27
--- /dev/null
+++ b/shared/package-files/04/04139d49b6dd2467002b721538a1054ff5e2adae0a73eeaa4cce328430af5332/_foreach_zero_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API void _foreach_zero_(at::TensorList self);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/04/04169386964dbb021006ab5530e101e1b50793741d1b6369b8b241ba6f0fe9a9/_tstutils.py b/shared/package-files/04/04169386964dbb021006ab5530e101e1b50793741d1b6369b8b241ba6f0fe9a9/_tstutils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f56e835e345d66023efae81114a45ed29269f18d
--- /dev/null
+++ b/shared/package-files/04/04169386964dbb021006ab5530e101e1b50793741d1b6369b8b241ba6f0fe9a9/_tstutils.py
@@ -0,0 +1,972 @@
+r"""
+Parameters used in test and benchmark methods.
+
+Collections of test cases suitable for testing 1-D root-finders
+  'original': The original benchmarking functions.
+     Real-valued functions of real-valued inputs on an interval
+     with a zero.
+     f1, .., f3 are continuous and infinitely differentiable
+     f4 has a left- and right- discontinuity at the root
+     f5 has a root at 1 replacing a 1st order pole
+     f6 is randomly positive on one side of the root,
+     randomly negative on the other.
+     f4 - f6 are not continuous at the root.
+
+  'aps': The test problems in the 1995 paper
+     TOMS "Algorithm 748: Enclosing Zeros of Continuous Functions"
+     by Alefeld, Potra and Shi. Real-valued functions of
+     real-valued inputs on an interval with a zero.
+     Suitable for methods which start with an enclosing interval, and
+     derivatives up to 2nd order.
+
+  'complex': Some complex-valued functions of complex-valued inputs.
+     No enclosing bracket is provided.
+     Suitable for methods which use one or more starting values, and
+     derivatives up to 2nd order.
+
+  The test cases are provided as a list of dictionaries. The dictionary
+  keys will be a subset of:
+  ["f", "fprime", "fprime2", "args", "bracket", "smoothness",
+  "a", "b", "x0", "x1", "root", "ID"]
+"""
+
+# Sources:
+#  [1] Alefeld, G. E. and Potra, F. A. and Shi, Yixun,
+#      "Algorithm 748: Enclosing Zeros of Continuous Functions",
+#      ACM Trans. Math. Softw. Volume 221(1995)
+#       doi = {10.1145/210089.210111},
+#  [2] Chandrupatla, Tirupathi R. "A new hybrid quadratic/bisection algorithm
+#      for finding the zero of a nonlinear function without using derivatives."
+#      Advances in Engineering Software 28.3 (1997): 145-149.
+
+from random import random
+
+import numpy as np
+
+from scipy.optimize import _zeros_py as cc
+from scipy._lib._array_api import array_namespace
+
+# "description" refers to the original functions
+description = """
+f2 is a symmetric parabola, x**2 - 1
+f3 is a quartic polynomial with large hump in interval
+f4 is step function with a discontinuity at 1
+f5 is a hyperbola with vertical asymptote at 1
+f6 has random values positive to left of 1, negative to right
+
+Of course, these are not real problems. They just test how the
+'good' solvers behave in bad circumstances where bisection is
+really the best. A good solver should not be much worse than
+bisection in such circumstance, while being faster for smooth
+monotone sorts of functions.
+"""
+
+
+def f1(x):
+    r"""f1 is a quadratic with roots at 0 and 1"""
+    return x * (x - 1.)
+
+
+def f1_fp(x):
+    return 2 * x - 1
+
+
+def f1_fpp(x):
+    return 2
+
+
+def f2(x):
+    r"""f2 is a symmetric parabola, x**2 - 1"""
+    return x**2 - 1
+
+
+def f2_fp(x):
+    return 2 * x
+
+
+def f2_fpp(x):
+    return 2
+
+
+def f3(x):
+    r"""A quartic with roots at 0, 1, 2 and 3"""
+    return x * (x - 1.) * (x - 2.) * (x - 3.)  # x**4 - 6x**3 + 11x**2 - 6x
+
+
+def f3_fp(x):
+    return 4 * x**3 - 18 * x**2 + 22 * x - 6
+
+
+def f3_fpp(x):
+    return 12 * x**2 - 36 * x + 22
+
+
+def f4(x):
+    r"""Piecewise linear, left- and right- discontinuous at x=1, the root."""
+    if x > 1:
+        return 1.0 + .1 * x
+    if x < 1:
+        return -1.0 + .1 * x
+    return 0
+
+
+def f5(x):
+    r"""
+    Hyperbola with a pole at x=1, but pole replaced with 0. Not continuous at root.
+    """
+    if x != 1:
+        return 1.0 / (1. - x)
+    return 0
+
+
+# f6(x) returns random value. Without memoization, calling twice with the
+# same x returns different values, hence a "random value", not a
+# "function with random values"
+_f6_cache = {}
+def f6(x):
+    v = _f6_cache.get(x, None)
+    if v is None:
+        if x > 1:
+            v = random()
+        elif x < 1:
+            v = -random()
+        else:
+            v = 0
+        _f6_cache[x] = v
+    return v
+
+
+# Each Original test case has
+# - a function and its two derivatives,
+# - additional arguments,
+# - a bracket enclosing a root,
+# - the order of differentiability (smoothness) on this interval
+# - a starting value for methods which don't require a bracket
+# - the root (inside the bracket)
+# - an Identifier of the test case
+
+_ORIGINAL_TESTS_KEYS = [
+    "f", "fprime", "fprime2", "args", "bracket", "smoothness", "x0", "root", "ID"
+]
+_ORIGINAL_TESTS = [
+    [f1, f1_fp, f1_fpp, (), [0.5, np.sqrt(3)], np.inf, 0.6, 1.0, "original.01.00"],
+    [f2, f2_fp, f2_fpp, (), [0.5, np.sqrt(3)], np.inf, 0.6, 1.0, "original.02.00"],
+    [f3, f3_fp, f3_fpp, (), [0.5, np.sqrt(3)], np.inf, 0.6, 1.0, "original.03.00"],
+    [f4, None, None, (), [0.5, np.sqrt(3)], -1, 0.6, 1.0, "original.04.00"],
+    [f5, None, None, (), [0.5, np.sqrt(3)], -1, 0.6, 1.0, "original.05.00"],
+    [f6, None, None, (), [0.5, np.sqrt(3)], -np.inf, 0.6, 1.0, "original.05.00"]
+]
+
+_ORIGINAL_TESTS_DICTS = [
+    dict(zip(_ORIGINAL_TESTS_KEYS, testcase)) for testcase in _ORIGINAL_TESTS
+]
+
+#   ##################
+#   "APS" test cases
+#   Functions and test cases that appear in [1]
+
+
+def aps01_f(x):
+    r"""Straightforward sum of trigonometric function and polynomial"""
+    return np.sin(x) - x / 2
+
+
+def aps01_fp(x):
+    return np.cos(x) - 1.0 / 2
+
+
+def aps01_fpp(x):
+    return -np.sin(x)
+
+
+def aps02_f(x):
+    r"""poles at x=n**2, 1st and 2nd derivatives at root are also close to 0"""
+    ii = np.arange(1, 21)
+    return -2 * np.sum((2 * ii - 5)**2 / (x - ii**2)**3)
+
+
+def aps02_fp(x):
+    ii = np.arange(1, 21)
+    return 6 * np.sum((2 * ii - 5)**2 / (x - ii**2)**4)
+
+
+def aps02_fpp(x):
+    ii = np.arange(1, 21)
+    return 24 * np.sum((2 * ii - 5)**2 / (x - ii**2)**5)
+
+
+def aps03_f(x, a, b):
+    r"""Rapidly changing at the root"""
+    return a * x * np.exp(b * x)
+
+
+def aps03_fp(x, a, b):
+    return a * (b * x + 1) * np.exp(b * x)
+
+
+def aps03_fpp(x, a, b):
+    return a * (b * (b * x + 1) + b) * np.exp(b * x)
+
+
+def aps04_f(x, n, a):
+    r"""Medium-degree polynomial"""
+    return x**n - a
+
+
+def aps04_fp(x, n, a):
+    return n * x**(n - 1)
+
+
+def aps04_fpp(x, n, a):
+    return n * (n - 1) * x**(n - 2)
+
+
+def aps05_f(x):
+    r"""Simple Trigonometric function"""
+    return np.sin(x) - 1.0 / 2
+
+
+def aps05_fp(x):
+    return np.cos(x)
+
+
+def aps05_fpp(x):
+    return -np.sin(x)
+
+
+def aps06_f(x, n):
+    r"""Exponential rapidly changing from -1 to 1 at x=0"""
+    return 2 * x * np.exp(-n) - 2 * np.exp(-n * x) + 1
+
+
+def aps06_fp(x, n):
+    return 2 * np.exp(-n) + 2 * n * np.exp(-n * x)
+
+
+def aps06_fpp(x, n):
+    return -2 * n * n * np.exp(-n * x)
+
+
+def aps07_f(x, n):
+    r"""Upside down parabola with parametrizable height"""
+    return (1 + (1 - n)**2) * x - (1 - n * x)**2
+
+
+def aps07_fp(x, n):
+    return (1 + (1 - n)**2) + 2 * n * (1 - n * x)
+
+
+def aps07_fpp(x, n):
+    return -2 * n * n
+
+
+def aps08_f(x, n):
+    r"""Degree n polynomial"""
+    return x * x - (1 - x)**n
+
+
+def aps08_fp(x, n):
+    return 2 * x + n * (1 - x)**(n - 1)
+
+
+def aps08_fpp(x, n):
+    return 2 - n * (n - 1) * (1 - x)**(n - 2)
+
+
+def aps09_f(x, n):
+    r"""Upside down quartic with parametrizable height"""
+    return (1 + (1 - n)**4) * x - (1 - n * x)**4
+
+
+def aps09_fp(x, n):
+    return (1 + (1 - n)**4) + 4 * n * (1 - n * x)**3
+
+
+def aps09_fpp(x, n):
+    return -12 * n * (1 - n * x)**2
+
+
+def aps10_f(x, n):
+    r"""Exponential plus a polynomial"""
+    return np.exp(-n * x) * (x - 1) + x**n
+
+
+def aps10_fp(x, n):
+    return np.exp(-n * x) * (-n * (x - 1) + 1) + n * x**(n - 1)
+
+
+def aps10_fpp(x, n):
+    return (np.exp(-n * x) * (-n * (-n * (x - 1) + 1) + -n * x)
+            + n * (n - 1) * x**(n - 2))
+
+
+def aps11_f(x, n):
+    r"""Rational function with a zero at x=1/n and a pole at x=0"""
+    return (n * x - 1) / ((n - 1) * x)
+
+
+def aps11_fp(x, n):
+    return 1 / (n - 1) / x**2
+
+
+def aps11_fpp(x, n):
+    return -2 / (n - 1) / x**3
+
+
+def aps12_f(x, n):
+    r"""nth root of x, with a zero at x=n"""
+    return np.power(x, 1.0 / n) - np.power(n, 1.0 / n)
+
+
+def aps12_fp(x, n):
+    return np.power(x, (1.0 - n) / n) / n
+
+
+def aps12_fpp(x, n):
+    return np.power(x, (1.0 - 2 * n) / n) * (1.0 / n) * (1.0 - n) / n
+
+
+_MAX_EXPABLE = np.log(np.finfo(float).max)
+
+
+def aps13_f(x):
+    r"""Function with *all* derivatives 0 at the root"""
+    if x == 0:
+        return 0
+    # x2 = 1.0/x**2
+    # if x2 > 708:
+    #     return 0
+    y = 1 / x**2
+    if y > _MAX_EXPABLE:
+        return 0
+    return x / np.exp(y)
+
+
+def aps13_fp(x):
+    if x == 0:
+        return 0
+    y = 1 / x**2
+    if y > _MAX_EXPABLE:
+        return 0
+    return (1 + 2 / x**2) / np.exp(y)
+
+
+def aps13_fpp(x):
+    if x == 0:
+        return 0
+    y = 1 / x**2
+    if y > _MAX_EXPABLE:
+        return 0
+    return 2 * (2 - x**2) / x**5 / np.exp(y)
+
+
+def aps14_f(x, n):
+    r"""0 for negative x-values, trigonometric+linear for x positive"""
+    if x <= 0:
+        return -n / 20.0
+    return n / 20.0 * (x / 1.5 + np.sin(x) - 1)
+
+
+def aps14_fp(x, n):
+    if x <= 0:
+        return 0
+    return n / 20.0 * (1.0 / 1.5 + np.cos(x))
+
+
+def aps14_fpp(x, n):
+    if x <= 0:
+        return 0
+    return -n / 20.0 * (np.sin(x))
+
+
+def aps15_f(x, n):
+    r"""piecewise linear, constant outside of [0, 0.002/(1+n)]"""
+    if x < 0:
+        return -0.859
+    if x > 2 * 1e-3 / (1 + n):
+        return np.e - 1.859
+    return np.exp((n + 1) * x / 2 * 1000) - 1.859
+
+
+def aps15_fp(x, n):
+    if not 0 <= x <= 2 * 1e-3 / (1 + n):
+        return np.e - 1.859
+    return np.exp((n + 1) * x / 2 * 1000) * (n + 1) / 2 * 1000
+
+
+def aps15_fpp(x, n):
+    if not 0 <= x <= 2 * 1e-3 / (1 + n):
+        return np.e - 1.859
+    return np.exp((n + 1) * x / 2 * 1000) * (n + 1) / 2 * 1000 * (n + 1) / 2 * 1000
+
+
+# Each APS test case has
+# - a function and its two derivatives,
+# - additional arguments,
+# - a bracket enclosing a root,
+# - the order of differentiability of the function on this interval
+# - a starting value for methods which don't require a bracket
+# - the root (inside the bracket)
+# - an Identifier of the test case
+#
+# Algorithm 748 is a bracketing algorithm so a bracketing interval was provided
+# in [1] for each test case. Newton and Halley methods need a single
+# starting point x0, which was chosen to be near the middle of the interval,
+# unless that would have made the problem too easy.
+
+_APS_TESTS_KEYS = [
+    "f", "fprime", "fprime2", "args", "bracket", "smoothness", "x0", "root", "ID"
+]
+_APS_TESTS = [
+    [aps01_f, aps01_fp, aps01_fpp, (), [np.pi / 2, np.pi], np.inf,
+     3, 1.89549426703398094e+00, "aps.01.00"],
+    [aps02_f, aps02_fp, aps02_fpp, (), [1 + 1e-9, 4 - 1e-9], np.inf,
+     2, 3.02291534727305677e+00, "aps.02.00"],
+    [aps02_f, aps02_fp, aps02_fpp, (), [4 + 1e-9, 9 - 1e-9], np.inf,
+     5, 6.68375356080807848e+00, "aps.02.01"],
+    [aps02_f, aps02_fp, aps02_fpp, (), [9 + 1e-9, 16 - 1e-9], np.inf,
+     10, 1.12387016550022114e+01, "aps.02.02"],
+    [aps02_f, aps02_fp, aps02_fpp, (), [16 + 1e-9, 25 - 1e-9], np.inf,
+     17, 1.96760000806234103e+01, "aps.02.03"],
+    [aps02_f, aps02_fp, aps02_fpp, (), [25 + 1e-9, 36 - 1e-9], np.inf,
+     26, 2.98282273265047557e+01, "aps.02.04"],
+    [aps02_f, aps02_fp, aps02_fpp, (), [36 + 1e-9, 49 - 1e-9], np.inf,
+     37, 4.19061161952894139e+01, "aps.02.05"],
+    [aps02_f, aps02_fp, aps02_fpp, (), [49 + 1e-9, 64 - 1e-9], np.inf,
+     50, 5.59535958001430913e+01, "aps.02.06"],
+    [aps02_f, aps02_fp, aps02_fpp, (), [64 + 1e-9, 81 - 1e-9], np.inf,
+     65, 7.19856655865877997e+01, "aps.02.07"],
+    [aps02_f, aps02_fp, aps02_fpp, (), [81 + 1e-9, 100 - 1e-9], np.inf,
+     82, 9.00088685391666701e+01, "aps.02.08"],
+    [aps02_f, aps02_fp, aps02_fpp, (), [100 + 1e-9, 121 - 1e-9], np.inf,
+     101, 1.10026532748330197e+02, "aps.02.09"],
+    [aps03_f, aps03_fp, aps03_fpp, (-40, -1), [-9, 31], np.inf,
+     -2, 0, "aps.03.00"],
+    [aps03_f, aps03_fp, aps03_fpp, (-100, -2), [-9, 31], np.inf,
+     -2, 0, "aps.03.01"],
+    [aps03_f, aps03_fp, aps03_fpp, (-200, -3), [-9, 31], np.inf,
+     -2, 0, "aps.03.02"],
+    [aps04_f, aps04_fp, aps04_fpp, (4, 0.2), [0, 5], np.inf,
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+     -2, 1.22779942324615231e-05, "aps.15.21"],
+    [aps15_f, aps15_fp, aps15_fpp, (200,), [-1000, 1e-4], 0,
+     -2, 6.16953939044086617e-06, "aps.15.22"],
+    [aps15_f, aps15_fp, aps15_fpp, (300,), [-1000, 1e-4], 0,
+     -2, 4.11985852982928163e-06, "aps.15.23"],
+    [aps15_f, aps15_fp, aps15_fpp, (400,), [-1000, 1e-4], 0,
+     -2, 3.09246238772721682e-06, "aps.15.24"],
+    [aps15_f, aps15_fp, aps15_fpp, (500,), [-1000, 1e-4], 0,
+     -2, 2.47520442610501789e-06, "aps.15.25"],
+    [aps15_f, aps15_fp, aps15_fpp, (600,), [-1000, 1e-4], 0,
+     -2, 2.06335676785127107e-06, "aps.15.26"],
+    [aps15_f, aps15_fp, aps15_fpp, (700,), [-1000, 1e-4], 0,
+     -2, 1.76901200781542651e-06, "aps.15.27"],
+    [aps15_f, aps15_fp, aps15_fpp, (800,), [-1000, 1e-4], 0,
+     -2, 1.54816156988591016e-06, "aps.15.28"],
+    [aps15_f, aps15_fp, aps15_fpp, (900,), [-1000, 1e-4], 0,
+     -2, 1.37633453660223511e-06, "aps.15.29"],
+    [aps15_f, aps15_fp, aps15_fpp, (1000,), [-1000, 1e-4], 0,
+     -2, 1.23883857889971403e-06, "aps.15.30"]
+]
+
+_APS_TESTS_DICTS = [dict(zip(_APS_TESTS_KEYS, testcase)) for testcase in _APS_TESTS]
+
+
+#   ##################
+#   "complex" test cases
+#   A few simple, complex-valued, functions, defined on the complex plane.
+
+
+def cplx01_f(z, n, a):
+    r"""z**n-a:  Use to find the nth root of a"""
+    return z**n - a
+
+
+def cplx01_fp(z, n, a):
+    return n * z**(n - 1)
+
+
+def cplx01_fpp(z, n, a):
+    return n * (n - 1) * z**(n - 2)
+
+
+def cplx02_f(z, a):
+    r"""e**z - a: Use to find the log of a"""
+    return np.exp(z) - a
+
+
+def cplx02_fp(z, a):
+    return np.exp(z)
+
+
+def cplx02_fpp(z, a):
+    return np.exp(z)
+
+
+# Each "complex" test case has
+# - a function and its two derivatives,
+# - additional arguments,
+# - the order of differentiability of the function on this interval
+# - two starting values x0 and x1
+# - the root
+# - an Identifier of the test case
+#
+# Algorithm 748 is a bracketing algorithm so a bracketing interval was provided
+# in [1] for each test case. Newton and Halley need a single starting point
+# x0, which was chosen to be near the middle of the interval, unless that
+# would make the problem too easy.
+
+
+_COMPLEX_TESTS_KEYS = [
+    "f", "fprime", "fprime2", "args", "smoothness", "x0", "x1", "root", "ID"
+]
+_COMPLEX_TESTS = [
+    [cplx01_f, cplx01_fp, cplx01_fpp, (2, -1), np.inf,
+     (1 + 1j), (0.5 + 0.5j), 1j, "complex.01.00"],
+    [cplx01_f, cplx01_fp, cplx01_fpp, (3, 1), np.inf,
+     (-1 + 1j), (-0.5 + 2.0j), (-0.5 + np.sqrt(3) / 2 * 1.0j),
+     "complex.01.01"],
+    [cplx01_f, cplx01_fp, cplx01_fpp, (3, -1), np.inf,
+     1j, (0.5 + 0.5j), (0.5 + np.sqrt(3) / 2 * 1.0j),
+     "complex.01.02"],
+    [cplx01_f, cplx01_fp, cplx01_fpp, (3, 8), np.inf,
+     5, 4, 2, "complex.01.03"],
+    [cplx02_f, cplx02_fp, cplx02_fpp, (-1,), np.inf,
+     (1 + 2j), (0.5 + 0.5j), np.pi * 1.0j, "complex.02.00"],
+    [cplx02_f, cplx02_fp, cplx02_fpp, (1j,), np.inf,
+     (1 + 2j), (0.5 + 0.5j), np.pi * 0.5j, "complex.02.01"],
+]
+
+_COMPLEX_TESTS_DICTS = [
+    dict(zip(_COMPLEX_TESTS_KEYS, testcase)) for testcase in _COMPLEX_TESTS
+]
+
+
+def _add_a_b(tests):
+    r"""Add "a" and "b" keys to each test from the "bracket" value"""
+    for d in tests:
+        for k, v in zip(['a', 'b'], d.get('bracket', [])):
+            d[k] = v
+
+
+_add_a_b(_ORIGINAL_TESTS_DICTS)
+_add_a_b(_APS_TESTS_DICTS)
+_add_a_b(_COMPLEX_TESTS_DICTS)
+
+
+def get_tests(collection='original', smoothness=None):
+    r"""Return the requested collection of test cases, as an array of dicts with subset-specific keys
+
+    Allowed values of collection:
+    'original': The original benchmarking functions.
+         Real-valued functions of real-valued inputs on an interval with a zero.
+         f1, .., f3 are continuous and infinitely differentiable
+         f4 has a single discontinuity at the root
+         f5 has a root at 1 replacing a 1st order pole
+         f6 is randomly positive on one side of the root, randomly negative on the other
+    'aps': The test problems in the TOMS "Algorithm 748: Enclosing Zeros of Continuous Functions"
+         paper by Alefeld, Potra and Shi. Real-valued functions of
+         real-valued inputs on an interval with a zero.
+         Suitable for methods which start with an enclosing interval, and
+         derivatives up to 2nd order.
+    'complex': Some complex-valued functions of complex-valued inputs.
+         No enclosing bracket is provided.
+         Suitable for methods which use one or more starting values, and
+         derivatives up to 2nd order.
+
+    The dictionary keys will be a subset of
+    ["f", "fprime", "fprime2", "args", "bracket", "a", b", "smoothness", "x0", "x1", "root", "ID"]
+    """  # noqa: E501
+    collection = collection or "original"
+    subsets = {"aps": _APS_TESTS_DICTS,
+               "complex": _COMPLEX_TESTS_DICTS,
+               "original": _ORIGINAL_TESTS_DICTS,
+               "chandrupatla": _CHANDRUPATLA_TESTS_DICTS}
+    tests = subsets.get(collection, [])
+    if smoothness is not None:
+        tests = [tc for tc in tests if tc['smoothness'] >= smoothness]
+    return tests
+
+
+# Backwards compatibility
+methods = [cc.bisect, cc.ridder, cc.brenth, cc.brentq]
+mstrings = ['cc.bisect', 'cc.ridder', 'cc.brenth', 'cc.brentq']
+functions = [f2, f3, f4, f5, f6]
+fstrings = ['f2', 'f3', 'f4', 'f5', 'f6']
+
+#   ##################
+#   "Chandrupatla" test cases
+#   Functions and test cases that appear in [2]
+
+def fun1(x):
+    return x**3 - 2*x - 5
+fun1.root = 2.0945514815423265  # additional precision using mpmath.findroot
+
+
+def fun2(x):
+    return 1 - 1/x**2
+fun2.root = 1
+
+
+def fun3(x):
+    return (x-3)**3
+fun3.root = 3
+
+
+def fun4(x):
+    return 6*(x-2)**5
+fun4.root = 2
+
+
+def fun5(x):
+    return x**9
+fun5.root = 0
+
+
+def fun6(x):
+    return x**19
+fun6.root = 0
+
+
+def fun7(x):
+    xp = array_namespace(x)
+    return 0 if xp.abs(x) < 3.8e-4 else x*xp.exp(-x**(-2))
+fun7.root = 0
+
+
+def fun8(x):
+    xp = array_namespace(x)
+    xi = 0.61489
+    return -(3062*(1-xi)*xp.exp(-x))/(xi + (1-xi)*xp.exp(-x)) - 1013 + 1628/x
+fun8.root = 1.0375360332870405
+
+
+def fun9(x):
+    xp = array_namespace(x)
+    return xp.exp(x) - 2 - 0.01/x**2 + .000002/x**3
+fun9.root = 0.7032048403631358
+
+# Each "chandropatla" test case has
+# - a function,
+# - two starting values x0 and x1
+# - the root
+# - the number of function evaluations required by Chandrupatla's algorithm
+# - an Identifier of the test case
+#
+# Chandrupatla's is a bracketing algorithm, so a bracketing interval was
+# provided in [2] for each test case. No special support for testing with
+# secant/Newton/Halley is provided.
+
+_CHANDRUPATLA_TESTS_KEYS = ["f", "bracket", "root", "nfeval", "ID"]
+_CHANDRUPATLA_TESTS = [
+    [fun1, [2, 3], fun1.root, 7],
+    [fun1, [1, 10], fun1.root, 11],
+    [fun1, [1, 100], fun1.root, 14],
+    [fun1, [-1e4, 1e4], fun1.root, 23],
+    [fun1, [-1e10, 1e10], fun1.root, 43],
+    [fun2, [0.5, 1.51], fun2.root, 8],
+    [fun2, [1e-4, 1e4], fun2.root, 22],
+    [fun2, [1e-6, 1e6], fun2.root, 28],
+    [fun2, [1e-10, 1e10], fun2.root, 41],
+    [fun2, [1e-12, 1e12], fun2.root, 48],
+    [fun3, [0, 5], fun3.root, 21],
+    [fun3, [-10, 10], fun3.root, 23],
+    [fun3, [-1e4, 1e4], fun3.root, 36],
+    [fun3, [-1e6, 1e6], fun3.root, 45],
+    [fun3, [-1e10, 1e10], fun3.root, 55],
+    [fun4, [0, 5], fun4.root, 21],
+    [fun4, [-10, 10], fun4.root, 23],
+    [fun4, [-1e4, 1e4], fun4.root, 33],
+    [fun4, [-1e6, 1e6], fun4.root, 43],
+    [fun4, [-1e10, 1e10], fun4.root, 54],
+    [fun5, [-1, 4], fun5.root, 21],
+    [fun5, [-2, 5], fun5.root, 22],
+    [fun5, [-1, 10], fun5.root, 23],
+    [fun5, [-5, 50], fun5.root, 25],
+    [fun5, [-10, 100], fun5.root, 26],
+    [fun6, [-1., 4.], fun6.root, 21],
+    [fun6, [-2., 5.], fun6.root, 22],
+    [fun6, [-1., 10.], fun6.root, 23],
+    [fun6, [-5., 50.], fun6.root, 25],
+    [fun6, [-10., 100.], fun6.root, 26],
+    [fun7, [-1, 4], fun7.root, 8],
+    [fun7, [-2, 5], fun7.root, 8],
+    [fun7, [-1, 10], fun7.root, 11],
+    [fun7, [-5, 50], fun7.root, 18],
+    [fun7, [-10, 100], fun7.root, 19],
+    [fun8, [2e-4, 2], fun8.root, 9],
+    [fun8, [2e-4, 3], fun8.root, 10],
+    [fun8, [2e-4, 9], fun8.root, 11],
+    [fun8, [2e-4, 27], fun8.root, 12],
+    [fun8, [2e-4, 81], fun8.root, 14],
+    [fun9, [2e-4, 1], fun9.root, 7],
+    [fun9, [2e-4, 3], fun9.root, 8],
+    [fun9, [2e-4, 9], fun9.root, 10],
+    [fun9, [2e-4, 27], fun9.root, 11],
+    [fun9, [2e-4, 81], fun9.root, 13],
+]
+_CHANDRUPATLA_TESTS = [test + [f'{test[0].__name__}.{i%5+1}']
+                       for i, test in enumerate(_CHANDRUPATLA_TESTS)]
+
+_CHANDRUPATLA_TESTS_DICTS = [dict(zip(_CHANDRUPATLA_TESTS_KEYS, testcase))
+                             for testcase in _CHANDRUPATLA_TESTS]
+_add_a_b(_CHANDRUPATLA_TESTS_DICTS)
diff --git a/shared/package-files/04/0416aa1dc3dedf41822da7389d5c52bb36305f62747e69f230456b541482dc49/basic_ops.h b/shared/package-files/04/0416aa1dc3dedf41822da7389d5c52bb36305f62747e69f230456b541482dc49/basic_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..2248c7082d8cb6e963b14d68fc71e00432141e53
--- /dev/null
+++ b/shared/package-files/04/0416aa1dc3dedf41822da7389d5c52bb36305f62747e69f230456b541482dc49/basic_ops.h
@@ -0,0 +1,111 @@
+#pragma once
+
+#include 
+#include 
+#include 
+#include 
+
+#include 
+#include 
+#include 
+
+namespace torch {
+namespace autograd {
+
+struct TORCH_API Error : public Node {
+  Error(std::string msg, edge_list&& next_edges)
+      : Node(std::move(next_edges)), msg(std::move(msg)) {}
+
+  Error(std::string msg) : msg(std::move(msg)) {}
+
+  variable_list apply(variable_list&& inputs) override;
+
+  void compiled_args(CompiledNodeArgs& args) override;
+  variable_list apply_with_saved(
+      const variable_list& inputs,
+      SwapSavedVariables& saved) override;
+
+  std::string msg;
+};
+
+// We print grad_fn names in tensor printing. For functions with backward
+// NYI, grad_fn= will be printed if we use Error, which is confusing. So
+// special case with a new NotImplemented function here.
+struct TORCH_API NotImplemented : public Error {
+  NotImplemented(const std::string& forward_fn, edge_list&& next_edges)
+      : Error(
+            "derivative for " + forward_fn + " is not implemented",
+            std::move(next_edges)) {}
+
+  NotImplemented(const std::string& forward_fn)
+      : Error("derivative for " + forward_fn + " is not implemented") {}
+};
+
+// Identity in forward, Error in backward. Used to implement
+// @once_differentiable
+struct TORCH_API DelayedError : public Node {
+  DelayedError(std::string msg, int64_t num_inputs) : msg(std::move(msg)) {
+    // NOLINTNEXTLINE(clang-analyzer-deadcode.DeadStores)
+    for (const auto i : c10::irange(num_inputs)) {
+      (void)i; // Suppress unused variable warning
+      add_input_metadata(Node::undefined_input());
+    }
+  }
+
+  variable_list apply(variable_list&& inputs) override;
+
+  std::string msg;
+};
+
+struct TORCH_API UndefinedGrad : public Node {
+  UndefinedGrad() {
+    add_input_metadata(Node::undefined_input());
+  }
+
+  variable_list apply(variable_list&& inputs) override;
+};
+
+struct TORCH_API UndefinedGradBackward : public Node {
+  UndefinedGradBackward(edge_list&& next_edges) : Node(std::move(next_edges)) {}
+
+  UndefinedGradBackward() = default;
+
+  variable_list apply(variable_list&& inputs) override;
+
+  void compiled_args(CompiledNodeArgs& args) override {}
+  variable_list apply_with_saved(
+      const variable_list& inputs,
+      SwapSavedVariables& saved) override {
+    return apply(variable_list(inputs));
+  }
+};
+
+struct TORCH_API GraphRoot : public Node {
+  GraphRoot(edge_list functions, variable_list inputs)
+      : Node(std::move(functions)), outputs(std::move(inputs)) {
+    // Ensures calls to stream() on a GraphRoot instance reflect current
+    // stream(s) on devices of root grad tensors at the time the instance is
+    // constructed.
+    for (const auto& t : outputs) {
+      add_input_metadata(t);
+    }
+  }
+
+  variable_list apply(variable_list&& inputs) override {
+    return outputs;
+  }
+
+  void compiled_args(CompiledNodeArgs& args) override;
+  variable_list apply_with_saved(
+      const variable_list& inputs,
+      SwapSavedVariables& saved) override;
+
+  variable_list outputs;
+};
+
+struct TORCH_API Identity : public Node {
+  variable_list apply(variable_list&& inputs) override;
+};
+
+} // namespace autograd
+} // namespace torch
diff --git a/shared/package-files/04/04182ecad5bdc529d75bd8052568ab08369fbbd796a06fda026825c0606120b2/pad_ops.h b/shared/package-files/04/04182ecad5bdc529d75bd8052568ab08369fbbd796a06fda026825c0606120b2/pad_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..dfd172839b4eb8620fe5183afe30f68cf6732cf7
--- /dev/null
+++ b/shared/package-files/04/04182ecad5bdc529d75bd8052568ab08369fbbd796a06fda026825c0606120b2/pad_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API pad {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::string_view, c10::optional);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::pad")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "pad(Tensor self, SymInt[] pad, str mode=\"constant\", float? value=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef pad, c10::string_view mode, c10::optional value);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef pad, c10::string_view mode, c10::optional value);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/04/04194fe6bf88daad8345126d5364d1f35d1ca113a22ed66a466f04b39174fb58/vec256_bfloat16_vsx.h b/shared/package-files/04/04194fe6bf88daad8345126d5364d1f35d1ca113a22ed66a466f04b39174fb58/vec256_bfloat16_vsx.h
new file mode 100644
index 0000000000000000000000000000000000000000..2d8afd9ef29525e1acedd10f35cb3e7c21e646af
--- /dev/null
+++ b/shared/package-files/04/04194fe6bf88daad8345126d5364d1f35d1ca113a22ed66a466f04b39174fb58/vec256_bfloat16_vsx.h
@@ -0,0 +1,73 @@
+#pragma once
+
+#include 
+#include 
+#include 
+#include 
+
+namespace at {
+namespace vec {
+// See Note [CPU_CAPABILITY namespace]
+inline namespace CPU_CAPABILITY {
+
+inline std::tuple, Vectorized> convert_bfloat16_float(
+    const Vectorized& a) {
+  constexpr int64_t K = Vectorized::size();
+  __at_align__ float arr[K];
+  __at_align__ BFloat16 arr2[K];
+  a.store(arr2);
+  convert(arr2, arr, K);
+  return std::make_tuple(
+      Vectorized::loadu(arr),
+      Vectorized::loadu(arr + Vectorized::size()));
+}
+
+inline Vectorized convert_float_bfloat16(
+    const Vectorized& a,
+    const Vectorized& b) {
+  constexpr int64_t K = Vectorized::size();
+  __at_align__ float arr[K];
+  __at_align__ BFloat16 arr2[K];
+  a.store(arr);
+  b.store(arr + Vectorized::size());
+  convert(arr, arr2, K);
+  return Vectorized::loadu(arr2);
+}
+
+inline void load_fp32_from_bf16(const c10::BFloat16* data, Vectorized& out) {
+  __at_align__ float values[Vectorized::size()];
+  for (const auto k : c10::irange(Vectorized::size())) {
+    values[k] = data[k];
+  }
+  out = Vectorized::loadu(values);
+}
+
+inline void load_fp32_from_bf16(
+    const c10::BFloat16* data,
+    Vectorized& out1,
+    Vectorized& out2) {
+  load_fp32_from_bf16(data, out1);
+  data += Vectorized::size();
+  load_fp32_from_bf16(data, out2);
+}
+
+inline void load_fp32_from_fp16(const c10::Half* data, Vectorized& out) {
+  __at_align__ float values[Vectorized::size()];
+  for (const auto k : c10::irange(Vectorized::size())) {
+    values[k] = data[k];
+  }
+  out = Vectorized::loadu(values);
+}
+
+inline void load_fp32_from_fp16(
+    const c10::Half* data,
+    Vectorized& out1,
+    Vectorized& out2) {
+  load_fp32_from_fp16(data, out1);
+  data += Vectorized::size();
+  load_fp32_from_fp16(data, out2);
+}
+
+} // namespace
+} // namespace vec
+} // namespace at
diff --git a/shared/package-files/04/0419b25b01132bca2f5d3de01c8dda59b380cab7860dbac40ce009fb084f67cd/codegen.h b/shared/package-files/04/0419b25b01132bca2f5d3de01c8dda59b380cab7860dbac40ce009fb084f67cd/codegen.h
new file mode 100644
index 0000000000000000000000000000000000000000..fdcf3425e3abcba05d88d5637f53e14d7346a0d3
--- /dev/null
+++ b/shared/package-files/04/0419b25b01132bca2f5d3de01c8dda59b380cab7860dbac40ce009fb084f67cd/codegen.h
@@ -0,0 +1,283 @@
+#pragma once
+
+#include 
+#include 
+#include 
+
+#include 
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+template 
+class PaddedBuffer;
+
+class TORCH_API CodeGen {
+ public:
+  class BufferArg;
+  class CallArg;
+
+  template 
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CodeGen(StmtPtr stmt, Ts... ts)
+      : stmt_(std::move(stmt)), buffer_args_({BufferArg(ts)...}) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CodeGen(
+      StmtPtr stmt,
+      std::vector buffer_args,
+      at::Device device = at::kCPU,
+      std::string kernel_func_name = "func");
+
+  virtual ~CodeGen() = default;
+
+  StmtPtr stmt() const {
+    return stmt_;
+  }
+
+  void set_stmt(StmtPtr s) {
+    stmt_ = s;
+  }
+
+  void apply_mutator(IRMutator* mutator) {
+    stmt_ = stmt_->accept_mutator(mutator);
+  }
+
+  void apply_visitor(IRVisitor* visitor) {
+    stmt_->accept(visitor);
+  }
+
+  std::vector& buffer_args() {
+    return buffer_args_;
+  }
+
+  const std::vector& buffer_args() const {
+    return buffer_args_;
+  }
+
+  at::Device device() {
+    return device_;
+  }
+
+  // This function returns the generated code as
+  // a string.
+  virtual std::string getCodeText(const std::string& attr = "") {
+    return ("");
+  }
+
+  // TODO: Figure out how to unify these call interfaces.
+
+  /// Call a function with a vector of CallArgs, which are tagged
+  /// unions that properly type the arguments.
+  virtual void call(const std::vector& args) = 0;
+
+  /// Call a function faster than a regular `call` by assuming that
+  /// the generated kernel already knows the type of the arguments, so
+  /// they can be type-punned with `void*`s.
+  virtual void call_raw(const std::vector& args) = 0;
+
+  /// Call a function even faster than a regular call, by assuming
+  /// that the number of thread blocks can be derived from `numel` via
+  /// a simple division, rather than evaluating an expression.
+  virtual void call_with_numel(void** args, int64_t numel);
+
+  virtual at::Tensor empty_strided(
+      c10::IntArrayRef size,
+      c10::IntArrayRef stride,
+      c10::optional dtype_opt,
+      c10::optional layout_opt,
+      c10::optional device_opt,
+      c10::optional pin_memory_opt) {
+    return at::empty_strided(
+        size, stride, dtype_opt, layout_opt, device_opt, pin_memory_opt);
+  }
+
+  const std::string& kernel_func_name() const {
+    return kernel_func_name_;
+  }
+
+  void allocIntermediateBufs();
+
+ protected:
+  static void* argToPtr(const BufferArg& bufferArg, const CallArg& callArg);
+
+ private:
+  StmtPtr stmt_;
+  std::vector buffer_args_;
+  at::Device device_ = at::kCPU;
+  std::string kernel_func_name_ = "func";
+};
+
+class TORCH_API ExtCallMemoryReuse : public IRMutator {
+  static std::unordered_map makeExtCallFuncNameMap();
+  static const std::unordered_map extCallFuncNameMap_;
+
+ public:
+  explicit ExtCallMemoryReuse(
+      const std::vector& bufferArgs);
+  ~ExtCallMemoryReuse() override = default;
+  StmtPtr mutate(ExternalCallPtr v) override;
+
+ private:
+  std::unordered_set bufferArgs_;
+};
+
+class CodeGen::BufferArg {
+ public:
+  BufferArg(const Tensor& tensor) : buf_(tensor.buf()) {}
+  BufferArg(const VarHandle& var) : var_(var.node()), isVar_(true) {}
+  BufferArg(const BufHandle& buf) : buf_(buf.node()) {}
+  BufferArg(BufPtr buf) : buf_(std::move(buf)) {}
+
+  VarPtr var() const {
+    return isVar_ ? var_ : buf_->base_handle();
+  }
+
+  BufPtr buf() const {
+    return buf_;
+  }
+
+  bool isVar() const {
+    return isVar_;
+  }
+
+  Dtype dtype() const {
+    return isVar_ ? var_->dtype() : buf_->dtype();
+  }
+
+ private:
+  VarPtr var_ = nullptr;
+  BufPtr buf_ = nullptr;
+  bool isVar_ = false;
+};
+
+class CodeGen::CallArg {
+ public:
+  template 
+  CallArg(const PaddedBuffer& buffer);
+
+  template 
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init,cppcoreguidelines-pro-type-const-cast)
+  CallArg(const std::vector& buffer)
+      // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+      : data_(const_cast(buffer.data())) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CallArg(void* ptr) : data_(ptr) {}
+
+#define ARG_TYPE_CTOR(Type, Name)      \
+  CallArg(Type v) {                    \
+    memcpy(buffer_, &v, sizeof(Type)); \
+    data_ = (void*)buffer_;            \
+  }
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, ARG_TYPE_CTOR);
+#undef ARG_TYPE_CTOR
+
+  void* data() const {
+    return data_;
+  }
+
+  CallArg(const CallArg& rhs) {
+    if (rhs.data_ == rhs.buffer_) {
+      memcpy(this->buffer_, rhs.buffer_, sizeof(rhs.buffer_));
+      this->data_ = (void*)(this->buffer_);
+    } else {
+      this->data_ = rhs.data_;
+    }
+  }
+
+  CallArg& operator=(const CallArg& rhs) {
+    if (rhs.data_ == rhs.buffer_) {
+      memcpy(this->buffer_, rhs.buffer_, sizeof(rhs.buffer_));
+      this->data_ = (void*)(this->buffer_);
+    } else {
+      this->data_ = rhs.data_;
+    }
+    return *this;
+  }
+
+#define ARG_PTR_DEFINE(Type, Name)                  \
+  Type* Name##Ptr() const {                         \
+    TORCH_INTERNAL_ASSERT(data_ == (void*)buffer_); \
+    return (Type*)data_;                            \
+  }
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, ARG_PTR_DEFINE);
+#undef ARG_PTR_DEFINE
+
+ private:
+  void* data_;
+  // Regarding a scalar value, CallArg uses void**=&data_ to store it. But the
+  // bit width of a pointer is 32bit on a 32bit platform. It cannot store the
+  // scalar if the bit width of the scalar is larger than 32bit, such as double
+  // and long. Hence, we add 8 bytes buffer dedicated to storing the scalar
+  // value regardless its bit width is less or greater than 32bits.
+  char buffer_[8] = {0}; // 64bits
+};
+
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+class RegisterCodeGenList {
+ public:
+  TORCH_API static RegisterCodeGenList& GetInstance() {
+    static RegisterCodeGenList codegen_list;
+    return codegen_list;
+  }
+
+  using StmtFactoryMethod = std::function(
+      StmtPtr stmt,
+      const std::vector&,
+      at::Device device,
+      const std::string& kernel_func_name)>;
+
+  TORCH_API StmtFactoryMethod FindStmtFactoryMethod(const std::string& name);
+  RegisterCodeGenList(const RegisterCodeGenList&) = delete;
+  RegisterCodeGenList& operator=(const RegisterCodeGenList&) = delete;
+
+ private:
+  template 
+  friend class RegisterCodeGen;
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  RegisterCodeGenList() = default;
+  TORCH_API void AddStmtFactoryMethod(
+      const std::string& name,
+      const StmtFactoryMethod& stmt_factory_method);
+
+  std::unordered_map stmt_factory_methods_;
+};
+
+template 
+class RegisterCodeGen {
+ public:
+  explicit RegisterCodeGen(const std::string& name) {
+    RegisterCodeGenList& codegen_list = RegisterCodeGenList::GetInstance();
+    codegen_list.AddStmtFactoryMethod(
+        name,
+        [](StmtPtr stmt,
+           const std::vector& params,
+           at::Device device,
+           const std::string& kernel_func_name) {
+          // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+          std::unique_ptr method(
+              new CodeGenType(stmt, params, device, kernel_func_name));
+          return method;
+        });
+  }
+};
+
+TORCH_API std::unique_ptr CreateCodeGen(
+    const std::string& name,
+    StmtPtr stmt,
+    const std::vector& params,
+    at::Device device = at::kCPU,
+    const std::string& kernel_func_name = "func");
+
+class TORCH_API GenericIntrinsicsExpander : public IRMutator {
+ protected:
+  ExprPtr mutate(IntrinsicsPtr v) override;
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/04/041b220996da3e9182639d3a7e49aa65d06424d09772095cdf071206e00008d9/extras.pyi b/shared/package-files/04/041b220996da3e9182639d3a7e49aa65d06424d09772095cdf071206e00008d9/extras.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..56228b927080a3963159206a9afc830d6d7335cc
--- /dev/null
+++ b/shared/package-files/04/041b220996da3e9182639d3a7e49aa65d06424d09772095cdf071206e00008d9/extras.pyi
@@ -0,0 +1,85 @@
+from typing import Any
+from numpy.lib.index_tricks import AxisConcatenator
+
+from numpy.ma.core import (
+    dot as dot,
+    mask_rowcols as mask_rowcols,
+)
+
+__all__: list[str]
+
+def count_masked(arr, axis=...): ...
+def masked_all(shape, dtype = ...): ...
+def masked_all_like(arr): ...
+
+class _fromnxfunction:
+    __name__: Any
+    __doc__: Any
+    def __init__(self, funcname): ...
+    def getdoc(self): ...
+    def __call__(self, *args, **params): ...
+
+class _fromnxfunction_single(_fromnxfunction):
+    def __call__(self, x, *args, **params): ...
+
+class _fromnxfunction_seq(_fromnxfunction):
+    def __call__(self, x, *args, **params): ...
+
+class _fromnxfunction_allargs(_fromnxfunction):
+    def __call__(self, *args, **params): ...
+
+atleast_1d: _fromnxfunction_allargs
+atleast_2d: _fromnxfunction_allargs
+atleast_3d: _fromnxfunction_allargs
+
+vstack: _fromnxfunction_seq
+row_stack: _fromnxfunction_seq
+hstack: _fromnxfunction_seq
+column_stack: _fromnxfunction_seq
+dstack: _fromnxfunction_seq
+stack: _fromnxfunction_seq
+
+hsplit: _fromnxfunction_single
+diagflat: _fromnxfunction_single
+
+def apply_along_axis(func1d, axis, arr, *args, **kwargs): ...
+def apply_over_axes(func, a, axes): ...
+def average(a, axis=..., weights=..., returned=..., keepdims=...): ...
+def median(a, axis=..., out=..., overwrite_input=..., keepdims=...): ...
+def compress_nd(x, axis=...): ...
+def compress_rowcols(x, axis=...): ...
+def compress_rows(a): ...
+def compress_cols(a): ...
+def mask_rows(a, axis = ...): ...
+def mask_cols(a, axis = ...): ...
+def ediff1d(arr, to_end=..., to_begin=...): ...
+def unique(ar1, return_index=..., return_inverse=...): ...
+def intersect1d(ar1, ar2, assume_unique=...): ...
+def setxor1d(ar1, ar2, assume_unique=...): ...
+def in1d(ar1, ar2, assume_unique=..., invert=...): ...
+def isin(element, test_elements, assume_unique=..., invert=...): ...
+def union1d(ar1, ar2): ...
+def setdiff1d(ar1, ar2, assume_unique=...): ...
+def cov(x, y=..., rowvar=..., bias=..., allow_masked=..., ddof=...): ...
+def corrcoef(x, y=..., rowvar=..., bias = ..., allow_masked=..., ddof = ...): ...
+
+class MAxisConcatenator(AxisConcatenator):
+    concatenate: Any
+    @classmethod
+    def makemat(cls, arr): ...
+    def __getitem__(self, key): ...
+
+class mr_class(MAxisConcatenator):
+    def __init__(self): ...
+
+mr_: mr_class
+
+def ndenumerate(a, compressed=...): ...
+def flatnotmasked_edges(a): ...
+def notmasked_edges(a, axis=...): ...
+def flatnotmasked_contiguous(a): ...
+def notmasked_contiguous(a, axis=...): ...
+def clump_unmasked(a): ...
+def clump_masked(a): ...
+def vander(x, n=...): ...
+def polyfit(x, y, deg, rcond=..., full=..., w=..., cov=...): ...
diff --git a/shared/package-files/04/041e12ea530f544abe062b9112bdfba887ddd7a199d23bd7af96df92234200c8/fft_hfft_ops.h b/shared/package-files/04/041e12ea530f544abe062b9112bdfba887ddd7a199d23bd7af96df92234200c8/fft_hfft_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..e25eb14ab3f4ca7e5673d601ab88755a0a5d68ff
--- /dev/null
+++ b/shared/package-files/04/041e12ea530f544abe062b9112bdfba887ddd7a199d23bd7af96df92234200c8/fft_hfft_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API fft_hfft {
+  using schema = at::Tensor (const at::Tensor &, c10::optional, int64_t, c10::optional);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::fft_hfft")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "fft_hfft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm);
+};
+
+struct TORCH_API fft_hfft_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::optional, int64_t, c10::optional, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::fft_hfft")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "fft_hfft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/04/041f9fe9080338f890fef51250a4c035982979e18e37b125a5eebecb3f16c06d/histogram.pyx b/shared/package-files/04/041f9fe9080338f890fef51250a4c035982979e18e37b125a5eebecb3f16c06d/histogram.pyx
new file mode 100644
index 0000000000000000000000000000000000000000..e204eec6b97850f696ef61898562bb65bc908ed6
--- /dev/null
+++ b/shared/package-files/04/041f9fe9080338f890fef51250a4c035982979e18e37b125a5eebecb3f16c06d/histogram.pyx
@@ -0,0 +1,520 @@
+"""This module contains routines for building histograms."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+cimport cython
+from cython.parallel import prange
+from libc.string cimport memset
+
+import numpy as np
+
+from .common import HISTOGRAM_DTYPE
+from .common cimport hist_struct
+from .common cimport X_BINNED_DTYPE_C
+from .common cimport G_H_DTYPE_C
+from ...utils._typedefs cimport uint8_t
+
+
+# Notes:
+# - IN views are read-only, OUT views are write-only
+# - In a lot of functions here, we pass feature_idx and the whole 2d
+#   histograms arrays instead of just histograms[feature_idx]. This is because
+#   Cython generated C code will have strange Python interactions (likely
+#   related to the GIL release and the custom histogram dtype) when using 1d
+#   histogram arrays that come from 2d arrays.
+# - The for loops are un-wrapped, for example:
+#
+#   for i in range(n):
+#       array[i] = i
+#
+#   will become
+#
+#   for i in range(n // 4):
+#       array[i] = i
+#       array[i + 1] = i + 1
+#       array[i + 2] = i + 2
+#       array[i + 3] = i + 3
+#
+#   This is to hint gcc that it can auto-vectorize these 4 operations and
+#   perform them all at once.
+
+
+@cython.final
+cdef class HistogramBuilder:
+    """A Histogram builder... used to build histograms.
+
+    A histogram is an array with n_bins entries of type HISTOGRAM_DTYPE. Each
+    feature has its own histogram. A histogram contains the sum of gradients
+    and hessians of all the samples belonging to each bin.
+
+    There are different ways to build a histogram:
+    - by subtraction: hist(child) = hist(parent) - hist(sibling)
+    - from scratch. In this case we have routines that update the hessians
+      or not (not useful when hessians are constant for some losses e.g.
+      least squares). Also, there's a special case for the root which
+      contains all the samples, leading to some possible optimizations.
+      Overall all the implementations look the same, and are optimized for
+      cache hit.
+
+    Parameters
+    ----------
+    X_binned : ndarray of int, shape (n_samples, n_features)
+        The binned input samples. Must be Fortran-aligned.
+    n_bins : int
+        The total number of bins, including the bin for missing values. Used
+        to define the shape of the histograms.
+    gradients : ndarray, shape (n_samples,)
+        The gradients of each training sample. Those are the gradients of the
+        loss w.r.t the predictions, evaluated at iteration i - 1.
+    hessians : ndarray, shape (n_samples,)
+        The hessians of each training sample. Those are the hessians of the
+        loss w.r.t the predictions, evaluated at iteration i - 1.
+    hessians_are_constant : bool
+        Whether hessians are constant.
+    """
+    cdef public:
+        const X_BINNED_DTYPE_C [::1, :] X_binned
+        unsigned int n_features
+        unsigned int n_bins
+        G_H_DTYPE_C [::1] gradients
+        G_H_DTYPE_C [::1] hessians
+        G_H_DTYPE_C [::1] ordered_gradients
+        G_H_DTYPE_C [::1] ordered_hessians
+        uint8_t hessians_are_constant
+        int n_threads
+
+    def __init__(self, const X_BINNED_DTYPE_C [::1, :] X_binned,
+                 unsigned int n_bins, G_H_DTYPE_C [::1] gradients,
+                 G_H_DTYPE_C [::1] hessians,
+                 uint8_t hessians_are_constant,
+                 int n_threads):
+
+        self.X_binned = X_binned
+        self.n_features = X_binned.shape[1]
+        # Note: all histograms will have  bins, but some of the
+        # bins may be unused if a feature has a small number of unique values.
+        self.n_bins = n_bins
+        self.gradients = gradients
+        self.hessians = hessians
+        # for root node, gradients and hessians are already ordered
+        self.ordered_gradients = gradients.copy()
+        self.ordered_hessians = hessians.copy()
+        self.hessians_are_constant = hessians_are_constant
+        self.n_threads = n_threads
+
+    def compute_histograms_brute(
+        HistogramBuilder self,
+        const unsigned int [::1] sample_indices,       # IN
+        const unsigned int [:] allowed_features=None,  # IN
+    ):
+        """Compute the histograms of the node by scanning through all the data.
+
+        For a given feature, the complexity is O(n_samples)
+
+        Parameters
+        ----------
+        sample_indices : array of int, shape (n_samples_at_node,)
+            The indices of the samples at the node to split.
+
+        allowed_features : None or ndarray, dtype=np.uint32
+            Indices of the features that are allowed by interaction constraints to be
+            split.
+
+        Returns
+        -------
+        histograms : ndarray of HISTOGRAM_DTYPE, shape (n_features, n_bins)
+            The computed histograms of the current node.
+        """
+        cdef:
+            int n_samples
+            int feature_idx
+            int f_idx
+            int i
+            # need local views to avoid python interactions
+            uint8_t hessians_are_constant = self.hessians_are_constant
+            int n_allowed_features = self.n_features
+            G_H_DTYPE_C [::1] ordered_gradients = self.ordered_gradients
+            G_H_DTYPE_C [::1] gradients = self.gradients
+            G_H_DTYPE_C [::1] ordered_hessians = self.ordered_hessians
+            G_H_DTYPE_C [::1] hessians = self.hessians
+            # Histograms will be initialized to zero later within a prange
+            hist_struct [:, ::1] histograms = np.empty(
+                shape=(self.n_features, self.n_bins),
+                dtype=HISTOGRAM_DTYPE
+            )
+            bint has_interaction_cst = allowed_features is not None
+            int n_threads = self.n_threads
+
+        if has_interaction_cst:
+            n_allowed_features = allowed_features.shape[0]
+
+        with nogil:
+            n_samples = sample_indices.shape[0]
+
+            # Populate ordered_gradients and ordered_hessians. (Already done
+            # for root) Ordering the gradients and hessians helps to improve
+            # cache hit.
+            if sample_indices.shape[0] != gradients.shape[0]:
+                if hessians_are_constant:
+                    for i in prange(n_samples, schedule='static',
+                                    num_threads=n_threads):
+                        ordered_gradients[i] = gradients[sample_indices[i]]
+                else:
+                    for i in prange(n_samples, schedule='static',
+                                    num_threads=n_threads):
+                        ordered_gradients[i] = gradients[sample_indices[i]]
+                        ordered_hessians[i] = hessians[sample_indices[i]]
+
+            # Compute histogram of each feature
+            for f_idx in prange(
+                n_allowed_features, schedule='static', num_threads=n_threads
+            ):
+                if has_interaction_cst:
+                    feature_idx = allowed_features[f_idx]
+                else:
+                    feature_idx = f_idx
+
+                self._compute_histogram_brute_single_feature(
+                    feature_idx, sample_indices, histograms
+                )
+
+        return histograms
+
+    cdef void _compute_histogram_brute_single_feature(
+            HistogramBuilder self,
+            const int feature_idx,
+            const unsigned int [::1] sample_indices,  # IN
+            hist_struct [:, ::1] histograms) noexcept nogil:  # OUT
+        """Compute the histogram for a given feature."""
+
+        cdef:
+            unsigned int n_samples = sample_indices.shape[0]
+            const X_BINNED_DTYPE_C [::1] X_binned = \
+                self.X_binned[:, feature_idx]
+            unsigned int root_node = X_binned.shape[0] == n_samples
+            G_H_DTYPE_C [::1] ordered_gradients = \
+                self.ordered_gradients[:n_samples]
+            G_H_DTYPE_C [::1] ordered_hessians = \
+                self.ordered_hessians[:n_samples]
+            uint8_t hessians_are_constant = \
+                self.hessians_are_constant
+
+        # Set histograms to zero.
+        memset(&histograms[feature_idx, 0], 0, self.n_bins * sizeof(hist_struct))
+
+        if root_node:
+            if hessians_are_constant:
+                _build_histogram_root_no_hessian(feature_idx, X_binned,
+                                                 ordered_gradients,
+                                                 histograms)
+            else:
+                _build_histogram_root(feature_idx, X_binned,
+                                      ordered_gradients, ordered_hessians,
+                                      histograms)
+        else:
+            if hessians_are_constant:
+                _build_histogram_no_hessian(feature_idx,
+                                            sample_indices, X_binned,
+                                            ordered_gradients, histograms)
+            else:
+                _build_histogram(feature_idx, sample_indices,
+                                 X_binned, ordered_gradients,
+                                 ordered_hessians, histograms)
+
+    def compute_histograms_subtraction(
+        HistogramBuilder self,
+        hist_struct [:, ::1] parent_histograms,        # IN and OUT
+        hist_struct [:, ::1] sibling_histograms,       # IN
+        const unsigned int [:] allowed_features=None,  # IN
+    ):
+        """Compute the histograms of the node using the subtraction trick.
+
+        hist(parent) = hist(left_child) + hist(right_child)
+
+        For a given feature, the complexity is O(n_bins). This is much more
+        efficient than compute_histograms_brute, but it's only possible for one
+        of the siblings.
+
+        Parameters
+        ----------
+        parent_histograms : ndarray of HISTOGRAM_DTYPE, \
+                shape (n_features, n_bins)
+            The histograms of the parent.
+        sibling_histograms : ndarray of HISTOGRAM_DTYPE, \
+                shape (n_features, n_bins)
+            The histograms of the sibling.
+        allowed_features : None or ndarray, dtype=np.uint32
+            Indices of the features that are allowed by interaction constraints to be
+            split.
+
+        Returns
+        -------
+        histograms : ndarray of HISTOGRAM_DTYPE, shape(n_features, n_bins)
+            The computed histograms of the current node.
+            We repurpose parent_histograms for this and don't need to allocate new
+            memory.
+        """
+
+        cdef:
+            int feature_idx
+            int f_idx
+            int n_allowed_features = self.n_features
+            bint has_interaction_cst = allowed_features is not None
+            int n_threads = self.n_threads
+
+        if has_interaction_cst:
+            n_allowed_features = allowed_features.shape[0]
+
+        # Compute histogram of each feature
+        for f_idx in prange(n_allowed_features, schedule='static', nogil=True,
+                            num_threads=n_threads):
+            if has_interaction_cst:
+                feature_idx = allowed_features[f_idx]
+            else:
+                feature_idx = f_idx
+
+            _subtract_histograms(
+                feature_idx,
+                self.n_bins,
+                parent_histograms,
+                sibling_histograms,
+            )
+        return parent_histograms
+
+
+cpdef void _build_histogram_naive(
+        const int feature_idx,
+        unsigned int [:] sample_indices,  # IN
+        X_BINNED_DTYPE_C [:] binned_feature,  # IN
+        G_H_DTYPE_C [:] ordered_gradients,  # IN
+        G_H_DTYPE_C [:] ordered_hessians,  # IN
+        hist_struct [:, :] out) noexcept nogil:  # OUT
+    """Build histogram in a naive way, without optimizing for cache hit.
+
+    Used in tests to compare with the optimized version."""
+    cdef:
+        unsigned int i
+        unsigned int n_samples = sample_indices.shape[0]
+        unsigned int sample_idx
+        unsigned int bin_idx
+
+    for i in range(n_samples):
+        sample_idx = sample_indices[i]
+        bin_idx = binned_feature[sample_idx]
+        out[feature_idx, bin_idx].sum_gradients += ordered_gradients[i]
+        out[feature_idx, bin_idx].sum_hessians += ordered_hessians[i]
+        out[feature_idx, bin_idx].count += 1
+
+
+cpdef void _subtract_histograms(
+        const int feature_idx,
+        unsigned int n_bins,
+        hist_struct [:, ::1] hist_a,  # IN and OUT
+        hist_struct [:, ::1] hist_b,  # IN
+) noexcept nogil:  # OUT
+    """compute hist_a = hist_a - hist_b"""
+    # Note that subtraction of large sums of floating point numbers, as we have here,
+    # can exhibit catastrophic cancallation. This is in particular true for gradients
+    # as they can be positive and negative, while hessians are non-negative.
+    # Remember that gradients and hessians are originally computed in
+    # G_H_DTYPE_C = float32 precision. Therefore, if sum_gradients and sum_hessians are
+    # float64, we don't loose precision. But if we also used float32 for summation, we
+    # would need to take care of floating point errors.
+    #
+    # Note that we could protect for negative hessians by setting:
+    #     sum_hessians = max(0, sum_hessians)
+    # But as we use float64 for summing float32, that's veeeery unlikely.
+    cdef:
+        unsigned int i = 0
+    for i in range(n_bins):
+        hist_a[feature_idx, i].sum_gradients -= hist_b[feature_idx, i].sum_gradients
+        hist_a[feature_idx, i].sum_hessians -= hist_b[feature_idx, i].sum_hessians
+        hist_a[feature_idx, i].count -= hist_b[feature_idx, i].count
+
+
+cpdef void _build_histogram(
+        const int feature_idx,
+        const unsigned int [::1] sample_indices,  # IN
+        const X_BINNED_DTYPE_C [::1] binned_feature,  # IN
+        const G_H_DTYPE_C [::1] ordered_gradients,  # IN
+        const G_H_DTYPE_C [::1] ordered_hessians,  # IN
+        hist_struct [:, ::1] out) noexcept nogil:  # OUT
+    """Return histogram for a given feature."""
+    cdef:
+        unsigned int i = 0
+        unsigned int n_node_samples = sample_indices.shape[0]
+        unsigned int unrolled_upper = (n_node_samples // 4) * 4
+
+        unsigned int bin_0
+        unsigned int bin_1
+        unsigned int bin_2
+        unsigned int bin_3
+        unsigned int bin_idx
+
+    for i in range(0, unrolled_upper, 4):
+        bin_0 = binned_feature[sample_indices[i]]
+        bin_1 = binned_feature[sample_indices[i + 1]]
+        bin_2 = binned_feature[sample_indices[i + 2]]
+        bin_3 = binned_feature[sample_indices[i + 3]]
+
+        out[feature_idx, bin_0].sum_gradients += ordered_gradients[i]
+        out[feature_idx, bin_1].sum_gradients += ordered_gradients[i + 1]
+        out[feature_idx, bin_2].sum_gradients += ordered_gradients[i + 2]
+        out[feature_idx, bin_3].sum_gradients += ordered_gradients[i + 3]
+
+        out[feature_idx, bin_0].sum_hessians += ordered_hessians[i]
+        out[feature_idx, bin_1].sum_hessians += ordered_hessians[i + 1]
+        out[feature_idx, bin_2].sum_hessians += ordered_hessians[i + 2]
+        out[feature_idx, bin_3].sum_hessians += ordered_hessians[i + 3]
+
+        out[feature_idx, bin_0].count += 1
+        out[feature_idx, bin_1].count += 1
+        out[feature_idx, bin_2].count += 1
+        out[feature_idx, bin_3].count += 1
+
+    for i in range(unrolled_upper, n_node_samples):
+        bin_idx = binned_feature[sample_indices[i]]
+        out[feature_idx, bin_idx].sum_gradients += ordered_gradients[i]
+        out[feature_idx, bin_idx].sum_hessians += ordered_hessians[i]
+        out[feature_idx, bin_idx].count += 1
+
+
+cpdef void _build_histogram_no_hessian(
+        const int feature_idx,
+        const unsigned int [::1] sample_indices,  # IN
+        const X_BINNED_DTYPE_C [::1] binned_feature,  # IN
+        const G_H_DTYPE_C [::1] ordered_gradients,  # IN
+        hist_struct [:, ::1] out) noexcept nogil:  # OUT
+    """Return histogram for a given feature, not updating hessians.
+
+    Used when the hessians of the loss are constant (typically LS loss).
+    """
+
+    cdef:
+        unsigned int i = 0
+        unsigned int n_node_samples = sample_indices.shape[0]
+        unsigned int unrolled_upper = (n_node_samples // 4) * 4
+
+        unsigned int bin_0
+        unsigned int bin_1
+        unsigned int bin_2
+        unsigned int bin_3
+        unsigned int bin_idx
+
+    for i in range(0, unrolled_upper, 4):
+        bin_0 = binned_feature[sample_indices[i]]
+        bin_1 = binned_feature[sample_indices[i + 1]]
+        bin_2 = binned_feature[sample_indices[i + 2]]
+        bin_3 = binned_feature[sample_indices[i + 3]]
+
+        out[feature_idx, bin_0].sum_gradients += ordered_gradients[i]
+        out[feature_idx, bin_1].sum_gradients += ordered_gradients[i + 1]
+        out[feature_idx, bin_2].sum_gradients += ordered_gradients[i + 2]
+        out[feature_idx, bin_3].sum_gradients += ordered_gradients[i + 3]
+
+        out[feature_idx, bin_0].count += 1
+        out[feature_idx, bin_1].count += 1
+        out[feature_idx, bin_2].count += 1
+        out[feature_idx, bin_3].count += 1
+
+    for i in range(unrolled_upper, n_node_samples):
+        bin_idx = binned_feature[sample_indices[i]]
+        out[feature_idx, bin_idx].sum_gradients += ordered_gradients[i]
+        out[feature_idx, bin_idx].count += 1
+
+
+cpdef void _build_histogram_root(
+        const int feature_idx,
+        const X_BINNED_DTYPE_C [::1] binned_feature,  # IN
+        const G_H_DTYPE_C [::1] all_gradients,  # IN
+        const G_H_DTYPE_C [::1] all_hessians,  # IN
+        hist_struct [:, ::1] out) noexcept nogil:  # OUT
+    """Compute histogram of the root node.
+
+    Unlike other nodes, the root node has to find the split among *all* the
+    samples from the training set. binned_feature and all_gradients /
+    all_hessians already have a consistent ordering.
+    """
+
+    cdef:
+        unsigned int i = 0
+        unsigned int n_samples = binned_feature.shape[0]
+        unsigned int unrolled_upper = (n_samples // 4) * 4
+
+        unsigned int bin_0
+        unsigned int bin_1
+        unsigned int bin_2
+        unsigned int bin_3
+        unsigned int bin_idx
+
+    for i in range(0, unrolled_upper, 4):
+
+        bin_0 = binned_feature[i]
+        bin_1 = binned_feature[i + 1]
+        bin_2 = binned_feature[i + 2]
+        bin_3 = binned_feature[i + 3]
+
+        out[feature_idx, bin_0].sum_gradients += all_gradients[i]
+        out[feature_idx, bin_1].sum_gradients += all_gradients[i + 1]
+        out[feature_idx, bin_2].sum_gradients += all_gradients[i + 2]
+        out[feature_idx, bin_3].sum_gradients += all_gradients[i + 3]
+
+        out[feature_idx, bin_0].sum_hessians += all_hessians[i]
+        out[feature_idx, bin_1].sum_hessians += all_hessians[i + 1]
+        out[feature_idx, bin_2].sum_hessians += all_hessians[i + 2]
+        out[feature_idx, bin_3].sum_hessians += all_hessians[i + 3]
+
+        out[feature_idx, bin_0].count += 1
+        out[feature_idx, bin_1].count += 1
+        out[feature_idx, bin_2].count += 1
+        out[feature_idx, bin_3].count += 1
+
+    for i in range(unrolled_upper, n_samples):
+        bin_idx = binned_feature[i]
+        out[feature_idx, bin_idx].sum_gradients += all_gradients[i]
+        out[feature_idx, bin_idx].sum_hessians += all_hessians[i]
+        out[feature_idx, bin_idx].count += 1
+
+
+cpdef void _build_histogram_root_no_hessian(
+        const int feature_idx,
+        const X_BINNED_DTYPE_C [::1] binned_feature,  # IN
+        const G_H_DTYPE_C [::1] all_gradients,  # IN
+        hist_struct [:, ::1] out) noexcept nogil:  # OUT
+    """Compute histogram of the root node, not updating hessians.
+
+    Used when the hessians of the loss are constant (typically LS loss).
+    """
+
+    cdef:
+        unsigned int i = 0
+        unsigned int n_samples = binned_feature.shape[0]
+        unsigned int unrolled_upper = (n_samples // 4) * 4
+
+        unsigned int bin_0
+        unsigned int bin_1
+        unsigned int bin_2
+        unsigned int bin_3
+        unsigned int bin_idx
+
+    for i in range(0, unrolled_upper, 4):
+        bin_0 = binned_feature[i]
+        bin_1 = binned_feature[i + 1]
+        bin_2 = binned_feature[i + 2]
+        bin_3 = binned_feature[i + 3]
+
+        out[feature_idx, bin_0].sum_gradients += all_gradients[i]
+        out[feature_idx, bin_1].sum_gradients += all_gradients[i + 1]
+        out[feature_idx, bin_2].sum_gradients += all_gradients[i + 2]
+        out[feature_idx, bin_3].sum_gradients += all_gradients[i + 3]
+
+        out[feature_idx, bin_0].count += 1
+        out[feature_idx, bin_1].count += 1
+        out[feature_idx, bin_2].count += 1
+        out[feature_idx, bin_3].count += 1
+
+    for i in range(unrolled_upper, n_samples):
+        bin_idx = binned_feature[i]
+        out[feature_idx, bin_idx].sum_gradients += all_gradients[i]
+        out[feature_idx, bin_idx].count += 1
diff --git a/shared/package-files/04/042223e56632d8ff283e63e190e0496f9af6945f0d6dfb692ac9d129aabc85dc/opcode.py b/shared/package-files/04/042223e56632d8ff283e63e190e0496f9af6945f0d6dfb692ac9d129aabc85dc/opcode.py
new file mode 100644
index 0000000000000000000000000000000000000000..37e88e92df70ecc58ea9f59bea40fa20a209f916
--- /dev/null
+++ b/shared/package-files/04/042223e56632d8ff283e63e190e0496f9af6945f0d6dfb692ac9d129aabc85dc/opcode.py
@@ -0,0 +1,216 @@
+
+"""
+opcode module - potentially shared between dis and other modules which
+operate on bytecodes (e.g. peephole optimizers).
+"""
+
+__all__ = ["cmp_op", "hasconst", "hasname", "hasjrel", "hasjabs",
+           "haslocal", "hascompare", "hasfree", "opname", "opmap",
+           "HAVE_ARGUMENT", "EXTENDED_ARG", "hasnargs"]
+
+# It's a chicken-and-egg I'm afraid:
+# We're imported before _opcode's made.
+# With exception unheeded
+# (stack_effect is not needed)
+# Both our chickens and eggs are allayed.
+#     --Larry Hastings, 2013/11/23
+
+try:
+    from _opcode import stack_effect
+    __all__.append('stack_effect')
+except ImportError:
+    pass
+
+cmp_op = ('<', '<=', '==', '!=', '>', '>=')
+
+hasconst = []
+hasname = []
+hasjrel = []
+hasjabs = []
+haslocal = []
+hascompare = []
+hasfree = []
+hasnargs = [] # unused
+
+opmap = {}
+opname = ['<%r>' % (op,) for op in range(256)]
+
+def def_op(name, op):
+    opname[op] = name
+    opmap[name] = op
+
+def name_op(name, op):
+    def_op(name, op)
+    hasname.append(op)
+
+def jrel_op(name, op):
+    def_op(name, op)
+    hasjrel.append(op)
+
+def jabs_op(name, op):
+    def_op(name, op)
+    hasjabs.append(op)
+
+# Instruction opcodes for compiled code
+# Blank lines correspond to available opcodes
+
+def_op('POP_TOP', 1)
+def_op('ROT_TWO', 2)
+def_op('ROT_THREE', 3)
+def_op('DUP_TOP', 4)
+def_op('DUP_TOP_TWO', 5)
+def_op('ROT_FOUR', 6)
+
+def_op('NOP', 9)
+def_op('UNARY_POSITIVE', 10)
+def_op('UNARY_NEGATIVE', 11)
+def_op('UNARY_NOT', 12)
+
+def_op('UNARY_INVERT', 15)
+def_op('BINARY_MATRIX_MULTIPLY', 16)
+def_op('INPLACE_MATRIX_MULTIPLY', 17)
+
+def_op('BINARY_POWER', 19)
+def_op('BINARY_MULTIPLY', 20)
+
+def_op('BINARY_MODULO', 22)
+def_op('BINARY_ADD', 23)
+def_op('BINARY_SUBTRACT', 24)
+def_op('BINARY_SUBSCR', 25)
+def_op('BINARY_FLOOR_DIVIDE', 26)
+def_op('BINARY_TRUE_DIVIDE', 27)
+def_op('INPLACE_FLOOR_DIVIDE', 28)
+def_op('INPLACE_TRUE_DIVIDE', 29)
+def_op('GET_LEN', 30)
+def_op('MATCH_MAPPING', 31)
+def_op('MATCH_SEQUENCE', 32)
+def_op('MATCH_KEYS', 33)
+def_op('COPY_DICT_WITHOUT_KEYS', 34)
+
+def_op('WITH_EXCEPT_START', 49)
+def_op('GET_AITER', 50)
+def_op('GET_ANEXT', 51)
+def_op('BEFORE_ASYNC_WITH', 52)
+
+def_op('END_ASYNC_FOR', 54)
+def_op('INPLACE_ADD', 55)
+def_op('INPLACE_SUBTRACT', 56)
+def_op('INPLACE_MULTIPLY', 57)
+
+def_op('INPLACE_MODULO', 59)
+def_op('STORE_SUBSCR', 60)
+def_op('DELETE_SUBSCR', 61)
+def_op('BINARY_LSHIFT', 62)
+def_op('BINARY_RSHIFT', 63)
+def_op('BINARY_AND', 64)
+def_op('BINARY_XOR', 65)
+def_op('BINARY_OR', 66)
+def_op('INPLACE_POWER', 67)
+def_op('GET_ITER', 68)
+def_op('GET_YIELD_FROM_ITER', 69)
+def_op('PRINT_EXPR', 70)
+def_op('LOAD_BUILD_CLASS', 71)
+def_op('YIELD_FROM', 72)
+def_op('GET_AWAITABLE', 73)
+def_op('LOAD_ASSERTION_ERROR', 74)
+def_op('INPLACE_LSHIFT', 75)
+def_op('INPLACE_RSHIFT', 76)
+def_op('INPLACE_AND', 77)
+def_op('INPLACE_XOR', 78)
+def_op('INPLACE_OR', 79)
+
+def_op('LIST_TO_TUPLE', 82)
+def_op('RETURN_VALUE', 83)
+def_op('IMPORT_STAR', 84)
+def_op('SETUP_ANNOTATIONS', 85)
+def_op('YIELD_VALUE', 86)
+def_op('POP_BLOCK', 87)
+
+def_op('POP_EXCEPT', 89)
+
+HAVE_ARGUMENT = 90              # Opcodes from here have an argument:
+
+name_op('STORE_NAME', 90)       # Index in name list
+name_op('DELETE_NAME', 91)      # ""
+def_op('UNPACK_SEQUENCE', 92)   # Number of tuple items
+jrel_op('FOR_ITER', 93)
+def_op('UNPACK_EX', 94)
+name_op('STORE_ATTR', 95)       # Index in name list
+name_op('DELETE_ATTR', 96)      # ""
+name_op('STORE_GLOBAL', 97)     # ""
+name_op('DELETE_GLOBAL', 98)    # ""
+def_op('ROT_N', 99)
+def_op('LOAD_CONST', 100)       # Index in const list
+hasconst.append(100)
+name_op('LOAD_NAME', 101)       # Index in name list
+def_op('BUILD_TUPLE', 102)      # Number of tuple items
+def_op('BUILD_LIST', 103)       # Number of list items
+def_op('BUILD_SET', 104)        # Number of set items
+def_op('BUILD_MAP', 105)        # Number of dict entries
+name_op('LOAD_ATTR', 106)       # Index in name list
+def_op('COMPARE_OP', 107)       # Comparison operator
+hascompare.append(107)
+name_op('IMPORT_NAME', 108)     # Index in name list
+name_op('IMPORT_FROM', 109)     # Index in name list
+jrel_op('JUMP_FORWARD', 110)    # Number of bytes to skip
+jabs_op('JUMP_IF_FALSE_OR_POP', 111) # Target byte offset from beginning of code
+jabs_op('JUMP_IF_TRUE_OR_POP', 112)  # ""
+jabs_op('JUMP_ABSOLUTE', 113)        # ""
+jabs_op('POP_JUMP_IF_FALSE', 114)    # ""
+jabs_op('POP_JUMP_IF_TRUE', 115)     # ""
+name_op('LOAD_GLOBAL', 116)     # Index in name list
+def_op('IS_OP', 117)
+def_op('CONTAINS_OP', 118)
+def_op('RERAISE', 119)
+
+jabs_op('JUMP_IF_NOT_EXC_MATCH', 121)
+jrel_op('SETUP_FINALLY', 122)   # Distance to target address
+
+def_op('LOAD_FAST', 124)        # Local variable number
+haslocal.append(124)
+def_op('STORE_FAST', 125)       # Local variable number
+haslocal.append(125)
+def_op('DELETE_FAST', 126)      # Local variable number
+haslocal.append(126)
+
+def_op('GEN_START', 129)        # Kind of generator/coroutine
+def_op('RAISE_VARARGS', 130)    # Number of raise arguments (1, 2, or 3)
+def_op('CALL_FUNCTION', 131)    # #args
+def_op('MAKE_FUNCTION', 132)    # Flags
+def_op('BUILD_SLICE', 133)      # Number of items
+
+def_op('LOAD_CLOSURE', 135)
+hasfree.append(135)
+def_op('LOAD_DEREF', 136)
+hasfree.append(136)
+def_op('STORE_DEREF', 137)
+hasfree.append(137)
+def_op('DELETE_DEREF', 138)
+hasfree.append(138)
+
+def_op('CALL_FUNCTION_KW', 141)  # #args + #kwargs
+def_op('CALL_FUNCTION_EX', 142)  # Flags
+jrel_op('SETUP_WITH', 143)
+def_op('EXTENDED_ARG', 144)
+EXTENDED_ARG = 144
+def_op('LIST_APPEND', 145)
+def_op('SET_ADD', 146)
+def_op('MAP_ADD', 147)
+def_op('LOAD_CLASSDEREF', 148)
+hasfree.append(148)
+
+def_op('MATCH_CLASS', 152)
+
+jrel_op('SETUP_ASYNC_WITH', 154)
+def_op('FORMAT_VALUE', 155)
+def_op('BUILD_CONST_KEY_MAP', 156)
+def_op('BUILD_STRING', 157)
+
+name_op('LOAD_METHOD', 160)
+def_op('CALL_METHOD', 161)
+def_op('LIST_EXTEND', 162)
+def_op('SET_UPDATE', 163)
+def_op('DICT_MERGE', 164)
+def_op('DICT_UPDATE', 165)
+
+del def_op, name_op, jrel_op, jabs_op
diff --git a/shared/package-files/04/04223ecc5e4afe63963f31958b132f557a884aa4b15a89068d1d1a568b32bcad/types.pyi b/shared/package-files/04/04223ecc5e4afe63963f31958b132f557a884aa4b15a89068d1d1a568b32bcad/types.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..14195f04f5691ae35578a49869f452dbef5de62d
--- /dev/null
+++ b/shared/package-files/04/04223ecc5e4afe63963f31958b132f557a884aa4b15a89068d1d1a568b32bcad/types.pyi
@@ -0,0 +1,5 @@
+STRING: str
+NUMBER: str
+BINARY: str
+HASH: str
+RANGE: str
diff --git a/shared/package-files/04/04265f681d39ee8cf775fa9d1d289379a2954868fb38b563da93a0af229b65d3/test_data.txt b/shared/package-files/04/04265f681d39ee8cf775fa9d1d289379a2954868fb38b563da93a0af229b65d3/test_data.txt
new file mode 100644
index 0000000000000000000000000000000000000000..de835be14623ba476efc2fc5ac7d3608dfb4d1fa
--- /dev/null
+++ b/shared/package-files/04/04265f681d39ee8cf775fa9d1d289379a2954868fb38b563da93a0af229b65d3/test_data.txt
@@ -0,0 +1,5000 @@
+        1          1  -1.113973   2.251535
+        1          2  -.0808538   1.242346
+        1          3  -.2376072  -1.426376
+        1          4  -.1524857  -1.109394
+        1          5  -.0014262   .9146864
+        1          6  -1.212737  -1.424686
+        1          7  -.1272733   .7589449
+        1          8  -1.433539   .9296525
+        1          9  -.2421959   1.056465
+        1         10   .4609221   3.308434
+        2          1  -.5507909  -2.545477
+        2          2  -1.287685   -3.02192
+        2          3   -.220503  -1.003296
+        2          4   .8143178   -.118388
+        2          5  -.0463721   -1.27967
+        2          6   .6220436  -3.539696
+        2          7  -.6530094  -2.235361
+        2          8   .0294105  -2.552972
+        2          9  -.5297469  -2.697836
+        2         10   1.062629  -.2633513
+        3          1  -1.068723   1.526512
+        3          2  -1.487991  -1.168439
+        3          3   -.814688   1.043587
+        3          4  -.2338627  -.1197725
+        3          5   .1520617   .8082583
+        3          6  -.7190769   .4063513
+        3          7   -.789831   .1766931
+        3          8   .2342699  -.0555624
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diff --git a/shared/package-files/04/0427bc2bb65cd1dad80b9fc9299041315ae12bce0046598fa64f6f4963d885a1/pprint.py b/shared/package-files/04/0427bc2bb65cd1dad80b9fc9299041315ae12bce0046598fa64f6f4963d885a1/pprint.py
new file mode 100644
index 0000000000000000000000000000000000000000..fc29989be0b7e0f324138ca40fa31af737afe5ab
--- /dev/null
+++ b/shared/package-files/04/0427bc2bb65cd1dad80b9fc9299041315ae12bce0046598fa64f6f4963d885a1/pprint.py
@@ -0,0 +1,673 @@
+# mypy: allow-untyped-defs
+# This module was imported from the cpython standard library
+# (https://github.com/python/cpython/) at commit
+# c5140945c723ae6c4b7ee81ff720ac8ea4b52cfd (python3.12).
+#
+#
+#  Original Author:      Fred L. Drake, Jr.
+#                        fdrake@acm.org
+#
+#  This is a simple little module I wrote to make life easier.  I didn't
+#  see anything quite like it in the library, though I may have overlooked
+#  something.  I wrote this when I was trying to read some heavily nested
+#  tuples with fairly non-descriptive content.  This is modeled very much
+#  after Lisp/Scheme - style pretty-printing of lists.  If you find it
+#  useful, thank small children who sleep at night.
+from __future__ import annotations
+
+import collections as _collections
+import dataclasses as _dataclasses
+from io import StringIO as _StringIO
+import re
+import types as _types
+from typing import Any
+from typing import Callable
+from typing import IO
+from typing import Iterator
+
+
+class _safe_key:
+    """Helper function for key functions when sorting unorderable objects.
+
+    The wrapped-object will fallback to a Py2.x style comparison for
+    unorderable types (sorting first comparing the type name and then by
+    the obj ids).  Does not work recursively, so dict.items() must have
+    _safe_key applied to both the key and the value.
+
+    """
+
+    __slots__ = ["obj"]
+
+    def __init__(self, obj):
+        self.obj = obj
+
+    def __lt__(self, other):
+        try:
+            return self.obj < other.obj
+        except TypeError:
+            return (str(type(self.obj)), id(self.obj)) < (
+                str(type(other.obj)),
+                id(other.obj),
+            )
+
+
+def _safe_tuple(t):
+    """Helper function for comparing 2-tuples"""
+    return _safe_key(t[0]), _safe_key(t[1])
+
+
+class PrettyPrinter:
+    def __init__(
+        self,
+        indent: int = 4,
+        width: int = 80,
+        depth: int | None = None,
+    ) -> None:
+        """Handle pretty printing operations onto a stream using a set of
+        configured parameters.
+
+        indent
+            Number of spaces to indent for each level of nesting.
+
+        width
+            Attempted maximum number of columns in the output.
+
+        depth
+            The maximum depth to print out nested structures.
+
+        """
+        if indent < 0:
+            raise ValueError("indent must be >= 0")
+        if depth is not None and depth <= 0:
+            raise ValueError("depth must be > 0")
+        if not width:
+            raise ValueError("width must be != 0")
+        self._depth = depth
+        self._indent_per_level = indent
+        self._width = width
+
+    def pformat(self, object: Any) -> str:
+        sio = _StringIO()
+        self._format(object, sio, 0, 0, set(), 0)
+        return sio.getvalue()
+
+    def _format(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        objid = id(object)
+        if objid in context:
+            stream.write(_recursion(object))
+            return
+
+        p = self._dispatch.get(type(object).__repr__, None)
+        if p is not None:
+            context.add(objid)
+            p(self, object, stream, indent, allowance, context, level + 1)
+            context.remove(objid)
+        elif (
+            _dataclasses.is_dataclass(object)  # type:ignore[unreachable]
+            and not isinstance(object, type)
+            and object.__dataclass_params__.repr
+            and
+            # Check dataclass has generated repr method.
+            hasattr(object.__repr__, "__wrapped__")
+            and "__create_fn__" in object.__repr__.__wrapped__.__qualname__
+        ):
+            context.add(objid)  # type:ignore[unreachable]
+            self._pprint_dataclass(
+                object, stream, indent, allowance, context, level + 1
+            )
+            context.remove(objid)
+        else:
+            stream.write(self._repr(object, context, level))
+
+    def _pprint_dataclass(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        cls_name = object.__class__.__name__
+        items = [
+            (f.name, getattr(object, f.name))
+            for f in _dataclasses.fields(object)
+            if f.repr
+        ]
+        stream.write(cls_name + "(")
+        self._format_namespace_items(items, stream, indent, allowance, context, level)
+        stream.write(")")
+
+    _dispatch: dict[
+        Callable[..., str],
+        Callable[[PrettyPrinter, Any, IO[str], int, int, set[int], int], None],
+    ] = {}
+
+    def _pprint_dict(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        write = stream.write
+        write("{")
+        items = sorted(object.items(), key=_safe_tuple)
+        self._format_dict_items(items, stream, indent, allowance, context, level)
+        write("}")
+
+    _dispatch[dict.__repr__] = _pprint_dict
+
+    def _pprint_ordered_dict(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        if not len(object):
+            stream.write(repr(object))
+            return
+        cls = object.__class__
+        stream.write(cls.__name__ + "(")
+        self._pprint_dict(object, stream, indent, allowance, context, level)
+        stream.write(")")
+
+    _dispatch[_collections.OrderedDict.__repr__] = _pprint_ordered_dict
+
+    def _pprint_list(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        stream.write("[")
+        self._format_items(object, stream, indent, allowance, context, level)
+        stream.write("]")
+
+    _dispatch[list.__repr__] = _pprint_list
+
+    def _pprint_tuple(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        stream.write("(")
+        self._format_items(object, stream, indent, allowance, context, level)
+        stream.write(")")
+
+    _dispatch[tuple.__repr__] = _pprint_tuple
+
+    def _pprint_set(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        if not len(object):
+            stream.write(repr(object))
+            return
+        typ = object.__class__
+        if typ is set:
+            stream.write("{")
+            endchar = "}"
+        else:
+            stream.write(typ.__name__ + "({")
+            endchar = "})"
+        object = sorted(object, key=_safe_key)
+        self._format_items(object, stream, indent, allowance, context, level)
+        stream.write(endchar)
+
+    _dispatch[set.__repr__] = _pprint_set
+    _dispatch[frozenset.__repr__] = _pprint_set
+
+    def _pprint_str(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        write = stream.write
+        if not len(object):
+            write(repr(object))
+            return
+        chunks = []
+        lines = object.splitlines(True)
+        if level == 1:
+            indent += 1
+            allowance += 1
+        max_width1 = max_width = self._width - indent
+        for i, line in enumerate(lines):
+            rep = repr(line)
+            if i == len(lines) - 1:
+                max_width1 -= allowance
+            if len(rep) <= max_width1:
+                chunks.append(rep)
+            else:
+                # A list of alternating (non-space, space) strings
+                parts = re.findall(r"\S*\s*", line)
+                assert parts
+                assert not parts[-1]
+                parts.pop()  # drop empty last part
+                max_width2 = max_width
+                current = ""
+                for j, part in enumerate(parts):
+                    candidate = current + part
+                    if j == len(parts) - 1 and i == len(lines) - 1:
+                        max_width2 -= allowance
+                    if len(repr(candidate)) > max_width2:
+                        if current:
+                            chunks.append(repr(current))
+                        current = part
+                    else:
+                        current = candidate
+                if current:
+                    chunks.append(repr(current))
+        if len(chunks) == 1:
+            write(rep)
+            return
+        if level == 1:
+            write("(")
+        for i, rep in enumerate(chunks):
+            if i > 0:
+                write("\n" + " " * indent)
+            write(rep)
+        if level == 1:
+            write(")")
+
+    _dispatch[str.__repr__] = _pprint_str
+
+    def _pprint_bytes(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        write = stream.write
+        if len(object) <= 4:
+            write(repr(object))
+            return
+        parens = level == 1
+        if parens:
+            indent += 1
+            allowance += 1
+            write("(")
+        delim = ""
+        for rep in _wrap_bytes_repr(object, self._width - indent, allowance):
+            write(delim)
+            write(rep)
+            if not delim:
+                delim = "\n" + " " * indent
+        if parens:
+            write(")")
+
+    _dispatch[bytes.__repr__] = _pprint_bytes
+
+    def _pprint_bytearray(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        write = stream.write
+        write("bytearray(")
+        self._pprint_bytes(
+            bytes(object), stream, indent + 10, allowance + 1, context, level + 1
+        )
+        write(")")
+
+    _dispatch[bytearray.__repr__] = _pprint_bytearray
+
+    def _pprint_mappingproxy(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        stream.write("mappingproxy(")
+        self._format(object.copy(), stream, indent, allowance, context, level)
+        stream.write(")")
+
+    _dispatch[_types.MappingProxyType.__repr__] = _pprint_mappingproxy
+
+    def _pprint_simplenamespace(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        if type(object) is _types.SimpleNamespace:
+            # The SimpleNamespace repr is "namespace" instead of the class
+            # name, so we do the same here. For subclasses; use the class name.
+            cls_name = "namespace"
+        else:
+            cls_name = object.__class__.__name__
+        items = object.__dict__.items()
+        stream.write(cls_name + "(")
+        self._format_namespace_items(items, stream, indent, allowance, context, level)
+        stream.write(")")
+
+    _dispatch[_types.SimpleNamespace.__repr__] = _pprint_simplenamespace
+
+    def _format_dict_items(
+        self,
+        items: list[tuple[Any, Any]],
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        if not items:
+            return
+
+        write = stream.write
+        item_indent = indent + self._indent_per_level
+        delimnl = "\n" + " " * item_indent
+        for key, ent in items:
+            write(delimnl)
+            write(self._repr(key, context, level))
+            write(": ")
+            self._format(ent, stream, item_indent, 1, context, level)
+            write(",")
+
+        write("\n" + " " * indent)
+
+    def _format_namespace_items(
+        self,
+        items: list[tuple[Any, Any]],
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        if not items:
+            return
+
+        write = stream.write
+        item_indent = indent + self._indent_per_level
+        delimnl = "\n" + " " * item_indent
+        for key, ent in items:
+            write(delimnl)
+            write(key)
+            write("=")
+            if id(ent) in context:
+                # Special-case representation of recursion to match standard
+                # recursive dataclass repr.
+                write("...")
+            else:
+                self._format(
+                    ent,
+                    stream,
+                    item_indent + len(key) + 1,
+                    1,
+                    context,
+                    level,
+                )
+
+            write(",")
+
+        write("\n" + " " * indent)
+
+    def _format_items(
+        self,
+        items: list[Any],
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        if not items:
+            return
+
+        write = stream.write
+        item_indent = indent + self._indent_per_level
+        delimnl = "\n" + " " * item_indent
+
+        for item in items:
+            write(delimnl)
+            self._format(item, stream, item_indent, 1, context, level)
+            write(",")
+
+        write("\n" + " " * indent)
+
+    def _repr(self, object: Any, context: set[int], level: int) -> str:
+        return self._safe_repr(object, context.copy(), self._depth, level)
+
+    def _pprint_default_dict(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        rdf = self._repr(object.default_factory, context, level)
+        stream.write(f"{object.__class__.__name__}({rdf}, ")
+        self._pprint_dict(object, stream, indent, allowance, context, level)
+        stream.write(")")
+
+    _dispatch[_collections.defaultdict.__repr__] = _pprint_default_dict
+
+    def _pprint_counter(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        stream.write(object.__class__.__name__ + "(")
+
+        if object:
+            stream.write("{")
+            items = object.most_common()
+            self._format_dict_items(items, stream, indent, allowance, context, level)
+            stream.write("}")
+
+        stream.write(")")
+
+    _dispatch[_collections.Counter.__repr__] = _pprint_counter
+
+    def _pprint_chain_map(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        if not len(object.maps) or (len(object.maps) == 1 and not len(object.maps[0])):
+            stream.write(repr(object))
+            return
+
+        stream.write(object.__class__.__name__ + "(")
+        self._format_items(object.maps, stream, indent, allowance, context, level)
+        stream.write(")")
+
+    _dispatch[_collections.ChainMap.__repr__] = _pprint_chain_map
+
+    def _pprint_deque(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        stream.write(object.__class__.__name__ + "(")
+        if object.maxlen is not None:
+            stream.write("maxlen=%d, " % object.maxlen)
+        stream.write("[")
+
+        self._format_items(object, stream, indent, allowance + 1, context, level)
+        stream.write("])")
+
+    _dispatch[_collections.deque.__repr__] = _pprint_deque
+
+    def _pprint_user_dict(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        self._format(object.data, stream, indent, allowance, context, level - 1)
+
+    _dispatch[_collections.UserDict.__repr__] = _pprint_user_dict
+
+    def _pprint_user_list(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        self._format(object.data, stream, indent, allowance, context, level - 1)
+
+    _dispatch[_collections.UserList.__repr__] = _pprint_user_list
+
+    def _pprint_user_string(
+        self,
+        object: Any,
+        stream: IO[str],
+        indent: int,
+        allowance: int,
+        context: set[int],
+        level: int,
+    ) -> None:
+        self._format(object.data, stream, indent, allowance, context, level - 1)
+
+    _dispatch[_collections.UserString.__repr__] = _pprint_user_string
+
+    def _safe_repr(
+        self, object: Any, context: set[int], maxlevels: int | None, level: int
+    ) -> str:
+        typ = type(object)
+        if typ in _builtin_scalars:
+            return repr(object)
+
+        r = getattr(typ, "__repr__", None)
+
+        if issubclass(typ, dict) and r is dict.__repr__:
+            if not object:
+                return "{}"
+            objid = id(object)
+            if maxlevels and level >= maxlevels:
+                return "{...}"
+            if objid in context:
+                return _recursion(object)
+            context.add(objid)
+            components: list[str] = []
+            append = components.append
+            level += 1
+            for k, v in sorted(object.items(), key=_safe_tuple):
+                krepr = self._safe_repr(k, context, maxlevels, level)
+                vrepr = self._safe_repr(v, context, maxlevels, level)
+                append(f"{krepr}: {vrepr}")
+            context.remove(objid)
+            return "{{{}}}".format(", ".join(components))
+
+        if (issubclass(typ, list) and r is list.__repr__) or (
+            issubclass(typ, tuple) and r is tuple.__repr__
+        ):
+            if issubclass(typ, list):
+                if not object:
+                    return "[]"
+                format = "[%s]"
+            elif len(object) == 1:
+                format = "(%s,)"
+            else:
+                if not object:
+                    return "()"
+                format = "(%s)"
+            objid = id(object)
+            if maxlevels and level >= maxlevels:
+                return format % "..."
+            if objid in context:
+                return _recursion(object)
+            context.add(objid)
+            components = []
+            append = components.append
+            level += 1
+            for o in object:
+                orepr = self._safe_repr(o, context, maxlevels, level)
+                append(orepr)
+            context.remove(objid)
+            return format % ", ".join(components)
+
+        return repr(object)
+
+
+_builtin_scalars = frozenset(
+    {str, bytes, bytearray, float, complex, bool, type(None), int}
+)
+
+
+def _recursion(object: Any) -> str:
+    return f""
+
+
+def _wrap_bytes_repr(object: Any, width: int, allowance: int) -> Iterator[str]:
+    current = b""
+    last = len(object) // 4 * 4
+    for i in range(0, len(object), 4):
+        part = object[i : i + 4]
+        candidate = current + part
+        if i == last:
+            width -= allowance
+        if len(repr(candidate)) > width:
+            if current:
+                yield repr(current)
+            current = part
+        else:
+            current = candidate
+    if current:
+        yield repr(current)
diff --git a/shared/package-files/04/0427d3854833e446f97d625228155e25a03993092cc3c8c942d37a83ad713173/dummy.py b/shared/package-files/04/0427d3854833e446f97d625228155e25a03993092cc3c8c942d37a83ad713173/dummy.py
new file mode 100644
index 0000000000000000000000000000000000000000..43819e1e71cafc0ed18740b9d6472bd6d187eeba
--- /dev/null
+++ b/shared/package-files/04/0427d3854833e446f97d625228155e25a03993092cc3c8c942d37a83ad713173/dummy.py
@@ -0,0 +1,55 @@
+from __future__ import annotations
+
+from typing import Callable
+
+from prompt_toolkit.eventloop import InputHook
+from prompt_toolkit.formatted_text import AnyFormattedText
+from prompt_toolkit.input import DummyInput
+from prompt_toolkit.output import DummyOutput
+
+from .application import Application
+
+__all__ = [
+    "DummyApplication",
+]
+
+
+class DummyApplication(Application[None]):
+    """
+    When no :class:`.Application` is running,
+    :func:`.get_app` will run an instance of this :class:`.DummyApplication` instead.
+    """
+
+    def __init__(self) -> None:
+        super().__init__(output=DummyOutput(), input=DummyInput())
+
+    def run(
+        self,
+        pre_run: Callable[[], None] | None = None,
+        set_exception_handler: bool = True,
+        handle_sigint: bool = True,
+        in_thread: bool = False,
+        inputhook: InputHook | None = None,
+    ) -> None:
+        raise NotImplementedError("A DummyApplication is not supposed to run.")
+
+    async def run_async(
+        self,
+        pre_run: Callable[[], None] | None = None,
+        set_exception_handler: bool = True,
+        handle_sigint: bool = True,
+        slow_callback_duration: float = 0.5,
+    ) -> None:
+        raise NotImplementedError("A DummyApplication is not supposed to run.")
+
+    async def run_system_command(
+        self,
+        command: str,
+        wait_for_enter: bool = True,
+        display_before_text: AnyFormattedText = "",
+        wait_text: str = "",
+    ) -> None:
+        raise NotImplementedError
+
+    def suspend_to_background(self, suspend_group: bool = True) -> None:
+        raise NotImplementedError
diff --git a/shared/package-files/04/04292f15d8530b2712f5c001179afc309fa0500d2411403610282a206a20c925/upsample_trilinear3d_backward_native.h b/shared/package-files/04/04292f15d8530b2712f5c001179afc309fa0500d2411403610282a206a20c925/upsample_trilinear3d_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..2724cecf448a85d5117e36e0df35b8934a43bb64
--- /dev/null
+++ b/shared/package-files/04/04292f15d8530b2712f5c001179afc309fa0500d2411403610282a206a20c925/upsample_trilinear3d_backward_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+namespace at {
+namespace native {
+struct TORCH_API structured_upsample_trilinear3d_backward_out_cpu : public at::meta::structured_upsample_trilinear3d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef output_size, at::ArrayRef input_size, bool align_corners, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, const at::Tensor & grad_input);
+};
+struct TORCH_API structured_upsample_trilinear3d_backward_out_cuda : public at::meta::structured_upsample_trilinear3d_backward {
+void impl(const at::Tensor & grad_output, at::ArrayRef output_size, at::ArrayRef input_size, bool align_corners, c10::optional scales_d, c10::optional scales_h, c10::optional scales_w, const at::Tensor & grad_input);
+};
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/04/042acb60984c6f721fa89c42cebe22572acc9434cc7f27d2fb4362b3602deb1d/fix_last.py b/shared/package-files/04/042acb60984c6f721fa89c42cebe22572acc9434cc7f27d2fb4362b3602deb1d/fix_last.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a077d41594da65e463664e80eea7918d56a736b
--- /dev/null
+++ b/shared/package-files/04/042acb60984c6f721fa89c42cebe22572acc9434cc7f27d2fb4362b3602deb1d/fix_last.py
@@ -0,0 +1,7 @@
+from lib2to3.fixer_base import BaseFix
+
+class FixLast(BaseFix):
+
+    run_order = 10
+
+    def match(self, node): return False
diff --git a/shared/package-files/04/042be9a4684ed7dfa3fafc40bdb76c391222cae26acc2b90a20f40a3c57596f0/testonechar_4.2c_SOL2.mat b/shared/package-files/04/042be9a4684ed7dfa3fafc40bdb76c391222cae26acc2b90a20f40a3c57596f0/testonechar_4.2c_SOL2.mat
new file mode 100644
index 0000000000000000000000000000000000000000..cdb4191c7d2eb0ac66d4f6add250e1f6a604d892
Binary files /dev/null and b/shared/package-files/04/042be9a4684ed7dfa3fafc40bdb76c391222cae26acc2b90a20f40a3c57596f0/testonechar_4.2c_SOL2.mat differ
diff --git a/shared/package-files/04/042bf52db7bee801f1b5a1ee84819dde73ad37e79f466648fb1be9c7029de059/install.pyi b/shared/package-files/04/042bf52db7bee801f1b5a1ee84819dde73ad37e79f466648fb1be9c7029de059/install.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..dc6d96b82a62e6509bc12b9b3f87daa349174197
--- /dev/null
+++ b/shared/package-files/04/042bf52db7bee801f1b5a1ee84819dde73ad37e79f466648fb1be9c7029de059/install.pyi
@@ -0,0 +1,12 @@
+from distutils.cmd import Command
+from typing import Optional, Text
+
+class install(Command):
+    user: bool
+    prefix: Optional[Text]
+    home: Optional[Text]
+    root: Optional[Text]
+    install_lib: Optional[Text]
+    def initialize_options(self) -> None: ...
+    def finalize_options(self) -> None: ...
+    def run(self) -> None: ...
diff --git a/shared/package-files/04/042c78792746f0c43c652515d655847770b36d9e0888c89df934f13e857a1475/lambdarepr.py b/shared/package-files/04/042c78792746f0c43c652515d655847770b36d9e0888c89df934f13e857a1475/lambdarepr.py
new file mode 100644
index 0000000000000000000000000000000000000000..87fa0988d138d54d68ab8aef1bbc0f27b243b472
--- /dev/null
+++ b/shared/package-files/04/042c78792746f0c43c652515d655847770b36d9e0888c89df934f13e857a1475/lambdarepr.py
@@ -0,0 +1,251 @@
+from .pycode import (
+    PythonCodePrinter,
+    MpmathPrinter,
+)
+from .numpy import NumPyPrinter  # NumPyPrinter is imported for backward compatibility
+from sympy.core.sorting import default_sort_key
+
+
+__all__ = [
+    'PythonCodePrinter',
+    'MpmathPrinter',  # MpmathPrinter is published for backward compatibility
+    'NumPyPrinter',
+    'LambdaPrinter',
+    'NumPyPrinter',
+    'IntervalPrinter',
+    'lambdarepr',
+]
+
+
+class LambdaPrinter(PythonCodePrinter):
+    """
+    This printer converts expressions into strings that can be used by
+    lambdify.
+    """
+    printmethod = "_lambdacode"
+
+
+    def _print_And(self, expr):
+        result = ['(']
+        for arg in sorted(expr.args, key=default_sort_key):
+            result.extend(['(', self._print(arg), ')'])
+            result.append(' and ')
+        result = result[:-1]
+        result.append(')')
+        return ''.join(result)
+
+    def _print_Or(self, expr):
+        result = ['(']
+        for arg in sorted(expr.args, key=default_sort_key):
+            result.extend(['(', self._print(arg), ')'])
+            result.append(' or ')
+        result = result[:-1]
+        result.append(')')
+        return ''.join(result)
+
+    def _print_Not(self, expr):
+        result = ['(', 'not (', self._print(expr.args[0]), '))']
+        return ''.join(result)
+
+    def _print_BooleanTrue(self, expr):
+        return "True"
+
+    def _print_BooleanFalse(self, expr):
+        return "False"
+
+    def _print_ITE(self, expr):
+        result = [
+            '((', self._print(expr.args[1]),
+            ') if (', self._print(expr.args[0]),
+            ') else (', self._print(expr.args[2]), '))'
+        ]
+        return ''.join(result)
+
+    def _print_NumberSymbol(self, expr):
+        return str(expr)
+
+    def _print_Pow(self, expr, **kwargs):
+        # XXX Temporary workaround. Should Python math printer be
+        # isolated from PythonCodePrinter?
+        return super(PythonCodePrinter, self)._print_Pow(expr, **kwargs)
+
+
+# numexpr works by altering the string passed to numexpr.evaluate
+# rather than by populating a namespace.  Thus a special printer...
+class NumExprPrinter(LambdaPrinter):
+    # key, value pairs correspond to SymPy name and numexpr name
+    # functions not appearing in this dict will raise a TypeError
+    printmethod = "_numexprcode"
+
+    _numexpr_functions = {
+        'sin' : 'sin',
+        'cos' : 'cos',
+        'tan' : 'tan',
+        'asin': 'arcsin',
+        'acos': 'arccos',
+        'atan': 'arctan',
+        'atan2' : 'arctan2',
+        'sinh' : 'sinh',
+        'cosh' : 'cosh',
+        'tanh' : 'tanh',
+        'asinh': 'arcsinh',
+        'acosh': 'arccosh',
+        'atanh': 'arctanh',
+        'ln' : 'log',
+        'log': 'log',
+        'exp': 'exp',
+        'sqrt' : 'sqrt',
+        'Abs' : 'abs',
+        'conjugate' : 'conj',
+        'im' : 'imag',
+        're' : 'real',
+        'where' : 'where',
+        'complex' : 'complex',
+        'contains' : 'contains',
+    }
+
+    module = 'numexpr'
+
+    def _print_ImaginaryUnit(self, expr):
+        return '1j'
+
+    def _print_seq(self, seq, delimiter=', '):
+        # simplified _print_seq taken from pretty.py
+        s = [self._print(item) for item in seq]
+        if s:
+            return delimiter.join(s)
+        else:
+            return ""
+
+    def _print_Function(self, e):
+        func_name = e.func.__name__
+
+        nstr = self._numexpr_functions.get(func_name, None)
+        if nstr is None:
+            # check for implemented_function
+            if hasattr(e, '_imp_'):
+                return "(%s)" % self._print(e._imp_(*e.args))
+            else:
+                raise TypeError("numexpr does not support function '%s'" %
+                                func_name)
+        return "%s(%s)" % (nstr, self._print_seq(e.args))
+
+    def _print_Piecewise(self, expr):
+        "Piecewise function printer"
+        exprs = [self._print(arg.expr) for arg in expr.args]
+        conds = [self._print(arg.cond) for arg in expr.args]
+        # If [default_value, True] is a (expr, cond) sequence in a Piecewise object
+        #     it will behave the same as passing the 'default' kwarg to select()
+        #     *as long as* it is the last element in expr.args.
+        # If this is not the case, it may be triggered prematurely.
+        ans = []
+        parenthesis_count = 0
+        is_last_cond_True = False
+        for cond, expr in zip(conds, exprs):
+            if cond == 'True':
+                ans.append(expr)
+                is_last_cond_True = True
+                break
+            else:
+                ans.append('where(%s, %s, ' % (cond, expr))
+                parenthesis_count += 1
+        if not is_last_cond_True:
+            # See https://github.com/pydata/numexpr/issues/298
+            #
+            # simplest way to put a nan but raises
+            # 'RuntimeWarning: invalid value encountered in log'
+            #
+            # There are other ways to do this such as
+            #
+            #   >>> import numexpr as ne
+            #   >>> nan = float('nan')
+            #   >>> ne.evaluate('where(x < 0, -1, nan)', {'x': [-1, 2, 3], 'nan':nan})
+            #   array([-1., nan, nan])
+            #
+            # That needs to be handled in the lambdified function though rather
+            # than here in the printer.
+            ans.append('log(-1)')
+        return ''.join(ans) + ')' * parenthesis_count
+
+    def _print_ITE(self, expr):
+        from sympy.functions.elementary.piecewise import Piecewise
+        return self._print(expr.rewrite(Piecewise))
+
+    def blacklisted(self, expr):
+        raise TypeError("numexpr cannot be used with %s" %
+                        expr.__class__.__name__)
+
+    # blacklist all Matrix printing
+    _print_SparseRepMatrix = \
+    _print_MutableSparseMatrix = \
+    _print_ImmutableSparseMatrix = \
+    _print_Matrix = \
+    _print_DenseMatrix = \
+    _print_MutableDenseMatrix = \
+    _print_ImmutableMatrix = \
+    _print_ImmutableDenseMatrix = \
+    blacklisted
+    # blacklist some Python expressions
+    _print_list = \
+    _print_tuple = \
+    _print_Tuple = \
+    _print_dict = \
+    _print_Dict = \
+    blacklisted
+
+    def _print_NumExprEvaluate(self, expr):
+        evaluate = self._module_format(self.module +".evaluate")
+        return "%s('%s', truediv=True)" % (evaluate, self._print(expr.expr))
+
+    def doprint(self, expr):
+        from sympy.codegen.ast import CodegenAST
+        from sympy.codegen.pynodes import NumExprEvaluate
+        if not isinstance(expr, CodegenAST):
+            expr = NumExprEvaluate(expr)
+        return super().doprint(expr)
+
+    def _print_Return(self, expr):
+        from sympy.codegen.pynodes import NumExprEvaluate
+        r, = expr.args
+        if not isinstance(r, NumExprEvaluate):
+            expr = expr.func(NumExprEvaluate(r))
+        return super()._print_Return(expr)
+
+    def _print_Assignment(self, expr):
+        from sympy.codegen.pynodes import NumExprEvaluate
+        lhs, rhs, *args = expr.args
+        if not isinstance(rhs, NumExprEvaluate):
+            expr = expr.func(lhs, NumExprEvaluate(rhs), *args)
+        return super()._print_Assignment(expr)
+
+    def _print_CodeBlock(self, expr):
+        from sympy.codegen.ast import CodegenAST
+        from sympy.codegen.pynodes import NumExprEvaluate
+        args = [ arg if isinstance(arg, CodegenAST) else NumExprEvaluate(arg) for arg in expr.args ]
+        return super()._print_CodeBlock(self, expr.func(*args))
+
+
+class IntervalPrinter(MpmathPrinter, LambdaPrinter):
+    """Use ``lambda`` printer but print numbers as ``mpi`` intervals. """
+
+    def _print_Integer(self, expr):
+        return "mpi('%s')" % super(PythonCodePrinter, self)._print_Integer(expr)
+
+    def _print_Rational(self, expr):
+        return "mpi('%s')" % super(PythonCodePrinter, self)._print_Rational(expr)
+
+    def _print_Half(self, expr):
+        return "mpi('%s')" % super(PythonCodePrinter, self)._print_Rational(expr)
+
+    def _print_Pow(self, expr):
+        return super(MpmathPrinter, self)._print_Pow(expr, rational=True)
+
+
+for k in NumExprPrinter._numexpr_functions:
+    setattr(NumExprPrinter, '_print_%s' % k, NumExprPrinter._print_Function)
+
+def lambdarepr(expr, **settings):
+    """
+    Returns a string usable for lambdifying.
+    """
+    return LambdaPrinter(settings).doprint(expr)
diff --git a/shared/package-files/04/042cbcec75ea23658ed3f70218afab79dbe2d924c9cc2c6cc1862cd3a61dc41e/datetimelike_accumulations.py b/shared/package-files/04/042cbcec75ea23658ed3f70218afab79dbe2d924c9cc2c6cc1862cd3a61dc41e/datetimelike_accumulations.py
new file mode 100644
index 0000000000000000000000000000000000000000..825fe60ee6cf88a2186a5f501c8696cecaf2657d
--- /dev/null
+++ b/shared/package-files/04/042cbcec75ea23658ed3f70218afab79dbe2d924c9cc2c6cc1862cd3a61dc41e/datetimelike_accumulations.py
@@ -0,0 +1,67 @@
+"""
+datetimelke_accumulations.py is for accumulations of datetimelike extension arrays
+"""
+
+from __future__ import annotations
+
+from typing import Callable
+
+import numpy as np
+
+from pandas._libs import iNaT
+
+from pandas.core.dtypes.missing import isna
+
+
+def _cum_func(
+    func: Callable,
+    values: np.ndarray,
+    *,
+    skipna: bool = True,
+):
+    """
+    Accumulations for 1D datetimelike arrays.
+
+    Parameters
+    ----------
+    func : np.cumsum, np.maximum.accumulate, np.minimum.accumulate
+    values : np.ndarray
+        Numpy array with the values (can be of any dtype that support the
+        operation). Values is changed is modified inplace.
+    skipna : bool, default True
+        Whether to skip NA.
+    """
+    try:
+        fill_value = {
+            np.maximum.accumulate: np.iinfo(np.int64).min,
+            np.cumsum: 0,
+            np.minimum.accumulate: np.iinfo(np.int64).max,
+        }[func]
+    except KeyError:
+        raise ValueError(f"No accumulation for {func} implemented on BaseMaskedArray")
+
+    mask = isna(values)
+    y = values.view("i8")
+    y[mask] = fill_value
+
+    if not skipna:
+        mask = np.maximum.accumulate(mask)
+
+    result = func(y)
+    result[mask] = iNaT
+
+    if values.dtype.kind in "mM":
+        return result.view(values.dtype.base)
+    return result
+
+
+def cumsum(values: np.ndarray, *, skipna: bool = True) -> np.ndarray:
+    return _cum_func(np.cumsum, values, skipna=skipna)
+
+
+def cummin(values: np.ndarray, *, skipna: bool = True):
+    return _cum_func(np.minimum.accumulate, values, skipna=skipna)
+
+
+def cummax(values: np.ndarray, *, skipna: bool = True):
+    return _cum_func(np.maximum.accumulate, values, skipna=skipna)
diff --git a/shared/package-files/04/042d8b7f553dc139fc4d04ef40820e6e9c723134334236578b98ad0e0caa4951/symbolic_opset17.py b/shared/package-files/04/042d8b7f553dc139fc4d04ef40820e6e9c723134334236578b98ad0e0caa4951/symbolic_opset17.py
new file mode 100644
index 0000000000000000000000000000000000000000..3aad249a1126556f3cb0834991c82ea19650ac31
--- /dev/null
+++ b/shared/package-files/04/042d8b7f553dc139fc4d04ef40820e6e9c723134334236578b98ad0e0caa4951/symbolic_opset17.py
@@ -0,0 +1,211 @@
+"""This file exports ONNX ops for opset 17.
+
+Note [ONNX Operators that are added/updated in opset 17]
+
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+https://github.com/onnx/onnx/blob/main/docs/Changelog.md#version-17-of-the-default-onnx-operator-set
+New operators:
+    BlackmanWindow
+    DFT
+    HammingWindow
+    HannWindow
+    LayerNormalization
+    MelWeightMatrix
+    STFT
+    SequenceMap
+"""
+
+import functools
+from typing import Optional, Sequence
+
+import torch
+from torch import _C
+from torch.onnx import _type_utils, errors, symbolic_helper
+from torch.onnx._internal import _beartype, jit_utils, registration
+
+# EDITING THIS FILE? READ THIS FIRST!
+# see Note [Edit Symbolic Files] in README.md
+
+__all__ = ["layer_norm", "stft"]
+
+_onnx_symbolic = functools.partial(registration.onnx_symbolic, opset=17)
+
+
+@_onnx_symbolic("aten::layer_norm")
+@symbolic_helper.parse_args("v", "is", "v", "v", "f", "none")
+def layer_norm(
+    g: jit_utils.GraphContext,
+    input: _C.Value,
+    normalized_shape: Sequence[int],
+    weight: _C.Value,
+    bias: _C.Value,
+    eps: float,
+    cudnn_enable: bool,
+):
+    # normalized_shape: input shape from an expected input of size
+    # axis: The first normalization dimension.
+    # layer_norm normalizes on the last D dimensions,
+    # where D is the size of normalized_shape
+    axis = -len(normalized_shape)
+    scalar_type = _type_utils.JitScalarType.from_value(
+        input, _type_utils.JitScalarType.FLOAT
+    )
+    dtype = scalar_type.dtype()
+    if symbolic_helper._is_none(weight):
+        weight_value = torch.ones(normalized_shape, dtype=dtype)
+        weight = g.op("Constant", value_t=weight_value)
+    if symbolic_helper._is_none(bias):
+        bias_value = torch.zeros(normalized_shape, dtype=dtype)
+        bias = g.op("Constant", value_t=bias_value)
+    return g.op(
+        "LayerNormalization",
+        input,
+        weight,
+        bias,
+        epsilon_f=eps,
+        axis_i=axis,
+    )
+
+
+def _compute_edge_sizes(n_fft, window_size):
+    """Helper function to compute the sizes of the edges (left and right)
+    of a given window centered within an FFT size."""
+    left = (n_fft - window_size) // 2
+    right = n_fft - left - window_size
+    return left, right
+
+
+@_onnx_symbolic("aten::stft")
+@symbolic_helper.parse_args("v", "i", "i", "i", "v", "b", "b", "b")
+@_beartype.beartype
+def stft(
+    g: jit_utils.GraphContext,
+    input: _C.Value,
+    n_fft: int,
+    hop_length: Optional[int] = None,
+    win_length: Optional[int] = None,
+    window: Optional[_C.Value] = None,
+    normalized: bool = False,
+    onesided: Optional[bool] = True,
+    return_complex: Optional[bool] = False,
+) -> _C.Value:
+    """Associates `torch.stft` with the `STFT` ONNX operator.
+    Note that torch.stft calls _VF.stft, without centering or padding options.
+    Hence, this function does not contain these two arguments.
+    See torch.stft source code for more info.
+
+    Args:
+        g: Graph to write the ONNX representation into
+        input: Input tensor for the transformation
+        n_fft: FFT size
+        hop_length: Size of the hop. Defaults to `floot(n_fft // 4)`
+        win_length: Size of the analysis window. Defaults to `n_fft`
+        window: Analysis window. Defaults to a window of all ones
+        normalized: Whether to return a normalized STFT
+        onesided: Whether to return only half (+1) of the results, given the
+            symmetry of the STFT
+        return_complex: Whether to return the complex value (Note: Must be
+            `False` or `None`)
+
+    Returns:
+        op: Operator for torch.stft associated with STFT (ONNX)
+    """
+    # Checks
+    if return_complex:
+        raise errors.SymbolicValueError(
+            msg="STFT does not currently support complex types", value=input
+        )
+
+    # Get STFT sizes
+    frame_step_value = hop_length if hop_length is not None else n_fft // 4
+    frame_step_const = g.op(
+        "Constant", value_t=torch.tensor(frame_step_value, dtype=torch.int64)
+    )
+    frame_length_const = g.op(
+        "Constant", value_t=torch.tensor(n_fft, dtype=torch.int64)
+    )
+
+    # Pre-process input if needed
+    signal = input
+    signal_rank = symbolic_helper._get_tensor_rank(signal)
+    if signal_rank == 1:
+        # Add batch dimension
+        signal = g.op(
+            "Unsqueeze",
+            signal,
+            g.op("Constant", value_t=torch.tensor([0], dtype=torch.int64)),
+        )
+    elif signal_rank > 2:
+        raise errors.SymbolicValueError(
+            msg="STFT can only take inputs of 1 [signal] or 2 [batch, signal] dimensions. "
+            f"Current rank of signal is {signal_rank}, please reduce it.",
+            value=input,
+        )
+
+    # Get window and make sure it's the same size as `win_length` or `n_fft`
+    n_win = symbolic_helper._get_tensor_dim_size(window, dim=0)
+    if n_win is not None:
+        win_length_default = win_length if win_length else n_fft
+        assert n_win == win_length_default, (
+            "Analysis window size must equal `win_length` or `n_fft`. "
+            f"Please, set `win_length` or `n_fft` to match `window` size ({n_win})",
+        )
+
+        # Center window around zeros if needed (required by ONNX's STFT)
+        if n_win < n_fft:
+            left, right = _compute_edge_sizes(n_fft, n_win)
+            left_win = g.op("Constant", value_t=torch.zeros(left))
+            right_win = g.op("Constant", value_t=torch.zeros(right))
+            window = g.op("Concat", left_win, window, right_win, axis_i=0)
+
+    # Create window, if needed
+    if symbolic_helper._is_none(window):
+        if win_length:
+            if win_length > n_fft:
+                raise errors.SymbolicValueError(
+                    msg="The analysis window can't be longer than the size of the FFT. "
+                    f"Please set `win_length` ({win_length}) to `n_fft` ({n_fft}) or less.",
+                    value=input,
+                )
+
+            # Center window, if needed
+            left, right = _compute_edge_sizes(n_fft, win_length)
+            torch_window = torch.hstack(
+                (torch.zeros(left), torch.ones(win_length), torch.zeros(right))
+            )
+        else:
+            # Rectangle window
+            torch_window = torch.ones(n_fft)
+        assert torch_window.shape[0] == n_fft
+        window = g.op("Constant", value_t=torch_window)
+    window = g.op(
+        "Cast", window, to_i=_type_utils.JitScalarType.from_value(signal).onnx_type()
+    )
+
+    # Run STFT
+    result = g.op(
+        "STFT",
+        signal,
+        frame_step_const,
+        window,
+        frame_length_const,
+        onesided_i=1 if onesided is None or onesided else 0,
+    )
+
+    # Transpose to mimic torch.stft's behavior
+    result = g.op("Transpose", result, perm_i=[0, 2, 1, 3])
+
+    # Remove batch dimension, if needed
+    if signal_rank == 1:
+        result = g.op(
+            "Squeeze",
+            result,
+            g.op("Constant", value_t=torch.tensor([0], dtype=torch.int64)),
+        )
+
+    # Normalize, if needed
+    if normalized:
+        sqrt_nfft = torch.sqrt(torch.tensor(n_fft, dtype=signal.type().dtype()))
+        result = g.op("Div", result, g.op("Constant", value_t=sqrt_nfft))
+
+    return result
diff --git a/shared/package-files/04/042e8c985f8ecc9dc6ac10fc4d80d9f9509237850ec74a4dd2bde8178192a22c/lazyTools.py b/shared/package-files/04/042e8c985f8ecc9dc6ac10fc4d80d9f9509237850ec74a4dd2bde8178192a22c/lazyTools.py
new file mode 100644
index 0000000000000000000000000000000000000000..91cb80c99262d7d3b6a998847df8de295470d9b9
--- /dev/null
+++ b/shared/package-files/04/042e8c985f8ecc9dc6ac10fc4d80d9f9509237850ec74a4dd2bde8178192a22c/lazyTools.py
@@ -0,0 +1,42 @@
+from collections import UserDict, UserList
+
+__all__ = ["LazyDict", "LazyList"]
+
+
+class LazyDict(UserDict):
+    def __init__(self, data):
+        super().__init__()
+        self.data = data
+
+    def __getitem__(self, k):
+        v = self.data[k]
+        if callable(v):
+            v = v(k)
+            self.data[k] = v
+        return v
+
+
+class LazyList(UserList):
+    def __getitem__(self, k):
+        if isinstance(k, slice):
+            indices = range(*k.indices(len(self)))
+            return [self[i] for i in indices]
+        v = self.data[k]
+        if callable(v):
+            v = v(k)
+            self.data[k] = v
+        return v
+
+    def __add__(self, other):
+        if isinstance(other, LazyList):
+            other = list(other)
+        elif isinstance(other, list):
+            pass
+        else:
+            return NotImplemented
+        return list(self) + other
+
+    def __radd__(self, other):
+        if not isinstance(other, list):
+            return NotImplemented
+        return other + list(self)
diff --git a/shared/package-files/04/0432302f32baaebff4579721e79edf4b3b22bc2125a5daa15ebe5d67d4def841/callbacks.py b/shared/package-files/04/0432302f32baaebff4579721e79edf4b3b22bc2125a5daa15ebe5d67d4def841/callbacks.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ca99ca6ac3cd69b28bcd1550f6550e8e648c5fe
--- /dev/null
+++ b/shared/package-files/04/0432302f32baaebff4579721e79edf4b3b22bc2125a5daa15ebe5d67d4def841/callbacks.py
@@ -0,0 +1,324 @@
+from functools import wraps
+
+
+class Callback:
+    """
+    Base class and interface for callback mechanism
+
+    This class can be used directly for monitoring file transfers by
+    providing ``callback=Callback(hooks=...)`` (see the ``hooks`` argument,
+    below), or subclassed for more specialised behaviour.
+
+    Parameters
+    ----------
+    size: int (optional)
+        Nominal quantity for the value that corresponds to a complete
+        transfer, e.g., total number of tiles or total number of
+        bytes
+    value: int (0)
+        Starting internal counter value
+    hooks: dict or None
+        A dict of named functions to be called on each update. The signature
+        of these must be ``f(size, value, **kwargs)``
+    """
+
+    def __init__(self, size=None, value=0, hooks=None, **kwargs):
+        self.size = size
+        self.value = value
+        self.hooks = hooks or {}
+        self.kw = kwargs
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *exc_args):
+        self.close()
+
+    def close(self):
+        """Close callback."""
+
+    def branched(self, path_1, path_2, **kwargs):
+        """
+        Return callback for child transfers
+
+        If this callback is operating at a higher level, e.g., put, which may
+        trigger transfers that can also be monitored. The function returns a callback
+        that has to be passed to the child method, e.g., put_file,
+        as `callback=` argument.
+
+        The implementation uses `callback.branch` for compatibility.
+        When implementing callbacks, it is recommended to override this function instead
+        of `branch` and avoid calling `super().branched(...)`.
+
+        Prefer using this function over `branch`.
+
+        Parameters
+        ----------
+        path_1: str
+            Child's source path
+        path_2: str
+            Child's destination path
+        **kwargs:
+            Arbitrary keyword arguments
+
+        Returns
+        -------
+        callback: Callback
+            A callback instance to be passed to the child method
+        """
+        self.branch(path_1, path_2, kwargs)
+        # mutate kwargs so that we can force the caller to pass "callback=" explicitly
+        return kwargs.pop("callback", DEFAULT_CALLBACK)
+
+    def branch_coro(self, fn):
+        """
+        Wraps a coroutine, and pass a new child callback to it.
+        """
+
+        @wraps(fn)
+        async def func(path1, path2: str, **kwargs):
+            with self.branched(path1, path2, **kwargs) as child:
+                return await fn(path1, path2, callback=child, **kwargs)
+
+        return func
+
+    def set_size(self, size):
+        """
+        Set the internal maximum size attribute
+
+        Usually called if not initially set at instantiation. Note that this
+        triggers a ``call()``.
+
+        Parameters
+        ----------
+        size: int
+        """
+        self.size = size
+        self.call()
+
+    def absolute_update(self, value):
+        """
+        Set the internal value state
+
+        Triggers ``call()``
+
+        Parameters
+        ----------
+        value: int
+        """
+        self.value = value
+        self.call()
+
+    def relative_update(self, inc=1):
+        """
+        Delta increment the internal counter
+
+        Triggers ``call()``
+
+        Parameters
+        ----------
+        inc: int
+        """
+        self.value += inc
+        self.call()
+
+    def call(self, hook_name=None, **kwargs):
+        """
+        Execute hook(s) with current state
+
+        Each function is passed the internal size and current value
+
+        Parameters
+        ----------
+        hook_name: str or None
+            If given, execute on this hook
+        kwargs: passed on to (all) hook(s)
+        """
+        if not self.hooks:
+            return
+        kw = self.kw.copy()
+        kw.update(kwargs)
+        if hook_name:
+            if hook_name not in self.hooks:
+                return
+            return self.hooks[hook_name](self.size, self.value, **kw)
+        for hook in self.hooks.values() or []:
+            hook(self.size, self.value, **kw)
+
+    def wrap(self, iterable):
+        """
+        Wrap an iterable to call ``relative_update`` on each iterations
+
+        Parameters
+        ----------
+        iterable: Iterable
+            The iterable that is being wrapped
+        """
+        for item in iterable:
+            self.relative_update()
+            yield item
+
+    def branch(self, path_1, path_2, kwargs):
+        """
+        Set callbacks for child transfers
+
+        If this callback is operating at a higher level, e.g., put, which may
+        trigger transfers that can also be monitored. The passed kwargs are
+        to be *mutated* to add ``callback=``, if this class supports branching
+        to children.
+
+        Parameters
+        ----------
+        path_1: str
+            Child's source path
+        path_2: str
+            Child's destination path
+        kwargs: dict
+            arguments passed to child method, e.g., put_file.
+
+        Returns
+        -------
+
+        """
+        return None
+
+    def no_op(self, *_, **__):
+        pass
+
+    def __getattr__(self, item):
+        """
+        If undefined methods are called on this class, nothing happens
+        """
+        return self.no_op
+
+    @classmethod
+    def as_callback(cls, maybe_callback=None):
+        """Transform callback=... into Callback instance
+
+        For the special value of ``None``, return the global instance of
+        ``NoOpCallback``. This is an alternative to including
+        ``callback=DEFAULT_CALLBACK`` directly in a method signature.
+        """
+        if maybe_callback is None:
+            return DEFAULT_CALLBACK
+        return maybe_callback
+
+
+class NoOpCallback(Callback):
+    """
+    This implementation of Callback does exactly nothing
+    """
+
+    def call(self, *args, **kwargs):
+        return None
+
+
+class DotPrinterCallback(Callback):
+    """
+    Simple example Callback implementation
+
+    Almost identical to Callback with a hook that prints a char; here we
+    demonstrate how the outer layer may print "#" and the inner layer "."
+    """
+
+    def __init__(self, chr_to_print="#", **kwargs):
+        self.chr = chr_to_print
+        super().__init__(**kwargs)
+
+    def branch(self, path_1, path_2, kwargs):
+        """Mutate kwargs to add new instance with different print char"""
+        kwargs["callback"] = DotPrinterCallback(".")
+
+    def call(self, **kwargs):
+        """Just outputs a character"""
+        print(self.chr, end="")
+
+
+class TqdmCallback(Callback):
+    """
+    A callback to display a progress bar using tqdm
+
+    Parameters
+    ----------
+    tqdm_kwargs : dict, (optional)
+        Any argument accepted by the tqdm constructor.
+        See the `tqdm doc `_.
+        Will be forwarded to `tqdm_cls`.
+    tqdm_cls: (optional)
+        subclass of `tqdm.tqdm`. If not passed, it will default to `tqdm.tqdm`.
+
+    Examples
+    --------
+    >>> import fsspec
+    >>> from fsspec.callbacks import TqdmCallback
+    >>> fs = fsspec.filesystem("memory")
+    >>> path2distant_data = "/your-path"
+    >>> fs.upload(
+            ".",
+            path2distant_data,
+            recursive=True,
+            callback=TqdmCallback(),
+        )
+
+    You can forward args to tqdm using the ``tqdm_kwargs`` parameter.
+
+    >>> fs.upload(
+            ".",
+            path2distant_data,
+            recursive=True,
+            callback=TqdmCallback(tqdm_kwargs={"desc": "Your tqdm description"}),
+        )
+
+    You can also customize the progress bar by passing a subclass of `tqdm`.
+
+    .. code-block:: python
+
+        class TqdmFormat(tqdm):
+            '''Provides a `total_time` format parameter'''
+            @property
+            def format_dict(self):
+                d = super().format_dict
+                total_time = d["elapsed"] * (d["total"] or 0) / max(d["n"], 1)
+                d.update(total_time=self.format_interval(total_time) + " in total")
+                return d
+
+    >>> with TqdmCallback(
+            tqdm_kwargs={
+                "desc": "desc",
+                "bar_format": "{total_time}: {percentage:.0f}%|{bar}{r_bar}",
+            },
+            tqdm_cls=TqdmFormat,
+        ) as callback:
+            fs.upload(".", path2distant_data, recursive=True, callback=callback)
+    """
+
+    def __init__(self, tqdm_kwargs=None, *args, **kwargs):
+        try:
+            from tqdm import tqdm
+
+        except ImportError as exce:
+            raise ImportError(
+                "Using TqdmCallback requires tqdm to be installed"
+            ) from exce
+
+        self._tqdm_cls = kwargs.pop("tqdm_cls", tqdm)
+        self._tqdm_kwargs = tqdm_kwargs or {}
+        self.tqdm = None
+        super().__init__(*args, **kwargs)
+
+    def call(self, *args, **kwargs):
+        if self.tqdm is None:
+            self.tqdm = self._tqdm_cls(total=self.size, **self._tqdm_kwargs)
+        self.tqdm.total = self.size
+        self.tqdm.update(self.value - self.tqdm.n)
+
+    def close(self):
+        if self.tqdm is not None:
+            self.tqdm.close()
+            self.tqdm = None
+
+    def __del__(self):
+        return self.close()
+
+
+DEFAULT_CALLBACK = _DEFAULT_CALLBACK = NoOpCallback()
diff --git a/shared/package-files/04/04373e77aced8ab288052c0177ead08bf54079e64a863505b608022ec7c74521/nll_loss_backward_ops.h b/shared/package-files/04/04373e77aced8ab288052c0177ead08bf54079e64a863505b608022ec7c74521/nll_loss_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..aa950ae077158805edd176a52976b6f311364c9b
--- /dev/null
+++ b/shared/package-files/04/04373e77aced8ab288052c0177ead08bf54079e64a863505b608022ec7c74521/nll_loss_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API nll_loss_backward_grad_input {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Tensor &, const c10::optional &, int64_t, c10::SymInt, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::nll_loss_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "grad_input")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "nll_loss_backward.grad_input(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight, *, Tensor(a!) grad_input) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight, at::Tensor & grad_input);
+};
+
+struct TORCH_API nll_loss_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, const c10::optional &, int64_t, c10::SymInt, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::nll_loss_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "nll_loss_backward(Tensor grad_output, Tensor self, Tensor target, Tensor? weight, int reduction, SymInt ignore_index, Tensor total_weight) -> Tensor")
+  static at::Tensor call(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & target, const c10::optional & weight, int64_t reduction, c10::SymInt ignore_index, const at::Tensor & total_weight);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/04/0439ad63657d2104bdde4e3d672c857423747a546d349acbac52ece95ebc2ec3/lu_unpack.h b/shared/package-files/04/0439ad63657d2104bdde4e3d672c857423747a546d349acbac52ece95ebc2ec3/lu_unpack.h
new file mode 100644
index 0000000000000000000000000000000000000000..55b7577a8a6e634ce8805a42bbfb4fec9edc5882
--- /dev/null
+++ b/shared/package-files/04/0439ad63657d2104bdde4e3d672c857423747a546d349acbac52ece95ebc2ec3/lu_unpack.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::lu_unpack(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True) -> (Tensor P, Tensor L, Tensor U)
+inline ::std::tuple lu_unpack(const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data=true, bool unpack_pivots=true) {
+    return at::_ops::lu_unpack::call(LU_data, LU_pivots, unpack_data, unpack_pivots);
+}
+
+// aten::lu_unpack.out(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True, *, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U)
+inline ::std::tuple lu_unpack_out(at::Tensor & P, at::Tensor & L, at::Tensor & U, const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data=true, bool unpack_pivots=true) {
+    return at::_ops::lu_unpack_out::call(LU_data, LU_pivots, unpack_data, unpack_pivots, P, L, U);
+}
+// aten::lu_unpack.out(Tensor LU_data, Tensor LU_pivots, bool unpack_data=True, bool unpack_pivots=True, *, Tensor(a!) P, Tensor(b!) L, Tensor(c!) U) -> (Tensor(a!) P, Tensor(b!) L, Tensor(c!) U)
+inline ::std::tuple lu_unpack_outf(const at::Tensor & LU_data, const at::Tensor & LU_pivots, bool unpack_data, bool unpack_pivots, at::Tensor & P, at::Tensor & L, at::Tensor & U) {
+    return at::_ops::lu_unpack_out::call(LU_data, LU_pivots, unpack_data, unpack_pivots, P, L, U);
+}
+
+}
diff --git a/shared/package-files/04/043a06503b9bec31532a10ffb735d6e836c3dee197d795239b60f3b8517f72f0/spwd.pyi b/shared/package-files/04/043a06503b9bec31532a10ffb735d6e836c3dee197d795239b60f3b8517f72f0/spwd.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..756c142a61da04fd011d51efc32f169d3553c256
--- /dev/null
+++ b/shared/package-files/04/043a06503b9bec31532a10ffb735d6e836c3dee197d795239b60f3b8517f72f0/spwd.pyi
@@ -0,0 +1,15 @@
+from typing import List, NamedTuple
+
+class struct_spwd(NamedTuple):
+    sp_nam: str
+    sp_pwd: str
+    sp_lstchg: int
+    sp_min: int
+    sp_max: int
+    sp_warn: int
+    sp_inact: int
+    sp_expire: int
+    sp_flag: int
+
+def getspall() -> List[struct_spwd]: ...
+def getspnam(name: str) -> struct_spwd: ...
diff --git a/shared/package-files/04/043b74b34f209506b8946f7934058e929995e73ec87acdbda0d49658d5e00e8f/fft_rfftn.h b/shared/package-files/04/043b74b34f209506b8946f7934058e929995e73ec87acdbda0d49658d5e00e8f/fft_rfftn.h
new file mode 100644
index 0000000000000000000000000000000000000000..2d236a3157004e4b1a100e010c676d5ca18947f5
--- /dev/null
+++ b/shared/package-files/04/043b74b34f209506b8946f7934058e929995e73ec87acdbda0d49658d5e00e8f/fft_rfftn.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::fft_rfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
+inline at::Tensor fft_rfftn(const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) {
+    return at::_ops::fft_rfftn::call(self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm);
+}
+namespace symint {
+  template ::value>>
+  at::Tensor fft_rfftn(const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) {
+    return at::_ops::fft_rfftn::call(self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm);
+  }
+}
+
+// aten::fft_rfftn(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None) -> Tensor
+inline at::Tensor fft_rfftn_symint(const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) {
+    return at::_ops::fft_rfftn::call(self, s, dim, norm);
+}
+namespace symint {
+  template ::value>>
+  at::Tensor fft_rfftn(const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) {
+    return at::_ops::fft_rfftn::call(self, s, dim, norm);
+  }
+}
+
+// aten::fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & fft_rfftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) {
+    return at::_ops::fft_rfftn_out::call(self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out);
+}
+namespace symint {
+  template ::value>>
+  at::Tensor & fft_rfftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) {
+    return at::_ops::fft_rfftn_out::call(self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out);
+  }
+}
+
+// aten::fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & fft_rfftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, at::Tensor & out) {
+    return at::_ops::fft_rfftn_out::call(self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out);
+}
+namespace symint {
+  template ::value>>
+  at::Tensor & fft_rfftn_outf(const at::Tensor & self, at::OptionalIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, at::Tensor & out) {
+    return at::_ops::fft_rfftn_out::call(self, s.has_value() ? c10::make_optional(c10::fromIntArrayRefSlow(*s)) : c10::nullopt, dim, norm, out);
+  }
+}
+
+// aten::fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & fft_rfftn_symint_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) {
+    return at::_ops::fft_rfftn_out::call(self, s, dim, norm, out);
+}
+namespace symint {
+  template ::value>>
+  at::Tensor & fft_rfftn_out(at::Tensor & out, const at::Tensor & self, at::OptionalSymIntArrayRef s=c10::nullopt, at::OptionalIntArrayRef dim=c10::nullopt, c10::optional norm=c10::nullopt) {
+    return at::_ops::fft_rfftn_out::call(self, s, dim, norm, out);
+  }
+}
+
+// aten::fft_rfftn.out(Tensor self, SymInt[1]? s=None, int[1]? dim=None, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & fft_rfftn_symint_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, at::Tensor & out) {
+    return at::_ops::fft_rfftn_out::call(self, s, dim, norm, out);
+}
+namespace symint {
+  template ::value>>
+  at::Tensor & fft_rfftn_outf(const at::Tensor & self, at::OptionalSymIntArrayRef s, at::OptionalIntArrayRef dim, c10::optional norm, at::Tensor & out) {
+    return at::_ops::fft_rfftn_out::call(self, s, dim, norm, out);
+  }
+}
+
+}
diff --git a/shared/package-files/04/043d8ee6fafaecf82073f396d493fb66beb8793ad1f9fcaed34ccfbf71d6c9ce/_embedding_bag_ops.h b/shared/package-files/04/043d8ee6fafaecf82073f396d493fb66beb8793ad1f9fcaed34ccfbf71d6c9ce/_embedding_bag_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..0cfc049c5c614697a46b3db0eefd648967a84eb4
--- /dev/null
+++ b/shared/package-files/04/043d8ee6fafaecf82073f396d493fb66beb8793ad1f9fcaed34ccfbf71d6c9ce/_embedding_bag_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _embedding_bag {
+  using schema = ::std::tuple (const at::Tensor &, const at::Tensor &, const at::Tensor &, bool, int64_t, bool, const c10::optional &, bool, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_embedding_bag")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1) -> (Tensor, Tensor, Tensor, Tensor)")
+  static ::std::tuple call(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, bool include_last_offset, int64_t padding_idx);
+  static ::std::tuple redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, bool include_last_offset, int64_t padding_idx);
+};
+
+struct TORCH_API _embedding_bag_out {
+  using schema = ::std::tuple (const at::Tensor &, const at::Tensor &, const at::Tensor &, bool, int64_t, bool, const c10::optional &, bool, int64_t, at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_embedding_bag")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_embedding_bag.out(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None, bool include_last_offset=False, int padding_idx=-1, *, Tensor(a!) out0, Tensor(b!) out1, Tensor(c!) out2, Tensor(d!) out3) -> (Tensor(a!), Tensor(b!), Tensor(c!), Tensor(d!))")
+  static ::std::tuple call(const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, bool include_last_offset, int64_t padding_idx, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3);
+  static ::std::tuple redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, bool scale_grad_by_freq, int64_t mode, bool sparse, const c10::optional & per_sample_weights, bool include_last_offset, int64_t padding_idx, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/04/043d9940fe3318f778ac96acf654b90befeaa775d70cbdae754f2dcc3f5b4084/arraylike.py b/shared/package-files/04/043d9940fe3318f778ac96acf654b90befeaa775d70cbdae754f2dcc3f5b4084/arraylike.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd585b3e1946880ca79f97b0b6d4a2990bdd32d1
--- /dev/null
+++ b/shared/package-files/04/043d9940fe3318f778ac96acf654b90befeaa775d70cbdae754f2dcc3f5b4084/arraylike.py
@@ -0,0 +1,530 @@
+"""
+Methods that can be shared by many array-like classes or subclasses:
+    Series
+    Index
+    ExtensionArray
+"""
+from __future__ import annotations
+
+import operator
+from typing import Any
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas._libs.ops_dispatch import maybe_dispatch_ufunc_to_dunder_op
+
+from pandas.core.dtypes.generic import ABCNDFrame
+
+from pandas.core import roperator
+from pandas.core.construction import extract_array
+from pandas.core.ops.common import unpack_zerodim_and_defer
+
+REDUCTION_ALIASES = {
+    "maximum": "max",
+    "minimum": "min",
+    "add": "sum",
+    "multiply": "prod",
+}
+
+
+class OpsMixin:
+    # -------------------------------------------------------------
+    # Comparisons
+
+    def _cmp_method(self, other, op):
+        return NotImplemented
+
+    @unpack_zerodim_and_defer("__eq__")
+    def __eq__(self, other):
+        return self._cmp_method(other, operator.eq)
+
+    @unpack_zerodim_and_defer("__ne__")
+    def __ne__(self, other):
+        return self._cmp_method(other, operator.ne)
+
+    @unpack_zerodim_and_defer("__lt__")
+    def __lt__(self, other):
+        return self._cmp_method(other, operator.lt)
+
+    @unpack_zerodim_and_defer("__le__")
+    def __le__(self, other):
+        return self._cmp_method(other, operator.le)
+
+    @unpack_zerodim_and_defer("__gt__")
+    def __gt__(self, other):
+        return self._cmp_method(other, operator.gt)
+
+    @unpack_zerodim_and_defer("__ge__")
+    def __ge__(self, other):
+        return self._cmp_method(other, operator.ge)
+
+    # -------------------------------------------------------------
+    # Logical Methods
+
+    def _logical_method(self, other, op):
+        return NotImplemented
+
+    @unpack_zerodim_and_defer("__and__")
+    def __and__(self, other):
+        return self._logical_method(other, operator.and_)
+
+    @unpack_zerodim_and_defer("__rand__")
+    def __rand__(self, other):
+        return self._logical_method(other, roperator.rand_)
+
+    @unpack_zerodim_and_defer("__or__")
+    def __or__(self, other):
+        return self._logical_method(other, operator.or_)
+
+    @unpack_zerodim_and_defer("__ror__")
+    def __ror__(self, other):
+        return self._logical_method(other, roperator.ror_)
+
+    @unpack_zerodim_and_defer("__xor__")
+    def __xor__(self, other):
+        return self._logical_method(other, operator.xor)
+
+    @unpack_zerodim_and_defer("__rxor__")
+    def __rxor__(self, other):
+        return self._logical_method(other, roperator.rxor)
+
+    # -------------------------------------------------------------
+    # Arithmetic Methods
+
+    def _arith_method(self, other, op):
+        return NotImplemented
+
+    @unpack_zerodim_and_defer("__add__")
+    def __add__(self, other):
+        """
+        Get Addition of DataFrame and other, column-wise.
+
+        Equivalent to ``DataFrame.add(other)``.
+
+        Parameters
+        ----------
+        other : scalar, sequence, Series, dict or DataFrame
+            Object to be added to the DataFrame.
+
+        Returns
+        -------
+        DataFrame
+            The result of adding ``other`` to DataFrame.
+
+        See Also
+        --------
+        DataFrame.add : Add a DataFrame and another object, with option for index-
+            or column-oriented addition.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({'height': [1.5, 2.6], 'weight': [500, 800]},
+        ...                   index=['elk', 'moose'])
+        >>> df
+               height  weight
+        elk       1.5     500
+        moose     2.6     800
+
+        Adding a scalar affects all rows and columns.
+
+        >>> df[['height', 'weight']] + 1.5
+               height  weight
+        elk       3.0   501.5
+        moose     4.1   801.5
+
+        Each element of a list is added to a column of the DataFrame, in order.
+
+        >>> df[['height', 'weight']] + [0.5, 1.5]
+               height  weight
+        elk       2.0   501.5
+        moose     3.1   801.5
+
+        Keys of a dictionary are aligned to the DataFrame, based on column names;
+        each value in the dictionary is added to the corresponding column.
+
+        >>> df[['height', 'weight']] + {'height': 0.5, 'weight': 1.5}
+               height  weight
+        elk       2.0   501.5
+        moose     3.1   801.5
+
+        When `other` is a :class:`Series`, the index of `other` is aligned with the
+        columns of the DataFrame.
+
+        >>> s1 = pd.Series([0.5, 1.5], index=['weight', 'height'])
+        >>> df[['height', 'weight']] + s1
+               height  weight
+        elk       3.0   500.5
+        moose     4.1   800.5
+
+        Even when the index of `other` is the same as the index of the DataFrame,
+        the :class:`Series` will not be reoriented. If index-wise alignment is desired,
+        :meth:`DataFrame.add` should be used with `axis='index'`.
+
+        >>> s2 = pd.Series([0.5, 1.5], index=['elk', 'moose'])
+        >>> df[['height', 'weight']] + s2
+               elk  height  moose  weight
+        elk    NaN     NaN    NaN     NaN
+        moose  NaN     NaN    NaN     NaN
+
+        >>> df[['height', 'weight']].add(s2, axis='index')
+               height  weight
+        elk       2.0   500.5
+        moose     4.1   801.5
+
+        When `other` is a :class:`DataFrame`, both columns names and the
+        index are aligned.
+
+        >>> other = pd.DataFrame({'height': [0.2, 0.4, 0.6]},
+        ...                      index=['elk', 'moose', 'deer'])
+        >>> df[['height', 'weight']] + other
+               height  weight
+        deer      NaN     NaN
+        elk       1.7     NaN
+        moose     3.0     NaN
+        """
+        return self._arith_method(other, operator.add)
+
+    @unpack_zerodim_and_defer("__radd__")
+    def __radd__(self, other):
+        return self._arith_method(other, roperator.radd)
+
+    @unpack_zerodim_and_defer("__sub__")
+    def __sub__(self, other):
+        return self._arith_method(other, operator.sub)
+
+    @unpack_zerodim_and_defer("__rsub__")
+    def __rsub__(self, other):
+        return self._arith_method(other, roperator.rsub)
+
+    @unpack_zerodim_and_defer("__mul__")
+    def __mul__(self, other):
+        return self._arith_method(other, operator.mul)
+
+    @unpack_zerodim_and_defer("__rmul__")
+    def __rmul__(self, other):
+        return self._arith_method(other, roperator.rmul)
+
+    @unpack_zerodim_and_defer("__truediv__")
+    def __truediv__(self, other):
+        return self._arith_method(other, operator.truediv)
+
+    @unpack_zerodim_and_defer("__rtruediv__")
+    def __rtruediv__(self, other):
+        return self._arith_method(other, roperator.rtruediv)
+
+    @unpack_zerodim_and_defer("__floordiv__")
+    def __floordiv__(self, other):
+        return self._arith_method(other, operator.floordiv)
+
+    @unpack_zerodim_and_defer("__rfloordiv")
+    def __rfloordiv__(self, other):
+        return self._arith_method(other, roperator.rfloordiv)
+
+    @unpack_zerodim_and_defer("__mod__")
+    def __mod__(self, other):
+        return self._arith_method(other, operator.mod)
+
+    @unpack_zerodim_and_defer("__rmod__")
+    def __rmod__(self, other):
+        return self._arith_method(other, roperator.rmod)
+
+    @unpack_zerodim_and_defer("__divmod__")
+    def __divmod__(self, other):
+        return self._arith_method(other, divmod)
+
+    @unpack_zerodim_and_defer("__rdivmod__")
+    def __rdivmod__(self, other):
+        return self._arith_method(other, roperator.rdivmod)
+
+    @unpack_zerodim_and_defer("__pow__")
+    def __pow__(self, other):
+        return self._arith_method(other, operator.pow)
+
+    @unpack_zerodim_and_defer("__rpow__")
+    def __rpow__(self, other):
+        return self._arith_method(other, roperator.rpow)
+
+
+# -----------------------------------------------------------------------------
+# Helpers to implement __array_ufunc__
+
+
+def array_ufunc(self, ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any):
+    """
+    Compatibility with numpy ufuncs.
+
+    See also
+    --------
+    numpy.org/doc/stable/reference/arrays.classes.html#numpy.class.__array_ufunc__
+    """
+    from pandas.core.frame import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.generic import NDFrame
+    from pandas.core.internals import (
+        ArrayManager,
+        BlockManager,
+    )
+
+    cls = type(self)
+
+    kwargs = _standardize_out_kwarg(**kwargs)
+
+    # for binary ops, use our custom dunder methods
+    result = maybe_dispatch_ufunc_to_dunder_op(self, ufunc, method, *inputs, **kwargs)
+    if result is not NotImplemented:
+        return result
+
+    # Determine if we should defer.
+    no_defer = (
+        np.ndarray.__array_ufunc__,
+        cls.__array_ufunc__,
+    )
+
+    for item in inputs:
+        higher_priority = (
+            hasattr(item, "__array_priority__")
+            and item.__array_priority__ > self.__array_priority__
+        )
+        has_array_ufunc = (
+            hasattr(item, "__array_ufunc__")
+            and type(item).__array_ufunc__ not in no_defer
+            and not isinstance(item, self._HANDLED_TYPES)
+        )
+        if higher_priority or has_array_ufunc:
+            return NotImplemented
+
+    # align all the inputs.
+    types = tuple(type(x) for x in inputs)
+    alignable = [x for x, t in zip(inputs, types) if issubclass(t, NDFrame)]
+
+    if len(alignable) > 1:
+        # This triggers alignment.
+        # At the moment, there aren't any ufuncs with more than two inputs
+        # so this ends up just being x1.index | x2.index, but we write
+        # it to handle *args.
+        set_types = set(types)
+        if len(set_types) > 1 and {DataFrame, Series}.issubset(set_types):
+            # We currently don't handle ufunc(DataFrame, Series)
+            # well. Previously this raised an internal ValueError. We might
+            # support it someday, so raise a NotImplementedError.
+            raise NotImplementedError(
+                f"Cannot apply ufunc {ufunc} to mixed DataFrame and Series inputs."
+            )
+        axes = self.axes
+        for obj in alignable[1:]:
+            # this relies on the fact that we aren't handling mixed
+            # series / frame ufuncs.
+            for i, (ax1, ax2) in enumerate(zip(axes, obj.axes)):
+                axes[i] = ax1.union(ax2)
+
+        reconstruct_axes = dict(zip(self._AXIS_ORDERS, axes))
+        inputs = tuple(
+            x.reindex(**reconstruct_axes) if issubclass(t, NDFrame) else x
+            for x, t in zip(inputs, types)
+        )
+    else:
+        reconstruct_axes = dict(zip(self._AXIS_ORDERS, self.axes))
+
+    if self.ndim == 1:
+        names = [getattr(x, "name") for x in inputs if hasattr(x, "name")]
+        name = names[0] if len(set(names)) == 1 else None
+        reconstruct_kwargs = {"name": name}
+    else:
+        reconstruct_kwargs = {}
+
+    def reconstruct(result):
+        if ufunc.nout > 1:
+            # np.modf, np.frexp, np.divmod
+            return tuple(_reconstruct(x) for x in result)
+
+        return _reconstruct(result)
+
+    def _reconstruct(result):
+        if lib.is_scalar(result):
+            return result
+
+        if result.ndim != self.ndim:
+            if method == "outer":
+                raise NotImplementedError
+            return result
+        if isinstance(result, (BlockManager, ArrayManager)):
+            # we went through BlockManager.apply e.g. np.sqrt
+            result = self._constructor_from_mgr(result, axes=result.axes)
+        else:
+            # we converted an array, lost our axes
+            result = self._constructor(
+                result, **reconstruct_axes, **reconstruct_kwargs, copy=False
+            )
+        # TODO: When we support multiple values in __finalize__, this
+        # should pass alignable to `__finalize__` instead of self.
+        # Then `np.add(a, b)` would consider attrs from both a and b
+        # when a and b are NDFrames.
+        if len(alignable) == 1:
+            result = result.__finalize__(self)
+        return result
+
+    if "out" in kwargs:
+        # e.g. test_multiindex_get_loc
+        result = dispatch_ufunc_with_out(self, ufunc, method, *inputs, **kwargs)
+        return reconstruct(result)
+
+    if method == "reduce":
+        # e.g. test.series.test_ufunc.test_reduce
+        result = dispatch_reduction_ufunc(self, ufunc, method, *inputs, **kwargs)
+        if result is not NotImplemented:
+            return result
+
+    # We still get here with kwargs `axis` for e.g. np.maximum.accumulate
+    #  and `dtype` and `keepdims` for np.ptp
+
+    if self.ndim > 1 and (len(inputs) > 1 or ufunc.nout > 1):
+        # Just give up on preserving types in the complex case.
+        # In theory we could preserve them for them.
+        # * nout>1 is doable if BlockManager.apply took nout and
+        #   returned a Tuple[BlockManager].
+        # * len(inputs) > 1 is doable when we know that we have
+        #   aligned blocks / dtypes.
+
+        # e.g. my_ufunc, modf, logaddexp, heaviside, subtract, add
+        inputs = tuple(np.asarray(x) for x in inputs)
+        # Note: we can't use default_array_ufunc here bc reindexing means
+        #  that `self` may not be among `inputs`
+        result = getattr(ufunc, method)(*inputs, **kwargs)
+    elif self.ndim == 1:
+        # ufunc(series, ...)
+        inputs = tuple(extract_array(x, extract_numpy=True) for x in inputs)
+        result = getattr(ufunc, method)(*inputs, **kwargs)
+    else:
+        # ufunc(dataframe)
+        if method == "__call__" and not kwargs:
+            # for np.(..) calls
+            # kwargs cannot necessarily be handled block-by-block, so only
+            # take this path if there are no kwargs
+            mgr = inputs[0]._mgr
+            result = mgr.apply(getattr(ufunc, method))
+        else:
+            # otherwise specific ufunc methods (eg np..accumulate(..))
+            # Those can have an axis keyword and thus can't be called block-by-block
+            result = default_array_ufunc(inputs[0], ufunc, method, *inputs, **kwargs)
+            # e.g. np.negative (only one reached), with "where" and "out" in kwargs
+
+    result = reconstruct(result)
+    return result
+
+
+def _standardize_out_kwarg(**kwargs) -> dict:
+    """
+    If kwargs contain "out1" and "out2", replace that with a tuple "out"
+
+    np.divmod, np.modf, np.frexp can have either `out=(out1, out2)` or
+    `out1=out1, out2=out2)`
+    """
+    if "out" not in kwargs and "out1" in kwargs and "out2" in kwargs:
+        out1 = kwargs.pop("out1")
+        out2 = kwargs.pop("out2")
+        out = (out1, out2)
+        kwargs["out"] = out
+    return kwargs
+
+
+def dispatch_ufunc_with_out(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+    """
+    If we have an `out` keyword, then call the ufunc without `out` and then
+    set the result into the given `out`.
+    """
+
+    # Note: we assume _standardize_out_kwarg has already been called.
+    out = kwargs.pop("out")
+    where = kwargs.pop("where", None)
+
+    result = getattr(ufunc, method)(*inputs, **kwargs)
+
+    if result is NotImplemented:
+        return NotImplemented
+
+    if isinstance(result, tuple):
+        # i.e. np.divmod, np.modf, np.frexp
+        if not isinstance(out, tuple) or len(out) != len(result):
+            raise NotImplementedError
+
+        for arr, res in zip(out, result):
+            _assign_where(arr, res, where)
+
+        return out
+
+    if isinstance(out, tuple):
+        if len(out) == 1:
+            out = out[0]
+        else:
+            raise NotImplementedError
+
+    _assign_where(out, result, where)
+    return out
+
+
+def _assign_where(out, result, where) -> None:
+    """
+    Set a ufunc result into 'out', masking with a 'where' argument if necessary.
+    """
+    if where is None:
+        # no 'where' arg passed to ufunc
+        out[:] = result
+    else:
+        np.putmask(out, where, result)
+
+
+def default_array_ufunc(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+    """
+    Fallback to the behavior we would get if we did not define __array_ufunc__.
+
+    Notes
+    -----
+    We are assuming that `self` is among `inputs`.
+    """
+    if not any(x is self for x in inputs):
+        raise NotImplementedError
+
+    new_inputs = [x if x is not self else np.asarray(x) for x in inputs]
+
+    return getattr(ufunc, method)(*new_inputs, **kwargs)
+
+
+def dispatch_reduction_ufunc(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
+    """
+    Dispatch ufunc reductions to self's reduction methods.
+    """
+    assert method == "reduce"
+
+    if len(inputs) != 1 or inputs[0] is not self:
+        return NotImplemented
+
+    if ufunc.__name__ not in REDUCTION_ALIASES:
+        return NotImplemented
+
+    method_name = REDUCTION_ALIASES[ufunc.__name__]
+
+    # NB: we are assuming that min/max represent minimum/maximum methods,
+    #  which would not be accurate for e.g. Timestamp.min
+    if not hasattr(self, method_name):
+        return NotImplemented
+
+    if self.ndim > 1:
+        if isinstance(self, ABCNDFrame):
+            # TODO: test cases where this doesn't hold, i.e. 2D DTA/TDA
+            kwargs["numeric_only"] = False
+
+        if "axis" not in kwargs:
+            # For DataFrame reductions we don't want the default axis=0
+            # Note: np.min is not a ufunc, but uses array_function_dispatch,
+            #  so calls DataFrame.min (without ever getting here) with the np.min
+            #  default of axis=None, which DataFrame.min catches and changes to axis=0.
+            # np.minimum.reduce(df) gets here bc axis is not in kwargs,
+            #  so we set axis=0 to match the behaviorof np.minimum.reduce(df.values)
+            kwargs["axis"] = 0
+
+    # By default, numpy's reductions do not skip NaNs, so we have to
+    #  pass skipna=False
+    return getattr(self, method_name)(skipna=False, **kwargs)
diff --git a/shared/package-files/04/043dece6ea7c83956b3300b95f8a0e92badaa8fc29d6c510706649d1d810679a/sk.msg b/shared/package-files/04/043dece6ea7c83956b3300b95f8a0e92badaa8fc29d6c510706649d1d810679a/sk.msg
new file mode 100644
index 0000000000000000000000000000000000000000..9b2f0aadd2de5b7bd3aedf9e1fdc47caf6704a07
--- /dev/null
+++ b/shared/package-files/04/043dece6ea7c83956b3300b95f8a0e92badaa8fc29d6c510706649d1d810679a/sk.msg
@@ -0,0 +1,52 @@
+# created by tools/loadICU.tcl -- do not edit
+namespace eval ::tcl::clock {
+    ::msgcat::mcset sk DAYS_OF_WEEK_ABBREV [list \
+        "Ne"\
+        "Po"\
+        "Ut"\
+        "St"\
+        "\u0160t"\
+        "Pa"\
+        "So"]
+    ::msgcat::mcset sk DAYS_OF_WEEK_FULL [list \
+        "Nede\u013ee"\
+        "Pondelok"\
+        "Utorok"\
+        "Streda"\
+        "\u0160tvrtok"\
+        "Piatok"\
+        "Sobota"]
+    ::msgcat::mcset sk MONTHS_ABBREV [list \
+        "jan"\
+        "feb"\
+        "mar"\
+        "apr"\
+        "m\u00e1j"\
+        "j\u00fan"\
+        "j\u00fal"\
+        "aug"\
+        "sep"\
+        "okt"\
+        "nov"\
+        "dec"\
+        ""]
+    ::msgcat::mcset sk MONTHS_FULL [list \
+        "janu\u00e1r"\
+        "febru\u00e1r"\
+        "marec"\
+        "apr\u00edl"\
+        "m\u00e1j"\
+        "j\u00fan"\
+        "j\u00fal"\
+        "august"\
+        "september"\
+        "okt\u00f3ber"\
+        "november"\
+        "december"\
+        ""]
+    ::msgcat::mcset sk BCE "pred n.l."
+    ::msgcat::mcset sk CE "n.l."
+    ::msgcat::mcset sk DATE_FORMAT "%e.%m.%Y"
+    ::msgcat::mcset sk TIME_FORMAT "%k:%M:%S"
+    ::msgcat::mcset sk DATE_TIME_FORMAT "%e.%m.%Y %k:%M:%S %z"
+}
diff --git a/shared/package-files/04/0440062e50dba136cbaa1c009e8cb0a54569a91bbab2a8f37bf89ac9a9ab1e63/to_sparse_csc.h b/shared/package-files/04/0440062e50dba136cbaa1c009e8cb0a54569a91bbab2a8f37bf89ac9a9ab1e63/to_sparse_csc.h
new file mode 100644
index 0000000000000000000000000000000000000000..9a9c7879687c05d2efad0b44c334068efe7d79f9
--- /dev/null
+++ b/shared/package-files/04/0440062e50dba136cbaa1c009e8cb0a54569a91bbab2a8f37bf89ac9a9ab1e63/to_sparse_csc.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+
+}
diff --git a/shared/package-files/04/044104b912239c33badda0ed0a4984eb7d52a316d3bc77b09ad3d0c2f894d346/syspathcontext.py b/shared/package-files/04/044104b912239c33badda0ed0a4984eb7d52a316d3bc77b09ad3d0c2f894d346/syspathcontext.py
new file mode 100644
index 0000000000000000000000000000000000000000..7af1ab60af9c797596e9c27036f9db96a8fe70ee
--- /dev/null
+++ b/shared/package-files/04/044104b912239c33badda0ed0a4984eb7d52a316d3bc77b09ad3d0c2f894d346/syspathcontext.py
@@ -0,0 +1,71 @@
+# encoding: utf-8
+"""
+Context managers for adding things to sys.path temporarily.
+
+Authors:
+
+* Brian Granger
+"""
+
+#-----------------------------------------------------------------------------
+#  Copyright (C) 2008-2011  The IPython Development Team
+#
+#  Distributed under the terms of the BSD License.  The full license is in
+#  the file COPYING, distributed as part of this software.
+#-----------------------------------------------------------------------------
+
+import sys
+import warnings
+
+
+class appended_to_syspath(object):
+    """
+    Deprecated since IPython 8.1, no replacements.
+
+    A context for appending a directory to sys.path for a second."""
+
+    def __init__(self, dir):
+        warnings.warn(
+            "`appended_to_syspath` is deprecated since IPython 8.1, and has no replacements",
+            DeprecationWarning,
+            stacklevel=2,
+        )
+        self.dir = dir
+
+    def __enter__(self):
+        if self.dir not in sys.path:
+            sys.path.append(self.dir)
+            self.added = True
+        else:
+            self.added = False
+
+    def __exit__(self, type, value, traceback):
+        if self.added:
+            try:
+                sys.path.remove(self.dir)
+            except ValueError:
+                pass
+        # Returning False causes any exceptions to be re-raised.
+        return False
+
+class prepended_to_syspath(object):
+    """A context for prepending a directory to sys.path for a second."""
+
+    def __init__(self, dir):
+        self.dir = dir
+
+    def __enter__(self):
+        if self.dir not in sys.path:
+            sys.path.insert(0,self.dir)
+            self.added = True
+        else:
+            self.added = False
+
+    def __exit__(self, type, value, traceback):
+        if self.added:
+            try:
+                sys.path.remove(self.dir)
+            except ValueError:
+                pass
+        # Returning False causes any exceptions to be re-raised.
+        return False
diff --git a/shared/package-files/04/04428db3bad416777f71967bc10cf4026289ed4fbb2ac108e95dde0296f5721c/test_to_dict.py b/shared/package-files/04/04428db3bad416777f71967bc10cf4026289ed4fbb2ac108e95dde0296f5721c/test_to_dict.py
new file mode 100644
index 0000000000000000000000000000000000000000..570f85a4a31ee5f210a6ccd9c8c52a95b5c09b8d
--- /dev/null
+++ b/shared/package-files/04/04428db3bad416777f71967bc10cf4026289ed4fbb2ac108e95dde0296f5721c/test_to_dict.py
@@ -0,0 +1,535 @@
+from collections import (
+    OrderedDict,
+    defaultdict,
+)
+from datetime import datetime
+
+import numpy as np
+import pytest
+import pytz
+
+from pandas import (
+    NA,
+    DataFrame,
+    Index,
+    Interval,
+    MultiIndex,
+    Period,
+    Series,
+    Timedelta,
+    Timestamp,
+)
+import pandas._testing as tm
+
+
+class TestDataFrameToDict:
+    def test_to_dict_timestamp(self):
+        # GH#11247
+        # split/records producing np.datetime64 rather than Timestamps
+        # on datetime64[ns] dtypes only
+
+        tsmp = Timestamp("20130101")
+        test_data = DataFrame({"A": [tsmp, tsmp], "B": [tsmp, tsmp]})
+        test_data_mixed = DataFrame({"A": [tsmp, tsmp], "B": [1, 2]})
+
+        expected_records = [{"A": tsmp, "B": tsmp}, {"A": tsmp, "B": tsmp}]
+        expected_records_mixed = [{"A": tsmp, "B": 1}, {"A": tsmp, "B": 2}]
+
+        assert test_data.to_dict(orient="records") == expected_records
+        assert test_data_mixed.to_dict(orient="records") == expected_records_mixed
+
+        expected_series = {
+            "A": Series([tsmp, tsmp], name="A"),
+            "B": Series([tsmp, tsmp], name="B"),
+        }
+        expected_series_mixed = {
+            "A": Series([tsmp, tsmp], name="A"),
+            "B": Series([1, 2], name="B"),
+        }
+
+        tm.assert_dict_equal(test_data.to_dict(orient="series"), expected_series)
+        tm.assert_dict_equal(
+            test_data_mixed.to_dict(orient="series"), expected_series_mixed
+        )
+
+        expected_split = {
+            "index": [0, 1],
+            "data": [[tsmp, tsmp], [tsmp, tsmp]],
+            "columns": ["A", "B"],
+        }
+        expected_split_mixed = {
+            "index": [0, 1],
+            "data": [[tsmp, 1], [tsmp, 2]],
+            "columns": ["A", "B"],
+        }
+
+        tm.assert_dict_equal(test_data.to_dict(orient="split"), expected_split)
+        tm.assert_dict_equal(
+            test_data_mixed.to_dict(orient="split"), expected_split_mixed
+        )
+
+    def test_to_dict_index_not_unique_with_index_orient(self):
+        # GH#22801
+        # Data loss when indexes are not unique. Raise ValueError.
+        df = DataFrame({"a": [1, 2], "b": [0.5, 0.75]}, index=["A", "A"])
+        msg = "DataFrame index must be unique for orient='index'"
+        with pytest.raises(ValueError, match=msg):
+            df.to_dict(orient="index")
+
+    def test_to_dict_invalid_orient(self):
+        df = DataFrame({"A": [0, 1]})
+        msg = "orient 'xinvalid' not understood"
+        with pytest.raises(ValueError, match=msg):
+            df.to_dict(orient="xinvalid")
+
+    @pytest.mark.parametrize("orient", ["d", "l", "r", "sp", "s", "i"])
+    def test_to_dict_short_orient_raises(self, orient):
+        # GH#32515
+        df = DataFrame({"A": [0, 1]})
+        with pytest.raises(ValueError, match="not understood"):
+            df.to_dict(orient=orient)
+
+    @pytest.mark.parametrize("mapping", [dict, defaultdict(list), OrderedDict])
+    def test_to_dict(self, mapping):
+        # orient= should only take the listed options
+        # see GH#32515
+        test_data = {"A": {"1": 1, "2": 2}, "B": {"1": "1", "2": "2", "3": "3"}}
+
+        # GH#16122
+        recons_data = DataFrame(test_data).to_dict(into=mapping)
+
+        for k, v in test_data.items():
+            for k2, v2 in v.items():
+                assert v2 == recons_data[k][k2]
+
+        recons_data = DataFrame(test_data).to_dict("list", into=mapping)
+
+        for k, v in test_data.items():
+            for k2, v2 in v.items():
+                assert v2 == recons_data[k][int(k2) - 1]
+
+        recons_data = DataFrame(test_data).to_dict("series", into=mapping)
+
+        for k, v in test_data.items():
+            for k2, v2 in v.items():
+                assert v2 == recons_data[k][k2]
+
+        recons_data = DataFrame(test_data).to_dict("split", into=mapping)
+        expected_split = {
+            "columns": ["A", "B"],
+            "index": ["1", "2", "3"],
+            "data": [[1.0, "1"], [2.0, "2"], [np.nan, "3"]],
+        }
+        tm.assert_dict_equal(recons_data, expected_split)
+
+        recons_data = DataFrame(test_data).to_dict("records", into=mapping)
+        expected_records = [
+            {"A": 1.0, "B": "1"},
+            {"A": 2.0, "B": "2"},
+            {"A": np.nan, "B": "3"},
+        ]
+        assert isinstance(recons_data, list)
+        assert len(recons_data) == 3
+        for left, right in zip(recons_data, expected_records):
+            tm.assert_dict_equal(left, right)
+
+        # GH#10844
+        recons_data = DataFrame(test_data).to_dict("index")
+
+        for k, v in test_data.items():
+            for k2, v2 in v.items():
+                assert v2 == recons_data[k2][k]
+
+        df = DataFrame(test_data)
+        df["duped"] = df[df.columns[0]]
+        recons_data = df.to_dict("index")
+        comp_data = test_data.copy()
+        comp_data["duped"] = comp_data[df.columns[0]]
+        for k, v in comp_data.items():
+            for k2, v2 in v.items():
+                assert v2 == recons_data[k2][k]
+
+    @pytest.mark.parametrize("mapping", [list, defaultdict, []])
+    def test_to_dict_errors(self, mapping):
+        # GH#16122
+        df = DataFrame(np.random.default_rng(2).standard_normal((3, 3)))
+        msg = "|".join(
+            [
+                "unsupported type: ",
+                r"to_dict\(\) only accepts initialized defaultdicts",
+            ]
+        )
+        with pytest.raises(TypeError, match=msg):
+            df.to_dict(into=mapping)
+
+    def test_to_dict_not_unique_warning(self):
+        # GH#16927: When converting to a dict, if a column has a non-unique name
+        # it will be dropped, throwing a warning.
+        df = DataFrame([[1, 2, 3]], columns=["a", "a", "b"])
+        with tm.assert_produces_warning(UserWarning):
+            df.to_dict()
+
+    @pytest.mark.filterwarnings("ignore::UserWarning")
+    @pytest.mark.parametrize(
+        "orient,expected",
+        [
+            ("list", {"A": [2, 5], "B": [3, 6]}),
+            ("dict", {"A": {0: 2, 1: 5}, "B": {0: 3, 1: 6}}),
+        ],
+    )
+    def test_to_dict_not_unique(self, orient, expected):
+        # GH#54824: This is to make sure that dataframes with non-unique column
+        # would have uniform behavior throughout different orients
+        df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "A", "B"])
+        result = df.to_dict(orient)
+        assert result == expected
+
+    # orient - orient argument to to_dict function
+    # item_getter - function for extracting value from
+    # the resulting dict using column name and index
+    @pytest.mark.parametrize(
+        "orient,item_getter",
+        [
+            ("dict", lambda d, col, idx: d[col][idx]),
+            ("records", lambda d, col, idx: d[idx][col]),
+            ("list", lambda d, col, idx: d[col][idx]),
+            ("split", lambda d, col, idx: d["data"][idx][d["columns"].index(col)]),
+            ("index", lambda d, col, idx: d[idx][col]),
+        ],
+    )
+    def test_to_dict_box_scalars(self, orient, item_getter):
+        # GH#14216, GH#23753
+        # make sure that we are boxing properly
+        df = DataFrame({"a": [1, 2], "b": [0.1, 0.2]})
+        result = df.to_dict(orient=orient)
+        assert isinstance(item_getter(result, "a", 0), int)
+        assert isinstance(item_getter(result, "b", 0), float)
+
+    def test_to_dict_tz(self):
+        # GH#18372 When converting to dict with orient='records' columns of
+        # datetime that are tz-aware were not converted to required arrays
+        data = [
+            (datetime(2017, 11, 18, 21, 53, 0, 219225, tzinfo=pytz.utc),),
+            (datetime(2017, 11, 18, 22, 6, 30, 61810, tzinfo=pytz.utc),),
+        ]
+        df = DataFrame(list(data), columns=["d"])
+
+        result = df.to_dict(orient="records")
+        expected = [
+            {"d": Timestamp("2017-11-18 21:53:00.219225+0000", tz=pytz.utc)},
+            {"d": Timestamp("2017-11-18 22:06:30.061810+0000", tz=pytz.utc)},
+        ]
+        tm.assert_dict_equal(result[0], expected[0])
+        tm.assert_dict_equal(result[1], expected[1])
+
+    @pytest.mark.parametrize(
+        "into, expected",
+        [
+            (
+                dict,
+                {
+                    0: {"int_col": 1, "float_col": 1.0},
+                    1: {"int_col": 2, "float_col": 2.0},
+                    2: {"int_col": 3, "float_col": 3.0},
+                },
+            ),
+            (
+                OrderedDict,
+                OrderedDict(
+                    [
+                        (0, {"int_col": 1, "float_col": 1.0}),
+                        (1, {"int_col": 2, "float_col": 2.0}),
+                        (2, {"int_col": 3, "float_col": 3.0}),
+                    ]
+                ),
+            ),
+            (
+                defaultdict(dict),
+                defaultdict(
+                    dict,
+                    {
+                        0: {"int_col": 1, "float_col": 1.0},
+                        1: {"int_col": 2, "float_col": 2.0},
+                        2: {"int_col": 3, "float_col": 3.0},
+                    },
+                ),
+            ),
+        ],
+    )
+    def test_to_dict_index_dtypes(self, into, expected):
+        # GH#18580
+        # When using to_dict(orient='index') on a dataframe with int
+        # and float columns only the int columns were cast to float
+
+        df = DataFrame({"int_col": [1, 2, 3], "float_col": [1.0, 2.0, 3.0]})
+
+        result = df.to_dict(orient="index", into=into)
+        cols = ["int_col", "float_col"]
+        result = DataFrame.from_dict(result, orient="index")[cols]
+        expected = DataFrame.from_dict(expected, orient="index")[cols]
+        tm.assert_frame_equal(result, expected)
+
+    def test_to_dict_numeric_names(self):
+        # GH#24940
+        df = DataFrame({str(i): [i] for i in range(5)})
+        result = set(df.to_dict("records")[0].keys())
+        expected = set(df.columns)
+        assert result == expected
+
+    def test_to_dict_wide(self):
+        # GH#24939
+        df = DataFrame({(f"A_{i:d}"): [i] for i in range(256)})
+        result = df.to_dict("records")[0]
+        expected = {f"A_{i:d}": i for i in range(256)}
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "data,dtype",
+        (
+            ([True, True, False], bool),
+            [
+                [
+                    datetime(2018, 1, 1),
+                    datetime(2019, 2, 2),
+                    datetime(2020, 3, 3),
+                ],
+                Timestamp,
+            ],
+            [[1.0, 2.0, 3.0], float],
+            [[1, 2, 3], int],
+            [["X", "Y", "Z"], str],
+        ),
+    )
+    def test_to_dict_orient_dtype(self, data, dtype):
+        # GH22620 & GH21256
+
+        df = DataFrame({"a": data})
+        d = df.to_dict(orient="records")
+        assert all(type(record["a"]) is dtype for record in d)
+
+    @pytest.mark.parametrize(
+        "data,expected_dtype",
+        (
+            [np.uint64(2), int],
+            [np.int64(-9), int],
+            [np.float64(1.1), float],
+            [np.bool_(True), bool],
+            [np.datetime64("2005-02-25"), Timestamp],
+        ),
+    )
+    def test_to_dict_scalar_constructor_orient_dtype(self, data, expected_dtype):
+        # GH22620 & GH21256
+
+        df = DataFrame({"a": data}, index=[0])
+        d = df.to_dict(orient="records")
+        result = type(d[0]["a"])
+        assert result is expected_dtype
+
+    def test_to_dict_mixed_numeric_frame(self):
+        # GH 12859
+        df = DataFrame({"a": [1.0], "b": [9.0]})
+        result = df.reset_index().to_dict("records")
+        expected = [{"index": 0, "a": 1.0, "b": 9.0}]
+        assert result == expected
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            None,
+            Index(["aa", "bb"]),
+            Index(["aa", "bb"], name="cc"),
+            MultiIndex.from_tuples([("a", "b"), ("a", "c")]),
+            MultiIndex.from_tuples([("a", "b"), ("a", "c")], names=["n1", "n2"]),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "columns",
+        [
+            ["x", "y"],
+            Index(["x", "y"]),
+            Index(["x", "y"], name="z"),
+            MultiIndex.from_tuples([("x", 1), ("y", 2)]),
+            MultiIndex.from_tuples([("x", 1), ("y", 2)], names=["z1", "z2"]),
+        ],
+    )
+    def test_to_dict_orient_tight(self, index, columns):
+        df = DataFrame.from_records(
+            [[1, 3], [2, 4]],
+            columns=columns,
+            index=index,
+        )
+        roundtrip = DataFrame.from_dict(df.to_dict(orient="tight"), orient="tight")
+
+        tm.assert_frame_equal(df, roundtrip)
+
+    @pytest.mark.parametrize(
+        "orient",
+        ["dict", "list", "split", "records", "index", "tight"],
+    )
+    @pytest.mark.parametrize(
+        "data,expected_types",
+        (
+            (
+                {
+                    "a": [np.int64(1), 1, np.int64(3)],
+                    "b": [np.float64(1.0), 2.0, np.float64(3.0)],
+                    "c": [np.float64(1.0), 2, np.int64(3)],
+                    "d": [np.float64(1.0), "a", np.int64(3)],
+                    "e": [np.float64(1.0), ["a"], np.int64(3)],
+                    "f": [np.float64(1.0), ("a",), np.int64(3)],
+                },
+                {
+                    "a": [int, int, int],
+                    "b": [float, float, float],
+                    "c": [float, float, float],
+                    "d": [float, str, int],
+                    "e": [float, list, int],
+                    "f": [float, tuple, int],
+                },
+            ),
+            (
+                {
+                    "a": [1, 2, 3],
+                    "b": [1.1, 2.2, 3.3],
+                },
+                {
+                    "a": [int, int, int],
+                    "b": [float, float, float],
+                },
+            ),
+            (  # Make sure we have one df which is all object type cols
+                {
+                    "a": [1, "hello", 3],
+                    "b": [1.1, "world", 3.3],
+                },
+                {
+                    "a": [int, str, int],
+                    "b": [float, str, float],
+                },
+            ),
+        ),
+    )
+    def test_to_dict_returns_native_types(self, orient, data, expected_types):
+        # GH 46751
+        # Tests we get back native types for all orient types
+        df = DataFrame(data)
+        result = df.to_dict(orient)
+        if orient == "dict":
+            assertion_iterator = (
+                (i, key, value)
+                for key, index_value_map in result.items()
+                for i, value in index_value_map.items()
+            )
+        elif orient == "list":
+            assertion_iterator = (
+                (i, key, value)
+                for key, values in result.items()
+                for i, value in enumerate(values)
+            )
+        elif orient in {"split", "tight"}:
+            assertion_iterator = (
+                (i, key, result["data"][i][j])
+                for i in result["index"]
+                for j, key in enumerate(result["columns"])
+            )
+        elif orient == "records":
+            assertion_iterator = (
+                (i, key, value)
+                for i, record in enumerate(result)
+                for key, value in record.items()
+            )
+        elif orient == "index":
+            assertion_iterator = (
+                (i, key, value)
+                for i, record in result.items()
+                for key, value in record.items()
+            )
+
+        for i, key, value in assertion_iterator:
+            assert value == data[key][i]
+            assert type(value) is expected_types[key][i]
+
+    @pytest.mark.parametrize("orient", ["dict", "list", "series", "records", "index"])
+    def test_to_dict_index_false_error(self, orient):
+        # GH#46398
+        df = DataFrame({"col1": [1, 2], "col2": [3, 4]}, index=["row1", "row2"])
+        msg = "'index=False' is only valid when 'orient' is 'split' or 'tight'"
+        with pytest.raises(ValueError, match=msg):
+            df.to_dict(orient=orient, index=False)
+
+    @pytest.mark.parametrize(
+        "orient, expected",
+        [
+            ("split", {"columns": ["col1", "col2"], "data": [[1, 3], [2, 4]]}),
+            (
+                "tight",
+                {
+                    "columns": ["col1", "col2"],
+                    "data": [[1, 3], [2, 4]],
+                    "column_names": [None],
+                },
+            ),
+        ],
+    )
+    def test_to_dict_index_false(self, orient, expected):
+        # GH#46398
+        df = DataFrame({"col1": [1, 2], "col2": [3, 4]}, index=["row1", "row2"])
+        result = df.to_dict(orient=orient, index=False)
+        tm.assert_dict_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "orient, expected",
+        [
+            ("dict", {"a": {0: 1, 1: None}}),
+            ("list", {"a": [1, None]}),
+            ("split", {"index": [0, 1], "columns": ["a"], "data": [[1], [None]]}),
+            (
+                "tight",
+                {
+                    "index": [0, 1],
+                    "columns": ["a"],
+                    "data": [[1], [None]],
+                    "index_names": [None],
+                    "column_names": [None],
+                },
+            ),
+            ("records", [{"a": 1}, {"a": None}]),
+            ("index", {0: {"a": 1}, 1: {"a": None}}),
+        ],
+    )
+    def test_to_dict_na_to_none(self, orient, expected):
+        # GH#50795
+        df = DataFrame({"a": [1, NA]}, dtype="Int64")
+        result = df.to_dict(orient=orient)
+        assert result == expected
+
+    def test_to_dict_masked_native_python(self):
+        # GH#34665
+        df = DataFrame({"a": Series([1, 2], dtype="Int64"), "B": 1})
+        result = df.to_dict(orient="records")
+        assert isinstance(result[0]["a"], int)
+
+        df = DataFrame({"a": Series([1, NA], dtype="Int64"), "B": 1})
+        result = df.to_dict(orient="records")
+        assert isinstance(result[0]["a"], int)
+
+    def test_to_dict_pos_args_deprecation(self):
+        # GH-54229
+        df = DataFrame({"a": [1, 2, 3]})
+        msg = (
+            r"Starting with pandas version 3.0 all arguments of to_dict except for the "
+            r"argument 'orient' will be keyword-only."
+        )
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            df.to_dict("records", {})
+
+
+@pytest.mark.parametrize(
+    "val", [Timestamp(2020, 1, 1), Timedelta(1), Period("2020"), Interval(1, 2)]
+)
+def test_to_dict_list_pd_scalars(val):
+    # GH 54824
+    df = DataFrame({"a": [val]})
+    result = df.to_dict(orient="list")
+    expected = {"a": [val]}
+    assert result == expected
diff --git a/shared/package-files/04/0446de61f489aecab4a90711770cfe3acfdf094fe9ba65957cb23e403a4bfd69/complex_cuda_dispatch.h b/shared/package-files/04/0446de61f489aecab4a90711770cfe3acfdf094fe9ba65957cb23e403a4bfd69/complex_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..3dcf615a42e94eefcc2e849806fbaa9f6a092674
--- /dev/null
+++ b/shared/package-files/04/0446de61f489aecab4a90711770cfe3acfdf094fe9ba65957cb23e403a4bfd69/complex_cuda_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor & complex_out(at::Tensor & out, const at::Tensor & real, const at::Tensor & imag);
+TORCH_API at::Tensor & complex_outf(const at::Tensor & real, const at::Tensor & imag, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/04/04472d73a2b75f5aa226cdfab81c59a84b248c63bff7e1821cd5fd35dec875ff/integration.py b/shared/package-files/04/04472d73a2b75f5aa226cdfab81c59a84b248c63bff7e1821cd5fd35dec875ff/integration.py
new file mode 100644
index 0000000000000000000000000000000000000000..e95c15632b4e8a71b6ff00704c45255767d20573
--- /dev/null
+++ b/shared/package-files/04/04472d73a2b75f5aa226cdfab81c59a84b248c63bff7e1821cd5fd35dec875ff/integration.py
@@ -0,0 +1,479 @@
+# Copyright (C) 2012 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+"""
+These helpers were originally defined in tests/test_create.py,
+but were refactored here so downstream projects can benefit from
+them too.
+"""
+
+import json
+import os
+import sys
+from contextlib import contextmanager
+from functools import lru_cache
+from logging import getLogger
+from os.path import dirname, exists, isdir, join, lexists
+from random import sample
+from shutil import copyfile, rmtree
+from subprocess import check_output
+from tempfile import gettempdir
+from uuid import uuid4
+
+import pytest
+
+from conda.auxlib.compat import Utf8NamedTemporaryFile
+from conda.auxlib.entity import EntityEncoder
+from conda.base.constants import PACKAGE_CACHE_MAGIC_FILE
+from conda.base.context import conda_tests_ctxt_mgmt_def_pol, context, reset_context
+from conda.cli.conda_argparse import do_call, generate_parser
+from conda.cli.main import init_loggers
+from conda.common.compat import encode_arguments, on_win
+from conda.common.io import (
+    argv,
+    captured,
+    dashlist,
+    disable_logger,
+    env_var,
+    stderr_log_level,
+)
+from conda.common.url import escape_channel_url, path_to_url
+from conda.core.package_cache_data import PackageCacheData
+from conda.core.prefix_data import PrefixData
+from conda.deprecations import deprecated
+from conda.exceptions import conda_exception_handler
+from conda.gateways.disk.create import mkdir_p
+from conda.gateways.disk.delete import rm_rf
+from conda.gateways.disk.link import link
+from conda.gateways.disk.update import touch
+from conda.gateways.logging import DEBUG
+from conda.models.match_spec import MatchSpec
+from conda.models.records import PackageRecord
+from conda.utils import massage_arguments
+
+TEST_LOG_LEVEL = DEBUG
+PYTHON_BINARY = "python.exe" if on_win else "bin/python"
+BIN_DIRECTORY = "Scripts" if on_win else "bin"
+UNICODE_CHARACTERS = "Åγђ家固한áêñßôç"
+# UNICODE_CHARACTERS_RESTRICTED = u"áêñßôç"
+UNICODE_CHARACTERS_RESTRICTED = "abcdef"
+which_or_where = "which" if not on_win else "where"
+cp_or_copy = "cp" if not on_win else "copy"
+env_or_set = "env" if not on_win else "set"
+
+# UNICODE_CHARACTERS = u"12345678abcdef"
+# UNICODE_CHARACTERS_RESTRICTED = UNICODE_CHARACTERS
+
+# When testing for bugs, you may want to change this to a _,
+# for example to see if a bug is related to spaces in prefixes.
+SPACER_CHARACTER = " "
+
+log = getLogger(__name__)
+
+
+def escape_for_winpath(p):
+    return p.replace("\\", "\\\\")
+
+
+@lru_cache(maxsize=None)
+def running_a_python_capable_of_unicode_subprocessing():
+    name = None
+    # try:
+    # UNICODE_CHARACTERS + os.sep +
+    with Utf8NamedTemporaryFile(
+        mode="w", suffix=UNICODE_CHARACTERS + ".bat", delete=False
+    ) as batch_file:
+        batch_file.write("@echo Hello World\n")
+        batch_file.write("@exit 0\n")
+        name = batch_file.name
+    if name:
+        try:
+            out = check_output(name, cwd=dirname(name), stderr=None, shell=False)
+            out = out.decode("utf-8") if hasattr(out, "decode") else out
+            if out.startswith("Hello World"):
+                return True
+            return False
+        except Exception:
+            return False
+        finally:
+            os.unlink(name)
+    return False
+
+
+tmpdir_in_use = None
+
+
+@pytest.fixture(autouse=True)
+def set_tmpdir(tmpdir):
+    global tmpdir_in_use
+    if not tmpdir:
+        return tmpdir_in_use
+    td = tmpdir.strpath
+    assert os.sep in td
+    tmpdir_in_use = td
+
+
+def _get_temp_prefix(name=None, use_restricted_unicode=False):
+    tmpdir = tmpdir_in_use or gettempdir()
+    capable = running_a_python_capable_of_unicode_subprocessing()
+
+    if not capable or use_restricted_unicode:
+        RESTRICTED = UNICODE_CHARACTERS_RESTRICTED
+        random_unicode = "".join(sample(RESTRICTED, len(RESTRICTED)))
+    else:
+        random_unicode = "".join(sample(UNICODE_CHARACTERS, len(UNICODE_CHARACTERS)))
+    tmpdir_name = os.environ.get(
+        "CONDA_TEST_TMPDIR_NAME",
+        (str(uuid4())[:4] + SPACER_CHARACTER + random_unicode)
+        if name is None
+        else name,
+    )
+    prefix = join(tmpdir, tmpdir_name)
+
+    # Exit immediately if we cannot use hardlinks, on Windows, we get permissions errors if we use
+    # sys.executable so instead use the pdb files.
+    src = sys.executable.replace(".exe", ".pdb") if on_win else sys.executable
+    dst = os.path.join(tmpdir, os.path.basename(sys.executable))
+
+    try:
+        link(src, dst)
+    except OSError:
+        print(
+            "\nWARNING :: You are testing `conda` with `tmpdir`:-\n           {}\n"
+            "           not on the same FS as `sys.prefix`:\n           {}\n"
+            "           this will be slow and unlike the majority of end-user installs.\n"
+            "           Please pass `--basetemp=` instead.".format(
+                tmpdir, sys.prefix
+            )
+        )
+    try:
+        rm_rf(dst)
+    except Exception as e:
+        print(e)
+        pass
+
+    return prefix
+
+
+def make_temp_prefix(name=None, use_restricted_unicode=False, _temp_prefix=None):
+    """
+    When the env. you are creating will be used to install Python 2.7 on Windows
+    only a restricted amount of Unicode will work, and probably only those chars
+    in your current codepage, so the characters in UNICODE_CHARACTERS_RESTRICTED
+    should probably be randomly generated from that instead. The problem here is
+    that the current codepage needs to be able to handle 'sys.prefix' otherwise
+    ntpath will fall over.
+    """
+    if not _temp_prefix:
+        _temp_prefix = _get_temp_prefix(
+            name=name, use_restricted_unicode=use_restricted_unicode
+        )
+    try:
+        os.makedirs(_temp_prefix)
+    except:
+        pass
+    assert isdir(_temp_prefix)
+    return _temp_prefix
+
+
+def FORCE_temp_prefix(name=None, use_restricted_unicode=False):
+    _temp_prefix = _get_temp_prefix(
+        name=name, use_restricted_unicode=use_restricted_unicode
+    )
+    rm_rf(_temp_prefix)
+    os.makedirs(_temp_prefix)
+    assert isdir(_temp_prefix)
+    return _temp_prefix
+
+
+class Commands:
+    COMPARE = "compare"
+    CONFIG = "config"
+    CLEAN = "clean"
+    CREATE = "create"
+    INFO = "info"
+    INSTALL = "install"
+    LIST = "list"
+    REMOVE = "remove"
+    SEARCH = "search"
+    UPDATE = "update"
+    RUN = "run"
+
+
+@deprecated("23.9", "24.3", addendum="Use `monkeypatch.chdir` instead.")
+@contextmanager
+def temp_chdir(target_dir):
+    curdir = os.getcwd()
+    if not target_dir:
+        target_dir = curdir
+    try:
+        os.chdir(target_dir)
+        yield
+    finally:
+        os.chdir(curdir)
+
+
+@deprecated("23.9", "24.3", addendum="Use `conda.testing.conda_cli` instead.")
+def run_command(command, prefix, *arguments, **kwargs):
+    assert isinstance(arguments, tuple), "run_command() arguments must be tuples"
+    arguments = massage_arguments(arguments)
+
+    use_exception_handler = kwargs.get("use_exception_handler", False)
+    # These commands require 'dev' mode to be enabled during testing because
+    # they end up calling run_script() in link.py and that uses wrapper scripts for e.g. activate.
+    # Setting `dev` means that, in these scripts, conda is executed via:
+    #   `sys.prefix/bin/python -m conda` (or the Windows equivalent).
+    # .. and the source code for `conda` is put on `sys.path` via `PYTHONPATH` (a bit gross but
+    # less so than always requiring `cwd` to be the root of the conda source tree in every case).
+    # If you do not want this to happen for some test you must pass dev=False as a kwarg, though
+    # for nearly all tests, you want to make sure you are running *this* conda and not some old
+    # conda (it was random which you'd get depending on the initial values of PATH and PYTHONPATH
+    # - and likely more variables - before `dev` came along). Setting CONDA_EXE is not enough
+    # either because in the 4.5 days that would just run whatever Python was found first on PATH.
+    command_defaults_to_dev = command in (
+        Commands.CREATE,
+        Commands.INSTALL,
+        Commands.REMOVE,
+        Commands.RUN,
+    )
+    dev = kwargs.get("dev", True if command_defaults_to_dev else False)
+    debug = kwargs.get("debug_wrapper_scripts", False)
+
+    p = generate_parser()
+
+    if command is Commands.CONFIG:
+        arguments.append("--file")
+        arguments.append(join(prefix, "condarc"))
+    if command in (
+        Commands.LIST,
+        Commands.COMPARE,
+        Commands.CREATE,
+        Commands.INSTALL,
+        Commands.REMOVE,
+        Commands.UPDATE,
+        Commands.RUN,
+    ):
+        arguments.insert(0, "-p")
+        arguments.insert(1, prefix)
+    if command in (Commands.CREATE, Commands.INSTALL, Commands.REMOVE, Commands.UPDATE):
+        arguments.extend(["-y", "-q"])
+
+    arguments.insert(0, command)
+    if dev:
+        arguments.insert(1, "--dev")
+    if debug:
+        arguments.insert(1, "--debug-wrapper-scripts")
+
+    # It would be nice at this point to re-use:
+    # from conda.cli.python_api import run_command as python_api_run_command
+    # python_api_run_command
+    # .. but that does not support no_capture and probably more stuff.
+
+    args = p.parse_args(arguments)
+    context._set_argparse_args(args)
+    init_loggers(context)
+    cap_args = () if not kwargs.get("no_capture") else (None, None)
+    # list2cmdline is not exact, but it is only informational.
+    print(
+        "\n\nEXECUTING COMMAND >>> $ conda %s\n\n" % " ".join(arguments),
+        file=sys.stderr,
+    )
+    with stderr_log_level(TEST_LOG_LEVEL, "conda"), stderr_log_level(
+        TEST_LOG_LEVEL, "requests"
+    ):
+        arguments = encode_arguments(arguments)
+        with argv(["python_api"] + arguments), captured(*cap_args) as c:
+            if use_exception_handler:
+                result = conda_exception_handler(do_call, args, p)
+            else:
+                result = do_call(args, p)
+        stdout = c.stdout
+        stderr = c.stderr
+        print(stdout, file=sys.stdout)
+        print(stderr, file=sys.stderr)
+
+    # Unfortunately there are other ways to change context, such as Commands.CREATE --offline.
+    # You will probably end up playing whack-a-bug here adding more and more the tuple here.
+    if command in (Commands.CONFIG,):
+        reset_context([os.path.join(prefix + os.sep, "condarc")], args)
+    return stdout, stderr, result
+
+
+@contextmanager
+def make_temp_env(*packages, **kwargs):
+    name = kwargs.pop("name", None)
+    use_restricted_unicode = kwargs.pop("use_restricted_unicode", False)
+
+    prefix = kwargs.pop("prefix", None) or _get_temp_prefix(
+        name=name, use_restricted_unicode=use_restricted_unicode
+    )
+    clean_prefix = kwargs.pop("clean_prefix", None)
+    if clean_prefix:
+        if os.path.exists(prefix):
+            rm_rf(prefix)
+    if not isdir(prefix):
+        make_temp_prefix(name, use_restricted_unicode, prefix)
+    with disable_logger("fetch"), disable_logger("dotupdate"):
+        try:
+            # try to clear any config that's been set by other tests
+            # CAUTION :: This does not partake in the context stack management code
+            #            of env_{var,vars,unmodified} and, when used in conjunction
+            #            with that code, this *must* be called first.
+            reset_context([os.path.join(prefix + os.sep, "condarc")])
+            run_command(Commands.CREATE, prefix, *packages, **kwargs)
+            yield prefix
+        finally:
+            if "CONDA_TEST_SAVE_TEMPS" not in os.environ:
+                rmtree(prefix, ignore_errors=True)
+            else:
+                log.warning(
+                    f"CONDA_TEST_SAVE_TEMPS :: retaining make_temp_env {prefix}"
+                )
+
+
+@contextmanager
+def make_temp_package_cache():
+    prefix = make_temp_prefix(use_restricted_unicode=on_win)
+    pkgs_dir = join(prefix, "pkgs")
+    mkdir_p(pkgs_dir)
+    touch(join(pkgs_dir, PACKAGE_CACHE_MAGIC_FILE))
+
+    try:
+        with env_var(
+            "CONDA_PKGS_DIRS", pkgs_dir, stack_callback=conda_tests_ctxt_mgmt_def_pol
+        ):
+            assert context.pkgs_dirs == (pkgs_dir,)
+            yield pkgs_dir
+    finally:
+        rmtree(prefix, ignore_errors=True)
+        if pkgs_dir in PackageCacheData._cache_:
+            del PackageCacheData._cache_[pkgs_dir]
+
+
+@contextmanager
+def make_temp_channel(packages):
+    package_reqs = [pkg.replace("-", "=") for pkg in packages]
+    package_names = [pkg.split("-")[0] for pkg in packages]
+
+    with make_temp_env(*package_reqs) as prefix:
+        for package in packages:
+            assert package_is_installed(prefix, package.replace("-", "="))
+        data = [
+            p for p in PrefixData(prefix).iter_records() if p["name"] in package_names
+        ]
+        run_command(Commands.REMOVE, prefix, *package_names)
+        for package in packages:
+            assert not package_is_installed(prefix, package.replace("-", "="))
+
+    repodata = {"info": {}, "packages": {}}
+    tarfiles = {}
+    for package_data in data:
+        pkg_data = package_data
+        fname = pkg_data["fn"]
+        tarfiles[fname] = join(PackageCacheData.first_writable().pkgs_dir, fname)
+
+        pkg_data = pkg_data.dump()
+        for field in ("url", "channel", "schannel"):
+            pkg_data.pop(field, None)
+        repodata["packages"][fname] = PackageRecord(**pkg_data)
+
+    with make_temp_env() as channel:
+        subchan = join(channel, context.subdir)
+        noarch_dir = join(channel, "noarch")
+        channel = path_to_url(channel)
+        os.makedirs(subchan)
+        os.makedirs(noarch_dir)
+        for fname, tar_old_path in tarfiles.items():
+            tar_new_path = join(subchan, fname)
+            copyfile(tar_old_path, tar_new_path)
+
+        with open(join(subchan, "repodata.json"), "w") as f:
+            f.write(json.dumps(repodata, cls=EntityEncoder))
+        with open(join(noarch_dir, "repodata.json"), "w") as f:
+            f.write(json.dumps({}, cls=EntityEncoder))
+
+        yield channel
+
+
+def create_temp_location():
+    return _get_temp_prefix()
+
+
+@contextmanager
+def tempdir():
+    prefix = create_temp_location()
+    try:
+        os.makedirs(prefix)
+        yield prefix
+    finally:
+        if lexists(prefix):
+            rm_rf(prefix)
+
+
+def reload_config(prefix):
+    prefix_condarc = join(prefix + os.sep, "condarc")
+    reset_context([prefix_condarc])
+
+
+def package_is_installed(prefix, spec):
+    is_installed = _package_is_installed(prefix, spec)
+
+    # Mamba needs to escape the URL (e.g. space -> %20)
+    # Which ends up rendered in the package spec
+    # Let's try query with a escaped spec in case we are
+    # testing for Mamba or other implementations that need this
+    if not is_installed and "::" in spec:
+        channel, pkg = spec.split("::", 1)
+        escaped_channel = escape_channel_url(channel)
+        escaped_spec = escaped_channel + "::" + pkg
+        is_installed = _package_is_installed(prefix, escaped_spec)
+
+        # Workaround for https://github.com/mamba-org/mamba/issues/1324
+        if not is_installed and channel.startswith("file:"):
+            components = channel.split("/")
+            lowercase_channel = "/".join(components[:-1] + [components[-1].lower()])
+            spec = lowercase_channel + "::" + pkg
+            is_installed = _package_is_installed(prefix, spec)
+
+    return is_installed
+
+
+def _package_is_installed(prefix, spec):
+    spec = MatchSpec(spec)
+    prefix_recs = tuple(PrefixData(prefix).query(spec))
+    if len(prefix_recs) > 1:
+        raise AssertionError(
+            "Multiple packages installed.%s"
+            % (dashlist(prec.dist_str() for prec in prefix_recs))
+        )
+    return bool(len(prefix_recs))
+
+
+@deprecated(
+    "23.9",
+    "24.3",
+    addendum="Use `conda.core.prefix_data.PrefixData().get()` instead.",
+)
+def get_conda_list_tuple(prefix, package_name):
+    stdout, stderr, _ = run_command(Commands.LIST, prefix)
+    stdout_lines = stdout.split("\n")
+    package_line = next(
+        (line for line in stdout_lines if line.lower().startswith(package_name + " ")),
+        None,
+    )
+    return package_line.split()
+
+
+def get_shortcut_dir():
+    assert on_win
+    user_mode = "user" if exists(join(sys.prefix, ".nonadmin")) else "system"
+    try:
+        from menuinst.win32 import dirs_src as win_locations
+
+        return win_locations[user_mode]["start"][0]
+    except ImportError:
+        try:
+            from menuinst.win32 import dirs as win_locations
+
+            return win_locations[user_mode]["start"]
+        except ImportError:
+            raise
diff --git a/shared/package-files/04/0448fb91f3a2985fd08bad91f81f1b9981b2ff94081358d2db435fae7344f52b/ooc.py b/shared/package-files/04/0448fb91f3a2985fd08bad91f81f1b9981b2ff94081358d2db435fae7344f52b/ooc.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4600eaeed5c9e32bcebd894b1260082d16f1ec6
--- /dev/null
+++ b/shared/package-files/04/0448fb91f3a2985fd08bad91f81f1b9981b2ff94081358d2db435fae7344f52b/ooc.py
@@ -0,0 +1,85 @@
+"""
+    pygments.lexers.ooc
+    ~~~~~~~~~~~~~~~~~~~
+
+    Lexers for the Ooc language.
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+from pygments.lexer import RegexLexer, bygroups, words
+from pygments.token import Text, Comment, Operator, Keyword, Name, String, \
+    Number, Punctuation
+
+__all__ = ['OocLexer']
+
+
+class OocLexer(RegexLexer):
+    """
+    For Ooc source code
+
+    .. versionadded:: 1.2
+    """
+    name = 'Ooc'
+    url = 'http://ooc-lang.org/'
+    aliases = ['ooc']
+    filenames = ['*.ooc']
+    mimetypes = ['text/x-ooc']
+
+    tokens = {
+        'root': [
+            (words((
+                'class', 'interface', 'implement', 'abstract', 'extends', 'from',
+                'this', 'super', 'new', 'const', 'final', 'static', 'import',
+                'use', 'extern', 'inline', 'proto', 'break', 'continue',
+                'fallthrough', 'operator', 'if', 'else', 'for', 'while', 'do',
+                'switch', 'case', 'as', 'in', 'version', 'return', 'true',
+                'false', 'null'), prefix=r'\b', suffix=r'\b'),
+             Keyword),
+            (r'include\b', Keyword, 'include'),
+            (r'(cover)([ \t]+)(from)([ \t]+)(\w+[*@]?)',
+             bygroups(Keyword, Text, Keyword, Text, Name.Class)),
+            (r'(func)((?:[ \t]|\\\n)+)(~[a-z_]\w*)',
+             bygroups(Keyword, Text, Name.Function)),
+            (r'\bfunc\b', Keyword),
+            # Note: %= and ^= not listed on http://ooc-lang.org/syntax
+            (r'//.*', Comment),
+            (r'(?s)/\*.*?\*/', Comment.Multiline),
+            (r'(==?|\+=?|-[=>]?|\*=?|/=?|:=|!=?|%=?|\?|>{1,3}=?|<{1,3}=?|\.\.|'
+             r'&&?|\|\|?|\^=?)', Operator),
+            (r'(\.)([ \t]*)([a-z]\w*)', bygroups(Operator, Text,
+                                                 Name.Function)),
+            (r'[A-Z][A-Z0-9_]+', Name.Constant),
+            (r'[A-Z]\w*([@*]|\[[ \t]*\])?', Name.Class),
+
+            (r'([a-z]\w*(?:~[a-z]\w*)?)((?:[ \t]|\\\n)*)(?=\()',
+             bygroups(Name.Function, Text)),
+            (r'[a-z]\w*', Name.Variable),
+
+            # : introduces types
+            (r'[:(){}\[\];,]', Punctuation),
+
+            (r'0x[0-9a-fA-F]+', Number.Hex),
+            (r'0c[0-9]+', Number.Oct),
+            (r'0b[01]+', Number.Bin),
+            (r'[0-9_]\.[0-9_]*(?!\.)', Number.Float),
+            (r'[0-9_]+', Number.Decimal),
+
+            (r'"(?:\\.|\\[0-7]{1,3}|\\x[a-fA-F0-9]{1,2}|[^\\"])*"',
+             String.Double),
+            (r"'(?:\\.|\\[0-9]{1,3}|\\x[a-fA-F0-9]{1,2}|[^\\\'\n])'",
+             String.Char),
+            (r'@', Punctuation),  # pointer dereference
+            (r'\.', Punctuation),  # imports or chain operator
+
+            (r'\\[ \t\n]', Text),
+            (r'[ \t]+', Text),
+        ],
+        'include': [
+            (r'[\w/]+', Name),
+            (r',', Punctuation),
+            (r'[ \t]', Text),
+            (r'[;\n]', Text, '#pop'),
+        ],
+    }
diff --git a/shared/package-files/04/0449630fb2bec1b781f7387e953897809a075c7e04ebccd3a7657d4758466c2d/hardshrink_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/04/0449630fb2bec1b781f7387e953897809a075c7e04ebccd3a7657d4758466c2d/hardshrink_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..0ac5cfccd87dc62a9dd9584839b937bd4aab978d
--- /dev/null
+++ b/shared/package-files/04/0449630fb2bec1b781f7387e953897809a075c7e04ebccd3a7657d4758466c2d/hardshrink_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor hardshrink(const at::Tensor & self, const at::Scalar & lambd=0.5);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/04/044d201f7f267b79fca55a5e7e21fff96aa61ae62e2e279398a493f72befe5a1/fmin.h b/shared/package-files/04/044d201f7f267b79fca55a5e7e21fff96aa61ae62e2e279398a493f72befe5a1/fmin.h
new file mode 100644
index 0000000000000000000000000000000000000000..b421e06a6b286494dc0d7e27b18fd31a6a831d1b
--- /dev/null
+++ b/shared/package-files/04/044d201f7f267b79fca55a5e7e21fff96aa61ae62e2e279398a493f72befe5a1/fmin.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::fmin(Tensor self, Tensor other) -> Tensor
+inline at::Tensor fmin(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::fmin::call(self, other);
+}
+
+// aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & fmin_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::fmin_out::call(self, other, out);
+}
+// aten::fmin.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & fmin_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::fmin_out::call(self, other, out);
+}
+
+}
diff --git a/shared/package-files/04/044ef6246fac8bd19b4af7575814cdd9ab15f6362b6488761a57535527200b52/mixins.pyi b/shared/package-files/04/044ef6246fac8bd19b4af7575814cdd9ab15f6362b6488761a57535527200b52/mixins.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..538ba87c40b1f6e3575843f241834dc69dc7f85c
--- /dev/null
+++ b/shared/package-files/04/044ef6246fac8bd19b4af7575814cdd9ab15f6362b6488761a57535527200b52/mixins.pyi
@@ -0,0 +1 @@
+class OrderableAggMixin: ...
diff --git a/shared/package-files/04/04532539259b52e1d75794a26fcbf74e498ae18f10f7a9267f78a58aebbe6526/squeeze_copy_native.h b/shared/package-files/04/04532539259b52e1d75794a26fcbf74e498ae18f10f7a9267f78a58aebbe6526/squeeze_copy_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..595780318afdbdc5026e7d38de88a1214b34d4b0
--- /dev/null
+++ b/shared/package-files/04/04532539259b52e1d75794a26fcbf74e498ae18f10f7a9267f78a58aebbe6526/squeeze_copy_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & squeeze_copy_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor squeeze_copy(const at::Tensor & self);
+TORCH_API at::Tensor & squeeze_copy_dim_out(const at::Tensor & self, int64_t dim, at::Tensor & out);
+TORCH_API at::Tensor squeeze_copy_dim(const at::Tensor & self, int64_t dim);
+TORCH_API at::Tensor & squeeze_copy_dims_out(const at::Tensor & self, at::IntArrayRef dim, at::Tensor & out);
+TORCH_API at::Tensor squeeze_copy_dims(const at::Tensor & self, at::IntArrayRef dim);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/04/045342e0c6d3dbc9d19719211977a6c56ebc482faf13f8d29a657116e6e53ccd/output_nr.h b/shared/package-files/04/045342e0c6d3dbc9d19719211977a6c56ebc482faf13f8d29a657116e6e53ccd/output_nr.h
new file mode 100644
index 0000000000000000000000000000000000000000..24bfdcd8f41758ce8ee437252447d5ac775b723a
--- /dev/null
+++ b/shared/package-files/04/045342e0c6d3dbc9d19719211977a6c56ebc482faf13f8d29a657116e6e53ccd/output_nr.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+
+}
diff --git a/shared/package-files/04/0454159276b9ead94efaff6a68d2551c8705df4848546acff52b7fbcca78c3df/cacheprovider.py b/shared/package-files/04/0454159276b9ead94efaff6a68d2551c8705df4848546acff52b7fbcca78c3df/cacheprovider.py
new file mode 100644
index 0000000000000000000000000000000000000000..1b236efdc9b3196a0e3238306b627c03fee69fa1
--- /dev/null
+++ b/shared/package-files/04/0454159276b9ead94efaff6a68d2551c8705df4848546acff52b7fbcca78c3df/cacheprovider.py
@@ -0,0 +1,626 @@
+# mypy: allow-untyped-defs
+"""Implementation of the cache provider."""
+
+# This plugin was not named "cache" to avoid conflicts with the external
+# pytest-cache version.
+from __future__ import annotations
+
+import dataclasses
+import errno
+import json
+import os
+from pathlib import Path
+import tempfile
+from typing import final
+from typing import Generator
+from typing import Iterable
+
+from .pathlib import resolve_from_str
+from .pathlib import rm_rf
+from .reports import CollectReport
+from _pytest import nodes
+from _pytest._io import TerminalWriter
+from _pytest.config import Config
+from _pytest.config import ExitCode
+from _pytest.config import hookimpl
+from _pytest.config.argparsing import Parser
+from _pytest.deprecated import check_ispytest
+from _pytest.fixtures import fixture
+from _pytest.fixtures import FixtureRequest
+from _pytest.main import Session
+from _pytest.nodes import Directory
+from _pytest.nodes import File
+from _pytest.reports import TestReport
+
+
+README_CONTENT = """\
+# pytest cache directory #
+
+This directory contains data from the pytest's cache plugin,
+which provides the `--lf` and `--ff` options, as well as the `cache` fixture.
+
+**Do not** commit this to version control.
+
+See [the docs](https://docs.pytest.org/en/stable/how-to/cache.html) for more information.
+"""
+
+CACHEDIR_TAG_CONTENT = b"""\
+Signature: 8a477f597d28d172789f06886806bc55
+# This file is a cache directory tag created by pytest.
+# For information about cache directory tags, see:
+#	https://bford.info/cachedir/spec.html
+"""
+
+
+@final
+@dataclasses.dataclass
+class Cache:
+    """Instance of the `cache` fixture."""
+
+    _cachedir: Path = dataclasses.field(repr=False)
+    _config: Config = dataclasses.field(repr=False)
+
+    # Sub-directory under cache-dir for directories created by `mkdir()`.
+    _CACHE_PREFIX_DIRS = "d"
+
+    # Sub-directory under cache-dir for values created by `set()`.
+    _CACHE_PREFIX_VALUES = "v"
+
+    def __init__(
+        self, cachedir: Path, config: Config, *, _ispytest: bool = False
+    ) -> None:
+        check_ispytest(_ispytest)
+        self._cachedir = cachedir
+        self._config = config
+
+    @classmethod
+    def for_config(cls, config: Config, *, _ispytest: bool = False) -> Cache:
+        """Create the Cache instance for a Config.
+
+        :meta private:
+        """
+        check_ispytest(_ispytest)
+        cachedir = cls.cache_dir_from_config(config, _ispytest=True)
+        if config.getoption("cacheclear") and cachedir.is_dir():
+            cls.clear_cache(cachedir, _ispytest=True)
+        return cls(cachedir, config, _ispytest=True)
+
+    @classmethod
+    def clear_cache(cls, cachedir: Path, _ispytest: bool = False) -> None:
+        """Clear the sub-directories used to hold cached directories and values.
+
+        :meta private:
+        """
+        check_ispytest(_ispytest)
+        for prefix in (cls._CACHE_PREFIX_DIRS, cls._CACHE_PREFIX_VALUES):
+            d = cachedir / prefix
+            if d.is_dir():
+                rm_rf(d)
+
+    @staticmethod
+    def cache_dir_from_config(config: Config, *, _ispytest: bool = False) -> Path:
+        """Get the path to the cache directory for a Config.
+
+        :meta private:
+        """
+        check_ispytest(_ispytest)
+        return resolve_from_str(config.getini("cache_dir"), config.rootpath)
+
+    def warn(self, fmt: str, *, _ispytest: bool = False, **args: object) -> None:
+        """Issue a cache warning.
+
+        :meta private:
+        """
+        check_ispytest(_ispytest)
+        import warnings
+
+        from _pytest.warning_types import PytestCacheWarning
+
+        warnings.warn(
+            PytestCacheWarning(fmt.format(**args) if args else fmt),
+            self._config.hook,
+            stacklevel=3,
+        )
+
+    def _mkdir(self, path: Path) -> None:
+        self._ensure_cache_dir_and_supporting_files()
+        path.mkdir(exist_ok=True, parents=True)
+
+    def mkdir(self, name: str) -> Path:
+        """Return a directory path object with the given name.
+
+        If the directory does not yet exist, it will be created. You can use
+        it to manage files to e.g. store/retrieve database dumps across test
+        sessions.
+
+        .. versionadded:: 7.0
+
+        :param name:
+            Must be a string not containing a ``/`` separator.
+            Make sure the name contains your plugin or application
+            identifiers to prevent clashes with other cache users.
+        """
+        path = Path(name)
+        if len(path.parts) > 1:
+            raise ValueError("name is not allowed to contain path separators")
+        res = self._cachedir.joinpath(self._CACHE_PREFIX_DIRS, path)
+        self._mkdir(res)
+        return res
+
+    def _getvaluepath(self, key: str) -> Path:
+        return self._cachedir.joinpath(self._CACHE_PREFIX_VALUES, Path(key))
+
+    def get(self, key: str, default):
+        """Return the cached value for the given key.
+
+        If no value was yet cached or the value cannot be read, the specified
+        default is returned.
+
+        :param key:
+            Must be a ``/`` separated value. Usually the first
+            name is the name of your plugin or your application.
+        :param default:
+            The value to return in case of a cache-miss or invalid cache value.
+        """
+        path = self._getvaluepath(key)
+        try:
+            with path.open("r", encoding="UTF-8") as f:
+                return json.load(f)
+        except (ValueError, OSError):
+            return default
+
+    def set(self, key: str, value: object) -> None:
+        """Save value for the given key.
+
+        :param key:
+            Must be a ``/`` separated value. Usually the first
+            name is the name of your plugin or your application.
+        :param value:
+            Must be of any combination of basic python types,
+            including nested types like lists of dictionaries.
+        """
+        path = self._getvaluepath(key)
+        try:
+            self._mkdir(path.parent)
+        except OSError as exc:
+            self.warn(
+                f"could not create cache path {path}: {exc}",
+                _ispytest=True,
+            )
+            return
+        data = json.dumps(value, ensure_ascii=False, indent=2)
+        try:
+            f = path.open("w", encoding="UTF-8")
+        except OSError as exc:
+            self.warn(
+                f"cache could not write path {path}: {exc}",
+                _ispytest=True,
+            )
+        else:
+            with f:
+                f.write(data)
+
+    def _ensure_cache_dir_and_supporting_files(self) -> None:
+        """Create the cache dir and its supporting files."""
+        if self._cachedir.is_dir():
+            return
+
+        self._cachedir.parent.mkdir(parents=True, exist_ok=True)
+        with tempfile.TemporaryDirectory(
+            prefix="pytest-cache-files-",
+            dir=self._cachedir.parent,
+        ) as newpath:
+            path = Path(newpath)
+
+            # Reset permissions to the default, see #12308.
+            # Note: there's no way to get the current umask atomically, eek.
+            umask = os.umask(0o022)
+            os.umask(umask)
+            path.chmod(0o777 - umask)
+
+            with open(path.joinpath("README.md"), "x", encoding="UTF-8") as f:
+                f.write(README_CONTENT)
+            with open(path.joinpath(".gitignore"), "x", encoding="UTF-8") as f:
+                f.write("# Created by pytest automatically.\n*\n")
+            with open(path.joinpath("CACHEDIR.TAG"), "xb") as f:
+                f.write(CACHEDIR_TAG_CONTENT)
+
+            try:
+                path.rename(self._cachedir)
+            except OSError as e:
+                # If 2 concurrent pytests both race to the rename, the loser
+                # gets "Directory not empty" from the rename. In this case,
+                # everything is handled so just continue (while letting the
+                # temporary directory be cleaned up).
+                # On Windows, the error is a FileExistsError which translates to EEXIST.
+                if e.errno not in (errno.ENOTEMPTY, errno.EEXIST):
+                    raise
+            else:
+                # Create a directory in place of the one we just moved so that
+                # `TemporaryDirectory`'s cleanup doesn't complain.
+                #
+                # TODO: pass ignore_cleanup_errors=True when we no longer support python < 3.10.
+                # See https://github.com/python/cpython/issues/74168. Note that passing
+                # delete=False would do the wrong thing in case of errors and isn't supported
+                # until python 3.12.
+                path.mkdir()
+
+
+class LFPluginCollWrapper:
+    def __init__(self, lfplugin: LFPlugin) -> None:
+        self.lfplugin = lfplugin
+        self._collected_at_least_one_failure = False
+
+    @hookimpl(wrapper=True)
+    def pytest_make_collect_report(
+        self, collector: nodes.Collector
+    ) -> Generator[None, CollectReport, CollectReport]:
+        res = yield
+        if isinstance(collector, (Session, Directory)):
+            # Sort any lf-paths to the beginning.
+            lf_paths = self.lfplugin._last_failed_paths
+
+            # Use stable sort to prioritize last failed.
+            def sort_key(node: nodes.Item | nodes.Collector) -> bool:
+                return node.path in lf_paths
+
+            res.result = sorted(
+                res.result,
+                key=sort_key,
+                reverse=True,
+            )
+
+        elif isinstance(collector, File):
+            if collector.path in self.lfplugin._last_failed_paths:
+                result = res.result
+                lastfailed = self.lfplugin.lastfailed
+
+                # Only filter with known failures.
+                if not self._collected_at_least_one_failure:
+                    if not any(x.nodeid in lastfailed for x in result):
+                        return res
+                    self.lfplugin.config.pluginmanager.register(
+                        LFPluginCollSkipfiles(self.lfplugin), "lfplugin-collskip"
+                    )
+                    self._collected_at_least_one_failure = True
+
+                session = collector.session
+                result[:] = [
+                    x
+                    for x in result
+                    if x.nodeid in lastfailed
+                    # Include any passed arguments (not trivial to filter).
+                    or session.isinitpath(x.path)
+                    # Keep all sub-collectors.
+                    or isinstance(x, nodes.Collector)
+                ]
+
+        return res
+
+
+class LFPluginCollSkipfiles:
+    def __init__(self, lfplugin: LFPlugin) -> None:
+        self.lfplugin = lfplugin
+
+    @hookimpl
+    def pytest_make_collect_report(
+        self, collector: nodes.Collector
+    ) -> CollectReport | None:
+        if isinstance(collector, File):
+            if collector.path not in self.lfplugin._last_failed_paths:
+                self.lfplugin._skipped_files += 1
+
+                return CollectReport(
+                    collector.nodeid, "passed", longrepr=None, result=[]
+                )
+        return None
+
+
+class LFPlugin:
+    """Plugin which implements the --lf (run last-failing) option."""
+
+    def __init__(self, config: Config) -> None:
+        self.config = config
+        active_keys = "lf", "failedfirst"
+        self.active = any(config.getoption(key) for key in active_keys)
+        assert config.cache
+        self.lastfailed: dict[str, bool] = config.cache.get("cache/lastfailed", {})
+        self._previously_failed_count: int | None = None
+        self._report_status: str | None = None
+        self._skipped_files = 0  # count skipped files during collection due to --lf
+
+        if config.getoption("lf"):
+            self._last_failed_paths = self.get_last_failed_paths()
+            config.pluginmanager.register(
+                LFPluginCollWrapper(self), "lfplugin-collwrapper"
+            )
+
+    def get_last_failed_paths(self) -> set[Path]:
+        """Return a set with all Paths of the previously failed nodeids and
+        their parents."""
+        rootpath = self.config.rootpath
+        result = set()
+        for nodeid in self.lastfailed:
+            path = rootpath / nodeid.split("::")[0]
+            result.add(path)
+            result.update(path.parents)
+        return {x for x in result if x.exists()}
+
+    def pytest_report_collectionfinish(self) -> str | None:
+        if self.active and self.config.get_verbosity() >= 0:
+            return f"run-last-failure: {self._report_status}"
+        return None
+
+    def pytest_runtest_logreport(self, report: TestReport) -> None:
+        if (report.when == "call" and report.passed) or report.skipped:
+            self.lastfailed.pop(report.nodeid, None)
+        elif report.failed:
+            self.lastfailed[report.nodeid] = True
+
+    def pytest_collectreport(self, report: CollectReport) -> None:
+        passed = report.outcome in ("passed", "skipped")
+        if passed:
+            if report.nodeid in self.lastfailed:
+                self.lastfailed.pop(report.nodeid)
+                self.lastfailed.update((item.nodeid, True) for item in report.result)
+        else:
+            self.lastfailed[report.nodeid] = True
+
+    @hookimpl(wrapper=True, tryfirst=True)
+    def pytest_collection_modifyitems(
+        self, config: Config, items: list[nodes.Item]
+    ) -> Generator[None]:
+        res = yield
+
+        if not self.active:
+            return res
+
+        if self.lastfailed:
+            previously_failed = []
+            previously_passed = []
+            for item in items:
+                if item.nodeid in self.lastfailed:
+                    previously_failed.append(item)
+                else:
+                    previously_passed.append(item)
+            self._previously_failed_count = len(previously_failed)
+
+            if not previously_failed:
+                # Running a subset of all tests with recorded failures
+                # only outside of it.
+                self._report_status = "%d known failures not in selected tests" % (
+                    len(self.lastfailed),
+                )
+            else:
+                if self.config.getoption("lf"):
+                    items[:] = previously_failed
+                    config.hook.pytest_deselected(items=previously_passed)
+                else:  # --failedfirst
+                    items[:] = previously_failed + previously_passed
+
+                noun = "failure" if self._previously_failed_count == 1 else "failures"
+                suffix = " first" if self.config.getoption("failedfirst") else ""
+                self._report_status = (
+                    f"rerun previous {self._previously_failed_count} {noun}{suffix}"
+                )
+
+            if self._skipped_files > 0:
+                files_noun = "file" if self._skipped_files == 1 else "files"
+                self._report_status += f" (skipped {self._skipped_files} {files_noun})"
+        else:
+            self._report_status = "no previously failed tests, "
+            if self.config.getoption("last_failed_no_failures") == "none":
+                self._report_status += "deselecting all items."
+                config.hook.pytest_deselected(items=items[:])
+                items[:] = []
+            else:
+                self._report_status += "not deselecting items."
+
+        return res
+
+    def pytest_sessionfinish(self, session: Session) -> None:
+        config = self.config
+        if config.getoption("cacheshow") or hasattr(config, "workerinput"):
+            return
+
+        assert config.cache is not None
+        saved_lastfailed = config.cache.get("cache/lastfailed", {})
+        if saved_lastfailed != self.lastfailed:
+            config.cache.set("cache/lastfailed", self.lastfailed)
+
+
+class NFPlugin:
+    """Plugin which implements the --nf (run new-first) option."""
+
+    def __init__(self, config: Config) -> None:
+        self.config = config
+        self.active = config.option.newfirst
+        assert config.cache is not None
+        self.cached_nodeids = set(config.cache.get("cache/nodeids", []))
+
+    @hookimpl(wrapper=True, tryfirst=True)
+    def pytest_collection_modifyitems(self, items: list[nodes.Item]) -> Generator[None]:
+        res = yield
+
+        if self.active:
+            new_items: dict[str, nodes.Item] = {}
+            other_items: dict[str, nodes.Item] = {}
+            for item in items:
+                if item.nodeid not in self.cached_nodeids:
+                    new_items[item.nodeid] = item
+                else:
+                    other_items[item.nodeid] = item
+
+            items[:] = self._get_increasing_order(
+                new_items.values()
+            ) + self._get_increasing_order(other_items.values())
+            self.cached_nodeids.update(new_items)
+        else:
+            self.cached_nodeids.update(item.nodeid for item in items)
+
+        return res
+
+    def _get_increasing_order(self, items: Iterable[nodes.Item]) -> list[nodes.Item]:
+        return sorted(items, key=lambda item: item.path.stat().st_mtime, reverse=True)
+
+    def pytest_sessionfinish(self) -> None:
+        config = self.config
+        if config.getoption("cacheshow") or hasattr(config, "workerinput"):
+            return
+
+        if config.getoption("collectonly"):
+            return
+
+        assert config.cache is not None
+        config.cache.set("cache/nodeids", sorted(self.cached_nodeids))
+
+
+def pytest_addoption(parser: Parser) -> None:
+    group = parser.getgroup("general")
+    group.addoption(
+        "--lf",
+        "--last-failed",
+        action="store_true",
+        dest="lf",
+        help="Rerun only the tests that failed "
+        "at the last run (or all if none failed)",
+    )
+    group.addoption(
+        "--ff",
+        "--failed-first",
+        action="store_true",
+        dest="failedfirst",
+        help="Run all tests, but run the last failures first. "
+        "This may re-order tests and thus lead to "
+        "repeated fixture setup/teardown.",
+    )
+    group.addoption(
+        "--nf",
+        "--new-first",
+        action="store_true",
+        dest="newfirst",
+        help="Run tests from new files first, then the rest of the tests "
+        "sorted by file mtime",
+    )
+    group.addoption(
+        "--cache-show",
+        action="append",
+        nargs="?",
+        dest="cacheshow",
+        help=(
+            "Show cache contents, don't perform collection or tests. "
+            "Optional argument: glob (default: '*')."
+        ),
+    )
+    group.addoption(
+        "--cache-clear",
+        action="store_true",
+        dest="cacheclear",
+        help="Remove all cache contents at start of test run",
+    )
+    cache_dir_default = ".pytest_cache"
+    if "TOX_ENV_DIR" in os.environ:
+        cache_dir_default = os.path.join(os.environ["TOX_ENV_DIR"], cache_dir_default)
+    parser.addini("cache_dir", default=cache_dir_default, help="Cache directory path")
+    group.addoption(
+        "--lfnf",
+        "--last-failed-no-failures",
+        action="store",
+        dest="last_failed_no_failures",
+        choices=("all", "none"),
+        default="all",
+        help="With ``--lf``, determines whether to execute tests when there "
+        "are no previously (known) failures or when no "
+        "cached ``lastfailed`` data was found. "
+        "``all`` (the default) runs the full test suite again. "
+        "``none`` just emits a message about no known failures and exits successfully.",
+    )
+
+
+def pytest_cmdline_main(config: Config) -> int | ExitCode | None:
+    if config.option.cacheshow and not config.option.help:
+        from _pytest.main import wrap_session
+
+        return wrap_session(config, cacheshow)
+    return None
+
+
+@hookimpl(tryfirst=True)
+def pytest_configure(config: Config) -> None:
+    config.cache = Cache.for_config(config, _ispytest=True)
+    config.pluginmanager.register(LFPlugin(config), "lfplugin")
+    config.pluginmanager.register(NFPlugin(config), "nfplugin")
+
+
+@fixture
+def cache(request: FixtureRequest) -> Cache:
+    """Return a cache object that can persist state between testing sessions.
+
+    cache.get(key, default)
+    cache.set(key, value)
+
+    Keys must be ``/`` separated strings, where the first part is usually the
+    name of your plugin or application to avoid clashes with other cache users.
+
+    Values can be any object handled by the json stdlib module.
+    """
+    assert request.config.cache is not None
+    return request.config.cache
+
+
+def pytest_report_header(config: Config) -> str | None:
+    """Display cachedir with --cache-show and if non-default."""
+    if config.option.verbose > 0 or config.getini("cache_dir") != ".pytest_cache":
+        assert config.cache is not None
+        cachedir = config.cache._cachedir
+        # TODO: evaluate generating upward relative paths
+        # starting with .., ../.. if sensible
+
+        try:
+            displaypath = cachedir.relative_to(config.rootpath)
+        except ValueError:
+            displaypath = cachedir
+        return f"cachedir: {displaypath}"
+    return None
+
+
+def cacheshow(config: Config, session: Session) -> int:
+    from pprint import pformat
+
+    assert config.cache is not None
+
+    tw = TerminalWriter()
+    tw.line("cachedir: " + str(config.cache._cachedir))
+    if not config.cache._cachedir.is_dir():
+        tw.line("cache is empty")
+        return 0
+
+    glob = config.option.cacheshow[0]
+    if glob is None:
+        glob = "*"
+
+    dummy = object()
+    basedir = config.cache._cachedir
+    vdir = basedir / Cache._CACHE_PREFIX_VALUES
+    tw.sep("-", f"cache values for {glob!r}")
+    for valpath in sorted(x for x in vdir.rglob(glob) if x.is_file()):
+        key = str(valpath.relative_to(vdir))
+        val = config.cache.get(key, dummy)
+        if val is dummy:
+            tw.line(f"{key} contains unreadable content, will be ignored")
+        else:
+            tw.line(f"{key} contains:")
+            for line in pformat(val).splitlines():
+                tw.line("  " + line)
+
+    ddir = basedir / Cache._CACHE_PREFIX_DIRS
+    if ddir.is_dir():
+        contents = sorted(ddir.rglob(glob))
+        tw.sep("-", f"cache directories for {glob!r}")
+        for p in contents:
+            # if p.is_dir():
+            #    print("%s/" % p.relative_to(basedir))
+            if p.is_file():
+                key = str(p.relative_to(basedir))
+                tw.line(f"{key} is a file of length {p.stat().st_size:d}")
+    return 0
diff --git a/shared/package-files/04/04561e18003d0c68996f8b551692a2fffd10b44084164e3baa43d0159e313aeb/test_fastica.py b/shared/package-files/04/04561e18003d0c68996f8b551692a2fffd10b44084164e3baa43d0159e313aeb/test_fastica.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f8c9c55db621a36b6153aeef28a830ac7675a25
--- /dev/null
+++ b/shared/package-files/04/04561e18003d0c68996f8b551692a2fffd10b44084164e3baa43d0159e313aeb/test_fastica.py
@@ -0,0 +1,457 @@
+"""
+Test the fastica algorithm.
+"""
+
+import itertools
+import os
+import warnings
+
+import numpy as np
+import pytest
+from scipy import stats
+
+from sklearn.decomposition import PCA, FastICA, fastica
+from sklearn.decomposition._fastica import _gs_decorrelation
+from sklearn.exceptions import ConvergenceWarning
+from sklearn.utils._testing import assert_allclose, ignore_warnings
+
+
+def center_and_norm(x, axis=-1):
+    """Centers and norms x **in place**
+
+    Parameters
+    -----------
+    x: ndarray
+        Array with an axis of observations (statistical units) measured on
+        random variables.
+    axis: int, optional
+        Axis along which the mean and variance are calculated.
+    """
+    x = np.rollaxis(x, axis)
+    x -= x.mean(axis=0)
+    x /= x.std(axis=0)
+
+
+def test_gs(global_random_seed):
+    # Test gram schmidt orthonormalization
+    # generate a random orthogonal  matrix
+    rng = np.random.RandomState(global_random_seed)
+    W, _, _ = np.linalg.svd(rng.randn(10, 10))
+    w = rng.randn(10)
+    _gs_decorrelation(w, W, 10)
+    assert (w**2).sum() < 1.0e-10
+    w = rng.randn(10)
+    u = _gs_decorrelation(w, W, 5)
+    tmp = np.dot(u, W.T)
+    assert (tmp[:5] ** 2).sum() < 1.0e-10
+
+
+def test_fastica_attributes_dtypes(global_dtype):
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((100, 10)).astype(global_dtype, copy=False)
+    fica = FastICA(
+        n_components=5, max_iter=1000, whiten="unit-variance", random_state=0
+    ).fit(X)
+    assert fica.components_.dtype == global_dtype
+    assert fica.mixing_.dtype == global_dtype
+    assert fica.mean_.dtype == global_dtype
+    assert fica.whitening_.dtype == global_dtype
+
+
+def test_fastica_return_dtypes(global_dtype):
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((100, 10)).astype(global_dtype, copy=False)
+    k_, mixing_, s_ = fastica(
+        X, max_iter=1000, whiten="unit-variance", random_state=rng
+    )
+    assert k_.dtype == global_dtype
+    assert mixing_.dtype == global_dtype
+    assert s_.dtype == global_dtype
+
+
+@pytest.mark.parametrize("add_noise", [True, False])
+def test_fastica_simple(add_noise, global_random_seed, global_dtype):
+    if (
+        global_random_seed == 20
+        and global_dtype == np.float32
+        and not add_noise
+        and os.getenv("DISTRIB") == "ubuntu"
+    ):
+        pytest.xfail(
+            "FastICA instability with Ubuntu Atlas build with float32 "
+            "global_dtype. For more details, see "
+            "https://github.com/scikit-learn/scikit-learn/issues/24131#issuecomment-1208091119"
+        )
+
+    # Test the FastICA algorithm on very simple data.
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 1000
+    # Generate two sources:
+    s1 = (2 * np.sin(np.linspace(0, 100, n_samples)) > 0) - 1
+    s2 = stats.t.rvs(1, size=n_samples, random_state=global_random_seed)
+    s = np.c_[s1, s2].T
+    center_and_norm(s)
+    s = s.astype(global_dtype)
+    s1, s2 = s
+
+    # Mixing angle
+    phi = 0.6
+    mixing = np.array([[np.cos(phi), np.sin(phi)], [np.sin(phi), -np.cos(phi)]])
+    mixing = mixing.astype(global_dtype)
+    m = np.dot(mixing, s)
+
+    if add_noise:
+        m += 0.1 * rng.randn(2, 1000)
+
+    center_and_norm(m)
+
+    # function as fun arg
+    def g_test(x):
+        return x**3, (3 * x**2).mean(axis=-1)
+
+    algos = ["parallel", "deflation"]
+    nls = ["logcosh", "exp", "cube", g_test]
+    whitening = ["arbitrary-variance", "unit-variance", False]
+    for algo, nl, whiten in itertools.product(algos, nls, whitening):
+        if whiten:
+            k_, mixing_, s_ = fastica(
+                m.T, fun=nl, whiten=whiten, algorithm=algo, random_state=rng
+            )
+            with pytest.raises(ValueError):
+                fastica(m.T, fun=np.tanh, whiten=whiten, algorithm=algo)
+        else:
+            pca = PCA(n_components=2, whiten=True, random_state=rng)
+            X = pca.fit_transform(m.T)
+            k_, mixing_, s_ = fastica(
+                X, fun=nl, algorithm=algo, whiten=False, random_state=rng
+            )
+            with pytest.raises(ValueError):
+                fastica(X, fun=np.tanh, algorithm=algo)
+        s_ = s_.T
+        # Check that the mixing model described in the docstring holds:
+        if whiten:
+            # XXX: exact reconstruction to standard relative tolerance is not
+            # possible. This is probably expected when add_noise is True but we
+            # also need a non-trivial atol in float32 when add_noise is False.
+            #
+            # Note that the 2 sources are non-Gaussian in this test.
+            atol = 1e-5 if global_dtype == np.float32 else 0
+            assert_allclose(np.dot(np.dot(mixing_, k_), m), s_, atol=atol)
+
+        center_and_norm(s_)
+        s1_, s2_ = s_
+        # Check to see if the sources have been estimated
+        # in the wrong order
+        if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
+            s2_, s1_ = s_
+        s1_ *= np.sign(np.dot(s1_, s1))
+        s2_ *= np.sign(np.dot(s2_, s2))
+
+        # Check that we have estimated the original sources
+        if not add_noise:
+            assert_allclose(np.dot(s1_, s1) / n_samples, 1, atol=1e-2)
+            assert_allclose(np.dot(s2_, s2) / n_samples, 1, atol=1e-2)
+        else:
+            assert_allclose(np.dot(s1_, s1) / n_samples, 1, atol=1e-1)
+            assert_allclose(np.dot(s2_, s2) / n_samples, 1, atol=1e-1)
+
+    # Test FastICA class
+    _, _, sources_fun = fastica(
+        m.T, fun=nl, algorithm=algo, random_state=global_random_seed
+    )
+    ica = FastICA(fun=nl, algorithm=algo, random_state=global_random_seed)
+    sources = ica.fit_transform(m.T)
+    assert ica.components_.shape == (2, 2)
+    assert sources.shape == (1000, 2)
+
+    assert_allclose(sources_fun, sources)
+    # Set atol to account for the different magnitudes of the elements in sources
+    # (from 1e-4 to 1e1).
+    atol = np.max(np.abs(sources)) * (1e-5 if global_dtype == np.float32 else 1e-7)
+    assert_allclose(sources, ica.transform(m.T), atol=atol)
+
+    assert ica.mixing_.shape == (2, 2)
+
+    ica = FastICA(fun=np.tanh, algorithm=algo)
+    with pytest.raises(ValueError):
+        ica.fit(m.T)
+
+
+def test_fastica_nowhiten():
+    m = [[0, 1], [1, 0]]
+
+    # test for issue #697
+    ica = FastICA(n_components=1, whiten=False, random_state=0)
+    warn_msg = "Ignoring n_components with whiten=False."
+    with pytest.warns(UserWarning, match=warn_msg):
+        ica.fit(m)
+    assert hasattr(ica, "mixing_")
+
+
+def test_fastica_convergence_fail(global_random_seed):
+    # Test the FastICA algorithm on very simple data
+    # (see test_non_square_fastica).
+    # Ensure a ConvergenceWarning raised if the tolerance is sufficiently low.
+    rng = np.random.RandomState(global_random_seed)
+
+    n_samples = 1000
+    # Generate two sources:
+    t = np.linspace(0, 100, n_samples)
+    s1 = np.sin(t)
+    s2 = np.ceil(np.sin(np.pi * t))
+    s = np.c_[s1, s2].T
+    center_and_norm(s)
+
+    # Mixing matrix
+    mixing = rng.randn(6, 2)
+    m = np.dot(mixing, s)
+
+    # Do fastICA with tolerance 0. to ensure failing convergence
+    warn_msg = (
+        "FastICA did not converge. Consider increasing tolerance "
+        "or the maximum number of iterations."
+    )
+    with pytest.warns(ConvergenceWarning, match=warn_msg):
+        ica = FastICA(
+            algorithm="parallel", n_components=2, random_state=rng, max_iter=2, tol=0.0
+        )
+        ica.fit(m.T)
+
+
+@pytest.mark.parametrize("add_noise", [True, False])
+def test_non_square_fastica(global_random_seed, add_noise):
+    # Test the FastICA algorithm on very simple data.
+    rng = np.random.RandomState(global_random_seed)
+
+    n_samples = 1000
+    # Generate two sources:
+    t = np.linspace(0, 100, n_samples)
+    s1 = np.sin(t)
+    s2 = np.ceil(np.sin(np.pi * t))
+    s = np.c_[s1, s2].T
+    center_and_norm(s)
+    s1, s2 = s
+
+    # Mixing matrix
+    mixing = rng.randn(6, 2)
+    m = np.dot(mixing, s)
+
+    if add_noise:
+        m += 0.1 * rng.randn(6, n_samples)
+
+    center_and_norm(m)
+
+    k_, mixing_, s_ = fastica(
+        m.T, n_components=2, whiten="unit-variance", random_state=rng
+    )
+    s_ = s_.T
+
+    # Check that the mixing model described in the docstring holds:
+    assert_allclose(s_, np.dot(np.dot(mixing_, k_), m))
+
+    center_and_norm(s_)
+    s1_, s2_ = s_
+    # Check to see if the sources have been estimated
+    # in the wrong order
+    if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
+        s2_, s1_ = s_
+    s1_ *= np.sign(np.dot(s1_, s1))
+    s2_ *= np.sign(np.dot(s2_, s2))
+
+    # Check that we have estimated the original sources
+    if not add_noise:
+        assert_allclose(np.dot(s1_, s1) / n_samples, 1, atol=1e-3)
+        assert_allclose(np.dot(s2_, s2) / n_samples, 1, atol=1e-3)
+
+
+def test_fit_transform(global_random_seed, global_dtype):
+    """Test unit variance of transformed data using FastICA algorithm.
+
+    Check that `fit_transform` gives the same result as applying
+    `fit` and then `transform`.
+
+    Bug #13056
+    """
+    # multivariate uniform data in [0, 1]
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.random_sample((100, 10)).astype(global_dtype)
+    max_iter = 300
+    for whiten, n_components in [["unit-variance", 5], [False, None]]:
+        n_components_ = n_components if n_components is not None else X.shape[1]
+
+        ica = FastICA(
+            n_components=n_components, max_iter=max_iter, whiten=whiten, random_state=0
+        )
+        with warnings.catch_warnings():
+            # make sure that numerical errors do not cause sqrt of negative
+            # values
+            warnings.simplefilter("error", RuntimeWarning)
+            # XXX: for some seeds, the model does not converge.
+            # However this is not what we test here.
+            warnings.simplefilter("ignore", ConvergenceWarning)
+            Xt = ica.fit_transform(X)
+        assert ica.components_.shape == (n_components_, 10)
+        assert Xt.shape == (X.shape[0], n_components_)
+
+        ica2 = FastICA(
+            n_components=n_components, max_iter=max_iter, whiten=whiten, random_state=0
+        )
+        with warnings.catch_warnings():
+            # make sure that numerical errors do not cause sqrt of negative
+            # values
+            warnings.simplefilter("error", RuntimeWarning)
+            warnings.simplefilter("ignore", ConvergenceWarning)
+            ica2.fit(X)
+        assert ica2.components_.shape == (n_components_, 10)
+        Xt2 = ica2.transform(X)
+
+        # XXX: we have to set atol for this test to pass for all seeds when
+        # fitting with float32 data. Is this revealing a bug?
+        if global_dtype:
+            atol = np.abs(Xt2).mean() / 1e6
+        else:
+            atol = 0.0  # the default rtol is enough for float64 data
+        assert_allclose(Xt, Xt2, atol=atol)
+
+
+@pytest.mark.filterwarnings("ignore:Ignoring n_components with whiten=False.")
+@pytest.mark.parametrize(
+    "whiten, n_components, expected_mixing_shape",
+    [
+        ("arbitrary-variance", 5, (10, 5)),
+        ("arbitrary-variance", 10, (10, 10)),
+        ("unit-variance", 5, (10, 5)),
+        ("unit-variance", 10, (10, 10)),
+        (False, 5, (10, 10)),
+        (False, 10, (10, 10)),
+    ],
+)
+def test_inverse_transform(
+    whiten, n_components, expected_mixing_shape, global_random_seed, global_dtype
+):
+    # Test FastICA.inverse_transform
+    n_samples = 100
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.random_sample((n_samples, 10)).astype(global_dtype)
+
+    ica = FastICA(n_components=n_components, random_state=rng, whiten=whiten)
+    with warnings.catch_warnings():
+        # For some dataset (depending on the value of global_dtype) the model
+        # can fail to converge but this should not impact the definition of
+        # a valid inverse transform.
+        warnings.simplefilter("ignore", ConvergenceWarning)
+        Xt = ica.fit_transform(X)
+    assert ica.mixing_.shape == expected_mixing_shape
+    X2 = ica.inverse_transform(Xt)
+    assert X.shape == X2.shape
+
+    # reversibility test in non-reduction case
+    if n_components == X.shape[1]:
+        # XXX: we have to set atol for this test to pass for all seeds when
+        # fitting with float32 data. Is this revealing a bug?
+        if global_dtype:
+            # XXX: dividing by a smaller number makes
+            # tests fail for some seeds.
+            atol = np.abs(X2).mean() / 1e5
+        else:
+            atol = 0.0  # the default rtol is enough for float64 data
+        assert_allclose(X, X2, atol=atol)
+
+
+def test_fastica_errors():
+    n_features = 3
+    n_samples = 10
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((n_samples, n_features))
+    w_init = rng.randn(n_features + 1, n_features + 1)
+    with pytest.raises(ValueError, match=r"alpha must be in \[1,2\]"):
+        fastica(X, fun_args={"alpha": 0})
+    with pytest.raises(
+        ValueError, match=r"w_init has invalid shape.+should be \(3L?, 3L?\)"
+    ):
+        fastica(X, w_init=w_init)
+
+
+def test_fastica_whiten_unit_variance(global_random_seed):
+    """Test unit variance of transformed data using FastICA algorithm.
+
+    Bug #13056
+    """
+    rng = np.random.RandomState(global_random_seed)
+    X = rng.random_sample((100, 10))
+    n_components = X.shape[1]
+    ica = FastICA(n_components=n_components, whiten="unit-variance", random_state=0)
+    Xt = ica.fit_transform(X)
+
+    assert np.var(Xt) == pytest.approx(1.0)
+
+
+@pytest.mark.parametrize("whiten", ["arbitrary-variance", "unit-variance", False])
+@pytest.mark.parametrize("return_X_mean", [True, False])
+@pytest.mark.parametrize("return_n_iter", [True, False])
+def test_fastica_output_shape(whiten, return_X_mean, return_n_iter):
+    n_features = 3
+    n_samples = 10
+    rng = np.random.RandomState(0)
+    X = rng.random_sample((n_samples, n_features))
+
+    expected_len = 3 + return_X_mean + return_n_iter
+
+    out = fastica(
+        X, whiten=whiten, return_n_iter=return_n_iter, return_X_mean=return_X_mean
+    )
+
+    assert len(out) == expected_len
+    if not whiten:
+        assert out[0] is None
+
+
+@pytest.mark.parametrize("add_noise", [True, False])
+def test_fastica_simple_different_solvers(add_noise, global_random_seed):
+    """Test FastICA is consistent between whiten_solvers."""
+    rng = np.random.RandomState(global_random_seed)
+    n_samples = 1000
+    # Generate two sources:
+    s1 = (2 * np.sin(np.linspace(0, 100, n_samples)) > 0) - 1
+    s2 = stats.t.rvs(1, size=n_samples, random_state=rng)
+    s = np.c_[s1, s2].T
+    center_and_norm(s)
+    s1, s2 = s
+
+    # Mixing angle
+    phi = rng.rand() * 2 * np.pi
+    mixing = np.array([[np.cos(phi), np.sin(phi)], [np.sin(phi), -np.cos(phi)]])
+    m = np.dot(mixing, s)
+
+    if add_noise:
+        m += 0.1 * rng.randn(2, 1000)
+
+    center_and_norm(m)
+
+    outs = {}
+    for solver in ("svd", "eigh"):
+        ica = FastICA(random_state=0, whiten="unit-variance", whiten_solver=solver)
+        sources = ica.fit_transform(m.T)
+        outs[solver] = sources
+        assert ica.components_.shape == (2, 2)
+        assert sources.shape == (1000, 2)
+
+    # compared numbers are not all on the same magnitude. Using a small atol to
+    # make the test less brittle
+    assert_allclose(outs["eigh"], outs["svd"], atol=1e-12)
+
+
+def test_fastica_eigh_low_rank_warning(global_random_seed):
+    """Test FastICA eigh solver raises warning for low-rank data."""
+    rng = np.random.RandomState(global_random_seed)
+    A = rng.randn(10, 2)
+    X = A @ A.T
+    ica = FastICA(random_state=0, whiten="unit-variance", whiten_solver="eigh")
+    msg = "There are some small singular values"
+
+    with pytest.warns(UserWarning, match=msg):
+        with ignore_warnings(category=ConvergenceWarning):
+            # The FastICA solver may not converge for some data with specific
+            # random seeds but this happens after the whiten step so this is
+            # not want we want to test here.
+            ica.fit(X)
diff --git a/shared/package-files/04/045852c783af99ff4ae5dcbfe807ad33b2e7dae983560391a3976d1cee268727/_nested_sum_backward_ops.h b/shared/package-files/04/045852c783af99ff4ae5dcbfe807ad33b2e7dae983560391a3976d1cee268727/_nested_sum_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..ca7754b6390232be7b4de7a501900444b5cc6000
--- /dev/null
+++ b/shared/package-files/04/045852c783af99ff4ae5dcbfe807ad33b2e7dae983560391a3976d1cee268727/_nested_sum_backward_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _nested_sum_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, at::OptionalIntArrayRef, bool);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_nested_sum_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_nested_sum_backward(Tensor grad, Tensor self, int[1]? dim, bool keepdim=False) -> Tensor")
+  static at::Tensor call(const at::Tensor & grad, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & self, at::OptionalIntArrayRef dim, bool keepdim);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/04/04587fa204f4de99ca7e9a946f40d8e4b2f0f53df7d1eacadcdcce04c3a4235a/md_in_html.pyi b/shared/package-files/04/04587fa204f4de99ca7e9a946f40d8e4b2f0f53df7d1eacadcdcce04c3a4235a/md_in_html.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3991a94dfda9b9a9a3ec5d872a4c57f317f6700e
--- /dev/null
+++ b/shared/package-files/04/04587fa204f4de99ca7e9a946f40d8e4b2f0f53df7d1eacadcdcce04c3a4235a/md_in_html.pyi
@@ -0,0 +1,9 @@
+from typing import Any, Optional
+
+from markdown.blockprocessors import BlockProcessor
+from markdown.extensions import Extension
+
+class MarkdownInHtmlProcessor(BlockProcessor): ...
+class MarkdownInHtmlExtension(Extension): ...
+
+def makeExtension(**kwargs): ...
diff --git a/shared/package-files/04/0458edcc6f9440ac61be7f07789c35d4971adc9464d54efe028036e7fea52e6b/upsample_nearest1d_backward_meta.h b/shared/package-files/04/0458edcc6f9440ac61be7f07789c35d4971adc9464d54efe028036e7fea52e6b/upsample_nearest1d_backward_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..a195ce2027016b1296f5cc0f7a48dfb96dc2fcc7
--- /dev/null
+++ b/shared/package-files/04/0458edcc6f9440ac61be7f07789c35d4971adc9464d54efe028036e7fea52e6b/upsample_nearest1d_backward_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_upsample_nearest1d_backward : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & grad_output, at::ArrayRef output_size, at::ArrayRef input_size, c10::optional scales);
+};
+
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/04/045a5cd67e33a72c87c8cd8390db3ced345c2e615f1a8b80ceb7a92850fd57a1/kermit b/shared/package-files/04/045a5cd67e33a72c87c8cd8390db3ced345c2e615f1a8b80ceb7a92850fd57a1/kermit
new file mode 100644
index 0000000000000000000000000000000000000000..6cad0d04d84ec57936f7ebfe71ba66585031a413
Binary files /dev/null and b/shared/package-files/04/045a5cd67e33a72c87c8cd8390db3ced345c2e615f1a8b80ceb7a92850fd57a1/kermit differ
diff --git a/shared/package-files/04/0460758f4be648c4bc946c1256604e73d6a923534cf36aae1701761e1af66c31/test_order.py b/shared/package-files/04/0460758f4be648c4bc946c1256604e73d6a923534cf36aae1701761e1af66c31/test_order.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd4cd9938d6ebbc4d8d915e09ec6e9c02c6fe599
--- /dev/null
+++ b/shared/package-files/04/0460758f4be648c4bc946c1256604e73d6a923534cf36aae1701761e1af66c31/test_order.py
@@ -0,0 +1,477 @@
+from sympy.core.add import Add
+from sympy.core.function import (Function, expand)
+from sympy.core.numbers import (I, Rational, nan, oo, pi)
+from sympy.core.singleton import S
+from sympy.core.symbol import (Symbol, symbols)
+from sympy.functions.elementary.complexes import (conjugate, transpose)
+from sympy.functions.elementary.exponential import (exp, log)
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import (cos, sin)
+from sympy.integrals.integrals import Integral
+from sympy.series.order import O, Order
+from sympy.core.expr import unchanged
+from sympy.testing.pytest import raises
+from sympy.abc import w, x, y, z
+
+
+def test_caching_bug():
+    #needs to be a first test, so that all caches are clean
+    #cache it
+    O(w)
+    #and test that this won't raise an exception
+    O(w**(-1/x/log(3)*log(5)), w)
+
+
+def test_free_symbols():
+    assert Order(1).free_symbols == set()
+    assert Order(x).free_symbols == {x}
+    assert Order(1, x).free_symbols == {x}
+    assert Order(x*y).free_symbols == {x, y}
+    assert Order(x, x, y).free_symbols == {x, y}
+
+
+def test_simple_1():
+    o = Rational(0)
+    assert Order(2*x) == Order(x)
+    assert Order(x)*3 == Order(x)
+    assert -28*Order(x) == Order(x)
+    assert Order(Order(x)) == Order(x)
+    assert Order(Order(x), y) == Order(Order(x), x, y)
+    assert Order(-23) == Order(1)
+    assert Order(exp(x)) == Order(1, x)
+    assert Order(exp(1/x)).expr == exp(1/x)
+    assert Order(x*exp(1/x)).expr == x*exp(1/x)
+    assert Order(x**(o/3)).expr == x**(o/3)
+    assert Order(x**(o*Rational(5, 3))).expr == x**(o*Rational(5, 3))
+    assert Order(x**2 + x + y, x) == O(1, x)
+    assert Order(x**2 + x + y, y) == O(1, y)
+    raises(ValueError, lambda: Order(exp(x), x, x))
+    raises(TypeError, lambda: Order(x, 2 - x))
+
+
+def test_simple_2():
+    assert Order(2*x)*x == Order(x**2)
+    assert Order(2*x)/x == Order(1, x)
+    assert Order(2*x)*x*exp(1/x) == Order(x**2*exp(1/x))
+    assert (Order(2*x)*x*exp(1/x)/log(x)**3).expr == x**2*exp(1/x)*log(x)**-3
+
+
+def test_simple_3():
+    assert Order(x) + x == Order(x)
+    assert Order(x) + 2 == 2 + Order(x)
+    assert Order(x) + x**2 == Order(x)
+    assert Order(x) + 1/x == 1/x + Order(x)
+    assert Order(1/x) + 1/x**2 == 1/x**2 + Order(1/x)
+    assert Order(x) + exp(1/x) == Order(x) + exp(1/x)
+
+
+def test_simple_4():
+    assert Order(x)**2 == Order(x**2)
+
+
+def test_simple_5():
+    assert Order(x) + Order(x**2) == Order(x)
+    assert Order(x) + Order(x**-2) == Order(x**-2)
+    assert Order(x) + Order(1/x) == Order(1/x)
+
+
+def test_simple_6():
+    assert Order(x) - Order(x) == Order(x)
+    assert Order(x) + Order(1) == Order(1)
+    assert Order(x) + Order(x**2) == Order(x)
+    assert Order(1/x) + Order(1) == Order(1/x)
+    assert Order(x) + Order(exp(1/x)) == Order(exp(1/x))
+    assert Order(x**3) + Order(exp(2/x)) == Order(exp(2/x))
+    assert Order(x**-3) + Order(exp(2/x)) == Order(exp(2/x))
+
+
+def test_simple_7():
+    assert 1 + O(1) == O(1)
+    assert 2 + O(1) == O(1)
+    assert x + O(1) == O(1)
+    assert 1/x + O(1) == 1/x + O(1)
+
+
+def test_simple_8():
+    assert O(sqrt(-x)) == O(sqrt(x))
+    assert O(x**2*sqrt(x)) == O(x**Rational(5, 2))
+    assert O(x**3*sqrt(-(-x)**3)) == O(x**Rational(9, 2))
+    assert O(x**Rational(3, 2)*sqrt((-x)**3)) == O(x**3)
+    assert O(x*(-2*x)**(I/2)) == O(x*(-x)**(I/2))
+
+
+def test_as_expr_variables():
+    assert Order(x).as_expr_variables(None) == (x, ((x, 0),))
+    assert Order(x).as_expr_variables(((x, 0),)) == (x, ((x, 0),))
+    assert Order(y).as_expr_variables(((x, 0),)) == (y, ((x, 0), (y, 0)))
+    assert Order(y).as_expr_variables(((x, 0), (y, 0))) == (y, ((x, 0), (y, 0)))
+
+
+def test_contains_0():
+    assert Order(1, x).contains(Order(1, x))
+    assert Order(1, x).contains(Order(1))
+    assert Order(1).contains(Order(1, x)) is False
+
+
+def test_contains_1():
+    assert Order(x).contains(Order(x))
+    assert Order(x).contains(Order(x**2))
+    assert not Order(x**2).contains(Order(x))
+    assert not Order(x).contains(Order(1/x))
+    assert not Order(1/x).contains(Order(exp(1/x)))
+    assert not Order(x).contains(Order(exp(1/x)))
+    assert Order(1/x).contains(Order(x))
+    assert Order(exp(1/x)).contains(Order(x))
+    assert Order(exp(1/x)).contains(Order(1/x))
+    assert Order(exp(1/x)).contains(Order(exp(1/x)))
+    assert Order(exp(2/x)).contains(Order(exp(1/x)))
+    assert not Order(exp(1/x)).contains(Order(exp(2/x)))
+
+
+def test_contains_2():
+    assert Order(x).contains(Order(y)) is None
+    assert Order(x).contains(Order(y*x))
+    assert Order(y*x).contains(Order(x))
+    assert Order(y).contains(Order(x*y))
+    assert Order(x).contains(Order(y**2*x))
+
+
+def test_contains_3():
+    assert Order(x*y**2).contains(Order(x**2*y)) is None
+    assert Order(x**2*y).contains(Order(x*y**2)) is None
+
+
+def test_contains_4():
+    assert Order(sin(1/x**2)).contains(Order(cos(1/x**2))) is True
+    assert Order(cos(1/x**2)).contains(Order(sin(1/x**2))) is True
+
+
+def test_contains():
+    assert Order(1, x) not in Order(1)
+    assert Order(1) in Order(1, x)
+    raises(TypeError, lambda: Order(x*y**2) in Order(x**2*y))
+
+
+def test_add_1():
+    assert Order(x + x) == Order(x)
+    assert Order(3*x - 2*x**2) == Order(x)
+    assert Order(1 + x) == Order(1, x)
+    assert Order(1 + 1/x) == Order(1/x)
+    # TODO : A better output for Order(log(x) + 1/log(x))
+    # could be Order(log(x)). Currently Order for expressions
+    # where all arguments would involve a log term would fall
+    # in this category and outputs for these should be improved.
+    assert Order(log(x) + 1/log(x)) == Order((log(x)**2 + 1)/log(x))
+    assert Order(exp(1/x) + x) == Order(exp(1/x))
+    assert Order(exp(1/x) + 1/x**20) == Order(exp(1/x))
+
+
+def test_ln_args():
+    assert O(log(x)) + O(log(2*x)) == O(log(x))
+    assert O(log(x)) + O(log(x**3)) == O(log(x))
+    assert O(log(x*y)) + O(log(x) + log(y)) == O(log(x) + log(y), x, y)
+
+
+def test_multivar_0():
+    assert Order(x*y).expr == x*y
+    assert Order(x*y**2).expr == x*y**2
+    assert Order(x*y, x).expr == x
+    assert Order(x*y**2, y).expr == y**2
+    assert Order(x*y*z).expr == x*y*z
+    assert Order(x/y).expr == x/y
+    assert Order(x*exp(1/y)).expr == x*exp(1/y)
+    assert Order(exp(x)*exp(1/y)).expr == exp(x)*exp(1/y)
+
+
+def test_multivar_0a():
+    assert Order(exp(1/x)*exp(1/y)).expr == exp(1/x)*exp(1/y)
+
+
+def test_multivar_1():
+    assert Order(x + y).expr == x + y
+    assert Order(x + 2*y).expr == x + y
+    assert (Order(x + y) + x).expr == (x + y)
+    assert (Order(x + y) + x**2) == Order(x + y)
+    assert (Order(x + y) + 1/x) == 1/x + Order(x + y)
+    assert Order(x**2 + y*x).expr == x**2 + y*x
+
+
+def test_multivar_2():
+    assert Order(x**2*y + y**2*x, x, y).expr == x**2*y + y**2*x
+
+
+def test_multivar_mul_1():
+    assert Order(x + y)*x == Order(x**2 + y*x, x, y)
+
+
+def test_multivar_3():
+    assert (Order(x) + Order(y)).args in [
+        (Order(x), Order(y)),
+        (Order(y), Order(x))]
+    assert Order(x) + Order(y) + Order(x + y) == Order(x + y)
+    assert (Order(x**2*y) + Order(y**2*x)).args in [
+        (Order(x*y**2), Order(y*x**2)),
+        (Order(y*x**2), Order(x*y**2))]
+    assert (Order(x**2*y) + Order(y*x)) == Order(x*y)
+
+
+def test_issue_3468():
+    y = Symbol('y', negative=True)
+    z = Symbol('z', complex=True)
+
+    # check that Order does not modify assumptions about symbols
+    Order(x)
+    Order(y)
+    Order(z)
+
+    assert x.is_positive is None
+    assert y.is_positive is False
+    assert z.is_positive is None
+
+
+def test_leading_order():
+    assert (x + 1 + 1/x**5).extract_leading_order(x) == ((1/x**5, O(1/x**5)),)
+    assert (1 + 1/x).extract_leading_order(x) == ((1/x, O(1/x)),)
+    assert (1 + x).extract_leading_order(x) == ((1, O(1, x)),)
+    assert (1 + x**2).extract_leading_order(x) == ((1, O(1, x)),)
+    assert (2 + x**2).extract_leading_order(x) == ((2, O(1, x)),)
+    assert (x + x**2).extract_leading_order(x) == ((x, O(x)),)
+
+
+def test_leading_order2():
+    assert set((2 + pi + x**2).extract_leading_order(x)) == {(pi, O(1, x)),
+            (S(2), O(1, x))}
+    assert set((2*x + pi*x + x**2).extract_leading_order(x)) == {(2*x, O(x)),
+            (x*pi, O(x))}
+
+
+def test_order_leadterm():
+    assert O(x**2)._eval_as_leading_term(x) == O(x**2)
+
+
+def test_order_symbols():
+    e = x*y*sin(x)*Integral(x, (x, 1, 2))
+    assert O(e) == O(x**2*y, x, y)
+    assert O(e, x) == O(x**2)
+
+
+def test_nan():
+    assert O(nan) is nan
+    assert not O(x).contains(nan)
+
+
+def test_O1():
+    assert O(1, x) * x == O(x)
+    assert O(1, y) * x == O(1, y)
+
+
+def test_getn():
+    # other lines are tested incidentally by the suite
+    assert O(x).getn() == 1
+    assert O(x/log(x)).getn() == 1
+    assert O(x**2/log(x)**2).getn() == 2
+    assert O(x*log(x)).getn() == 1
+    raises(NotImplementedError, lambda: (O(x) + O(y)).getn())
+
+
+def test_diff():
+    assert O(x**2).diff(x) == O(x)
+
+
+def test_getO():
+    assert (x).getO() is None
+    assert (x).removeO() == x
+    assert (O(x)).getO() == O(x)
+    assert (O(x)).removeO() == 0
+    assert (z + O(x) + O(y)).getO() == O(x) + O(y)
+    assert (z + O(x) + O(y)).removeO() == z
+    raises(NotImplementedError, lambda: (O(x) + O(y)).getn())
+
+
+def test_leading_term():
+    from sympy.functions.special.gamma_functions import digamma
+    assert O(1/digamma(1/x)) == O(1/log(x))
+
+
+def test_eval():
+    assert Order(x).subs(Order(x), 1) == 1
+    assert Order(x).subs(x, y) == Order(y)
+    assert Order(x).subs(y, x) == Order(x)
+    assert Order(x).subs(x, x + y) == Order(x + y, (x, -y))
+    assert (O(1)**x).is_Pow
+
+
+def test_issue_4279():
+    a, b = symbols('a b')
+    assert O(a, a, b) + O(1, a, b) == O(1, a, b)
+    assert O(b, a, b) + O(1, a, b) == O(1, a, b)
+    assert O(a + b, a, b) + O(1, a, b) == O(1, a, b)
+    assert O(1, a, b) + O(a, a, b) == O(1, a, b)
+    assert O(1, a, b) + O(b, a, b) == O(1, a, b)
+    assert O(1, a, b) + O(a + b, a, b) == O(1, a, b)
+
+
+def test_issue_4855():
+    assert 1/O(1) != O(1)
+    assert 1/O(x) != O(1/x)
+    assert 1/O(x, (x, oo)) != O(1/x, (x, oo))
+
+    f = Function('f')
+    assert 1/O(f(x)) != O(1/x)
+
+
+def test_order_conjugate_transpose():
+    x = Symbol('x', real=True)
+    y = Symbol('y', imaginary=True)
+    assert conjugate(Order(x)) == Order(conjugate(x))
+    assert conjugate(Order(y)) == Order(conjugate(y))
+    assert conjugate(Order(x**2)) == Order(conjugate(x)**2)
+    assert conjugate(Order(y**2)) == Order(conjugate(y)**2)
+    assert transpose(Order(x)) == Order(transpose(x))
+    assert transpose(Order(y)) == Order(transpose(y))
+    assert transpose(Order(x**2)) == Order(transpose(x)**2)
+    assert transpose(Order(y**2)) == Order(transpose(y)**2)
+
+
+def test_order_noncommutative():
+    A = Symbol('A', commutative=False)
+    assert Order(A + A*x, x) == Order(1, x)
+    assert (A + A*x)*Order(x) == Order(x)
+    assert (A*x)*Order(x) == Order(x**2, x)
+    assert expand((1 + Order(x))*A*A*x) == A*A*x + Order(x**2, x)
+    assert expand((A*A + Order(x))*x) == A*A*x + Order(x**2, x)
+    assert expand((A + Order(x))*A*x) == A*A*x + Order(x**2, x)
+
+
+def test_issue_6753():
+    assert (1 + x**2)**10000*O(x) == O(x)
+
+
+def test_order_at_infinity():
+    assert Order(1 + x, (x, oo)) == Order(x, (x, oo))
+    assert Order(3*x, (x, oo)) == Order(x, (x, oo))
+    assert Order(x, (x, oo))*3 == Order(x, (x, oo))
+    assert -28*Order(x, (x, oo)) == Order(x, (x, oo))
+    assert Order(Order(x, (x, oo)), (x, oo)) == Order(x, (x, oo))
+    assert Order(Order(x, (x, oo)), (y, oo)) == Order(x, (x, oo), (y, oo))
+    assert Order(3, (x, oo)) == Order(1, (x, oo))
+    assert Order(x**2 + x + y, (x, oo)) == O(x**2, (x, oo))
+    assert Order(x**2 + x + y, (y, oo)) == O(y, (y, oo))
+
+    assert Order(2*x, (x, oo))*x == Order(x**2, (x, oo))
+    assert Order(2*x, (x, oo))/x == Order(1, (x, oo))
+    assert Order(2*x, (x, oo))*x*exp(1/x) == Order(x**2*exp(1/x), (x, oo))
+    assert Order(2*x, (x, oo))*x*exp(1/x)/log(x)**3 == Order(x**2*exp(1/x)*log(x)**-3, (x, oo))
+
+    assert Order(x, (x, oo)) + 1/x == 1/x + Order(x, (x, oo)) == Order(x, (x, oo))
+    assert Order(x, (x, oo)) + 1 == 1 + Order(x, (x, oo)) == Order(x, (x, oo))
+    assert Order(x, (x, oo)) + x == x + Order(x, (x, oo)) == Order(x, (x, oo))
+    assert Order(x, (x, oo)) + x**2 == x**2 + Order(x, (x, oo))
+    assert Order(1/x, (x, oo)) + 1/x**2 == 1/x**2 + Order(1/x, (x, oo)) == Order(1/x, (x, oo))
+    assert Order(x, (x, oo)) + exp(1/x) == exp(1/x) + Order(x, (x, oo))
+
+    assert Order(x, (x, oo))**2 == Order(x**2, (x, oo))
+
+    assert Order(x, (x, oo)) + Order(x**2, (x, oo)) == Order(x**2, (x, oo))
+    assert Order(x, (x, oo)) + Order(x**-2, (x, oo)) == Order(x, (x, oo))
+    assert Order(x, (x, oo)) + Order(1/x, (x, oo)) == Order(x, (x, oo))
+
+    assert Order(x, (x, oo)) - Order(x, (x, oo)) == Order(x, (x, oo))
+    assert Order(x, (x, oo)) + Order(1, (x, oo)) == Order(x, (x, oo))
+    assert Order(x, (x, oo)) + Order(x**2, (x, oo)) == Order(x**2, (x, oo))
+    assert Order(1/x, (x, oo)) + Order(1, (x, oo)) == Order(1, (x, oo))
+    assert Order(x, (x, oo)) + Order(exp(1/x), (x, oo)) == Order(x, (x, oo))
+    assert Order(x**3, (x, oo)) + Order(exp(2/x), (x, oo)) == Order(x**3, (x, oo))
+    assert Order(x**-3, (x, oo)) + Order(exp(2/x), (x, oo)) == Order(exp(2/x), (x, oo))
+
+    # issue 7207
+    assert Order(exp(x), (x, oo)).expr == Order(2*exp(x), (x, oo)).expr == exp(x)
+    assert Order(y**x, (x, oo)).expr == Order(2*y**x, (x, oo)).expr == exp(x*log(y))
+
+    # issue 19545
+    assert Order(1/x - 3/(3*x + 2), (x, oo)).expr == x**(-2)
+
+def test_mixing_order_at_zero_and_infinity():
+    assert (Order(x, (x, 0)) + Order(x, (x, oo))).is_Add
+    assert Order(x, (x, 0)) + Order(x, (x, oo)) == Order(x, (x, oo)) + Order(x, (x, 0))
+    assert Order(Order(x, (x, oo))) == Order(x, (x, oo))
+
+    # not supported (yet)
+    raises(NotImplementedError, lambda: Order(x, (x, 0))*Order(x, (x, oo)))
+    raises(NotImplementedError, lambda: Order(x, (x, oo))*Order(x, (x, 0)))
+    raises(NotImplementedError, lambda: Order(Order(x, (x, oo)), y))
+    raises(NotImplementedError, lambda: Order(Order(x), (x, oo)))
+
+
+def test_order_at_some_point():
+    assert Order(x, (x, 1)) == Order(1, (x, 1))
+    assert Order(2*x - 2, (x, 1)) == Order(x - 1, (x, 1))
+    assert Order(-x + 1, (x, 1)) == Order(x - 1, (x, 1))
+    assert Order(x - 1, (x, 1))**2 == Order((x - 1)**2, (x, 1))
+    assert Order(x - 2, (x, 2)) - O(x - 2, (x, 2)) == Order(x - 2, (x, 2))
+
+
+def test_order_subs_limits():
+    # issue 3333
+    assert (1 + Order(x)).subs(x, 1/x) == 1 + Order(1/x, (x, oo))
+    assert (1 + Order(x)).limit(x, 0) == 1
+    # issue 5769
+    assert ((x + Order(x**2))/x).limit(x, 0) == 1
+
+    assert Order(x**2).subs(x, y - 1) == Order((y - 1)**2, (y, 1))
+    assert Order(10*x**2, (x, 2)).subs(x, y - 1) == Order(1, (y, 3))
+
+
+def test_issue_9351():
+    assert exp(x).series(x, 10, 1) == exp(10) + Order(x - 10, (x, 10))
+
+
+def test_issue_9192():
+    assert O(1)*O(1) == O(1)
+    assert O(1)**O(1) == O(1)
+
+
+def test_issue_9910():
+    assert O(x*log(x) + sin(x), (x, oo)) == O(x*log(x), (x, oo))
+
+
+def test_performance_of_adding_order():
+    l = [x**i for i in range(1000)]
+    l.append(O(x**1001))
+    assert Add(*l).subs(x,1) == O(1)
+
+def test_issue_14622():
+    assert (x**(-4) + x**(-3) + x**(-1) + O(x**(-6), (x, oo))).as_numer_denom() == (
+        x**4 + x**5 + x**7 + O(x**2, (x, oo)), x**8)
+    assert (x**3 + O(x**2, (x, oo))).is_Add
+    assert O(x**2, (x, oo)).contains(x**3) is False
+    assert O(x, (x, oo)).contains(O(x, (x, 0))) is None
+    assert O(x, (x, 0)).contains(O(x, (x, oo))) is None
+    raises(NotImplementedError, lambda: O(x**3).contains(x**w))
+
+
+def test_issue_15539():
+    assert O(1/x**2 + 1/x**4, (x, -oo)) == O(1/x**2, (x, -oo))
+    assert O(1/x**4 + exp(x), (x, -oo)) == O(1/x**4, (x, -oo))
+    assert O(1/x**4 + exp(-x), (x, -oo)) == O(exp(-x), (x, -oo))
+    assert O(1/x, (x, oo)).subs(x, -x) == O(-1/x, (x, -oo))
+
+def test_issue_18606():
+    assert unchanged(Order, 0)
+
+
+def test_issue_22165():
+    assert O(log(x)).contains(2)
+
+
+def test_issue_23231():
+    # This test checks Order for expressions having
+    # arguments containing variables in exponents/powers.
+    assert O(x**x + 2**x, (x, oo)) == O(exp(x*log(x)), (x, oo))
+    assert O(x**x + x**2, (x, oo)) == O(exp(x*log(x)), (x, oo))
+    assert O(x**x + 1/x**2, (x, oo)) == O(exp(x*log(x)), (x, oo))
+    assert O(2**x + 3**x , (x, oo)) == O(exp(x*log(3)), (x, oo))
+
+
+def test_issue_9917():
+    assert O(x*sin(x) + 1, (x, oo)) == O(x, (x, oo))
diff --git a/shared/package-files/04/0463c623897237a20517f4f4931d6ada587753948485bc83a8b16e5bc10509a5/Guatemala b/shared/package-files/04/0463c623897237a20517f4f4931d6ada587753948485bc83a8b16e5bc10509a5/Guatemala
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diff --git a/shared/package-files/04/046407fe31a1b3416acbd34df0c4622c7f7e6fecf9859bcebd4214b6b62c505f/test_build_scripts.py b/shared/package-files/04/046407fe31a1b3416acbd34df0c4622c7f7e6fecf9859bcebd4214b6b62c505f/test_build_scripts.py
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index 0000000000000000000000000000000000000000..954fc763987c2a97ad03fb7576feb0b60f314a4b
--- /dev/null
+++ b/shared/package-files/04/046407fe31a1b3416acbd34df0c4622c7f7e6fecf9859bcebd4214b6b62c505f/test_build_scripts.py
@@ -0,0 +1,112 @@
+"""Tests for distutils.command.build_scripts."""
+
+import os
+import unittest
+
+from distutils.command.build_scripts import build_scripts
+from distutils.core import Distribution
+from distutils import sysconfig
+
+from distutils.tests import support
+from test.support import run_unittest
+
+
+class BuildScriptsTestCase(support.TempdirManager,
+                           support.LoggingSilencer,
+                           unittest.TestCase):
+
+    def test_default_settings(self):
+        cmd = self.get_build_scripts_cmd("/foo/bar", [])
+        self.assertFalse(cmd.force)
+        self.assertIsNone(cmd.build_dir)
+
+        cmd.finalize_options()
+
+        self.assertTrue(cmd.force)
+        self.assertEqual(cmd.build_dir, "/foo/bar")
+
+    def test_build(self):
+        source = self.mkdtemp()
+        target = self.mkdtemp()
+        expected = self.write_sample_scripts(source)
+
+        cmd = self.get_build_scripts_cmd(target,
+                                         [os.path.join(source, fn)
+                                          for fn in expected])
+        cmd.finalize_options()
+        cmd.run()
+
+        built = os.listdir(target)
+        for name in expected:
+            self.assertIn(name, built)
+
+    def get_build_scripts_cmd(self, target, scripts):
+        import sys
+        dist = Distribution()
+        dist.scripts = scripts
+        dist.command_obj["build"] = support.DummyCommand(
+            build_scripts=target,
+            force=1,
+            executable=sys.executable
+            )
+        return build_scripts(dist)
+
+    def write_sample_scripts(self, dir):
+        expected = []
+        expected.append("script1.py")
+        self.write_script(dir, "script1.py",
+                          ("#! /usr/bin/env python2.3\n"
+                           "# bogus script w/ Python sh-bang\n"
+                           "pass\n"))
+        expected.append("script2.py")
+        self.write_script(dir, "script2.py",
+                          ("#!/usr/bin/python\n"
+                           "# bogus script w/ Python sh-bang\n"
+                           "pass\n"))
+        expected.append("shell.sh")
+        self.write_script(dir, "shell.sh",
+                          ("#!/bin/sh\n"
+                           "# bogus shell script w/ sh-bang\n"
+                           "exit 0\n"))
+        return expected
+
+    def write_script(self, dir, name, text):
+        f = open(os.path.join(dir, name), "w")
+        try:
+            f.write(text)
+        finally:
+            f.close()
+
+    def test_version_int(self):
+        source = self.mkdtemp()
+        target = self.mkdtemp()
+        expected = self.write_sample_scripts(source)
+
+
+        cmd = self.get_build_scripts_cmd(target,
+                                         [os.path.join(source, fn)
+                                          for fn in expected])
+        cmd.finalize_options()
+
+        # http://bugs.python.org/issue4524
+        #
+        # On linux-g++-32 with command line `./configure --enable-ipv6
+        # --with-suffix=3`, python is compiled okay but the build scripts
+        # failed when writing the name of the executable
+        old = sysconfig.get_config_vars().get('VERSION')
+        sysconfig._config_vars['VERSION'] = 4
+        try:
+            cmd.run()
+        finally:
+            if old is not None:
+                sysconfig._config_vars['VERSION'] = old
+
+        built = os.listdir(target)
+        for name in expected:
+            self.assertIn(name, built)
+
+def test_suite():
+    return unittest.makeSuite(BuildScriptsTestCase)
+
+if __name__ == "__main__":
+    run_unittest(test_suite())
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diff --git a/shared/package-files/04/0469d968b0b08293d31d9672f6a8efcb63454935f8c50657aec061420755b499/dummy.py b/shared/package-files/04/0469d968b0b08293d31d9672f6a8efcb63454935f8c50657aec061420755b499/dummy.py
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index 0000000000000000000000000000000000000000..7d44fa2e473bba9110eb2d998eb4b7eee4d2d543
--- /dev/null
+++ b/shared/package-files/04/0469d968b0b08293d31d9672f6a8efcb63454935f8c50657aec061420755b499/dummy.py
@@ -0,0 +1,704 @@
+"""Dummy estimators that implement simple rules of thumb."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import warnings
+from numbers import Integral, Real
+
+import numpy as np
+import scipy.sparse as sp
+
+from .base import (
+    BaseEstimator,
+    ClassifierMixin,
+    MultiOutputMixin,
+    RegressorMixin,
+    _fit_context,
+)
+from .utils import check_random_state
+from .utils._param_validation import Interval, StrOptions
+from .utils.multiclass import class_distribution
+from .utils.random import _random_choice_csc
+from .utils.stats import _weighted_percentile
+from .utils.validation import (
+    _check_sample_weight,
+    _num_samples,
+    check_array,
+    check_consistent_length,
+    check_is_fitted,
+    validate_data,
+)
+
+
+class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
+    """DummyClassifier makes predictions that ignore the input features.
+
+    This classifier serves as a simple baseline to compare against other more
+    complex classifiers.
+
+    The specific behavior of the baseline is selected with the `strategy`
+    parameter.
+
+    All strategies make predictions that ignore the input feature values passed
+    as the `X` argument to `fit` and `predict`. The predictions, however,
+    typically depend on values observed in the `y` parameter passed to `fit`.
+
+    Note that the "stratified" and "uniform" strategies lead to
+    non-deterministic predictions that can be rendered deterministic by setting
+    the `random_state` parameter if needed. The other strategies are naturally
+    deterministic and, once fit, always return the same constant prediction
+    for any value of `X`.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.13
+
+    Parameters
+    ----------
+    strategy : {"most_frequent", "prior", "stratified", "uniform", \
+            "constant"}, default="prior"
+        Strategy to use to generate predictions.
+
+        * "most_frequent": the `predict` method always returns the most
+          frequent class label in the observed `y` argument passed to `fit`.
+          The `predict_proba` method returns the matching one-hot encoded
+          vector.
+        * "prior": the `predict` method always returns the most frequent
+          class label in the observed `y` argument passed to `fit` (like
+          "most_frequent"). ``predict_proba`` always returns the empirical
+          class distribution of `y` also known as the empirical class prior
+          distribution.
+        * "stratified": the `predict_proba` method randomly samples one-hot
+          vectors from a multinomial distribution parametrized by the empirical
+          class prior probabilities.
+          The `predict` method returns the class label which got probability
+          one in the one-hot vector of `predict_proba`.
+          Each sampled row of both methods is therefore independent and
+          identically distributed.
+        * "uniform": generates predictions uniformly at random from the list
+          of unique classes observed in `y`, i.e. each class has equal
+          probability.
+        * "constant": always predicts a constant label that is provided by
+          the user. This is useful for metrics that evaluate a non-majority
+          class.
+
+          .. versionchanged:: 0.24
+             The default value of `strategy` has changed to "prior" in version
+             0.24.
+
+    random_state : int, RandomState instance or None, default=None
+        Controls the randomness to generate the predictions when
+        ``strategy='stratified'`` or ``strategy='uniform'``.
+        Pass an int for reproducible output across multiple function calls.
+        See :term:`Glossary `.
+
+    constant : int or str or array-like of shape (n_outputs,), default=None
+        The explicit constant as predicted by the "constant" strategy. This
+        parameter is useful only for the "constant" strategy.
+
+    Attributes
+    ----------
+    classes_ : ndarray of shape (n_classes,) or list of such arrays
+        Unique class labels observed in `y`. For multi-output classification
+        problems, this attribute is a list of arrays as each output has an
+        independent set of possible classes.
+
+    n_classes_ : int or list of int
+        Number of label for each output.
+
+    class_prior_ : ndarray of shape (n_classes,) or list of such arrays
+        Frequency of each class observed in `y`. For multioutput classification
+        problems, this is computed independently for each output.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X` has
+        feature names that are all strings.
+
+    n_outputs_ : int
+        Number of outputs.
+
+    sparse_output_ : bool
+        True if the array returned from predict is to be in sparse CSC format.
+        Is automatically set to True if the input `y` is passed in sparse
+        format.
+
+    See Also
+    --------
+    DummyRegressor : Regressor that makes predictions using simple rules.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.dummy import DummyClassifier
+    >>> X = np.array([-1, 1, 1, 1])
+    >>> y = np.array([0, 1, 1, 1])
+    >>> dummy_clf = DummyClassifier(strategy="most_frequent")
+    >>> dummy_clf.fit(X, y)
+    DummyClassifier(strategy='most_frequent')
+    >>> dummy_clf.predict(X)
+    array([1, 1, 1, 1])
+    >>> dummy_clf.score(X, y)
+    0.75
+    """
+
+    _parameter_constraints: dict = {
+        "strategy": [
+            StrOptions({"most_frequent", "prior", "stratified", "uniform", "constant"})
+        ],
+        "random_state": ["random_state"],
+        "constant": [Integral, str, "array-like", None],
+    }
+
+    def __init__(self, *, strategy="prior", random_state=None, constant=None):
+        self.strategy = strategy
+        self.random_state = random_state
+        self.constant = constant
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit the baseline classifier.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            Target values.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights.
+
+        Returns
+        -------
+        self : object
+            Returns the instance itself.
+        """
+        validate_data(self, X, skip_check_array=True)
+
+        self._strategy = self.strategy
+
+        if self._strategy == "uniform" and sp.issparse(y):
+            y = y.toarray()
+            warnings.warn(
+                (
+                    "A local copy of the target data has been converted "
+                    "to a numpy array. Predicting on sparse target data "
+                    "with the uniform strategy would not save memory "
+                    "and would be slower."
+                ),
+                UserWarning,
+            )
+
+        self.sparse_output_ = sp.issparse(y)
+
+        if not self.sparse_output_:
+            y = np.asarray(y)
+            y = np.atleast_1d(y)
+
+        if y.ndim == 1:
+            y = np.reshape(y, (-1, 1))
+
+        self.n_outputs_ = y.shape[1]
+
+        check_consistent_length(X, y)
+
+        if sample_weight is not None:
+            sample_weight = _check_sample_weight(sample_weight, X)
+
+        if self._strategy == "constant":
+            if self.constant is None:
+                raise ValueError(
+                    "Constant target value has to be specified "
+                    "when the constant strategy is used."
+                )
+            else:
+                constant = np.reshape(np.atleast_1d(self.constant), (-1, 1))
+                if constant.shape[0] != self.n_outputs_:
+                    raise ValueError(
+                        "Constant target value should have shape (%d, 1)."
+                        % self.n_outputs_
+                    )
+
+        (self.classes_, self.n_classes_, self.class_prior_) = class_distribution(
+            y, sample_weight
+        )
+
+        if self._strategy == "constant":
+            for k in range(self.n_outputs_):
+                if not any(constant[k][0] == c for c in self.classes_[k]):
+                    # Checking in case of constant strategy if the constant
+                    # provided by the user is in y.
+                    err_msg = (
+                        "The constant target value must be present in "
+                        "the training data. You provided constant={}. "
+                        "Possible values are: {}.".format(
+                            self.constant, self.classes_[k].tolist()
+                        )
+                    )
+                    raise ValueError(err_msg)
+
+        if self.n_outputs_ == 1:
+            self.n_classes_ = self.n_classes_[0]
+            self.classes_ = self.classes_[0]
+            self.class_prior_ = self.class_prior_[0]
+
+        return self
+
+    def predict(self, X):
+        """Perform classification on test vectors X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test data.
+
+        Returns
+        -------
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            Predicted target values for X.
+        """
+        check_is_fitted(self)
+
+        # numpy random_state expects Python int and not long as size argument
+        # under Windows
+        n_samples = _num_samples(X)
+        rs = check_random_state(self.random_state)
+
+        n_classes_ = self.n_classes_
+        classes_ = self.classes_
+        class_prior_ = self.class_prior_
+        constant = self.constant
+        if self.n_outputs_ == 1:
+            # Get same type even for self.n_outputs_ == 1
+            n_classes_ = [n_classes_]
+            classes_ = [classes_]
+            class_prior_ = [class_prior_]
+            constant = [constant]
+        # Compute probability only once
+        if self._strategy == "stratified":
+            proba = self.predict_proba(X)
+            if self.n_outputs_ == 1:
+                proba = [proba]
+
+        if self.sparse_output_:
+            class_prob = None
+            if self._strategy in ("most_frequent", "prior"):
+                classes_ = [np.array([cp.argmax()]) for cp in class_prior_]
+
+            elif self._strategy == "stratified":
+                class_prob = class_prior_
+
+            elif self._strategy == "uniform":
+                raise ValueError(
+                    "Sparse target prediction is not "
+                    "supported with the uniform strategy"
+                )
+
+            elif self._strategy == "constant":
+                classes_ = [np.array([c]) for c in constant]
+
+            y = _random_choice_csc(n_samples, classes_, class_prob, self.random_state)
+        else:
+            if self._strategy in ("most_frequent", "prior"):
+                y = np.tile(
+                    [
+                        classes_[k][class_prior_[k].argmax()]
+                        for k in range(self.n_outputs_)
+                    ],
+                    [n_samples, 1],
+                )
+
+            elif self._strategy == "stratified":
+                y = np.vstack(
+                    [
+                        classes_[k][proba[k].argmax(axis=1)]
+                        for k in range(self.n_outputs_)
+                    ]
+                ).T
+
+            elif self._strategy == "uniform":
+                ret = [
+                    classes_[k][rs.randint(n_classes_[k], size=n_samples)]
+                    for k in range(self.n_outputs_)
+                ]
+                y = np.vstack(ret).T
+
+            elif self._strategy == "constant":
+                y = np.tile(self.constant, (n_samples, 1))
+
+            if self.n_outputs_ == 1:
+                y = np.ravel(y)
+
+        return y
+
+    def predict_proba(self, X):
+        """
+        Return probability estimates for the test vectors X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test data.
+
+        Returns
+        -------
+        P : ndarray of shape (n_samples, n_classes) or list of such arrays
+            Returns the probability of the sample for each class in
+            the model, where classes are ordered arithmetically, for each
+            output.
+        """
+        check_is_fitted(self)
+
+        # numpy random_state expects Python int and not long as size argument
+        # under Windows
+        n_samples = _num_samples(X)
+        rs = check_random_state(self.random_state)
+
+        n_classes_ = self.n_classes_
+        classes_ = self.classes_
+        class_prior_ = self.class_prior_
+        constant = self.constant
+        if self.n_outputs_ == 1:
+            # Get same type even for self.n_outputs_ == 1
+            n_classes_ = [n_classes_]
+            classes_ = [classes_]
+            class_prior_ = [class_prior_]
+            constant = [constant]
+
+        P = []
+        for k in range(self.n_outputs_):
+            if self._strategy == "most_frequent":
+                ind = class_prior_[k].argmax()
+                out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64)
+                out[:, ind] = 1.0
+            elif self._strategy == "prior":
+                out = np.ones((n_samples, 1)) * class_prior_[k]
+
+            elif self._strategy == "stratified":
+                out = rs.multinomial(1, class_prior_[k], size=n_samples)
+                out = out.astype(np.float64)
+
+            elif self._strategy == "uniform":
+                out = np.ones((n_samples, n_classes_[k]), dtype=np.float64)
+                out /= n_classes_[k]
+
+            elif self._strategy == "constant":
+                ind = np.where(classes_[k] == constant[k])
+                out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64)
+                out[:, ind] = 1.0
+
+            P.append(out)
+
+        if self.n_outputs_ == 1:
+            P = P[0]
+
+        return P
+
+    def predict_log_proba(self, X):
+        """
+        Return log probability estimates for the test vectors X.
+
+        Parameters
+        ----------
+        X : {array-like, object with finite length or shape}
+            Training data.
+
+        Returns
+        -------
+        P : ndarray of shape (n_samples, n_classes) or list of such arrays
+            Returns the log probability of the sample for each class in
+            the model, where classes are ordered arithmetically for each
+            output.
+        """
+        proba = self.predict_proba(X)
+        if self.n_outputs_ == 1:
+            return np.log(proba)
+        else:
+            return [np.log(p) for p in proba]
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        tags.classifier_tags.poor_score = True
+        tags.no_validation = True
+        return tags
+
+    def score(self, X, y, sample_weight=None):
+        """Return the mean accuracy on the given test data and labels.
+
+        In multi-label classification, this is the subset accuracy
+        which is a harsh metric since you require for each sample that
+        each label set be correctly predicted.
+
+        Parameters
+        ----------
+        X : None or array-like of shape (n_samples, n_features)
+            Test samples. Passing None as test samples gives the same result
+            as passing real test samples, since DummyClassifier
+            operates independently of the sampled observations.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            True labels for X.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights.
+
+        Returns
+        -------
+        score : float
+            Mean accuracy of self.predict(X) w.r.t. y.
+        """
+        if X is None:
+            X = np.zeros(shape=(len(y), 1))
+        return super().score(X, y, sample_weight)
+
+
+class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
+    """Regressor that makes predictions using simple rules.
+
+    This regressor is useful as a simple baseline to compare with other
+    (real) regressors. Do not use it for real problems.
+
+    Read more in the :ref:`User Guide `.
+
+    .. versionadded:: 0.13
+
+    Parameters
+    ----------
+    strategy : {"mean", "median", "quantile", "constant"}, default="mean"
+        Strategy to use to generate predictions.
+
+        * "mean": always predicts the mean of the training set
+        * "median": always predicts the median of the training set
+        * "quantile": always predicts a specified quantile of the training set,
+          provided with the quantile parameter.
+        * "constant": always predicts a constant value that is provided by
+          the user.
+
+    constant : int or float or array-like of shape (n_outputs,), default=None
+        The explicit constant as predicted by the "constant" strategy. This
+        parameter is useful only for the "constant" strategy.
+
+    quantile : float in [0.0, 1.0], default=None
+        The quantile to predict using the "quantile" strategy. A quantile of
+        0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the
+        maximum.
+
+    Attributes
+    ----------
+    constant_ : ndarray of shape (1, n_outputs)
+        Mean or median or quantile of the training targets or constant value
+        given by the user.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X` has
+        feature names that are all strings.
+
+    n_outputs_ : int
+        Number of outputs.
+
+    See Also
+    --------
+    DummyClassifier: Classifier that makes predictions using simple rules.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.dummy import DummyRegressor
+    >>> X = np.array([1.0, 2.0, 3.0, 4.0])
+    >>> y = np.array([2.0, 3.0, 5.0, 10.0])
+    >>> dummy_regr = DummyRegressor(strategy="mean")
+    >>> dummy_regr.fit(X, y)
+    DummyRegressor()
+    >>> dummy_regr.predict(X)
+    array([5., 5., 5., 5.])
+    >>> dummy_regr.score(X, y)
+    0.0
+    """
+
+    _parameter_constraints: dict = {
+        "strategy": [StrOptions({"mean", "median", "quantile", "constant"})],
+        "quantile": [Interval(Real, 0.0, 1.0, closed="both"), None],
+        "constant": [
+            Interval(Real, None, None, closed="neither"),
+            "array-like",
+            None,
+        ],
+    }
+
+    def __init__(self, *, strategy="mean", constant=None, quantile=None):
+        self.strategy = strategy
+        self.constant = constant
+        self.quantile = quantile
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit the baseline regressor.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Training data.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            Target values.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights.
+
+        Returns
+        -------
+        self : object
+            Fitted estimator.
+        """
+        validate_data(self, X, skip_check_array=True)
+
+        y = check_array(y, ensure_2d=False, input_name="y")
+        if len(y) == 0:
+            raise ValueError("y must not be empty.")
+
+        if y.ndim == 1:
+            y = np.reshape(y, (-1, 1))
+        self.n_outputs_ = y.shape[1]
+
+        check_consistent_length(X, y, sample_weight)
+
+        if sample_weight is not None:
+            sample_weight = _check_sample_weight(sample_weight, X)
+
+        if self.strategy == "mean":
+            self.constant_ = np.average(y, axis=0, weights=sample_weight)
+
+        elif self.strategy == "median":
+            if sample_weight is None:
+                self.constant_ = np.median(y, axis=0)
+            else:
+                self.constant_ = [
+                    _weighted_percentile(y[:, k], sample_weight, percentile_rank=50.0)
+                    for k in range(self.n_outputs_)
+                ]
+
+        elif self.strategy == "quantile":
+            if self.quantile is None:
+                raise ValueError(
+                    "When using `strategy='quantile', you have to specify the desired "
+                    "quantile in the range [0, 1]."
+                )
+            percentile_rank = self.quantile * 100.0
+            if sample_weight is None:
+                self.constant_ = np.percentile(y, axis=0, q=percentile_rank)
+            else:
+                self.constant_ = [
+                    _weighted_percentile(
+                        y[:, k], sample_weight, percentile_rank=percentile_rank
+                    )
+                    for k in range(self.n_outputs_)
+                ]
+
+        elif self.strategy == "constant":
+            if self.constant is None:
+                raise TypeError(
+                    "Constant target value has to be specified "
+                    "when the constant strategy is used."
+                )
+
+            self.constant_ = check_array(
+                self.constant,
+                accept_sparse=["csr", "csc", "coo"],
+                ensure_2d=False,
+                ensure_min_samples=0,
+            )
+
+            if self.n_outputs_ != 1 and self.constant_.shape[0] != y.shape[1]:
+                raise ValueError(
+                    "Constant target value should have shape (%d, 1)." % y.shape[1]
+                )
+
+        self.constant_ = np.reshape(self.constant_, (1, -1))
+        return self
+
+    def predict(self, X, return_std=False):
+        """Perform classification on test vectors X.
+
+        Parameters
+        ----------
+        X : array-like of shape (n_samples, n_features)
+            Test data.
+
+        return_std : bool, default=False
+            Whether to return the standard deviation of posterior prediction.
+            All zeros in this case.
+
+            .. versionadded:: 0.20
+
+        Returns
+        -------
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            Predicted target values for X.
+
+        y_std : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            Standard deviation of predictive distribution of query points.
+        """
+        check_is_fitted(self)
+        n_samples = _num_samples(X)
+
+        y = np.full(
+            (n_samples, self.n_outputs_),
+            self.constant_,
+            dtype=np.array(self.constant_).dtype,
+        )
+        y_std = np.zeros((n_samples, self.n_outputs_))
+
+        if self.n_outputs_ == 1:
+            y = np.ravel(y)
+            y_std = np.ravel(y_std)
+
+        return (y, y_std) if return_std else y
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        tags.regressor_tags.poor_score = True
+        tags.no_validation = True
+        return tags
+
+    def score(self, X, y, sample_weight=None):
+        """Return the coefficient of determination R^2 of the prediction.
+
+        The coefficient R^2 is defined as `(1 - u/v)`, where `u` is the
+        residual sum of squares `((y_true - y_pred) ** 2).sum()` and `v` is the
+        total sum of squares `((y_true - y_true.mean()) ** 2).sum()`. The best
+        possible score is 1.0 and it can be negative (because the model can be
+        arbitrarily worse). A constant model that always predicts the expected
+        value of y, disregarding the input features, would get a R^2 score of
+        0.0.
+
+        Parameters
+        ----------
+        X : None or array-like of shape (n_samples, n_features)
+            Test samples. Passing None as test samples gives the same result
+            as passing real test samples, since `DummyRegressor`
+            operates independently of the sampled observations.
+
+        y : array-like of shape (n_samples,) or (n_samples, n_outputs)
+            True values for X.
+
+        sample_weight : array-like of shape (n_samples,), default=None
+            Sample weights.
+
+        Returns
+        -------
+        score : float
+            R^2 of `self.predict(X)` w.r.t. y.
+        """
+        if X is None:
+            X = np.zeros(shape=(len(y), 1))
+        return super().score(X, y, sample_weight)
diff --git a/shared/package-files/04/046ae550ba573e4d762f76a14fb82c0d29a64d013cf7044f01f2c58e6c04ba5f/series_str.py b/shared/package-files/04/046ae550ba573e4d762f76a14fb82c0d29a64d013cf7044f01f2c58e6c04ba5f/series_str.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c474a23deaa03ebbd203c0a3910f5ade73a02b3
--- /dev/null
+++ b/shared/package-files/04/046ae550ba573e4d762f76a14fb82c0d29a64d013cf7044f01f2c58e6c04ba5f/series_str.py
@@ -0,0 +1,158 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import pyarrow as pa
+import pyarrow.compute as pc
+
+from narwhals._arrow.utils import (
+    ArrowSeriesNamespace,
+    extract_native,
+    lit,
+    parse_datetime_format,
+    parse_time_format,
+)
+from narwhals._compliant.any_namespace import StringNamespace
+from narwhals._utils import parse_str_strip_chars, str_slice_stop
+
+if TYPE_CHECKING:
+    from narwhals._arrow.series import ArrowSeries
+    from narwhals._arrow.typing import Incomplete
+
+
+class ArrowSeriesStringNamespace(ArrowSeriesNamespace, StringNamespace["ArrowSeries"]):
+    def len_chars(self) -> ArrowSeries:
+        return self.with_native(pc.utf8_length(self.native))
+
+    def replace(
+        self, value: ArrowSeries, pattern: str, *, literal: bool, n: int
+    ) -> ArrowSeries:
+        fn = pc.replace_substring if literal else pc.replace_substring_regex
+        _, value_native = extract_native(self.compliant, value)
+        if not isinstance(value_native, pa.StringScalar):
+            msg = "PyArrow backed `.str.replace` only supports str replacement values"
+            raise TypeError(msg)
+        arr = fn(
+            self.native, pattern, replacement=value_native.as_py(), max_replacements=n
+        )
+        return self.with_native(arr)
+
+    def replace_all(
+        self, value: ArrowSeries, pattern: str, *, literal: bool
+    ) -> ArrowSeries:
+        return self.replace(value, pattern, literal=literal, n=-1)
+
+    def strip_chars(self, characters: str | None) -> ArrowSeries:
+        return self.with_native(
+            pc.utf8_trim(self.native, parse_str_strip_chars(characters))
+        )
+
+    def strip_chars_start(self, characters: str) -> ArrowSeries:
+        return self.with_native(pc.utf8_ltrim(self.native, characters))
+
+    def strip_chars_end(self, characters: str) -> ArrowSeries:
+        return self.with_native(pc.utf8_rtrim(self.native, characters))
+
+    def starts_with(self, prefix: ArrowSeries) -> ArrowSeries:
+        _, prefix_native = extract_native(self.compliant, prefix)
+        if not isinstance(prefix_native, pa.StringScalar):
+            msg = "`.str.starts_with` only supports str prefix values for pyarrow backend"
+            raise TypeError(msg)
+        return self.with_native(pc.starts_with(self.native, prefix_native.as_py()))
+
+    def ends_with(self, suffix: ArrowSeries) -> ArrowSeries:
+        _, suffix_native = extract_native(self.compliant, suffix)
+        if not isinstance(suffix_native, pa.StringScalar):
+            msg = "`.str.ends_with` only supports str suffix values for pyarrow backend"
+            raise TypeError(msg)
+        return self.with_native(pc.ends_with(self.native, suffix_native.as_py()))
+
+    def contains(self, pattern: ArrowSeries, *, literal: bool) -> ArrowSeries:
+        _, pattern_native = extract_native(self.compliant, pattern)
+        if not isinstance(pattern_native, pa.StringScalar):
+            msg = "`.str.contains` only supports str pattern values for pyarrow backend"
+            raise TypeError(msg)
+        fn = pc.match_substring if literal else pc.match_substring_regex
+        return self.with_native(fn(self.native, pattern_native.as_py()))
+
+    def slice(self, offset: int, length: int | None) -> ArrowSeries:
+        stop = str_slice_stop(offset, length)
+        return self.with_native(
+            pc.utf8_slice_codeunits(self.native, start=offset, stop=stop)
+        )
+
+    def split(self, by: str) -> ArrowSeries:
+        split_series = pc.split_pattern(self.native, by)  # type: ignore[call-overload]
+        return self.with_native(split_series)
+
+    def to_datetime(self, format: str | None) -> ArrowSeries:
+        format = parse_datetime_format(self.native) if format is None else format
+        timestamp_array = pc.strptime(self.native, format=format, unit="us")
+        return self.with_native(timestamp_array)
+
+    def to_date(self, format: str | None) -> ArrowSeries:
+        return self.to_datetime(format=format).dt.date()
+
+    def to_time(self, format: str | None) -> ArrowSeries:
+        format = parse_time_format(self.native) if format is None else format
+        timestamp_array = pc.strptime(self.native, format=format, unit="us")
+
+        nw_time_dtype = self.version.dtypes.Time()
+        return self.with_native(timestamp_array).cast(nw_time_dtype)
+
+    def to_uppercase(self) -> ArrowSeries:
+        return self.with_native(pc.utf8_upper(self.native))
+
+    def to_lowercase(self) -> ArrowSeries:
+        return self.with_native(pc.utf8_lower(self.native))
+
+    def to_titlecase(self) -> ArrowSeries:
+        return self.with_native(pc.utf8_title(self.native))
+
+    def zfill(self, width: int) -> ArrowSeries:
+        if width == 0:
+            return self.compliant
+        binary_join: Incomplete = pc.binary_join_element_wise
+        native = self.native
+        hyphen, plus = lit("-"), lit("+")
+        first_char, remaining_chars = (
+            self.slice(0, 1).native,
+            self.slice(1, None).native,
+        )
+
+        # Conditions
+        less_than_width = pc.less(pc.utf8_length(native), lit(width))
+        starts_with_hyphen = pc.equal(first_char, hyphen)
+        starts_with_plus = pc.equal(first_char, plus)
+
+        conditions = pc.make_struct(
+            pc.and_(starts_with_hyphen, less_than_width),
+            pc.and_(starts_with_plus, less_than_width),
+            less_than_width,
+        )
+
+        # Cases
+        padded_remaining_chars = pc.utf8_lpad(remaining_chars, width - 1, padding="0")
+
+        result = pc.case_when(
+            conditions,
+            binary_join(
+                pa.repeat(hyphen, len(native)), padded_remaining_chars, ""
+            ),  # starts with hyphen and less than width
+            binary_join(
+                pa.repeat(plus, len(native)), padded_remaining_chars, ""
+            ),  # starts with plus and less than width
+            pc.utf8_lpad(native, width=width, padding="0"),  # less than width
+            native,
+        )
+        return self.with_native(result)
+
+    def pad_start(self, length: int, fill_char: str) -> ArrowSeries:
+        return self.with_native(
+            pc.utf8_lpad(self.native, width=length, padding=fill_char)
+        )
+
+    def pad_end(self, length: int, fill_char: str) -> ArrowSeries:
+        return self.with_native(
+            pc.utf8_rpad(self.native, width=length, padding=fill_char)
+        )
diff --git a/shared/package-files/04/046ca1d7b1f5423014b91566403a9bd31e14b08d8b61891c55664e964854bdb6/_pslinux.py b/shared/package-files/04/046ca1d7b1f5423014b91566403a9bd31e14b08d8b61891c55664e964854bdb6/_pslinux.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a35691207902610899e0445fc7e5f0c16b4a315
--- /dev/null
+++ b/shared/package-files/04/046ca1d7b1f5423014b91566403a9bd31e14b08d8b61891c55664e964854bdb6/_pslinux.py
@@ -0,0 +1,2243 @@
+# Copyright (c) 2009, Giampaolo Rodola'. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+"""Linux platform implementation."""
+
+from __future__ import division
+
+import base64
+import collections
+import errno
+import functools
+import glob
+import os
+import re
+import socket
+import struct
+import sys
+import traceback
+import warnings
+from collections import defaultdict
+from collections import namedtuple
+
+from . import _common
+from . import _psposix
+from . import _psutil_linux as cext
+from . import _psutil_posix as cext_posix
+from ._common import NIC_DUPLEX_FULL
+from ._common import NIC_DUPLEX_HALF
+from ._common import NIC_DUPLEX_UNKNOWN
+from ._common import AccessDenied
+from ._common import NoSuchProcess
+from ._common import ZombieProcess
+from ._common import debug
+from ._common import decode
+from ._common import get_procfs_path
+from ._common import isfile_strict
+from ._common import memoize
+from ._common import memoize_when_activated
+from ._common import open_binary
+from ._common import open_text
+from ._common import parse_environ_block
+from ._common import path_exists_strict
+from ._common import supports_ipv6
+from ._common import usage_percent
+from ._compat import PY3
+from ._compat import FileNotFoundError
+from ._compat import PermissionError
+from ._compat import ProcessLookupError
+from ._compat import b
+from ._compat import basestring
+
+
+if sys.version_info >= (3, 4):
+    import enum
+else:
+    enum = None
+
+
+__extra__all__ = [
+    #
+    'PROCFS_PATH',
+    # io prio constants
+    "IOPRIO_CLASS_NONE", "IOPRIO_CLASS_RT", "IOPRIO_CLASS_BE",
+    "IOPRIO_CLASS_IDLE",
+    # connection status constants
+    "CONN_ESTABLISHED", "CONN_SYN_SENT", "CONN_SYN_RECV", "CONN_FIN_WAIT1",
+    "CONN_FIN_WAIT2", "CONN_TIME_WAIT", "CONN_CLOSE", "CONN_CLOSE_WAIT",
+    "CONN_LAST_ACK", "CONN_LISTEN", "CONN_CLOSING", ]
+
+
+# =====================================================================
+# --- globals
+# =====================================================================
+
+
+POWER_SUPPLY_PATH = "/sys/class/power_supply"
+HAS_SMAPS = os.path.exists('/proc/%s/smaps' % os.getpid())
+HAS_PROC_IO_PRIORITY = hasattr(cext, "proc_ioprio_get")
+HAS_CPU_AFFINITY = hasattr(cext, "proc_cpu_affinity_get")
+_DEFAULT = object()
+
+# Number of clock ticks per second
+CLOCK_TICKS = os.sysconf("SC_CLK_TCK")
+PAGESIZE = cext_posix.getpagesize()
+BOOT_TIME = None  # set later
+# Used when reading "big" files, namely /proc/{pid}/smaps and /proc/net/*.
+# On Python 2, using a buffer with open() for such files may result in a
+# speedup, see: https://github.com/giampaolo/psutil/issues/708
+BIGFILE_BUFFERING = -1 if PY3 else 8192
+LITTLE_ENDIAN = sys.byteorder == 'little'
+
+# "man iostat" states that sectors are equivalent with blocks and have
+# a size of 512 bytes. Despite this value can be queried at runtime
+# via /sys/block/{DISK}/queue/hw_sector_size and results may vary
+# between 1k, 2k, or 4k... 512 appears to be a magic constant used
+# throughout Linux source code:
+# * https://stackoverflow.com/a/38136179/376587
+# * https://lists.gt.net/linux/kernel/2241060
+# * https://github.com/giampaolo/psutil/issues/1305
+# * https://github.com/torvalds/linux/blob/
+#     4f671fe2f9523a1ea206f63fe60a7c7b3a56d5c7/include/linux/bio.h#L99
+# * https://lkml.org/lkml/2015/8/17/234
+DISK_SECTOR_SIZE = 512
+
+if enum is None:
+    AF_LINK = socket.AF_PACKET
+else:
+    AddressFamily = enum.IntEnum('AddressFamily',
+                                 {'AF_LINK': int(socket.AF_PACKET)})
+    AF_LINK = AddressFamily.AF_LINK
+
+# ioprio_* constants http://linux.die.net/man/2/ioprio_get
+if enum is None:
+    IOPRIO_CLASS_NONE = 0
+    IOPRIO_CLASS_RT = 1
+    IOPRIO_CLASS_BE = 2
+    IOPRIO_CLASS_IDLE = 3
+else:
+    class IOPriority(enum.IntEnum):
+        IOPRIO_CLASS_NONE = 0
+        IOPRIO_CLASS_RT = 1
+        IOPRIO_CLASS_BE = 2
+        IOPRIO_CLASS_IDLE = 3
+
+    globals().update(IOPriority.__members__)
+
+# See:
+# https://github.com/torvalds/linux/blame/master/fs/proc/array.c
+# ...and (TASK_* constants):
+# https://github.com/torvalds/linux/blob/master/include/linux/sched.h
+PROC_STATUSES = {
+    "R": _common.STATUS_RUNNING,
+    "S": _common.STATUS_SLEEPING,
+    "D": _common.STATUS_DISK_SLEEP,
+    "T": _common.STATUS_STOPPED,
+    "t": _common.STATUS_TRACING_STOP,
+    "Z": _common.STATUS_ZOMBIE,
+    "X": _common.STATUS_DEAD,
+    "x": _common.STATUS_DEAD,
+    "K": _common.STATUS_WAKE_KILL,
+    "W": _common.STATUS_WAKING,
+    "I": _common.STATUS_IDLE,
+    "P": _common.STATUS_PARKED,
+}
+
+# https://github.com/torvalds/linux/blob/master/include/net/tcp_states.h
+TCP_STATUSES = {
+    "01": _common.CONN_ESTABLISHED,
+    "02": _common.CONN_SYN_SENT,
+    "03": _common.CONN_SYN_RECV,
+    "04": _common.CONN_FIN_WAIT1,
+    "05": _common.CONN_FIN_WAIT2,
+    "06": _common.CONN_TIME_WAIT,
+    "07": _common.CONN_CLOSE,
+    "08": _common.CONN_CLOSE_WAIT,
+    "09": _common.CONN_LAST_ACK,
+    "0A": _common.CONN_LISTEN,
+    "0B": _common.CONN_CLOSING
+}
+
+
+# =====================================================================
+# --- named tuples
+# =====================================================================
+
+
+# psutil.virtual_memory()
+svmem = namedtuple(
+    'svmem', ['total', 'available', 'percent', 'used', 'free',
+              'active', 'inactive', 'buffers', 'cached', 'shared', 'slab'])
+# psutil.disk_io_counters()
+sdiskio = namedtuple(
+    'sdiskio', ['read_count', 'write_count',
+                'read_bytes', 'write_bytes',
+                'read_time', 'write_time',
+                'read_merged_count', 'write_merged_count',
+                'busy_time'])
+# psutil.Process().open_files()
+popenfile = namedtuple(
+    'popenfile', ['path', 'fd', 'position', 'mode', 'flags'])
+# psutil.Process().memory_info()
+pmem = namedtuple('pmem', 'rss vms shared text lib data dirty')
+# psutil.Process().memory_full_info()
+pfullmem = namedtuple('pfullmem', pmem._fields + ('uss', 'pss', 'swap'))
+# psutil.Process().memory_maps(grouped=True)
+pmmap_grouped = namedtuple(
+    'pmmap_grouped',
+    ['path', 'rss', 'size', 'pss', 'shared_clean', 'shared_dirty',
+     'private_clean', 'private_dirty', 'referenced', 'anonymous', 'swap'])
+# psutil.Process().memory_maps(grouped=False)
+pmmap_ext = namedtuple(
+    'pmmap_ext', 'addr perms ' + ' '.join(pmmap_grouped._fields))
+# psutil.Process.io_counters()
+pio = namedtuple('pio', ['read_count', 'write_count',
+                         'read_bytes', 'write_bytes',
+                         'read_chars', 'write_chars'])
+# psutil.Process.cpu_times()
+pcputimes = namedtuple('pcputimes',
+                       ['user', 'system', 'children_user', 'children_system',
+                        'iowait'])
+
+
+# =====================================================================
+# --- utils
+# =====================================================================
+
+
+def readlink(path):
+    """Wrapper around os.readlink()."""
+    assert isinstance(path, basestring), path
+    path = os.readlink(path)
+    # readlink() might return paths containing null bytes ('\x00')
+    # resulting in "TypeError: must be encoded string without NULL
+    # bytes, not str" errors when the string is passed to other
+    # fs-related functions (os.*, open(), ...).
+    # Apparently everything after '\x00' is garbage (we can have
+    # ' (deleted)', 'new' and possibly others), see:
+    # https://github.com/giampaolo/psutil/issues/717
+    path = path.split('\x00')[0]
+    # Certain paths have ' (deleted)' appended. Usually this is
+    # bogus as the file actually exists. Even if it doesn't we
+    # don't care.
+    if path.endswith(' (deleted)') and not path_exists_strict(path):
+        path = path[:-10]
+    return path
+
+
+def file_flags_to_mode(flags):
+    """Convert file's open() flags into a readable string.
+    Used by Process.open_files().
+    """
+    modes_map = {os.O_RDONLY: 'r', os.O_WRONLY: 'w', os.O_RDWR: 'w+'}
+    mode = modes_map[flags & (os.O_RDONLY | os.O_WRONLY | os.O_RDWR)]
+    if flags & os.O_APPEND:
+        mode = mode.replace('w', 'a', 1)
+    mode = mode.replace('w+', 'r+')
+    # possible values: r, w, a, r+, a+
+    return mode
+
+
+def is_storage_device(name):
+    """Return True if the given name refers to a root device (e.g.
+    "sda", "nvme0n1") as opposed to a logical partition (e.g.  "sda1",
+    "nvme0n1p1"). If name is a virtual device (e.g. "loop1", "ram")
+    return True.
+    """
+    # Readapted from iostat source code, see:
+    # https://github.com/sysstat/sysstat/blob/
+    #     97912938cd476645b267280069e83b1c8dc0e1c7/common.c#L208
+    # Some devices may have a slash in their name (e.g. cciss/c0d0...).
+    name = name.replace('/', '!')
+    including_virtual = True
+    if including_virtual:
+        path = "/sys/block/%s" % name
+    else:
+        path = "/sys/block/%s/device" % name
+    return os.access(path, os.F_OK)
+
+
+@memoize
+def set_scputimes_ntuple(procfs_path):
+    """Set a namedtuple of variable fields depending on the CPU times
+    available on this Linux kernel version which may be:
+    (user, nice, system, idle, iowait, irq, softirq, [steal, [guest,
+     [guest_nice]]])
+    Used by cpu_times() function.
+    """
+    global scputimes
+    with open_binary('%s/stat' % procfs_path) as f:
+        values = f.readline().split()[1:]
+    fields = ['user', 'nice', 'system', 'idle', 'iowait', 'irq', 'softirq']
+    vlen = len(values)
+    if vlen >= 8:
+        # Linux >= 2.6.11
+        fields.append('steal')
+    if vlen >= 9:
+        # Linux >= 2.6.24
+        fields.append('guest')
+    if vlen >= 10:
+        # Linux >= 3.2.0
+        fields.append('guest_nice')
+    scputimes = namedtuple('scputimes', fields)
+
+
+def cat(fname, fallback=_DEFAULT, binary=True):
+    """Return file content.
+    fallback: the value returned in case the file does not exist or
+              cannot be read
+    binary: whether to open the file in binary or text mode.
+    """
+    try:
+        with open_binary(fname) if binary else open_text(fname) as f:
+            return f.read().strip()
+    except (IOError, OSError):
+        if fallback is not _DEFAULT:
+            return fallback
+        else:
+            raise
+
+
+try:
+    set_scputimes_ntuple("/proc")
+except Exception:  # pragma: no cover
+    # Don't want to crash at import time.
+    traceback.print_exc()
+    scputimes = namedtuple('scputimes', 'user system idle')(0.0, 0.0, 0.0)
+
+
+# =====================================================================
+# --- prlimit
+# =====================================================================
+
+# Backport of resource.prlimit() for Python 2. Originally this was done
+# in C, but CentOS-6 which we use to create manylinux wheels is too old
+# and does not support prlimit() syscall. As such the resulting wheel
+# would not include prlimit(), even when installed on newer systems.
+# This is the only part of psutil using ctypes.
+
+prlimit = None
+try:
+    from resource import prlimit  # python >= 3.4
+except ImportError:
+    import ctypes
+
+    libc = ctypes.CDLL(None, use_errno=True)
+
+    if hasattr(libc, "prlimit"):
+
+        def prlimit(pid, resource_, limits=None):
+            class StructRlimit(ctypes.Structure):
+                _fields_ = [('rlim_cur', ctypes.c_longlong),
+                            ('rlim_max', ctypes.c_longlong)]
+
+            current = StructRlimit()
+            if limits is None:
+                # get
+                ret = libc.prlimit(pid, resource_, None, ctypes.byref(current))
+            else:
+                # set
+                new = StructRlimit()
+                new.rlim_cur = limits[0]
+                new.rlim_max = limits[1]
+                ret = libc.prlimit(
+                    pid, resource_, ctypes.byref(new), ctypes.byref(current))
+
+            if ret != 0:
+                errno = ctypes.get_errno()
+                raise OSError(errno, os.strerror(errno))
+            return (current.rlim_cur, current.rlim_max)
+
+
+if prlimit is not None:
+    __extra__all__.extend(
+        [x for x in dir(cext) if x.startswith('RLIM') and x.isupper()])
+
+
+# =====================================================================
+# --- system memory
+# =====================================================================
+
+
+def calculate_avail_vmem(mems):
+    """Fallback for kernels < 3.14 where /proc/meminfo does not provide
+    "MemAvailable:" column, see:
+    https://blog.famzah.net/2014/09/24/
+    This code reimplements the algorithm outlined here:
+    https://git.kernel.org/cgit/linux/kernel/git/torvalds/linux.git/
+        commit/?id=34e431b0ae398fc54ea69ff85ec700722c9da773
+
+    XXX: on recent kernels this calculation differs by ~1.5% than
+    "MemAvailable:" as it's calculated slightly differently, see:
+    https://gitlab.com/procps-ng/procps/issues/42
+    https://github.com/famzah/linux-memavailable-procfs/issues/2
+    It is still way more realistic than doing (free + cached) though.
+    """
+    # Fallback for very old distros. According to
+    # https://git.kernel.org/cgit/linux/kernel/git/torvalds/linux.git/
+    #     commit/?id=34e431b0ae398fc54ea69ff85ec700722c9da773
+    # ...long ago "avail" was calculated as (free + cached).
+    # We might fallback in such cases:
+    # "Active(file)" not available: 2.6.28 / Dec 2008
+    # "Inactive(file)" not available: 2.6.28 / Dec 2008
+    # "SReclaimable:" not available: 2.6.19 / Nov 2006
+    # /proc/zoneinfo not available: 2.6.13 / Aug 2005
+    free = mems[b'MemFree:']
+    fallback = free + mems.get(b"Cached:", 0)
+    try:
+        lru_active_file = mems[b'Active(file):']
+        lru_inactive_file = mems[b'Inactive(file):']
+        slab_reclaimable = mems[b'SReclaimable:']
+    except KeyError:
+        return fallback
+    try:
+        f = open_binary('%s/zoneinfo' % get_procfs_path())
+    except IOError:
+        return fallback  # kernel 2.6.13
+
+    watermark_low = 0
+    with f:
+        for line in f:
+            line = line.strip()
+            if line.startswith(b'low'):
+                watermark_low += int(line.split()[1])
+    watermark_low *= PAGESIZE
+
+    avail = free - watermark_low
+    pagecache = lru_active_file + lru_inactive_file
+    pagecache -= min(pagecache / 2, watermark_low)
+    avail += pagecache
+    avail += slab_reclaimable - min(slab_reclaimable / 2.0, watermark_low)
+    return int(avail)
+
+
+def virtual_memory():
+    """Report virtual memory stats.
+    This implementation matches "free" and "vmstat -s" cmdline
+    utility values and procps-ng-3.3.12 source was used as a reference
+    (2016-09-18):
+    https://gitlab.com/procps-ng/procps/blob/
+        24fd2605c51fccc375ab0287cec33aa767f06718/proc/sysinfo.c
+    For reference, procps-ng-3.3.10 is the version available on Ubuntu
+    16.04.
+
+    Note about "available" memory: up until psutil 4.3 it was
+    calculated as "avail = (free + buffers + cached)". Now
+    "MemAvailable:" column (kernel 3.14) from /proc/meminfo is used as
+    it's more accurate.
+    That matches "available" column in newer versions of "free".
+    """
+    missing_fields = []
+    mems = {}
+    with open_binary('%s/meminfo' % get_procfs_path()) as f:
+        for line in f:
+            fields = line.split()
+            mems[fields[0]] = int(fields[1]) * 1024
+
+    # /proc doc states that the available fields in /proc/meminfo vary
+    # by architecture and compile options, but these 3 values are also
+    # returned by sysinfo(2); as such we assume they are always there.
+    total = mems[b'MemTotal:']
+    free = mems[b'MemFree:']
+    try:
+        buffers = mems[b'Buffers:']
+    except KeyError:
+        # https://github.com/giampaolo/psutil/issues/1010
+        buffers = 0
+        missing_fields.append('buffers')
+    try:
+        cached = mems[b"Cached:"]
+    except KeyError:
+        cached = 0
+        missing_fields.append('cached')
+    else:
+        # "free" cmdline utility sums reclaimable to cached.
+        # Older versions of procps used to add slab memory instead.
+        # This got changed in:
+        # https://gitlab.com/procps-ng/procps/commit/
+        #     05d751c4f076a2f0118b914c5e51cfbb4762ad8e
+        cached += mems.get(b"SReclaimable:", 0)  # since kernel 2.6.19
+
+    try:
+        shared = mems[b'Shmem:']  # since kernel 2.6.32
+    except KeyError:
+        try:
+            shared = mems[b'MemShared:']  # kernels 2.4
+        except KeyError:
+            shared = 0
+            missing_fields.append('shared')
+
+    try:
+        active = mems[b"Active:"]
+    except KeyError:
+        active = 0
+        missing_fields.append('active')
+
+    try:
+        inactive = mems[b"Inactive:"]
+    except KeyError:
+        try:
+            inactive = \
+                mems[b"Inact_dirty:"] + \
+                mems[b"Inact_clean:"] + \
+                mems[b"Inact_laundry:"]
+        except KeyError:
+            inactive = 0
+            missing_fields.append('inactive')
+
+    try:
+        slab = mems[b"Slab:"]
+    except KeyError:
+        slab = 0
+
+    used = total - free - cached - buffers
+    if used < 0:
+        # May be symptomatic of running within a LCX container where such
+        # values will be dramatically distorted over those of the host.
+        used = total - free
+
+    # - starting from 4.4.0 we match free's "available" column.
+    #   Before 4.4.0 we calculated it as (free + buffers + cached)
+    #   which matched htop.
+    # - free and htop available memory differs as per:
+    #   http://askubuntu.com/a/369589
+    #   http://unix.stackexchange.com/a/65852/168884
+    # - MemAvailable has been introduced in kernel 3.14
+    try:
+        avail = mems[b'MemAvailable:']
+    except KeyError:
+        avail = calculate_avail_vmem(mems)
+
+    if avail < 0:
+        avail = 0
+        missing_fields.append('available')
+
+    # If avail is greater than total or our calculation overflows,
+    # that's symptomatic of running within a LCX container where such
+    # values will be dramatically distorted over those of the host.
+    # https://gitlab.com/procps-ng/procps/blob/
+    #     24fd2605c51fccc375ab0287cec33aa767f06718/proc/sysinfo.c#L764
+    if avail > total:
+        avail = free
+
+    percent = usage_percent((total - avail), total, round_=1)
+
+    # Warn about missing metrics which are set to 0.
+    if missing_fields:
+        msg = "%s memory stats couldn't be determined and %s set to 0" % (
+            ", ".join(missing_fields),
+            "was" if len(missing_fields) == 1 else "were")
+        warnings.warn(msg, RuntimeWarning)
+
+    return svmem(total, avail, percent, used, free,
+                 active, inactive, buffers, cached, shared, slab)
+
+
+def swap_memory():
+    """Return swap memory metrics."""
+    mems = {}
+    with open_binary('%s/meminfo' % get_procfs_path()) as f:
+        for line in f:
+            fields = line.split()
+            mems[fields[0]] = int(fields[1]) * 1024
+    # We prefer /proc/meminfo over sysinfo() syscall so that
+    # psutil.PROCFS_PATH can be used in order to allow retrieval
+    # for linux containers, see:
+    # https://github.com/giampaolo/psutil/issues/1015
+    try:
+        total = mems[b'SwapTotal:']
+        free = mems[b'SwapFree:']
+    except KeyError:
+        _, _, _, _, total, free, unit_multiplier = cext.linux_sysinfo()
+        total *= unit_multiplier
+        free *= unit_multiplier
+
+    used = total - free
+    percent = usage_percent(used, total, round_=1)
+    # get pgin/pgouts
+    try:
+        f = open_binary("%s/vmstat" % get_procfs_path())
+    except IOError as err:
+        # see https://github.com/giampaolo/psutil/issues/722
+        msg = "'sin' and 'sout' swap memory stats couldn't " \
+              "be determined and were set to 0 (%s)" % str(err)
+        warnings.warn(msg, RuntimeWarning)
+        sin = sout = 0
+    else:
+        with f:
+            sin = sout = None
+            for line in f:
+                # values are expressed in 4 kilo bytes, we want
+                # bytes instead
+                if line.startswith(b'pswpin'):
+                    sin = int(line.split(b' ')[1]) * 4 * 1024
+                elif line.startswith(b'pswpout'):
+                    sout = int(line.split(b' ')[1]) * 4 * 1024
+                if sin is not None and sout is not None:
+                    break
+            else:
+                # we might get here when dealing with exotic Linux
+                # flavors, see:
+                # https://github.com/giampaolo/psutil/issues/313
+                msg = "'sin' and 'sout' swap memory stats couldn't " \
+                      "be determined and were set to 0"
+                warnings.warn(msg, RuntimeWarning)
+                sin = sout = 0
+    return _common.sswap(total, used, free, percent, sin, sout)
+
+
+# =====================================================================
+# --- CPU
+# =====================================================================
+
+
+def cpu_times():
+    """Return a named tuple representing the following system-wide
+    CPU times:
+    (user, nice, system, idle, iowait, irq, softirq [steal, [guest,
+     [guest_nice]]])
+    Last 3 fields may not be available on all Linux kernel versions.
+    """
+    procfs_path = get_procfs_path()
+    set_scputimes_ntuple(procfs_path)
+    with open_binary('%s/stat' % procfs_path) as f:
+        values = f.readline().split()
+    fields = values[1:len(scputimes._fields) + 1]
+    fields = [float(x) / CLOCK_TICKS for x in fields]
+    return scputimes(*fields)
+
+
+def per_cpu_times():
+    """Return a list of namedtuple representing the CPU times
+    for every CPU available on the system.
+    """
+    procfs_path = get_procfs_path()
+    set_scputimes_ntuple(procfs_path)
+    cpus = []
+    with open_binary('%s/stat' % procfs_path) as f:
+        # get rid of the first line which refers to system wide CPU stats
+        f.readline()
+        for line in f:
+            if line.startswith(b'cpu'):
+                values = line.split()
+                fields = values[1:len(scputimes._fields) + 1]
+                fields = [float(x) / CLOCK_TICKS for x in fields]
+                entry = scputimes(*fields)
+                cpus.append(entry)
+        return cpus
+
+
+def cpu_count_logical():
+    """Return the number of logical CPUs in the system."""
+    try:
+        return os.sysconf("SC_NPROCESSORS_ONLN")
+    except ValueError:
+        # as a second fallback we try to parse /proc/cpuinfo
+        num = 0
+        with open_binary('%s/cpuinfo' % get_procfs_path()) as f:
+            for line in f:
+                if line.lower().startswith(b'processor'):
+                    num += 1
+
+        # unknown format (e.g. amrel/sparc architectures), see:
+        # https://github.com/giampaolo/psutil/issues/200
+        # try to parse /proc/stat as a last resort
+        if num == 0:
+            search = re.compile(r'cpu\d')
+            with open_text('%s/stat' % get_procfs_path()) as f:
+                for line in f:
+                    line = line.split(' ')[0]
+                    if search.match(line):
+                        num += 1
+
+        if num == 0:
+            # mimic os.cpu_count()
+            return None
+        return num
+
+
+def cpu_count_cores():
+    """Return the number of CPU cores in the system."""
+    # Method #1
+    ls = set()
+    # These 2 files are the same but */core_cpus_list is newer while
+    # */thread_siblings_list is deprecated and may disappear in the future.
+    # https://www.kernel.org/doc/Documentation/admin-guide/cputopology.rst
+    # https://github.com/giampaolo/psutil/pull/1727#issuecomment-707624964
+    # https://lkml.org/lkml/2019/2/26/41
+    p1 = "/sys/devices/system/cpu/cpu[0-9]*/topology/core_cpus_list"
+    p2 = "/sys/devices/system/cpu/cpu[0-9]*/topology/thread_siblings_list"
+    for path in glob.glob(p1) or glob.glob(p2):
+        with open_binary(path) as f:
+            ls.add(f.read().strip())
+    result = len(ls)
+    if result != 0:
+        return result
+
+    # Method #2
+    mapping = {}
+    current_info = {}
+    with open_binary('%s/cpuinfo' % get_procfs_path()) as f:
+        for line in f:
+            line = line.strip().lower()
+            if not line:
+                # new section
+                try:
+                    mapping[current_info[b'physical id']] = \
+                        current_info[b'cpu cores']
+                except KeyError:
+                    pass
+                current_info = {}
+            else:
+                # ongoing section
+                if line.startswith((b'physical id', b'cpu cores')):
+                    key, value = line.split(b'\t:', 1)
+                    current_info[key] = int(value)
+
+    result = sum(mapping.values())
+    return result or None  # mimic os.cpu_count()
+
+
+def cpu_stats():
+    """Return various CPU stats as a named tuple."""
+    with open_binary('%s/stat' % get_procfs_path()) as f:
+        ctx_switches = None
+        interrupts = None
+        soft_interrupts = None
+        for line in f:
+            if line.startswith(b'ctxt'):
+                ctx_switches = int(line.split()[1])
+            elif line.startswith(b'intr'):
+                interrupts = int(line.split()[1])
+            elif line.startswith(b'softirq'):
+                soft_interrupts = int(line.split()[1])
+            if ctx_switches is not None and soft_interrupts is not None \
+                    and interrupts is not None:
+                break
+    syscalls = 0
+    return _common.scpustats(
+        ctx_switches, interrupts, soft_interrupts, syscalls)
+
+
+def _cpu_get_cpuinfo_freq():
+    """Return current CPU frequency from cpuinfo if available.
+    """
+    ret = []
+    with open_binary('%s/cpuinfo' % get_procfs_path()) as f:
+        for line in f:
+            if line.lower().startswith(b'cpu mhz'):
+                ret.append(float(line.split(b':', 1)[1]))
+    return ret
+
+
+if os.path.exists("/sys/devices/system/cpu/cpufreq/policy0") or \
+        os.path.exists("/sys/devices/system/cpu/cpu0/cpufreq"):
+    def cpu_freq():
+        """Return frequency metrics for all CPUs.
+        Contrarily to other OSes, Linux updates these values in
+        real-time.
+        """
+        cpuinfo_freqs = _cpu_get_cpuinfo_freq()
+        paths = \
+            glob.glob("/sys/devices/system/cpu/cpufreq/policy[0-9]*") or \
+            glob.glob("/sys/devices/system/cpu/cpu[0-9]*/cpufreq")
+        paths.sort(key=lambda x: int(re.search(r"[0-9]+", x).group()))
+        ret = []
+        pjoin = os.path.join
+        for i, path in enumerate(paths):
+            if len(paths) == len(cpuinfo_freqs):
+                # take cached value from cpuinfo if available, see:
+                # https://github.com/giampaolo/psutil/issues/1851
+                curr = cpuinfo_freqs[i]
+            else:
+                curr = cat(pjoin(path, "scaling_cur_freq"), fallback=None)
+            if curr is None:
+                # Likely an old RedHat, see:
+                # https://github.com/giampaolo/psutil/issues/1071
+                curr = cat(pjoin(path, "cpuinfo_cur_freq"), fallback=None)
+                if curr is None:
+                    raise NotImplementedError(
+                        "can't find current frequency file")
+            curr = int(curr) / 1000
+            max_ = int(cat(pjoin(path, "scaling_max_freq"))) / 1000
+            min_ = int(cat(pjoin(path, "scaling_min_freq"))) / 1000
+            ret.append(_common.scpufreq(curr, min_, max_))
+        return ret
+
+else:
+    def cpu_freq():
+        """Alternate implementation using /proc/cpuinfo.
+        min and max frequencies are not available and are set to None.
+        """
+        return [_common.scpufreq(x, 0., 0.) for x in _cpu_get_cpuinfo_freq()]
+
+
+# =====================================================================
+# --- network
+# =====================================================================
+
+
+net_if_addrs = cext_posix.net_if_addrs
+
+
+class _Ipv6UnsupportedError(Exception):
+    pass
+
+
+class Connections:
+    """A wrapper on top of /proc/net/* files, retrieving per-process
+    and system-wide open connections (TCP, UDP, UNIX) similarly to
+    "netstat -an".
+
+    Note: in case of UNIX sockets we're only able to determine the
+    local endpoint/path, not the one it's connected to.
+    According to [1] it would be possible but not easily.
+
+    [1] http://serverfault.com/a/417946
+    """
+
+    def __init__(self):
+        # The string represents the basename of the corresponding
+        # /proc/net/{proto_name} file.
+        tcp4 = ("tcp", socket.AF_INET, socket.SOCK_STREAM)
+        tcp6 = ("tcp6", socket.AF_INET6, socket.SOCK_STREAM)
+        udp4 = ("udp", socket.AF_INET, socket.SOCK_DGRAM)
+        udp6 = ("udp6", socket.AF_INET6, socket.SOCK_DGRAM)
+        unix = ("unix", socket.AF_UNIX, None)
+        self.tmap = {
+            "all": (tcp4, tcp6, udp4, udp6, unix),
+            "tcp": (tcp4, tcp6),
+            "tcp4": (tcp4,),
+            "tcp6": (tcp6,),
+            "udp": (udp4, udp6),
+            "udp4": (udp4,),
+            "udp6": (udp6,),
+            "unix": (unix,),
+            "inet": (tcp4, tcp6, udp4, udp6),
+            "inet4": (tcp4, udp4),
+            "inet6": (tcp6, udp6),
+        }
+        self._procfs_path = None
+
+    def get_proc_inodes(self, pid):
+        inodes = defaultdict(list)
+        for fd in os.listdir("%s/%s/fd" % (self._procfs_path, pid)):
+            try:
+                inode = readlink("%s/%s/fd/%s" % (self._procfs_path, pid, fd))
+            except (FileNotFoundError, ProcessLookupError):
+                # ENOENT == file which is gone in the meantime;
+                # os.stat('/proc/%s' % self.pid) will be done later
+                # to force NSP (if it's the case)
+                continue
+            except OSError as err:
+                if err.errno == errno.EINVAL:
+                    # not a link
+                    continue
+                if err.errno == errno.ENAMETOOLONG:
+                    # file name too long
+                    debug(err)
+                    continue
+                raise
+            else:
+                if inode.startswith('socket:['):
+                    # the process is using a socket
+                    inode = inode[8:][:-1]
+                    inodes[inode].append((pid, int(fd)))
+        return inodes
+
+    def get_all_inodes(self):
+        inodes = {}
+        for pid in pids():
+            try:
+                inodes.update(self.get_proc_inodes(pid))
+            except (FileNotFoundError, ProcessLookupError, PermissionError):
+                # os.listdir() is gonna raise a lot of access denied
+                # exceptions in case of unprivileged user; that's fine
+                # as we'll just end up returning a connection with PID
+                # and fd set to None anyway.
+                # Both netstat -an and lsof does the same so it's
+                # unlikely we can do any better.
+                # ENOENT just means a PID disappeared on us.
+                continue
+        return inodes
+
+    @staticmethod
+    def decode_address(addr, family):
+        """Accept an "ip:port" address as displayed in /proc/net/*
+        and convert it into a human readable form, like:
+
+        "0500000A:0016" -> ("10.0.0.5", 22)
+        "0000000000000000FFFF00000100007F:9E49" -> ("::ffff:127.0.0.1", 40521)
+
+        The IP address portion is a little or big endian four-byte
+        hexadecimal number; that is, the least significant byte is listed
+        first, so we need to reverse the order of the bytes to convert it
+        to an IP address.
+        The port is represented as a two-byte hexadecimal number.
+
+        Reference:
+        http://linuxdevcenter.com/pub/a/linux/2000/11/16/LinuxAdmin.html
+        """
+        ip, port = addr.split(':')
+        port = int(port, 16)
+        # this usually refers to a local socket in listen mode with
+        # no end-points connected
+        if not port:
+            return ()
+        if PY3:
+            ip = ip.encode('ascii')
+        if family == socket.AF_INET:
+            # see: https://github.com/giampaolo/psutil/issues/201
+            if LITTLE_ENDIAN:
+                ip = socket.inet_ntop(family, base64.b16decode(ip)[::-1])
+            else:
+                ip = socket.inet_ntop(family, base64.b16decode(ip))
+        else:  # IPv6
+            ip = base64.b16decode(ip)
+            try:
+                # see: https://github.com/giampaolo/psutil/issues/201
+                if LITTLE_ENDIAN:
+                    ip = socket.inet_ntop(
+                        socket.AF_INET6,
+                        struct.pack('>4I', *struct.unpack('<4I', ip)))
+                else:
+                    ip = socket.inet_ntop(
+                        socket.AF_INET6,
+                        struct.pack('<4I', *struct.unpack('<4I', ip)))
+            except ValueError:
+                # see: https://github.com/giampaolo/psutil/issues/623
+                if not supports_ipv6():
+                    raise _Ipv6UnsupportedError
+                else:
+                    raise
+        return _common.addr(ip, port)
+
+    @staticmethod
+    def process_inet(file, family, type_, inodes, filter_pid=None):
+        """Parse /proc/net/tcp* and /proc/net/udp* files."""
+        if file.endswith('6') and not os.path.exists(file):
+            # IPv6 not supported
+            return
+        with open_text(file, buffering=BIGFILE_BUFFERING) as f:
+            f.readline()  # skip the first line
+            for lineno, line in enumerate(f, 1):
+                try:
+                    _, laddr, raddr, status, _, _, _, _, _, inode = \
+                        line.split()[:10]
+                except ValueError:
+                    raise RuntimeError(
+                        "error while parsing %s; malformed line %s %r" % (
+                            file, lineno, line))
+                if inode in inodes:
+                    # # We assume inet sockets are unique, so we error
+                    # # out if there are multiple references to the
+                    # # same inode. We won't do this for UNIX sockets.
+                    # if len(inodes[inode]) > 1 and family != socket.AF_UNIX:
+                    #     raise ValueError("ambiguos inode with multiple "
+                    #                      "PIDs references")
+                    pid, fd = inodes[inode][0]
+                else:
+                    pid, fd = None, -1
+                if filter_pid is not None and filter_pid != pid:
+                    continue
+                else:
+                    if type_ == socket.SOCK_STREAM:
+                        status = TCP_STATUSES[status]
+                    else:
+                        status = _common.CONN_NONE
+                    try:
+                        laddr = Connections.decode_address(laddr, family)
+                        raddr = Connections.decode_address(raddr, family)
+                    except _Ipv6UnsupportedError:
+                        continue
+                    yield (fd, family, type_, laddr, raddr, status, pid)
+
+    @staticmethod
+    def process_unix(file, family, inodes, filter_pid=None):
+        """Parse /proc/net/unix files."""
+        with open_text(file, buffering=BIGFILE_BUFFERING) as f:
+            f.readline()  # skip the first line
+            for line in f:
+                tokens = line.split()
+                try:
+                    _, _, _, _, type_, _, inode = tokens[0:7]
+                except ValueError:
+                    if ' ' not in line:
+                        # see: https://github.com/giampaolo/psutil/issues/766
+                        continue
+                    raise RuntimeError(
+                        "error while parsing %s; malformed line %r" % (
+                            file, line))
+                if inode in inodes:
+                    # With UNIX sockets we can have a single inode
+                    # referencing many file descriptors.
+                    pairs = inodes[inode]
+                else:
+                    pairs = [(None, -1)]
+                for pid, fd in pairs:
+                    if filter_pid is not None and filter_pid != pid:
+                        continue
+                    else:
+                        if len(tokens) == 8:
+                            path = tokens[-1]
+                        else:
+                            path = ""
+                        type_ = _common.socktype_to_enum(int(type_))
+                        # XXX: determining the remote endpoint of a
+                        # UNIX socket on Linux is not possible, see:
+                        # https://serverfault.com/questions/252723/
+                        raddr = ""
+                        status = _common.CONN_NONE
+                        yield (fd, family, type_, path, raddr, status, pid)
+
+    def retrieve(self, kind, pid=None):
+        if kind not in self.tmap:
+            raise ValueError("invalid %r kind argument; choose between %s"
+                             % (kind, ', '.join([repr(x) for x in self.tmap])))
+        self._procfs_path = get_procfs_path()
+        if pid is not None:
+            inodes = self.get_proc_inodes(pid)
+            if not inodes:
+                # no connections for this process
+                return []
+        else:
+            inodes = self.get_all_inodes()
+        ret = set()
+        for proto_name, family, type_ in self.tmap[kind]:
+            path = "%s/net/%s" % (self._procfs_path, proto_name)
+            if family in (socket.AF_INET, socket.AF_INET6):
+                ls = self.process_inet(
+                    path, family, type_, inodes, filter_pid=pid)
+            else:
+                ls = self.process_unix(
+                    path, family, inodes, filter_pid=pid)
+            for fd, family, type_, laddr, raddr, status, bound_pid in ls:
+                if pid:
+                    conn = _common.pconn(fd, family, type_, laddr, raddr,
+                                         status)
+                else:
+                    conn = _common.sconn(fd, family, type_, laddr, raddr,
+                                         status, bound_pid)
+                ret.add(conn)
+        return list(ret)
+
+
+_connections = Connections()
+
+
+def net_connections(kind='inet'):
+    """Return system-wide open connections."""
+    return _connections.retrieve(kind)
+
+
+def net_io_counters():
+    """Return network I/O statistics for every network interface
+    installed on the system as a dict of raw tuples.
+    """
+    with open_text("%s/net/dev" % get_procfs_path()) as f:
+        lines = f.readlines()
+    retdict = {}
+    for line in lines[2:]:
+        colon = line.rfind(':')
+        assert colon > 0, repr(line)
+        name = line[:colon].strip()
+        fields = line[colon + 1:].strip().split()
+
+        # in
+        (bytes_recv,
+         packets_recv,
+         errin,
+         dropin,
+         fifoin,  # unused
+         framein,  # unused
+         compressedin,  # unused
+         multicastin,  # unused
+         # out
+         bytes_sent,
+         packets_sent,
+         errout,
+         dropout,
+         fifoout,  # unused
+         collisionsout,  # unused
+         carrierout,  # unused
+         compressedout) = map(int, fields)
+
+        retdict[name] = (bytes_sent, bytes_recv, packets_sent, packets_recv,
+                         errin, errout, dropin, dropout)
+    return retdict
+
+
+def net_if_stats():
+    """Get NIC stats (isup, duplex, speed, mtu)."""
+    duplex_map = {cext.DUPLEX_FULL: NIC_DUPLEX_FULL,
+                  cext.DUPLEX_HALF: NIC_DUPLEX_HALF,
+                  cext.DUPLEX_UNKNOWN: NIC_DUPLEX_UNKNOWN}
+    names = net_io_counters().keys()
+    ret = {}
+    for name in names:
+        try:
+            mtu = cext_posix.net_if_mtu(name)
+            isup = cext_posix.net_if_is_running(name)
+            duplex, speed = cext.net_if_duplex_speed(name)
+        except OSError as err:
+            # https://github.com/giampaolo/psutil/issues/1279
+            if err.errno != errno.ENODEV:
+                raise
+            else:
+                debug(err)
+        else:
+            ret[name] = _common.snicstats(isup, duplex_map[duplex], speed, mtu)
+    return ret
+
+
+# =====================================================================
+# --- disks
+# =====================================================================
+
+
+disk_usage = _psposix.disk_usage
+
+
+def disk_io_counters(perdisk=False):
+    """Return disk I/O statistics for every disk installed on the
+    system as a dict of raw tuples.
+    """
+    def read_procfs():
+        # OK, this is a bit confusing. The format of /proc/diskstats can
+        # have 3 variations.
+        # On Linux 2.4 each line has always 15 fields, e.g.:
+        # "3     0   8 hda 8 8 8 8 8 8 8 8 8 8 8"
+        # On Linux 2.6+ each line *usually* has 14 fields, and the disk
+        # name is in another position, like this:
+        # "3    0   hda 8 8 8 8 8 8 8 8 8 8 8"
+        # ...unless (Linux 2.6) the line refers to a partition instead
+        # of a disk, in which case the line has less fields (7):
+        # "3    1   hda1 8 8 8 8"
+        # 4.18+ has 4 fields added:
+        # "3    0   hda 8 8 8 8 8 8 8 8 8 8 8 0 0 0 0"
+        # 5.5 has 2 more fields.
+        # See:
+        # https://www.kernel.org/doc/Documentation/iostats.txt
+        # https://www.kernel.org/doc/Documentation/ABI/testing/procfs-diskstats
+        with open_text("%s/diskstats" % get_procfs_path()) as f:
+            lines = f.readlines()
+        for line in lines:
+            fields = line.split()
+            flen = len(fields)
+            if flen == 15:
+                # Linux 2.4
+                name = fields[3]
+                reads = int(fields[2])
+                (reads_merged, rbytes, rtime, writes, writes_merged,
+                    wbytes, wtime, _, busy_time, _) = map(int, fields[4:14])
+            elif flen == 14 or flen >= 18:
+                # Linux 2.6+, line referring to a disk
+                name = fields[2]
+                (reads, reads_merged, rbytes, rtime, writes, writes_merged,
+                    wbytes, wtime, _, busy_time, _) = map(int, fields[3:14])
+            elif flen == 7:
+                # Linux 2.6+, line referring to a partition
+                name = fields[2]
+                reads, rbytes, writes, wbytes = map(int, fields[3:])
+                rtime = wtime = reads_merged = writes_merged = busy_time = 0
+            else:
+                raise ValueError("not sure how to interpret line %r" % line)
+            yield (name, reads, writes, rbytes, wbytes, rtime, wtime,
+                   reads_merged, writes_merged, busy_time)
+
+    def read_sysfs():
+        for block in os.listdir('/sys/block'):
+            for root, _, files in os.walk(os.path.join('/sys/block', block)):
+                if 'stat' not in files:
+                    continue
+                with open_text(os.path.join(root, 'stat')) as f:
+                    fields = f.read().strip().split()
+                name = os.path.basename(root)
+                (reads, reads_merged, rbytes, rtime, writes, writes_merged,
+                    wbytes, wtime, _, busy_time) = map(int, fields[:10])
+                yield (name, reads, writes, rbytes, wbytes, rtime,
+                       wtime, reads_merged, writes_merged, busy_time)
+
+    if os.path.exists('%s/diskstats' % get_procfs_path()):
+        gen = read_procfs()
+    elif os.path.exists('/sys/block'):
+        gen = read_sysfs()
+    else:
+        raise NotImplementedError(
+            "%s/diskstats nor /sys/block filesystem are available on this "
+            "system" % get_procfs_path())
+
+    retdict = {}
+    for entry in gen:
+        (name, reads, writes, rbytes, wbytes, rtime, wtime, reads_merged,
+            writes_merged, busy_time) = entry
+        if not perdisk and not is_storage_device(name):
+            # perdisk=False means we want to calculate totals so we skip
+            # partitions (e.g. 'sda1', 'nvme0n1p1') and only include
+            # base disk devices (e.g. 'sda', 'nvme0n1'). Base disks
+            # include a total of all their partitions + some extra size
+            # of their own:
+            #     $ cat /proc/diskstats
+            #     259       0 sda 10485760 ...
+            #     259       1 sda1 5186039 ...
+            #     259       1 sda2 5082039 ...
+            # See:
+            # https://github.com/giampaolo/psutil/pull/1313
+            continue
+
+        rbytes *= DISK_SECTOR_SIZE
+        wbytes *= DISK_SECTOR_SIZE
+        retdict[name] = (reads, writes, rbytes, wbytes, rtime, wtime,
+                         reads_merged, writes_merged, busy_time)
+
+    return retdict
+
+
+class RootFsDeviceFinder:
+    """disk_partitions() may return partitions with device == "/dev/root"
+    or "rootfs". This container class uses different strategies to try to
+    obtain the real device path. Resources:
+    https://bootlin.com/blog/find-root-device/
+    https://www.systutorials.com/how-to-find-the-disk-where-root-is-on-in-bash-on-linux/
+    """
+    __slots__ = ['major', 'minor']
+
+    def __init__(self):
+        dev = os.stat("/").st_dev
+        self.major = os.major(dev)
+        self.minor = os.minor(dev)
+
+    def ask_proc_partitions(self):
+        with open_text("%s/partitions" % get_procfs_path()) as f:
+            for line in f.readlines()[2:]:
+                fields = line.split()
+                if len(fields) < 4:  # just for extra safety
+                    continue
+                major = int(fields[0]) if fields[0].isdigit() else None
+                minor = int(fields[1]) if fields[1].isdigit() else None
+                name = fields[3]
+                if major == self.major and minor == self.minor:
+                    if name:  # just for extra safety
+                        return "/dev/%s" % name
+
+    def ask_sys_dev_block(self):
+        path = "/sys/dev/block/%s:%s/uevent" % (self.major, self.minor)
+        with open_text(path) as f:
+            for line in f:
+                if line.startswith("DEVNAME="):
+                    name = line.strip().rpartition("DEVNAME=")[2]
+                    if name:  # just for extra safety
+                        return "/dev/%s" % name
+
+    def ask_sys_class_block(self):
+        needle = "%s:%s" % (self.major, self.minor)
+        files = glob.iglob("/sys/class/block/*/dev")
+        for file in files:
+            try:
+                f = open_text(file)
+            except FileNotFoundError:  # race condition
+                continue
+            else:
+                with f:
+                    data = f.read().strip()
+                    if data == needle:
+                        name = os.path.basename(os.path.dirname(file))
+                        return "/dev/%s" % name
+
+    def find(self):
+        path = None
+        if path is None:
+            try:
+                path = self.ask_proc_partitions()
+            except (IOError, OSError) as err:
+                debug(err)
+        if path is None:
+            try:
+                path = self.ask_sys_dev_block()
+            except (IOError, OSError) as err:
+                debug(err)
+        if path is None:
+            try:
+                path = self.ask_sys_class_block()
+            except (IOError, OSError) as err:
+                debug(err)
+        # We use exists() because the "/dev/*" part of the path is hard
+        # coded, so we want to be sure.
+        if path is not None and os.path.exists(path):
+            return path
+
+
+def disk_partitions(all=False):
+    """Return mounted disk partitions as a list of namedtuples."""
+    fstypes = set()
+    procfs_path = get_procfs_path()
+    with open_text("%s/filesystems" % procfs_path) as f:
+        for line in f:
+            line = line.strip()
+            if not line.startswith("nodev"):
+                fstypes.add(line.strip())
+            else:
+                # ignore all lines starting with "nodev" except "nodev zfs"
+                fstype = line.split("\t")[1]
+                if fstype == "zfs":
+                    fstypes.add("zfs")
+
+    # See: https://github.com/giampaolo/psutil/issues/1307
+    if procfs_path == "/proc" and os.path.isfile('/etc/mtab'):
+        mounts_path = os.path.realpath("/etc/mtab")
+    else:
+        mounts_path = os.path.realpath("%s/self/mounts" % procfs_path)
+
+    retlist = []
+    partitions = cext.disk_partitions(mounts_path)
+    for partition in partitions:
+        device, mountpoint, fstype, opts = partition
+        if device == 'none':
+            device = ''
+        if device in ("/dev/root", "rootfs"):
+            device = RootFsDeviceFinder().find() or device
+        if not all:
+            if device == '' or fstype not in fstypes:
+                continue
+        maxfile = maxpath = None  # set later
+        ntuple = _common.sdiskpart(device, mountpoint, fstype, opts,
+                                   maxfile, maxpath)
+        retlist.append(ntuple)
+
+    return retlist
+
+
+# =====================================================================
+# --- sensors
+# =====================================================================
+
+
+def sensors_temperatures():
+    """Return hardware (CPU and others) temperatures as a dict
+    including hardware name, label, current, max and critical
+    temperatures.
+
+    Implementation notes:
+    - /sys/class/hwmon looks like the most recent interface to
+      retrieve this info, and this implementation relies on it
+      only (old distros will probably use something else)
+    - lm-sensors on Ubuntu 16.04 relies on /sys/class/hwmon
+    - /sys/class/thermal/thermal_zone* is another one but it's more
+      difficult to parse
+    """
+    ret = collections.defaultdict(list)
+    basenames = glob.glob('/sys/class/hwmon/hwmon*/temp*_*')
+    # CentOS has an intermediate /device directory:
+    # https://github.com/giampaolo/psutil/issues/971
+    # https://github.com/nicolargo/glances/issues/1060
+    basenames.extend(glob.glob('/sys/class/hwmon/hwmon*/device/temp*_*'))
+    basenames = sorted(set([x.split('_')[0] for x in basenames]))
+
+    # Only add the coretemp hwmon entries if they're not already in
+    # /sys/class/hwmon/
+    # https://github.com/giampaolo/psutil/issues/1708
+    # https://github.com/giampaolo/psutil/pull/1648
+    basenames2 = glob.glob(
+        '/sys/devices/platform/coretemp.*/hwmon/hwmon*/temp*_*')
+    repl = re.compile('/sys/devices/platform/coretemp.*/hwmon/')
+    for name in basenames2:
+        altname = repl.sub('/sys/class/hwmon/', name)
+        if altname not in basenames:
+            basenames.append(name)
+
+    for base in basenames:
+        try:
+            path = base + '_input'
+            current = float(cat(path)) / 1000.0
+            path = os.path.join(os.path.dirname(base), 'name')
+            unit_name = cat(path, binary=False)
+        except (IOError, OSError, ValueError):
+            # A lot of things can go wrong here, so let's just skip the
+            # whole entry. Sure thing is Linux's /sys/class/hwmon really
+            # is a stinky broken mess.
+            # https://github.com/giampaolo/psutil/issues/1009
+            # https://github.com/giampaolo/psutil/issues/1101
+            # https://github.com/giampaolo/psutil/issues/1129
+            # https://github.com/giampaolo/psutil/issues/1245
+            # https://github.com/giampaolo/psutil/issues/1323
+            continue
+
+        high = cat(base + '_max', fallback=None)
+        critical = cat(base + '_crit', fallback=None)
+        label = cat(base + '_label', fallback='', binary=False)
+
+        if high is not None:
+            try:
+                high = float(high) / 1000.0
+            except ValueError:
+                high = None
+        if critical is not None:
+            try:
+                critical = float(critical) / 1000.0
+            except ValueError:
+                critical = None
+
+        ret[unit_name].append((label, current, high, critical))
+
+    # Indication that no sensors were detected in /sys/class/hwmon/
+    if not basenames:
+        basenames = glob.glob('/sys/class/thermal/thermal_zone*')
+        basenames = sorted(set(basenames))
+
+        for base in basenames:
+            try:
+                path = os.path.join(base, 'temp')
+                current = float(cat(path)) / 1000.0
+                path = os.path.join(base, 'type')
+                unit_name = cat(path, binary=False)
+            except (IOError, OSError, ValueError) as err:
+                debug(err)
+                continue
+
+            trip_paths = glob.glob(base + '/trip_point*')
+            trip_points = set(['_'.join(
+                os.path.basename(p).split('_')[0:3]) for p in trip_paths])
+            critical = None
+            high = None
+            for trip_point in trip_points:
+                path = os.path.join(base, trip_point + "_type")
+                trip_type = cat(path, fallback='', binary=False)
+                if trip_type == 'critical':
+                    critical = cat(os.path.join(base, trip_point + "_temp"),
+                                   fallback=None)
+                elif trip_type == 'high':
+                    high = cat(os.path.join(base, trip_point + "_temp"),
+                               fallback=None)
+
+                if high is not None:
+                    try:
+                        high = float(high) / 1000.0
+                    except ValueError:
+                        high = None
+                if critical is not None:
+                    try:
+                        critical = float(critical) / 1000.0
+                    except ValueError:
+                        critical = None
+
+            ret[unit_name].append(('', current, high, critical))
+
+    return dict(ret)
+
+
+def sensors_fans():
+    """Return hardware fans info (for CPU and other peripherals) as a
+    dict including hardware label and current speed.
+
+    Implementation notes:
+    - /sys/class/hwmon looks like the most recent interface to
+      retrieve this info, and this implementation relies on it
+      only (old distros will probably use something else)
+    - lm-sensors on Ubuntu 16.04 relies on /sys/class/hwmon
+    """
+    ret = collections.defaultdict(list)
+    basenames = glob.glob('/sys/class/hwmon/hwmon*/fan*_*')
+    if not basenames:
+        # CentOS has an intermediate /device directory:
+        # https://github.com/giampaolo/psutil/issues/971
+        basenames = glob.glob('/sys/class/hwmon/hwmon*/device/fan*_*')
+
+    basenames = sorted(set([x.split('_')[0] for x in basenames]))
+    for base in basenames:
+        try:
+            current = int(cat(base + '_input'))
+        except (IOError, OSError) as err:
+            debug(err)
+            continue
+        unit_name = cat(os.path.join(os.path.dirname(base), 'name'),
+                        binary=False)
+        label = cat(base + '_label', fallback='', binary=False)
+        ret[unit_name].append(_common.sfan(label, current))
+
+    return dict(ret)
+
+
+def sensors_battery():
+    """Return battery information.
+    Implementation note: it appears /sys/class/power_supply/BAT0/
+    directory structure may vary and provide files with the same
+    meaning but under different names, see:
+    https://github.com/giampaolo/psutil/issues/966
+    """
+    null = object()
+
+    def multi_cat(*paths):
+        """Attempt to read the content of multiple files which may
+        not exist. If none of them exist return None.
+        """
+        for path in paths:
+            ret = cat(path, fallback=null)
+            if ret != null:
+                try:
+                    return int(ret)
+                except ValueError:
+                    return ret
+        return None
+
+    bats = [x for x in os.listdir(POWER_SUPPLY_PATH) if x.startswith('BAT') or
+            'battery' in x.lower()]
+    if not bats:
+        return None
+    # Get the first available battery. Usually this is "BAT0", except
+    # some rare exceptions:
+    # https://github.com/giampaolo/psutil/issues/1238
+    root = os.path.join(POWER_SUPPLY_PATH, sorted(bats)[0])
+
+    # Base metrics.
+    energy_now = multi_cat(
+        root + "/energy_now",
+        root + "/charge_now")
+    power_now = multi_cat(
+        root + "/power_now",
+        root + "/current_now")
+    energy_full = multi_cat(
+        root + "/energy_full",
+        root + "/charge_full")
+    time_to_empty = multi_cat(root + "/time_to_empty_now")
+
+    # Percent. If we have energy_full the percentage will be more
+    # accurate compared to reading /capacity file (float vs. int).
+    if energy_full is not None and energy_now is not None:
+        try:
+            percent = 100.0 * energy_now / energy_full
+        except ZeroDivisionError:
+            percent = 0.0
+    else:
+        percent = int(cat(root + "/capacity", fallback=-1))
+        if percent == -1:
+            return None
+
+    # Is AC power cable plugged in?
+    # Note: AC0 is not always available and sometimes (e.g. CentOS7)
+    # it's called "AC".
+    power_plugged = None
+    online = multi_cat(
+        os.path.join(POWER_SUPPLY_PATH, "AC0/online"),
+        os.path.join(POWER_SUPPLY_PATH, "AC/online"))
+    if online is not None:
+        power_plugged = online == 1
+    else:
+        status = cat(root + "/status", fallback="", binary=False).lower()
+        if status == "discharging":
+            power_plugged = False
+        elif status in ("charging", "full"):
+            power_plugged = True
+
+    # Seconds left.
+    # Note to self: we may also calculate the charging ETA as per:
+    # https://github.com/thialfihar/dotfiles/blob/
+    #     013937745fd9050c30146290e8f963d65c0179e6/bin/battery.py#L55
+    if power_plugged:
+        secsleft = _common.POWER_TIME_UNLIMITED
+    elif energy_now is not None and power_now is not None:
+        try:
+            secsleft = int(energy_now / power_now * 3600)
+        except ZeroDivisionError:
+            secsleft = _common.POWER_TIME_UNKNOWN
+    elif time_to_empty is not None:
+        secsleft = int(time_to_empty * 60)
+        if secsleft < 0:
+            secsleft = _common.POWER_TIME_UNKNOWN
+    else:
+        secsleft = _common.POWER_TIME_UNKNOWN
+
+    return _common.sbattery(percent, secsleft, power_plugged)
+
+
+# =====================================================================
+# --- other system functions
+# =====================================================================
+
+
+def users():
+    """Return currently connected users as a list of namedtuples."""
+    retlist = []
+    rawlist = cext.users()
+    for item in rawlist:
+        user, tty, hostname, tstamp, user_process, pid = item
+        # note: the underlying C function includes entries about
+        # system boot, run level and others.  We might want
+        # to use them in the future.
+        if not user_process:
+            continue
+        if hostname in (':0.0', ':0'):
+            hostname = 'localhost'
+        nt = _common.suser(user, tty or None, hostname, tstamp, pid)
+        retlist.append(nt)
+    return retlist
+
+
+def boot_time():
+    """Return the system boot time expressed in seconds since the epoch."""
+    global BOOT_TIME
+    path = '%s/stat' % get_procfs_path()
+    with open_binary(path) as f:
+        for line in f:
+            if line.startswith(b'btime'):
+                ret = float(line.strip().split()[1])
+                BOOT_TIME = ret
+                return ret
+        raise RuntimeError(
+            "line 'btime' not found in %s" % path)
+
+
+# =====================================================================
+# --- processes
+# =====================================================================
+
+
+def pids():
+    """Returns a list of PIDs currently running on the system."""
+    return [int(x) for x in os.listdir(b(get_procfs_path())) if x.isdigit()]
+
+
+def pid_exists(pid):
+    """Check for the existence of a unix PID. Linux TIDs are not
+    supported (always return False).
+    """
+    if not _psposix.pid_exists(pid):
+        return False
+    else:
+        # Linux's apparently does not distinguish between PIDs and TIDs
+        # (thread IDs).
+        # listdir("/proc") won't show any TID (only PIDs) but
+        # os.stat("/proc/{tid}") will succeed if {tid} exists.
+        # os.kill() can also be passed a TID. This is quite confusing.
+        # In here we want to enforce this distinction and support PIDs
+        # only, see:
+        # https://github.com/giampaolo/psutil/issues/687
+        try:
+            # Note: already checked that this is faster than using a
+            # regular expr. Also (a lot) faster than doing
+            # 'return pid in pids()'
+            path = "%s/%s/status" % (get_procfs_path(), pid)
+            with open_binary(path) as f:
+                for line in f:
+                    if line.startswith(b"Tgid:"):
+                        tgid = int(line.split()[1])
+                        # If tgid and pid are the same then we're
+                        # dealing with a process PID.
+                        return tgid == pid
+                raise ValueError("'Tgid' line not found in %s" % path)
+        except (EnvironmentError, ValueError):
+            return pid in pids()
+
+
+def ppid_map():
+    """Obtain a {pid: ppid, ...} dict for all running processes in
+    one shot. Used to speed up Process.children().
+    """
+    ret = {}
+    procfs_path = get_procfs_path()
+    for pid in pids():
+        try:
+            with open_binary("%s/%s/stat" % (procfs_path, pid)) as f:
+                data = f.read()
+        except (FileNotFoundError, ProcessLookupError):
+            # Note: we should be able to access /stat for all processes
+            # aka it's unlikely we'll bump into EPERM, which is good.
+            pass
+        else:
+            rpar = data.rfind(b')')
+            dset = data[rpar + 2:].split()
+            ppid = int(dset[1])
+            ret[pid] = ppid
+    return ret
+
+
+def wrap_exceptions(fun):
+    """Decorator which translates bare OSError and IOError exceptions
+    into NoSuchProcess and AccessDenied.
+    """
+    @functools.wraps(fun)
+    def wrapper(self, *args, **kwargs):
+        try:
+            return fun(self, *args, **kwargs)
+        except PermissionError:
+            raise AccessDenied(self.pid, self._name)
+        except ProcessLookupError:
+            raise NoSuchProcess(self.pid, self._name)
+        except FileNotFoundError:
+            if not os.path.exists("%s/%s" % (self._procfs_path, self.pid)):
+                raise NoSuchProcess(self.pid, self._name)
+            # Note: zombies will keep existing under /proc until they're
+            # gone so there's no way to distinguish them in here.
+            raise
+    return wrapper
+
+
+class Process(object):
+    """Linux process implementation."""
+
+    __slots__ = ["pid", "_name", "_ppid", "_procfs_path", "_cache"]
+
+    def __init__(self, pid):
+        self.pid = pid
+        self._name = None
+        self._ppid = None
+        self._procfs_path = get_procfs_path()
+
+    def _assert_alive(self):
+        """Raise NSP if the process disappeared on us."""
+        # For those C function who do not raise NSP, possibly returning
+        # incorrect or incomplete result.
+        os.stat('%s/%s' % (self._procfs_path, self.pid))
+
+    @wrap_exceptions
+    @memoize_when_activated
+    def _parse_stat_file(self):
+        """Parse /proc/{pid}/stat file and return a dict with various
+        process info.
+        Using "man proc" as a reference: where "man proc" refers to
+        position N always substract 3 (e.g ppid position 4 in
+        'man proc' == position 1 in here).
+        The return value is cached in case oneshot() ctx manager is
+        in use.
+        """
+        with open_binary("%s/%s/stat" % (self._procfs_path, self.pid)) as f:
+            data = f.read()
+        # Process name is between parentheses. It can contain spaces and
+        # other parentheses. This is taken into account by looking for
+        # the first occurrence of "(" and the last occurence of ")".
+        rpar = data.rfind(b')')
+        name = data[data.find(b'(') + 1:rpar]
+        fields = data[rpar + 2:].split()
+
+        ret = {}
+        ret['name'] = name
+        ret['status'] = fields[0]
+        ret['ppid'] = fields[1]
+        ret['ttynr'] = fields[4]
+        ret['utime'] = fields[11]
+        ret['stime'] = fields[12]
+        ret['children_utime'] = fields[13]
+        ret['children_stime'] = fields[14]
+        ret['create_time'] = fields[19]
+        ret['cpu_num'] = fields[36]
+        ret['blkio_ticks'] = fields[39]  # aka 'delayacct_blkio_ticks'
+
+        return ret
+
+    @wrap_exceptions
+    @memoize_when_activated
+    def _read_status_file(self):
+        """Read /proc/{pid}/stat file and return its content.
+        The return value is cached in case oneshot() ctx manager is
+        in use.
+        """
+        with open_binary("%s/%s/status" % (self._procfs_path, self.pid)) as f:
+            return f.read()
+
+    @wrap_exceptions
+    @memoize_when_activated
+    def _read_smaps_file(self):
+        with open_binary("%s/%s/smaps" % (self._procfs_path, self.pid),
+                         buffering=BIGFILE_BUFFERING) as f:
+            return f.read().strip()
+
+    def oneshot_enter(self):
+        self._parse_stat_file.cache_activate(self)
+        self._read_status_file.cache_activate(self)
+        self._read_smaps_file.cache_activate(self)
+
+    def oneshot_exit(self):
+        self._parse_stat_file.cache_deactivate(self)
+        self._read_status_file.cache_deactivate(self)
+        self._read_smaps_file.cache_deactivate(self)
+
+    @wrap_exceptions
+    def name(self):
+        name = self._parse_stat_file()['name']
+        if PY3:
+            name = decode(name)
+        # XXX - gets changed later and probably needs refactoring
+        return name
+
+    def exe(self):
+        try:
+            return readlink("%s/%s/exe" % (self._procfs_path, self.pid))
+        except (FileNotFoundError, ProcessLookupError):
+            # no such file error; might be raised also if the
+            # path actually exists for system processes with
+            # low pids (about 0-20)
+            if os.path.lexists("%s/%s" % (self._procfs_path, self.pid)):
+                return ""
+            else:
+                if not pid_exists(self.pid):
+                    raise NoSuchProcess(self.pid, self._name)
+                else:
+                    raise ZombieProcess(self.pid, self._name, self._ppid)
+        except PermissionError:
+            raise AccessDenied(self.pid, self._name)
+
+    @wrap_exceptions
+    def cmdline(self):
+        with open_text("%s/%s/cmdline" % (self._procfs_path, self.pid)) as f:
+            data = f.read()
+        if not data:
+            # may happen in case of zombie process
+            return []
+        # 'man proc' states that args are separated by null bytes '\0'
+        # and last char is supposed to be a null byte. Nevertheless
+        # some processes may change their cmdline after being started
+        # (via setproctitle() or similar), they are usually not
+        # compliant with this rule and use spaces instead. Google
+        # Chrome process is an example. See:
+        # https://github.com/giampaolo/psutil/issues/1179
+        sep = '\x00' if data.endswith('\x00') else ' '
+        if data.endswith(sep):
+            data = data[:-1]
+        cmdline = data.split(sep)
+        # Sometimes last char is a null byte '\0' but the args are
+        # separated by spaces, see: https://github.com/giampaolo/psutil/
+        # issues/1179#issuecomment-552984549
+        if sep == '\x00' and len(cmdline) == 1 and ' ' in data:
+            cmdline = data.split(' ')
+        return cmdline
+
+    @wrap_exceptions
+    def environ(self):
+        with open_text("%s/%s/environ" % (self._procfs_path, self.pid)) as f:
+            data = f.read()
+        return parse_environ_block(data)
+
+    @wrap_exceptions
+    def terminal(self):
+        tty_nr = int(self._parse_stat_file()['ttynr'])
+        tmap = _psposix.get_terminal_map()
+        try:
+            return tmap[tty_nr]
+        except KeyError:
+            return None
+
+    # May not be available on old kernels.
+    if os.path.exists('/proc/%s/io' % os.getpid()):
+        @wrap_exceptions
+        def io_counters(self):
+            fname = "%s/%s/io" % (self._procfs_path, self.pid)
+            fields = {}
+            with open_binary(fname) as f:
+                for line in f:
+                    # https://github.com/giampaolo/psutil/issues/1004
+                    line = line.strip()
+                    if line:
+                        try:
+                            name, value = line.split(b': ')
+                        except ValueError:
+                            # https://github.com/giampaolo/psutil/issues/1004
+                            continue
+                        else:
+                            fields[name] = int(value)
+            if not fields:
+                raise RuntimeError("%s file was empty" % fname)
+            try:
+                return pio(
+                    fields[b'syscr'],  # read syscalls
+                    fields[b'syscw'],  # write syscalls
+                    fields[b'read_bytes'],  # read bytes
+                    fields[b'write_bytes'],  # write bytes
+                    fields[b'rchar'],  # read chars
+                    fields[b'wchar'],  # write chars
+                )
+            except KeyError as err:
+                raise ValueError("%r field was not found in %s; found fields "
+                                 "are %r" % (err[0], fname, fields))
+
+    @wrap_exceptions
+    def cpu_times(self):
+        values = self._parse_stat_file()
+        utime = float(values['utime']) / CLOCK_TICKS
+        stime = float(values['stime']) / CLOCK_TICKS
+        children_utime = float(values['children_utime']) / CLOCK_TICKS
+        children_stime = float(values['children_stime']) / CLOCK_TICKS
+        iowait = float(values['blkio_ticks']) / CLOCK_TICKS
+        return pcputimes(utime, stime, children_utime, children_stime, iowait)
+
+    @wrap_exceptions
+    def cpu_num(self):
+        """What CPU the process is on."""
+        return int(self._parse_stat_file()['cpu_num'])
+
+    @wrap_exceptions
+    def wait(self, timeout=None):
+        return _psposix.wait_pid(self.pid, timeout, self._name)
+
+    @wrap_exceptions
+    def create_time(self):
+        ctime = float(self._parse_stat_file()['create_time'])
+        # According to documentation, starttime is in field 21 and the
+        # unit is jiffies (clock ticks).
+        # We first divide it for clock ticks and then add uptime returning
+        # seconds since the epoch.
+        # Also use cached value if available.
+        bt = BOOT_TIME or boot_time()
+        return (ctime / CLOCK_TICKS) + bt
+
+    @wrap_exceptions
+    def memory_info(self):
+        #  ============================================================
+        # | FIELD  | DESCRIPTION                         | AKA  | TOP  |
+        #  ============================================================
+        # | rss    | resident set size                   |      | RES  |
+        # | vms    | total program size                  | size | VIRT |
+        # | shared | shared pages (from shared mappings) |      | SHR  |
+        # | text   | text ('code')                       | trs  | CODE |
+        # | lib    | library (unused in Linux 2.6)       | lrs  |      |
+        # | data   | data + stack                        | drs  | DATA |
+        # | dirty  | dirty pages (unused in Linux 2.6)   | dt   |      |
+        #  ============================================================
+        with open_binary("%s/%s/statm" % (self._procfs_path, self.pid)) as f:
+            vms, rss, shared, text, lib, data, dirty = \
+                [int(x) * PAGESIZE for x in f.readline().split()[:7]]
+        return pmem(rss, vms, shared, text, lib, data, dirty)
+
+    # /proc/pid/smaps does not exist on kernels < 2.6.14 or if
+    # CONFIG_MMU kernel configuration option is not enabled.
+    if HAS_SMAPS:
+
+        @wrap_exceptions
+        def memory_full_info(
+                self,
+                # Gets Private_Clean, Private_Dirty, Private_Hugetlb.
+                _private_re=re.compile(br"\nPrivate.*:\s+(\d+)"),
+                _pss_re=re.compile(br"\nPss\:\s+(\d+)"),
+                _swap_re=re.compile(br"\nSwap\:\s+(\d+)")):
+            basic_mem = self.memory_info()
+            # Note: using 3 regexes is faster than reading the file
+            # line by line.
+            # XXX: on Python 3 the 2 regexes are 30% slower than on
+            # Python 2 though. Figure out why.
+            #
+            # You might be tempted to calculate USS by subtracting
+            # the "shared" value from the "resident" value in
+            # /proc//statm. But at least on Linux, statm's "shared"
+            # value actually counts pages backed by files, which has
+            # little to do with whether the pages are actually shared.
+            # /proc/self/smaps on the other hand appears to give us the
+            # correct information.
+            smaps_data = self._read_smaps_file()
+            # Note: smaps file can be empty for certain processes.
+            # The code below will not crash though and will result to 0.
+            uss = sum(map(int, _private_re.findall(smaps_data))) * 1024
+            pss = sum(map(int, _pss_re.findall(smaps_data))) * 1024
+            swap = sum(map(int, _swap_re.findall(smaps_data))) * 1024
+            return pfullmem(*basic_mem + (uss, pss, swap))
+
+    else:
+        memory_full_info = memory_info
+
+    if HAS_SMAPS:
+
+        @wrap_exceptions
+        def memory_maps(self):
+            """Return process's mapped memory regions as a list of named
+            tuples. Fields are explained in 'man proc'; here is an updated
+            (Apr 2012) version: http://goo.gl/fmebo
+
+            /proc/{PID}/smaps does not exist on kernels < 2.6.14 or if
+            CONFIG_MMU kernel configuration option is not enabled.
+            """
+            def get_blocks(lines, current_block):
+                data = {}
+                for line in lines:
+                    fields = line.split(None, 5)
+                    if not fields[0].endswith(b':'):
+                        # new block section
+                        yield (current_block.pop(), data)
+                        current_block.append(line)
+                    else:
+                        try:
+                            data[fields[0]] = int(fields[1]) * 1024
+                        except ValueError:
+                            if fields[0].startswith(b'VmFlags:'):
+                                # see issue #369
+                                continue
+                            else:
+                                raise ValueError("don't know how to inte"
+                                                 "rpret line %r" % line)
+                yield (current_block.pop(), data)
+
+            data = self._read_smaps_file()
+            # Note: smaps file can be empty for certain processes.
+            if not data:
+                return []
+            lines = data.split(b'\n')
+            ls = []
+            first_line = lines.pop(0)
+            current_block = [first_line]
+            for header, data in get_blocks(lines, current_block):
+                hfields = header.split(None, 5)
+                try:
+                    addr, perms, offset, dev, inode, path = hfields
+                except ValueError:
+                    addr, perms, offset, dev, inode, path = \
+                        hfields + ['']
+                if not path:
+                    path = '[anon]'
+                else:
+                    if PY3:
+                        path = decode(path)
+                    path = path.strip()
+                    if (path.endswith(' (deleted)') and not
+                            path_exists_strict(path)):
+                        path = path[:-10]
+                ls.append((
+                    decode(addr), decode(perms), path,
+                    data.get(b'Rss:', 0),
+                    data.get(b'Size:', 0),
+                    data.get(b'Pss:', 0),
+                    data.get(b'Shared_Clean:', 0),
+                    data.get(b'Shared_Dirty:', 0),
+                    data.get(b'Private_Clean:', 0),
+                    data.get(b'Private_Dirty:', 0),
+                    data.get(b'Referenced:', 0),
+                    data.get(b'Anonymous:', 0),
+                    data.get(b'Swap:', 0)
+                ))
+            return ls
+
+    @wrap_exceptions
+    def cwd(self):
+        try:
+            return readlink("%s/%s/cwd" % (self._procfs_path, self.pid))
+        except (FileNotFoundError, ProcessLookupError):
+            # https://github.com/giampaolo/psutil/issues/986
+            if not pid_exists(self.pid):
+                raise NoSuchProcess(self.pid, self._name)
+            else:
+                raise ZombieProcess(self.pid, self._name, self._ppid)
+
+    @wrap_exceptions
+    def num_ctx_switches(self,
+                         _ctxsw_re=re.compile(br'ctxt_switches:\t(\d+)')):
+        data = self._read_status_file()
+        ctxsw = _ctxsw_re.findall(data)
+        if not ctxsw:
+            raise NotImplementedError(
+                "'voluntary_ctxt_switches' and 'nonvoluntary_ctxt_switches'"
+                "lines were not found in %s/%s/status; the kernel is "
+                "probably older than 2.6.23" % (
+                    self._procfs_path, self.pid))
+        else:
+            return _common.pctxsw(int(ctxsw[0]), int(ctxsw[1]))
+
+    @wrap_exceptions
+    def num_threads(self, _num_threads_re=re.compile(br'Threads:\t(\d+)')):
+        # Note: on Python 3 using a re is faster than iterating over file
+        # line by line. On Python 2 is the exact opposite, and iterating
+        # over a file on Python 3 is slower than on Python 2.
+        data = self._read_status_file()
+        return int(_num_threads_re.findall(data)[0])
+
+    @wrap_exceptions
+    def threads(self):
+        thread_ids = os.listdir("%s/%s/task" % (self._procfs_path, self.pid))
+        thread_ids.sort()
+        retlist = []
+        hit_enoent = False
+        for thread_id in thread_ids:
+            fname = "%s/%s/task/%s/stat" % (
+                self._procfs_path, self.pid, thread_id)
+            try:
+                with open_binary(fname) as f:
+                    st = f.read().strip()
+            except FileNotFoundError:
+                # no such file or directory; it means thread
+                # disappeared on us
+                hit_enoent = True
+                continue
+            # ignore the first two values ("pid (exe)")
+            st = st[st.find(b')') + 2:]
+            values = st.split(b' ')
+            utime = float(values[11]) / CLOCK_TICKS
+            stime = float(values[12]) / CLOCK_TICKS
+            ntuple = _common.pthread(int(thread_id), utime, stime)
+            retlist.append(ntuple)
+        if hit_enoent:
+            self._assert_alive()
+        return retlist
+
+    @wrap_exceptions
+    def nice_get(self):
+        # with open_text('%s/%s/stat' % (self._procfs_path, self.pid)) as f:
+        #   data = f.read()
+        #   return int(data.split()[18])
+
+        # Use C implementation
+        return cext_posix.getpriority(self.pid)
+
+    @wrap_exceptions
+    def nice_set(self, value):
+        return cext_posix.setpriority(self.pid, value)
+
+    # starting from CentOS 6.
+    if HAS_CPU_AFFINITY:
+
+        @wrap_exceptions
+        def cpu_affinity_get(self):
+            return cext.proc_cpu_affinity_get(self.pid)
+
+        def _get_eligible_cpus(
+                self, _re=re.compile(br"Cpus_allowed_list:\t(\d+)-(\d+)")):
+            # See: https://github.com/giampaolo/psutil/issues/956
+            data = self._read_status_file()
+            match = _re.findall(data)
+            if match:
+                return list(range(int(match[0][0]), int(match[0][1]) + 1))
+            else:
+                return list(range(len(per_cpu_times())))
+
+        @wrap_exceptions
+        def cpu_affinity_set(self, cpus):
+            try:
+                cext.proc_cpu_affinity_set(self.pid, cpus)
+            except (OSError, ValueError) as err:
+                if isinstance(err, ValueError) or err.errno == errno.EINVAL:
+                    eligible_cpus = self._get_eligible_cpus()
+                    all_cpus = tuple(range(len(per_cpu_times())))
+                    for cpu in cpus:
+                        if cpu not in all_cpus:
+                            raise ValueError(
+                                "invalid CPU number %r; choose between %s" % (
+                                    cpu, eligible_cpus))
+                        if cpu not in eligible_cpus:
+                            raise ValueError(
+                                "CPU number %r is not eligible; choose "
+                                "between %s" % (cpu, eligible_cpus))
+                raise
+
+    # only starting from kernel 2.6.13
+    if HAS_PROC_IO_PRIORITY:
+
+        @wrap_exceptions
+        def ionice_get(self):
+            ioclass, value = cext.proc_ioprio_get(self.pid)
+            if enum is not None:
+                ioclass = IOPriority(ioclass)
+            return _common.pionice(ioclass, value)
+
+        @wrap_exceptions
+        def ionice_set(self, ioclass, value):
+            if value is None:
+                value = 0
+            if value and ioclass in (IOPRIO_CLASS_IDLE, IOPRIO_CLASS_NONE):
+                raise ValueError("%r ioclass accepts no value" % ioclass)
+            if value < 0 or value > 7:
+                raise ValueError("value not in 0-7 range")
+            return cext.proc_ioprio_set(self.pid, ioclass, value)
+
+    if prlimit is not None:
+
+        @wrap_exceptions
+        def rlimit(self, resource_, limits=None):
+            # If pid is 0 prlimit() applies to the calling process and
+            # we don't want that. We should never get here though as
+            # PID 0 is not supported on Linux.
+            if self.pid == 0:
+                raise ValueError("can't use prlimit() against PID 0 process")
+            try:
+                if limits is None:
+                    # get
+                    return prlimit(self.pid, resource_)
+                else:
+                    # set
+                    if len(limits) != 2:
+                        raise ValueError(
+                            "second argument must be a (soft, hard) tuple, "
+                            "got %s" % repr(limits))
+                    prlimit(self.pid, resource_, limits)
+            except OSError as err:
+                if err.errno == errno.ENOSYS and pid_exists(self.pid):
+                    # I saw this happening on Travis:
+                    # https://travis-ci.org/giampaolo/psutil/jobs/51368273
+                    raise ZombieProcess(self.pid, self._name, self._ppid)
+                else:
+                    raise
+
+    @wrap_exceptions
+    def status(self):
+        letter = self._parse_stat_file()['status']
+        if PY3:
+            letter = letter.decode()
+        # XXX is '?' legit? (we're not supposed to return it anyway)
+        return PROC_STATUSES.get(letter, '?')
+
+    @wrap_exceptions
+    def open_files(self):
+        retlist = []
+        files = os.listdir("%s/%s/fd" % (self._procfs_path, self.pid))
+        hit_enoent = False
+        for fd in files:
+            file = "%s/%s/fd/%s" % (self._procfs_path, self.pid, fd)
+            try:
+                path = readlink(file)
+            except (FileNotFoundError, ProcessLookupError):
+                # ENOENT == file which is gone in the meantime
+                hit_enoent = True
+                continue
+            except OSError as err:
+                if err.errno == errno.EINVAL:
+                    # not a link
+                    continue
+                if err.errno == errno.ENAMETOOLONG:
+                    # file name too long
+                    debug(err)
+                    continue
+                raise
+            else:
+                # If path is not an absolute there's no way to tell
+                # whether it's a regular file or not, so we skip it.
+                # A regular file is always supposed to be have an
+                # absolute path though.
+                if path.startswith('/') and isfile_strict(path):
+                    # Get file position and flags.
+                    file = "%s/%s/fdinfo/%s" % (
+                        self._procfs_path, self.pid, fd)
+                    try:
+                        with open_binary(file) as f:
+                            pos = int(f.readline().split()[1])
+                            flags = int(f.readline().split()[1], 8)
+                    except FileNotFoundError:
+                        # fd gone in the meantime; process may
+                        # still be alive
+                        hit_enoent = True
+                    else:
+                        mode = file_flags_to_mode(flags)
+                        ntuple = popenfile(
+                            path, int(fd), int(pos), mode, flags)
+                        retlist.append(ntuple)
+        if hit_enoent:
+            self._assert_alive()
+        return retlist
+
+    @wrap_exceptions
+    def connections(self, kind='inet'):
+        ret = _connections.retrieve(kind, self.pid)
+        self._assert_alive()
+        return ret
+
+    @wrap_exceptions
+    def num_fds(self):
+        return len(os.listdir("%s/%s/fd" % (self._procfs_path, self.pid)))
+
+    @wrap_exceptions
+    def ppid(self):
+        return int(self._parse_stat_file()['ppid'])
+
+    @wrap_exceptions
+    def uids(self, _uids_re=re.compile(br'Uid:\t(\d+)\t(\d+)\t(\d+)')):
+        data = self._read_status_file()
+        real, effective, saved = _uids_re.findall(data)[0]
+        return _common.puids(int(real), int(effective), int(saved))
+
+    @wrap_exceptions
+    def gids(self, _gids_re=re.compile(br'Gid:\t(\d+)\t(\d+)\t(\d+)')):
+        data = self._read_status_file()
+        real, effective, saved = _gids_re.findall(data)[0]
+        return _common.pgids(int(real), int(effective), int(saved))
diff --git a/shared/package-files/04/046df1853bb3603163cdffa4e4271073f57b5b81e5425e022fb442410cf4102f/lines.py b/shared/package-files/04/046df1853bb3603163cdffa4e4271073f57b5b81e5425e022fb442410cf4102f/lines.py
new file mode 100644
index 0000000000000000000000000000000000000000..21dd91b89f49348c0165ef351e6f1494fed549e3
--- /dev/null
+++ b/shared/package-files/04/046df1853bb3603163cdffa4e4271073f57b5b81e5425e022fb442410cf4102f/lines.py
@@ -0,0 +1,1720 @@
+"""
+2D lines with support for a variety of line styles, markers, colors, etc.
+"""
+
+import copy
+
+from numbers import Integral, Number, Real
+import logging
+
+import numpy as np
+
+import matplotlib as mpl
+from . import _api, cbook, colors as mcolors, _docstring
+from .artist import Artist, allow_rasterization
+from .cbook import (
+    _to_unmasked_float_array, ls_mapper, ls_mapper_r, STEP_LOOKUP_MAP)
+from .markers import MarkerStyle
+from .path import Path
+from .transforms import Bbox, BboxTransformTo, TransformedPath
+from ._enums import JoinStyle, CapStyle
+
+# Imported here for backward compatibility, even though they don't
+# really belong.
+from . import _path
+from .markers import (  # noqa
+    CARETLEFT, CARETRIGHT, CARETUP, CARETDOWN,
+    CARETLEFTBASE, CARETRIGHTBASE, CARETUPBASE, CARETDOWNBASE,
+    TICKLEFT, TICKRIGHT, TICKUP, TICKDOWN)
+
+_log = logging.getLogger(__name__)
+
+
+def _get_dash_pattern(style):
+    """Convert linestyle to dash pattern."""
+    # go from short hand -> full strings
+    if isinstance(style, str):
+        style = ls_mapper.get(style, style)
+    # un-dashed styles
+    if style in ['solid', 'None']:
+        offset = 0
+        dashes = None
+    # dashed styles
+    elif style in ['dashed', 'dashdot', 'dotted']:
+        offset = 0
+        dashes = tuple(mpl.rcParams[f'lines.{style}_pattern'])
+    #
+    elif isinstance(style, tuple):
+        offset, dashes = style
+        if offset is None:
+            raise ValueError(f'Unrecognized linestyle: {style!r}')
+    else:
+        raise ValueError(f'Unrecognized linestyle: {style!r}')
+
+    # normalize offset to be positive and shorter than the dash cycle
+    if dashes is not None:
+        dsum = sum(dashes)
+        if dsum:
+            offset %= dsum
+
+    return offset, dashes
+
+
+def _get_dash_patterns(styles):
+    """Convert linestyle or sequence of linestyles to list of dash patterns."""
+    try:
+        patterns = [_get_dash_pattern(styles)]
+    except ValueError:
+        try:
+            patterns = [_get_dash_pattern(x) for x in styles]
+        except ValueError as err:
+            emsg = f'Do not know how to convert {styles!r} to dashes'
+            raise ValueError(emsg) from err
+
+    return patterns
+
+
+def _get_inverse_dash_pattern(offset, dashes):
+    """Return the inverse of the given dash pattern, for filling the gaps."""
+    # Define the inverse pattern by moving the last gap to the start of the
+    # sequence.
+    gaps = dashes[-1:] + dashes[:-1]
+    # Set the offset so that this new first segment is skipped
+    # (see backend_bases.GraphicsContextBase.set_dashes for offset definition).
+    offset_gaps = offset + dashes[-1]
+
+    return offset_gaps, gaps
+
+
+def _scale_dashes(offset, dashes, lw):
+    if not mpl.rcParams['lines.scale_dashes']:
+        return offset, dashes
+    scaled_offset = offset * lw
+    scaled_dashes = ([x * lw if x is not None else None for x in dashes]
+                     if dashes is not None else None)
+    return scaled_offset, scaled_dashes
+
+
+def segment_hits(cx, cy, x, y, radius):
+    """
+    Return the indices of the segments in the polyline with coordinates (*cx*,
+    *cy*) that are within a distance *radius* of the point (*x*, *y*).
+    """
+    # Process single points specially
+    if len(x) <= 1:
+        res, = np.nonzero((cx - x) ** 2 + (cy - y) ** 2 <= radius ** 2)
+        return res
+
+    # We need to lop the last element off a lot.
+    xr, yr = x[:-1], y[:-1]
+
+    # Only look at line segments whose nearest point to C on the line
+    # lies within the segment.
+    dx, dy = x[1:] - xr, y[1:] - yr
+    Lnorm_sq = dx ** 2 + dy ** 2  # Possibly want to eliminate Lnorm==0
+    u = ((cx - xr) * dx + (cy - yr) * dy) / Lnorm_sq
+    candidates = (u >= 0) & (u <= 1)
+
+    # Note that there is a little area near one side of each point
+    # which will be near neither segment, and another which will
+    # be near both, depending on the angle of the lines.  The
+    # following radius test eliminates these ambiguities.
+    point_hits = (cx - x) ** 2 + (cy - y) ** 2 <= radius ** 2
+    candidates = candidates & ~(point_hits[:-1] | point_hits[1:])
+
+    # For those candidates which remain, determine how far they lie away
+    # from the line.
+    px, py = xr + u * dx, yr + u * dy
+    line_hits = (cx - px) ** 2 + (cy - py) ** 2 <= radius ** 2
+    line_hits = line_hits & candidates
+    points, = point_hits.ravel().nonzero()
+    lines, = line_hits.ravel().nonzero()
+    return np.concatenate((points, lines))
+
+
+def _mark_every_path(markevery, tpath, affine, ax):
+    """
+    Helper function that sorts out how to deal the input
+    `markevery` and returns the points where markers should be drawn.
+
+    Takes in the `markevery` value and the line path and returns the
+    sub-sampled path.
+    """
+    # pull out the two bits of data we want from the path
+    codes, verts = tpath.codes, tpath.vertices
+
+    def _slice_or_none(in_v, slc):
+        """Helper function to cope with `codes` being an ndarray or `None`."""
+        if in_v is None:
+            return None
+        return in_v[slc]
+
+    # if just an int, assume starting at 0 and make a tuple
+    if isinstance(markevery, Integral):
+        markevery = (0, markevery)
+    # if just a float, assume starting at 0.0 and make a tuple
+    elif isinstance(markevery, Real):
+        markevery = (0.0, markevery)
+
+    if isinstance(markevery, tuple):
+        if len(markevery) != 2:
+            raise ValueError('`markevery` is a tuple but its len is not 2; '
+                             f'markevery={markevery}')
+        start, step = markevery
+        # if step is an int, old behavior
+        if isinstance(step, Integral):
+            # tuple of 2 int is for backwards compatibility,
+            if not isinstance(start, Integral):
+                raise ValueError(
+                    '`markevery` is a tuple with len 2 and second element is '
+                    'an int, but the first element is not an int; '
+                    f'markevery={markevery}')
+            # just return, we are done here
+
+            return Path(verts[slice(start, None, step)],
+                        _slice_or_none(codes, slice(start, None, step)))
+
+        elif isinstance(step, Real):
+            if not isinstance(start, Real):
+                raise ValueError(
+                    '`markevery` is a tuple with len 2 and second element is '
+                    'a float, but the first element is not a float or an int; '
+                    f'markevery={markevery}')
+            if ax is None:
+                raise ValueError(
+                    "markevery is specified relative to the Axes size, but "
+                    "the line does not have a Axes as parent")
+
+            # calc cumulative distance along path (in display coords):
+            fin = np.isfinite(verts).all(axis=1)
+            fverts = verts[fin]
+            disp_coords = affine.transform(fverts)
+
+            delta = np.empty((len(disp_coords), 2))
+            delta[0, :] = 0
+            delta[1:, :] = disp_coords[1:, :] - disp_coords[:-1, :]
+            delta = np.hypot(*delta.T).cumsum()
+            # calc distance between markers along path based on the Axes
+            # bounding box diagonal being a distance of unity:
+            (x0, y0), (x1, y1) = ax.transAxes.transform([[0, 0], [1, 1]])
+            scale = np.hypot(x1 - x0, y1 - y0)
+            marker_delta = np.arange(start * scale, delta[-1], step * scale)
+            # find closest actual data point that is closest to
+            # the theoretical distance along the path:
+            inds = np.abs(delta[np.newaxis, :] - marker_delta[:, np.newaxis])
+            inds = inds.argmin(axis=1)
+            inds = np.unique(inds)
+            # return, we are done here
+            return Path(fverts[inds], _slice_or_none(codes, inds))
+        else:
+            raise ValueError(
+                f"markevery={markevery!r} is a tuple with len 2, but its "
+                f"second element is not an int or a float")
+
+    elif isinstance(markevery, slice):
+        # mazol tov, it's already a slice, just return
+        return Path(verts[markevery], _slice_or_none(codes, markevery))
+
+    elif np.iterable(markevery):
+        # fancy indexing
+        try:
+            return Path(verts[markevery], _slice_or_none(codes, markevery))
+        except (ValueError, IndexError) as err:
+            raise ValueError(
+                f"markevery={markevery!r} is iterable but not a valid numpy "
+                f"fancy index") from err
+    else:
+        raise ValueError(f"markevery={markevery!r} is not a recognized value")
+
+
+@_docstring.interpd
+@_api.define_aliases({
+    "antialiased": ["aa"],
+    "color": ["c"],
+    "drawstyle": ["ds"],
+    "linestyle": ["ls"],
+    "linewidth": ["lw"],
+    "markeredgecolor": ["mec"],
+    "markeredgewidth": ["mew"],
+    "markerfacecolor": ["mfc"],
+    "markerfacecoloralt": ["mfcalt"],
+    "markersize": ["ms"],
+})
+class Line2D(Artist):
+    """
+    A line - the line can have both a solid linestyle connecting all
+    the vertices, and a marker at each vertex.  Additionally, the
+    drawing of the solid line is influenced by the drawstyle, e.g., one
+    can create "stepped" lines in various styles.
+    """
+
+    lineStyles = _lineStyles = {  # hidden names deprecated
+        '-':    '_draw_solid',
+        '--':   '_draw_dashed',
+        '-.':   '_draw_dash_dot',
+        ':':    '_draw_dotted',
+        'None': '_draw_nothing',
+        ' ':    '_draw_nothing',
+        '':     '_draw_nothing',
+    }
+
+    _drawStyles_l = {
+        'default':    '_draw_lines',
+        'steps-mid':  '_draw_steps_mid',
+        'steps-pre':  '_draw_steps_pre',
+        'steps-post': '_draw_steps_post',
+    }
+
+    _drawStyles_s = {
+        'steps': '_draw_steps_pre',
+    }
+
+    # drawStyles should now be deprecated.
+    drawStyles = {**_drawStyles_l, **_drawStyles_s}
+    # Need a list ordered with long names first:
+    drawStyleKeys = [*_drawStyles_l, *_drawStyles_s]
+
+    # Referenced here to maintain API.  These are defined in
+    # MarkerStyle
+    markers = MarkerStyle.markers
+    filled_markers = MarkerStyle.filled_markers
+    fillStyles = MarkerStyle.fillstyles
+
+    zorder = 2
+
+    _subslice_optim_min_size = 1000
+
+    def __str__(self):
+        if self._label != "":
+            return f"Line2D({self._label})"
+        elif self._x is None:
+            return "Line2D()"
+        elif len(self._x) > 3:
+            return "Line2D(({:g},{:g}),({:g},{:g}),...,({:g},{:g}))".format(
+                self._x[0], self._y[0],
+                self._x[1], self._y[1],
+                self._x[-1], self._y[-1])
+        else:
+            return "Line2D(%s)" % ",".join(
+                map("({:g},{:g})".format, self._x, self._y))
+
+    def __init__(self, xdata, ydata, *,
+                 linewidth=None,  # all Nones default to rc
+                 linestyle=None,
+                 color=None,
+                 gapcolor=None,
+                 marker=None,
+                 markersize=None,
+                 markeredgewidth=None,
+                 markeredgecolor=None,
+                 markerfacecolor=None,
+                 markerfacecoloralt='none',
+                 fillstyle=None,
+                 antialiased=None,
+                 dash_capstyle=None,
+                 solid_capstyle=None,
+                 dash_joinstyle=None,
+                 solid_joinstyle=None,
+                 pickradius=5,
+                 drawstyle=None,
+                 markevery=None,
+                 **kwargs
+                 ):
+        """
+        Create a `.Line2D` instance with *x* and *y* data in sequences of
+        *xdata*, *ydata*.
+
+        Additional keyword arguments are `.Line2D` properties:
+
+        %(Line2D:kwdoc)s
+
+        See :meth:`set_linestyle` for a description of the line styles,
+        :meth:`set_marker` for a description of the markers, and
+        :meth:`set_drawstyle` for a description of the draw styles.
+
+        """
+        super().__init__()
+
+        # Convert sequences to NumPy arrays.
+        if not np.iterable(xdata):
+            raise RuntimeError('xdata must be a sequence')
+        if not np.iterable(ydata):
+            raise RuntimeError('ydata must be a sequence')
+
+        if linewidth is None:
+            linewidth = mpl.rcParams['lines.linewidth']
+
+        if linestyle is None:
+            linestyle = mpl.rcParams['lines.linestyle']
+        if marker is None:
+            marker = mpl.rcParams['lines.marker']
+        if color is None:
+            color = mpl.rcParams['lines.color']
+
+        if markersize is None:
+            markersize = mpl.rcParams['lines.markersize']
+        if antialiased is None:
+            antialiased = mpl.rcParams['lines.antialiased']
+        if dash_capstyle is None:
+            dash_capstyle = mpl.rcParams['lines.dash_capstyle']
+        if dash_joinstyle is None:
+            dash_joinstyle = mpl.rcParams['lines.dash_joinstyle']
+        if solid_capstyle is None:
+            solid_capstyle = mpl.rcParams['lines.solid_capstyle']
+        if solid_joinstyle is None:
+            solid_joinstyle = mpl.rcParams['lines.solid_joinstyle']
+
+        if drawstyle is None:
+            drawstyle = 'default'
+
+        self._dashcapstyle = None
+        self._dashjoinstyle = None
+        self._solidjoinstyle = None
+        self._solidcapstyle = None
+        self.set_dash_capstyle(dash_capstyle)
+        self.set_dash_joinstyle(dash_joinstyle)
+        self.set_solid_capstyle(solid_capstyle)
+        self.set_solid_joinstyle(solid_joinstyle)
+
+        self._linestyles = None
+        self._drawstyle = None
+        self._linewidth = linewidth
+        self._unscaled_dash_pattern = (0, None)  # offset, dash
+        self._dash_pattern = (0, None)  # offset, dash (scaled by linewidth)
+
+        self.set_linewidth(linewidth)
+        self.set_linestyle(linestyle)
+        self.set_drawstyle(drawstyle)
+
+        self._color = None
+        self.set_color(color)
+        if marker is None:
+            marker = 'none'  # Default.
+        if not isinstance(marker, MarkerStyle):
+            self._marker = MarkerStyle(marker, fillstyle)
+        else:
+            self._marker = marker
+
+        self._gapcolor = None
+        self.set_gapcolor(gapcolor)
+
+        self._markevery = None
+        self._markersize = None
+        self._antialiased = None
+
+        self.set_markevery(markevery)
+        self.set_antialiased(antialiased)
+        self.set_markersize(markersize)
+
+        self._markeredgecolor = None
+        self._markeredgewidth = None
+        self._markerfacecolor = None
+        self._markerfacecoloralt = None
+
+        self.set_markerfacecolor(markerfacecolor)  # Normalizes None to rc.
+        self.set_markerfacecoloralt(markerfacecoloralt)
+        self.set_markeredgecolor(markeredgecolor)  # Normalizes None to rc.
+        self.set_markeredgewidth(markeredgewidth)
+
+        # update kwargs before updating data to give the caller a
+        # chance to init axes (and hence unit support)
+        self._internal_update(kwargs)
+        self.pickradius = pickradius
+        self.ind_offset = 0
+        if (isinstance(self._picker, Number) and
+                not isinstance(self._picker, bool)):
+            self._pickradius = self._picker
+
+        self._xorig = np.asarray([])
+        self._yorig = np.asarray([])
+        self._invalidx = True
+        self._invalidy = True
+        self._x = None
+        self._y = None
+        self._xy = None
+        self._path = None
+        self._transformed_path = None
+        self._subslice = False
+        self._x_filled = None  # used in subslicing; only x is needed
+
+        self.set_data(xdata, ydata)
+
+    def contains(self, mouseevent):
+        """
+        Test whether *mouseevent* occurred on the line.
+
+        An event is deemed to have occurred "on" the line if it is less
+        than ``self.pickradius`` (default: 5 points) away from it.  Use
+        `~.Line2D.get_pickradius` or `~.Line2D.set_pickradius` to get or set
+        the pick radius.
+
+        Parameters
+        ----------
+        mouseevent : `~matplotlib.backend_bases.MouseEvent`
+
+        Returns
+        -------
+        contains : bool
+            Whether any values are within the radius.
+        details : dict
+            A dictionary ``{'ind': pointlist}``, where *pointlist* is a
+            list of points of the line that are within the pickradius around
+            the event position.
+
+            TODO: sort returned indices by distance
+        """
+        if self._different_canvas(mouseevent):
+            return False, {}
+
+        # Make sure we have data to plot
+        if self._invalidy or self._invalidx:
+            self.recache()
+        if len(self._xy) == 0:
+            return False, {}
+
+        # Convert points to pixels
+        transformed_path = self._get_transformed_path()
+        path, affine = transformed_path.get_transformed_path_and_affine()
+        path = affine.transform_path(path)
+        xy = path.vertices
+        xt = xy[:, 0]
+        yt = xy[:, 1]
+
+        # Convert pick radius from points to pixels
+        fig = self.get_figure(root=True)
+        if fig is None:
+            _log.warning('no figure set when check if mouse is on line')
+            pixels = self._pickradius
+        else:
+            pixels = fig.dpi / 72. * self._pickradius
+
+        # The math involved in checking for containment (here and inside of
+        # segment_hits) assumes that it is OK to overflow, so temporarily set
+        # the error flags accordingly.
+        with np.errstate(all='ignore'):
+            # Check for collision
+            if self._linestyle in ['None', None]:
+                # If no line, return the nearby point(s)
+                ind, = np.nonzero(
+                    (xt - mouseevent.x) ** 2 + (yt - mouseevent.y) ** 2
+                    <= pixels ** 2)
+            else:
+                # If line, return the nearby segment(s)
+                ind = segment_hits(mouseevent.x, mouseevent.y, xt, yt, pixels)
+                if self._drawstyle.startswith("steps"):
+                    ind //= 2
+
+        ind += self.ind_offset
+
+        # Return the point(s) within radius
+        return len(ind) > 0, dict(ind=ind)
+
+    def get_pickradius(self):
+        """
+        Return the pick radius used for containment tests.
+
+        See `.contains` for more details.
+        """
+        return self._pickradius
+
+    def set_pickradius(self, pickradius):
+        """
+        Set the pick radius used for containment tests.
+
+        See `.contains` for more details.
+
+        Parameters
+        ----------
+        pickradius : float
+            Pick radius, in points.
+        """
+        if not isinstance(pickradius, Real) or pickradius < 0:
+            raise ValueError("pick radius should be a distance")
+        self._pickradius = pickradius
+
+    pickradius = property(get_pickradius, set_pickradius)
+
+    def get_fillstyle(self):
+        """
+        Return the marker fill style.
+
+        See also `~.Line2D.set_fillstyle`.
+        """
+        return self._marker.get_fillstyle()
+
+    def set_fillstyle(self, fs):
+        """
+        Set the marker fill style.
+
+        Parameters
+        ----------
+        fs : {'full', 'left', 'right', 'bottom', 'top', 'none'}
+            Possible values:
+
+            - 'full': Fill the whole marker with the *markerfacecolor*.
+            - 'left', 'right', 'bottom', 'top': Fill the marker half at
+              the given side with the *markerfacecolor*. The other
+              half of the marker is filled with *markerfacecoloralt*.
+            - 'none': No filling.
+
+            For examples see :ref:`marker_fill_styles`.
+        """
+        self.set_marker(MarkerStyle(self._marker.get_marker(), fs))
+        self.stale = True
+
+    def set_markevery(self, every):
+        """
+        Set the markevery property to subsample the plot when using markers.
+
+        e.g., if ``every=5``, every 5-th marker will be plotted.
+
+        Parameters
+        ----------
+        every : None or int or (int, int) or slice or list[int] or float or \
+(float, float) or list[bool]
+            Which markers to plot.
+
+            - ``every=None``: every point will be plotted.
+            - ``every=N``: every N-th marker will be plotted starting with
+              marker 0.
+            - ``every=(start, N)``: every N-th marker, starting at index
+              *start*, will be plotted.
+            - ``every=slice(start, end, N)``: every N-th marker, starting at
+              index *start*, up to but not including index *end*, will be
+              plotted.
+            - ``every=[i, j, m, ...]``: only markers at the given indices
+              will be plotted.
+            - ``every=[True, False, True, ...]``: only positions that are True
+              will be plotted. The list must have the same length as the data
+              points.
+            - ``every=0.1``, (i.e. a float): markers will be spaced at
+              approximately equal visual distances along the line; the distance
+              along the line between markers is determined by multiplying the
+              display-coordinate distance of the Axes bounding-box diagonal
+              by the value of *every*.
+            - ``every=(0.5, 0.1)`` (i.e. a length-2 tuple of float): similar
+              to ``every=0.1`` but the first marker will be offset along the
+              line by 0.5 multiplied by the
+              display-coordinate-diagonal-distance along the line.
+
+            For examples see
+            :doc:`/gallery/lines_bars_and_markers/markevery_demo`.
+
+        Notes
+        -----
+        Setting *markevery* will still only draw markers at actual data points.
+        While the float argument form aims for uniform visual spacing, it has
+        to coerce from the ideal spacing to the nearest available data point.
+        Depending on the number and distribution of data points, the result
+        may still not look evenly spaced.
+
+        When using a start offset to specify the first marker, the offset will
+        be from the first data point which may be different from the first
+        the visible data point if the plot is zoomed in.
+
+        If zooming in on a plot when using float arguments then the actual
+        data points that have markers will change because the distance between
+        markers is always determined from the display-coordinates
+        axes-bounding-box-diagonal regardless of the actual axes data limits.
+
+        """
+        self._markevery = every
+        self.stale = True
+
+    def get_markevery(self):
+        """
+        Return the markevery setting for marker subsampling.
+
+        See also `~.Line2D.set_markevery`.
+        """
+        return self._markevery
+
+    def set_picker(self, p):
+        """
+        Set the event picker details for the line.
+
+        Parameters
+        ----------
+        p : float or callable[[Artist, Event], tuple[bool, dict]]
+            If a float, it is used as the pick radius in points.
+        """
+        if not callable(p):
+            self.set_pickradius(p)
+        self._picker = p
+
+    def get_bbox(self):
+        """Get the bounding box of this line."""
+        bbox = Bbox([[0, 0], [0, 0]])
+        bbox.update_from_data_xy(self.get_xydata())
+        return bbox
+
+    def get_window_extent(self, renderer=None):
+        bbox = Bbox([[0, 0], [0, 0]])
+        trans_data_to_xy = self.get_transform().transform
+        bbox.update_from_data_xy(trans_data_to_xy(self.get_xydata()),
+                                 ignore=True)
+        # correct for marker size, if any
+        if self._marker:
+            ms = (self._markersize / 72.0 * self.get_figure(root=True).dpi) * 0.5
+            bbox = bbox.padded(ms)
+        return bbox
+
+    def set_data(self, *args):
+        """
+        Set the x and y data.
+
+        Parameters
+        ----------
+        *args : (2, N) array or two 1D arrays
+
+        See Also
+        --------
+        set_xdata
+        set_ydata
+        """
+        if len(args) == 1:
+            (x, y), = args
+        else:
+            x, y = args
+
+        self.set_xdata(x)
+        self.set_ydata(y)
+
+    def recache_always(self):
+        self.recache(always=True)
+
+    def recache(self, always=False):
+        if always or self._invalidx:
+            xconv = self.convert_xunits(self._xorig)
+            x = _to_unmasked_float_array(xconv).ravel()
+        else:
+            x = self._x
+        if always or self._invalidy:
+            yconv = self.convert_yunits(self._yorig)
+            y = _to_unmasked_float_array(yconv).ravel()
+        else:
+            y = self._y
+
+        self._xy = np.column_stack(np.broadcast_arrays(x, y)).astype(float)
+        self._x, self._y = self._xy.T  # views
+
+        self._subslice = False
+        if (self.axes
+                and len(x) > self._subslice_optim_min_size
+                and _path.is_sorted_and_has_non_nan(x)
+                and self.axes.name == 'rectilinear'
+                and self.axes.get_xscale() == 'linear'
+                and self._markevery is None
+                and self.get_clip_on()
+                and self.get_transform() == self.axes.transData):
+            self._subslice = True
+            nanmask = np.isnan(x)
+            if nanmask.any():
+                self._x_filled = self._x.copy()
+                indices = np.arange(len(x))
+                self._x_filled[nanmask] = np.interp(
+                    indices[nanmask], indices[~nanmask], self._x[~nanmask])
+            else:
+                self._x_filled = self._x
+
+        if self._path is not None:
+            interpolation_steps = self._path._interpolation_steps
+        else:
+            interpolation_steps = 1
+        xy = STEP_LOOKUP_MAP[self._drawstyle](*self._xy.T)
+        self._path = Path(np.asarray(xy).T,
+                          _interpolation_steps=interpolation_steps)
+        self._transformed_path = None
+        self._invalidx = False
+        self._invalidy = False
+
+    def _transform_path(self, subslice=None):
+        """
+        Put a TransformedPath instance at self._transformed_path;
+        all invalidation of the transform is then handled by the
+        TransformedPath instance.
+        """
+        # Masked arrays are now handled by the Path class itself
+        if subslice is not None:
+            xy = STEP_LOOKUP_MAP[self._drawstyle](*self._xy[subslice, :].T)
+            _path = Path(np.asarray(xy).T,
+                         _interpolation_steps=self._path._interpolation_steps)
+        else:
+            _path = self._path
+        self._transformed_path = TransformedPath(_path, self.get_transform())
+
+    def _get_transformed_path(self):
+        """Return this line's `~matplotlib.transforms.TransformedPath`."""
+        if self._transformed_path is None:
+            self._transform_path()
+        return self._transformed_path
+
+    def set_transform(self, t):
+        # docstring inherited
+        self._invalidx = True
+        self._invalidy = True
+        super().set_transform(t)
+
+    @allow_rasterization
+    def draw(self, renderer):
+        # docstring inherited
+
+        if not self.get_visible():
+            return
+
+        if self._invalidy or self._invalidx:
+            self.recache()
+        self.ind_offset = 0  # Needed for contains() method.
+        if self._subslice and self.axes:
+            x0, x1 = self.axes.get_xbound()
+            i0 = self._x_filled.searchsorted(x0, 'left')
+            i1 = self._x_filled.searchsorted(x1, 'right')
+            subslice = slice(max(i0 - 1, 0), i1 + 1)
+            self.ind_offset = subslice.start
+            self._transform_path(subslice)
+        else:
+            subslice = None
+
+        if self.get_path_effects():
+            from matplotlib.patheffects import PathEffectRenderer
+            renderer = PathEffectRenderer(self.get_path_effects(), renderer)
+
+        renderer.open_group('line2d', self.get_gid())
+        if self._lineStyles[self._linestyle] != '_draw_nothing':
+            tpath, affine = (self._get_transformed_path()
+                             .get_transformed_path_and_affine())
+            if len(tpath.vertices):
+                gc = renderer.new_gc()
+                self._set_gc_clip(gc)
+                gc.set_url(self.get_url())
+
+                gc.set_antialiased(self._antialiased)
+                gc.set_linewidth(self._linewidth)
+
+                if self.is_dashed():
+                    cap = self._dashcapstyle
+                    join = self._dashjoinstyle
+                else:
+                    cap = self._solidcapstyle
+                    join = self._solidjoinstyle
+                gc.set_joinstyle(join)
+                gc.set_capstyle(cap)
+                gc.set_snap(self.get_snap())
+                if self.get_sketch_params() is not None:
+                    gc.set_sketch_params(*self.get_sketch_params())
+
+                # We first draw a path within the gaps if needed.
+                if self.is_dashed() and self._gapcolor is not None:
+                    lc_rgba = mcolors.to_rgba(self._gapcolor, self._alpha)
+                    gc.set_foreground(lc_rgba, isRGBA=True)
+
+                    offset_gaps, gaps = _get_inverse_dash_pattern(
+                        *self._dash_pattern)
+
+                    gc.set_dashes(offset_gaps, gaps)
+                    renderer.draw_path(gc, tpath, affine.frozen())
+
+                lc_rgba = mcolors.to_rgba(self._color, self._alpha)
+                gc.set_foreground(lc_rgba, isRGBA=True)
+
+                gc.set_dashes(*self._dash_pattern)
+                renderer.draw_path(gc, tpath, affine.frozen())
+                gc.restore()
+
+        if self._marker and self._markersize > 0:
+            gc = renderer.new_gc()
+            self._set_gc_clip(gc)
+            gc.set_url(self.get_url())
+            gc.set_linewidth(self._markeredgewidth)
+            gc.set_antialiased(self._antialiased)
+
+            ec_rgba = mcolors.to_rgba(
+                self.get_markeredgecolor(), self._alpha)
+            fc_rgba = mcolors.to_rgba(
+                self._get_markerfacecolor(), self._alpha)
+            fcalt_rgba = mcolors.to_rgba(
+                self._get_markerfacecolor(alt=True), self._alpha)
+            # If the edgecolor is "auto", it is set according to the *line*
+            # color but inherits the alpha value of the *face* color, if any.
+            if (cbook._str_equal(self._markeredgecolor, "auto")
+                    and not cbook._str_lower_equal(
+                        self.get_markerfacecolor(), "none")):
+                ec_rgba = ec_rgba[:3] + (fc_rgba[3],)
+            gc.set_foreground(ec_rgba, isRGBA=True)
+            if self.get_sketch_params() is not None:
+                scale, length, randomness = self.get_sketch_params()
+                gc.set_sketch_params(scale/2, length/2, 2*randomness)
+
+            marker = self._marker
+
+            # Markers *must* be drawn ignoring the drawstyle (but don't pay the
+            # recaching if drawstyle is already "default").
+            if self.get_drawstyle() != "default":
+                with cbook._setattr_cm(
+                        self, _drawstyle="default", _transformed_path=None):
+                    self.recache()
+                    self._transform_path(subslice)
+                    tpath, affine = (self._get_transformed_path()
+                                     .get_transformed_points_and_affine())
+            else:
+                tpath, affine = (self._get_transformed_path()
+                                 .get_transformed_points_and_affine())
+
+            if len(tpath.vertices):
+                # subsample the markers if markevery is not None
+                markevery = self.get_markevery()
+                if markevery is not None:
+                    subsampled = _mark_every_path(
+                        markevery, tpath, affine, self.axes)
+                else:
+                    subsampled = tpath
+
+                snap = marker.get_snap_threshold()
+                if isinstance(snap, Real):
+                    snap = renderer.points_to_pixels(self._markersize) >= snap
+                gc.set_snap(snap)
+                gc.set_joinstyle(marker.get_joinstyle())
+                gc.set_capstyle(marker.get_capstyle())
+                marker_path = marker.get_path()
+                marker_trans = marker.get_transform()
+                w = renderer.points_to_pixels(self._markersize)
+
+                if cbook._str_equal(marker.get_marker(), ","):
+                    gc.set_linewidth(0)
+                else:
+                    # Don't scale for pixels, and don't stroke them
+                    marker_trans = marker_trans.scale(w)
+                renderer.draw_markers(gc, marker_path, marker_trans,
+                                      subsampled, affine.frozen(),
+                                      fc_rgba)
+
+                alt_marker_path = marker.get_alt_path()
+                if alt_marker_path:
+                    alt_marker_trans = marker.get_alt_transform()
+                    alt_marker_trans = alt_marker_trans.scale(w)
+                    renderer.draw_markers(
+                            gc, alt_marker_path, alt_marker_trans, subsampled,
+                            affine.frozen(), fcalt_rgba)
+
+            gc.restore()
+
+        renderer.close_group('line2d')
+        self.stale = False
+
+    def get_antialiased(self):
+        """Return whether antialiased rendering is used."""
+        return self._antialiased
+
+    def get_color(self):
+        """
+        Return the line color.
+
+        See also `~.Line2D.set_color`.
+        """
+        return self._color
+
+    def get_drawstyle(self):
+        """
+        Return the drawstyle.
+
+        See also `~.Line2D.set_drawstyle`.
+        """
+        return self._drawstyle
+
+    def get_gapcolor(self):
+        """
+        Return the line gapcolor.
+
+        See also `~.Line2D.set_gapcolor`.
+        """
+        return self._gapcolor
+
+    def get_linestyle(self):
+        """
+        Return the linestyle.
+
+        See also `~.Line2D.set_linestyle`.
+        """
+        return self._linestyle
+
+    def get_linewidth(self):
+        """
+        Return the linewidth in points.
+
+        See also `~.Line2D.set_linewidth`.
+        """
+        return self._linewidth
+
+    def get_marker(self):
+        """
+        Return the line marker.
+
+        See also `~.Line2D.set_marker`.
+        """
+        return self._marker.get_marker()
+
+    def get_markeredgecolor(self):
+        """
+        Return the marker edge color.
+
+        See also `~.Line2D.set_markeredgecolor`.
+        """
+        mec = self._markeredgecolor
+        if cbook._str_equal(mec, 'auto'):
+            if mpl.rcParams['_internal.classic_mode']:
+                if self._marker.get_marker() in ('.', ','):
+                    return self._color
+                if (self._marker.is_filled()
+                        and self._marker.get_fillstyle() != 'none'):
+                    return 'k'  # Bad hard-wired default...
+            return self._color
+        else:
+            return mec
+
+    def get_markeredgewidth(self):
+        """
+        Return the marker edge width in points.
+
+        See also `~.Line2D.set_markeredgewidth`.
+        """
+        return self._markeredgewidth
+
+    def _get_markerfacecolor(self, alt=False):
+        if self._marker.get_fillstyle() == 'none':
+            return 'none'
+        fc = self._markerfacecoloralt if alt else self._markerfacecolor
+        if cbook._str_lower_equal(fc, 'auto'):
+            return self._color
+        else:
+            return fc
+
+    def get_markerfacecolor(self):
+        """
+        Return the marker face color.
+
+        See also `~.Line2D.set_markerfacecolor`.
+        """
+        return self._get_markerfacecolor(alt=False)
+
+    def get_markerfacecoloralt(self):
+        """
+        Return the alternate marker face color.
+
+        See also `~.Line2D.set_markerfacecoloralt`.
+        """
+        return self._get_markerfacecolor(alt=True)
+
+    def get_markersize(self):
+        """
+        Return the marker size in points.
+
+        See also `~.Line2D.set_markersize`.
+        """
+        return self._markersize
+
+    def get_data(self, orig=True):
+        """
+        Return the line data as an ``(xdata, ydata)`` pair.
+
+        If *orig* is *True*, return the original data.
+        """
+        return self.get_xdata(orig=orig), self.get_ydata(orig=orig)
+
+    def get_xdata(self, orig=True):
+        """
+        Return the xdata.
+
+        If *orig* is *True*, return the original data, else the
+        processed data.
+        """
+        if orig:
+            return self._xorig
+        if self._invalidx:
+            self.recache()
+        return self._x
+
+    def get_ydata(self, orig=True):
+        """
+        Return the ydata.
+
+        If *orig* is *True*, return the original data, else the
+        processed data.
+        """
+        if orig:
+            return self._yorig
+        if self._invalidy:
+            self.recache()
+        return self._y
+
+    def get_path(self):
+        """Return the `~matplotlib.path.Path` associated with this line."""
+        if self._invalidy or self._invalidx:
+            self.recache()
+        return self._path
+
+    def get_xydata(self):
+        """Return the *xy* data as a (N, 2) array."""
+        if self._invalidy or self._invalidx:
+            self.recache()
+        return self._xy
+
+    def set_antialiased(self, b):
+        """
+        Set whether to use antialiased rendering.
+
+        Parameters
+        ----------
+        b : bool
+        """
+        if self._antialiased != b:
+            self.stale = True
+        self._antialiased = b
+
+    def set_color(self, color):
+        """
+        Set the color of the line.
+
+        Parameters
+        ----------
+        color : :mpltype:`color`
+        """
+        mcolors._check_color_like(color=color)
+        self._color = color
+        self.stale = True
+
+    def set_drawstyle(self, drawstyle):
+        """
+        Set the drawstyle of the plot.
+
+        The drawstyle determines how the points are connected.
+
+        Parameters
+        ----------
+        drawstyle : {'default', 'steps', 'steps-pre', 'steps-mid', \
+'steps-post'}, default: 'default'
+            For 'default', the points are connected with straight lines.
+
+            The steps variants connect the points with step-like lines,
+            i.e. horizontal lines with vertical steps. They differ in the
+            location of the step:
+
+            - 'steps-pre': The step is at the beginning of the line segment,
+              i.e. the line will be at the y-value of point to the right.
+            - 'steps-mid': The step is halfway between the points.
+            - 'steps-post: The step is at the end of the line segment,
+              i.e. the line will be at the y-value of the point to the left.
+            - 'steps' is equal to 'steps-pre' and is maintained for
+              backward-compatibility.
+
+            For examples see :doc:`/gallery/lines_bars_and_markers/step_demo`.
+        """
+        if drawstyle is None:
+            drawstyle = 'default'
+        _api.check_in_list(self.drawStyles, drawstyle=drawstyle)
+        if self._drawstyle != drawstyle:
+            self.stale = True
+            # invalidate to trigger a recache of the path
+            self._invalidx = True
+        self._drawstyle = drawstyle
+
+    def set_gapcolor(self, gapcolor):
+        """
+        Set a color to fill the gaps in the dashed line style.
+
+        .. note::
+
+            Striped lines are created by drawing two interleaved dashed lines.
+            There can be overlaps between those two, which may result in
+            artifacts when using transparency.
+
+            This functionality is experimental and may change.
+
+        Parameters
+        ----------
+        gapcolor : :mpltype:`color` or None
+            The color with which to fill the gaps. If None, the gaps are
+            unfilled.
+        """
+        if gapcolor is not None:
+            mcolors._check_color_like(color=gapcolor)
+        self._gapcolor = gapcolor
+        self.stale = True
+
+    def set_linewidth(self, w):
+        """
+        Set the line width in points.
+
+        Parameters
+        ----------
+        w : float
+            Line width, in points.
+        """
+        w = float(w)
+        if self._linewidth != w:
+            self.stale = True
+        self._linewidth = w
+        self._dash_pattern = _scale_dashes(*self._unscaled_dash_pattern, w)
+
+    def set_linestyle(self, ls):
+        """
+        Set the linestyle of the line.
+
+        Parameters
+        ----------
+        ls : {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
+            Possible values:
+
+            - A string:
+
+              ==========================================  =================
+              linestyle                                   description
+              ==========================================  =================
+              ``'-'`` or ``'solid'``                      solid line
+              ``'--'`` or  ``'dashed'``                   dashed line
+              ``'-.'`` or  ``'dashdot'``                  dash-dotted line
+              ``':'`` or ``'dotted'``                     dotted line
+              ``'none'``, ``'None'``, ``' '``, or ``''``  draw nothing
+              ==========================================  =================
+
+            - Alternatively a dash tuple of the following form can be
+              provided::
+
+                  (offset, onoffseq)
+
+              where ``onoffseq`` is an even length tuple of on and off ink
+              in points. See also :meth:`set_dashes`.
+
+            For examples see :doc:`/gallery/lines_bars_and_markers/linestyles`.
+        """
+        if isinstance(ls, str):
+            if ls in [' ', '', 'none']:
+                ls = 'None'
+            _api.check_in_list([*self._lineStyles, *ls_mapper_r], ls=ls)
+            if ls not in self._lineStyles:
+                ls = ls_mapper_r[ls]
+            self._linestyle = ls
+        else:
+            self._linestyle = '--'
+        self._unscaled_dash_pattern = _get_dash_pattern(ls)
+        self._dash_pattern = _scale_dashes(
+            *self._unscaled_dash_pattern, self._linewidth)
+        self.stale = True
+
+    @_docstring.interpd
+    def set_marker(self, marker):
+        """
+        Set the line marker.
+
+        Parameters
+        ----------
+        marker : marker style string, `~.path.Path` or `~.markers.MarkerStyle`
+            See `~matplotlib.markers` for full description of possible
+            arguments.
+        """
+        self._marker = MarkerStyle(marker, self._marker.get_fillstyle())
+        self.stale = True
+
+    def _set_markercolor(self, name, has_rcdefault, val):
+        if val is None:
+            val = mpl.rcParams[f"lines.{name}"] if has_rcdefault else "auto"
+        attr = f"_{name}"
+        current = getattr(self, attr)
+        if current is None:
+            self.stale = True
+        else:
+            neq = current != val
+            # Much faster than `np.any(current != val)` if no arrays are used.
+            if neq.any() if isinstance(neq, np.ndarray) else neq:
+                self.stale = True
+        setattr(self, attr, val)
+
+    def set_markeredgecolor(self, ec):
+        """
+        Set the marker edge color.
+
+        Parameters
+        ----------
+        ec : :mpltype:`color`
+        """
+        self._set_markercolor("markeredgecolor", True, ec)
+
+    def set_markerfacecolor(self, fc):
+        """
+        Set the marker face color.
+
+        Parameters
+        ----------
+        fc : :mpltype:`color`
+        """
+        self._set_markercolor("markerfacecolor", True, fc)
+
+    def set_markerfacecoloralt(self, fc):
+        """
+        Set the alternate marker face color.
+
+        Parameters
+        ----------
+        fc : :mpltype:`color`
+        """
+        self._set_markercolor("markerfacecoloralt", False, fc)
+
+    def set_markeredgewidth(self, ew):
+        """
+        Set the marker edge width in points.
+
+        Parameters
+        ----------
+        ew : float
+             Marker edge width, in points.
+        """
+        if ew is None:
+            ew = mpl.rcParams['lines.markeredgewidth']
+        if self._markeredgewidth != ew:
+            self.stale = True
+        self._markeredgewidth = ew
+
+    def set_markersize(self, sz):
+        """
+        Set the marker size in points.
+
+        Parameters
+        ----------
+        sz : float
+             Marker size, in points.
+        """
+        sz = float(sz)
+        if self._markersize != sz:
+            self.stale = True
+        self._markersize = sz
+
+    def set_xdata(self, x):
+        """
+        Set the data array for x.
+
+        Parameters
+        ----------
+        x : 1D array
+
+        See Also
+        --------
+        set_data
+        set_ydata
+        """
+        if not np.iterable(x):
+            raise RuntimeError('x must be a sequence')
+        self._xorig = copy.copy(x)
+        self._invalidx = True
+        self.stale = True
+
+    def set_ydata(self, y):
+        """
+        Set the data array for y.
+
+        Parameters
+        ----------
+        y : 1D array
+
+        See Also
+        --------
+        set_data
+        set_xdata
+        """
+        if not np.iterable(y):
+            raise RuntimeError('y must be a sequence')
+        self._yorig = copy.copy(y)
+        self._invalidy = True
+        self.stale = True
+
+    def set_dashes(self, seq):
+        """
+        Set the dash sequence.
+
+        The dash sequence is a sequence of floats of even length describing
+        the length of dashes and spaces in points.
+
+        For example, (5, 2, 1, 2) describes a sequence of 5 point and 1 point
+        dashes separated by 2 point spaces.
+
+        See also `~.Line2D.set_gapcolor`, which allows those spaces to be
+        filled with a color.
+
+        Parameters
+        ----------
+        seq : sequence of floats (on/off ink in points) or (None, None)
+            If *seq* is empty or ``(None, None)``, the linestyle will be set
+            to solid.
+        """
+        if seq == (None, None) or len(seq) == 0:
+            self.set_linestyle('-')
+        else:
+            self.set_linestyle((0, seq))
+
+    def update_from(self, other):
+        """Copy properties from *other* to self."""
+        super().update_from(other)
+        self._linestyle = other._linestyle
+        self._linewidth = other._linewidth
+        self._color = other._color
+        self._gapcolor = other._gapcolor
+        self._markersize = other._markersize
+        self._markerfacecolor = other._markerfacecolor
+        self._markerfacecoloralt = other._markerfacecoloralt
+        self._markeredgecolor = other._markeredgecolor
+        self._markeredgewidth = other._markeredgewidth
+        self._unscaled_dash_pattern = other._unscaled_dash_pattern
+        self._dash_pattern = other._dash_pattern
+        self._dashcapstyle = other._dashcapstyle
+        self._dashjoinstyle = other._dashjoinstyle
+        self._solidcapstyle = other._solidcapstyle
+        self._solidjoinstyle = other._solidjoinstyle
+
+        self._linestyle = other._linestyle
+        self._marker = MarkerStyle(marker=other._marker)
+        self._drawstyle = other._drawstyle
+
+    @_docstring.interpd
+    def set_dash_joinstyle(self, s):
+        """
+        How to join segments of the line if it `~Line2D.is_dashed`.
+
+        The default joinstyle is :rc:`lines.dash_joinstyle`.
+
+        Parameters
+        ----------
+        s : `.JoinStyle` or %(JoinStyle)s
+        """
+        js = JoinStyle(s)
+        if self._dashjoinstyle != js:
+            self.stale = True
+        self._dashjoinstyle = js
+
+    @_docstring.interpd
+    def set_solid_joinstyle(self, s):
+        """
+        How to join segments if the line is solid (not `~Line2D.is_dashed`).
+
+        The default joinstyle is :rc:`lines.solid_joinstyle`.
+
+        Parameters
+        ----------
+        s : `.JoinStyle` or %(JoinStyle)s
+        """
+        js = JoinStyle(s)
+        if self._solidjoinstyle != js:
+            self.stale = True
+        self._solidjoinstyle = js
+
+    def get_dash_joinstyle(self):
+        """
+        Return the `.JoinStyle` for dashed lines.
+
+        See also `~.Line2D.set_dash_joinstyle`.
+        """
+        return self._dashjoinstyle.name
+
+    def get_solid_joinstyle(self):
+        """
+        Return the `.JoinStyle` for solid lines.
+
+        See also `~.Line2D.set_solid_joinstyle`.
+        """
+        return self._solidjoinstyle.name
+
+    @_docstring.interpd
+    def set_dash_capstyle(self, s):
+        """
+        How to draw the end caps if the line is `~Line2D.is_dashed`.
+
+        The default capstyle is :rc:`lines.dash_capstyle`.
+
+        Parameters
+        ----------
+        s : `.CapStyle` or %(CapStyle)s
+        """
+        cs = CapStyle(s)
+        if self._dashcapstyle != cs:
+            self.stale = True
+        self._dashcapstyle = cs
+
+    @_docstring.interpd
+    def set_solid_capstyle(self, s):
+        """
+        How to draw the end caps if the line is solid (not `~Line2D.is_dashed`)
+
+        The default capstyle is :rc:`lines.solid_capstyle`.
+
+        Parameters
+        ----------
+        s : `.CapStyle` or %(CapStyle)s
+        """
+        cs = CapStyle(s)
+        if self._solidcapstyle != cs:
+            self.stale = True
+        self._solidcapstyle = cs
+
+    def get_dash_capstyle(self):
+        """
+        Return the `.CapStyle` for dashed lines.
+
+        See also `~.Line2D.set_dash_capstyle`.
+        """
+        return self._dashcapstyle.name
+
+    def get_solid_capstyle(self):
+        """
+        Return the `.CapStyle` for solid lines.
+
+        See also `~.Line2D.set_solid_capstyle`.
+        """
+        return self._solidcapstyle.name
+
+    def is_dashed(self):
+        """
+        Return whether line has a dashed linestyle.
+
+        A custom linestyle is assumed to be dashed, we do not inspect the
+        ``onoffseq`` directly.
+
+        See also `~.Line2D.set_linestyle`.
+        """
+        return self._linestyle in ('--', '-.', ':')
+
+
+class AxLine(Line2D):
+    """
+    A helper class that implements `~.Axes.axline`, by recomputing the artist
+    transform at draw time.
+    """
+
+    def __init__(self, xy1, xy2, slope, **kwargs):
+        """
+        Parameters
+        ----------
+        xy1 : (float, float)
+            The first set of (x, y) coordinates for the line to pass through.
+        xy2 : (float, float) or None
+            The second set of (x, y) coordinates for the line to pass through.
+            Both *xy2* and *slope* must be passed, but one of them must be None.
+        slope : float or None
+            The slope of the line. Both *xy2* and *slope* must be passed, but one of
+            them must be None.
+        """
+        super().__init__([0, 1], [0, 1], **kwargs)
+
+        if (xy2 is None and slope is None or
+                xy2 is not None and slope is not None):
+            raise TypeError(
+                "Exactly one of 'xy2' and 'slope' must be given")
+
+        self._slope = slope
+        self._xy1 = xy1
+        self._xy2 = xy2
+
+    def get_transform(self):
+        ax = self.axes
+        points_transform = self._transform - ax.transData + ax.transScale
+
+        if self._xy2 is not None:
+            # two points were given
+            (x1, y1), (x2, y2) = \
+                points_transform.transform([self._xy1, self._xy2])
+            dx = x2 - x1
+            dy = y2 - y1
+            if dx == 0:
+                if dy == 0:
+                    raise ValueError(
+                        f"Cannot draw a line through two identical points "
+                        f"(x={(x1, x2)}, y={(y1, y2)})")
+                slope = np.inf
+            else:
+                slope = dy / dx
+        else:
+            # one point and a slope were given
+            x1, y1 = points_transform.transform(self._xy1)
+            slope = self._slope
+        (vxlo, vylo), (vxhi, vyhi) = ax.transScale.transform(ax.viewLim)
+        # General case: find intersections with view limits in either
+        # direction, and draw between the middle two points.
+        if slope == 0:
+            start = vxlo, y1
+            stop = vxhi, y1
+        elif np.isinf(slope):
+            start = x1, vylo
+            stop = x1, vyhi
+        else:
+            _, start, stop, _ = sorted([
+                (vxlo, y1 + (vxlo - x1) * slope),
+                (vxhi, y1 + (vxhi - x1) * slope),
+                (x1 + (vylo - y1) / slope, vylo),
+                (x1 + (vyhi - y1) / slope, vyhi),
+            ])
+        return (BboxTransformTo(Bbox([start, stop]))
+                + ax.transLimits + ax.transAxes)
+
+    def draw(self, renderer):
+        self._transformed_path = None  # Force regen.
+        super().draw(renderer)
+
+    def get_xy1(self):
+        """Return the *xy1* value of the line."""
+        return self._xy1
+
+    def get_xy2(self):
+        """Return the *xy2* value of the line."""
+        return self._xy2
+
+    def get_slope(self):
+        """Return the *slope* value of the line."""
+        return self._slope
+
+    def set_xy1(self, *args, **kwargs):
+        """
+        Set the *xy1* value of the line.
+
+        Parameters
+        ----------
+        xy1 : tuple[float, float]
+            Points for the line to pass through.
+        """
+        params = _api.select_matching_signature([
+            lambda self, x, y: locals(), lambda self, xy1: locals(),
+        ], self, *args, **kwargs)
+        if "x" in params:
+            _api.warn_deprecated("3.10", message=(
+                "Passing x and y separately to AxLine.set_xy1 is deprecated since "
+                "%(since)s; pass them as a single tuple instead."))
+            xy1 = params["x"], params["y"]
+        else:
+            xy1 = params["xy1"]
+        self._xy1 = xy1
+
+    def set_xy2(self, *args, **kwargs):
+        """
+        Set the *xy2* value of the line.
+
+        .. note::
+
+            You can only set *xy2* if the line was created using the *xy2*
+            parameter. If the line was created using *slope*, please use
+            `~.AxLine.set_slope`.
+
+        Parameters
+        ----------
+        xy2 : tuple[float, float]
+            Points for the line to pass through.
+        """
+        if self._slope is None:
+            params = _api.select_matching_signature([
+                lambda self, x, y: locals(), lambda self, xy2: locals(),
+            ], self, *args, **kwargs)
+            if "x" in params:
+                _api.warn_deprecated("3.10", message=(
+                    "Passing x and y separately to AxLine.set_xy2 is deprecated since "
+                    "%(since)s; pass them as a single tuple instead."))
+                xy2 = params["x"], params["y"]
+            else:
+                xy2 = params["xy2"]
+            self._xy2 = xy2
+        else:
+            raise ValueError("Cannot set an 'xy2' value while 'slope' is set;"
+                             " they differ but their functionalities overlap")
+
+    def set_slope(self, slope):
+        """
+        Set the *slope* value of the line.
+
+        .. note::
+
+            You can only set *slope* if the line was created using the *slope*
+            parameter. If the line was created using *xy2*, please use
+            `~.AxLine.set_xy2`.
+
+        Parameters
+        ----------
+        slope : float
+            The slope of the line.
+        """
+        if self._xy2 is None:
+            self._slope = slope
+        else:
+            raise ValueError("Cannot set a 'slope' value while 'xy2' is set;"
+                             " they differ but their functionalities overlap")
+
+
+class VertexSelector:
+    """
+    Manage the callbacks to maintain a list of selected vertices for `.Line2D`.
+    Derived classes should override the `process_selected` method to do
+    something with the picks.
+
+    Here is an example which highlights the selected verts with red circles::
+
+        import numpy as np
+        import matplotlib.pyplot as plt
+        import matplotlib.lines as lines
+
+        class HighlightSelected(lines.VertexSelector):
+            def __init__(self, line, fmt='ro', **kwargs):
+                super().__init__(line)
+                self.markers, = self.axes.plot([], [], fmt, **kwargs)
+
+            def process_selected(self, ind, xs, ys):
+                self.markers.set_data(xs, ys)
+                self.canvas.draw()
+
+        fig, ax = plt.subplots()
+        x, y = np.random.rand(2, 30)
+        line, = ax.plot(x, y, 'bs-', picker=5)
+
+        selector = HighlightSelected(line)
+        plt.show()
+    """
+
+    def __init__(self, line):
+        """
+        Parameters
+        ----------
+        line : `~matplotlib.lines.Line2D`
+            The line must already have been added to an `~.axes.Axes` and must
+            have its picker property set.
+        """
+        if line.axes is None:
+            raise RuntimeError('You must first add the line to the Axes')
+        if line.get_picker() is None:
+            raise RuntimeError('You must first set the picker property '
+                               'of the line')
+        self.axes = line.axes
+        self.line = line
+        self.cid = self.canvas.callbacks._connect_picklable(
+            'pick_event', self.onpick)
+        self.ind = set()
+
+    canvas = property(lambda self: self.axes.get_figure(root=True).canvas)
+
+    def process_selected(self, ind, xs, ys):
+        """
+        Default "do nothing" implementation of the `process_selected` method.
+
+        Parameters
+        ----------
+        ind : list of int
+            The indices of the selected vertices.
+        xs, ys : array-like
+            The coordinates of the selected vertices.
+        """
+        pass
+
+    def onpick(self, event):
+        """When the line is picked, update the set of selected indices."""
+        if event.artist is not self.line:
+            return
+        self.ind ^= set(event.ind)
+        ind = sorted(self.ind)
+        xdata, ydata = self.line.get_data()
+        self.process_selected(ind, xdata[ind], ydata[ind])
+
+
+lineStyles = Line2D._lineStyles
+lineMarkers = MarkerStyle.markers
+drawStyles = Line2D.drawStyles
+fillStyles = MarkerStyle.fillstyles
diff --git a/shared/package-files/04/046e60e26bc957b9427421635942547e9a2b5940487ead84421bdd6d3e959ae1/sdpa.py b/shared/package-files/04/046e60e26bc957b9427421635942547e9a2b5940487ead84421bdd6d3e959ae1/sdpa.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a6af76690df6cbbaa7538bb4adfa6acc5b3ce24
--- /dev/null
+++ b/shared/package-files/04/046e60e26bc957b9427421635942547e9a2b5940487ead84421bdd6d3e959ae1/sdpa.py
@@ -0,0 +1,84 @@
+# mypy: ignore-errors
+
+from inspect import getattr_static
+
+from ..bytecode_transformation import create_call_function
+from ..exc import Unsupported
+from .base import VariableTracker
+
+
+class SDPAParamsVariable(VariableTracker):
+    """Represents the c++ params struct for scaled dot product attention.
+    This is a read-only container."""
+
+    @staticmethod
+    def create(tx, value, source):
+        from torch.backends.cuda import SDPAParams
+        from ..source import AttrSource
+        from .builder import VariableBuilder
+        from .torch import TorchInGraphFunctionVariable
+
+        query_var = VariableBuilder(tx, AttrSource(source, "query"))(value.query)
+        key_var = VariableBuilder(tx, AttrSource(source, "key"))(value.key)
+        value_var = VariableBuilder(tx, AttrSource(source, "value"))(value.value)
+        attn_mask_var = VariableBuilder(tx, AttrSource(source, "attn_mask"))(
+            value.attn_mask
+        )
+        dropout_var = VariableBuilder(tx, AttrSource(source, "dropout"))(value.dropout)
+        is_causal_var = VariableBuilder(tx, AttrSource(source, "is_causal"))(
+            value.is_causal
+        )
+        param_vars = [
+            query_var,
+            key_var,
+            value_var,
+            attn_mask_var,
+            dropout_var,
+            is_causal_var,
+        ]
+        return TorchInGraphFunctionVariable(SDPAParams).call_function(
+            tx, param_vars, {}
+        )
+
+    def __init__(self, proxy, param_vars, **kwargs):
+        self.proxy = proxy
+        self.param_vars = param_vars
+        super().__init__(**kwargs)
+
+    def reconstruct(self, codegen):
+        assert self.source is None
+        assert self.param_vars is not None
+        codegen.load_import_from("torch._C", "_SDPAParams")
+        codegen.foreach(self.param_vars)
+        codegen.extend_output(create_call_function(len(self.param_vars), True))
+
+    def as_proxy(self):
+        return self.proxy
+
+    def var_getattr(self, tx, name: str) -> VariableTracker:
+        import torch._C
+        from ..source import AttrSource
+        from .builder import wrap_fx_proxy
+        from .misc import GetAttrVariable
+
+        try:
+            getattr_static(torch._C._SDPAParams, name)
+        except AttributeError:
+            # Using raise from is too verbose here
+            raise Unsupported(  # noqa: TRY200
+                f"Unsupported torch._C._SDPAParams attribute {name}"
+            )
+
+        proxy = GetAttrVariable.create_getattr_proxy(self.as_proxy(), name)
+        if self.source is not None:
+            return wrap_fx_proxy(
+                tx=tx, proxy=proxy, source=AttrSource(self.source, name)
+            )
+        else:
+            return wrap_fx_proxy(tx=tx, proxy=proxy)
+
+    @staticmethod
+    def is_sdpa_params(value):
+        from torch.backends.cuda import SDPAParams
+
+        return value is SDPAParams
diff --git a/shared/package-files/04/046f76f4d0b2ea0dcb1e1a63f11fee7945aa2dbb88b74932089871475439489a/_vim_builtins.py b/shared/package-files/04/046f76f4d0b2ea0dcb1e1a63f11fee7945aa2dbb88b74932089871475439489a/_vim_builtins.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccc8740f47a2e66bd69113f17d8fecae4507d866
--- /dev/null
+++ b/shared/package-files/04/046f76f4d0b2ea0dcb1e1a63f11fee7945aa2dbb88b74932089871475439489a/_vim_builtins.py
@@ -0,0 +1,1938 @@
+"""
+    pygments.lexers._vim_builtins
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+    This file is autogenerated by scripts/get_vimkw.py
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+# Split up in multiple functions so it's importable by jython, which has a
+# per-method size limit.
+
+def _getauto():
+    var = (
+        ('BufAdd','BufAdd'),
+        ('BufCreate','BufCreate'),
+        ('BufDelete','BufDelete'),
+        ('BufEnter','BufEnter'),
+        ('BufFilePost','BufFilePost'),
+        ('BufFilePre','BufFilePre'),
+        ('BufHidden','BufHidden'),
+        ('BufLeave','BufLeave'),
+        ('BufNew','BufNew'),
+        ('BufNewFile','BufNewFile'),
+        ('BufRead','BufRead'),
+        ('BufReadCmd','BufReadCmd'),
+        ('BufReadPost','BufReadPost'),
+        ('BufReadPre','BufReadPre'),
+        ('BufUnload','BufUnload'),
+        ('BufWinEnter','BufWinEnter'),
+        ('BufWinLeave','BufWinLeave'),
+        ('BufWipeout','BufWipeout'),
+        ('BufWrite','BufWrite'),
+        ('BufWriteCmd','BufWriteCmd'),
+        ('BufWritePost','BufWritePost'),
+        ('BufWritePre','BufWritePre'),
+        ('Cmd','Cmd'),
+        ('CmdwinEnter','CmdwinEnter'),
+        ('CmdwinLeave','CmdwinLeave'),
+        ('ColorScheme','ColorScheme'),
+        ('CompleteDone','CompleteDone'),
+        ('CursorHold','CursorHold'),
+        ('CursorHoldI','CursorHoldI'),
+        ('CursorMoved','CursorMoved'),
+        ('CursorMovedI','CursorMovedI'),
+        ('EncodingChanged','EncodingChanged'),
+        ('FileAppendCmd','FileAppendCmd'),
+        ('FileAppendPost','FileAppendPost'),
+        ('FileAppendPre','FileAppendPre'),
+        ('FileChangedRO','FileChangedRO'),
+        ('FileChangedShell','FileChangedShell'),
+        ('FileChangedShellPost','FileChangedShellPost'),
+        ('FileEncoding','FileEncoding'),
+        ('FileReadCmd','FileReadCmd'),
+        ('FileReadPost','FileReadPost'),
+        ('FileReadPre','FileReadPre'),
+        ('FileType','FileType'),
+        ('FileWriteCmd','FileWriteCmd'),
+        ('FileWritePost','FileWritePost'),
+        ('FileWritePre','FileWritePre'),
+        ('FilterReadPost','FilterReadPost'),
+        ('FilterReadPre','FilterReadPre'),
+        ('FilterWritePost','FilterWritePost'),
+        ('FilterWritePre','FilterWritePre'),
+        ('FocusGained','FocusGained'),
+        ('FocusLost','FocusLost'),
+        ('FuncUndefined','FuncUndefined'),
+        ('GUIEnter','GUIEnter'),
+        ('GUIFailed','GUIFailed'),
+        ('InsertChange','InsertChange'),
+        ('InsertCharPre','InsertCharPre'),
+        ('InsertEnter','InsertEnter'),
+        ('InsertLeave','InsertLeave'),
+        ('MenuPopup','MenuPopup'),
+        ('QuickFixCmdPost','QuickFixCmdPost'),
+        ('QuickFixCmdPre','QuickFixCmdPre'),
+        ('QuitPre','QuitPre'),
+        ('RemoteReply','RemoteReply'),
+        ('SessionLoadPost','SessionLoadPost'),
+        ('ShellCmdPost','ShellCmdPost'),
+        ('ShellFilterPost','ShellFilterPost'),
+        ('SourceCmd','SourceCmd'),
+        ('SourcePre','SourcePre'),
+        ('SpellFileMissing','SpellFileMissing'),
+        ('StdinReadPost','StdinReadPost'),
+        ('StdinReadPre','StdinReadPre'),
+        ('SwapExists','SwapExists'),
+        ('Syntax','Syntax'),
+        ('TabEnter','TabEnter'),
+        ('TabLeave','TabLeave'),
+        ('TermChanged','TermChanged'),
+        ('TermResponse','TermResponse'),
+        ('TextChanged','TextChanged'),
+        ('TextChangedI','TextChangedI'),
+        ('User','User'),
+        ('UserGettingBored','UserGettingBored'),
+        ('VimEnter','VimEnter'),
+        ('VimLeave','VimLeave'),
+        ('VimLeavePre','VimLeavePre'),
+        ('VimResized','VimResized'),
+        ('WinEnter','WinEnter'),
+        ('WinLeave','WinLeave'),
+        ('event','event'),
+    )
+    return var
+auto = _getauto()
+
+def _getcommand():
+    var = (
+        ('a','a'),
+        ('ab','ab'),
+        ('abc','abclear'),
+        ('abo','aboveleft'),
+        ('al','all'),
+        ('ar','ar'),
+        ('ar','args'),
+        ('arga','argadd'),
+        ('argd','argdelete'),
+        ('argdo','argdo'),
+        ('arge','argedit'),
+        ('argg','argglobal'),
+        ('argl','arglocal'),
+        ('argu','argument'),
+        ('as','ascii'),
+        ('au','au'),
+        ('b','buffer'),
+        ('bN','bNext'),
+        ('ba','ball'),
+        ('bad','badd'),
+        ('bd','bdelete'),
+        ('bel','belowright'),
+        ('bf','bfirst'),
+        ('bl','blast'),
+        ('bm','bmodified'),
+        ('bn','bnext'),
+        ('bo','botright'),
+        ('bp','bprevious'),
+        ('br','br'),
+        ('br','brewind'),
+        ('brea','break'),
+        ('breaka','breakadd'),
+        ('breakd','breakdel'),
+        ('breakl','breaklist'),
+        ('bro','browse'),
+        ('bu','bu'),
+        ('buf','buf'),
+        ('bufdo','bufdo'),
+        ('buffers','buffers'),
+        ('bun','bunload'),
+        ('bw','bwipeout'),
+        ('c','c'),
+        ('c','change'),
+        ('cN','cN'),
+        ('cN','cNext'),
+        ('cNf','cNf'),
+        ('cNf','cNfile'),
+        ('cabc','cabclear'),
+        ('cad','cad'),
+        ('cad','caddexpr'),
+        ('caddb','caddbuffer'),
+        ('caddf','caddfile'),
+        ('cal','call'),
+        ('cat','catch'),
+        ('cb','cbuffer'),
+        ('cc','cc'),
+        ('ccl','cclose'),
+        ('cd','cd'),
+        ('ce','center'),
+        ('cex','cexpr'),
+        ('cf','cfile'),
+        ('cfir','cfirst'),
+        ('cg','cgetfile'),
+        ('cgetb','cgetbuffer'),
+        ('cgete','cgetexpr'),
+        ('changes','changes'),
+        ('chd','chdir'),
+        ('che','checkpath'),
+        ('checkt','checktime'),
+        ('cl','cl'),
+        ('cl','clist'),
+        ('cla','clast'),
+        ('clo','close'),
+        ('cmapc','cmapclear'),
+        ('cn','cn'),
+        ('cn','cnext'),
+        ('cnew','cnewer'),
+        ('cnf','cnf'),
+        ('cnf','cnfile'),
+        ('co','copy'),
+        ('col','colder'),
+        ('colo','colorscheme'),
+        ('com','com'),
+        ('comc','comclear'),
+        ('comp','compiler'),
+        ('con','con'),
+        ('con','continue'),
+        ('conf','confirm'),
+        ('cope','copen'),
+        ('cp','cprevious'),
+        ('cpf','cpfile'),
+        ('cq','cquit'),
+        ('cr','crewind'),
+        ('cs','cs'),
+        ('cscope','cscope'),
+        ('cstag','cstag'),
+        ('cuna','cunabbrev'),
+        ('cw','cwindow'),
+        ('d','d'),
+        ('d','delete'),
+        ('de','de'),
+        ('debug','debug'),
+        ('debugg','debuggreedy'),
+        ('del','del'),
+        ('delc','delcommand'),
+        ('delel','delel'),
+        ('delep','delep'),
+        ('deletel','deletel'),
+        ('deletep','deletep'),
+        ('deletl','deletl'),
+        ('deletp','deletp'),
+        ('delf','delf'),
+        ('delf','delfunction'),
+        ('dell','dell'),
+        ('delm','delmarks'),
+        ('delp','delp'),
+        ('dep','dep'),
+        ('di','di'),
+        ('di','display'),
+        ('diffg','diffget'),
+        ('diffo','diffoff'),
+        ('diffp','diffpatch'),
+        ('diffpu','diffput'),
+        ('diffs','diffsplit'),
+        ('difft','diffthis'),
+        ('diffu','diffupdate'),
+        ('dig','dig'),
+        ('dig','digraphs'),
+        ('dir','dir'),
+        ('dj','djump'),
+        ('dl','dl'),
+        ('dli','dlist'),
+        ('do','do'),
+        ('doau','doau'),
+        ('dp','dp'),
+        ('dr','drop'),
+        ('ds','dsearch'),
+        ('dsp','dsplit'),
+        ('e','e'),
+        ('e','edit'),
+        ('ea','ea'),
+        ('earlier','earlier'),
+        ('ec','ec'),
+        ('echoe','echoerr'),
+        ('echom','echomsg'),
+        ('echon','echon'),
+        ('el','else'),
+        ('elsei','elseif'),
+        ('em','emenu'),
+        ('en','en'),
+        ('en','endif'),
+        ('endf','endf'),
+        ('endf','endfunction'),
+        ('endfo','endfor'),
+        ('endfun','endfun'),
+        ('endt','endtry'),
+        ('endw','endwhile'),
+        ('ene','enew'),
+        ('ex','ex'),
+        ('exi','exit'),
+        ('exu','exusage'),
+        ('f','f'),
+        ('f','file'),
+        ('files','files'),
+        ('filet','filet'),
+        ('filetype','filetype'),
+        ('fin','fin'),
+        ('fin','find'),
+        ('fina','finally'),
+        ('fini','finish'),
+        ('fir','first'),
+        ('fix','fixdel'),
+        ('fo','fold'),
+        ('foldc','foldclose'),
+        ('foldd','folddoopen'),
+        ('folddoc','folddoclosed'),
+        ('foldo','foldopen'),
+        ('for','for'),
+        ('fu','fu'),
+        ('fu','function'),
+        ('fun','fun'),
+        ('g','g'),
+        ('go','goto'),
+        ('gr','grep'),
+        ('grepa','grepadd'),
+        ('gui','gui'),
+        ('gvim','gvim'),
+        ('h','h'),
+        ('h','help'),
+        ('ha','hardcopy'),
+        ('helpf','helpfind'),
+        ('helpg','helpgrep'),
+        ('helpt','helptags'),
+        ('hi','hi'),
+        ('hid','hide'),
+        ('his','history'),
+        ('i','i'),
+        ('ia','ia'),
+        ('iabc','iabclear'),
+        ('if','if'),
+        ('ij','ijump'),
+        ('il','ilist'),
+        ('imapc','imapclear'),
+        ('in','in'),
+        ('intro','intro'),
+        ('is','isearch'),
+        ('isp','isplit'),
+        ('iuna','iunabbrev'),
+        ('j','join'),
+        ('ju','jumps'),
+        ('k','k'),
+        ('kee','keepmarks'),
+        ('keepa','keepa'),
+        ('keepalt','keepalt'),
+        ('keepj','keepjumps'),
+        ('keepp','keeppatterns'),
+        ('l','l'),
+        ('l','list'),
+        ('lN','lN'),
+        ('lN','lNext'),
+        ('lNf','lNf'),
+        ('lNf','lNfile'),
+        ('la','la'),
+        ('la','last'),
+        ('lad','lad'),
+        ('lad','laddexpr'),
+        ('laddb','laddbuffer'),
+        ('laddf','laddfile'),
+        ('lan','lan'),
+        ('lan','language'),
+        ('lat','lat'),
+        ('later','later'),
+        ('lb','lbuffer'),
+        ('lc','lcd'),
+        ('lch','lchdir'),
+        ('lcl','lclose'),
+        ('lcs','lcs'),
+        ('lcscope','lcscope'),
+        ('le','left'),
+        ('lefta','leftabove'),
+        ('lex','lexpr'),
+        ('lf','lfile'),
+        ('lfir','lfirst'),
+        ('lg','lgetfile'),
+        ('lgetb','lgetbuffer'),
+        ('lgete','lgetexpr'),
+        ('lgr','lgrep'),
+        ('lgrepa','lgrepadd'),
+        ('lh','lhelpgrep'),
+        ('ll','ll'),
+        ('lla','llast'),
+        ('lli','llist'),
+        ('lmak','lmake'),
+        ('lmapc','lmapclear'),
+        ('lne','lne'),
+        ('lne','lnext'),
+        ('lnew','lnewer'),
+        ('lnf','lnf'),
+        ('lnf','lnfile'),
+        ('lo','lo'),
+        ('lo','loadview'),
+        ('loadk','loadk'),
+        ('loadkeymap','loadkeymap'),
+        ('loc','lockmarks'),
+        ('lockv','lockvar'),
+        ('lol','lolder'),
+        ('lop','lopen'),
+        ('lp','lprevious'),
+        ('lpf','lpfile'),
+        ('lr','lrewind'),
+        ('ls','ls'),
+        ('lt','ltag'),
+        ('lua','lua'),
+        ('luado','luado'),
+        ('luafile','luafile'),
+        ('lv','lvimgrep'),
+        ('lvimgrepa','lvimgrepadd'),
+        ('lw','lwindow'),
+        ('m','move'),
+        ('ma','ma'),
+        ('ma','mark'),
+        ('mak','make'),
+        ('marks','marks'),
+        ('mat','match'),
+        ('menut','menut'),
+        ('menut','menutranslate'),
+        ('mes','mes'),
+        ('messages','messages'),
+        ('mk','mk'),
+        ('mk','mkexrc'),
+        ('mks','mksession'),
+        ('mksp','mkspell'),
+        ('mkv','mkv'),
+        ('mkv','mkvimrc'),
+        ('mkvie','mkview'),
+        ('mo','mo'),
+        ('mod','mode'),
+        ('mz','mz'),
+        ('mz','mzscheme'),
+        ('mzf','mzfile'),
+        ('n','n'),
+        ('n','next'),
+        ('nb','nbkey'),
+        ('nbc','nbclose'),
+        ('nbs','nbstart'),
+        ('ne','ne'),
+        ('new','new'),
+        ('nmapc','nmapclear'),
+        ('noa','noa'),
+        ('noautocmd','noautocmd'),
+        ('noh','nohlsearch'),
+        ('nu','number'),
+        ('o','o'),
+        ('o','open'),
+        ('ol','oldfiles'),
+        ('omapc','omapclear'),
+        ('on','only'),
+        ('opt','options'),
+        ('ownsyntax','ownsyntax'),
+        ('p','p'),
+        ('p','print'),
+        ('pc','pclose'),
+        ('pe','pe'),
+        ('pe','perl'),
+        ('ped','pedit'),
+        ('perld','perldo'),
+        ('po','pop'),
+        ('popu','popu'),
+        ('popu','popup'),
+        ('pp','ppop'),
+        ('pr','pr'),
+        ('pre','preserve'),
+        ('prev','previous'),
+        ('pro','pro'),
+        ('prof','profile'),
+        ('profd','profdel'),
+        ('promptf','promptfind'),
+        ('promptr','promptrepl'),
+        ('ps','psearch'),
+        ('ptN','ptN'),
+        ('ptN','ptNext'),
+        ('pta','ptag'),
+        ('ptf','ptfirst'),
+        ('ptj','ptjump'),
+        ('ptl','ptlast'),
+        ('ptn','ptn'),
+        ('ptn','ptnext'),
+        ('ptp','ptprevious'),
+        ('ptr','ptrewind'),
+        ('pts','ptselect'),
+        ('pu','put'),
+        ('pw','pwd'),
+        ('py','py'),
+        ('py','python'),
+        ('py3','py3'),
+        ('py3','py3'),
+        ('py3do','py3do'),
+        ('pydo','pydo'),
+        ('pyf','pyfile'),
+        ('python3','python3'),
+        ('q','q'),
+        ('q','quit'),
+        ('qa','qall'),
+        ('quita','quitall'),
+        ('r','r'),
+        ('r','read'),
+        ('re','re'),
+        ('rec','recover'),
+        ('red','red'),
+        ('red','redo'),
+        ('redi','redir'),
+        ('redr','redraw'),
+        ('redraws','redrawstatus'),
+        ('reg','registers'),
+        ('res','resize'),
+        ('ret','retab'),
+        ('retu','return'),
+        ('rew','rewind'),
+        ('ri','right'),
+        ('rightb','rightbelow'),
+        ('ru','ru'),
+        ('ru','runtime'),
+        ('rub','ruby'),
+        ('rubyd','rubydo'),
+        ('rubyf','rubyfile'),
+        ('rundo','rundo'),
+        ('rv','rviminfo'),
+        ('sN','sNext'),
+        ('sa','sargument'),
+        ('sal','sall'),
+        ('san','sandbox'),
+        ('sav','saveas'),
+        ('sb','sbuffer'),
+        ('sbN','sbNext'),
+        ('sba','sball'),
+        ('sbf','sbfirst'),
+        ('sbl','sblast'),
+        ('sbm','sbmodified'),
+        ('sbn','sbnext'),
+        ('sbp','sbprevious'),
+        ('sbr','sbrewind'),
+        ('scrip','scrip'),
+        ('scrip','scriptnames'),
+        ('scripte','scriptencoding'),
+        ('scs','scs'),
+        ('scscope','scscope'),
+        ('se','set'),
+        ('setf','setfiletype'),
+        ('setg','setglobal'),
+        ('setl','setlocal'),
+        ('sf','sfind'),
+        ('sfir','sfirst'),
+        ('sh','shell'),
+        ('si','si'),
+        ('sig','sig'),
+        ('sign','sign'),
+        ('sil','silent'),
+        ('sim','simalt'),
+        ('sl','sl'),
+        ('sl','sleep'),
+        ('sla','slast'),
+        ('sm','smagic'),
+        ('sm','smap'),
+        ('sme','sme'),
+        ('smenu','smenu'),
+        ('sn','snext'),
+        ('sni','sniff'),
+        ('sno','snomagic'),
+        ('snoreme','snoreme'),
+        ('snoremenu','snoremenu'),
+        ('so','so'),
+        ('so','source'),
+        ('sor','sort'),
+        ('sp','split'),
+        ('spe','spe'),
+        ('spe','spellgood'),
+        ('spelld','spelldump'),
+        ('spelli','spellinfo'),
+        ('spellr','spellrepall'),
+        ('spellu','spellundo'),
+        ('spellw','spellwrong'),
+        ('spr','sprevious'),
+        ('sre','srewind'),
+        ('st','st'),
+        ('st','stop'),
+        ('sta','stag'),
+        ('star','star'),
+        ('star','startinsert'),
+        ('start','start'),
+        ('startg','startgreplace'),
+        ('startr','startreplace'),
+        ('stj','stjump'),
+        ('stopi','stopinsert'),
+        ('sts','stselect'),
+        ('sun','sunhide'),
+        ('sunme','sunme'),
+        ('sunmenu','sunmenu'),
+        ('sus','suspend'),
+        ('sv','sview'),
+        ('sw','swapname'),
+        ('sy','sy'),
+        ('syn','syn'),
+        ('sync','sync'),
+        ('syncbind','syncbind'),
+        ('syntime','syntime'),
+        ('t','t'),
+        ('tN','tN'),
+        ('tN','tNext'),
+        ('ta','ta'),
+        ('ta','tag'),
+        ('tab','tab'),
+        ('tabN','tabN'),
+        ('tabN','tabNext'),
+        ('tabc','tabclose'),
+        ('tabd','tabdo'),
+        ('tabe','tabedit'),
+        ('tabf','tabfind'),
+        ('tabfir','tabfirst'),
+        ('tabl','tablast'),
+        ('tabm','tabmove'),
+        ('tabn','tabnext'),
+        ('tabnew','tabnew'),
+        ('tabo','tabonly'),
+        ('tabp','tabprevious'),
+        ('tabr','tabrewind'),
+        ('tabs','tabs'),
+        ('tags','tags'),
+        ('tc','tcl'),
+        ('tcld','tcldo'),
+        ('tclf','tclfile'),
+        ('te','tearoff'),
+        ('tf','tfirst'),
+        ('th','throw'),
+        ('tj','tjump'),
+        ('tl','tlast'),
+        ('tm','tm'),
+        ('tm','tmenu'),
+        ('tn','tn'),
+        ('tn','tnext'),
+        ('to','topleft'),
+        ('tp','tprevious'),
+        ('tr','tr'),
+        ('tr','trewind'),
+        ('try','try'),
+        ('ts','tselect'),
+        ('tu','tu'),
+        ('tu','tunmenu'),
+        ('u','u'),
+        ('u','undo'),
+        ('un','un'),
+        ('una','unabbreviate'),
+        ('undoj','undojoin'),
+        ('undol','undolist'),
+        ('unh','unhide'),
+        ('unl','unl'),
+        ('unlo','unlockvar'),
+        ('uns','unsilent'),
+        ('up','update'),
+        ('v','v'),
+        ('ve','ve'),
+        ('ve','version'),
+        ('verb','verbose'),
+        ('vert','vertical'),
+        ('vi','vi'),
+        ('vi','visual'),
+        ('vie','view'),
+        ('vim','vimgrep'),
+        ('vimgrepa','vimgrepadd'),
+        ('viu','viusage'),
+        ('vmapc','vmapclear'),
+        ('vne','vnew'),
+        ('vs','vsplit'),
+        ('w','w'),
+        ('w','write'),
+        ('wN','wNext'),
+        ('wa','wall'),
+        ('wh','while'),
+        ('win','win'),
+        ('win','winsize'),
+        ('winc','wincmd'),
+        ('windo','windo'),
+        ('winp','winpos'),
+        ('wn','wnext'),
+        ('wp','wprevious'),
+        ('wq','wq'),
+        ('wqa','wqall'),
+        ('ws','wsverb'),
+        ('wundo','wundo'),
+        ('wv','wviminfo'),
+        ('x','x'),
+        ('x','xit'),
+        ('xa','xall'),
+        ('xmapc','xmapclear'),
+        ('xme','xme'),
+        ('xmenu','xmenu'),
+        ('xnoreme','xnoreme'),
+        ('xnoremenu','xnoremenu'),
+        ('xunme','xunme'),
+        ('xunmenu','xunmenu'),
+        ('xwininfo','xwininfo'),
+        ('y','yank'),
+    )
+    return var
+command = _getcommand()
+
+def _getoption():
+    var = (
+        ('acd','acd'),
+        ('ai','ai'),
+        ('akm','akm'),
+        ('al','al'),
+        ('aleph','aleph'),
+        ('allowrevins','allowrevins'),
+        ('altkeymap','altkeymap'),
+        ('ambiwidth','ambiwidth'),
+        ('ambw','ambw'),
+        ('anti','anti'),
+        ('antialias','antialias'),
+        ('ar','ar'),
+        ('arab','arab'),
+        ('arabic','arabic'),
+        ('arabicshape','arabicshape'),
+        ('ari','ari'),
+        ('arshape','arshape'),
+        ('autochdir','autochdir'),
+        ('autoindent','autoindent'),
+        ('autoread','autoread'),
+        ('autowrite','autowrite'),
+        ('autowriteall','autowriteall'),
+        ('aw','aw'),
+        ('awa','awa'),
+        ('background','background'),
+        ('backspace','backspace'),
+        ('backup','backup'),
+        ('backupcopy','backupcopy'),
+        ('backupdir','backupdir'),
+        ('backupext','backupext'),
+        ('backupskip','backupskip'),
+        ('balloondelay','balloondelay'),
+        ('ballooneval','ballooneval'),
+        ('balloonexpr','balloonexpr'),
+        ('bdir','bdir'),
+        ('bdlay','bdlay'),
+        ('beval','beval'),
+        ('bex','bex'),
+        ('bexpr','bexpr'),
+        ('bg','bg'),
+        ('bh','bh'),
+        ('bin','bin'),
+        ('binary','binary'),
+        ('biosk','biosk'),
+        ('bioskey','bioskey'),
+        ('bk','bk'),
+        ('bkc','bkc'),
+        ('bl','bl'),
+        ('bomb','bomb'),
+        ('breakat','breakat'),
+        ('brk','brk'),
+        ('browsedir','browsedir'),
+        ('bs','bs'),
+        ('bsdir','bsdir'),
+        ('bsk','bsk'),
+        ('bt','bt'),
+        ('bufhidden','bufhidden'),
+        ('buflisted','buflisted'),
+        ('buftype','buftype'),
+        ('casemap','casemap'),
+        ('cb','cb'),
+        ('cc','cc'),
+        ('ccv','ccv'),
+        ('cd','cd'),
+        ('cdpath','cdpath'),
+        ('cedit','cedit'),
+        ('cf','cf'),
+        ('cfu','cfu'),
+        ('ch','ch'),
+        ('charconvert','charconvert'),
+        ('ci','ci'),
+        ('cin','cin'),
+        ('cindent','cindent'),
+        ('cink','cink'),
+        ('cinkeys','cinkeys'),
+        ('cino','cino'),
+        ('cinoptions','cinoptions'),
+        ('cinw','cinw'),
+        ('cinwords','cinwords'),
+        ('clipboard','clipboard'),
+        ('cmdheight','cmdheight'),
+        ('cmdwinheight','cmdwinheight'),
+        ('cmp','cmp'),
+        ('cms','cms'),
+        ('co','co'),
+        ('cocu','cocu'),
+        ('cole','cole'),
+        ('colorcolumn','colorcolumn'),
+        ('columns','columns'),
+        ('com','com'),
+        ('comments','comments'),
+        ('commentstring','commentstring'),
+        ('compatible','compatible'),
+        ('complete','complete'),
+        ('completefunc','completefunc'),
+        ('completeopt','completeopt'),
+        ('concealcursor','concealcursor'),
+        ('conceallevel','conceallevel'),
+        ('confirm','confirm'),
+        ('consk','consk'),
+        ('conskey','conskey'),
+        ('copyindent','copyindent'),
+        ('cot','cot'),
+        ('cp','cp'),
+        ('cpo','cpo'),
+        ('cpoptions','cpoptions'),
+        ('cpt','cpt'),
+        ('crb','crb'),
+        ('cryptmethod','cryptmethod'),
+        ('cscopepathcomp','cscopepathcomp'),
+        ('cscopeprg','cscopeprg'),
+        ('cscopequickfix','cscopequickfix'),
+        ('cscoperelative','cscoperelative'),
+        ('cscopetag','cscopetag'),
+        ('cscopetagorder','cscopetagorder'),
+        ('cscopeverbose','cscopeverbose'),
+        ('cspc','cspc'),
+        ('csprg','csprg'),
+        ('csqf','csqf'),
+        ('csre','csre'),
+        ('cst','cst'),
+        ('csto','csto'),
+        ('csverb','csverb'),
+        ('cuc','cuc'),
+        ('cul','cul'),
+        ('cursorbind','cursorbind'),
+        ('cursorcolumn','cursorcolumn'),
+        ('cursorline','cursorline'),
+        ('cwh','cwh'),
+        ('debug','debug'),
+        ('deco','deco'),
+        ('def','def'),
+        ('define','define'),
+        ('delcombine','delcombine'),
+        ('dex','dex'),
+        ('dg','dg'),
+        ('dict','dict'),
+        ('dictionary','dictionary'),
+        ('diff','diff'),
+        ('diffexpr','diffexpr'),
+        ('diffopt','diffopt'),
+        ('digraph','digraph'),
+        ('dip','dip'),
+        ('dir','dir'),
+        ('directory','directory'),
+        ('display','display'),
+        ('dy','dy'),
+        ('ea','ea'),
+        ('ead','ead'),
+        ('eadirection','eadirection'),
+        ('eb','eb'),
+        ('ed','ed'),
+        ('edcompatible','edcompatible'),
+        ('ef','ef'),
+        ('efm','efm'),
+        ('ei','ei'),
+        ('ek','ek'),
+        ('enc','enc'),
+        ('encoding','encoding'),
+        ('endofline','endofline'),
+        ('eol','eol'),
+        ('ep','ep'),
+        ('equalalways','equalalways'),
+        ('equalprg','equalprg'),
+        ('errorbells','errorbells'),
+        ('errorfile','errorfile'),
+        ('errorformat','errorformat'),
+        ('esckeys','esckeys'),
+        ('et','et'),
+        ('eventignore','eventignore'),
+        ('ex','ex'),
+        ('expandtab','expandtab'),
+        ('exrc','exrc'),
+        ('fcl','fcl'),
+        ('fcs','fcs'),
+        ('fdc','fdc'),
+        ('fde','fde'),
+        ('fdi','fdi'),
+        ('fdl','fdl'),
+        ('fdls','fdls'),
+        ('fdm','fdm'),
+        ('fdn','fdn'),
+        ('fdo','fdo'),
+        ('fdt','fdt'),
+        ('fen','fen'),
+        ('fenc','fenc'),
+        ('fencs','fencs'),
+        ('fex','fex'),
+        ('ff','ff'),
+        ('ffs','ffs'),
+        ('fic','fic'),
+        ('fileencoding','fileencoding'),
+        ('fileencodings','fileencodings'),
+        ('fileformat','fileformat'),
+        ('fileformats','fileformats'),
+        ('fileignorecase','fileignorecase'),
+        ('filetype','filetype'),
+        ('fillchars','fillchars'),
+        ('fk','fk'),
+        ('fkmap','fkmap'),
+        ('flp','flp'),
+        ('fml','fml'),
+        ('fmr','fmr'),
+        ('fo','fo'),
+        ('foldclose','foldclose'),
+        ('foldcolumn','foldcolumn'),
+        ('foldenable','foldenable'),
+        ('foldexpr','foldexpr'),
+        ('foldignore','foldignore'),
+        ('foldlevel','foldlevel'),
+        ('foldlevelstart','foldlevelstart'),
+        ('foldmarker','foldmarker'),
+        ('foldmethod','foldmethod'),
+        ('foldminlines','foldminlines'),
+        ('foldnestmax','foldnestmax'),
+        ('foldopen','foldopen'),
+        ('foldtext','foldtext'),
+        ('formatexpr','formatexpr'),
+        ('formatlistpat','formatlistpat'),
+        ('formatoptions','formatoptions'),
+        ('formatprg','formatprg'),
+        ('fp','fp'),
+        ('fs','fs'),
+        ('fsync','fsync'),
+        ('ft','ft'),
+        ('gcr','gcr'),
+        ('gd','gd'),
+        ('gdefault','gdefault'),
+        ('gfm','gfm'),
+        ('gfn','gfn'),
+        ('gfs','gfs'),
+        ('gfw','gfw'),
+        ('ghr','ghr'),
+        ('go','go'),
+        ('gp','gp'),
+        ('grepformat','grepformat'),
+        ('grepprg','grepprg'),
+        ('gtl','gtl'),
+        ('gtt','gtt'),
+        ('guicursor','guicursor'),
+        ('guifont','guifont'),
+        ('guifontset','guifontset'),
+        ('guifontwide','guifontwide'),
+        ('guiheadroom','guiheadroom'),
+        ('guioptions','guioptions'),
+        ('guipty','guipty'),
+        ('guitablabel','guitablabel'),
+        ('guitabtooltip','guitabtooltip'),
+        ('helpfile','helpfile'),
+        ('helpheight','helpheight'),
+        ('helplang','helplang'),
+        ('hf','hf'),
+        ('hh','hh'),
+        ('hi','hi'),
+        ('hid','hid'),
+        ('hidden','hidden'),
+        ('highlight','highlight'),
+        ('history','history'),
+        ('hk','hk'),
+        ('hkmap','hkmap'),
+        ('hkmapp','hkmapp'),
+        ('hkp','hkp'),
+        ('hl','hl'),
+        ('hlg','hlg'),
+        ('hls','hls'),
+        ('hlsearch','hlsearch'),
+        ('ic','ic'),
+        ('icon','icon'),
+        ('iconstring','iconstring'),
+        ('ignorecase','ignorecase'),
+        ('im','im'),
+        ('imactivatefunc','imactivatefunc'),
+        ('imactivatekey','imactivatekey'),
+        ('imaf','imaf'),
+        ('imak','imak'),
+        ('imc','imc'),
+        ('imcmdline','imcmdline'),
+        ('imd','imd'),
+        ('imdisable','imdisable'),
+        ('imi','imi'),
+        ('iminsert','iminsert'),
+        ('ims','ims'),
+        ('imsearch','imsearch'),
+        ('imsf','imsf'),
+        ('imstatusfunc','imstatusfunc'),
+        ('inc','inc'),
+        ('include','include'),
+        ('includeexpr','includeexpr'),
+        ('incsearch','incsearch'),
+        ('inde','inde'),
+        ('indentexpr','indentexpr'),
+        ('indentkeys','indentkeys'),
+        ('indk','indk'),
+        ('inex','inex'),
+        ('inf','inf'),
+        ('infercase','infercase'),
+        ('inoremap','inoremap'),
+        ('insertmode','insertmode'),
+        ('invacd','invacd'),
+        ('invai','invai'),
+        ('invakm','invakm'),
+        ('invallowrevins','invallowrevins'),
+        ('invaltkeymap','invaltkeymap'),
+        ('invanti','invanti'),
+        ('invantialias','invantialias'),
+        ('invar','invar'),
+        ('invarab','invarab'),
+        ('invarabic','invarabic'),
+        ('invarabicshape','invarabicshape'),
+        ('invari','invari'),
+        ('invarshape','invarshape'),
+        ('invautochdir','invautochdir'),
+        ('invautoindent','invautoindent'),
+        ('invautoread','invautoread'),
+        ('invautowrite','invautowrite'),
+        ('invautowriteall','invautowriteall'),
+        ('invaw','invaw'),
+        ('invawa','invawa'),
+        ('invbackup','invbackup'),
+        ('invballooneval','invballooneval'),
+        ('invbeval','invbeval'),
+        ('invbin','invbin'),
+        ('invbinary','invbinary'),
+        ('invbiosk','invbiosk'),
+        ('invbioskey','invbioskey'),
+        ('invbk','invbk'),
+        ('invbl','invbl'),
+        ('invbomb','invbomb'),
+        ('invbuflisted','invbuflisted'),
+        ('invcf','invcf'),
+        ('invci','invci'),
+        ('invcin','invcin'),
+        ('invcindent','invcindent'),
+        ('invcompatible','invcompatible'),
+        ('invconfirm','invconfirm'),
+        ('invconsk','invconsk'),
+        ('invconskey','invconskey'),
+        ('invcopyindent','invcopyindent'),
+        ('invcp','invcp'),
+        ('invcrb','invcrb'),
+        ('invcscoperelative','invcscoperelative'),
+        ('invcscopetag','invcscopetag'),
+        ('invcscopeverbose','invcscopeverbose'),
+        ('invcsre','invcsre'),
+        ('invcst','invcst'),
+        ('invcsverb','invcsverb'),
+        ('invcuc','invcuc'),
+        ('invcul','invcul'),
+        ('invcursorbind','invcursorbind'),
+        ('invcursorcolumn','invcursorcolumn'),
+        ('invcursorline','invcursorline'),
+        ('invdeco','invdeco'),
+        ('invdelcombine','invdelcombine'),
+        ('invdg','invdg'),
+        ('invdiff','invdiff'),
+        ('invdigraph','invdigraph'),
+        ('invea','invea'),
+        ('inveb','inveb'),
+        ('inved','inved'),
+        ('invedcompatible','invedcompatible'),
+        ('invek','invek'),
+        ('invendofline','invendofline'),
+        ('inveol','inveol'),
+        ('invequalalways','invequalalways'),
+        ('inverrorbells','inverrorbells'),
+        ('invesckeys','invesckeys'),
+        ('invet','invet'),
+        ('invex','invex'),
+        ('invexpandtab','invexpandtab'),
+        ('invexrc','invexrc'),
+        ('invfen','invfen'),
+        ('invfic','invfic'),
+        ('invfileignorecase','invfileignorecase'),
+        ('invfk','invfk'),
+        ('invfkmap','invfkmap'),
+        ('invfoldenable','invfoldenable'),
+        ('invgd','invgd'),
+        ('invgdefault','invgdefault'),
+        ('invguipty','invguipty'),
+        ('invhid','invhid'),
+        ('invhidden','invhidden'),
+        ('invhk','invhk'),
+        ('invhkmap','invhkmap'),
+        ('invhkmapp','invhkmapp'),
+        ('invhkp','invhkp'),
+        ('invhls','invhls'),
+        ('invhlsearch','invhlsearch'),
+        ('invic','invic'),
+        ('invicon','invicon'),
+        ('invignorecase','invignorecase'),
+        ('invim','invim'),
+        ('invimc','invimc'),
+        ('invimcmdline','invimcmdline'),
+        ('invimd','invimd'),
+        ('invimdisable','invimdisable'),
+        ('invincsearch','invincsearch'),
+        ('invinf','invinf'),
+        ('invinfercase','invinfercase'),
+        ('invinsertmode','invinsertmode'),
+        ('invis','invis'),
+        ('invjoinspaces','invjoinspaces'),
+        ('invjs','invjs'),
+        ('invlazyredraw','invlazyredraw'),
+        ('invlbr','invlbr'),
+        ('invlinebreak','invlinebreak'),
+        ('invlisp','invlisp'),
+        ('invlist','invlist'),
+        ('invloadplugins','invloadplugins'),
+        ('invlpl','invlpl'),
+        ('invlz','invlz'),
+        ('invma','invma'),
+        ('invmacatsui','invmacatsui'),
+        ('invmagic','invmagic'),
+        ('invmh','invmh'),
+        ('invml','invml'),
+        ('invmod','invmod'),
+        ('invmodeline','invmodeline'),
+        ('invmodifiable','invmodifiable'),
+        ('invmodified','invmodified'),
+        ('invmore','invmore'),
+        ('invmousef','invmousef'),
+        ('invmousefocus','invmousefocus'),
+        ('invmousehide','invmousehide'),
+        ('invnu','invnu'),
+        ('invnumber','invnumber'),
+        ('invodev','invodev'),
+        ('invopendevice','invopendevice'),
+        ('invpaste','invpaste'),
+        ('invpi','invpi'),
+        ('invpreserveindent','invpreserveindent'),
+        ('invpreviewwindow','invpreviewwindow'),
+        ('invprompt','invprompt'),
+        ('invpvw','invpvw'),
+        ('invreadonly','invreadonly'),
+        ('invrelativenumber','invrelativenumber'),
+        ('invremap','invremap'),
+        ('invrestorescreen','invrestorescreen'),
+        ('invrevins','invrevins'),
+        ('invri','invri'),
+        ('invrightleft','invrightleft'),
+        ('invrl','invrl'),
+        ('invrnu','invrnu'),
+        ('invro','invro'),
+        ('invrs','invrs'),
+        ('invru','invru'),
+        ('invruler','invruler'),
+        ('invsb','invsb'),
+        ('invsc','invsc'),
+        ('invscb','invscb'),
+        ('invscrollbind','invscrollbind'),
+        ('invscs','invscs'),
+        ('invsecure','invsecure'),
+        ('invsft','invsft'),
+        ('invshellslash','invshellslash'),
+        ('invshelltemp','invshelltemp'),
+        ('invshiftround','invshiftround'),
+        ('invshortname','invshortname'),
+        ('invshowcmd','invshowcmd'),
+        ('invshowfulltag','invshowfulltag'),
+        ('invshowmatch','invshowmatch'),
+        ('invshowmode','invshowmode'),
+        ('invsi','invsi'),
+        ('invsm','invsm'),
+        ('invsmartcase','invsmartcase'),
+        ('invsmartindent','invsmartindent'),
+        ('invsmarttab','invsmarttab'),
+        ('invsmd','invsmd'),
+        ('invsn','invsn'),
+        ('invsol','invsol'),
+        ('invspell','invspell'),
+        ('invsplitbelow','invsplitbelow'),
+        ('invsplitright','invsplitright'),
+        ('invspr','invspr'),
+        ('invsr','invsr'),
+        ('invssl','invssl'),
+        ('invsta','invsta'),
+        ('invstartofline','invstartofline'),
+        ('invstmp','invstmp'),
+        ('invswapfile','invswapfile'),
+        ('invswf','invswf'),
+        ('invta','invta'),
+        ('invtagbsearch','invtagbsearch'),
+        ('invtagrelative','invtagrelative'),
+        ('invtagstack','invtagstack'),
+        ('invtbi','invtbi'),
+        ('invtbidi','invtbidi'),
+        ('invtbs','invtbs'),
+        ('invtermbidi','invtermbidi'),
+        ('invterse','invterse'),
+        ('invtextauto','invtextauto'),
+        ('invtextmode','invtextmode'),
+        ('invtf','invtf'),
+        ('invtgst','invtgst'),
+        ('invtildeop','invtildeop'),
+        ('invtimeout','invtimeout'),
+        ('invtitle','invtitle'),
+        ('invto','invto'),
+        ('invtop','invtop'),
+        ('invtr','invtr'),
+        ('invttimeout','invttimeout'),
+        ('invttybuiltin','invttybuiltin'),
+        ('invttyfast','invttyfast'),
+        ('invtx','invtx'),
+        ('invudf','invudf'),
+        ('invundofile','invundofile'),
+        ('invvb','invvb'),
+        ('invvisualbell','invvisualbell'),
+        ('invwa','invwa'),
+        ('invwarn','invwarn'),
+        ('invwb','invwb'),
+        ('invweirdinvert','invweirdinvert'),
+        ('invwfh','invwfh'),
+        ('invwfw','invwfw'),
+        ('invwic','invwic'),
+        ('invwildignorecase','invwildignorecase'),
+        ('invwildmenu','invwildmenu'),
+        ('invwinfixheight','invwinfixheight'),
+        ('invwinfixwidth','invwinfixwidth'),
+        ('invwiv','invwiv'),
+        ('invwmnu','invwmnu'),
+        ('invwrap','invwrap'),
+        ('invwrapscan','invwrapscan'),
+        ('invwrite','invwrite'),
+        ('invwriteany','invwriteany'),
+        ('invwritebackup','invwritebackup'),
+        ('invws','invws'),
+        ('is','is'),
+        ('isf','isf'),
+        ('isfname','isfname'),
+        ('isi','isi'),
+        ('isident','isident'),
+        ('isk','isk'),
+        ('iskeyword','iskeyword'),
+        ('isp','isp'),
+        ('isprint','isprint'),
+        ('joinspaces','joinspaces'),
+        ('js','js'),
+        ('key','key'),
+        ('keymap','keymap'),
+        ('keymodel','keymodel'),
+        ('keywordprg','keywordprg'),
+        ('km','km'),
+        ('kmp','kmp'),
+        ('kp','kp'),
+        ('langmap','langmap'),
+        ('langmenu','langmenu'),
+        ('laststatus','laststatus'),
+        ('lazyredraw','lazyredraw'),
+        ('lbr','lbr'),
+        ('lcs','lcs'),
+        ('linebreak','linebreak'),
+        ('lines','lines'),
+        ('linespace','linespace'),
+        ('lisp','lisp'),
+        ('lispwords','lispwords'),
+        ('list','list'),
+        ('listchars','listchars'),
+        ('lm','lm'),
+        ('lmap','lmap'),
+        ('loadplugins','loadplugins'),
+        ('lpl','lpl'),
+        ('ls','ls'),
+        ('lsp','lsp'),
+        ('lw','lw'),
+        ('lz','lz'),
+        ('ma','ma'),
+        ('macatsui','macatsui'),
+        ('magic','magic'),
+        ('makeef','makeef'),
+        ('makeprg','makeprg'),
+        ('mat','mat'),
+        ('matchpairs','matchpairs'),
+        ('matchtime','matchtime'),
+        ('maxcombine','maxcombine'),
+        ('maxfuncdepth','maxfuncdepth'),
+        ('maxmapdepth','maxmapdepth'),
+        ('maxmem','maxmem'),
+        ('maxmempattern','maxmempattern'),
+        ('maxmemtot','maxmemtot'),
+        ('mco','mco'),
+        ('mef','mef'),
+        ('menuitems','menuitems'),
+        ('mfd','mfd'),
+        ('mh','mh'),
+        ('mis','mis'),
+        ('mkspellmem','mkspellmem'),
+        ('ml','ml'),
+        ('mls','mls'),
+        ('mm','mm'),
+        ('mmd','mmd'),
+        ('mmp','mmp'),
+        ('mmt','mmt'),
+        ('mod','mod'),
+        ('modeline','modeline'),
+        ('modelines','modelines'),
+        ('modifiable','modifiable'),
+        ('modified','modified'),
+        ('more','more'),
+        ('mouse','mouse'),
+        ('mousef','mousef'),
+        ('mousefocus','mousefocus'),
+        ('mousehide','mousehide'),
+        ('mousem','mousem'),
+        ('mousemodel','mousemodel'),
+        ('mouses','mouses'),
+        ('mouseshape','mouseshape'),
+        ('mouset','mouset'),
+        ('mousetime','mousetime'),
+        ('mp','mp'),
+        ('mps','mps'),
+        ('msm','msm'),
+        ('mzq','mzq'),
+        ('mzquantum','mzquantum'),
+        ('nf','nf'),
+        ('nnoremap','nnoremap'),
+        ('noacd','noacd'),
+        ('noai','noai'),
+        ('noakm','noakm'),
+        ('noallowrevins','noallowrevins'),
+        ('noaltkeymap','noaltkeymap'),
+        ('noanti','noanti'),
+        ('noantialias','noantialias'),
+        ('noar','noar'),
+        ('noarab','noarab'),
+        ('noarabic','noarabic'),
+        ('noarabicshape','noarabicshape'),
+        ('noari','noari'),
+        ('noarshape','noarshape'),
+        ('noautochdir','noautochdir'),
+        ('noautoindent','noautoindent'),
+        ('noautoread','noautoread'),
+        ('noautowrite','noautowrite'),
+        ('noautowriteall','noautowriteall'),
+        ('noaw','noaw'),
+        ('noawa','noawa'),
+        ('nobackup','nobackup'),
+        ('noballooneval','noballooneval'),
+        ('nobeval','nobeval'),
+        ('nobin','nobin'),
+        ('nobinary','nobinary'),
+        ('nobiosk','nobiosk'),
+        ('nobioskey','nobioskey'),
+        ('nobk','nobk'),
+        ('nobl','nobl'),
+        ('nobomb','nobomb'),
+        ('nobuflisted','nobuflisted'),
+        ('nocf','nocf'),
+        ('noci','noci'),
+        ('nocin','nocin'),
+        ('nocindent','nocindent'),
+        ('nocompatible','nocompatible'),
+        ('noconfirm','noconfirm'),
+        ('noconsk','noconsk'),
+        ('noconskey','noconskey'),
+        ('nocopyindent','nocopyindent'),
+        ('nocp','nocp'),
+        ('nocrb','nocrb'),
+        ('nocscoperelative','nocscoperelative'),
+        ('nocscopetag','nocscopetag'),
+        ('nocscopeverbose','nocscopeverbose'),
+        ('nocsre','nocsre'),
+        ('nocst','nocst'),
+        ('nocsverb','nocsverb'),
+        ('nocuc','nocuc'),
+        ('nocul','nocul'),
+        ('nocursorbind','nocursorbind'),
+        ('nocursorcolumn','nocursorcolumn'),
+        ('nocursorline','nocursorline'),
+        ('nodeco','nodeco'),
+        ('nodelcombine','nodelcombine'),
+        ('nodg','nodg'),
+        ('nodiff','nodiff'),
+        ('nodigraph','nodigraph'),
+        ('noea','noea'),
+        ('noeb','noeb'),
+        ('noed','noed'),
+        ('noedcompatible','noedcompatible'),
+        ('noek','noek'),
+        ('noendofline','noendofline'),
+        ('noeol','noeol'),
+        ('noequalalways','noequalalways'),
+        ('noerrorbells','noerrorbells'),
+        ('noesckeys','noesckeys'),
+        ('noet','noet'),
+        ('noex','noex'),
+        ('noexpandtab','noexpandtab'),
+        ('noexrc','noexrc'),
+        ('nofen','nofen'),
+        ('nofic','nofic'),
+        ('nofileignorecase','nofileignorecase'),
+        ('nofk','nofk'),
+        ('nofkmap','nofkmap'),
+        ('nofoldenable','nofoldenable'),
+        ('nogd','nogd'),
+        ('nogdefault','nogdefault'),
+        ('noguipty','noguipty'),
+        ('nohid','nohid'),
+        ('nohidden','nohidden'),
+        ('nohk','nohk'),
+        ('nohkmap','nohkmap'),
+        ('nohkmapp','nohkmapp'),
+        ('nohkp','nohkp'),
+        ('nohls','nohls'),
+        ('nohlsearch','nohlsearch'),
+        ('noic','noic'),
+        ('noicon','noicon'),
+        ('noignorecase','noignorecase'),
+        ('noim','noim'),
+        ('noimc','noimc'),
+        ('noimcmdline','noimcmdline'),
+        ('noimd','noimd'),
+        ('noimdisable','noimdisable'),
+        ('noincsearch','noincsearch'),
+        ('noinf','noinf'),
+        ('noinfercase','noinfercase'),
+        ('noinsertmode','noinsertmode'),
+        ('nois','nois'),
+        ('nojoinspaces','nojoinspaces'),
+        ('nojs','nojs'),
+        ('nolazyredraw','nolazyredraw'),
+        ('nolbr','nolbr'),
+        ('nolinebreak','nolinebreak'),
+        ('nolisp','nolisp'),
+        ('nolist','nolist'),
+        ('noloadplugins','noloadplugins'),
+        ('nolpl','nolpl'),
+        ('nolz','nolz'),
+        ('noma','noma'),
+        ('nomacatsui','nomacatsui'),
+        ('nomagic','nomagic'),
+        ('nomh','nomh'),
+        ('noml','noml'),
+        ('nomod','nomod'),
+        ('nomodeline','nomodeline'),
+        ('nomodifiable','nomodifiable'),
+        ('nomodified','nomodified'),
+        ('nomore','nomore'),
+        ('nomousef','nomousef'),
+        ('nomousefocus','nomousefocus'),
+        ('nomousehide','nomousehide'),
+        ('nonu','nonu'),
+        ('nonumber','nonumber'),
+        ('noodev','noodev'),
+        ('noopendevice','noopendevice'),
+        ('nopaste','nopaste'),
+        ('nopi','nopi'),
+        ('nopreserveindent','nopreserveindent'),
+        ('nopreviewwindow','nopreviewwindow'),
+        ('noprompt','noprompt'),
+        ('nopvw','nopvw'),
+        ('noreadonly','noreadonly'),
+        ('norelativenumber','norelativenumber'),
+        ('noremap','noremap'),
+        ('norestorescreen','norestorescreen'),
+        ('norevins','norevins'),
+        ('nori','nori'),
+        ('norightleft','norightleft'),
+        ('norl','norl'),
+        ('nornu','nornu'),
+        ('noro','noro'),
+        ('nors','nors'),
+        ('noru','noru'),
+        ('noruler','noruler'),
+        ('nosb','nosb'),
+        ('nosc','nosc'),
+        ('noscb','noscb'),
+        ('noscrollbind','noscrollbind'),
+        ('noscs','noscs'),
+        ('nosecure','nosecure'),
+        ('nosft','nosft'),
+        ('noshellslash','noshellslash'),
+        ('noshelltemp','noshelltemp'),
+        ('noshiftround','noshiftround'),
+        ('noshortname','noshortname'),
+        ('noshowcmd','noshowcmd'),
+        ('noshowfulltag','noshowfulltag'),
+        ('noshowmatch','noshowmatch'),
+        ('noshowmode','noshowmode'),
+        ('nosi','nosi'),
+        ('nosm','nosm'),
+        ('nosmartcase','nosmartcase'),
+        ('nosmartindent','nosmartindent'),
+        ('nosmarttab','nosmarttab'),
+        ('nosmd','nosmd'),
+        ('nosn','nosn'),
+        ('nosol','nosol'),
+        ('nospell','nospell'),
+        ('nosplitbelow','nosplitbelow'),
+        ('nosplitright','nosplitright'),
+        ('nospr','nospr'),
+        ('nosr','nosr'),
+        ('nossl','nossl'),
+        ('nosta','nosta'),
+        ('nostartofline','nostartofline'),
+        ('nostmp','nostmp'),
+        ('noswapfile','noswapfile'),
+        ('noswf','noswf'),
+        ('nota','nota'),
+        ('notagbsearch','notagbsearch'),
+        ('notagrelative','notagrelative'),
+        ('notagstack','notagstack'),
+        ('notbi','notbi'),
+        ('notbidi','notbidi'),
+        ('notbs','notbs'),
+        ('notermbidi','notermbidi'),
+        ('noterse','noterse'),
+        ('notextauto','notextauto'),
+        ('notextmode','notextmode'),
+        ('notf','notf'),
+        ('notgst','notgst'),
+        ('notildeop','notildeop'),
+        ('notimeout','notimeout'),
+        ('notitle','notitle'),
+        ('noto','noto'),
+        ('notop','notop'),
+        ('notr','notr'),
+        ('nottimeout','nottimeout'),
+        ('nottybuiltin','nottybuiltin'),
+        ('nottyfast','nottyfast'),
+        ('notx','notx'),
+        ('noudf','noudf'),
+        ('noundofile','noundofile'),
+        ('novb','novb'),
+        ('novisualbell','novisualbell'),
+        ('nowa','nowa'),
+        ('nowarn','nowarn'),
+        ('nowb','nowb'),
+        ('noweirdinvert','noweirdinvert'),
+        ('nowfh','nowfh'),
+        ('nowfw','nowfw'),
+        ('nowic','nowic'),
+        ('nowildignorecase','nowildignorecase'),
+        ('nowildmenu','nowildmenu'),
+        ('nowinfixheight','nowinfixheight'),
+        ('nowinfixwidth','nowinfixwidth'),
+        ('nowiv','nowiv'),
+        ('nowmnu','nowmnu'),
+        ('nowrap','nowrap'),
+        ('nowrapscan','nowrapscan'),
+        ('nowrite','nowrite'),
+        ('nowriteany','nowriteany'),
+        ('nowritebackup','nowritebackup'),
+        ('nows','nows'),
+        ('nrformats','nrformats'),
+        ('nu','nu'),
+        ('number','number'),
+        ('numberwidth','numberwidth'),
+        ('nuw','nuw'),
+        ('odev','odev'),
+        ('oft','oft'),
+        ('ofu','ofu'),
+        ('omnifunc','omnifunc'),
+        ('opendevice','opendevice'),
+        ('operatorfunc','operatorfunc'),
+        ('opfunc','opfunc'),
+        ('osfiletype','osfiletype'),
+        ('pa','pa'),
+        ('para','para'),
+        ('paragraphs','paragraphs'),
+        ('paste','paste'),
+        ('pastetoggle','pastetoggle'),
+        ('patchexpr','patchexpr'),
+        ('patchmode','patchmode'),
+        ('path','path'),
+        ('pdev','pdev'),
+        ('penc','penc'),
+        ('pex','pex'),
+        ('pexpr','pexpr'),
+        ('pfn','pfn'),
+        ('ph','ph'),
+        ('pheader','pheader'),
+        ('pi','pi'),
+        ('pm','pm'),
+        ('pmbcs','pmbcs'),
+        ('pmbfn','pmbfn'),
+        ('popt','popt'),
+        ('preserveindent','preserveindent'),
+        ('previewheight','previewheight'),
+        ('previewwindow','previewwindow'),
+        ('printdevice','printdevice'),
+        ('printencoding','printencoding'),
+        ('printexpr','printexpr'),
+        ('printfont','printfont'),
+        ('printheader','printheader'),
+        ('printmbcharset','printmbcharset'),
+        ('printmbfont','printmbfont'),
+        ('printoptions','printoptions'),
+        ('prompt','prompt'),
+        ('pt','pt'),
+        ('pumheight','pumheight'),
+        ('pvh','pvh'),
+        ('pvw','pvw'),
+        ('qe','qe'),
+        ('quoteescape','quoteescape'),
+        ('rdt','rdt'),
+        ('re','re'),
+        ('readonly','readonly'),
+        ('redrawtime','redrawtime'),
+        ('regexpengine','regexpengine'),
+        ('relativenumber','relativenumber'),
+        ('remap','remap'),
+        ('report','report'),
+        ('restorescreen','restorescreen'),
+        ('revins','revins'),
+        ('ri','ri'),
+        ('rightleft','rightleft'),
+        ('rightleftcmd','rightleftcmd'),
+        ('rl','rl'),
+        ('rlc','rlc'),
+        ('rnu','rnu'),
+        ('ro','ro'),
+        ('rs','rs'),
+        ('rtp','rtp'),
+        ('ru','ru'),
+        ('ruf','ruf'),
+        ('ruler','ruler'),
+        ('rulerformat','rulerformat'),
+        ('runtimepath','runtimepath'),
+        ('sb','sb'),
+        ('sbo','sbo'),
+        ('sbr','sbr'),
+        ('sc','sc'),
+        ('scb','scb'),
+        ('scr','scr'),
+        ('scroll','scroll'),
+        ('scrollbind','scrollbind'),
+        ('scrolljump','scrolljump'),
+        ('scrolloff','scrolloff'),
+        ('scrollopt','scrollopt'),
+        ('scs','scs'),
+        ('sect','sect'),
+        ('sections','sections'),
+        ('secure','secure'),
+        ('sel','sel'),
+        ('selection','selection'),
+        ('selectmode','selectmode'),
+        ('sessionoptions','sessionoptions'),
+        ('sft','sft'),
+        ('sh','sh'),
+        ('shcf','shcf'),
+        ('shell','shell'),
+        ('shellcmdflag','shellcmdflag'),
+        ('shellpipe','shellpipe'),
+        ('shellquote','shellquote'),
+        ('shellredir','shellredir'),
+        ('shellslash','shellslash'),
+        ('shelltemp','shelltemp'),
+        ('shelltype','shelltype'),
+        ('shellxescape','shellxescape'),
+        ('shellxquote','shellxquote'),
+        ('shiftround','shiftround'),
+        ('shiftwidth','shiftwidth'),
+        ('shm','shm'),
+        ('shortmess','shortmess'),
+        ('shortname','shortname'),
+        ('showbreak','showbreak'),
+        ('showcmd','showcmd'),
+        ('showfulltag','showfulltag'),
+        ('showmatch','showmatch'),
+        ('showmode','showmode'),
+        ('showtabline','showtabline'),
+        ('shq','shq'),
+        ('si','si'),
+        ('sidescroll','sidescroll'),
+        ('sidescrolloff','sidescrolloff'),
+        ('siso','siso'),
+        ('sj','sj'),
+        ('slm','slm'),
+        ('sm','sm'),
+        ('smartcase','smartcase'),
+        ('smartindent','smartindent'),
+        ('smarttab','smarttab'),
+        ('smc','smc'),
+        ('smd','smd'),
+        ('sn','sn'),
+        ('so','so'),
+        ('softtabstop','softtabstop'),
+        ('sol','sol'),
+        ('sp','sp'),
+        ('spc','spc'),
+        ('spell','spell'),
+        ('spellcapcheck','spellcapcheck'),
+        ('spellfile','spellfile'),
+        ('spelllang','spelllang'),
+        ('spellsuggest','spellsuggest'),
+        ('spf','spf'),
+        ('spl','spl'),
+        ('splitbelow','splitbelow'),
+        ('splitright','splitright'),
+        ('spr','spr'),
+        ('sps','sps'),
+        ('sr','sr'),
+        ('srr','srr'),
+        ('ss','ss'),
+        ('ssl','ssl'),
+        ('ssop','ssop'),
+        ('st','st'),
+        ('sta','sta'),
+        ('stal','stal'),
+        ('startofline','startofline'),
+        ('statusline','statusline'),
+        ('stl','stl'),
+        ('stmp','stmp'),
+        ('sts','sts'),
+        ('su','su'),
+        ('sua','sua'),
+        ('suffixes','suffixes'),
+        ('suffixesadd','suffixesadd'),
+        ('sw','sw'),
+        ('swapfile','swapfile'),
+        ('swapsync','swapsync'),
+        ('swb','swb'),
+        ('swf','swf'),
+        ('switchbuf','switchbuf'),
+        ('sws','sws'),
+        ('sxe','sxe'),
+        ('sxq','sxq'),
+        ('syn','syn'),
+        ('synmaxcol','synmaxcol'),
+        ('syntax','syntax'),
+        ('t_AB','t_AB'),
+        ('t_AF','t_AF'),
+        ('t_AL','t_AL'),
+        ('t_CS','t_CS'),
+        ('t_CV','t_CV'),
+        ('t_Ce','t_Ce'),
+        ('t_Co','t_Co'),
+        ('t_Cs','t_Cs'),
+        ('t_DL','t_DL'),
+        ('t_EI','t_EI'),
+        ('t_F1','t_F1'),
+        ('t_F2','t_F2'),
+        ('t_F3','t_F3'),
+        ('t_F4','t_F4'),
+        ('t_F5','t_F5'),
+        ('t_F6','t_F6'),
+        ('t_F7','t_F7'),
+        ('t_F8','t_F8'),
+        ('t_F9','t_F9'),
+        ('t_IE','t_IE'),
+        ('t_IS','t_IS'),
+        ('t_K1','t_K1'),
+        ('t_K3','t_K3'),
+        ('t_K4','t_K4'),
+        ('t_K5','t_K5'),
+        ('t_K6','t_K6'),
+        ('t_K7','t_K7'),
+        ('t_K8','t_K8'),
+        ('t_K9','t_K9'),
+        ('t_KA','t_KA'),
+        ('t_KB','t_KB'),
+        ('t_KC','t_KC'),
+        ('t_KD','t_KD'),
+        ('t_KE','t_KE'),
+        ('t_KF','t_KF'),
+        ('t_KG','t_KG'),
+        ('t_KH','t_KH'),
+        ('t_KI','t_KI'),
+        ('t_KJ','t_KJ'),
+        ('t_KK','t_KK'),
+        ('t_KL','t_KL'),
+        ('t_RI','t_RI'),
+        ('t_RV','t_RV'),
+        ('t_SI','t_SI'),
+        ('t_Sb','t_Sb'),
+        ('t_Sf','t_Sf'),
+        ('t_WP','t_WP'),
+        ('t_WS','t_WS'),
+        ('t_ZH','t_ZH'),
+        ('t_ZR','t_ZR'),
+        ('t_al','t_al'),
+        ('t_bc','t_bc'),
+        ('t_cd','t_cd'),
+        ('t_ce','t_ce'),
+        ('t_cl','t_cl'),
+        ('t_cm','t_cm'),
+        ('t_cs','t_cs'),
+        ('t_da','t_da'),
+        ('t_db','t_db'),
+        ('t_dl','t_dl'),
+        ('t_fs','t_fs'),
+        ('t_k1','t_k1'),
+        ('t_k2','t_k2'),
+        ('t_k3','t_k3'),
+        ('t_k4','t_k4'),
+        ('t_k5','t_k5'),
+        ('t_k6','t_k6'),
+        ('t_k7','t_k7'),
+        ('t_k8','t_k8'),
+        ('t_k9','t_k9'),
+        ('t_kB','t_kB'),
+        ('t_kD','t_kD'),
+        ('t_kI','t_kI'),
+        ('t_kN','t_kN'),
+        ('t_kP','t_kP'),
+        ('t_kb','t_kb'),
+        ('t_kd','t_kd'),
+        ('t_ke','t_ke'),
+        ('t_kh','t_kh'),
+        ('t_kl','t_kl'),
+        ('t_kr','t_kr'),
+        ('t_ks','t_ks'),
+        ('t_ku','t_ku'),
+        ('t_le','t_le'),
+        ('t_mb','t_mb'),
+        ('t_md','t_md'),
+        ('t_me','t_me'),
+        ('t_mr','t_mr'),
+        ('t_ms','t_ms'),
+        ('t_nd','t_nd'),
+        ('t_op','t_op'),
+        ('t_se','t_se'),
+        ('t_so','t_so'),
+        ('t_sr','t_sr'),
+        ('t_te','t_te'),
+        ('t_ti','t_ti'),
+        ('t_ts','t_ts'),
+        ('t_u7','t_u7'),
+        ('t_ue','t_ue'),
+        ('t_us','t_us'),
+        ('t_ut','t_ut'),
+        ('t_vb','t_vb'),
+        ('t_ve','t_ve'),
+        ('t_vi','t_vi'),
+        ('t_vs','t_vs'),
+        ('t_xs','t_xs'),
+        ('ta','ta'),
+        ('tabline','tabline'),
+        ('tabpagemax','tabpagemax'),
+        ('tabstop','tabstop'),
+        ('tag','tag'),
+        ('tagbsearch','tagbsearch'),
+        ('taglength','taglength'),
+        ('tagrelative','tagrelative'),
+        ('tags','tags'),
+        ('tagstack','tagstack'),
+        ('tal','tal'),
+        ('tb','tb'),
+        ('tbi','tbi'),
+        ('tbidi','tbidi'),
+        ('tbis','tbis'),
+        ('tbs','tbs'),
+        ('tenc','tenc'),
+        ('term','term'),
+        ('termbidi','termbidi'),
+        ('termencoding','termencoding'),
+        ('terse','terse'),
+        ('textauto','textauto'),
+        ('textmode','textmode'),
+        ('textwidth','textwidth'),
+        ('tf','tf'),
+        ('tgst','tgst'),
+        ('thesaurus','thesaurus'),
+        ('tildeop','tildeop'),
+        ('timeout','timeout'),
+        ('timeoutlen','timeoutlen'),
+        ('title','title'),
+        ('titlelen','titlelen'),
+        ('titleold','titleold'),
+        ('titlestring','titlestring'),
+        ('tl','tl'),
+        ('tm','tm'),
+        ('to','to'),
+        ('toolbar','toolbar'),
+        ('toolbariconsize','toolbariconsize'),
+        ('top','top'),
+        ('tpm','tpm'),
+        ('tr','tr'),
+        ('ts','ts'),
+        ('tsl','tsl'),
+        ('tsr','tsr'),
+        ('ttimeout','ttimeout'),
+        ('ttimeoutlen','ttimeoutlen'),
+        ('ttm','ttm'),
+        ('tty','tty'),
+        ('ttybuiltin','ttybuiltin'),
+        ('ttyfast','ttyfast'),
+        ('ttym','ttym'),
+        ('ttymouse','ttymouse'),
+        ('ttyscroll','ttyscroll'),
+        ('ttytype','ttytype'),
+        ('tw','tw'),
+        ('tx','tx'),
+        ('uc','uc'),
+        ('udf','udf'),
+        ('udir','udir'),
+        ('ul','ul'),
+        ('undodir','undodir'),
+        ('undofile','undofile'),
+        ('undolevels','undolevels'),
+        ('undoreload','undoreload'),
+        ('updatecount','updatecount'),
+        ('updatetime','updatetime'),
+        ('ur','ur'),
+        ('ut','ut'),
+        ('vb','vb'),
+        ('vbs','vbs'),
+        ('vdir','vdir'),
+        ('ve','ve'),
+        ('verbose','verbose'),
+        ('verbosefile','verbosefile'),
+        ('vfile','vfile'),
+        ('vi','vi'),
+        ('viewdir','viewdir'),
+        ('viewoptions','viewoptions'),
+        ('viminfo','viminfo'),
+        ('virtualedit','virtualedit'),
+        ('visualbell','visualbell'),
+        ('vnoremap','vnoremap'),
+        ('vop','vop'),
+        ('wa','wa'),
+        ('wak','wak'),
+        ('warn','warn'),
+        ('wb','wb'),
+        ('wc','wc'),
+        ('wcm','wcm'),
+        ('wd','wd'),
+        ('weirdinvert','weirdinvert'),
+        ('wfh','wfh'),
+        ('wfw','wfw'),
+        ('wh','wh'),
+        ('whichwrap','whichwrap'),
+        ('wi','wi'),
+        ('wic','wic'),
+        ('wig','wig'),
+        ('wildchar','wildchar'),
+        ('wildcharm','wildcharm'),
+        ('wildignore','wildignore'),
+        ('wildignorecase','wildignorecase'),
+        ('wildmenu','wildmenu'),
+        ('wildmode','wildmode'),
+        ('wildoptions','wildoptions'),
+        ('wim','wim'),
+        ('winaltkeys','winaltkeys'),
+        ('window','window'),
+        ('winfixheight','winfixheight'),
+        ('winfixwidth','winfixwidth'),
+        ('winheight','winheight'),
+        ('winminheight','winminheight'),
+        ('winminwidth','winminwidth'),
+        ('winwidth','winwidth'),
+        ('wiv','wiv'),
+        ('wiw','wiw'),
+        ('wm','wm'),
+        ('wmh','wmh'),
+        ('wmnu','wmnu'),
+        ('wmw','wmw'),
+        ('wop','wop'),
+        ('wrap','wrap'),
+        ('wrapmargin','wrapmargin'),
+        ('wrapscan','wrapscan'),
+        ('write','write'),
+        ('writeany','writeany'),
+        ('writebackup','writebackup'),
+        ('writedelay','writedelay'),
+        ('ws','ws'),
+        ('ww','ww'),
+    )
+    return var
+option = _getoption()
+
diff --git a/shared/package-files/04/046fe62124d0275ff7746bed2ae9eac63226d0f635eed45acf34faeda04d1208/_sparse_sparse_matmul_compositeexplicitautograd_dispatch.h b/shared/package-files/04/046fe62124d0275ff7746bed2ae9eac63226d0f635eed45acf34faeda04d1208/_sparse_sparse_matmul_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..ada52eef057dfebe7bf8e6c4491d8f1fad7236cb
--- /dev/null
+++ b/shared/package-files/04/046fe62124d0275ff7746bed2ae9eac63226d0f635eed45acf34faeda04d1208/_sparse_sparse_matmul_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & _sparse_sparse_matmul_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & _sparse_sparse_matmul_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/04/04706940aa10bf81234f452958103e0a4e0be4eb0ca9323622e636e29fae7179/__init__.py b/shared/package-files/04/04706940aa10bf81234f452958103e0a4e0be4eb0ca9323622e636e29fae7179/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a7597d30387c98c0e7e66a0bfc82f5e64823d95
--- /dev/null
+++ b/shared/package-files/04/04706940aa10bf81234f452958103e0a4e0be4eb0ca9323622e636e29fae7179/__init__.py
@@ -0,0 +1,11 @@
+"""Sub-package containing the matrix class and related functions.
+
+"""
+from . import defmatrix
+from .defmatrix import *
+
+__all__ = defmatrix.__all__
+
+from numpy._pytesttester import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/shared/package-files/04/04717236aa281dc9294098afb4076faa99f925b8e96a4c681816988c60d0a3a9/view_as_real_ops.h b/shared/package-files/04/04717236aa281dc9294098afb4076faa99f925b8e96a4c681816988c60d0a3a9/view_as_real_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..8384a56e8c98bfa30107659257cb814b68dbb82e
--- /dev/null
+++ b/shared/package-files/04/04717236aa281dc9294098afb4076faa99f925b8e96a4c681816988c60d0a3a9/view_as_real_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API view_as_real {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::view_as_real")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "view_as_real(Tensor(a) self) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/04/047576813bd4faf55ed699500b77933810cb368d5b328bf56cea489ad539b037/rename_compositeimplicitautograd_dispatch.h b/shared/package-files/04/047576813bd4faf55ed699500b77933810cb368d5b328bf56cea489ad539b037/rename_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9df3d9d7205b065ac37dadd80c1fa2788beb2290
--- /dev/null
+++ b/shared/package-files/04/047576813bd4faf55ed699500b77933810cb368d5b328bf56cea489ad539b037/rename_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor & rename_(at::Tensor & self, c10::optional names);
+TORCH_API at::Tensor rename(const at::Tensor & self, c10::optional names);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/04/047f307ca328d1a27dfd97918916e35c21886aa8dcd1dc502c78c966da6f63f9/abs_cuda_dispatch.h b/shared/package-files/04/047f307ca328d1a27dfd97918916e35c21886aa8dcd1dc502c78c966da6f63f9/abs_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..00e5a7b1c264ce43bca5e01ecbe88a563d14c8e0
--- /dev/null
+++ b/shared/package-files/04/047f307ca328d1a27dfd97918916e35c21886aa8dcd1dc502c78c966da6f63f9/abs_cuda_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor & abs_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & abs_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/04/0480219fb0292a9dac13648f23ee8569c82f3ac1d5acf979660fed9710c6e0d0/feature_dropout.h b/shared/package-files/04/0480219fb0292a9dac13648f23ee8569c82f3ac1d5acf979660fed9710c6e0d0/feature_dropout.h
new file mode 100644
index 0000000000000000000000000000000000000000..d19cade9aa98321e927921dcdfe796a9d8633b77
--- /dev/null
+++ b/shared/package-files/04/0480219fb0292a9dac13648f23ee8569c82f3ac1d5acf979660fed9710c6e0d0/feature_dropout.h
@@ -0,0 +1,35 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::feature_dropout(Tensor input, float p, bool train) -> Tensor
+inline at::Tensor feature_dropout(const at::Tensor & input, double p, bool train) {
+    return at::_ops::feature_dropout::call(input, p, train);
+}
+
+// aten::feature_dropout_(Tensor(a!) self, float p, bool train) -> Tensor(a!)
+inline at::Tensor & feature_dropout_(at::Tensor & self, double p, bool train) {
+    return at::_ops::feature_dropout_::call(self, p, train);
+}
+
+}
diff --git a/shared/package-files/04/04813d5d281bc0a2daa4ead8652ddfc8f876ea926c0c705db2d6bd3bc8b0c0ad/_pin_memory_native.h b/shared/package-files/04/04813d5d281bc0a2daa4ead8652ddfc8f876ea926c0c705db2d6bd3bc8b0c0ad/_pin_memory_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..1aa883f6682ccf181a8dbe048f25888c38782a8b
--- /dev/null
+++ b/shared/package-files/04/04813d5d281bc0a2daa4ead8652ddfc8f876ea926c0c705db2d6bd3bc8b0c0ad/_pin_memory_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & _pin_memory_out(const at::Tensor & self, c10::optional device, at::Tensor & out);
+TORCH_API at::Tensor _pin_memory_cuda(const at::Tensor & self, c10::optional device=c10::nullopt);
+TORCH_API at::Tensor _pin_memory_nested(const at::Tensor & self, c10::optional device=c10::nullopt);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/04/0481ef8c59df1f75f1b87b696f85b1c5d938e8bc407266320fc3860dbfea5f00/datetimes.py b/shared/package-files/04/0481ef8c59df1f75f1b87b696f85b1c5d938e8bc407266320fc3860dbfea5f00/datetimes.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f700cfa631327fb09b1d6d50f86eefb212f3f87
--- /dev/null
+++ b/shared/package-files/04/0481ef8c59df1f75f1b87b696f85b1c5d938e8bc407266320fc3860dbfea5f00/datetimes.py
@@ -0,0 +1,1240 @@
+from __future__ import annotations
+
+from collections import abc
+from datetime import date
+from functools import partial
+from itertools import islice
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    TypedDict,
+    Union,
+    cast,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import (
+    lib,
+    tslib,
+)
+from pandas._libs.tslibs import (
+    OutOfBoundsDatetime,
+    Timedelta,
+    Timestamp,
+    astype_overflowsafe,
+    is_supported_dtype,
+    timezones as libtimezones,
+)
+from pandas._libs.tslibs.conversion import cast_from_unit_vectorized
+from pandas._libs.tslibs.parsing import (
+    DateParseError,
+    guess_datetime_format,
+)
+from pandas._libs.tslibs.strptime import array_strptime
+from pandas._typing import (
+    AnyArrayLike,
+    ArrayLike,
+    DateTimeErrorChoices,
+)
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import (
+    ensure_object,
+    is_float,
+    is_integer,
+    is_integer_dtype,
+    is_list_like,
+    is_numeric_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    ArrowDtype,
+    DatetimeTZDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+from pandas.arrays import (
+    DatetimeArray,
+    IntegerArray,
+    NumpyExtensionArray,
+)
+from pandas.core.algorithms import unique
+from pandas.core.arrays import ArrowExtensionArray
+from pandas.core.arrays.base import ExtensionArray
+from pandas.core.arrays.datetimes import (
+    maybe_convert_dtype,
+    objects_to_datetime64,
+    tz_to_dtype,
+)
+from pandas.core.construction import extract_array
+from pandas.core.indexes.base import Index
+from pandas.core.indexes.datetimes import DatetimeIndex
+
+if TYPE_CHECKING:
+    from collections.abc import Hashable
+
+    from pandas._libs.tslibs.nattype import NaTType
+    from pandas._libs.tslibs.timedeltas import UnitChoices
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+
+# ---------------------------------------------------------------------
+# types used in annotations
+
+ArrayConvertible = Union[list, tuple, AnyArrayLike]
+Scalar = Union[float, str]
+DatetimeScalar = Union[Scalar, date, np.datetime64]
+
+DatetimeScalarOrArrayConvertible = Union[DatetimeScalar, ArrayConvertible]
+
+DatetimeDictArg = Union[list[Scalar], tuple[Scalar, ...], AnyArrayLike]
+
+
+class YearMonthDayDict(TypedDict, total=True):
+    year: DatetimeDictArg
+    month: DatetimeDictArg
+    day: DatetimeDictArg
+
+
+class FulldatetimeDict(YearMonthDayDict, total=False):
+    hour: DatetimeDictArg
+    hours: DatetimeDictArg
+    minute: DatetimeDictArg
+    minutes: DatetimeDictArg
+    second: DatetimeDictArg
+    seconds: DatetimeDictArg
+    ms: DatetimeDictArg
+    us: DatetimeDictArg
+    ns: DatetimeDictArg
+
+
+DictConvertible = Union[FulldatetimeDict, "DataFrame"]
+start_caching_at = 50
+
+
+# ---------------------------------------------------------------------
+
+
+def _guess_datetime_format_for_array(arr, dayfirst: bool | None = False) -> str | None:
+    # Try to guess the format based on the first non-NaN element, return None if can't
+    if (first_non_null := tslib.first_non_null(arr)) != -1:
+        if type(first_non_nan_element := arr[first_non_null]) is str:  # noqa: E721
+            # GH#32264 np.str_ object
+            guessed_format = guess_datetime_format(
+                first_non_nan_element, dayfirst=dayfirst
+            )
+            if guessed_format is not None:
+                return guessed_format
+            # If there are multiple non-null elements, warn about
+            # how parsing might not be consistent
+            if tslib.first_non_null(arr[first_non_null + 1 :]) != -1:
+                warnings.warn(
+                    "Could not infer format, so each element will be parsed "
+                    "individually, falling back to `dateutil`. To ensure parsing is "
+                    "consistent and as-expected, please specify a format.",
+                    UserWarning,
+                    stacklevel=find_stack_level(),
+                )
+    return None
+
+
+def should_cache(
+    arg: ArrayConvertible, unique_share: float = 0.7, check_count: int | None = None
+) -> bool:
+    """
+    Decides whether to do caching.
+
+    If the percent of unique elements among `check_count` elements less
+    than `unique_share * 100` then we can do caching.
+
+    Parameters
+    ----------
+    arg: listlike, tuple, 1-d array, Series
+    unique_share: float, default=0.7, optional
+        0 < unique_share < 1
+    check_count: int, optional
+        0 <= check_count <= len(arg)
+
+    Returns
+    -------
+    do_caching: bool
+
+    Notes
+    -----
+    By default for a sequence of less than 50 items in size, we don't do
+    caching; for the number of elements less than 5000, we take ten percent of
+    all elements to check for a uniqueness share; if the sequence size is more
+    than 5000, then we check only the first 500 elements.
+    All constants were chosen empirically by.
+    """
+    do_caching = True
+
+    # default realization
+    if check_count is None:
+        # in this case, the gain from caching is negligible
+        if len(arg) <= start_caching_at:
+            return False
+
+        if len(arg) <= 5000:
+            check_count = len(arg) // 10
+        else:
+            check_count = 500
+    else:
+        assert (
+            0 <= check_count <= len(arg)
+        ), "check_count must be in next bounds: [0; len(arg)]"
+        if check_count == 0:
+            return False
+
+    assert 0 < unique_share < 1, "unique_share must be in next bounds: (0; 1)"
+
+    try:
+        # We can't cache if the items are not hashable.
+        unique_elements = set(islice(arg, check_count))
+    except TypeError:
+        return False
+    if len(unique_elements) > check_count * unique_share:
+        do_caching = False
+    return do_caching
+
+
+def _maybe_cache(
+    arg: ArrayConvertible,
+    format: str | None,
+    cache: bool,
+    convert_listlike: Callable,
+) -> Series:
+    """
+    Create a cache of unique dates from an array of dates
+
+    Parameters
+    ----------
+    arg : listlike, tuple, 1-d array, Series
+    format : string
+        Strftime format to parse time
+    cache : bool
+        True attempts to create a cache of converted values
+    convert_listlike : function
+        Conversion function to apply on dates
+
+    Returns
+    -------
+    cache_array : Series
+        Cache of converted, unique dates. Can be empty
+    """
+    from pandas import Series
+
+    cache_array = Series(dtype=object)
+
+    if cache:
+        # Perform a quicker unique check
+        if not should_cache(arg):
+            return cache_array
+
+        if not isinstance(arg, (np.ndarray, ExtensionArray, Index, ABCSeries)):
+            arg = np.array(arg)
+
+        unique_dates = unique(arg)
+        if len(unique_dates) < len(arg):
+            cache_dates = convert_listlike(unique_dates, format)
+            # GH#45319
+            try:
+                cache_array = Series(cache_dates, index=unique_dates, copy=False)
+            except OutOfBoundsDatetime:
+                return cache_array
+            # GH#39882 and GH#35888 in case of None and NaT we get duplicates
+            if not cache_array.index.is_unique:
+                cache_array = cache_array[~cache_array.index.duplicated()]
+    return cache_array
+
+
+def _box_as_indexlike(
+    dt_array: ArrayLike, utc: bool = False, name: Hashable | None = None
+) -> Index:
+    """
+    Properly boxes the ndarray of datetimes to DatetimeIndex
+    if it is possible or to generic Index instead
+
+    Parameters
+    ----------
+    dt_array: 1-d array
+        Array of datetimes to be wrapped in an Index.
+    utc : bool
+        Whether to convert/localize timestamps to UTC.
+    name : string, default None
+        Name for a resulting index
+
+    Returns
+    -------
+    result : datetime of converted dates
+        - DatetimeIndex if convertible to sole datetime64 type
+        - general Index otherwise
+    """
+
+    if lib.is_np_dtype(dt_array.dtype, "M"):
+        tz = "utc" if utc else None
+        return DatetimeIndex(dt_array, tz=tz, name=name)
+    return Index(dt_array, name=name, dtype=dt_array.dtype)
+
+
+def _convert_and_box_cache(
+    arg: DatetimeScalarOrArrayConvertible,
+    cache_array: Series,
+    name: Hashable | None = None,
+) -> Index:
+    """
+    Convert array of dates with a cache and wrap the result in an Index.
+
+    Parameters
+    ----------
+    arg : integer, float, string, datetime, list, tuple, 1-d array, Series
+    cache_array : Series
+        Cache of converted, unique dates
+    name : string, default None
+        Name for a DatetimeIndex
+
+    Returns
+    -------
+    result : Index-like of converted dates
+    """
+    from pandas import Series
+
+    result = Series(arg, dtype=cache_array.index.dtype).map(cache_array)
+    return _box_as_indexlike(result._values, utc=False, name=name)
+
+
+def _convert_listlike_datetimes(
+    arg,
+    format: str | None,
+    name: Hashable | None = None,
+    utc: bool = False,
+    unit: str | None = None,
+    errors: DateTimeErrorChoices = "raise",
+    dayfirst: bool | None = None,
+    yearfirst: bool | None = None,
+    exact: bool = True,
+):
+    """
+    Helper function for to_datetime. Performs the conversions of 1D listlike
+    of dates
+
+    Parameters
+    ----------
+    arg : list, tuple, ndarray, Series, Index
+        date to be parsed
+    name : object
+        None or string for the Index name
+    utc : bool
+        Whether to convert/localize timestamps to UTC.
+    unit : str
+        None or string of the frequency of the passed data
+    errors : str
+        error handing behaviors from to_datetime, 'raise', 'coerce', 'ignore'
+    dayfirst : bool
+        dayfirst parsing behavior from to_datetime
+    yearfirst : bool
+        yearfirst parsing behavior from to_datetime
+    exact : bool, default True
+        exact format matching behavior from to_datetime
+
+    Returns
+    -------
+    Index-like of parsed dates
+    """
+    if isinstance(arg, (list, tuple)):
+        arg = np.array(arg, dtype="O")
+    elif isinstance(arg, NumpyExtensionArray):
+        arg = np.array(arg)
+
+    arg_dtype = getattr(arg, "dtype", None)
+    # these are shortcutable
+    tz = "utc" if utc else None
+    if isinstance(arg_dtype, DatetimeTZDtype):
+        if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
+            return DatetimeIndex(arg, tz=tz, name=name)
+        if utc:
+            arg = arg.tz_convert(None).tz_localize("utc")
+        return arg
+
+    elif isinstance(arg_dtype, ArrowDtype) and arg_dtype.type is Timestamp:
+        # TODO: Combine with above if DTI/DTA supports Arrow timestamps
+        if utc:
+            # pyarrow uses UTC, not lowercase utc
+            if isinstance(arg, Index):
+                arg_array = cast(ArrowExtensionArray, arg.array)
+                if arg_dtype.pyarrow_dtype.tz is not None:
+                    arg_array = arg_array._dt_tz_convert("UTC")
+                else:
+                    arg_array = arg_array._dt_tz_localize("UTC")
+                arg = Index(arg_array)
+            else:
+                # ArrowExtensionArray
+                if arg_dtype.pyarrow_dtype.tz is not None:
+                    arg = arg._dt_tz_convert("UTC")
+                else:
+                    arg = arg._dt_tz_localize("UTC")
+        return arg
+
+    elif lib.is_np_dtype(arg_dtype, "M"):
+        if not is_supported_dtype(arg_dtype):
+            # We go to closest supported reso, i.e. "s"
+            arg = astype_overflowsafe(
+                # TODO: looks like we incorrectly raise with errors=="ignore"
+                np.asarray(arg),
+                np.dtype("M8[s]"),
+                is_coerce=errors == "coerce",
+            )
+
+        if not isinstance(arg, (DatetimeArray, DatetimeIndex)):
+            return DatetimeIndex(arg, tz=tz, name=name)
+        elif utc:
+            # DatetimeArray, DatetimeIndex
+            return arg.tz_localize("utc")
+
+        return arg
+
+    elif unit is not None:
+        if format is not None:
+            raise ValueError("cannot specify both format and unit")
+        return _to_datetime_with_unit(arg, unit, name, utc, errors)
+    elif getattr(arg, "ndim", 1) > 1:
+        raise TypeError(
+            "arg must be a string, datetime, list, tuple, 1-d array, or Series"
+        )
+
+    # warn if passing timedelta64, raise for PeriodDtype
+    # NB: this must come after unit transformation
+    try:
+        arg, _ = maybe_convert_dtype(arg, copy=False, tz=libtimezones.maybe_get_tz(tz))
+    except TypeError:
+        if errors == "coerce":
+            npvalues = np.array(["NaT"], dtype="datetime64[ns]").repeat(len(arg))
+            return DatetimeIndex(npvalues, name=name)
+        elif errors == "ignore":
+            idx = Index(arg, name=name)
+            return idx
+        raise
+
+    arg = ensure_object(arg)
+
+    if format is None:
+        format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
+
+    # `format` could be inferred, or user didn't ask for mixed-format parsing.
+    if format is not None and format != "mixed":
+        return _array_strptime_with_fallback(arg, name, utc, format, exact, errors)
+
+    result, tz_parsed = objects_to_datetime64(
+        arg,
+        dayfirst=dayfirst,
+        yearfirst=yearfirst,
+        utc=utc,
+        errors=errors,
+        allow_object=True,
+    )
+
+    if tz_parsed is not None:
+        # We can take a shortcut since the datetime64 numpy array
+        # is in UTC
+        out_unit = np.datetime_data(result.dtype)[0]
+        dtype = cast(DatetimeTZDtype, tz_to_dtype(tz_parsed, out_unit))
+        dt64_values = result.view(f"M8[{dtype.unit}]")
+        dta = DatetimeArray._simple_new(dt64_values, dtype=dtype)
+        return DatetimeIndex._simple_new(dta, name=name)
+
+    return _box_as_indexlike(result, utc=utc, name=name)
+
+
+def _array_strptime_with_fallback(
+    arg,
+    name,
+    utc: bool,
+    fmt: str,
+    exact: bool,
+    errors: str,
+) -> Index:
+    """
+    Call array_strptime, with fallback behavior depending on 'errors'.
+    """
+    result, tz_out = array_strptime(arg, fmt, exact=exact, errors=errors, utc=utc)
+    if tz_out is not None:
+        unit = np.datetime_data(result.dtype)[0]
+        dtype = DatetimeTZDtype(tz=tz_out, unit=unit)
+        dta = DatetimeArray._simple_new(result, dtype=dtype)
+        if utc:
+            dta = dta.tz_convert("UTC")
+        return Index(dta, name=name)
+    elif result.dtype != object and utc:
+        unit = np.datetime_data(result.dtype)[0]
+        res = Index(result, dtype=f"M8[{unit}, UTC]", name=name)
+        return res
+    elif using_string_dtype() and result.dtype == object:
+        if lib.is_string_array(result):
+            return Index(result, dtype="str", name=name)
+    return Index(result, dtype=result.dtype, name=name)
+
+
+def _to_datetime_with_unit(arg, unit, name, utc: bool, errors: str) -> Index:
+    """
+    to_datetime specalized to the case where a 'unit' is passed.
+    """
+    arg = extract_array(arg, extract_numpy=True)
+
+    # GH#30050 pass an ndarray to tslib.array_with_unit_to_datetime
+    # because it expects an ndarray argument
+    if isinstance(arg, IntegerArray):
+        arr = arg.astype(f"datetime64[{unit}]")
+        tz_parsed = None
+    else:
+        arg = np.asarray(arg)
+
+        if arg.dtype.kind in "iu":
+            # Note we can't do "f" here because that could induce unwanted
+            #  rounding GH#14156, GH#20445
+            arr = arg.astype(f"datetime64[{unit}]", copy=False)
+            try:
+                arr = astype_overflowsafe(arr, np.dtype("M8[ns]"), copy=False)
+            except OutOfBoundsDatetime:
+                if errors == "raise":
+                    raise
+                arg = arg.astype(object)
+                return _to_datetime_with_unit(arg, unit, name, utc, errors)
+            tz_parsed = None
+
+        elif arg.dtype.kind == "f":
+            with np.errstate(over="raise"):
+                try:
+                    arr = cast_from_unit_vectorized(arg, unit=unit)
+                except OutOfBoundsDatetime:
+                    if errors != "raise":
+                        return _to_datetime_with_unit(
+                            arg.astype(object), unit, name, utc, errors
+                        )
+                    raise OutOfBoundsDatetime(
+                        f"cannot convert input with unit '{unit}'"
+                    )
+
+            arr = arr.view("M8[ns]")
+            tz_parsed = None
+        else:
+            arg = arg.astype(object, copy=False)
+            arr, tz_parsed = tslib.array_with_unit_to_datetime(arg, unit, errors=errors)
+
+    if errors == "ignore":
+        # Index constructor _may_ infer to DatetimeIndex
+        result = Index._with_infer(arr, name=name)
+    else:
+        result = DatetimeIndex(arr, name=name)
+
+    if not isinstance(result, DatetimeIndex):
+        return result
+
+    # GH#23758: We may still need to localize the result with tz
+    # GH#25546: Apply tz_parsed first (from arg), then tz (from caller)
+    # result will be naive but in UTC
+    result = result.tz_localize("UTC").tz_convert(tz_parsed)
+
+    if utc:
+        if result.tz is None:
+            result = result.tz_localize("utc")
+        else:
+            result = result.tz_convert("utc")
+    return result
+
+
+def _adjust_to_origin(arg, origin, unit):
+    """
+    Helper function for to_datetime.
+    Adjust input argument to the specified origin
+
+    Parameters
+    ----------
+    arg : list, tuple, ndarray, Series, Index
+        date to be adjusted
+    origin : 'julian' or Timestamp
+        origin offset for the arg
+    unit : str
+        passed unit from to_datetime, must be 'D'
+
+    Returns
+    -------
+    ndarray or scalar of adjusted date(s)
+    """
+    if origin == "julian":
+        original = arg
+        j0 = Timestamp(0).to_julian_date()
+        if unit != "D":
+            raise ValueError("unit must be 'D' for origin='julian'")
+        try:
+            arg = arg - j0
+        except TypeError as err:
+            raise ValueError(
+                "incompatible 'arg' type for given 'origin'='julian'"
+            ) from err
+
+        # preemptively check this for a nice range
+        j_max = Timestamp.max.to_julian_date() - j0
+        j_min = Timestamp.min.to_julian_date() - j0
+        if np.any(arg > j_max) or np.any(arg < j_min):
+            raise OutOfBoundsDatetime(
+                f"{original} is Out of Bounds for origin='julian'"
+            )
+    else:
+        # arg must be numeric
+        if not (
+            (is_integer(arg) or is_float(arg)) or is_numeric_dtype(np.asarray(arg))
+        ):
+            raise ValueError(
+                f"'{arg}' is not compatible with origin='{origin}'; "
+                "it must be numeric with a unit specified"
+            )
+
+        # we are going to offset back to unix / epoch time
+        try:
+            offset = Timestamp(origin, unit=unit)
+        except OutOfBoundsDatetime as err:
+            raise OutOfBoundsDatetime(f"origin {origin} is Out of Bounds") from err
+        except ValueError as err:
+            raise ValueError(
+                f"origin {origin} cannot be converted to a Timestamp"
+            ) from err
+
+        if offset.tz is not None:
+            raise ValueError(f"origin offset {offset} must be tz-naive")
+        td_offset = offset - Timestamp(0)
+
+        # convert the offset to the unit of the arg
+        # this should be lossless in terms of precision
+        ioffset = td_offset // Timedelta(1, unit=unit)
+
+        # scalars & ndarray-like can handle the addition
+        if is_list_like(arg) and not isinstance(arg, (ABCSeries, Index, np.ndarray)):
+            arg = np.asarray(arg)
+        arg = arg + ioffset
+    return arg
+
+
+@overload
+def to_datetime(
+    arg: DatetimeScalar,
+    errors: DateTimeErrorChoices = ...,
+    dayfirst: bool = ...,
+    yearfirst: bool = ...,
+    utc: bool = ...,
+    format: str | None = ...,
+    exact: bool = ...,
+    unit: str | None = ...,
+    infer_datetime_format: bool = ...,
+    origin=...,
+    cache: bool = ...,
+) -> Timestamp:
+    ...
+
+
+@overload
+def to_datetime(
+    arg: Series | DictConvertible,
+    errors: DateTimeErrorChoices = ...,
+    dayfirst: bool = ...,
+    yearfirst: bool = ...,
+    utc: bool = ...,
+    format: str | None = ...,
+    exact: bool = ...,
+    unit: str | None = ...,
+    infer_datetime_format: bool = ...,
+    origin=...,
+    cache: bool = ...,
+) -> Series:
+    ...
+
+
+@overload
+def to_datetime(
+    arg: list | tuple | Index | ArrayLike,
+    errors: DateTimeErrorChoices = ...,
+    dayfirst: bool = ...,
+    yearfirst: bool = ...,
+    utc: bool = ...,
+    format: str | None = ...,
+    exact: bool = ...,
+    unit: str | None = ...,
+    infer_datetime_format: bool = ...,
+    origin=...,
+    cache: bool = ...,
+) -> DatetimeIndex:
+    ...
+
+
+def to_datetime(
+    arg: DatetimeScalarOrArrayConvertible | DictConvertible,
+    errors: DateTimeErrorChoices = "raise",
+    dayfirst: bool = False,
+    yearfirst: bool = False,
+    utc: bool = False,
+    format: str | None = None,
+    exact: bool | lib.NoDefault = lib.no_default,
+    unit: str | None = None,
+    infer_datetime_format: lib.NoDefault | bool = lib.no_default,
+    origin: str = "unix",
+    cache: bool = True,
+) -> DatetimeIndex | Series | DatetimeScalar | NaTType | None:
+    """
+    Convert argument to datetime.
+
+    This function converts a scalar, array-like, :class:`Series` or
+    :class:`DataFrame`/dict-like to a pandas datetime object.
+
+    Parameters
+    ----------
+    arg : int, float, str, datetime, list, tuple, 1-d array, Series, DataFrame/dict-like
+        The object to convert to a datetime. If a :class:`DataFrame` is provided, the
+        method expects minimally the following columns: :const:`"year"`,
+        :const:`"month"`, :const:`"day"`. The column "year"
+        must be specified in 4-digit format.
+    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+        - If :const:`'raise'`, then invalid parsing will raise an exception.
+        - If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT`.
+        - If :const:`'ignore'`, then invalid parsing will return the input.
+    dayfirst : bool, default False
+        Specify a date parse order if `arg` is str or is list-like.
+        If :const:`True`, parses dates with the day first, e.g. :const:`"10/11/12"`
+        is parsed as :const:`2012-11-10`.
+
+        .. warning::
+
+            ``dayfirst=True`` is not strict, but will prefer to parse
+            with day first.
+
+    yearfirst : bool, default False
+        Specify a date parse order if `arg` is str or is list-like.
+
+        - If :const:`True` parses dates with the year first, e.g.
+          :const:`"10/11/12"` is parsed as :const:`2010-11-12`.
+        - If both `dayfirst` and `yearfirst` are :const:`True`, `yearfirst` is
+          preceded (same as :mod:`dateutil`).
+
+        .. warning::
+
+            ``yearfirst=True`` is not strict, but will prefer to parse
+            with year first.
+
+    utc : bool, default False
+        Control timezone-related parsing, localization and conversion.
+
+        - If :const:`True`, the function *always* returns a timezone-aware
+          UTC-localized :class:`Timestamp`, :class:`Series` or
+          :class:`DatetimeIndex`. To do this, timezone-naive inputs are
+          *localized* as UTC, while timezone-aware inputs are *converted* to UTC.
+
+        - If :const:`False` (default), inputs will not be coerced to UTC.
+          Timezone-naive inputs will remain naive, while timezone-aware ones
+          will keep their time offsets. Limitations exist for mixed
+          offsets (typically, daylight savings), see :ref:`Examples
+          ` section for details.
+
+        .. warning::
+
+            In a future version of pandas, parsing datetimes with mixed time
+            zones will raise an error unless `utc=True`.
+            Please specify `utc=True` to opt in to the new behaviour
+            and silence this warning. To create a `Series` with mixed offsets and
+            `object` dtype, please use `apply` and `datetime.datetime.strptime`.
+
+        See also: pandas general documentation about `timezone conversion and
+        localization
+        `_.
+
+    format : str, default None
+        The strftime to parse time, e.g. :const:`"%d/%m/%Y"`. See
+        `strftime documentation
+        `_ for more information on choices, though
+        note that :const:`"%f"` will parse all the way up to nanoseconds.
+        You can also pass:
+
+        - "ISO8601", to parse any `ISO8601 `_
+          time string (not necessarily in exactly the same format);
+        - "mixed", to infer the format for each element individually. This is risky,
+          and you should probably use it along with `dayfirst`.
+
+        .. note::
+
+            If a :class:`DataFrame` is passed, then `format` has no effect.
+
+    exact : bool, default True
+        Control how `format` is used:
+
+        - If :const:`True`, require an exact `format` match.
+        - If :const:`False`, allow the `format` to match anywhere in the target
+          string.
+
+        Cannot be used alongside ``format='ISO8601'`` or ``format='mixed'``.
+    unit : str, default 'ns'
+        The unit of the arg (D,s,ms,us,ns) denote the unit, which is an
+        integer or float number. This will be based off the origin.
+        Example, with ``unit='ms'`` and ``origin='unix'``, this would calculate
+        the number of milliseconds to the unix epoch start.
+    infer_datetime_format : bool, default False
+        If :const:`True` and no `format` is given, attempt to infer the format
+        of the datetime strings based on the first non-NaN element,
+        and if it can be inferred, switch to a faster method of parsing them.
+        In some cases this can increase the parsing speed by ~5-10x.
+
+        .. deprecated:: 2.0.0
+            A strict version of this argument is now the default, passing it has
+            no effect.
+
+    origin : scalar, default 'unix'
+        Define the reference date. The numeric values would be parsed as number
+        of units (defined by `unit`) since this reference date.
+
+        - If :const:`'unix'` (or POSIX) time; origin is set to 1970-01-01.
+        - If :const:`'julian'`, unit must be :const:`'D'`, and origin is set to
+          beginning of Julian Calendar. Julian day number :const:`0` is assigned
+          to the day starting at noon on January 1, 4713 BC.
+        - If Timestamp convertible (Timestamp, dt.datetime, np.datetimt64 or date
+          string), origin is set to Timestamp identified by origin.
+        - If a float or integer, origin is the difference
+          (in units determined by the ``unit`` argument) relative to 1970-01-01.
+    cache : bool, default True
+        If :const:`True`, use a cache of unique, converted dates to apply the
+        datetime conversion. May produce significant speed-up when parsing
+        duplicate date strings, especially ones with timezone offsets. The cache
+        is only used when there are at least 50 values. The presence of
+        out-of-bounds values will render the cache unusable and may slow down
+        parsing.
+
+    Returns
+    -------
+    datetime
+        If parsing succeeded.
+        Return type depends on input (types in parenthesis correspond to
+        fallback in case of unsuccessful timezone or out-of-range timestamp
+        parsing):
+
+        - scalar: :class:`Timestamp` (or :class:`datetime.datetime`)
+        - array-like: :class:`DatetimeIndex` (or :class:`Series` with
+          :class:`object` dtype containing :class:`datetime.datetime`)
+        - Series: :class:`Series` of :class:`datetime64` dtype (or
+          :class:`Series` of :class:`object` dtype containing
+          :class:`datetime.datetime`)
+        - DataFrame: :class:`Series` of :class:`datetime64` dtype (or
+          :class:`Series` of :class:`object` dtype containing
+          :class:`datetime.datetime`)
+
+    Raises
+    ------
+    ParserError
+        When parsing a date from string fails.
+    ValueError
+        When another datetime conversion error happens. For example when one
+        of 'year', 'month', day' columns is missing in a :class:`DataFrame`, or
+        when a Timezone-aware :class:`datetime.datetime` is found in an array-like
+        of mixed time offsets, and ``utc=False``.
+
+    See Also
+    --------
+    DataFrame.astype : Cast argument to a specified dtype.
+    to_timedelta : Convert argument to timedelta.
+    convert_dtypes : Convert dtypes.
+
+    Notes
+    -----
+
+    Many input types are supported, and lead to different output types:
+
+    - **scalars** can be int, float, str, datetime object (from stdlib :mod:`datetime`
+      module or :mod:`numpy`). They are converted to :class:`Timestamp` when
+      possible, otherwise they are converted to :class:`datetime.datetime`.
+      None/NaN/null scalars are converted to :const:`NaT`.
+
+    - **array-like** can contain int, float, str, datetime objects. They are
+      converted to :class:`DatetimeIndex` when possible, otherwise they are
+      converted to :class:`Index` with :class:`object` dtype, containing
+      :class:`datetime.datetime`. None/NaN/null entries are converted to
+      :const:`NaT` in both cases.
+
+    - **Series** are converted to :class:`Series` with :class:`datetime64`
+      dtype when possible, otherwise they are converted to :class:`Series` with
+      :class:`object` dtype, containing :class:`datetime.datetime`. None/NaN/null
+      entries are converted to :const:`NaT` in both cases.
+
+    - **DataFrame/dict-like** are converted to :class:`Series` with
+      :class:`datetime64` dtype. For each row a datetime is created from assembling
+      the various dataframe columns. Column keys can be common abbreviations
+      like ['year', 'month', 'day', 'minute', 'second', 'ms', 'us', 'ns']) or
+      plurals of the same.
+
+    The following causes are responsible for :class:`datetime.datetime` objects
+    being returned (possibly inside an :class:`Index` or a :class:`Series` with
+    :class:`object` dtype) instead of a proper pandas designated type
+    (:class:`Timestamp`, :class:`DatetimeIndex` or :class:`Series`
+    with :class:`datetime64` dtype):
+
+    - when any input element is before :const:`Timestamp.min` or after
+      :const:`Timestamp.max`, see `timestamp limitations
+      `_.
+
+    - when ``utc=False`` (default) and the input is an array-like or
+      :class:`Series` containing mixed naive/aware datetime, or aware with mixed
+      time offsets. Note that this happens in the (quite frequent) situation when
+      the timezone has a daylight savings policy. In that case you may wish to
+      use ``utc=True``.
+
+    Examples
+    --------
+
+    **Handling various input formats**
+
+    Assembling a datetime from multiple columns of a :class:`DataFrame`. The keys
+    can be common abbreviations like ['year', 'month', 'day', 'minute', 'second',
+    'ms', 'us', 'ns']) or plurals of the same
+
+    >>> df = pd.DataFrame({'year': [2015, 2016],
+    ...                    'month': [2, 3],
+    ...                    'day': [4, 5]})
+    >>> pd.to_datetime(df)
+    0   2015-02-04
+    1   2016-03-05
+    dtype: datetime64[ns]
+
+    Using a unix epoch time
+
+    >>> pd.to_datetime(1490195805, unit='s')
+    Timestamp('2017-03-22 15:16:45')
+    >>> pd.to_datetime(1490195805433502912, unit='ns')
+    Timestamp('2017-03-22 15:16:45.433502912')
+
+    .. warning:: For float arg, precision rounding might happen. To prevent
+        unexpected behavior use a fixed-width exact type.
+
+    Using a non-unix epoch origin
+
+    >>> pd.to_datetime([1, 2, 3], unit='D',
+    ...                origin=pd.Timestamp('1960-01-01'))
+    DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'],
+                  dtype='datetime64[ns]', freq=None)
+
+    **Differences with strptime behavior**
+
+    :const:`"%f"` will parse all the way up to nanoseconds.
+
+    >>> pd.to_datetime('2018-10-26 12:00:00.0000000011',
+    ...                format='%Y-%m-%d %H:%M:%S.%f')
+    Timestamp('2018-10-26 12:00:00.000000001')
+
+    **Non-convertible date/times**
+
+    Passing ``errors='coerce'`` will force an out-of-bounds date to :const:`NaT`,
+    in addition to forcing non-dates (or non-parseable dates) to :const:`NaT`.
+
+    >>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
+    NaT
+
+    .. _to_datetime_tz_examples:
+
+    **Timezones and time offsets**
+
+    The default behaviour (``utc=False``) is as follows:
+
+    - Timezone-naive inputs are converted to timezone-naive :class:`DatetimeIndex`:
+
+    >>> pd.to_datetime(['2018-10-26 12:00:00', '2018-10-26 13:00:15'])
+    DatetimeIndex(['2018-10-26 12:00:00', '2018-10-26 13:00:15'],
+                  dtype='datetime64[ns]', freq=None)
+
+    - Timezone-aware inputs *with constant time offset* are converted to
+      timezone-aware :class:`DatetimeIndex`:
+
+    >>> pd.to_datetime(['2018-10-26 12:00 -0500', '2018-10-26 13:00 -0500'])
+    DatetimeIndex(['2018-10-26 12:00:00-05:00', '2018-10-26 13:00:00-05:00'],
+                  dtype='datetime64[ns, UTC-05:00]', freq=None)
+
+    - However, timezone-aware inputs *with mixed time offsets* (for example
+      issued from a timezone with daylight savings, such as Europe/Paris)
+      are **not successfully converted** to a :class:`DatetimeIndex`.
+      Parsing datetimes with mixed time zones will show a warning unless
+      `utc=True`. If you specify `utc=False` the warning below will be shown
+      and a simple :class:`Index` containing :class:`datetime.datetime`
+      objects will be returned:
+
+    >>> pd.to_datetime(['2020-10-25 02:00 +0200',
+    ...                 '2020-10-25 04:00 +0100'])  # doctest: +SKIP
+    FutureWarning: In a future version of pandas, parsing datetimes with mixed
+    time zones will raise an error unless `utc=True`. Please specify `utc=True`
+    to opt in to the new behaviour and silence this warning. To create a `Series`
+    with mixed offsets and `object` dtype, please use `apply` and
+    `datetime.datetime.strptime`.
+    Index([2020-10-25 02:00:00+02:00, 2020-10-25 04:00:00+01:00],
+          dtype='object')
+
+    - A mix of timezone-aware and timezone-naive inputs is also converted to
+      a simple :class:`Index` containing :class:`datetime.datetime` objects:
+
+    >>> from datetime import datetime
+    >>> pd.to_datetime(["2020-01-01 01:00:00-01:00",
+    ...                 datetime(2020, 1, 1, 3, 0)])  # doctest: +SKIP
+    FutureWarning: In a future version of pandas, parsing datetimes with mixed
+    time zones will raise an error unless `utc=True`. Please specify `utc=True`
+    to opt in to the new behaviour and silence this warning. To create a `Series`
+    with mixed offsets and `object` dtype, please use `apply` and
+    `datetime.datetime.strptime`.
+    Index([2020-01-01 01:00:00-01:00, 2020-01-01 03:00:00], dtype='object')
+
+    |
+
+    Setting ``utc=True`` solves most of the above issues:
+
+    - Timezone-naive inputs are *localized* as UTC
+
+    >>> pd.to_datetime(['2018-10-26 12:00', '2018-10-26 13:00'], utc=True)
+    DatetimeIndex(['2018-10-26 12:00:00+00:00', '2018-10-26 13:00:00+00:00'],
+                  dtype='datetime64[ns, UTC]', freq=None)
+
+    - Timezone-aware inputs are *converted* to UTC (the output represents the
+      exact same datetime, but viewed from the UTC time offset `+00:00`).
+
+    >>> pd.to_datetime(['2018-10-26 12:00 -0530', '2018-10-26 12:00 -0500'],
+    ...                utc=True)
+    DatetimeIndex(['2018-10-26 17:30:00+00:00', '2018-10-26 17:00:00+00:00'],
+                  dtype='datetime64[ns, UTC]', freq=None)
+
+    - Inputs can contain both string or datetime, the above
+      rules still apply
+
+    >>> pd.to_datetime(['2018-10-26 12:00', datetime(2020, 1, 1, 18)], utc=True)
+    DatetimeIndex(['2018-10-26 12:00:00+00:00', '2020-01-01 18:00:00+00:00'],
+                  dtype='datetime64[ns, UTC]', freq=None)
+    """
+    if exact is not lib.no_default and format in {"mixed", "ISO8601"}:
+        raise ValueError("Cannot use 'exact' when 'format' is 'mixed' or 'ISO8601'")
+    if infer_datetime_format is not lib.no_default:
+        warnings.warn(
+            "The argument 'infer_datetime_format' is deprecated and will "
+            "be removed in a future version. "
+            "A strict version of it is now the default, see "
+            "https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. "
+            "You can safely remove this argument.",
+            stacklevel=find_stack_level(),
+        )
+    if errors == "ignore":
+        # GH#54467
+        warnings.warn(
+            "errors='ignore' is deprecated and will raise in a future version. "
+            "Use to_datetime without passing `errors` and catch exceptions "
+            "explicitly instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+
+    if arg is None:
+        return None
+
+    if origin != "unix":
+        arg = _adjust_to_origin(arg, origin, unit)
+
+    convert_listlike = partial(
+        _convert_listlike_datetimes,
+        utc=utc,
+        unit=unit,
+        dayfirst=dayfirst,
+        yearfirst=yearfirst,
+        errors=errors,
+        exact=exact,
+    )
+    # pylint: disable-next=used-before-assignment
+    result: Timestamp | NaTType | Series | Index
+
+    if isinstance(arg, Timestamp):
+        result = arg
+        if utc:
+            if arg.tz is not None:
+                result = arg.tz_convert("utc")
+            else:
+                result = arg.tz_localize("utc")
+    elif isinstance(arg, ABCSeries):
+        cache_array = _maybe_cache(arg, format, cache, convert_listlike)
+        if not cache_array.empty:
+            result = arg.map(cache_array)
+        else:
+            values = convert_listlike(arg._values, format)
+            result = arg._constructor(values, index=arg.index, name=arg.name)
+    elif isinstance(arg, (ABCDataFrame, abc.MutableMapping)):
+        result = _assemble_from_unit_mappings(arg, errors, utc)
+    elif isinstance(arg, Index):
+        cache_array = _maybe_cache(arg, format, cache, convert_listlike)
+        if not cache_array.empty:
+            result = _convert_and_box_cache(arg, cache_array, name=arg.name)
+        else:
+            result = convert_listlike(arg, format, name=arg.name)
+    elif is_list_like(arg):
+        try:
+            # error: Argument 1 to "_maybe_cache" has incompatible type
+            # "Union[float, str, datetime, List[Any], Tuple[Any, ...], ExtensionArray,
+            # ndarray[Any, Any], Series]"; expected "Union[List[Any], Tuple[Any, ...],
+            # Union[Union[ExtensionArray, ndarray[Any, Any]], Index, Series], Series]"
+            argc = cast(
+                Union[list, tuple, ExtensionArray, np.ndarray, "Series", Index], arg
+            )
+            cache_array = _maybe_cache(argc, format, cache, convert_listlike)
+        except OutOfBoundsDatetime:
+            # caching attempts to create a DatetimeIndex, which may raise
+            # an OOB. If that's the desired behavior, then just reraise...
+            if errors == "raise":
+                raise
+            # ... otherwise, continue without the cache.
+            from pandas import Series
+
+            cache_array = Series([], dtype=object)  # just an empty array
+        if not cache_array.empty:
+            result = _convert_and_box_cache(argc, cache_array)
+        else:
+            result = convert_listlike(argc, format)
+    else:
+        result = convert_listlike(np.array([arg]), format)[0]
+        if isinstance(arg, bool) and isinstance(result, np.bool_):
+            result = bool(result)  # TODO: avoid this kludge.
+
+    #  error: Incompatible return value type (got "Union[Timestamp, NaTType,
+    # Series, Index]", expected "Union[DatetimeIndex, Series, float, str,
+    # NaTType, None]")
+    return result  # type: ignore[return-value]
+
+
+# mappings for assembling units
+_unit_map = {
+    "year": "year",
+    "years": "year",
+    "month": "month",
+    "months": "month",
+    "day": "day",
+    "days": "day",
+    "hour": "h",
+    "hours": "h",
+    "minute": "m",
+    "minutes": "m",
+    "second": "s",
+    "seconds": "s",
+    "ms": "ms",
+    "millisecond": "ms",
+    "milliseconds": "ms",
+    "us": "us",
+    "microsecond": "us",
+    "microseconds": "us",
+    "ns": "ns",
+    "nanosecond": "ns",
+    "nanoseconds": "ns",
+}
+
+
+def _assemble_from_unit_mappings(arg, errors: DateTimeErrorChoices, utc: bool):
+    """
+    assemble the unit specified fields from the arg (DataFrame)
+    Return a Series for actual parsing
+
+    Parameters
+    ----------
+    arg : DataFrame
+    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+
+        - If :const:`'raise'`, then invalid parsing will raise an exception
+        - If :const:`'coerce'`, then invalid parsing will be set as :const:`NaT`
+        - If :const:`'ignore'`, then invalid parsing will return the input
+    utc : bool
+        Whether to convert/localize timestamps to UTC.
+
+    Returns
+    -------
+    Series
+    """
+    from pandas import (
+        DataFrame,
+        to_numeric,
+        to_timedelta,
+    )
+
+    arg = DataFrame(arg)
+    if not arg.columns.is_unique:
+        raise ValueError("cannot assemble with duplicate keys")
+
+    # replace passed unit with _unit_map
+    def f(value):
+        if value in _unit_map:
+            return _unit_map[value]
+
+        # m is case significant
+        if value.lower() in _unit_map:
+            return _unit_map[value.lower()]
+
+        return value
+
+    unit = {k: f(k) for k in arg.keys()}
+    unit_rev = {v: k for k, v in unit.items()}
+
+    # we require at least Ymd
+    required = ["year", "month", "day"]
+    req = sorted(set(required) - set(unit_rev.keys()))
+    if len(req):
+        _required = ",".join(req)
+        raise ValueError(
+            "to assemble mappings requires at least that "
+            f"[year, month, day] be specified: [{_required}] is missing"
+        )
+
+    # keys we don't recognize
+    excess = sorted(set(unit_rev.keys()) - set(_unit_map.values()))
+    if len(excess):
+        _excess = ",".join(excess)
+        raise ValueError(
+            f"extra keys have been passed to the datetime assemblage: [{_excess}]"
+        )
+
+    def coerce(values):
+        # we allow coercion to if errors allows
+        values = to_numeric(values, errors=errors)
+
+        # prevent overflow in case of int8 or int16
+        if is_integer_dtype(values.dtype):
+            values = values.astype("int64", copy=False)
+        return values
+
+    values = (
+        coerce(arg[unit_rev["year"]]) * 10000
+        + coerce(arg[unit_rev["month"]]) * 100
+        + coerce(arg[unit_rev["day"]])
+    )
+    try:
+        values = to_datetime(values, format="%Y%m%d", errors=errors, utc=utc)
+    except (TypeError, ValueError) as err:
+        raise ValueError(f"cannot assemble the datetimes: {err}") from err
+
+    units: list[UnitChoices] = ["h", "m", "s", "ms", "us", "ns"]
+    for u in units:
+        value = unit_rev.get(u)
+        if value is not None and value in arg:
+            try:
+                values += to_timedelta(coerce(arg[value]), unit=u, errors=errors)
+            except (TypeError, ValueError) as err:
+                raise ValueError(
+                    f"cannot assemble the datetimes [{value}]: {err}"
+                ) from err
+    return values
+
+
+__all__ = [
+    "DateParseError",
+    "should_cache",
+    "to_datetime",
+]
diff --git a/shared/package-files/04/0482d3f7e849c4da4d6f84d345b66705fa8960f9ca2982ef21fce3ebd25f4544/_scaled_dot_product_flash_attention_for_cpu.h b/shared/package-files/04/0482d3f7e849c4da4d6f84d345b66705fa8960f9ca2982ef21fce3ebd25f4544/_scaled_dot_product_flash_attention_for_cpu.h
new file mode 100644
index 0000000000000000000000000000000000000000..22c9dd7f186016b0ad89440c2df362a75e2672e1
--- /dev/null
+++ b/shared/package-files/04/0482d3f7e849c4da4d6f84d345b66705fa8960f9ca2982ef21fce3ebd25f4544/_scaled_dot_product_flash_attention_for_cpu.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::_scaled_dot_product_flash_attention_for_cpu(Tensor query, Tensor key, Tensor value, float dropout_p=0.0, bool is_causal=False, *, Tensor? attn_mask=None, float? scale=None) -> (Tensor output, Tensor logsumexp)
+inline ::std::tuple _scaled_dot_product_flash_attention_for_cpu(const at::Tensor & query, const at::Tensor & key, const at::Tensor & value, double dropout_p=0.0, bool is_causal=false, const c10::optional & attn_mask={}, c10::optional scale=c10::nullopt) {
+    return at::_ops::_scaled_dot_product_flash_attention_for_cpu::call(query, key, value, dropout_p, is_causal, attn_mask, scale);
+}
+
+}
diff --git a/shared/package-files/04/04857bfd0f2b2c370e8ed9d08cf1dd8baabfbedb26072ac798e3148b8a25901e/sinc_cuda_dispatch.h b/shared/package-files/04/04857bfd0f2b2c370e8ed9d08cf1dd8baabfbedb26072ac798e3148b8a25901e/sinc_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..97c2167b9d293f218d17596ed2e08d1a9e146ae2
--- /dev/null
+++ b/shared/package-files/04/04857bfd0f2b2c370e8ed9d08cf1dd8baabfbedb26072ac798e3148b8a25901e/sinc_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor sinc(const at::Tensor & self);
+TORCH_API at::Tensor & sinc_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & sinc_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & sinc_(at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/04/04889f7c401de9a2d437bb91187ce7c1944f4dd265ab5456586966dd801c453b/_unique_native.h b/shared/package-files/04/04889f7c401de9a2d437bb91187ce7c1944f4dd265ab5456586966dd801c453b/_unique_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..b816d1122e7da8333b7840e472233990a7497856
--- /dev/null
+++ b/shared/package-files/04/04889f7c401de9a2d437bb91187ce7c1944f4dd265ab5456586966dd801c453b/_unique_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple _unique_out(const at::Tensor & self, bool sorted, bool return_inverse, at::Tensor & out0, at::Tensor & out1);
+TORCH_API ::std::tuple _unique_cpu(const at::Tensor & self, bool sorted=true, bool return_inverse=false);
+TORCH_API ::std::tuple _unique_cuda(const at::Tensor & self, bool sorted=true, bool return_inverse=false);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/04/048aee6f31c4a79428c8c68f8186c2d7349afc5dcbc295f328cf311f7ac7b292/Petersburg b/shared/package-files/04/048aee6f31c4a79428c8c68f8186c2d7349afc5dcbc295f328cf311f7ac7b292/Petersburg
new file mode 100644
index 0000000000000000000000000000000000000000..341a0235ef488ee623aba36a8b5928122774d2fb
Binary files /dev/null and b/shared/package-files/04/048aee6f31c4a79428c8c68f8186c2d7349afc5dcbc295f328cf311f7ac7b292/Petersburg differ
diff --git a/shared/package-files/04/048ba846e73030ea77938f7fa71e40ebdebb7dcaefb6b1f9c05c6a194a8ebdad/embedding.h b/shared/package-files/04/048ba846e73030ea77938f7fa71e40ebdebb7dcaefb6b1f9c05c6a194a8ebdad/embedding.h
new file mode 100644
index 0000000000000000000000000000000000000000..fcaddd46e83b7fdac612eb72da14ef4d0d157948
--- /dev/null
+++ b/shared/package-files/04/048ba846e73030ea77938f7fa71e40ebdebb7dcaefb6b1f9c05c6a194a8ebdad/embedding.h
@@ -0,0 +1,171 @@
+#pragma once
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+#include 
+
+namespace torch {
+namespace nn {
+
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Embedding
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+/// Performs a lookup in a fixed size embedding table.
+/// See https://pytorch.org/docs/master/nn.html#torch.nn.Embedding to learn
+/// about the exact behavior of this module.
+///
+/// See the documentation for `torch::nn::EmbeddingOptions` class to learn what
+/// constructor arguments are supported for this module.
+///
+/// Example:
+/// ```
+/// Embedding model(EmbeddingOptions(10,
+/// 2).padding_idx(3).max_norm(2).norm_type(2.5).scale_grad_by_freq(true).sparse(true));
+/// ```
+class TORCH_API EmbeddingImpl : public torch::nn::Cloneable {
+ public:
+  EmbeddingImpl(int64_t num_embeddings, int64_t embedding_dim)
+      : EmbeddingImpl(EmbeddingOptions(num_embeddings, embedding_dim)) {}
+  explicit EmbeddingImpl(EmbeddingOptions options_);
+
+  void reset() override;
+
+  void reset_parameters();
+
+  /// Pretty prints the `Embedding` module into the given `stream`.
+  void pretty_print(std::ostream& stream) const override;
+
+  /// Performs a lookup on the embedding table stored in `weight` using the
+  /// `indices` supplied and returns the result.
+  Tensor forward(const Tensor& indices);
+
+  /// The `Options` used to configure this `Embedding` module.
+  /// Changes to `EmbeddingOptions` *after construction* have no effect.
+  EmbeddingOptions options;
+
+  /// The embedding table.
+  Tensor weight;
+};
+
+/// A `ModuleHolder` subclass for `EmbeddingImpl`.
+/// See the documentation for `EmbeddingImpl` class to learn what methods it
+/// provides, and examples of how to use `Embedding` with
+/// `torch::nn::EmbeddingOptions`. See the documentation for `ModuleHolder` to
+/// learn about PyTorch's module storage semantics.
+class Embedding : public torch::nn::ModuleHolder {
+ public:
+  using torch::nn::ModuleHolder::ModuleHolder;
+
+  /// See the documentation for `torch::nn::EmbeddingFromPretrainedOptions`
+  /// class to learn what optional arguments are supported for this function.
+  static Embedding from_pretrained(
+      const torch::Tensor& embeddings,
+      const EmbeddingFromPretrainedOptions& options = {}) {
+    TORCH_CHECK(
+        embeddings.dim() == 2,
+        "Embeddings parameter is expected to be 2-dimensional");
+
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    int64_t rows, cols;
+    rows = embeddings.size(0);
+    cols = embeddings.size(1);
+
+    Embedding embedding(EmbeddingOptions(rows, cols)
+                            ._weight(embeddings)
+                            .padding_idx(options.padding_idx())
+                            .max_norm(options.max_norm())
+                            .norm_type(options.norm_type())
+                            .scale_grad_by_freq(options.scale_grad_by_freq())
+                            .sparse(options.sparse()));
+    embedding->weight.set_requires_grad(!options.freeze());
+    return embedding;
+  }
+};
+
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ EmbeddingBag
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+/// Computes sums or means of 'bags' of embeddings, without instantiating the
+/// intermediate embeddings.
+/// See https://pytorch.org/docs/master/nn.html#torch.nn.EmbeddingBag to learn
+/// about the exact behavior of this module.
+///
+/// See the documentation for `torch::nn::EmbeddingBagOptions` class to learn
+/// what constructor arguments are supported for this module.
+///
+/// Example:
+/// ```
+/// EmbeddingBag model(EmbeddingBagOptions(10,
+/// 2).max_norm(2).norm_type(2.5).scale_grad_by_freq(true).sparse(true).mode(torch::kSum).padding_idx(1));
+/// ```
+class TORCH_API EmbeddingBagImpl
+    : public torch::nn::Cloneable {
+ public:
+  EmbeddingBagImpl(int64_t num_embeddings, int64_t embedding_dim)
+      : EmbeddingBagImpl(EmbeddingBagOptions(num_embeddings, embedding_dim)) {}
+  explicit EmbeddingBagImpl(EmbeddingBagOptions options_);
+
+  void reset() override;
+
+  void reset_parameters();
+
+  /// Pretty prints the `EmbeddingBag` module into the given `stream`.
+  void pretty_print(std::ostream& stream) const override;
+
+  /// The `Options` used to configure this `EmbeddingBag` module.
+  EmbeddingBagOptions options;
+  /// The embedding table.
+  Tensor weight;
+
+  Tensor forward(
+      const Tensor& input,
+      const Tensor& offsets = {},
+      const Tensor& per_sample_weights = {});
+
+ protected:
+  FORWARD_HAS_DEFAULT_ARGS({1, AnyValue(Tensor())}, {2, AnyValue(Tensor())})
+};
+
+/// A `ModuleHolder` subclass for `EmbeddingBagImpl`.
+/// See the documentation for `EmbeddingBagImpl` class to learn what methods it
+/// provides, and examples of how to use `EmbeddingBag` with
+/// `torch::nn::EmbeddingBagOptions`. See the documentation for `ModuleHolder`
+/// to learn about PyTorch's module storage semantics.
+class EmbeddingBag : public torch::nn::ModuleHolder {
+ public:
+  using torch::nn::ModuleHolder::ModuleHolder;
+
+  /// See the documentation for `torch::nn::EmbeddingBagFromPretrainedOptions`
+  /// class to learn what optional arguments are supported for this function.
+  static EmbeddingBag from_pretrained(
+      const torch::Tensor& embeddings,
+      const EmbeddingBagFromPretrainedOptions& options = {}) {
+    TORCH_CHECK(
+        embeddings.dim() == 2,
+        "Embeddings parameter is expected to be 2-dimensional");
+
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    int64_t rows, cols;
+    rows = embeddings.size(0);
+    cols = embeddings.size(1);
+
+    EmbeddingBag embeddingbag(
+        EmbeddingBagOptions(rows, cols)
+            ._weight(embeddings)
+            .max_norm(options.max_norm())
+            .norm_type(options.norm_type())
+            .scale_grad_by_freq(options.scale_grad_by_freq())
+            .mode(options.mode())
+            .sparse(options.sparse())
+            .padding_idx(options.padding_idx()));
+    embeddingbag->weight.set_requires_grad(!options.freeze());
+    return embeddingbag;
+  }
+};
+} // namespace nn
+} // namespace torch
diff --git a/shared/package-files/04/048c08530459c0d0742fcea8fcbd1a699ec5900470383a9eafdcf62a4db33e8b/mkldnn_reorder_conv2d_weight_native.h b/shared/package-files/04/048c08530459c0d0742fcea8fcbd1a699ec5900470383a9eafdcf62a4db33e8b/mkldnn_reorder_conv2d_weight_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..f8e8a9e6046f8107845edb9c6ce59a8e98dc2977
--- /dev/null
+++ b/shared/package-files/04/048c08530459c0d0742fcea8fcbd1a699ec5900470383a9eafdcf62a4db33e8b/mkldnn_reorder_conv2d_weight_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & mkldnn_reorder_conv2d_weight_out_symint(const at::Tensor & self, c10::SymIntArrayRef padding, c10::SymIntArrayRef stride, c10::SymIntArrayRef dilation, c10::SymInt groups, at::OptionalSymIntArrayRef input_size, at::Tensor & out);
+TORCH_API at::Tensor mkldnn_reorder_conv2d_weight(const at::Tensor & self, at::IntArrayRef padding=0, at::IntArrayRef stride=1, at::IntArrayRef dilation=1, int64_t groups=1, at::OptionalIntArrayRef input_size=c10::nullopt);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/04/048c2542bed1282f1b64dde647c2020b94d8382f6e6ac61c57465cd5534a9a21/conv_array_to_indexed.py b/shared/package-files/04/048c2542bed1282f1b64dde647c2020b94d8382f6e6ac61c57465cd5534a9a21/conv_array_to_indexed.py
new file mode 100644
index 0000000000000000000000000000000000000000..1929c3401e131cca0a83080131ead9198b37bcbb
--- /dev/null
+++ b/shared/package-files/04/048c2542bed1282f1b64dde647c2020b94d8382f6e6ac61c57465cd5534a9a21/conv_array_to_indexed.py
@@ -0,0 +1,12 @@
+from sympy.tensor.array.expressions import from_array_to_indexed
+from sympy.utilities.decorator import deprecated
+
+
+_conv_to_from_decorator = deprecated(
+    "module has been renamed by replacing 'conv_' with 'from_' in its name",
+    deprecated_since_version="1.11",
+    active_deprecations_target="deprecated-conv-array-expr-module-names",
+)
+
+
+convert_array_to_indexed = _conv_to_from_decorator(from_array_to_indexed.convert_array_to_indexed)
diff --git a/shared/package-files/04/048d4bcba49afd4d730f36b4d334e50b7a4f17e6c8e1e28a7afe0bd9b4e59aa1/lookups.pyi b/shared/package-files/04/048d4bcba49afd4d730f36b4d334e50b7a4f17e6c8e1e28a7afe0bd9b4e59aa1/lookups.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..6bdf9d9dfbd0aa0aa47536c6747a454193213d2a
--- /dev/null
+++ b/shared/package-files/04/048d4bcba49afd4d730f36b4d334e50b7a4f17e6c8e1e28a7afe0bd9b4e59aa1/lookups.pyi
@@ -0,0 +1,104 @@
+from datetime import datetime
+from typing import Any, Iterable, List, Optional, Tuple, Type, Union, Mapping, TypeVar, Generic
+
+from django.db.backends.sqlite3.base import DatabaseWrapper
+from django.db.models.expressions import Expression, Func
+from django.db.models.query_utils import RegisterLookupMixin
+from django.db.models.sql.compiler import SQLCompiler
+from django.utils.datastructures import OrderedSet
+from django.utils.safestring import SafeText
+
+from django.db.models.fields import TextField, related_lookups
+
+_T = TypeVar("_T")
+
+class Lookup(Generic[_T]):
+    lookup_name: str = ...
+    prepare_rhs: bool = ...
+    can_use_none_as_rhs: bool = ...
+    lhs: Any = ...
+    rhs: Any = ...
+    bilateral_transforms: List[Type[Transform]] = ...
+    def __init__(self, lhs: Union[Expression, TextField, related_lookups.MultiColSource], rhs: Any) -> None: ...
+    def apply_bilateral_transforms(self, value: Expression) -> Transform: ...
+    def batch_process_rhs(
+        self, compiler: SQLCompiler, connection: DatabaseWrapper, rhs: Optional[OrderedSet] = ...
+    ) -> Tuple[List[str], List[str]]: ...
+    def get_source_expressions(self) -> List[Expression]: ...
+    def set_source_expressions(self, new_exprs: List[Expression]) -> None: ...
+    def get_prep_lookup(self) -> Any: ...
+    def get_db_prep_lookup(self, value: Union[int, str], connection: DatabaseWrapper) -> Tuple[str, List[SafeText]]: ...
+    def process_lhs(
+        self, compiler: SQLCompiler, connection: DatabaseWrapper, lhs: Optional[Expression] = ...
+    ) -> Tuple[str, List[Union[int, str]]]: ...
+    def process_rhs(self, compiler: SQLCompiler, connection: DatabaseWrapper) -> Tuple[str, List[Union[int, str]]]: ...
+    def rhs_is_direct_value(self) -> bool: ...
+    def relabeled_clone(self: _T, relabels: Mapping[str, str]) -> _T: ...
+    def get_group_by_cols(self) -> List[Expression]: ...
+    def as_sql(self, compiler: Any, connection: Any) -> Any: ...
+    def contains_aggregate(self) -> bool: ...
+    def contains_over_clause(self) -> bool: ...
+    @property
+    def is_summary(self) -> bool: ...
+
+class Transform(RegisterLookupMixin, Func):
+    bilateral: bool = ...
+    @property
+    def lhs(self) -> Expression: ...
+    def get_bilateral_transforms(self) -> List[Type[Transform]]: ...
+
+class BuiltinLookup(Lookup[_T]):
+    def get_rhs_op(self, connection: DatabaseWrapper, rhs: str) -> str: ...
+
+class FieldGetDbPrepValueMixin:
+    get_db_prep_lookup_value_is_iterable: bool = ...
+
+class FieldGetDbPrepValueIterableMixin(FieldGetDbPrepValueMixin):
+    def get_prep_lookup(self) -> Iterable[Any]: ...
+    def resolve_expression_parameter(
+        self, compiler: SQLCompiler, connection: DatabaseWrapper, sql: str, param: Any
+    ) -> Any: ...
+
+class Exact(FieldGetDbPrepValueMixin, BuiltinLookup): ...
+class IExact(BuiltinLookup): ...
+class GreaterThan(FieldGetDbPrepValueMixin, BuiltinLookup): ...
+class GreaterThanOrEqual(FieldGetDbPrepValueMixin, BuiltinLookup[_T]): ...
+class LessThan(FieldGetDbPrepValueMixin, BuiltinLookup[_T]): ...
+class LessThanOrEqual(FieldGetDbPrepValueMixin, BuiltinLookup): ...
+
+class IntegerFieldFloatRounding:
+    rhs: Any = ...
+    def get_prep_lookup(self) -> Any: ...
+
+class IntegerGreaterThanOrEqual(IntegerFieldFloatRounding, GreaterThanOrEqual[Union[int, float]]): ...
+class IntegerLessThan(IntegerFieldFloatRounding, LessThan[Union[int, float]]): ...
+
+class In(FieldGetDbPrepValueIterableMixin, BuiltinLookup):
+    def split_parameter_list_as_sql(self, compiler: Any, connection: Any): ...
+
+class PatternLookup(BuiltinLookup[str]):
+    param_pattern: str = ...
+
+class Contains(PatternLookup): ...
+class IContains(Contains): ...
+class StartsWith(PatternLookup): ...
+class IStartsWith(StartsWith): ...
+class EndsWith(PatternLookup): ...
+class IEndsWith(EndsWith): ...
+class Range(FieldGetDbPrepValueIterableMixin, BuiltinLookup): ...
+class IsNull(BuiltinLookup[bool]): ...
+class Regex(BuiltinLookup[str]): ...
+class IRegex(Regex): ...
+
+class YearLookup(Lookup):
+    def year_lookup_bounds(self, connection: DatabaseWrapper, year: int) -> List[str]: ...
+
+class YearComparisonLookup(YearLookup):
+    def get_rhs_op(self, connection: DatabaseWrapper, rhs: str) -> str: ...
+    def get_bound(self, start: datetime, finish: datetime) -> Any: ...
+
+class YearExact(YearLookup, Exact): ...
+class YearGt(YearComparisonLookup): ...
+class YearGte(YearComparisonLookup): ...
+class YearLt(YearComparisonLookup): ...
+class YearLte(YearComparisonLookup): ...
diff --git a/shared/package-files/04/048e34dc5c2c6c95dcd888a00b48ff4bea245d3b4a6cdf0ac62e63bc8a601896/reflection_pad1d_backward_meta.h b/shared/package-files/04/048e34dc5c2c6c95dcd888a00b48ff4bea245d3b4a6cdf0ac62e63bc8a601896/reflection_pad1d_backward_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..765f3aba51e913bbe312f40e3b5d03526c530bd5
--- /dev/null
+++ b/shared/package-files/04/048e34dc5c2c6c95dcd888a00b48ff4bea245d3b4a6cdf0ac62e63bc8a601896/reflection_pad1d_backward_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_reflection_pad1d_backward : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & grad_output, const at::Tensor & self, at::ArrayRef padding);
+};
+
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/04/04907375fa84540fc5b18d54e68a61cf3663dd89eb7f9a2dca2491dd73a1ac3e/moveaxis_native.h b/shared/package-files/04/04907375fa84540fc5b18d54e68a61cf3663dd89eb7f9a2dca2491dd73a1ac3e/moveaxis_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..94bae7e82e4b3a124c25192220928da49a711c82
--- /dev/null
+++ b/shared/package-files/04/04907375fa84540fc5b18d54e68a61cf3663dd89eb7f9a2dca2491dd73a1ac3e/moveaxis_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor moveaxis(const at::Tensor & self, at::IntArrayRef source, at::IntArrayRef destination);
+TORCH_API at::Tensor moveaxis(const at::Tensor & self, int64_t source, int64_t destination);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/04/04921e1372b3862154ccf16b731a4f33409be0fd030bf16812e0c5c4ef591ea6/diag_embed_native.h b/shared/package-files/04/04921e1372b3862154ccf16b731a4f33409be0fd030bf16812e0c5c4ef591ea6/diag_embed_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..3e2f3f4e044bbd4222b2254f5357e8fe2dbcf922
--- /dev/null
+++ b/shared/package-files/04/04921e1372b3862154ccf16b731a4f33409be0fd030bf16812e0c5c4ef591ea6/diag_embed_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & diag_embed_out(const at::Tensor & self, int64_t offset, int64_t dim1, int64_t dim2, at::Tensor & out);
+TORCH_API at::Tensor diag_embed(const at::Tensor & self, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/04/049221f56496139864b1b923a4d2457257e87c45d292f07f90b60d19cad69348/slicing.py b/shared/package-files/04/049221f56496139864b1b923a4d2457257e87c45d292f07f90b60d19cad69348/slicing.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5c38e2d97992d1dc5565f8c7e61c3b866c3ac75
--- /dev/null
+++ b/shared/package-files/04/049221f56496139864b1b923a4d2457257e87c45d292f07f90b60d19cad69348/slicing.py
@@ -0,0 +1,302 @@
+"""
+Implement slices and various slice computations.
+"""
+
+from itertools import zip_longest
+
+from llvmlite import ir
+from numba.core import cgutils, types, typing, utils
+from numba.core.imputils import (impl_ret_borrowed, impl_ret_new_ref,
+                                 impl_ret_untracked, iternext_impl,
+                                 lower_builtin, lower_cast, lower_constant,
+                                 lower_getattr)
+
+
+def fix_index(builder, idx, size):
+    """
+    Fix negative index by adding *size* to it.  Positive
+    indices are left untouched.
+    """
+    is_negative = builder.icmp_signed('<', idx, ir.Constant(size.type, 0))
+    wrapped_index = builder.add(idx, size)
+    return builder.select(is_negative, wrapped_index, idx)
+
+
+def fix_slice(builder, slice, size):
+    """
+    Fix *slice* start and stop to be valid (inclusive and exclusive, resp)
+    indexing bounds for a sequence of the given *size*.
+    """
+    # See PySlice_GetIndicesEx()
+    zero = ir.Constant(size.type, 0)
+    minus_one = ir.Constant(size.type, -1)
+
+    def fix_bound(bound_name, lower_repl, upper_repl):
+        bound = getattr(slice, bound_name)
+        bound = fix_index(builder, bound, size)
+        # Store value
+        setattr(slice, bound_name, bound)
+        # Still negative? => clamp to lower_repl
+        underflow = builder.icmp_signed('<', bound, zero)
+        with builder.if_then(underflow, likely=False):
+            setattr(slice, bound_name, lower_repl)
+        # Greater than size? => clamp to upper_repl
+        overflow = builder.icmp_signed('>=', bound, size)
+        with builder.if_then(overflow, likely=False):
+            setattr(slice, bound_name, upper_repl)
+
+    with builder.if_else(cgutils.is_neg_int(builder, slice.step)) as (if_neg_step, if_pos_step):
+        with if_pos_step:
+            # < 0 => 0; >= size => size
+            fix_bound('start', zero, size)
+            fix_bound('stop', zero, size)
+        with if_neg_step:
+            # < 0 => -1; >= size => size - 1
+            lower = minus_one
+            upper = builder.add(size, minus_one)
+            fix_bound('start', lower, upper)
+            fix_bound('stop', lower, upper)
+
+
+def get_slice_length(builder, slicestruct):
+    """
+    Given a slice, compute the number of indices it spans, i.e. the
+    number of iterations that for_range_slice() will execute.
+
+    Pseudo-code:
+        assert step != 0
+        if step > 0:
+            if stop <= start:
+                return 0
+            else:
+                return (stop - start - 1) // step + 1
+        else:
+            if stop >= start:
+                return 0
+            else:
+                return (stop - start + 1) // step + 1
+
+    (see PySlice_GetIndicesEx() in CPython)
+    """
+    start = slicestruct.start
+    stop = slicestruct.stop
+    step = slicestruct.step
+    one = ir.Constant(start.type, 1)
+    zero = ir.Constant(start.type, 0)
+
+    is_step_negative = cgutils.is_neg_int(builder, step)
+    delta = builder.sub(stop, start)
+
+    # Nominal case
+    pos_dividend = builder.sub(delta, one)
+    neg_dividend = builder.add(delta, one)
+    dividend  = builder.select(is_step_negative, neg_dividend, pos_dividend)
+    nominal_length = builder.add(one, builder.sdiv(dividend, step))
+
+    # Catch zero length
+    is_zero_length = builder.select(is_step_negative,
+                                    builder.icmp_signed('>=', delta, zero),
+                                    builder.icmp_signed('<=', delta, zero))
+
+    # Clamp to 0 if is_zero_length
+    return builder.select(is_zero_length, zero, nominal_length)
+
+
+def get_slice_bounds(builder, slicestruct):
+    """
+    Return the [lower, upper) indexing bounds of a slice.
+    """
+    start = slicestruct.start
+    stop = slicestruct.stop
+    zero = start.type(0)
+    one = start.type(1)
+    # This is a bit pessimal, e.g. it will return [1, 5) instead
+    # of [1, 4) for `1:5:2`
+    is_step_negative = builder.icmp_signed('<', slicestruct.step, zero)
+    lower = builder.select(is_step_negative,
+                           builder.add(stop, one), start)
+    upper = builder.select(is_step_negative,
+                           builder.add(start, one), stop)
+    return lower, upper
+
+
+def fix_stride(builder, slice, stride):
+    """
+    Fix the given stride for the slice's step.
+    """
+    return builder.mul(slice.step, stride)
+
+def guard_invalid_slice(context, builder, typ, slicestruct):
+    """
+    Guard against *slicestruct* having a zero step (and raise ValueError).
+    """
+    if typ.has_step:
+        cgutils.guard_null(context, builder, slicestruct.step,
+                           (ValueError, "slice step cannot be zero"))
+
+
+def get_defaults(context):
+    """
+    Get the default values for a slice's members:
+    (start for positive step, start for negative step,
+     stop for positive step, stop for negative step, step)
+    """
+    maxint = (1 << (context.address_size - 1)) - 1
+    return (0, maxint, maxint, - maxint - 1, 1)
+
+
+#---------------------------------------------------------------------------
+# The slice structure
+
+@lower_builtin(slice, types.VarArg(types.Any))
+def slice_constructor_impl(context, builder, sig, args):
+    (
+        default_start_pos,
+        default_start_neg,
+        default_stop_pos,
+        default_stop_neg,
+        default_step,
+    ) = [context.get_constant(types.intp, x) for x in get_defaults(context)]
+
+    slice_args = [None] * 3
+
+    # Fetch non-None arguments
+    if len(args) == 1 and sig.args[0] is not types.none:
+        slice_args[1] = args[0]
+    else:
+        for i, (ty, val) in enumerate(zip(sig.args, args)):
+            if ty is not types.none:
+                slice_args[i] = val
+
+    # Fill omitted arguments
+    def get_arg_value(i, default):
+        val = slice_args[i]
+        if val is None:
+            return default
+        else:
+            return val
+
+    step = get_arg_value(2, default_step)
+    is_step_negative = builder.icmp_signed('<', step,
+                                           context.get_constant(types.intp, 0))
+    default_stop = builder.select(is_step_negative,
+                                  default_stop_neg, default_stop_pos)
+    default_start = builder.select(is_step_negative,
+                                   default_start_neg, default_start_pos)
+    stop = get_arg_value(1, default_stop)
+    start = get_arg_value(0, default_start)
+
+    ty = sig.return_type
+    sli = context.make_helper(builder, sig.return_type)
+    sli.start = start
+    sli.stop = stop
+    sli.step = step
+
+    res = sli._getvalue()
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_getattr(types.SliceType, "start")
+def slice_start_impl(context, builder, typ, value):
+    sli = context.make_helper(builder, typ, value)
+    return sli.start
+
+@lower_getattr(types.SliceType, "stop")
+def slice_stop_impl(context, builder, typ, value):
+    sli = context.make_helper(builder, typ, value)
+    return sli.stop
+
+@lower_getattr(types.SliceType, "step")
+def slice_step_impl(context, builder, typ, value):
+    if typ.has_step:
+        sli = context.make_helper(builder, typ, value)
+        return sli.step
+    else:
+        return context.get_constant(types.intp, 1)
+
+
+@lower_builtin("slice.indices", types.SliceType, types.Integer)
+def slice_indices(context, builder, sig, args):
+    length = args[1]
+    sli = context.make_helper(builder, sig.args[0], args[0])
+
+    with builder.if_then(cgutils.is_neg_int(builder, length), likely=False):
+        context.call_conv.return_user_exc(
+            builder, ValueError,
+            ("length should not be negative",)
+        )
+    with builder.if_then(cgutils.is_scalar_zero(builder, sli.step), likely=False):
+        context.call_conv.return_user_exc(
+            builder, ValueError,
+            ("slice step cannot be zero",)
+        )
+
+    fix_slice(builder, sli, length)
+
+    return context.make_tuple(
+        builder,
+        sig.return_type,
+        (sli.start, sli.stop, sli.step)
+    )
+
+
+def make_slice_from_constant(context, builder, ty, pyval):
+    sli = context.make_helper(builder, ty)
+    lty = context.get_value_type(types.intp)
+
+    (
+        default_start_pos,
+        default_start_neg,
+        default_stop_pos,
+        default_stop_neg,
+        default_step,
+    ) = [context.get_constant(types.intp, x) for x in get_defaults(context)]
+
+    step = pyval.step
+    if step is None:
+        step_is_neg = False
+        step = default_step
+    else:
+        step_is_neg = step < 0
+        step = lty(step)
+
+    start = pyval.start
+    if start is None:
+        if step_is_neg:
+            start = default_start_neg
+        else:
+            start = default_start_pos
+    else:
+        start = lty(start)
+
+    stop = pyval.stop
+    if stop is None:
+        if step_is_neg:
+            stop = default_stop_neg
+        else:
+            stop = default_stop_pos
+    else:
+        stop = lty(stop)
+
+    sli.start = start
+    sli.stop = stop
+    sli.step = step
+
+    return sli._getvalue()
+
+
+@lower_constant(types.SliceType)
+def constant_slice(context, builder, ty, pyval):
+    if isinstance(ty, types.Literal):
+        typ = ty.literal_type
+    else:
+        typ = ty
+
+    return make_slice_from_constant(context, builder, typ, pyval)
+
+
+@lower_cast(types.misc.SliceLiteral, types.SliceType)
+def cast_from_literal(context, builder, fromty, toty, val):
+    return make_slice_from_constant(
+        context, builder, toty, fromty.literal_value,
+    )
diff --git a/shared/package-files/04/0495480d88dd79daf7ab6a0550e0b8a978313884edfc59bb0d75beb0ae5cff05/__init__.py b/shared/package-files/04/0495480d88dd79daf7ab6a0550e0b8a978313884edfc59bb0d75beb0ae5cff05/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8554be0a18b1bb6a7ad6a0b43fe5cfc29f27864
--- /dev/null
+++ b/shared/package-files/04/0495480d88dd79daf7ab6a0550e0b8a978313884edfc59bb0d75beb0ae5cff05/__init__.py
@@ -0,0 +1,213 @@
+"""
+compat
+======
+
+Cross-compatible functions for different versions of Python.
+
+Other items:
+* platform checker
+"""
+from __future__ import annotations
+
+import os
+import platform
+import sys
+from typing import TYPE_CHECKING
+
+from pandas.compat._constants import (
+    IS64,
+    ISMUSL,
+    PY310,
+    PY311,
+    PY312,
+    PY314,
+    PYPY,
+    WARNING_CHECK_DISABLED,
+)
+import pandas.compat.compressors
+from pandas.compat.numpy import is_numpy_dev
+from pandas.compat.pyarrow import (
+    HAS_PYARROW,
+    pa_version_under10p1,
+    pa_version_under11p0,
+    pa_version_under13p0,
+    pa_version_under14p0,
+    pa_version_under14p1,
+    pa_version_under16p0,
+    pa_version_under17p0,
+    pa_version_under18p0,
+    pa_version_under19p0,
+    pa_version_under20p0,
+    pa_version_under21p0,
+)
+
+if TYPE_CHECKING:
+    from pandas._typing import F
+
+
+def set_function_name(f: F, name: str, cls: type) -> F:
+    """
+    Bind the name/qualname attributes of the function.
+    """
+    f.__name__ = name
+    f.__qualname__ = f"{cls.__name__}.{name}"
+    f.__module__ = cls.__module__
+    return f
+
+
+def is_platform_little_endian() -> bool:
+    """
+    Checking if the running platform is little endian.
+
+    Returns
+    -------
+    bool
+        True if the running platform is little endian.
+    """
+    return sys.byteorder == "little"
+
+
+def is_platform_windows() -> bool:
+    """
+    Checking if the running platform is windows.
+
+    Returns
+    -------
+    bool
+        True if the running platform is windows.
+    """
+    return sys.platform in ["win32", "cygwin"]
+
+
+def is_platform_linux() -> bool:
+    """
+    Checking if the running platform is linux.
+
+    Returns
+    -------
+    bool
+        True if the running platform is linux.
+    """
+    return sys.platform == "linux"
+
+
+def is_platform_mac() -> bool:
+    """
+    Checking if the running platform is mac.
+
+    Returns
+    -------
+    bool
+        True if the running platform is mac.
+    """
+    return sys.platform == "darwin"
+
+
+def is_platform_arm() -> bool:
+    """
+    Checking if the running platform use ARM architecture.
+
+    Returns
+    -------
+    bool
+        True if the running platform uses ARM architecture.
+    """
+    return platform.machine() in ("arm64", "aarch64") or platform.machine().startswith(
+        "armv"
+    )
+
+
+def is_platform_power() -> bool:
+    """
+    Checking if the running platform use Power architecture.
+
+    Returns
+    -------
+    bool
+        True if the running platform uses ARM architecture.
+    """
+    return platform.machine() in ("ppc64", "ppc64le")
+
+
+def is_ci_environment() -> bool:
+    """
+    Checking if running in a continuous integration environment by checking
+    the PANDAS_CI environment variable.
+
+    Returns
+    -------
+    bool
+        True if the running in a continuous integration environment.
+    """
+    return os.environ.get("PANDAS_CI", "0") == "1"
+
+
+def get_lzma_file() -> type[pandas.compat.compressors.LZMAFile]:
+    """
+    Importing the `LZMAFile` class from the `lzma` module.
+
+    Returns
+    -------
+    class
+        The `LZMAFile` class from the `lzma` module.
+
+    Raises
+    ------
+    RuntimeError
+        If the `lzma` module was not imported correctly, or didn't exist.
+    """
+    if not pandas.compat.compressors.has_lzma:
+        raise RuntimeError(
+            "lzma module not available. "
+            "A Python re-install with the proper dependencies, "
+            "might be required to solve this issue."
+        )
+    return pandas.compat.compressors.LZMAFile
+
+
+def get_bz2_file() -> type[pandas.compat.compressors.BZ2File]:
+    """
+    Importing the `BZ2File` class from the `bz2` module.
+
+    Returns
+    -------
+    class
+        The `BZ2File` class from the `bz2` module.
+
+    Raises
+    ------
+    RuntimeError
+        If the `bz2` module was not imported correctly, or didn't exist.
+    """
+    if not pandas.compat.compressors.has_bz2:
+        raise RuntimeError(
+            "bz2 module not available. "
+            "A Python re-install with the proper dependencies, "
+            "might be required to solve this issue."
+        )
+    return pandas.compat.compressors.BZ2File
+
+
+__all__ = [
+    "is_numpy_dev",
+    "pa_version_under10p1",
+    "pa_version_under11p0",
+    "pa_version_under13p0",
+    "pa_version_under14p0",
+    "pa_version_under14p1",
+    "pa_version_under16p0",
+    "pa_version_under17p0",
+    "pa_version_under18p0",
+    "pa_version_under19p0",
+    "pa_version_under20p0",
+    "pa_version_under21p0",
+    "HAS_PYARROW",
+    "IS64",
+    "ISMUSL",
+    "PY310",
+    "PY311",
+    "PY312",
+    "PY314",
+    "PYPY",
+    "WARNING_CHECK_DISABLED",
+]
diff --git a/shared/package-files/04/0495732e9b5e24575da64730a9bb73cd15d9f38a054f4509705b51d4f55e2deb/_cudnn_rnn_flatten_weight_compositeexplicitautograd_dispatch.h b/shared/package-files/04/0495732e9b5e24575da64730a9bb73cd15d9f38a054f4509705b51d4f55e2deb/_cudnn_rnn_flatten_weight_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e2996f1796030958bab86073d9505081d41fbbdc
--- /dev/null
+++ b/shared/package-files/04/0495732e9b5e24575da64730a9bb73cd15d9f38a054f4509705b51d4f55e2deb/_cudnn_rnn_flatten_weight_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & _cudnn_rnn_flatten_weight_out(at::Tensor & out, at::TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, bool bidirectional);
+TORCH_API at::Tensor & _cudnn_rnn_flatten_weight_outf(at::TensorList weight_arr, int64_t weight_stride0, int64_t input_size, int64_t mode, int64_t hidden_size, int64_t proj_size, int64_t num_layers, bool batch_first, bool bidirectional, at::Tensor & out);
+TORCH_API at::Tensor & _cudnn_rnn_flatten_weight_symint_out(at::Tensor & out, at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional);
+TORCH_API at::Tensor & _cudnn_rnn_flatten_weight_symint_outf(at::TensorList weight_arr, int64_t weight_stride0, c10::SymInt input_size, int64_t mode, c10::SymInt hidden_size, c10::SymInt proj_size, int64_t num_layers, bool batch_first, bool bidirectional, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/04/049a59cee60b80b2bef8641f4d71fe61c16d5b6f3af52efd514bb14a26ffb131/CompositeExplicitAutogradFunctions.h b/shared/package-files/04/049a59cee60b80b2bef8641f4d71fe61c16d5b6f3af52efd514bb14a26ffb131/CompositeExplicitAutogradFunctions.h
new file mode 100644
index 0000000000000000000000000000000000000000..bdaee888e89bd9e1dd22c54a72d5d1b96affeda1
--- /dev/null
+++ b/shared/package-files/04/049a59cee60b80b2bef8641f4d71fe61c16d5b6f3af52efd514bb14a26ffb131/CompositeExplicitAutogradFunctions.h
@@ -0,0 +1,29 @@
+#include 
+
+// TODO Undo all logic introduced for Note [Avoiding Include Cycles In Static Dispatch]
+// Code introduced to avoid cyclic dependency in static dispatch is no longer
+// needed as static dispatch logic is moved from TensorBody.h, which caused cycles in the first place,
+// to Operators.cpp for supporting multiple backends with multiple kernels.
+//
+// Note [Avoiding Include Cycles In Static Dispatch]
+// In order to avoid #include cycles in the static dispatch build, we've carefully split out
+// the static function definition files into {DispatchKey}Functions.h and {DispatchKey}Functions_inl.h.
+//
+// Without this split, the include cycle looks like TensorBody.h -> CPUFunctions.h -> TensorBody.h.
+// - TensorBody.h #includes CPUFunctions.h in the static dispatch build, because the tensor methods
+//   all need to call into the fastpath C++ API defined in CPUFunctions.h. The methods are also all
+//   directly inlined into TensorBody.h.
+// - CPUFunctions.h #includes TensorBody.h because it contains function declarations for the entire C++ API,
+//   which include functions that have defaultable optional arguments.
+//   That requires knowing the full Tensor class definition.
+//
+// We break the cycle by doing the following:
+// - Split out CPUFunction.h into two files: CPUFunctions.h and CPUFunctions_inl.h
+// - CPUFunction.h is a dummy file that just includes the Tensor class and includes CPUFunctions_inl.,
+// - CPUFunctions_inl.h includes everything else
+// - (only in the static dispatch build) TensorBody.h makes sure to finish defining the Tensor class,
+//   and then it includes CPUFunctions_inl.h.
+// - All other files that want the cpu fastpath functions can include CPUFunctions.h directly.
+// - This also means that static dispatch build, CPUFunctions.h only needs to
+//   #include TensorBody.h, and it will automatically bring in CPUFunctions_inl.h.
+#include 
diff --git a/shared/package-files/04/049d1dfe2d47e6c2f3c715a8220eee0321c40437312748727756400f79e0fd99/LICENSE b/shared/package-files/04/049d1dfe2d47e6c2f3c715a8220eee0321c40437312748727756400f79e0fd99/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..c0df70a5ff96316a367b51d658e070ae008d04de
--- /dev/null
+++ b/shared/package-files/04/049d1dfe2d47e6c2f3c715a8220eee0321c40437312748727756400f79e0fd99/LICENSE
@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
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+      "License" shall mean the terms and conditions for use, reproduction,
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+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
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+      the terms of any separate license agreement you may have executed
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+   6. Trademarks. This License does not grant permission to use the trade
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+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
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+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
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+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
diff --git a/shared/package-files/04/049d5c889a09b2aae554721a800b2d44f2b1980314f5c6ced37d2b3f91995d13/redistribute.py b/shared/package-files/04/049d5c889a09b2aae554721a800b2d44f2b1980314f5c6ced37d2b3f91995d13/redistribute.py
new file mode 100644
index 0000000000000000000000000000000000000000..58f3f42c94b70f129899ba33dafbf8b4e349a900
--- /dev/null
+++ b/shared/package-files/04/049d5c889a09b2aae554721a800b2d44f2b1980314f5c6ced37d2b3f91995d13/redistribute.py
@@ -0,0 +1,337 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from functools import lru_cache
+from typing import cast, Dict, List, NamedTuple, Tuple
+
+import torch
+import torch.distributed._functional_collectives as funcol
+import torch.distributed._tensor.api as dtensor
+from torch.distributed._tensor.device_mesh import DeviceMesh
+from torch.distributed._tensor.placement_types import (
+    _Partial,
+    DTensorSpec,
+    Placement,
+    Replicate,
+    Shard,
+)
+
+
+class _TransformInfo(NamedTuple):
+    mesh_dim: int
+    src_dst_placements: Tuple[Placement, Placement]
+    # logical_shape on this mesh dimension
+    logical_shape: List[int]
+
+
+def _replicate_then_shard(val: _TransformInfo) -> int:
+    """
+    This is a helper function to allow reordering _TransformInfo list. The high level
+    idea is that we want to reorder the sharding redistributions so that the DTensor
+    redistribution is consistent with its full tensor. This is built on top of two simple
+    assumptions:
+    1. Replication happens from inner to outer dimension. i.e. Shard -> Replicate
+    2. Sharding happens from outer to inner dimension, i.e. Replicate -> Shard
+
+    So we always put the replication first and put sharding later.
+    """
+    mesh_dim = val.mesh_dim
+    src, dst = val.src_dst_placements
+    if (dst.is_replicate() or dst.is_partial()) and src.is_shard():
+        return -mesh_dim
+    elif (src.is_replicate() or src.is_partial()) and dst.is_shard():
+        return mesh_dim
+    else:
+        return 0
+
+
+@lru_cache(maxsize=None)
+def _gen_transform_infos(
+    src_spec: DTensorSpec,
+    dst_spec: DTensorSpec,
+) -> List[_TransformInfo]:
+    """
+    Generate the transform infos from the source placements to the target placements, to
+    transform from source to target placement it might have multipl steps, i.e. it might
+    decompose Si -> Sj into Si -> R -> Sj.
+    This would detects if there're mis-aligned shardings between src/dst placements.
+    i.e. (Shard(0), Shard(0)) -> (Replicate(), Shard(0)), in this case Shard(0) -> Shard(0)
+    for mesh dimension 1 actually needs reshard, because in the first case it's a sub-sharding
+    of an already tensor dimension 0, and in the second case, it's the first sharding on tensor
+    dimension 0.
+
+    Note that we also currently handles sharding on different tensor dimensions, e.g.
+    Shard(0) -> Shard(1) in this pass
+    """
+    src_dim_counts: Dict[int, int] = {}
+    dst_dim_counts: Dict[int, int] = {}
+    transform_infos: List[_TransformInfo] = []
+
+    src_placements = src_spec.placements
+    dst_placements = dst_spec.placements
+    device_mesh = src_spec.device_mesh
+    my_coordinate = device_mesh.get_coordinate()
+    assert my_coordinate is not None
+
+    # logical shape records the logic tensor shape on the mesh dimension
+    # this is useful to ensure uneven sharding gets correct output shape
+    initial_logical_shape = list(src_spec.shape)
+    mesh_dims_to_logical_shape = [initial_logical_shape]
+    mesh_ndim = len(src_placements)
+
+    for i, (src, dst) in enumerate(zip(src_placements, dst_placements)):
+        # detect mis-aligned sharding and build logical shapes
+        current_logical_shape = mesh_dims_to_logical_shape[i]
+        if isinstance(src, Shard):
+            src_dim_counts[src.dim] = src_dim_counts.get(src.dim, 0) + 1
+
+            if i < mesh_ndim - 1:
+                # calculate and save the logical shape for this sharding
+                mesh_dim_size = device_mesh.size(mesh_dim=i)
+                local_shard_size, _ = src._local_shard_size_on_dim(
+                    current_logical_shape[src.dim],
+                    mesh_dim_size,
+                    my_coordinate[i],
+                )
+                new_logical_shape = list(current_logical_shape)
+                new_logical_shape[src.dim] = local_shard_size
+                mesh_dims_to_logical_shape.append(new_logical_shape)
+        else:
+            mesh_dims_to_logical_shape.append(current_logical_shape)
+
+        if isinstance(dst, Shard):
+            dst_dim_counts[dst.dim] = dst_dim_counts.get(dst.dim, 0) + 1
+
+        if (
+            isinstance(src, Shard)
+            and isinstance(dst, Shard)
+            and (
+                src.dim != dst.dim or src_dim_counts[src.dim] != dst_dim_counts[dst.dim]
+            )
+        ):
+            # decompose Shard(i) -> Shard(j) into Shard(i) -> Replicate() -> Shard(j)
+            transform_infos.append(
+                _TransformInfo(
+                    mesh_dim=i,
+                    src_dst_placements=(src, Replicate()),
+                    logical_shape=mesh_dims_to_logical_shape[i],
+                )
+            )
+            transform_infos.append(
+                _TransformInfo(
+                    mesh_dim=i,
+                    src_dst_placements=(Replicate(), dst),
+                    logical_shape=mesh_dims_to_logical_shape[i],
+                )
+            )
+        else:
+            transform_infos.append(
+                _TransformInfo(
+                    mesh_dim=i,
+                    src_dst_placements=(src, dst),
+                    logical_shape=mesh_dims_to_logical_shape[i],
+                )
+            )
+
+    # sort the pairs by first perform replication then sharding
+    transform_infos.sort(key=_replicate_then_shard)
+    return transform_infos
+
+
+def redistribute_local_tensor(
+    local_tensor: torch.Tensor,
+    current_spec: DTensorSpec,
+    target_spec: DTensorSpec,
+    *,
+    async_op: bool = False,
+    is_backward: bool = False,
+) -> torch.Tensor:
+    """
+    This redistribute the local tensor (torch.Tensor) from the current DTensorSpec to
+    the target DTensorSpec, which involves the necessary collective calls to transform
+    the local shard of the DTensor from its current spec to the target spec.
+    """
+
+    if current_spec.mesh != target_spec.mesh:
+        # TODO: alltoall/permute reshuffling to change device_mesh if they are not the same
+        raise NotImplementedError("Cross device mesh comm not supported yet!")
+
+    new_local_tensor = None
+    device_mesh = current_spec.mesh
+
+    my_coordinate = device_mesh.get_coordinate()
+
+    if my_coordinate is None:
+        # if rank is not part of mesh, we skip redistribute and simply return local_tensor,
+        # which should be an empty tensor
+        return local_tensor
+
+    transform_infos = _gen_transform_infos(current_spec, target_spec)
+
+    for transform_info in transform_infos:
+        i = transform_info.mesh_dim
+        current, target = transform_info.src_dst_placements
+        num_chunks = device_mesh.size(mesh_dim=i)
+
+        if current == target:
+            # short cut, just use the original local tensor
+            new_local_tensor = local_tensor
+            continue
+
+        if target.is_replicate():
+            # Case 1: target is Replicate
+            if current.is_partial():
+                partial_spec = cast(_Partial, current)
+                new_local_tensor = partial_spec._reduce_value(
+                    local_tensor, device_mesh, i
+                )
+            elif current.is_shard():
+                current_placement = cast(Shard, current)
+                new_local_tensor = current_placement._to_replicate_tensor(
+                    local_tensor, device_mesh, i, transform_info.logical_shape
+                )
+            else:
+                raise RuntimeError(
+                    f"redistribute from {current} to {target} not supported yet"
+                )
+        elif target.is_shard():
+            # Case 2: target is Shard
+            target_placement = cast(Shard, target)
+            target_dim = target_placement.dim
+            if current.is_partial():
+                partial_spec = cast(_Partial, current)
+                new_local_tensor = partial_spec._reduce_shard_value(
+                    local_tensor, device_mesh, i, target_placement
+                )
+            elif current.is_replicate():
+                # split the tensor and return the corresponding cloned local shard
+                new_local_tensor = target_placement._replicate_to_shard(
+                    local_tensor, device_mesh, i, my_coordinate[i]
+                )
+            else:
+                # NOTE: we don't support this case efficiently yet, the fallback path we are going here is
+                # to decompose Shard(0) -> Shard(1) into Shard(0) -> Replicate -> Shard(1)
+                # TODO: enable this with all_to_all
+                assert (
+                    current.is_shard()
+                ), f"Current placement should be shard but found {current}"
+                shard_spec = cast(Shard, current)
+                if shard_spec.dim != target_placement.dim:
+                    new_local_tensor = shard_spec._to_replicate_tensor(
+                        local_tensor, device_mesh, i, transform_info.logical_shape
+                    )
+                    shards, _ = target_placement._split_tensor(
+                        new_local_tensor,
+                        num_chunks,
+                        with_padding=False,
+                        contiguous=False,
+                    )
+                    new_local_tensor = shards[my_coordinate[i]]
+        elif target.is_partial():
+            if current.is_replicate():
+                partial_spec = cast(_Partial, target)
+                # skip the replicate to partial transformation when we are in backward pass
+                # In this case we keep the grad as replicate, this is because we don't
+                # want to convert the replicated gradients back to partial, although
+                # that's logically conform with the same layout, converting the gradients
+                # back to partial is actually useless as you would have to do reduce later
+                # which would be more expensive than keeping it replicate! For this reason,
+                # we keep the replicate grad here.
+                new_local_tensor = (
+                    partial_spec._partition_value(local_tensor, device_mesh, i)
+                    if not is_backward
+                    else local_tensor
+                )
+            elif current.is_shard():
+                if not is_backward:
+                    raise RuntimeError(
+                        f"redistribute from {current} to {target} not supported yet"
+                    )
+                # for backward shard -> partial, we just need to convert the shard to replicate
+                current_placement = cast(Shard, current)
+                new_local_tensor = current_placement._to_replicate_tensor(
+                    local_tensor, device_mesh, i, transform_info.logical_shape
+                )
+            else:
+                # partial -> partial no op, should never hit
+                new_local_tensor = local_tensor
+
+        assert new_local_tensor is not None
+        local_tensor = new_local_tensor
+
+    assert new_local_tensor is not None, "redistribute failed!"
+
+    if not async_op and isinstance(new_local_tensor, funcol.AsyncCollectiveTensor):
+        new_local_tensor = new_local_tensor.wait()
+
+    return new_local_tensor
+
+
+class Redistribute(torch.autograd.Function):
+    @staticmethod
+    def forward(  # type: ignore[override]
+        # pyre-fixme[2]: Parameter must be annotated.
+        ctx,
+        input: "dtensor.DTensor",
+        device_mesh: DeviceMesh,
+        placements: Tuple[Placement, ...],
+        async_op: bool = False,
+    ):
+        current_spec = input._spec
+        ctx.current_spec = current_spec
+        ctx.async_op = async_op
+        target_spec = DTensorSpec(
+            device_mesh, placements, tensor_meta=input._spec.tensor_meta
+        )
+
+        local_tensor = input._local_tensor
+        output = redistribute_local_tensor(
+            local_tensor, current_spec, target_spec, async_op=async_op
+        )
+
+        return dtensor.DTensor(
+            output,
+            device_mesh,
+            target_spec.placements,
+            shape=input.shape,
+            dtype=input.dtype,
+            requires_grad=input.requires_grad,
+            stride=input.stride(),
+        )
+
+    @staticmethod
+    def backward(ctx, grad_output: "dtensor.DTensor"):  # type: ignore[override]
+        previous_spec = ctx.current_spec
+        current_spec = grad_output._spec
+        async_op = ctx.async_op
+
+        local_tensor = grad_output._local_tensor
+        output = redistribute_local_tensor(
+            local_tensor,
+            current_spec,
+            previous_spec,
+            async_op=async_op,
+            is_backward=True,
+        )
+        # normalize the target placement to replicate if it is partial
+        normalized_placements: List[Placement] = []
+        for previous_placement in previous_spec.placements:
+            if previous_placement.is_partial():
+                # keep target placement to replicate instead of partial in this case
+                normalized_placements.append(Replicate())
+            else:
+                normalized_placements.append(previous_placement)
+        output_dtensor = dtensor.DTensor(
+            output,
+            previous_spec.mesh,
+            tuple(normalized_placements),
+            shape=grad_output.shape,
+            dtype=grad_output.dtype,
+            requires_grad=grad_output.requires_grad,
+            stride=grad_output.stride(),
+        )
+
+        return (
+            output_dtensor,
+            None,
+            None,
+            None,
+        )
diff --git a/shared/package-files/04/049eab53ce9576c076f3532a1a61f0524caf7faeb565ae07fea8521e2e678af5/trigger.pyi b/shared/package-files/04/049eab53ce9576c076f3532a1a61f0524caf7faeb565ae07fea8521e2e678af5/trigger.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1638972ed16070adb7259e01848cd4851d562ed2
--- /dev/null
+++ b/shared/package-files/04/049eab53ce9576c076f3532a1a61f0524caf7faeb565ae07fea8521e2e678af5/trigger.pyi
@@ -0,0 +1,31 @@
+import sys
+from socket import SocketType
+from threading import Lock
+from typing import Callable, Mapping, Optional
+from typing_extensions import Literal
+
+from . import wasyncore as wasyncore
+
+class _triggerbase:
+    kind: Optional[str] = ...
+    lock: Lock = ...
+    thunks: Callable[[None], None] = ...
+    def __init__(self) -> None: ...
+    def readable(self) -> Literal[True]: ...
+    def writable(self) -> Literal[False]: ...
+    def handle_connect(self) -> None: ...
+    def handle_close(self) -> None: ...
+    def close(self) -> None: ...
+    def pull_trigger(self, thunk: Optional[Callable[[None], None]] = ...) -> None: ...
+    def handle_read(self) -> None: ...
+
+if sys.platform == "linux" or sys.platform == "darwin":
+    class trigger(_triggerbase, wasyncore.file_dispatcher):
+        kind: str = ...
+        def __init__(self, map: Mapping[str, _triggerbase]) -> None: ...
+
+else:
+    class trigger(_triggerbase, wasyncore.dispatcher):
+        kind: str = ...
+        trigger: SocketType = ...
+        def __init__(self, map: Mapping[str, _triggerbase]) -> None: ...
diff --git a/shared/package-files/04/049f5164320274c03ff3543ef0cded2528694ec449f9fbcafeb303c6e52c7510/special_polygamma.h b/shared/package-files/04/049f5164320274c03ff3543ef0cded2528694ec449f9fbcafeb303c6e52c7510/special_polygamma.h
new file mode 100644
index 0000000000000000000000000000000000000000..b459a97f6e71dd47f9d87cbec00cfaf3767a3bb1
--- /dev/null
+++ b/shared/package-files/04/049f5164320274c03ff3543ef0cded2528694ec449f9fbcafeb303c6e52c7510/special_polygamma.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::special_polygamma(int n, Tensor self) -> Tensor
+inline at::Tensor special_polygamma(int64_t n, const at::Tensor & self) {
+    return at::_ops::special_polygamma::call(n, self);
+}
+
+// aten::special_polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_polygamma_out(at::Tensor & out, int64_t n, const at::Tensor & self) {
+    return at::_ops::special_polygamma_out::call(n, self, out);
+}
+// aten::special_polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & special_polygamma_outf(int64_t n, const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::special_polygamma_out::call(n, self, out);
+}
+
+}
diff --git a/shared/package-files/04/04a34003a0802a5cbb9f508f1ca75c7197511dc7f6b5a484b9b46dcdd1d36e5b/test_metaestimators_metadata_routing.py b/shared/package-files/04/04a34003a0802a5cbb9f508f1ca75c7197511dc7f6b5a484b9b46dcdd1d36e5b/test_metaestimators_metadata_routing.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4ed228ec2f9d4ba87def991783d1a5b2237329f
--- /dev/null
+++ b/shared/package-files/04/04a34003a0802a5cbb9f508f1ca75c7197511dc7f6b5a484b9b46dcdd1d36e5b/test_metaestimators_metadata_routing.py
@@ -0,0 +1,927 @@
+import copy
+import re
+
+import numpy as np
+import pytest
+
+from sklearn import config_context
+from sklearn.base import BaseEstimator, is_classifier
+from sklearn.calibration import CalibratedClassifierCV
+from sklearn.compose import TransformedTargetRegressor
+from sklearn.covariance import GraphicalLassoCV
+from sklearn.ensemble import (
+    AdaBoostClassifier,
+    AdaBoostRegressor,
+    BaggingClassifier,
+    BaggingRegressor,
+)
+from sklearn.exceptions import UnsetMetadataPassedError
+from sklearn.experimental import (
+    enable_halving_search_cv,  # noqa: F401
+    enable_iterative_imputer,  # noqa: F401
+)
+from sklearn.feature_selection import (
+    RFE,
+    RFECV,
+    SelectFromModel,
+    SequentialFeatureSelector,
+)
+from sklearn.impute import IterativeImputer
+from sklearn.linear_model import (
+    ElasticNetCV,
+    LarsCV,
+    LassoCV,
+    LassoLarsCV,
+    LogisticRegressionCV,
+    MultiTaskElasticNetCV,
+    MultiTaskLassoCV,
+    OrthogonalMatchingPursuitCV,
+    RANSACRegressor,
+    RidgeClassifierCV,
+    RidgeCV,
+)
+from sklearn.metrics._regression import mean_squared_error
+from sklearn.metrics._scorer import make_scorer
+from sklearn.model_selection import (
+    FixedThresholdClassifier,
+    GridSearchCV,
+    GroupKFold,
+    HalvingGridSearchCV,
+    HalvingRandomSearchCV,
+    RandomizedSearchCV,
+    TunedThresholdClassifierCV,
+    cross_validate,
+)
+from sklearn.multiclass import (
+    OneVsOneClassifier,
+    OneVsRestClassifier,
+    OutputCodeClassifier,
+)
+from sklearn.multioutput import (
+    ClassifierChain,
+    MultiOutputClassifier,
+    MultiOutputRegressor,
+    RegressorChain,
+)
+from sklearn.semi_supervised import SelfTrainingClassifier
+from sklearn.tests.metadata_routing_common import (
+    ConsumingClassifier,
+    ConsumingRegressor,
+    ConsumingScorer,
+    ConsumingSplitter,
+    NonConsumingClassifier,
+    NonConsumingRegressor,
+    _Registry,
+    assert_request_is_empty,
+    check_recorded_metadata,
+)
+from sklearn.utils.metadata_routing import MetadataRouter
+
+rng = np.random.RandomState(42)
+N, M = 100, 4
+X = rng.rand(N, M)
+y = rng.randint(0, 3, size=N)
+y_binary = (y >= 1).astype(int)
+classes = np.unique(y)
+y_multi = rng.randint(0, 3, size=(N, 3))
+classes_multi = [np.unique(y_multi[:, i]) for i in range(y_multi.shape[1])]
+metadata = rng.randint(0, 10, size=N)
+sample_weight = rng.rand(N)
+groups = rng.randint(0, 10, size=len(y))
+
+
+METAESTIMATORS: list = [
+    {
+        "metaestimator": MultiOutputRegressor,
+        "estimator_name": "estimator",
+        "estimator": "regressor",
+        "X": X,
+        "y": y_multi,
+        "estimator_routing_methods": ["fit", "partial_fit"],
+    },
+    {
+        "metaestimator": MultiOutputClassifier,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "X": X,
+        "y": y_multi,
+        "estimator_routing_methods": ["fit", "partial_fit"],
+        "method_args": {"partial_fit": {"classes": classes_multi}},
+    },
+    {
+        "metaestimator": CalibratedClassifierCV,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit"],
+        "preserves_metadata": "subset",
+    },
+    {
+        "metaestimator": ClassifierChain,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "X": X,
+        "y": y_multi,
+        "estimator_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": RegressorChain,
+        "estimator_name": "estimator",
+        "estimator": "regressor",
+        "X": X,
+        "y": y_multi,
+        "estimator_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": LogisticRegressionCV,
+        "X": X,
+        "y": y,
+        "scorer_name": "scoring",
+        "scorer_routing_methods": ["fit", "score"],
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": GridSearchCV,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "init_args": {"param_grid": {"alpha": [0.1, 0.2]}},
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit"],
+        "preserves_metadata": "subset",
+        "scorer_name": "scoring",
+        "scorer_routing_methods": ["fit", "score"],
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": RandomizedSearchCV,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "init_args": {"param_distributions": {"alpha": [0.1, 0.2]}},
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit"],
+        "preserves_metadata": "subset",
+        "scorer_name": "scoring",
+        "scorer_routing_methods": ["fit", "score"],
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": HalvingGridSearchCV,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "init_args": {"param_grid": {"alpha": [0.1, 0.2]}},
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit"],
+        "preserves_metadata": "subset",
+        "scorer_name": "scoring",
+        "scorer_routing_methods": ["fit", "score"],
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": HalvingRandomSearchCV,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "init_args": {"param_distributions": {"alpha": [0.1, 0.2]}},
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit"],
+        "preserves_metadata": "subset",
+        "scorer_name": "scoring",
+        "scorer_routing_methods": ["fit", "score"],
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": FixedThresholdClassifier,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "X": X,
+        "y": y_binary,
+        "estimator_routing_methods": ["fit"],
+        "preserves_metadata": "subset",
+    },
+    {
+        "metaestimator": TunedThresholdClassifierCV,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "X": X,
+        "y": y_binary,
+        "estimator_routing_methods": ["fit"],
+        "preserves_metadata": "subset",
+    },
+    {
+        "metaestimator": OneVsRestClassifier,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit", "partial_fit"],
+        "method_args": {"partial_fit": {"classes": classes}},
+    },
+    {
+        "metaestimator": OneVsOneClassifier,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit", "partial_fit"],
+        "preserves_metadata": "subset",
+        "method_args": {"partial_fit": {"classes": classes}},
+    },
+    {
+        "metaestimator": OutputCodeClassifier,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "init_args": {"random_state": 42},
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": SelectFromModel,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit", "partial_fit"],
+        "method_args": {"partial_fit": {"classes": classes}},
+    },
+    {
+        "metaestimator": OrthogonalMatchingPursuitCV,
+        "X": X,
+        "y": y,
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": ElasticNetCV,
+        "X": X,
+        "y": y,
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": LassoCV,
+        "X": X,
+        "y": y,
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": MultiTaskElasticNetCV,
+        "X": X,
+        "y": y_multi,
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": MultiTaskLassoCV,
+        "X": X,
+        "y": y_multi,
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": LarsCV,
+        "X": X,
+        "y": y,
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": LassoLarsCV,
+        "X": X,
+        "y": y,
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": RANSACRegressor,
+        "estimator_name": "estimator",
+        "estimator": "regressor",
+        "init_args": {"min_samples": 0.5},
+        "X": X,
+        "y": y,
+        "preserves_metadata": "subset",
+        "estimator_routing_methods": ["fit", "predict", "score"],
+        "method_mapping": {"fit": ["fit", "score"]},
+    },
+    {
+        "metaestimator": IterativeImputer,
+        "estimator_name": "estimator",
+        "estimator": "regressor",
+        "init_args": {"skip_complete": False},
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": BaggingClassifier,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "X": X,
+        "y": y,
+        "preserves_metadata": False,
+        "estimator_routing_methods": [
+            "fit",
+            "predict",
+            "predict_proba",
+            "predict_log_proba",
+            "decision_function",
+        ],
+        "method_mapping": {
+            "predict": ["predict", "predict_proba"],
+            "predict_proba": ["predict", "predict_proba"],
+            "predict_log_proba": ["predict", "predict_proba", "predict_log_proba"],
+        },
+    },
+    {
+        "metaestimator": BaggingRegressor,
+        "estimator_name": "estimator",
+        "estimator": "regressor",
+        "X": X,
+        "y": y,
+        "preserves_metadata": False,
+        "estimator_routing_methods": ["fit", "predict"],
+    },
+    {
+        "metaestimator": RidgeCV,
+        "X": X,
+        "y": y,
+        "scorer_name": "scoring",
+        "scorer_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": RidgeClassifierCV,
+        "X": X,
+        "y": y,
+        "scorer_name": "scoring",
+        "scorer_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": RidgeCV,
+        "X": X,
+        "y": y,
+        "scorer_name": "scoring",
+        "scorer_routing_methods": ["fit"],
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": RidgeClassifierCV,
+        "X": X,
+        "y": y,
+        "scorer_name": "scoring",
+        "scorer_routing_methods": ["fit"],
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": GraphicalLassoCV,
+        "X": X,
+        "y": y,
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": TransformedTargetRegressor,
+        "estimator": "regressor",
+        "estimator_name": "regressor",
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit", "predict"],
+    },
+    {
+        "metaestimator": SelfTrainingClassifier,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "X": X,
+        "y": y,
+        "preserves_metadata": True,
+        "estimator_routing_methods": [
+            "fit",
+            "predict",
+            "predict_proba",
+            "predict_log_proba",
+            "decision_function",
+            "score",
+        ],
+        "method_mapping": {"fit": ["fit", "score"]},
+    },
+    {
+        "metaestimator": SequentialFeatureSelector,
+        "estimator_name": "estimator",
+        "estimator": "classifier",
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit"],
+        "scorer_name": "scoring",
+        "scorer_routing_methods": ["fit"],
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+    },
+    {
+        "metaestimator": RFE,
+        "estimator": "classifier",
+        "estimator_name": "estimator",
+        "X": X,
+        "y": y,
+        "estimator_routing_methods": ["fit", "predict", "score"],
+    },
+    {
+        "metaestimator": RFECV,
+        "estimator": "classifier",
+        "estimator_name": "estimator",
+        "estimator_routing_methods": ["fit"],
+        "cv_name": "cv",
+        "cv_routing_methods": ["fit"],
+        "scorer_name": "scoring",
+        "scorer_routing_methods": ["fit", "score"],
+        "X": X,
+        "y": y,
+    },
+]
+"""List containing all metaestimators to be tested and their settings
+
+The keys are as follows:
+
+- metaestimator: The metaestimator to be tested
+- estimator_name: The name of the argument for the sub-estimator
+- estimator: The sub-estimator type, either "regressor" or "classifier"
+- init_args: The arguments to be passed to the metaestimator's constructor
+- X: X-data to fit and predict
+- y: y-data to fit
+- estimator_routing_methods: list of all methods to check for routing metadata
+  to the sub-estimator
+- preserves_metadata:
+    - True (default): the metaestimator passes the metadata to the
+      sub-estimator without modification. We check that the values recorded by
+      the sub-estimator are identical to what we've passed to the
+      metaestimator.
+    - False: no check is performed regarding values, we only check that a
+      metadata with the expected names/keys are passed.
+    - "subset": we check that the recorded metadata by the sub-estimator is a
+      subset of what is passed to the metaestimator.
+- scorer_name: The name of the argument for the scorer
+- scorer_routing_methods: list of all methods to check for routing metadata
+  to the scorer
+- cv_name: The name of the argument for the CV splitter
+- cv_routing_methods: list of all methods to check for routing metadata
+  to the splitter
+- method_args: a dict of dicts, defining extra arguments needed to be passed to
+  methods, such as passing `classes` to `partial_fit`.
+- method_mapping: a dict of the form `{caller: [callee1, ...]}` which signals
+  which `.set_{method}_request` methods should be called to set request values.
+  If not present, a one-to-one mapping is assumed.
+"""
+
+# IDs used by pytest to get meaningful verbose messages when running the tests
+METAESTIMATOR_IDS = [str(row["metaestimator"].__name__) for row in METAESTIMATORS]
+
+UNSUPPORTED_ESTIMATORS = [
+    AdaBoostClassifier(),
+    AdaBoostRegressor(),
+]
+
+
+def get_init_args(metaestimator_info, sub_estimator_consumes):
+    """Get the init args for a metaestimator
+
+    This is a helper function to get the init args for a metaestimator from
+    the METAESTIMATORS list. It returns an empty dict if no init args are
+    required.
+
+    Parameters
+    ----------
+    metaestimator_info : dict
+        The metaestimator info from METAESTIMATORS
+
+    sub_estimator_consumes : bool
+        Whether the sub-estimator consumes metadata or not.
+
+    Returns
+    -------
+    kwargs : dict
+        The init args for the metaestimator.
+
+    (estimator, estimator_registry) : (estimator, registry)
+        The sub-estimator and the corresponding registry.
+
+    (scorer, scorer_registry) : (scorer, registry)
+        The scorer and the corresponding registry.
+
+    (cv, cv_registry) : (CV splitter, registry)
+        The CV splitter and the corresponding registry.
+    """
+    kwargs = metaestimator_info.get("init_args", {})
+    estimator, estimator_registry = None, None
+    scorer, scorer_registry = None, None
+    cv, cv_registry = None, None
+    if "estimator" in metaestimator_info:
+        estimator_name = metaestimator_info["estimator_name"]
+        estimator_registry = _Registry()
+        sub_estimator_type = metaestimator_info["estimator"]
+        if sub_estimator_consumes:
+            if sub_estimator_type == "regressor":
+                estimator = ConsumingRegressor(estimator_registry)
+            elif sub_estimator_type == "classifier":
+                estimator = ConsumingClassifier(estimator_registry)
+            else:
+                raise ValueError("Unpermitted `sub_estimator_type`.")  # pragma: nocover
+        else:
+            if sub_estimator_type == "regressor":
+                estimator = NonConsumingRegressor()
+            elif sub_estimator_type == "classifier":
+                estimator = NonConsumingClassifier()
+            else:
+                raise ValueError("Unpermitted `sub_estimator_type`.")  # pragma: nocover
+        kwargs[estimator_name] = estimator
+    if "scorer_name" in metaestimator_info:
+        scorer_name = metaestimator_info["scorer_name"]
+        scorer_registry = _Registry()
+        scorer = ConsumingScorer(registry=scorer_registry)
+        kwargs[scorer_name] = scorer
+    if "cv_name" in metaestimator_info:
+        cv_name = metaestimator_info["cv_name"]
+        cv_registry = _Registry()
+        cv = ConsumingSplitter(registry=cv_registry)
+        kwargs[cv_name] = cv
+
+    return (
+        kwargs,
+        (estimator, estimator_registry),
+        (scorer, scorer_registry),
+        (cv, cv_registry),
+    )
+
+
+def set_requests(obj, *, method_mapping, methods, metadata_name, value=True):
+    """Call `set_{method}_request` on a list of methods from the sub-estimator.
+
+    Parameters
+    ----------
+    obj : BaseEstimator
+        The object for which `set_{method}_request` methods are called.
+
+    method_mapping : dict
+        The method mapping in the form of `{caller: [callee, ...]}`.
+        If a "caller" is not present in the method mapping, a one-to-one mapping is
+        assumed.
+
+    methods : list of str
+        The list of methods as "caller"s for which the request for the child should
+        be set.
+
+    metadata_name : str
+        The name of the metadata to be routed, usually either `"metadata"` or
+        `"sample_weight"` in our tests.
+
+    value : None, bool, or str
+        The request value to be set, by default it's `True`
+    """
+    for caller in methods:
+        for callee in method_mapping.get(caller, [caller]):
+            set_request_for_method = getattr(obj, f"set_{callee}_request")
+            set_request_for_method(**{metadata_name: value})
+            if (
+                isinstance(obj, BaseEstimator)
+                and is_classifier(obj)
+                and callee == "partial_fit"
+            ):
+                set_request_for_method(classes=True)
+
+
+@pytest.mark.parametrize("estimator", UNSUPPORTED_ESTIMATORS)
+@config_context(enable_metadata_routing=True)
+def test_unsupported_estimators_get_metadata_routing(estimator):
+    """Test that get_metadata_routing is not implemented on meta-estimators for
+    which we haven't implemented routing yet."""
+    with pytest.raises(NotImplementedError):
+        estimator.get_metadata_routing()
+
+
+@pytest.mark.parametrize("estimator", UNSUPPORTED_ESTIMATORS)
+@config_context(enable_metadata_routing=True)
+def test_unsupported_estimators_fit_with_metadata(estimator):
+    """Test that fit raises NotImplementedError when metadata routing is
+    enabled and a metadata is passed on meta-estimators for which we haven't
+    implemented routing yet."""
+    with pytest.raises(NotImplementedError):
+        try:
+            estimator.fit([[1]], [1], sample_weight=[1])
+        except TypeError:
+            # not all meta-estimators in the list support sample_weight,
+            # and for those we skip this test.
+            raise NotImplementedError
+
+
+@config_context(enable_metadata_routing=True)
+def test_registry_copy():
+    # test that _Registry is not copied into a new instance.
+    a = _Registry()
+    b = _Registry()
+    assert a is not b
+    assert a is copy.copy(a)
+    assert a is copy.deepcopy(a)
+
+
+@pytest.mark.parametrize("metaestimator", METAESTIMATORS, ids=METAESTIMATOR_IDS)
+@config_context(enable_metadata_routing=True)
+def test_default_request(metaestimator):
+    # Check that by default request is empty and the right type
+    metaestimator_class = metaestimator["metaestimator"]
+    kwargs, *_ = get_init_args(metaestimator, sub_estimator_consumes=True)
+    instance = metaestimator_class(**kwargs)
+    if "cv_name" in metaestimator:
+        # Our GroupCV splitters request groups by default, which we should
+        # ignore in this test.
+        exclude = {"splitter": ["split"]}
+    else:
+        exclude = None
+    assert_request_is_empty(instance.get_metadata_routing(), exclude=exclude)
+    assert isinstance(instance.get_metadata_routing(), MetadataRouter)
+
+
+@pytest.mark.parametrize("metaestimator", METAESTIMATORS, ids=METAESTIMATOR_IDS)
+@config_context(enable_metadata_routing=True)
+def test_error_on_missing_requests_for_sub_estimator(metaestimator):
+    # Test that a UnsetMetadataPassedError is raised when the sub-estimator's
+    # requests are not set
+    if "estimator" not in metaestimator:
+        # This test only makes sense for metaestimators which have a
+        # sub-estimator, e.g. MyMetaEstimator(estimator=MySubEstimator())
+        return
+
+    metaestimator_class = metaestimator["metaestimator"]
+    X = metaestimator["X"]
+    y = metaestimator["y"]
+    routing_methods = metaestimator["estimator_routing_methods"]
+
+    for method_name in routing_methods:
+        for key in ["sample_weight", "metadata"]:
+            kwargs, (estimator, _), (scorer, _), *_ = get_init_args(
+                metaestimator, sub_estimator_consumes=True
+            )
+            if scorer:
+                scorer.set_score_request(**{key: True})
+            val = {"sample_weight": sample_weight, "metadata": metadata}[key]
+            method_kwargs = {key: val}
+            instance = metaestimator_class(**kwargs)
+            msg = (
+                f"[{key}] are passed but are not explicitly set as requested or not"
+                f" requested for {estimator.__class__.__name__}.{method_name}"
+            )
+            with pytest.raises(UnsetMetadataPassedError, match=re.escape(msg)):
+                method = getattr(instance, method_name)
+                if "fit" not in method_name:
+                    # set request on fit
+                    set_requests(
+                        estimator,
+                        method_mapping=metaestimator.get("method_mapping", {}),
+                        methods=["fit"],
+                        metadata_name=key,
+                    )
+                    instance.fit(X, y, **method_kwargs)
+                # making sure the requests are unset, in case they were set as a
+                # side effect of setting them for fit. For instance, if method
+                # mapping for fit is: `"fit": ["fit", "score"]`, that would mean
+                # calling `.score` here would not raise, because we have already
+                # set request value for child estimator's `score`.
+                set_requests(
+                    estimator,
+                    method_mapping=metaestimator.get("method_mapping", {}),
+                    methods=["fit"],
+                    metadata_name=key,
+                    value=None,
+                )
+                try:
+                    # `fit`, `partial_fit`, 'score' accept y, others don't.
+                    method(X, y, **method_kwargs)
+                except TypeError:
+                    method(X, **method_kwargs)
+
+
+@pytest.mark.parametrize("metaestimator", METAESTIMATORS, ids=METAESTIMATOR_IDS)
+@config_context(enable_metadata_routing=True)
+def test_setting_request_on_sub_estimator_removes_error(metaestimator):
+    # When the metadata is explicitly requested on the sub-estimator, there
+    # should be no errors.
+    if "estimator" not in metaestimator:
+        # This test only makes sense for metaestimators which have a
+        # sub-estimator, e.g. MyMetaEstimator(estimator=MySubEstimator())
+        return
+
+    metaestimator_class = metaestimator["metaestimator"]
+    X = metaestimator["X"]
+    y = metaestimator["y"]
+    routing_methods = metaestimator["estimator_routing_methods"]
+    method_mapping = metaestimator.get("method_mapping", {})
+    preserves_metadata = metaestimator.get("preserves_metadata", True)
+
+    for method_name in routing_methods:
+        for key in ["sample_weight", "metadata"]:
+            val = {"sample_weight": sample_weight, "metadata": metadata}[key]
+            method_kwargs = {key: val}
+
+            kwargs, (estimator, registry), (scorer, _), (cv, _) = get_init_args(
+                metaestimator, sub_estimator_consumes=True
+            )
+            if scorer:
+                set_requests(
+                    scorer, method_mapping={}, methods=["score"], metadata_name=key
+                )
+            if cv:
+                cv.set_split_request(groups=True, metadata=True)
+
+            # `set_{method}_request({metadata}==True)` on the underlying objects
+            set_requests(
+                estimator,
+                method_mapping=method_mapping,
+                methods=[method_name],
+                metadata_name=key,
+            )
+
+            instance = metaestimator_class(**kwargs)
+            method = getattr(instance, method_name)
+            extra_method_args = metaestimator.get("method_args", {}).get(
+                method_name, {}
+            )
+            if "fit" not in method_name:
+                # fit before calling method
+                instance.fit(X, y)
+            try:
+                # `fit` and `partial_fit` accept y, others don't.
+                method(X, y, **method_kwargs, **extra_method_args)
+            except TypeError:
+                method(X, **method_kwargs, **extra_method_args)
+
+            # sanity check that registry is not empty, or else the test passes
+            # trivially
+            assert registry
+            split_params = (
+                method_kwargs.keys() if preserves_metadata == "subset" else ()
+            )
+            for estimator in registry:
+                check_recorded_metadata(
+                    estimator,
+                    method=method_name,
+                    parent=method_name,
+                    split_params=split_params,
+                    **method_kwargs,
+                )
+
+
+@pytest.mark.parametrize("metaestimator", METAESTIMATORS, ids=METAESTIMATOR_IDS)
+@config_context(enable_metadata_routing=True)
+def test_non_consuming_estimator_works(metaestimator):
+    # Test that when a non-consuming estimator is given, the meta-estimator
+    # works w/o setting any requests.
+    # Regression test for https://github.com/scikit-learn/scikit-learn/issues/28239
+    if "estimator" not in metaestimator:
+        # This test only makes sense for metaestimators which have a
+        # sub-estimator, e.g. MyMetaEstimator(estimator=MySubEstimator())
+        return
+
+    def set_request(estimator, method_name):
+        # e.g. call set_fit_request on estimator
+        if is_classifier(estimator) and method_name == "partial_fit":
+            estimator.set_partial_fit_request(classes=True)
+
+    metaestimator_class = metaestimator["metaestimator"]
+    X = metaestimator["X"]
+    y = metaestimator["y"]
+    routing_methods = metaestimator["estimator_routing_methods"]
+
+    for method_name in routing_methods:
+        kwargs, (estimator, _), (_, _), (_, _) = get_init_args(
+            metaestimator, sub_estimator_consumes=False
+        )
+        instance = metaestimator_class(**kwargs)
+        set_request(estimator, method_name)
+        method = getattr(instance, method_name)
+        extra_method_args = metaestimator.get("method_args", {}).get(method_name, {})
+        if "fit" not in method_name:
+            instance.fit(X, y, **extra_method_args)
+        # The following should pass w/o raising a routing error.
+        try:
+            # `fit` and `partial_fit` accept y, others don't.
+            method(X, y, **extra_method_args)
+        except TypeError:
+            method(X, **extra_method_args)
+
+
+@pytest.mark.parametrize("metaestimator", METAESTIMATORS, ids=METAESTIMATOR_IDS)
+@config_context(enable_metadata_routing=True)
+def test_metadata_is_routed_correctly_to_scorer(metaestimator):
+    """Test that any requested metadata is correctly routed to the underlying
+    scorers in CV estimators.
+    """
+    if "scorer_name" not in metaestimator:
+        # This test only makes sense for CV estimators
+        return
+
+    metaestimator_class = metaestimator["metaestimator"]
+    routing_methods = metaestimator["scorer_routing_methods"]
+    method_mapping = metaestimator.get("method_mapping", {})
+
+    for method_name in routing_methods:
+        kwargs, (estimator, _), (scorer, registry), (cv, _) = get_init_args(
+            metaestimator, sub_estimator_consumes=True
+        )
+        scorer.set_score_request(sample_weight=True)
+        if cv:
+            cv.set_split_request(groups=True, metadata=True)
+        if estimator is not None:
+            set_requests(
+                estimator,
+                method_mapping=method_mapping,
+                methods=[method_name],
+                metadata_name="sample_weight",
+            )
+        instance = metaestimator_class(**kwargs)
+        method = getattr(instance, method_name)
+        method_kwargs = {"sample_weight": sample_weight}
+        if "fit" not in method_name:
+            instance.fit(X, y)
+        method(X, y, **method_kwargs)
+
+        assert registry
+        for _scorer in registry:
+            check_recorded_metadata(
+                obj=_scorer,
+                method="score",
+                parent=method_name,
+                split_params=("sample_weight",),
+                **method_kwargs,
+            )
+
+
+@pytest.mark.parametrize("metaestimator", METAESTIMATORS, ids=METAESTIMATOR_IDS)
+@config_context(enable_metadata_routing=True)
+def test_metadata_is_routed_correctly_to_splitter(metaestimator):
+    """Test that any requested metadata is correctly routed to the underlying
+    splitters in CV estimators.
+    """
+    if "cv_routing_methods" not in metaestimator:
+        # This test is only for metaestimators accepting a CV splitter
+        return
+
+    metaestimator_class = metaestimator["metaestimator"]
+    routing_methods = metaestimator["cv_routing_methods"]
+    X_ = metaestimator["X"]
+    y_ = metaestimator["y"]
+
+    for method_name in routing_methods:
+        kwargs, (estimator, _), (scorer, _), (cv, registry) = get_init_args(
+            metaestimator, sub_estimator_consumes=True
+        )
+        if estimator:
+            estimator.set_fit_request(sample_weight=False, metadata=False)
+        if scorer:
+            scorer.set_score_request(sample_weight=False, metadata=False)
+        cv.set_split_request(groups=True, metadata=True)
+        instance = metaestimator_class(**kwargs)
+        method_kwargs = {"groups": groups, "metadata": metadata}
+        method = getattr(instance, method_name)
+        method(X_, y_, **method_kwargs)
+        assert registry
+        for _splitter in registry:
+            check_recorded_metadata(
+                obj=_splitter, method="split", parent=method_name, **method_kwargs
+            )
+
+
+@pytest.mark.parametrize("metaestimator", METAESTIMATORS, ids=METAESTIMATOR_IDS)
+@config_context(enable_metadata_routing=True)
+def test_metadata_routed_to_group_splitter(metaestimator):
+    """Test that groups are routed correctly if group splitter of CV estimator is used
+    within cross_validate. Regression test for issue described in PR #29634 to test that
+    `ValueError: The 'groups' parameter should not be None.` is not raised."""
+
+    if "cv_routing_methods" not in metaestimator:
+        # This test is only for metaestimators accepting a CV splitter
+        return
+
+    metaestimator_class = metaestimator["metaestimator"]
+    X_ = metaestimator["X"]
+    y_ = metaestimator["y"]
+
+    kwargs, *_ = get_init_args(metaestimator, sub_estimator_consumes=True)
+    # remove `ConsumingSplitter` from kwargs, so 'cv' param isn't passed twice:
+    kwargs.pop("cv", None)
+    instance = metaestimator_class(cv=GroupKFold(n_splits=2), **kwargs)
+    cross_validate(
+        instance,
+        X_,
+        y_,
+        params={"groups": groups},
+        cv=GroupKFold(n_splits=2),
+        scoring=make_scorer(mean_squared_error, response_method="predict"),
+    )
diff --git a/shared/package-files/04/04a68139735b2d76f63076294fc0700a1efc8fc1da80b42ae75b996340eae675/orjson.pyi b/shared/package-files/04/04a68139735b2d76f63076294fc0700a1efc8fc1da80b42ae75b996340eae675/orjson.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3fcbd7b668ce295ef6fd06369b84a433c0b8020a
--- /dev/null
+++ b/shared/package-files/04/04a68139735b2d76f63076294fc0700a1efc8fc1da80b42ae75b996340eae675/orjson.pyi
@@ -0,0 +1,24 @@
+from typing import Any, Callable, Optional, Union
+
+__version__: str
+
+def dumps(__obj: Any, default: Optional[Callable[[Any], Any]] = ..., option: Optional[int] = ...) -> bytes: ...
+def loads(__obj: Union[bytes, bytearray, str]) -> Any: ...
+
+class JSONDecodeError(ValueError): ...
+class JSONEncodeError(TypeError): ...
+
+OPT_APPEND_NEWLINE: int
+OPT_INDENT_2: int
+OPT_NAIVE_UTC: int
+OPT_NON_STR_KEYS: int
+OPT_OMIT_MICROSECONDS: int
+OPT_PASSTHROUGH_DATACLASS: int
+OPT_PASSTHROUGH_DATETIME: int
+OPT_PASSTHROUGH_SUBCLASS: int
+OPT_SERIALIZE_DATACLASS: int
+OPT_SERIALIZE_NUMPY: int
+OPT_SERIALIZE_UUID: int
+OPT_SORT_KEYS: int
+OPT_STRICT_INTEGER: int
+OPT_UTC_Z: int
diff --git a/shared/package-files/04/04a6a961b713f342b07bf4180d54dbf3db06222fa143eaf4c74abc9b096a7d44/test_literal_container_usage.py b/shared/package-files/04/04a6a961b713f342b07bf4180d54dbf3db06222fa143eaf4c74abc9b096a7d44/test_literal_container_usage.py
new file mode 100644
index 0000000000000000000000000000000000000000..4872faa4c0aa689e1a83a1b570a4fbed0d540964
--- /dev/null
+++ b/shared/package-files/04/04a6a961b713f342b07bf4180d54dbf3db06222fa143eaf4c74abc9b096a7d44/test_literal_container_usage.py
@@ -0,0 +1,161 @@
+# Contents in this file are referenced from the sphinx-generated docs.
+# "magictoken" is used for markers as beginning and ending of example text.
+
+import unittest
+from numba.tests.support import captured_stdout
+from numba import typed
+
+
+class DocsLiteralContainerUsageTest(unittest.TestCase):
+
+    def test_ex_literal_dict_compile_time_consts(self):
+        with captured_stdout():
+            # magictoken.test_ex_literal_dict_compile_time_consts.begin
+            import numpy as np
+            from numba import njit, types
+            from numba.extending import overload
+
+            # overload this function
+            def specialize(x):
+                pass
+
+            @overload(specialize)
+            def ol_specialize(x):
+                ld = x.literal_value
+                const_expr = []
+                for k, v in ld.items():
+                    if isinstance(v, types.Literal):
+                        lv = v.literal_value
+                        if lv == 'cat':
+                            const_expr.append("Meow!")
+                        elif lv == 'dog':
+                            const_expr.append("Woof!")
+                        elif isinstance(lv, int):
+                            const_expr.append(k.literal_value * lv)
+                    else: # it's an array
+                        const_expr.append("Array(dim={dim}".format(dim=v.ndim))
+                const_strings = tuple(const_expr)
+
+                def impl(x):
+                    return const_strings
+                return impl
+
+            @njit
+            def foo():
+                pets_ints_and_array = {'a': 1,
+                                       'b': 2,
+                                       'c': 'cat',
+                                       'd': 'dog',
+                                       'e': np.ones(5,)}
+                return specialize(pets_ints_and_array)
+
+            result = foo()
+            print(result) # ('a', 'bb', 'Meow!', 'Woof!', 'Array(dim=1')
+            # magictoken.test_ex_literal_dict_compile_time_consts.end
+
+        self.assertEqual(result, ('a', 'bb', 'Meow!', 'Woof!', 'Array(dim=1'))
+
+    def test_ex_initial_value_dict_compile_time_consts(self):
+        with captured_stdout():
+            # magictoken.test_ex_initial_value_dict_compile_time_consts.begin
+            from numba import njit, literally
+            from numba.extending import overload
+
+            # overload this function
+            def specialize(x):
+                pass
+
+            @overload(specialize)
+            def ol_specialize(x):
+                iv = x.initial_value
+                if iv is None:
+                    return lambda x: literally(x) # Force literal dispatch
+                assert iv == {'a': 1, 'b': 2, 'c': 3} # INITIAL VALUE
+                return lambda x: literally(x)
+
+            @njit
+            def foo():
+                d = {'a': 1, 'b': 2, 'c': 3}
+                d['c'] = 20 # no impact on .initial_value
+                d['d'] = 30 # no impact on .initial_value
+                return specialize(d)
+
+            result = foo()
+            print(result) # {a: 1, b: 2, c: 20, d: 30} # NOT INITIAL VALUE!
+            # magictoken.test_ex_initial_value_dict_compile_time_consts.end
+
+        expected = typed.Dict()
+        for k, v in {'a': 1, 'b': 2, 'c': 20, 'd': 30}.items():
+            expected[k] = v
+        self.assertEqual(result, expected)
+
+    def test_ex_literal_list(self):
+        with captured_stdout():
+            # magictoken.test_ex_literal_list.begin
+            from numba import njit
+            from numba.extending import overload
+
+            # overload this function
+            def specialize(x):
+                pass
+
+            @overload(specialize)
+            def ol_specialize(x):
+                l = x.literal_value
+                const_expr = []
+                for v in l:
+                    const_expr.append(str(v))
+                const_strings = tuple(const_expr)
+
+                def impl(x):
+                    return const_strings
+                return impl
+
+            @njit
+            def foo():
+                const_list = ['a', 10, 1j, ['another', 'list']]
+                return specialize(const_list)
+
+            result = foo()
+            print(result) # ('Literal[str](a)', 'Literal[int](10)', 'complex128', 'list(unicode_type)') # noqa E501
+            # magictoken.test_ex_literal_list.end
+
+        expected = ('Literal[str](a)', 'Literal[int](10)', 'complex128',
+                    "list(unicode_type)")
+        self.assertEqual(result, expected)
+
+    def test_ex_initial_value_list_compile_time_consts(self):
+        with captured_stdout():
+            # magictoken.test_ex_initial_value_list_compile_time_consts.begin
+            from numba import njit, literally
+            from numba.extending import overload
+
+            # overload this function
+            def specialize(x):
+                pass
+
+            @overload(specialize)
+            def ol_specialize(x):
+                iv = x.initial_value
+                if iv is None:
+                    return lambda x: literally(x) # Force literal dispatch
+                assert iv == [1, 2, 3] # INITIAL VALUE
+                return lambda x: x
+
+            @njit
+            def foo():
+                l = [1, 2, 3]
+                l[2] = 20 # no impact on .initial_value
+                l.append(30) # no impact on .initial_value
+                return specialize(l)
+
+            result = foo()
+            print(result) # [1, 2, 20, 30] # NOT INITIAL VALUE!
+            # magictoken.test_ex_initial_value_list_compile_time_consts.end
+
+        expected = [1, 2, 20, 30]
+        self.assertEqual(result, expected)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/shared/package-files/04/04a835fdccab283d865d8317fc40740e75ee6bc0c8d8ed4a58e0d77ff05186b9/umath-validation-set-exp.csv b/shared/package-files/04/04a835fdccab283d865d8317fc40740e75ee6bc0c8d8ed4a58e0d77ff05186b9/umath-validation-set-exp.csv
new file mode 100644
index 0000000000000000000000000000000000000000..071fb312932f3d59c92d03c11d505a78b96c9257
--- /dev/null
+++ b/shared/package-files/04/04a835fdccab283d865d8317fc40740e75ee6bc0c8d8ed4a58e0d77ff05186b9/umath-validation-set-exp.csv
@@ -0,0 +1,412 @@
+dtype,input,output,ulperrortol
+## +ve denormals ##
+np.float32,0x004b4716,0x3f800000,3
+np.float32,0x007b2490,0x3f800000,3
+np.float32,0x007c99fa,0x3f800000,3
+np.float32,0x00734a0c,0x3f800000,3
+np.float32,0x0070de24,0x3f800000,3
+np.float32,0x00495d65,0x3f800000,3
+np.float32,0x006894f6,0x3f800000,3
+np.float32,0x00555a76,0x3f800000,3
+np.float32,0x004e1fb8,0x3f800000,3
+np.float32,0x00687de9,0x3f800000,3
+## -ve denormals ##
+np.float32,0x805b59af,0x3f800000,3
+np.float32,0x807ed8ed,0x3f800000,3
+np.float32,0x807142ad,0x3f800000,3
+np.float32,0x80772002,0x3f800000,3
+np.float32,0x8062abcb,0x3f800000,3
+np.float32,0x8045e31c,0x3f800000,3
+np.float32,0x805f01c2,0x3f800000,3
+np.float32,0x80506432,0x3f800000,3
+np.float32,0x8060089d,0x3f800000,3
+np.float32,0x8071292f,0x3f800000,3
+## floats that output a denormal ##
+np.float32,0xc2cf3fc1,0x00000001,3
+np.float32,0xc2c79726,0x00000021,3
+np.float32,0xc2cb295d,0x00000005,3
+np.float32,0xc2b49e6b,0x00068c4c,3
+np.float32,0xc2ca8116,0x00000008,3
+np.float32,0xc2c23f82,0x000001d7,3
+np.float32,0xc2cb69c0,0x00000005,3
+np.float32,0xc2cc1f4d,0x00000003,3
+np.float32,0xc2ae094e,0x00affc4c,3
+np.float32,0xc2c86c44,0x00000015,3
+## random floats between -87.0f and 88.0f ##
+np.float32,0x4030d7e0,0x417d9a05,3
+np.float32,0x426f60e8,0x6aa1be2c,3
+np.float32,0x41a1b220,0x4e0efc11,3
+np.float32,0xc20cc722,0x26159da7,3
+np.float32,0x41c492bc,0x512ec79d,3
+np.float32,0x40980210,0x42e73a0e,3
+np.float32,0xbf1f7b80,0x3f094de3,3
+np.float32,0x42a678a4,0x7b87a383,3
+np.float32,0xc20f3cfd,0x25a1c304,3
+np.float32,0x423ff34c,0x6216467f,3
+np.float32,0x00000000,0x3f800000,3
+## floats that cause an overflow ##
+np.float32,0x7f06d8c1,0x7f800000,3
+np.float32,0x7f451912,0x7f800000,3
+np.float32,0x7ecceac3,0x7f800000,3
+np.float32,0x7f643b45,0x7f800000,3
+np.float32,0x7e910ea0,0x7f800000,3
+np.float32,0x7eb4756b,0x7f800000,3
+np.float32,0x7f4ec708,0x7f800000,3
+np.float32,0x7f6b4551,0x7f800000,3
+np.float32,0x7d8edbda,0x7f800000,3
+np.float32,0x7f730718,0x7f800000,3
+np.float32,0x42b17217,0x7f7fff84,3
+np.float32,0x42b17218,0x7f800000,3
+np.float32,0x42b17219,0x7f800000,3
+np.float32,0xfef2b0bc,0x00000000,3
+np.float32,0xff69f83e,0x00000000,3
+np.float32,0xff4ecb12,0x00000000,3
+np.float32,0xfeac6d86,0x00000000,3
+np.float32,0xfde0cdb8,0x00000000,3
+np.float32,0xff26aef4,0x00000000,3
+np.float32,0xff6f9277,0x00000000,3
+np.float32,0xff7adfc4,0x00000000,3
+np.float32,0xff0ad40e,0x00000000,3
+np.float32,0xff6fd8f3,0x00000000,3
+np.float32,0xc2cff1b4,0x00000001,3
+np.float32,0xc2cff1b5,0x00000000,3
+np.float32,0xc2cff1b6,0x00000000,3
+np.float32,0x7f800000,0x7f800000,3
+np.float32,0xff800000,0x00000000,3
+np.float32,0x4292f27c,0x7480000a,3
+np.float32,0x42a920be,0x7c7fff94,3
+np.float32,0x41c214c9,0x50ffffd9,3
+np.float32,0x41abe686,0x4effffd9,3
+np.float32,0x4287db5a,0x707fffd3,3
+np.float32,0x41902cbb,0x4c800078,3
+np.float32,0x42609466,0x67ffffeb,3
+np.float32,0x41a65af5,0x4e7fffd1,3
+np.float32,0x417f13ff,0x4affffc9,3
+np.float32,0x426d0e6c,0x6a3504f2,3
+np.float32,0x41bc8934,0x507fff51,3
+np.float32,0x42a7bdde,0x7c0000d6,3
+np.float32,0x4120cf66,0x46b504f6,3
+np.float32,0x4244da8f,0x62ffff1a,3
+np.float32,0x41a0cf69,0x4e000034,3
+np.float32,0x41cd2bec,0x52000005,3
+np.float32,0x42893e41,0x7100009e,3
+np.float32,0x41b437e1,0x4fb50502,3
+np.float32,0x41d8430f,0x5300001d,3
+np.float32,0x4244da92,0x62ffffda,3
+np.float32,0x41a0cf63,0x4dffffa9,3
+np.float32,0x3eb17218,0x3fb504f3,3
+np.float32,0x428729e8,0x703504dc,3
+np.float32,0x41a0cf67,0x4e000014,3
+np.float32,0x4252b77d,0x65800011,3
+np.float32,0x41902cb9,0x4c800058,3
+np.float32,0x42a0cf67,0x79800052,3
+np.float32,0x4152b77b,0x48ffffe9,3
+np.float32,0x41265af3,0x46ffffc8,3
+np.float32,0x42187e0b,0x5affff9a,3
+np.float32,0xc0d2b77c,0x3ab504f6,3
+np.float32,0xc283b2ac,0x10000072,3
+np.float32,0xc1cff1b4,0x2cb504f5,3
+np.float32,0xc05dce9e,0x3d000000,3
+np.float32,0xc28ec9d2,0x0bfffea5,3
+np.float32,0xc23c893a,0x1d7fffde,3
+np.float32,0xc2a920c0,0x027fff6c,3
+np.float32,0xc1f9886f,0x2900002b,3
+np.float32,0xc2c42920,0x000000b5,3
+np.float32,0xc2893e41,0x0dfffec5,3
+np.float32,0xc2c4da93,0x00000080,3
+np.float32,0xc17f1401,0x3400000c,3
+np.float32,0xc1902cb6,0x327fffaf,3
+np.float32,0xc27c4e3b,0x11ffffc5,3
+np.float32,0xc268e5c5,0x157ffe9d,3
+np.float32,0xc2b4e953,0x0005a826,3
+np.float32,0xc287db5a,0x0e800016,3
+np.float32,0xc207db5a,0x2700000b,3
+np.float32,0xc2b2d4fe,0x000ffff1,3
+np.float32,0xc268e5c0,0x157fffdd,3
+np.float32,0xc22920bd,0x2100003b,3
+np.float32,0xc2902caf,0x0b80011e,3
+np.float32,0xc1902cba,0x327fff2f,3
+np.float32,0xc2ca6625,0x00000008,3
+np.float32,0xc280ece8,0x10fffeb5,3
+np.float32,0xc2918f94,0x0b0000ea,3
+np.float32,0xc29b43d5,0x077ffffc,3
+np.float32,0xc1e61ff7,0x2ab504f5,3
+np.float32,0xc2867878,0x0effff15,3
+np.float32,0xc2a2324a,0x04fffff4,3
+#float64
+## near zero ##
+np.float64,0x8000000000000000,0x3ff0000000000000,2
+np.float64,0x8010000000000000,0x3ff0000000000000,2
+np.float64,0x8000000000000001,0x3ff0000000000000,2
+np.float64,0x8360000000000000,0x3ff0000000000000,2
+np.float64,0x9a70000000000000,0x3ff0000000000000,2
+np.float64,0xb9b0000000000000,0x3ff0000000000000,2
+np.float64,0xb810000000000000,0x3ff0000000000000,2
+np.float64,0xbc30000000000000,0x3ff0000000000000,2
+np.float64,0xb6a0000000000000,0x3ff0000000000000,2
+np.float64,0x0000000000000000,0x3ff0000000000000,2
+np.float64,0x0010000000000000,0x3ff0000000000000,2
+np.float64,0x0000000000000001,0x3ff0000000000000,2
+np.float64,0x0360000000000000,0x3ff0000000000000,2
+np.float64,0x1a70000000000000,0x3ff0000000000000,2
+np.float64,0x3c30000000000000,0x3ff0000000000000,2
+np.float64,0x36a0000000000000,0x3ff0000000000000,2
+np.float64,0x39b0000000000000,0x3ff0000000000000,2
+np.float64,0x3810000000000000,0x3ff0000000000000,2
+## underflow ##
+np.float64,0xc0c6276800000000,0x0000000000000000,2
+np.float64,0xc0c62d918ce2421d,0x0000000000000000,2
+np.float64,0xc0c62d918ce2421e,0x0000000000000000,2
+np.float64,0xc0c62d91a0000000,0x0000000000000000,2
+np.float64,0xc0c62d9180000000,0x0000000000000000,2
+np.float64,0xc0c62dea45ee3e06,0x0000000000000000,2
+np.float64,0xc0c62dea45ee3e07,0x0000000000000000,2
+np.float64,0xc0c62dea40000000,0x0000000000000000,2
+np.float64,0xc0c62dea60000000,0x0000000000000000,2
+np.float64,0xc0875f1120000000,0x0000000000000000,2
+np.float64,0xc0875f113c30b1c8,0x0000000000000000,2
+np.float64,0xc0875f1140000000,0x0000000000000000,2
+np.float64,0xc093480000000000,0x0000000000000000,2
+np.float64,0xffefffffffffffff,0x0000000000000000,2
+np.float64,0xc7efffffe0000000,0x0000000000000000,2
+## overflow ##
+np.float64,0x40862e52fefa39ef,0x7ff0000000000000,2
+np.float64,0x40872e42fefa39ef,0x7ff0000000000000,2
+## +/- INF, +/- NAN ##
+np.float64,0x7ff0000000000000,0x7ff0000000000000,2
+np.float64,0xfff0000000000000,0x0000000000000000,2
+np.float64,0x7ff8000000000000,0x7ff8000000000000,2
+np.float64,0xfff8000000000000,0xfff8000000000000,2
+## output denormal ##
+np.float64,0xc087438520000000,0x0000000000000001,2
+np.float64,0xc08743853f2f4461,0x0000000000000001,2
+np.float64,0xc08743853f2f4460,0x0000000000000001,2
+np.float64,0xc087438540000000,0x0000000000000001,2
+## between -745.13321910 and 709.78271289 ##
+np.float64,0xbff760cd14774bd9,0x3fcdb14ced00ceb6,2
+np.float64,0xbff760cd20000000,0x3fcdb14cd7993879,2
+np.float64,0xbff760cd00000000,0x3fcdb14d12fbd264,2
+np.float64,0xc07f1cf360000000,0x130c1b369af14fda,2
+np.float64,0xbeb0000000000000,0x3feffffe00001000,2
+np.float64,0xbd70000000000000,0x3fefffffffffe000,2
+np.float64,0xc084fd46e5c84952,0x0360000000000139,2
+np.float64,0xc084fd46e5c84953,0x035ffffffffffe71,2
+np.float64,0xc084fd46e0000000,0x0360000b9096d32c,2
+np.float64,0xc084fd4700000000,0x035fff9721d12104,2
+np.float64,0xc086232bc0000000,0x0010003af5e64635,2
+np.float64,0xc086232bdd7abcd2,0x001000000000007c,2
+np.float64,0xc086232bdd7abcd3,0x000ffffffffffe7c,2
+np.float64,0xc086232be0000000,0x000ffffaf57a6fc9,2
+np.float64,0xc086233920000000,0x000fe590e3b45eb0,2
+np.float64,0xc086233938000000,0x000fe56133493c57,2
+np.float64,0xc086233940000000,0x000fe5514deffbbc,2
+np.float64,0xc086234c98000000,0x000fbf1024c32ccb,2
+np.float64,0xc086234ca0000000,0x000fbf0065bae78d,2
+np.float64,0xc086234c80000000,0x000fbf3f623a7724,2
+np.float64,0xc086234ec0000000,0x000fbad237c846f9,2
+np.float64,0xc086234ec8000000,0x000fbac27cfdec97,2
+np.float64,0xc086234ee0000000,0x000fba934cfd3dc2,2
+np.float64,0xc086234ef0000000,0x000fba73d7f618d9,2
+np.float64,0xc086234f00000000,0x000fba54632dddc0,2
+np.float64,0xc0862356e0000000,0x000faae0945b761a,2
+np.float64,0xc0862356f0000000,0x000faac13eb9a310,2
+np.float64,0xc086235700000000,0x000faaa1e9567b0a,2
+np.float64,0xc086236020000000,0x000f98cd75c11ed7,2
+np.float64,0xc086236ca0000000,0x000f8081b4d93f89,2
+np.float64,0xc086236cb0000000,0x000f8062b3f4d6c5,2
+np.float64,0xc086236cc0000000,0x000f8043b34e6f8c,2
+np.float64,0xc086238d98000000,0x000f41220d9b0d2c,2
+np.float64,0xc086238da0000000,0x000f4112cc80a01f,2
+np.float64,0xc086238d80000000,0x000f414fd145db5b,2
+np.float64,0xc08624fd00000000,0x000cbfce8ea1e6c4,2
+np.float64,0xc086256080000000,0x000c250747fcd46e,2
+np.float64,0xc08626c480000000,0x000a34f4bd975193,2
+np.float64,0xbf50000000000000,0x3feff800ffeaac00,2
+np.float64,0xbe10000000000000,0x3fefffffff800000,2
+np.float64,0xbcd0000000000000,0x3feffffffffffff8,2
+np.float64,0xc055d589e0000000,0x38100004bf94f63e,2
+np.float64,0xc055d58a00000000,0x380ffff97f292ce8,2
+np.float64,0xbfd962d900000000,0x3fe585a4b00110e1,2
+np.float64,0x3ff4bed280000000,0x400d411e7a58a303,2
+np.float64,0x3fff0b3620000000,0x401bd7737ffffcf3,2
+np.float64,0x3ff0000000000000,0x4005bf0a8b145769,2
+np.float64,0x3eb0000000000000,0x3ff0000100000800,2
+np.float64,0x3d70000000000000,0x3ff0000000001000,2
+np.float64,0x40862e42e0000000,0x7fefff841808287f,2
+np.float64,0x40862e42fefa39ef,0x7fefffffffffff2a,2
+np.float64,0x40862e0000000000,0x7feef85a11e73f2d,2
+np.float64,0x4000000000000000,0x401d8e64b8d4ddae,2
+np.float64,0x4009242920000000,0x40372a52c383a488,2
+np.float64,0x4049000000000000,0x44719103e4080b45,2
+np.float64,0x4008000000000000,0x403415e5bf6fb106,2
+np.float64,0x3f50000000000000,0x3ff00400800aab55,2
+np.float64,0x3e10000000000000,0x3ff0000000400000,2
+np.float64,0x3cd0000000000000,0x3ff0000000000004,2
+np.float64,0x40562e40a0000000,0x47effed088821c3f,2
+np.float64,0x40562e42e0000000,0x47effff082e6c7ff,2
+np.float64,0x40562e4300000000,0x47f00000417184b8,2
+np.float64,0x3fe8000000000000,0x4000ef9db467dcf8,2
+np.float64,0x402b12e8d4f33589,0x412718f68c71a6fe,2
+np.float64,0x402b12e8d4f3358a,0x412718f68c71a70a,2
+np.float64,0x402b12e8c0000000,0x412718f59a7f472e,2
+np.float64,0x402b12e8e0000000,0x412718f70c0eac62,2
+##use 1th entry
+np.float64,0x40631659AE147CB4,0x4db3a95025a4890f,2
+np.float64,0xC061B87D2E85A4E2,0x332640c8e2de2c51,2
+np.float64,0x405A4A50BE243AF4,0x496a45e4b7f0339a,2
+np.float64,0xC0839898B98EC5C6,0x0764027828830df4,2
+#use 2th entry
+np.float64,0xC072428C44B6537C,0x2596ade838b96f3e,2
+np.float64,0xC053057C5E1AE9BF,0x3912c8fad18fdadf,2
+np.float64,0x407E89C78328BAA3,0x6bfe35d5b9a1a194,2
+np.float64,0x4083501B6DD87112,0x77a855503a38924e,2
+#use 3th entry
+np.float64,0x40832C6195F24540,0x7741e73c80e5eb2f,2
+np.float64,0xC083D4CD557C2EC9,0x06b61727c2d2508e,2
+np.float64,0x400C48F5F67C99BD,0x404128820f02b92e,2
+np.float64,0x4056E36D9B2DF26A,0x4830f52ff34a8242,2
+#use 4th entry
+np.float64,0x4080FF700D8CBD06,0x70fa70df9bc30f20,2
+np.float64,0x406C276D39E53328,0x543eb8e20a8f4741,2
+np.float64,0xC070D6159BBD8716,0x27a4a0548c904a75,2
+np.float64,0xC052EBCF8ED61F83,0x391c0e92368d15e4,2
+#use 5th entry
+np.float64,0xC061F892A8AC5FBE,0x32f807a89efd3869,2
+np.float64,0x4021D885D2DBA085,0x40bd4dc86d3e3270,2
+np.float64,0x40767AEEEE7D4FCF,0x605e22851ee2afb7,2
+np.float64,0xC0757C5D75D08C80,0x20f0751599b992a2,2
+#use 6th entry
+np.float64,0x405ACF7A284C4CE3,0x499a4e0b7a27027c,2
+np.float64,0xC085A6C9E80D7AF5,0x0175914009d62ec2,2
+np.float64,0xC07E4C02F86F1DAE,0x1439269b29a9231e,2
+np.float64,0x4080D80F9691CC87,0x7088a6cdafb041de,2
+#use 7th entry
+np.float64,0x407FDFD84FBA0AC1,0x6deb1ae6f9bc4767,2
+np.float64,0x40630C06A1A2213D,0x4dac7a9d51a838b7,2
+np.float64,0x40685FDB30BB8B4F,0x5183f5cc2cac9e79,2
+np.float64,0x408045A2208F77F4,0x6ee299e08e2aa2f0,2
+#use 8th entry
+np.float64,0xC08104E391F5078B,0x0ed397b7cbfbd230,2
+np.float64,0xC031501CAEFAE395,0x3e6040fd1ea35085,2
+np.float64,0xC079229124F6247C,0x1babf4f923306b1e,2
+np.float64,0x407FB65F44600435,0x6db03beaf2512b8a,2
+#use 9th entry
+np.float64,0xC07EDEE8E8E8A5AC,0x136536cec9cbef48,2
+np.float64,0x4072BB4086099A14,0x5af4d3c3008b56cc,2
+np.float64,0x4050442A2EC42CB4,0x45cd393bd8fad357,2
+np.float64,0xC06AC28FB3D419B4,0x2ca1b9d3437df85f,2
+#use 10th entry
+np.float64,0x40567FC6F0A68076,0x480c977fd5f3122e,2
+np.float64,0x40620A2F7EDA59BB,0x4cf278e96f4ce4d7,2
+np.float64,0xC085044707CD557C,0x034aad6c968a045a,2
+np.float64,0xC07374EA5AC516AA,0x23dd6afdc03e83d5,2
+#use 11th entry
+np.float64,0x4073CC95332619C1,0x5c804b1498bbaa54,2
+np.float64,0xC0799FEBBE257F31,0x1af6a954c43b87d2,2
+np.float64,0x408159F19EA424F6,0x7200858efcbfc84d,2
+np.float64,0x404A81F6F24C0792,0x44b664a07ce5bbfa,2
+#use 12th entry
+np.float64,0x40295FF1EFB9A741,0x4113c0e74c52d7b0,2
+np.float64,0x4073975F4CC411DA,0x5c32be40b4fec2c1,2
+np.float64,0x406E9DE52E82A77E,0x56049c9a3f1ae089,2
+np.float64,0x40748C2F52560ED9,0x5d93bc14fd4cd23b,2
+#use 13th entry
+np.float64,0x4062A553CDC4D04C,0x4d6266bfde301318,2
+np.float64,0xC079EC1D63598AB7,0x1a88cb184dab224c,2
+np.float64,0xC0725C1CB3167427,0x25725b46f8a081f6,2
+np.float64,0x407888771D9B45F9,0x6353b1ec6bd7ce80,2
+#use 14th entry
+np.float64,0xC082CBA03AA89807,0x09b383723831ce56,2
+np.float64,0xC083A8961BB67DD7,0x0735b118d5275552,2
+np.float64,0xC076BC6ECA12E7E3,0x1f2222679eaef615,2
+np.float64,0xC072752503AA1A5B,0x254eb832242c77e1,2
+#use 15th entry
+np.float64,0xC058800792125DEC,0x371882372a0b48d4,2
+np.float64,0x4082909FD863E81C,0x7580d5f386920142,2
+np.float64,0xC071616F8FB534F9,0x26dbe20ef64a412b,2
+np.float64,0x406D1AB571CAA747,0x54ee0d55cb38ac20,2
+#use 16th entry
+np.float64,0x406956428B7DAD09,0x52358682c271237f,2
+np.float64,0xC07EFC2D9D17B621,0x133b3e77c27a4d45,2
+np.float64,0xC08469BAC5BA3CCA,0x050863e5f42cc52f,2
+np.float64,0x407189D9626386A5,0x593cb1c0b3b5c1d3,2
+#use 17th entry
+np.float64,0x4077E652E3DEB8C6,0x6269a10dcbd3c752,2
+np.float64,0x407674C97DB06878,0x605485dcc2426ec2,2
+np.float64,0xC07CE9969CF4268D,0x16386cf8996669f2,2
+np.float64,0x40780EE32D5847C4,0x62a436bd1abe108d,2
+#use 18th entry
+np.float64,0x4076C3AA5E1E8DA1,0x60c62f56a5e72e24,2
+np.float64,0xC0730AFC7239B9BE,0x24758ead095cec1e,2
+np.float64,0xC085CC2B9C420DDB,0x0109cdaa2e5694c1,2
+np.float64,0x406D0765CB6D7AA4,0x54e06f8dd91bd945,2
+#use 19th entry
+np.float64,0xC082D011F3B495E7,0x09a6647661d279c2,2
+np.float64,0xC072826AF8F6AFBC,0x253acd3cd224507e,2
+np.float64,0x404EB9C4810CEA09,0x457933dbf07e8133,2
+np.float64,0x408284FBC97C58CE,0x755f6eb234aa4b98,2
+#use 20th entry
+np.float64,0x40856008CF6EDC63,0x7d9c0b3c03f4f73c,2
+np.float64,0xC077CB2E9F013B17,0x1d9b3d3a166a55db,2
+np.float64,0xC0479CA3C20AD057,0x3bad40e081555b99,2
+np.float64,0x40844CD31107332A,0x7a821d70aea478e2,2
+#use 21th entry
+np.float64,0xC07C8FCC0BFCC844,0x16ba1cc8c539d19b,2
+np.float64,0xC085C4E9A3ABA488,0x011ff675ba1a2217,2
+np.float64,0x4074D538B32966E5,0x5dfd9d78043c6ad9,2
+np.float64,0xC0630CA16902AD46,0x3231a446074cede6,2
+#use 22th entry
+np.float64,0xC06C826733D7D0B7,0x2b5f1078314d41e1,2
+np.float64,0xC0520DF55B2B907F,0x396c13a6ce8e833e,2
+np.float64,0xC080712072B0F437,0x107eae02d11d98ea,2
+np.float64,0x40528A6150E19EFB,0x469fdabda02228c5,2
+#use 23th entry
+np.float64,0xC07B1D74B6586451,0x18d1253883ae3b48,2
+np.float64,0x4045AFD7867DAEC0,0x43d7d634fc4c5d98,2
+np.float64,0xC07A08B91F9ED3E2,0x1a60973e6397fc37,2
+np.float64,0x407B3ECF0AE21C8C,0x673e03e9d98d7235,2
+#use 24th entry
+np.float64,0xC078AEB6F30CEABF,0x1c530b93ab54a1b3,2
+np.float64,0x4084495006A41672,0x7a775b6dc7e63064,2
+np.float64,0x40830B1C0EBF95DD,0x76e1e6eed77cfb89,2
+np.float64,0x407D93E8F33D8470,0x6a9adbc9e1e4f1e5,2
+#use 25th entry
+np.float64,0x4066B11A09EFD9E8,0x504dd528065c28a7,2
+np.float64,0x408545823723AEEB,0x7d504a9b1844f594,2
+np.float64,0xC068C711F2CA3362,0x2e104f3496ea118e,2
+np.float64,0x407F317FCC3CA873,0x6cf0732c9948ebf4,2
+#use 26th entry
+np.float64,0x407AFB3EBA2ED50F,0x66dc28a129c868d5,2
+np.float64,0xC075377037708ADE,0x21531a329f3d793e,2
+np.float64,0xC07C30066A1F3246,0x174448baa16ded2b,2
+np.float64,0xC06689A75DE2ABD3,0x2fad70662fae230b,2
+#use 27th entry
+np.float64,0x4081514E9FCCF1E0,0x71e673b9efd15f44,2
+np.float64,0xC0762C710AF68460,0x1ff1ed7d8947fe43,2
+np.float64,0xC0468102FF70D9C4,0x3be0c3a8ff3419a3,2
+np.float64,0xC07EA4CEEF02A83E,0x13b908f085102c61,2
+#use 28th entry
+np.float64,0xC06290B04AE823C4,0x328a83da3c2e3351,2
+np.float64,0xC0770EB1D1C395FB,0x1eab281c1f1db5fe,2
+np.float64,0xC06F5D4D838A5BAE,0x29500ea32fb474ea,2
+np.float64,0x40723B3133B54C5D,0x5a3c82c7c3a2b848,2
+#use 29th entry
+np.float64,0x4085E6454CE3B4AA,0x7f20319b9638d06a,2
+np.float64,0x408389F2A0585D4B,0x7850667c58aab3d0,2
+np.float64,0xC0382798F9C8AE69,0x3dc1c79fe8739d6d,2
+np.float64,0xC08299D827608418,0x0a4335f76cdbaeb5,2
+#use 30th entry
+np.float64,0xC06F3DED43301BF1,0x2965670ae46750a8,2
+np.float64,0xC070CAF6BDD577D9,0x27b4aa4ffdd29981,2
+np.float64,0x4078529AD4B2D9F2,0x6305c12755d5e0a6,2
+np.float64,0xC055B14E75A31B96,0x381c2eda6d111e5d,2
+#use 31th entry
+np.float64,0x407B13EE414FA931,0x6700772c7544564d,2
+np.float64,0x407EAFDE9DE3EC54,0x6c346a0e49724a3c,2
+np.float64,0xC08362F398B9530D,0x07ffeddbadf980cb,2
+np.float64,0x407E865CDD9EEB86,0x6bf866cac5e0d126,2
+#use 32th entry
+np.float64,0x407FB62DBC794C86,0x6db009f708ac62cb,2
+np.float64,0xC063D0BAA68CDDDE,0x31a3b2a51ce50430,2
+np.float64,0xC05E7706A2231394,0x34f24bead6fab5c9,2
+np.float64,0x4083E3A06FDE444E,0x79527b7a386d1937,2
diff --git a/shared/package-files/04/04a8caaf496473a4ef6b4253773946971ea29d790a59a60a46dbb9b9cc59d966/special_xlogy_compositeimplicitautograd_dispatch.h b/shared/package-files/04/04a8caaf496473a4ef6b4253773946971ea29d790a59a60a46dbb9b9cc59d966/special_xlogy_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d81fe60e3167d8c37e9fd3d8e12945b7881fe3d8
--- /dev/null
+++ b/shared/package-files/04/04a8caaf496473a4ef6b4253773946971ea29d790a59a60a46dbb9b9cc59d966/special_xlogy_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,31 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor special_xlogy(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlogy_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor special_xlogy(const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlogy_out(at::Tensor & out, const at::Scalar & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlogy_outf(const at::Scalar & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor special_xlogy(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & special_xlogy_out(at::Tensor & out, const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & special_xlogy_outf(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/04/04aaf4108bbc83568b885e9afe0eb5cc5d8d1cf558f0297a09170d9c72d0ff8a/test_usecols_basic.py b/shared/package-files/04/04aaf4108bbc83568b885e9afe0eb5cc5d8d1cf558f0297a09170d9c72d0ff8a/test_usecols_basic.py
new file mode 100644
index 0000000000000000000000000000000000000000..767fba666e41769a2fa1c756a5e93b5e1720cd9c
--- /dev/null
+++ b/shared/package-files/04/04aaf4108bbc83568b885e9afe0eb5cc5d8d1cf558f0297a09170d9c72d0ff8a/test_usecols_basic.py
@@ -0,0 +1,563 @@
+"""
+Tests the usecols functionality during parsing
+for all of the parsers defined in parsers.py
+"""
+from io import StringIO
+
+import numpy as np
+import pytest
+
+from pandas.errors import ParserError
+
+from pandas import (
+    DataFrame,
+    Index,
+    array,
+)
+import pandas._testing as tm
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
+)
+
+_msg_validate_usecols_arg = (
+    "'usecols' must either be list-like "
+    "of all strings, all unicode, all "
+    "integers or a callable."
+)
+_msg_validate_usecols_names = (
+    "Usecols do not match columns, columns expected but not found: {0}"
+)
+_msg_pyarrow_requires_names = (
+    "The pyarrow engine does not allow 'usecols' to be integer column "
+    "positions. Pass a list of string column names instead."
+)
+
+xfail_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")
+skip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")
+
+pytestmark = pytest.mark.filterwarnings(
+    "ignore:Passing a BlockManager to DataFrame is deprecated:DeprecationWarning"
+)
+
+
+def test_raise_on_mixed_dtype_usecols(all_parsers):
+    # See gh-12678
+    data = """a,b,c
+        1000,2000,3000
+        4000,5000,6000
+        """
+    usecols = [0, "b", 2]
+    parser = all_parsers
+
+    with pytest.raises(ValueError, match=_msg_validate_usecols_arg):
+        parser.read_csv(StringIO(data), usecols=usecols)
+
+
+@pytest.mark.parametrize("usecols", [(1, 2), ("b", "c")])
+def test_usecols(all_parsers, usecols, request):
+    data = """\
+a,b,c
+1,2,3
+4,5,6
+7,8,9
+10,11,12"""
+    parser = all_parsers
+    if parser.engine == "pyarrow" and isinstance(usecols[0], int):
+        with pytest.raises(ValueError, match=_msg_pyarrow_requires_names):
+            parser.read_csv(StringIO(data), usecols=usecols)
+        return
+
+    result = parser.read_csv(StringIO(data), usecols=usecols)
+
+    expected = DataFrame([[2, 3], [5, 6], [8, 9], [11, 12]], columns=["b", "c"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_usecols_with_names(all_parsers):
+    data = """\
+a,b,c
+1,2,3
+4,5,6
+7,8,9
+10,11,12"""
+    parser = all_parsers
+    names = ["foo", "bar"]
+
+    if parser.engine == "pyarrow":
+        with pytest.raises(ValueError, match=_msg_pyarrow_requires_names):
+            parser.read_csv(StringIO(data), names=names, usecols=[1, 2], header=0)
+        return
+
+    result = parser.read_csv(StringIO(data), names=names, usecols=[1, 2], header=0)
+
+    expected = DataFrame([[2, 3], [5, 6], [8, 9], [11, 12]], columns=names)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "names,usecols", [(["b", "c"], [1, 2]), (["a", "b", "c"], ["b", "c"])]
+)
+def test_usecols_relative_to_names(all_parsers, names, usecols):
+    data = """\
+1,2,3
+4,5,6
+7,8,9
+10,11,12"""
+    parser = all_parsers
+    if parser.engine == "pyarrow" and not isinstance(usecols[0], int):
+        # ArrowKeyError: Column 'fb' in include_columns does not exist
+        pytest.skip(reason="https://github.com/apache/arrow/issues/38676")
+
+    result = parser.read_csv(StringIO(data), names=names, header=None, usecols=usecols)
+
+    expected = DataFrame([[2, 3], [5, 6], [8, 9], [11, 12]], columns=["b", "c"])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_usecols_relative_to_names2(all_parsers):
+    # see gh-5766
+    data = """\
+1,2,3
+4,5,6
+7,8,9
+10,11,12"""
+    parser = all_parsers
+
+    result = parser.read_csv(
+        StringIO(data), names=["a", "b"], header=None, usecols=[0, 1]
+    )
+
+    expected = DataFrame([[1, 2], [4, 5], [7, 8], [10, 11]], columns=["a", "b"])
+    tm.assert_frame_equal(result, expected)
+
+
+# regex mismatch: "Length mismatch: Expected axis has 1 elements"
+@xfail_pyarrow
+def test_usecols_name_length_conflict(all_parsers):
+    data = """\
+1,2,3
+4,5,6
+7,8,9
+10,11,12"""
+    parser = all_parsers
+    msg = "Number of passed names did not match number of header fields in the file"
+    with pytest.raises(ValueError, match=msg):
+        parser.read_csv(StringIO(data), names=["a", "b"], header=None, usecols=[1])
+
+
+def test_usecols_single_string(all_parsers):
+    # see gh-20558
+    parser = all_parsers
+    data = """foo, bar, baz
+1000, 2000, 3000
+4000, 5000, 6000"""
+
+    with pytest.raises(ValueError, match=_msg_validate_usecols_arg):
+        parser.read_csv(StringIO(data), usecols="foo")
+
+
+@skip_pyarrow  # CSV parse error in one case, AttributeError in another
+@pytest.mark.parametrize(
+    "data", ["a,b,c,d\n1,2,3,4\n5,6,7,8", "a,b,c,d\n1,2,3,4,\n5,6,7,8,"]
+)
+def test_usecols_index_col_false(all_parsers, data):
+    # see gh-9082
+    parser = all_parsers
+    usecols = ["a", "c", "d"]
+    expected = DataFrame({"a": [1, 5], "c": [3, 7], "d": [4, 8]})
+
+    result = parser.read_csv(StringIO(data), usecols=usecols, index_col=False)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("index_col", ["b", 0])
+@pytest.mark.parametrize("usecols", [["b", "c"], [1, 2]])
+def test_usecols_index_col_conflict(all_parsers, usecols, index_col, request):
+    # see gh-4201: test that index_col as integer reflects usecols
+    parser = all_parsers
+    data = "a,b,c,d\nA,a,1,one\nB,b,2,two"
+
+    if parser.engine == "pyarrow" and isinstance(usecols[0], int):
+        with pytest.raises(ValueError, match=_msg_pyarrow_requires_names):
+            parser.read_csv(StringIO(data), usecols=usecols, index_col=index_col)
+        return
+
+    expected = DataFrame({"c": [1, 2]}, index=Index(["a", "b"], name="b"))
+
+    result = parser.read_csv(StringIO(data), usecols=usecols, index_col=index_col)
+    tm.assert_frame_equal(result, expected)
+
+
+def test_usecols_index_col_conflict2(all_parsers):
+    # see gh-4201: test that index_col as integer reflects usecols
+    parser = all_parsers
+    data = "a,b,c,d\nA,a,1,one\nB,b,2,two"
+
+    expected = DataFrame({"b": ["a", "b"], "c": [1, 2], "d": ("one", "two")})
+    expected = expected.set_index(["b", "c"])
+
+    result = parser.read_csv(
+        StringIO(data), usecols=["b", "c", "d"], index_col=["b", "c"]
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@skip_pyarrow  # CSV parse error: Expected 3 columns, got 4
+def test_usecols_implicit_index_col(all_parsers):
+    # see gh-2654
+    parser = all_parsers
+    data = "a,b,c\n4,apple,bat,5.7\n8,orange,cow,10"
+
+    result = parser.read_csv(StringIO(data), usecols=["a", "b"])
+    expected = DataFrame({"a": ["apple", "orange"], "b": ["bat", "cow"]}, index=[4, 8])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_usecols_index_col_middle(all_parsers):
+    # GH#9098
+    parser = all_parsers
+    data = """a,b,c,d
+1,2,3,4
+"""
+    result = parser.read_csv(StringIO(data), usecols=["b", "c", "d"], index_col="c")
+    expected = DataFrame({"b": [2], "d": [4]}, index=Index([3], name="c"))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_usecols_index_col_end(all_parsers):
+    # GH#9098
+    parser = all_parsers
+    data = """a,b,c,d
+1,2,3,4
+"""
+    result = parser.read_csv(StringIO(data), usecols=["b", "c", "d"], index_col="d")
+    expected = DataFrame({"b": [2], "c": [3]}, index=Index([4], name="d"))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_usecols_regex_sep(all_parsers):
+    # see gh-2733
+    parser = all_parsers
+    data = "a  b  c\n4  apple  bat  5.7\n8  orange  cow  10"
+
+    if parser.engine == "pyarrow":
+        msg = "the 'pyarrow' engine does not support regex separators"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), sep=r"\s+", usecols=("a", "b"))
+        return
+
+    result = parser.read_csv(StringIO(data), sep=r"\s+", usecols=("a", "b"))
+
+    expected = DataFrame({"a": ["apple", "orange"], "b": ["bat", "cow"]}, index=[4, 8])
+    tm.assert_frame_equal(result, expected)
+
+
+def test_usecols_with_whitespace(all_parsers):
+    parser = all_parsers
+    data = "a  b  c\n4  apple  bat  5.7\n8  orange  cow  10"
+
+    depr_msg = "The 'delim_whitespace' keyword in pd.read_csv is deprecated"
+
+    if parser.engine == "pyarrow":
+        msg = "The 'delim_whitespace' option is not supported with the 'pyarrow' engine"
+        with pytest.raises(ValueError, match=msg):
+            with tm.assert_produces_warning(
+                FutureWarning, match=depr_msg, check_stacklevel=False
+            ):
+                parser.read_csv(
+                    StringIO(data), delim_whitespace=True, usecols=("a", "b")
+                )
+        return
+
+    with tm.assert_produces_warning(
+        FutureWarning, match=depr_msg, check_stacklevel=False
+    ):
+        result = parser.read_csv(
+            StringIO(data), delim_whitespace=True, usecols=("a", "b")
+        )
+    expected = DataFrame({"a": ["apple", "orange"], "b": ["bat", "cow"]}, index=[4, 8])
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "usecols,expected",
+    [
+        # Column selection by index.
+        ([0, 1], DataFrame(data=[[1000, 2000], [4000, 5000]], columns=["2", "0"])),
+        # Column selection by name.
+        (
+            ["0", "1"],
+            DataFrame(data=[[2000, 3000], [5000, 6000]], columns=["0", "1"]),
+        ),
+    ],
+)
+def test_usecols_with_integer_like_header(all_parsers, usecols, expected, request):
+    parser = all_parsers
+    data = """2,0,1
+1000,2000,3000
+4000,5000,6000"""
+
+    if parser.engine == "pyarrow" and isinstance(usecols[0], int):
+        with pytest.raises(ValueError, match=_msg_pyarrow_requires_names):
+            parser.read_csv(StringIO(data), usecols=usecols)
+        return
+
+    result = parser.read_csv(StringIO(data), usecols=usecols)
+    tm.assert_frame_equal(result, expected)
+
+
+@xfail_pyarrow  # mismatched shape
+def test_empty_usecols(all_parsers):
+    data = "a,b,c\n1,2,3\n4,5,6"
+    expected = DataFrame(columns=Index([]))
+    parser = all_parsers
+
+    result = parser.read_csv(StringIO(data), usecols=set())
+    tm.assert_frame_equal(result, expected)
+
+
+def test_np_array_usecols(all_parsers):
+    # see gh-12546
+    parser = all_parsers
+    data = "a,b,c\n1,2,3"
+    usecols = np.array(["a", "b"])
+
+    expected = DataFrame([[1, 2]], columns=usecols)
+    result = parser.read_csv(StringIO(data), usecols=usecols)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "usecols,expected",
+    [
+        (
+            lambda x: x.upper() in ["AAA", "BBB", "DDD"],
+            DataFrame(
+                {
+                    "AaA": {
+                        0: 0.056674972999999997,
+                        1: 2.6132309819999997,
+                        2: 3.5689350380000002,
+                    },
+                    "bBb": {0: 8, 1: 2, 2: 7},
+                    "ddd": {0: "a", 1: "b", 2: "a"},
+                }
+            ),
+        ),
+        (lambda x: False, DataFrame(columns=Index([]))),
+    ],
+)
+def test_callable_usecols(all_parsers, usecols, expected):
+    # see gh-14154
+    data = """AaA,bBb,CCC,ddd
+0.056674973,8,True,a
+2.613230982,2,False,b
+3.568935038,7,False,a"""
+    parser = all_parsers
+
+    if parser.engine == "pyarrow":
+        msg = "The pyarrow engine does not allow 'usecols' to be a callable"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), usecols=usecols)
+        return
+
+    result = parser.read_csv(StringIO(data), usecols=usecols)
+    tm.assert_frame_equal(result, expected)
+
+
+# ArrowKeyError: Column 'fa' in include_columns does not exist in CSV file
+@skip_pyarrow
+@pytest.mark.parametrize("usecols", [["a", "c"], lambda x: x in ["a", "c"]])
+def test_incomplete_first_row(all_parsers, usecols):
+    # see gh-6710
+    data = "1,2\n1,2,3"
+    parser = all_parsers
+    names = ["a", "b", "c"]
+    expected = DataFrame({"a": [1, 1], "c": [np.nan, 3]})
+
+    result = parser.read_csv(StringIO(data), names=names, usecols=usecols)
+    tm.assert_frame_equal(result, expected)
+
+
+@skip_pyarrow  # CSV parse error: Expected 3 columns, got 4
+@pytest.mark.parametrize(
+    "data,usecols,kwargs,expected",
+    [
+        # see gh-8985
+        (
+            "19,29,39\n" * 2 + "10,20,30,40",
+            [0, 1, 2],
+            {"header": None},
+            DataFrame([[19, 29, 39], [19, 29, 39], [10, 20, 30]]),
+        ),
+        # see gh-9549
+        (
+            ("A,B,C\n1,2,3\n3,4,5\n1,2,4,5,1,6\n1,2,3,,,1,\n1,2,3\n5,6,7"),
+            ["A", "B", "C"],
+            {},
+            DataFrame(
+                {
+                    "A": [1, 3, 1, 1, 1, 5],
+                    "B": [2, 4, 2, 2, 2, 6],
+                    "C": [3, 5, 4, 3, 3, 7],
+                }
+            ),
+        ),
+    ],
+)
+def test_uneven_length_cols(all_parsers, data, usecols, kwargs, expected):
+    # see gh-8985
+    parser = all_parsers
+    result = parser.read_csv(StringIO(data), usecols=usecols, **kwargs)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "usecols,kwargs,expected,msg",
+    [
+        (
+            ["a", "b", "c", "d"],
+            {},
+            DataFrame({"a": [1, 5], "b": [2, 6], "c": [3, 7], "d": [4, 8]}),
+            None,
+        ),
+        (
+            ["a", "b", "c", "f"],
+            {},
+            None,
+            _msg_validate_usecols_names.format(r"\['f'\]"),
+        ),
+        (["a", "b", "f"], {}, None, _msg_validate_usecols_names.format(r"\['f'\]")),
+        (
+            ["a", "b", "f", "g"],
+            {},
+            None,
+            _msg_validate_usecols_names.format(r"\[('f', 'g'|'g', 'f')\]"),
+        ),
+        # see gh-14671
+        (
+            None,
+            {"header": 0, "names": ["A", "B", "C", "D"]},
+            DataFrame({"A": [1, 5], "B": [2, 6], "C": [3, 7], "D": [4, 8]}),
+            None,
+        ),
+        (
+            ["A", "B", "C", "f"],
+            {"header": 0, "names": ["A", "B", "C", "D"]},
+            None,
+            _msg_validate_usecols_names.format(r"\['f'\]"),
+        ),
+        (
+            ["A", "B", "f"],
+            {"names": ["A", "B", "C", "D"]},
+            None,
+            _msg_validate_usecols_names.format(r"\['f'\]"),
+        ),
+    ],
+)
+def test_raises_on_usecols_names_mismatch(
+    all_parsers, usecols, kwargs, expected, msg, request
+):
+    data = "a,b,c,d\n1,2,3,4\n5,6,7,8"
+    kwargs.update(usecols=usecols)
+    parser = all_parsers
+
+    if parser.engine == "pyarrow" and not (
+        usecols is not None and expected is not None
+    ):
+        # everything but the first case
+        # ArrowKeyError: Column 'f' in include_columns does not exist in CSV file
+        pytest.skip(reason="https://github.com/apache/arrow/issues/38676")
+
+    if expected is None:
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO(data), **kwargs)
+    else:
+        result = parser.read_csv(StringIO(data), **kwargs)
+        tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("usecols", [["A", "C"], [0, 2]])
+def test_usecols_subset_names_mismatch_orig_columns(all_parsers, usecols, request):
+    data = "a,b,c,d\n1,2,3,4\n5,6,7,8"
+    names = ["A", "B", "C", "D"]
+    parser = all_parsers
+
+    if parser.engine == "pyarrow":
+        if isinstance(usecols[0], int):
+            with pytest.raises(ValueError, match=_msg_pyarrow_requires_names):
+                parser.read_csv(StringIO(data), header=0, names=names, usecols=usecols)
+            return
+        # "pyarrow.lib.ArrowKeyError: Column 'A' in include_columns does not exist"
+        pytest.skip(reason="https://github.com/apache/arrow/issues/38676")
+
+    result = parser.read_csv(StringIO(data), header=0, names=names, usecols=usecols)
+    expected = DataFrame({"A": [1, 5], "C": [3, 7]})
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("names", [None, ["a", "b"]])
+def test_usecols_indices_out_of_bounds(all_parsers, names):
+    # GH#25623 & GH 41130; enforced in 2.0
+    parser = all_parsers
+    data = """
+a,b
+1,2
+    """
+
+    err = ParserError
+    msg = "Defining usecols with out-of-bounds"
+    if parser.engine == "pyarrow":
+        err = ValueError
+        msg = _msg_pyarrow_requires_names
+
+    with pytest.raises(err, match=msg):
+        parser.read_csv(StringIO(data), usecols=[0, 2], names=names, header=0)
+
+
+def test_usecols_additional_columns(all_parsers):
+    # GH#46997
+    parser = all_parsers
+    usecols = lambda header: header.strip() in ["a", "b", "c"]
+
+    if parser.engine == "pyarrow":
+        msg = "The pyarrow engine does not allow 'usecols' to be a callable"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO("a,b\nx,y,z"), index_col=False, usecols=usecols)
+        return
+    result = parser.read_csv(StringIO("a,b\nx,y,z"), index_col=False, usecols=usecols)
+    expected = DataFrame({"a": ["x"], "b": "y"})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_usecols_additional_columns_integer_columns(all_parsers):
+    # GH#46997
+    parser = all_parsers
+    usecols = lambda header: header.strip() in ["0", "1"]
+    if parser.engine == "pyarrow":
+        msg = "The pyarrow engine does not allow 'usecols' to be a callable"
+        with pytest.raises(ValueError, match=msg):
+            parser.read_csv(StringIO("0,1\nx,y,z"), index_col=False, usecols=usecols)
+        return
+    result = parser.read_csv(StringIO("0,1\nx,y,z"), index_col=False, usecols=usecols)
+    expected = DataFrame({"0": ["x"], "1": "y"})
+    tm.assert_frame_equal(result, expected)
+
+
+def test_usecols_dtype(all_parsers):
+    parser = all_parsers
+    data = """
+col1,col2,col3
+a,1,x
+b,2,y
+"""
+    result = parser.read_csv(
+        StringIO(data),
+        usecols=["col1", "col2"],
+        dtype={"col1": "string", "col2": "uint8", "col3": "string"},
+    )
+    expected = DataFrame(
+        {"col1": array(["a", "b"]), "col2": np.array([1, 2], dtype="uint8")}
+    )
+    tm.assert_frame_equal(result, expected)
diff --git a/shared/package-files/04/04ac4f022e21dc839e6dc6581b109bbad62e017d2d95be343f40c491f122f75f/fisher_exact_results_from_r.py b/shared/package-files/04/04ac4f022e21dc839e6dc6581b109bbad62e017d2d95be343f40c491f122f75f/fisher_exact_results_from_r.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7dd8936018eae2f74cc6f5966235a86fa821793
--- /dev/null
+++ b/shared/package-files/04/04ac4f022e21dc839e6dc6581b109bbad62e017d2d95be343f40c491f122f75f/fisher_exact_results_from_r.py
@@ -0,0 +1,607 @@
+# DO NOT EDIT THIS FILE!
+# This file was generated by the R script
+#     generate_fisher_exact_results_from_r.R
+# The script was run with R version 3.6.2 (2019-12-12) at 2020-11-09 06:16:09
+
+
+from collections import namedtuple
+import numpy as np
+
+
+Inf = np.inf
+
+Parameters = namedtuple('Parameters',
+                        ['table', 'confidence_level', 'alternative'])
+RResults = namedtuple('RResults',
+                      ['pvalue', 'conditional_odds_ratio',
+                       'conditional_odds_ratio_ci'])
+data = [
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.1300759363430016,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0.04035202926536294,
+                                         2.662846672960251))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.02301413756522116,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0.004668988338943325,
+                                         0.895792956493601))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.1973244147157191,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0.4153910882532168,
+                                         259.2593661129417))),
+    (Parameters(table=[[5, 15], [20, 20]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.09580440012477633,
+              conditional_odds_ratio=0.3394396617440851,
+              conditional_odds_ratio_ci=(0.08056337526385809,
+                                         1.22704788545557))),
+    (Parameters(table=[[5, 16], [16, 25]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.2697004098849359,
+              conditional_odds_ratio=0.4937791394540491,
+              conditional_odds_ratio_ci=(0.1176691231650079,
+                                         1.787463657995973))),
+    (Parameters(table=[[10, 5], [10, 1]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.1973244147157192,
+              conditional_odds_ratio=0.2116112781158479,
+              conditional_odds_ratio_ci=(0.003857141267422399,
+                                         2.407369893767229))),
+    (Parameters(table=[[10, 5], [10, 0]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.06126482213438735,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         1.451643573543705))),
+    (Parameters(table=[[5, 0], [1, 4]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.04761904761904762,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(1.024822256141754,
+                                         Inf))),
+    (Parameters(table=[[0, 5], [1, 4]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         39.00054996869288))),
+    (Parameters(table=[[5, 1], [0, 4]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.04761904761904761,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(1.024822256141754,
+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         39.00054996869287))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=2.005657880389071e-122,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(349.2595113327733,
+                                         3630.382605689872))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=5.728437460831947e-44,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(152.4166024390096,
+                                         1425.700792178893))),
+    (Parameters(table=[[190, 800], [200, 900]],
+                confidence_level=0.95,
+                alternative='two.sided'),
+     RResults(pvalue=0.574111858126088,
+              conditional_odds_ratio=1.068697577856801,
+              conditional_odds_ratio_ci=(0.8520462587912048,
+                                         1.340148950273938))),
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.1300759363430016,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0.02502345007115455,
+                                         6.304424772117853))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.02301413756522116,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0.001923034001462487,
+                                         1.53670836950172))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.1973244147157191,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0.2397970951413721,
+                                         1291.342011095509))),
+    (Parameters(table=[[5, 15], [20, 20]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.09580440012477633,
+              conditional_odds_ratio=0.3394396617440851,
+              conditional_odds_ratio_ci=(0.05127576113762925,
+                                         1.717176678806983))),
+    (Parameters(table=[[5, 16], [16, 25]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.2697004098849359,
+              conditional_odds_ratio=0.4937791394540491,
+              conditional_odds_ratio_ci=(0.07498546954483619,
+                                         2.506969905199901))),
+    (Parameters(table=[[10, 5], [10, 1]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.1973244147157192,
+              conditional_odds_ratio=0.2116112781158479,
+              conditional_odds_ratio_ci=(0.0007743881879531337,
+                                         4.170192301163831))),
+    (Parameters(table=[[10, 5], [10, 0]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.06126482213438735,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         2.642491011905582))),
+    (Parameters(table=[[5, 0], [1, 4]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.04761904761904762,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0.496935393325443,
+                                         Inf))),
+    (Parameters(table=[[0, 5], [1, 4]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         198.019801980198))),
+    (Parameters(table=[[5, 1], [0, 4]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.04761904761904761,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0.496935393325443,
+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         198.019801980198))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=2.005657880389071e-122,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(270.0334165523604,
+                                         5461.333333326708))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=5.728437460831947e-44,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(116.7944750275836,
+                                         1931.995993191814))),
+    (Parameters(table=[[190, 800], [200, 900]],
+                confidence_level=0.99,
+                alternative='two.sided'),
+     RResults(pvalue=0.574111858126088,
+              conditional_odds_ratio=1.068697577856801,
+              conditional_odds_ratio_ci=(0.7949398282935892,
+                                         1.436229679394333))),
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.1300759363430016,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0,
+                                         1.797867027270803))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.0185217259520665,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0,
+                                         0.6785254803404526))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.9782608695652173,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0,
+                                         127.8497388102893))),
+    (Parameters(table=[[5, 15], [20, 20]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.05625775074399956,
+              conditional_odds_ratio=0.3394396617440851,
+              conditional_odds_ratio_ci=(0,
+                                         1.032332939718425))),
+    (Parameters(table=[[5, 16], [16, 25]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.1808979350599346,
+              conditional_odds_ratio=0.4937791394540491,
+              conditional_odds_ratio_ci=(0,
+                                         1.502407513296985))),
+    (Parameters(table=[[10, 5], [10, 1]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.1652173913043479,
+              conditional_odds_ratio=0.2116112781158479,
+              conditional_odds_ratio_ci=(0,
+                                         1.820421051562392))),
+    (Parameters(table=[[10, 5], [10, 0]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.0565217391304348,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         1.06224603077045))),
+    (Parameters(table=[[5, 0], [1, 4]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[0, 5], [1, 4]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.5,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         19.00192394479939))),
+    (Parameters(table=[[5, 1], [0, 4]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.4999999999999999,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         19.00192394479939))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(0,
+                                         3045.460216525746))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(0,
+                                         1186.440170942579))),
+    (Parameters(table=[[190, 800], [200, 900]],
+                confidence_level=0.95,
+                alternative='less'),
+     RResults(pvalue=0.7416227010368963,
+              conditional_odds_ratio=1.068697577856801,
+              conditional_odds_ratio_ci=(0,
+                                         1.293551891610822))),
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.1300759363430016,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0,
+                                         4.375946050832565))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.0185217259520665,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0,
+                                         1.235282118191202))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.9782608695652173,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0,
+                                         657.2063583945989))),
+    (Parameters(table=[[5, 15], [20, 20]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.05625775074399956,
+              conditional_odds_ratio=0.3394396617440851,
+              conditional_odds_ratio_ci=(0,
+                                         1.498867660683128))),
+    (Parameters(table=[[5, 16], [16, 25]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.1808979350599346,
+              conditional_odds_ratio=0.4937791394540491,
+              conditional_odds_ratio_ci=(0,
+                                         2.186159386716762))),
+    (Parameters(table=[[10, 5], [10, 1]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.1652173913043479,
+              conditional_odds_ratio=0.2116112781158479,
+              conditional_odds_ratio_ci=(0,
+                                         3.335351451901569))),
+    (Parameters(table=[[10, 5], [10, 0]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.0565217391304348,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         2.075407697450433))),
+    (Parameters(table=[[5, 0], [1, 4]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[0, 5], [1, 4]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.5,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         99.00009507969122))),
+    (Parameters(table=[[5, 1], [0, 4]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.4999999999999999,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         99.00009507969123))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(0,
+                                         4503.078257659934))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(0,
+                                         1811.766127544222))),
+    (Parameters(table=[[190, 800], [200, 900]],
+                confidence_level=0.99,
+                alternative='less'),
+     RResults(pvalue=0.7416227010368963,
+              conditional_odds_ratio=1.068697577856801,
+              conditional_odds_ratio_ci=(0,
+                                         1.396522811516685))),
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.979790445314723,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0.05119649909830196,
+                                         Inf))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.9990149169715733,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0.007163749169069961,
+                                         Inf))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.1652173913043478,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0.5493234651081089,
+                                         Inf))),
+    (Parameters(table=[[5, 15], [20, 20]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.9849086665340765,
+              conditional_odds_ratio=0.3394396617440851,
+              conditional_odds_ratio_ci=(0.1003538933958604,
+                                         Inf))),
+    (Parameters(table=[[5, 16], [16, 25]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.9330176609214881,
+              conditional_odds_ratio=0.4937791394540491,
+              conditional_odds_ratio_ci=(0.146507416280863,
+                                         Inf))),
+    (Parameters(table=[[10, 5], [10, 1]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.9782608695652174,
+              conditional_odds_ratio=0.2116112781158479,
+              conditional_odds_ratio_ci=(0.007821681994077808,
+                                         Inf))),
+    (Parameters(table=[[10, 5], [10, 0]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[5, 0], [1, 4]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.02380952380952382,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(1.487678929918272,
+                                         Inf))),
+    (Parameters(table=[[0, 5], [1, 4]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[5, 1], [0, 4]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.0238095238095238,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(1.487678929918272,
+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=2.005657880388915e-122,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(397.784359748113,
+                                         Inf))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=5.728437460831983e-44,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(174.7148056880929,
+                                         Inf))),
+    (Parameters(table=[[190, 800], [200, 900]],
+                confidence_level=0.95,
+                alternative='greater'),
+     RResults(pvalue=0.2959825901308897,
+              conditional_odds_ratio=1.068697577856801,
+              conditional_odds_ratio_ci=(0.8828406663967776,
+                                         Inf))),
+    (Parameters(table=[[100, 2], [1000, 5]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.979790445314723,
+              conditional_odds_ratio=0.25055839934223,
+              conditional_odds_ratio_ci=(0.03045407081240429,
+                                         Inf))),
+    (Parameters(table=[[2, 7], [8, 2]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.9990149169715733,
+              conditional_odds_ratio=0.0858623513573622,
+              conditional_odds_ratio_ci=(0.002768053063547901,
+                                         Inf))),
+    (Parameters(table=[[5, 1], [10, 10]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.1652173913043478,
+              conditional_odds_ratio=4.725646047336587,
+              conditional_odds_ratio_ci=(0.2998184792279909,
+                                         Inf))),
+    (Parameters(table=[[5, 15], [20, 20]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.9849086665340765,
+              conditional_odds_ratio=0.3394396617440851,
+              conditional_odds_ratio_ci=(0.06180414342643172,
+                                         Inf))),
+    (Parameters(table=[[5, 16], [16, 25]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.9330176609214881,
+              conditional_odds_ratio=0.4937791394540491,
+              conditional_odds_ratio_ci=(0.09037094010066403,
+                                         Inf))),
+    (Parameters(table=[[10, 5], [10, 1]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.9782608695652174,
+              conditional_odds_ratio=0.2116112781158479,
+              conditional_odds_ratio_ci=(0.001521592095430679,
+                                         Inf))),
+    (Parameters(table=[[10, 5], [10, 0]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[5, 0], [1, 4]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.02380952380952382,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0.6661157890359722,
+                                         Inf))),
+    (Parameters(table=[[0, 5], [1, 4]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[5, 1], [0, 4]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.0238095238095238,
+              conditional_odds_ratio=Inf,
+              conditional_odds_ratio_ci=(0.6661157890359725,
+                                         Inf))),
+    (Parameters(table=[[0, 1], [3, 2]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=1,
+              conditional_odds_ratio=0,
+              conditional_odds_ratio_ci=(0,
+                                         Inf))),
+    (Parameters(table=[[200, 7], [8, 300]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=2.005657880388915e-122,
+              conditional_odds_ratio=977.7866978606228,
+              conditional_odds_ratio_ci=(297.9619252357688,
+                                         Inf))),
+    (Parameters(table=[[28, 21], [6, 1957]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=5.728437460831983e-44,
+              conditional_odds_ratio=425.2403028434684,
+              conditional_odds_ratio_ci=(130.3213490295859,
+                                         Inf))),
+    (Parameters(table=[[190, 800], [200, 900]],
+                confidence_level=0.99,
+                alternative='greater'),
+     RResults(pvalue=0.2959825901308897,
+              conditional_odds_ratio=1.068697577856801,
+              conditional_odds_ratio_ci=(0.8176272148267533,
+                                         Inf))),
+]
diff --git a/shared/package-files/04/04acea9c3302c0035580d61330a20bbcfbc19ea243058d2b33b78f518c358d1a/thnn.py b/shared/package-files/04/04acea9c3302c0035580d61330a20bbcfbc19ea243058d2b33b78f518c358d1a/thnn.py
new file mode 100644
index 0000000000000000000000000000000000000000..5250b4bff1674880c97be7b36ca81d6cd6b665a4
--- /dev/null
+++ b/shared/package-files/04/04acea9c3302c0035580d61330a20bbcfbc19ea243058d2b33b78f518c358d1a/thnn.py
@@ -0,0 +1,4 @@
+# this is for historical pickle deserialization, it is not used otherwise
+
+def _get_thnn_function_backend():
+    pass
diff --git a/shared/package-files/04/04ae8993c19e4bc824d26a1b751b197ee0654a48d5b5d96e9c2054e8b6807861/_nested_tensor_from_mask.h b/shared/package-files/04/04ae8993c19e4bc824d26a1b751b197ee0654a48d5b5d96e9c2054e8b6807861/_nested_tensor_from_mask.h
new file mode 100644
index 0000000000000000000000000000000000000000..4acb55513c96e2a08f9793de81e9e0736d022b39
--- /dev/null
+++ b/shared/package-files/04/04ae8993c19e4bc824d26a1b751b197ee0654a48d5b5d96e9c2054e8b6807861/_nested_tensor_from_mask.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::_nested_tensor_from_mask(Tensor t, Tensor mask, bool mask_check=True) -> Tensor
+inline at::Tensor _nested_tensor_from_mask(const at::Tensor & t, const at::Tensor & mask, bool mask_check=true) {
+    return at::_ops::_nested_tensor_from_mask::call(t, mask, mask_check);
+}
+
+// aten::_nested_tensor_from_mask.out(Tensor t, Tensor mask, bool mask_check=True, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _nested_tensor_from_mask_out(at::Tensor & out, const at::Tensor & t, const at::Tensor & mask, bool mask_check=true) {
+    return at::_ops::_nested_tensor_from_mask_out::call(t, mask, mask_check, out);
+}
+// aten::_nested_tensor_from_mask.out(Tensor t, Tensor mask, bool mask_check=True, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & _nested_tensor_from_mask_outf(const at::Tensor & t, const at::Tensor & mask, bool mask_check, at::Tensor & out) {
+    return at::_ops::_nested_tensor_from_mask_out::call(t, mask, mask_check, out);
+}
+
+}
diff --git a/shared/package-files/04/04afa11df208ae409ea7e8cd992e7f0b0ab9a044afb2f5f757fd170c0913d4e9/test_exec_command.py b/shared/package-files/04/04afa11df208ae409ea7e8cd992e7f0b0ab9a044afb2f5f757fd170c0913d4e9/test_exec_command.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1a20056a5a2a78a76cf36d1bde31a0e82cbb873
--- /dev/null
+++ b/shared/package-files/04/04afa11df208ae409ea7e8cd992e7f0b0ab9a044afb2f5f757fd170c0913d4e9/test_exec_command.py
@@ -0,0 +1,217 @@
+import os
+import pytest
+import sys
+from tempfile import TemporaryFile
+
+from numpy.distutils import exec_command
+from numpy.distutils.exec_command import get_pythonexe
+from numpy.testing import tempdir, assert_, assert_warns, IS_WASM
+
+
+# In python 3 stdout, stderr are text (unicode compliant) devices, so to
+# emulate them import StringIO from the io module.
+from io import StringIO
+
+class redirect_stdout:
+    """Context manager to redirect stdout for exec_command test."""
+    def __init__(self, stdout=None):
+        self._stdout = stdout or sys.stdout
+
+    def __enter__(self):
+        self.old_stdout = sys.stdout
+        sys.stdout = self._stdout
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self._stdout.flush()
+        sys.stdout = self.old_stdout
+        # note: closing sys.stdout won't close it.
+        self._stdout.close()
+
+class redirect_stderr:
+    """Context manager to redirect stderr for exec_command test."""
+    def __init__(self, stderr=None):
+        self._stderr = stderr or sys.stderr
+
+    def __enter__(self):
+        self.old_stderr = sys.stderr
+        sys.stderr = self._stderr
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self._stderr.flush()
+        sys.stderr = self.old_stderr
+        # note: closing sys.stderr won't close it.
+        self._stderr.close()
+
+class emulate_nonposix:
+    """Context manager to emulate os.name != 'posix' """
+    def __init__(self, osname='non-posix'):
+        self._new_name = osname
+
+    def __enter__(self):
+        self._old_name = os.name
+        os.name = self._new_name
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        os.name = self._old_name
+
+
+def test_exec_command_stdout():
+    # Regression test for gh-2999 and gh-2915.
+    # There are several packages (nose, scipy.weave.inline, Sage inline
+    # Fortran) that replace stdout, in which case it doesn't have a fileno
+    # method.  This is tested here, with a do-nothing command that fails if the
+    # presence of fileno() is assumed in exec_command.
+
+    # The code has a special case for posix systems, so if we are on posix test
+    # both that the special case works and that the generic code works.
+
+    # Test posix version:
+    with redirect_stdout(StringIO()):
+        with redirect_stderr(TemporaryFile()):
+            with assert_warns(DeprecationWarning):
+                exec_command.exec_command("cd '.'")
+
+    if os.name == 'posix':
+        # Test general (non-posix) version:
+        with emulate_nonposix():
+            with redirect_stdout(StringIO()):
+                with redirect_stderr(TemporaryFile()):
+                    with assert_warns(DeprecationWarning):
+                        exec_command.exec_command("cd '.'")
+
+def test_exec_command_stderr():
+    # Test posix version:
+    with redirect_stdout(TemporaryFile(mode='w+')):
+        with redirect_stderr(StringIO()):
+            with assert_warns(DeprecationWarning):
+                exec_command.exec_command("cd '.'")
+
+    if os.name == 'posix':
+        # Test general (non-posix) version:
+        with emulate_nonposix():
+            with redirect_stdout(TemporaryFile()):
+                with redirect_stderr(StringIO()):
+                    with assert_warns(DeprecationWarning):
+                        exec_command.exec_command("cd '.'")
+
+
+@pytest.mark.skipif(IS_WASM, reason="Cannot start subprocess")
+class TestExecCommand:
+    def setup_method(self):
+        self.pyexe = get_pythonexe()
+
+    def check_nt(self, **kws):
+        s, o = exec_command.exec_command('cmd /C echo path=%path%')
+        assert_(s == 0)
+        assert_(o != '')
+
+        s, o = exec_command.exec_command(
+         '"%s" -c "import sys;sys.stderr.write(sys.platform)"' % self.pyexe)
+        assert_(s == 0)
+        assert_(o == 'win32')
+
+    def check_posix(self, **kws):
+        s, o = exec_command.exec_command("echo Hello", **kws)
+        assert_(s == 0)
+        assert_(o == 'Hello')
+
+        s, o = exec_command.exec_command('echo $AAA', **kws)
+        assert_(s == 0)
+        assert_(o == '')
+
+        s, o = exec_command.exec_command('echo "$AAA"', AAA='Tere', **kws)
+        assert_(s == 0)
+        assert_(o == 'Tere')
+
+        s, o = exec_command.exec_command('echo "$AAA"', **kws)
+        assert_(s == 0)
+        assert_(o == '')
+
+        if 'BBB' not in os.environ:
+            os.environ['BBB'] = 'Hi'
+            s, o = exec_command.exec_command('echo "$BBB"', **kws)
+            assert_(s == 0)
+            assert_(o == 'Hi')
+
+            s, o = exec_command.exec_command('echo "$BBB"', BBB='Hey', **kws)
+            assert_(s == 0)
+            assert_(o == 'Hey')
+
+            s, o = exec_command.exec_command('echo "$BBB"', **kws)
+            assert_(s == 0)
+            assert_(o == 'Hi')
+
+            del os.environ['BBB']
+
+            s, o = exec_command.exec_command('echo "$BBB"', **kws)
+            assert_(s == 0)
+            assert_(o == '')
+
+
+        s, o = exec_command.exec_command('this_is_not_a_command', **kws)
+        assert_(s != 0)
+        assert_(o != '')
+
+        s, o = exec_command.exec_command('echo path=$PATH', **kws)
+        assert_(s == 0)
+        assert_(o != '')
+
+        s, o = exec_command.exec_command(
+             '"%s" -c "import sys,os;sys.stderr.write(os.name)"' %
+             self.pyexe, **kws)
+        assert_(s == 0)
+        assert_(o == 'posix')
+
+    def check_basic(self, *kws):
+        s, o = exec_command.exec_command(
+                     '"%s" -c "raise \'Ignore me.\'"' % self.pyexe, **kws)
+        assert_(s != 0)
+        assert_(o != '')
+
+        s, o = exec_command.exec_command(
+             '"%s" -c "import sys;sys.stderr.write(\'0\');'
+             'sys.stderr.write(\'1\');sys.stderr.write(\'2\')"' %
+             self.pyexe, **kws)
+        assert_(s == 0)
+        assert_(o == '012')
+
+        s, o = exec_command.exec_command(
+                 '"%s" -c "import sys;sys.exit(15)"' % self.pyexe, **kws)
+        assert_(s == 15)
+        assert_(o == '')
+
+        s, o = exec_command.exec_command(
+                     '"%s" -c "print(\'Heipa\'")' % self.pyexe, **kws)
+        assert_(s == 0)
+        assert_(o == 'Heipa')
+
+    def check_execute_in(self, **kws):
+        with tempdir() as tmpdir:
+            fn = "file"
+            tmpfile = os.path.join(tmpdir, fn)
+            with open(tmpfile, 'w') as f:
+                f.write('Hello')
+
+            s, o = exec_command.exec_command(
+                 '"%s" -c "f = open(\'%s\', \'r\'); f.close()"' %
+                 (self.pyexe, fn), **kws)
+            assert_(s != 0)
+            assert_(o != '')
+            s, o = exec_command.exec_command(
+                     '"%s" -c "f = open(\'%s\', \'r\'); print(f.read()); '
+                     'f.close()"' % (self.pyexe, fn), execute_in=tmpdir, **kws)
+            assert_(s == 0)
+            assert_(o == 'Hello')
+
+    def test_basic(self):
+        with redirect_stdout(StringIO()):
+            with redirect_stderr(StringIO()):
+                with assert_warns(DeprecationWarning):
+                    if os.name == "posix":
+                        self.check_posix(use_tee=0)
+                        self.check_posix(use_tee=1)
+                    elif os.name == "nt":
+                        self.check_nt(use_tee=0)
+                        self.check_nt(use_tee=1)
+                    self.check_execute_in(use_tee=0)
+                    self.check_execute_in(use_tee=1)
diff --git a/shared/package-files/04/04afdc41fb8f8c9564e6107c019afba3481e3641c0ed1d720c9bcbc3a1d0b6e2/_unicodetype_db.h b/shared/package-files/04/04afdc41fb8f8c9564e6107c019afba3481e3641c0ed1d720c9bcbc3a1d0b6e2/_unicodetype_db.h
new file mode 100644
index 0000000000000000000000000000000000000000..d4dca060d776ed479d272cdd7514d95a54839724
--- /dev/null
+++ b/shared/package-files/04/04afdc41fb8f8c9564e6107c019afba3481e3641c0ed1d720c9bcbc3a1d0b6e2/_unicodetype_db.h
@@ -0,0 +1,6091 @@
+/* This file is from CPython:
+ * https://github.com/python/cpython/blob/3.7/Objects/unicodetype_db.h
+ * As of Commit SHA: 1d4b6ba19466aba0eb91c4ba01ba509acf18c723
+ *
+ * Changes made include:
+ *  - Renaming all functions and structures with a `numba` prefix to prevent
+ *    collisions.
+ *
+ * NOTE: Numba devs, this may need updating from time to time as the unicode
+ * standard is updated.
+ */
+
+#ifndef _UNICODETYPE_DB_H
+#define _UNICODETYPE_DB_H
+
+/*Py_UCS4 definition from Include/unicodeobject.h */
+#define Py_UCS4 uint32_t
+
+typedef struct {
+    /*
+       These are either deltas to the character or offsets in
+       _PyUnicode_ExtendedCase.
+    */
+    const int upper;
+    const int lower;
+    const int title;
+    /* Note if more flag space is needed, decimal and digit could be unified. */
+    const unsigned char decimal;
+    const unsigned char digit;
+    const unsigned short flags;
+} numba_PyUnicode_TypeRecord;
+
+/* -------------------------------------------------------------------------- */
+/* CPython unicodetype_db.h definitions start here                            */
+/* -------------------------------------------------------------------------- */
+
+/* this file was generated by Tools/unicode/makeunicodedata.py 3.2 */
+
+/* a list of unique character type descriptors */
+const numba_PyUnicode_TypeRecord numba_PyUnicode_TypeRecords[] = {
+    {0, 0, 0, 0, 0, 0},
+    {0, 0, 0, 0, 0, 0},
+    {0, 0, 0, 0, 0, 32},
+    {0, 0, 0, 0, 0, 48},
+    {0, 0, 0, 0, 0, 1056},
+    {0, 0, 0, 0, 0, 1024},
+    {0, 0, 0, 0, 0, 5120},
+    {0, 0, 0, 0, 0, 3590},
+    {0, 0, 0, 1, 1, 3590},
+    {0, 0, 0, 2, 2, 3590},
+    {0, 0, 0, 3, 3, 3590},
+    {0, 0, 0, 4, 4, 3590},
+    {0, 0, 0, 5, 5, 3590},
+    {0, 0, 0, 6, 6, 3590},
+    {0, 0, 0, 7, 7, 3590},
+    {0, 0, 0, 8, 8, 3590},
+    {0, 0, 0, 9, 9, 3590},
+    {0, 32, 0, 0, 0, 10113},
+    {0, 0, 0, 0, 0, 1536},
+    {-32, 0, -32, 0, 0, 9993},
+    {0, 0, 0, 0, 0, 9993},
+    {0, 0, 0, 0, 0, 4096},
+    {0, 0, 0, 0, 2, 3076},
+    {0, 0, 0, 0, 3, 3076},
+    {16777218, 17825792, 16777218, 0, 0, 26377},
+    {0, 0, 0, 0, 0, 5632},
+    {0, 0, 0, 0, 1, 3076},
+    {0, 0, 0, 0, 0, 3072},
+    {33554438, 18874371, 33554440, 0, 0, 26377},
+    {121, 0, 121, 0, 0, 9993},
+    {0, 1, 0, 0, 0, 10113},
+    {-1, 0, -1, 0, 0, 9993},
+    {16777228, 33554442, 16777228, 0, 0, 26497},
+    {-232, 0, -232, 0, 0, 9993},
+    {33554448, 18874381, 33554448, 0, 0, 26377},
+    {0, -121, 0, 0, 0, 10113},
+    {16777236, 17825810, 16777236, 0, 0, 26377},
+    {195, 0, 195, 0, 0, 9993},
+    {0, 210, 0, 0, 0, 10113},
+    {0, 206, 0, 0, 0, 10113},
+    {0, 205, 0, 0, 0, 10113},
+    {0, 79, 0, 0, 0, 10113},
+    {0, 202, 0, 0, 0, 10113},
+    {0, 203, 0, 0, 0, 10113},
+    {0, 207, 0, 0, 0, 10113},
+    {97, 0, 97, 0, 0, 9993},
+    {0, 211, 0, 0, 0, 10113},
+    {0, 209, 0, 0, 0, 10113},
+    {163, 0, 163, 0, 0, 9993},
+    {0, 213, 0, 0, 0, 10113},
+    {130, 0, 130, 0, 0, 9993},
+    {0, 214, 0, 0, 0, 10113},
+    {0, 218, 0, 0, 0, 10113},
+    {0, 217, 0, 0, 0, 10113},
+    {0, 219, 0, 0, 0, 10113},
+    {0, 0, 0, 0, 0, 1793},
+    {56, 0, 56, 0, 0, 9993},
+    {0, 2, 1, 0, 0, 10113},
+    {-1, 1, 0, 0, 0, 10049},
+    {-2, 0, -1, 0, 0, 9993},
+    {-79, 0, -79, 0, 0, 9993},
+    {33554456, 18874389, 33554456, 0, 0, 26377},
+    {0, -97, 0, 0, 0, 10113},
+    {0, -56, 0, 0, 0, 10113},
+    {0, -130, 0, 0, 0, 10113},
+    {0, 10795, 0, 0, 0, 10113},
+    {0, -163, 0, 0, 0, 10113},
+    {0, 10792, 0, 0, 0, 10113},
+    {10815, 0, 10815, 0, 0, 9993},
+    {0, -195, 0, 0, 0, 10113},
+    {0, 69, 0, 0, 0, 10113},
+    {0, 71, 0, 0, 0, 10113},
+    {10783, 0, 10783, 0, 0, 9993},
+    {10780, 0, 10780, 0, 0, 9993},
+    {10782, 0, 10782, 0, 0, 9993},
+    {-210, 0, -210, 0, 0, 9993},
+    {-206, 0, -206, 0, 0, 9993},
+    {-205, 0, -205, 0, 0, 9993},
+    {-202, 0, -202, 0, 0, 9993},
+    {-203, 0, -203, 0, 0, 9993},
+    {42319, 0, 42319, 0, 0, 9993},
+    {42315, 0, 42315, 0, 0, 9993},
+    {-207, 0, -207, 0, 0, 9993},
+    {42280, 0, 42280, 0, 0, 9993},
+    {42308, 0, 42308, 0, 0, 9993},
+    {-209, 0, -209, 0, 0, 9993},
+    {-211, 0, -211, 0, 0, 9993},
+    {10743, 0, 10743, 0, 0, 9993},
+    {42305, 0, 42305, 0, 0, 9993},
+    {10749, 0, 10749, 0, 0, 9993},
+    {-213, 0, -213, 0, 0, 9993},
+    {-214, 0, -214, 0, 0, 9993},
+    {10727, 0, 10727, 0, 0, 9993},
+    {-218, 0, -218, 0, 0, 9993},
+    {42282, 0, 42282, 0, 0, 9993},
+    {-69, 0, -69, 0, 0, 9993},
+    {-217, 0, -217, 0, 0, 9993},
+    {-71, 0, -71, 0, 0, 9993},
+    {-219, 0, -219, 0, 0, 9993},
+    {42261, 0, 42261, 0, 0, 9993},
+    {42258, 0, 42258, 0, 0, 9993},
+    {0, 0, 0, 0, 0, 14089},
+    {0, 0, 0, 0, 0, 5889},
+    {16777244, 17825818, 16777244, 0, 0, 30216},
+    {0, 0, 0, 0, 0, 13321},
+    {0, 116, 0, 0, 0, 10113},
+    {0, 38, 0, 0, 0, 10113},
+    {0, 37, 0, 0, 0, 10113},
+    {0, 64, 0, 0, 0, 10113},
+    {0, 63, 0, 0, 0, 10113},
+    {50331681, 19922973, 50331681, 0, 0, 26377},
+    {-38, 0, -38, 0, 0, 9993},
+    {-37, 0, -37, 0, 0, 9993},
+    {50331688, 19922980, 50331688, 0, 0, 26377},
+    {16777261, 17825835, 16777261, 0, 0, 26377},
+    {-64, 0, -64, 0, 0, 9993},
+    {-63, 0, -63, 0, 0, 9993},
+    {0, 8, 0, 0, 0, 10113},
+    {16777264, 17825838, 16777264, 0, 0, 26377},
+    {16777267, 17825841, 16777267, 0, 0, 26377},
+    {0, 0, 0, 0, 0, 10113},
+    {16777270, 17825844, 16777270, 0, 0, 26377},
+    {16777273, 17825847, 16777273, 0, 0, 26377},
+    {-8, 0, -8, 0, 0, 9993},
+    {16777276, 17825850, 16777276, 0, 0, 26377},
+    {16777279, 17825853, 16777279, 0, 0, 26377},
+    {7, 0, 7, 0, 0, 9993},
+    {-116, 0, -116, 0, 0, 9993},
+    {0, -60, 0, 0, 0, 10113},
+    {16777282, 17825856, 16777282, 0, 0, 26377},
+    {0, -7, 0, 0, 0, 10113},
+    {0, 80, 0, 0, 0, 10113},
+    {-80, 0, -80, 0, 0, 9993},
+    {0, 15, 0, 0, 0, 10113},
+    {-15, 0, -15, 0, 0, 9993},
+    {0, 48, 0, 0, 0, 10113},
+    {-48, 0, -48, 0, 0, 9993},
+    {33554502, 18874435, 33554504, 0, 0, 26377},
+    {0, 0, 0, 0, 0, 1537},
+    {0, 7264, 0, 0, 0, 10113},
+    {3008, 0, 0, 0, 0, 9993},
+    {0, 0, 0, 0, 1, 3588},
+    {0, 0, 0, 0, 2, 3588},
+    {0, 0, 0, 0, 3, 3588},
+    {0, 0, 0, 0, 4, 3588},
+    {0, 0, 0, 0, 5, 3588},
+    {0, 0, 0, 0, 6, 3588},
+    {0, 0, 0, 0, 7, 3588},
+    {0, 0, 0, 0, 8, 3588},
+    {0, 0, 0, 0, 9, 3588},
+    {16777292, 17825866, 16777292, 0, 0, 26497},
+    {16777295, 17825869, 16777295, 0, 0, 26497},
+    {16777298, 17825872, 16777298, 0, 0, 26497},
+    {16777301, 17825875, 16777301, 0, 0, 26497},
+    {16777304, 17825878, 16777304, 0, 0, 26497},
+    {16777307, 17825881, 16777307, 0, 0, 26497},
+    {16777310, 17825884, 16777310, 0, 0, 26497},
+    {16777313, 17825887, 16777313, 0, 0, 26497},
+    {16777316, 17825890, 16777316, 0, 0, 26497},
+    {16777319, 17825893, 16777319, 0, 0, 26497},
+    {16777322, 17825896, 16777322, 0, 0, 26497},
+    {16777325, 17825899, 16777325, 0, 0, 26497},
+    {16777328, 17825902, 16777328, 0, 0, 26497},
+    {16777331, 17825905, 16777331, 0, 0, 26497},
+    {16777334, 17825908, 16777334, 0, 0, 26497},
+    {16777337, 17825911, 16777337, 0, 0, 26497},
+    {16777340, 17825914, 16777340, 0, 0, 26497},
+    {16777343, 17825917, 16777343, 0, 0, 26497},
+    {16777346, 17825920, 16777346, 0, 0, 26497},
+    {16777349, 17825923, 16777349, 0, 0, 26497},
+    {16777352, 17825926, 16777352, 0, 0, 26497},
+    {16777355, 17825929, 16777355, 0, 0, 26497},
+    {16777358, 17825932, 16777358, 0, 0, 26497},
+    {16777361, 17825935, 16777361, 0, 0, 26497},
+    {16777364, 17825938, 16777364, 0, 0, 26497},
+    {16777367, 17825941, 16777367, 0, 0, 26497},
+    {16777370, 17825944, 16777370, 0, 0, 26497},
+    {16777373, 17825947, 16777373, 0, 0, 26497},
+    {16777376, 17825950, 16777376, 0, 0, 26497},
+    {16777379, 17825953, 16777379, 0, 0, 26497},
+    {16777382, 17825956, 16777382, 0, 0, 26497},
+    {16777385, 17825959, 16777385, 0, 0, 26497},
+    {16777388, 17825962, 16777388, 0, 0, 26497},
+    {16777391, 17825965, 16777391, 0, 0, 26497},
+    {16777394, 17825968, 16777394, 0, 0, 26497},
+    {16777397, 17825971, 16777397, 0, 0, 26497},
+    {16777400, 17825974, 16777400, 0, 0, 26497},
+    {16777403, 17825977, 16777403, 0, 0, 26497},
+    {16777406, 17825980, 16777406, 0, 0, 26497},
+    {16777409, 17825983, 16777409, 0, 0, 26497},
+    {16777412, 17825986, 16777412, 0, 0, 26497},
+    {16777415, 17825989, 16777415, 0, 0, 26497},
+    {16777418, 17825992, 16777418, 0, 0, 26497},
+    {16777421, 17825995, 16777421, 0, 0, 26497},
+    {16777424, 17825998, 16777424, 0, 0, 26497},
+    {16777427, 17826001, 16777427, 0, 0, 26497},
+    {16777430, 17826004, 16777430, 0, 0, 26497},
+    {16777433, 17826007, 16777433, 0, 0, 26497},
+    {16777436, 17826010, 16777436, 0, 0, 26497},
+    {16777439, 17826013, 16777439, 0, 0, 26497},
+    {16777442, 17826016, 16777442, 0, 0, 26497},
+    {16777445, 17826019, 16777445, 0, 0, 26497},
+    {16777448, 17826022, 16777448, 0, 0, 26497},
+    {16777451, 17826025, 16777451, 0, 0, 26497},
+    {16777454, 17826028, 16777454, 0, 0, 26497},
+    {16777457, 17826031, 16777457, 0, 0, 26497},
+    {16777460, 17826034, 16777460, 0, 0, 26497},
+    {16777463, 17826037, 16777463, 0, 0, 26497},
+    {16777466, 17826040, 16777466, 0, 0, 26497},
+    {16777469, 17826043, 16777469, 0, 0, 26497},
+    {16777472, 17826046, 16777472, 0, 0, 26497},
+    {16777475, 17826049, 16777475, 0, 0, 26497},
+    {16777478, 17826052, 16777478, 0, 0, 26497},
+    {16777481, 17826055, 16777481, 0, 0, 26497},
+    {16777484, 17826058, 16777484, 0, 0, 26497},
+    {16777487, 17826061, 16777487, 0, 0, 26497},
+    {16777490, 17826064, 16777490, 0, 0, 26497},
+    {16777493, 17826067, 16777493, 0, 0, 26497},
+    {16777496, 17826070, 16777496, 0, 0, 26497},
+    {16777499, 17826073, 16777499, 0, 0, 26497},
+    {16777502, 17826076, 16777502, 0, 0, 26497},
+    {16777505, 17826079, 16777505, 0, 0, 26497},
+    {16777508, 17826082, 16777508, 0, 0, 26497},
+    {16777511, 17826085, 16777511, 0, 0, 26497},
+    {16777514, 17826088, 16777514, 0, 0, 26497},
+    {16777517, 17826091, 16777517, 0, 0, 26497},
+    {16777520, 17826094, 16777520, 0, 0, 26497},
+    {16777523, 17826097, 16777523, 0, 0, 26497},
+    {16777526, 17826100, 16777526, 0, 0, 26497},
+    {16777529, 17826103, 16777529, 0, 0, 26497},
+    {16777532, 17826106, 16777532, 0, 0, 26497},
+    {16777535, 17826109, 16777535, 0, 0, 26497},
+    {16777538, 17826112, 16777538, 0, 0, 26497},
+    {16777541, 17826115, 16777541, 0, 0, 26497},
+    {16777544, 17826118, 16777544, 0, 0, 26497},
+    {16777547, 17826121, 16777547, 0, 0, 26497},
+    {16777550, 17826124, 16777550, 0, 0, 26377},
+    {16777553, 17826127, 16777553, 0, 0, 26377},
+    {16777556, 17826130, 16777556, 0, 0, 26377},
+    {16777559, 17826133, 16777559, 0, 0, 26377},
+    {16777562, 17826136, 16777562, 0, 0, 26377},
+    {16777565, 17826139, 16777565, 0, 0, 26377},
+    {0, 0, 0, 0, 0, 3840},
+    {0, 0, 0, 0, 0, 5888},
+    {16777568, 17826142, 16777568, 0, 0, 26377},
+    {16777571, 17826145, 16777571, 0, 0, 26377},
+    {16777574, 17826148, 16777574, 0, 0, 26377},
+    {16777577, 17826151, 16777577, 0, 0, 26377},
+    {16777580, 17826154, 16777580, 0, 0, 26377},
+    {16777583, 17826157, 16777583, 0, 0, 26377},
+    {16777586, 17826160, 16777586, 0, 0, 26377},
+    {16777589, 17826163, 16777589, 0, 0, 26377},
+    {16777592, 17826166, 16777592, 0, 0, 26377},
+    {0, -3008, 0, 0, 0, 10113},
+    {35332, 0, 35332, 0, 0, 9993},
+    {3814, 0, 3814, 0, 0, 9993},
+    {33554812, 18874745, 33554812, 0, 0, 26377},
+    {33554817, 18874750, 33554817, 0, 0, 26377},
+    {33554822, 18874755, 33554822, 0, 0, 26377},
+    {33554827, 18874760, 33554827, 0, 0, 26377},
+    {33554832, 18874765, 33554832, 0, 0, 26377},
+    {16777620, 17826194, 16777620, 0, 0, 26377},
+    {16777624, 18874773, 16777624, 0, 0, 26497},
+    {8, 0, 8, 0, 0, 9993},
+    {0, -8, 0, 0, 0, 10113},
+    {33554844, 18874777, 33554844, 0, 0, 26377},
+    {50332066, 19923358, 50332066, 0, 0, 26377},
+    {50332073, 19923365, 50332073, 0, 0, 26377},
+    {50332080, 19923372, 50332080, 0, 0, 26377},
+    {74, 0, 74, 0, 0, 9993},
+    {86, 0, 86, 0, 0, 9993},
+    {100, 0, 100, 0, 0, 9993},
+    {128, 0, 128, 0, 0, 9993},
+    {112, 0, 112, 0, 0, 9993},
+    {126, 0, 126, 0, 0, 9993},
+    {33554870, 18874803, 16777656, 0, 0, 26377},
+    {33554876, 18874809, 16777662, 0, 0, 26377},
+    {33554882, 18874815, 16777668, 0, 0, 26377},
+    {33554888, 18874821, 16777674, 0, 0, 26377},
+    {33554894, 18874827, 16777680, 0, 0, 26377},
+    {33554900, 18874833, 16777686, 0, 0, 26377},
+    {33554906, 18874839, 16777692, 0, 0, 26377},
+    {33554912, 18874845, 16777698, 0, 0, 26377},
+    {33554918, 18874851, 16777704, 0, 0, 26433},
+    {33554924, 18874857, 16777710, 0, 0, 26433},
+    {33554930, 18874863, 16777716, 0, 0, 26433},
+    {33554936, 18874869, 16777722, 0, 0, 26433},
+    {33554942, 18874875, 16777728, 0, 0, 26433},
+    {33554948, 18874881, 16777734, 0, 0, 26433},
+    {33554954, 18874887, 16777740, 0, 0, 26433},
+    {33554960, 18874893, 16777746, 0, 0, 26433},
+    {33554966, 18874899, 16777752, 0, 0, 26377},
+    {33554972, 18874905, 16777758, 0, 0, 26377},
+    {33554978, 18874911, 16777764, 0, 0, 26377},
+    {33554984, 18874917, 16777770, 0, 0, 26377},
+    {33554990, 18874923, 16777776, 0, 0, 26377},
+    {33554996, 18874929, 16777782, 0, 0, 26377},
+    {33555002, 18874935, 16777788, 0, 0, 26377},
+    {33555008, 18874941, 16777794, 0, 0, 26377},
+    {33555014, 18874947, 16777800, 0, 0, 26433},
+    {33555020, 18874953, 16777806, 0, 0, 26433},
+    {33555026, 18874959, 16777812, 0, 0, 26433},
+    {33555032, 18874965, 16777818, 0, 0, 26433},
+    {33555038, 18874971, 16777824, 0, 0, 26433},
+    {33555044, 18874977, 16777830, 0, 0, 26433},
+    {33555050, 18874983, 16777836, 0, 0, 26433},
+    {33555056, 18874989, 16777842, 0, 0, 26433},
+    {33555062, 18874995, 16777848, 0, 0, 26377},
+    {33555068, 18875001, 16777854, 0, 0, 26377},
+    {33555074, 18875007, 16777860, 0, 0, 26377},
+    {33555080, 18875013, 16777866, 0, 0, 26377},
+    {33555086, 18875019, 16777872, 0, 0, 26377},
+    {33555092, 18875025, 16777878, 0, 0, 26377},
+    {33555098, 18875031, 16777884, 0, 0, 26377},
+    {33555104, 18875037, 16777890, 0, 0, 26377},
+    {33555110, 18875043, 16777896, 0, 0, 26433},
+    {33555116, 18875049, 16777902, 0, 0, 26433},
+    {33555122, 18875055, 16777908, 0, 0, 26433},
+    {33555128, 18875061, 16777914, 0, 0, 26433},
+    {33555134, 18875067, 16777920, 0, 0, 26433},
+    {33555140, 18875073, 16777926, 0, 0, 26433},
+    {33555146, 18875079, 16777932, 0, 0, 26433},
+    {33555152, 18875085, 16777938, 0, 0, 26433},
+    {33555158, 18875091, 33555160, 0, 0, 26377},
+    {33555165, 18875098, 16777951, 0, 0, 26377},
+    {33555171, 18875104, 33555173, 0, 0, 26377},
+    {33555178, 18875111, 33555178, 0, 0, 26377},
+    {50332400, 19923692, 50332403, 0, 0, 26377},
+    {0, -74, 0, 0, 0, 10113},
+    {33555193, 18875126, 16777979, 0, 0, 26433},
+    {16777982, 17826556, 16777982, 0, 0, 26377},
+    {33555202, 18875135, 33555204, 0, 0, 26377},
+    {33555209, 18875142, 16777995, 0, 0, 26377},
+    {33555215, 18875148, 33555217, 0, 0, 26377},
+    {33555222, 18875155, 33555222, 0, 0, 26377},
+    {50332444, 19923736, 50332447, 0, 0, 26377},
+    {0, -86, 0, 0, 0, 10113},
+    {33555237, 18875170, 16778023, 0, 0, 26433},
+    {50332460, 19923752, 50332460, 0, 0, 26377},
+    {50332467, 19923759, 50332467, 0, 0, 26377},
+    {33555257, 18875190, 33555257, 0, 0, 26377},
+    {50332479, 19923771, 50332479, 0, 0, 26377},
+    {0, -100, 0, 0, 0, 10113},
+    {50332486, 19923778, 50332486, 0, 0, 26377},
+    {50332493, 19923785, 50332493, 0, 0, 26377},
+    {33555283, 18875216, 33555283, 0, 0, 26377},
+    {33555288, 18875221, 33555288, 0, 0, 26377},
+    {50332510, 19923802, 50332510, 0, 0, 26377},
+    {0, -112, 0, 0, 0, 10113},
+    {33555300, 18875233, 33555302, 0, 0, 26377},
+    {33555307, 18875240, 16778093, 0, 0, 26377},
+    {33555313, 18875246, 33555315, 0, 0, 26377},
+    {33555320, 18875253, 33555320, 0, 0, 26377},
+    {50332542, 19923834, 50332545, 0, 0, 26377},
+    {0, -128, 0, 0, 0, 10113},
+    {0, -126, 0, 0, 0, 10113},
+    {33555335, 18875268, 16778121, 0, 0, 26433},
+    {0, 0, 0, 0, 0, 3076},
+    {0, 0, 0, 0, 4, 3076},
+    {0, 0, 0, 0, 5, 3076},
+    {0, 0, 0, 0, 6, 3076},
+    {0, 0, 0, 0, 7, 3076},
+    {0, 0, 0, 0, 8, 3076},
+    {0, 0, 0, 0, 9, 3076},
+    {0, 0, 0, 0, 0, 1792},
+    {0, -7517, 0, 0, 0, 10113},
+    {0, -8383, 0, 0, 0, 10113},
+    {0, -8262, 0, 0, 0, 10113},
+    {0, 28, 0, 0, 0, 10113},
+    {-28, 0, -28, 0, 0, 9993},
+    {0, 16, 0, 0, 0, 12160},
+    {-16, 0, -16, 0, 0, 12040},
+    {0, 26, 0, 0, 0, 9344},
+    {-26, 0, -26, 0, 0, 9224},
+    {0, -10743, 0, 0, 0, 10113},
+    {0, -3814, 0, 0, 0, 10113},
+    {0, -10727, 0, 0, 0, 10113},
+    {-10795, 0, -10795, 0, 0, 9993},
+    {-10792, 0, -10792, 0, 0, 9993},
+    {0, -10780, 0, 0, 0, 10113},
+    {0, -10749, 0, 0, 0, 10113},
+    {0, -10783, 0, 0, 0, 10113},
+    {0, -10782, 0, 0, 0, 10113},
+    {0, -10815, 0, 0, 0, 10113},
+    {-7264, 0, -7264, 0, 0, 9993},
+    {0, 0, 0, 0, 0, 5121},
+    {0, 0, 0, 0, 0, 3841},
+    {0, -35332, 0, 0, 0, 10113},
+    {0, -42280, 0, 0, 0, 10113},
+    {0, -42308, 0, 0, 0, 10113},
+    {0, -42319, 0, 0, 0, 10113},
+    {0, -42315, 0, 0, 0, 10113},
+    {0, -42305, 0, 0, 0, 10113},
+    {0, -42258, 0, 0, 0, 10113},
+    {0, -42282, 0, 0, 0, 10113},
+    {0, -42261, 0, 0, 0, 10113},
+    {0, 928, 0, 0, 0, 10113},
+    {-928, 0, -928, 0, 0, 9993},
+    {16778124, 17826698, 16778124, 0, 0, 26377},
+    {16778127, 17826701, 16778127, 0, 0, 26377},
+    {16778130, 17826704, 16778130, 0, 0, 26377},
+    {16778133, 17826707, 16778133, 0, 0, 26377},
+    {16778136, 17826710, 16778136, 0, 0, 26377},
+    {16778139, 17826713, 16778139, 0, 0, 26377},
+    {16778142, 17826716, 16778142, 0, 0, 26377},
+    {16778145, 17826719, 16778145, 0, 0, 26377},
+    {16778148, 17826722, 16778148, 0, 0, 26377},
+    {16778151, 17826725, 16778151, 0, 0, 26377},
+    {16778154, 17826728, 16778154, 0, 0, 26377},
+    {16778157, 17826731, 16778157, 0, 0, 26377},
+    {16778160, 17826734, 16778160, 0, 0, 26377},
+    {16778163, 17826737, 16778163, 0, 0, 26377},
+    {16778166, 17826740, 16778166, 0, 0, 26377},
+    {16778169, 17826743, 16778169, 0, 0, 26377},
+    {16778172, 17826746, 16778172, 0, 0, 26377},
+    {16778175, 17826749, 16778175, 0, 0, 26377},
+    {16778178, 17826752, 16778178, 0, 0, 26377},
+    {16778181, 17826755, 16778181, 0, 0, 26377},
+    {16778184, 17826758, 16778184, 0, 0, 26377},
+    {16778187, 17826761, 16778187, 0, 0, 26377},
+    {16778190, 17826764, 16778190, 0, 0, 26377},
+    {16778193, 17826767, 16778193, 0, 0, 26377},
+    {16778196, 17826770, 16778196, 0, 0, 26377},
+    {16778199, 17826773, 16778199, 0, 0, 26377},
+    {16778202, 17826776, 16778202, 0, 0, 26377},
+    {16778205, 17826779, 16778205, 0, 0, 26377},
+    {16778208, 17826782, 16778208, 0, 0, 26377},
+    {16778211, 17826785, 16778211, 0, 0, 26377},
+    {16778214, 17826788, 16778214, 0, 0, 26377},
+    {16778217, 17826791, 16778217, 0, 0, 26377},
+    {16778220, 17826794, 16778220, 0, 0, 26377},
+    {16778223, 17826797, 16778223, 0, 0, 26377},
+    {16778226, 17826800, 16778226, 0, 0, 26377},
+    {16778229, 17826803, 16778229, 0, 0, 26377},
+    {16778232, 17826806, 16778232, 0, 0, 26377},
+    {16778235, 17826809, 16778235, 0, 0, 26377},
+    {16778238, 17826812, 16778238, 0, 0, 26377},
+    {16778241, 17826815, 16778241, 0, 0, 26377},
+    {16778244, 17826818, 16778244, 0, 0, 26377},
+    {16778247, 17826821, 16778247, 0, 0, 26377},
+    {16778250, 17826824, 16778250, 0, 0, 26377},
+    {16778253, 17826827, 16778253, 0, 0, 26377},
+    {16778256, 17826830, 16778256, 0, 0, 26377},
+    {16778259, 17826833, 16778259, 0, 0, 26377},
+    {16778262, 17826836, 16778262, 0, 0, 26377},
+    {16778265, 17826839, 16778265, 0, 0, 26377},
+    {16778268, 17826842, 16778268, 0, 0, 26377},
+    {16778271, 17826845, 16778271, 0, 0, 26377},
+    {16778274, 17826848, 16778274, 0, 0, 26377},
+    {16778277, 17826851, 16778277, 0, 0, 26377},
+    {16778280, 17826854, 16778280, 0, 0, 26377},
+    {16778283, 17826857, 16778283, 0, 0, 26377},
+    {16778286, 17826860, 16778286, 0, 0, 26377},
+    {16778289, 17826863, 16778289, 0, 0, 26377},
+    {16778292, 17826866, 16778292, 0, 0, 26377},
+    {16778295, 17826869, 16778295, 0, 0, 26377},
+    {16778298, 17826872, 16778298, 0, 0, 26377},
+    {16778301, 17826875, 16778301, 0, 0, 26377},
+    {16778304, 17826878, 16778304, 0, 0, 26377},
+    {16778307, 17826881, 16778307, 0, 0, 26377},
+    {16778310, 17826884, 16778310, 0, 0, 26377},
+    {16778313, 17826887, 16778313, 0, 0, 26377},
+    {16778316, 17826890, 16778316, 0, 0, 26377},
+    {16778319, 17826893, 16778319, 0, 0, 26377},
+    {16778322, 17826896, 16778322, 0, 0, 26377},
+    {16778325, 17826899, 16778325, 0, 0, 26377},
+    {16778328, 17826902, 16778328, 0, 0, 26377},
+    {16778331, 17826905, 16778331, 0, 0, 26377},
+    {16778334, 17826908, 16778334, 0, 0, 26377},
+    {16778337, 17826911, 16778337, 0, 0, 26377},
+    {16778340, 17826914, 16778340, 0, 0, 26377},
+    {16778343, 17826917, 16778343, 0, 0, 26377},
+    {16778346, 17826920, 16778346, 0, 0, 26377},
+    {16778349, 17826923, 16778349, 0, 0, 26377},
+    {16778352, 17826926, 16778352, 0, 0, 26377},
+    {16778355, 17826929, 16778355, 0, 0, 26377},
+    {16778358, 17826932, 16778358, 0, 0, 26377},
+    {16778361, 17826935, 16778361, 0, 0, 26377},
+    {33555581, 18875514, 33555583, 0, 0, 26377},
+    {33555588, 18875521, 33555590, 0, 0, 26377},
+    {33555595, 18875528, 33555597, 0, 0, 26377},
+    {50332819, 19924111, 50332822, 0, 0, 26377},
+    {50332829, 19924121, 50332832, 0, 0, 26377},
+    {33555622, 18875555, 33555624, 0, 0, 26377},
+    {33555629, 18875562, 33555631, 0, 0, 26377},
+    {33555636, 18875569, 33555638, 0, 0, 26377},
+    {33555643, 18875576, 33555645, 0, 0, 26377},
+    {33555650, 18875583, 33555652, 0, 0, 26377},
+    {33555657, 18875590, 33555659, 0, 0, 26377},
+    {33555664, 18875597, 33555666, 0, 0, 26377},
+    {0, 0, 0, 0, 0, 1025},
+    {0, 0, 0, 0, 0, 5633},
+    {0, 40, 0, 0, 0, 10113},
+    {-40, 0, -40, 0, 0, 9993},
+    {0, 34, 0, 0, 0, 10113},
+    {-34, 0, -34, 0, 0, 9993},
+    {0, 0, 0, 0, 0, 9344},
+};
+
+/* extended case mappings */
+
+const Py_UCS4 numba_PyUnicode_ExtendedCase[] = {
+    181,
+    956,
+    924,
+    223,
+    115,
+    115,
+    83,
+    83,
+    83,
+    115,
+    105,
+    775,
+    304,
+    329,
+    700,
+    110,
+    700,
+    78,
+    383,
+    115,
+    83,
+    496,
+    106,
+    780,
+    74,
+    780,
+    837,
+    953,
+    921,
+    912,
+    953,
+    776,
+    769,
+    921,
+    776,
+    769,
+    944,
+    965,
+    776,
+    769,
+    933,
+    776,
+    769,
+    962,
+    963,
+    931,
+    976,
+    946,
+    914,
+    977,
+    952,
+    920,
+    981,
+    966,
+    934,
+    982,
+    960,
+    928,
+    1008,
+    954,
+    922,
+    1009,
+    961,
+    929,
+    1013,
+    949,
+    917,
+    1415,
+    1381,
+    1410,
+    1333,
+    1362,
+    1333,
+    1410,
+    43888,
+    5024,
+    5024,
+    43889,
+    5025,
+    5025,
+    43890,
+    5026,
+    5026,
+    43891,
+    5027,
+    5027,
+    43892,
+    5028,
+    5028,
+    43893,
+    5029,
+    5029,
+    43894,
+    5030,
+    5030,
+    43895,
+    5031,
+    5031,
+    43896,
+    5032,
+    5032,
+    43897,
+    5033,
+    5033,
+    43898,
+    5034,
+    5034,
+    43899,
+    5035,
+    5035,
+    43900,
+    5036,
+    5036,
+    43901,
+    5037,
+    5037,
+    43902,
+    5038,
+    5038,
+    43903,
+    5039,
+    5039,
+    43904,
+    5040,
+    5040,
+    43905,
+    5041,
+    5041,
+    43906,
+    5042,
+    5042,
+    43907,
+    5043,
+    5043,
+    43908,
+    5044,
+    5044,
+    43909,
+    5045,
+    5045,
+    43910,
+    5046,
+    5046,
+    43911,
+    5047,
+    5047,
+    43912,
+    5048,
+    5048,
+    43913,
+    5049,
+    5049,
+    43914,
+    5050,
+    5050,
+    43915,
+    5051,
+    5051,
+    43916,
+    5052,
+    5052,
+    43917,
+    5053,
+    5053,
+    43918,
+    5054,
+    5054,
+    43919,
+    5055,
+    5055,
+    43920,
+    5056,
+    5056,
+    43921,
+    5057,
+    5057,
+    43922,
+    5058,
+    5058,
+    43923,
+    5059,
+    5059,
+    43924,
+    5060,
+    5060,
+    43925,
+    5061,
+    5061,
+    43926,
+    5062,
+    5062,
+    43927,
+    5063,
+    5063,
+    43928,
+    5064,
+    5064,
+    43929,
+    5065,
+    5065,
+    43930,
+    5066,
+    5066,
+    43931,
+    5067,
+    5067,
+    43932,
+    5068,
+    5068,
+    43933,
+    5069,
+    5069,
+    43934,
+    5070,
+    5070,
+    43935,
+    5071,
+    5071,
+    43936,
+    5072,
+    5072,
+    43937,
+    5073,
+    5073,
+    43938,
+    5074,
+    5074,
+    43939,
+    5075,
+    5075,
+    43940,
+    5076,
+    5076,
+    43941,
+    5077,
+    5077,
+    43942,
+    5078,
+    5078,
+    43943,
+    5079,
+    5079,
+    43944,
+    5080,
+    5080,
+    43945,
+    5081,
+    5081,
+    43946,
+    5082,
+    5082,
+    43947,
+    5083,
+    5083,
+    43948,
+    5084,
+    5084,
+    43949,
+    5085,
+    5085,
+    43950,
+    5086,
+    5086,
+    43951,
+    5087,
+    5087,
+    43952,
+    5088,
+    5088,
+    43953,
+    5089,
+    5089,
+    43954,
+    5090,
+    5090,
+    43955,
+    5091,
+    5091,
+    43956,
+    5092,
+    5092,
+    43957,
+    5093,
+    5093,
+    43958,
+    5094,
+    5094,
+    43959,
+    5095,
+    5095,
+    43960,
+    5096,
+    5096,
+    43961,
+    5097,
+    5097,
+    43962,
+    5098,
+    5098,
+    43963,
+    5099,
+    5099,
+    43964,
+    5100,
+    5100,
+    43965,
+    5101,
+    5101,
+    43966,
+    5102,
+    5102,
+    43967,
+    5103,
+    5103,
+    5112,
+    5104,
+    5104,
+    5113,
+    5105,
+    5105,
+    5114,
+    5106,
+    5106,
+    5115,
+    5107,
+    5107,
+    5116,
+    5108,
+    5108,
+    5117,
+    5109,
+    5109,
+    5112,
+    5104,
+    5104,
+    5113,
+    5105,
+    5105,
+    5114,
+    5106,
+    5106,
+    5115,
+    5107,
+    5107,
+    5116,
+    5108,
+    5108,
+    5117,
+    5109,
+    5109,
+    7296,
+    1074,
+    1042,
+    7297,
+    1076,
+    1044,
+    7298,
+    1086,
+    1054,
+    7299,
+    1089,
+    1057,
+    7300,
+    1090,
+    1058,
+    7301,
+    1090,
+    1058,
+    7302,
+    1098,
+    1066,
+    7303,
+    1123,
+    1122,
+    7304,
+    42571,
+    42570,
+    7830,
+    104,
+    817,
+    72,
+    817,
+    7831,
+    116,
+    776,
+    84,
+    776,
+    7832,
+    119,
+    778,
+    87,
+    778,
+    7833,
+    121,
+    778,
+    89,
+    778,
+    7834,
+    97,
+    702,
+    65,
+    702,
+    7835,
+    7777,
+    7776,
+    223,
+    115,
+    115,
+    7838,
+    8016,
+    965,
+    787,
+    933,
+    787,
+    8018,
+    965,
+    787,
+    768,
+    933,
+    787,
+    768,
+    8020,
+    965,
+    787,
+    769,
+    933,
+    787,
+    769,
+    8022,
+    965,
+    787,
+    834,
+    933,
+    787,
+    834,
+    8064,
+    7936,
+    953,
+    7944,
+    921,
+    8072,
+    8065,
+    7937,
+    953,
+    7945,
+    921,
+    8073,
+    8066,
+    7938,
+    953,
+    7946,
+    921,
+    8074,
+    8067,
+    7939,
+    953,
+    7947,
+    921,
+    8075,
+    8068,
+    7940,
+    953,
+    7948,
+    921,
+    8076,
+    8069,
+    7941,
+    953,
+    7949,
+    921,
+    8077,
+    8070,
+    7942,
+    953,
+    7950,
+    921,
+    8078,
+    8071,
+    7943,
+    953,
+    7951,
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+    8079,
+    8064,
+    7936,
+    953,
+    7944,
+    921,
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+    953,
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+    7938,
+    953,
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+    7939,
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+    7942,
+    953,
+    7950,
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+    7943,
+    953,
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+    8079,
+    8080,
+    7968,
+    953,
+    7976,
+    921,
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+    8081,
+    7969,
+    953,
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+    7970,
+    953,
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+    7971,
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+    7972,
+    953,
+    7980,
+    921,
+    8092,
+    8085,
+    7973,
+    953,
+    7981,
+    921,
+    8093,
+    8086,
+    7974,
+    953,
+    7982,
+    921,
+    8094,
+    8087,
+    7975,
+    953,
+    7983,
+    921,
+    8095,
+    8080,
+    7968,
+    953,
+    7976,
+    921,
+    8088,
+    8081,
+    7969,
+    953,
+    7977,
+    921,
+    8089,
+    8082,
+    7970,
+    953,
+    7978,
+    921,
+    8090,
+    8083,
+    7971,
+    953,
+    7979,
+    921,
+    8091,
+    8084,
+    7972,
+    953,
+    7980,
+    921,
+    8092,
+    8085,
+    7973,
+    953,
+    7981,
+    921,
+    8093,
+    8086,
+    7974,
+    953,
+    7982,
+    921,
+    8094,
+    8087,
+    7975,
+    953,
+    7983,
+    921,
+    8095,
+    8096,
+    8032,
+    953,
+    8040,
+    921,
+    8104,
+    8097,
+    8033,
+    953,
+    8041,
+    921,
+    8105,
+    8098,
+    8034,
+    953,
+    8042,
+    921,
+    8106,
+    8099,
+    8035,
+    953,
+    8043,
+    921,
+    8107,
+    8100,
+    8036,
+    953,
+    8044,
+    921,
+    8108,
+    8101,
+    8037,
+    953,
+    8045,
+    921,
+    8109,
+    8102,
+    8038,
+    953,
+    8046,
+    921,
+    8110,
+    8103,
+    8039,
+    953,
+    8047,
+    921,
+    8111,
+    8096,
+    8032,
+    953,
+    8040,
+    921,
+    8104,
+    8097,
+    8033,
+    953,
+    8041,
+    921,
+    8105,
+    8098,
+    8034,
+    953,
+    8042,
+    921,
+    8106,
+    8099,
+    8035,
+    953,
+    8043,
+    921,
+    8107,
+    8100,
+    8036,
+    953,
+    8044,
+    921,
+    8108,
+    8101,
+    8037,
+    953,
+    8045,
+    921,
+    8109,
+    8102,
+    8038,
+    953,
+    8046,
+    921,
+    8110,
+    8103,
+    8039,
+    953,
+    8047,
+    921,
+    8111,
+    8114,
+    8048,
+    953,
+    8122,
+    921,
+    8122,
+    837,
+    8115,
+    945,
+    953,
+    913,
+    921,
+    8124,
+    8116,
+    940,
+    953,
+    902,
+    921,
+    902,
+    837,
+    8118,
+    945,
+    834,
+    913,
+    834,
+    8119,
+    945,
+    834,
+    953,
+    913,
+    834,
+    921,
+    913,
+    834,
+    837,
+    8115,
+    945,
+    953,
+    913,
+    921,
+    8124,
+    8126,
+    953,
+    921,
+    8130,
+    8052,
+    953,
+    8138,
+    921,
+    8138,
+    837,
+    8131,
+    951,
+    953,
+    919,
+    921,
+    8140,
+    8132,
+    942,
+    953,
+    905,
+    921,
+    905,
+    837,
+    8134,
+    951,
+    834,
+    919,
+    834,
+    8135,
+    951,
+    834,
+    953,
+    919,
+    834,
+    921,
+    919,
+    834,
+    837,
+    8131,
+    951,
+    953,
+    919,
+    921,
+    8140,
+    8146,
+    953,
+    776,
+    768,
+    921,
+    776,
+    768,
+    8147,
+    953,
+    776,
+    769,
+    921,
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+    769,
+    8150,
+    953,
+    834,
+    921,
+    834,
+    8151,
+    953,
+    776,
+    834,
+    921,
+    776,
+    834,
+    8162,
+    965,
+    776,
+    768,
+    933,
+    776,
+    768,
+    8163,
+    965,
+    776,
+    769,
+    933,
+    776,
+    769,
+    8164,
+    961,
+    787,
+    929,
+    787,
+    8166,
+    965,
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+    933,
+    834,
+    8167,
+    965,
+    776,
+    834,
+    933,
+    776,
+    834,
+    8178,
+    8060,
+    953,
+    8186,
+    921,
+    8186,
+    837,
+    8179,
+    969,
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+    937,
+    921,
+    8188,
+    8180,
+    974,
+    953,
+    911,
+    921,
+    911,
+    837,
+    8182,
+    969,
+    834,
+    937,
+    834,
+    8183,
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+    834,
+    953,
+    937,
+    834,
+    921,
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+    834,
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+    8179,
+    969,
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+    937,
+    921,
+    8188,
+    43888,
+    5024,
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+    5025,
+    5025,
+    43890,
+    5026,
+    5026,
+    43891,
+    5027,
+    5027,
+    43892,
+    5028,
+    5028,
+    43893,
+    5029,
+    5029,
+    43894,
+    5030,
+    5030,
+    43895,
+    5031,
+    5031,
+    43896,
+    5032,
+    5032,
+    43897,
+    5033,
+    5033,
+    43898,
+    5034,
+    5034,
+    43899,
+    5035,
+    5035,
+    43900,
+    5036,
+    5036,
+    43901,
+    5037,
+    5037,
+    43902,
+    5038,
+    5038,
+    43903,
+    5039,
+    5039,
+    43904,
+    5040,
+    5040,
+    43905,
+    5041,
+    5041,
+    43906,
+    5042,
+    5042,
+    43907,
+    5043,
+    5043,
+    43908,
+    5044,
+    5044,
+    43909,
+    5045,
+    5045,
+    43910,
+    5046,
+    5046,
+    43911,
+    5047,
+    5047,
+    43912,
+    5048,
+    5048,
+    43913,
+    5049,
+    5049,
+    43914,
+    5050,
+    5050,
+    43915,
+    5051,
+    5051,
+    43916,
+    5052,
+    5052,
+    43917,
+    5053,
+    5053,
+    43918,
+    5054,
+    5054,
+    43919,
+    5055,
+    5055,
+    43920,
+    5056,
+    5056,
+    43921,
+    5057,
+    5057,
+    43922,
+    5058,
+    5058,
+    43923,
+    5059,
+    5059,
+    43924,
+    5060,
+    5060,
+    43925,
+    5061,
+    5061,
+    43926,
+    5062,
+    5062,
+    43927,
+    5063,
+    5063,
+    43928,
+    5064,
+    5064,
+    43929,
+    5065,
+    5065,
+    43930,
+    5066,
+    5066,
+    43931,
+    5067,
+    5067,
+    43932,
+    5068,
+    5068,
+    43933,
+    5069,
+    5069,
+    43934,
+    5070,
+    5070,
+    43935,
+    5071,
+    5071,
+    43936,
+    5072,
+    5072,
+    43937,
+    5073,
+    5073,
+    43938,
+    5074,
+    5074,
+    43939,
+    5075,
+    5075,
+    43940,
+    5076,
+    5076,
+    43941,
+    5077,
+    5077,
+    43942,
+    5078,
+    5078,
+    43943,
+    5079,
+    5079,
+    43944,
+    5080,
+    5080,
+    43945,
+    5081,
+    5081,
+    43946,
+    5082,
+    5082,
+    43947,
+    5083,
+    5083,
+    43948,
+    5084,
+    5084,
+    43949,
+    5085,
+    5085,
+    43950,
+    5086,
+    5086,
+    43951,
+    5087,
+    5087,
+    43952,
+    5088,
+    5088,
+    43953,
+    5089,
+    5089,
+    43954,
+    5090,
+    5090,
+    43955,
+    5091,
+    5091,
+    43956,
+    5092,
+    5092,
+    43957,
+    5093,
+    5093,
+    43958,
+    5094,
+    5094,
+    43959,
+    5095,
+    5095,
+    43960,
+    5096,
+    5096,
+    43961,
+    5097,
+    5097,
+    43962,
+    5098,
+    5098,
+    43963,
+    5099,
+    5099,
+    43964,
+    5100,
+    5100,
+    43965,
+    5101,
+    5101,
+    43966,
+    5102,
+    5102,
+    43967,
+    5103,
+    5103,
+    64256,
+    102,
+    102,
+    70,
+    70,
+    70,
+    102,
+    64257,
+    102,
+    105,
+    70,
+    73,
+    70,
+    105,
+    64258,
+    102,
+    108,
+    70,
+    76,
+    70,
+    108,
+    64259,
+    102,
+    102,
+    105,
+    70,
+    70,
+    73,
+    70,
+    102,
+    105,
+    64260,
+    102,
+    102,
+    108,
+    70,
+    70,
+    76,
+    70,
+    102,
+    108,
+    64261,
+    115,
+    116,
+    83,
+    84,
+    83,
+    116,
+    64262,
+    115,
+    116,
+    83,
+    84,
+    83,
+    116,
+    64275,
+    1396,
+    1398,
+    1348,
+    1350,
+    1348,
+    1398,
+    64276,
+    1396,
+    1381,
+    1348,
+    1333,
+    1348,
+    1381,
+    64277,
+    1396,
+    1387,
+    1348,
+    1339,
+    1348,
+    1387,
+    64278,
+    1406,
+    1398,
+    1358,
+    1350,
+    1358,
+    1398,
+    64279,
+    1396,
+    1389,
+    1348,
+    1341,
+    1348,
+    1389,
+};
+
+/* type indexes */
+#define SHIFT 7
+static unsigned short index1[] = {
+    0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
+    21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 34, 35, 36, 37,
+    38, 39, 34, 34, 34, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
+    53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 64, 64, 64, 65, 66, 64,
+    64, 64, 64, 67, 68, 64, 64, 64, 64, 64, 64, 69, 70, 71, 72, 73, 74, 75,
+    76, 64, 77, 78, 79, 80, 81, 82, 83, 64, 64, 84, 85, 34, 34, 34, 34, 34,
+    34, 86, 34, 34, 34, 34, 34, 87, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 88, 89, 90, 91, 34, 34, 34, 92, 34, 34,
+    34, 93, 94, 34, 34, 34, 34, 34, 95, 34, 34, 34, 96, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 97, 98, 99, 34, 34, 34, 34, 34, 34, 100, 101, 34, 34,
+    34, 34, 34, 34, 34, 34, 102, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 103, 34, 34, 34, 34, 34, 34, 34, 34, 104, 34, 34, 34, 34,
+    100, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 103, 34, 34, 34, 34, 34, 34, 105, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 106, 107, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 108, 109, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 110, 111, 34, 34, 34, 34, 34,
+    34, 34, 34, 112, 34, 34, 113, 114, 115, 116, 117, 118, 119, 120, 121,
+    122, 123, 124, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 125, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 127, 128, 129,
+    130, 131, 132, 133, 34, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143,
+    144, 145, 146, 147, 148, 149, 150, 144, 34, 34, 151, 144, 152, 153, 154,
+    155, 156, 157, 158, 159, 160, 161, 162, 144, 163, 144, 164, 144, 165,
+    166, 167, 168, 169, 170, 171, 144, 172, 173, 144, 174, 175, 176, 177,
+    144, 178, 179, 144, 144, 180, 181, 144, 144, 182, 183, 184, 185, 144,
+    186, 144, 144, 34, 34, 34, 34, 34, 34, 34, 187, 188, 34, 189, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 34, 34, 34, 34, 34, 34, 34, 34, 190, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 34, 34, 34, 34, 191, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 34, 34, 34, 34, 192, 193, 194, 195, 144, 144, 144, 144, 196,
+    197, 198, 199, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34,
+    34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, 200, 34, 34,
+    34, 34, 34, 201, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 34, 34, 202, 34, 34, 203, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 204, 205, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 64,
+    206, 207, 208, 209, 210, 211, 144, 212, 213, 214, 215, 216, 217, 218,
+    219, 64, 64, 64, 64, 220, 221, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 222, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144, 144,
+    144, 144, 144, 144, 34, 223, 224, 144, 144, 144, 144, 144, 225, 226, 144,
+    144, 227, 228, 144, 144, 229, 230, 231, 232, 233, 144, 64, 234, 64, 64,
+    64, 64, 64, 235, 236, 237, 238, 239, 240, 241, 242, 144, 144, 144, 144,
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+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 262,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126, 126,
+    126, 126, 126, 126, 126, 126, 126, 262,
+};
+
+static unsigned short index2[] = {
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 3, 3, 3, 2, 4, 5, 5, 5, 5, 5, 5, 6, 5, 5, 5, 5, 5, 5, 6, 5,
+    7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 6, 5, 5, 5, 5, 5, 5, 17, 17, 17, 17,
+    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
+    17, 17, 17, 17, 5, 5, 5, 6, 18, 6, 19, 19, 19, 19, 19, 19, 19, 19, 19,
+    19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 5, 5,
+    5, 5, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 5, 5, 5, 5, 5, 5, 5, 6, 5, 20, 5, 5,
+    21, 5, 6, 5, 5, 22, 23, 6, 24, 5, 25, 6, 26, 20, 5, 27, 27, 27, 5, 17,
+    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
+    17, 17, 17, 17, 5, 17, 17, 17, 17, 17, 17, 17, 28, 19, 19, 19, 19, 19,
+    19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,
+    5, 19, 19, 19, 19, 19, 19, 19, 29, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 32, 33, 30, 31, 30, 31, 30, 31, 20, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 34, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 35, 30, 31, 30, 31, 30, 31, 36, 37, 38, 30, 31, 30, 31, 39,
+    30, 31, 40, 40, 30, 31, 20, 41, 42, 43, 30, 31, 40, 44, 45, 46, 47, 30,
+    31, 48, 20, 46, 49, 50, 51, 30, 31, 30, 31, 30, 31, 52, 30, 31, 52, 20,
+    20, 30, 31, 52, 30, 31, 53, 53, 30, 31, 30, 31, 54, 30, 31, 20, 55, 30,
+    31, 20, 56, 55, 55, 55, 55, 57, 58, 59, 57, 58, 59, 57, 58, 59, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 60, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 61, 57, 58,
+    59, 30, 31, 62, 63, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 64, 20, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 20, 20, 20, 20, 20, 20, 65,
+    30, 31, 66, 67, 68, 68, 30, 31, 69, 70, 71, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 72, 73, 74, 75, 76, 20, 77, 77, 20, 78, 20, 79, 80, 20, 20,
+    20, 77, 81, 20, 82, 20, 83, 84, 20, 85, 86, 84, 87, 88, 20, 20, 86, 20,
+    89, 90, 20, 20, 91, 20, 20, 20, 20, 20, 20, 20, 92, 20, 20, 93, 20, 20,
+    93, 20, 20, 20, 94, 93, 95, 96, 96, 97, 20, 20, 20, 20, 20, 98, 20, 55,
+    20, 20, 20, 20, 20, 20, 20, 20, 99, 100, 20, 20, 20, 20, 20, 20, 20, 20,
+    20, 20, 20, 20, 20, 20, 20, 20, 20, 101, 101, 101, 101, 101, 101, 101,
+    101, 101, 102, 102, 102, 102, 102, 102, 102, 101, 101, 6, 6, 6, 6, 102,
+    102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 102, 6, 6, 6, 6, 6, 6,
+    6, 6, 6, 6, 6, 6, 6, 6, 101, 101, 101, 101, 101, 6, 6, 6, 6, 6, 6, 6,
+    102, 6, 102, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 103, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 30, 31, 30, 31, 102, 6, 30, 31, 0, 0, 104, 50, 50, 50, 5, 105, 0,
+    0, 0, 0, 6, 6, 106, 25, 107, 107, 107, 0, 108, 0, 109, 109, 110, 17, 17,
+    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 0, 17, 17,
+    17, 17, 17, 17, 17, 17, 17, 111, 112, 112, 112, 113, 19, 19, 19, 19, 19,
+    19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 114, 19, 19, 19, 19, 19,
+    19, 19, 19, 19, 115, 116, 116, 117, 118, 119, 120, 120, 120, 121, 122,
+    123, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 124, 125, 126, 127, 128, 129, 5, 30, 31, 130,
+    30, 31, 20, 64, 64, 64, 131, 131, 131, 131, 131, 131, 131, 131, 131, 131,
+    131, 131, 131, 131, 131, 131, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
+    17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17,
+    17, 17, 17, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19,
+    19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 132,
+    132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132, 132,
+    132, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 5,
+    25, 25, 25, 25, 25, 6, 6, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 133, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30,
+    31, 30, 31, 134, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 30, 31,
+    30, 31, 30, 31, 30, 31, 30, 31, 30, 31, 0, 135, 135, 135, 135, 135, 135,
+    135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135,
+    135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135, 135,
+    135, 135, 135, 135, 0, 0, 102, 5, 5, 5, 5, 5, 5, 20, 136, 136, 136, 136,
+    136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
+    136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136,
+    136, 136, 136, 136, 136, 136, 137, 20, 5, 5, 0, 0, 5, 5, 5, 0, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 5, 25, 5, 25, 25, 5, 25, 25, 5, 25, 0, 0, 0,
+    0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 55, 55,
+    55, 55, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 21, 21, 21, 21, 21, 21, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 5, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 5,
+    21, 0, 5, 5, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 102,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 7, 8, 9, 10, 11, 12,
+    13, 14, 15, 16, 5, 5, 5, 5, 55, 55, 25, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 5, 55, 25, 25, 25, 25, 25, 25, 25, 21, 5, 25, 25, 25, 25, 25, 25,
+    102, 102, 25, 25, 5, 25, 25, 25, 25, 55, 55, 7, 8, 9, 10, 11, 12, 13, 14,
+    15, 16, 55, 55, 55, 5, 5, 55, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
+    0, 21, 55, 25, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13,
+    14, 15, 16, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 102, 102, 5, 5, 5, 5, 102, 0, 0, 25,
+    5, 5, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 25, 25, 25, 25, 102, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 102, 25, 25, 25, 102, 25, 25, 25, 25, 25, 0, 0, 5, 5, 5, 5, 5, 5, 5,
+    5, 5, 5, 5, 5, 5, 5, 5, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 25, 25, 25, 0, 0,
+    5, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 0, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 21, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 18, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 25, 18, 25, 55, 18, 18, 18, 25, 25, 25, 25, 25, 25, 25, 25, 18,
+    18, 18, 18, 25, 18, 18, 55, 25, 25, 25, 25, 25, 25, 25, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 25, 25, 5, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+    16, 5, 102, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    25, 18, 18, 0, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 55, 55, 0, 0, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 0, 55, 0, 0, 0, 55, 55, 55,
+    55, 0, 0, 25, 55, 18, 18, 18, 25, 25, 25, 25, 0, 0, 18, 18, 0, 0, 18, 18,
+    25, 55, 0, 0, 0, 0, 0, 0, 0, 0, 18, 0, 0, 0, 0, 55, 55, 0, 55, 55, 55,
+    25, 25, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 55, 55, 5, 5, 27, 27,
+    27, 27, 27, 27, 5, 5, 55, 5, 25, 0, 0, 25, 25, 18, 0, 55, 55, 55, 55, 55,
+    55, 0, 0, 0, 0, 55, 55, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55,
+    55, 0, 55, 55, 0, 55, 55, 0, 55, 55, 0, 0, 25, 0, 18, 18, 18, 25, 25, 0,
+    0, 0, 0, 25, 25, 0, 0, 25, 25, 25, 0, 0, 0, 25, 0, 0, 0, 0, 0, 0, 0, 55,
+    55, 55, 55, 0, 55, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+    16, 25, 25, 55, 55, 55, 25, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 25, 18,
+    0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 0, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    0, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 0, 55, 55, 55, 55, 55, 0, 0,
+    25, 55, 18, 18, 18, 25, 25, 25, 25, 25, 0, 25, 25, 18, 0, 18, 18, 25, 0,
+    0, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 25, 25, 0, 0,
+    7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 5, 5, 0, 0, 0, 0, 0, 0, 0, 55, 25,
+    25, 25, 25, 25, 25, 0, 25, 18, 18, 0, 55, 55, 55, 55, 55, 55, 55, 55, 0,
+    0, 55, 55, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55, 55, 55, 55, 55, 55, 0, 55, 55,
+    0, 55, 55, 55, 55, 55, 0, 0, 25, 55, 18, 25, 18, 25, 25, 25, 25, 0, 0,
+    18, 18, 0, 0, 18, 18, 25, 0, 0, 0, 0, 0, 0, 0, 0, 25, 18, 0, 0, 0, 0, 55,
+    55, 0, 55, 55, 55, 25, 25, 0, 0, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 5,
+    55, 27, 27, 27, 27, 27, 27, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 25, 55, 0, 55,
+    55, 55, 55, 55, 55, 0, 0, 0, 55, 55, 55, 0, 55, 55, 55, 55, 0, 0, 0, 55,
+    55, 0, 55, 0, 55, 55, 0, 0, 0, 55, 55, 0, 0, 0, 55, 55, 55, 0, 0, 0, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 18, 18, 25, 18,
+    18, 0, 0, 0, 18, 18, 18, 0, 18, 18, 18, 25, 0, 0, 55, 0, 0, 0, 0, 0, 0,
+    18, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 9, 10, 11, 12, 13,
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+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 386, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
+    55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 21, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+    0, 0, 0, 0, 0, 0, 0, 0, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
+    21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
+    21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
+    21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
+    21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21,
+    21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25,
+    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
+    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,
+};
+
+/* Returns the numeric value as double for Unicode characters
+ * having this property, -1.0 otherwise.
+ */
+double numba_PyUnicode_ToNumeric(Py_UCS4 ch)
+{
+    switch (ch) {
+    case 0x0F33:
+        return (double) -1.0/2.0;
+    case 0x0030:
+    case 0x0660:
+    case 0x06F0:
+    case 0x07C0:
+    case 0x0966:
+    case 0x09E6:
+    case 0x0A66:
+    case 0x0AE6:
+    case 0x0B66:
+    case 0x0BE6:
+    case 0x0C66:
+    case 0x0C78:
+    case 0x0CE6:
+    case 0x0D66:
+    case 0x0DE6:
+    case 0x0E50:
+    case 0x0ED0:
+    case 0x0F20:
+    case 0x1040:
+    case 0x1090:
+    case 0x17E0:
+    case 0x17F0:
+    case 0x1810:
+    case 0x1946:
+    case 0x19D0:
+    case 0x1A80:
+    case 0x1A90:
+    case 0x1B50:
+    case 0x1BB0:
+    case 0x1C40:
+    case 0x1C50:
+    case 0x2070:
+    case 0x2080:
+    case 0x2189:
+    case 0x24EA:
+    case 0x24FF:
+    case 0x3007:
+    case 0x96F6:
+    case 0xA620:
+    case 0xA6EF:
+    case 0xA8D0:
+    case 0xA900:
+    case 0xA9D0:
+    case 0xA9F0:
+    case 0xAA50:
+    case 0xABF0:
+    case 0xF9B2:
+    case 0xFF10:
+    case 0x1018A:
+    case 0x104A0:
+    case 0x10D30:
+    case 0x11066:
+    case 0x110F0:
+    case 0x11136:
+    case 0x111D0:
+    case 0x112F0:
+    case 0x11450:
+    case 0x114D0:
+    case 0x11650:
+    case 0x116C0:
+    case 0x11730:
+    case 0x118E0:
+    case 0x11C50:
+    case 0x11D50:
+    case 0x11DA0:
+    case 0x16A60:
+    case 0x16B50:
+    case 0x16E80:
+    case 0x1D2E0:
+    case 0x1D7CE:
+    case 0x1D7D8:
+    case 0x1D7E2:
+    case 0x1D7EC:
+    case 0x1D7F6:
+    case 0x1E950:
+    case 0x1F100:
+    case 0x1F101:
+    case 0x1F10B:
+    case 0x1F10C:
+        return (double) 0.0;
+    case 0x0031:
+    case 0x00B9:
+    case 0x0661:
+    case 0x06F1:
+    case 0x07C1:
+    case 0x0967:
+    case 0x09E7:
+    case 0x0A67:
+    case 0x0AE7:
+    case 0x0B67:
+    case 0x0BE7:
+    case 0x0C67:
+    case 0x0C79:
+    case 0x0C7C:
+    case 0x0CE7:
+    case 0x0D67:
+    case 0x0DE7:
+    case 0x0E51:
+    case 0x0ED1:
+    case 0x0F21:
+    case 0x1041:
+    case 0x1091:
+    case 0x1369:
+    case 0x17E1:
+    case 0x17F1:
+    case 0x1811:
+    case 0x1947:
+    case 0x19D1:
+    case 0x19DA:
+    case 0x1A81:
+    case 0x1A91:
+    case 0x1B51:
+    case 0x1BB1:
+    case 0x1C41:
+    case 0x1C51:
+    case 0x2081:
+    case 0x215F:
+    case 0x2160:
+    case 0x2170:
+    case 0x2460:
+    case 0x2474:
+    case 0x2488:
+    case 0x24F5:
+    case 0x2776:
+    case 0x2780:
+    case 0x278A:
+    case 0x3021:
+    case 0x3192:
+    case 0x3220:
+    case 0x3280:
+    case 0x4E00:
+    case 0x58F1:
+    case 0x58F9:
+    case 0x5E7A:
+    case 0x5F0C:
+    case 0xA621:
+    case 0xA6E6:
+    case 0xA8D1:
+    case 0xA901:
+    case 0xA9D1:
+    case 0xA9F1:
+    case 0xAA51:
+    case 0xABF1:
+    case 0xFF11:
+    case 0x10107:
+    case 0x10142:
+    case 0x10158:
+    case 0x10159:
+    case 0x1015A:
+    case 0x102E1:
+    case 0x10320:
+    case 0x103D1:
+    case 0x104A1:
+    case 0x10858:
+    case 0x10879:
+    case 0x108A7:
+    case 0x108FB:
+    case 0x10916:
+    case 0x109C0:
+    case 0x10A40:
+    case 0x10A7D:
+    case 0x10A9D:
+    case 0x10AEB:
+    case 0x10B58:
+    case 0x10B78:
+    case 0x10BA9:
+    case 0x10CFA:
+    case 0x10D31:
+    case 0x10E60:
+    case 0x10F1D:
+    case 0x10F51:
+    case 0x11052:
+    case 0x11067:
+    case 0x110F1:
+    case 0x11137:
+    case 0x111D1:
+    case 0x111E1:
+    case 0x112F1:
+    case 0x11451:
+    case 0x114D1:
+    case 0x11651:
+    case 0x116C1:
+    case 0x11731:
+    case 0x118E1:
+    case 0x11C51:
+    case 0x11C5A:
+    case 0x11D51:
+    case 0x11DA1:
+    case 0x12415:
+    case 0x1241E:
+    case 0x1242C:
+    case 0x12434:
+    case 0x1244F:
+    case 0x12458:
+    case 0x16A61:
+    case 0x16B51:
+    case 0x16E81:
+    case 0x16E94:
+    case 0x1D2E1:
+    case 0x1D360:
+    case 0x1D372:
+    case 0x1D377:
+    case 0x1D7CF:
+    case 0x1D7D9:
+    case 0x1D7E3:
+    case 0x1D7ED:
+    case 0x1D7F7:
+    case 0x1E8C7:
+    case 0x1E951:
+    case 0x1EC71:
+    case 0x1ECA3:
+    case 0x1ECB1:
+    case 0x1F102:
+    case 0x2092A:
+        return (double) 1.0;
+    case 0x0D5C:
+    case 0x2152:
+        return (double) 1.0/10.0;
+    case 0x109F6:
+        return (double) 1.0/12.0;
+    case 0x09F4:
+    case 0x0B75:
+    case 0x0D76:
+    case 0xA833:
+        return (double) 1.0/16.0;
+    case 0x0D58:
+        return (double) 1.0/160.0;
+    case 0x00BD:
+    case 0x0B73:
+    case 0x0D74:
+    case 0x0F2A:
+    case 0x2CFD:
+    case 0xA831:
+    case 0x10141:
+    case 0x10175:
+    case 0x10176:
+    case 0x109BD:
+    case 0x10A48:
+    case 0x10E7B:
+    case 0x10F26:
+    case 0x12464:
+    case 0x1ECAE:
+        return (double) 1.0/2.0;
+    case 0x0D5B:
+        return (double) 1.0/20.0;
+    case 0x2153:
+    case 0x10E7D:
+    case 0x1245A:
+    case 0x1245D:
+    case 0x12465:
+        return (double) 1.0/3.0;
+    case 0x00BC:
+    case 0x09F7:
+    case 0x0B72:
+    case 0x0D73:
+    case 0xA830:
+    case 0x10140:
+    case 0x1018B:
+    case 0x10E7C:
+    case 0x12460:
+    case 0x12462:
+    case 0x12463:
+    case 0x1ECAD:
+        return (double) 1.0/4.0;
+    case 0x0D59:
+        return (double) 1.0/40.0;
+    case 0x0D5E:
+    case 0x2155:
+        return (double) 1.0/5.0;
+    case 0x2159:
+    case 0x12461:
+        return (double) 1.0/6.0;
+    case 0x2150:
+        return (double) 1.0/7.0;
+    case 0x09F5:
+    case 0x0B76:
+    case 0x0D77:
+    case 0x215B:
+    case 0xA834:
+    case 0x1245F:
+        return (double) 1.0/8.0;
+    case 0x2151:
+        return (double) 1.0/9.0;
+    case 0x0BF0:
+    case 0x0D70:
+    case 0x1372:
+    case 0x2169:
+    case 0x2179:
+    case 0x2469:
+    case 0x247D:
+    case 0x2491:
+    case 0x24FE:
+    case 0x277F:
+    case 0x2789:
+    case 0x2793:
+    case 0x3038:
+    case 0x3229:
+    case 0x3248:
+    case 0x3289:
+    case 0x4EC0:
+    case 0x5341:
+    case 0x62FE:
+    case 0xF973:
+    case 0xF9FD:
+    case 0x10110:
+    case 0x10149:
+    case 0x10150:
+    case 0x10157:
+    case 0x10160:
+    case 0x10161:
+    case 0x10162:
+    case 0x10163:
+    case 0x10164:
+    case 0x102EA:
+    case 0x10322:
+    case 0x103D3:
+    case 0x1085B:
+    case 0x1087E:
+    case 0x108AD:
+    case 0x108FD:
+    case 0x10917:
+    case 0x109C9:
+    case 0x10A44:
+    case 0x10A9E:
+    case 0x10AED:
+    case 0x10B5C:
+    case 0x10B7C:
+    case 0x10BAD:
+    case 0x10CFC:
+    case 0x10E69:
+    case 0x10F22:
+    case 0x10F52:
+    case 0x1105B:
+    case 0x111EA:
+    case 0x1173A:
+    case 0x118EA:
+    case 0x11C63:
+    case 0x16B5B:
+    case 0x16E8A:
+    case 0x1D2EA:
+    case 0x1D369:
+    case 0x1EC7A:
+        return (double) 10.0;
+    case 0x109FF:
+        return (double) 10.0/12.0;
+    case 0x0BF1:
+    case 0x0D71:
+    case 0x137B:
+    case 0x216D:
+    case 0x217D:
+    case 0x4F70:
+    case 0x767E:
+    case 0x964C:
+    case 0x10119:
+    case 0x1014B:
+    case 0x10152:
+    case 0x1016A:
+    case 0x102F3:
+    case 0x103D5:
+    case 0x1085D:
+    case 0x108AF:
+    case 0x108FF:
+    case 0x10919:
+    case 0x109D2:
+    case 0x10A46:
+    case 0x10AEF:
+    case 0x10B5E:
+    case 0x10B7E:
+    case 0x10BAF:
+    case 0x10CFE:
+    case 0x10E72:
+    case 0x10F25:
+    case 0x10F54:
+    case 0x11064:
+    case 0x111F3:
+    case 0x11C6C:
+    case 0x16B5C:
+    case 0x1EC83:
+        return (double) 100.0;
+    case 0x0BF2:
+    case 0x0D72:
+    case 0x216F:
+    case 0x217F:
+    case 0x2180:
+    case 0x4EDF:
+    case 0x5343:
+    case 0x9621:
+    case 0x10122:
+    case 0x1014D:
+    case 0x10154:
+    case 0x10171:
+    case 0x1085E:
+    case 0x109DB:
+    case 0x10A47:
+    case 0x10B5F:
+    case 0x10B7F:
+    case 0x10CFF:
+    case 0x11065:
+    case 0x111F4:
+    case 0x1EC8C:
+        return (double) 1000.0;
+    case 0x137C:
+    case 0x2182:
+    case 0x4E07:
+    case 0x842C:
+    case 0x1012B:
+    case 0x10155:
+    case 0x1085F:
+    case 0x109E4:
+    case 0x16B5D:
+    case 0x1EC95:
+    case 0x1ECB3:
+        return (double) 10000.0;
+    case 0x2188:
+    case 0x109ED:
+    case 0x1EC9E:
+    case 0x1ECA0:
+    case 0x1ECB4:
+        return (double) 100000.0;
+    case 0x16B5E:
+        return (double) 1000000.0;
+    case 0x1ECA1:
+        return (double) 10000000.0;
+    case 0x4EBF:
+    case 0x5104:
+    case 0x16B5F:
+        return (double) 100000000.0;
+    case 0x16B60:
+        return (double) 10000000000.0;
+    case 0x5146:
+    case 0x16B61:
+        return (double) 1000000000000.0;
+    case 0x216A:
+    case 0x217A:
+    case 0x246A:
+    case 0x247E:
+    case 0x2492:
+    case 0x24EB:
+    case 0x16E8B:
+    case 0x1D2EB:
+        return (double) 11.0;
+    case 0x109BC:
+        return (double) 11.0/12.0;
+    case 0x0F2F:
+        return (double) 11.0/2.0;
+    case 0x216B:
+    case 0x217B:
+    case 0x246B:
+    case 0x247F:
+    case 0x2493:
+    case 0x24EC:
+    case 0x16E8C:
+    case 0x1D2EC:
+        return (double) 12.0;
+    case 0x246C:
+    case 0x2480:
+    case 0x2494:
+    case 0x24ED:
+    case 0x16E8D:
+    case 0x1D2ED:
+        return (double) 13.0;
+    case 0x0F30:
+        return (double) 13.0/2.0;
+    case 0x246D:
+    case 0x2481:
+    case 0x2495:
+    case 0x24EE:
+    case 0x16E8E:
+    case 0x1D2EE:
+        return (double) 14.0;
+    case 0x246E:
+    case 0x2482:
+    case 0x2496:
+    case 0x24EF:
+    case 0x16E8F:
+    case 0x1D2EF:
+        return (double) 15.0;
+    case 0x0F31:
+        return (double) 15.0/2.0;
+    case 0x09F9:
+    case 0x246F:
+    case 0x2483:
+    case 0x2497:
+    case 0x24F0:
+    case 0x16E90:
+    case 0x1D2F0:
+        return (double) 16.0;
+    case 0x16EE:
+    case 0x2470:
+    case 0x2484:
+    case 0x2498:
+    case 0x24F1:
+    case 0x16E91:
+    case 0x1D2F1:
+        return (double) 17.0;
+    case 0x0F32:
+        return (double) 17.0/2.0;
+    case 0x16EF:
+    case 0x2471:
+    case 0x2485:
+    case 0x2499:
+    case 0x24F2:
+    case 0x16E92:
+    case 0x1D2F2:
+        return (double) 18.0;
+    case 0x16F0:
+    case 0x2472:
+    case 0x2486:
+    case 0x249A:
+    case 0x24F3:
+    case 0x16E93:
+    case 0x1D2F3:
+        return (double) 19.0;
+    case 0x0032:
+    case 0x00B2:
+    case 0x0662:
+    case 0x06F2:
+    case 0x07C2:
+    case 0x0968:
+    case 0x09E8:
+    case 0x0A68:
+    case 0x0AE8:
+    case 0x0B68:
+    case 0x0BE8:
+    case 0x0C68:
+    case 0x0C7A:
+    case 0x0C7D:
+    case 0x0CE8:
+    case 0x0D68:
+    case 0x0DE8:
+    case 0x0E52:
+    case 0x0ED2:
+    case 0x0F22:
+    case 0x1042:
+    case 0x1092:
+    case 0x136A:
+    case 0x17E2:
+    case 0x17F2:
+    case 0x1812:
+    case 0x1948:
+    case 0x19D2:
+    case 0x1A82:
+    case 0x1A92:
+    case 0x1B52:
+    case 0x1BB2:
+    case 0x1C42:
+    case 0x1C52:
+    case 0x2082:
+    case 0x2161:
+    case 0x2171:
+    case 0x2461:
+    case 0x2475:
+    case 0x2489:
+    case 0x24F6:
+    case 0x2777:
+    case 0x2781:
+    case 0x278B:
+    case 0x3022:
+    case 0x3193:
+    case 0x3221:
+    case 0x3281:
+    case 0x3483:
+    case 0x4E8C:
+    case 0x5169:
+    case 0x5F0D:
+    case 0x5F10:
+    case 0x8CAE:
+    case 0x8CB3:
+    case 0x8D30:
+    case 0xA622:
+    case 0xA6E7:
+    case 0xA8D2:
+    case 0xA902:
+    case 0xA9D2:
+    case 0xA9F2:
+    case 0xAA52:
+    case 0xABF2:
+    case 0xF978:
+    case 0xFF12:
+    case 0x10108:
+    case 0x1015B:
+    case 0x1015C:
+    case 0x1015D:
+    case 0x1015E:
+    case 0x102E2:
+    case 0x103D2:
+    case 0x104A2:
+    case 0x10859:
+    case 0x1087A:
+    case 0x108A8:
+    case 0x1091A:
+    case 0x109C1:
+    case 0x10A41:
+    case 0x10B59:
+    case 0x10B79:
+    case 0x10BAA:
+    case 0x10D32:
+    case 0x10E61:
+    case 0x10F1E:
+    case 0x11053:
+    case 0x11068:
+    case 0x110F2:
+    case 0x11138:
+    case 0x111D2:
+    case 0x111E2:
+    case 0x112F2:
+    case 0x11452:
+    case 0x114D2:
+    case 0x11652:
+    case 0x116C2:
+    case 0x11732:
+    case 0x118E2:
+    case 0x11C52:
+    case 0x11C5B:
+    case 0x11D52:
+    case 0x11DA2:
+    case 0x12400:
+    case 0x12416:
+    case 0x1241F:
+    case 0x12423:
+    case 0x1242D:
+    case 0x12435:
+    case 0x1244A:
+    case 0x12450:
+    case 0x12456:
+    case 0x12459:
+    case 0x16A62:
+    case 0x16B52:
+    case 0x16E82:
+    case 0x16E95:
+    case 0x1D2E2:
+    case 0x1D361:
+    case 0x1D373:
+    case 0x1D7D0:
+    case 0x1D7DA:
+    case 0x1D7E4:
+    case 0x1D7EE:
+    case 0x1D7F8:
+    case 0x1E8C8:
+    case 0x1E952:
+    case 0x1EC72:
+    case 0x1ECA4:
+    case 0x1ECB2:
+    case 0x1F103:
+    case 0x22390:
+        return (double) 2.0;
+    case 0x109F7:
+        return (double) 2.0/12.0;
+    case 0x2154:
+    case 0x10177:
+    case 0x10E7E:
+    case 0x1245B:
+    case 0x1245E:
+    case 0x12466:
+        return (double) 2.0/3.0;
+    case 0x2156:
+        return (double) 2.0/5.0;
+    case 0x1373:
+    case 0x2473:
+    case 0x2487:
+    case 0x249B:
+    case 0x24F4:
+    case 0x3039:
+    case 0x3249:
+    case 0x5344:
+    case 0x5EFF:
+    case 0x10111:
+    case 0x102EB:
+    case 0x103D4:
+    case 0x1085C:
+    case 0x1087F:
+    case 0x108AE:
+    case 0x108FE:
+    case 0x10918:
+    case 0x109CA:
+    case 0x10A45:
+    case 0x10A9F:
+    case 0x10AEE:
+    case 0x10B5D:
+    case 0x10B7D:
+    case 0x10BAE:
+    case 0x10E6A:
+    case 0x10F23:
+    case 0x10F53:
+    case 0x1105C:
+    case 0x111EB:
+    case 0x1173B:
+    case 0x118EB:
+    case 0x11C64:
+    case 0x1D36A:
+    case 0x1EC7B:
+        return (double) 20.0;
+    case 0x1011A:
+    case 0x102F4:
+    case 0x109D3:
+    case 0x10E73:
+    case 0x1EC84:
+        return (double) 200.0;
+    case 0x10123:
+    case 0x109DC:
+    case 0x1EC8D:
+        return (double) 2000.0;
+    case 0x1012C:
+    case 0x109E5:
+    case 0x1EC96:
+        return (double) 20000.0;
+    case 0x109EE:
+    case 0x1EC9F:
+        return (double) 200000.0;
+    case 0x1ECA2:
+        return (double) 20000000.0;
+    case 0x3251:
+        return (double) 21.0;
+    case 0x12432:
+        return (double) 216000.0;
+    case 0x3252:
+        return (double) 22.0;
+    case 0x3253:
+        return (double) 23.0;
+    case 0x3254:
+        return (double) 24.0;
+    case 0x3255:
+        return (double) 25.0;
+    case 0x3256:
+        return (double) 26.0;
+    case 0x3257:
+        return (double) 27.0;
+    case 0x3258:
+        return (double) 28.0;
+    case 0x3259:
+        return (double) 29.0;
+    case 0x0033:
+    case 0x00B3:
+    case 0x0663:
+    case 0x06F3:
+    case 0x07C3:
+    case 0x0969:
+    case 0x09E9:
+    case 0x0A69:
+    case 0x0AE9:
+    case 0x0B69:
+    case 0x0BE9:
+    case 0x0C69:
+    case 0x0C7B:
+    case 0x0C7E:
+    case 0x0CE9:
+    case 0x0D69:
+    case 0x0DE9:
+    case 0x0E53:
+    case 0x0ED3:
+    case 0x0F23:
+    case 0x1043:
+    case 0x1093:
+    case 0x136B:
+    case 0x17E3:
+    case 0x17F3:
+    case 0x1813:
+    case 0x1949:
+    case 0x19D3:
+    case 0x1A83:
+    case 0x1A93:
+    case 0x1B53:
+    case 0x1BB3:
+    case 0x1C43:
+    case 0x1C53:
+    case 0x2083:
+    case 0x2162:
+    case 0x2172:
+    case 0x2462:
+    case 0x2476:
+    case 0x248A:
+    case 0x24F7:
+    case 0x2778:
+    case 0x2782:
+    case 0x278C:
+    case 0x3023:
+    case 0x3194:
+    case 0x3222:
+    case 0x3282:
+    case 0x4E09:
+    case 0x4EE8:
+    case 0x53C1:
+    case 0x53C2:
+    case 0x53C3:
+    case 0x53C4:
+    case 0x5F0E:
+    case 0xA623:
+    case 0xA6E8:
+    case 0xA8D3:
+    case 0xA903:
+    case 0xA9D3:
+    case 0xA9F3:
+    case 0xAA53:
+    case 0xABF3:
+    case 0xF96B:
+    case 0xFF13:
+    case 0x10109:
+    case 0x102E3:
+    case 0x104A3:
+    case 0x1085A:
+    case 0x1087B:
+    case 0x108A9:
+    case 0x1091B:
+    case 0x109C2:
+    case 0x10A42:
+    case 0x10B5A:
+    case 0x10B7A:
+    case 0x10BAB:
+    case 0x10D33:
+    case 0x10E62:
+    case 0x10F1F:
+    case 0x11054:
+    case 0x11069:
+    case 0x110F3:
+    case 0x11139:
+    case 0x111D3:
+    case 0x111E3:
+    case 0x112F3:
+    case 0x11453:
+    case 0x114D3:
+    case 0x11653:
+    case 0x116C3:
+    case 0x11733:
+    case 0x118E3:
+    case 0x11C53:
+    case 0x11C5C:
+    case 0x11D53:
+    case 0x11DA3:
+    case 0x12401:
+    case 0x12408:
+    case 0x12417:
+    case 0x12420:
+    case 0x12424:
+    case 0x12425:
+    case 0x1242E:
+    case 0x1242F:
+    case 0x12436:
+    case 0x12437:
+    case 0x1243A:
+    case 0x1243B:
+    case 0x1244B:
+    case 0x12451:
+    case 0x12457:
+    case 0x16A63:
+    case 0x16B53:
+    case 0x16E83:
+    case 0x16E96:
+    case 0x1D2E3:
+    case 0x1D362:
+    case 0x1D374:
+    case 0x1D7D1:
+    case 0x1D7DB:
+    case 0x1D7E5:
+    case 0x1D7EF:
+    case 0x1D7F9:
+    case 0x1E8C9:
+    case 0x1E953:
+    case 0x1EC73:
+    case 0x1ECA5:
+    case 0x1F104:
+    case 0x20AFD:
+    case 0x20B19:
+    case 0x22998:
+    case 0x23B1B:
+        return (double) 3.0;
+    case 0x109F8:
+        return (double) 3.0/12.0;
+    case 0x09F6:
+    case 0x0B77:
+    case 0x0D78:
+    case 0xA835:
+        return (double) 3.0/16.0;
+    case 0x0F2B:
+        return (double) 3.0/2.0;
+    case 0x0D5D:
+        return (double) 3.0/20.0;
+    case 0x00BE:
+    case 0x09F8:
+    case 0x0B74:
+    case 0x0D75:
+    case 0xA832:
+    case 0x10178:
+    case 0x1ECAF:
+        return (double) 3.0/4.0;
+    case 0x2157:
+        return (double) 3.0/5.0;
+    case 0x215C:
+        return (double) 3.0/8.0;
+    case 0x0D5A:
+        return (double) 3.0/80.0;
+    case 0x1374:
+    case 0x303A:
+    case 0x324A:
+    case 0x325A:
+    case 0x5345:
+    case 0x10112:
+    case 0x10165:
+    case 0x102EC:
+    case 0x109CB:
+    case 0x10E6B:
+    case 0x10F24:
+    case 0x1105D:
+    case 0x111EC:
+    case 0x118EC:
+    case 0x11C65:
+    case 0x1D36B:
+    case 0x1EC7C:
+    case 0x20983:
+        return (double) 30.0;
+    case 0x1011B:
+    case 0x1016B:
+    case 0x102F5:
+    case 0x109D4:
+    case 0x10E74:
+    case 0x1EC85:
+        return (double) 300.0;
+    case 0x10124:
+    case 0x109DD:
+    case 0x1EC8E:
+        return (double) 3000.0;
+    case 0x1012D:
+    case 0x109E6:
+    case 0x1EC97:
+        return (double) 30000.0;
+    case 0x109EF:
+        return (double) 300000.0;
+    case 0x325B:
+        return (double) 31.0;
+    case 0x325C:
+        return (double) 32.0;
+    case 0x325D:
+        return (double) 33.0;
+    case 0x325E:
+        return (double) 34.0;
+    case 0x325F:
+        return (double) 35.0;
+    case 0x32B1:
+        return (double) 36.0;
+    case 0x32B2:
+        return (double) 37.0;
+    case 0x32B3:
+        return (double) 38.0;
+    case 0x32B4:
+        return (double) 39.0;
+    case 0x0034:
+    case 0x0664:
+    case 0x06F4:
+    case 0x07C4:
+    case 0x096A:
+    case 0x09EA:
+    case 0x0A6A:
+    case 0x0AEA:
+    case 0x0B6A:
+    case 0x0BEA:
+    case 0x0C6A:
+    case 0x0CEA:
+    case 0x0D6A:
+    case 0x0DEA:
+    case 0x0E54:
+    case 0x0ED4:
+    case 0x0F24:
+    case 0x1044:
+    case 0x1094:
+    case 0x136C:
+    case 0x17E4:
+    case 0x17F4:
+    case 0x1814:
+    case 0x194A:
+    case 0x19D4:
+    case 0x1A84:
+    case 0x1A94:
+    case 0x1B54:
+    case 0x1BB4:
+    case 0x1C44:
+    case 0x1C54:
+    case 0x2074:
+    case 0x2084:
+    case 0x2163:
+    case 0x2173:
+    case 0x2463:
+    case 0x2477:
+    case 0x248B:
+    case 0x24F8:
+    case 0x2779:
+    case 0x2783:
+    case 0x278D:
+    case 0x3024:
+    case 0x3195:
+    case 0x3223:
+    case 0x3283:
+    case 0x4E96:
+    case 0x56DB:
+    case 0x8086:
+    case 0xA624:
+    case 0xA6E9:
+    case 0xA8D4:
+    case 0xA904:
+    case 0xA9D4:
+    case 0xA9F4:
+    case 0xAA54:
+    case 0xABF4:
+    case 0xFF14:
+    case 0x1010A:
+    case 0x102E4:
+    case 0x104A4:
+    case 0x1087C:
+    case 0x108AA:
+    case 0x108AB:
+    case 0x109C3:
+    case 0x10A43:
+    case 0x10B5B:
+    case 0x10B7B:
+    case 0x10BAC:
+    case 0x10D34:
+    case 0x10E63:
+    case 0x10F20:
+    case 0x11055:
+    case 0x1106A:
+    case 0x110F4:
+    case 0x1113A:
+    case 0x111D4:
+    case 0x111E4:
+    case 0x112F4:
+    case 0x11454:
+    case 0x114D4:
+    case 0x11654:
+    case 0x116C4:
+    case 0x11734:
+    case 0x118E4:
+    case 0x11C54:
+    case 0x11C5D:
+    case 0x11D54:
+    case 0x11DA4:
+    case 0x12402:
+    case 0x12409:
+    case 0x1240F:
+    case 0x12418:
+    case 0x12421:
+    case 0x12426:
+    case 0x12430:
+    case 0x12438:
+    case 0x1243C:
+    case 0x1243D:
+    case 0x1243E:
+    case 0x1243F:
+    case 0x1244C:
+    case 0x12452:
+    case 0x12453:
+    case 0x12469:
+    case 0x16A64:
+    case 0x16B54:
+    case 0x16E84:
+    case 0x1D2E4:
+    case 0x1D363:
+    case 0x1D375:
+    case 0x1D7D2:
+    case 0x1D7DC:
+    case 0x1D7E6:
+    case 0x1D7F0:
+    case 0x1D7FA:
+    case 0x1E8CA:
+    case 0x1E954:
+    case 0x1EC74:
+    case 0x1ECA6:
+    case 0x1F105:
+    case 0x20064:
+    case 0x200E2:
+    case 0x2626D:
+        return (double) 4.0;
+    case 0x109F9:
+        return (double) 4.0/12.0;
+    case 0x2158:
+        return (double) 4.0/5.0;
+    case 0x1375:
+    case 0x324B:
+    case 0x32B5:
+    case 0x534C:
+    case 0x10113:
+    case 0x102ED:
+    case 0x109CC:
+    case 0x10E6C:
+    case 0x1105E:
+    case 0x111ED:
+    case 0x118ED:
+    case 0x11C66:
+    case 0x12467:
+    case 0x1D36C:
+    case 0x1EC7D:
+    case 0x2098C:
+    case 0x2099C:
+        return (double) 40.0;
+    case 0x1011C:
+    case 0x102F6:
+    case 0x109D5:
+    case 0x10E75:
+    case 0x1EC86:
+        return (double) 400.0;
+    case 0x10125:
+    case 0x109DE:
+    case 0x1EC8F:
+        return (double) 4000.0;
+    case 0x1012E:
+    case 0x109E7:
+    case 0x1EC98:
+        return (double) 40000.0;
+    case 0x109F0:
+        return (double) 400000.0;
+    case 0x32B6:
+        return (double) 41.0;
+    case 0x32B7:
+        return (double) 42.0;
+    case 0x32B8:
+        return (double) 43.0;
+    case 0x12433:
+        return (double) 432000.0;
+    case 0x32B9:
+        return (double) 44.0;
+    case 0x32BA:
+        return (double) 45.0;
+    case 0x32BB:
+        return (double) 46.0;
+    case 0x32BC:
+        return (double) 47.0;
+    case 0x32BD:
+        return (double) 48.0;
+    case 0x32BE:
+        return (double) 49.0;
+    case 0x0035:
+    case 0x0665:
+    case 0x06F5:
+    case 0x07C5:
+    case 0x096B:
+    case 0x09EB:
+    case 0x0A6B:
+    case 0x0AEB:
+    case 0x0B6B:
+    case 0x0BEB:
+    case 0x0C6B:
+    case 0x0CEB:
+    case 0x0D6B:
+    case 0x0DEB:
+    case 0x0E55:
+    case 0x0ED5:
+    case 0x0F25:
+    case 0x1045:
+    case 0x1095:
+    case 0x136D:
+    case 0x17E5:
+    case 0x17F5:
+    case 0x1815:
+    case 0x194B:
+    case 0x19D5:
+    case 0x1A85:
+    case 0x1A95:
+    case 0x1B55:
+    case 0x1BB5:
+    case 0x1C45:
+    case 0x1C55:
+    case 0x2075:
+    case 0x2085:
+    case 0x2164:
+    case 0x2174:
+    case 0x2464:
+    case 0x2478:
+    case 0x248C:
+    case 0x24F9:
+    case 0x277A:
+    case 0x2784:
+    case 0x278E:
+    case 0x3025:
+    case 0x3224:
+    case 0x3284:
+    case 0x3405:
+    case 0x382A:
+    case 0x4E94:
+    case 0x4F0D:
+    case 0xA625:
+    case 0xA6EA:
+    case 0xA8D5:
+    case 0xA905:
+    case 0xA9D5:
+    case 0xA9F5:
+    case 0xAA55:
+    case 0xABF5:
+    case 0xFF15:
+    case 0x1010B:
+    case 0x10143:
+    case 0x10148:
+    case 0x1014F:
+    case 0x1015F:
+    case 0x10173:
+    case 0x102E5:
+    case 0x10321:
+    case 0x104A5:
+    case 0x1087D:
+    case 0x108AC:
+    case 0x108FC:
+    case 0x109C4:
+    case 0x10AEC:
+    case 0x10CFB:
+    case 0x10D35:
+    case 0x10E64:
+    case 0x10F21:
+    case 0x11056:
+    case 0x1106B:
+    case 0x110F5:
+    case 0x1113B:
+    case 0x111D5:
+    case 0x111E5:
+    case 0x112F5:
+    case 0x11455:
+    case 0x114D5:
+    case 0x11655:
+    case 0x116C5:
+    case 0x11735:
+    case 0x118E5:
+    case 0x11C55:
+    case 0x11C5E:
+    case 0x11D55:
+    case 0x11DA5:
+    case 0x12403:
+    case 0x1240A:
+    case 0x12410:
+    case 0x12419:
+    case 0x12422:
+    case 0x12427:
+    case 0x12431:
+    case 0x12439:
+    case 0x1244D:
+    case 0x12454:
+    case 0x12455:
+    case 0x1246A:
+    case 0x16A65:
+    case 0x16B55:
+    case 0x16E85:
+    case 0x1D2E5:
+    case 0x1D364:
+    case 0x1D376:
+    case 0x1D378:
+    case 0x1D7D3:
+    case 0x1D7DD:
+    case 0x1D7E7:
+    case 0x1D7F1:
+    case 0x1D7FB:
+    case 0x1E8CB:
+    case 0x1E955:
+    case 0x1EC75:
+    case 0x1ECA7:
+    case 0x1F106:
+    case 0x20121:
+        return (double) 5.0;
+    case 0x109FA:
+        return (double) 5.0/12.0;
+    case 0x0F2C:
+        return (double) 5.0/2.0;
+    case 0x215A:
+    case 0x1245C:
+        return (double) 5.0/6.0;
+    case 0x215D:
+        return (double) 5.0/8.0;
+    case 0x1376:
+    case 0x216C:
+    case 0x217C:
+    case 0x2186:
+    case 0x324C:
+    case 0x32BF:
+    case 0x10114:
+    case 0x10144:
+    case 0x1014A:
+    case 0x10151:
+    case 0x10166:
+    case 0x10167:
+    case 0x10168:
+    case 0x10169:
+    case 0x10174:
+    case 0x102EE:
+    case 0x10323:
+    case 0x109CD:
+    case 0x10A7E:
+    case 0x10CFD:
+    case 0x10E6D:
+    case 0x1105F:
+    case 0x111EE:
+    case 0x118EE:
+    case 0x11C67:
+    case 0x12468:
+    case 0x1D36D:
+    case 0x1EC7E:
+        return (double) 50.0;
+    case 0x216E:
+    case 0x217E:
+    case 0x1011D:
+    case 0x10145:
+    case 0x1014C:
+    case 0x10153:
+    case 0x1016C:
+    case 0x1016D:
+    case 0x1016E:
+    case 0x1016F:
+    case 0x10170:
+    case 0x102F7:
+    case 0x109D6:
+    case 0x10E76:
+    case 0x1EC87:
+        return (double) 500.0;
+    case 0x2181:
+    case 0x10126:
+    case 0x10146:
+    case 0x1014E:
+    case 0x10172:
+    case 0x109DF:
+    case 0x1EC90:
+        return (double) 5000.0;
+    case 0x2187:
+    case 0x1012F:
+    case 0x10147:
+    case 0x10156:
+    case 0x109E8:
+    case 0x1EC99:
+        return (double) 50000.0;
+    case 0x109F1:
+        return (double) 500000.0;
+    case 0x0036:
+    case 0x0666:
+    case 0x06F6:
+    case 0x07C6:
+    case 0x096C:
+    case 0x09EC:
+    case 0x0A6C:
+    case 0x0AEC:
+    case 0x0B6C:
+    case 0x0BEC:
+    case 0x0C6C:
+    case 0x0CEC:
+    case 0x0D6C:
+    case 0x0DEC:
+    case 0x0E56:
+    case 0x0ED6:
+    case 0x0F26:
+    case 0x1046:
+    case 0x1096:
+    case 0x136E:
+    case 0x17E6:
+    case 0x17F6:
+    case 0x1816:
+    case 0x194C:
+    case 0x19D6:
+    case 0x1A86:
+    case 0x1A96:
+    case 0x1B56:
+    case 0x1BB6:
+    case 0x1C46:
+    case 0x1C56:
+    case 0x2076:
+    case 0x2086:
+    case 0x2165:
+    case 0x2175:
+    case 0x2185:
+    case 0x2465:
+    case 0x2479:
+    case 0x248D:
+    case 0x24FA:
+    case 0x277B:
+    case 0x2785:
+    case 0x278F:
+    case 0x3026:
+    case 0x3225:
+    case 0x3285:
+    case 0x516D:
+    case 0x9646:
+    case 0x9678:
+    case 0xA626:
+    case 0xA6EB:
+    case 0xA8D6:
+    case 0xA906:
+    case 0xA9D6:
+    case 0xA9F6:
+    case 0xAA56:
+    case 0xABF6:
+    case 0xF9D1:
+    case 0xF9D3:
+    case 0xFF16:
+    case 0x1010C:
+    case 0x102E6:
+    case 0x104A6:
+    case 0x109C5:
+    case 0x10D36:
+    case 0x10E65:
+    case 0x11057:
+    case 0x1106C:
+    case 0x110F6:
+    case 0x1113C:
+    case 0x111D6:
+    case 0x111E6:
+    case 0x112F6:
+    case 0x11456:
+    case 0x114D6:
+    case 0x11656:
+    case 0x116C6:
+    case 0x11736:
+    case 0x118E6:
+    case 0x11C56:
+    case 0x11C5F:
+    case 0x11D56:
+    case 0x11DA6:
+    case 0x12404:
+    case 0x1240B:
+    case 0x12411:
+    case 0x1241A:
+    case 0x12428:
+    case 0x12440:
+    case 0x1244E:
+    case 0x1246B:
+    case 0x16A66:
+    case 0x16B56:
+    case 0x16E86:
+    case 0x1D2E6:
+    case 0x1D365:
+    case 0x1D7D4:
+    case 0x1D7DE:
+    case 0x1D7E8:
+    case 0x1D7F2:
+    case 0x1D7FC:
+    case 0x1E8CC:
+    case 0x1E956:
+    case 0x1EC76:
+    case 0x1ECA8:
+    case 0x1F107:
+    case 0x20AEA:
+        return (double) 6.0;
+    case 0x109FB:
+        return (double) 6.0/12.0;
+    case 0x1377:
+    case 0x324D:
+    case 0x10115:
+    case 0x102EF:
+    case 0x109CE:
+    case 0x10E6E:
+    case 0x11060:
+    case 0x111EF:
+    case 0x118EF:
+    case 0x11C68:
+    case 0x1D36E:
+    case 0x1EC7F:
+        return (double) 60.0;
+    case 0x1011E:
+    case 0x102F8:
+    case 0x109D7:
+    case 0x10E77:
+    case 0x1EC88:
+        return (double) 600.0;
+    case 0x10127:
+    case 0x109E0:
+    case 0x1EC91:
+        return (double) 6000.0;
+    case 0x10130:
+    case 0x109E9:
+    case 0x1EC9A:
+        return (double) 60000.0;
+    case 0x109F2:
+        return (double) 600000.0;
+    case 0x0037:
+    case 0x0667:
+    case 0x06F7:
+    case 0x07C7:
+    case 0x096D:
+    case 0x09ED:
+    case 0x0A6D:
+    case 0x0AED:
+    case 0x0B6D:
+    case 0x0BED:
+    case 0x0C6D:
+    case 0x0CED:
+    case 0x0D6D:
+    case 0x0DED:
+    case 0x0E57:
+    case 0x0ED7:
+    case 0x0F27:
+    case 0x1047:
+    case 0x1097:
+    case 0x136F:
+    case 0x17E7:
+    case 0x17F7:
+    case 0x1817:
+    case 0x194D:
+    case 0x19D7:
+    case 0x1A87:
+    case 0x1A97:
+    case 0x1B57:
+    case 0x1BB7:
+    case 0x1C47:
+    case 0x1C57:
+    case 0x2077:
+    case 0x2087:
+    case 0x2166:
+    case 0x2176:
+    case 0x2466:
+    case 0x247A:
+    case 0x248E:
+    case 0x24FB:
+    case 0x277C:
+    case 0x2786:
+    case 0x2790:
+    case 0x3027:
+    case 0x3226:
+    case 0x3286:
+    case 0x3B4D:
+    case 0x4E03:
+    case 0x67D2:
+    case 0x6F06:
+    case 0xA627:
+    case 0xA6EC:
+    case 0xA8D7:
+    case 0xA907:
+    case 0xA9D7:
+    case 0xA9F7:
+    case 0xAA57:
+    case 0xABF7:
+    case 0xFF17:
+    case 0x1010D:
+    case 0x102E7:
+    case 0x104A7:
+    case 0x109C6:
+    case 0x10D37:
+    case 0x10E66:
+    case 0x11058:
+    case 0x1106D:
+    case 0x110F7:
+    case 0x1113D:
+    case 0x111D7:
+    case 0x111E7:
+    case 0x112F7:
+    case 0x11457:
+    case 0x114D7:
+    case 0x11657:
+    case 0x116C7:
+    case 0x11737:
+    case 0x118E7:
+    case 0x11C57:
+    case 0x11C60:
+    case 0x11D57:
+    case 0x11DA7:
+    case 0x12405:
+    case 0x1240C:
+    case 0x12412:
+    case 0x1241B:
+    case 0x12429:
+    case 0x12441:
+    case 0x12442:
+    case 0x12443:
+    case 0x1246C:
+    case 0x16A67:
+    case 0x16B57:
+    case 0x16E87:
+    case 0x1D2E7:
+    case 0x1D366:
+    case 0x1D7D5:
+    case 0x1D7DF:
+    case 0x1D7E9:
+    case 0x1D7F3:
+    case 0x1D7FD:
+    case 0x1E8CD:
+    case 0x1E957:
+    case 0x1EC77:
+    case 0x1ECA9:
+    case 0x1F108:
+    case 0x20001:
+        return (double) 7.0;
+    case 0x109FC:
+        return (double) 7.0/12.0;
+    case 0x0F2D:
+        return (double) 7.0/2.0;
+    case 0x215E:
+        return (double) 7.0/8.0;
+    case 0x1378:
+    case 0x324E:
+    case 0x10116:
+    case 0x102F0:
+    case 0x109CF:
+    case 0x10E6F:
+    case 0x11061:
+    case 0x111F0:
+    case 0x118F0:
+    case 0x11C69:
+    case 0x1D36F:
+    case 0x1EC80:
+        return (double) 70.0;
+    case 0x1011F:
+    case 0x102F9:
+    case 0x109D8:
+    case 0x10E78:
+    case 0x1EC89:
+        return (double) 700.0;
+    case 0x10128:
+    case 0x109E1:
+    case 0x1EC92:
+        return (double) 7000.0;
+    case 0x10131:
+    case 0x109EA:
+    case 0x1EC9B:
+        return (double) 70000.0;
+    case 0x109F3:
+        return (double) 700000.0;
+    case 0x0038:
+    case 0x0668:
+    case 0x06F8:
+    case 0x07C8:
+    case 0x096E:
+    case 0x09EE:
+    case 0x0A6E:
+    case 0x0AEE:
+    case 0x0B6E:
+    case 0x0BEE:
+    case 0x0C6E:
+    case 0x0CEE:
+    case 0x0D6E:
+    case 0x0DEE:
+    case 0x0E58:
+    case 0x0ED8:
+    case 0x0F28:
+    case 0x1048:
+    case 0x1098:
+    case 0x1370:
+    case 0x17E8:
+    case 0x17F8:
+    case 0x1818:
+    case 0x194E:
+    case 0x19D8:
+    case 0x1A88:
+    case 0x1A98:
+    case 0x1B58:
+    case 0x1BB8:
+    case 0x1C48:
+    case 0x1C58:
+    case 0x2078:
+    case 0x2088:
+    case 0x2167:
+    case 0x2177:
+    case 0x2467:
+    case 0x247B:
+    case 0x248F:
+    case 0x24FC:
+    case 0x277D:
+    case 0x2787:
+    case 0x2791:
+    case 0x3028:
+    case 0x3227:
+    case 0x3287:
+    case 0x516B:
+    case 0x634C:
+    case 0xA628:
+    case 0xA6ED:
+    case 0xA8D8:
+    case 0xA908:
+    case 0xA9D8:
+    case 0xA9F8:
+    case 0xAA58:
+    case 0xABF8:
+    case 0xFF18:
+    case 0x1010E:
+    case 0x102E8:
+    case 0x104A8:
+    case 0x109C7:
+    case 0x10D38:
+    case 0x10E67:
+    case 0x11059:
+    case 0x1106E:
+    case 0x110F8:
+    case 0x1113E:
+    case 0x111D8:
+    case 0x111E8:
+    case 0x112F8:
+    case 0x11458:
+    case 0x114D8:
+    case 0x11658:
+    case 0x116C8:
+    case 0x11738:
+    case 0x118E8:
+    case 0x11C58:
+    case 0x11C61:
+    case 0x11D58:
+    case 0x11DA8:
+    case 0x12406:
+    case 0x1240D:
+    case 0x12413:
+    case 0x1241C:
+    case 0x1242A:
+    case 0x12444:
+    case 0x12445:
+    case 0x1246D:
+    case 0x16A68:
+    case 0x16B58:
+    case 0x16E88:
+    case 0x1D2E8:
+    case 0x1D367:
+    case 0x1D7D6:
+    case 0x1D7E0:
+    case 0x1D7EA:
+    case 0x1D7F4:
+    case 0x1D7FE:
+    case 0x1E8CE:
+    case 0x1E958:
+    case 0x1EC78:
+    case 0x1ECAA:
+    case 0x1F109:
+        return (double) 8.0;
+    case 0x109FD:
+        return (double) 8.0/12.0;
+    case 0x1379:
+    case 0x324F:
+    case 0x10117:
+    case 0x102F1:
+    case 0x10E70:
+    case 0x11062:
+    case 0x111F1:
+    case 0x118F1:
+    case 0x11C6A:
+    case 0x1D370:
+    case 0x1EC81:
+        return (double) 80.0;
+    case 0x10120:
+    case 0x102FA:
+    case 0x109D9:
+    case 0x10E79:
+    case 0x1EC8A:
+        return (double) 800.0;
+    case 0x10129:
+    case 0x109E2:
+    case 0x1EC93:
+        return (double) 8000.0;
+    case 0x10132:
+    case 0x109EB:
+    case 0x1EC9C:
+        return (double) 80000.0;
+    case 0x109F4:
+        return (double) 800000.0;
+    case 0x0039:
+    case 0x0669:
+    case 0x06F9:
+    case 0x07C9:
+    case 0x096F:
+    case 0x09EF:
+    case 0x0A6F:
+    case 0x0AEF:
+    case 0x0B6F:
+    case 0x0BEF:
+    case 0x0C6F:
+    case 0x0CEF:
+    case 0x0D6F:
+    case 0x0DEF:
+    case 0x0E59:
+    case 0x0ED9:
+    case 0x0F29:
+    case 0x1049:
+    case 0x1099:
+    case 0x1371:
+    case 0x17E9:
+    case 0x17F9:
+    case 0x1819:
+    case 0x194F:
+    case 0x19D9:
+    case 0x1A89:
+    case 0x1A99:
+    case 0x1B59:
+    case 0x1BB9:
+    case 0x1C49:
+    case 0x1C59:
+    case 0x2079:
+    case 0x2089:
+    case 0x2168:
+    case 0x2178:
+    case 0x2468:
+    case 0x247C:
+    case 0x2490:
+    case 0x24FD:
+    case 0x277E:
+    case 0x2788:
+    case 0x2792:
+    case 0x3029:
+    case 0x3228:
+    case 0x3288:
+    case 0x4E5D:
+    case 0x5EFE:
+    case 0x7396:
+    case 0xA629:
+    case 0xA6EE:
+    case 0xA8D9:
+    case 0xA909:
+    case 0xA9D9:
+    case 0xA9F9:
+    case 0xAA59:
+    case 0xABF9:
+    case 0xFF19:
+    case 0x1010F:
+    case 0x102E9:
+    case 0x104A9:
+    case 0x109C8:
+    case 0x10D39:
+    case 0x10E68:
+    case 0x1105A:
+    case 0x1106F:
+    case 0x110F9:
+    case 0x1113F:
+    case 0x111D9:
+    case 0x111E9:
+    case 0x112F9:
+    case 0x11459:
+    case 0x114D9:
+    case 0x11659:
+    case 0x116C9:
+    case 0x11739:
+    case 0x118E9:
+    case 0x11C59:
+    case 0x11C62:
+    case 0x11D59:
+    case 0x11DA9:
+    case 0x12407:
+    case 0x1240E:
+    case 0x12414:
+    case 0x1241D:
+    case 0x1242B:
+    case 0x12446:
+    case 0x12447:
+    case 0x12448:
+    case 0x12449:
+    case 0x1246E:
+    case 0x16A69:
+    case 0x16B59:
+    case 0x16E89:
+    case 0x1D2E9:
+    case 0x1D368:
+    case 0x1D7D7:
+    case 0x1D7E1:
+    case 0x1D7EB:
+    case 0x1D7F5:
+    case 0x1D7FF:
+    case 0x1E8CF:
+    case 0x1E959:
+    case 0x1EC79:
+    case 0x1ECAB:
+    case 0x1F10A:
+    case 0x2F890:
+        return (double) 9.0;
+    case 0x109FE:
+        return (double) 9.0/12.0;
+    case 0x0F2E:
+        return (double) 9.0/2.0;
+    case 0x137A:
+    case 0x10118:
+    case 0x102F2:
+    case 0x10341:
+    case 0x10E71:
+    case 0x11063:
+    case 0x111F2:
+    case 0x118F2:
+    case 0x11C6B:
+    case 0x1D371:
+    case 0x1EC82:
+        return (double) 90.0;
+    case 0x10121:
+    case 0x102FB:
+    case 0x1034A:
+    case 0x109DA:
+    case 0x10E7A:
+    case 0x1EC8B:
+        return (double) 900.0;
+    case 0x1012A:
+    case 0x109E3:
+    case 0x1EC94:
+        return (double) 9000.0;
+    case 0x10133:
+    case 0x109EC:
+    case 0x1EC9D:
+        return (double) 90000.0;
+    case 0x109F5:
+        return (double) 900000.0;
+    }
+    return -1.0;
+}
+
+/* Returns 1 for Unicode characters having the bidirectional
+ * type 'WS', 'B' or 'S' or the category 'Zs', 0 otherwise.
+ */
+int numba_PyUnicode_IsWhitespace(const Py_UCS4 ch)
+{
+    switch (ch) {
+    case 0x0009:
+    case 0x000A:
+    case 0x000B:
+    case 0x000C:
+    case 0x000D:
+    case 0x001C:
+    case 0x001D:
+    case 0x001E:
+    case 0x001F:
+    case 0x0020:
+    case 0x0085:
+    case 0x00A0:
+    case 0x1680:
+    case 0x2000:
+    case 0x2001:
+    case 0x2002:
+    case 0x2003:
+    case 0x2004:
+    case 0x2005:
+    case 0x2006:
+    case 0x2007:
+    case 0x2008:
+    case 0x2009:
+    case 0x200A:
+    case 0x2028:
+    case 0x2029:
+    case 0x202F:
+    case 0x205F:
+    case 0x3000:
+        return 1;
+    }
+    return 0;
+}
+
+/* Returns 1 for Unicode characters having the line break
+ * property 'BK', 'CR', 'LF' or 'NL' or having bidirectional
+ * type 'B', 0 otherwise.
+ */
+int numba_PyUnicode_IsLinebreak(const Py_UCS4 ch)
+{
+    switch (ch) {
+    case 0x000A:
+    case 0x000B:
+    case 0x000C:
+    case 0x000D:
+    case 0x001C:
+    case 0x001D:
+    case 0x001E:
+    case 0x0085:
+    case 0x2028:
+    case 0x2029:
+        return 1;
+    }
+    return 0;
+}
+
+#endif /* _UNICODETYPE_DB_H */
diff --git a/shared/package-files/04/04b2330eb45c6830d80695e3f277f0dda21769ba2ce989703e3a277589a1e882/_core.py b/shared/package-files/04/04b2330eb45c6830d80695e3f277f0dda21769ba2ce989703e3a277589a1e882/_core.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb5598a98d5afbc93954d74e3ecc78b4e572606d
--- /dev/null
+++ b/shared/package-files/04/04b2330eb45c6830d80695e3f277f0dda21769ba2ce989703e3a277589a1e882/_core.py
@@ -0,0 +1,1946 @@
+from __future__ import annotations
+
+import importlib
+from typing import (
+    TYPE_CHECKING,
+    Callable,
+    Literal,
+)
+
+from pandas._config import get_option
+
+from pandas.util._decorators import (
+    Appender,
+    Substitution,
+)
+
+from pandas.core.dtypes.common import (
+    is_integer,
+    is_list_like,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCSeries,
+)
+
+from pandas.core.base import PandasObject
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Sequence,
+    )
+    import types
+
+    from matplotlib.axes import Axes
+    import numpy as np
+
+    from pandas._typing import IndexLabel
+
+    from pandas import (
+        DataFrame,
+        Series,
+    )
+    from pandas.core.groupby.generic import DataFrameGroupBy
+
+
+def hist_series(
+    self: Series,
+    by=None,
+    ax=None,
+    grid: bool = True,
+    xlabelsize: int | None = None,
+    xrot: float | None = None,
+    ylabelsize: int | None = None,
+    yrot: float | None = None,
+    figsize: tuple[int, int] | None = None,
+    bins: int | Sequence[int] = 10,
+    backend: str | None = None,
+    legend: bool = False,
+    **kwargs,
+):
+    """
+    Draw histogram of the input series using matplotlib.
+
+    Parameters
+    ----------
+    by : object, optional
+        If passed, then used to form histograms for separate groups.
+    ax : matplotlib axis object
+        If not passed, uses gca().
+    grid : bool, default True
+        Whether to show axis grid lines.
+    xlabelsize : int, default None
+        If specified changes the x-axis label size.
+    xrot : float, default None
+        Rotation of x axis labels.
+    ylabelsize : int, default None
+        If specified changes the y-axis label size.
+    yrot : float, default None
+        Rotation of y axis labels.
+    figsize : tuple, default None
+        Figure size in inches by default.
+    bins : int or sequence, default 10
+        Number of histogram bins to be used. If an integer is given, bins + 1
+        bin edges are calculated and returned. If bins is a sequence, gives
+        bin edges, including left edge of first bin and right edge of last
+        bin. In this case, bins is returned unmodified.
+    backend : str, default None
+        Backend to use instead of the backend specified in the option
+        ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
+        specify the ``plotting.backend`` for the whole session, set
+        ``pd.options.plotting.backend``.
+    legend : bool, default False
+        Whether to show the legend.
+
+    **kwargs
+        To be passed to the actual plotting function.
+
+    Returns
+    -------
+    matplotlib.AxesSubplot
+        A histogram plot.
+
+    See Also
+    --------
+    matplotlib.axes.Axes.hist : Plot a histogram using matplotlib.
+
+    Examples
+    --------
+    For Series:
+
+    .. plot::
+        :context: close-figs
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 2, 4, 6, 6], index=lst)
+        >>> hist = ser.hist()
+
+    For Groupby:
+
+    .. plot::
+        :context: close-figs
+
+        >>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
+        >>> ser = pd.Series([1, 2, 2, 4, 6, 6], index=lst)
+        >>> hist = ser.groupby(level=0).hist()
+    """
+    plot_backend = _get_plot_backend(backend)
+    return plot_backend.hist_series(
+        self,
+        by=by,
+        ax=ax,
+        grid=grid,
+        xlabelsize=xlabelsize,
+        xrot=xrot,
+        ylabelsize=ylabelsize,
+        yrot=yrot,
+        figsize=figsize,
+        bins=bins,
+        legend=legend,
+        **kwargs,
+    )
+
+
+def hist_frame(
+    data: DataFrame,
+    column: IndexLabel | None = None,
+    by=None,
+    grid: bool = True,
+    xlabelsize: int | None = None,
+    xrot: float | None = None,
+    ylabelsize: int | None = None,
+    yrot: float | None = None,
+    ax=None,
+    sharex: bool = False,
+    sharey: bool = False,
+    figsize: tuple[int, int] | None = None,
+    layout: tuple[int, int] | None = None,
+    bins: int | Sequence[int] = 10,
+    backend: str | None = None,
+    legend: bool = False,
+    **kwargs,
+):
+    """
+    Make a histogram of the DataFrame's columns.
+
+    A `histogram`_ is a representation of the distribution of data.
+    This function calls :meth:`matplotlib.pyplot.hist`, on each series in
+    the DataFrame, resulting in one histogram per column.
+
+    .. _histogram: https://en.wikipedia.org/wiki/Histogram
+
+    Parameters
+    ----------
+    data : DataFrame
+        The pandas object holding the data.
+    column : str or sequence, optional
+        If passed, will be used to limit data to a subset of columns.
+    by : object, optional
+        If passed, then used to form histograms for separate groups.
+    grid : bool, default True
+        Whether to show axis grid lines.
+    xlabelsize : int, default None
+        If specified changes the x-axis label size.
+    xrot : float, default None
+        Rotation of x axis labels. For example, a value of 90 displays the
+        x labels rotated 90 degrees clockwise.
+    ylabelsize : int, default None
+        If specified changes the y-axis label size.
+    yrot : float, default None
+        Rotation of y axis labels. For example, a value of 90 displays the
+        y labels rotated 90 degrees clockwise.
+    ax : Matplotlib axes object, default None
+        The axes to plot the histogram on.
+    sharex : bool, default True if ax is None else False
+        In case subplots=True, share x axis and set some x axis labels to
+        invisible; defaults to True if ax is None otherwise False if an ax
+        is passed in.
+        Note that passing in both an ax and sharex=True will alter all x axis
+        labels for all subplots in a figure.
+    sharey : bool, default False
+        In case subplots=True, share y axis and set some y axis labels to
+        invisible.
+    figsize : tuple, optional
+        The size in inches of the figure to create. Uses the value in
+        `matplotlib.rcParams` by default.
+    layout : tuple, optional
+        Tuple of (rows, columns) for the layout of the histograms.
+    bins : int or sequence, default 10
+        Number of histogram bins to be used. If an integer is given, bins + 1
+        bin edges are calculated and returned. If bins is a sequence, gives
+        bin edges, including left edge of first bin and right edge of last
+        bin. In this case, bins is returned unmodified.
+
+    backend : str, default None
+        Backend to use instead of the backend specified in the option
+        ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
+        specify the ``plotting.backend`` for the whole session, set
+        ``pd.options.plotting.backend``.
+
+    legend : bool, default False
+        Whether to show the legend.
+
+    **kwargs
+        All other plotting keyword arguments to be passed to
+        :meth:`matplotlib.pyplot.hist`.
+
+    Returns
+    -------
+    matplotlib.AxesSubplot or numpy.ndarray of them
+
+    See Also
+    --------
+    matplotlib.pyplot.hist : Plot a histogram using matplotlib.
+
+    Examples
+    --------
+    This example draws a histogram based on the length and width of
+    some animals, displayed in three bins
+
+    .. plot::
+        :context: close-figs
+
+        >>> data = {'length': [1.5, 0.5, 1.2, 0.9, 3],
+        ...         'width': [0.7, 0.2, 0.15, 0.2, 1.1]}
+        >>> index = ['pig', 'rabbit', 'duck', 'chicken', 'horse']
+        >>> df = pd.DataFrame(data, index=index)
+        >>> hist = df.hist(bins=3)
+    """
+    plot_backend = _get_plot_backend(backend)
+    return plot_backend.hist_frame(
+        data,
+        column=column,
+        by=by,
+        grid=grid,
+        xlabelsize=xlabelsize,
+        xrot=xrot,
+        ylabelsize=ylabelsize,
+        yrot=yrot,
+        ax=ax,
+        sharex=sharex,
+        sharey=sharey,
+        figsize=figsize,
+        layout=layout,
+        legend=legend,
+        bins=bins,
+        **kwargs,
+    )
+
+
+_boxplot_doc = """
+Make a box plot from DataFrame columns.
+
+Make a box-and-whisker plot from DataFrame columns, optionally grouped
+by some other columns. A box plot is a method for graphically depicting
+groups of numerical data through their quartiles.
+The box extends from the Q1 to Q3 quartile values of the data,
+with a line at the median (Q2). The whiskers extend from the edges
+of box to show the range of the data. By default, they extend no more than
+`1.5 * IQR (IQR = Q3 - Q1)` from the edges of the box, ending at the farthest
+data point within that interval. Outliers are plotted as separate dots.
+
+For further details see
+Wikipedia's entry for `boxplot `_.
+
+Parameters
+----------
+%(data)s\
+column : str or list of str, optional
+    Column name or list of names, or vector.
+    Can be any valid input to :meth:`pandas.DataFrame.groupby`.
+by : str or array-like, optional
+    Column in the DataFrame to :meth:`pandas.DataFrame.groupby`.
+    One box-plot will be done per value of columns in `by`.
+ax : object of class matplotlib.axes.Axes, optional
+    The matplotlib axes to be used by boxplot.
+fontsize : float or str
+    Tick label font size in points or as a string (e.g., `large`).
+rot : float, default 0
+    The rotation angle of labels (in degrees)
+    with respect to the screen coordinate system.
+grid : bool, default True
+    Setting this to True will show the grid.
+figsize : A tuple (width, height) in inches
+    The size of the figure to create in matplotlib.
+layout : tuple (rows, columns), optional
+    For example, (3, 5) will display the subplots
+    using 3 rows and 5 columns, starting from the top-left.
+return_type : {'axes', 'dict', 'both'} or None, default 'axes'
+    The kind of object to return. The default is ``axes``.
+
+    * 'axes' returns the matplotlib axes the boxplot is drawn on.
+    * 'dict' returns a dictionary whose values are the matplotlib
+      Lines of the boxplot.
+    * 'both' returns a namedtuple with the axes and dict.
+    * when grouping with ``by``, a Series mapping columns to
+      ``return_type`` is returned.
+
+      If ``return_type`` is `None`, a NumPy array
+      of axes with the same shape as ``layout`` is returned.
+%(backend)s\
+
+**kwargs
+    All other plotting keyword arguments to be passed to
+    :func:`matplotlib.pyplot.boxplot`.
+
+Returns
+-------
+result
+    See Notes.
+
+See Also
+--------
+pandas.Series.plot.hist: Make a histogram.
+matplotlib.pyplot.boxplot : Matplotlib equivalent plot.
+
+Notes
+-----
+The return type depends on the `return_type` parameter:
+
+* 'axes' : object of class matplotlib.axes.Axes
+* 'dict' : dict of matplotlib.lines.Line2D objects
+* 'both' : a namedtuple with structure (ax, lines)
+
+For data grouped with ``by``, return a Series of the above or a numpy
+array:
+
+* :class:`~pandas.Series`
+* :class:`~numpy.array` (for ``return_type = None``)
+
+Use ``return_type='dict'`` when you want to tweak the appearance
+of the lines after plotting. In this case a dict containing the Lines
+making up the boxes, caps, fliers, medians, and whiskers is returned.
+
+Examples
+--------
+
+Boxplots can be created for every column in the dataframe
+by ``df.boxplot()`` or indicating the columns to be used:
+
+.. plot::
+    :context: close-figs
+
+    >>> np.random.seed(1234)
+    >>> df = pd.DataFrame(np.random.randn(10, 4),
+    ...                   columns=['Col1', 'Col2', 'Col3', 'Col4'])
+    >>> boxplot = df.boxplot(column=['Col1', 'Col2', 'Col3'])  # doctest: +SKIP
+
+Boxplots of variables distributions grouped by the values of a third
+variable can be created using the option ``by``. For instance:
+
+.. plot::
+    :context: close-figs
+
+    >>> df = pd.DataFrame(np.random.randn(10, 2),
+    ...                   columns=['Col1', 'Col2'])
+    >>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A',
+    ...                      'B', 'B', 'B', 'B', 'B'])
+    >>> boxplot = df.boxplot(by='X')
+
+A list of strings (i.e. ``['X', 'Y']``) can be passed to boxplot
+in order to group the data by combination of the variables in the x-axis:
+
+.. plot::
+    :context: close-figs
+
+    >>> df = pd.DataFrame(np.random.randn(10, 3),
+    ...                   columns=['Col1', 'Col2', 'Col3'])
+    >>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A',
+    ...                      'B', 'B', 'B', 'B', 'B'])
+    >>> df['Y'] = pd.Series(['A', 'B', 'A', 'B', 'A',
+    ...                      'B', 'A', 'B', 'A', 'B'])
+    >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by=['X', 'Y'])
+
+The layout of boxplot can be adjusted giving a tuple to ``layout``:
+
+.. plot::
+    :context: close-figs
+
+    >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
+    ...                      layout=(2, 1))
+
+Additional formatting can be done to the boxplot, like suppressing the grid
+(``grid=False``), rotating the labels in the x-axis (i.e. ``rot=45``)
+or changing the fontsize (i.e. ``fontsize=15``):
+
+.. plot::
+    :context: close-figs
+
+    >>> boxplot = df.boxplot(grid=False, rot=45, fontsize=15)  # doctest: +SKIP
+
+The parameter ``return_type`` can be used to select the type of element
+returned by `boxplot`.  When ``return_type='axes'`` is selected,
+the matplotlib axes on which the boxplot is drawn are returned:
+
+    >>> boxplot = df.boxplot(column=['Col1', 'Col2'], return_type='axes')
+    >>> type(boxplot)
+    
+
+When grouping with ``by``, a Series mapping columns to ``return_type``
+is returned:
+
+    >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
+    ...                      return_type='axes')
+    >>> type(boxplot)
+    
+
+If ``return_type`` is `None`, a NumPy array of axes with the same shape
+as ``layout`` is returned:
+
+    >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
+    ...                      return_type=None)
+    >>> type(boxplot)
+    
+"""
+
+_backend_doc = """\
+backend : str, default None
+    Backend to use instead of the backend specified in the option
+    ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
+    specify the ``plotting.backend`` for the whole session, set
+    ``pd.options.plotting.backend``.
+"""
+
+
+_bar_or_line_doc = """
+        Parameters
+        ----------
+        x : label or position, optional
+            Allows plotting of one column versus another. If not specified,
+            the index of the DataFrame is used.
+        y : label or position, optional
+            Allows plotting of one column versus another. If not specified,
+            all numerical columns are used.
+        color : str, array-like, or dict, optional
+            The color for each of the DataFrame's columns. Possible values are:
+
+            - A single color string referred to by name, RGB or RGBA code,
+                for instance 'red' or '#a98d19'.
+
+            - A sequence of color strings referred to by name, RGB or RGBA
+                code, which will be used for each column recursively. For
+                instance ['green','yellow'] each column's %(kind)s will be filled in
+                green or yellow, alternatively. If there is only a single column to
+                be plotted, then only the first color from the color list will be
+                used.
+
+            - A dict of the form {column name : color}, so that each column will be
+                colored accordingly. For example, if your columns are called `a` and
+                `b`, then passing {'a': 'green', 'b': 'red'} will color %(kind)ss for
+                column `a` in green and %(kind)ss for column `b` in red.
+
+        **kwargs
+            Additional keyword arguments are documented in
+            :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        matplotlib.axes.Axes or np.ndarray of them
+            An ndarray is returned with one :class:`matplotlib.axes.Axes`
+            per column when ``subplots=True``.
+"""
+
+
+@Substitution(data="data : DataFrame\n    The data to visualize.\n", backend="")
+@Appender(_boxplot_doc)
+def boxplot(
+    data: DataFrame,
+    column: str | list[str] | None = None,
+    by: str | list[str] | None = None,
+    ax: Axes | None = None,
+    fontsize: float | str | None = None,
+    rot: int = 0,
+    grid: bool = True,
+    figsize: tuple[float, float] | None = None,
+    layout: tuple[int, int] | None = None,
+    return_type: str | None = None,
+    **kwargs,
+):
+    plot_backend = _get_plot_backend("matplotlib")
+    return plot_backend.boxplot(
+        data,
+        column=column,
+        by=by,
+        ax=ax,
+        fontsize=fontsize,
+        rot=rot,
+        grid=grid,
+        figsize=figsize,
+        layout=layout,
+        return_type=return_type,
+        **kwargs,
+    )
+
+
+@Substitution(data="", backend=_backend_doc)
+@Appender(_boxplot_doc)
+def boxplot_frame(
+    self: DataFrame,
+    column=None,
+    by=None,
+    ax=None,
+    fontsize: int | None = None,
+    rot: int = 0,
+    grid: bool = True,
+    figsize: tuple[float, float] | None = None,
+    layout=None,
+    return_type=None,
+    backend=None,
+    **kwargs,
+):
+    plot_backend = _get_plot_backend(backend)
+    return plot_backend.boxplot_frame(
+        self,
+        column=column,
+        by=by,
+        ax=ax,
+        fontsize=fontsize,
+        rot=rot,
+        grid=grid,
+        figsize=figsize,
+        layout=layout,
+        return_type=return_type,
+        **kwargs,
+    )
+
+
+def boxplot_frame_groupby(
+    grouped: DataFrameGroupBy,
+    subplots: bool = True,
+    column=None,
+    fontsize: int | None = None,
+    rot: int = 0,
+    grid: bool = True,
+    ax=None,
+    figsize: tuple[float, float] | None = None,
+    layout=None,
+    sharex: bool = False,
+    sharey: bool = True,
+    backend=None,
+    **kwargs,
+):
+    """
+    Make box plots from DataFrameGroupBy data.
+
+    Parameters
+    ----------
+    grouped : Grouped DataFrame
+    subplots : bool
+        * ``False`` - no subplots will be used
+        * ``True`` - create a subplot for each group.
+
+    column : column name or list of names, or vector
+        Can be any valid input to groupby.
+    fontsize : float or str
+    rot : label rotation angle
+    grid : Setting this to True will show the grid
+    ax : Matplotlib axis object, default None
+    figsize : A tuple (width, height) in inches
+    layout : tuple (optional)
+        The layout of the plot: (rows, columns).
+    sharex : bool, default False
+        Whether x-axes will be shared among subplots.
+    sharey : bool, default True
+        Whether y-axes will be shared among subplots.
+    backend : str, default None
+        Backend to use instead of the backend specified in the option
+        ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
+        specify the ``plotting.backend`` for the whole session, set
+        ``pd.options.plotting.backend``.
+    **kwargs
+        All other plotting keyword arguments to be passed to
+        matplotlib's boxplot function.
+
+    Returns
+    -------
+    dict of key/value = group key/DataFrame.boxplot return value
+    or DataFrame.boxplot return value in case subplots=figures=False
+
+    Examples
+    --------
+    You can create boxplots for grouped data and show them as separate subplots:
+
+    .. plot::
+        :context: close-figs
+
+        >>> import itertools
+        >>> tuples = [t for t in itertools.product(range(1000), range(4))]
+        >>> index = pd.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1'])
+        >>> data = np.random.randn(len(index), 4)
+        >>> df = pd.DataFrame(data, columns=list('ABCD'), index=index)
+        >>> grouped = df.groupby(level='lvl1')
+        >>> grouped.boxplot(rot=45, fontsize=12, figsize=(8, 10))  # doctest: +SKIP
+
+    The ``subplots=False`` option shows the boxplots in a single figure.
+
+    .. plot::
+        :context: close-figs
+
+        >>> grouped.boxplot(subplots=False, rot=45, fontsize=12)  # doctest: +SKIP
+    """
+    plot_backend = _get_plot_backend(backend)
+    return plot_backend.boxplot_frame_groupby(
+        grouped,
+        subplots=subplots,
+        column=column,
+        fontsize=fontsize,
+        rot=rot,
+        grid=grid,
+        ax=ax,
+        figsize=figsize,
+        layout=layout,
+        sharex=sharex,
+        sharey=sharey,
+        **kwargs,
+    )
+
+
+class PlotAccessor(PandasObject):
+    """
+    Make plots of Series or DataFrame.
+
+    Uses the backend specified by the
+    option ``plotting.backend``. By default, matplotlib is used.
+
+    Parameters
+    ----------
+    data : Series or DataFrame
+        The object for which the method is called.
+    x : label or position, default None
+        Only used if data is a DataFrame.
+    y : label, position or list of label, positions, default None
+        Allows plotting of one column versus another. Only used if data is a
+        DataFrame.
+    kind : str
+        The kind of plot to produce:
+
+        - 'line' : line plot (default)
+        - 'bar' : vertical bar plot
+        - 'barh' : horizontal bar plot
+        - 'hist' : histogram
+        - 'box' : boxplot
+        - 'kde' : Kernel Density Estimation plot
+        - 'density' : same as 'kde'
+        - 'area' : area plot
+        - 'pie' : pie plot
+        - 'scatter' : scatter plot (DataFrame only)
+        - 'hexbin' : hexbin plot (DataFrame only)
+    ax : matplotlib axes object, default None
+        An axes of the current figure.
+    subplots : bool or sequence of iterables, default False
+        Whether to group columns into subplots:
+
+        - ``False`` : No subplots will be used
+        - ``True`` : Make separate subplots for each column.
+        - sequence of iterables of column labels: Create a subplot for each
+          group of columns. For example `[('a', 'c'), ('b', 'd')]` will
+          create 2 subplots: one with columns 'a' and 'c', and one
+          with columns 'b' and 'd'. Remaining columns that aren't specified
+          will be plotted in additional subplots (one per column).
+
+          .. versionadded:: 1.5.0
+
+    sharex : bool, default True if ax is None else False
+        In case ``subplots=True``, share x axis and set some x axis labels
+        to invisible; defaults to True if ax is None otherwise False if
+        an ax is passed in; Be aware, that passing in both an ax and
+        ``sharex=True`` will alter all x axis labels for all axis in a figure.
+    sharey : bool, default False
+        In case ``subplots=True``, share y axis and set some y axis labels to invisible.
+    layout : tuple, optional
+        (rows, columns) for the layout of subplots.
+    figsize : a tuple (width, height) in inches
+        Size of a figure object.
+    use_index : bool, default True
+        Use index as ticks for x axis.
+    title : str or list
+        Title to use for the plot. If a string is passed, print the string
+        at the top of the figure. If a list is passed and `subplots` is
+        True, print each item in the list above the corresponding subplot.
+    grid : bool, default None (matlab style default)
+        Axis grid lines.
+    legend : bool or {'reverse'}
+        Place legend on axis subplots.
+    style : list or dict
+        The matplotlib line style per column.
+    logx : bool or 'sym', default False
+        Use log scaling or symlog scaling on x axis.
+
+    logy : bool or 'sym' default False
+        Use log scaling or symlog scaling on y axis.
+
+    loglog : bool or 'sym', default False
+        Use log scaling or symlog scaling on both x and y axes.
+
+    xticks : sequence
+        Values to use for the xticks.
+    yticks : sequence
+        Values to use for the yticks.
+    xlim : 2-tuple/list
+        Set the x limits of the current axes.
+    ylim : 2-tuple/list
+        Set the y limits of the current axes.
+    xlabel : label, optional
+        Name to use for the xlabel on x-axis. Default uses index name as xlabel, or the
+        x-column name for planar plots.
+
+        .. versionchanged:: 2.0.0
+
+            Now applicable to histograms.
+
+    ylabel : label, optional
+        Name to use for the ylabel on y-axis. Default will show no ylabel, or the
+        y-column name for planar plots.
+
+        .. versionchanged:: 2.0.0
+
+            Now applicable to histograms.
+
+    rot : float, default None
+        Rotation for ticks (xticks for vertical, yticks for horizontal
+        plots).
+    fontsize : float, default None
+        Font size for xticks and yticks.
+    colormap : str or matplotlib colormap object, default None
+        Colormap to select colors from. If string, load colormap with that
+        name from matplotlib.
+    colorbar : bool, optional
+        If True, plot colorbar (only relevant for 'scatter' and 'hexbin'
+        plots).
+    position : float
+        Specify relative alignments for bar plot layout.
+        From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5
+        (center).
+    table : bool, Series or DataFrame, default False
+        If True, draw a table using the data in the DataFrame and the data
+        will be transposed to meet matplotlib's default layout.
+        If a Series or DataFrame is passed, use passed data to draw a
+        table.
+    yerr : DataFrame, Series, array-like, dict and str
+        See :ref:`Plotting with Error Bars ` for
+        detail.
+    xerr : DataFrame, Series, array-like, dict and str
+        Equivalent to yerr.
+    stacked : bool, default False in line and bar plots, and True in area plot
+        If True, create stacked plot.
+    secondary_y : bool or sequence, default False
+        Whether to plot on the secondary y-axis if a list/tuple, which
+        columns to plot on secondary y-axis.
+    mark_right : bool, default True
+        When using a secondary_y axis, automatically mark the column
+        labels with "(right)" in the legend.
+    include_bool : bool, default is False
+        If True, boolean values can be plotted.
+    backend : str, default None
+        Backend to use instead of the backend specified in the option
+        ``plotting.backend``. For instance, 'matplotlib'. Alternatively, to
+        specify the ``plotting.backend`` for the whole session, set
+        ``pd.options.plotting.backend``.
+    **kwargs
+        Options to pass to matplotlib plotting method.
+
+    Returns
+    -------
+    :class:`matplotlib.axes.Axes` or numpy.ndarray of them
+        If the backend is not the default matplotlib one, the return value
+        will be the object returned by the backend.
+
+    Notes
+    -----
+    - See matplotlib documentation online for more on this subject
+    - If `kind` = 'bar' or 'barh', you can specify relative alignments
+      for bar plot layout by `position` keyword.
+      From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5
+      (center)
+
+    Examples
+    --------
+    For Series:
+
+    .. plot::
+        :context: close-figs
+
+        >>> ser = pd.Series([1, 2, 3, 3])
+        >>> plot = ser.plot(kind='hist', title="My plot")
+
+    For DataFrame:
+
+    .. plot::
+        :context: close-figs
+
+        >>> df = pd.DataFrame({'length': [1.5, 0.5, 1.2, 0.9, 3],
+        ...                   'width': [0.7, 0.2, 0.15, 0.2, 1.1]},
+        ...                   index=['pig', 'rabbit', 'duck', 'chicken', 'horse'])
+        >>> plot = df.plot(title="DataFrame Plot")
+
+    For SeriesGroupBy:
+
+    .. plot::
+        :context: close-figs
+
+        >>> lst = [-1, -2, -3, 1, 2, 3]
+        >>> ser = pd.Series([1, 2, 2, 4, 6, 6], index=lst)
+        >>> plot = ser.groupby(lambda x: x > 0).plot(title="SeriesGroupBy Plot")
+
+    For DataFrameGroupBy:
+
+    .. plot::
+        :context: close-figs
+
+        >>> df = pd.DataFrame({"col1" : [1, 2, 3, 4],
+        ...                   "col2" : ["A", "B", "A", "B"]})
+        >>> plot = df.groupby("col2").plot(kind="bar", title="DataFrameGroupBy Plot")
+    """
+
+    _common_kinds = ("line", "bar", "barh", "kde", "density", "area", "hist", "box")
+    _series_kinds = ("pie",)
+    _dataframe_kinds = ("scatter", "hexbin")
+    _kind_aliases = {"density": "kde"}
+    _all_kinds = _common_kinds + _series_kinds + _dataframe_kinds
+
+    def __init__(self, data: Series | DataFrame) -> None:
+        self._parent = data
+
+    @staticmethod
+    def _get_call_args(backend_name: str, data: Series | DataFrame, args, kwargs):
+        """
+        This function makes calls to this accessor `__call__` method compatible
+        with the previous `SeriesPlotMethods.__call__` and
+        `DataFramePlotMethods.__call__`. Those had slightly different
+        signatures, since `DataFramePlotMethods` accepted `x` and `y`
+        parameters.
+        """
+        if isinstance(data, ABCSeries):
+            arg_def = [
+                ("kind", "line"),
+                ("ax", None),
+                ("figsize", None),
+                ("use_index", True),
+                ("title", None),
+                ("grid", None),
+                ("legend", False),
+                ("style", None),
+                ("logx", False),
+                ("logy", False),
+                ("loglog", False),
+                ("xticks", None),
+                ("yticks", None),
+                ("xlim", None),
+                ("ylim", None),
+                ("rot", None),
+                ("fontsize", None),
+                ("colormap", None),
+                ("table", False),
+                ("yerr", None),
+                ("xerr", None),
+                ("label", None),
+                ("secondary_y", False),
+                ("xlabel", None),
+                ("ylabel", None),
+            ]
+        elif isinstance(data, ABCDataFrame):
+            arg_def = [
+                ("x", None),
+                ("y", None),
+                ("kind", "line"),
+                ("ax", None),
+                ("subplots", False),
+                ("sharex", None),
+                ("sharey", False),
+                ("layout", None),
+                ("figsize", None),
+                ("use_index", True),
+                ("title", None),
+                ("grid", None),
+                ("legend", True),
+                ("style", None),
+                ("logx", False),
+                ("logy", False),
+                ("loglog", False),
+                ("xticks", None),
+                ("yticks", None),
+                ("xlim", None),
+                ("ylim", None),
+                ("rot", None),
+                ("fontsize", None),
+                ("colormap", None),
+                ("table", False),
+                ("yerr", None),
+                ("xerr", None),
+                ("secondary_y", False),
+                ("xlabel", None),
+                ("ylabel", None),
+            ]
+        else:
+            raise TypeError(
+                f"Called plot accessor for type {type(data).__name__}, "
+                "expected Series or DataFrame"
+            )
+
+        if args and isinstance(data, ABCSeries):
+            positional_args = str(args)[1:-1]
+            keyword_args = ", ".join(
+                [f"{name}={repr(value)}" for (name, _), value in zip(arg_def, args)]
+            )
+            msg = (
+                "`Series.plot()` should not be called with positional "
+                "arguments, only keyword arguments. The order of "
+                "positional arguments will change in the future. "
+                f"Use `Series.plot({keyword_args})` instead of "
+                f"`Series.plot({positional_args})`."
+            )
+            raise TypeError(msg)
+
+        pos_args = {name: value for (name, _), value in zip(arg_def, args)}
+        if backend_name == "pandas.plotting._matplotlib":
+            kwargs = dict(arg_def, **pos_args, **kwargs)
+        else:
+            kwargs = dict(pos_args, **kwargs)
+
+        x = kwargs.pop("x", None)
+        y = kwargs.pop("y", None)
+        kind = kwargs.pop("kind", "line")
+        return x, y, kind, kwargs
+
+    def __call__(self, *args, **kwargs):
+        plot_backend = _get_plot_backend(kwargs.pop("backend", None))
+
+        x, y, kind, kwargs = self._get_call_args(
+            plot_backend.__name__, self._parent, args, kwargs
+        )
+
+        kind = self._kind_aliases.get(kind, kind)
+
+        # when using another backend, get out of the way
+        if plot_backend.__name__ != "pandas.plotting._matplotlib":
+            return plot_backend.plot(self._parent, x=x, y=y, kind=kind, **kwargs)
+
+        if kind not in self._all_kinds:
+            raise ValueError(
+                f"{kind} is not a valid plot kind "
+                f"Valid plot kinds: {self._all_kinds}"
+            )
+
+        # The original data structured can be transformed before passed to the
+        # backend. For example, for DataFrame is common to set the index as the
+        # `x` parameter, and return a Series with the parameter `y` as values.
+        data = self._parent.copy()
+
+        if isinstance(data, ABCSeries):
+            kwargs["reuse_plot"] = True
+
+        if kind in self._dataframe_kinds:
+            if isinstance(data, ABCDataFrame):
+                return plot_backend.plot(data, x=x, y=y, kind=kind, **kwargs)
+            else:
+                raise ValueError(f"plot kind {kind} can only be used for data frames")
+        elif kind in self._series_kinds:
+            if isinstance(data, ABCDataFrame):
+                if y is None and kwargs.get("subplots") is False:
+                    raise ValueError(
+                        f"{kind} requires either y column or 'subplots=True'"
+                    )
+                if y is not None:
+                    if is_integer(y) and not data.columns._holds_integer():
+                        y = data.columns[y]
+                    # converted to series actually. copy to not modify
+                    data = data[y].copy()
+                    data.index.name = y
+        elif isinstance(data, ABCDataFrame):
+            data_cols = data.columns
+            if x is not None:
+                if is_integer(x) and not data.columns._holds_integer():
+                    x = data_cols[x]
+                elif not isinstance(data[x], ABCSeries):
+                    raise ValueError("x must be a label or position")
+                data = data.set_index(x)
+            if y is not None:
+                # check if we have y as int or list of ints
+                int_ylist = is_list_like(y) and all(is_integer(c) for c in y)
+                int_y_arg = is_integer(y) or int_ylist
+                if int_y_arg and not data.columns._holds_integer():
+                    y = data_cols[y]
+
+                label_kw = kwargs["label"] if "label" in kwargs else False
+                for kw in ["xerr", "yerr"]:
+                    if kw in kwargs and (
+                        isinstance(kwargs[kw], str) or is_integer(kwargs[kw])
+                    ):
+                        try:
+                            kwargs[kw] = data[kwargs[kw]]
+                        except (IndexError, KeyError, TypeError):
+                            pass
+
+                # don't overwrite
+                data = data[y].copy()
+
+                if isinstance(data, ABCSeries):
+                    label_name = label_kw or y
+                    data.name = label_name
+                else:
+                    match = is_list_like(label_kw) and len(label_kw) == len(y)
+                    if label_kw and not match:
+                        raise ValueError(
+                            "label should be list-like and same length as y"
+                        )
+                    label_name = label_kw or data.columns
+                    data.columns = label_name
+
+        return plot_backend.plot(data, kind=kind, **kwargs)
+
+    __call__.__doc__ = __doc__
+
+    @Appender(
+        """
+        See Also
+        --------
+        matplotlib.pyplot.plot : Plot y versus x as lines and/or markers.
+
+        Examples
+        --------
+
+        .. plot::
+            :context: close-figs
+
+            >>> s = pd.Series([1, 3, 2])
+            >>> s.plot.line()  # doctest: +SKIP
+
+        .. plot::
+            :context: close-figs
+
+            The following example shows the populations for some animals
+            over the years.
+
+            >>> df = pd.DataFrame({
+            ...    'pig': [20, 18, 489, 675, 1776],
+            ...    'horse': [4, 25, 281, 600, 1900]
+            ...    }, index=[1990, 1997, 2003, 2009, 2014])
+            >>> lines = df.plot.line()
+
+        .. plot::
+           :context: close-figs
+
+           An example with subplots, so an array of axes is returned.
+
+           >>> axes = df.plot.line(subplots=True)
+           >>> type(axes)
+           
+
+        .. plot::
+           :context: close-figs
+
+           Let's repeat the same example, but specifying colors for
+           each column (in this case, for each animal).
+
+           >>> axes = df.plot.line(
+           ...     subplots=True, color={"pig": "pink", "horse": "#742802"}
+           ... )
+
+        .. plot::
+            :context: close-figs
+
+            The following example shows the relationship between both
+            populations.
+
+            >>> lines = df.plot.line(x='pig', y='horse')
+        """
+    )
+    @Substitution(kind="line")
+    @Appender(_bar_or_line_doc)
+    def line(
+        self, x: Hashable | None = None, y: Hashable | None = None, **kwargs
+    ) -> PlotAccessor:
+        """
+        Plot Series or DataFrame as lines.
+
+        This function is useful to plot lines using DataFrame's values
+        as coordinates.
+        """
+        return self(kind="line", x=x, y=y, **kwargs)
+
+    @Appender(
+        """
+        See Also
+        --------
+        DataFrame.plot.barh : Horizontal bar plot.
+        DataFrame.plot : Make plots of a DataFrame.
+        matplotlib.pyplot.bar : Make a bar plot with matplotlib.
+
+        Examples
+        --------
+        Basic plot.
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})
+            >>> ax = df.plot.bar(x='lab', y='val', rot=0)
+
+        Plot a whole dataframe to a bar plot. Each column is assigned a
+        distinct color, and each row is nested in a group along the
+        horizontal axis.
+
+        .. plot::
+            :context: close-figs
+
+            >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
+            >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
+            >>> index = ['snail', 'pig', 'elephant',
+            ...          'rabbit', 'giraffe', 'coyote', 'horse']
+            >>> df = pd.DataFrame({'speed': speed,
+            ...                    'lifespan': lifespan}, index=index)
+            >>> ax = df.plot.bar(rot=0)
+
+        Plot stacked bar charts for the DataFrame
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.bar(stacked=True)
+
+        Instead of nesting, the figure can be split by column with
+        ``subplots=True``. In this case, a :class:`numpy.ndarray` of
+        :class:`matplotlib.axes.Axes` are returned.
+
+        .. plot::
+            :context: close-figs
+
+            >>> axes = df.plot.bar(rot=0, subplots=True)
+            >>> axes[1].legend(loc=2)  # doctest: +SKIP
+
+        If you don't like the default colours, you can specify how you'd
+        like each column to be colored.
+
+        .. plot::
+            :context: close-figs
+
+            >>> axes = df.plot.bar(
+            ...     rot=0, subplots=True, color={"speed": "red", "lifespan": "green"}
+            ... )
+            >>> axes[1].legend(loc=2)  # doctest: +SKIP
+
+        Plot a single column.
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.bar(y='speed', rot=0)
+
+        Plot only selected categories for the DataFrame.
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.bar(x='lifespan', rot=0)
+    """
+    )
+    @Substitution(kind="bar")
+    @Appender(_bar_or_line_doc)
+    def bar(  # pylint: disable=disallowed-name
+        self, x: Hashable | None = None, y: Hashable | None = None, **kwargs
+    ) -> PlotAccessor:
+        """
+        Vertical bar plot.
+
+        A bar plot is a plot that presents categorical data with
+        rectangular bars with lengths proportional to the values that they
+        represent. A bar plot shows comparisons among discrete categories. One
+        axis of the plot shows the specific categories being compared, and the
+        other axis represents a measured value.
+        """
+        return self(kind="bar", x=x, y=y, **kwargs)
+
+    @Appender(
+        """
+        See Also
+        --------
+        DataFrame.plot.bar: Vertical bar plot.
+        DataFrame.plot : Make plots of DataFrame using matplotlib.
+        matplotlib.axes.Axes.bar : Plot a vertical bar plot using matplotlib.
+
+        Examples
+        --------
+        Basic example
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame({'lab': ['A', 'B', 'C'], 'val': [10, 30, 20]})
+            >>> ax = df.plot.barh(x='lab', y='val')
+
+        Plot a whole DataFrame to a horizontal bar plot
+
+        .. plot::
+            :context: close-figs
+
+            >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
+            >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
+            >>> index = ['snail', 'pig', 'elephant',
+            ...          'rabbit', 'giraffe', 'coyote', 'horse']
+            >>> df = pd.DataFrame({'speed': speed,
+            ...                    'lifespan': lifespan}, index=index)
+            >>> ax = df.plot.barh()
+
+        Plot stacked barh charts for the DataFrame
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.barh(stacked=True)
+
+        We can specify colors for each column
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.barh(color={"speed": "red", "lifespan": "green"})
+
+        Plot a column of the DataFrame to a horizontal bar plot
+
+        .. plot::
+            :context: close-figs
+
+            >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
+            >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
+            >>> index = ['snail', 'pig', 'elephant',
+            ...          'rabbit', 'giraffe', 'coyote', 'horse']
+            >>> df = pd.DataFrame({'speed': speed,
+            ...                    'lifespan': lifespan}, index=index)
+            >>> ax = df.plot.barh(y='speed')
+
+        Plot DataFrame versus the desired column
+
+        .. plot::
+            :context: close-figs
+
+            >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
+            >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
+            >>> index = ['snail', 'pig', 'elephant',
+            ...          'rabbit', 'giraffe', 'coyote', 'horse']
+            >>> df = pd.DataFrame({'speed': speed,
+            ...                    'lifespan': lifespan}, index=index)
+            >>> ax = df.plot.barh(x='lifespan')
+    """
+    )
+    @Substitution(kind="bar")
+    @Appender(_bar_or_line_doc)
+    def barh(
+        self, x: Hashable | None = None, y: Hashable | None = None, **kwargs
+    ) -> PlotAccessor:
+        """
+        Make a horizontal bar plot.
+
+        A horizontal bar plot is a plot that presents quantitative data with
+        rectangular bars with lengths proportional to the values that they
+        represent. A bar plot shows comparisons among discrete categories. One
+        axis of the plot shows the specific categories being compared, and the
+        other axis represents a measured value.
+        """
+        return self(kind="barh", x=x, y=y, **kwargs)
+
+    def box(self, by: IndexLabel | None = None, **kwargs) -> PlotAccessor:
+        r"""
+        Make a box plot of the DataFrame columns.
+
+        A box plot is a method for graphically depicting groups of numerical
+        data through their quartiles.
+        The box extends from the Q1 to Q3 quartile values of the data,
+        with a line at the median (Q2). The whiskers extend from the edges
+        of box to show the range of the data. The position of the whiskers
+        is set by default to 1.5*IQR (IQR = Q3 - Q1) from the edges of the
+        box. Outlier points are those past the end of the whiskers.
+
+        For further details see Wikipedia's
+        entry for `boxplot `__.
+
+        A consideration when using this chart is that the box and the whiskers
+        can overlap, which is very common when plotting small sets of data.
+
+        Parameters
+        ----------
+        by : str or sequence
+            Column in the DataFrame to group by.
+
+            .. versionchanged:: 1.4.0
+
+               Previously, `by` is silently ignore and makes no groupings
+
+        **kwargs
+            Additional keywords are documented in
+            :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        :class:`matplotlib.axes.Axes` or numpy.ndarray of them
+
+        See Also
+        --------
+        DataFrame.boxplot: Another method to draw a box plot.
+        Series.plot.box: Draw a box plot from a Series object.
+        matplotlib.pyplot.boxplot: Draw a box plot in matplotlib.
+
+        Examples
+        --------
+        Draw a box plot from a DataFrame with four columns of randomly
+        generated data.
+
+        .. plot::
+            :context: close-figs
+
+            >>> data = np.random.randn(25, 4)
+            >>> df = pd.DataFrame(data, columns=list('ABCD'))
+            >>> ax = df.plot.box()
+
+        You can also generate groupings if you specify the `by` parameter (which
+        can take a column name, or a list or tuple of column names):
+
+        .. versionchanged:: 1.4.0
+
+        .. plot::
+            :context: close-figs
+
+            >>> age_list = [8, 10, 12, 14, 72, 74, 76, 78, 20, 25, 30, 35, 60, 85]
+            >>> df = pd.DataFrame({"gender": list("MMMMMMMMFFFFFF"), "age": age_list})
+            >>> ax = df.plot.box(column="age", by="gender", figsize=(10, 8))
+        """
+        return self(kind="box", by=by, **kwargs)
+
+    def hist(
+        self, by: IndexLabel | None = None, bins: int = 10, **kwargs
+    ) -> PlotAccessor:
+        """
+        Draw one histogram of the DataFrame's columns.
+
+        A histogram is a representation of the distribution of data.
+        This function groups the values of all given Series in the DataFrame
+        into bins and draws all bins in one :class:`matplotlib.axes.Axes`.
+        This is useful when the DataFrame's Series are in a similar scale.
+
+        Parameters
+        ----------
+        by : str or sequence, optional
+            Column in the DataFrame to group by.
+
+            .. versionchanged:: 1.4.0
+
+               Previously, `by` is silently ignore and makes no groupings
+
+        bins : int, default 10
+            Number of histogram bins to be used.
+        **kwargs
+            Additional keyword arguments are documented in
+            :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        class:`matplotlib.AxesSubplot`
+            Return a histogram plot.
+
+        See Also
+        --------
+        DataFrame.hist : Draw histograms per DataFrame's Series.
+        Series.hist : Draw a histogram with Series' data.
+
+        Examples
+        --------
+        When we roll a die 6000 times, we expect to get each value around 1000
+        times. But when we roll two dice and sum the result, the distribution
+        is going to be quite different. A histogram illustrates those
+        distributions.
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame(np.random.randint(1, 7, 6000), columns=['one'])
+            >>> df['two'] = df['one'] + np.random.randint(1, 7, 6000)
+            >>> ax = df.plot.hist(bins=12, alpha=0.5)
+
+        A grouped histogram can be generated by providing the parameter `by` (which
+        can be a column name, or a list of column names):
+
+        .. plot::
+            :context: close-figs
+
+            >>> age_list = [8, 10, 12, 14, 72, 74, 76, 78, 20, 25, 30, 35, 60, 85]
+            >>> df = pd.DataFrame({"gender": list("MMMMMMMMFFFFFF"), "age": age_list})
+            >>> ax = df.plot.hist(column=["age"], by="gender", figsize=(10, 8))
+        """
+        return self(kind="hist", by=by, bins=bins, **kwargs)
+
+    def kde(
+        self,
+        bw_method: Literal["scott", "silverman"] | float | Callable | None = None,
+        ind: np.ndarray | int | None = None,
+        **kwargs,
+    ) -> PlotAccessor:
+        """
+        Generate Kernel Density Estimate plot using Gaussian kernels.
+
+        In statistics, `kernel density estimation`_ (KDE) is a non-parametric
+        way to estimate the probability density function (PDF) of a random
+        variable. This function uses Gaussian kernels and includes automatic
+        bandwidth determination.
+
+        .. _kernel density estimation:
+            https://en.wikipedia.org/wiki/Kernel_density_estimation
+
+        Parameters
+        ----------
+        bw_method : str, scalar or callable, optional
+            The method used to calculate the estimator bandwidth. This can be
+            'scott', 'silverman', a scalar constant or a callable.
+            If None (default), 'scott' is used.
+            See :class:`scipy.stats.gaussian_kde` for more information.
+        ind : NumPy array or int, optional
+            Evaluation points for the estimated PDF. If None (default),
+            1000 equally spaced points are used. If `ind` is a NumPy array, the
+            KDE is evaluated at the points passed. If `ind` is an integer,
+            `ind` number of equally spaced points are used.
+        **kwargs
+            Additional keyword arguments are documented in
+            :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        matplotlib.axes.Axes or numpy.ndarray of them
+
+        See Also
+        --------
+        scipy.stats.gaussian_kde : Representation of a kernel-density
+            estimate using Gaussian kernels. This is the function used
+            internally to estimate the PDF.
+
+        Examples
+        --------
+        Given a Series of points randomly sampled from an unknown
+        distribution, estimate its PDF using KDE with automatic
+        bandwidth determination and plot the results, evaluating them at
+        1000 equally spaced points (default):
+
+        .. plot::
+            :context: close-figs
+
+            >>> s = pd.Series([1, 2, 2.5, 3, 3.5, 4, 5])
+            >>> ax = s.plot.kde()
+
+        A scalar bandwidth can be specified. Using a small bandwidth value can
+        lead to over-fitting, while using a large bandwidth value may result
+        in under-fitting:
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = s.plot.kde(bw_method=0.3)
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = s.plot.kde(bw_method=3)
+
+        Finally, the `ind` parameter determines the evaluation points for the
+        plot of the estimated PDF:
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = s.plot.kde(ind=[1, 2, 3, 4, 5])
+
+        For DataFrame, it works in the same way:
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame({
+            ...     'x': [1, 2, 2.5, 3, 3.5, 4, 5],
+            ...     'y': [4, 4, 4.5, 5, 5.5, 6, 6],
+            ... })
+            >>> ax = df.plot.kde()
+
+        A scalar bandwidth can be specified. Using a small bandwidth value can
+        lead to over-fitting, while using a large bandwidth value may result
+        in under-fitting:
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.kde(bw_method=0.3)
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.kde(bw_method=3)
+
+        Finally, the `ind` parameter determines the evaluation points for the
+        plot of the estimated PDF:
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.kde(ind=[1, 2, 3, 4, 5, 6])
+        """
+        return self(kind="kde", bw_method=bw_method, ind=ind, **kwargs)
+
+    density = kde
+
+    def area(
+        self,
+        x: Hashable | None = None,
+        y: Hashable | None = None,
+        stacked: bool = True,
+        **kwargs,
+    ) -> PlotAccessor:
+        """
+        Draw a stacked area plot.
+
+        An area plot displays quantitative data visually.
+        This function wraps the matplotlib area function.
+
+        Parameters
+        ----------
+        x : label or position, optional
+            Coordinates for the X axis. By default uses the index.
+        y : label or position, optional
+            Column to plot. By default uses all columns.
+        stacked : bool, default True
+            Area plots are stacked by default. Set to False to create a
+            unstacked plot.
+        **kwargs
+            Additional keyword arguments are documented in
+            :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        matplotlib.axes.Axes or numpy.ndarray
+            Area plot, or array of area plots if subplots is True.
+
+        See Also
+        --------
+        DataFrame.plot : Make plots of DataFrame using matplotlib / pylab.
+
+        Examples
+        --------
+        Draw an area plot based on basic business metrics:
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame({
+            ...     'sales': [3, 2, 3, 9, 10, 6],
+            ...     'signups': [5, 5, 6, 12, 14, 13],
+            ...     'visits': [20, 42, 28, 62, 81, 50],
+            ... }, index=pd.date_range(start='2018/01/01', end='2018/07/01',
+            ...                        freq='ME'))
+            >>> ax = df.plot.area()
+
+        Area plots are stacked by default. To produce an unstacked plot,
+        pass ``stacked=False``:
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.area(stacked=False)
+
+        Draw an area plot for a single column:
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.area(y='sales')
+
+        Draw with a different `x`:
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame({
+            ...     'sales': [3, 2, 3],
+            ...     'visits': [20, 42, 28],
+            ...     'day': [1, 2, 3],
+            ... })
+            >>> ax = df.plot.area(x='day')
+        """
+        return self(kind="area", x=x, y=y, stacked=stacked, **kwargs)
+
+    def pie(self, **kwargs) -> PlotAccessor:
+        """
+        Generate a pie plot.
+
+        A pie plot is a proportional representation of the numerical data in a
+        column. This function wraps :meth:`matplotlib.pyplot.pie` for the
+        specified column. If no column reference is passed and
+        ``subplots=True`` a pie plot is drawn for each numerical column
+        independently.
+
+        Parameters
+        ----------
+        y : int or label, optional
+            Label or position of the column to plot.
+            If not provided, ``subplots=True`` argument must be passed.
+        **kwargs
+            Keyword arguments to pass on to :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        matplotlib.axes.Axes or np.ndarray of them
+            A NumPy array is returned when `subplots` is True.
+
+        See Also
+        --------
+        Series.plot.pie : Generate a pie plot for a Series.
+        DataFrame.plot : Make plots of a DataFrame.
+
+        Examples
+        --------
+        In the example below we have a DataFrame with the information about
+        planet's mass and radius. We pass the 'mass' column to the
+        pie function to get a pie plot.
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame({'mass': [0.330, 4.87 , 5.97],
+            ...                    'radius': [2439.7, 6051.8, 6378.1]},
+            ...                   index=['Mercury', 'Venus', 'Earth'])
+            >>> plot = df.plot.pie(y='mass', figsize=(5, 5))
+
+        .. plot::
+            :context: close-figs
+
+            >>> plot = df.plot.pie(subplots=True, figsize=(11, 6))
+        """
+        if (
+            isinstance(self._parent, ABCDataFrame)
+            and kwargs.get("y", None) is None
+            and not kwargs.get("subplots", False)
+        ):
+            raise ValueError("pie requires either y column or 'subplots=True'")
+        return self(kind="pie", **kwargs)
+
+    def scatter(
+        self,
+        x: Hashable,
+        y: Hashable,
+        s: Hashable | Sequence[Hashable] | None = None,
+        c: Hashable | Sequence[Hashable] | None = None,
+        **kwargs,
+    ) -> PlotAccessor:
+        """
+        Create a scatter plot with varying marker point size and color.
+
+        The coordinates of each point are defined by two dataframe columns and
+        filled circles are used to represent each point. This kind of plot is
+        useful to see complex correlations between two variables. Points could
+        be for instance natural 2D coordinates like longitude and latitude in
+        a map or, in general, any pair of metrics that can be plotted against
+        each other.
+
+        Parameters
+        ----------
+        x : int or str
+            The column name or column position to be used as horizontal
+            coordinates for each point.
+        y : int or str
+            The column name or column position to be used as vertical
+            coordinates for each point.
+        s : str, scalar or array-like, optional
+            The size of each point. Possible values are:
+
+            - A string with the name of the column to be used for marker's size.
+
+            - A single scalar so all points have the same size.
+
+            - A sequence of scalars, which will be used for each point's size
+              recursively. For instance, when passing [2,14] all points size
+              will be either 2 or 14, alternatively.
+
+        c : str, int or array-like, optional
+            The color of each point. Possible values are:
+
+            - A single color string referred to by name, RGB or RGBA code,
+              for instance 'red' or '#a98d19'.
+
+            - A sequence of color strings referred to by name, RGB or RGBA
+              code, which will be used for each point's color recursively. For
+              instance ['green','yellow'] all points will be filled in green or
+              yellow, alternatively.
+
+            - A column name or position whose values will be used to color the
+              marker points according to a colormap.
+
+        **kwargs
+            Keyword arguments to pass on to :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        :class:`matplotlib.axes.Axes` or numpy.ndarray of them
+
+        See Also
+        --------
+        matplotlib.pyplot.scatter : Scatter plot using multiple input data
+            formats.
+
+        Examples
+        --------
+        Let's see how to draw a scatter plot using coordinates from the values
+        in a DataFrame's columns.
+
+        .. plot::
+            :context: close-figs
+
+            >>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],
+            ...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],
+            ...                   columns=['length', 'width', 'species'])
+            >>> ax1 = df.plot.scatter(x='length',
+            ...                       y='width',
+            ...                       c='DarkBlue')
+
+        And now with the color determined by a column as well.
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax2 = df.plot.scatter(x='length',
+            ...                       y='width',
+            ...                       c='species',
+            ...                       colormap='viridis')
+        """
+        return self(kind="scatter", x=x, y=y, s=s, c=c, **kwargs)
+
+    def hexbin(
+        self,
+        x: Hashable,
+        y: Hashable,
+        C: Hashable | None = None,
+        reduce_C_function: Callable | None = None,
+        gridsize: int | tuple[int, int] | None = None,
+        **kwargs,
+    ) -> PlotAccessor:
+        """
+        Generate a hexagonal binning plot.
+
+        Generate a hexagonal binning plot of `x` versus `y`. If `C` is `None`
+        (the default), this is a histogram of the number of occurrences
+        of the observations at ``(x[i], y[i])``.
+
+        If `C` is specified, specifies values at given coordinates
+        ``(x[i], y[i])``. These values are accumulated for each hexagonal
+        bin and then reduced according to `reduce_C_function`,
+        having as default the NumPy's mean function (:meth:`numpy.mean`).
+        (If `C` is specified, it must also be a 1-D sequence
+        of the same length as `x` and `y`, or a column label.)
+
+        Parameters
+        ----------
+        x : int or str
+            The column label or position for x points.
+        y : int or str
+            The column label or position for y points.
+        C : int or str, optional
+            The column label or position for the value of `(x, y)` point.
+        reduce_C_function : callable, default `np.mean`
+            Function of one argument that reduces all the values in a bin to
+            a single number (e.g. `np.mean`, `np.max`, `np.sum`, `np.std`).
+        gridsize : int or tuple of (int, int), default 100
+            The number of hexagons in the x-direction.
+            The corresponding number of hexagons in the y-direction is
+            chosen in a way that the hexagons are approximately regular.
+            Alternatively, gridsize can be a tuple with two elements
+            specifying the number of hexagons in the x-direction and the
+            y-direction.
+        **kwargs
+            Additional keyword arguments are documented in
+            :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        matplotlib.AxesSubplot
+            The matplotlib ``Axes`` on which the hexbin is plotted.
+
+        See Also
+        --------
+        DataFrame.plot : Make plots of a DataFrame.
+        matplotlib.pyplot.hexbin : Hexagonal binning plot using matplotlib,
+            the matplotlib function that is used under the hood.
+
+        Examples
+        --------
+        The following examples are generated with random data from
+        a normal distribution.
+
+        .. plot::
+            :context: close-figs
+
+            >>> n = 10000
+            >>> df = pd.DataFrame({'x': np.random.randn(n),
+            ...                    'y': np.random.randn(n)})
+            >>> ax = df.plot.hexbin(x='x', y='y', gridsize=20)
+
+        The next example uses `C` and `np.sum` as `reduce_C_function`.
+        Note that `'observations'` values ranges from 1 to 5 but the result
+        plot shows values up to more than 25. This is because of the
+        `reduce_C_function`.
+
+        .. plot::
+            :context: close-figs
+
+            >>> n = 500
+            >>> df = pd.DataFrame({
+            ...     'coord_x': np.random.uniform(-3, 3, size=n),
+            ...     'coord_y': np.random.uniform(30, 50, size=n),
+            ...     'observations': np.random.randint(1,5, size=n)
+            ...     })
+            >>> ax = df.plot.hexbin(x='coord_x',
+            ...                     y='coord_y',
+            ...                     C='observations',
+            ...                     reduce_C_function=np.sum,
+            ...                     gridsize=10,
+            ...                     cmap="viridis")
+        """
+        if reduce_C_function is not None:
+            kwargs["reduce_C_function"] = reduce_C_function
+        if gridsize is not None:
+            kwargs["gridsize"] = gridsize
+
+        return self(kind="hexbin", x=x, y=y, C=C, **kwargs)
+
+
+_backends: dict[str, types.ModuleType] = {}
+
+
+def _load_backend(backend: str) -> types.ModuleType:
+    """
+    Load a pandas plotting backend.
+
+    Parameters
+    ----------
+    backend : str
+        The identifier for the backend. Either an entrypoint item registered
+        with importlib.metadata, "matplotlib", or a module name.
+
+    Returns
+    -------
+    types.ModuleType
+        The imported backend.
+    """
+    from importlib.metadata import entry_points
+
+    if backend == "matplotlib":
+        # Because matplotlib is an optional dependency and first-party backend,
+        # we need to attempt an import here to raise an ImportError if needed.
+        try:
+            module = importlib.import_module("pandas.plotting._matplotlib")
+        except ImportError:
+            raise ImportError(
+                "matplotlib is required for plotting when the "
+                'default backend "matplotlib" is selected.'
+            ) from None
+        return module
+
+    found_backend = False
+
+    eps = entry_points()
+    key = "pandas_plotting_backends"
+    # entry_points lost dict API ~ PY 3.10
+    # https://github.com/python/importlib_metadata/issues/298
+    if hasattr(eps, "select"):
+        entry = eps.select(group=key)
+    else:
+        # Argument 2 to "get" of "dict" has incompatible type "Tuple[]";
+        # expected "EntryPoints"  [arg-type]
+        entry = eps.get(key, ())  # type: ignore[arg-type]
+    for entry_point in entry:
+        found_backend = entry_point.name == backend
+        if found_backend:
+            module = entry_point.load()
+            break
+
+    if not found_backend:
+        # Fall back to unregistered, module name approach.
+        try:
+            module = importlib.import_module(backend)
+            found_backend = True
+        except ImportError:
+            # We re-raise later on.
+            pass
+
+    if found_backend:
+        if hasattr(module, "plot"):
+            # Validate that the interface is implemented when the option is set,
+            # rather than at plot time.
+            return module
+
+    raise ValueError(
+        f"Could not find plotting backend '{backend}'. Ensure that you've "
+        f"installed the package providing the '{backend}' entrypoint, or that "
+        "the package has a top-level `.plot` method."
+    )
+
+
+def _get_plot_backend(backend: str | None = None):
+    """
+    Return the plotting backend to use (e.g. `pandas.plotting._matplotlib`).
+
+    The plotting system of pandas uses matplotlib by default, but the idea here
+    is that it can also work with other third-party backends. This function
+    returns the module which provides a top-level `.plot` method that will
+    actually do the plotting. The backend is specified from a string, which
+    either comes from the keyword argument `backend`, or, if not specified, from
+    the option `pandas.options.plotting.backend`. All the rest of the code in
+    this file uses the backend specified there for the plotting.
+
+    The backend is imported lazily, as matplotlib is a soft dependency, and
+    pandas can be used without it being installed.
+
+    Notes
+    -----
+    Modifies `_backends` with imported backend as a side effect.
+    """
+    backend_str: str = backend or get_option("plotting.backend")
+
+    if backend_str in _backends:
+        return _backends[backend_str]
+
+    module = _load_backend(backend_str)
+    _backends[backend_str] = module
+    return module
diff --git a/shared/package-files/04/04b26eef07ed14ed7db604b5a3ff20f55a39f2f2a806ab5d2e21f4a583026ddc/variable_tensor_list.h b/shared/package-files/04/04b26eef07ed14ed7db604b5a3ff20f55a39f2f2a806ab5d2e21f4a583026ddc/variable_tensor_list.h
new file mode 100644
index 0000000000000000000000000000000000000000..e8dcd4f2c5b0b95f0f727e97794ddc321fe49fc7
--- /dev/null
+++ b/shared/package-files/04/04b26eef07ed14ed7db604b5a3ff20f55a39f2f2a806ab5d2e21f4a583026ddc/variable_tensor_list.h
@@ -0,0 +1,17 @@
+#pragma once
+#include 
+
+namespace torch::jit {
+
+// a wrapper to mark places where we expect all the at::Tensors to be
+// variables
+struct variable_tensor_list : public std::vector {
+  variable_tensor_list() = default;
+  template 
+  variable_tensor_list(InputIt first, InputIt last)
+      : std::vector(first, last) {}
+  explicit variable_tensor_list(std::vector&& tensor)
+      : std::vector(std::move(tensor)) {}
+};
+
+} // namespace torch::jit
diff --git a/shared/package-files/04/04b2ee67aa146a3d4a27b2d544c324ca87f6dc1cb212f14c83f779360941fc09/NamedTensor.h b/shared/package-files/04/04b2ee67aa146a3d4a27b2d544c324ca87f6dc1cb212f14c83f779360941fc09/NamedTensor.h
new file mode 100644
index 0000000000000000000000000000000000000000..eb119132e28ccd36919d94a15d49b94e45c9513a
--- /dev/null
+++ b/shared/package-files/04/04b2ee67aa146a3d4a27b2d544c324ca87f6dc1cb212f14c83f779360941fc09/NamedTensor.h
@@ -0,0 +1,139 @@
+#pragma once
+
+#include 
+#include 
+
+namespace at {
+
+class TensorBase;
+
+// XXX: This file exists because TensorImpl is in c10, but Dimname is in ATen.
+// Due to the c10/ATen library split, TensorImpl cannot depend on Dimname,
+// so we have a couple of workarounds.
+//
+// In the long term, we'll move Dimname to c10 and everything in this file
+// can be refactored out. The main blocker for that is that "c10::Symbol"
+// actually exists outside of c10 and needs to be moved in.
+
+// TensorImpl has a unique_ptr field.
+// XXX: Ideally we would just put optional> into TensorImpl.
+//
+// This class has an important invariant: there must be at least ONE
+// non-wildcard
+struct TORCH_API NamedTensorMeta final : public c10::NamedTensorMetaInterface {
+  // This enum is to remind people that the invariant on constructors is that
+  // the list of dimnames must have at least one non-wildcard
+  enum HAS_NON_WILDCARD {
+    HasNonWildcard
+  };
+
+  explicit NamedTensorMeta(HAS_NON_WILDCARD, DimnameList names)
+    : names_(names.vec()) {
+    check_invariants();
+  }
+  explicit NamedTensorMeta(HAS_NON_WILDCARD, std::vector&& names)
+    : names_(std::move(names)) {
+    check_invariants();
+  }
+
+  std::unique_ptr clone() const override {
+    return std::make_unique(HasNonWildcard, names_);
+  }
+
+  DimnameList names() const { return names_; }
+
+  // Used for an assertion in TensorImpl.h
+  int64_t slow_dim() const override {
+    return names_.size();
+  }
+
+  void check_invariants() const {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+      std::any_of(names_.begin(), names_.end(), [](const Dimname& n) { return !n.isWildcard(); }));
+  }
+
+  void set_names(HAS_NON_WILDCARD, DimnameList new_names) {
+    TORCH_INTERNAL_ASSERT(new_names.size() == names_.size());
+    std::copy(new_names.begin(), new_names.end(), names_.begin());
+    check_invariants();
+  }
+
+  void set_names(HAS_NON_WILDCARD, std::vector&& new_names) {
+    TORCH_INTERNAL_ASSERT(new_names.size() == names_.size());
+    names_ = std::move(new_names);
+    check_invariants();
+  }
+
+  // INVARIANT: at least one Dimname is non-WILDCARD
+  std::vector names_;
+};
+
+// When NamesMode is disabled, then all operations ignore tensors' names fields.
+// Concretely speaking, all tensors are treated as having nullopt names.
+struct TORCH_API NamesMode {
+  static bool is_enabled();
+  static void set_enabled(bool enabled);
+};
+
+
+// A RAII, thread local (!) guard that enables or disables names upon
+// construction, and sets it back to the original value upon destruction.
+struct TORCH_API NoNamesGuard {
+  NoNamesGuard() : prev_mode(NamesMode::is_enabled()), initialized(true) {
+    NamesMode::set_enabled(false);
+  }
+  ~NoNamesGuard() {
+    if (initialized) {
+      reset();
+    }
+  }
+  void reset() {
+    TORCH_INTERNAL_ASSERT(initialized);
+    NamesMode::set_enabled(prev_mode);
+  }
+ private:
+  bool prev_mode;
+  bool initialized;
+};
+
+void check_names_valid_for(const TensorBase& tensor, DimnameList names);
+void check_names_valid_for(size_t tensor_dim, DimnameList names);
+
+// Sets the names of `tensor` to be `names`.
+TORCH_API const TensorBase& internal_set_names_inplace(const TensorBase& tensor, c10::optional names);
+TORCH_API const TensorBase& internal_set_names_inplace(const TensorBase& tensor, std::vector&& names, bool validate_names);
+
+constexpr size_t kMaxNamedTensorDim = 64;
+
+DimnameList default_names(size_t len);
+
+namespace impl {
+
+// Some helper functions on TensorImpl. Useful for working with names in TH.
+// XXX: Ideally these would exist as methods on TensorImpl
+TORCH_API void internal_set_names_inplace(TensorImpl* impl, c10::optional names, bool validate_names);
+TORCH_API void internal_set_names_inplace(TensorImpl* impl, std::vector&& names, bool validate_names);
+
+void check_names_valid_for(TensorImpl* impl, DimnameList names);
+
+// Returns true if the tensor's names exist and are not all 'None'.
+// Returns false if the tensor's names don't exist (were not allocated),
+// or if all names are 'None'.
+// We treat not-allocated-names the same as allocated names that are all 'None'.
+TORCH_API bool has_names(const TensorImpl* impl);
+
+// Returns the names of the tensor's dimensions.
+// Unnamed tensors are treated as having 'None' in all dimension; this method
+// would return a DimnameList of all 'None's for an unnamed tensor.
+TORCH_API DimnameList get_names(const TensorImpl* impl);
+
+// This is more of an implementation detail; one should use impl::get_names /
+// Tensor::names() whenever possible because it provides a cleaner API.
+// Returns the names of the tensor if they have been allocated; returns nullopt
+// instead if the haven't been. The names of a tensor are not allocated if a
+// tensor is constructed with names=None.
+TORCH_API c10::optional get_opt_names(const TensorImpl* impl);
+
+} // namespace impl
+
+} // namespace at
diff --git a/shared/package-files/04/04b53116e748e0bb4623384e3aba5a03e6c7e8f73c5219a319075f7fee128500/kfas_helpers.py b/shared/package-files/04/04b53116e748e0bb4623384e3aba5a03e6c7e8f73c5219a319075f7fee128500/kfas_helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7c3a38516ab3d20fff2de941487393c92ab74c2
--- /dev/null
+++ b/shared/package-files/04/04b53116e748e0bb4623384e3aba5a03e6c7e8f73c5219a319075f7fee128500/kfas_helpers.py
@@ -0,0 +1,97 @@
+# Helper functions for working with KFAS output
+
+import numpy as np
+import pandas as pd
+
+from statsmodels.tools.tools import Bunch
+
+
+def parse(path, ssm):
+    # Dimensions
+    n = ssm.nobs
+    p = ssm.k_endog
+    m = ssm.k_states
+    r = ssm.k_posdef
+
+    p2 = p**2
+    m2 = m**2
+    mp = m * p
+    r2 = r**2
+
+    # Extract the different pieces of output from KFAS
+    kfas = pd.read_csv(path)
+    components = [("r", m), ("r0", m), ("r1", m),
+                  ("N", m2), ("N0", m2), ("N1", m2), ("N2", m2),
+                  ("m", p), ("v", p), ("F", p), ("Finf", p),
+                  ("K", mp), ("Kinf", mp), ("a", m), ("P", m2),
+                  ("Pinf", m2), ("att", m), ("Ptt", m2),
+                  ("alphahat", m), ("V", m2), ("muhat", p),
+                  ("V_mu", p2), ("etahat", r), ("V_eta", r2), ("epshat", p),
+                  ("V_eps", p), ("llf", 1)]
+    dta = {}
+    ix = 0
+    for key, length in components:
+        dta[key] = kfas.iloc[:, ix:ix + length].fillna(0)
+        dta[key].name = None
+        ix += length
+
+    # Reformat the KFAS output to compare with statsmodels output
+    res = Bunch()
+    d = len(kfas["Pinf_1"].dropna())
+
+    # forecasts
+    res["forecasts"] = dta["m"].values[:n].T
+    res["forecasts_error"] = dta["v"].values[:n].T
+    res["forecasts_error_cov"] = np.c_[
+        [np.diag(x) for y, x in dta["F"].iloc[:n].iterrows()]].T
+    res["forecasts_error_diffuse_cov"] = np.c_[
+        [np.diag(x) for y, x in dta["Finf"].iloc[:n].iterrows()]].T
+
+    res["kalman_gain"] = dta["K"].values[:n].reshape(n, m, p, order="F").T
+    res["Kinf"] = dta["Kinf"].values[:n].reshape(n, m, p, order="F")
+
+    # filtered
+    res["filtered_state"] = dta["att"].values[:n].T
+    res["filtered_state_cov"] = dta["Ptt"].values[:n].reshape(
+        n, m, m, order="F").T
+    # predicted
+    res["predicted_state"] = dta["a"].values.T
+    res["predicted_state_cov"] = dta["P"].values.reshape(
+        n + 1, m, m, order="F").T
+    res["predicted_diffuse_state_cov"] = dta["Pinf"].values.reshape(
+        n + 1, m, m, order="F").T
+    # loglike
+    # Note: KFAS only gives the total loglikelihood
+    res["llf_obs"] = dta["llf"].values[0, 0]
+
+    # smoothed
+    res["smoothed_state"] = dta["alphahat"].values[:n].T
+    res["smoothed_state_cov"] = dta["V"].values[:n].reshape(
+        n, m, m, order="F").T
+
+    res["smoothed_measurement_disturbance"] = dta["epshat"].values[:n].T
+    res["smoothed_measurement_disturbance_cov"] = np.c_[
+        [np.diag(x) for y, x in dta["V_eps"].iloc[:n].iterrows()]].T
+    res["smoothed_state_disturbance"] = dta["etahat"].values[:n].T
+    res["smoothed_state_disturbance_cov"] = dta["V_eta"].values[:n].reshape(
+        n, r, r, order="F").T
+
+    # scaled smoothed estimator
+    # Note: we store both r and r0 together as "scaled smoothed estimator"
+    # while "scaled smoothed diffuse estimator" corresponds to r1
+    res["scaled_smoothed_estimator"] = np.c_[dta["r0"][:d].T,
+                                             dta["r"][d:].T][..., 1:]
+    res["scaled_smoothed_diffuse_estimator"] = dta["r1"].values.T
+
+    N0 = dta["N0"].values[:d].reshape(d, m, m, order="F")
+    N = dta["N"].values[d:].reshape(n + 1 - d, m, m, order="F")
+    res["scaled_smoothed_estimator_cov"] = np.c_[N0.T, N.T][..., 1:]
+    # Have to do a more precise transpose for these since they may not be
+    # symmetric
+    res["scaled_smoothed_diffuse1_estimator_cov"] = dta["N1"].values.reshape(
+        n + 1, m, m, order="F").transpose(1, 2, 0)
+    res["scaled_smoothed_diffuse2_estimator_cov"] = dta["N2"].values.reshape(
+        n + 1, m, m, order="F").transpose(1, 2, 0)
+
+    # Save the results object for the tests
+    return res
diff --git a/shared/package-files/04/04b568a4dd324850217d38da4a0fbd72dca0369a0cc53a3118dce883fd9e1761/server.pyi b/shared/package-files/04/04b568a4dd324850217d38da4a0fbd72dca0369a0cc53a3118dce883fd9e1761/server.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..eb4faab23d3aed36adcf8d2ba5f821ff0542e3c3
--- /dev/null
+++ b/shared/package-files/04/04b568a4dd324850217d38da4a0fbd72dca0369a0cc53a3118dce883fd9e1761/server.pyi
@@ -0,0 +1,103 @@
+from socket import SocketType
+from typing import Any, Optional, Sequence, Tuple, Union
+
+from waitress.adjustments import Adjustments
+from waitress.channel import HTTPChannel
+from waitress.task import Task, ThreadedTaskDispatcher
+
+from . import wasyncore
+
+def create_server(
+    application: Any,
+    map: Optional[Any] = ...,
+    _start: bool = ...,
+    _sock: Optional[SocketType] = ...,
+    _dispatcher: Optional[ThreadedTaskDispatcher] = ...,
+    **kw: Any,
+) -> Union[MultiSocketServer, BaseWSGIServer]: ...
+
+class MultiSocketServer:
+    asyncore: Any = ...
+    adj: Adjustments = ...
+    map: Any = ...
+    effective_listen: Sequence[Tuple[str, int]] = ...
+    task_dispatcher: ThreadedTaskDispatcher = ...
+    def __init__(
+        self,
+        map: Optional[Any] = ...,
+        adj: Optional[Adjustments] = ...,
+        effective_listen: Optional[Sequence[Tuple[str, int]]] = ...,
+        dispatcher: Optional[ThreadedTaskDispatcher] = ...,
+    ) -> None: ...
+    def print_listen(self, format_str: str) -> None: ...
+    def run(self) -> None: ...
+    def close(self) -> None: ...
+
+class BaseWSGIServer(wasyncore.dispatcher):
+    channel_class: HTTPChannel = ...
+    next_channel_cleanup: int = ...
+    socketmod: SocketType = ...
+    asyncore: Any = ...
+    sockinfo: Tuple[int, int, int, Tuple[str, int]] = ...
+    family: int = ...
+    socktype: int = ...
+    application: Any = ...
+    adj: Adjustments = ...
+    trigger: int = ...
+    task_dispatcher: ThreadedTaskDispatcher = ...
+    server_name: str = ...
+    active_channels: HTTPChannel = ...
+    def __init__(
+        self,
+        application: Any,
+        map: Optional[Any] = ...,
+        _start: bool = ...,
+        _sock: Optional[Any] = ...,
+        dispatcher: Optional[ThreadedTaskDispatcher] = ...,
+        adj: Optional[Adjustments] = ...,
+        sockinfo: Optional[Any] = ...,
+        bind_socket: bool = ...,
+        **kw: Any,
+    ) -> None: ...
+    def bind_server_socket(self) -> None: ...
+    def get_server_name(self, ip: str) -> str: ...
+    def getsockname(self) -> Any: ...
+    accepting: bool = ...
+    def accept_connections(self) -> None: ...
+    def add_task(self, task: Task) -> None: ...
+    def readable(self) -> bool: ...
+    def writable(self) -> bool: ...
+    def handle_read(self) -> None: ...
+    def handle_connect(self) -> None: ...
+    def handle_accept(self) -> None: ...
+    def run(self) -> None: ...
+    def pull_trigger(self) -> None: ...
+    def set_socket_options(self, conn: Any) -> None: ...
+    def fix_addr(self, addr: Any) -> Any: ...
+    def maintenance(self, now: int) -> None: ...
+    def print_listen(self, format_str: str) -> None: ...
+    def close(self) -> None: ...
+
+class TcpWSGIServer(BaseWSGIServer):
+    def bind_server_socket(self) -> None: ...
+    def getsockname(self) -> Tuple[str, Tuple[str, int]]: ...
+    def set_socket_options(self, conn: SocketType) -> None: ...
+
+class UnixWSGIServer(BaseWSGIServer):
+    def __init__(
+        self,
+        application: Any,
+        map: Optional[Any] = ...,
+        _start: bool = ...,
+        _sock: Optional[Any] = ...,
+        dispatcher: Optional[Any] = ...,
+        adj: Optional[Adjustments] = ...,
+        sockinfo: Optional[Any] = ...,
+        **kw: Any,
+    ) -> None: ...
+    def bind_server_socket(self) -> None: ...
+    def getsockname(self) -> Tuple[str, Tuple[str, int]]: ...
+    def fix_addr(self, addr: Any) -> Tuple[str, None]: ...
+    def get_server_name(self, ip: Any) -> str: ...
+
+WSGIServer: TcpWSGIServer
diff --git a/shared/package-files/04/04b7c12423e0f95679109f82585a5c7ca4dca67052e36a3d0816dca94eaeb255/attrmatrix.py b/shared/package-files/04/04b7c12423e0f95679109f82585a5c7ca4dca67052e36a3d0816dca94eaeb255/attrmatrix.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2029996de7ad3ddcb1d2908c8d2d6d8eb36046d
--- /dev/null
+++ b/shared/package-files/04/04b7c12423e0f95679109f82585a5c7ca4dca67052e36a3d0816dca94eaeb255/attrmatrix.py
@@ -0,0 +1,462 @@
+"""
+    Functions for constructing matrix-like objects from graph attributes.
+"""
+
+__all__ = ["attr_matrix", "attr_sparse_matrix"]
+
+
+def _node_value(G, node_attr):
+    """Returns a function that returns a value from G.nodes[u].
+
+    We return a function expecting a node as its sole argument. Then, in the
+    simplest scenario, the returned function will return G.nodes[u][node_attr].
+    However, we also handle the case when `node_attr` is None or when it is a
+    function itself.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX graph
+
+    node_attr : {None, str, callable}
+        Specification of how the value of the node attribute should be obtained
+        from the node attribute dictionary.
+
+    Returns
+    -------
+    value : function
+        A function expecting a node as its sole argument. The function will
+        returns a value from G.nodes[u] that depends on `edge_attr`.
+
+    """
+    if node_attr is None:
+
+        def value(u):
+            return u
+
+    elif not callable(node_attr):
+        # assume it is a key for the node attribute dictionary
+        def value(u):
+            return G.nodes[u][node_attr]
+
+    else:
+        # Advanced:  Allow users to specify something else.
+        #
+        # For example,
+        #     node_attr = lambda u: G.nodes[u].get('size', .5) * 3
+        #
+        value = node_attr
+
+    return value
+
+
+def _edge_value(G, edge_attr):
+    """Returns a function that returns a value from G[u][v].
+
+    Suppose there exists an edge between u and v.  Then we return a function
+    expecting u and v as arguments.  For Graph and DiGraph, G[u][v] is
+    the edge attribute dictionary, and the function (essentially) returns
+    G[u][v][edge_attr].  However, we also handle cases when `edge_attr` is None
+    and when it is a function itself. For MultiGraph and MultiDiGraph, G[u][v]
+    is a dictionary of all edges between u and v.  In this case, the returned
+    function sums the value of `edge_attr` for every edge between u and v.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    edge_attr : {None, str, callable}
+        Specification of how the value of the edge attribute should be obtained
+        from the edge attribute dictionary, G[u][v].  For multigraphs, G[u][v]
+        is a dictionary of all the edges between u and v.  This allows for
+        special treatment of multiedges.
+
+    Returns
+    -------
+    value : function
+        A function expecting two nodes as parameters. The nodes should
+        represent the from- and to- node of an edge. The function will
+        return a value from G[u][v] that depends on `edge_attr`.
+
+    """
+
+    if edge_attr is None:
+        # topological count of edges
+
+        if G.is_multigraph():
+
+            def value(u, v):
+                return len(G[u][v])
+
+        else:
+
+            def value(u, v):
+                return 1
+
+    elif not callable(edge_attr):
+        # assume it is a key for the edge attribute dictionary
+
+        if edge_attr == "weight":
+            # provide a default value
+            if G.is_multigraph():
+
+                def value(u, v):
+                    return sum(d.get(edge_attr, 1) for d in G[u][v].values())
+
+            else:
+
+                def value(u, v):
+                    return G[u][v].get(edge_attr, 1)
+
+        else:
+            # otherwise, the edge attribute MUST exist for each edge
+            if G.is_multigraph():
+
+                def value(u, v):
+                    return sum(d[edge_attr] for d in G[u][v].values())
+
+            else:
+
+                def value(u, v):
+                    return G[u][v][edge_attr]
+
+    else:
+        # Advanced:  Allow users to specify something else.
+        #
+        # Alternative default value:
+        #     edge_attr = lambda u,v: G[u][v].get('thickness', .5)
+        #
+        # Function on an attribute:
+        #     edge_attr = lambda u,v: abs(G[u][v]['weight'])
+        #
+        # Handle Multi(Di)Graphs differently:
+        #     edge_attr = lambda u,v: numpy.prod([d['size'] for d in G[u][v].values()])
+        #
+        # Ignore multiple edges
+        #     edge_attr = lambda u,v: 1 if len(G[u][v]) else 0
+        #
+        value = edge_attr
+
+    return value
+
+
+def attr_matrix(
+    G,
+    edge_attr=None,
+    node_attr=None,
+    normalized=False,
+    rc_order=None,
+    dtype=None,
+    order=None,
+):
+    """Returns the attribute matrix using attributes from `G` as a numpy array.
+
+    If only `G` is passed in, then the adjacency matrix is constructed.
+
+    Let A be a discrete set of values for the node attribute `node_attr`. Then
+    the elements of A represent the rows and columns of the constructed matrix.
+    Now, iterate through every edge e=(u,v) in `G` and consider the value
+    of the edge attribute `edge_attr`.  If ua and va are the values of the
+    node attribute `node_attr` for u and v, respectively, then the value of
+    the edge attribute is added to the matrix element at (ua, va).
+
+    Parameters
+    ----------
+    G : graph
+        The NetworkX graph used to construct the attribute matrix.
+
+    edge_attr : str, optional
+        Each element of the matrix represents a running total of the
+        specified edge attribute for edges whose node attributes correspond
+        to the rows/cols of the matrix. The attribute must be present for
+        all edges in the graph. If no attribute is specified, then we
+        just count the number of edges whose node attributes correspond
+        to the matrix element.
+
+    node_attr : str, optional
+        Each row and column in the matrix represents a particular value
+        of the node attribute.  The attribute must be present for all nodes
+        in the graph. Note, the values of this attribute should be reliably
+        hashable. So, float values are not recommended. If no attribute is
+        specified, then the rows and columns will be the nodes of the graph.
+
+    normalized : bool, optional
+        If True, then each row is normalized by the summation of its values.
+
+    rc_order : list, optional
+        A list of the node attribute values. This list specifies the ordering
+        of rows and columns of the array. If no ordering is provided, then
+        the ordering will be random (and also, a return value).
+
+    Other Parameters
+    ----------------
+    dtype : NumPy data-type, optional
+        A valid NumPy dtype used to initialize the array. Keep in mind certain
+        dtypes can yield unexpected results if the array is to be normalized.
+        The parameter is passed to numpy.zeros(). If unspecified, the NumPy
+        default is used.
+
+    order : {'C', 'F'}, optional
+        Whether to store multidimensional data in C- or Fortran-contiguous
+        (row- or column-wise) order in memory. This parameter is passed to
+        numpy.zeros(). If unspecified, the NumPy default is used.
+
+    Returns
+    -------
+    M : 2D NumPy ndarray
+        The attribute matrix.
+
+    ordering : list
+        If `rc_order` was specified, then only the attribute matrix is returned.
+        However, if `rc_order` was None, then the ordering used to construct
+        the matrix is returned as well.
+
+    Examples
+    --------
+    Construct an adjacency matrix:
+
+    >>> G = nx.Graph()
+    >>> G.add_edge(0, 1, thickness=1, weight=3)
+    >>> G.add_edge(0, 2, thickness=2)
+    >>> G.add_edge(1, 2, thickness=3)
+    >>> nx.attr_matrix(G, rc_order=[0, 1, 2])
+    array([[0., 1., 1.],
+           [1., 0., 1.],
+           [1., 1., 0.]])
+
+    Alternatively, we can obtain the matrix describing edge thickness.
+
+    >>> nx.attr_matrix(G, edge_attr="thickness", rc_order=[0, 1, 2])
+    array([[0., 1., 2.],
+           [1., 0., 3.],
+           [2., 3., 0.]])
+
+    We can also color the nodes and ask for the probability distribution over
+    all edges (u,v) describing:
+
+        Pr(v has color Y | u has color X)
+
+    >>> G.nodes[0]["color"] = "red"
+    >>> G.nodes[1]["color"] = "red"
+    >>> G.nodes[2]["color"] = "blue"
+    >>> rc = ["red", "blue"]
+    >>> nx.attr_matrix(G, node_attr="color", normalized=True, rc_order=rc)
+    array([[0.33333333, 0.66666667],
+           [1.        , 0.        ]])
+
+    For example, the above tells us that for all edges (u,v):
+
+        Pr( v is red  | u is red)  = 1/3
+        Pr( v is blue | u is red)  = 2/3
+
+        Pr( v is red  | u is blue) = 1
+        Pr( v is blue | u is blue) = 0
+
+    Finally, we can obtain the total weights listed by the node colors.
+
+    >>> nx.attr_matrix(G, edge_attr="weight", node_attr="color", rc_order=rc)
+    array([[3., 2.],
+           [2., 0.]])
+
+    Thus, the total weight over all edges (u,v) with u and v having colors:
+
+        (red, red)   is 3   # the sole contribution is from edge (0,1)
+        (red, blue)  is 2   # contributions from edges (0,2) and (1,2)
+        (blue, red)  is 2   # same as (red, blue) since graph is undirected
+        (blue, blue) is 0   # there are no edges with blue endpoints
+
+    """
+    import numpy as np
+
+    edge_value = _edge_value(G, edge_attr)
+    node_value = _node_value(G, node_attr)
+
+    if rc_order is None:
+        ordering = list({node_value(n) for n in G})
+    else:
+        ordering = rc_order
+
+    N = len(ordering)
+    undirected = not G.is_directed()
+    index = dict(zip(ordering, range(N)))
+    M = np.zeros((N, N), dtype=dtype, order=order)
+
+    seen = set()
+    for u, nbrdict in G.adjacency():
+        for v in nbrdict:
+            # Obtain the node attribute values.
+            i, j = index[node_value(u)], index[node_value(v)]
+            if v not in seen:
+                M[i, j] += edge_value(u, v)
+                if undirected:
+                    M[j, i] = M[i, j]
+
+        if undirected:
+            seen.add(u)
+
+    if normalized:
+        M /= M.sum(axis=1).reshape((N, 1))
+
+    if rc_order is None:
+        return M, ordering
+    else:
+        return M
+
+
+def attr_sparse_matrix(
+    G, edge_attr=None, node_attr=None, normalized=False, rc_order=None, dtype=None
+):
+    """Returns a SciPy sparse array using attributes from G.
+
+    If only `G` is passed in, then the adjacency matrix is constructed.
+
+    Let A be a discrete set of values for the node attribute `node_attr`. Then
+    the elements of A represent the rows and columns of the constructed matrix.
+    Now, iterate through every edge e=(u,v) in `G` and consider the value
+    of the edge attribute `edge_attr`.  If ua and va are the values of the
+    node attribute `node_attr` for u and v, respectively, then the value of
+    the edge attribute is added to the matrix element at (ua, va).
+
+    Parameters
+    ----------
+    G : graph
+        The NetworkX graph used to construct the NumPy matrix.
+
+    edge_attr : str, optional
+        Each element of the matrix represents a running total of the
+        specified edge attribute for edges whose node attributes correspond
+        to the rows/cols of the matrix. The attribute must be present for
+        all edges in the graph. If no attribute is specified, then we
+        just count the number of edges whose node attributes correspond
+        to the matrix element.
+
+    node_attr : str, optional
+        Each row and column in the matrix represents a particular value
+        of the node attribute.  The attribute must be present for all nodes
+        in the graph. Note, the values of this attribute should be reliably
+        hashable. So, float values are not recommended. If no attribute is
+        specified, then the rows and columns will be the nodes of the graph.
+
+    normalized : bool, optional
+        If True, then each row is normalized by the summation of its values.
+
+    rc_order : list, optional
+        A list of the node attribute values. This list specifies the ordering
+        of rows and columns of the array. If no ordering is provided, then
+        the ordering will be random (and also, a return value).
+
+    Other Parameters
+    ----------------
+    dtype : NumPy data-type, optional
+        A valid NumPy dtype used to initialize the array. Keep in mind certain
+        dtypes can yield unexpected results if the array is to be normalized.
+        The parameter is passed to numpy.zeros(). If unspecified, the NumPy
+        default is used.
+
+    Returns
+    -------
+    M : SciPy sparse array
+        The attribute matrix.
+
+    ordering : list
+        If `rc_order` was specified, then only the matrix is returned.
+        However, if `rc_order` was None, then the ordering used to construct
+        the matrix is returned as well.
+
+    Examples
+    --------
+    Construct an adjacency matrix:
+
+    >>> G = nx.Graph()
+    >>> G.add_edge(0, 1, thickness=1, weight=3)
+    >>> G.add_edge(0, 2, thickness=2)
+    >>> G.add_edge(1, 2, thickness=3)
+    >>> M = nx.attr_sparse_matrix(G, rc_order=[0, 1, 2])
+    >>> M.toarray()
+    array([[0., 1., 1.],
+           [1., 0., 1.],
+           [1., 1., 0.]])
+
+    Alternatively, we can obtain the matrix describing edge thickness.
+
+    >>> M = nx.attr_sparse_matrix(G, edge_attr="thickness", rc_order=[0, 1, 2])
+    >>> M.toarray()
+    array([[0., 1., 2.],
+           [1., 0., 3.],
+           [2., 3., 0.]])
+
+    We can also color the nodes and ask for the probability distribution over
+    all edges (u,v) describing:
+
+        Pr(v has color Y | u has color X)
+
+    >>> G.nodes[0]["color"] = "red"
+    >>> G.nodes[1]["color"] = "red"
+    >>> G.nodes[2]["color"] = "blue"
+    >>> rc = ["red", "blue"]
+    >>> M = nx.attr_sparse_matrix(G, node_attr="color", normalized=True, rc_order=rc)
+    >>> M.toarray()
+    array([[0.33333333, 0.66666667],
+           [1.        , 0.        ]])
+
+    For example, the above tells us that for all edges (u,v):
+
+        Pr( v is red  | u is red)  = 1/3
+        Pr( v is blue | u is red)  = 2/3
+
+        Pr( v is red  | u is blue) = 1
+        Pr( v is blue | u is blue) = 0
+
+    Finally, we can obtain the total weights listed by the node colors.
+
+    >>> M = nx.attr_sparse_matrix(G, edge_attr="weight", node_attr="color", rc_order=rc)
+    >>> M.toarray()
+    array([[3., 2.],
+           [2., 0.]])
+
+    Thus, the total weight over all edges (u,v) with u and v having colors:
+
+        (red, red)   is 3   # the sole contribution is from edge (0,1)
+        (red, blue)  is 2   # contributions from edges (0,2) and (1,2)
+        (blue, red)  is 2   # same as (red, blue) since graph is undirected
+        (blue, blue) is 0   # there are no edges with blue endpoints
+
+    """
+    import numpy as np
+    import scipy as sp
+    import scipy.sparse  # call as sp.sparse
+
+    edge_value = _edge_value(G, edge_attr)
+    node_value = _node_value(G, node_attr)
+
+    if rc_order is None:
+        ordering = list({node_value(n) for n in G})
+    else:
+        ordering = rc_order
+
+    N = len(ordering)
+    undirected = not G.is_directed()
+    index = dict(zip(ordering, range(N)))
+    M = sp.sparse.lil_array((N, N), dtype=dtype)
+
+    seen = set()
+    for u, nbrdict in G.adjacency():
+        for v in nbrdict:
+            # Obtain the node attribute values.
+            i, j = index[node_value(u)], index[node_value(v)]
+            if v not in seen:
+                M[i, j] += edge_value(u, v)
+                if undirected:
+                    M[j, i] = M[i, j]
+
+        if undirected:
+            seen.add(u)
+
+    if normalized:
+        M *= 1 / M.sum(axis=1)[:, np.newaxis]  # in-place mult preserves sparse
+
+    if rc_order is None:
+        return M, ordering
+    else:
+        return M
diff --git a/shared/package-files/04/04b7eefad8abb7c90411c7253dcc032f1d8eb2add9c31fa391cb77a91228cee1/analysis.py b/shared/package-files/04/04b7eefad8abb7c90411c7253dcc032f1d8eb2add9c31fa391cb77a91228cee1/analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b1686b0db7b8924856b53beaf49ade968a4797b
--- /dev/null
+++ b/shared/package-files/04/04b7eefad8abb7c90411c7253dcc032f1d8eb2add9c31fa391cb77a91228cee1/analysis.py
@@ -0,0 +1,735 @@
+"""
+Utils for IR analysis
+"""
+import operator
+from functools import reduce
+from collections import namedtuple, defaultdict
+
+from .controlflow import CFGraph
+from numba.core import types, errors, ir, consts
+from numba.misc import special
+
+#
+# Analysis related to variable lifetime
+#
+
+_use_defs_result = namedtuple('use_defs_result', 'usemap,defmap')
+
+# other packages that define new nodes add calls for finding defs
+# format: {type:function}
+ir_extension_usedefs = {}
+
+
+def compute_use_defs(blocks):
+    """
+    Find variable use/def per block.
+    """
+
+    var_use_map = {}   # { block offset -> set of vars }
+    var_def_map = {}   # { block offset -> set of vars }
+    for offset, ir_block in blocks.items():
+        var_use_map[offset] = use_set = set()
+        var_def_map[offset] = def_set = set()
+        for stmt in ir_block.body:
+            if type(stmt) in ir_extension_usedefs:
+                func = ir_extension_usedefs[type(stmt)]
+                func(stmt, use_set, def_set)
+                continue
+            if isinstance(stmt, ir.Assign):
+                if isinstance(stmt.value, ir.Inst):
+                    rhs_set = set(var.name for var in stmt.value.list_vars())
+                elif isinstance(stmt.value, ir.Var):
+                    rhs_set = set([stmt.value.name])
+                elif isinstance(stmt.value, (ir.Arg, ir.Const, ir.Global,
+                                             ir.FreeVar)):
+                    rhs_set = ()
+                else:
+                    raise AssertionError('unreachable', type(stmt.value))
+                # If lhs not in rhs of the assignment
+                if stmt.target.name not in rhs_set:
+                    def_set.add(stmt.target.name)
+
+            for var in stmt.list_vars():
+                # do not include locally defined vars to use-map
+                if var.name not in def_set:
+                    use_set.add(var.name)
+
+    return _use_defs_result(usemap=var_use_map, defmap=var_def_map)
+
+
+def compute_live_map(cfg, blocks, var_use_map, var_def_map):
+    """
+    Find variables that must be alive at the ENTRY of each block.
+    We use a simple fix-point algorithm that iterates until the set of
+    live variables is unchanged for each block.
+    """
+    # Topological order for forward analysis, reversed for backward.
+    # Proper ordering reduces fix-point iterations from O(loop_depth)
+    # to typically 2-3 passes on reducible CFGs.
+    topo_order = cfg.topo_order()
+
+    def fix_point_progress(dct):
+        """Helper function to determine if a fix-point has been reached.
+        """
+        return tuple(len(v) for v in dct.values())
+
+    def fix_point(fn, dct):
+        """Helper function to run fix-point algorithm.
+        """
+        old_point = None
+        new_point = fix_point_progress(dct)
+        while old_point != new_point:
+            fn(dct)
+            old_point = new_point
+            new_point = fix_point_progress(dct)
+
+    def def_reach(dct):
+        """Find all variable definition reachable at the entry of a block
+        """
+        for offset in topo_order:
+            used_or_defined = var_def_map[offset] | var_use_map[offset]
+            dct[offset] |= used_or_defined
+            # Propagate to outgoing nodes
+            for out_blk, _ in cfg.successors(offset):
+                dct[out_blk] |= dct[offset]
+
+    def liveness(dct):
+        """Find live variables.
+
+        Push var usage backward.
+        """
+        # Backward pass: fresh reverse iterator each call (fix_point may
+        # invoke this several times, so a one-shot iterator can't be reused).
+        for offset in reversed(topo_order):
+            # Live vars here
+            live_vars = dct[offset]
+            for inc_blk, _data in cfg.predecessors(offset):
+                # Reachable at the predecessor
+                reachable = live_vars & def_reach_map[inc_blk]
+                # But not defined in the predecessor
+                dct[inc_blk] |= reachable - var_def_map[inc_blk]
+
+    live_map = {}
+    for offset in blocks.keys():
+        live_map[offset] = set(var_use_map[offset])
+
+    def_reach_map = defaultdict(set)
+    fix_point(def_reach, def_reach_map)
+    fix_point(liveness, live_map)
+    return live_map
+
+
+_dead_maps_result = namedtuple('dead_maps_result', 'internal,escaping,combined')
+
+
+def compute_dead_maps(cfg, blocks, live_map, var_def_map):
+    """
+    Compute the end-of-live information for variables.
+    `live_map` contains a mapping of block offset to all the living
+    variables at the ENTRY of the block.
+    """
+    # The following three dictionaries will be
+    # { block offset -> set of variables to delete }
+    # all vars that should be deleted at the start of the successors
+    escaping_dead_map = defaultdict(set)
+    # all vars that should be deleted within this block
+    internal_dead_map = defaultdict(set)
+    # all vars that should be deleted after the function exit
+    exit_dead_map = defaultdict(set)
+
+    for offset, ir_block in blocks.items():
+        # live vars WITHIN the block will include all the locally
+        # defined variables
+        cur_live_set = live_map[offset] | var_def_map[offset]
+        # vars alive in the outgoing blocks
+        outgoing_live_map = dict((out_blk, live_map[out_blk])
+                                 for out_blk, _data in cfg.successors(offset))
+        # vars to keep alive for the terminator
+        terminator_liveset = set(v.name
+                                 for v in ir_block.terminator.list_vars())
+        # vars to keep alive in the successors
+        combined_liveset = reduce(operator.or_, outgoing_live_map.values(),
+                                  set())
+        # include variables used in terminator
+        combined_liveset |= terminator_liveset
+        # vars that are dead within the block because they are not
+        # propagated to any outgoing blocks
+        internal_set = cur_live_set - combined_liveset
+        internal_dead_map[offset] = internal_set
+        # vars that escape this block
+        escaping_live_set = cur_live_set - internal_set
+        for out_blk, new_live_set in outgoing_live_map.items():
+            # successor should delete the unused escaped vars
+            new_live_set = new_live_set | var_def_map[out_blk]
+            escaping_dead_map[out_blk] |= escaping_live_set - new_live_set
+
+        # if no outgoing blocks
+        if not outgoing_live_map:
+            # insert var used by terminator
+            exit_dead_map[offset] = terminator_liveset
+
+    # Verify that the dead maps cover all live variables
+    all_vars = reduce(operator.or_, live_map.values(), set())
+    internal_dead_vars = reduce(operator.or_, internal_dead_map.values(),
+                                set())
+    escaping_dead_vars = reduce(operator.or_, escaping_dead_map.values(),
+                                set())
+    exit_dead_vars = reduce(operator.or_, exit_dead_map.values(), set())
+    dead_vars = (internal_dead_vars | escaping_dead_vars | exit_dead_vars)
+    missing_vars = all_vars - dead_vars
+    if missing_vars:
+        # There are no exit points
+        if not cfg.exit_points():
+            # We won't be able to verify this
+            pass
+        else:
+            msg = 'liveness info missing for vars: {0}'.format(missing_vars)
+            raise RuntimeError(msg)
+
+    combined = dict((k, internal_dead_map[k] | escaping_dead_map[k])
+                    for k in blocks)
+
+    return _dead_maps_result(internal=internal_dead_map,
+                             escaping=escaping_dead_map,
+                             combined=combined)
+
+
+def compute_live_variables(cfg, blocks, var_def_map, var_dead_map):
+    """
+    Compute the live variables at the beginning of each block
+    and at each yield point.
+    The ``var_def_map`` and ``var_dead_map`` indicates the variable defined
+    and deleted at each block, respectively.
+    """
+    # live var at the entry per block
+    block_entry_vars = defaultdict(set)
+
+    def fix_point_progress():
+        return tuple(map(len, block_entry_vars.values()))
+
+    old_point = None
+    new_point = fix_point_progress()
+
+    # Propagate defined variables and still live the successors.
+    # (note the entry block automatically gets an empty set)
+
+    # Note: This is finding the actual available variables at the entry
+    #       of each block. The algorithm in compute_live_map() is finding
+    #       the variable that must be available at the entry of each block.
+    #       This is top-down in the dataflow.  The other one is bottom-up.
+    # Topological order lets this forward analysis converge in fewer passes.
+    topo_order = cfg.topo_order()
+    while old_point != new_point:
+        # We iterate until the result stabilizes.  This is necessary
+        # because of loops in the graphself.
+        for offset in topo_order:
+            # vars available + variable defined
+            avail = block_entry_vars[offset] | var_def_map[offset]
+            # subtract variables deleted
+            avail -= var_dead_map[offset]
+            # add ``avail`` to each successors
+            for succ, _data in cfg.successors(offset):
+                block_entry_vars[succ] |= avail
+
+        old_point = new_point
+        new_point = fix_point_progress()
+
+    return block_entry_vars
+
+
+#
+# Analysis related to controlflow
+#
+
+def compute_cfg_from_blocks(blocks):
+    cfg = CFGraph()
+    for k in blocks:
+        cfg.add_node(k)
+
+    for k, b in blocks.items():
+        term = b.terminator
+        for target in term.get_targets():
+            cfg.add_edge(k, target)
+
+    cfg.set_entry_point(min(blocks))
+    cfg.process()
+    return cfg
+
+
+def find_top_level_loops(cfg):
+    """
+    A generator that yields toplevel loops given a control-flow-graph
+    """
+    blocks_in_loop = set()
+    # get loop bodies
+    for loop in cfg.loops().values():
+        insiders = set(loop.body) | set(loop.entries) | set(loop.exits)
+        insiders.discard(loop.header)
+        blocks_in_loop |= insiders
+    # find loop that is not part of other loops
+    for loop in cfg.loops().values():
+        if loop.header not in blocks_in_loop:
+            yield _fix_loop_exit(cfg, loop)
+
+
+def _fix_loop_exit(cfg, loop):
+    """
+    Fixes loop.exits for Py3.8+ bytecode CFG changes.
+    This is to handle `break` inside loops.
+    """
+    # Computes the common postdoms of exit nodes
+    postdoms = cfg.post_dominators()
+    exits = reduce(
+        operator.and_,
+        [postdoms[b] for b in loop.exits],
+        loop.exits,
+    )
+    if exits:
+        # Put the non-common-exits as body nodes
+        body = loop.body | loop.exits - exits
+        return loop._replace(exits=exits, body=body)
+    else:
+        return loop
+
+
+# Used to describe a nullified condition in dead branch pruning
+nullified = namedtuple('nullified', 'condition, taken_br, rewrite_stmt')
+
+
+# Functions to manipulate IR
+def dead_branch_prune(func_ir, called_args):
+    """
+    Removes dead branches based on constant inference from function args.
+    This directly mutates the IR.
+
+    func_ir is the IR
+    called_args are the actual arguments with which the function is called
+    """
+    from numba.core.ir_utils import (get_definition, guard, find_const,
+                                     GuardException)
+
+    DEBUG = 0
+
+    def find_branches(func_ir):
+        # find *all* branches
+        branches = []
+        for blk in func_ir.blocks.values():
+            branch_or_jump = blk.body[-1]
+            if isinstance(branch_or_jump, ir.Branch):
+                branch = branch_or_jump
+                pred = guard(get_definition, func_ir, branch.cond.name)
+                if pred is not None and getattr(pred, "op", None) == "call":
+                    function = guard(get_definition, func_ir, pred.func)
+                    if (function is not None and
+                        isinstance(function, ir.Global) and
+                            function.value is bool):
+                        condition = guard(get_definition, func_ir, pred.args[0])
+                        if condition is not None:
+                            branches.append((branch, condition, blk))
+        return branches
+
+    def do_prune(take_truebr, blk):
+        keep = branch.truebr if take_truebr else branch.falsebr
+        # replace the branch with a direct jump
+        jmp = ir.Jump(keep, loc=branch.loc)
+        blk.body[-1] = jmp
+        return 1 if keep == branch.truebr else 0
+
+    def prune_by_type(branch, condition, blk, *conds):
+        # this prunes a given branch and fixes up the IR
+        # at least one needs to be a NoneType
+        lhs_cond, rhs_cond = conds
+        lhs_none = isinstance(lhs_cond, types.NoneType)
+        rhs_none = isinstance(rhs_cond, types.NoneType)
+        if lhs_none or rhs_none:
+            try:
+                take_truebr = condition.fn(lhs_cond, rhs_cond)
+            except Exception:
+                return False, None
+            if DEBUG > 0:
+                kill = branch.falsebr if take_truebr else branch.truebr
+                print("Pruning %s" % kill, branch, lhs_cond, rhs_cond,
+                      condition.fn)
+            taken = do_prune(take_truebr, blk)
+            return True, taken
+        return False, None
+
+    def prune_by_value(branch, condition, blk, *conds):
+        lhs_cond, rhs_cond = conds
+        try:
+            take_truebr = condition.fn(lhs_cond, rhs_cond)
+        except Exception:
+            return False, None
+        if DEBUG > 0:
+            kill = branch.falsebr if take_truebr else branch.truebr
+            print("Pruning %s" % kill, branch, lhs_cond, rhs_cond, condition.fn)
+        do_prune(take_truebr, blk)
+        # It is not safe to rewrite the predicate to a nominal value based on
+        # which branch is taken, the rewritten const predicate needs to
+        # hold the actual computed const value as something else may refer to
+        # it!
+        return True, take_truebr
+
+    def prune_by_predicate(branch, pred, blk):
+        try:
+            # Just to prevent accidents, whilst already guarded, ensure this
+            # is an ir.Const
+            if not isinstance(pred, (ir.Const, ir.FreeVar, ir.Global)):
+                raise TypeError('Expected constant Numba IR node')
+            take_truebr = bool(pred.value)
+        except TypeError:
+            return False, None
+        if DEBUG > 0:
+            kill = branch.falsebr if take_truebr else branch.truebr
+            print("Pruning %s" % kill, branch, pred)
+        taken = do_prune(take_truebr, blk)
+        return True, taken
+
+    class Unknown(object):
+        pass
+
+    def resolve_input_arg_const(input_arg_idx):
+        """
+        Resolves an input arg to a constant (if possible)
+        """
+        input_arg_ty = called_args[input_arg_idx]
+
+        # comparing to None?
+        if isinstance(input_arg_ty, types.NoneType):
+            return input_arg_ty
+
+        # is it a kwarg default
+        if isinstance(input_arg_ty, types.Omitted):
+            val = input_arg_ty.value
+            if isinstance(val, types.NoneType):
+                return val
+            elif val is None:
+                return types.NoneType('none')
+
+        # literal type, return the type itself so comparisons like `x == None`
+        # still work as e.g. x = types.int64 will never be None/NoneType so
+        # the branch can still be pruned
+        return getattr(input_arg_ty, 'literal_type', Unknown())
+
+    if DEBUG > 1:
+        print("before".center(80, '-'))
+        print(func_ir.dump())
+
+    phi2lbl = dict()
+    phi2asgn = dict()
+    for lbl, blk in func_ir.blocks.items():
+        for stmt in blk.body:
+            if isinstance(stmt, ir.Assign):
+                if isinstance(stmt.value, ir.Expr) and stmt.value.op == 'phi':
+                    phi2lbl[stmt.value] = lbl
+                    phi2asgn[stmt.value] = stmt
+
+    # This looks for branches where:
+    # at least one arg of the condition is in input args and const
+    # at least one an arg of the condition is a const
+    # if the condition is met it will replace the branch with a jump
+    branch_info = find_branches(func_ir)
+    # stores conditions that have no impact post prune
+    nullified_conditions = []
+
+    for branch, condition, blk in branch_info:
+        const_conds = []
+        if isinstance(condition, ir.Expr) and condition.op == 'binop':
+            prune = prune_by_value
+            for arg in [condition.lhs, condition.rhs]:
+                resolved_const = Unknown()
+                arg_def = guard(get_definition, func_ir, arg)
+                if isinstance(arg_def, ir.Arg):
+                    # it's an e.g. literal argument to the function
+                    resolved_const = resolve_input_arg_const(arg_def.index)
+                    prune = prune_by_type
+                else:
+                    # it's some const argument to the function, cannot use guard
+                    # here as the const itself may be None
+                    try:
+                        resolved_const = find_const(func_ir, arg)
+                        if resolved_const is None:
+                            resolved_const = types.NoneType('none')
+                    except GuardException:
+                        pass
+
+                if not isinstance(resolved_const, Unknown):
+                    const_conds.append(resolved_const)
+
+            # lhs/rhs are consts
+            if len(const_conds) == 2:
+                # prune the branch, switch the branch for an unconditional jump
+                prune_stat, taken = prune(branch, condition, blk, *const_conds)
+                if (prune_stat):
+                    # add the condition to the list of nullified conditions
+                    nullified_conditions.append(nullified(condition, taken,
+                                                          True))
+        else:
+            # see if this is a branch on a constant value predicate
+            resolved_const = Unknown()
+            try:
+                pred_call = get_definition(func_ir, branch.cond)
+                resolved_const = find_const(func_ir, pred_call.args[0])
+                if resolved_const is None:
+                    resolved_const = types.NoneType('none')
+            except GuardException:
+                pass
+
+            if not isinstance(resolved_const, Unknown):
+                prune_stat, taken = prune_by_predicate(branch, condition, blk)
+                if (prune_stat):
+                    # add the condition to the list of nullified conditions
+                    nullified_conditions.append(nullified(condition, taken,
+                                                          False))
+
+    # 'ERE BE DRAGONS...
+    # It is the evaluation of the condition expression that often trips up type
+    # inference, so ideally it would be removed as it is effectively rendered
+    # dead by the unconditional jump if a branch was pruned. However, there may
+    # be references to the condition that exist in multiple places (e.g. dels)
+    # and we cannot run DCE here as typing has not taken place to give enough
+    # information to run DCE safely. Upshot of all this is the condition gets
+    # rewritten below into a benign const that typing will be happy with and DCE
+    # can remove it and its reference post typing when it is safe to do so
+    # (if desired). It is required that the const is assigned a value that
+    # indicates the branch taken as its mutated value would be read in the case
+    # of object mode fall back in place of the condition itself. For
+    # completeness the func_ir._definitions and ._consts are also updated to
+    # make the IR state self consistent.
+
+    deadcond = [x.condition for x in nullified_conditions]
+    for _, cond, blk in branch_info:
+        if cond in deadcond:
+            for x in blk.body:
+                if isinstance(x, ir.Assign) and x.value is cond:
+                    # rewrite the condition as a true/false bit
+                    nullified_info = nullified_conditions[deadcond.index(cond)]
+                    # only do a rewrite of conditions, predicates need to retain
+                    # their value as they may be used later.
+                    if nullified_info.rewrite_stmt:
+                        branch_bit = nullified_info.taken_br
+                        x.value = ir.Const(branch_bit, loc=x.loc)
+                        # update the specific definition to the new const
+                        defns = func_ir._definitions[x.target.name]
+                        repl_idx = defns.index(cond)
+                        defns[repl_idx] = x.value
+
+    # Check post dominators of dead nodes from in the original CFG for use of
+    # vars that are being removed in the dead blocks which might be referred to
+    # by phi nodes.
+    #
+    # Multiple things to fix up:
+    #
+    # 1. Cases like:
+    #
+    # A        A
+    # |\       |
+    # | B  --> B
+    # |/       |
+    # C        C
+    #
+    # i.e. the branch is dead but the block is still alive. In this case CFG
+    # simplification will fuse A-B-C and any phi in C can be updated as an
+    # direct assignment from the last assigned version in the dominators of the
+    # fused block.
+    #
+    # 2. Cases like:
+    #
+    #   A        A
+    #  / \       |
+    # B   C  --> B
+    #  \ /       |
+    #   D        D
+    #
+    # i.e. the block C is dead. In this case the phis in D need updating to
+    # reflect the collapse of the phi condition. This should result in a direct
+    # assignment of the surviving version in B to the LHS of the phi in D.
+
+    new_cfg = compute_cfg_from_blocks(func_ir.blocks)
+    dead_blocks = new_cfg.dead_nodes()
+
+    # for all phis that are still in live blocks.
+    for phi, lbl in phi2lbl.items():
+        if lbl in dead_blocks:
+            continue
+        new_incoming = [x[0] for x in new_cfg.predecessors(lbl)]
+        if set(new_incoming) != set(phi.incoming_blocks):
+            # Something has changed in the CFG...
+            if len(new_incoming) == 1:
+                # There's now just one incoming. Replace the PHI node by a
+                # direct assignment
+                idx = phi.incoming_blocks.index(new_incoming[0])
+                phi2asgn[phi].value = phi.incoming_values[idx]
+            else:
+                # There's more than one incoming still, then look through the
+                # incoming and remove dead
+                ic_val_tmp = []
+                ic_blk_tmp = []
+                for ic_val, ic_blk in zip(phi.incoming_values,
+                                          phi.incoming_blocks):
+                    if ic_blk in dead_blocks:
+                        continue
+                    else:
+                        ic_val_tmp.append(ic_val)
+                        ic_blk_tmp.append(ic_blk)
+                phi.incoming_values.clear()
+                phi.incoming_values.extend(ic_val_tmp)
+                phi.incoming_blocks.clear()
+                phi.incoming_blocks.extend(ic_blk_tmp)
+
+    # Remove dead blocks, this is safe as it relies on the CFG only.
+    for dead in dead_blocks:
+        del func_ir.blocks[dead]
+
+    # if conditions were nullified then consts were rewritten, update
+    if nullified_conditions:
+        func_ir._consts = consts.ConstantInference(func_ir)
+
+    if DEBUG > 1:
+        print("after".center(80, '-'))
+        print(func_ir.dump())
+
+
+def rewrite_semantic_constants(func_ir, called_args):
+    """
+    This rewrites values known to be constant by their semantics as ir.Const
+    nodes, this is to give branch pruning the best chance possible of killing
+    branches. An example might be rewriting len(tuple) as the literal length.
+
+    func_ir is the IR
+    called_args are the actual arguments with which the function is called
+    """
+    DEBUG = 0
+
+    if DEBUG > 1:
+        print(("rewrite_semantic_constants: " +
+               func_ir.func_id.func_name).center(80, '-'))
+        print("before".center(80, '*'))
+        func_ir.dump()
+
+    def rewrite_statement(func_ir, stmt, new_val):
+        """
+        Rewrites the stmt as a ir.Const new_val and fixes up the entries in
+        func_ir._definitions
+        """
+        stmt.value = ir.Const(new_val, stmt.loc)
+        defns = func_ir._definitions[stmt.target.name]
+        repl_idx = defns.index(val)
+        defns[repl_idx] = stmt.value
+
+    def rewrite_array_ndim(val, func_ir, called_args):
+        # rewrite Array.ndim as const(ndim)
+        if getattr(val, 'op', None) == 'getattr':
+            if val.attr == 'ndim':
+                arg_def = guard(get_definition, func_ir, val.value)
+                if isinstance(arg_def, ir.Arg):
+                    argty = called_args[arg_def.index]
+                    if isinstance(argty, types.Array):
+                        rewrite_statement(func_ir, stmt, argty.ndim)
+
+    def rewrite_tuple_len(val, func_ir, called_args):
+        # rewrite len(tuple) as const(len(tuple))
+        if getattr(val, 'op', None) == 'call':
+            func = guard(get_definition, func_ir, val.func)
+            if (func is not None and isinstance(func, ir.Global) and
+                    getattr(func, 'value', None) is len):
+
+                (arg,) = val.args
+                arg_def = guard(get_definition, func_ir, arg)
+                if isinstance(arg_def, ir.Arg):
+                    argty = called_args[arg_def.index]
+                    if isinstance(argty, types.BaseTuple):
+                        rewrite_statement(func_ir, stmt, argty.count)
+                elif (isinstance(arg_def, ir.Expr) and
+                      arg_def.op == 'typed_getitem'):
+                    argty = arg_def.dtype
+                    if isinstance(argty, types.BaseTuple):
+                        rewrite_statement(func_ir, stmt, argty.count)
+
+    from numba.core.ir_utils import get_definition, guard
+    for blk in func_ir.blocks.values():
+        for stmt in blk.body:
+            if isinstance(stmt, ir.Assign):
+                val = stmt.value
+                if isinstance(val, ir.Expr):
+                    rewrite_array_ndim(val, func_ir, called_args)
+                    rewrite_tuple_len(val, func_ir, called_args)
+
+    if DEBUG > 1:
+        print("after".center(80, '*'))
+        func_ir.dump()
+        print('-' * 80)
+
+
+def find_literally_calls(func_ir, argtypes):
+    """An analysis to find `numba.literally` call inside the given IR.
+    When an unsatisfied literal typing request is found, a `ForceLiteralArg`
+    exception is raised.
+
+    Parameters
+    ----------
+
+    func_ir : numba.ir.FunctionIR
+
+    argtypes : Sequence[numba.types.Type]
+        The argument types.
+    """
+    from numba.core import ir_utils
+
+    marked_args = set()
+    first_loc = {}
+    # Scan for literally calls
+    for blk in func_ir.blocks.values():
+        for assign in blk.find_exprs(op='call'):
+            var = ir_utils.guard(ir_utils.get_definition, func_ir, assign.func)
+            if isinstance(var, (ir.Global, ir.FreeVar)):
+                fnobj = var.value
+            else:
+                fnobj = ir_utils.guard(ir_utils.resolve_func_from_module,
+                                       func_ir, var)
+            if fnobj is special.literally:
+                # Found
+                [arg] = assign.args
+                defarg = func_ir.get_definition(arg)
+                if isinstance(defarg, ir.Arg):
+                    argindex = defarg.index
+                    marked_args.add(argindex)
+                    first_loc.setdefault(argindex, assign.loc)
+    # Signal the dispatcher to force literal typing
+    for pos in marked_args:
+        query_arg = argtypes[pos]
+        do_raise = (isinstance(query_arg, types.InitialValue) and
+                    query_arg.initial_value is None)
+        if do_raise:
+            loc = first_loc[pos]
+            raise errors.ForceLiteralArg(marked_args, loc=loc)
+
+        if not isinstance(query_arg, (types.Literal, types.InitialValue)):
+            loc = first_loc[pos]
+            raise errors.ForceLiteralArg(marked_args, loc=loc)
+
+
+ir_extension_use_alloca = {}
+
+
+def must_use_alloca(blocks):
+    """
+    Analyzes a dictionary of blocks to find variables that must be
+    stack allocated with alloca.  For each statement in the blocks,
+    determine if that statement requires certain variables to be
+    stack allocated.  This function uses the extension point
+    ir_extension_use_alloca to allow other IR node types like parfors
+    to register to be processed by this analysis function.  At the
+    moment, parfors are the only IR node types that may require
+    something to be stack allocated.
+    """
+    use_alloca_vars = set()
+
+    for ir_block in blocks.values():
+        for stmt in ir_block.body:
+            if type(stmt) in ir_extension_use_alloca:
+                func = ir_extension_use_alloca[type(stmt)]
+                func(stmt, use_alloca_vars)
+                continue
+
+    return use_alloca_vars
diff --git a/shared/package-files/04/04b80f5b077bbed68808cfebadeb5e3523f2a8c9a96495c587bd96df1eac2a33/LICENSE b/shared/package-files/04/04b80f5b077bbed68808cfebadeb5e3523f2a8c9a96495c587bd96df1eac2a33/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..29225eee9edcd72c6a354550a5a3bedf1932b2ef
--- /dev/null
+++ b/shared/package-files/04/04b80f5b077bbed68808cfebadeb5e3523f2a8c9a96495c587bd96df1eac2a33/LICENSE
@@ -0,0 +1,26 @@
+
+Except when otherwise stated (look for LICENSE files in directories or
+information at the beginning of each file) all software and
+documentation is licensed as follows: 
+
+    The MIT License
+
+    Permission is hereby granted, free of charge, to any person 
+    obtaining a copy of this software and associated documentation 
+    files (the "Software"), to deal in the Software without 
+    restriction, including without limitation the rights to use, 
+    copy, modify, merge, publish, distribute, sublicense, and/or 
+    sell copies of the Software, and to permit persons to whom the 
+    Software is furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included 
+    in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS 
+    OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL 
+    THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING 
+    FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER 
+    DEALINGS IN THE SOFTWARE.
+
diff --git a/shared/package-files/04/04bce83f38d314e3b195eb11c1fc86320a78e244d4b1e5fbede0ac6aac56764a/default_planner.py b/shared/package-files/04/04bce83f38d314e3b195eb11c1fc86320a78e244d4b1e5fbede0ac6aac56764a/default_planner.py
new file mode 100644
index 0000000000000000000000000000000000000000..22f9be1964f85577a597345ec91a017842caa539
--- /dev/null
+++ b/shared/package-files/04/04bce83f38d314e3b195eb11c1fc86320a78e244d4b1e5fbede0ac6aac56764a/default_planner.py
@@ -0,0 +1,420 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import dataclasses
+import io
+import logging
+import operator
+from collections import ChainMap
+from functools import reduce
+from typing import Any, cast, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch.distributed._shard._utils import narrow_tensor_by_index
+from torch.distributed._tensor import DTensor
+from torch.distributed.checkpoint._dedup_save_plans import dedup_save_plans
+from torch.distributed.checkpoint._nested_dict import (
+    FLATTEN_MAPPING,
+    flatten_state_dict,
+)
+from torch.distributed.checkpoint._sharded_tensor_utils import _flatten_sharded_tensors
+from torch.distributed.checkpoint._traverse import set_element
+from torch.distributed.checkpoint.metadata import (
+    BytesStorageMetadata,
+    ChunkStorageMetadata,
+    Metadata,
+    MetadataIndex,
+    STATE_DICT_TYPE,
+    STORAGE_TYPES,
+    TensorStorageMetadata,
+)
+from torch.distributed.checkpoint.planner import (
+    LoadPlan,
+    LoadPlanner,
+    ReadItem,
+    SavePlan,
+    SavePlanner,
+    WriteItem,
+    WriteItemType,
+)
+from torch.distributed.checkpoint.planner_helpers import (
+    _create_default_metadata_only_plan,
+    _create_read_items,
+    _create_write_items,
+    _init_state_dict,
+)
+from torch.distributed.checkpoint.utils import find_state_dict_object
+
+logger: logging.Logger = logging.getLogger(__name__)
+
+
+__all__ = [
+    "DefaultSavePlanner",
+    "DefaultLoadPlanner",
+    "create_default_local_load_plan",
+    "create_default_global_load_plan",
+    "create_default_local_save_plan",
+    "create_default_global_save_plan",
+]
+
+
+# TODO: Update docstrings for default_planner.py
+class DefaultSavePlanner(SavePlanner):
+    mappings: FLATTEN_MAPPING
+
+    def __init__(
+        self,
+        flatten_state_dict: bool = True,
+        flatten_sharded_tensors: bool = True,
+        dedup_replicated_tensors: Optional[bool] = None,
+    ) -> None:
+        self.flatten_state_dict = flatten_state_dict
+        self.flatten_sharded_tensors = flatten_sharded_tensors
+        self.mappings = {}
+
+        if dedup_replicated_tensors is not None:
+            logger.warning(
+                "DefaultSavePlanner's `dedup_replicated_tensors` argument is being "
+                "deprecated, and no longer has any effect. Please remove this argument "
+                "from your call."
+            )
+
+    def set_up_planner(self, state_dict: STATE_DICT_TYPE, is_coordinator: bool) -> None:
+        if self.flatten_state_dict:
+            state_dict, self.mappings = flatten_state_dict(state_dict)
+        if self.flatten_sharded_tensors:
+            state_dict = _flatten_sharded_tensors(state_dict)
+        self.state_dict = state_dict
+        self.is_coordinator = is_coordinator
+
+    def create_local_plan(self) -> SavePlan:
+        plan = create_default_local_save_plan(self.state_dict, self.is_coordinator)
+        if self.flatten_state_dict:
+            plan = dataclasses.replace(plan, planner_data=self.mappings)
+        self.plan = plan
+
+        return self.plan
+
+    def create_global_plan(
+        self, all_plans: List[SavePlan]
+    ) -> Tuple[List[SavePlan], Metadata]:
+        all_plans = dedup_save_plans(all_plans)
+
+        global_plan, metadata = create_default_global_save_plan(all_plans)
+
+        if self.flatten_state_dict:
+            # | does not work for Python 3.8 or older version.
+            # merged_mappings = reduce(
+            #     lambda x, y: x | y, (p.planner_data for p in global_plan)
+            # )
+            planner_data_dict = [p.planner_data for p in global_plan]
+            merged_mappings = dict(ChainMap(*planner_data_dict))
+            metadata = dataclasses.replace(metadata, planner_data=merged_mappings)
+
+        if not _validate_global_plan(global_plan, metadata):
+            raise ValueError("Failed to validate global plan")
+
+        self.global_plan = global_plan
+        self.metadata = metadata
+
+        return self.global_plan, self.metadata
+
+    def finish_plan(self, new_plan: SavePlan) -> SavePlan:
+        self.plan = new_plan
+        return new_plan
+
+    def resolve_data(self, write_item: WriteItem) -> Union[torch.Tensor, io.BytesIO]:
+        object = self.lookup_object(write_item.index)
+        return self.transform_object(write_item, object)
+
+    def lookup_object(self, index: MetadataIndex) -> Any:
+        """Extension from the planner interface to make it easy to extend the default planner."""
+        return find_state_dict_object(self.state_dict, index)
+
+    def transform_object(self, write_item: WriteItem, object: Any):
+        """Extension from the planner interface to make it easy to extend the default planner."""
+        if write_item.type == WriteItemType.BYTE_IO:
+            bytes = io.BytesIO()
+            torch.save(object, bytes)
+            object = bytes
+        return object
+
+
+class DefaultLoadPlanner(LoadPlanner):
+    """
+    DefaultLoadPlanner that adds multiple features on top of LoadPlanner.
+
+    In particular it adds the following:
+
+    flatten_state_dict: Handle state_dict with nested dicts
+    flatten_sharded_tensors: For FSDP in 2D parallel mode
+    """
+
+    original_state_dict: STATE_DICT_TYPE
+    mappings: FLATTEN_MAPPING
+
+    def __init__(
+        self,
+        flatten_state_dict: bool = True,
+        flatten_sharded_tensors: bool = True,
+    ) -> None:
+        self.flatten_state_dict = flatten_state_dict
+        self.flatten_sharded_tensors = flatten_sharded_tensors
+        self.original_state_dict = {}
+        self.mappings = {}
+
+    def set_up_planner(
+        self,
+        state_dict: STATE_DICT_TYPE,
+        metadata: Metadata,
+        is_coordinator: bool,
+    ) -> None:
+        _init_state_dict(state_dict)
+        self.original_state_dict = state_dict
+
+        if self.flatten_sharded_tensors:
+            state_dict = _flatten_sharded_tensors(state_dict)
+
+        if self.flatten_state_dict:
+            state_dict, self.mappings = flatten_state_dict(state_dict)
+
+        self.state_dict = state_dict
+        self.metadata = metadata
+        self.is_coordinator = is_coordinator
+
+    def create_local_plan(self) -> LoadPlan:
+        return create_default_local_load_plan(self.state_dict, self.metadata)
+
+    def create_global_plan(self, global_plan: List[LoadPlan]) -> List[LoadPlan]:
+        return create_default_global_load_plan(global_plan)
+
+    def finish_plan(self, new_plan: LoadPlan) -> LoadPlan:
+        return new_plan
+
+    def load_bytes(self, read_item: ReadItem, value: io.BytesIO) -> None:
+        if self.flatten_state_dict:
+            set_element(
+                self.original_state_dict,
+                self.mappings[read_item.dest_index.fqn],
+                torch.load(value),
+            )
+        else:
+            self.state_dict[read_item.dest_index.fqn] = torch.load(value)
+
+    def resolve_tensor(self, read_item: ReadItem):
+        tensor = self.lookup_tensor(read_item.dest_index)
+        return self.transform_tensor(read_item, tensor)
+
+    def commit_tensor(self, read_item: ReadItem, tensor: torch.Tensor) -> None:
+        pass
+
+    def lookup_tensor(self, index: MetadataIndex) -> torch.Tensor:
+        """Extension from the planner interface to make it easy to extend the default planner."""
+        return find_state_dict_object(self.state_dict, index)
+
+    def transform_tensor(self, read_item: ReadItem, tensor: torch.Tensor):
+        """Extension from the planner interface to make it easy to extend the default planner."""
+        return narrow_tensor_by_index(tensor, read_item.dest_offsets, read_item.lengths)
+
+
+def create_default_local_load_plan(
+    state_dict: Dict[str, Any],
+    metadata: Metadata,
+) -> LoadPlan:
+    requests = []
+    """
+    Create the ``LoadPlan`` used by DefaultLoadPlanner.
+
+    It produces one read item per value in ``state_dict`` using the metadata in ``metadata``.
+
+    The default behavior is to match key exactly between state_dict and metadata.
+    It handles resharding by issuing multiple read requests against storage in order to match
+    load requirements.
+    """
+
+    for fqn, obj in state_dict.items():
+        md = metadata.state_dict_metadata[fqn]
+        # Since DTensor supports submesh, adding extra check to ensure _create_read_items()
+        # gets called only when the current rank is part of the mesh for the corresponding DTensor.
+        if isinstance(obj, DTensor):
+            if obj.device_mesh.get_coordinate() is not None:
+                requests += _create_read_items(fqn, md, obj)
+        else:
+            requests += _create_read_items(fqn, md, obj)
+
+    return LoadPlan(requests)
+
+
+def create_default_global_load_plan(
+    all_plans: List[LoadPlan],
+) -> List[LoadPlan]:
+    """
+    Create global load plan used by DefaultLoadPlanner.
+
+    The default load behavior involved no global coordination and this function
+    currently doesn't change the local plans.
+    """
+    return all_plans
+
+
+def create_default_local_save_plan(
+    state_dict: Dict[str, Any], is_coordinator: bool
+) -> SavePlan:
+    """
+    Create the ``SavePlan`` used by DefaultSavePlanner.
+
+    On non-coordinator ranks, this function ignores tensors and non-tensor objects,
+    only producing writes for ShardedTensor objects.
+
+    On the coordinator rank, produce writes for all values.
+    """
+    requests = []
+    for fqn, obj in state_dict.items():
+        # Since DTensor supports submesh, adding extra check to ensure _create_write_items()
+        # gets called only when the current rank is part of the mesh for the corresponding DTensor.
+        if isinstance(obj, DTensor):
+            if obj.device_mesh.get_coordinate() is not None:
+                requests += _create_write_items(fqn, obj)
+        elif isinstance(obj, (torch.Tensor)) or is_coordinator:
+            requests += _create_write_items(fqn, obj)
+
+    return SavePlan(requests)
+
+
+def create_default_global_save_plan(
+    all_plans: List[SavePlan],
+    rewrite_index_hints: bool = True,
+) -> Tuple[List[SavePlan], Metadata]:
+    """
+    Create the global plan and metadata used by DefaultSavePlanner.
+
+    Metadata is produced by concatenating the metadata of all ``WriteItem`` from the supplied plans.
+
+    The only global planning change is to update index hints in all ``MetadataIndex`` objects if
+    ``rewrite_index_hints`` is True.
+    """
+    md: Dict[str, STORAGE_TYPES] = {}
+    new_plans = []
+    for plan in all_plans:
+        new_items = []
+        for item in plan.items:
+            if not item.type == WriteItemType.SHARD:
+                assert item.index.fqn not in md
+
+            if item.type == WriteItemType.BYTE_IO:
+                md[item.index.fqn] = BytesStorageMetadata()
+                new_items.append(item)
+            else:
+                assert item.tensor_data is not None
+                tensor_md = cast(
+                    TensorStorageMetadata,
+                    md.setdefault(
+                        item.index.fqn,
+                        TensorStorageMetadata(
+                            properties=item.tensor_data.properties,
+                            size=item.tensor_data.size,
+                            chunks=[],
+                        ),
+                    ),
+                )
+                new_item = item
+                if rewrite_index_hints:
+                    new_index = dataclasses.replace(
+                        item.index, index=len(tensor_md.chunks)
+                    )
+                    new_item = dataclasses.replace(item, index=new_index)
+                new_items.append(new_item)
+
+                assert (
+                    item.tensor_data.chunk is not None
+                ), f"""
+                    Cannot create MD for tensor without bounds.
+                    FQN: {item.index.fqn}
+                """
+                tensor_md.chunks.append(item.tensor_data.chunk)
+        new_plans.append(dataclasses.replace(plan, items=new_items))
+    return (new_plans, Metadata(md))
+
+
+def _create_default_local_metadata(state_dict: STATE_DICT_TYPE) -> Metadata:
+    """Return the ``Metadata`` if DefaultSavePlanner was used to checkpoint ``state_dict``."""
+    plan = _create_default_metadata_only_plan(state_dict)
+    _, md = create_default_global_save_plan([plan])
+    return md
+
+
+def _check_box_overlap(box0: ChunkStorageMetadata, box1: ChunkStorageMetadata) -> bool:
+    """Check if two boxes overlap. Tuples are (offset, lengths)."""
+    # For each dim of each shard, check if one shard resides on the other
+    # end of second shard with respect to that dim. As an example for a 2D
+    # shard, we would check if one shard is above or on the left of the
+    # other shard.
+    ndims = len(box0.offsets)
+    for i in range(ndims):
+        if box0.offsets[i] >= box1.offsets[i] + box1.sizes[i]:
+            return False
+        if box1.offsets[i] >= box0.offsets[i] + box0.sizes[i]:
+            return False
+
+    return True
+
+
+def _check_box_bounds(
+    outer_box_size: torch.Size, inner_box: ChunkStorageMetadata
+) -> bool:
+    for i in range(len(outer_box_size)):
+        if inner_box.offsets[i] < 0:
+            return False
+        if inner_box.sizes[i] < 0:
+            return False
+        if inner_box.offsets[i] + inner_box.sizes[i] > outer_box_size[i]:
+            return False
+
+    return True
+
+
+def _validate_global_plan(global_plan: List[SavePlan], metadata: Metadata) -> bool:
+    all_good = True
+    for key, value in metadata.state_dict_metadata.items():
+        if isinstance(value, BytesStorageMetadata):
+            continue
+        if len(value.size) == 0:
+            continue
+        chunks_volume = 0
+        for chunk_idx, chunk0 in enumerate(value.chunks):
+            # Compute the volume
+            if not _check_box_bounds(value.size, chunk0):
+                logger.warning(
+                    """
+                        key:%s has out of bounds chunk:
+                        tensor-size:%s chunk: %s
+                    """,
+                    key,
+                    value.size,
+                    chunk0,
+                )
+                all_good = False
+            chunks_volume += reduce(operator.mul, chunk0.sizes, 1)
+
+            # Check for overlap
+            for chunk1 in value.chunks[chunk_idx + 1 :]:
+                if _check_box_overlap(chunk0, chunk1):
+                    logger.warning(
+                        "key:%s has overlapping chunks: %s %s", key, chunk0, chunk1
+                    )
+                    all_good = False
+
+        # Check whether combined chunk cover the whole tensor
+        tensor_volume = reduce(operator.mul, value.size, 1)
+        if chunks_volume != tensor_volume:
+            logger.warning(
+                """
+                    key:%s invalid fill tensor-volume:
+                    %s chunks-volume: %s
+                """,
+                key,
+                tensor_volume,
+                chunks_volume,
+            )
+            all_good = False
+
+    return all_good
diff --git a/shared/package-files/04/04bea6c67b8688e434e4c1e134a2d570d48f1ee823b8d259fc8e96c000ae2f3f/sym_size_compositeimplicitautograd_dispatch.h b/shared/package-files/04/04bea6c67b8688e434e4c1e134a2d570d48f1ee823b8d259fc8e96c000ae2f3f/sym_size_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..44634d72c4541d512729ac492766758ccfc3c657
--- /dev/null
+++ b/shared/package-files/04/04bea6c67b8688e434e4c1e134a2d570d48f1ee823b8d259fc8e96c000ae2f3f/sym_size_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API c10::SymInt sym_size(const at::Tensor & self, int64_t dim);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/04/04c044ba5c2042d9bf93d97cadfe4335e67e2a92afb61ee4a9fee4cc42f6f74f/utils.pyi b/shared/package-files/04/04c044ba5c2042d9bf93d97cadfe4335e67e2a92afb61ee4a9fee4cc42f6f74f/utils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..1f29b13af633482d3a9f5f0d0013d09e1962caa1
--- /dev/null
+++ b/shared/package-files/04/04c044ba5c2042d9bf93d97cadfe4335e67e2a92afb61ee4a9fee4cc42f6f74f/utils.pyi
@@ -0,0 +1,3 @@
+from typing import Dict
+
+def get_level_tags() -> Dict[int, str]: ...
diff --git a/shared/package-files/04/04c06744ee3fe078ef3b8b779e38eb30666bf993e67a092fcaf2fc28f63a64ce/East b/shared/package-files/04/04c06744ee3fe078ef3b8b779e38eb30666bf993e67a092fcaf2fc28f63a64ce/East
new file mode 100644
index 0000000000000000000000000000000000000000..67935ff4da8f527e03cd05ed2aa20e601e10f921
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diff --git a/shared/package-files/04/04c06744ee3fe078ef3b8b779e38eb30666bf993e67a092fcaf2fc28f63a64ce/Sao_Paulo b/shared/package-files/04/04c06744ee3fe078ef3b8b779e38eb30666bf993e67a092fcaf2fc28f63a64ce/Sao_Paulo
new file mode 100644
index 0000000000000000000000000000000000000000..67935ff4da8f527e03cd05ed2aa20e601e10f921
Binary files /dev/null and b/shared/package-files/04/04c06744ee3fe078ef3b8b779e38eb30666bf993e67a092fcaf2fc28f63a64ce/Sao_Paulo differ
diff --git a/shared/package-files/04/04c138a3fca699bf2d726514693cd98b5d310e38761654704486f67de090c81f/le_native.h b/shared/package-files/04/04c138a3fca699bf2d726514693cd98b5d310e38761654704486f67de090c81f/le_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..004218b618b9f40fcdaf1a1aec2306dd7e91ec53
--- /dev/null
+++ b/shared/package-files/04/04c138a3fca699bf2d726514693cd98b5d310e38761654704486f67de090c81f/le_native.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+namespace at {
+namespace native {
+struct TORCH_API structured_le_Scalar_out : public at::meta::structured_le_Scalar {
+void impl(const at::Tensor & self, const at::Scalar & other, const at::Tensor & out);
+};
+TORCH_API at::Tensor le_quantized_cpu(const at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & le_out_quantized_cpu(const at::Tensor & self, const at::Scalar & other, at::Tensor & out);
+struct TORCH_API structured_le_Tensor_out : public at::meta::structured_le_Tensor {
+void impl(const at::Tensor & self, const at::Tensor & other, const at::Tensor & out);
+};
+TORCH_API at::Tensor le_quantized_cpu(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & le_out_quantized_cpu(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/04/04c26202404339e79100771477660661355686fda2b48924c1ba3cdcbea50f1e/utils.py b/shared/package-files/04/04c26202404339e79100771477660661355686fda2b48924c1ba3cdcbea50f1e/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a6d31a05722c182738ed25584de9ea75890eb28
--- /dev/null
+++ b/shared/package-files/04/04c26202404339e79100771477660661355686fda2b48924c1ba3cdcbea50f1e/utils.py
@@ -0,0 +1,177 @@
+from functools import update_wrapper
+from numbers import Number
+from typing import Any, Dict
+
+import torch
+import torch.nn.functional as F
+from torch.overrides import is_tensor_like
+
+euler_constant = 0.57721566490153286060  # Euler Mascheroni Constant
+
+__all__ = [
+    "broadcast_all",
+    "logits_to_probs",
+    "clamp_probs",
+    "probs_to_logits",
+    "lazy_property",
+    "tril_matrix_to_vec",
+    "vec_to_tril_matrix",
+]
+
+
+def broadcast_all(*values):
+    r"""
+    Given a list of values (possibly containing numbers), returns a list where each
+    value is broadcasted based on the following rules:
+      - `torch.*Tensor` instances are broadcasted as per :ref:`_broadcasting-semantics`.
+      - numbers.Number instances (scalars) are upcast to tensors having
+        the same size and type as the first tensor passed to `values`.  If all the
+        values are scalars, then they are upcasted to scalar Tensors.
+
+    Args:
+        values (list of `numbers.Number`, `torch.*Tensor` or objects implementing __torch_function__)
+
+    Raises:
+        ValueError: if any of the values is not a `numbers.Number` instance,
+            a `torch.*Tensor` instance, or an instance implementing __torch_function__
+    """
+    if not all(is_tensor_like(v) or isinstance(v, Number) for v in values):
+        raise ValueError(
+            "Input arguments must all be instances of numbers.Number, "
+            "torch.Tensor or objects implementing __torch_function__."
+        )
+    if not all(is_tensor_like(v) for v in values):
+        options: Dict[str, Any] = dict(dtype=torch.get_default_dtype())
+        for value in values:
+            if isinstance(value, torch.Tensor):
+                options = dict(dtype=value.dtype, device=value.device)
+                break
+        new_values = [
+            v if is_tensor_like(v) else torch.tensor(v, **options) for v in values
+        ]
+        return torch.broadcast_tensors(*new_values)
+    return torch.broadcast_tensors(*values)
+
+
+def _standard_normal(shape, dtype, device):
+    if torch._C._get_tracing_state():
+        # [JIT WORKAROUND] lack of support for .normal_()
+        return torch.normal(
+            torch.zeros(shape, dtype=dtype, device=device),
+            torch.ones(shape, dtype=dtype, device=device),
+        )
+    return torch.empty(shape, dtype=dtype, device=device).normal_()
+
+
+def _sum_rightmost(value, dim):
+    r"""
+    Sum out ``dim`` many rightmost dimensions of a given tensor.
+
+    Args:
+        value (Tensor): A tensor of ``.dim()`` at least ``dim``.
+        dim (int): The number of rightmost dims to sum out.
+    """
+    if dim == 0:
+        return value
+    required_shape = value.shape[:-dim] + (-1,)
+    return value.reshape(required_shape).sum(-1)
+
+
+def logits_to_probs(logits, is_binary=False):
+    r"""
+    Converts a tensor of logits into probabilities. Note that for the
+    binary case, each value denotes log odds, whereas for the
+    multi-dimensional case, the values along the last dimension denote
+    the log probabilities (possibly unnormalized) of the events.
+    """
+    if is_binary:
+        return torch.sigmoid(logits)
+    return F.softmax(logits, dim=-1)
+
+
+def clamp_probs(probs):
+    eps = torch.finfo(probs.dtype).eps
+    return probs.clamp(min=eps, max=1 - eps)
+
+
+def probs_to_logits(probs, is_binary=False):
+    r"""
+    Converts a tensor of probabilities into logits. For the binary case,
+    this denotes the probability of occurrence of the event indexed by `1`.
+    For the multi-dimensional case, the values along the last dimension
+    denote the probabilities of occurrence of each of the events.
+    """
+    ps_clamped = clamp_probs(probs)
+    if is_binary:
+        return torch.log(ps_clamped) - torch.log1p(-ps_clamped)
+    return torch.log(ps_clamped)
+
+
+class lazy_property:
+    r"""
+    Used as a decorator for lazy loading of class attributes. This uses a
+    non-data descriptor that calls the wrapped method to compute the property on
+    first call; thereafter replacing the wrapped method into an instance
+    attribute.
+    """
+
+    def __init__(self, wrapped):
+        self.wrapped = wrapped
+        update_wrapper(self, wrapped)
+
+    def __get__(self, instance, obj_type=None):
+        if instance is None:
+            return _lazy_property_and_property(self.wrapped)
+        with torch.enable_grad():
+            value = self.wrapped(instance)
+        setattr(instance, self.wrapped.__name__, value)
+        return value
+
+
+class _lazy_property_and_property(lazy_property, property):
+    """We want lazy properties to look like multiple things.
+
+    * property when Sphinx autodoc looks
+    * lazy_property when Distribution validate_args looks
+    """
+
+    def __init__(self, wrapped):
+        property.__init__(self, wrapped)
+
+
+def tril_matrix_to_vec(mat: torch.Tensor, diag: int = 0) -> torch.Tensor:
+    r"""
+    Convert a `D x D` matrix or a batch of matrices into a (batched) vector
+    which comprises of lower triangular elements from the matrix in row order.
+    """
+    n = mat.shape[-1]
+    if not torch._C._get_tracing_state() and (diag < -n or diag >= n):
+        raise ValueError(f"diag ({diag}) provided is outside [{-n}, {n-1}].")
+    arange = torch.arange(n, device=mat.device)
+    tril_mask = arange < arange.view(-1, 1) + (diag + 1)
+    vec = mat[..., tril_mask]
+    return vec
+
+
+def vec_to_tril_matrix(vec: torch.Tensor, diag: int = 0) -> torch.Tensor:
+    r"""
+    Convert a vector or a batch of vectors into a batched `D x D`
+    lower triangular matrix containing elements from the vector in row order.
+    """
+    # +ve root of D**2 + (1+2*diag)*D - |diag| * (diag+1) - 2*vec.shape[-1] = 0
+    n = (
+        -(1 + 2 * diag)
+        + ((1 + 2 * diag) ** 2 + 8 * vec.shape[-1] + 4 * abs(diag) * (diag + 1)) ** 0.5
+    ) / 2
+    eps = torch.finfo(vec.dtype).eps
+    if not torch._C._get_tracing_state() and (round(n) - n > eps):
+        raise ValueError(
+            f"The size of last dimension is {vec.shape[-1]} which cannot be expressed as "
+            + "the lower triangular part of a square D x D matrix."
+        )
+    n = round(n.item()) if isinstance(n, torch.Tensor) else round(n)
+    mat = vec.new_zeros(vec.shape[:-1] + torch.Size((n, n)))
+    arange = torch.arange(n, device=vec.device)
+    tril_mask = arange < arange.view(-1, 1) + (diag + 1)
+    mat[..., tril_mask] = vec
+    return mat
diff --git a/shared/package-files/04/04c69c8d5df2f47e4863cd5de3677d48bfa055c6acdc700b99e988ab15a043d0/special_modified_bessel_k0_cuda_dispatch.h b/shared/package-files/04/04c69c8d5df2f47e4863cd5de3677d48bfa055c6acdc700b99e988ab15a043d0/special_modified_bessel_k0_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4da8524994ead6cd4f85a8f2debcd7aecdbb0ac5
--- /dev/null
+++ b/shared/package-files/04/04c69c8d5df2f47e4863cd5de3677d48bfa055c6acdc700b99e988ab15a043d0/special_modified_bessel_k0_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_modified_bessel_k0(const at::Tensor & self);
+TORCH_API at::Tensor & special_modified_bessel_k0_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_modified_bessel_k0_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/04/04c6d460d8d2f6ea1d34f7efb58fe8766534f4603943370c6d0e5c2598659502/tree.py b/shared/package-files/04/04c6d460d8d2f6ea1d34f7efb58fe8766534f4603943370c6d0e5c2598659502/tree.py
new file mode 100644
index 0000000000000000000000000000000000000000..afe8da1a4a30daf6e48ffba514656e7c86c9abaa
--- /dev/null
+++ b/shared/package-files/04/04c6d460d8d2f6ea1d34f7efb58fe8766534f4603943370c6d0e5c2598659502/tree.py
@@ -0,0 +1,251 @@
+from typing import Iterator, List, Optional, Tuple
+
+from ._loop import loop_first, loop_last
+from .console import Console, ConsoleOptions, RenderableType, RenderResult
+from .jupyter import JupyterMixin
+from .measure import Measurement
+from .segment import Segment
+from .style import Style, StyleStack, StyleType
+from .styled import Styled
+
+
+class Tree(JupyterMixin):
+    """A renderable for a tree structure.
+
+    Args:
+        label (RenderableType): The renderable or str for the tree label.
+        style (StyleType, optional): Style of this tree. Defaults to "tree".
+        guide_style (StyleType, optional): Style of the guide lines. Defaults to "tree.line".
+        expanded (bool, optional): Also display children. Defaults to True.
+        highlight (bool, optional): Highlight renderable (if str). Defaults to False.
+    """
+
+    def __init__(
+        self,
+        label: RenderableType,
+        *,
+        style: StyleType = "tree",
+        guide_style: StyleType = "tree.line",
+        expanded: bool = True,
+        highlight: bool = False,
+        hide_root: bool = False,
+    ) -> None:
+        self.label = label
+        self.style = style
+        self.guide_style = guide_style
+        self.children: List[Tree] = []
+        self.expanded = expanded
+        self.highlight = highlight
+        self.hide_root = hide_root
+
+    def add(
+        self,
+        label: RenderableType,
+        *,
+        style: Optional[StyleType] = None,
+        guide_style: Optional[StyleType] = None,
+        expanded: bool = True,
+        highlight: Optional[bool] = False,
+    ) -> "Tree":
+        """Add a child tree.
+
+        Args:
+            label (RenderableType): The renderable or str for the tree label.
+            style (StyleType, optional): Style of this tree. Defaults to "tree".
+            guide_style (StyleType, optional): Style of the guide lines. Defaults to "tree.line".
+            expanded (bool, optional): Also display children. Defaults to True.
+            highlight (Optional[bool], optional): Highlight renderable (if str). Defaults to False.
+
+        Returns:
+            Tree: A new child Tree, which may be further modified.
+        """
+        node = Tree(
+            label,
+            style=self.style if style is None else style,
+            guide_style=self.guide_style if guide_style is None else guide_style,
+            expanded=expanded,
+            highlight=self.highlight if highlight is None else highlight,
+        )
+        self.children.append(node)
+        return node
+
+    def __rich_console__(
+        self, console: "Console", options: "ConsoleOptions"
+    ) -> "RenderResult":
+
+        stack: List[Iterator[Tuple[bool, Tree]]] = []
+        pop = stack.pop
+        push = stack.append
+        new_line = Segment.line()
+
+        get_style = console.get_style
+        null_style = Style.null()
+        guide_style = get_style(self.guide_style, default="") or null_style
+        SPACE, CONTINUE, FORK, END = range(4)
+
+        ASCII_GUIDES = ("    ", "|   ", "+-- ", "`-- ")
+        TREE_GUIDES = [
+            ("    ", "│   ", "├── ", "└── "),
+            ("    ", "┃   ", "┣â”â” ", "â”—â”â” "),
+            ("    ", "â•‘   ", "â• â•â• ", "â•šâ•â• "),
+        ]
+        _Segment = Segment
+
+        def make_guide(index: int, style: Style) -> Segment:
+            """Make a Segment for a level of the guide lines."""
+            if options.ascii_only:
+                line = ASCII_GUIDES[index]
+            else:
+                guide = 1 if style.bold else (2 if style.underline2 else 0)
+                line = TREE_GUIDES[0 if options.legacy_windows else guide][index]
+            return _Segment(line, style)
+
+        levels: List[Segment] = [make_guide(CONTINUE, guide_style)]
+        push(iter(loop_last([self])))
+
+        guide_style_stack = StyleStack(get_style(self.guide_style))
+        style_stack = StyleStack(get_style(self.style))
+        remove_guide_styles = Style(bold=False, underline2=False)
+
+        depth = 0
+
+        while stack:
+            stack_node = pop()
+            try:
+                last, node = next(stack_node)
+            except StopIteration:
+                levels.pop()
+                if levels:
+                    guide_style = levels[-1].style or null_style
+                    levels[-1] = make_guide(FORK, guide_style)
+                    guide_style_stack.pop()
+                    style_stack.pop()
+                continue
+            push(stack_node)
+            if last:
+                levels[-1] = make_guide(END, levels[-1].style or null_style)
+
+            guide_style = guide_style_stack.current + get_style(node.guide_style)
+            style = style_stack.current + get_style(node.style)
+            prefix = levels[(2 if self.hide_root else 1) :]
+            renderable_lines = console.render_lines(
+                Styled(node.label, style),
+                options.update(
+                    width=options.max_width
+                    - sum(level.cell_length for level in prefix),
+                    highlight=self.highlight,
+                    height=None,
+                ),
+                pad=options.justify is not None,
+            )
+
+            if not (depth == 0 and self.hide_root):
+                for first, line in loop_first(renderable_lines):
+                    if prefix:
+                        yield from _Segment.apply_style(
+                            prefix,
+                            style.background_style,
+                            post_style=remove_guide_styles,
+                        )
+                    yield from line
+                    yield new_line
+                    if first and prefix:
+                        prefix[-1] = make_guide(
+                            SPACE if last else CONTINUE, prefix[-1].style or null_style
+                        )
+
+            if node.expanded and node.children:
+                levels[-1] = make_guide(
+                    SPACE if last else CONTINUE, levels[-1].style or null_style
+                )
+                levels.append(
+                    make_guide(END if len(node.children) == 1 else FORK, guide_style)
+                )
+                style_stack.push(get_style(node.style))
+                guide_style_stack.push(get_style(node.guide_style))
+                push(iter(loop_last(node.children)))
+                depth += 1
+
+    def __rich_measure__(
+        self, console: "Console", options: "ConsoleOptions"
+    ) -> "Measurement":
+        stack: List[Iterator[Tree]] = [iter([self])]
+        pop = stack.pop
+        push = stack.append
+        minimum = 0
+        maximum = 0
+        measure = Measurement.get
+        level = 0
+        while stack:
+            iter_tree = pop()
+            try:
+                tree = next(iter_tree)
+            except StopIteration:
+                level -= 1
+                continue
+            push(iter_tree)
+            min_measure, max_measure = measure(console, options, tree.label)
+            indent = level * 4
+            minimum = max(min_measure + indent, minimum)
+            maximum = max(max_measure + indent, maximum)
+            if tree.expanded and tree.children:
+                push(iter(tree.children))
+                level += 1
+        return Measurement(minimum, maximum)
+
+
+if __name__ == "__main__":  # pragma: no cover
+
+    from pip._vendor.rich.console import Group
+    from pip._vendor.rich.markdown import Markdown
+    from pip._vendor.rich.panel import Panel
+    from pip._vendor.rich.syntax import Syntax
+    from pip._vendor.rich.table import Table
+
+    table = Table(row_styles=["", "dim"])
+
+    table.add_column("Released", style="cyan", no_wrap=True)
+    table.add_column("Title", style="magenta")
+    table.add_column("Box Office", justify="right", style="green")
+
+    table.add_row("Dec 20, 2019", "Star Wars: The Rise of Skywalker", "$952,110,690")
+    table.add_row("May 25, 2018", "Solo: A Star Wars Story", "$393,151,347")
+    table.add_row("Dec 15, 2017", "Star Wars Ep. V111: The Last Jedi", "$1,332,539,889")
+    table.add_row("Dec 16, 2016", "Rogue One: A Star Wars Story", "$1,332,439,889")
+
+    code = """\
+class Segment(NamedTuple):
+    text: str = ""
+    style: Optional[Style] = None
+    is_control: bool = False
+"""
+    syntax = Syntax(code, "python", theme="monokai", line_numbers=True)
+
+    markdown = Markdown(
+        """\
+### example.md
+> Hello, World!
+>
+> Markdown _all_ the things
+"""
+    )
+
+    root = Tree("🌲 [b green]Rich Tree", highlight=True, hide_root=True)
+
+    node = root.add(":file_folder: Renderables", guide_style="red")
+    simple_node = node.add(":file_folder: [bold yellow]Atomic", guide_style="uu green")
+    simple_node.add(Group("📄 Syntax", syntax))
+    simple_node.add(Group("📄 Markdown", Panel(markdown, border_style="green")))
+
+    containers_node = node.add(
+        ":file_folder: [bold magenta]Containers", guide_style="bold magenta"
+    )
+    containers_node.expanded = True
+    panel = Panel.fit("Just a panel", border_style="red")
+    containers_node.add(Group("📄 Panels", panel))
+
+    containers_node.add(Group("📄 [b magenta]Table", table))
+
+    console = Console()
+
+    console.print(root)
diff --git a/shared/package-files/04/04c7b57894f704caa03b9fb118156fbbf2a16d6463fac7dd76848362993675aa/CUDAGuard.h b/shared/package-files/04/04c7b57894f704caa03b9fb118156fbbf2a16d6463fac7dd76848362993675aa/CUDAGuard.h
new file mode 100644
index 0000000000000000000000000000000000000000..aebd59dd161cf7b94683345c4eac728aab0f49f8
--- /dev/null
+++ b/shared/package-files/04/04c7b57894f704caa03b9fb118156fbbf2a16d6463fac7dd76848362993675aa/CUDAGuard.h
@@ -0,0 +1,303 @@
+#pragma once
+
+#include 
+#include 
+#include 
+#include 
+#include 
+
+#include 
+
+namespace c10::cuda {
+
+// This code is kind of boilerplatey.  See Note [Whither the DeviceGuard
+// boilerplate]
+
+/// A variant of DeviceGuard that is specialized for CUDA.  It accepts
+/// integer indices (interpreting them as CUDA devices) and is a little
+/// more efficient than DeviceGuard (it compiles to straight line
+/// cudaSetDevice/cudaGetDevice calls); however, it can only be used
+/// from code that links against CUDA directly.
+struct CUDAGuard {
+  /// No default constructor; see Note [Omitted default constructor from RAII]
+  explicit CUDAGuard() = delete;
+
+  /// Set the current CUDA device to the passed device index.
+  explicit CUDAGuard(DeviceIndex device_index) : guard_(device_index) {}
+
+  /// Sets the current CUDA device to the passed device.  Errors if the passed
+  /// device is not a CUDA device.
+  explicit CUDAGuard(Device device) : guard_(device) {}
+
+  // Copy is not allowed
+  CUDAGuard(const CUDAGuard&) = delete;
+  CUDAGuard& operator=(const CUDAGuard&) = delete;
+
+  // Move is not allowed (there is no uninitialized state)
+  CUDAGuard(CUDAGuard&& other) = delete;
+  CUDAGuard& operator=(CUDAGuard&& other) = delete;
+
+  /// Sets the CUDA device to the given device.  Errors if the given device
+  /// is not a CUDA device.
+  void set_device(Device device) {
+    guard_.set_device(device);
+  }
+
+  /// Sets the CUDA device to the given device.  Errors if the given device
+  /// is not a CUDA device.  (This method is provided for uniformity with
+  /// DeviceGuard).
+  void reset_device(Device device) {
+    guard_.reset_device(device);
+  }
+
+  /// Sets the CUDA device to the given device index.
+  void set_index(DeviceIndex device_index) {
+    guard_.set_index(device_index);
+  }
+
+  /// Returns the device that was set upon construction of the guard
+  Device original_device() const {
+    return guard_.original_device();
+  }
+
+  /// Returns the last device that was set via `set_device`, if any, otherwise
+  /// the device passed during construction.
+  Device current_device() const {
+    return guard_.current_device();
+  }
+
+ private:
+  /// The guard for the current device.
+  c10::impl::InlineDeviceGuard guard_;
+};
+
+/// A variant of OptionalDeviceGuard that is specialized for CUDA.  See
+/// CUDAGuard for when you can use this.
+struct OptionalCUDAGuard {
+  /// Create an uninitialized OptionalCUDAGuard.
+  explicit OptionalCUDAGuard() : guard_() {}
+
+  /// Set the current CUDA device to the passed Device, if it is not nullopt.
+  explicit OptionalCUDAGuard(optional device_opt)
+      : guard_(device_opt) {}
+
+  /// Set the current CUDA device to the passed device index, if it is not
+  /// nullopt
+  explicit OptionalCUDAGuard(optional device_index_opt)
+      : guard_(device_index_opt) {}
+
+  // Copy is not allowed
+  OptionalCUDAGuard(const OptionalCUDAGuard&) = delete;
+  OptionalCUDAGuard& operator=(const OptionalCUDAGuard&) = delete;
+
+  // See Note [Move construction for RAII guards is tricky]
+  OptionalCUDAGuard(OptionalCUDAGuard&& other) = delete;
+
+  // See Note [Move assignment for RAII guards is tricky]
+  OptionalCUDAGuard& operator=(OptionalCUDAGuard&& other) = delete;
+
+  /// Sets the CUDA device to the given device, initializing the guard if it
+  /// is not already initialized.  Errors if the given device is not a CUDA
+  /// device.
+  void set_device(Device device) {
+    guard_.set_device(device);
+  }
+
+  /// Sets the CUDA device to the given device, initializing the guard if it is
+  /// not already initialized.  Errors if the given device is not a CUDA device.
+  /// (This method is provided for uniformity with OptionalDeviceGuard).
+  void reset_device(Device device) {
+    guard_.reset_device(device);
+  }
+
+  /// Sets the CUDA device to the given device index, initializing the guard if
+  /// it is not already initialized.
+  void set_index(DeviceIndex device_index) {
+    guard_.set_index(device_index);
+  }
+
+  /// Returns the device that was set immediately prior to initialization of the
+  /// guard, or nullopt if the guard is uninitialized.
+  optional original_device() const {
+    return guard_.original_device();
+  }
+
+  /// Returns the most recent device that was set using this device guard,
+  /// either from construction, or via set_device, if the guard is initialized,
+  /// or nullopt if the guard is uninitialized.
+  optional current_device() const {
+    return guard_.current_device();
+  }
+
+  /// Restore the original CUDA device, resetting this guard to uninitialized
+  /// state.
+  void reset() {
+    guard_.reset();
+  }
+
+ private:
+  c10::impl::InlineOptionalDeviceGuard guard_;
+};
+
+/// A variant of StreamGuard that is specialized for CUDA.  See CUDAGuard
+/// for when you can use this.
+struct CUDAStreamGuard {
+  /// No default constructor, see Note [Omitted default constructor from RAII]
+  explicit CUDAStreamGuard() = delete;
+
+  /// Set the current CUDA device to the device associated with the passed
+  /// stream, and set the current CUDA stream on that device to the passed
+  /// stream. Errors if the Stream is not a CUDA stream.
+  explicit CUDAStreamGuard(Stream stream) : guard_(stream) {}
+
+  /// Copy is disallowed
+  CUDAStreamGuard(const CUDAStreamGuard&) = delete;
+  CUDAStreamGuard& operator=(const CUDAStreamGuard&) = delete;
+
+  /// Move is disallowed, as CUDAStreamGuard does not have an uninitialized
+  /// state, which is required for moves on types with nontrivial destructors.
+  CUDAStreamGuard(CUDAStreamGuard&& other) = delete;
+  CUDAStreamGuard& operator=(CUDAStreamGuard&& other) = delete;
+
+  /// Resets the currently set stream to the original stream and
+  /// the currently set device to the original device.  Then,
+  /// set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  /// Errors if the stream passed is not a CUDA stream.
+  ///
+  /// NOTE: this implementation may skip some stream/device setting if
+  /// it can prove that it is unnecessary.
+  ///
+  /// WARNING: reset_stream does NOT preserve previously set streams on
+  /// different devices.  If you need to set streams on multiple devices
+  /// on CUDA, use CUDAMultiStreamGuard instead.
+  void reset_stream(Stream stream) {
+    guard_.reset_stream(stream);
+  }
+
+  /// Returns the CUDA stream that was set at the time the guard was
+  /// constructed.
+  CUDAStream original_stream() const {
+    return CUDAStream(CUDAStream::UNCHECKED, guard_.original_stream());
+  }
+
+  /// Returns the most recent CUDA stream that was set using this device guard,
+  /// either from construction, or via set_stream.
+  CUDAStream current_stream() const {
+    return CUDAStream(CUDAStream::UNCHECKED, guard_.current_stream());
+  }
+
+  /// Returns the most recent CUDA device that was set using this device guard,
+  /// either from construction, or via set_device/reset_device/set_index.
+  Device current_device() const {
+    return guard_.current_device();
+  }
+
+  /// Returns the CUDA device that was set at the most recent reset_stream(),
+  /// or otherwise the device at construction time.
+  Device original_device() const {
+    return guard_.original_device();
+  }
+
+ private:
+  c10::impl::InlineStreamGuard guard_;
+};
+
+/// A variant of OptionalStreamGuard that is specialized for CUDA.  See
+/// CUDAGuard for when you can use this.
+struct OptionalCUDAStreamGuard {
+  /// Create an uninitialized guard.
+  explicit OptionalCUDAStreamGuard() : guard_() {}
+
+  /// Set the current CUDA device to the device associated with the passed
+  /// stream, and set the current CUDA stream on that device to the passed
+  /// stream. Errors if the Stream is not a CUDA stream.
+  explicit OptionalCUDAStreamGuard(Stream stream) : guard_(stream) {}
+
+  /// Set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream,
+  /// if the passed stream is not nullopt.
+  explicit OptionalCUDAStreamGuard(optional stream_opt)
+      : guard_(stream_opt) {}
+
+  /// Copy is disallowed
+  OptionalCUDAStreamGuard(const OptionalCUDAStreamGuard&) = delete;
+  OptionalCUDAStreamGuard& operator=(const OptionalCUDAStreamGuard&) = delete;
+
+  // See Note [Move construction for RAII guards is tricky]
+  OptionalCUDAStreamGuard(OptionalCUDAStreamGuard&& other) = delete;
+
+  // See Note [Move assignment for RAII guards is tricky]
+  OptionalCUDAStreamGuard& operator=(OptionalCUDAStreamGuard&& other) = delete;
+
+  /// Resets the currently set CUDA stream to the original stream and
+  /// the currently set device to the original device.  Then,
+  /// set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  /// Initializes the guard if it was not previously initialized.
+  void reset_stream(Stream stream) {
+    guard_.reset_stream(stream);
+  }
+
+  /// Returns the CUDA stream that was set at the time the guard was most
+  /// recently initialized, or nullopt if the guard is uninitialized.
+  optional original_stream() const {
+    auto r = guard_.original_stream();
+    if (r.has_value()) {
+      return make_optional(CUDAStream(CUDAStream::UNCHECKED, r.value()));
+    } else {
+      return nullopt;
+    }
+  }
+
+  /// Returns the most recent CUDA stream that was set using this stream guard,
+  /// either from construction, or via reset_stream, if the guard is
+  /// initialized, or nullopt if the guard is uninitialized.
+  optional current_stream() const {
+    auto r = guard_.current_stream();
+    if (r.has_value()) {
+      return make_optional(CUDAStream(CUDAStream::UNCHECKED, r.value()));
+    } else {
+      return nullopt;
+    }
+  }
+
+  /// Restore the original CUDA device and stream, resetting this guard to
+  /// uninitialized state.
+  void reset() {
+    guard_.reset();
+  }
+
+ private:
+  c10::impl::InlineOptionalStreamGuard guard_;
+};
+
+/// A variant of MultiStreamGuard that is specialized for CUDA.
+struct CUDAMultiStreamGuard {
+  explicit CUDAMultiStreamGuard(ArrayRef streams)
+      : guard_(unwrapStreams(streams)) {}
+
+  /// Copy is disallowed
+  CUDAMultiStreamGuard(const CUDAMultiStreamGuard&) = delete;
+  CUDAMultiStreamGuard& operator=(const CUDAMultiStreamGuard&) = delete;
+
+  // See Note [Move construction for RAII guards is tricky]
+  CUDAMultiStreamGuard(CUDAMultiStreamGuard&& other) = delete;
+
+  // See Note [Move assignment for RAII guards is tricky]
+  CUDAMultiStreamGuard& operator=(CUDAMultiStreamGuard&& other) = delete;
+
+ private:
+  c10::impl::InlineMultiStreamGuard guard_;
+
+  static std::vector unwrapStreams(ArrayRef cudaStreams) {
+    std::vector streams;
+    streams.reserve(cudaStreams.size());
+    for (const CUDAStream& cudaStream : cudaStreams) {
+      streams.push_back(cudaStream);
+    }
+    return streams;
+  }
+};
+
+} // namespace c10::cuda
diff --git a/shared/package-files/04/04ca3d2d5d119b372c13605dae2ac5a67afb414fee90d7820929bc00749e75b8/special_bessel_y1_ops.h b/shared/package-files/04/04ca3d2d5d119b372c13605dae2ac5a67afb414fee90d7820929bc00749e75b8/special_bessel_y1_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..e852b8fc352d1507d82691f818b4a032895567b8
--- /dev/null
+++ b/shared/package-files/04/04ca3d2d5d119b372c13605dae2ac5a67afb414fee90d7820929bc00749e75b8/special_bessel_y1_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_bessel_y1 {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_bessel_y1")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_bessel_y1(Tensor self) -> Tensor")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API special_bessel_y1_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_bessel_y1")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_bessel_y1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/04/04cafda56881bf660865dab4e17e1cdc280be8d3cb50fae5eb8695d4de30f548/STIXNonUniIta.ttf b/shared/package-files/04/04cafda56881bf660865dab4e17e1cdc280be8d3cb50fae5eb8695d4de30f548/STIXNonUniIta.ttf
new file mode 100644
index 0000000000000000000000000000000000000000..c27d42fb250a5881efeccbf9c9dabca18efe958b
Binary files /dev/null and b/shared/package-files/04/04cafda56881bf660865dab4e17e1cdc280be8d3cb50fae5eb8695d4de30f548/STIXNonUniIta.ttf differ
diff --git a/shared/package-files/04/04ccf5400ee6bb10a2d44a0a85349fce323f66cafe10ce87bd57c846b29a9812/CompositeRandomAccessor.h b/shared/package-files/04/04ccf5400ee6bb10a2d44a0a85349fce323f66cafe10ce87bd57c846b29a9812/CompositeRandomAccessor.h
new file mode 100644
index 0000000000000000000000000000000000000000..970b7da5cb70931ccb450a6ec24d511f975248c6
--- /dev/null
+++ b/shared/package-files/04/04ccf5400ee6bb10a2d44a0a85349fce323f66cafe10ce87bd57c846b29a9812/CompositeRandomAccessor.h
@@ -0,0 +1,34 @@
+#pragma once
+
+#include 
+
+namespace at::native {
+
+struct TupleInfoCPU {
+  template 
+  using tuple = std::tuple;
+
+  template 
+  static constexpr auto tie(Types&... args) noexcept {
+    return std::tie(args...);
+  }
+};
+
+template 
+using CompositeRandomAccessorCPU =
+  CompositeRandomAccessor;
+
+template 
+void swap(
+  references_holder rh1,
+  references_holder rh2
+) {
+  return std::swap(rh1.data(), rh2.data());
+}
+
+template 
+auto get(references_holder rh) -> decltype(std::get(rh.data())) {
+  return std::get(rh.data());
+}
+
+} // namespace at::native
diff --git a/shared/package-files/04/04cf7755e9c103556cfaa93600948b04c24d9f90436f25592c867949c4f81485/prefix.py b/shared/package-files/04/04cf7755e9c103556cfaa93600948b04c24d9f90436f25592c867949c4f81485/prefix.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b8d59e69e37f971e7967585ed00c54735c6dc29
--- /dev/null
+++ b/shared/package-files/04/04cf7755e9c103556cfaa93600948b04c24d9f90436f25592c867949c4f81485/prefix.py
@@ -0,0 +1,106 @@
+import re
+from codecs import BOM_UTF8
+from typing import Tuple
+
+from parso.python.tokenize import group
+
+unicode_bom = BOM_UTF8.decode('utf-8')
+
+
+class PrefixPart:
+    def __init__(self, leaf, typ, value, spacing='', start_pos=None):
+        assert start_pos is not None
+        self.parent = leaf
+        self.type = typ
+        self.value = value
+        self.spacing = spacing
+        self.start_pos: Tuple[int, int] = start_pos
+
+    @property
+    def end_pos(self) -> Tuple[int, int]:
+        if self.value.endswith('\n') or self.value.endswith('\r'):
+            return self.start_pos[0] + 1, 0
+        if self.value == unicode_bom:
+            # The bom doesn't have a length at the start of a Python file.
+            return self.start_pos
+        return self.start_pos[0], self.start_pos[1] + len(self.value)
+
+    def create_spacing_part(self):
+        column = self.start_pos[1] - len(self.spacing)
+        return PrefixPart(
+            self.parent, 'spacing', self.spacing,
+            start_pos=(self.start_pos[0], column)
+        )
+
+    def __repr__(self):
+        return '%s(%s, %s, %s)' % (
+            self.__class__.__name__,
+            self.type,
+            repr(self.value),
+            self.start_pos
+        )
+
+    def search_ancestor(self, *node_types):
+        node = self.parent
+        while node is not None:
+            if node.type in node_types:
+                return node
+            node = node.parent
+        return None
+
+
+_comment = r'#[^\n\r\f]*'
+_backslash = r'\\\r?\n|\\\r'
+_newline = r'\r?\n|\r'
+_form_feed = r'\f'
+_only_spacing = '$'
+_spacing = r'[ \t]*'
+_bom = unicode_bom
+
+_regex = group(
+    _comment, _backslash, _newline, _form_feed, _only_spacing, _bom,
+    capture=True
+)
+_regex = re.compile(group(_spacing, capture=True) + _regex)
+
+
+_types = {
+    '#': 'comment',
+    '\\': 'backslash',
+    '\f': 'formfeed',
+    '\n': 'newline',
+    '\r': 'newline',
+    unicode_bom: 'bom'
+}
+
+
+def split_prefix(leaf, start_pos):
+    line, column = start_pos
+    start = 0
+    value = spacing = ''
+    bom = False
+    while start != len(leaf.prefix):
+        match = _regex.match(leaf.prefix, start)
+        spacing = match.group(1)
+        value = match.group(2)
+        if not value:
+            break
+        type_ = _types[value[0]]
+        yield PrefixPart(
+            leaf, type_, value, spacing,
+            start_pos=(line, column + start - int(bom) + len(spacing))
+        )
+        if type_ == 'bom':
+            bom = True
+
+        start = match.end(0)
+        if value.endswith('\n') or value.endswith('\r'):
+            line += 1
+            column = -start
+
+    if value:
+        spacing = ''
+    yield PrefixPart(
+        leaf, 'spacing', spacing,
+        start_pos=(line, column + start)
+    )
diff --git a/shared/package-files/04/04d30d1aec29240151b1db701d843ab25182964ad44eedf5ffb99787b163795e/__init__.py b/shared/package-files/04/04d30d1aec29240151b1db701d843ab25182964ad44eedf5ffb99787b163795e/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d344400d3b8771d2c1b93ba48def361615a132f
--- /dev/null
+++ b/shared/package-files/04/04d30d1aec29240151b1db701d843ab25182964ad44eedf5ffb99787b163795e/__init__.py
@@ -0,0 +1,85 @@
+from ._hdemucs import HDemucs, hdemucs_high, hdemucs_low, hdemucs_medium
+from .conformer import Conformer
+from .conv_tasnet import conv_tasnet_base, ConvTasNet
+from .deepspeech import DeepSpeech
+from .emformer import Emformer
+from .rnnt import emformer_rnnt_base, emformer_rnnt_model, RNNT
+from .rnnt_decoder import Hypothesis, RNNTBeamSearch
+from .squim import (
+    squim_objective_base,
+    squim_objective_model,
+    squim_subjective_base,
+    squim_subjective_model,
+    SquimObjective,
+    SquimSubjective,
+)
+from .tacotron2 import Tacotron2
+from .wav2letter import Wav2Letter
+from .wav2vec2 import (
+    hubert_base,
+    hubert_large,
+    hubert_pretrain_base,
+    hubert_pretrain_large,
+    hubert_pretrain_model,
+    hubert_pretrain_xlarge,
+    hubert_xlarge,
+    HuBERTPretrainModel,
+    wav2vec2_base,
+    wav2vec2_large,
+    wav2vec2_large_lv60k,
+    wav2vec2_model,
+    wav2vec2_xlsr_1b,
+    wav2vec2_xlsr_2b,
+    wav2vec2_xlsr_300m,
+    Wav2Vec2Model,
+    wavlm_base,
+    wavlm_large,
+    wavlm_model,
+)
+from .wavernn import WaveRNN
+
+
+__all__ = [
+    "Wav2Letter",
+    "WaveRNN",
+    "ConvTasNet",
+    "conv_tasnet_base",
+    "DeepSpeech",
+    "Wav2Vec2Model",
+    "HuBERTPretrainModel",
+    "wavlm_model",
+    "wavlm_base",
+    "wavlm_large",
+    "wav2vec2_model",
+    "wav2vec2_base",
+    "wav2vec2_large",
+    "wav2vec2_large_lv60k",
+    "hubert_base",
+    "hubert_large",
+    "hubert_xlarge",
+    "hubert_pretrain_model",
+    "hubert_pretrain_base",
+    "hubert_pretrain_large",
+    "hubert_pretrain_xlarge",
+    "wav2vec2_xlsr_300m",
+    "wav2vec2_xlsr_1b",
+    "wav2vec2_xlsr_2b",
+    "Tacotron2",
+    "Conformer",
+    "Emformer",
+    "Hypothesis",
+    "RNNT",
+    "RNNTBeamSearch",
+    "emformer_rnnt_base",
+    "emformer_rnnt_model",
+    "HDemucs",
+    "hdemucs_low",
+    "hdemucs_medium",
+    "hdemucs_high",
+    "squim_objective_base",
+    "squim_objective_model",
+    "squim_subjective_base",
+    "squim_subjective_model",
+    "SquimObjective",
+    "SquimSubjective",
+]
diff --git a/shared/package-files/04/04d51b2a5891de1d95c199d012cd5974d9d53c43b5b5dca2bc5d5a09c00d3806/rnn_relu_native.h b/shared/package-files/04/04d51b2a5891de1d95c199d012cd5974d9d53c43b5b5dca2bc5d5a09c00d3806/rnn_relu_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..32dc2a990cc272f021e1f53d7f394ac9d5237d0b
--- /dev/null
+++ b/shared/package-files/04/04d51b2a5891de1d95c199d012cd5974d9d53c43b5b5dca2bc5d5a09c00d3806/rnn_relu_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple rnn_relu(const at::Tensor & input, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional, bool batch_first);
+TORCH_API ::std::tuple rnn_relu(const at::Tensor & data, const at::Tensor & batch_sizes, const at::Tensor & hx, at::TensorList params, bool has_biases, int64_t num_layers, double dropout, bool train, bool bidirectional);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/04/04d5cc88a71ca234567ac71ac9890991243e030e78d7b25889641874d1eb0db5/frozen_linear_folding.h b/shared/package-files/04/04d5cc88a71ca234567ac71ac9890991243e030e78d7b25889641874d1eb0db5/frozen_linear_folding.h
new file mode 100644
index 0000000000000000000000000000000000000000..bac4bedd53a6bb45999d5d276986d0946e1a2f0b
--- /dev/null
+++ b/shared/package-files/04/04d5cc88a71ca234567ac71ac9890991243e030e78d7b25889641874d1eb0db5/frozen_linear_folding.h
@@ -0,0 +1,14 @@
+#pragma once
+
+#include 
+
+namespace torch {
+namespace jit {
+
+// Fuses Linear -> BatchNormNd into a single Linear by
+// folding batchnorm weights into linear weights.
+// This pass only works on Frozen Graphs; otherwise it is a No-Op.
+TORCH_API bool FoldFrozenLinearBatchnorm(std::shared_ptr& graph);
+
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/04/04d704871458985880d07cb2d9df2e680342315780ddeee11ba80a221b0d85a6/linalg_lu_solve.h b/shared/package-files/04/04d704871458985880d07cb2d9df2e680342315780ddeee11ba80a221b0d85a6/linalg_lu_solve.h
new file mode 100644
index 0000000000000000000000000000000000000000..d4288474e0c3f64429285f60772cfe164e19ddc3
--- /dev/null
+++ b/shared/package-files/04/04d704871458985880d07cb2d9df2e680342315780ddeee11ba80a221b0d85a6/linalg_lu_solve.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::linalg_lu_solve(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False) -> Tensor
+inline at::Tensor linalg_lu_solve(const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left=true, bool adjoint=false) {
+    return at::_ops::linalg_lu_solve::call(LU, pivots, B, left, adjoint);
+}
+
+// aten::linalg_lu_solve.out(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & linalg_lu_solve_out(at::Tensor & out, const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left=true, bool adjoint=false) {
+    return at::_ops::linalg_lu_solve_out::call(LU, pivots, B, left, adjoint, out);
+}
+// aten::linalg_lu_solve.out(Tensor LU, Tensor pivots, Tensor B, *, bool left=True, bool adjoint=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & linalg_lu_solve_outf(const at::Tensor & LU, const at::Tensor & pivots, const at::Tensor & B, bool left, bool adjoint, at::Tensor & out) {
+    return at::_ops::linalg_lu_solve_out::call(LU, pivots, B, left, adjoint, out);
+}
+
+}
diff --git a/shared/package-files/04/04dbc43b308cdbbb3b1171bb545aae4978c1865546457ef124267f0db5196fee/argmin_meta_dispatch.h b/shared/package-files/04/04dbc43b308cdbbb3b1171bb545aae4978c1865546457ef124267f0db5196fee/argmin_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..1bede8da2134ee04fbf12b4ed5143a9726503a02
--- /dev/null
+++ b/shared/package-files/04/04dbc43b308cdbbb3b1171bb545aae4978c1865546457ef124267f0db5196fee/argmin_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor argmin(const at::Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & argmin_out(at::Tensor & out, const at::Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & argmin_outf(const at::Tensor & self, c10::optional dim, bool keepdim, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/04/04dc1709edb080d3c4f28df9fac84fb058fc975f5cfd01ba61ef2d9081a985ff/test_dense_numpy.py b/shared/package-files/04/04dc1709edb080d3c4f28df9fac84fb058fc975f5cfd01ba61ef2d9081a985ff/test_dense_numpy.py
new file mode 100644
index 0000000000000000000000000000000000000000..1316e23e654a775e949cdbd34a86a474597f993a
--- /dev/null
+++ b/shared/package-files/04/04dc1709edb080d3c4f28df9fac84fb058fc975f5cfd01ba61ef2d9081a985ff/test_dense_numpy.py
@@ -0,0 +1,89 @@
+import pytest
+
+np = pytest.importorskip("numpy")
+
+
+import networkx as nx
+
+
+def test_cycle_numpy():
+    dist = nx.floyd_warshall_numpy(nx.cycle_graph(7))
+    assert dist[0, 3] == 3
+    assert dist[0, 4] == 3
+
+
+def test_weighted_numpy_three_edges():
+    XG3 = nx.Graph()
+    XG3.add_weighted_edges_from(
+        [[0, 1, 2], [1, 2, 12], [2, 3, 1], [3, 4, 5], [4, 5, 1], [5, 0, 10]]
+    )
+    dist = nx.floyd_warshall_numpy(XG3)
+    assert dist[0, 3] == 15
+
+
+def test_weighted_numpy_two_edges():
+    XG4 = nx.Graph()
+    XG4.add_weighted_edges_from(
+        [
+            [0, 1, 2],
+            [1, 2, 2],
+            [2, 3, 1],
+            [3, 4, 1],
+            [4, 5, 1],
+            [5, 6, 1],
+            [6, 7, 1],
+            [7, 0, 1],
+        ]
+    )
+    dist = nx.floyd_warshall_numpy(XG4)
+    assert dist[0, 2] == 4
+
+
+def test_weight_parameter_numpy():
+    XG4 = nx.Graph()
+    XG4.add_edges_from(
+        [
+            (0, 1, {"heavy": 2}),
+            (1, 2, {"heavy": 2}),
+            (2, 3, {"heavy": 1}),
+            (3, 4, {"heavy": 1}),
+            (4, 5, {"heavy": 1}),
+            (5, 6, {"heavy": 1}),
+            (6, 7, {"heavy": 1}),
+            (7, 0, {"heavy": 1}),
+        ]
+    )
+    dist = nx.floyd_warshall_numpy(XG4, weight="heavy")
+    assert dist[0, 2] == 4
+
+
+def test_directed_cycle_numpy():
+    G = nx.DiGraph()
+    nx.add_cycle(G, [0, 1, 2, 3])
+    pred, dist = nx.floyd_warshall_predecessor_and_distance(G)
+    D = nx.utils.dict_to_numpy_array(dist)
+    np.testing.assert_equal(nx.floyd_warshall_numpy(G), D)
+
+
+def test_zero_weight():
+    G = nx.DiGraph()
+    edges = [(1, 2, -2), (2, 3, -4), (1, 5, 1), (5, 4, 0), (4, 3, -5), (2, 5, -7)]
+    G.add_weighted_edges_from(edges)
+    dist = nx.floyd_warshall_numpy(G)
+    assert int(np.min(dist)) == -14
+
+    G = nx.MultiDiGraph()
+    edges.append((2, 5, -7))
+    G.add_weighted_edges_from(edges)
+    dist = nx.floyd_warshall_numpy(G)
+    assert int(np.min(dist)) == -14
+
+
+def test_nodelist():
+    G = nx.path_graph(7)
+    dist = nx.floyd_warshall_numpy(G, nodelist=[3, 5, 4, 6, 2, 1, 0])
+    assert dist[0, 3] == 3
+    assert dist[0, 1] == 2
+    assert dist[6, 2] == 4
+    pytest.raises(nx.NetworkXError, nx.floyd_warshall_numpy, G, [1, 3])
+    pytest.raises(nx.NetworkXError, nx.floyd_warshall_numpy, G, list(range(9)))
diff --git a/shared/package-files/04/04dd07f6f56e9a87760ddc9cc8d088b792466d432167c5659b1a3fcf92b14c3d/view_as_real_native.h b/shared/package-files/04/04dd07f6f56e9a87760ddc9cc8d088b792466d432167c5659b1a3fcf92b14c3d/view_as_real_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..75f7d135806b9499a176979889f64b4e66e6ce30
--- /dev/null
+++ b/shared/package-files/04/04dd07f6f56e9a87760ddc9cc8d088b792466d432167c5659b1a3fcf92b14c3d/view_as_real_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor view_as_real(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/04/04df48926b15d1685adae71f241642f6ac57ff5f42c980cf5b2783ff3ac42a03/index_ops.h b/shared/package-files/04/04df48926b15d1685adae71f241642f6ac57ff5f42c980cf5b2783ff3ac42a03/index_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..032d44bef7c22a45482e332b14c15425444e5d41
--- /dev/null
+++ b/shared/package-files/04/04df48926b15d1685adae71f241642f6ac57ff5f42c980cf5b2783ff3ac42a03/index_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API index_Tensor {
+  using schema = at::Tensor (const at::Tensor &, const c10::List> &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::index")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Tensor")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "index.Tensor(Tensor self, Tensor?[] indices) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const c10::List> & indices);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List> & indices);
+};
+
+struct TORCH_API index_Tensor_out {
+  using schema = at::Tensor & (const at::Tensor &, const c10::List> &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::index")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Tensor_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "index.Tensor_out(Tensor self, Tensor?[] indices, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, const c10::List> & indices, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const c10::List> & indices, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/04/04e1bc38935df20db2742a61d49020a2285190d792b97d63aa192abad38ec000/krb5.pc b/shared/package-files/04/04e1bc38935df20db2742a61d49020a2285190d792b97d63aa192abad38ec000/krb5.pc
new file mode 100644
index 0000000000000000000000000000000000000000..49dd638d159ed662516a508bda680630a845a809
--- /dev/null
+++ b/shared/package-files/04/04e1bc38935df20db2742a61d49020a2285190d792b97d63aa192abad38ec000/krb5.pc
@@ -0,0 +1,14 @@
+prefix=/opt/conda
+exec_prefix=${prefix}
+libdir=${exec_prefix}/lib
+includedir=${prefix}/include
+vendor=MIT
+
+defccname=FILE:/tmp/krb5cc_%{uid}
+defktname=FILE:/etc/krb5.keytab
+defcktname=FILE:/opt/conda/var/krb5/user/%{euid}/client.keytab
+
+Name: krb5
+Description: An implementation of Kerberos network authentication
+Version: 1.20.1
+Requires: mit-krb5
diff --git a/shared/package-files/04/04e8dfd104389511037e8c5cbe4ab7c4be75772b1494cc590360b59353ffe177/cauchy.h b/shared/package-files/04/04e8dfd104389511037e8c5cbe4ab7c4be75772b1494cc590360b59353ffe177/cauchy.h
new file mode 100644
index 0000000000000000000000000000000000000000..7bada5f6343b365c7867259208e8191be8c7615c
--- /dev/null
+++ b/shared/package-files/04/04e8dfd104389511037e8c5cbe4ab7c4be75772b1494cc590360b59353ffe177/cauchy.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::cauchy.out(Tensor self, float median=0, float sigma=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & cauchy_out(at::Tensor & out, const at::Tensor & self, double median=0, double sigma=1, c10::optional generator=c10::nullopt) {
+    return at::_ops::cauchy_out::call(self, median, sigma, generator, out);
+}
+// aten::cauchy.out(Tensor self, float median=0, float sigma=1, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & cauchy_outf(const at::Tensor & self, double median, double sigma, c10::optional generator, at::Tensor & out) {
+    return at::_ops::cauchy_out::call(self, median, sigma, generator, out);
+}
+
+// aten::cauchy(Tensor self, float median=0, float sigma=1, *, Generator? generator=None) -> Tensor
+inline at::Tensor cauchy(const at::Tensor & self, double median=0, double sigma=1, c10::optional generator=c10::nullopt) {
+    return at::_ops::cauchy::call(self, median, sigma, generator);
+}
+
+}
diff --git a/shared/package-files/04/04e9522c626a980ef68f05709e4112d0877c810036ac573d3be08c013d61ab38/rewrite.py b/shared/package-files/04/04e9522c626a980ef68f05709e4112d0877c810036ac573d3be08c013d61ab38/rewrite.py
new file mode 100644
index 0000000000000000000000000000000000000000..36ef2ecbdcdc129db094dfdf54d876d8b9faee37
--- /dev/null
+++ b/shared/package-files/04/04e9522c626a980ef68f05709e4112d0877c810036ac573d3be08c013d61ab38/rewrite.py
@@ -0,0 +1,600 @@
+import torch
+from torch.fx import GraphModule
+from ..export_utils import _WrapperModule
+from ..utils import (
+    get_aten_graph_module,
+    remove_tensor_overload_for_qdq_ops,
+    _replace_literals_with_new_placeholders,
+    _replace_literals_with_existing_placeholders,
+)
+from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib  # noqa: F401
+from torch.fx.subgraph_rewriter import replace_pattern
+from torch._higher_order_ops.out_dtype import out_dtype
+from typing import Optional, Callable, Tuple, Any
+from dataclasses import dataclass
+
+from functools import partial
+
+__all__ = [
+    "reference_representation_rewrite",
+]
+
+
+_QUANTIZED_LINEAR_EXAMPLE_INPUTS = (
+    torch.randint(-128, 127, (2, 5), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+    torch.randint(-128, 127, (5, 5), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-127], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+    torch.randn(1, dtype=torch.float),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+)
+
+def _qdq_quantized_linear(
+    x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max,
+    weight_i8, weight_scale, weight_zero_point, weight_quant_min, weight_quant_max,
+    bias_fp32,
+    out_scale, out_zero_point, out_quant_min, out_quant_max
+):
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8)
+    weight_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        weight_i8, weight_scale, weight_zero_point, weight_quant_min, weight_quant_max, torch.int8)
+    out_fp32 = torch.ops.aten.linear.default(x_fp32, weight_fp32, bias_fp32)
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, out_quant_min, out_quant_max, torch.int8)
+    return out_i8
+
+def _reference_quantized_linear(
+    x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max,
+    weight_i8, weight_scale, weight_zero_point, weight_quant_min, weight_quant_max,
+    bias_fp32,
+    out_scale, out_zero_point, out_quant_min, out_quant_max
+):
+    # without using quant_min/max in clamp, the traced graph will not have quant_mi/max args.
+    # This results in failure to match the pattern.
+    # Therefore, we call a torch.ops.aten.clamp here
+    x_i8 = torch.ops.aten.clamp(x_i8, x_quant_min, x_quant_max)
+    weight_i8 = torch.ops.aten.clamp(weight_i8, weight_quant_min, weight_quant_max)
+
+    x_i16 = x_i8.to(torch.int16)
+    weight_i16 = weight_i8.to(torch.int16)
+    # always set bias to None so that the same representation can work for the case
+    # no matter if bias_scale == x_scale * weight_scale or not
+    acc_i32 = out_dtype(
+        torch.ops.aten.linear.default,
+        torch.int32,
+        x_i16 - x_zero_point,
+        weight_i16 - weight_zero_point,
+        None)
+    # TODO: change to mul.Scalar
+    # Note: we are quantizing bias with these scales without signal from user, but it might be OK
+    bias_scale = x_scale * weight_scale
+    bias_i32 = out_dtype(torch.ops.aten.div.Tensor, torch.int32, bias_fp32, bias_scale)
+    acc_i32 = acc_i32 + bias_i32
+    # TODO: change to mul.Scalar when we make x_scale/weight_scale etc. Scalar values
+    acc_i32 = out_dtype(torch.ops.aten.mul.Tensor, torch.int32, acc_i32, x_scale * weight_scale / out_scale) + out_zero_point
+    out_i8 = torch.ops.aten.clamp(acc_i32, out_quant_min, out_quant_max).to(torch.int8)
+    return out_i8
+
+
+_DYNAMIC_QUANTIZED_LINEAR_EXAMPLE_INPUTS = (
+    torch.randn((2, 5), dtype=torch.float),
+    -128,
+    127,
+    torch.finfo(torch.float32).eps,
+    torch.randint(-128, 127, (5, 5), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-127], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+    torch.randn(1, dtype=torch.float),
+)
+
+
+def _qdq_dynamic_quantized_linear(
+    x_fp32, x_quant_min, x_quant_max, x_eps,
+    weight_i8, weight_scale, weight_zero_point, weight_quant_min, weight_quant_max,
+    bias_fp32,
+):
+    x_scale, x_zero_point = torch.ops.quantized_decomposed.choose_qparams(x_fp32, x_quant_min, x_quant_max, x_eps, torch.int8)
+    x_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        x_fp32, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8)
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8)
+    weight_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        weight_i8, weight_scale, weight_zero_point, weight_quant_min, weight_quant_max, torch.int8)
+    out_fp32 = torch.ops.aten.linear.default(x_fp32, weight_fp32, bias_fp32)
+    return out_fp32
+
+def _reference_dynamic_quantized_linear(
+    x_fp32, x_quant_min, x_quant_max, x_eps,
+    weight_i8, weight_scale, weight_zero_point, weight_quant_min, weight_quant_max,
+    bias_fp32,
+):
+    x_scale, x_zero_point = torch.ops.quantized_decomposed.choose_qparams(x_fp32, x_quant_min, x_quant_max, x_eps, torch.int8)
+    # decomposed representation for quantize_per_tensor
+    # TODO: use out_dtype(mul, ...) here when the op is ready
+    x_fp32 = x_fp32 / x_scale  # fp32
+    # round modes might be different here
+    # pytorch is rounding to even, which is also common for most of the backends
+    x_fp32 = torch.round(x_fp32)  # fp32
+    x_i32 = x_fp32.to(dtype=torch.int32)  # int32
+    x_i32 = x_i32 + x_zero_point  # int32
+    # clamp works for fp32, int32 and int8 dtypes
+    x_i32 = torch.clamp(x_i32, x_quant_min, x_quant_max)  # int32
+    x_i8 = x_i32.to(dtype=torch.int8)
+
+    weight_i8 = torch.ops.aten.clamp(weight_i8, weight_quant_min, weight_quant_max)
+
+    x_i16 = x_i8.to(torch.int16)
+    weight_i16 = weight_i8.to(torch.int16)
+    # always set bias to None so that the same representation can work for the case
+    # no matter if bias_scale == x_scale * weight_scale or not
+    acc_i32 = out_dtype(
+        torch.ops.aten.linear.default,
+        torch.int32,
+        x_i16 - x_zero_point,
+        weight_i16 - weight_zero_point,
+        None)
+    bias_scale = x_scale * weight_scale
+    bias_i32 = out_dtype(torch.ops.aten.div.Tensor, torch.int32, bias_fp32, bias_scale)
+    acc_i32 = acc_i32 + bias_i32
+    out_fp32 = acc_i32 * (x_scale * weight_scale)
+    return out_fp32
+
+
+_QUANTIZED_CONV2d_EXAMPLE_INPUTS = (
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-127], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+    torch.randn(1, dtype=torch.float),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+)
+
+def _qdq_quantized_conv2d(
+    x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max,
+    weight_i8, weight_scale, weight_zero_point, weight_quant_min, weight_quant_max,
+    bias_fp32,
+    out_scale, out_zero_point, out_quant_min, out_quant_max
+):
+    stride = [1, 1]
+    padding = [0, 0]
+    dilation = [1, 1]
+    transposed = False
+    output_padding = [0, 0]
+    groups = 1
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8)
+    weight_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(
+        weight_i8, weight_scale, weight_zero_point, weight_quant_min, weight_quant_max, torch.int8)
+    out_fp32 = torch.ops.aten.convolution.default(
+        x_fp32, weight_fp32, bias_fp32, stride, padding, dilation, transposed, output_padding, groups)
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, out_quant_min, out_quant_max, torch.int8)
+    return out_i8
+
+def _reference_quantized_conv2d(
+    x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max,
+    weight_i8, weight_scale, weight_zero_point, weight_quant_min, weight_quant_max,
+    bias_fp32,
+    out_scale, out_zero_point, out_quant_min, out_quant_max
+):
+    stride = [1, 1]
+    padding = [0, 0]
+    dilation = [1, 1]
+    transposed = False
+    output_padding = [0, 0]
+    groups = 1
+    # without using quant_min/max in clamp, the traced graph will not have quant_mi/max args.
+    # This results in failure to match the pattern.
+    # Therefore, we call a torch.ops.aten.clamp here
+    x_i8 = torch.ops.aten.clamp(x_i8, x_quant_min, x_quant_max)
+    weight_i8 = torch.ops.aten.clamp(weight_i8, weight_quant_min, weight_quant_max)
+
+    x_i16 = x_i8.to(torch.int16)
+    weight_i16 = weight_i8.to(torch.int16)
+    # always set bias to None so that the same representation can work for the case
+    # no matter if bias_scale == x_scale * weight_scale or not
+    acc_i32 = out_dtype(
+        torch.ops.aten.convolution.default,
+        torch.int32,
+        x_i16 - x_zero_point,
+        weight_i16 - weight_zero_point,
+        None, stride, padding, dilation, transposed, output_padding, groups)
+    # Note: we are quantizing bias with these scales without signal from user, but it might be OK
+    bias_scale = x_scale * weight_scale
+    # bias quantization to int32 uses bias_scale = x_scale * weight_scale due to:
+    # Take linear calculation for example
+    # Out_(i, j)_fp32 = Sum_(over k)[X_(i, k)_fp32 * W_(i, k)_fp32] + bias_(i)_fp32
+    # Represent X, W fp32 as their dequant transforms
+    # A_fp32 = (A_q - A_zero_point)/A_scale
+    # Out_(i, j)_fp32 = Sum_(over k)[(X_(i, k)_fp32 - X_zp) * X_scale * (W_(i, k)_fp32 - W_zp) * W_scale] + bias_(i)_fp32
+    # Factor out X_scale and W_scale
+    # Out_(i, j)_fp32 = ((X_scale * W_scale) * Sum_(over k)[(X_(i, k)_fp32 - X_zp) * (W_(i, k)_fp32 - W_zp)]) + bias_(i)_fp32
+    # In order to addition of bias_(i)_fp32 inside, we must do
+    # Out_(i, j)_fp32 = (X_scale * W_scale) * (Sum_(over k)[(X_(i, k)_fp32 - X_zp) * (W_(i, k)_fp32 - W_zp)] + (1 / (X_scale * W_scale)) * bias_(i)_fp32)W_scale  # noqa: B950
+    # Note we had to multiply bias_fp32 qith X_scale * W_scale = bias_scale
+    # Thus bias quantization to int32 must be with X_scale * W_scale
+
+    bias_i32 = out_dtype(torch.ops.aten.div.Tensor, torch.int32, bias_fp32, bias_scale)
+    # Unsqueeze to match broadcast dims
+    # Unfortnuately I cannot do bias_i32.unsqueeze(0) due to literal matching nightmare
+    # in graph pattern replacement
+    bias_i32 = bias_i32.unsqueeze(-1)
+    bias_i32 = bias_i32.unsqueeze(-1)
+    acc_i32 = acc_i32 + bias_i32
+    # TODO: change to mul.Scalar when we make x_scale/weight_scale etc. Scalar values
+    acc_i32 = out_dtype(
+        torch.ops.aten.mul.Tensor, torch.int32, acc_i32, x_scale * weight_scale / out_scale) + out_zero_point
+    out_i8 = torch.ops.aten.clamp(acc_i32, out_quant_min, out_quant_max).to(torch.int8)
+    return out_i8
+
+
+_QUANTIZED_ADD_OR_ADD_RELU_EXAMPLE_INPUTS = (
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+)
+
+def _qdq_quantized_add_relu(
+    x_i8, x_scale, x_zero_point, y_i8, y_scale, y_zero_point,
+    out_scale, out_zero_point, quant_min, quant_max
+):
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(x_i8, x_scale, x_zero_point, quant_min, quant_max, torch.int8)
+    y_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(y_i8, y_scale, y_zero_point, quant_min, quant_max, torch.int8)
+    out_fp32 = x_fp32 + y_fp32
+    out_fp32 = torch.ops.aten.relu(out_fp32)
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, quant_min, quant_max, torch.int8
+    )
+    return out_i8
+
+def _reference_quantized_add_relu(
+    x_i8, x_scale, x_zero_point, y_i8, y_scale, y_zero_point,
+    out_scale, out_zero_point, quant_min, quant_max
+):
+    """
+    See comments for `_reference_quantized_add` for more information on
+    how to derive the formula for out_i8 based on x_i8 and y_i8
+    """
+    x_i32 = x_i8.to(torch.int32)
+    y_i32 = y_i8.to(torch.int32)
+    # TODO: change this to mul.Scalar?
+    x_i32 = out_dtype(torch.ops.aten.mul.Tensor, torch.int32, (x_i32 - x_zero_point), (x_scale / out_scale))
+    y_i32 = out_dtype(torch.ops.aten.mul.Tensor, torch.int32, (y_i32 - y_zero_point), (y_scale / out_scale))
+    out_i32 = x_i32 + y_i32 + out_zero_point
+    # out_i32 = torch.ops.aten.clamp(out_i32, out_zero_point)
+    out_i8 = torch.ops.aten.clamp(out_i32, out_zero_point, quant_max).to(torch.int8)
+    return out_i8
+
+def _qdq_quantized_add(x_i8, x_scale, x_zero_point, y_i8, y_scale, y_zero_point, out_scale, out_zero_point, quant_min, quant_max):
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(x_i8, x_scale, x_zero_point, quant_min, quant_max, torch.int8)
+    y_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(y_i8, y_scale, y_zero_point, quant_min, quant_max, torch.int8)
+    out_fp32 = x_fp32 + y_fp32
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, quant_min, quant_max, torch.int8
+    )
+    return out_i8
+
+def _reference_quantized_add(
+    x_i8, x_scale, x_zero_point, y_i8, y_scale, y_zero_point,
+    out_scale, out_zero_point, quant_min, quant_max
+):
+    """
+    # How to Derive the formula for out_i8 based on x_i8 and y_i8
+    # (since quantized add takes x_i8, y_i8 and their quantization parameters, and produce an out_i8)
+
+    # out_i8 is quantized output, we can write down the formula for it first:
+out_i8 = out_f32 / out_scale + out_zero_point           (1)
+
+    # then out_fp32 is computed from x_f32 + y_f32, and the x_fp32 and y_fp32 are the dequantized x_i8 and y_i8
+    out_f32 = x_f32 + y_f32           (2)
+    x_fp32 = (x_i8 - x_zero_point) * x_scale         (3)
+    y_fp32 = (y_i8 - y_zero_point) * y_scale         (4)
+
+    # applying the above fomula to the out_i8 equation we can get the following:
+    out_i8 = out_fp32 / out_scale + out_zero_point             # (1)
+       = (x_f32 + y_f32) / out_scale + out_zero_point      # applying (2) to substitute out_fp32 with x_fp32 + y_fp32
+       = ((x_i8 - x_zero_point) * x_scale + (y_i8 - y_zero_point) * y_scale) / out_scale + out_zero_point  # apply (3) and (4)
+    """
+    x_i32 = x_i8.to(torch.int32)
+    y_i32 = y_i8.to(torch.int32)
+    # TODO: use out_dtype op
+    x_i32 = torch.round((x_scale / out_scale) * (x_i32 - x_zero_point)).to(torch.int32)
+    y_i32 = torch.round((y_scale / out_scale) * (y_i32 - y_zero_point)).to(torch.int32)
+    out_i32 = x_i32 + y_i32 + out_zero_point
+    quant_min = -128
+    quant_max = 127
+    out_i8 = torch.ops.aten.clamp(out_i32, quant_min, quant_max).to(torch.int8)
+    return out_i8
+
+_QUANTIZED_MAX_POOL2D_EXAMPLE_INPUTS = (
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+)
+
+def _qdq_quantized_max_pool2d(
+        x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, out_scale, out_zero_point, out_quant_min, out_quant_max):
+    kernel_size = 1
+    stride = 1
+    padding = 0
+    dilation = 1
+    ceil_mode = False
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8)
+    out_fp32, _ = torch.ops.aten.max_pool2d_with_indices.default(x_fp32, kernel_size, stride, padding, dilation, ceil_mode)
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor(
+        out_fp32, out_scale, out_zero_point, out_quant_min, out_quant_max, torch.int8)
+    return out_i8
+
+def _reference_quantized_max_pool2d(
+        x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, out_scale, out_zero_point, out_quant_min, out_quant_max):
+    kernel_size = 1
+    stride = 1
+    padding = 0
+    dilation = 1
+    ceil_mode = False
+    # to preserve x_quant_min, x_quant_max in the graph for pattern matching
+    x_i8 = torch.clamp(x_i8, x_quant_min, x_quant_max)
+    x_i32 = x_i8.to(torch.int32)
+    out_i32, _ = torch.ops.aten.max_pool2d_with_indices.default(
+        x_i32 - x_zero_point,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        ceil_mode
+    )
+    out_fp32 = out_i32 * (x_scale / out_scale) + out_zero_point
+    out_fp32 = torch.clamp(out_fp32, out_quant_min, out_quant_max)
+    out_i8 = out_fp32.to(torch.int8)
+    return out_i8
+
+_QUANTIZE_PER_TENSOR_INT8_EXAMPLE_INPUTS = (
+    torch.randn(1, 3, 3, 3, dtype=torch.float),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+)
+
+def _quantize_per_tensor_int8(x_fp32, scale, zero_point, quant_min, quant_max):
+    x = torch.ops.quantized_decomposed.quantize_per_tensor(x_fp32, scale, zero_point, quant_min, quant_max, torch.int8)
+    return x
+
+def _reference_quantize_per_tensor_int8(x_fp32, scale, zero_point, quant_min, quant_max):
+    # TODO: use out_dtype(mul, ...) here when the op is ready
+    x = x_fp32 / scale  # fp32
+    # round modes might be different here
+    # pytorch is rounding to even, which is also common for most of the backends
+    x = torch.round(x)  # fp32
+    x = x.to(dtype=torch.int32)  # int32
+    x = x + zero_point  # int32
+    # clamp works for fp32, int32 and int8 dtypes
+    x = torch.clamp(x, quant_min, quant_max)  # int32
+    x = x.to(dtype=torch.int8)
+    return x
+
+_DEQUANTIZE_PER_TENSOR_INT8_EXAMPLE_INPUTS = (
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(1, dtype=torch.float),
+    torch.zeros(1, dtype=torch.int),
+    torch.tensor([-128], dtype=torch.int),
+    torch.tensor([127], dtype=torch.int),
+)
+
+def _dequantize_per_tensor_int8(x_i8, scale, zero_point, quant_min, quant_max):
+    x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor(x_i8, scale, zero_point, quant_min, quant_max, torch.int8)
+    return x_fp32
+
+def _reference_dequantize_per_tensor_int8(x_i8, scale, zero_point, quant_min, quant_max):
+    # without using quant_min/max in clamp, the traced graph will not have quant_mi/max args.
+    # This results in failure to match the pattern.
+    # Therefore, we call a torch.ops.aten.clamp here
+    x_i8 = torch.ops.aten.clamp(x_i8, quant_min, quant_max)
+    # TODO: use out_dtype op
+    # note: x_i8.to(torch.int32) does not work here
+    # TODO: debug the implementation later when torchdynamo time out issue is resolved
+    return ((x_i8.to(torch.float32) - zero_point) * scale).to(dtype=torch.float32)
+
+_QUANTIZE_PER_CHANNEL_INT8_EXAMPLE_INPUTS = (
+    torch.randn(1, 3, 3, 3, dtype=torch.float),
+    torch.randn(3, dtype=torch.float),
+    torch.zeros(3, dtype=torch.int),
+    1,
+    -128,
+    127,
+)
+
+def _quantize_per_channel_int8(x_fp32, scales, zero_points, ch_axis, quant_min, quant_max):
+    out_i8 = torch.ops.quantized_decomposed.quantize_per_channel(
+        x_fp32, scales, zero_points, ch_axis, quant_min, quant_max, torch.int8
+    )
+    return out_i8
+
+def _reference_quantize_per_channel_int8(x_fp32, scales, zero_points, ch_axis, quant_min, quant_max):
+    x_fp32 = torch.transpose(x_fp32, ch_axis, -1)
+    out_i32 = torch.ops.aten.clamp(torch.round(x_fp32 / scales).to(torch.int32) + zero_points, quant_min, quant_max)
+    out_i32 = torch.transpose(out_i32, ch_axis, -1)
+    return out_i32.to(torch.int8)
+
+_DEQUANTIZE_PER_CHANNEL_INT8_EXAMPLE_INPUTS = (
+    torch.randint(-128, 127, (1, 3, 3, 3), dtype=torch.int8),
+    torch.randn(3, dtype=torch.float),
+    torch.zeros(3, dtype=torch.int),
+    1,
+    -128,
+    127,
+)
+
+def _dequantize_per_channel_int8(x_i8, scales, zero_points, ch_axis, quant_min, quant_max):
+    # the following will be replaced as placeholders
+    out_fp32 = torch.ops.quantized_decomposed.dequantize_per_channel(
+        x_i8, scales, zero_points, ch_axis, quant_min, quant_max, torch.int8
+    )
+    return out_fp32
+
+def _reference_dequantize_per_channel_int8(x_i8, scales, zero_points, ch_axis, quant_min, quant_max):
+    # the following will be replaced as placeholders
+    # in order to preserve the quant_min/quant_max args for pattern matching (e.g. matching for int4 quantized ops)
+    # we call a torch.ops.aten.clamp here
+    x_i8 = torch.ops.aten.clamp(x_i8, quant_min, quant_max)
+    x_i8 = torch.transpose(x_i8, ch_axis, -1)
+    x_i32 = x_i8.to(torch.int32)
+    out_fp32 = (x_i32 - zero_points).to(torch.float) * scales
+    out_fp32 = torch.transpose(out_fp32, ch_axis, -1)
+    return out_fp32
+
+def _replace_ph_qdq_per_channel_replacement(gm: torch.fx.GraphModule):
+    return _replace_literals_with_existing_placeholders(
+        gm,
+        exclude_literals=[-1],
+        literal_to_ph_idx={1: 3, -128: 4, 127: 5}
+    )
+
+
+@dataclass
+class _RewriteInfo:
+    """Data needed for rewrite, this includes example inputs, pattern and replacement functions
+    and post transformation functions for the exported pattern and replacement GraphModule
+    """
+
+    # example inputs used for exporting the pattern into GraphModule
+    example_inputs: Tuple[Any, ...]
+    pattern: Callable
+    replacement: Callable
+    # post transformation on the exported pattern and replacement GraphModule
+    pattern_post_trans: Optional[Callable[[GraphModule], GraphModule]] = None
+    replacement_post_trans: Optional[Callable[[GraphModule], GraphModule]] = None
+
+_REWRITE_INFO_LIST = [
+    _RewriteInfo(
+        _DYNAMIC_QUANTIZED_LINEAR_EXAMPLE_INPUTS,
+        _WrapperModule(_qdq_dynamic_quantized_linear),
+        _WrapperModule(_reference_dynamic_quantized_linear),
+        partial(
+            _replace_literals_with_existing_placeholders,
+            literal_to_ph_idx={
+                -128: 1,
+                127: 2,
+                torch.finfo(torch.float32).eps: 3
+            }
+        ),
+        partial(
+            _replace_literals_with_existing_placeholders,
+            literal_to_ph_idx={
+                -128: 1,
+                127: 2,
+                torch.finfo(torch.float32).eps: 3
+            }
+        ),
+    ),
+    _RewriteInfo(
+        _QUANTIZED_LINEAR_EXAMPLE_INPUTS,
+        _WrapperModule(_qdq_quantized_linear),
+        _WrapperModule(_reference_quantized_linear),
+        _replace_literals_with_new_placeholders,
+        _replace_literals_with_new_placeholders,
+    ),
+    _RewriteInfo(
+        _QUANTIZED_CONV2d_EXAMPLE_INPUTS,
+        _WrapperModule(_qdq_quantized_conv2d),
+        _WrapperModule(_reference_quantized_conv2d),
+        partial(_replace_literals_with_new_placeholders, exclude_literals=[-1]),
+        partial(_replace_literals_with_new_placeholders, exclude_literals=[-1]),
+    ),
+    _RewriteInfo(
+        _QUANTIZED_ADD_OR_ADD_RELU_EXAMPLE_INPUTS,
+        _WrapperModule(_qdq_quantized_add_relu),
+        _WrapperModule(_reference_quantized_add_relu),
+    ),
+    _RewriteInfo(
+        _QUANTIZED_ADD_OR_ADD_RELU_EXAMPLE_INPUTS,
+        _WrapperModule(_qdq_quantized_add),
+        _WrapperModule(_reference_quantized_add),
+    ),
+    _RewriteInfo(
+        _QUANTIZED_MAX_POOL2D_EXAMPLE_INPUTS,
+        _WrapperModule(_qdq_quantized_max_pool2d),
+        _WrapperModule(_reference_quantized_max_pool2d),
+        _replace_literals_with_new_placeholders,
+        _replace_literals_with_new_placeholders
+    ),
+    _RewriteInfo(
+        _QUANTIZE_PER_TENSOR_INT8_EXAMPLE_INPUTS,
+        _WrapperModule(_quantize_per_tensor_int8),
+        _WrapperModule(_reference_quantize_per_tensor_int8),
+    ),
+    _RewriteInfo(
+        _DEQUANTIZE_PER_TENSOR_INT8_EXAMPLE_INPUTS,
+        _WrapperModule(_dequantize_per_tensor_int8),
+        _WrapperModule(_reference_dequantize_per_tensor_int8),
+    ),
+    _RewriteInfo(
+        _QUANTIZE_PER_CHANNEL_INT8_EXAMPLE_INPUTS,
+        _WrapperModule(_quantize_per_channel_int8),
+        _WrapperModule(_reference_quantize_per_channel_int8),
+        _replace_ph_qdq_per_channel_replacement,
+        _replace_ph_qdq_per_channel_replacement
+    ),
+    _RewriteInfo(
+        _DEQUANTIZE_PER_CHANNEL_INT8_EXAMPLE_INPUTS,
+        _WrapperModule(_dequantize_per_channel_int8),
+        _WrapperModule(_reference_dequantize_per_channel_int8),
+        _replace_ph_qdq_per_channel_replacement,
+        _replace_ph_qdq_per_channel_replacement
+    ),
+]
+
+def reference_representation_rewrite(model: GraphModule) -> GraphModule:
+    remove_tensor_overload_for_qdq_ops(model)
+    for rewrite_info in _REWRITE_INFO_LIST:
+        example_inputs = rewrite_info.example_inputs
+        pattern = rewrite_info.pattern
+        replacement = rewrite_info.replacement
+        pattern_post_trans = rewrite_info.pattern_post_trans
+        replacement_post_trans = rewrite_info.replacement_post_trans
+        pattern = get_aten_graph_module(pattern, example_inputs)  # type: ignore[arg-type, assignment]
+        remove_tensor_overload_for_qdq_ops(pattern)  # type: ignore[arg-type]
+        replacement = get_aten_graph_module(replacement, example_inputs)  # type: ignore[arg-type, assignment]
+        remove_tensor_overload_for_qdq_ops(replacement)  # type: ignore[arg-type]
+        if pattern_post_trans:
+            pattern = pattern_post_trans(pattern)
+        if replacement_post_trans:
+            replacement = replacement_post_trans(replacement)
+        pattern.recompile()  # type: ignore[attr-defined]
+        replacement.recompile()  # type: ignore[attr-defined]
+        matches = replace_pattern(model, pattern, replacement)
+    return model
diff --git a/shared/package-files/04/04ea2bb253df1917eddbf2d2526439cedb0b4d11cec56d45858a748e9ec3d201/meson.build b/shared/package-files/04/04ea2bb253df1917eddbf2d2526439cedb0b4d11cec56d45858a748e9ec3d201/meson.build
new file mode 100644
index 0000000000000000000000000000000000000000..3d51e06bfc0206c230c9922516caed415de85516
--- /dev/null
+++ b/shared/package-files/04/04ea2bb253df1917eddbf2d2526439cedb0b4d11cec56d45858a748e9ec3d201/meson.build
@@ -0,0 +1,17 @@
+py.extension_module(
+    '_smoothers_lowess',
+    '_smoothers_lowess.pyx',
+    install: true,
+    include_directories: [inc_np],
+    subdir: 'statsmodels/nonparametric',
+    c_args: cython_c_args,
+)
+
+py.extension_module(
+    'linbin',
+    'linbin.pyx',
+    install: true,
+    include_directories: [inc_np],
+    subdir: 'statsmodels/nonparametric',
+    c_args: cython_c_args,
+)
diff --git a/shared/package-files/04/04ee1095a3d2747d48dfb908add522f3c083a0b0e6dcc61a298fbb6233053462/dedup_module_uses.h b/shared/package-files/04/04ee1095a3d2747d48dfb908add522f3c083a0b0e6dcc61a298fbb6233053462/dedup_module_uses.h
new file mode 100644
index 0000000000000000000000000000000000000000..0204d5f73f04f3420f8d822c782739417b31b7e6
--- /dev/null
+++ b/shared/package-files/04/04ee1095a3d2747d48dfb908add522f3c083a0b0e6dcc61a298fbb6233053462/dedup_module_uses.h
@@ -0,0 +1,28 @@
+#pragma once
+
+#include 
+
+namespace torch {
+namespace jit {
+
+/** Recursively deduplicate multiple uses of the same module by
+ *  creating an instance clone for each use of the module, which means
+ *  the type will be the same as before and all the attributes will be
+ *  copied, then we'll change the use of the original module to the use
+ *  of cloned module in the Graph.
+ *
+ *  This is done to ensure that modules can survive destructive passes
+ *  without changing model behavior. For example, here:
+ *
+ *    x = self.conv1(x)
+ *    x = self.relu(x)
+ *    x = self.conv2(x)
+ *    x = self.relu(x)
+ *
+ *  self.relu needs to be deduplicated for potential future destructive passes
+ *  to work properly.
+ */
+TORCH_API void DedupModuleUses(Module& module);
+
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/04/04f01e96fd4c48c5adca2cd0bbdf56de000eae14ae61e67833911174863a8a11/RECORD b/shared/package-files/04/04f01e96fd4c48c5adca2cd0bbdf56de000eae14ae61e67833911174863a8a11/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..19c05021fd2104bab4bc1df638b2dd2defb5a103
--- /dev/null
+++ b/shared/package-files/04/04f01e96fd4c48c5adca2cd0bbdf56de000eae14ae61e67833911174863a8a11/RECORD
@@ -0,0 +1,121 @@
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+formulaic-1.2.2.dist-info/RECORD,,
+formulaic-1.2.2.dist-info/WHEEL,sha256=mffPy8wBnZQn2VnJUU5jE99KsxaSfiyMHV9Yt0aLVxs,87
+formulaic-1.2.2.dist-info/licenses/LICENSE,sha256=UjDNkKF1poCozrGHT6ZQjSWrtNC4c6MPEP4oiPPX4oA,1072
+formulaic/__init__.py,sha256=t_JRYWulqY5bHM_cfjbRjOE1ugK9wnbuZymH3Ja-Ey0,826
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+formulaic/formula.py,sha256=EDo8nUFOIu9gVZ4s9QFKhf6Q4areMYVQtKvmzXqw1z4,28944
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diff --git a/shared/package-files/04/04f0bf220f65460dbc9951c8a9d4aa5a7c0128bba44d71646eb76a518bac92ae/test_gil.py b/shared/package-files/04/04f0bf220f65460dbc9951c8a9d4aa5a7c0128bba44d71646eb76a518bac92ae/test_gil.py
new file mode 100644
index 0000000000000000000000000000000000000000..48197062e0b83a9ef54e45089d9089d49b8ad367
--- /dev/null
+++ b/shared/package-files/04/04f0bf220f65460dbc9951c8a9d4aa5a7c0128bba44d71646eb76a518bac92ae/test_gil.py
@@ -0,0 +1,64 @@
+import itertools
+import threading
+import time
+
+import numpy as np
+import pytest
+import scipy.interpolate
+
+
+class TestGIL:
+    """Check if the GIL is properly released by scipy.interpolate functions."""
+
+    def setup_method(self):
+        self.messages = []
+
+    def log(self, message):
+        self.messages.append(message)
+
+    def make_worker_thread(self, target, args):
+        log = self.log
+
+        class WorkerThread(threading.Thread):
+            def run(self):
+                log('interpolation started')
+                target(*args)
+                log('interpolation complete')
+
+        return WorkerThread()
+
+    @pytest.mark.xslow
+    @pytest.mark.xfail(reason='race conditions, may depend on system load')
+    def test_rectbivariatespline(self):
+        def generate_params(n_points):
+            x = y = np.linspace(0, 1000, n_points)
+            x_grid, y_grid = np.meshgrid(x, y)
+            z = x_grid * y_grid
+            return x, y, z
+
+        def calibrate_delay(requested_time):
+            for n_points in itertools.count(5000, 1000):
+                args = generate_params(n_points)
+                time_started = time.time()
+                interpolate(*args)
+                if time.time() - time_started > requested_time:
+                    return args
+
+        def interpolate(x, y, z):
+            scipy.interpolate.RectBivariateSpline(x, y, z)
+
+        args = calibrate_delay(requested_time=3)
+        worker_thread = self.make_worker_thread(interpolate, args)
+        worker_thread.start()
+        for i in range(3):
+            time.sleep(0.5)
+            self.log('working')
+        worker_thread.join()
+        assert self.messages == [
+            'interpolation started',
+            'working',
+            'working',
+            'working',
+            'interpolation complete',
+        ]
+
diff --git a/shared/package-files/04/04f62e6b76daf9018af1f99c10ec73324454750eaa68ac6a20ed6f878fc45aae/__init__.py b/shared/package-files/04/04f62e6b76daf9018af1f99c10ec73324454750eaa68ac6a20ed6f878fc45aae/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f7a6f98e2d12628c6095c761d936b35f0ec891d
--- /dev/null
+++ b/shared/package-files/04/04f62e6b76daf9018af1f99c10ec73324454750eaa68ac6a20ed6f878fc45aae/__init__.py
@@ -0,0 +1,5 @@
+from .base_data_scheduler import BaseDataScheduler
+
+__all__ = [
+    "BaseDataScheduler",
+]
diff --git a/shared/package-files/04/04f6cc295b72046a330cbca9a09499e184f70744d89177bc289afd90f674013e/unbind_copy_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/04/04f6cc295b72046a330cbca9a09499e184f70744d89177bc289afd90f674013e/unbind_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..51983d8d2a7c11aabef3b42cfc868dd86797bd62
--- /dev/null
+++ b/shared/package-files/04/04f6cc295b72046a330cbca9a09499e184f70744d89177bc289afd90f674013e/unbind_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API ::std::vector unbind_copy(const at::Tensor & self, int64_t dim=0);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/04/04f780c697e866ef47d92f3cd0079502d5a520a3bd14cf43af2586ea78857986/test_parallel_low_work.py b/shared/package-files/04/04f780c697e866ef47d92f3cd0079502d5a520a3bd14cf43af2586ea78857986/test_parallel_low_work.py
new file mode 100644
index 0000000000000000000000000000000000000000..cab4d42749f643ca1b38f74f3f6ef400661c5023
--- /dev/null
+++ b/shared/package-files/04/04f780c697e866ef47d92f3cd0079502d5a520a3bd14cf43af2586ea78857986/test_parallel_low_work.py
@@ -0,0 +1,44 @@
+"""
+There was a deadlock problem when work count is smaller than number of threads.
+"""
+
+import numpy as np
+
+from numba import float32, float64, int32, uint32
+from numba.np.ufunc import Vectorize
+import unittest
+
+
+def vector_add(a, b):
+    return a + b
+
+
+class TestParallelLowWorkCount(unittest.TestCase):
+
+    _numba_parallel_test_ = False
+
+    def test_low_workcount(self):
+        # build parallel native code ufunc
+        pv = Vectorize(vector_add, target='parallel')
+        for ty in (int32, uint32, float32, float64):
+            pv.add(ty(ty, ty))
+        para_ufunc = pv.build_ufunc()
+
+        # build python ufunc
+        np_ufunc = np.vectorize(vector_add)
+
+        # test it out
+        def test(ty):
+            data = np.arange(1).astype(ty) # just one item
+            result = para_ufunc(data, data)
+            gold = np_ufunc(data, data)
+            np.testing.assert_allclose(gold, result)
+
+        test(np.double)
+        test(np.float32)
+        test(np.int32)
+        test(np.uint32)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/shared/package-files/04/04f830fe52a0921867b8c423f2a1f055b3112e680dc248aeca72eb1cd87378fe/liblzma.pc b/shared/package-files/04/04f830fe52a0921867b8c423f2a1f055b3112e680dc248aeca72eb1cd87378fe/liblzma.pc
new file mode 100644
index 0000000000000000000000000000000000000000..87c83fc9a65bcfa52f9736dd885c75a4c65430ca
--- /dev/null
+++ b/shared/package-files/04/04f830fe52a0921867b8c423f2a1f055b3112e680dc248aeca72eb1cd87378fe/liblzma.pc
@@ -0,0 +1,20 @@
+#
+# Author: Lasse Collin
+#
+# This file has been put into the public domain.
+# You can do whatever you want with this file.
+#
+
+prefix=/opt/conda
+exec_prefix=/opt/conda
+libdir=/opt/conda/lib
+includedir=/opt/conda/include
+
+Name: liblzma
+Description: General purpose data compression library
+URL: https://xz.tukaani.org/xz-utils/
+Version: 5.4.6
+Cflags: -I${includedir}
+Cflags.private: -DLZMA_API_STATIC
+Libs: -L${libdir} -llzma
+Libs.private: -pthread -lpthread
diff --git a/shared/package-files/04/04f89eeaaf0db4159dd3c6d9e2188751f3e2df0ca16a484290ed70a45465d6bd/compilerop.py b/shared/package-files/04/04f89eeaaf0db4159dd3c6d9e2188751f3e2df0ca16a484290ed70a45465d6bd/compilerop.py
new file mode 100644
index 0000000000000000000000000000000000000000..7799a4fc99e94adb71cf4867af8e29e71757eeb9
--- /dev/null
+++ b/shared/package-files/04/04f89eeaaf0db4159dd3c6d9e2188751f3e2df0ca16a484290ed70a45465d6bd/compilerop.py
@@ -0,0 +1,214 @@
+"""Compiler tools with improved interactive support.
+
+Provides compilation machinery similar to codeop, but with caching support so
+we can provide interactive tracebacks.
+
+Authors
+-------
+* Robert Kern
+* Fernando Perez
+* Thomas Kluyver
+"""
+
+# Note: though it might be more natural to name this module 'compiler', that
+# name is in the stdlib and name collisions with the stdlib tend to produce
+# weird problems (often with third-party tools).
+
+#-----------------------------------------------------------------------------
+#  Copyright (C) 2010-2011 The IPython Development Team.
+#
+#  Distributed under the terms of the BSD License.
+#
+#  The full license is in the file COPYING.txt, distributed with this software.
+#-----------------------------------------------------------------------------
+
+#-----------------------------------------------------------------------------
+# Imports
+#-----------------------------------------------------------------------------
+
+# Stdlib imports
+import __future__
+from ast import PyCF_ONLY_AST
+import codeop
+import functools
+import hashlib
+import linecache
+import operator
+import time
+from contextlib import contextmanager
+
+#-----------------------------------------------------------------------------
+# Constants
+#-----------------------------------------------------------------------------
+
+# Roughly equal to PyCF_MASK | PyCF_MASK_OBSOLETE as defined in pythonrun.h,
+# this is used as a bitmask to extract future-related code flags.
+PyCF_MASK = functools.reduce(operator.or_,
+                             (getattr(__future__, fname).compiler_flag
+                              for fname in __future__.all_feature_names))
+
+#-----------------------------------------------------------------------------
+# Local utilities
+#-----------------------------------------------------------------------------
+
+def code_name(code, number=0):
+    """ Compute a (probably) unique name for code for caching.
+
+    This now expects code to be unicode.
+    """
+    hash_digest = hashlib.sha1(code.encode("utf-8")).hexdigest()
+    # Include the number and 12 characters of the hash in the name.  It's
+    # pretty much impossible that in a single session we'll have collisions
+    # even with truncated hashes, and the full one makes tracebacks too long
+    return ''.format(number, hash_digest[:12])
+
+#-----------------------------------------------------------------------------
+# Classes and functions
+#-----------------------------------------------------------------------------
+
+class CachingCompiler(codeop.Compile):
+    """A compiler that caches code compiled from interactive statements.
+    """
+
+    def __init__(self):
+        codeop.Compile.__init__(self)
+
+        # Caching a dictionary { filename: execution_count } for nicely
+        # rendered tracebacks. The filename corresponds to the filename
+        # argument used for the builtins.compile function.
+        self._filename_map = {}
+
+    def ast_parse(self, source, filename='', symbol='exec'):
+        """Parse code to an AST with the current compiler flags active.
+
+        Arguments are exactly the same as ast.parse (in the standard library),
+        and are passed to the built-in compile function."""
+        return compile(source, filename, symbol, self.flags | PyCF_ONLY_AST, 1)
+
+    def reset_compiler_flags(self):
+        """Reset compiler flags to default state."""
+        # This value is copied from codeop.Compile.__init__, so if that ever
+        # changes, it will need to be updated.
+        self.flags = codeop.PyCF_DONT_IMPLY_DEDENT
+
+    @property
+    def compiler_flags(self):
+        """Flags currently active in the compilation process.
+        """
+        return self.flags
+
+    def get_code_name(self, raw_code, transformed_code, number):
+        """Compute filename given the code, and the cell number.
+
+        Parameters
+        ----------
+        raw_code : str
+            The raw cell code.
+        transformed_code : str
+            The executable Python source code to cache and compile.
+        number : int
+            A number which forms part of the code's name. Used for the execution
+            counter.
+
+        Returns
+        -------
+        The computed filename.
+        """
+        return code_name(transformed_code, number)
+
+    def format_code_name(self, name):
+        """Return a user-friendly label and name for a code block.
+
+        Parameters
+        ----------
+        name : str
+            The name for the code block returned from get_code_name
+
+        Returns
+        -------
+        A (label, name) pair that can be used in tracebacks, or None if the default formatting should be used.
+        """
+        if name in self._filename_map:
+            return "Cell", "In[%s]" % self._filename_map[name]
+
+    def cache(self, transformed_code, number=0, raw_code=None):
+        """Make a name for a block of code, and cache the code.
+
+        Parameters
+        ----------
+        transformed_code : str
+            The executable Python source code to cache and compile.
+        number : int
+            A number which forms part of the code's name. Used for the execution
+            counter.
+        raw_code : str
+            The raw code before transformation, if None, set to `transformed_code`.
+
+        Returns
+        -------
+        The name of the cached code (as a string). Pass this as the filename
+        argument to compilation, so that tracebacks are correctly hooked up.
+        """
+        if raw_code is None:
+            raw_code = transformed_code
+
+        name = self.get_code_name(raw_code, transformed_code, number)
+
+        # Save the execution count
+        self._filename_map[name] = number
+
+        # Since Python 2.5, setting mtime to `None` means the lines will
+        # never be removed by `linecache.checkcache`.  This means all the
+        # monkeypatching has *never* been necessary, since this code was
+        # only added in 2010, at which point IPython had already stopped
+        # supporting Python 2.4.
+        #
+        # Note that `linecache.clearcache` and `linecache.updatecache` may
+        # still remove our code from the cache, but those show explicit
+        # intent, and we should not try to interfere.  Normally the former
+        # is never called except when out of memory, and the latter is only
+        # called for lines *not* in the cache.
+        entry = (
+            len(transformed_code),
+            None,
+            [line + "\n" for line in transformed_code.splitlines()],
+            name,
+        )
+        linecache.cache[name] = entry
+        return name
+
+    @contextmanager
+    def extra_flags(self, flags):
+        ## bits that we'll set to 1
+        turn_on_bits = ~self.flags & flags
+
+
+        self.flags = self.flags | flags
+        try:
+            yield
+        finally:
+            # turn off only the bits we turned on so that something like
+            # __future__ that set flags stays.
+            self.flags &= ~turn_on_bits
+
+
+def check_linecache_ipython(*args):
+    """Deprecated since IPython 8.6.  Call linecache.checkcache() directly.
+
+    It was already not necessary to call this function directly.  If no
+    CachingCompiler had been created, this function would fail badly.  If
+    an instance had been created, this function would've been monkeypatched
+    into place.
+
+    As of IPython 8.6, the monkeypatching has gone away entirely.  But there
+    were still internal callers of this function, so maybe external callers
+    also existed?
+    """
+    import warnings
+
+    warnings.warn(
+        "Deprecated Since IPython 8.6, Just call linecache.checkcache() directly.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    linecache.checkcache()
diff --git a/shared/package-files/04/04f9d3c040a425f6a9d23cf4322d51208dcf2c778723a787dd9de69c166816f4/views.pyi b/shared/package-files/04/04f9d3c040a425f6a9d23cf4322d51208dcf2c778723a787dd9de69c166816f4/views.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..124c01c54328a26629c67b885a48b67f2d38c692
--- /dev/null
+++ b/shared/package-files/04/04f9d3c040a425f6a9d23cf4322d51208dcf2c778723a787dd9de69c166816f4/views.pyi
@@ -0,0 +1,72 @@
+from typing import Any, Optional, Set
+
+from django.contrib.auth.base_user import AbstractBaseUser
+from django.core.handlers.wsgi import WSGIRequest
+from django.http.request import HttpRequest
+from django.http.response import HttpResponseRedirect
+from django.template.response import TemplateResponse
+from django.views.generic.base import TemplateView
+from django.views.generic.edit import FormView
+
+UserModel: Any
+
+class SuccessURLAllowedHostsMixin:
+    success_url_allowed_hosts: Any = ...
+    def get_success_url_allowed_hosts(self) -> Set[str]: ...
+
+class LoginView(SuccessURLAllowedHostsMixin, FormView):
+    authentication_form: Any = ...
+    redirect_field_name: Any = ...
+    redirect_authenticated_user: bool = ...
+    extra_context: Any = ...
+    def get_redirect_url(self) -> str: ...
+
+class LogoutView(SuccessURLAllowedHostsMixin, TemplateView):
+    next_page: Any = ...
+    redirect_field_name: Any = ...
+    extra_context: Any = ...
+    def post(self, request: WSGIRequest, *args: Any, **kwargs: Any) -> TemplateResponse: ...
+    def get_next_page(self) -> Optional[str]: ...
+
+def logout_then_login(request: HttpRequest, login_url: Optional[str] = ...) -> HttpResponseRedirect: ...
+def redirect_to_login(
+    next: str, login_url: Optional[str] = ..., redirect_field_name: Optional[str] = ...
+) -> HttpResponseRedirect: ...
+
+class PasswordContextMixin:
+    extra_context: Any = ...
+    def get_context_data(self, **kwargs: Any): ...
+
+class PasswordResetView(PasswordContextMixin, FormView):
+    email_template_name: str = ...
+    extra_email_context: Any = ...
+    from_email: Any = ...
+    html_email_template_name: Any = ...
+    subject_template_name: str = ...
+    title: Any = ...
+    token_generator: Any = ...
+
+INTERNAL_RESET_URL_TOKEN: str
+INTERNAL_RESET_SESSION_TOKEN: str
+
+class PasswordResetDoneView(PasswordContextMixin, TemplateView):
+    title: Any = ...
+
+class PasswordResetConfirmView(PasswordContextMixin, FormView):
+    post_reset_login: bool = ...
+    post_reset_login_backend: Any = ...
+    reset_url_token: str = ...
+    title: Any = ...
+    token_generator: Any = ...
+    validlink: bool = ...
+    user: Any = ...
+    def get_user(self, uidb64: str) -> Optional[AbstractBaseUser]: ...
+
+class PasswordResetCompleteView(PasswordContextMixin, TemplateView):
+    title: Any = ...
+
+class PasswordChangeView(PasswordContextMixin, FormView):
+    title: Any = ...
+
+class PasswordChangeDoneView(PasswordContextMixin, TemplateView):
+    title: Any = ...
diff --git a/shared/package-files/04/04fc2215d77e8833b458640a7e124a218a38030762be48a18af31c1161d03055/block_codegen.h b/shared/package-files/04/04fc2215d77e8833b458640a7e124a218a38030762be48a18af31c1161d03055/block_codegen.h
new file mode 100644
index 0000000000000000000000000000000000000000..fca5fdc9fd0e2eeff5613bbfeba1b96f270b29a7
--- /dev/null
+++ b/shared/package-files/04/04fc2215d77e8833b458640a7e124a218a38030762be48a18af31c1161d03055/block_codegen.h
@@ -0,0 +1,150 @@
+#pragma once
+
+#include 
+#include 
+#include 
+#include 
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// A class that analyzes the given program relevant for Block backend.
+class BlockAnalysis : public IRVisitor {
+ public:
+  bool is_buf_store_target(BufPtr buf) const {
+    return store_targets_.count(buf) > 0;
+  }
+
+  const std::unordered_set& loads() const {
+    return loads_;
+  }
+
+  const std::unordered_set& stores() const {
+    return store_targets_;
+  }
+
+  int block_size() const {
+    return block_size_;
+  }
+
+  bool areBufsInMap(const std::unordered_set& bufs) const;
+
+  BufPtr getMultiDimBuf(BufPtr buf) const;
+
+  std::string getInputName(BufPtr buf) const;
+
+  std::string getFlatInputName(BufPtr buf) const {
+    return getInputName(std::move(buf)) + "_flat";
+  }
+
+  std::unordered_map getBufferMap() const {
+    return map_input_to_tensor_bufs_;
+  }
+
+ private:
+  void visit(StorePtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(ForPtr v) override;
+
+  std::unordered_map map_input_to_tensor_bufs_;
+  std::unordered_set store_targets_;
+  std::unordered_set loads_;
+  int block_size_ = 32;
+};
+
+// A class that overrides the underlying IRPrinter to produce Block.
+class BlockPrinter : public IRPrinter {
+ public:
+  BlockPrinter(std::ostream* os, BlockAnalysis* block_analysis)
+      : IRPrinter(*os), block_analysis_(block_analysis) {}
+
+  using IRPrinter::name_manager;
+  using IRPrinter::visit;
+
+ private:
+  BlockAnalysis* block_analysis_;
+  std::unordered_map dim_values_map;
+  std::vector dim_names = {"N", "H", "W", "C"};
+  std::vector flat_dim_names = {"N", "NH", "NHW", "NHWC"};
+  void PrintTensorInfo(const std::unordered_set& bufs);
+  void PrintArguments(const std::unordered_set& bufs);
+  void PrintBufferInfo(const std::unordered_set& bufs);
+  void PrintDistribution(const std::unordered_set& bufs);
+  void PrintLoop(const std::unordered_set& bufs, bool block_idx = true);
+  void PrintReshapeInfo(
+      const std::unordered_set& bufs,
+      bool reverse = false);
+  void PrintDMAs(const std::unordered_set& bufs);
+  void PrintAdjustBuffers(const std::unordered_set& bufs);
+
+  void visit(ForPtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(StorePtr v) override;
+  void visit(BlockPtr v) override;
+  void visit(AddPtr v) override;
+  void visit(MulPtr v) override;
+};
+
+class TORCH_API BlockCodeGen : public CodeGen {
+ public:
+  template 
+  /* implicit */
+  BlockCodeGen(StmtPtr stmt, Ts... ts)
+      : CodeGen(
+            stmt,
+            std::vector({BufferArg(ts)...}),
+            at::Device(at::kCPU)) {
+    Initialize();
+  }
+
+  BlockCodeGen(
+      StmtPtr stmt,
+      const std::vector& buffer_args,
+      at::Device device = at::Device(at::kCPU),
+      const std::string& kernel_func_name = "func")
+      : CodeGen(stmt, buffer_args, device, kernel_func_name) {
+    Initialize();
+  }
+
+  ~BlockCodeGen() override;
+
+  void call(const std::vector& args) override;
+  void call_raw(const std::vector& args) override;
+
+  void Initialize();
+
+  std::string getCodeText(const std::string& attr = "") override {
+    return oss_.str();
+  }
+
+ private:
+  UniqueNameManager* name_manager() {
+    if (!printer_) {
+      throw std::runtime_error("Null IRPrinter is not expected");
+    }
+    return printer_->name_manager();
+  }
+
+  std::ostream& os() {
+    return printer_->os();
+  }
+
+  std::ostringstream oss_;
+  std::unique_ptr printer_;
+  std::unique_ptr block_analysis_;
+
+  std::string GetUniqueFuncName(const std::string& func_prefix);
+};
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/04/04fcdb9abeceee28668cb284f40df5724e7800d8c0b6ba7cf9d553db4311e5f4/missing.py b/shared/package-files/04/04fcdb9abeceee28668cb284f40df5724e7800d8c0b6ba7cf9d553db4311e5f4/missing.py
new file mode 100644
index 0000000000000000000000000000000000000000..c341ff9dff7e613d8db2209efb5c10f170a9cd47
--- /dev/null
+++ b/shared/package-files/04/04fcdb9abeceee28668cb284f40df5724e7800d8c0b6ba7cf9d553db4311e5f4/missing.py
@@ -0,0 +1,810 @@
+"""
+missing types & inference
+"""
+from __future__ import annotations
+
+from decimal import Decimal
+from functools import partial
+from typing import (
+    TYPE_CHECKING,
+    overload,
+)
+import warnings
+
+import numpy as np
+
+from pandas._config import get_option
+
+from pandas._libs import lib
+import pandas._libs.missing as libmissing
+from pandas._libs.tslibs import (
+    NaT,
+    iNaT,
+)
+
+from pandas.core.dtypes.common import (
+    DT64NS_DTYPE,
+    TD64NS_DTYPE,
+    ensure_object,
+    is_scalar,
+    is_string_or_object_np_dtype,
+)
+from pandas.core.dtypes.dtypes import (
+    CategoricalDtype,
+    DatetimeTZDtype,
+    ExtensionDtype,
+    IntervalDtype,
+    PeriodDtype,
+)
+from pandas.core.dtypes.generic import (
+    ABCDataFrame,
+    ABCExtensionArray,
+    ABCIndex,
+    ABCMultiIndex,
+    ABCSeries,
+)
+from pandas.core.dtypes.inference import is_list_like
+
+if TYPE_CHECKING:
+    from re import Pattern
+
+    from pandas._typing import (
+        ArrayLike,
+        DtypeObj,
+        NDFrame,
+        NDFrameT,
+        Scalar,
+        npt,
+    )
+
+    from pandas import Series
+    from pandas.core.indexes.base import Index
+
+
+isposinf_scalar = libmissing.isposinf_scalar
+isneginf_scalar = libmissing.isneginf_scalar
+
+nan_checker = np.isnan
+INF_AS_NA = False
+_dtype_object = np.dtype("object")
+_dtype_str = np.dtype(str)
+
+
+@overload
+def isna(obj: Scalar | Pattern) -> bool:
+    ...
+
+
+@overload
+def isna(
+    obj: ArrayLike | Index | list,
+) -> npt.NDArray[np.bool_]:
+    ...
+
+
+@overload
+def isna(obj: NDFrameT) -> NDFrameT:
+    ...
+
+
+# handle unions
+@overload
+def isna(obj: NDFrameT | ArrayLike | Index | list) -> NDFrameT | npt.NDArray[np.bool_]:
+    ...
+
+
+@overload
+def isna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
+    ...
+
+
+def isna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
+    """
+    Detect missing values for an array-like object.
+
+    This function takes a scalar or array-like object and indicates
+    whether values are missing (``NaN`` in numeric arrays, ``None`` or ``NaN``
+    in object arrays, ``NaT`` in datetimelike).
+
+    Parameters
+    ----------
+    obj : scalar or array-like
+        Object to check for null or missing values.
+
+    Returns
+    -------
+    bool or array-like of bool
+        For scalar input, returns a scalar boolean.
+        For array input, returns an array of boolean indicating whether each
+        corresponding element is missing.
+
+    See Also
+    --------
+    notna : Boolean inverse of pandas.isna.
+    Series.isna : Detect missing values in a Series.
+    DataFrame.isna : Detect missing values in a DataFrame.
+    Index.isna : Detect missing values in an Index.
+
+    Examples
+    --------
+    Scalar arguments (including strings) result in a scalar boolean.
+
+    >>> pd.isna('dog')
+    False
+
+    >>> pd.isna(pd.NA)
+    True
+
+    >>> pd.isna(np.nan)
+    True
+
+    ndarrays result in an ndarray of booleans.
+
+    >>> array = np.array([[1, np.nan, 3], [4, 5, np.nan]])
+    >>> array
+    array([[ 1., nan,  3.],
+           [ 4.,  5., nan]])
+    >>> pd.isna(array)
+    array([[False,  True, False],
+           [False, False,  True]])
+
+    For indexes, an ndarray of booleans is returned.
+
+    >>> index = pd.DatetimeIndex(["2017-07-05", "2017-07-06", None,
+    ...                           "2017-07-08"])
+    >>> index
+    DatetimeIndex(['2017-07-05', '2017-07-06', 'NaT', '2017-07-08'],
+                  dtype='datetime64[ns]', freq=None)
+    >>> pd.isna(index)
+    array([False, False,  True, False])
+
+    For Series and DataFrame, the same type is returned, containing booleans.
+
+    >>> df = pd.DataFrame([['ant', 'bee', 'cat'], ['dog', None, 'fly']])
+    >>> df
+         0     1    2
+    0  ant   bee  cat
+    1  dog  None  fly
+    >>> pd.isna(df)
+           0      1      2
+    0  False  False  False
+    1  False   True  False
+
+    >>> pd.isna(df[1])
+    0    False
+    1     True
+    Name: 1, dtype: bool
+    """
+    return _isna(obj)
+
+
+isnull = isna
+
+
+def _isna(obj, inf_as_na: bool = False):
+    """
+    Detect missing values, treating None, NaN or NA as null. Infinite
+    values will also be treated as null if inf_as_na is True.
+
+    Parameters
+    ----------
+    obj: ndarray or object value
+        Input array or scalar value.
+    inf_as_na: bool
+        Whether to treat infinity as null.
+
+    Returns
+    -------
+    boolean ndarray or boolean
+    """
+    if is_scalar(obj):
+        return libmissing.checknull(obj, inf_as_na=inf_as_na)
+    elif isinstance(obj, ABCMultiIndex):
+        raise NotImplementedError("isna is not defined for MultiIndex")
+    elif isinstance(obj, type):
+        return False
+    elif isinstance(obj, (np.ndarray, ABCExtensionArray)):
+        return _isna_array(obj, inf_as_na=inf_as_na)
+    elif isinstance(obj, ABCIndex):
+        # Try to use cached isna, which also short-circuits for integer dtypes
+        #  and avoids materializing RangeIndex._values
+        if not obj._can_hold_na:
+            return obj.isna()
+        return _isna_array(obj._values, inf_as_na=inf_as_na)
+
+    elif isinstance(obj, ABCSeries):
+        result = _isna_array(obj._values, inf_as_na=inf_as_na)
+        # box
+        result = obj._constructor(result, index=obj.index, name=obj.name, copy=False)
+        return result
+    elif isinstance(obj, ABCDataFrame):
+        return obj.isna()
+    elif isinstance(obj, list):
+        return _isna_array(np.asarray(obj, dtype=object), inf_as_na=inf_as_na)
+    elif hasattr(obj, "__array__"):
+        return _isna_array(np.asarray(obj), inf_as_na=inf_as_na)
+    else:
+        return False
+
+
+def _use_inf_as_na(key) -> None:
+    """
+    Option change callback for na/inf behaviour.
+
+    Choose which replacement for numpy.isnan / -numpy.isfinite is used.
+
+    Parameters
+    ----------
+    flag: bool
+        True means treat None, NaN, INF, -INF as null (old way),
+        False means None and NaN are null, but INF, -INF are not null
+        (new way).
+
+    Notes
+    -----
+    This approach to setting global module values is discussed and
+    approved here:
+
+    * https://stackoverflow.com/questions/4859217/
+      programmatically-creating-variables-in-python/4859312#4859312
+    """
+    inf_as_na = get_option(key)
+    globals()["_isna"] = partial(_isna, inf_as_na=inf_as_na)
+    if inf_as_na:
+        globals()["nan_checker"] = lambda x: ~np.isfinite(x)
+        globals()["INF_AS_NA"] = True
+    else:
+        globals()["nan_checker"] = np.isnan
+        globals()["INF_AS_NA"] = False
+
+
+def _isna_array(values: ArrayLike, inf_as_na: bool = False):
+    """
+    Return an array indicating which values of the input array are NaN / NA.
+
+    Parameters
+    ----------
+    obj: ndarray or ExtensionArray
+        The input array whose elements are to be checked.
+    inf_as_na: bool
+        Whether or not to treat infinite values as NA.
+
+    Returns
+    -------
+    array-like
+        Array of boolean values denoting the NA status of each element.
+    """
+    dtype = values.dtype
+
+    if not isinstance(values, np.ndarray):
+        # i.e. ExtensionArray
+        if inf_as_na and isinstance(dtype, CategoricalDtype):
+            result = libmissing.isnaobj(values.to_numpy(), inf_as_na=inf_as_na)
+        else:
+            # error: Incompatible types in assignment (expression has type
+            # "Union[ndarray[Any, Any], ExtensionArraySupportsAnyAll]", variable has
+            # type "ndarray[Any, dtype[bool_]]")
+            result = values.isna()  # type: ignore[assignment]
+    elif isinstance(values, np.rec.recarray):
+        # GH 48526
+        result = _isna_recarray_dtype(values, inf_as_na=inf_as_na)
+    elif is_string_or_object_np_dtype(values.dtype):
+        result = _isna_string_dtype(values, inf_as_na=inf_as_na)
+    elif dtype.kind in "mM":
+        # this is the NaT pattern
+        result = values.view("i8") == iNaT
+    else:
+        if inf_as_na:
+            result = ~np.isfinite(values)
+        else:
+            result = np.isnan(values)
+
+    return result
+
+
+def _isna_string_dtype(values: np.ndarray, inf_as_na: bool) -> npt.NDArray[np.bool_]:
+    # Working around NumPy ticket 1542
+    dtype = values.dtype
+
+    if dtype.kind in ("S", "U"):
+        result = np.zeros(values.shape, dtype=bool)
+    else:
+        if values.ndim in {1, 2}:
+            result = libmissing.isnaobj(values, inf_as_na=inf_as_na)
+        else:
+            # 0-D, reached via e.g. mask_missing
+            result = libmissing.isnaobj(values.ravel(), inf_as_na=inf_as_na)
+            result = result.reshape(values.shape)
+
+    return result
+
+
+def _has_record_inf_value(record_as_array: np.ndarray) -> np.bool_:
+    is_inf_in_record = np.zeros(len(record_as_array), dtype=bool)
+    for i, value in enumerate(record_as_array):
+        is_element_inf = False
+        try:
+            is_element_inf = np.isinf(value)
+        except TypeError:
+            is_element_inf = False
+        is_inf_in_record[i] = is_element_inf
+
+    return np.any(is_inf_in_record)
+
+
+def _isna_recarray_dtype(
+    values: np.rec.recarray, inf_as_na: bool
+) -> npt.NDArray[np.bool_]:
+    result = np.zeros(values.shape, dtype=bool)
+    for i, record in enumerate(values):
+        record_as_array = np.array(record.tolist())
+        does_record_contain_nan = isna_all(record_as_array)
+        does_record_contain_inf = False
+        if inf_as_na:
+            does_record_contain_inf = bool(_has_record_inf_value(record_as_array))
+        result[i] = np.any(
+            np.logical_or(does_record_contain_nan, does_record_contain_inf)
+        )
+
+    return result
+
+
+@overload
+def notna(obj: Scalar) -> bool:
+    ...
+
+
+@overload
+def notna(
+    obj: ArrayLike | Index | list,
+) -> npt.NDArray[np.bool_]:
+    ...
+
+
+@overload
+def notna(obj: NDFrameT) -> NDFrameT:
+    ...
+
+
+# handle unions
+@overload
+def notna(obj: NDFrameT | ArrayLike | Index | list) -> NDFrameT | npt.NDArray[np.bool_]:
+    ...
+
+
+@overload
+def notna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
+    ...
+
+
+def notna(obj: object) -> bool | npt.NDArray[np.bool_] | NDFrame:
+    """
+    Detect non-missing values for an array-like object.
+
+    This function takes a scalar or array-like object and indicates
+    whether values are valid (not missing, which is ``NaN`` in numeric
+    arrays, ``None`` or ``NaN`` in object arrays, ``NaT`` in datetimelike).
+
+    Parameters
+    ----------
+    obj : array-like or object value
+        Object to check for *not* null or *non*-missing values.
+
+    Returns
+    -------
+    bool or array-like of bool
+        For scalar input, returns a scalar boolean.
+        For array input, returns an array of boolean indicating whether each
+        corresponding element is valid.
+
+    See Also
+    --------
+    isna : Boolean inverse of pandas.notna.
+    Series.notna : Detect valid values in a Series.
+    DataFrame.notna : Detect valid values in a DataFrame.
+    Index.notna : Detect valid values in an Index.
+
+    Examples
+    --------
+    Scalar arguments (including strings) result in a scalar boolean.
+
+    >>> pd.notna('dog')
+    True
+
+    >>> pd.notna(pd.NA)
+    False
+
+    >>> pd.notna(np.nan)
+    False
+
+    ndarrays result in an ndarray of booleans.
+
+    >>> array = np.array([[1, np.nan, 3], [4, 5, np.nan]])
+    >>> array
+    array([[ 1., nan,  3.],
+           [ 4.,  5., nan]])
+    >>> pd.notna(array)
+    array([[ True, False,  True],
+           [ True,  True, False]])
+
+    For indexes, an ndarray of booleans is returned.
+
+    >>> index = pd.DatetimeIndex(["2017-07-05", "2017-07-06", None,
+    ...                          "2017-07-08"])
+    >>> index
+    DatetimeIndex(['2017-07-05', '2017-07-06', 'NaT', '2017-07-08'],
+                  dtype='datetime64[ns]', freq=None)
+    >>> pd.notna(index)
+    array([ True,  True, False,  True])
+
+    For Series and DataFrame, the same type is returned, containing booleans.
+
+    >>> df = pd.DataFrame([['ant', 'bee', 'cat'], ['dog', None, 'fly']])
+    >>> df
+         0     1    2
+    0  ant   bee  cat
+    1  dog  None  fly
+    >>> pd.notna(df)
+          0      1     2
+    0  True   True  True
+    1  True  False  True
+
+    >>> pd.notna(df[1])
+    0     True
+    1    False
+    Name: 1, dtype: bool
+    """
+    res = isna(obj)
+    if isinstance(res, bool):
+        return not res
+    return ~res
+
+
+notnull = notna
+
+
+def array_equivalent(
+    left,
+    right,
+    strict_nan: bool = False,
+    dtype_equal: bool = False,
+) -> bool:
+    """
+    True if two arrays, left and right, have equal non-NaN elements, and NaNs
+    in corresponding locations.  False otherwise. It is assumed that left and
+    right are NumPy arrays of the same dtype. The behavior of this function
+    (particularly with respect to NaNs) is not defined if the dtypes are
+    different.
+
+    Parameters
+    ----------
+    left, right : ndarrays
+    strict_nan : bool, default False
+        If True, consider NaN and None to be different.
+    dtype_equal : bool, default False
+        Whether `left` and `right` are known to have the same dtype
+        according to `is_dtype_equal`. Some methods like `BlockManager.equals`.
+        require that the dtypes match. Setting this to ``True`` can improve
+        performance, but will give different results for arrays that are
+        equal but different dtypes.
+
+    Returns
+    -------
+    b : bool
+        Returns True if the arrays are equivalent.
+
+    Examples
+    --------
+    >>> array_equivalent(
+    ...     np.array([1, 2, np.nan]),
+    ...     np.array([1, 2, np.nan]))
+    True
+    >>> array_equivalent(
+    ...     np.array([1, np.nan, 2]),
+    ...     np.array([1, 2, np.nan]))
+    False
+    """
+    left, right = np.asarray(left), np.asarray(right)
+
+    # shape compat
+    if left.shape != right.shape:
+        return False
+
+    if dtype_equal:
+        # fastpath when we require that the dtypes match (Block.equals)
+        if left.dtype.kind in "fc":
+            return _array_equivalent_float(left, right)
+        elif left.dtype.kind in "mM":
+            return _array_equivalent_datetimelike(left, right)
+        elif is_string_or_object_np_dtype(left.dtype):
+            # TODO: fastpath for pandas' StringDtype
+            return _array_equivalent_object(left, right, strict_nan)
+        else:
+            return np.array_equal(left, right)
+
+    # Slow path when we allow comparing different dtypes.
+    # Object arrays can contain None, NaN and NaT.
+    # string dtypes must be come to this path for NumPy 1.7.1 compat
+    if left.dtype.kind in "OSU" or right.dtype.kind in "OSU":
+        # Note: `in "OSU"` is non-trivially faster than `in ["O", "S", "U"]`
+        #  or `in ("O", "S", "U")`
+        return _array_equivalent_object(left, right, strict_nan)
+
+    # NaNs can occur in float and complex arrays.
+    if left.dtype.kind in "fc":
+        if not (left.size and right.size):
+            return True
+        return ((left == right) | (isna(left) & isna(right))).all()
+
+    elif left.dtype.kind in "mM" or right.dtype.kind in "mM":
+        # datetime64, timedelta64, Period
+        if left.dtype != right.dtype:
+            return False
+
+        left = left.view("i8")
+        right = right.view("i8")
+
+    # if we have structured dtypes, compare first
+    if (
+        left.dtype.type is np.void or right.dtype.type is np.void
+    ) and left.dtype != right.dtype:
+        return False
+
+    return np.array_equal(left, right)
+
+
+def _array_equivalent_float(left: np.ndarray, right: np.ndarray) -> bool:
+    return bool(((left == right) | (np.isnan(left) & np.isnan(right))).all())
+
+
+def _array_equivalent_datetimelike(left: np.ndarray, right: np.ndarray):
+    return np.array_equal(left.view("i8"), right.view("i8"))
+
+
+def _array_equivalent_object(left: np.ndarray, right: np.ndarray, strict_nan: bool):
+    left = ensure_object(left)
+    right = ensure_object(right)
+
+    mask: npt.NDArray[np.bool_] | None = None
+    if strict_nan:
+        mask = isna(left) & isna(right)
+        if not mask.any():
+            mask = None
+
+    try:
+        if mask is None:
+            return lib.array_equivalent_object(left, right)
+        if not lib.array_equivalent_object(left[~mask], right[~mask]):
+            return False
+        left_remaining = left[mask]
+        right_remaining = right[mask]
+    except ValueError:
+        # can raise a ValueError if left and right cannot be
+        # compared (e.g. nested arrays)
+        left_remaining = left
+        right_remaining = right
+
+    for left_value, right_value in zip(left_remaining, right_remaining):
+        if left_value is NaT and right_value is not NaT:
+            return False
+
+        elif left_value is libmissing.NA and right_value is not libmissing.NA:
+            return False
+
+        elif isinstance(left_value, float) and np.isnan(left_value):
+            if not isinstance(right_value, float) or not np.isnan(right_value):
+                return False
+        else:
+            with warnings.catch_warnings():
+                # suppress numpy's "elementwise comparison failed"
+                warnings.simplefilter("ignore", DeprecationWarning)
+                try:
+                    if np.any(np.asarray(left_value != right_value)):
+                        return False
+                except TypeError as err:
+                    if "boolean value of NA is ambiguous" in str(err):
+                        return False
+                    raise
+                except ValueError:
+                    # numpy can raise a ValueError if left and right cannot be
+                    # compared (e.g. nested arrays)
+                    return False
+    return True
+
+
+def array_equals(left: ArrayLike, right: ArrayLike) -> bool:
+    """
+    ExtensionArray-compatible implementation of array_equivalent.
+    """
+    if left.dtype != right.dtype:
+        return False
+    elif isinstance(left, ABCExtensionArray):
+        return left.equals(right)
+    else:
+        return array_equivalent(left, right, dtype_equal=True)
+
+
+def infer_fill_value(val):
+    """
+    infer the fill value for the nan/NaT from the provided
+    scalar/ndarray/list-like if we are a NaT, return the correct dtyped
+    element to provide proper block construction
+    """
+    if not is_list_like(val):
+        val = [val]
+    val = np.asarray(val)
+    if val.dtype.kind in "mM":
+        return np.array("NaT", dtype=val.dtype)
+    elif val.dtype == object:
+        dtype = lib.infer_dtype(ensure_object(val), skipna=False)
+        if dtype in ["datetime", "datetime64"]:
+            return np.array("NaT", dtype=DT64NS_DTYPE)
+        elif dtype in ["timedelta", "timedelta64"]:
+            return np.array("NaT", dtype=TD64NS_DTYPE)
+        return np.array(np.nan, dtype=object)
+    elif val.dtype.kind == "U":
+        return np.array(np.nan, dtype=val.dtype)
+    return np.nan
+
+
+def construct_1d_array_from_inferred_fill_value(
+    value: object, length: int
+) -> ArrayLike:
+    # Find our empty_value dtype by constructing an array
+    #  from our value and doing a .take on it
+    from pandas.core.algorithms import take_nd
+    from pandas.core.construction import sanitize_array
+    from pandas.core.indexes.base import Index
+
+    arr = sanitize_array(value, Index(range(1)), copy=False)
+    taker = -1 * np.ones(length, dtype=np.intp)
+    return take_nd(arr, taker)
+
+
+def maybe_fill(arr: np.ndarray) -> np.ndarray:
+    """
+    Fill numpy.ndarray with NaN, unless we have a integer or boolean dtype.
+    """
+    if arr.dtype.kind not in "iub":
+        arr.fill(np.nan)
+    return arr
+
+
+def na_value_for_dtype(dtype: DtypeObj, compat: bool = True):
+    """
+    Return a dtype compat na value
+
+    Parameters
+    ----------
+    dtype : string / dtype
+    compat : bool, default True
+
+    Returns
+    -------
+    np.dtype or a pandas dtype
+
+    Examples
+    --------
+    >>> na_value_for_dtype(np.dtype('int64'))
+    0
+    >>> na_value_for_dtype(np.dtype('int64'), compat=False)
+    nan
+    >>> na_value_for_dtype(np.dtype('float64'))
+    nan
+    >>> na_value_for_dtype(np.dtype('bool'))
+    False
+    >>> na_value_for_dtype(np.dtype('datetime64[ns]'))
+    numpy.datetime64('NaT')
+    """
+
+    if isinstance(dtype, ExtensionDtype):
+        return dtype.na_value
+    elif dtype.kind in "mM":
+        unit = np.datetime_data(dtype)[0]
+        return dtype.type("NaT", unit)
+    elif dtype.kind == "f":
+        return np.nan
+    elif dtype.kind in "iu":
+        if compat:
+            return 0
+        return np.nan
+    elif dtype.kind == "b":
+        if compat:
+            return False
+        return np.nan
+    return np.nan
+
+
+def remove_na_arraylike(arr: Series | Index | np.ndarray):
+    """
+    Return array-like containing only true/non-NaN values, possibly empty.
+    """
+    if isinstance(arr.dtype, ExtensionDtype):
+        return arr[notna(arr)]
+    else:
+        return arr[notna(np.asarray(arr))]
+
+
+def is_valid_na_for_dtype(obj, dtype: DtypeObj) -> bool:
+    """
+    isna check that excludes incompatible dtypes
+
+    Parameters
+    ----------
+    obj : object
+    dtype : np.datetime64, np.timedelta64, DatetimeTZDtype, or PeriodDtype
+
+    Returns
+    -------
+    bool
+    """
+    if not lib.is_scalar(obj) or not isna(obj):
+        return False
+    elif dtype.kind == "M":
+        if isinstance(dtype, np.dtype):
+            # i.e. not tzaware
+            return not isinstance(obj, (np.timedelta64, Decimal))
+        # we have to rule out tznaive dt64("NaT")
+        return not isinstance(obj, (np.timedelta64, np.datetime64, Decimal))
+    elif dtype.kind == "m":
+        return not isinstance(obj, (np.datetime64, Decimal))
+    elif dtype.kind in "iufc":
+        # Numeric
+        return obj is not NaT and not isinstance(obj, (np.datetime64, np.timedelta64))
+    elif dtype.kind == "b":
+        # We allow pd.NA, None, np.nan in BooleanArray (same as IntervalDtype)
+        return lib.is_float(obj) or obj is None or obj is libmissing.NA
+
+    elif dtype == _dtype_str:
+        # numpy string dtypes to avoid float np.nan
+        return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal, float))
+
+    elif dtype == _dtype_object:
+        # This is needed for Categorical, but is kind of weird
+        return True
+
+    elif isinstance(dtype, PeriodDtype):
+        return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal))
+
+    elif isinstance(dtype, IntervalDtype):
+        return lib.is_float(obj) or obj is None or obj is libmissing.NA
+
+    elif isinstance(dtype, CategoricalDtype):
+        return is_valid_na_for_dtype(obj, dtype.categories.dtype)
+
+    # fallback, default to allowing NaN, None, NA, NaT
+    return not isinstance(obj, (np.datetime64, np.timedelta64, Decimal))
+
+
+def isna_all(arr: ArrayLike) -> bool:
+    """
+    Optimized equivalent to isna(arr).all()
+    """
+    total_len = len(arr)
+
+    # Usually it's enough to check but a small fraction of values to see if
+    #  a block is NOT null, chunks should help in such cases.
+    #  parameters 1000 and 40 were chosen arbitrarily
+    chunk_len = max(total_len // 40, 1000)
+
+    dtype = arr.dtype
+    if lib.is_np_dtype(dtype, "f"):
+        checker = nan_checker
+
+    elif (lib.is_np_dtype(dtype, "mM")) or isinstance(
+        dtype, (DatetimeTZDtype, PeriodDtype)
+    ):
+        # error: Incompatible types in assignment (expression has type
+        # "Callable[[Any], Any]", variable has type "ufunc")
+        checker = lambda x: np.asarray(x.view("i8")) == iNaT  # type: ignore[assignment]
+
+    else:
+        # error: Incompatible types in assignment (expression has type "Callable[[Any],
+        # Any]", variable has type "ufunc")
+        checker = lambda x: _isna_array(  # type: ignore[assignment]
+            x, inf_as_na=INF_AS_NA
+        )
+
+    return all(
+        checker(arr[i : i + chunk_len]).all() for i in range(0, total_len, chunk_len)
+    )
diff --git a/shared/package-files/04/04ff671937eb31692f90d7961d70a5f5b780591beaf8442d9cda3e0eb09a08a6/_functions.h b/shared/package-files/04/04ff671937eb31692f90d7961d70a5f5b780591beaf8442d9cda3e0eb09a08a6/_functions.h
new file mode 100644
index 0000000000000000000000000000000000000000..5bf1ce2dcb28537a0f37939c686450292a92c930
--- /dev/null
+++ b/shared/package-files/04/04ff671937eb31692f90d7961d70a5f5b780591beaf8442d9cda3e0eb09a08a6/_functions.h
@@ -0,0 +1,26 @@
+#pragma once
+
+#include 
+#include 
+#include 
+#include 
+
+namespace torch {
+namespace nn {
+namespace functions {
+
+class CrossMapLRN2d : public torch::autograd::Function {
+ public:
+  static torch::autograd::Variable forward(
+      torch::autograd::AutogradContext* ctx,
+      const torch::autograd::Variable& input,
+      const CrossMapLRN2dOptions& options);
+
+  static torch::autograd::variable_list backward(
+      torch::autograd::AutogradContext* ctx,
+      torch::autograd::variable_list grad_output);
+};
+
+} // namespace functions
+} // namespace nn
+} // namespace torch
diff --git a/shared/package-files/04/04ffad7031d92d1915c52d15c8e64360be35768481c15d213aa96a15e5539056/arrayobj.py b/shared/package-files/04/04ffad7031d92d1915c52d15c8e64360be35768481c15d213aa96a15e5539056/arrayobj.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d8721f10a29b2d50c013122059b6f0ec2c89951
--- /dev/null
+++ b/shared/package-files/04/04ffad7031d92d1915c52d15c8e64360be35768481c15d213aa96a15e5539056/arrayobj.py
@@ -0,0 +1,7583 @@
+"""
+Implementation of operations on Array objects and objects supporting
+the buffer protocol.
+"""
+
+import functools
+import math
+import operator
+import textwrap
+
+from llvmlite import ir
+from llvmlite.ir import Constant
+
+import numpy as np
+
+from numba import pndindex, literal_unroll
+from numba.core import types, typing, errors, cgutils, extending
+from numba.np.numpy_support import (as_dtype, from_dtype, carray, farray,
+                                    is_contiguous, is_fortran,
+                                    check_is_integer, type_is_scalar,
+                                    lt_complex, lt_floats)
+from numba.np.numpy_support import type_can_asarray, is_nonelike, numpy_version
+from numba.core.imputils import (lower_builtin, lower_getattr,
+                                 lower_getattr_generic,
+                                 lower_setattr_generic,
+                                 lower_cast, lower_constant,
+                                 iternext_impl, impl_ret_borrowed,
+                                 impl_ret_new_ref, impl_ret_untracked,
+                                 RefType)
+from numba.core.typing import signature
+from numba.core.types import StringLiteral
+from numba.core.extending import (register_jitable, overload, overload_method,
+                                  intrinsic, overload_attribute)
+from numba.misc import quicksort, mergesort
+from numba.cpython import slicing
+from numba.cpython.charseq import _make_constant_bytes, bytes_type
+from numba.cpython.unsafe.tuple import tuple_setitem, build_full_slice_tuple
+from numba.core.extending import overload_classmethod
+from numba.core.typing.npydecl import (parse_dtype as ty_parse_dtype,
+                                       parse_shape as ty_parse_shape,
+                                       _parse_nested_sequence,
+                                       _sequence_of_arrays,
+                                       _choose_concatenation_layout)
+from numba.np import types as npy_types
+
+
+def set_range_metadata(builder, load, lower_bound, upper_bound):
+    """
+    Set the "range" metadata on a load instruction.
+    Note the interval is in the form [lower_bound, upper_bound).
+    """
+    range_operands = [Constant(load.type, lower_bound),
+                      Constant(load.type, upper_bound)]
+    md = builder.module.add_metadata(range_operands)
+    load.set_metadata("range", md)
+
+
+def mark_positive(builder, load):
+    """
+    Mark the result of a load instruction as positive (or zero).
+    """
+    upper_bound = (1 << (load.type.width - 1)) - 1
+    set_range_metadata(builder, load, 0, upper_bound)
+
+
+def make_array(array_type):
+    """
+    Return the Structure representation of the given *array_type*
+    (an instance of types.ArrayCompatible).
+
+    Note this does not call __array_wrap__ in case a new array structure
+    is being created (rather than populated).
+    """
+    real_array_type = array_type.as_array
+    base = cgutils.create_struct_proxy(real_array_type)
+    ndim = real_array_type.ndim
+
+    class ArrayStruct(base):
+
+        def _make_refs(self, ref):
+            sig = signature(real_array_type, array_type)
+            try:
+                array_impl = self._context.get_function('__array__', sig)
+            except NotImplementedError:
+                return super(ArrayStruct, self)._make_refs(ref)
+
+            # Return a wrapped structure and its unwrapped reference
+            datamodel = self._context.data_model_manager[array_type]
+            be_type = self._get_be_type(datamodel)
+            if ref is None:
+                outer_ref = cgutils.alloca_once(self._builder, be_type,
+                                                zfill=True)
+            else:
+                outer_ref = ref
+            # NOTE: __array__ is called with a pointer and expects a pointer
+            # in return!
+            ref = array_impl(self._builder, (outer_ref,))
+            return outer_ref, ref
+
+        @property
+        def shape(self):
+            """
+            Override .shape to inform LLVM that its elements are all positive.
+            """
+            builder = self._builder
+            if ndim == 0:
+                return base.__getattr__(self, "shape")
+
+            # Unfortunately, we can't use llvm.assume as its presence can
+            # seriously pessimize performance,
+            # *and* the range metadata currently isn't improving anything here,
+            # see https://llvm.org/bugs/show_bug.cgi?id=23848 !
+            ptr = self._get_ptr_by_name("shape")
+            dims = []
+            for i in range(ndim):
+                dimptr = cgutils.gep_inbounds(builder, ptr, 0, i)
+                load = builder.load(dimptr)
+                dims.append(load)
+                mark_positive(builder, load)
+
+            return cgutils.pack_array(builder, dims)
+
+    return ArrayStruct
+
+
+def get_itemsize(context, array_type):
+    """
+    Return the item size for the given array or buffer type.
+    """
+    llty = context.get_data_type(array_type.dtype)
+    return context.get_abi_sizeof(llty)
+
+
+def load_item(context, builder, arrayty, ptr):
+    """
+    Load the item at the given array pointer.
+    """
+    align = None if arrayty.aligned else 1
+    return context.unpack_value(builder, arrayty.dtype, ptr,
+                                align=align)
+
+
+def store_item(context, builder, arrayty, val, ptr):
+    """
+    Store the item at the given array pointer.
+    """
+    align = None if arrayty.aligned else 1
+    return context.pack_value(builder, arrayty.dtype, val, ptr, align=align)
+
+
+def fix_integer_index(context, builder, idxty, idx, size):
+    """
+    Fix the integer index' type and value for the given dimension size.
+    """
+    if idxty.signed:
+        ind = context.cast(builder, idx, idxty, types.intp)
+        ind = slicing.fix_index(builder, ind, size)
+    else:
+        ind = context.cast(builder, idx, idxty, types.uintp)
+    return ind
+
+
+def normalize_index(context, builder, idxty, idx):
+    """
+    Normalize the index type and value.  0-d arrays are converted to scalars.
+    """
+    if isinstance(idxty, types.Array) and idxty.ndim == 0:
+        assert isinstance(idxty.dtype, types.Integer)
+        idxary = make_array(idxty)(context, builder, idx)
+        idxval = load_item(context, builder, idxty, idxary.data)
+        return idxty.dtype, idxval
+    else:
+        return idxty, idx
+
+
+def normalize_indices(context, builder, index_types, indices):
+    """
+    Same as normalize_index(), but operating on sequences of
+    index types and values.
+    """
+    if len(indices):
+        index_types, indices = zip(*[normalize_index(context, builder, idxty,
+                                                     idx)
+                                     for idxty, idx in zip(index_types, indices)
+                                     ])
+    return index_types, indices
+
+
+def populate_array(array, data, shape, strides, itemsize, meminfo,
+                   parent=None):
+    """
+    Helper function for populating array structures.
+    This avoids forgetting to set fields.
+
+    *shape* and *strides* can be Python tuples or LLVM arrays.
+    """
+    context = array._context
+    builder = array._builder
+    datamodel = array._datamodel
+    # doesn't matter what this array type instance is, it's just to get the
+    # fields for the datamodel of the standard array type in this context
+    standard_array = types.Array(types.float64, 1, 'C')
+    standard_array_type_datamodel = context.data_model_manager[standard_array]
+    required_fields = set(standard_array_type_datamodel._fields)
+    datamodel_fields = set(datamodel._fields)
+    # Make sure that the presented array object has a data model that is close
+    # enough to an array for this function to proceed.
+    if (required_fields & datamodel_fields) != required_fields:
+        missing = required_fields - datamodel_fields
+        msg = (f"The datamodel for type {array._fe_type} is missing "
+               f"field{'s' if len(missing) > 1 else ''} {missing}.")
+        raise ValueError(msg)
+
+    if meminfo is None:
+        meminfo = Constant(context.get_value_type(
+            datamodel.get_type('meminfo')), None)
+
+    intp_t = context.get_value_type(types.intp)
+    if isinstance(shape, (tuple, list)):
+        shape = cgutils.pack_array(builder, shape, intp_t)
+    if isinstance(strides, (tuple, list)):
+        strides = cgutils.pack_array(builder, strides, intp_t)
+    if isinstance(itemsize, int):
+        itemsize = intp_t(itemsize)
+
+    attrs = dict(shape=shape,
+                 strides=strides,
+                 data=data,
+                 itemsize=itemsize,
+                 meminfo=meminfo,)
+
+    # Set `parent` attribute
+    if parent is None:
+        attrs['parent'] = Constant(context.get_value_type(
+            datamodel.get_type('parent')), None)
+    else:
+        attrs['parent'] = parent
+    # Calc num of items from shape
+    nitems = context.get_constant(types.intp, 1)
+    unpacked_shape = cgutils.unpack_tuple(builder, shape, shape.type.count)
+    # (note empty shape => 0d array therefore nitems = 1)
+    for axlen in unpacked_shape:
+        nitems = builder.mul(nitems, axlen, flags=['nsw'])
+    attrs['nitems'] = nitems
+
+    # Make sure that we have all the fields
+    got_fields = set(attrs.keys())
+    if got_fields != required_fields:
+        raise ValueError("missing {0}".format(required_fields - got_fields))
+
+    # Set field value
+    for k, v in attrs.items():
+        setattr(array, k, v)
+
+    return array
+
+
+def update_array_info(aryty, array):
+    """
+    Update some auxiliary information in *array* after some of its fields
+    were changed.  `itemsize` and `nitems` are updated.
+    """
+    context = array._context
+    builder = array._builder
+
+    # Calc num of items from shape
+    nitems = context.get_constant(types.intp, 1)
+    unpacked_shape = cgutils.unpack_tuple(builder, array.shape, aryty.ndim)
+    for axlen in unpacked_shape:
+        nitems = builder.mul(nitems, axlen, flags=['nsw'])
+    array.nitems = nitems
+
+    array.itemsize = context.get_constant(types.intp,
+                                          get_itemsize(context, aryty))
+
+
+def normalize_axis(func_name, arg_name, ndim, axis):
+    """Constrain axis values to valid positive values."""
+    raise NotImplementedError()
+
+
+@overload(normalize_axis)
+def normalize_axis_overloads(func_name, arg_name, ndim, axis):
+    if not isinstance(func_name, StringLiteral):
+        raise errors.TypingError("func_name must be a str literal.")
+    if not isinstance(arg_name, StringLiteral):
+        raise errors.TypingError("arg_name must be a str literal.")
+
+    msg = (
+        f"{func_name.literal_value}: Argument {arg_name.literal_value} "
+        "out of bounds for dimensions of the array"
+    )
+
+    def impl(func_name, arg_name, ndim, axis):
+        if axis < 0:
+            axis += ndim
+        if axis < 0 or axis >= ndim:
+            raise ValueError(msg)
+
+        return axis
+
+    return impl
+
+
+def normalize_axis_tuple(func_name, arg_name, ndim, axis):
+    """Normalizes an axis argument into a tuple of non-negative integer axes."""
+    raise NotImplementedError()
+
+
+@overload(normalize_axis_tuple)
+def normalize_axis_tuple_overloads(func_name, arg_name, ndim, axis):
+    if not isinstance(func_name, StringLiteral):
+        raise errors.TypingError("func_name must be a str literal.")
+    if not isinstance(arg_name, StringLiteral):
+        raise errors.TypingError("arg_name must be a str literal.")
+
+    invalid_axis_msg = (
+        f"{func_name.literal_value}: Argument {arg_name.literal_value} "
+        "out of bounds for dimensions of the array"
+    )
+
+    repeated_axis_msg = (
+        f"{func_name.literal_value}: repeated axis in "
+        f"{arg_name.literal_value} argument"
+    )
+
+    if isinstance(axis, types.Integer):
+
+        def impl(func_name, arg_name, ndim, axis):
+            if axis < 0:
+                axis += ndim
+            if axis < 0 or axis >= ndim:
+                raise ValueError(invalid_axis_msg)
+
+            return (axis,)
+
+    else:
+        axis_len = len(axis)
+        norm_axis_init = (0,) * axis_len
+
+        def impl(func_name, arg_name, ndim, axis):
+            norm_axis = norm_axis_init
+            for i, ax in enumerate(axis):
+                if ax < 0:
+                    ax += ndim
+                if ax < 0 or ax >= ndim:
+                    raise ValueError(invalid_axis_msg)
+                norm_axis = tuple_setitem(norm_axis, i, ax)
+
+            if len(set(norm_axis)) != axis_len:
+                raise ValueError(repeated_axis_msg)
+
+            return norm_axis
+
+    return impl
+
+
+@lower_builtin('getiter', types.Buffer)
+def getiter_array(context, builder, sig, args):
+    [arrayty] = sig.args
+    [array] = args
+
+    iterobj = context.make_helper(builder, sig.return_type)
+
+    zero = context.get_constant(types.intp, 0)
+    indexptr = cgutils.alloca_once_value(builder, zero)
+
+    iterobj.index = indexptr
+    iterobj.array = array
+
+    # Incref array
+    if context.enable_nrt:
+        context.nrt.incref(builder, arrayty, array)
+
+    res = iterobj._getvalue()
+
+    # Note: a decref on the iterator will dereference all internal MemInfo*
+    out = impl_ret_new_ref(context, builder, sig.return_type, res)
+    return out
+
+
+def _getitem_array_single_int(context, builder, return_type, aryty, ary, idx):
+    """ Evaluate `ary[idx]`, where idx is a single int. """
+    # optimized form of _getitem_array_generic
+    shapes = cgutils.unpack_tuple(builder, ary.shape, count=aryty.ndim)
+    strides = cgutils.unpack_tuple(builder, ary.strides, count=aryty.ndim)
+    offset = builder.mul(strides[0], idx)
+    dataptr = cgutils.pointer_add(builder, ary.data, offset)
+    view_shapes = shapes[1:]
+    view_strides = strides[1:]
+
+    if isinstance(return_type, types.Buffer):
+        # Build array view
+        retary = make_view(context, builder, aryty, ary, return_type,
+                           dataptr, view_shapes, view_strides)
+        return retary._getvalue()
+    else:
+        # Load scalar from 0-d result
+        assert not view_shapes
+        return load_item(context, builder, aryty, dataptr)
+
+
+@lower_builtin('iternext', types.ArrayIterator)
+@iternext_impl(RefType.BORROWED)
+def iternext_array(context, builder, sig, args, result):
+    [iterty] = sig.args
+    [iter] = args
+    arrayty = iterty.array_type
+
+    iterobj = context.make_helper(builder, iterty, value=iter)
+    ary = make_array(arrayty)(context, builder, value=iterobj.array)
+
+    nitems, = cgutils.unpack_tuple(builder, ary.shape, count=1)
+
+    index = builder.load(iterobj.index)
+    is_valid = builder.icmp_signed('<', index, nitems)
+    result.set_valid(is_valid)
+
+    with builder.if_then(is_valid):
+        value = _getitem_array_single_int(
+            context, builder, iterty.yield_type, arrayty, ary, index
+        )
+        result.yield_(value)
+        nindex = cgutils.increment_index(builder, index)
+        builder.store(nindex, iterobj.index)
+
+
+# ------------------------------------------------------------------------------
+# Basic indexing (with integers and slices only)
+
+def basic_indexing(context, builder, aryty, ary, index_types, indices,
+                   boundscheck=None):
+    """
+    Perform basic indexing on the given array.
+    A (data pointer, shapes, strides) tuple is returned describing
+    the corresponding view.
+    """
+    zero = context.get_constant(types.intp, 0)
+    one = context.get_constant(types.intp, 1)
+
+    shapes = cgutils.unpack_tuple(builder, ary.shape, aryty.ndim)
+    strides = cgutils.unpack_tuple(builder, ary.strides, aryty.ndim)
+
+    output_indices = []
+    output_shapes = []
+    output_strides = []
+
+    num_newaxes = len([idx for idx in index_types if is_nonelike(idx)])
+    ax = 0
+    for indexval, idxty in zip(indices, index_types):
+        if idxty is types.ellipsis:
+            # Fill up missing dimensions at the middle
+            n_missing = aryty.ndim - len(indices) + 1 + num_newaxes
+            for i in range(n_missing):
+                output_indices.append(zero)
+                output_shapes.append(shapes[ax])
+                output_strides.append(strides[ax])
+                ax += 1
+            continue
+        # Regular index value
+        if isinstance(idxty, types.SliceType):
+            slice = context.make_helper(builder, idxty, value=indexval)
+            slicing.guard_invalid_slice(context, builder, idxty, slice)
+            slicing.fix_slice(builder, slice, shapes[ax])
+            output_indices.append(slice.start)
+            sh = slicing.get_slice_length(builder, slice)
+            st = slicing.fix_stride(builder, slice, strides[ax])
+            output_shapes.append(sh)
+            output_strides.append(st)
+        elif isinstance(idxty, types.Integer):
+            ind = fix_integer_index(context, builder, idxty, indexval,
+                                    shapes[ax])
+            if boundscheck:
+                cgutils.do_boundscheck(context, builder, ind, shapes[ax], ax)
+            output_indices.append(ind)
+        elif is_nonelike(idxty):
+            output_shapes.append(one)
+            output_strides.append(zero)
+            ax -= 1
+        else:
+            raise NotImplementedError("unexpected index type: %s" % (idxty,))
+        ax += 1
+
+    # Fill up missing dimensions at the end
+    assert ax <= aryty.ndim
+    while ax < aryty.ndim:
+        output_shapes.append(shapes[ax])
+        output_strides.append(strides[ax])
+        ax += 1
+
+    # No need to check wraparound, as negative indices were already
+    # fixed in the loop above.
+    dataptr = cgutils.get_item_pointer(context, builder, aryty, ary,
+                                       output_indices,
+                                       wraparound=False, boundscheck=False)
+    return (dataptr, output_shapes, output_strides)
+
+
+def make_view(context, builder, aryty, ary, return_type,
+              data, shapes, strides):
+    """
+    Build a view over the given array with the given parameters.
+    """
+    retary = make_array(return_type)(context, builder)
+    populate_array(retary,
+                   data=data,
+                   shape=shapes,
+                   strides=strides,
+                   itemsize=ary.itemsize,
+                   meminfo=ary.meminfo,
+                   parent=ary.parent)
+    return retary
+
+
+def _getitem_array_generic(context, builder, return_type, aryty, ary,
+                           index_types, indices):
+    """
+    Return the result of indexing *ary* with the given *indices*,
+    returning either a scalar or a view.
+    """
+    dataptr, view_shapes, view_strides = \
+        basic_indexing(context, builder, aryty, ary, index_types, indices,
+                       boundscheck=context.enable_boundscheck)
+
+    if isinstance(return_type, types.Buffer):
+        # Build array view
+        retary = make_view(context, builder, aryty, ary, return_type,
+                           dataptr, view_shapes, view_strides)
+        return retary._getvalue()
+    else:
+        # Load scalar from 0-d result
+        assert not view_shapes
+        return load_item(context, builder, aryty, dataptr)
+
+
+@lower_builtin(operator.getitem, types.Buffer, types.Integer)
+@lower_builtin(operator.getitem, types.Buffer, types.SliceType)
+@lower_builtin(operator.getitem, types.Buffer, types.NoneType)
+def getitem_arraynd_intp(context, builder, sig, args):
+    """
+    Basic indexing with an integer, slice, or None.
+    """
+    aryty, idxty = sig.args
+    ary, idx = args
+
+    # for 0d arrays, a[None] is valid
+    if not isinstance(idxty, types.NoneType):
+        assert aryty.ndim >= 1
+    ary = make_array(aryty)(context, builder, ary)
+
+    res = _getitem_array_generic(context, builder, sig.return_type,
+                                 aryty, ary, (idxty,), (idx,))
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.getitem, types.Buffer, types.BaseTuple)
+def getitem_array_tuple(context, builder, sig, args):
+    """
+    Basic or advanced indexing with a tuple.
+    """
+    aryty, tupty = sig.args
+    ary, tup = args
+    ary = make_array(aryty)(context, builder, ary)
+
+    index_types = tupty.types
+    indices = cgutils.unpack_tuple(builder, tup, count=len(tupty))
+
+    index_types, indices = normalize_indices(context, builder,
+                                             index_types, indices)
+
+    if any(isinstance(ty, types.Array) for ty in index_types):
+        # Advanced indexing
+        return fancy_getitem(context, builder, sig, args,
+                             aryty, ary, index_types, indices)
+
+    res = _getitem_array_generic(context, builder, sig.return_type,
+                                 aryty, ary, index_types, indices)
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.setitem, types.Buffer, types.Any, types.Any)
+def setitem_array(context, builder, sig, args):
+    """
+    array[a] = scalar_or_array
+    array[a,..,b] = scalar_or_array
+    """
+    aryty, idxty, valty = sig.args
+    ary, idx, val = args
+
+    if isinstance(idxty, types.BaseTuple):
+        index_types = idxty.types
+        indices = cgutils.unpack_tuple(builder, idx, count=len(idxty))
+    else:
+        index_types = (idxty,)
+        indices = (idx,)
+
+    ary = make_array(aryty)(context, builder, ary)
+
+    # First try basic indexing to see if a single array location is denoted.
+    index_types, indices = normalize_indices(context, builder,
+                                             index_types, indices)
+    try:
+        dataptr, shapes, strides = \
+            basic_indexing(context, builder, aryty, ary, index_types, indices,
+                           boundscheck=context.enable_boundscheck)
+    except NotImplementedError:
+        use_fancy_indexing = True
+    else:
+        use_fancy_indexing = bool(shapes)
+
+    if use_fancy_indexing:
+        # Index describes a non-trivial view => use generic slice assignment
+        # (NOTE: this also handles scalar broadcasting)
+        return fancy_setslice(context, builder, sig, args,
+                              index_types, indices)
+
+    # Store source value the given location
+    val = context.cast(builder, val, valty, aryty.dtype)
+    store_item(context, builder, aryty, val, dataptr)
+
+
+@lower_builtin(len, types.Buffer)
+def array_len(context, builder, sig, args):
+    (aryty,) = sig.args
+    (ary,) = args
+    arystty = make_array(aryty)
+    ary = arystty(context, builder, ary)
+    shapeary = ary.shape
+    res = builder.extract_value(shapeary, 0)
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_builtin("array.item", types.Array)
+def array_item(context, builder, sig, args):
+    aryty, = sig.args
+    ary, = args
+    ary = make_array(aryty)(context, builder, ary)
+
+    nitems = ary.nitems
+    with builder.if_then(builder.icmp_signed('!=', nitems, nitems.type(1)),
+                         likely=False):
+        msg = "item(): can only convert an array of size 1 to a Python scalar"
+        context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+    return load_item(context, builder, aryty, ary.data)
+
+
+if numpy_version < (2, 0):
+    @lower_builtin("array.itemset", types.Array, types.Any)
+    def array_itemset(context, builder, sig, args):
+        aryty, valty = sig.args
+        ary, val = args
+        assert valty == aryty.dtype
+        ary = make_array(aryty)(context, builder, ary)
+
+        nitems = ary.nitems
+        with builder.if_then(builder.icmp_signed('!=', nitems, nitems.type(1)),
+                             likely=False):
+            msg = "itemset(): can only write to an array of size 1"
+            context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+        store_item(context, builder, aryty, val, ary.data)
+        return context.get_dummy_value()
+
+
+# ------------------------------------------------------------------------------
+# Advanced / fancy indexing
+
+
+class Indexer(object):
+    """
+    Generic indexer interface, for generating indices over a fancy indexed
+    array on a single dimension.
+    """
+
+    def prepare(self):
+        """
+        Prepare the indexer by initializing any required variables, basic
+        blocks...
+        """
+        raise NotImplementedError
+
+    def get_size(self):
+        """
+        Return this dimension's size as an integer.
+        """
+        raise NotImplementedError
+
+    def get_shape(self):
+        """
+        Return this dimension's shape as a tuple.
+        """
+        raise NotImplementedError
+
+    def get_index_bounds(self):
+        """
+        Return a half-open [lower, upper) range of indices this dimension
+        is guaranteed not to step out of.
+        """
+        raise NotImplementedError
+
+    def loop_head(self):
+        """
+        Start indexation loop.  Returns a index.
+        *index* is an integer LLVM value representing the index over this
+        dimension.
+        """
+        raise NotImplementedError
+
+    def loop_tail(self):
+        """
+        Finish indexation loop.
+        """
+        raise NotImplementedError
+
+    def get_src_idx(self):
+        """
+        Return the source index for this dimension, if applicable.
+        """
+        raise NotImplementedError
+
+
+class EntireIndexer(Indexer):
+    """
+    Compute indices along an entire array dimension.
+    """
+
+    def __init__(self, context, builder, aryty, ary, dim):
+        self.context = context
+        self.builder = builder
+        self.aryty = aryty
+        self.ary = ary
+        self.dim = dim
+        self.ll_intp = self.context.get_value_type(types.intp)
+
+    def prepare(self):
+        builder = self.builder
+        self.size = builder.extract_value(self.ary.shape, self.dim)
+        self.index = cgutils.alloca_once(builder, self.ll_intp)
+        self.bb_start = builder.append_basic_block()
+        self.bb_end = builder.append_basic_block()
+
+    def get_size(self):
+        return self.size
+
+    def get_shape(self):
+        return (self.size,)
+
+    def get_index_bounds(self):
+        # [0, size)
+        return (self.ll_intp(0), self.size)
+
+    def loop_head(self):
+        builder = self.builder
+        # Initialize loop variable
+        self.builder.store(Constant(self.ll_intp, 0), self.index)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_start)
+        cur_index = builder.load(self.index)
+        with builder.if_then(builder.icmp_signed('>=', cur_index, self.size),
+                             likely=False):
+            builder.branch(self.bb_end)
+        return cur_index
+
+    def loop_tail(self):
+        builder = self.builder
+        next_index = cgutils.increment_index(builder, builder.load(self.index))
+        builder.store(next_index, self.index)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_end)
+
+    def get_src_idx(self):
+        return (self.builder.load(self.index),)
+
+
+class IntegerIndexer(Indexer):
+    """
+    Compute indices from a single integer.
+    """
+
+    def __init__(self, context, builder, idx):
+        self.context = context
+        self.builder = builder
+        self.idx = idx
+        self.ll_intp = self.context.get_value_type(types.intp)
+
+    def prepare(self):
+        pass
+
+    def get_size(self):
+        return Constant(self.ll_intp, 1)
+
+    def get_shape(self):
+        return ()
+
+    def get_index_bounds(self):
+        # [idx, idx+1)
+        return (self.idx, self.builder.add(self.idx, self.get_size()))
+
+    def loop_head(self):
+        return self.idx
+
+    def loop_tail(self):
+        pass
+
+    def get_src_idx(self):
+        return ()
+
+
+class IntegerArrayIndexer(Indexer):
+    """
+    Compute indices from an array of integer indices.
+    """
+
+    def __init__(self, context, builder, idxty,
+                 idxary, size, global_ary_idx_list):
+        self.context = context
+        self.builder = builder
+        self.global_ary_idx_list = global_ary_idx_list
+
+        self.idx_shape = cgutils.unpack_tuple(builder, idxary.shape)
+        self.size = size
+        self.ll_intp = self.context.get_value_type(types.intp)
+
+        self.idxty = idxty
+        self.idxary = idxary
+        self.src_idx = None
+
+    def prepare(self):
+        builder = self.builder
+        self.idx_size = self.ll_intp(1)
+        for _shape in self.idx_shape:
+            self.idx_size = self.builder.mul(self.idx_size, _shape)
+        self.bb_start = builder.append_basic_block()
+        self.bb_end = builder.append_basic_block()
+
+    def get_size(self):
+        return self.idx_size
+
+    def get_shape(self):
+        return ()
+
+    def get_index_bounds(self):
+        # Pessimal heuristic, as we don't want to scan for the min and max
+        return (self.ll_intp(0), self.size)
+
+    def loop_head(self):
+        builder = self.builder
+        # Initialize loop variable
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_start)
+        # Load the actual index from the array of indices, last n indices
+        # from the global array index list
+        indices = [
+            builder.load(idx) for idx in self.global_ary_idx_list[
+                len(self.global_ary_idx_list) - len(self.idx_shape):]
+        ]
+        indices = [
+            builder.srem(indices[i], self.idx_shape[i])
+            for i in range(len(self.idx_shape))
+        ]
+
+        index = _getitem_array_generic(
+            self.context, self.builder, self.idxty.dtype,
+            self.idxty, self.idxary,
+            [types.intp for _ in self.idx_shape], tuple(indices)
+        )
+        index = fix_integer_index(self.context, builder,
+                                  self.idxty.dtype, index, self.size)
+        return index
+
+    def loop_tail(self):
+        builder = self.builder
+        builder.branch(self.bb_end)
+        builder.position_at_end(self.bb_end)
+
+    def get_src_idx(self):
+        return ()
+
+
+class BooleanArrayIndexer(Indexer):
+    """
+    Compute indices from an array of boolean predicates.
+    """
+
+    def __init__(self, context, builder, idxty, idxary):
+        self.context = context
+        self.builder = builder
+        self.idxty = idxty
+        self.idxary = idxary
+        assert idxty.ndim == 1
+        self.ll_intp = self.context.get_value_type(types.intp)
+        self.zero = Constant(self.ll_intp, 0)
+
+    def prepare(self):
+        builder = self.builder
+        self.size = cgutils.unpack_tuple(builder, self.idxary.shape)[0]
+        self.idx_index = cgutils.alloca_once(builder, self.ll_intp)
+        self.src_idx = cgutils.alloca_once(builder, self.ll_intp)
+
+        self.bb_start = builder.append_basic_block()
+        self.bb_tail = builder.append_basic_block()
+        self.bb_end = builder.append_basic_block()
+
+    def get_size(self):
+        builder = self.builder
+        count = cgutils.alloca_once_value(builder, self.zero)
+        # Sum all true values
+        with cgutils.for_range(builder, self.size) as loop:
+            c = builder.load(count)
+            pred = _getitem_array_single_int(
+                self.context, builder, self.idxty.dtype,
+                self.idxty, self.idxary, loop.index
+            )
+            c = builder.add(c, builder.zext(pred, c.type))
+            builder.store(c, count)
+
+        return builder.load(count)
+
+    def get_shape(self):
+        return (self.get_size(),)
+
+    def get_index_bounds(self):
+        # Pessimal heuristic, as we don't want to scan for the
+        # first and last true items
+        return (self.ll_intp(0), self.size)
+
+    def loop_head(self):
+        builder = self.builder
+        # Initialize loop variable
+        self.builder.store(self.zero, self.idx_index)
+        self.builder.store(self.zero, self.src_idx)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_start)
+        cur_index = builder.load(self.idx_index)
+        with builder.if_then(builder.icmp_signed('>=', cur_index, self.size),
+                             likely=False):
+            builder.branch(self.bb_end)
+        # Load the predicate and branch if false
+        pred = _getitem_array_single_int(
+            self.context, builder, self.idxty.dtype, self.idxty, self.idxary,
+            cur_index
+        )
+        with builder.if_then(builder.not_(pred)):
+            builder.branch(self.bb_tail)
+        return cur_index
+
+    def loop_tail(self):
+        builder = self.builder
+        src_idx = cgutils.increment_index(
+            builder,
+            builder.load(self.src_idx)
+        )
+        builder.store(src_idx, self.src_idx)
+        builder.branch(self.bb_tail)
+        builder.position_at_end(self.bb_tail)
+        next_index = cgutils.increment_index(
+            builder,
+            builder.load(self.idx_index)
+        )
+        builder.store(next_index, self.idx_index)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_end)
+
+    def get_src_idx(self):
+        # TODO: Implement own addition src index
+        return (self.builder.load(self.src_idx),)
+
+
+class SliceIndexer(Indexer):
+    """
+    Compute indices along a slice.
+    """
+
+    def __init__(self, context, builder, aryty, ary, dim, idxty, slice):
+        self.context = context
+        self.builder = builder
+        self.aryty = aryty
+        self.ary = ary
+        self.dim = dim
+        self.idxty = idxty
+        self.slice = slice
+        self.ll_intp = self.context.get_value_type(types.intp)
+        self.zero = Constant(self.ll_intp, 0)
+
+    def prepare(self):
+        builder = self.builder
+        # Fix slice for the dimension's size
+        self.dim_size = builder.extract_value(self.ary.shape, self.dim)
+        slicing.guard_invalid_slice(self.context, builder, self.idxty,
+                                    self.slice)
+        slicing.fix_slice(builder, self.slice, self.dim_size)
+        self.is_step_negative = cgutils.is_neg_int(builder, self.slice.step)
+        # Create loop entities
+        self.index = cgutils.alloca_once(builder, self.ll_intp)
+        self.src_idx = cgutils.alloca_once(builder, self.ll_intp)
+
+        self.bb_start = builder.append_basic_block()
+        self.bb_end = builder.append_basic_block()
+
+    def get_size(self):
+        return slicing.get_slice_length(self.builder, self.slice)
+
+    def get_shape(self):
+        return (self.get_size(),)
+
+    def get_index_bounds(self):
+        lower, upper = slicing.get_slice_bounds(self.builder, self.slice)
+        return lower, upper
+
+    def loop_head(self):
+        builder = self.builder
+        # Initialize loop variable
+        self.builder.store(self.slice.start, self.index)
+        self.builder.store(self.zero, self.src_idx)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_start)
+        cur_index = builder.load(self.index)
+        is_finished = builder.select(self.is_step_negative,
+                                     builder.icmp_signed('<=', cur_index,
+                                                         self.slice.stop),
+                                     builder.icmp_signed('>=', cur_index,
+                                                         self.slice.stop))
+        with builder.if_then(is_finished, likely=False):
+            builder.branch(self.bb_end)
+        return cur_index
+
+    def loop_tail(self):
+        builder = self.builder
+        next_index = builder.add(
+            builder.load(self.index),
+            self.slice.step,
+            flags=['nsw']
+        )
+        next_src_idx = builder.add(
+            builder.load(self.src_idx),
+            self.ll_intp(1),
+            flags=['nsw']
+        )
+        builder.store(next_src_idx, self.src_idx)
+        builder.store(next_index, self.index)
+        builder.branch(self.bb_start)
+        builder.position_at_end(self.bb_end)
+
+    def get_src_idx(self):
+        # TODO: Implement own addition src index
+        return (self.builder.load(self.src_idx),)
+
+
+class SubspaceIndexer(Indexer):
+    def __init__(self, context, builder, shape_tuple, global_ary_idx_list):
+        self.context = context
+        self.builder = builder
+        self.global_ary_idx_list = global_ary_idx_list
+
+        self.shape_tuple = shape_tuple
+        self.ll_intp = self.context.get_value_type(types.intp)
+        self.size = self.ll_intp(1)
+        for _shape in self.shape_tuple:
+            self.size = self.builder.mul(self.size, _shape)
+
+    def prepare(self):
+        builder = self.builder
+        self.bb_starts = [
+            builder.append_basic_block() for _ in self.global_ary_idx_list
+        ]
+        self.bb_ends = [
+            builder.append_basic_block() for _ in self.global_ary_idx_list
+        ]
+
+    def get_size(self):
+        return self.size
+
+    def get_shape(self):
+        return self.shape_tuple
+
+    def get_index_bounds(self):
+        # Pessimal heuristic, as we don't want to scan for the min and max
+        return (self.ll_intp(0), self.size)
+
+    def loop_head(self):
+        # Subspace indices are being tracked globally within
+        # self.global_ary_idx_list and are used by the IntegerArrayIndexers
+        # to access the elements of the respective index arrays. Over here
+        # we simply loop over the global indices and yield control to the
+        # next indexer, hence returning None.
+
+        builder = self.builder
+        # Initialize loop variables
+
+        for i in range(len(self.global_ary_idx_list)):
+            builder.branch(self.bb_starts[i])
+            builder.position_at_end(self.bb_starts[i])
+            cur_index = builder.load(self.global_ary_idx_list[i])
+            with builder.if_then(
+                builder.icmp_signed('>=', cur_index, self.shape_tuple[i]),
+                likely=False
+            ):
+                self.builder.store(
+                    Constant(self.ll_intp, 0),
+                    self.global_ary_idx_list[i]
+                )
+                builder.branch(self.bb_ends[i])
+
+    def loop_tail(self):
+        builder = self.builder
+        for i in reversed(range(len(self.global_ary_idx_list))):
+            next_index = cgutils.increment_index(
+                builder,
+                builder.load(self.global_ary_idx_list[i])
+            )
+            builder.store(next_index, self.global_ary_idx_list[i])
+            builder.branch(self.bb_starts[i])
+            builder.position_at_end(self.bb_ends[i])
+
+    def get_src_idx(self):
+        return tuple(self.builder.load(idx) for idx in self.global_ary_idx_list)
+
+
+class FancyIndexer(object):
+    """
+    Perform fancy indexing on the given array.
+    """
+
+    def __init__(self, context, builder, aryty, ary, index_types,
+                 indices, subspace_shape_tuple):
+        self.context = context
+        self.builder = builder
+        self.aryty = aryty
+        self.shapes = cgutils.unpack_tuple(builder, ary.shape, aryty.ndim)
+        self.strides = cgutils.unpack_tuple(builder, ary.strides, aryty.ndim)
+        self.ll_intp = self.context.get_value_type(types.intp)
+        self.subspace_shape = subspace_shape_tuple
+        self.global_ary_idx_list = [
+            cgutils.alloca_once(builder, self.ll_intp)
+            for _ in range(len(subspace_shape_tuple))
+        ]
+        for idx in self.global_ary_idx_list:
+            self.builder.store(Constant(self.ll_intp, 0), idx)
+        indexers = []
+        num_newaxes = len([idx for idx in index_types if is_nonelike(idx)])
+        ax = 0 # keeps track of position of original axes
+        new_ax = 0 # keeps track of position for inserting new axes
+        self.newaxes = []
+
+        for indexval, idxty in zip(indices, index_types):
+            if idxty is types.ellipsis:
+                # Fill up missing dimensions at the middle
+                n_missing = aryty.ndim - len(indices) + 1 + num_newaxes
+                for i in range(n_missing):
+                    indexer = EntireIndexer(context, builder, aryty, ary, ax)
+                    indexers.append(indexer)
+                    ax += 1
+                    new_ax += 1
+                continue
+
+            # Regular index value
+            if isinstance(idxty, types.SliceType):
+                slice = context.make_helper(builder, idxty, indexval)
+                indexer = SliceIndexer(context, builder, aryty, ary, ax,
+                                       idxty, slice)
+                indexers.append(indexer)
+            elif isinstance(idxty, types.Integer):
+                ind = fix_integer_index(context, builder, idxty, indexval,
+                                        self.shapes[ax])
+                indexer = IntegerIndexer(context, builder, ind)
+                indexers.append(indexer)
+                new_ax -= 1
+            elif isinstance(idxty, types.Array):
+                idxary = make_array(idxty)(context, builder, indexval)
+                if isinstance(idxty.dtype, types.Integer):
+                    indexer = IntegerArrayIndexer(context, builder,
+                                                  idxty, idxary,
+                                                  self.shapes[ax],
+                                                  self.global_ary_idx_list)
+                elif isinstance(idxty.dtype, types.Boolean):
+                    indexer = BooleanArrayIndexer(context, builder,
+                                                  idxty, idxary)
+                else:
+                    assert 0
+                indexers.append(indexer)
+            elif is_nonelike(idxty):
+                self.newaxes.append(new_ax)
+                ax -= 1
+            else:
+                raise AssertionError("unexpected index type: %s" % (idxty,))
+            ax += 1
+            new_ax += 1
+
+        # Fill up missing dimensions at the end
+        assert ax <= aryty.ndim, (ax, aryty.ndim)
+        while ax < aryty.ndim:
+            indexer = EntireIndexer(context, builder, aryty, ary, ax)
+            indexers.append(indexer)
+            ax += 1
+
+        assert len(indexers) == aryty.ndim, (len(indexers), aryty.ndim)
+
+        self.subspace_indexer = SubspaceIndexer(
+            context, builder, subspace_shape_tuple, self.global_ary_idx_list
+        )
+
+        num_subspaces = 0
+        in_subspace = False
+        subspace_index = None
+        if any([isinstance(i, IntegerArrayIndexer) for i in indexers]):
+            for idx, i in enumerate(indexers):
+                if isinstance(i, (IntegerArrayIndexer, IntegerIndexer)):
+                    if not in_subspace:
+                        in_subspace = True
+                        num_subspaces += 1
+                    if subspace_index is None:
+                        subspace_index = idx
+                else:
+                    if in_subspace:
+                        in_subspace = False
+
+            if num_subspaces:
+                if num_subspaces > 1:
+                    subspace_index = 0
+
+        self.subspace_index = subspace_index
+        self.indexers = indexers
+
+    def prepare(self):
+        one = self.context.get_constant(types.intp, 1)
+        for idx, i in enumerate(self.indexers):
+            if idx == self.subspace_index:
+                self.subspace_indexer.prepare()
+            i.prepare()
+
+        # Compute the resulting shape
+        res_shape = []
+        for i in self.indexers:
+            res_shape.append(list(i.get_shape()))
+
+        if self.subspace_index is not None:
+            res_shape.insert(
+                self.subspace_index,
+                list(self.subspace_indexer.get_shape())
+            )
+
+        # Store the shape as a tuple, we can't do a simple
+        # tuple(res_shape) here since res_shape is a list
+        # of tuples which may be differently sized.
+        self.indexers_shape = sum(res_shape, [])
+
+        # At every position where newaxis/None is present insert
+        # one as a constant shape in the resulting list of shapes.
+        for i in self.newaxes:
+            self.indexers_shape.insert(i, one)
+
+    def get_shape(self):
+        """
+        Get the resulting data shape as Python tuple.
+        """
+        return self.indexers_shape
+
+    def get_offset_bounds(self, strides, itemsize):
+        """
+        Get a half-open [lower, upper) range of byte offsets spanned by
+        the indexer with the given strides and itemsize.  The indexer is
+        guaranteed to not go past those bounds.
+        """
+        assert len(strides) == self.aryty.ndim
+        builder = self.builder
+        is_empty = cgutils.false_bit
+        zero = self.ll_intp(0)
+        one = self.ll_intp(1)
+        lower = zero
+        upper = zero
+        for indexer, shape, stride in zip(self.indexers, self.indexers_shape,
+                                          strides):
+            is_empty = builder.or_(is_empty,
+                                   builder.icmp_unsigned('==', shape, zero))
+            # Compute [lower, upper) indices on this dimension
+            lower_index, upper_index = indexer.get_index_bounds()
+            lower_offset = builder.mul(stride, lower_index)
+            upper_offset = builder.mul(stride, builder.sub(upper_index, one))
+            # Adjust total interval
+            is_downwards = builder.icmp_signed('<', stride, zero)
+            lower = builder.add(lower,
+                                builder.select(is_downwards,
+                                               upper_offset,
+                                               lower_offset))
+            upper = builder.add(upper,
+                                builder.select(is_downwards,
+                                               lower_offset,
+                                               upper_offset))
+        # Make interval half-open
+        upper = builder.add(upper, itemsize)
+        # Adjust for empty shape
+        lower = builder.select(is_empty, zero, lower)
+        upper = builder.select(is_empty, zero, upper)
+        return lower, upper
+
+    def begin_loops(self):
+        indices = []
+        for idx, i in enumerate(self.indexers):
+            if idx == self.subspace_index:
+                self.subspace_indexer.loop_head()
+            indices.append(i.loop_head())
+        return tuple(indices)
+
+    def end_loops(self):
+        idx = len(self.indexers) - 1
+        for i in reversed(self.indexers):
+            i.loop_tail()
+            if idx == self.subspace_index:
+                self.subspace_indexer.loop_tail()
+            idx -= 1
+
+    def get_src_indices(self):
+        indices = [list(idx.get_src_idx()) for idx in self.indexers]
+        if self.subspace_index is not None:
+            indices.insert(
+                self.subspace_index,
+                list(self.subspace_indexer.get_src_idx())
+            )
+        indices = sum(indices, [])
+        for i in self.newaxes:
+            indices.insert(i, self.ll_intp(0))
+
+        return tuple(indices)
+
+
+def get_subspace_shape(context, builder, array_indices):
+    max_dims = max([ary[2].ndim for ary in array_indices])
+
+    def bdcast_idx_shapes(*args):
+        return np.broadcast_shapes(*args)
+
+    inpty = types.StarArgTuple(
+        tuple(types.UniTuple(
+            types.intp, count=ary_idx[2].ndim
+        ) for ary_idx in array_indices)
+    )
+    retty = types.UniTuple(types.intp, count=max_dims)
+    subspace_shape = context.compile_internal(
+        builder, bdcast_idx_shapes, signature(retty, inpty),
+        (cgutils.pack_struct(
+            builder, tuple([ary_idx[3].shape for ary_idx in array_indices])
+        ),)
+    )
+
+    subspace_shape = tuple(cgutils.unpack_tuple(builder, subspace_shape))
+
+    return subspace_shape
+
+
+def fancy_getitem(context, builder, sig, args,
+                  aryty, ary, index_types, indices):
+
+    shapes = cgutils.unpack_tuple(builder, ary.shape)
+    strides = cgutils.unpack_tuple(builder, ary.strides)
+    data = ary.data
+
+    array_indices = []
+    for i, idxty in enumerate(index_types):
+        idx = indices[i]
+        if isinstance(idxty, types.Array):
+            idx_make = make_array(idxty)(context, builder, idx)
+            array_indices.append((i, idx, idxty, idx_make))
+
+    subspace_shape_tuple = get_subspace_shape(context, builder, array_indices)
+
+    indexer = FancyIndexer(context, builder, aryty, ary,
+                           index_types, indices, subspace_shape_tuple)
+    indexer.prepare()
+
+    # Construct output array
+    out_ty = sig.return_type
+    out_shapes = indexer.get_shape()
+
+    out = _empty_nd_impl(context, builder, out_ty, out_shapes)
+    out_data = out.data
+    out_idx = cgutils.alloca_once_value(builder,
+                                        context.get_constant(types.intp, 0))
+
+    # Loop on source and copy to destination
+    indices = indexer.begin_loops()
+
+    # No need to check for wraparound, as the indexers all ensure
+    # a positive index is returned.
+    ptr = cgutils.get_item_pointer2(context, builder, data, shapes, strides,
+                                    aryty.layout, indices, wraparound=False,
+                                    boundscheck=context.enable_boundscheck)
+    val = load_item(context, builder, aryty, ptr)
+
+    # Since the destination is C-contiguous, no need for multi-dimensional
+    # indexing.
+    cur = builder.load(out_idx)
+    ptr = builder.gep(out_data, [cur])
+    store_item(context, builder, out_ty, val, ptr)
+    next_idx = cgutils.increment_index(builder, cur)
+    builder.store(next_idx, out_idx)
+
+    indexer.end_loops()
+
+    return impl_ret_new_ref(context, builder, out_ty, out._getvalue())
+
+
+@lower_builtin(operator.getitem, types.Buffer, types.Array)
+def fancy_getitem_array(context, builder, sig, args):
+    """
+    Advanced or basic indexing with an array.
+    """
+    aryty, idxty = sig.args
+    ary, idx = args
+    ary = make_array(aryty)(context, builder, ary)
+    if idxty.ndim == 0:
+        # 0-d array index acts as a basic integer index
+        idxty, idx = normalize_index(context, builder, idxty, idx)
+        res = _getitem_array_generic(context, builder, sig.return_type,
+                                     aryty, ary, (idxty,), (idx,))
+        return impl_ret_borrowed(context, builder, sig.return_type, res)
+    else:
+        # Advanced indexing
+        return fancy_getitem(context, builder, sig, args,
+                             aryty, ary, (idxty,), (idx,))
+
+
+def offset_bounds_from_strides(context, builder, arrty, arr, shapes, strides):
+    """
+    Compute a half-open range [lower, upper) of byte offsets from the
+    array's data pointer, that bound the in-memory extent of the array.
+
+    This mimics offset_bounds_from_strides() from
+    numpy/core/src/private/mem_overlap.c
+    """
+    itemsize = arr.itemsize
+    zero = itemsize.type(0)
+    one = zero.type(1)
+    if arrty.layout in 'CF':
+        # Array is contiguous: contents are laid out sequentially
+        # starting from arr.data and upwards
+        lower = zero
+        upper = builder.mul(itemsize, arr.nitems)
+    else:
+        # Non-contiguous array: need to examine strides
+        lower = zero
+        upper = zero
+        for i in range(arrty.ndim):
+            # Compute the largest byte offset on this dimension
+            #   max_axis_offset = strides[i] * (shapes[i] - 1)
+            # (shapes[i] == 0 is catered for by the empty array case below)
+            max_axis_offset = builder.mul(strides[i],
+                                          builder.sub(shapes[i], one))
+            is_upwards = builder.icmp_signed('>=', max_axis_offset, zero)
+            # Expand either upwards or downwards depending on stride
+            upper = builder.select(is_upwards,
+                                   builder.add(upper, max_axis_offset), upper)
+            lower = builder.select(is_upwards,
+                                   lower, builder.add(lower, max_axis_offset))
+        # Return a half-open range
+        upper = builder.add(upper, itemsize)
+        # Adjust for empty arrays
+        is_empty = builder.icmp_signed('==', arr.nitems, zero)
+        upper = builder.select(is_empty, zero, upper)
+        lower = builder.select(is_empty, zero, lower)
+
+    return lower, upper
+
+
+def compute_memory_extents(context, builder, lower, upper, data):
+    """
+    Given [lower, upper) byte offsets and a base data pointer,
+    compute the memory pointer bounds as pointer-sized integers.
+    """
+    data_ptr_as_int = builder.ptrtoint(data, lower.type)
+    start = builder.add(data_ptr_as_int, lower)
+    end = builder.add(data_ptr_as_int, upper)
+    return start, end
+
+
+def get_array_memory_extents(context, builder, arrty, arr, shapes, strides,
+                             data):
+    """
+    Compute a half-open range [start, end) of pointer-sized integers
+    which fully contain the array data.
+    """
+    lower, upper = offset_bounds_from_strides(context, builder, arrty, arr,
+                                              shapes, strides)
+    return compute_memory_extents(context, builder, lower, upper, data)
+
+
+def extents_may_overlap(context, builder, a_start, a_end, b_start, b_end):
+    """
+    Whether two memory extents [a_start, a_end) and [b_start, b_end)
+    may overlap.
+    """
+    # Comparisons are unsigned, since we are really comparing pointers
+    may_overlap = builder.and_(
+        builder.icmp_unsigned('<', a_start, b_end),
+        builder.icmp_unsigned('<', b_start, a_end),
+    )
+    return may_overlap
+
+
+def maybe_copy_source(context, builder, use_copy, indexer,
+                      srcty, src, src_shapes, src_strides, src_data):
+    ptrty = src_data.type
+
+    copy_layout = 'C'
+    copy_data = cgutils.alloca_once_value(builder, src_data)
+    copy_shapes = src_shapes
+    copy_strides = None  # unneeded for contiguous arrays
+
+    with builder.if_then(use_copy, likely=False):
+        # Allocate temporary scratchpad
+        # XXX: should we use a stack-allocated array for very small
+        # data sizes?
+        allocsize = builder.mul(src.itemsize, src.nitems)
+        data = context.nrt.allocate(builder, allocsize)
+        voidptrty = data.type
+        data = builder.bitcast(data, ptrty)
+        builder.store(data, copy_data)
+
+        # Copy source data into scratchpad
+        intp_t = context.get_value_type(types.intp)
+
+        with cgutils.loop_nest(builder, src_shapes, intp_t) as indices:
+            src_ptr = cgutils.get_item_pointer2(context, builder, src_data,
+                                                src_shapes, src_strides,
+                                                srcty.layout, indices)
+            dest_ptr = cgutils.get_item_pointer2(context, builder, data,
+                                                 copy_shapes, copy_strides,
+                                                 copy_layout, indices)
+            builder.store(builder.load(src_ptr), dest_ptr)
+
+    def src_getitem():
+        src_ptr = cgutils.alloca_once(builder, ptrty)
+        src_indices = indexer.get_src_indices()[-len(src_shapes):]
+        if len(src_shapes) > len(src_indices):
+            # If the source has fewer dimensions than the indexer, we need
+            # to add some leading zeros to the indices to get the correct
+            # broadcasting behavior.
+            zero = context.get_constant(types.intp, 0)
+            src_indices = (
+                [zero] * (len(src_shapes) -
+                          len(src_indices)) + list(src_indices)
+            )
+
+        src_indices = [
+            builder.srem(src_indices[i], src_shapes[i])
+            for i in range(len(src_shapes))
+        ]
+        with builder.if_else(use_copy, likely=False) as (if_copy, otherwise):
+            with if_copy:
+                builder.store(
+                    cgutils.get_item_pointer2(context, builder,
+                                              builder.load(copy_data),
+                                              copy_shapes, copy_strides,
+                                              copy_layout, src_indices,
+                                              wraparound=False),
+                    src_ptr)
+            with otherwise:
+                builder.store(
+                    cgutils.get_item_pointer2(context, builder, src_data,
+                                              src_shapes, src_strides,
+                                              srcty.layout, src_indices,
+                                              wraparound=False),
+                    src_ptr)
+        return load_item(context, builder, srcty, builder.load(src_ptr))
+
+    def src_cleanup():
+        # Deallocate memory
+        with builder.if_then(use_copy, likely=False):
+            data = builder.load(copy_data)
+            data = builder.bitcast(data, voidptrty)
+            context.nrt.free(builder, data)
+
+    return src_getitem, src_cleanup
+
+
+def _bc_adjust_dimension(context, builder, shapes, strides, target_shape):
+    """
+    Preprocess dimension for broadcasting.
+    Returns (shapes, strides) such that the ndim match *target_shape*.
+    When expanding to higher ndim, the returning shapes and strides are
+    prepended with ones and zeros, respectively.
+    When truncating to lower ndim, the shapes are checked (in runtime).
+    All extra dimension must have size of 1.
+    """
+    zero = context.get_constant(types.uintp, 0)
+    one = context.get_constant(types.uintp, 1)
+
+    # Adjust for broadcasting to higher dimension
+    if len(target_shape) > len(shapes):
+        nd_diff = len(target_shape) - len(shapes)
+        # Fill missing shapes with one, strides with zeros
+        shapes = [one] * nd_diff + shapes
+        strides = [zero] * nd_diff + strides
+    # Adjust for broadcasting to lower dimension
+    elif len(target_shape) < len(shapes):
+        # Accepted if all extra dims has shape 1
+        nd_diff = len(shapes) - len(target_shape)
+        dim_is_one = [builder.icmp_unsigned('==', sh, one)
+                      for sh in shapes[:nd_diff]]
+        accepted = functools.reduce(builder.and_, dim_is_one,
+                                    cgutils.true_bit)
+        # Check error
+        with builder.if_then(builder.not_(accepted), likely=False):
+            msg = "cannot broadcast source array for assignment"
+            context.call_conv.return_user_exc(builder, ValueError, (msg,))
+        # Truncate extra shapes, strides
+        shapes = shapes[nd_diff:]
+        strides = strides[nd_diff:]
+
+    return shapes, strides
+
+
+def _bc_adjust_shape_strides(context, builder, shapes, strides, target_shape):
+    """
+    Broadcast shapes and strides to target_shape given that their ndim already
+    matches.  For each location where the shape is 1 and does not match the
+    dim for target, it is set to the value at the target and the stride is
+    set to zero.
+    """
+    bc_shapes = []
+    bc_strides = []
+    zero = context.get_constant(types.uintp, 0)
+    one = context.get_constant(types.uintp, 1)
+    # Adjust all mismatching ones in shape
+    mismatch = [builder.icmp_signed('!=', tar, old)
+                for tar, old in zip(target_shape, shapes)]
+    src_is_one = [builder.icmp_signed('==', old, one) for old in shapes]
+    preds = [builder.and_(x, y) for x, y in zip(mismatch, src_is_one)]
+    bc_shapes = [builder.select(p, tar, old)
+                 for p, tar, old in zip(preds, target_shape, shapes)]
+    bc_strides = [builder.select(p, zero, old)
+                  for p, old in zip(preds, strides)]
+    return bc_shapes, bc_strides
+
+
+def _broadcast_to_shape(context, builder, arrtype, arr, target_shape):
+    """
+    Broadcast the given array to the target_shape.
+    Returns (array_type, array)
+    """
+    # Compute broadcasted shape and strides
+    shapes = cgutils.unpack_tuple(builder, arr.shape)
+    strides = cgutils.unpack_tuple(builder, arr.strides)
+
+    shapes, strides = _bc_adjust_dimension(context, builder, shapes, strides,
+                                           target_shape)
+    shapes, strides = _bc_adjust_shape_strides(context, builder, shapes,
+                                               strides, target_shape)
+    new_arrtype = arrtype.copy(ndim=len(target_shape), layout='A')
+    # Create new view
+    new_arr = make_array(new_arrtype)(context, builder)
+    populate_array(new_arr,
+                   data=arr.data,
+                   shape=cgutils.pack_array(builder, shapes),
+                   strides=cgutils.pack_array(builder, strides),
+                   itemsize=arr.itemsize,
+                   meminfo=arr.meminfo,
+                   parent=arr.parent)
+    return new_arrtype, new_arr
+
+
+@intrinsic
+def _numpy_broadcast_to(typingctx, array, shape):
+    ret = array.copy(ndim=shape.count, layout='A', readonly=True)
+    sig = ret(array, shape)
+
+    def codegen(context, builder, sig, args):
+        src, shape_ = args
+        srcty = sig.args[0]
+
+        src = make_array(srcty)(context, builder, src)
+        shape_ = cgutils.unpack_tuple(builder, shape_)
+        _, dest = _broadcast_to_shape(context, builder, srcty, src, shape_,)
+
+        # Hack to get np.broadcast_to to return a read-only array
+        setattr(dest, 'parent', Constant(
+                context.get_value_type(dest._datamodel.get_type('parent')),
+                None))
+
+        res = dest._getvalue()
+        return impl_ret_borrowed(context, builder, sig.return_type, res)
+    return sig, codegen
+
+
+@intrinsic
+def get_readonly_array(typingctx, arr):
+    # returns a copy of arr which is readonly
+    ret = arr.copy(readonly=True)
+    sig = ret(arr)
+
+    def codegen(context, builder, sig, args):
+        [src] = args
+        srcty = sig.args[0]
+
+        dest = make_array(srcty)(context, builder, src)
+        # Hack to return a read-only array
+        dest.parent = cgutils.get_null_value(dest.parent.type)
+        res = dest._getvalue()
+        return impl_ret_borrowed(context, builder, sig.return_type, res)
+    return sig, codegen
+
+
+@register_jitable
+def _can_broadcast(array, dest_shape):
+    src_shape = array.shape
+    src_ndim = len(src_shape)
+    dest_ndim = len(dest_shape)
+    if src_ndim > dest_ndim:
+        raise ValueError('input operand has more dimensions than allowed '
+                         'by the axis remapping')
+    for size in dest_shape:
+        if size < 0:
+            raise ValueError('all elements of broadcast shape must be '
+                             'non-negative')
+
+    # based on _broadcast_onto function in numba/np/npyimpl.py
+    src_index = 0
+    dest_index = dest_ndim - src_ndim
+    while src_index < src_ndim:
+        src_dim = src_shape[src_index]
+        dest_dim = dest_shape[dest_index]
+        # possible cases for (src_dim, dest_dim):
+        #  * (1, 1)   -> Ok
+        #  * (>1, 1)  -> Error!
+        #  * (>1, >1) -> src_dim == dest_dim else error!
+        #  * (1, >1)  -> Ok
+        if src_dim == dest_dim or src_dim == 1:
+            src_index += 1
+            dest_index += 1
+        else:
+            raise ValueError('operands could not be broadcast together '
+                             'with remapped shapes')
+
+
+def _default_broadcast_to_impl(array, shape):
+    array = np.asarray(array)
+    _can_broadcast(array, shape)
+    return _numpy_broadcast_to(array, shape)
+
+
+@overload(np.broadcast_to)
+def numpy_broadcast_to(array, shape):
+    if not type_can_asarray(array):
+        raise errors.TypingError('The first argument "array" must '
+                                 'be array-like')
+
+    if isinstance(shape, types.Integer):
+        def impl(array, shape):
+            return np.broadcast_to(array, (shape,))
+        return impl
+
+    elif isinstance(shape, types.UniTuple):
+        if not isinstance(shape.dtype, types.Integer):
+            msg = 'The second argument "shape" must be a tuple of integers'
+            raise errors.TypingError(msg)
+        return _default_broadcast_to_impl
+
+    elif isinstance(shape, types.Tuple) and shape.count > 0:
+        # check if all types are integers
+        if not all([isinstance(typ, types.IntegerLiteral) for typ in shape]):
+            msg = f'"{shape}" object cannot be interpreted as an integer'
+            raise errors.TypingError(msg)
+        return _default_broadcast_to_impl
+    elif isinstance(shape, types.Tuple) and shape.count == 0:
+        is_scalar_array = isinstance(array, types.Array) and array.ndim == 0
+        if type_is_scalar(array) or is_scalar_array:
+
+            def impl(array, shape):  # broadcast_to(array, ())
+                # Array type must be supported by "type_can_asarray"
+                # Quick note that unicode types are not supported!
+                array = np.asarray(array)
+                return get_readonly_array(array)
+            return impl
+
+        else:
+            msg = 'Cannot broadcast a non-scalar to a scalar array'
+            raise errors.TypingError(msg)
+    else:
+        msg = ('The argument "shape" must be a tuple or an integer. '
+               'Got %s' % shape)
+        raise errors.TypingError(msg)
+
+
+@register_jitable
+def numpy_broadcast_shapes_list(r, m, shape):
+    for i in range(len(shape)):
+        k = m - len(shape) + i
+        tmp = shape[i]
+        if tmp < 0:
+            raise ValueError("negative dimensions are not allowed")
+        if tmp == 1:
+            continue
+        if r[k] == 1:
+            r[k] = tmp
+        elif r[k] != tmp:
+            raise ValueError("shape mismatch: objects"
+                             " cannot be broadcast"
+                             " to a single shape")
+
+
+@overload(np.broadcast_shapes)
+def ol_numpy_broadcast_shapes(*args):
+    # Based on https://github.com/numpy/numpy/blob/f702b26fff3271ba6a6ba29a021fc19051d1f007/numpy/core/src/multiarray/iterators.c#L1129-L1212  # noqa
+    for idx, arg in enumerate(args):
+        is_int = isinstance(arg, types.Integer)
+        is_int_tuple = isinstance(arg, types.UniTuple) and \
+            isinstance(arg.dtype, types.Integer)
+        is_empty_tuple = isinstance(arg, types.Tuple) and len(arg.types) == 0
+        if not (is_int or is_int_tuple or is_empty_tuple):
+            msg = (f'Argument {idx} must be either an int or tuple[int]. '
+                   f'Got {arg}')
+            raise errors.TypingError(msg)
+
+    # discover the number of dimensions
+    m = 0
+    for arg in args:
+        if isinstance(arg, types.Integer):
+            m = max(m, 1)
+        elif isinstance(arg, types.BaseTuple):
+            m = max(m, len(arg))
+
+    if m == 0:
+        return lambda *args: ()
+    else:
+        tup_init = (1,) * m
+
+        def impl(*args):
+            # propagate args
+            r = [1] * m
+            tup = tup_init
+            for arg in literal_unroll(args):
+                if isinstance(arg, tuple) and len(arg) > 0:
+                    numpy_broadcast_shapes_list(r, m, arg)
+                elif isinstance(arg, int):
+                    numpy_broadcast_shapes_list(r, m, (arg,))
+            for idx, elem in enumerate(r):
+                tup = tuple_setitem(tup, idx, elem)
+            return tup
+        return impl
+
+
+@overload(np.broadcast_arrays)
+def numpy_broadcast_arrays(*args):
+
+    for idx, arg in enumerate(args):
+        if not type_can_asarray(arg):
+            raise errors.TypingError(f'Argument "{idx}" must '
+                                     'be array-like')
+
+    unified_dtype = None
+    dt = None
+    for arg in args:
+        if isinstance(arg, (types.Array, types.BaseTuple)):
+            dt = arg.dtype
+        else:
+            dt = arg
+
+        if unified_dtype is None:
+            unified_dtype = dt
+        elif unified_dtype != dt:
+            raise errors.TypingError('Mismatch of argument types. Numba cannot '
+                                     'broadcast arrays with different types. '
+                                     f'Got {args}')
+
+    # number of dimensions
+    m = 0
+    for idx, arg in enumerate(args):
+        if isinstance(arg, types.ArrayCompatible):
+            m = max(m, arg.ndim)
+        elif isinstance(arg, (types.Number, types.Boolean, types.BaseTuple)):
+            m = max(m, 1)
+        else:
+            raise errors.TypingError(f'Unhandled type {arg}')
+
+    tup_init = (0,) * m
+
+    def impl(*args):
+        # find out the output shape
+        # we can't call np.broadcast_shapes here since args may have arrays
+        # with different shapes and it is not possible to create a list
+        # with those shapes dynamically
+        shape = [1] * m
+        for array in literal_unroll(args):
+            numpy_broadcast_shapes_list(shape, m, np.asarray(array).shape)
+
+        tup = tup_init
+
+        for i in range(m):
+            tup = tuple_setitem(tup, i, shape[i])
+
+        # numpy checks if the input arrays have the same shape as `shape`
+        outs = []
+        for array in literal_unroll(args):
+            outs.append(np.broadcast_to(np.asarray(array), tup))
+        return outs
+
+    return impl
+
+
+def raise_with_shape_context(src_shapes, index_shape):
+    """Targets should implement this if they wish to specialize the error
+    handling/messages. The overload implementation takes two tuples as arguments
+    and should raise a ValueError."""
+    raise NotImplementedError
+
+
+@overload(raise_with_shape_context, target="generic")
+def ol_raise_with_shape_context_generic(src_shapes, index_shape):
+    # This overload is for a "generic" target, which makes no assumption about
+    # the NRT or string support, but does assume exceptions can be raised.
+    if (isinstance(src_shapes, types.UniTuple) and
+        isinstance(index_shape, types.UniTuple) and
+        src_shapes.dtype == index_shape.dtype and
+            isinstance(src_shapes.dtype, types.Integer)):
+
+        def impl(src_shapes, index_shape):
+            raise ValueError("cannot assign slice from input of different size")
+        return impl
+
+
+@overload(raise_with_shape_context, target="CPU")
+def ol_raise_with_shape_context_cpu(src_shapes, index_shape):
+    if (isinstance(src_shapes, types.UniTuple) and
+        isinstance(index_shape, types.UniTuple) and
+        src_shapes.dtype == index_shape.dtype and
+            isinstance(src_shapes.dtype, types.Integer)):
+
+        def impl(src_shapes, index_shape):
+            if len(src_shapes) == 1:
+                shape_str = f"({src_shapes[0]},)"
+            else:
+                shape_str = f"({', '.join([str(x) for x in src_shapes])})"
+            if len(index_shape) == 1:
+                index_str = f"({index_shape[0]},)"
+            else:
+                index_str = f"({', '.join([str(x) for x in index_shape])})"
+            msg = (f"cannot assign slice of shape {index_str} from input of "
+                   f"shape {shape_str}")
+            raise ValueError(msg)
+        return impl
+
+
+def fancy_setslice(context, builder, sig, args, index_types, indices):
+    """
+    Implement slice assignment for arrays.  This implementation works for
+    basic as well as fancy indexing, since there's no functional difference
+    between the two for indexed assignment.
+    """
+    aryty, _, srcty = sig.args
+    ary, _, src = args
+
+    ary = make_array(aryty)(context, builder, ary)
+    dest_shapes = cgutils.unpack_tuple(builder, ary.shape)
+    dest_strides = cgutils.unpack_tuple(builder, ary.strides)
+    dest_data = ary.data
+
+    array_indices = []
+    for i, idxty in enumerate(index_types):
+        idx = indices[i]
+        if isinstance(idxty, types.Array):
+            idx_make = make_array(idxty)(context, builder, idx)
+            array_indices.append((i, idx, idxty, idx_make))
+
+    if len(array_indices):
+        subspace_shape_tuple = get_subspace_shape(
+            context, builder, array_indices
+        )
+    else:
+        subspace_shape_tuple = ()
+
+    indexer = FancyIndexer(context, builder, aryty, ary,
+                           index_types, indices, subspace_shape_tuple)
+    indexer.prepare()
+
+    def raise_shape_mismatch_error(context, builder, src_shapes, index_shape):
+        # This acts as the "trampoline" to raise a ValueError in the case
+        # of the source and destination shapes mismatch at runtime. It resolves
+        # the public overload stub `raise_with_shape_context`
+        fnty = context.typing_context.resolve_value_type(
+            raise_with_shape_context)
+        argtys = (types.UniTuple(types.int64, len(src_shapes)),
+                  types.UniTuple(types.int64, len(index_shape)))
+        raise_sig = fnty.get_call_type(context.typing_context, argtys, {})
+        func = context.get_function(fnty, raise_sig)
+        func(builder, (context.make_tuple(builder, raise_sig.args[0],
+                                          src_shapes),
+                       context.make_tuple(builder, raise_sig.args[1],
+                                          index_shape)))
+
+    if isinstance(srcty, types.Buffer):
+        # Source is an array
+        src_dtype = srcty.dtype
+        index_shape = indexer.get_shape()
+        src = make_array(srcty)(context, builder, src)
+        src_shapes = cgutils.unpack_tuple(builder, src.shape)
+        src_strides = cgutils.unpack_tuple(builder, src.strides)
+        src_data = src.data
+
+        # Pad 1s to either source or destination shape to make their
+        # dimensions match.
+        one = context.get_constant(types.uintp, 1)
+        if len(index_shape) > len(src_shapes):
+            new_src_shapes = (one,) * (
+                len(index_shape) - len(src_shapes)
+            ) + tuple(src_shapes)
+            new_index_shape = tuple(index_shape)
+        elif len(index_shape) < len(src_shapes):
+            new_index_shape = (one,) * (
+                len(src_shapes) - len(index_shape)
+            ) + tuple(index_shape)
+            new_src_shapes = tuple(src_shapes)
+        else:
+            new_src_shapes = tuple(src_shapes)
+            new_index_shape = tuple(index_shape)
+
+        # The resulting shapes should be broadcastable, so check that
+        # the source shape is either 1 or the same as the destination
+        # shape at every dimension.
+        for i in range(len(new_src_shapes)):
+            dim_match = builder.icmp_signed(
+                '==', new_src_shapes[i], new_index_shape[i]
+            )
+            src_dim_is_one = builder.icmp_signed('==', new_src_shapes[i], one)
+            shape_error = builder.not_(builder.or_(dim_match, src_dim_is_one))
+
+            with builder.if_then(shape_error, likely=False):
+                raise_shape_mismatch_error(
+                    context, builder, src_shapes, index_shape
+                )
+
+        # Check for array overlap
+        src_start, src_end = get_array_memory_extents(context, builder, srcty,
+                                                      src, src_shapes,
+                                                      src_strides, src_data)
+        dest_lower, dest_upper = indexer.get_offset_bounds(dest_strides,
+                                                           ary.itemsize)
+        dest_start, dest_end = compute_memory_extents(context, builder,
+                                                      dest_lower, dest_upper,
+                                                      dest_data)
+        use_copy = extents_may_overlap(context, builder, src_start, src_end,
+                                       dest_start, dest_end)
+        src_getitem, src_cleanup = maybe_copy_source(context, builder, use_copy,
+                                                     indexer,
+                                                     srcty, src, src_shapes,
+                                                     src_strides, src_data)
+
+        # Loop on destination and copy from source to destination
+        dest_indices = indexer.begin_loops()
+
+        # No need to check for wraparound, as the indexers all ensure
+        # a positive index is returned.
+        dest_ptr = cgutils.get_item_pointer2(
+            context, builder, dest_data, dest_shapes, dest_strides,
+            aryty.layout, dest_indices, wraparound=False,
+            boundscheck=context.enable_boundscheck)
+
+        val = src_getitem()
+        val = context.cast(builder, val, src_dtype, aryty.dtype)
+        store_item(context, builder, aryty, val, dest_ptr)
+
+        indexer.end_loops()
+        src_cleanup()
+
+    elif isinstance(srcty, types.Sequence):
+        src_dtype = srcty.dtype
+
+        # Check shape is equal to sequence length
+        index_shape = indexer.get_shape()
+        assert len(index_shape) == 1
+        len_impl = context.get_function(len, signature(types.intp, srcty))
+        seq_len = len_impl(builder, (src,))
+
+        shape_error = builder.icmp_signed('!=', index_shape[0], seq_len)
+
+        with builder.if_then(shape_error, likely=False):
+            raise_shape_mismatch_error(context, builder, (seq_len,),
+                                       (index_shape[0],))
+
+        def src_getitem(src_idx):
+            cur = builder.load(src_idx)
+            getitem_impl = context.get_function(
+                operator.getitem,
+                signature(src_dtype, srcty, types.intp),
+            )
+            val = getitem_impl(builder, (src, cur))
+            next_idx = cgutils.increment_index(builder, cur)
+            builder.store(next_idx, src_idx)
+            return val
+
+        src_idx = cgutils.alloca_once_value(builder,
+                                            context.get_constant(types.intp, 0))
+        # Loop on destination and copy from source to destination
+        dest_indices = indexer.begin_loops()
+
+        # No need to check for wraparound, as the indexers all ensure
+        # a positive index is returned.
+        dest_ptr = cgutils.get_item_pointer2(
+            context, builder, dest_data, dest_shapes, dest_strides,
+            aryty.layout, dest_indices, wraparound=False,
+            boundscheck=context.enable_boundscheck)
+
+        val = src_getitem(src_idx)
+        val = context.cast(builder, val, src_dtype, aryty.dtype)
+        store_item(context, builder, aryty, val, dest_ptr)
+
+        indexer.end_loops()
+    else:
+        # Source is a scalar (broadcast or not, depending on destination
+        # shape).
+        dest_indices = indexer.begin_loops()
+        # No need to check for wraparound, as the indexers all ensure
+        # a positive index is returned.
+        dest_ptr = cgutils.get_item_pointer2(
+            context, builder, dest_data, dest_shapes, dest_strides,
+            aryty.layout, dest_indices, wraparound=False,
+            boundscheck=context.enable_boundscheck)
+        store_item(context, builder, aryty, src, dest_ptr)
+        indexer.end_loops()
+
+    return context.get_dummy_value()
+
+
+# ------------------------------------------------------------------------------
+# Shape / layout altering
+
+def vararg_to_tuple(context, builder, sig, args):
+    aryty = sig.args[0]
+    dimtys = sig.args[1:]
+    # values
+    ary = args[0]
+    dims = args[1:]
+    # coerce all types to intp
+    dims = [context.cast(builder, val, ty, types.intp)
+            for ty, val in zip(dimtys, dims)]
+    # make a tuple
+    shape = cgutils.pack_array(builder, dims, dims[0].type)
+
+    shapety = types.UniTuple(dtype=types.intp, count=len(dims))
+    new_sig = typing.signature(sig.return_type, aryty, shapety)
+    new_args = ary, shape
+
+    return new_sig, new_args
+
+
+@lower_builtin('array.transpose', types.Array)
+def array_transpose(context, builder, sig, args):
+    return array_T(context, builder, sig.args[0], args[0])
+
+
+def permute_arrays(axis, shape, strides):
+    if len(axis) != len(set(axis)):
+        raise ValueError("repeated axis in transpose")
+    dim = len(shape)
+    for x in axis:
+        if x >= dim or abs(x) > dim:
+            raise ValueError("axis is out of bounds for array of "
+                             "given dimension")
+
+    shape[:] = shape[axis]
+    strides[:] = strides[axis]
+
+
+# Transposing an array involves permuting the shape and strides of the array
+# based on the given axes.
+@lower_builtin('array.transpose', types.Array, types.BaseTuple)
+def array_transpose_tuple(context, builder, sig, args):
+    aryty = sig.args[0]
+    ary = make_array(aryty)(context, builder, args[0])
+
+    axisty, axis = sig.args[1], args[1]
+    num_axis, dtype = axisty.count, axisty.dtype
+
+    ll_intp = context.get_value_type(types.intp)
+    ll_ary_size = ir.ArrayType(ll_intp, num_axis)
+
+    # Allocate memory for axes, shapes, and strides arrays.
+    arys = [axis, ary.shape, ary.strides]
+    ll_arys = [cgutils.alloca_once(builder, ll_ary_size) for _ in arys]
+
+    # Store axes, shapes, and strides arrays to the allocated memory.
+    for src, dst in zip(arys, ll_arys):
+        builder.store(src, dst)
+
+    np_ary_ty = types.Array(dtype=dtype, ndim=1, layout='C')
+    np_itemsize = context.get_constant(types.intp,
+                                       context.get_abi_sizeof(ll_intp))
+
+    # Form NumPy arrays for axes, shapes, and strides arrays.
+    np_arys = [make_array(np_ary_ty)(context, builder) for _ in arys]
+
+    # Roughly, `np_ary = np.array(ll_ary)` for each of axes, shapes, and strides
+    for np_ary, ll_ary in zip(np_arys, ll_arys):
+        populate_array(np_ary,
+                       data=builder.bitcast(ll_ary, ll_intp.as_pointer()),
+                       shape=[context.get_constant(types.intp, num_axis)],
+                       strides=[np_itemsize],
+                       itemsize=np_itemsize,
+                       meminfo=None)
+
+    # Pass NumPy arrays formed above to permute_arrays function that permutes
+    # shapes and strides based on axis contents.
+    context.compile_internal(builder, permute_arrays,
+                             typing.signature(types.void,
+                                              np_ary_ty, np_ary_ty, np_ary_ty),
+                             [a._getvalue() for a in np_arys])
+
+    # Make a new array based on permuted shape and strides and return it.
+    ret = make_array(sig.return_type)(context, builder)
+    populate_array(ret,
+                   data=ary.data,
+                   shape=builder.load(ll_arys[1]),
+                   strides=builder.load(ll_arys[2]),
+                   itemsize=ary.itemsize,
+                   meminfo=ary.meminfo,
+                   parent=ary.parent)
+    res = ret._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin('array.transpose', types.Array, types.VarArg(types.Any))
+def array_transpose_vararg(context, builder, sig, args):
+    new_sig, new_args = vararg_to_tuple(context, builder, sig, args)
+    return array_transpose_tuple(context, builder, new_sig, new_args)
+
+
+@overload(np.transpose)
+def numpy_transpose(a, axes=None):
+    if isinstance(a, types.BaseTuple):
+        raise errors.TypingError("np.transpose does not accept tuples")
+
+    if axes is None:
+        def np_transpose_impl(a, axes=None):
+            return a.transpose()
+    else:
+        def np_transpose_impl(a, axes=None):
+            return a.transpose(axes)
+
+    return np_transpose_impl
+
+
+@lower_getattr(types.Array, 'T')
+def array_T(context, builder, typ, value):
+    if typ.ndim <= 1:
+        res = value
+    else:
+        ary = make_array(typ)(context, builder, value)
+        ret = make_array(typ)(context, builder)
+        shapes = cgutils.unpack_tuple(builder, ary.shape, typ.ndim)
+        strides = cgutils.unpack_tuple(builder, ary.strides, typ.ndim)
+        populate_array(ret,
+                       data=ary.data,
+                       shape=cgutils.pack_array(builder, shapes[::-1]),
+                       strides=cgutils.pack_array(builder, strides[::-1]),
+                       itemsize=ary.itemsize,
+                       meminfo=ary.meminfo,
+                       parent=ary.parent)
+        res = ret._getvalue()
+    return impl_ret_borrowed(context, builder, typ, res)
+
+
+@overload(np.logspace)
+def numpy_logspace(start, stop, num=50):
+    if not isinstance(start, types.Number):
+        raise errors.TypingError('The first argument "start" must be a number')
+    if not isinstance(stop, types.Number):
+        raise errors.TypingError('The second argument "stop" must be a number')
+    if not isinstance(num, (int, types.Integer)):
+        raise errors.TypingError('The third argument "num" must be an integer')
+
+    def impl(start, stop, num=50):
+        y = np.linspace(start, stop, num)
+        return np.power(10.0, y)
+
+    return impl
+
+
+@overload(np.geomspace)
+def numpy_geomspace(start, stop, num=50):
+    if not isinstance(start, types.Number):
+        msg = 'The argument "start" must be a number'
+        raise errors.TypingError(msg)
+
+    if not isinstance(stop, types.Number):
+        msg = 'The argument "stop" must be a number'
+        raise errors.TypingError(msg)
+
+    if not isinstance(num, (int, types.Integer)):
+        msg = 'The argument "num" must be an integer'
+        raise errors.TypingError(msg)
+
+    if any(isinstance(arg, types.Complex) for arg in [start, stop]):
+        result_dtype = from_dtype(np.result_type(as_dtype(start),
+                                                 as_dtype(stop), None))
+
+        def impl(start, stop, num=50):
+            if start == 0 or stop == 0:
+                raise ValueError('Geometric sequence cannot include zero')
+            start = result_dtype(start)
+            stop = result_dtype(stop)
+            if numpy_version < (2, 0):
+                both_imaginary = (start.real == 0) & (stop.real == 0)
+                both_negative = (np.sign(start) == -1) & (np.sign(stop) == -1)
+                out_sign = 1
+                if both_imaginary:
+                    start = start.imag
+                    stop = stop.imag
+                    out_sign = 1j
+                if both_negative:
+                    start = -start
+                    stop = -stop
+                    out_sign = -out_sign
+            else:
+                out_sign = np.sign(start)
+                start /= out_sign
+                stop /= out_sign
+
+            logstart = np.log10(start)
+            logstop = np.log10(stop)
+            result = np.logspace(logstart, logstop, num)
+            # Make sure the endpoints match the start and stop arguments.
+            # This is necessary because np.exp(np.log(x)) is not necessarily
+            # equal to x.
+            if num > 0:
+                result[0] = start
+                if num > 1:
+                    result[-1] = stop
+            return out_sign * result
+
+    else:
+        def impl(start, stop, num=50):
+            if start == 0 or stop == 0:
+                raise ValueError('Geometric sequence cannot include zero')
+            both_negative = (np.sign(start) == -1) & (np.sign(stop) == -1)
+            out_sign = 1
+            if both_negative:
+                start = -start
+                stop = -stop
+                out_sign = -out_sign
+            logstart = np.log10(start)
+            logstop = np.log10(stop)
+            result = np.logspace(logstart, logstop, num)
+            # Make sure the endpoints match the start and stop arguments.
+            # This is necessary because np.exp(np.log(x)) is not necessarily
+            # equal to x.
+            if num > 0:
+                result[0] = start
+                if num > 1:
+                    result[-1] = stop
+            return out_sign * result
+
+    return impl
+
+
+@overload(np.rot90)
+def numpy_rot90(m, k=1):
+    # supporting axes argument it needs to be included in np.flip
+    if not isinstance(k, (int, types.Integer)):
+        raise errors.TypingError('The second argument "k" must be an integer')
+    if not isinstance(m, types.Array):
+        raise errors.TypingError('The first argument "m" must be an array')
+
+    if m.ndim < 2:
+        raise errors.NumbaValueError('Input must be >= 2-d.')
+
+    def impl(m, k=1):
+        k = k % 4
+        if k == 0:
+            return m[:]
+        elif k == 1:
+            return np.swapaxes(np.fliplr(m), 0, 1)
+        elif k == 2:
+            return np.flipud(np.fliplr(m))
+        elif k == 3:
+            return np.fliplr(np.swapaxes(m, 0, 1))
+        else:
+            raise AssertionError  # unreachable
+
+    return impl
+
+
+def _attempt_nocopy_reshape(context, builder, aryty, ary,
+                            newnd, newshape, newstrides):
+    """
+    Call into Numba_attempt_nocopy_reshape() for the given array type
+    and instance, and the specified new shape.
+
+    Return value is non-zero if successful, and the array pointed to
+    by *newstrides* will be filled up with the computed results.
+    """
+    ll_intp = context.get_value_type(types.intp)
+    ll_intp_star = ll_intp.as_pointer()
+    ll_intc = context.get_value_type(types.intc)
+    fnty = ir.FunctionType(ll_intc, [
+        # nd, *dims, *strides
+        ll_intp, ll_intp_star, ll_intp_star,
+        # newnd, *newdims, *newstrides
+        ll_intp, ll_intp_star, ll_intp_star,
+        # itemsize, is_f_order
+        ll_intp, ll_intc])
+    fn = cgutils.get_or_insert_function(builder.module, fnty,
+                                        "numba_attempt_nocopy_reshape")
+
+    nd = ll_intp(aryty.ndim)
+    shape = cgutils.gep_inbounds(builder, ary._get_ptr_by_name('shape'), 0, 0)
+    strides = cgutils.gep_inbounds(builder, ary._get_ptr_by_name('strides'),
+                                   0, 0)
+    newnd = ll_intp(newnd)
+    newshape = cgutils.gep_inbounds(builder, newshape, 0, 0)
+    newstrides = cgutils.gep_inbounds(builder, newstrides, 0, 0)
+    is_f_order = ll_intc(0)
+    res = builder.call(fn, [nd, shape, strides,
+                            newnd, newshape, newstrides,
+                            ary.itemsize, is_f_order])
+    return res
+
+
+def normalize_reshape_value(origsize, shape):
+    num_neg_value = 0
+    known_size = 1
+    for ax, s in enumerate(shape):
+        if s < 0:
+            num_neg_value += 1
+            neg_ax = ax
+        else:
+            known_size *= s
+
+    if num_neg_value == 0:
+        if origsize != known_size:
+            raise ValueError("total size of new array must be unchanged")
+
+    elif num_neg_value == 1:
+        # Infer negative dimension
+        if known_size == 0:
+            inferred = 0
+            ok = origsize == 0
+        else:
+            inferred = origsize // known_size
+            ok = origsize % known_size == 0
+        if not ok:
+            raise ValueError("total size of new array must be unchanged")
+        shape[neg_ax] = inferred
+
+    else:
+        raise ValueError("multiple negative shape values")
+
+
+@lower_builtin('array.reshape', types.Array, types.BaseTuple)
+def array_reshape(context, builder, sig, args):
+    aryty = sig.args[0]
+    retty = sig.return_type
+
+    shapety = sig.args[1]
+    shape = args[1]
+
+    ll_intp = context.get_value_type(types.intp)
+    ll_shape = ir.ArrayType(ll_intp, shapety.count)
+
+    ary = make_array(aryty)(context, builder, args[0])
+
+    # We will change the target shape in this slot
+    # (see normalize_reshape_value() below)
+    newshape = cgutils.alloca_once(builder, ll_shape)
+    builder.store(shape, newshape)
+
+    # Create a shape array pointing to the value of newshape.
+    # (roughly, `shape_ary = np.array(ary.shape)`)
+    shape_ary_ty = types.Array(dtype=shapety.dtype, ndim=1, layout='C')
+    shape_ary = make_array(shape_ary_ty)(context, builder)
+    shape_itemsize = context.get_constant(types.intp,
+                                          context.get_abi_sizeof(ll_intp))
+    populate_array(shape_ary,
+                   data=builder.bitcast(newshape, ll_intp.as_pointer()),
+                   shape=[context.get_constant(types.intp, shapety.count)],
+                   strides=[shape_itemsize],
+                   itemsize=shape_itemsize,
+                   meminfo=None)
+
+    # Compute the original array size
+    size = ary.nitems
+
+    # Call our normalizer which will fix the shape array in case of negative
+    # shape value
+    context.compile_internal(builder, normalize_reshape_value,
+                             typing.signature(types.void,
+                                              types.uintp, shape_ary_ty),
+                             [size, shape_ary._getvalue()])
+
+    # Perform reshape (nocopy)
+    newnd = shapety.count
+    newstrides = cgutils.alloca_once(builder, ll_shape)
+
+    ok = _attempt_nocopy_reshape(context, builder, aryty, ary, newnd,
+                                 newshape, newstrides)
+    fail = builder.icmp_unsigned('==', ok, ok.type(0))
+
+    with builder.if_then(fail):
+        msg = "incompatible shape for array"
+        context.call_conv.return_user_exc(builder, NotImplementedError, (msg,))
+
+    ret = make_array(retty)(context, builder)
+    populate_array(ret,
+                   data=ary.data,
+                   shape=builder.load(newshape),
+                   strides=builder.load(newstrides),
+                   itemsize=ary.itemsize,
+                   meminfo=ary.meminfo,
+                   parent=ary.parent)
+    res = ret._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin('array.reshape', types.Array, types.VarArg(types.Any))
+def array_reshape_vararg(context, builder, sig, args):
+    new_sig, new_args = vararg_to_tuple(context, builder, sig, args)
+    return array_reshape(context, builder, new_sig, new_args)
+
+
+if numpy_version < (2, 1):
+    @overload(np.reshape)
+    def np_reshape(a, newshape):
+        def np_reshape_impl(a, newshape):
+            return a.reshape(newshape)
+        return np_reshape_impl
+else:
+    @overload(np.reshape)
+    def np_reshape(a, shape):
+        def np_reshape_impl(a, shape):
+            return a.reshape(shape)
+        return np_reshape_impl
+
+
+@overload(np.resize)
+def numpy_resize(a, new_shape):
+
+    if not type_can_asarray(a):
+        msg = 'The argument "a" must be array-like'
+        raise errors.TypingError(msg)
+
+    if not ((isinstance(new_shape, types.UniTuple)
+             and
+             isinstance(new_shape.dtype, types.Integer))
+            or
+            isinstance(new_shape, types.Integer)):
+        msg = ('The argument "new_shape" must be an integer or '
+               'a tuple of integers')
+        raise errors.TypingError(msg)
+
+    def impl(a, new_shape):
+        a = np.asarray(a)
+        a = np.ravel(a)
+
+        if isinstance(new_shape, tuple):
+            new_size = 1
+            for dim_length in np.asarray(new_shape):
+                new_size *= dim_length
+                if dim_length < 0:
+                    msg = 'All elements of `new_shape` must be non-negative'
+                    raise ValueError(msg)
+        else:
+            if new_shape < 0:
+                msg2 = 'All elements of `new_shape` must be non-negative'
+                raise ValueError(msg2)
+            new_size = new_shape
+
+        if a.size == 0:
+            return np.zeros(new_shape).astype(a.dtype)
+
+        repeats = -(-new_size // a.size)  # ceil division
+        res = a
+        for i in range(repeats - 1):
+            res = np.concatenate((res, a))
+        res = res[:new_size]
+
+        return np.reshape(res, new_shape)
+
+    return impl
+
+
+@overload(np.append)
+def np_append(arr, values, axis=None):
+
+    if not type_can_asarray(arr):
+        raise errors.TypingError('The first argument "arr" must be array-like')
+
+    if not type_can_asarray(values):
+        raise errors.TypingError('The second argument "values" must be '
+                                 'array-like')
+
+    if is_nonelike(axis):
+        def impl(arr, values, axis=None):
+            arr = np.ravel(np.asarray(arr))
+            values = np.ravel(np.asarray(values))
+            return np.concatenate((arr, values))
+    else:
+
+        if not isinstance(axis, types.Integer):
+            raise errors.TypingError('The third argument "axis" must be an '
+                                     'integer')
+
+        def impl(arr, values, axis=None):
+            return np.concatenate((arr, values), axis=axis)
+    return impl
+
+
+@lower_builtin('array.ravel', types.Array)
+def array_ravel(context, builder, sig, args):
+    # Only support no argument version (default order='C')
+    def imp_nocopy(ary):
+        """No copy version"""
+        return ary.reshape(ary.size)
+
+    def imp_copy(ary):
+        """Copy version"""
+        return ary.flatten()
+
+    # If the input array is C layout already, use the nocopy version
+    if sig.args[0].layout == 'C':
+        imp = imp_nocopy
+    # otherwise, use flatten under-the-hood
+    else:
+        imp = imp_copy
+
+    res = context.compile_internal(builder, imp, sig, args)
+    res = impl_ret_new_ref(context, builder, sig.return_type, res)
+    return res
+
+
+@lower_builtin(np.ravel, types.Array)
+def np_ravel(context, builder, sig, args):
+    def np_ravel_impl(a):
+        return a.ravel()
+
+    return context.compile_internal(builder, np_ravel_impl, sig, args)
+
+
+@lower_builtin('array.flatten', types.Array)
+def array_flatten(context, builder, sig, args):
+    # Only support flattening to C layout currently.
+    def imp(ary):
+        return ary.copy().reshape(ary.size)
+
+    res = context.compile_internal(builder, imp, sig, args)
+    res = impl_ret_new_ref(context, builder, sig.return_type, res)
+    return res
+
+
+@register_jitable
+def _np_clip_prepare_out(a, shape, out):
+    # Returns the array the result is written into. NumPy broadcasts the
+    # inputs onto a caller-provided output but never stretches the output
+    # itself, so its shape has to match the shape of the result exactly.
+    # Without this check the callers loop over the result shape and write
+    # past the end of a too-small `out` (issue #10682).
+    if out is None:
+        return np.empty_like(a)
+    if out.shape != shape:
+        raise ValueError("clip: the shape of the 'out' array does not "
+                         "match the shape of the result")
+    return out
+
+
+@register_jitable
+def _np_clip_impl(a, a_min, a_max, out):
+    # Both a_min and a_max are numpy arrays
+    a_b, a_min_b, a_max_b = np.broadcast_arrays(a, a_min, a_max)
+    ret = _np_clip_prepare_out(a, a_b.shape, out)
+    for index in np.ndindex(a_b.shape):
+        val_a = a_b[index]
+        val_a_min = a_min_b[index]
+        val_a_max = a_max_b[index]
+        ret[index] = min(max(val_a, val_a_min), val_a_max)
+
+    return ret
+
+
+@register_jitable
+def _np_clip_impl_none(a, b, use_min, out):
+    for index in np.ndindex(a.shape):
+        val_a = a[index]
+        val_b = b[index]
+        if use_min:
+            out[index] = min(val_a, val_b)
+        else:
+            out[index] = max(val_a, val_b)
+    return out
+
+
+@overload(np.clip)
+def np_clip(a, a_min, a_max, out=None):
+    if not type_can_asarray(a):
+        raise errors.TypingError('The argument "a" must be array-like')
+
+    if (not isinstance(a_min, types.NoneType) and
+            not type_can_asarray(a_min)):
+        raise errors.TypingError(('The argument "a_min" must be a number '
+                                 'or an array-like'))
+
+    if (not isinstance(a_max, types.NoneType) and
+            not type_can_asarray(a_max)):
+        raise errors.TypingError('The argument "a_max" must be a number '
+                                 'or an array-like')
+
+    if not (isinstance(out, types.Array) or is_nonelike(out)):
+        msg = 'The argument "out" must be an array if it is provided'
+        raise errors.TypingError(msg)
+
+    # The result has as many dimensions as the widest operand, and `out` is
+    # never broadcast, so a mismatch here can be reported at compile time.
+    result_ndim = max(getattr(t, 'ndim', 0) for t in (a, a_min, a_max))
+    if isinstance(out, types.Array) and out.ndim != result_ndim:
+        msg = ('The argument "out" must have the same number of dimensions '
+               'as the result')
+        raise errors.TypingError(msg)
+
+    # TODO: support scalar a (issue #3469)
+    a_min_is_none = a_min is None or isinstance(a_min, types.NoneType)
+    a_max_is_none = a_max is None or isinstance(a_max, types.NoneType)
+
+    if a_min_is_none and a_max_is_none:
+        # Raises value error when both a_min and a_max are None
+        def np_clip_nn(a, a_min, a_max, out=None):
+            raise ValueError("array_clip: must set either max or min")
+
+        return np_clip_nn
+
+    a_min_is_scalar = isinstance(a_min, types.Number)
+    a_max_is_scalar = isinstance(a_max, types.Number)
+
+    if a_min_is_scalar and a_max_is_scalar:
+        def np_clip_ss(a, a_min, a_max, out=None):
+            # a_min and a_max are scalars
+            # since their shape will be empty
+            # so broadcasting is not needed at all
+            ret = _np_clip_prepare_out(a, a.shape, out)
+            for index in np.ndindex(a.shape):
+                val_a = a[index]
+                ret[index] = min(max(val_a, a_min), a_max)
+
+            return ret
+
+        return np_clip_ss
+    elif a_min_is_scalar and not a_max_is_scalar:
+        if a_max_is_none:
+            def np_clip_sn(a, a_min, a_max, out=None):
+                # a_min is a scalar
+                # since its shape will be empty
+                # so broadcasting is not needed at all
+                ret = _np_clip_prepare_out(a, a.shape, out)
+                for index in np.ndindex(a.shape):
+                    val_a = a[index]
+                    ret[index] = max(val_a, a_min)
+
+                return ret
+
+            return np_clip_sn
+        else:
+            def np_clip_sa(a, a_min, a_max, out=None):
+                # a_min is a scalar
+                # since its shape will be empty
+                # broadcast it to shape of a
+                # by using np.full_like
+                a_min_full = np.full_like(a, a_min)
+                return _np_clip_impl(a, a_min_full, a_max, out)
+
+            return np_clip_sa
+    elif not a_min_is_scalar and a_max_is_scalar:
+        if a_min_is_none:
+            def np_clip_ns(a, a_min, a_max, out=None):
+                # a_max is a scalar
+                # since its shape will be empty
+                # so broadcasting is not needed at all
+                ret = _np_clip_prepare_out(a, a.shape, out)
+                for index in np.ndindex(a.shape):
+                    val_a = a[index]
+                    ret[index] = min(val_a, a_max)
+
+                return ret
+
+            return np_clip_ns
+        else:
+            def np_clip_as(a, a_min, a_max, out=None):
+                # a_max is a scalar
+                # since its shape will be empty
+                # broadcast it to shape of a
+                # by using np.full_like
+                a_max_full = np.full_like(a, a_max)
+                return _np_clip_impl(a, a_min, a_max_full, out)
+
+            return np_clip_as
+    else:
+        # Case where exactly one of a_min or a_max is None
+        if a_min_is_none:
+            def np_clip_na(a, a_min, a_max, out=None):
+                # a_max is a numpy array but a_min is None
+                a_b, a_max_b = np.broadcast_arrays(a, a_max)
+                ret = _np_clip_prepare_out(a, a_b.shape, out)
+                return _np_clip_impl_none(a_b, a_max_b, True, ret)
+
+            return np_clip_na
+        elif a_max_is_none:
+            def np_clip_an(a, a_min, a_max, out=None):
+                # a_min is a numpy array but a_max is None
+                a_b, a_min_b = np.broadcast_arrays(a, a_min)
+                ret = _np_clip_prepare_out(a, a_b.shape, out)
+                return _np_clip_impl_none(a_b, a_min_b, False, ret)
+
+            return np_clip_an
+        else:
+            def np_clip_aa(a, a_min, a_max, out=None):
+                # Both a_min and a_max are clearly arrays
+                # because none of the above branches
+                # returned
+                return _np_clip_impl(a, a_min, a_max, out)
+
+            return np_clip_aa
+
+
+@overload_method(types.Array, 'clip')
+def array_clip(a, a_min=None, a_max=None, out=None):
+    def impl(a, a_min=None, a_max=None, out=None):
+        return np.clip(a, a_min, a_max, out)
+    return impl
+
+
+def _change_dtype(context, builder, oldty, newty, ary):
+    """
+    Attempt to fix up *ary* for switching from *oldty* to *newty*.
+
+    See Numpy's array_descr_set()
+    (np/core/src/multiarray/getset.c).
+    Attempt to fix the array's shape and strides for a new dtype.
+    False is returned on failure, True on success.
+    """
+    assert oldty.ndim == newty.ndim
+    assert oldty.layout == newty.layout
+
+    new_layout = ord(newty.layout)
+    any_layout = ord('A')
+    c_layout = ord('C')
+    f_layout = ord('F')
+
+    int8 = types.int8
+
+    def imp(nd, dims, strides, old_itemsize, new_itemsize, layout):
+        # Attempt to update the layout due to limitation of the numba
+        # type system.
+        if layout == any_layout:
+            # Test rightmost stride to be contiguous
+            if strides[-1] == old_itemsize:
+                # Process this as if it is C contiguous
+                layout = int8(c_layout)
+            # Test leftmost stride to be F contiguous
+            elif strides[0] == old_itemsize:
+                # Process this as if it is F contiguous
+                layout = int8(f_layout)
+
+        if old_itemsize != new_itemsize and (layout == any_layout or nd == 0):
+            return False
+
+        if layout == c_layout:
+            i = nd - 1
+        else:
+            i = 0
+
+        if new_itemsize < old_itemsize:
+            # If it is compatible, increase the size of the dimension
+            # at the end (or at the front if F-contiguous)
+            if (old_itemsize % new_itemsize) != 0:
+                return False
+
+            newdim = old_itemsize // new_itemsize
+            dims[i] *= newdim
+            strides[i] = new_itemsize
+
+        elif new_itemsize > old_itemsize:
+            # Determine if last (or first if F-contiguous) dimension
+            # is compatible
+            bytelength = dims[i] * old_itemsize
+            if (bytelength % new_itemsize) != 0:
+                return False
+
+            dims[i] = bytelength // new_itemsize
+            strides[i] = new_itemsize
+
+        else:
+            # Same item size: nothing to do (this also works for
+            # non-contiguous arrays).
+            pass
+
+        return True
+
+    old_itemsize = context.get_constant(types.intp,
+                                        get_itemsize(context, oldty))
+    new_itemsize = context.get_constant(types.intp,
+                                        get_itemsize(context, newty))
+
+    nd = context.get_constant(types.intp, newty.ndim)
+    shape_data = cgutils.gep_inbounds(builder, ary._get_ptr_by_name('shape'),
+                                      0, 0)
+    strides_data = cgutils.gep_inbounds(builder,
+                                        ary._get_ptr_by_name('strides'), 0, 0)
+
+    shape_strides_array_type = types.Array(dtype=types.intp, ndim=1, layout='C')
+    arycls = context.make_array(shape_strides_array_type)
+
+    shape_constant = cgutils.pack_array(builder,
+                                        [context.get_constant(types.intp,
+                                                              newty.ndim)])
+
+    sizeof_intp = context.get_abi_sizeof(context.get_data_type(types.intp))
+    sizeof_intp = context.get_constant(types.intp, sizeof_intp)
+    strides_constant = cgutils.pack_array(builder, [sizeof_intp])
+
+    shape_ary = arycls(context, builder)
+
+    populate_array(shape_ary,
+                   data=shape_data,
+                   shape=shape_constant,
+                   strides=strides_constant,
+                   itemsize=sizeof_intp,
+                   meminfo=None)
+
+    strides_ary = arycls(context, builder)
+    populate_array(strides_ary,
+                   data=strides_data,
+                   shape=shape_constant,
+                   strides=strides_constant,
+                   itemsize=sizeof_intp,
+                   meminfo=None)
+
+    shape = shape_ary._getvalue()
+    strides = strides_ary._getvalue()
+    args = [nd, shape, strides, old_itemsize, new_itemsize,
+            context.get_constant(types.int8, new_layout)]
+
+    sig = signature(types.boolean,
+                    types.intp,  # nd
+                    shape_strides_array_type,  # dims
+                    shape_strides_array_type,  # strides
+                    types.intp,  # old_itemsize
+                    types.intp,  # new_itemsize
+                    types.int8,  # layout
+                    )
+
+    res = context.compile_internal(builder, imp, sig, args)
+    update_array_info(newty, ary)
+    res = impl_ret_borrowed(context, builder, sig.return_type, res)
+    return res
+
+
+@overload(np.shape)
+def np_shape(a):
+    if not type_can_asarray(a):
+        raise errors.TypingError("The argument to np.shape must be array-like")
+
+    def impl(a):
+        return np.asarray(a).shape
+    return impl
+
+
+@overload(np.size)
+def np_size(a):
+    if not type_can_asarray(a):
+        raise errors.TypingError("The argument to np.size must be array-like")
+
+    def impl(a):
+        return np.asarray(a).size
+    return impl
+
+# ------------------------------------------------------------------------------
+
+
+@overload(np.unique)
+def np_unique(ar):
+    def np_unique_impl(ar):
+        def isnan(x):
+            # instead of np.isnan because it can't handle non-numeric type
+            return not (x == x)
+        b = np.sort(ar.ravel())
+        head = list(b[:1])
+        tail = [
+            x for i, x in enumerate(b[1:])
+            if b[i] != x and not (isnan(b[i]) and isnan(x))
+        ]
+        return np.array(head + tail)
+    return np_unique_impl
+
+
+@overload(np.repeat)
+def np_repeat(a, repeats):
+    # Implementation for repeats being a scalar is a module global function
+    # (see below) because it might be called from the implementation below.
+
+    def np_repeat_impl_repeats_array_like(a, repeats):
+        # implementation if repeats is an array like
+        repeats_array = np.asarray(repeats, dtype=np.int64)
+        # if it is a singleton array, invoke the scalar implementation
+        if repeats_array.shape[0] == 1:
+            return np_repeat_impl_repeats_scaler(a, repeats_array[0])
+        if np.any(repeats_array < 0):
+            raise ValueError("negative dimensions are not allowed")
+        asa = np.asarray(a)
+        aravel = asa.ravel()
+        n = aravel.shape[0]
+        if aravel.shape != repeats_array.shape:
+            raise ValueError(
+                "operands could not be broadcast together")
+        to_return = np.empty(np.sum(repeats_array), dtype=asa.dtype)
+        pos = 0
+        for i in range(n):
+            to_return[pos : pos + repeats_array[i]] = aravel[i]
+            pos += repeats_array[i]
+        return to_return
+
+    # type checking
+    if isinstance(a, (types.Array,
+                      types.List,
+                      types.BaseTuple,
+                      types.Number,
+                      types.Boolean,
+                      )
+                  ):
+        if isinstance(repeats, types.Integer):
+            return np_repeat_impl_repeats_scaler
+        elif isinstance(repeats, (types.Array, types.List)):
+            if isinstance(repeats.dtype, types.Integer):
+                return np_repeat_impl_repeats_array_like
+
+        raise errors.TypingError(
+            "The repeats argument must be an integer "
+            "or an array-like of integer dtype")
+
+
+@register_jitable
+def np_repeat_impl_repeats_scaler(a, repeats):
+    if repeats < 0:
+        raise ValueError("negative dimensions are not allowed")
+    asa = np.asarray(a)
+    aravel = asa.ravel()
+    n = aravel.shape[0]
+    if repeats == 0:
+        return np.empty(0, dtype=asa.dtype)
+    elif repeats == 1:
+        return np.copy(aravel)
+    else:
+        to_return = np.empty(n * repeats, dtype=asa.dtype)
+        for i in range(n):
+            to_return[i * repeats : (i + 1) * repeats] = aravel[i]
+        return to_return
+
+
+@extending.overload_method(types.Array, 'repeat')
+def array_repeat(a, repeats):
+    def array_repeat_impl(a, repeats):
+        return np.repeat(a, repeats)
+
+    return array_repeat_impl
+
+
+@intrinsic
+def _intrin_get_itemsize(tyctx, dtype):
+    """Computes the itemsize of the dtype"""
+    sig = types.intp(dtype)
+
+    def codegen(cgctx, builder, sig, llargs):
+        llty = cgctx.get_data_type(sig.args[0].dtype)
+        llintp = cgctx.get_data_type(sig.return_type)
+        return llintp(cgctx.get_abi_sizeof(llty))
+    return sig, codegen
+
+
+def _compatible_view(a, dtype):
+    pass
+
+
+@overload(_compatible_view, target='generic')
+def ol_compatible_view(a, dtype):
+    """Determines if the array and dtype are compatible for forming a view."""
+    # NOTE: NumPy 1.23+ uses this check.
+    # Code based on:
+    # https://github.com/numpy/numpy/blob/750ad21258cfc00663586d5a466e24f91b48edc7/numpy/core/src/multiarray/getset.c#L500-L555  # noqa: E501
+    def impl(a, dtype):
+        dtype_size = _intrin_get_itemsize(dtype)
+        if dtype_size != a.itemsize:
+            # catch forbidden cases
+            if a.ndim == 0:
+                msg1 = ("Changing the dtype of a 0d array is only supported "
+                        "if the itemsize is unchanged")
+                raise ValueError(msg1)
+            else:
+                # NumPy has a check here for subarray type conversion which
+                # Numba doesn't support
+                pass
+
+            # Resize on last axis only
+            axis = a.ndim - 1
+            p1 = a.shape[axis] != 1
+            p2 = a.size != 0
+            p3 = a.strides[axis] != a.itemsize
+            if (p1 and p2 and p3):
+                msg2 = ("To change to a dtype of a different size, the last "
+                        "axis must be contiguous")
+                raise ValueError(msg2)
+
+            if dtype_size < a.itemsize:
+                if dtype_size == 0 or a.itemsize % dtype_size != 0:
+                    msg3 = ("When changing to a smaller dtype, its size must "
+                            "be a divisor of the size of original dtype")
+                    raise ValueError(msg3)
+            else:
+                newdim = a.shape[axis] * a.itemsize
+                if newdim % dtype_size != 0:
+                    msg4 = ("When changing to a larger dtype, its size must be "
+                            "a divisor of the total size in bytes of the last "
+                            "axis of the array.")
+                    raise ValueError(msg4)
+    return impl
+
+
+@lower_builtin('array.view', types.Array, types.DTypeSpec)
+def array_view(context, builder, sig, args):
+    aryty = sig.args[0]
+    retty = sig.return_type
+
+    ary = make_array(aryty)(context, builder, args[0])
+    ret = make_array(retty)(context, builder)
+    # Copy all fields, casting the "data" pointer appropriately
+    fields = set(ret._datamodel._fields)
+    for k in sorted(fields):
+        val = getattr(ary, k)
+        if k == 'data':
+            ptrty = ret.data.type
+            ret.data = builder.bitcast(val, ptrty)
+        else:
+            setattr(ret, k, val)
+
+    if numpy_version >= (1, 23):
+        # NumPy 1.23+ bans views using a dtype that is a different size to that
+        # of the array when the last axis is not contiguous. For example, this
+        # manifests at runtime when a dtype size altering view is requested
+        # on a Fortran ordered array.
+
+        tyctx = context.typing_context
+        fnty = tyctx.resolve_value_type(_compatible_view)
+        _compatible_view_sig = fnty.get_call_type(tyctx, (*sig.args,), {})
+        impl = context.get_function(fnty, _compatible_view_sig)
+        impl(builder, args)
+
+    ok = _change_dtype(context, builder, aryty, retty, ret)
+    fail = builder.icmp_unsigned('==', ok, Constant(ok.type, 0))
+
+    with builder.if_then(fail):
+        msg = "new type not compatible with array"
+        context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+    res = ret._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+# ------------------------------------------------------------------------------
+# Array attributes
+
+@lower_getattr(types.Array, "dtype")
+def array_dtype(context, builder, typ, value):
+    res = context.get_dummy_value()
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.Array, "shape")
+@lower_getattr(types.MemoryView, "shape")
+def array_shape(context, builder, typ, value):
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+    res = array.shape
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.Array, "strides")
+@lower_getattr(types.MemoryView, "strides")
+def array_strides(context, builder, typ, value):
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+    res = array.strides
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.Array, "ndim")
+@lower_getattr(types.MemoryView, "ndim")
+def array_ndim(context, builder, typ, value):
+    res = context.get_constant(types.intp, typ.ndim)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.Array, "size")
+def array_size(context, builder, typ, value):
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+    res = array.nitems
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.Array, "itemsize")
+@lower_getattr(types.MemoryView, "itemsize")
+def array_itemsize(context, builder, typ, value):
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+    res = array.itemsize
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.Array, "nbytes")
+@lower_getattr(types.MemoryView, "nbytes")
+def array_nbytes(context, builder, typ, value):
+    """
+    nbytes = size * itemsize
+    """
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+    res = builder.mul(array.nitems, array.itemsize)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.MemoryView, "contiguous")
+def array_contiguous(context, builder, typ, value):
+    res = context.get_constant(types.boolean, typ.is_contig)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.MemoryView, "c_contiguous")
+def array_c_contiguous(context, builder, typ, value):
+    res = context.get_constant(types.boolean, typ.is_c_contig)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.MemoryView, "f_contiguous")
+def array_f_contiguous(context, builder, typ, value):
+    res = context.get_constant(types.boolean, typ.is_f_contig)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.MemoryView, "readonly")
+def array_readonly(context, builder, typ, value):
+    res = context.get_constant(types.boolean, not typ.mutable)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+# array.ctypes
+
+@lower_getattr(types.Array, "ctypes")
+def array_ctypes(context, builder, typ, value):
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+    # Create new ArrayCType structure
+    act = types.ArrayCTypes(typ)
+    ctinfo = context.make_helper(builder, act)
+    ctinfo.data = array.data
+    ctinfo.meminfo = array.meminfo
+    res = ctinfo._getvalue()
+    return impl_ret_borrowed(context, builder, act, res)
+
+
+@lower_getattr(types.ArrayCTypes, "data")
+def array_ctypes_data(context, builder, typ, value):
+    ctinfo = context.make_helper(builder, typ, value=value)
+    res = ctinfo.data
+    # Convert it to an integer
+    res = builder.ptrtoint(res, context.get_value_type(types.intp))
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_cast(types.ArrayCTypes, types.CPointer)
+@lower_cast(types.ArrayCTypes, types.voidptr)
+def array_ctypes_to_pointer(context, builder, fromty, toty, val):
+    ctinfo = context.make_helper(builder, fromty, value=val)
+    res = ctinfo.data
+    res = builder.bitcast(res, context.get_value_type(toty))
+    return impl_ret_untracked(context, builder, toty, res)
+
+
+def _call_contiguous_check(checker, context, builder, aryty, ary):
+    """Helper to invoke the contiguous checker function on an array
+
+    Args
+    ----
+    checker :
+        ``numba.numpy_supports.is_contiguous``, or
+        ``numba.numpy_supports.is_fortran``.
+    context : target context
+    builder : llvm ir builder
+    aryty : numba type
+    ary : llvm value
+    """
+    ary = make_array(aryty)(context, builder, value=ary)
+    tup_intp = types.UniTuple(types.intp, aryty.ndim)
+    itemsize = context.get_abi_sizeof(context.get_value_type(aryty.dtype))
+    check_sig = signature(types.bool_, tup_intp, tup_intp, types.intp)
+    check_args = [ary.shape, ary.strides,
+                  context.get_constant(types.intp, itemsize)]
+    is_contig = context.compile_internal(builder, checker, check_sig,
+                                         check_args)
+    return is_contig
+
+
+# array.flags
+
+@lower_getattr(types.Array, "flags")
+def array_flags(context, builder, typ, value):
+    flagsobj = context.make_helper(builder, types.ArrayFlags(typ))
+    flagsobj.parent = value
+    res = flagsobj._getvalue()
+    context.nrt.incref(builder, typ, value)
+    return impl_ret_new_ref(context, builder, typ, res)
+
+
+@lower_getattr(types.ArrayFlags, "contiguous")
+@lower_getattr(types.ArrayFlags, "c_contiguous")
+def array_flags_c_contiguous(context, builder, typ, value):
+    if typ.array_type.layout != 'C':
+        # any layout can still be contiguous
+        flagsobj = context.make_helper(builder, typ, value=value)
+        res = _call_contiguous_check(is_contiguous, context, builder,
+                                     typ.array_type, flagsobj.parent)
+    else:
+        val = typ.array_type.layout == 'C'
+        res = context.get_constant(types.boolean, val)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+@lower_getattr(types.ArrayFlags, "f_contiguous")
+def array_flags_f_contiguous(context, builder, typ, value):
+    if typ.array_type.layout != 'F':
+        # any layout can still be contiguous
+        flagsobj = context.make_helper(builder, typ, value=value)
+        res = _call_contiguous_check(is_fortran, context, builder,
+                                     typ.array_type, flagsobj.parent)
+    else:
+        layout = typ.array_type.layout
+        val = layout == 'F' if typ.array_type.ndim > 1 else layout in 'CF'
+        res = context.get_constant(types.boolean, val)
+    return impl_ret_untracked(context, builder, typ, res)
+
+
+# ------------------------------------------------------------------------------
+# .real / .imag
+
+@lower_getattr(types.Array, "real")
+def array_real_part(context, builder, typ, value):
+    if typ.dtype in types.complex_domain:
+        return array_complex_attr(context, builder, typ, value, attr='real')
+    elif typ.dtype in types.number_domain:
+        # as an identity function
+        return impl_ret_borrowed(context, builder, typ, value)
+    else:
+        raise NotImplementedError('unsupported .real for {}'.format(type.dtype))
+
+
+@lower_getattr(types.Array, "imag")
+def array_imag_part(context, builder, typ, value):
+    if typ.dtype in types.complex_domain:
+        return array_complex_attr(context, builder, typ, value, attr='imag')
+    elif typ.dtype in types.number_domain:
+        # return a readonly zero array
+        sig = signature(typ.copy(readonly=True), typ)
+        arrtype, shapes = _parse_empty_like_args(context, builder, sig, [value])
+        ary = _empty_nd_impl(context, builder, arrtype, shapes)
+        cgutils.memset(builder, ary.data, builder.mul(ary.itemsize,
+                                                      ary.nitems), 0)
+        return impl_ret_new_ref(context, builder, sig.return_type,
+                                ary._getvalue())
+    else:
+        raise NotImplementedError('unsupported .imag for {}'.format(type.dtype))
+
+
+def array_complex_attr(context, builder, typ, value, attr):
+    """
+    Given a complex array, it's memory layout is:
+
+        R C R C R C
+        ^   ^   ^
+
+    (`R` indicates a float for the real part;
+     `C` indicates a float for the imaginary part;
+     the `^` indicates the start of each element)
+
+    To get the real part, we can simply change the dtype and itemsize to that
+    of the underlying float type.  The new layout is:
+
+        R x R x R x
+        ^   ^   ^
+
+    (`x` indicates unused)
+
+    A load operation will use the dtype to determine the number of bytes to
+    load.
+
+    To get the imaginary part, we shift the pointer by 1 float offset and
+    change the dtype and itemsize.  The new layout is:
+
+        x C x C x C
+          ^   ^   ^
+    """
+    if attr not in ['real', 'imag'] or typ.dtype not in types.complex_domain:
+        raise NotImplementedError("cannot get attribute `{}`".format(attr))
+
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+
+    # sizeof underlying float type
+    flty = typ.dtype.underlying_float
+    sizeof_flty = context.get_abi_sizeof(context.get_data_type(flty))
+    itemsize = array.itemsize.type(sizeof_flty)
+
+    # cast data pointer to float type
+    llfltptrty = context.get_value_type(flty).as_pointer()
+    dataptr = builder.bitcast(array.data, llfltptrty)
+
+    # add offset
+    if attr == 'imag':
+        dataptr = builder.gep(dataptr, [ir.IntType(32)(1)])
+
+    # make result
+    resultty = typ.copy(dtype=flty, layout='A')
+    result = make_array(resultty)(context, builder)
+    repl = dict(data=dataptr, itemsize=itemsize)
+    cgutils.copy_struct(result, array, repl)
+    return impl_ret_borrowed(context, builder, resultty, result._getvalue())
+
+
+@overload_method(types.Array, 'conj')
+@overload_method(types.Array, 'conjugate')
+def array_conj(arr):
+    def impl(arr):
+        return np.conj(arr)
+    return impl
+
+# ------------------------------------------------------------------------------
+# DType attribute
+
+
+def dtype_type(context, builder, dtypety, dtypeval):
+    # Just return a dummy opaque value
+    return context.get_dummy_value()
+
+
+lower_getattr(types.DType, 'type')(dtype_type)
+lower_getattr(types.DType, 'kind')(dtype_type)
+
+
+# ------------------------------------------------------------------------------
+# static_getitem on Numba numerical types to create "array" types
+
+
+@lower_builtin('static_getitem', types.NumberClass, types.Any)
+def static_getitem_number_clazz(context, builder, sig, args):
+    """This handles the "static_getitem" when a Numba type is subscripted e.g:
+    var = typed.List.empty_list(float64[::1, :])
+    It only allows this on simple numerical types. Compound types, like
+    records, are not supported.
+    """
+    retty = sig.return_type
+    if isinstance(retty, types.Array):
+        # This isn't used or practically accessible, but has to exist, so just
+        # put in a NULL of the right type.
+        res = context.get_value_type(retty)(None)
+        return impl_ret_untracked(context, builder, retty, res)
+    else:
+        # This should be unreachable unless the implementation on the Type
+        # metaclass is changed.
+        msg = ("Unreachable; the definition of __getitem__ on the "
+               "numba.types.abstract.Type metaclass should prevent access.")
+        raise errors.LoweringError(msg)
+
+
+# ------------------------------------------------------------------------------
+# Structured / record lookup
+
+@lower_getattr_generic(types.Array)
+def array_record_getattr(context, builder, typ, value, attr):
+    """
+    Generic getattr() implementation for record arrays: fetch the given
+    record member, i.e. a subarray.
+    """
+    arrayty = make_array(typ)
+    array = arrayty(context, builder, value)
+
+    rectype = typ.dtype
+    if not isinstance(rectype, types.Record):
+        raise NotImplementedError("attribute %r of %s not defined"
+                                  % (attr, typ))
+    dtype = rectype.typeof(attr)
+    offset = rectype.offset(attr)
+
+    if isinstance(dtype, types.NestedArray):
+        resty = typ.copy(
+            dtype=dtype.dtype, ndim=typ.ndim + dtype.ndim, layout='A')
+    else:
+        resty = typ.copy(dtype=dtype, layout='A')
+
+    raryty = make_array(resty)
+
+    rary = raryty(context, builder)
+
+    constoffset = context.get_constant(types.intp, offset)
+
+    newdataptr = cgutils.pointer_add(
+        builder, array.data, constoffset,  return_type=rary.data.type,
+    )
+    if isinstance(dtype, types.NestedArray):
+        # new shape = recarray shape + inner dimension from nestedarray
+        shape = cgutils.unpack_tuple(builder, array.shape, typ.ndim)
+        shape += [context.get_constant(types.intp, i) for i in dtype.shape]
+        # new strides = recarray strides + strides of the inner nestedarray
+        strides = cgutils.unpack_tuple(builder, array.strides, typ.ndim)
+        strides += [context.get_constant(types.intp, i) for i in dtype.strides]
+        # New datasize = size of elements of the nestedarray
+        datasize = context.get_abi_sizeof(context.get_data_type(dtype.dtype))
+    else:
+        # New shape, strides, and datasize match the underlying array
+        shape = array.shape
+        strides = array.strides
+        datasize = context.get_abi_sizeof(context.get_data_type(dtype))
+    populate_array(rary,
+                   data=newdataptr,
+                   shape=shape,
+                   strides=strides,
+                   itemsize=context.get_constant(types.intp, datasize),
+                   meminfo=array.meminfo,
+                   parent=array.parent)
+    res = rary._getvalue()
+    return impl_ret_borrowed(context, builder, resty, res)
+
+
+@lower_builtin('static_getitem', types.Array, types.StringLiteral)
+def array_record_getitem(context, builder, sig, args):
+    index = args[1]
+    if not isinstance(index, str):
+        # This will fallback to normal getitem
+        raise NotImplementedError
+    return array_record_getattr(context, builder, sig.args[0], args[0], index)
+
+
+@lower_getattr_generic(types.Record)
+def record_getattr(context, builder, typ, value, attr):
+    """
+    Generic getattr() implementation for records: get the given record member.
+    """
+    context.sentry_record_alignment(typ, attr)
+    offset = typ.offset(attr)
+    elemty = typ.typeof(attr)
+
+    if isinstance(elemty, types.NestedArray):
+        # Only a nested array's *data* is stored in a structured array,
+        # so we create an array structure to point to that data.
+        aryty = make_array(elemty)
+        ary = aryty(context, builder)
+        dtype = elemty.dtype
+        newshape = [context.get_constant(types.intp, s) for s in
+                    elemty.shape]
+        newstrides = [context.get_constant(types.intp, s) for s in
+                      elemty.strides]
+        newdata = cgutils.get_record_member(builder, value, offset,
+                                            context.get_data_type(dtype))
+        populate_array(
+            ary,
+            data=newdata,
+            shape=cgutils.pack_array(builder, newshape),
+            strides=cgutils.pack_array(builder, newstrides),
+            itemsize=context.get_constant(types.intp, elemty.size),
+            meminfo=None,
+            parent=None,
+        )
+        res = ary._getvalue()
+        return impl_ret_borrowed(context, builder, typ, res)
+    else:
+        dptr = cgutils.get_record_member(builder, value, offset,
+                                         context.get_data_type(elemty))
+        align = None if typ.aligned else 1
+        res = context.unpack_value(builder, elemty, dptr, align)
+        return impl_ret_borrowed(context, builder, typ, res)
+
+
+@lower_setattr_generic(types.Record)
+def record_setattr(context, builder, sig, args, attr):
+    """
+    Generic setattr() implementation for records: set the given record member.
+    """
+    typ, valty = sig.args
+    target, val = args
+
+    context.sentry_record_alignment(typ, attr)
+    offset = typ.offset(attr)
+    elemty = typ.typeof(attr)
+
+    if isinstance(elemty, types.NestedArray):
+        # Copy the data from the RHS into the nested array
+        val_struct = cgutils.create_struct_proxy(valty)(context, builder,
+                                                        value=args[1])
+        src = val_struct.data
+        dest = cgutils.get_record_member(builder, target, offset,
+                                         src.type.pointee)
+        cgutils.memcpy(builder, dest, src,
+                       context.get_constant(types.intp, elemty.nitems))
+    else:
+        # Set the given scalar record member
+        dptr = cgutils.get_record_member(builder, target, offset,
+                                         context.get_data_type(elemty))
+        val = context.cast(builder, val, valty, elemty)
+        align = None if typ.aligned else 1
+        context.pack_value(builder, elemty, val, dptr, align=align)
+
+
+@lower_builtin('static_getitem', types.Record, types.StringLiteral)
+def record_static_getitem_str(context, builder, sig, args):
+    """
+    Record.__getitem__ redirects to getattr()
+    """
+    impl = context.get_getattr(sig.args[0], args[1])
+    return impl(context, builder, sig.args[0], args[0], args[1])
+
+
+@lower_builtin('static_getitem', types.Record, types.IntegerLiteral)
+def record_static_getitem_int(context, builder, sig, args):
+    """
+    Record.__getitem__ redirects to getattr()
+    """
+    idx = sig.args[1].literal_value
+    fields = list(sig.args[0].fields)
+    ll_field = context.insert_const_string(builder.module, fields[idx])
+    impl = context.get_getattr(sig.args[0], ll_field)
+    return impl(context, builder, sig.args[0], args[0], fields[idx])
+
+
+@lower_builtin('static_setitem', types.Record, types.StringLiteral, types.Any)
+def record_static_setitem_str(context, builder, sig, args):
+    """
+    Record.__setitem__ redirects to setattr()
+    """
+    recty, _, valty = sig.args
+    rec, idx, val = args
+    getattr_sig = signature(sig.return_type, recty, valty)
+    impl = context.get_setattr(idx, getattr_sig)
+    assert impl is not None
+    return impl(builder, (rec, val))
+
+
+@lower_builtin('static_setitem', types.Record, types.IntegerLiteral, types.Any)
+def record_static_setitem_int(context, builder, sig, args):
+    """
+    Record.__setitem__ redirects to setattr()
+    """
+    recty, _, valty = sig.args
+    rec, idx, val = args
+    getattr_sig = signature(sig.return_type, recty, valty)
+    fields = list(sig.args[0].fields)
+    impl = context.get_setattr(fields[idx], getattr_sig)
+    assert impl is not None
+    return impl(builder, (rec, val))
+
+
+# ------------------------------------------------------------------------------
+# Constant arrays and records
+
+
+@lower_constant(types.Array)
+def constant_array(context, builder, ty, pyval):
+    """
+    Create a constant array (mechanism is target-dependent).
+    """
+    return context.make_constant_array(builder, ty, pyval)
+
+
+@lower_constant(types.Record)
+def constant_record(context, builder, ty, pyval):
+    """
+    Create a record constant as a stack-allocated array of bytes.
+    """
+    lty = ir.ArrayType(ir.IntType(8), pyval.nbytes)
+    val = lty(bytearray(pyval.tobytes()))
+    return cgutils.alloca_once_value(builder, val)
+
+
+@lower_constant(types.Bytes)
+def constant_bytes(context, builder, ty, pyval):
+    """
+    Create a constant array from bytes (mechanism is target-dependent).
+    """
+    buf = np.array(bytearray(pyval), dtype=np.uint8)
+    return context.make_constant_array(builder, ty, buf)
+
+# ------------------------------------------------------------------------------
+# Comparisons
+
+
+@lower_builtin(operator.is_, types.Array, types.Array)
+def array_is(context, builder, sig, args):
+    aty, bty = sig.args
+    if aty != bty:
+        return cgutils.false_bit
+
+    def array_is_impl(a, b):
+        return (a.shape == b.shape and
+                a.strides == b.strides and
+                a.ctypes.data == b.ctypes.data)
+
+    return context.compile_internal(builder, array_is_impl, sig, args)
+
+# ------------------------------------------------------------------------------
+# Hash
+
+
+@overload_attribute(types.Array, "__hash__")
+def ol_array_hash(arr):
+    return lambda arr: None
+
+
+# ------------------------------------------------------------------------------
+# builtin `np.flat` implementation
+
+def make_array_flat_cls(flatiterty):
+    """
+    Return the Structure representation of the given *flatiterty* (an
+    instance of types.NumpyFlatType).
+    """
+    return _make_flattening_iter_cls(flatiterty, 'flat')
+
+
+def make_array_ndenumerate_cls(nditerty):
+    """
+    Return the Structure representation of the given *nditerty* (an
+    instance of types.NumpyNdEnumerateType).
+    """
+    return _make_flattening_iter_cls(nditerty, 'ndenumerate')
+
+
+def _increment_indices(context, builder, ndim, shape, indices, end_flag=None,
+                       loop_continue=None, loop_break=None):
+    zero = context.get_constant(types.intp, 0)
+
+    bbend = builder.append_basic_block('end_increment')
+
+    if end_flag is not None:
+        builder.store(cgutils.false_byte, end_flag)
+
+    for dim in reversed(range(ndim)):
+        idxptr = cgutils.gep_inbounds(builder, indices, dim)
+        idx = cgutils.increment_index(builder, builder.load(idxptr))
+
+        count = shape[dim]
+        in_bounds = builder.icmp_signed('<', idx, count)
+        with cgutils.if_likely(builder, in_bounds):
+            # New index is still in bounds
+            builder.store(idx, idxptr)
+            if loop_continue is not None:
+                loop_continue(dim)
+            builder.branch(bbend)
+        # Index out of bounds => reset it and proceed it to outer index
+        builder.store(zero, idxptr)
+        if loop_break is not None:
+            loop_break(dim)
+
+    if end_flag is not None:
+        builder.store(cgutils.true_byte, end_flag)
+    builder.branch(bbend)
+
+    builder.position_at_end(bbend)
+
+
+def _increment_indices_array(context, builder, arrty, arr, indices,
+                             end_flag=None):
+    shape = cgutils.unpack_tuple(builder, arr.shape, arrty.ndim)
+    _increment_indices(context, builder, arrty.ndim, shape, indices, end_flag)
+
+
+def make_nditer_cls(nditerty):
+    """
+    Return the Structure representation of the given *nditerty* (an
+    instance of types.NumpyNdIterType).
+    """
+    ndim = nditerty.ndim
+    layout = nditerty.layout
+    narrays = len(nditerty.arrays)
+    nshapes = ndim if nditerty.need_shaped_indexing else 1
+
+    class BaseSubIter(object):
+        """
+        Base class for sub-iterators of a nditer() instance.
+        """
+
+        def __init__(self, nditer, member_name, start_dim, end_dim):
+            self.nditer = nditer
+            self.member_name = member_name
+            self.start_dim = start_dim
+            self.end_dim = end_dim
+            self.ndim = end_dim - start_dim
+
+        def set_member_ptr(self, ptr):
+            setattr(self.nditer, self.member_name, ptr)
+
+        @functools.cached_property
+        def member_ptr(self):
+            return getattr(self.nditer, self.member_name)
+
+        def init_specific(self, context, builder):
+            pass
+
+        def loop_continue(self, context, builder, logical_dim):
+            pass
+
+        def loop_break(self, context, builder, logical_dim):
+            pass
+
+    class FlatSubIter(BaseSubIter):
+        """
+        Sub-iterator walking a contiguous array in physical order, with
+        support for broadcasting (the index is reset on the outer dimension).
+        """
+
+        def init_specific(self, context, builder):
+            zero = context.get_constant(types.intp, 0)
+            self.set_member_ptr(cgutils.alloca_once_value(builder, zero))
+
+        def compute_pointer(self, context, builder, indices, arrty, arr):
+            index = builder.load(self.member_ptr)
+            return builder.gep(arr.data, [index])
+
+        def loop_continue(self, context, builder, logical_dim):
+            if logical_dim == self.ndim - 1:
+                # Only increment index inside innermost logical dimension
+                index = builder.load(self.member_ptr)
+                index = cgutils.increment_index(builder, index)
+                builder.store(index, self.member_ptr)
+
+        def loop_break(self, context, builder, logical_dim):
+            if logical_dim == 0:
+                # At the exit of outermost logical dimension, reset index
+                zero = context.get_constant(types.intp, 0)
+                builder.store(zero, self.member_ptr)
+            elif logical_dim == self.ndim - 1:
+                # Inside innermost logical dimension, increment index
+                index = builder.load(self.member_ptr)
+                index = cgutils.increment_index(builder, index)
+                builder.store(index, self.member_ptr)
+
+    class TrivialFlatSubIter(BaseSubIter):
+        """
+        Sub-iterator walking a contiguous array in physical order,
+        *without* support for broadcasting.
+        """
+
+        def init_specific(self, context, builder):
+            assert not nditerty.need_shaped_indexing
+
+        def compute_pointer(self, context, builder, indices, arrty, arr):
+            assert len(indices) <= 1, len(indices)
+            return builder.gep(arr.data, indices)
+
+    class IndexedSubIter(BaseSubIter):
+        """
+        Sub-iterator walking an array in logical order.
+        """
+
+        def compute_pointer(self, context, builder, indices, arrty, arr):
+            assert len(indices) == self.ndim
+            return cgutils.get_item_pointer(context, builder, arrty, arr,
+                                            indices, wraparound=False)
+
+    class ZeroDimSubIter(BaseSubIter):
+        """
+        Sub-iterator "walking" a 0-d array.
+        """
+
+        def compute_pointer(self, context, builder, indices, arrty, arr):
+            return arr.data
+
+    class ScalarSubIter(BaseSubIter):
+        """
+        Sub-iterator "walking" a scalar value.
+        """
+
+        def compute_pointer(self, context, builder, indices, arrty, arr):
+            return arr
+
+    class NdIter(cgutils.create_struct_proxy(nditerty)):
+        """
+        .nditer() implementation.
+
+        Note: 'F' layout means the shape is iterated in reverse logical order,
+        so indices and shapes arrays have to be reversed as well.
+        """
+
+        @functools.cached_property
+        def subiters(self):
+            l = []
+            factories = {'flat': FlatSubIter if nditerty.need_shaped_indexing
+                         else TrivialFlatSubIter,
+                         'indexed': IndexedSubIter,
+                         '0d': ZeroDimSubIter,
+                         'scalar': ScalarSubIter,
+                         }
+            for i, sub in enumerate(nditerty.indexers):
+                kind, start_dim, end_dim, _ = sub
+                member_name = 'index%d' % i
+                factory = factories[kind]
+                l.append(factory(self, member_name, start_dim, end_dim))
+            return l
+
+        def init_specific(self, context, builder, arrtys, arrays):
+            """
+            Initialize the nditer() instance for the specific array inputs.
+            """
+            zero = context.get_constant(types.intp, 0)
+
+            # Store inputs
+            self.arrays = context.make_tuple(builder, types.Tuple(arrtys),
+                                             arrays)
+            # Create slots for scalars
+            for i, ty in enumerate(arrtys):
+                if not isinstance(ty, types.Array):
+                    member_name = 'scalar%d' % i
+                    # XXX as_data()?
+                    slot = cgutils.alloca_once_value(builder, arrays[i])
+                    setattr(self, member_name, slot)
+
+            arrays = self._arrays_or_scalars(context, builder, arrtys, arrays)
+
+            # Extract iterator shape (the shape of the most-dimensional input)
+            main_shape_ty = types.UniTuple(types.intp, ndim)
+            main_shape = None
+            main_nitems = None
+            for i, arrty in enumerate(arrtys):
+                if isinstance(arrty, types.Array) and arrty.ndim == ndim:
+                    main_shape = arrays[i].shape
+                    main_nitems = arrays[i].nitems
+                    break
+            else:
+                # Only scalar inputs => synthesize a dummy shape
+                assert ndim == 0
+                main_shape = context.make_tuple(builder, main_shape_ty, ())
+                main_nitems = context.get_constant(types.intp, 1)
+
+            # Validate shapes of array inputs
+            def check_shape(shape, main_shape):
+                n = len(shape)
+                for i in range(n):
+                    if shape[i] != main_shape[len(main_shape) - n + i]:
+                        raise ValueError("nditer(): operands could not be "
+                                         "broadcast together")
+
+            for arrty, arr in zip(arrtys, arrays):
+                if isinstance(arrty, types.Array) and arrty.ndim > 0:
+                    sig = signature(types.none,
+                                    types.UniTuple(types.intp, arrty.ndim),
+                                    main_shape_ty)
+                    context.compile_internal(builder, check_shape,
+                                             sig, (arr.shape, main_shape))
+
+            # Compute shape and size
+            shapes = cgutils.unpack_tuple(builder, main_shape)
+            if layout == 'F':
+                shapes = shapes[::-1]
+
+            # If shape is empty, mark iterator exhausted
+            shape_is_empty = builder.icmp_signed('==', main_nitems, zero)
+            exhausted = builder.select(shape_is_empty, cgutils.true_byte,
+                                       cgutils.false_byte)
+
+            if not nditerty.need_shaped_indexing:
+                # Flatten shape to make iteration faster on small innermost
+                # dimensions (e.g. a (100000, 3) shape)
+                shapes = (main_nitems,)
+            assert len(shapes) == nshapes
+
+            indices = cgutils.alloca_once(builder, zero.type, size=nshapes)
+            for dim in range(nshapes):
+                idxptr = cgutils.gep_inbounds(builder, indices, dim)
+                builder.store(zero, idxptr)
+
+            self.indices = indices
+            self.shape = cgutils.pack_array(builder, shapes, zero.type)
+            self.exhausted = cgutils.alloca_once_value(builder, exhausted)
+
+            # Initialize subiterators
+            for subiter in self.subiters:
+                subiter.init_specific(context, builder)
+
+        def iternext_specific(self, context, builder, result):
+            """
+            Compute next iteration of the nditer() instance.
+            """
+            bbend = builder.append_basic_block('end')
+
+            # Branch early if exhausted
+            exhausted = cgutils.as_bool_bit(builder,
+                                            builder.load(self.exhausted))
+            with cgutils.if_unlikely(builder, exhausted):
+                result.set_valid(False)
+                builder.branch(bbend)
+
+            arrtys = nditerty.arrays
+            arrays = cgutils.unpack_tuple(builder, self.arrays)
+            arrays = self._arrays_or_scalars(context, builder, arrtys, arrays)
+            indices = self.indices
+
+            # Compute iterated results
+            result.set_valid(True)
+            views = self._make_views(context, builder, indices, arrtys, arrays)
+            views = [v._getvalue() for v in views]
+            if len(views) == 1:
+                result.yield_(views[0])
+            else:
+                result.yield_(context.make_tuple(builder, nditerty.yield_type,
+                                                 views))
+
+            shape = cgutils.unpack_tuple(builder, self.shape)
+            _increment_indices(context, builder, len(shape), shape,
+                               indices, self.exhausted,
+                               functools.partial(self._loop_continue,
+                                                 context,
+                                                 builder),
+                               functools.partial(self._loop_break,
+                                                 context,
+                                                 builder),
+                               )
+
+            builder.branch(bbend)
+            builder.position_at_end(bbend)
+
+        def _loop_continue(self, context, builder, dim):
+            for sub in self.subiters:
+                if sub.start_dim <= dim < sub.end_dim:
+                    sub.loop_continue(context, builder, dim - sub.start_dim)
+
+        def _loop_break(self, context, builder, dim):
+            for sub in self.subiters:
+                if sub.start_dim <= dim < sub.end_dim:
+                    sub.loop_break(context, builder, dim - sub.start_dim)
+
+        def _make_views(self, context, builder, indices, arrtys, arrays):
+            """
+            Compute the views to be yielded.
+            """
+            views = [None] * narrays
+            indexers = nditerty.indexers
+            subiters = self.subiters
+            rettys = nditerty.yield_type
+            if isinstance(rettys, types.BaseTuple):
+                rettys = list(rettys)
+            else:
+                rettys = [rettys]
+            indices = [builder.load(cgutils.gep_inbounds(builder, indices, i))
+                       for i in range(nshapes)]
+
+            for sub, subiter in zip(indexers, subiters):
+                _, _, _, array_indices = sub
+                sub_indices = indices[subiter.start_dim:subiter.end_dim]
+                if layout == 'F':
+                    sub_indices = sub_indices[::-1]
+                for i in array_indices:
+                    assert views[i] is None
+                    views[i] = self._make_view(context, builder, sub_indices,
+                                               rettys[i],
+                                               arrtys[i], arrays[i], subiter)
+            assert all(v for v in views)
+            return views
+
+        def _make_view(self, context, builder, indices, retty, arrty, arr,
+                       subiter):
+            """
+            Compute a 0d view for a given input array.
+            """
+            assert isinstance(retty, types.Array) and retty.ndim == 0
+
+            ptr = subiter.compute_pointer(context, builder, indices, arrty, arr)
+            view = context.make_array(retty)(context, builder)
+
+            itemsize = get_itemsize(context, retty)
+            shape = context.make_tuple(builder, types.UniTuple(types.intp, 0),
+                                       ())
+            strides = context.make_tuple(builder, types.UniTuple(types.intp, 0),
+                                         ())
+            # HACK: meminfo=None avoids expensive refcounting operations
+            # on ephemeral views
+            populate_array(view, ptr, shape, strides, itemsize, meminfo=None)
+            return view
+
+        def _arrays_or_scalars(self, context, builder, arrtys, arrays):
+            # Return a list of either array structures or pointers to
+            # scalar slots
+            l = []
+            for i, (arrty, arr) in enumerate(zip(arrtys, arrays)):
+                if isinstance(arrty, types.Array):
+                    l.append(context.make_array(arrty)(context,
+                                                       builder,
+                                                       value=arr))
+                else:
+                    l.append(getattr(self, "scalar%d" % i))
+            return l
+
+    return NdIter
+
+
+def make_ndindex_cls(nditerty):
+    """
+    Return the Structure representation of the given *nditerty* (an
+    instance of types.NumpyNdIndexType).
+    """
+    ndim = nditerty.ndim
+
+    class NdIndexIter(cgutils.create_struct_proxy(nditerty)):
+        """
+        .ndindex() implementation.
+        """
+
+        def init_specific(self, context, builder, shapes):
+            zero = context.get_constant(types.intp, 0)
+            indices = cgutils.alloca_once(builder, zero.type,
+                                          size=context.get_constant(types.intp,
+                                                                    ndim))
+            exhausted = cgutils.alloca_once_value(builder, cgutils.false_byte)
+
+            for dim in range(ndim):
+                idxptr = cgutils.gep_inbounds(builder, indices, dim)
+                builder.store(zero, idxptr)
+                # 0-sized dimensions really indicate an empty array,
+                # but we have to catch that condition early to avoid
+                # a bug inside the iteration logic.
+                dim_size = shapes[dim]
+                dim_is_empty = builder.icmp_unsigned('==', dim_size, zero)
+                with cgutils.if_unlikely(builder, dim_is_empty):
+                    builder.store(cgutils.true_byte, exhausted)
+
+            self.indices = indices
+            self.exhausted = exhausted
+            self.shape = cgutils.pack_array(builder, shapes, zero.type)
+
+        def iternext_specific(self, context, builder, result):
+            zero = context.get_constant(types.intp, 0)
+
+            bbend = builder.append_basic_block('end')
+
+            exhausted = cgutils.as_bool_bit(builder,
+                                            builder.load(self.exhausted))
+            with cgutils.if_unlikely(builder, exhausted):
+                result.set_valid(False)
+                builder.branch(bbend)
+
+            indices = [builder.load(cgutils.gep_inbounds(builder,
+                                                         self.indices,
+                                                         dim))
+                       for dim in range(ndim)]
+            for load in indices:
+                mark_positive(builder, load)
+
+            result.yield_(cgutils.pack_array(builder, indices, zero.type))
+            result.set_valid(True)
+
+            shape = cgutils.unpack_tuple(builder, self.shape, ndim)
+            _increment_indices(context, builder, ndim, shape,
+                               self.indices, self.exhausted)
+
+            builder.branch(bbend)
+            builder.position_at_end(bbend)
+
+    return NdIndexIter
+
+
+def _make_flattening_iter_cls(flatiterty, kind):
+    assert kind in ('flat', 'ndenumerate')
+
+    array_type = flatiterty.array_type
+
+    if array_type.layout == 'C':
+        class CContiguousFlatIter(cgutils.create_struct_proxy(flatiterty)):
+            """
+            .flat() / .ndenumerate() implementation for C-contiguous arrays.
+            """
+
+            def init_specific(self, context, builder, arrty, arr):
+                zero = context.get_constant(types.intp, 0)
+                self.index = cgutils.alloca_once_value(builder, zero)
+                # We can't trust strides[-1] to always contain the right
+                # step value, see
+                # http://docs.scipy.org/doc/numpy-dev/release.html#npy-relaxed-strides-checking    # noqa: E501
+                self.stride = arr.itemsize
+
+                if kind == 'ndenumerate':
+                    # Zero-initialize the indices array.
+                    indices = cgutils.alloca_once(
+                        builder, zero.type,
+                        size=context.get_constant(types.intp, arrty.ndim))
+
+                    for dim in range(arrty.ndim):
+                        idxptr = cgutils.gep_inbounds(builder, indices, dim)
+                        builder.store(zero, idxptr)
+
+                    self.indices = indices
+
+            # NOTE: Using gep() instead of explicit pointer addition helps
+            # LLVM vectorize the loop (since the stride is known and
+            # constant).  This is not possible in the non-contiguous case,
+            # where the strides are unknown at compile-time.
+
+            def iternext_specific(self, context, builder, arrty, arr, result):
+                ndim = arrty.ndim
+                nitems = arr.nitems
+
+                index = builder.load(self.index)
+                is_valid = builder.icmp_signed('<', index, nitems)
+                result.set_valid(is_valid)
+
+                with cgutils.if_likely(builder, is_valid):
+                    ptr = builder.gep(arr.data, [index])
+                    value = load_item(context, builder, arrty, ptr)
+                    if kind == 'flat':
+                        result.yield_(value)
+                    else:
+                        # ndenumerate(): fetch and increment indices
+                        indices = self.indices
+                        idxvals = [builder.load(cgutils.gep_inbounds(builder,
+                                                                     indices,
+                                                                     dim))
+                                   for dim in range(ndim)]
+                        idxtuple = cgutils.pack_array(builder, idxvals,
+                                                      ty=context.get_data_type(
+                                                          types.intp))
+                        result.yield_(
+                            cgutils.make_anonymous_struct(builder,
+                                                          [idxtuple, value]))
+                        _increment_indices_array(context, builder, arrty,
+                                                 arr, indices)
+
+                    index = cgutils.increment_index(builder, index)
+                    builder.store(index, self.index)
+
+            def getitem(self, context, builder, arrty, arr, index):
+                ptr = builder.gep(arr.data, [index])
+                return load_item(context, builder, arrty, ptr)
+
+            def setitem(self, context, builder, arrty, arr, index, value):
+                ptr = builder.gep(arr.data, [index])
+                store_item(context, builder, arrty, value, ptr)
+
+        return CContiguousFlatIter
+
+    else:
+        class FlatIter(cgutils.create_struct_proxy(flatiterty)):
+            """
+            Generic .flat() / .ndenumerate() implementation for
+            non-contiguous arrays.
+            It keeps track of pointers along each dimension in order to
+            minimize computations.
+            """
+
+            def init_specific(self, context, builder, arrty, arr):
+                zero = context.get_constant(types.intp, 0)
+                data = arr.data
+                ndim = arrty.ndim
+                shapes = cgutils.unpack_tuple(builder, arr.shape, ndim)
+
+                indices = cgutils.alloca_once(
+                    builder, zero.type, size=context.get_constant(types.intp,
+                                                                  arrty.ndim))
+                pointers = cgutils.alloca_once(
+                    builder, data.type, size=context.get_constant(types.intp,
+                                                                  arrty.ndim))
+                exhausted = cgutils.alloca_once_value(builder,
+                                                      cgutils.false_byte)
+
+                # Initialize indices and pointers with their start values.
+                for dim in range(ndim):
+                    idxptr = cgutils.gep_inbounds(builder, indices, dim)
+                    ptrptr = cgutils.gep_inbounds(builder, pointers, dim)
+                    builder.store(data, ptrptr)
+                    builder.store(zero, idxptr)
+                    # 0-sized dimensions really indicate an empty array,
+                    # but we have to catch that condition early to avoid
+                    # a bug inside the iteration logic (see issue #846).
+                    dim_size = shapes[dim]
+                    dim_is_empty = builder.icmp_unsigned('==', dim_size, zero)
+                    with cgutils.if_unlikely(builder, dim_is_empty):
+                        builder.store(cgutils.true_byte, exhausted)
+
+                self.indices = indices
+                self.pointers = pointers
+                self.exhausted = exhausted
+
+            def iternext_specific(self, context, builder, arrty, arr, result):
+                ndim = arrty.ndim
+                shapes = cgutils.unpack_tuple(builder, arr.shape, ndim)
+                strides = cgutils.unpack_tuple(builder, arr.strides, ndim)
+                indices = self.indices
+                pointers = self.pointers
+
+                zero = context.get_constant(types.intp, 0)
+
+                bbend = builder.append_basic_block('end')
+
+                # Catch already computed iterator exhaustion
+                is_exhausted = cgutils.as_bool_bit(
+                    builder, builder.load(self.exhausted))
+                with cgutils.if_unlikely(builder, is_exhausted):
+                    result.set_valid(False)
+                    builder.branch(bbend)
+                result.set_valid(True)
+
+                # Current pointer inside last dimension
+                last_ptr = cgutils.gep_inbounds(builder, pointers, ndim - 1)
+                ptr = builder.load(last_ptr)
+                value = load_item(context, builder, arrty, ptr)
+                if kind == 'flat':
+                    result.yield_(value)
+                else:
+                    # ndenumerate() => yield (indices, value)
+                    idxvals = [builder.load(cgutils.gep_inbounds(builder,
+                                                                 indices,
+                                                                 dim))
+                               for dim in range(ndim)]
+                    idxtuple = cgutils.pack_array(builder, idxvals)
+                    result.yield_(
+                        cgutils.make_anonymous_struct(builder,
+                                                      [idxtuple, value]))
+
+                # Update indices and pointers by walking from inner
+                # dimension to outer.
+                for dim in reversed(range(ndim)):
+                    idxptr = cgutils.gep_inbounds(builder, indices, dim)
+                    idx = cgutils.increment_index(builder,
+                                                  builder.load(idxptr))
+
+                    count = shapes[dim]
+                    stride = strides[dim]
+                    in_bounds = builder.icmp_signed('<', idx, count)
+                    with cgutils.if_likely(builder, in_bounds):
+                        # Index is valid => pointer can simply be incremented.
+                        builder.store(idx, idxptr)
+                        ptrptr = cgutils.gep_inbounds(builder, pointers, dim)
+                        ptr = builder.load(ptrptr)
+                        ptr = cgutils.pointer_add(builder, ptr, stride)
+                        builder.store(ptr, ptrptr)
+                        # Reset pointers in inner dimensions
+                        for inner_dim in range(dim + 1, ndim):
+                            ptrptr = cgutils.gep_inbounds(builder,
+                                                          pointers,
+                                                          inner_dim)
+                            builder.store(ptr, ptrptr)
+                        builder.branch(bbend)
+                    # Reset index and continue with next dimension
+                    builder.store(zero, idxptr)
+
+                # End of array
+                builder.store(cgutils.true_byte, self.exhausted)
+                builder.branch(bbend)
+
+                builder.position_at_end(bbend)
+
+            def _ptr_for_index(self, context, builder, arrty, arr, index):
+                ndim = arrty.ndim
+                shapes = cgutils.unpack_tuple(builder, arr.shape, count=ndim)
+                strides = cgutils.unpack_tuple(builder, arr.strides, count=ndim)
+
+                # First convert the flattened index into a regular n-dim index
+                indices = []
+                for dim in reversed(range(ndim)):
+                    indices.append(builder.urem(index, shapes[dim]))
+                    index = builder.udiv(index, shapes[dim])
+                indices.reverse()
+
+                ptr = cgutils.get_item_pointer2(context, builder, arr.data,
+                                                shapes, strides, arrty.layout,
+                                                indices)
+                return ptr
+
+            def getitem(self, context, builder, arrty, arr, index):
+                ptr = self._ptr_for_index(context, builder, arrty, arr, index)
+                return load_item(context, builder, arrty, ptr)
+
+            def setitem(self, context, builder, arrty, arr, index, value):
+                ptr = self._ptr_for_index(context, builder, arrty, arr, index)
+                store_item(context, builder, arrty, value, ptr)
+
+        return FlatIter
+
+
+@lower_getattr(types.Array, "flat")
+def make_array_flatiter(context, builder, arrty, arr):
+    flatitercls = make_array_flat_cls(types.NumpyFlatType(arrty))
+    flatiter = flatitercls(context, builder)
+
+    flatiter.array = arr
+
+    arrcls = context.make_array(arrty)
+    arr = arrcls(context, builder, ref=flatiter._get_ptr_by_name('array'))
+
+    flatiter.init_specific(context, builder, arrty, arr)
+
+    res = flatiter._getvalue()
+    return impl_ret_borrowed(context, builder, types.NumpyFlatType(arrty), res)
+
+
+@lower_builtin('iternext', types.NumpyFlatType)
+@iternext_impl(RefType.BORROWED)
+def iternext_numpy_flatiter(context, builder, sig, args, result):
+    [flatiterty] = sig.args
+    [flatiter] = args
+
+    flatitercls = make_array_flat_cls(flatiterty)
+    flatiter = flatitercls(context, builder, value=flatiter)
+
+    arrty = flatiterty.array_type
+    arrcls = context.make_array(arrty)
+    arr = arrcls(context, builder, value=flatiter.array)
+
+    flatiter.iternext_specific(context, builder, arrty, arr, result)
+
+
+@lower_builtin(operator.getitem, types.NumpyFlatType, types.Integer)
+def iternext_numpy_getitem(context, builder, sig, args):
+    flatiterty = sig.args[0]
+    flatiter, index = args
+
+    flatitercls = make_array_flat_cls(flatiterty)
+    flatiter = flatitercls(context, builder, value=flatiter)
+
+    arrty = flatiterty.array_type
+    arrcls = context.make_array(arrty)
+    arr = arrcls(context, builder, value=flatiter.array)
+
+    res = flatiter.getitem(context, builder, arrty, arr, index)
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(operator.setitem, types.NumpyFlatType, types.Integer,
+               types.Any)
+def iternext_numpy_getitem_any(context, builder, sig, args):
+    flatiterty = sig.args[0]
+    flatiter, index, value = args
+
+    flatitercls = make_array_flat_cls(flatiterty)
+    flatiter = flatitercls(context, builder, value=flatiter)
+
+    arrty = flatiterty.array_type
+    arrcls = context.make_array(arrty)
+    arr = arrcls(context, builder, value=flatiter.array)
+
+    flatiter.setitem(context, builder, arrty, arr, index, value)
+    return context.get_dummy_value()
+
+
+@lower_builtin(len, types.NumpyFlatType)
+def iternext_numpy_getitem_flat(context, builder, sig, args):
+    flatiterty = sig.args[0]
+    flatitercls = make_array_flat_cls(flatiterty)
+    flatiter = flatitercls(context, builder, value=args[0])
+
+    arrcls = context.make_array(flatiterty.array_type)
+    arr = arrcls(context, builder, value=flatiter.array)
+    return arr.nitems
+
+
+@lower_builtin(np.ndenumerate, types.Array)
+def make_array_ndenumerate(context, builder, sig, args):
+    arrty, = sig.args
+    arr, = args
+    nditercls = make_array_ndenumerate_cls(types.NumpyNdEnumerateType(arrty))
+    nditer = nditercls(context, builder)
+
+    nditer.array = arr
+
+    arrcls = context.make_array(arrty)
+    arr = arrcls(context, builder, ref=nditer._get_ptr_by_name('array'))
+
+    nditer.init_specific(context, builder, arrty, arr)
+
+    res = nditer._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin('iternext', types.NumpyNdEnumerateType)
+@iternext_impl(RefType.BORROWED)
+def iternext_numpy_nditer(context, builder, sig, args, result):
+    [nditerty] = sig.args
+    [nditer] = args
+
+    nditercls = make_array_ndenumerate_cls(nditerty)
+    nditer = nditercls(context, builder, value=nditer)
+
+    arrty = nditerty.array_type
+    arrcls = context.make_array(arrty)
+    arr = arrcls(context, builder, value=nditer.array)
+
+    nditer.iternext_specific(context, builder, arrty, arr, result)
+
+
+@lower_builtin(pndindex, types.VarArg(types.Integer))
+@lower_builtin(np.ndindex, types.VarArg(types.Integer))
+def make_array_ndindex(context, builder, sig, args):
+    """ndindex(*shape)"""
+    shape = [context.cast(builder, arg, argty, types.intp)
+             for argty, arg in zip(sig.args, args)]
+
+    nditercls = make_ndindex_cls(types.NumpyNdIndexType(len(shape)))
+    nditer = nditercls(context, builder)
+    nditer.init_specific(context, builder, shape)
+
+    res = nditer._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin(pndindex, types.BaseTuple)
+@lower_builtin(np.ndindex, types.BaseTuple)
+def make_array_ndindex_tuple(context, builder, sig, args):
+    """ndindex(shape)"""
+    ndim = sig.return_type.ndim
+    if ndim > 0:
+        idxty = sig.args[0].dtype
+        tup = args[0]
+
+        shape = cgutils.unpack_tuple(builder, tup, ndim)
+        shape = [context.cast(builder, idx, idxty, types.intp)
+                 for idx in shape]
+    else:
+        shape = []
+
+    nditercls = make_ndindex_cls(types.NumpyNdIndexType(len(shape)))
+    nditer = nditercls(context, builder)
+    nditer.init_specific(context, builder, shape)
+
+    res = nditer._getvalue()
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+@lower_builtin('iternext', types.NumpyNdIndexType)
+@iternext_impl(RefType.BORROWED)
+def iternext_numpy_ndindex(context, builder, sig, args, result):
+    [nditerty] = sig.args
+    [nditer] = args
+
+    nditercls = make_ndindex_cls(nditerty)
+    nditer = nditercls(context, builder, value=nditer)
+
+    nditer.iternext_specific(context, builder, result)
+
+
+@lower_builtin(np.nditer, types.Any)
+def make_array_nditer(context, builder, sig, args):
+    """
+    nditer(...)
+    """
+    nditerty = sig.return_type
+    arrtys = nditerty.arrays
+
+    if isinstance(sig.args[0], types.BaseTuple):
+        arrays = cgutils.unpack_tuple(builder, args[0])
+    else:
+        arrays = [args[0]]
+
+    nditer = make_nditer_cls(nditerty)(context, builder)
+    nditer.init_specific(context, builder, arrtys, arrays)
+
+    res = nditer._getvalue()
+    return impl_ret_borrowed(context, builder, nditerty, res)
+
+
+@lower_builtin('iternext', types.NumpyNdIterType)
+@iternext_impl(RefType.BORROWED)
+def iternext_numpy_nditer2(context, builder, sig, args, result):
+    [nditerty] = sig.args
+    [nditer] = args
+
+    nditer = make_nditer_cls(nditerty)(context, builder, value=nditer)
+    nditer.iternext_specific(context, builder, result)
+
+
+@lower_builtin(operator.eq, types.DType, types.DType)
+def dtype_eq_impl(context, builder, sig, args):
+    arg1, arg2 = sig.args
+    res = ir.Constant(ir.IntType(1), int(arg1 == arg2))
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+# ------------------------------------------------------------------------------
+# Numpy array constructors
+
+def _empty_nd_impl(context, builder, arrtype, shapes):
+    """Utility function used for allocating a new array during LLVM code
+    generation (lowering).  Given a target context, builder, array
+    type, and a tuple or list of lowered dimension sizes, returns a
+    LLVM value pointing at a Numba runtime allocated array.
+    """
+    arycls = make_array(arrtype)
+    ary = arycls(context, builder)
+
+    datatype = context.get_data_type(arrtype.dtype)
+    itemsize = context.get_constant(types.intp, get_itemsize(context, arrtype))
+
+    # compute array length
+    arrlen = context.get_constant(types.intp, 1)
+    overflow = Constant(ir.IntType(1), 0)
+    for s in shapes:
+        arrlen_mult = builder.smul_with_overflow(arrlen, s)
+        arrlen = builder.extract_value(arrlen_mult, 0)
+        overflow = builder.or_(
+            overflow, builder.extract_value(arrlen_mult, 1)
+        )
+
+    if arrtype.ndim == 0:
+        strides = ()
+    elif arrtype.layout == 'C':
+        strides = [itemsize]
+        for dimension_size in reversed(shapes[1:]):
+            strides.append(builder.mul(strides[-1], dimension_size))
+        strides = tuple(reversed(strides))
+    elif arrtype.layout == 'F':
+        strides = [itemsize]
+        for dimension_size in shapes[:-1]:
+            strides.append(builder.mul(strides[-1], dimension_size))
+        strides = tuple(strides)
+    else:
+        raise NotImplementedError(
+            "Don't know how to allocate array with layout '{0}'.".format(
+                arrtype.layout))
+
+    # Check overflow, numpy also does this after checking order
+    allocsize_mult = builder.smul_with_overflow(arrlen, itemsize)
+    allocsize = builder.extract_value(allocsize_mult, 0)
+    overflow = builder.or_(overflow, builder.extract_value(allocsize_mult, 1))
+
+    with builder.if_then(overflow, likely=False):
+        # Raise same error as numpy, see:
+        # https://github.com/numpy/numpy/blob/2a488fe76a0f732dc418d03b452caace161673da/numpy/core/src/multiarray/ctors.c#L1095-L1101    # noqa: E501
+        context.call_conv.return_user_exc(
+            builder, ValueError,
+            ("array is too big; `arr.size * arr.dtype.itemsize` is larger than"
+             " the maximum possible size.",)
+        )
+
+    dtype = arrtype.dtype
+    align_val = context.get_preferred_array_alignment(dtype)
+    align = context.get_constant(types.uint32, align_val)
+    args = (context.get_dummy_value(), allocsize, align)
+
+    mip = types.MemInfoPointer(types.voidptr)
+    arytypeclass = types.TypeRef(type(arrtype))
+    argtypes = signature(mip, arytypeclass, types.intp, types.uint32)
+
+    meminfo = context.compile_internal(builder, _call_allocator, argtypes, args)
+    data = context.nrt.meminfo_data(builder, meminfo)
+
+    intp_t = context.get_value_type(types.intp)
+    shape_array = cgutils.pack_array(builder, shapes, ty=intp_t)
+    strides_array = cgutils.pack_array(builder, strides, ty=intp_t)
+
+    populate_array(ary,
+                   data=builder.bitcast(data, datatype.as_pointer()),
+                   shape=shape_array,
+                   strides=strides_array,
+                   itemsize=itemsize,
+                   meminfo=meminfo)
+
+    return ary
+
+
+@overload_classmethod(types.Array, "_allocate", target="CPU")
+def _ol_array_allocate(cls, allocsize, align):
+    """Implements a Numba-only default target (cpu) classmethod on the array
+    type.
+    """
+    def impl(cls, allocsize, align):
+        return intrin_alloc(allocsize, align)
+    return impl
+
+
+def _call_allocator(arrtype, size, align):
+    """Trampoline to call the intrinsic used for allocation
+    """
+    return arrtype._allocate(size, align)
+
+
+@intrinsic
+def intrin_alloc(typingctx, allocsize, align):
+    """Intrinsic to call into the allocator for Array
+    """
+    def codegen(context, builder, signature, args):
+        [allocsize, align] = args
+        meminfo = context.nrt.meminfo_alloc_aligned(builder, allocsize, align)
+        return meminfo
+
+    mip = types.MemInfoPointer(types.voidptr)    # return untyped pointer
+    sig = signature(mip, allocsize, align)
+    return sig, codegen
+
+
+def _parse_shape(context, builder, ty, val):
+    """
+    Parse the shape argument to an array constructor.
+    """
+    def safecast_intp(context, builder, src_t, src):
+        """Cast src to intp only if value can be maintained"""
+        intp_t = context.get_value_type(types.intp)
+        intp_width = intp_t.width
+        intp_ir = ir.IntType(intp_width)
+        maxval = Constant(intp_ir, ((1 << intp_width - 1) - 1))
+        if src_t.width < intp_width:
+            res = builder.sext(src, intp_ir)
+        elif src_t.width >= intp_width:
+            is_larger = builder.icmp_signed(">", src, maxval)
+            with builder.if_then(is_larger, likely=False):
+                context.call_conv.return_user_exc(
+                    builder, ValueError,
+                    ("Cannot safely convert value to intp",)
+                )
+            if src_t.width > intp_width:
+                res = builder.trunc(src, intp_ir)
+            else:
+                res = src
+        return res
+
+    if isinstance(ty, types.Integer):
+        ndim = 1
+        passed_shapes = [context.cast(builder, val, ty, types.intp)]
+    else:
+        assert isinstance(ty, types.BaseTuple)
+        ndim = ty.count
+        passed_shapes = cgutils.unpack_tuple(builder, val, count=ndim)
+
+    shapes = []
+    for s in passed_shapes:
+        shapes.append(safecast_intp(context, builder, s.type, s))
+
+    zero = context.get_constant_generic(builder, types.intp, 0)
+    for dim in range(ndim):
+        is_neg = builder.icmp_signed('<', shapes[dim], zero)
+        with cgutils.if_unlikely(builder, is_neg):
+            context.call_conv.return_user_exc(
+                builder, ValueError, ("negative dimensions not allowed",)
+            )
+
+    return shapes
+
+
+def _parse_empty_args(context, builder, sig, args):
+    """
+    Parse the arguments of a np.empty(), np.zeros() or np.ones() call.
+    """
+    arrshapetype = sig.args[0]
+    arrshape = args[0]
+    arrtype = sig.return_type
+    return arrtype, _parse_shape(context, builder, arrshapetype, arrshape)
+
+
+def _parse_empty_like_args(context, builder, sig, args):
+    """
+    Parse the arguments of a np.empty_like(), np.zeros_like() or
+    np.ones_like() call.
+    """
+    arytype = sig.args[0]
+    if isinstance(arytype, types.Array):
+        ary = make_array(arytype)(context, builder, value=args[0])
+        shapes = cgutils.unpack_tuple(builder, ary.shape, count=arytype.ndim)
+        return sig.return_type, shapes
+    else:
+        return sig.return_type, ()
+
+
+def _check_const_str_dtype(fname, dtype):
+    if isinstance(dtype, types.UnicodeType):
+        msg = f"If np.{fname} dtype is a string it must be a string constant."
+        raise errors.TypingError(msg)
+
+
+@intrinsic
+def numpy_empty_nd(tyctx, ty_shape, ty_dtype, ty_retty_ref):
+    ty_retty = ty_retty_ref.instance_type
+    sig = ty_retty(ty_shape, ty_dtype, ty_retty_ref)
+
+    def codegen(cgctx, builder, sig, llargs):
+        arrtype, shapes = _parse_empty_args(cgctx, builder, sig, llargs)
+        ary = _empty_nd_impl(cgctx, builder, arrtype, shapes)
+        return ary._getvalue()
+    return sig, codegen
+
+
+@overload(np.empty)
+def ol_np_empty(shape, dtype=float):
+    _check_const_str_dtype("empty", dtype)
+    if (dtype is float or
+        (isinstance(dtype, types.Function) and dtype.typing_key is float) or
+            is_nonelike(dtype)): #default
+        nb_dtype = types.double
+    else:
+        nb_dtype = ty_parse_dtype(dtype)
+
+    ndim = ty_parse_shape(shape)
+    if nb_dtype is not None and ndim is not None:
+        retty = types.Array(dtype=nb_dtype, ndim=ndim, layout='C')
+
+        def impl(shape, dtype=float):
+            return numpy_empty_nd(shape, dtype, retty)
+        return impl
+    else:
+        msg = f"Cannot parse input types to function np.empty({shape}, {dtype})"
+        raise errors.TypingError(msg)
+
+
+@intrinsic
+def numpy_empty_like_nd(tyctx, ty_prototype, ty_dtype, ty_retty_ref):
+    ty_retty = ty_retty_ref.instance_type
+    sig = ty_retty(ty_prototype, ty_dtype, ty_retty_ref)
+
+    def codegen(cgctx, builder, sig, llargs):
+        arrtype, shapes = _parse_empty_like_args(cgctx, builder, sig, llargs)
+        ary = _empty_nd_impl(cgctx, builder, arrtype, shapes)
+        return ary._getvalue()
+    return sig, codegen
+
+
+@overload(np.empty_like)
+def ol_np_empty_like(arr, dtype=None):
+    _check_const_str_dtype("empty_like", dtype)
+    if not is_nonelike(dtype):
+        nb_dtype = ty_parse_dtype(dtype)
+    elif isinstance(arr, types.Array):
+        nb_dtype = arr.dtype
+    else:
+        nb_dtype = arr
+    if nb_dtype is not None:
+        if isinstance(arr, types.Array):
+            layout = arr.layout if arr.layout != 'A' else 'C'
+            retty = arr.copy(dtype=nb_dtype, layout=layout, readonly=False)
+        else:
+            retty = types.Array(nb_dtype, 0, 'C')
+    else:
+        msg = ("Cannot parse input types to function "
+               f"np.empty_like({arr}, {dtype})")
+        raise errors.TypingError(msg)
+
+    def impl(arr, dtype=None):
+        return numpy_empty_like_nd(arr, dtype, retty)
+    return impl
+
+
+@intrinsic
+def _zero_fill_array_method(tyctx, self):
+    sig = types.none(self)
+
+    def codegen(cgctx, builder, sig, llargs):
+        ary = make_array(sig.args[0])(cgctx, builder, llargs[0])
+        cgutils.memset(builder, ary.data, builder.mul(ary.itemsize, ary.nitems),
+                       0)
+    return sig, codegen
+
+
+@overload_method(types.Array, '_zero_fill')
+def ol_array_zero_fill(self):
+    """Adds a `._zero_fill` method to zero fill an array using memset."""
+    def impl(self):
+        _zero_fill_array_method(self)
+    return impl
+
+
+@overload(np.zeros)
+def ol_np_zeros(shape, dtype=float):
+    _check_const_str_dtype("zeros", dtype)
+
+    def impl(shape, dtype=float):
+        arr = np.empty(shape, dtype=dtype)
+        arr._zero_fill()
+        return arr
+    return impl
+
+
+@overload(np.zeros_like)
+def ol_np_zeros_like(a, dtype=None):
+    _check_const_str_dtype("zeros_like", dtype)
+
+    # NumPy uses 'a' as the arg name for the array-like
+    def impl(a, dtype=None):
+        arr = np.empty_like(a, dtype=dtype)
+        arr._zero_fill()
+        return arr
+    return impl
+
+
+@overload(np.ones_like)
+def ol_np_ones_like(a, dtype=None):
+    _check_const_str_dtype("ones_like", dtype)
+
+    # NumPy uses 'a' as the arg name for the array-like
+    def impl(a, dtype=None):
+        arr = np.empty_like(a, dtype=dtype)
+        arr_flat = arr.flat
+        for idx in range(len(arr_flat)):
+            arr_flat[idx] = 1
+        return arr
+    return impl
+
+
+@overload(np.full)
+def impl_np_full(shape, fill_value, dtype=None):
+    _check_const_str_dtype("full", dtype)
+    if not is_nonelike(dtype):
+        nb_dtype = ty_parse_dtype(dtype)
+    else:
+        nb_dtype = fill_value
+
+    def full(shape, fill_value, dtype=None):
+        arr = np.empty(shape, nb_dtype)
+        arr_flat = arr.flat
+        for idx in range(len(arr_flat)):
+            arr_flat[idx] = fill_value
+        return arr
+    return full
+
+
+@overload(np.full_like)
+def impl_np_full_like(a, fill_value, dtype=None):
+    _check_const_str_dtype("full_like", dtype)
+
+    def full_like(a, fill_value, dtype=None):
+        arr = np.empty_like(a, dtype)
+        arr_flat = arr.flat
+        for idx in range(len(arr_flat)):
+            arr_flat[idx] = fill_value
+        return arr
+
+    return full_like
+
+
+@overload(np.ones)
+def ol_np_ones(shape, dtype=None):
+    # for some reason the NumPy default for dtype is None in the source but
+    # ends up as np.float64 by definition.
+    _check_const_str_dtype("ones", dtype)
+
+    def impl(shape, dtype=None):
+        arr = np.empty(shape, dtype=dtype)
+        arr_flat = arr.flat
+        for idx in range(len(arr_flat)):
+            arr_flat[idx] = 1
+        return arr
+    return impl
+
+
+@overload(np.identity)
+def impl_np_identity(n, dtype=None):
+    _check_const_str_dtype("identity", dtype)
+    if not is_nonelike(dtype):
+        nb_dtype = ty_parse_dtype(dtype)
+    else:
+        nb_dtype = types.double
+
+    def identity(n, dtype=None):
+        arr = np.zeros((n, n), nb_dtype)
+        for i in range(n):
+            arr[i, i] = 1
+        return arr
+    return identity
+
+
+def _eye_none_handler(N, M):
+    pass
+
+
+@extending.overload(_eye_none_handler)
+def _eye_none_handler_impl(N, M):
+    if isinstance(M, types.NoneType):
+        def impl(N, M):
+            return N
+    else:
+        def impl(N, M):
+            return M
+    return impl
+
+
+@extending.overload(np.eye)
+def numpy_eye(N, M=None, k=0, dtype=float):
+
+    if dtype is None or isinstance(dtype, types.NoneType):
+        dt = np.dtype(float)
+    elif isinstance(dtype, (types.DTypeSpec, types.Number)):
+        # dtype or instance of dtype
+        dt = as_dtype(getattr(dtype, 'dtype', dtype))
+    else:
+        dt = np.dtype(dtype)
+
+    def impl(N, M=None, k=0, dtype=float):
+        _M = _eye_none_handler(N, M)
+        arr = np.zeros((N, _M), dt)
+        if k >= 0:
+            d = min(N, _M - k)
+            for i in range(d):
+                arr[i, i + k] = 1
+        else:
+            d = min(N + k, _M)
+            for i in range(d):
+                arr[i - k, i] = 1
+        return arr
+    return impl
+
+
+@overload(np.diag)
+def impl_np_diag(v, k=0):
+    if not type_can_asarray(v):
+        raise errors.TypingError('The argument "v" must be array-like')
+
+    if isinstance(v, types.Array):
+        if v.ndim not in (1, 2):
+            raise errors.NumbaTypeError("Input must be 1- or 2-d.")
+
+        def diag_impl(v, k=0):
+            if v.ndim == 1:
+                s = v.shape
+                n = s[0] + abs(k)
+                ret = np.zeros((n, n), v.dtype)
+                if k >= 0:
+                    for i in range(n - k):
+                        ret[i, k + i] = v[i]
+                else:
+                    for i in range(n + k):
+                        ret[i - k, i] = v[i]
+                return ret
+            else:  # 2-d
+                rows, cols = v.shape
+                if k < 0:
+                    rows = rows + k
+                if k > 0:
+                    cols = cols - k
+                n = max(min(rows, cols), 0)
+                ret = np.empty(n, v.dtype)
+                if k >= 0:
+                    for i in range(n):
+                        ret[i] = v[i, k + i]
+                else:
+                    for i in range(n):
+                        ret[i] = v[i - k, i]
+                return ret
+        return diag_impl
+
+
+@overload(np.indices)
+def numpy_indices(dimensions):
+    if not isinstance(dimensions, types.UniTuple):
+        msg = 'The argument "dimensions" must be a tuple of integers'
+        raise errors.TypingError(msg)
+
+    if not isinstance(dimensions.dtype, types.Integer):
+        msg = 'The argument "dimensions" must be a tuple of integers'
+        raise errors.TypingError(msg)
+
+    N = len(dimensions)
+    shape = (1,) * N
+
+    def impl(dimensions):
+        res = np.empty((N,) + dimensions, dtype=np.int64)
+        i = 0
+        for dim in dimensions:
+            idx = np.arange(dim, dtype=np.int64).reshape(
+                tuple_setitem(shape, i, dim)
+            )
+            res[i] = idx
+            i += 1
+
+        return res
+
+    return impl
+
+
+@overload(np.diagflat)
+def numpy_diagflat(v, k=0):
+    if not type_can_asarray(v):
+        msg = 'The argument "v" must be array-like'
+        raise errors.TypingError(msg)
+
+    if not isinstance(k, (int, types.Integer)):
+        msg = 'The argument "k" must be an integer'
+        raise errors.TypingError(msg)
+
+    def impl(v, k=0):
+        v = np.asarray(v)
+        v = v.ravel()
+        s = len(v)
+        abs_k = abs(k)
+        n = s + abs_k
+        res = np.zeros((n, n), v.dtype)
+        i = np.maximum(0, -k)
+        j = np.maximum(0, k)
+        for t in range(s):
+            res[i + t, j + t] = v[t]
+
+        return res
+
+    return impl
+
+
+def generate_getitem_setitem_with_axis(ndim, kind):
+    assert kind in ('getitem', 'setitem')
+
+    if kind == 'getitem':
+        fn = '''
+            def _getitem(a, idx, axis):
+                if axis == 0:
+                    return a[idx, ...]
+        '''
+        for i in range(1, ndim):
+            lst = (':',) * i
+            fn += f'''
+                elif axis == {i}:
+                    return a[{", ".join(lst)}, idx, ...]
+            '''
+    else:
+        fn = '''
+            def _setitem(a, idx, axis, vals):
+                if axis == 0:
+                    a[idx, ...] = vals
+        '''
+
+        for i in range(1, ndim):
+            lst = (':',) * i
+            fn += f'''
+                elif axis == {i}:
+                    a[{", ".join(lst)}, idx, ...] = vals
+            '''
+
+    fn = textwrap.dedent(fn)
+    exec(fn, globals())
+    fn = globals()[f'_{kind}']
+    return register_jitable(fn)
+
+
+@overload(np.take)
+@overload_method(types.Array, 'take')
+def numpy_take(a, indices, axis=None):
+
+    if cgutils.is_nonelike(axis):
+        if isinstance(a, types.Array) and isinstance(indices, types.Integer):
+            def take_impl(a, indices, axis=None):
+                if indices > (a.size - 1) or indices < -a.size:
+                    raise IndexError("Index out of bounds")
+                return a.ravel()[indices]
+            return take_impl
+
+        if isinstance(a, types.Array) and isinstance(indices, types.Array):
+            F_order = indices.layout == 'F'
+
+            def take_impl(a, indices, axis=None):
+                ret = np.empty(indices.size, dtype=a.dtype)
+                if F_order:
+                    walker = indices.copy()  # get C order
+                else:
+                    walker = indices
+                it = np.nditer(walker)
+                i = 0
+                flat = a.ravel()
+                for x in it:
+                    if x > (a.size - 1) or x < -a.size:
+                        raise IndexError("Index out of bounds")
+                    ret[i] = flat[x]
+                    i = i + 1
+                return ret.reshape(indices.shape)
+            return take_impl
+
+        if isinstance(a, types.Array) and \
+                isinstance(indices, (types.List, types.BaseTuple)):
+            def take_impl(a, indices, axis=None):
+                convert = np.array(indices)
+                return np.take(a, convert)
+            return take_impl
+    else:
+        if isinstance(a, types.Array) and isinstance(indices, types.Integer):
+            t = (0,) * (a.ndim - 1)
+
+            # np.squeeze is too hard to implement in Numba as the tuple "t"
+            # needs to be allocated beforehand we don't know it's size until
+            # code gets executed.
+            @register_jitable
+            def _squeeze(r, axis):
+                tup = tuple(t)
+                j = 0
+                assert axis < len(r.shape) and r.shape[axis] == 1, r.shape
+                for idx in range(len(r.shape)):
+                    s = r.shape[idx]
+                    if idx != axis:
+                        tup = tuple_setitem(tup, j, s)
+                        j += 1
+                return r.reshape(tup)
+
+            def take_impl(a, indices, axis=None):
+                r = np.take(a, (indices,), axis=axis)
+                if a.ndim == 1:
+                    # caveats
+                    # >>> isinstance(np.take(1d_arr, 0), int)
+                    # True
+                    # >>> isinstance(np.take(1d_arr, (0,)), int)
+                    # False
+                    # The latter returns an array
+                    return r[0]
+                if axis < 0:
+                    axis += a.ndim
+                return _squeeze(r, axis)
+            return take_impl
+
+        if isinstance(a, types.Array) and \
+                isinstance(indices, (types.Array, types.List, types.BaseTuple)):
+
+            ndim = a.ndim
+
+            _getitem = generate_getitem_setitem_with_axis(ndim, 'getitem')
+            _setitem = generate_getitem_setitem_with_axis(ndim, 'setitem')
+
+            def take_impl(a, indices, axis=None):
+                ax = axis
+                if axis < 0:
+                    axis += a.ndim
+
+                if axis < 0 or axis >= a.ndim:
+                    msg = (f"axis {ax} is out of bounds for array "
+                           f"of dimension {a.ndim}")
+                    raise ValueError(msg)
+
+                shape = tuple_setitem(a.shape, axis, len(indices))
+                out = np.empty(shape, dtype=a.dtype)
+                for i in range(len(indices)):
+                    y = _getitem(a, indices[i], axis)
+                    _setitem(out, i, axis, y)
+                return out
+            return take_impl
+
+
+def _arange_dtype(*args):
+    bounds = [a for a in args if not isinstance(a, types.NoneType)]
+
+    if any(isinstance(a, types.Complex) for a in bounds):
+        dtype = types.complex128
+    elif any(isinstance(a, types.Float) for a in bounds):
+        dtype = types.float64
+    else:
+        # `np.arange(10).dtype` is always `np.dtype(int)`, aka `np.int_`, which
+        # in all released versions of numpy corresponds to the C `long` type.
+        # Windows 64 is broken by default here because Numba (as of 0.47) does
+        # not differentiate between Python and NumPy integers, so a `typeof(1)`
+        # on w64 is `int64`, i.e. `intp`. This means an arange() will
+        # be typed as arange(int64) and the following will yield int64 opposed
+        # to int32. Example: without a load of analysis to work out of the args
+        # were wrapped in NumPy int*() calls it's not possible to detect the
+        # difference between `np.arange(10)` and `np.arange(np.int64(10)`.
+        NPY_TY = getattr(types, "int%s" % (8 * np.dtype(int).itemsize))
+
+        # unliteral these types such that `max` works.
+        unliteral_bounds = [types.unliteral(x) for x in bounds]
+        dtype = max(unliteral_bounds + [NPY_TY,])
+
+    return dtype
+
+
+@overload(np.arange)
+def np_arange(start, / ,stop=None, step=None, dtype=None):
+    if isinstance(stop, types.Optional):
+        stop = stop.type
+    if isinstance(step, types.Optional):
+        step = step.type
+    if isinstance(dtype, types.Optional):
+        dtype = dtype.type
+
+    if stop is None:
+        stop = types.none
+    if step is None:
+        step = types.none
+    if dtype is None:
+        dtype = types.none
+
+    if (not isinstance(start, types.Number) or
+        not isinstance(stop, (types.NoneType, types.Number)) or
+        not isinstance(step, (types.NoneType, types.Number)) or
+            not isinstance(dtype, (types.NoneType, types.DTypeSpec))):
+
+        return
+
+    if isinstance(dtype, types.NoneType):
+        true_dtype = _arange_dtype(start, stop, step)
+    else:
+        true_dtype = dtype.dtype
+
+    use_complex = any([isinstance(x, types.Complex)
+                       for x in (start, stop, step)])
+
+    start_value = getattr(start, "literal_value", None)
+    stop_value = getattr(stop, "literal_value", None)
+    step_value = getattr(step, "literal_value", None)
+
+    def impl(start, /, stop=None, step=None, dtype=None):
+        # Allow for improved performance if given literal arguments.
+        lit_start = start_value if start_value is not None else start
+        lit_stop = stop_value if stop_value is not None else stop
+        lit_step = step_value if step_value is not None else step
+
+        _step = lit_step if lit_step is not None else 1
+        if lit_stop is None:
+            _start, _stop = 0, lit_start
+        else:
+            _start, _stop = lit_start, lit_stop
+
+        if _step == 0:
+            raise ValueError("Maximum allowed size exceeded")
+
+        nitems_c = (_stop - _start) / _step
+        nitems_r = int(math.ceil(nitems_c.real))
+
+        # Binary operator needed for compiler branch pruning.
+        if use_complex is True:
+            nitems_i = int(math.ceil(nitems_c.imag))
+            nitems = max(min(nitems_i, nitems_r), 0)
+        else:
+            nitems = max(nitems_r, 0)
+        arr = np.empty(nitems, true_dtype)
+        val = _start
+        for i in range(nitems):
+            arr[i] = val + (i * _step)
+        return arr
+
+    return impl
+
+
+@overload(np.linspace)
+def numpy_linspace(start, stop, num=50):
+    if not all(isinstance(arg, types.Number) for arg in [start, stop]):
+        return
+
+    if not isinstance(num, (int, types.Integer)):
+        msg = 'The argument "num" must be an integer'
+        raise errors.TypingError(msg)
+
+    if any(isinstance(arg, types.Complex) for arg in [start, stop]):
+        dtype = types.complex128
+    else:
+        dtype = types.float64
+
+    # Implementation based on https://github.com/numpy/numpy/blob/v1.20.0/numpy/core/function_base.py#L24 # noqa: E501
+    def linspace(start, stop, num=50):
+        arr = np.empty(num, dtype)
+        # The multiply by 1.0 mirrors
+        # https://github.com/numpy/numpy/blob/v1.20.0/numpy/core/function_base.py#L125-L128  # noqa: E501
+        # the side effect of this is important... start and stop become the same
+        # type as `dtype` i.e. 64/128 bits wide (float/complex). This is
+        # important later when used in the `np.divide`.
+        start = start * 1.0
+        stop = stop * 1.0
+        if num == 0:
+            return arr
+        div = num - 1
+        if div > 0:
+            delta = stop - start
+            step = np.divide(delta, div)
+            for i in range(0, num):
+                arr[i] = start + (i * step)
+        else:
+            arr[0] = start
+        if num > 1:
+            arr[-1] = stop
+        return arr
+    return linspace
+
+
+def _array_copy(context, builder, sig, args):
+    """
+    Array copy.
+    """
+    arytype = sig.args[0]
+    ary = make_array(arytype)(context, builder, value=args[0])
+    shapes = cgutils.unpack_tuple(builder, ary.shape)
+
+    rettype = sig.return_type
+    ret = _empty_nd_impl(context, builder, rettype, shapes)
+
+    src_data = ary.data
+    dest_data = ret.data
+
+    assert rettype.layout in "CF"
+    if arytype.layout == rettype.layout:
+        # Fast path: memcpy
+        cgutils.raw_memcpy(builder, dest_data, src_data, ary.nitems,
+                           ary.itemsize, align=1)
+
+    else:
+        src_strides = cgutils.unpack_tuple(builder, ary.strides)
+        dest_strides = cgutils.unpack_tuple(builder, ret.strides)
+        intp_t = context.get_value_type(types.intp)
+
+        with cgutils.loop_nest(builder, shapes, intp_t) as indices:
+            src_ptr = cgutils.get_item_pointer2(context, builder, src_data,
+                                                shapes, src_strides,
+                                                arytype.layout, indices)
+            dest_ptr = cgutils.get_item_pointer2(context, builder, dest_data,
+                                                 shapes, dest_strides,
+                                                 rettype.layout, indices)
+            builder.store(builder.load(src_ptr), dest_ptr)
+
+    return impl_ret_new_ref(context, builder, sig.return_type, ret._getvalue())
+
+
+@intrinsic
+def _array_copy_intrinsic(typingctx, a):
+    assert isinstance(a, types.Array)
+    layout = 'F' if a.layout == 'F' else 'C'
+    ret = a.copy(layout=layout, readonly=False)
+    sig = ret(a)
+    return sig, _array_copy
+
+
+@lower_builtin("array.copy", types.Array)
+def array_copy(context, builder, sig, args):
+    return _array_copy(context, builder, sig, args)
+
+
+@overload(np.copy)
+def impl_numpy_copy(a):
+    if not type_can_asarray(a):
+        raise errors.TypingError('The argument "a" must '
+                                 'be array-like')
+
+    if isinstance(a, types.Array):
+        def numpy_copy(a):
+            return _array_copy_intrinsic(a)
+    else:
+        def numpy_copy(a):
+            # asarray automatically copies non-Array types
+            return np.asarray(a)
+    return numpy_copy
+
+
+def _as_layout_array(context, builder, sig, args, output_layout):
+    """
+    Common logic for layout conversion function;
+    e.g. ascontiguousarray and asfortranarray
+    """
+    retty = sig.return_type
+    aryty = sig.args[0]
+    assert retty.layout == output_layout, 'return-type has incorrect layout'
+
+    if aryty.ndim == 0:
+        # 0-dim input => asfortranarray() returns a 1-dim array
+        assert retty.ndim == 1
+        ary = make_array(aryty)(context, builder, value=args[0])
+        ret = make_array(retty)(context, builder)
+
+        shape = context.get_constant_generic(
+            builder, types.UniTuple(types.intp, 1), (1,),
+        )
+        strides = context.make_tuple(builder,
+                                     types.UniTuple(types.intp, 1),
+                                     (ary.itemsize,))
+        populate_array(ret, ary.data, shape, strides, ary.itemsize,
+                       ary.meminfo, ary.parent)
+        return impl_ret_borrowed(context, builder, retty, ret._getvalue())
+
+    elif (retty.layout == aryty.layout
+            or (aryty.ndim == 1 and aryty.layout in 'CF')):
+        # 1-dim contiguous input => return the same array
+        return impl_ret_borrowed(context, builder, retty, args[0])
+
+    else:
+        if aryty.layout == 'A':
+            # There's still chance the array is in contiguous layout,
+            # just that we don't know at compile time.
+            # We can do a runtime check.
+
+            # Prepare and call is_contiguous or is_fortran
+            assert output_layout in 'CF'
+            check_func = is_contiguous if output_layout == 'C' else is_fortran
+            is_contig = _call_contiguous_check(check_func,
+                                               context,
+                                               builder,
+                                               aryty,
+                                               args[0])
+            with builder.if_else(is_contig) as (then, orelse):
+                # If the array is already contiguous, just return it
+                with then:
+                    out_then = impl_ret_borrowed(context, builder, retty,
+                                                 args[0])
+                    then_blk = builder.block
+                # Otherwise, copy to a new contiguous region
+                with orelse:
+                    out_orelse = _array_copy(context, builder, sig, args)
+                    orelse_blk = builder.block
+            # Phi node for the return value
+            ret_phi = builder.phi(out_then.type)
+            ret_phi.add_incoming(out_then, then_blk)
+            ret_phi.add_incoming(out_orelse, orelse_blk)
+            return ret_phi
+
+        else:
+            # Return a copy with the right layout
+            return _array_copy(context, builder, sig, args)
+
+
+@intrinsic
+def _as_layout_array_intrinsic(typingctx, a, output_layout):
+    if not isinstance(output_layout, types.StringLiteral):
+        raise errors.RequireLiteralValue(output_layout)
+
+    # copies are writable
+    ret = a.copy(
+        layout=output_layout.literal_value,
+        ndim=max(a.ndim, 1),
+        readonly=False)
+    sig = ret(a, output_layout)
+
+    return sig, lambda c, b, s, a: _as_layout_array(
+        c, b, s, a, output_layout=output_layout.literal_value)
+
+
+@overload(np.ascontiguousarray)
+def array_ascontiguousarray(a):
+    if not type_can_asarray(a):
+        raise errors.TypingError('The argument "a" must be array-like')
+
+    if isinstance(a, (types.Number, types.Boolean,)):
+        def impl(a):
+            return np.ascontiguousarray(np.array(a))
+    elif isinstance(a, types.Array):
+        def impl(a):
+            return _as_layout_array_intrinsic(a, 'C')
+    return impl
+
+
+@overload(np.asfortranarray)
+def array_asfortranarray(a):
+    if not type_can_asarray(a):
+        raise errors.TypingError('The argument "a" must be array-like')
+
+    if isinstance(a, (types.Number, types.Boolean,)):
+        def impl(a):
+            return np.asfortranarray(np.array(a))
+        return impl
+    elif isinstance(a, types.Array):
+        def impl(a):
+            return _as_layout_array_intrinsic(a, 'F')
+        return impl
+
+
+@lower_builtin("array.astype", types.Array, types.DTypeSpec)
+@lower_builtin("array.astype", types.Array, types.StringLiteral)
+def array_astype(context, builder, sig, args):
+    arytype = sig.args[0]
+    ary = make_array(arytype)(context, builder, value=args[0])
+    shapes = cgutils.unpack_tuple(builder, ary.shape)
+
+    rettype = sig.return_type
+    ret = _empty_nd_impl(context, builder, rettype, shapes)
+
+    src_data = ary.data
+    dest_data = ret.data
+
+    src_strides = cgutils.unpack_tuple(builder, ary.strides)
+    dest_strides = cgutils.unpack_tuple(builder, ret.strides)
+    intp_t = context.get_value_type(types.intp)
+
+    with cgutils.loop_nest(builder, shapes, intp_t) as indices:
+        src_ptr = cgutils.get_item_pointer2(context, builder, src_data,
+                                            shapes, src_strides,
+                                            arytype.layout, indices)
+        dest_ptr = cgutils.get_item_pointer2(context, builder, dest_data,
+                                             shapes, dest_strides,
+                                             rettype.layout, indices)
+        item = load_item(context, builder, arytype, src_ptr)
+        item = context.cast(builder, item, arytype.dtype, rettype.dtype)
+        store_item(context, builder, rettype, item, dest_ptr)
+
+    return impl_ret_new_ref(context, builder, sig.return_type, ret._getvalue())
+
+
+@intrinsic
+def _array_tobytes_intrinsic(typingctx, b):
+    assert isinstance(b, types.Array)
+    sig = bytes_type(b)
+
+    def codegen(context, builder, sig, args):
+        [ty] = sig.args
+        arrty = make_array(ty)
+        arr = arrty(context, builder, args[0])
+
+        itemsize = arr.itemsize
+        nbytes = builder.mul(itemsize, arr.nitems)
+
+        bstr = _make_constant_bytes(context, builder, nbytes)
+
+        if (ty.is_c_contig and ty.layout == "C"):
+            cgutils.raw_memcpy(builder, bstr.data, arr.data, arr.nitems,
+                               itemsize)
+        else:
+            shape = cgutils.unpack_tuple(builder, arr.shape)
+            strides = cgutils.unpack_tuple(builder, arr.strides)
+            layout = ty.layout
+            intp_t = context.get_value_type(types.intp)
+
+            byteidx = cgutils.alloca_once(
+                builder, intp_t, name="byteptr", zfill=True
+            )
+            with cgutils.loop_nest(builder, shape, intp_t) as indices:
+                ptr = cgutils.get_item_pointer2(
+                    context, builder, arr.data, shape, strides, layout, indices
+                )
+                srcptr = builder.bitcast(ptr, bstr.data.type)
+
+                idx = builder.load(byteidx)
+                destptr = builder.gep(bstr.data, [idx])
+
+                cgutils.memcpy(builder, destptr, srcptr, itemsize)
+                builder.store(builder.add(idx, itemsize), byteidx)
+
+        return bstr._getvalue()
+    return sig, codegen
+
+
+@overload_method(types.Array, "tobytes")
+def impl_array_tobytes(arr):
+    if isinstance(arr, types.Array):
+        def impl(arr):
+            return _array_tobytes_intrinsic(arr)
+        return impl
+
+
+@intrinsic
+def np_frombuffer(typingctx, buffer, dtype, count, offset, retty):
+    ty = retty.instance_type
+    sig = ty(buffer, dtype, count, offset, retty)
+
+    def codegen(context, builder, sig, args):
+        bufty = sig.args[0]
+        arg_count = args[2]
+        arg_offset = args[3]
+        aryty = sig.return_type
+
+        buf = make_array(bufty)(context, builder, value=args[0])
+        out_ary_ty = make_array(aryty)
+        out_ary = out_ary_ty(context, builder)
+        out_datamodel = out_ary._datamodel
+
+        itemsize = get_itemsize(context, aryty)
+        ll_itemsize = Constant(buf.itemsize.type, itemsize)
+        nbytes = builder.mul(buf.nitems, buf.itemsize)
+        ll_offset_size = builder.mul(arg_offset, ll_itemsize)
+        nbytes = builder.sub(nbytes, ll_offset_size)
+
+        nbytes_is_negative = builder.icmp_signed(
+            '<',
+            nbytes,
+            ir.Constant(arg_count.type, 0),
+        )
+
+        msg = "offset must be non-negative and no greater than buffer length"
+        with builder.if_then(nbytes_is_negative, likely=False):
+            context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+        ll_count_is_negative = builder.icmp_signed(
+            '<',
+            arg_count,
+            ir.Constant(arg_count.type, 0),
+        )
+
+        # Check that the buffer size is compatible
+        rem = builder.srem(nbytes, ll_itemsize)
+        is_incompatible = cgutils.is_not_null(builder, rem)
+        with builder.if_then(is_incompatible, likely=False):
+            msg = "buffer size must be a multiple of element size"
+            context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+        # Compute number of elements based on count
+        with builder.if_else(ll_count_is_negative) as (then_block, else_block):
+            with then_block:
+                bb_if = builder.basic_block
+                num_whole = builder.sdiv(nbytes, ll_itemsize)
+            with else_block:
+                bb_else = builder.basic_block
+
+        ll_itemcount = builder.phi(arg_count.type)
+        ll_itemcount.add_incoming(num_whole, bb_if)
+        ll_itemcount.add_incoming(arg_count, bb_else)
+
+        # Ensure we don’t exceed the buffer size
+        ll_required_size = builder.mul(ll_itemcount, ll_itemsize)
+        is_too_large = builder.icmp_unsigned('>', ll_required_size, nbytes)
+
+        with builder.if_then(is_too_large, likely=False):
+            msg = "buffer is smaller than requested size"
+            context.call_conv.return_user_exc(builder, ValueError, (msg,))
+
+        # Set shape and strides
+        shape = cgutils.pack_array(builder, [ll_itemcount])
+        strides = cgutils.pack_array(builder, [ll_itemsize])
+
+        data = builder.gep(buf.data, [arg_offset])
+        data = builder.bitcast(
+            data, context.get_value_type(out_datamodel.get_type('data'))
+        )
+
+        populate_array(out_ary,
+                       data=data,
+                       shape=shape,
+                       strides=strides,
+                       itemsize=ll_itemsize,
+                       meminfo=buf.meminfo,
+                       parent=buf.parent)
+
+        res = out_ary._getvalue()
+        return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+    return sig, codegen
+
+
+@overload(np.frombuffer)
+def impl_np_frombuffer(buffer, dtype=float, count=-1, offset=0):
+    _check_const_str_dtype("frombuffer", dtype)
+
+    if not isinstance(buffer, types.Buffer) or buffer.layout != 'C':
+        msg = f'Argument "buffer" must be buffer-like. Got {buffer}'
+        raise errors.TypingError(msg)
+
+    if (dtype is float or
+            (isinstance(dtype, types.Function) and dtype.typing_key is float) or
+            is_nonelike(dtype)):  # default
+        nb_dtype = types.double
+    else:
+        nb_dtype = ty_parse_dtype(dtype)
+
+    if nb_dtype is not None:
+        retty = types.Array(dtype=nb_dtype, ndim=1, layout='C',
+                            readonly=not buffer.mutable)
+    else:
+        msg = ("Cannot parse input types to function "
+               f"np.frombuffer({buffer}, {dtype})")
+        raise errors.TypingError(msg)
+
+    def impl(buffer, dtype=float, count=-1, offset=0):
+        return np_frombuffer(buffer, dtype, count, offset, retty)
+
+    return impl
+
+
+@overload(carray)
+def impl_carray(ptr, shape, dtype=None):
+    if is_nonelike(dtype):
+        intrinsic_cfarray = get_cfarray_intrinsic('C', None)
+
+        def impl(ptr, shape, dtype=None):
+            return intrinsic_cfarray(ptr, shape)
+        return impl
+    elif isinstance(dtype, types.DTypeSpec):
+        intrinsic_cfarray = get_cfarray_intrinsic('C', dtype)
+
+        def impl(ptr, shape, dtype=None):
+            return intrinsic_cfarray(ptr, shape)
+        return impl
+
+
+@overload(farray)
+def impl_farray(ptr, shape, dtype=None):
+    if is_nonelike(dtype):
+        intrinsic_cfarray = get_cfarray_intrinsic('F', None)
+
+        def impl(ptr, shape, dtype=None):
+            return intrinsic_cfarray(ptr, shape)
+        return impl
+    elif isinstance(dtype, types.DTypeSpec):
+        intrinsic_cfarray = get_cfarray_intrinsic('F', dtype)
+
+        def impl(ptr, shape, dtype=None):
+            return intrinsic_cfarray(ptr, shape)
+        return impl
+
+
+def get_cfarray_intrinsic(layout, dtype_):
+    @intrinsic
+    def intrinsic_cfarray(typingctx, ptr, shape):
+        if ptr is types.voidptr:
+            ptr_dtype = None
+        elif isinstance(ptr, types.CPointer):
+            ptr_dtype = ptr.dtype
+        else:
+            msg = f"pointer argument expected, got '{ptr}'"
+            raise errors.NumbaTypeError(msg)
+
+        if dtype_ is None:
+            if ptr_dtype is None:
+                msg = "explicit dtype required for void* argument"
+                raise errors.NumbaTypeError(msg)
+            dtype = ptr_dtype
+        elif isinstance(dtype_, types.DTypeSpec):
+            dtype = dtype_.dtype
+            if ptr_dtype is not None and dtype != ptr_dtype:
+                msg = f"mismatching dtype '{dtype}' for pointer type '{ptr}'"
+                raise errors.NumbaTypeError(msg)
+        else:
+            msg = f"invalid dtype spec '{dtype_}'"
+            raise errors.NumbaTypeError(msg)
+
+        ndim = ty_parse_shape(shape)
+        if ndim is None:
+            msg = f"invalid shape '{shape}'"
+            raise errors.NumbaTypeError(msg)
+
+        retty = types.Array(dtype, ndim, layout)
+        sig = signature(retty, ptr, shape)
+        return sig, np_cfarray
+    return intrinsic_cfarray
+
+
+def np_cfarray(context, builder, sig, args):
+    """
+    numba.numpy_support.carray(...) and
+    numba.numpy_support.farray(...).
+    """
+    ptrty, shapety = sig.args[:2]
+    ptr, shape = args[:2]
+
+    aryty = sig.return_type
+    assert aryty.layout in 'CF'
+
+    out_ary = make_array(aryty)(context, builder)
+
+    itemsize = get_itemsize(context, aryty)
+    ll_itemsize = cgutils.intp_t(itemsize)
+
+    if isinstance(shapety, types.BaseTuple):
+        shapes = cgutils.unpack_tuple(builder, shape)
+    else:
+        shapety = (shapety,)
+        shapes = (shape,)
+    shapes = [context.cast(builder, value, fromty, types.intp)
+              for fromty, value in zip(shapety, shapes)]
+
+    off = ll_itemsize
+    strides = []
+    if aryty.layout == 'F':
+        for s in shapes:
+            strides.append(off)
+            off = builder.mul(off, s)
+    else:
+        for s in reversed(shapes):
+            strides.append(off)
+            off = builder.mul(off, s)
+        strides.reverse()
+
+    data = builder.bitcast(ptr,
+                           context.get_data_type(aryty.dtype).as_pointer())
+
+    populate_array(out_ary,
+                   data=data,
+                   shape=shapes,
+                   strides=strides,
+                   itemsize=ll_itemsize,
+                   # Array is not memory-managed
+                   meminfo=None,
+                   )
+
+    res = out_ary._getvalue()
+    return impl_ret_new_ref(context, builder, sig.return_type, res)
+
+
+def _get_seq_size(context, builder, seqty, seq):
+    if isinstance(seqty, types.BaseTuple):
+        return context.get_constant(types.intp, len(seqty))
+    elif isinstance(seqty, types.Sequence):
+        len_impl = context.get_function(len, signature(types.intp, seqty,))
+        return len_impl(builder, (seq,))
+    else:
+        assert 0
+
+
+def _get_borrowing_getitem(context, seqty):
+    """
+    Return a getitem() implementation that doesn't incref its result.
+    """
+    retty = seqty.dtype
+    getitem_impl = context.get_function(operator.getitem,
+                                        signature(retty, seqty, types.intp))
+
+    def wrap(builder, args):
+        ret = getitem_impl(builder, args)
+        if context.enable_nrt:
+            context.nrt.decref(builder, retty, ret)
+        return ret
+
+    return wrap
+
+
+def compute_sequence_shape(context, builder, ndim, seqty, seq):
+    """
+    Compute the likely shape of a nested sequence (possibly 0d).
+    """
+    intp_t = context.get_value_type(types.intp)
+    zero = Constant(intp_t, 0)
+
+    def get_first_item(seqty, seq):
+        if isinstance(seqty, types.BaseTuple):
+            if len(seqty) == 0:
+                return None, None
+            else:
+                return seqty[0], builder.extract_value(seq, 0)
+        else:
+            getitem_impl = _get_borrowing_getitem(context, seqty)
+            return seqty.dtype, getitem_impl(builder, (seq, zero))
+
+    # Compute shape by traversing the first element of each nested
+    # sequence
+    shapes = []
+    innerty, inner = seqty, seq
+
+    for i in range(ndim):
+        if i > 0:
+            innerty, inner = get_first_item(innerty, inner)
+        shapes.append(_get_seq_size(context, builder, innerty, inner))
+
+    return tuple(shapes)
+
+
+def check_sequence_shape(context, builder, seqty, seq, shapes):
+    """
+    Check the nested sequence matches the given *shapes*.
+    """
+
+    def _fail():
+        context.call_conv.return_user_exc(builder, ValueError,
+                                          ("incompatible sequence shape",))
+
+    def check_seq_size(seqty, seq, shapes):
+        if len(shapes) == 0:
+            return
+
+        size = _get_seq_size(context, builder, seqty, seq)
+        expected = shapes[0]
+        mismatch = builder.icmp_signed('!=', size, expected)
+        with builder.if_then(mismatch, likely=False):
+            _fail()
+
+        if len(shapes) == 1:
+            return
+
+        if isinstance(seqty, types.Sequence):
+            getitem_impl = _get_borrowing_getitem(context, seqty)
+            with cgutils.for_range(builder, size) as loop:
+                innerty = seqty.dtype
+                inner = getitem_impl(builder, (seq, loop.index))
+                check_seq_size(innerty, inner, shapes[1:])
+
+        elif isinstance(seqty, types.BaseTuple):
+            for i in range(len(seqty)):
+                innerty = seqty[i]
+                inner = builder.extract_value(seq, i)
+                check_seq_size(innerty, inner, shapes[1:])
+
+        else:
+            assert 0, seqty
+
+    check_seq_size(seqty, seq, shapes)
+
+
+def assign_sequence_to_array(context, builder, data, shapes, strides,
+                             arrty, seqty, seq):
+    """
+    Assign a nested sequence contents to an array.  The shape must match
+    the sequence's structure.
+    """
+
+    def assign_item(indices, valty, val):
+        ptr = cgutils.get_item_pointer2(context, builder, data, shapes, strides,
+                                        arrty.layout, indices, wraparound=False)
+        val = context.cast(builder, val, valty, arrty.dtype)
+        store_item(context, builder, arrty, val, ptr)
+
+    def assign(seqty, seq, shapes, indices):
+        if len(shapes) == 0:
+            assert not isinstance(seqty, (types.Sequence, types.BaseTuple))
+            assign_item(indices, seqty, seq)
+            return
+
+        size = shapes[0]
+
+        if isinstance(seqty, types.Sequence):
+            getitem_impl = _get_borrowing_getitem(context, seqty)
+            with cgutils.for_range(builder, size) as loop:
+                innerty = seqty.dtype
+                inner = getitem_impl(builder, (seq, loop.index))
+                assign(innerty, inner, shapes[1:], indices + (loop.index,))
+
+        elif isinstance(seqty, types.BaseTuple):
+            for i in range(len(seqty)):
+                innerty = seqty[i]
+                inner = builder.extract_value(seq, i)
+                index = context.get_constant(types.intp, i)
+                assign(innerty, inner, shapes[1:], indices + (index,))
+
+        else:
+            assert 0, seqty
+
+    assign(seqty, seq, shapes, ())
+
+
+def np_array_typer(typingctx, object, dtype):
+    ndim, seq_dtype = _parse_nested_sequence(typingctx, object)
+    if is_nonelike(dtype):
+        dtype = seq_dtype
+    else:
+        dtype = ty_parse_dtype(dtype)
+        if dtype is None:
+            return
+    return types.Array(dtype, ndim, 'C')
+
+
+@intrinsic
+def np_array(typingctx, obj, dtype):
+    _check_const_str_dtype("array", dtype)
+    ret = np_array_typer(typingctx, obj, dtype)
+    sig = ret(obj, dtype)
+
+    def codegen(context, builder, sig, args):
+        arrty = sig.return_type
+        ndim = arrty.ndim
+        seqty = sig.args[0]
+        seq = args[0]
+
+        shapes = compute_sequence_shape(context, builder, ndim, seqty, seq)
+        assert len(shapes) == ndim
+
+        check_sequence_shape(context, builder, seqty, seq, shapes)
+        arr = _empty_nd_impl(context, builder, arrty, shapes)
+        assign_sequence_to_array(context, builder, arr.data, shapes,
+                                 arr.strides, arrty, seqty, seq)
+
+        return impl_ret_new_ref(context, builder, sig.return_type,
+                                arr._getvalue())
+
+    return sig, codegen
+
+
+@overload(np.array)
+def impl_np_array(object, dtype=None):
+    _check_const_str_dtype("array", dtype)
+    if not type_can_asarray(object):
+        raise errors.TypingError('The argument "object" must '
+                                 'be array-like')
+    if not is_nonelike(dtype) and ty_parse_dtype(dtype) is None:
+        msg = 'The argument "dtype" must be a data-type if it is provided'
+        raise errors.TypingError(msg)
+
+    def impl(object, dtype=None):
+        return np_array(object, dtype)
+    return impl
+
+
+def _normalize_axis(context, builder, func_name, ndim, axis):
+    zero = axis.type(0)
+    ll_ndim = axis.type(ndim)
+
+    # Normalize negative axis
+    is_neg_axis = builder.icmp_signed('<', axis, zero)
+    axis = builder.select(is_neg_axis, builder.add(axis, ll_ndim), axis)
+
+    # Check axis for bounds
+    axis_out_of_bounds = builder.or_(
+        builder.icmp_signed('<', axis, zero),
+        builder.icmp_signed('>=', axis, ll_ndim))
+    with builder.if_then(axis_out_of_bounds, likely=False):
+        msg = "%s(): axis out of bounds" % func_name
+        context.call_conv.return_user_exc(builder, IndexError, (msg,))
+
+    return axis
+
+
+def _insert_axis_in_shape(context, builder, orig_shape, ndim, axis):
+    """
+    Compute shape with the new axis inserted
+    e.g. given original shape (2, 3, 4) and axis=2,
+    the returned new shape is (2, 3, 1, 4).
+    """
+    assert len(orig_shape) == ndim - 1
+
+    ll_shty = ir.ArrayType(cgutils.intp_t, ndim)
+    shapes = cgutils.alloca_once(builder, ll_shty)
+
+    one = cgutils.intp_t(1)
+
+    # 1. copy original sizes at appropriate places
+    for dim in range(ndim - 1):
+        ll_dim = cgutils.intp_t(dim)
+        after_axis = builder.icmp_signed('>=', ll_dim, axis)
+        sh = orig_shape[dim]
+        idx = builder.select(after_axis,
+                             builder.add(ll_dim, one),
+                             ll_dim)
+        builder.store(sh, cgutils.gep_inbounds(builder, shapes, 0, idx))
+
+    # 2. insert new size (1) at axis dimension
+    builder.store(one, cgutils.gep_inbounds(builder, shapes, 0, axis))
+
+    return cgutils.unpack_tuple(builder, builder.load(shapes))
+
+
+def _insert_axis_in_strides(context, builder, orig_strides, ndim, axis):
+    """
+    Same as _insert_axis_in_shape(), but with a strides array.
+    """
+    assert len(orig_strides) == ndim - 1
+
+    ll_shty = ir.ArrayType(cgutils.intp_t, ndim)
+    strides = cgutils.alloca_once(builder, ll_shty)
+
+    one = cgutils.intp_t(1)
+    zero = cgutils.intp_t(0)
+
+    # 1. copy original strides at appropriate places
+    for dim in range(ndim - 1):
+        ll_dim = cgutils.intp_t(dim)
+        after_axis = builder.icmp_signed('>=', ll_dim, axis)
+        idx = builder.select(after_axis,
+                             builder.add(ll_dim, one),
+                             ll_dim)
+        builder.store(orig_strides[dim],
+                      cgutils.gep_inbounds(builder, strides, 0, idx))
+
+    # 2. insert new stride at axis dimension
+    # (the value is indifferent for a 1-sized dimension, we use 0)
+    builder.store(zero, cgutils.gep_inbounds(builder, strides, 0, axis))
+
+    return cgutils.unpack_tuple(builder, builder.load(strides))
+
+
+def expand_dims(context, builder, sig, args, axis):
+    """
+    np.expand_dims() with the given axis.
+    """
+    retty = sig.return_type
+    ndim = retty.ndim
+    arrty = sig.args[0]
+
+    arr = make_array(arrty)(context, builder, value=args[0])
+    ret = make_array(retty)(context, builder)
+
+    shapes = cgutils.unpack_tuple(builder, arr.shape)
+    strides = cgutils.unpack_tuple(builder, arr.strides)
+
+    new_shapes = _insert_axis_in_shape(context, builder, shapes, ndim, axis)
+    new_strides = _insert_axis_in_strides(context, builder, strides, ndim, axis)
+
+    populate_array(ret,
+                   data=arr.data,
+                   shape=new_shapes,
+                   strides=new_strides,
+                   itemsize=arr.itemsize,
+                   meminfo=arr.meminfo,
+                   parent=arr.parent)
+
+    return ret._getvalue()
+
+
+@intrinsic
+def np_expand_dims(typingctx, a, axis):
+    layout = a.layout if a.ndim <= 1 else 'A'
+    ret = a.copy(ndim=a.ndim + 1, layout=layout)
+    sig = ret(a, axis)
+
+    def codegen(context, builder, sig, args):
+        axis = context.cast(builder, args[1], sig.args[1], types.intp)
+        axis = _normalize_axis(context, builder, "np.expand_dims",
+                               sig.return_type.ndim, axis)
+
+        ret = expand_dims(context, builder, sig, args, axis)
+        return impl_ret_borrowed(context, builder, sig.return_type, ret)
+
+    return sig, codegen
+
+
+@overload(np.expand_dims)
+def impl_np_expand_dims(a, axis):
+    if not isinstance(a, types.Array):
+        msg = f'First argument "a" must be an array. Got {a}'
+        raise errors.TypingError(msg)
+
+    if not isinstance(axis, types.Integer):
+        msg = f'Argument "axis" must be an integer. Got {axis}'
+        raise errors.TypingError(msg)
+
+    def impl(a, axis):
+        return np_expand_dims(a, axis)
+    return impl
+
+
+def _atleast_nd(minimum, axes):
+    @intrinsic
+    def impl(typingcontext, *args):
+        arrtys = args
+        rettys = [arg.copy(ndim=max(arg.ndim, minimum)) for arg in args]
+
+        def codegen(context, builder, sig, args):
+            transform = _atleast_nd_transform(minimum, axes)
+            arrs = cgutils.unpack_tuple(builder, args[0])
+
+            rets = [transform(context, builder, arr, arrty, retty)
+                    for arr, arrty, retty in zip(arrs, arrtys, rettys)]
+
+            if len(rets) > 1:
+                ret = context.make_tuple(builder, sig.return_type, rets)
+            else:
+                ret = rets[0]
+            return impl_ret_borrowed(context, builder, sig.return_type, ret)
+
+        return signature(types.Tuple(rettys) if len(rettys) > 1 else rettys[0],
+                         types.StarArgTuple.from_types(args)), codegen
+
+    return lambda *args: impl(*args)
+
+
+def _atleast_nd_transform(min_ndim, axes):
+    """
+    Return a callback successively inserting 1-sized dimensions at the
+    following axes.
+    """
+    assert min_ndim == len(axes)
+
+    def transform(context, builder, arr, arrty, retty):
+        for i in range(min_ndim):
+            ndim = i + 1
+            if arrty.ndim < ndim:
+                axis = cgutils.intp_t(axes[i])
+                newarrty = arrty.copy(ndim=arrty.ndim + 1)
+                arr = expand_dims(context, builder,
+                                  typing.signature(newarrty, arrty), (arr,),
+                                  axis)
+                arrty = newarrty
+
+        return arr
+
+    return transform
+
+
+@overload(np.atleast_1d)
+def np_atleast_1d(*args):
+    if all(isinstance(arg, types.Array) for arg in args):
+        return _atleast_nd(1, [0])
+
+
+@overload(np.atleast_2d)
+def np_atleast_2d(*args):
+    if all(isinstance(arg, types.Array) for arg in args):
+        return _atleast_nd(2, [0, 0])
+
+
+@overload(np.atleast_3d)
+def np_atleast_3d(*args):
+    if all(isinstance(arg, types.Array) for arg in args):
+        return _atleast_nd(3, [0, 0, 2])
+
+
+def _do_concatenate(context, builder, axis,
+                    arrtys, arrs, arr_shapes, arr_strides,
+                    retty, ret_shapes):
+    """
+    Concatenate arrays along the given axis.
+    """
+    assert len(arrtys) == len(arrs) == len(arr_shapes) == len(arr_strides)
+
+    zero = cgutils.intp_t(0)
+
+    # Allocate return array
+    ret = _empty_nd_impl(context, builder, retty, ret_shapes)
+    ret_strides = cgutils.unpack_tuple(builder, ret.strides)
+
+    # Compute the offset by which to bump the destination pointer
+    # after copying each input array.
+    # Morally, we need to copy each input array at different start indices
+    # into the destination array; bumping the destination pointer
+    # is simply easier than offsetting all destination indices.
+    copy_offsets = []
+
+    for arr_sh in arr_shapes:
+        # offset = ret_strides[axis] * input_shape[axis]
+        offset = zero
+        for dim, (size, stride) in enumerate(zip(arr_sh, ret_strides)):
+            is_axis = builder.icmp_signed('==', axis.type(dim), axis)
+            addend = builder.mul(size, stride)
+            offset = builder.select(is_axis,
+                                    builder.add(offset, addend),
+                                    offset)
+        copy_offsets.append(offset)
+
+    # Copy input arrays into the return array
+    ret_data = ret.data
+
+    for arrty, arr, arr_sh, arr_st, offset in zip(arrtys, arrs, arr_shapes,
+                                                  arr_strides, copy_offsets):
+        arr_data = arr.data
+
+        # Do the copy loop
+        # Note the loop nesting is optimized for the destination layout
+        loop_nest = cgutils.loop_nest(builder, arr_sh, cgutils.intp_t,
+                                      order=retty.layout)
+
+        with loop_nest as indices:
+            src_ptr = cgutils.get_item_pointer2(context, builder, arr_data,
+                                                arr_sh, arr_st,
+                                                arrty.layout, indices)
+            val = load_item(context, builder, arrty, src_ptr)
+            val = context.cast(builder, val, arrty.dtype, retty.dtype)
+            dest_ptr = cgutils.get_item_pointer2(context, builder, ret_data,
+                                                 ret_shapes, ret_strides,
+                                                 retty.layout, indices)
+            store_item(context, builder, retty, val, dest_ptr)
+
+        # Bump destination pointer
+        ret_data = cgutils.pointer_add(builder, ret_data, offset)
+
+    return ret
+
+
+def _np_concatenate(context, builder, arrtys, arrs, retty, axis):
+    ndim = retty.ndim
+
+    arrs = [make_array(aty)(context, builder, value=a)
+            for aty, a in zip(arrtys, arrs)]
+
+    axis = _normalize_axis(context, builder, "np.concatenate", ndim, axis)
+
+    # Get input shapes
+    arr_shapes = [cgutils.unpack_tuple(builder, arr.shape) for arr in arrs]
+    arr_strides = [cgutils.unpack_tuple(builder, arr.strides) for arr in arrs]
+
+    # Compute return shape:
+    # - the dimension for the concatenation axis is summed over all inputs
+    # - other dimensions must match exactly for each input
+    ret_shapes = [cgutils.alloca_once_value(builder, sh)
+                  for sh in arr_shapes[0]]
+
+    for dim in range(ndim):
+        is_axis = builder.icmp_signed('==', axis.type(dim), axis)
+        ret_shape_ptr = ret_shapes[dim]
+        ret_sh = builder.load(ret_shape_ptr)
+        other_shapes = [sh[dim] for sh in arr_shapes[1:]]
+
+        with builder.if_else(is_axis) as (on_axis, on_other_dim):
+            with on_axis:
+                sh = functools.reduce(
+                    builder.add,
+                    other_shapes + [ret_sh])
+                builder.store(sh, ret_shape_ptr)
+
+            with on_other_dim:
+                is_ok = cgutils.true_bit
+                for sh in other_shapes:
+                    is_ok = builder.and_(is_ok,
+                                         builder.icmp_signed('==', sh, ret_sh))
+                with builder.if_then(builder.not_(is_ok), likely=False):
+                    context.call_conv.return_user_exc(
+                        builder, ValueError,
+                        ("np.concatenate(): input sizes over "
+                         "dimension %d do not match" % dim,))
+
+    ret_shapes = [builder.load(sh) for sh in ret_shapes]
+
+    ret = _do_concatenate(context, builder, axis,
+                          arrtys, arrs, arr_shapes, arr_strides,
+                          retty, ret_shapes)
+    return impl_ret_new_ref(context, builder, retty, ret._getvalue())
+
+
+def _np_stack(context, builder, arrtys, arrs, retty, axis):
+    ndim = retty.ndim
+
+    zero = cgutils.intp_t(0)
+    one = cgutils.intp_t(1)
+    ll_narrays = cgutils.intp_t(len(arrs))
+
+    arrs = [make_array(aty)(context, builder, value=a)
+            for aty, a in zip(arrtys, arrs)]
+
+    axis = _normalize_axis(context, builder, "np.stack", ndim, axis)
+
+    # Check input arrays have the same shape
+    orig_shape = cgutils.unpack_tuple(builder, arrs[0].shape)
+
+    for arr in arrs[1:]:
+        is_ok = cgutils.true_bit
+        for sh, orig_sh in zip(cgutils.unpack_tuple(builder, arr.shape),
+                               orig_shape):
+            is_ok = builder.and_(is_ok, builder.icmp_signed('==', sh, orig_sh))
+            with builder.if_then(builder.not_(is_ok), likely=False):
+                context.call_conv.return_user_exc(
+                    builder, ValueError,
+                    ("np.stack(): all input arrays must have the same shape",))
+
+    orig_strides = [cgutils.unpack_tuple(builder, arr.strides) for arr in arrs]
+
+    # Compute input shapes and return shape with the new axis inserted
+    # e.g. given 5 input arrays of shape (2, 3, 4) and axis=1,
+    # corrected input shape is (2, 1, 3, 4) and return shape is (2, 5, 3, 4).
+    ll_shty = ir.ArrayType(cgutils.intp_t, ndim)
+
+    input_shapes = cgutils.alloca_once(builder, ll_shty)
+    ret_shapes = cgutils.alloca_once(builder, ll_shty)
+
+    # 1. copy original sizes at appropriate places
+    for dim in range(ndim - 1):
+        ll_dim = cgutils.intp_t(dim)
+        after_axis = builder.icmp_signed('>=', ll_dim, axis)
+        sh = orig_shape[dim]
+        idx = builder.select(after_axis,
+                             builder.add(ll_dim, one),
+                             ll_dim)
+        builder.store(sh, cgutils.gep_inbounds(builder, input_shapes, 0, idx))
+        builder.store(sh, cgutils.gep_inbounds(builder, ret_shapes, 0, idx))
+
+    # 2. insert new size at axis dimension
+    builder.store(one, cgutils.gep_inbounds(builder, input_shapes, 0, axis))
+    builder.store(ll_narrays, cgutils.gep_inbounds(builder,
+                                                   ret_shapes,
+                                                   0,
+                                                   axis))
+
+    input_shapes = cgutils.unpack_tuple(builder, builder.load(input_shapes))
+    input_shapes = [input_shapes] * len(arrs)
+    ret_shapes = cgutils.unpack_tuple(builder, builder.load(ret_shapes))
+
+    # Compute input strides for each array with the new axis inserted
+    input_strides = [cgutils.alloca_once(builder, ll_shty)
+                     for i in range(len(arrs))]
+
+    # 1. copy original strides at appropriate places
+    for dim in range(ndim - 1):
+        ll_dim = cgutils.intp_t(dim)
+        after_axis = builder.icmp_signed('>=', ll_dim, axis)
+        idx = builder.select(after_axis,
+                             builder.add(ll_dim, one),
+                             ll_dim)
+        for i in range(len(arrs)):
+            builder.store(orig_strides[i][dim],
+                          cgutils.gep_inbounds(builder, input_strides[i], 0,
+                                               idx))
+
+    # 2. insert new stride at axis dimension
+    # (the value is indifferent for a 1-sized dimension, we put 0)
+    for i in range(len(arrs)):
+        builder.store(zero, cgutils.gep_inbounds(builder, input_strides[i], 0,
+                                                 axis))
+
+    input_strides = [cgutils.unpack_tuple(builder, builder.load(st))
+                     for st in input_strides]
+
+    # Create concatenated array
+    ret = _do_concatenate(context, builder, axis,
+                          arrtys, arrs, input_shapes, input_strides,
+                          retty, ret_shapes)
+    return impl_ret_new_ref(context, builder, retty, ret._getvalue())
+
+
+def np_concatenate_typer(typingctx, arrays, axis):
+    if axis is not None and not isinstance(axis, types.Integer):
+        # Note Numpy allows axis=None, but it isn't documented:
+        # https://github.com/numpy/numpy/issues/7968
+        return
+
+    # does type checking
+    dtype, ndim = _sequence_of_arrays(typingctx,
+                                      "np.concatenate", arrays)
+    if ndim == 0:
+        msg = "zero-dimensional arrays cannot be concatenated"
+        raise errors.NumbaTypeError(msg)
+
+    layout = _choose_concatenation_layout(arrays)
+
+    return types.Array(dtype, ndim, layout)
+
+
+@intrinsic
+def np_concatenate(typingctx, arrays, axis):
+    ret = np_concatenate_typer(typingctx, arrays, axis)
+    assert isinstance(ret, types.Array)
+    sig = ret(arrays, axis)
+
+    def codegen(context, builder, sig, args):
+        axis = context.cast(builder, args[1], sig.args[1], types.intp)
+        return _np_concatenate(context, builder,
+                               list(sig.args[0]),
+                               cgutils.unpack_tuple(builder, args[0]),
+                               sig.return_type,
+                               axis)
+
+    return sig, codegen
+
+
+@overload(np.concatenate)
+def impl_np_concatenate(arrays, axis=0):
+    if isinstance(arrays, types.BaseTuple):
+        def impl(arrays, axis=0):
+            return np_concatenate(arrays, axis)
+        return impl
+
+
+def _column_stack_dims(context, func_name, arrays):
+    # column_stack() allows stacking 1-d and 2-d arrays together
+    for a in arrays:
+        if a.ndim < 1 or a.ndim > 2:
+            msg = "np.column_stack() is only defined on 1-d and 2-d arrays"
+            raise errors.NumbaTypeError(msg)
+    return 2
+
+
+@intrinsic
+def np_column_stack(typingctx, tup):
+    dtype, ndim = _sequence_of_arrays(typingctx,
+                                      "np.column_stack", tup,
+                                      dim_chooser=_column_stack_dims)
+    layout = _choose_concatenation_layout(tup)
+    ret = types.Array(dtype, ndim, layout)
+    sig = ret(tup)
+
+    def codegen(context, builder, sig, args):
+        orig_arrtys = list(sig.args[0])
+        orig_arrs = cgutils.unpack_tuple(builder, args[0])
+
+        arrtys = []
+        arrs = []
+
+        axis = context.get_constant(types.intp, 1)
+
+        for arrty, arr in zip(orig_arrtys, orig_arrs):
+            if arrty.ndim == 2:
+                arrtys.append(arrty)
+                arrs.append(arr)
+            else:
+                # Convert 1d array to 2d column array: np.expand_dims(a, 1)
+                assert arrty.ndim == 1
+                newty = arrty.copy(ndim=2)
+                expand_sig = typing.signature(newty, arrty)
+                newarr = expand_dims(context, builder, expand_sig, (arr,), axis)
+
+                arrtys.append(newty)
+                arrs.append(newarr)
+
+        return _np_concatenate(context, builder, arrtys, arrs,
+                               sig.return_type, axis)
+
+    return sig, codegen
+
+
+@overload(np.column_stack)
+def impl_column_stack(tup):
+    if isinstance(tup, types.BaseTuple):
+        def impl(tup):
+            return np_column_stack(tup)
+        return impl
+
+
+def _np_stack_common(context, builder, sig, args, axis):
+    """
+    np.stack() with the given axis value.
+    """
+    return _np_stack(context, builder,
+                     list(sig.args[0]),
+                     cgutils.unpack_tuple(builder, args[0]),
+                     sig.return_type,
+                     axis)
+
+
+@intrinsic
+def np_stack_common(typingctx, arrays, axis):
+    # does type checking
+    dtype, ndim = _sequence_of_arrays(typingctx,
+                                      "np.stack", arrays)
+    layout = 'F' if all(a.layout == 'F' for a in arrays) else 'C'
+    ret = types.Array(dtype, ndim + 1, layout)
+    sig = ret(arrays, axis)
+
+    def codegen(context, builder, sig, args):
+        axis = context.cast(builder, args[1], sig.args[1], types.intp)
+        return _np_stack_common(context, builder, sig, args, axis)
+
+    return sig, codegen
+
+
+@overload(np.stack)
+def impl_np_stack(arrays, axis=0):
+    if isinstance(arrays, types.BaseTuple):
+        def impl(arrays, axis=0):
+            return np_stack_common(arrays, axis)
+        return impl
+
+
+def NdStack_typer(typingctx, func_name, arrays, ndim_min):
+    # does type checking
+    dtype, ndim = _sequence_of_arrays(typingctx, func_name, arrays)
+    ndim = max(ndim, ndim_min)
+    layout = _choose_concatenation_layout(arrays)
+    ret = types.Array(dtype, ndim, layout)
+    return ret
+
+
+@intrinsic
+def _np_hstack(typingctx, tup):
+    ret = NdStack_typer(typingctx, "np.hstack", tup, 1)
+    sig = ret(tup)
+
+    def codegen(context, builder, sig, args):
+        tupty = sig.args[0]
+        ndim = tupty[0].ndim
+
+        if ndim == 0:
+            # hstack() on 0-d arrays returns a 1-d array
+            axis = context.get_constant(types.intp, 0)
+            return _np_stack_common(context, builder, sig, args, axis)
+
+        else:
+            # As a special case, dimension 0 of 1-dimensional arrays
+            # is "horizontal"
+            axis = 0 if ndim == 1 else 1
+
+            def np_hstack_impl(arrays):
+                return np.concatenate(arrays, axis=axis)
+
+            return context.compile_internal(builder, np_hstack_impl, sig, args)
+
+    return sig, codegen
+
+
+@overload(np.hstack)
+def impl_np_hstack(tup):
+    if isinstance(tup, types.BaseTuple):
+        def impl(tup):
+            return _np_hstack(tup)
+        return impl
+
+
+@intrinsic
+def _np_vstack(typingctx, tup):
+    ret = NdStack_typer(typingctx, "np.vstack", tup, 2)
+    sig = ret(tup)
+
+    def codegen(context, builder, sig, args):
+        tupty = sig.args[0]
+        ndim = tupty[0].ndim
+
+        if ndim == 0:
+            def np_vstack_impl(arrays):
+                return np.expand_dims(np.hstack(arrays), 1)
+
+        elif ndim == 1:
+            # np.stack(arrays, axis=0)
+            axis = context.get_constant(types.intp, 0)
+            return _np_stack_common(context, builder, sig, args, axis)
+
+        else:
+            def np_vstack_impl(arrays):
+                return np.concatenate(arrays, axis=0)
+
+        return context.compile_internal(builder, np_vstack_impl, sig, args)
+
+    return sig, codegen
+
+
+@overload(np.vstack)
+def impl_np_vstack(tup):
+    if isinstance(tup, types.BaseTuple):
+        def impl(tup):
+            return _np_vstack(tup)
+        return impl
+
+
+if numpy_version >= (2, 0) and numpy_version < (2, 5):
+    # Numpy 2.5 removed np.row_stack, so only test it if it's present.
+    overload(np.row_stack)(impl_np_vstack)
+
+
+@intrinsic
+def _np_dstack(typingctx, tup):
+    ret = NdStack_typer(typingctx, "np.dstack", tup, 3)
+    sig = ret(tup)
+
+    def codegen(context, builder, sig, args):
+        tupty = sig.args[0]
+        retty = sig.return_type
+        ndim = tupty[0].ndim
+
+        if ndim == 0:
+            def np_vstack_impl(arrays):
+                return np.hstack(arrays).reshape(1, 1, -1)
+
+            return context.compile_internal(builder, np_vstack_impl, sig, args)
+
+        elif ndim == 1:
+            # np.expand_dims(np.stack(arrays, axis=1), axis=0)
+            axis = context.get_constant(types.intp, 1)
+            stack_retty = retty.copy(ndim=retty.ndim - 1)
+            stack_sig = typing.signature(stack_retty, *sig.args)
+            stack_ret = _np_stack_common(context, builder, stack_sig, args,
+                                         axis)
+
+            axis = context.get_constant(types.intp, 0)
+            expand_sig = typing.signature(retty, stack_retty)
+            return expand_dims(context, builder, expand_sig, (stack_ret,), axis)
+
+        elif ndim == 2:
+            # np.stack(arrays, axis=2)
+            axis = context.get_constant(types.intp, 2)
+            return _np_stack_common(context, builder, sig, args, axis)
+
+        else:
+            def np_vstack_impl(arrays):
+                return np.concatenate(arrays, axis=2)
+
+            return context.compile_internal(builder, np_vstack_impl, sig, args)
+
+    return sig, codegen
+
+
+@overload(np.dstack)
+def impl_np_dstack(tup):
+    if isinstance(tup, types.BaseTuple):
+        def impl(tup):
+            return _np_dstack(tup)
+        return impl
+
+
+@extending.overload_method(types.Array, 'fill')
+def arr_fill(arr, val):
+
+    def fill_impl(arr, val):
+        arr[:] = val
+        return None
+
+    return fill_impl
+
+
+@extending.overload_method(types.Array, 'dot')
+def array_dot(arr, other):
+    def dot_impl(arr, other):
+        return np.dot(arr, other)
+
+    return dot_impl
+
+
+@overload(np.fliplr)
+def np_flip_lr(m):
+
+    if not type_can_asarray(m):
+        raise errors.TypingError("Cannot np.fliplr on %s type" % m)
+
+    def impl(m):
+        A = np.asarray(m)
+        # this handling is superfluous/dead as < 2d array cannot be indexed as
+        # present below and so typing fails. If the typing doesn't fail due to
+        # some future change, this will catch it.
+        if A.ndim < 2:
+            raise ValueError('Input must be >= 2-d.')
+        return A[::, ::-1, ...]
+    return impl
+
+
+@overload(np.flipud)
+def np_flip_ud(m):
+
+    if not type_can_asarray(m):
+        raise errors.TypingError("Cannot np.flipud on %s type" % m)
+
+    def impl(m):
+        A = np.asarray(m)
+        # this handling is superfluous/dead as a 0d array cannot be indexed as
+        # present below and so typing fails. If the typing doesn't fail due to
+        # some future change, this will catch it.
+        if A.ndim < 1:
+            raise ValueError('Input must be >= 1-d.')
+        return A[::-1, ...]
+    return impl
+
+
+@intrinsic
+def _build_flip_slice_tuple(tyctx, sz):
+    """ Creates a tuple of slices for np.flip indexing like
+    `(slice(None, None, -1),) * sz` """
+    if not isinstance(sz, types.IntegerLiteral):
+        raise errors.RequireLiteralValue(sz)
+    size = int(sz.literal_value)
+    tuple_type = types.UniTuple(dtype=types.slice3_type, count=size)
+    sig = tuple_type(sz)
+
+    def codegen(context, builder, signature, args):
+        def impl(length, empty_tuple):
+            out = empty_tuple
+            for i in range(length):
+                out = tuple_setitem(out, i, slice(None, None, -1))
+            return out
+
+        inner_argtypes = [types.intp, tuple_type]
+        inner_sig = typing.signature(tuple_type, *inner_argtypes)
+        ll_idx_type = context.get_value_type(types.intp)
+        # Allocate an empty tuple
+        empty_tuple = context.get_constant_undef(tuple_type)
+        inner_args = [ll_idx_type(size), empty_tuple]
+
+        res = context.compile_internal(builder, impl, inner_sig, inner_args)
+        return res
+
+    return sig, codegen
+
+
+@overload(np.flip)
+def np_flip(m):
+    # a constant value is needed for the tuple slice, types.Array.ndim can
+    # provide this and so at presnet only type.Array is support
+    if not isinstance(m, types.Array):
+        raise errors.TypingError("Cannot np.flip on %s type" % m)
+
+    def impl(m):
+        sl = _build_flip_slice_tuple(m.ndim)
+        return m[sl]
+
+    return impl
+
+
+@overload(np.array_split)
+def np_array_split(ary, indices_or_sections, axis=0):
+    if isinstance(ary, (types.UniTuple, types.ListType, types.List)):
+        def impl(ary, indices_or_sections, axis=0):
+            return np.array_split(
+                np.asarray(ary),
+                indices_or_sections,
+                axis=axis
+            )
+
+        return impl
+
+    if isinstance(indices_or_sections, types.Integer):
+        def impl(ary, indices_or_sections, axis=0):
+            l, rem = divmod(ary.shape[axis], indices_or_sections)
+            indices = np.cumsum(np.array(
+                [l + 1] * rem +
+                [l] * (indices_or_sections - rem - 1)
+            ))
+            return np.array_split(ary, indices, axis=axis)
+
+        return impl
+
+    elif (
+        isinstance(indices_or_sections, types.IterableType)
+        and isinstance(
+            indices_or_sections.iterator_type.yield_type,
+            types.Integer
+        )
+    ):
+        def impl(ary, indices_or_sections, axis=0):
+            slice_tup = build_full_slice_tuple(ary.ndim)
+            axis = normalize_axis("np.split", "axis", ary.ndim, axis)
+            out = []
+            prev = 0
+            for cur in indices_or_sections:
+                idx = tuple_setitem(slice_tup, axis, slice(prev, cur))
+                out.append(ary[idx])
+                prev = cur
+            out.append(ary[tuple_setitem(slice_tup, axis, slice(cur, None))])
+            return out
+
+        return impl
+
+    elif (
+        isinstance(indices_or_sections, types.Tuple)
+        and all(isinstance(t, types.Integer) for t in indices_or_sections.types)
+    ):
+        def impl(ary, indices_or_sections, axis=0):
+            slice_tup = build_full_slice_tuple(ary.ndim)
+            axis = normalize_axis("np.split", "axis", ary.ndim, axis)
+            out = []
+            prev = 0
+            for cur in literal_unroll(indices_or_sections):
+                idx = tuple_setitem(slice_tup, axis, slice(prev, cur))
+                out.append(ary[idx])
+                prev = cur
+            out.append(ary[tuple_setitem(slice_tup, axis, slice(cur, None))])
+            return out
+
+        return impl
+
+
+@overload(np.split)
+def np_split(ary, indices_or_sections, axis=0):
+    # This is just a wrapper of array_split, but with an extra error if
+    # indices is an int.
+    if isinstance(ary, (types.UniTuple, types.ListType, types.List)):
+        def impl(ary, indices_or_sections, axis=0):
+            return np.split(np.asarray(ary), indices_or_sections, axis=axis)
+
+        return impl
+
+    if isinstance(indices_or_sections, types.Integer):
+        def impl(ary, indices_or_sections, axis=0):
+            _, rem = divmod(ary.shape[axis], indices_or_sections)
+            if rem != 0:
+                raise ValueError(
+                    "array split does not result in an equal division"
+                )
+            return np.array_split(
+                ary, indices_or_sections, axis=axis
+            )
+
+        return impl
+
+    else:
+        return np_array_split(ary, indices_or_sections, axis=axis)
+
+
+@overload(np.vsplit)
+def numpy_vsplit(ary, indices_or_sections):
+    if not isinstance(ary, types.Array):
+        msg = 'The argument "ary" must be an array'
+        raise errors.TypingError(msg)
+
+    if not isinstance(indices_or_sections, (types.Integer, types.Array,
+                                            types.List, types.UniTuple)):
+        msg = ('The argument "indices_or_sections" must be int or 1d-array')
+        raise errors.TypingError(msg)
+
+    def impl(ary, indices_or_sections):
+        if ary.ndim < 2:
+            raise ValueError(('vsplit only works on '
+                              'arrays of 2 or more dimensions'))
+        return np.split(ary, indices_or_sections, axis=0)
+
+    return impl
+
+
+@overload(np.hsplit)
+def numpy_hsplit(ary, indices_or_sections):
+    if not isinstance(ary, types.Array):
+        msg = 'The argument "ary" must be an array'
+        raise errors.TypingError(msg)
+
+    if not isinstance(indices_or_sections, (types.Integer, types.Array,
+                                            types.List, types.UniTuple)):
+        msg = ('The argument "indices_or_sections" must be int or 1d-array')
+        raise errors.TypingError(msg)
+
+    def impl(ary, indices_or_sections):
+        if ary.ndim == 0:
+            raise ValueError(('hsplit only works on '
+                              'arrays of 1 or more dimensions'))
+        if ary.ndim > 1:
+            return np.split(ary, indices_or_sections, axis=1)
+        return np.split(ary, indices_or_sections, axis=0)
+
+    return impl
+
+
+@overload(np.dsplit)
+def numpy_dsplit(ary, indices_or_sections):
+    if not isinstance(ary, types.Array):
+        msg = 'The argument "ary" must be an array'
+        raise errors.TypingError(msg)
+
+    if not isinstance(indices_or_sections, (types.Integer, types.Array,
+                                            types.List, types.UniTuple)):
+        msg = ('The argument "indices_or_sections" must be int or 1d-array')
+        raise errors.TypingError(msg)
+
+    def impl(ary, indices_or_sections):
+        if ary.ndim < 3:
+            raise ValueError('dsplit only works on arrays of 3 or more '
+                             'dimensions')
+        return np.split(ary, indices_or_sections, axis=2)
+
+    return impl
+
+
+# -----------------------------------------------------------------------------
+# Sorting
+
+_sorts = {}
+
+
+def default_lt(a, b):
+    """
+    Trivial comparison function between two keys.
+    """
+    return a < b
+
+
+def get_sort_func(kind, lt_impl, is_argsort=False):
+    """
+    Get a sort implementation of the given kind.
+    """
+    if kind == 'stable':
+        # NumPy treats 'stable' as an alias for 'mergesort'
+        kind = 'mergesort'
+    key = kind, lt_impl.__name__, is_argsort
+
+    try:
+        return _sorts[key]
+    except KeyError:
+        if kind == 'quicksort':
+            sort = quicksort.make_jit_quicksort(
+                lt=lt_impl,
+                is_argsort=is_argsort,
+                is_np_array=True)
+            func = sort.run_quicksort
+        elif kind == 'mergesort':
+            sort = mergesort.make_jit_mergesort(
+                lt=lt_impl,
+                is_argsort=is_argsort)
+            func = sort.run_mergesort
+        _sorts[key] = func
+        return func
+
+
+def lt_implementation(dtype):
+    if isinstance(dtype, types.Float):
+        return lt_floats
+    elif isinstance(dtype, types.Complex):
+        return lt_complex
+    else:
+        return default_lt
+
+
+@lower_builtin("array.sort", types.Array, types.StringLiteral)
+def array_sort(context, builder, sig, args):
+    arytype, kind = sig.args
+    kind = kind.literal_value
+
+    sort_func = get_sort_func(kind=kind,
+                              lt_impl=lt_implementation(arytype.dtype))
+
+    if kind in ('mergesort', 'stable') and arytype.ndim > 1:
+        def array_sort_impl(arr):
+            # mergesort only sorts 1D arrays, so sort each slice along
+            # the last axis as quicksort does for multidimensional arrays
+            for idx in np.ndindex(arr.shape[:-1]):
+                sort_func(arr[idx])
+    else:
+        def array_sort_impl(arr):
+            # Note we clobber the return value
+            sort_func(arr)
+
+    innersig = sig.replace(args=sig.args[:1])
+    innerargs = args[:1]
+    return context.compile_internal(builder, array_sort_impl,
+                                    innersig, innerargs)
+
+
+@overload(np.sort)
+def impl_np_sort(a):
+    if not type_can_asarray(a):
+        raise errors.TypingError('Argument "a" must '
+                                 'be array-like')
+
+    def np_sort_impl(a):
+        res = a.copy()
+        res.sort()
+        return res
+    return np_sort_impl
+
+
+@lower_builtin("array.argsort", types.Array, types.StringLiteral)
+@lower_builtin(np.argsort, types.Array, types.StringLiteral)
+def array_argsort(context, builder, sig, args):
+    arytype, kind = sig.args
+
+    sort_func = get_sort_func(kind=kind.literal_value,
+                              lt_impl=lt_implementation(arytype.dtype),
+                              is_argsort=True)
+
+    def array_argsort_impl(arr):
+        return sort_func(arr)
+
+    innersig = sig.replace(args=sig.args[:1])
+    innerargs = args[:1]
+    return context.compile_internal(builder, array_argsort_impl,
+                                    innersig, innerargs)
+
+
+# ------------------------------------------------------------------------------
+# Implicit cast
+
+@lower_cast(types.Array, types.Array)
+def array_to_array(context, builder, fromty, toty, val):
+    # Type inference should have prevented illegal array casting.
+    assert fromty.mutable != toty.mutable or toty.layout == 'A'
+    return val
+
+
+@lower_cast(types.Array, types.UnicodeCharSeq)
+@lower_cast(types.Array, types.Float)
+@lower_cast(types.Array, types.Integer)
+@lower_cast(types.Array, types.Complex)
+@lower_cast(types.Array, types.Boolean)
+@lower_cast(types.Array, npy_types.NPTimedelta)
+@lower_cast(types.Array, npy_types.NPDatetime)
+def array0d_to_scalar(context, builder, fromty, toty, val):
+    def impl(a):
+        # a is an array(T, 0d, O), T is type, O is order
+        return a.take(0)
+
+    sig = signature(toty, fromty)
+    res = context.compile_internal(builder, impl, sig, [val])
+    return impl_ret_untracked(context, builder, sig.return_type, res)
+
+
+@lower_cast(types.Array, types.UnicodeCharSeq)
+def array_to_unichrseq(context, builder, fromty, toty, val):
+    def impl(a):
+        return str(a[()])
+
+    sig = signature(toty, fromty)
+    res = context.compile_internal(builder, impl, sig, [val])
+    return impl_ret_borrowed(context, builder, sig.return_type, res)
+
+
+# ------------------------------------------------------------------------------
+# Stride tricks
+
+def reshape_unchecked(a, shape, strides):
+    """
+    An intrinsic returning a derived array with the given shape and strides.
+    """
+    raise NotImplementedError
+
+
+@extending.type_callable(reshape_unchecked)
+def type_reshape_unchecked(context):
+    def check_shape(shape):
+        return (isinstance(shape, types.BaseTuple) and
+                all(isinstance(v, types.Integer) for v in shape))
+
+    def typer(a, shape, strides):
+        if not isinstance(a, types.Array):
+            return
+        if not check_shape(shape) or not check_shape(strides):
+            return
+        if len(shape) != len(strides):
+            return
+        return a.copy(ndim=len(shape), layout='A')
+
+    return typer
+
+
+@lower_builtin(reshape_unchecked, types.Array, types.BaseTuple, types.BaseTuple)
+def impl_shape_unchecked(context, builder, sig, args):
+    aryty = sig.args[0]
+    retty = sig.return_type
+
+    ary = make_array(aryty)(context, builder, args[0])
+    out = make_array(retty)(context, builder)
+    shape = cgutils.unpack_tuple(builder, args[1])
+    strides = cgutils.unpack_tuple(builder, args[2])
+
+    populate_array(out,
+                   data=ary.data,
+                   shape=shape,
+                   strides=strides,
+                   itemsize=ary.itemsize,
+                   meminfo=ary.meminfo,
+                   )
+
+    res = out._getvalue()
+    return impl_ret_borrowed(context, builder, retty, res)
+
+
+@extending.overload(np.lib.stride_tricks.as_strided)
+def as_strided(x, shape=None, strides=None):
+    if shape in (None, types.none):
+        @register_jitable
+        def get_shape(x, shape):
+            return x.shape
+    else:
+        @register_jitable
+        def get_shape(x, shape):
+            return shape
+
+    if strides in (None, types.none):
+        # When *strides* is not passed, as_strided() does a non-size-checking
+        # reshape(), possibly changing the original strides.  This is too
+        # cumbersome to support right now, and a Web search shows all example
+        # use cases of as_strided() pass explicit *strides*.
+        raise errors.TypingError("as_strided() strides argument cannot be None")
+    else:
+        @register_jitable
+        def get_strides(x, strides):
+            return strides
+
+    def as_strided_impl(x, shape=None, strides=None):
+        x = reshape_unchecked(x, get_shape(x, shape), get_strides(x, strides))
+        return x
+
+    return as_strided_impl
+
+
+@extending.overload(np.lib.stride_tricks.sliding_window_view)
+def sliding_window_view(x, window_shape, axis=None):
+
+    # Window shape must be given as either an integer or tuple of integers.
+    # We also need to generate buffer tuples we can modify to contain the
+    # final shape and strides (reshape_unchecked does not accept lists).
+    if isinstance(window_shape, types.Integer):
+        shape_buffer = tuple(range(x.ndim + 1))
+        stride_buffer = tuple(range(x.ndim + 1))
+
+        @register_jitable
+        def get_window_shape(window_shape):
+            return (window_shape,)
+
+    elif (isinstance(window_shape, types.UniTuple) and
+            isinstance(window_shape.dtype, types.Integer)):
+        shape_buffer = tuple(range(x.ndim + len(window_shape)))
+        stride_buffer = tuple(range(x.ndim + len(window_shape)))
+
+        @register_jitable
+        def get_window_shape(window_shape):
+            return window_shape
+
+    else:
+        raise errors.TypingError(
+            "window_shape must be an integer or tuple of integers"
+        )
+
+    # Axis must be integer, tuple of integers, or None for all axes.
+    if is_nonelike(axis):
+        @register_jitable
+        def get_axis(window_shape, axis, ndim):
+            return list(range(ndim))
+
+    elif isinstance(axis, types.Integer):
+        @register_jitable
+        def get_axis(window_shape, axis, ndim):
+            return [
+                normalize_axis("sliding_window_view", "axis", ndim, axis)
+            ]
+
+    elif (isinstance(axis, types.UniTuple) and
+            isinstance(axis.dtype, types.Integer)):
+        @register_jitable
+        def get_axis(window_shape, axis, ndim):
+            return [normalize_axis("sliding_window_view", "axis", ndim, a)
+                    for a in axis]
+
+    else:
+        raise errors.TypingError(
+            "axis must be None, an integer or tuple of integers"
+        )
+
+    def sliding_window_view_impl(x, window_shape, axis=None):
+        window_shape = get_window_shape(window_shape)
+        axis = get_axis(window_shape, axis, x.ndim)
+        if len(window_shape) != len(axis):
+            raise ValueError(
+                "Must provide matching length window_shape and axis"
+            )
+
+        # Initialise view details with shape and strides of x.
+        out_shape = shape_buffer
+        out_strides = stride_buffer
+        for i in range(x.ndim):
+            out_shape = tuple_setitem(out_shape, i, x.shape[i])
+            out_strides = tuple_setitem(out_strides, i, x.strides[i])
+
+        # Trim the dimensions being windowed and set the window shape and
+        # strides. Note: the same axis can be windowed repeatedly.
+        i = x.ndim
+        for ax, dim in zip(axis, window_shape):
+            if dim < 0:
+                raise ValueError(
+                    "`window_shape` cannot contain negative values"
+                )
+            if out_shape[ax] < dim:
+                raise ValueError(
+                    "window_shape cannot be larger than input array shape"
+                )
+
+            trimmed = out_shape[ax] - dim + 1
+            out_shape = tuple_setitem(out_shape, ax, trimmed)
+            out_shape = tuple_setitem(out_shape, i, dim)
+            out_strides = tuple_setitem(out_strides, i, x.strides[ax])
+            i += 1
+
+        # The NumPy version calls as_strided, but our implementation of
+        # as_strided is effectively a wrapper for reshape_unchecked.
+        view = reshape_unchecked(x, out_shape, out_strides)
+        return view
+
+    return sliding_window_view_impl
+
+
+@overload(bool)
+def ol_bool(arr):
+    if isinstance(arr, types.Array):
+        def impl(arr):
+            if arr.size == 0:
+                if numpy_version < (2, 2):
+                    return False # this is deprecated
+                else:
+                    raise ValueError(("The truth value of an empty array is "
+                                      "ambiguous. Use `array.size > 0` to "
+                                      "check that an array is not empty."))
+            elif arr.size == 1:
+                return bool(arr.take(0))
+            else:
+                raise ValueError(("The truth value of an array with more than"
+                                  " one element is ambiguous. Use a.any() or"
+                                  " a.all()"))
+        return impl
+
+
+@overload(np.swapaxes)
+def numpy_swapaxes(a, axis1, axis2):
+    if not isinstance(axis1, (int, types.Integer)):
+        raise errors.TypingError('The second argument "axis1" must be an '
+                                 'integer')
+    if not isinstance(axis2, (int, types.Integer)):
+        raise errors.TypingError('The third argument "axis2" must be an '
+                                 'integer')
+    if not isinstance(a, types.Array):
+        raise errors.TypingError('The first argument "a" must be an array')
+
+    # create tuple list for transpose
+    ndim = a.ndim
+    axes_list = tuple(range(ndim))
+
+    def impl(a, axis1, axis2):
+        axis1 = normalize_axis("np.swapaxes", "axis1", ndim, axis1)
+        axis2 = normalize_axis("np.swapaxes", "axis2", ndim, axis2)
+        axes_tuple = tuple_setitem(axes_list, axis1, axis2)
+        axes_tuple = tuple_setitem(axes_tuple, axis2, axis1)
+        return np.transpose(a, axes_tuple)
+
+    return impl
+
+
+@overload(np.moveaxis)
+def numpy_moveaxis(a, source, destination):
+    if not isinstance(a, types.Array):
+        raise errors.TypingError('The first argument "a" must be an array')
+    if not (
+        isinstance(source, types.Integer)
+        or (
+            isinstance(source, types.Sequence)
+            and isinstance(source.dtype, types.Integer)
+        )
+    ):
+        raise errors.TypingError(
+            'The second argument "source" must be an integer '
+            'or sequence of integers'
+        )
+    if not (
+        isinstance(destination, types.Integer)
+        or (
+            isinstance(destination, types.Sequence)
+            and isinstance(destination.dtype, types.Integer)
+        )
+    ):
+        raise errors.TypingError(
+            'The third argument "destination" must be an integer '
+            'or sequence of integers'
+        )
+
+    ndim = a.ndim
+    order_init = (1,) * a.ndim
+
+    def impl(a, source, destination):
+        source = normalize_axis_tuple("np.moveaxis", "source", ndim, source)
+        destination = normalize_axis_tuple(
+            "np.moveaxis", "destination", ndim, destination
+        )
+        if len(source) != len(destination):
+            raise ValueError(
+                "`source` and `destination` arguments must have "
+                "the same number of parameters"
+            )
+
+        order_list = [n for n in range(a.ndim) if n not in source]
+
+        for dest, src in sorted(zip(destination, source)):
+            order_list.insert(dest, src)
+
+        order = order_init
+        for i, o in enumerate(order_list):
+            order = tuple_setitem(order, i, o)
+
+        return a.transpose(order)
+
+    return impl
+
+
+@register_jitable
+def _take_along_axis_impl(
+        arr, indices, axis, Ni_orig, Nk_orig, indices_broadcast_shape
+):
+    # Based on example code in
+    # https://github.com/numpy/numpy/blob/623bc1fae1d47df24e7f1e29321d0c0ba2771ce0/numpy/lib/shape_base.py#L90-L103
+    # With addition of pre-broadcasting:
+    # https://github.com/numpy/numpy/issues/19704
+
+    # Wrap axis, it's used in tuple_setitem so must be (axis >= 0) to ensure
+    # the GEP is in bounds.
+    axis = normalize_axis("np.take_along_axis", "axis", arr.ndim, axis)
+
+    # Broadcast the two arrays to matching shapes:
+    arr_shape = list(arr.shape)
+    arr_shape[axis] = 1
+    for i, (d1, d2) in enumerate(zip(arr_shape, indices.shape)):
+        if d1 == 1:
+            new_val = d2
+        elif d2 == 1:
+            new_val = d1
+        else:
+            if d1 != d2:
+                raise ValueError(
+                    "`arr` and `indices` dimensions don't match"
+                )
+            new_val = d1
+        indices_broadcast_shape = tuple_setitem(
+            indices_broadcast_shape, i, new_val
+        )
+    arr_broadcast_shape = tuple_setitem(
+        indices_broadcast_shape, axis, arr.shape[axis]
+    )
+    arr = np.broadcast_to(arr, arr_broadcast_shape)
+    indices = np.broadcast_to(indices, indices_broadcast_shape)
+
+    Ni = Ni_orig
+    if len(Ni_orig) > 0:
+        for i in range(len(Ni)):
+            Ni = tuple_setitem(Ni, i, arr.shape[i])
+    Nk = Nk_orig
+    if len(Nk_orig) > 0:
+        for i in range(len(Nk)):
+            Nk = tuple_setitem(Nk, i, arr.shape[axis + 1 + i])
+
+    J = indices.shape[axis]  # Need not equal M
+    out = np.empty(Ni + (J,) + Nk, arr.dtype)
+
+    np_s_ = (slice(None, None, None),)
+
+    for ii in np.ndindex(Ni):
+        for kk in np.ndindex(Nk):
+            a_1d = arr[ii + np_s_ + kk]
+            indices_1d = indices[ii + np_s_ + kk]
+            out_1d = out[ii + np_s_ + kk]
+            for j in range(J):
+                out_1d[j] = a_1d[indices_1d[j]]
+    return out
+
+
+@overload(np.take_along_axis)
+def arr_take_along_axis(arr, indices, axis):
+    if not isinstance(arr, types.Array):
+        raise errors.TypingError('The first argument "arr" must be an array')
+    if not isinstance(indices, types.Array):
+        raise errors.TypingError(
+            'The second argument "indices" must be an array')
+    if not isinstance(indices.dtype, types.Integer):
+        raise errors.TypingError('The indices array must contain integers')
+    if is_nonelike(axis):
+        arr_ndim = 1
+    else:
+        arr_ndim = arr.ndim
+    if arr_ndim != indices.ndim:
+        # Matches NumPy error:
+        raise errors.TypingError(
+            "`indices` and `arr` must have the same number of dimensions"
+        )
+
+    indices_broadcast_shape = tuple(range(indices.ndim))
+    if is_nonelike(axis):
+        def take_along_axis_impl(arr, indices, axis):
+            return _take_along_axis_impl(arr.flatten(), indices, 0, (), (),
+                                         indices_broadcast_shape)
+    else:
+        check_is_integer(axis, "axis")
+        if not isinstance(axis, types.IntegerLiteral):
+            raise errors.NumbaValueError("axis must be a literal value")
+        axis = axis.literal_value
+        if axis < 0:
+            axis = arr.ndim + axis
+
+        if axis < 0 or axis >= arr.ndim:
+            raise errors.NumbaValueError("axis is out of bounds")
+
+        Ni = tuple(range(axis))
+        Nk = tuple(range(axis + 1, arr.ndim))
+
+        def take_along_axis_impl(arr, indices, axis):
+            return _take_along_axis_impl(arr, indices, axis, Ni, Nk,
+                                         indices_broadcast_shape)
+    return take_along_axis_impl
+
+
+@overload(np.nan_to_num)
+def nan_to_num_impl(x, copy=True, nan=0.0, posinf=None, neginf=None):
+    if isinstance(x, types.Number):
+        if isinstance(x, types.Integer):
+            # Integers do not have nans or infs
+            def impl(x, copy=True, nan=0.0, posinf=None, neginf=None):
+                return x
+
+        elif isinstance(x, types.Float):
+            def impl(x, copy=True, nan=0.0, posinf=None, neginf=None):
+                min_inf = (
+                    neginf
+                    if neginf is not None
+                    else np.finfo(type(x)).min
+                )
+                max_inf = (
+                    posinf
+                    if posinf is not None
+                    else np.finfo(type(x)).max
+                )
+
+                if np.isnan(x):
+                    return nan
+                elif np.isneginf(x):
+                    return min_inf
+                elif np.isposinf(x):
+                    return max_inf
+                return x
+        elif isinstance(x, types.Complex):
+            def impl(x, copy=True, nan=0.0, posinf=None, neginf=None):
+                r = np.nan_to_num(x.real, nan=nan, posinf=posinf, neginf=neginf)
+                c = np.nan_to_num(x.imag, nan=nan, posinf=posinf, neginf=neginf)
+                return complex(r, c)
+        else:
+            raise errors.TypingError(
+                "Only Integer, Float, and Complex values are accepted"
+            )
+
+    elif type_can_asarray(x):
+        if isinstance(x.dtype, types.Integer):
+            # Integers do not have nans or infs
+            def impl(x, copy=True, nan=0.0, posinf=None, neginf=None):
+                return x
+        elif isinstance(x.dtype, types.Float):
+            def impl(x, copy=True, nan=0.0, posinf=None, neginf=None):
+                min_inf = (
+                    neginf
+                    if neginf is not None
+                    else np.finfo(x.dtype).min
+                )
+                max_inf = (
+                    posinf
+                    if posinf is not None
+                    else np.finfo(x.dtype).max
+                )
+
+                x_ = np.asarray(x)
+                output = np.copy(x_) if copy else x_
+
+                output_flat = output.flat
+                for i in range(output.size):
+                    if np.isnan(output_flat[i]):
+                        output_flat[i] = nan
+                    elif np.isneginf(output_flat[i]):
+                        output_flat[i] = min_inf
+                    elif np.isposinf(output_flat[i]):
+                        output_flat[i] = max_inf
+                return output
+        elif isinstance(x.dtype, types.Complex):
+            def impl(x, copy=True, nan=0.0, posinf=None, neginf=None):
+                x_ = np.asarray(x)
+                output = np.copy(x_) if copy else x_
+
+                np.nan_to_num(
+                    output.real,
+                    copy=False,
+                    nan=nan,
+                    posinf=posinf,
+                    neginf=neginf
+                )
+                np.nan_to_num(
+                    output.imag,
+                    copy=False,
+                    nan=nan,
+                    posinf=posinf,
+                    neginf=neginf
+                )
+                return output
+        else:
+            raise errors.TypingError(
+                "Only Integer, Float, and Complex values are accepted"
+            )
+    else:
+        raise errors.TypingError("The first argument must be a scalar or an "
+                                 "array-like")
+    return impl
diff --git a/shared/package-files/04/04ffefce61d0dd6ee5c02d6a0aecbbfc248be76a56179fe4cb1f68911413d74d/admin_urls.pyi b/shared/package-files/04/04ffefce61d0dd6ee5c02d6a0aecbbfc248be76a56179fe4cb1f68911413d74d/admin_urls.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..020c2ca66b3b8c712c3b33d95244b989dcd26ce4
--- /dev/null
+++ b/shared/package-files/04/04ffefce61d0dd6ee5c02d6a0aecbbfc248be76a56179fe4cb1f68911413d74d/admin_urls.pyi
@@ -0,0 +1,17 @@
+from typing import Any, Dict, Optional, Union
+from uuid import UUID
+
+from django.db.models.options import Options
+from django.template.context import RequestContext
+from django.utils.safestring import SafeText
+
+register: Any
+
+def admin_urlname(value: Options, arg: SafeText) -> str: ...
+def admin_urlquote(value: Union[int, str, UUID]) -> Union[int, str, UUID]: ...
+def add_preserved_filters(
+    context: Union[Dict[str, Union[Options, str]], RequestContext],
+    url: str,
+    popup: bool = ...,
+    to_field: Optional[str] = ...,
+) -> str: ...
diff --git a/shared/package-files/09/09010af0a34857fa30ad9b982d0e2b7b0e6d2898bab34351a631fbe333c4581b/_multiprocessing.cpython-310-x86_64-linux-gnu.so b/shared/package-files/09/09010af0a34857fa30ad9b982d0e2b7b0e6d2898bab34351a631fbe333c4581b/_multiprocessing.cpython-310-x86_64-linux-gnu.so
new file mode 100644
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+"""Generators for some classic graphs.
+
+The typical graph builder function is called as follows:
+
+>>> G = nx.complete_graph(100)
+
+returning the complete graph on n nodes labeled 0, .., 99
+as a simple graph. Except for `empty_graph`, all the functions
+in this module return a Graph class (i.e. a simple, undirected graph).
+
+"""
+
+import itertools
+import numbers
+
+import networkx as nx
+from networkx.classes import Graph
+from networkx.exception import NetworkXError
+from networkx.utils import nodes_or_number, pairwise
+
+__all__ = [
+    "balanced_tree",
+    "barbell_graph",
+    "binomial_tree",
+    "complete_graph",
+    "complete_multipartite_graph",
+    "circular_ladder_graph",
+    "circulant_graph",
+    "cycle_graph",
+    "dorogovtsev_goltsev_mendes_graph",
+    "empty_graph",
+    "full_rary_tree",
+    "ladder_graph",
+    "lollipop_graph",
+    "null_graph",
+    "path_graph",
+    "star_graph",
+    "trivial_graph",
+    "turan_graph",
+    "wheel_graph",
+]
+
+
+# -------------------------------------------------------------------
+#   Some Classic Graphs
+# -------------------------------------------------------------------
+
+
+def _tree_edges(n, r):
+    if n == 0:
+        return
+    # helper function for trees
+    # yields edges in rooted tree at 0 with n nodes and branching ratio r
+    nodes = iter(range(n))
+    parents = [next(nodes)]  # stack of max length r
+    while parents:
+        source = parents.pop(0)
+        for i in range(r):
+            try:
+                target = next(nodes)
+                parents.append(target)
+                yield source, target
+            except StopIteration:
+                break
+
+
+def full_rary_tree(r, n, create_using=None):
+    """Creates a full r-ary tree of `n` nodes.
+
+    Sometimes called a k-ary, n-ary, or m-ary tree.
+    "... all non-leaf nodes have exactly r children and all levels
+    are full except for some rightmost position of the bottom level
+    (if a leaf at the bottom level is missing, then so are all of the
+    leaves to its right." [1]_
+
+    Parameters
+    ----------
+    r : int
+        branching factor of the tree
+    n : int
+        Number of nodes in the tree
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        An r-ary tree with n nodes
+
+    References
+    ----------
+    .. [1] An introduction to data structures and algorithms,
+           James Andrew Storer,  Birkhauser Boston 2001, (page 225).
+    """
+    G = empty_graph(n, create_using)
+    G.add_edges_from(_tree_edges(n, r))
+    return G
+
+
+def balanced_tree(r, h, create_using=None):
+    """Returns the perfectly balanced `r`-ary tree of height `h`.
+
+    Parameters
+    ----------
+    r : int
+        Branching factor of the tree; each node will have `r`
+        children.
+
+    h : int
+        Height of the tree.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : NetworkX graph
+        A balanced `r`-ary tree of height `h`.
+
+    Notes
+    -----
+    This is the rooted tree where all leaves are at distance `h` from
+    the root. The root has degree `r` and all other internal nodes
+    have degree `r + 1`.
+
+    Node labels are integers, starting from zero.
+
+    A balanced tree is also known as a *complete r-ary tree*.
+
+    """
+    # The number of nodes in the balanced tree is `1 + r + ... + r^h`,
+    # which is computed by using the closed-form formula for a geometric
+    # sum with ratio `r`. In the special case that `r` is 1, the number
+    # of nodes is simply `h + 1` (since the tree is actually a path
+    # graph).
+    if r == 1:
+        n = h + 1
+    else:
+        # This must be an integer if both `r` and `h` are integers. If
+        # they are not, we force integer division anyway.
+        n = (1 - r ** (h + 1)) // (1 - r)
+    return full_rary_tree(r, n, create_using=create_using)
+
+
+def barbell_graph(m1, m2, create_using=None):
+    """Returns the Barbell Graph: two complete graphs connected by a path.
+
+    Parameters
+    ----------
+    m1 : int
+        Size of the left and right barbells, must be greater than 2.
+
+    m2 : int
+        Length of the path connecting the barbells.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+       Only undirected Graphs are supported.
+
+    Returns
+    -------
+    G : NetworkX graph
+        A barbell graph.
+
+    Notes
+    -----
+
+
+    Two identical complete graphs $K_{m1}$ form the left and right bells,
+    and are connected by a path $P_{m2}$.
+
+    The `2*m1+m2`  nodes are numbered
+        `0, ..., m1-1` for the left barbell,
+        `m1, ..., m1+m2-1` for the path,
+        and `m1+m2, ..., 2*m1+m2-1` for the right barbell.
+
+    The 3 subgraphs are joined via the edges `(m1-1, m1)` and
+    `(m1+m2-1, m1+m2)`. If `m2=0`, this is merely two complete
+    graphs joined together.
+
+    This graph is an extremal example in David Aldous
+    and Jim Fill's e-text on Random Walks on Graphs.
+
+    """
+    if m1 < 2:
+        raise NetworkXError("Invalid graph description, m1 should be >=2")
+    if m2 < 0:
+        raise NetworkXError("Invalid graph description, m2 should be >=0")
+
+    # left barbell
+    G = complete_graph(m1, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+
+    # connecting path
+    G.add_nodes_from(range(m1, m1 + m2 - 1))
+    if m2 > 1:
+        G.add_edges_from(pairwise(range(m1, m1 + m2)))
+
+    # right barbell
+    G.add_edges_from(
+        (u, v) for u in range(m1 + m2, 2 * m1 + m2) for v in range(u + 1, 2 * m1 + m2)
+    )
+
+    # connect it up
+    G.add_edge(m1 - 1, m1)
+    if m2 > 0:
+        G.add_edge(m1 + m2 - 1, m1 + m2)
+
+    return G
+
+
+def binomial_tree(n, create_using=None):
+    """Returns the Binomial Tree of order n.
+
+    The binomial tree of order 0 consists of a single node. A binomial tree of order k
+    is defined recursively by linking two binomial trees of order k-1: the root of one is
+    the leftmost child of the root of the other.
+
+    Parameters
+    ----------
+    n : int
+        Order of the binomial tree.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : NetworkX graph
+        A binomial tree of $2^n$ nodes and $2^n - 1$ edges.
+
+    """
+    G = nx.empty_graph(1, create_using)
+
+    N = 1
+    for i in range(n):
+        # Use G.edges() to ensure 2-tuples. G.edges is 3-tuple for MultiGraph
+        edges = [(u + N, v + N) for (u, v) in G.edges()]
+        G.add_edges_from(edges)
+        G.add_edge(0, N)
+        N *= 2
+    return G
+
+
+@nodes_or_number(0)
+def complete_graph(n, create_using=None):
+    """Return the complete graph `K_n` with n nodes.
+
+    A complete graph on `n` nodes means that all pairs
+    of distinct nodes have an edge connecting them.
+
+    Parameters
+    ----------
+    n : int or iterable container of nodes
+        If n is an integer, nodes are from range(n).
+        If n is a container of nodes, those nodes appear in the graph.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Examples
+    --------
+    >>> G = nx.complete_graph(9)
+    >>> len(G)
+    9
+    >>> G.size()
+    36
+    >>> G = nx.complete_graph(range(11, 14))
+    >>> list(G.nodes())
+    [11, 12, 13]
+    >>> G = nx.complete_graph(4, nx.DiGraph())
+    >>> G.is_directed()
+    True
+
+    """
+    _, nodes = n
+    G = empty_graph(nodes, create_using)
+    if len(nodes) > 1:
+        if G.is_directed():
+            edges = itertools.permutations(nodes, 2)
+        else:
+            edges = itertools.combinations(nodes, 2)
+        G.add_edges_from(edges)
+    return G
+
+
+def circular_ladder_graph(n, create_using=None):
+    """Returns the circular ladder graph $CL_n$ of length n.
+
+    $CL_n$ consists of two concentric n-cycles in which
+    each of the n pairs of concentric nodes are joined by an edge.
+
+    Node labels are the integers 0 to n-1
+
+    """
+    G = ladder_graph(n, create_using)
+    G.add_edge(0, n - 1)
+    G.add_edge(n, 2 * n - 1)
+    return G
+
+
+def circulant_graph(n, offsets, create_using=None):
+    r"""Returns the circulant graph $Ci_n(x_1, x_2, ..., x_m)$ with $n$ nodes.
+
+    The circulant graph $Ci_n(x_1, ..., x_m)$ consists of $n$ nodes $0, ..., n-1$
+    such that node $i$ is connected to nodes $(i + x) \mod n$ and $(i - x) \mod n$
+    for all $x$ in $x_1, ..., x_m$. Thus $Ci_n(1)$ is a cycle graph.
+
+    Parameters
+    ----------
+    n : integer
+        The number of nodes in the graph.
+    offsets : list of integers
+        A list of node offsets, $x_1$ up to $x_m$, as described above.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    NetworkX Graph of type create_using
+
+    Examples
+    --------
+    Many well-known graph families are subfamilies of the circulant graphs;
+    for example, to create the cycle graph on n points, we connect every
+    node to nodes on either side (with offset plus or minus one). For n = 10,
+
+    >>> G = nx.circulant_graph(10, [1])
+    >>> edges = [
+    ...     (0, 9),
+    ...     (0, 1),
+    ...     (1, 2),
+    ...     (2, 3),
+    ...     (3, 4),
+    ...     (4, 5),
+    ...     (5, 6),
+    ...     (6, 7),
+    ...     (7, 8),
+    ...     (8, 9),
+    ... ]
+    ...
+    >>> sorted(edges) == sorted(G.edges())
+    True
+
+    Similarly, we can create the complete graph
+    on 5 points with the set of offsets [1, 2]:
+
+    >>> G = nx.circulant_graph(5, [1, 2])
+    >>> edges = [
+    ...     (0, 1),
+    ...     (0, 2),
+    ...     (0, 3),
+    ...     (0, 4),
+    ...     (1, 2),
+    ...     (1, 3),
+    ...     (1, 4),
+    ...     (2, 3),
+    ...     (2, 4),
+    ...     (3, 4),
+    ... ]
+    ...
+    >>> sorted(edges) == sorted(G.edges())
+    True
+
+    """
+    G = empty_graph(n, create_using)
+    for i in range(n):
+        for j in offsets:
+            G.add_edge(i, (i - j) % n)
+            G.add_edge(i, (i + j) % n)
+    return G
+
+
+@nodes_or_number(0)
+def cycle_graph(n, create_using=None):
+    """Returns the cycle graph $C_n$ of cyclically connected nodes.
+
+    $C_n$ is a path with its two end-nodes connected.
+
+    Parameters
+    ----------
+    n : int or iterable container of nodes
+        If n is an integer, nodes are from `range(n)`.
+        If n is a container of nodes, those nodes appear in the graph.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Notes
+    -----
+    If create_using is directed, the direction is in increasing order.
+
+    """
+    _, nodes = n
+    G = empty_graph(nodes, create_using)
+    G.add_edges_from(pairwise(nodes, cyclic=True))
+    return G
+
+
+def dorogovtsev_goltsev_mendes_graph(n, create_using=None):
+    """Returns the hierarchically constructed Dorogovtsev-Goltsev-Mendes graph.
+
+    The Dorogovtsev-Goltsev-Mendes [1]_ procedure produces a scale-free graph
+    deterministically with the following properties for a given `n`:
+    - Total number of nodes = ``3 * (3**n + 1) / 2``
+    - Total number of edges = ``3 ** (n + 1)``
+
+    Parameters
+    ----------
+    n : integer
+       The generation number.
+
+    create_using : NetworkX Graph, optional
+       Graph type to be returned. Directed graphs and multi graphs are not
+       supported.
+
+    Returns
+    -------
+    G : NetworkX Graph
+
+    Examples
+    --------
+    >>> G = nx.dorogovtsev_goltsev_mendes_graph(3)
+    >>> G.number_of_nodes()
+    15
+    >>> G.number_of_edges()
+    27
+    >>> nx.is_planar(G)
+    True
+
+    References
+    ----------
+    .. [1] Dorogotsev S.N., Goltsev A.V., and Mendes J.F.F "Pseudofractal
+       Scale-free Web". arXiv:cond-mat/0112143
+    """
+    G = empty_graph(0, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+    if G.is_multigraph():
+        raise NetworkXError("Multigraph not supported")
+
+    G.add_edge(0, 1)
+    if n == 0:
+        return G
+    new_node = 2  # next node to be added
+    for i in range(1, n + 1):  # iterate over number of generations.
+        last_generation_edges = list(G.edges())
+        number_of_edges_in_last_generation = len(last_generation_edges)
+        for j in range(0, number_of_edges_in_last_generation):
+            G.add_edge(new_node, last_generation_edges[j][0])
+            G.add_edge(new_node, last_generation_edges[j][1])
+            new_node += 1
+    return G
+
+
+@nodes_or_number(0)
+def empty_graph(n=0, create_using=None, default=Graph):
+    """Returns the empty graph with n nodes and zero edges.
+
+    Parameters
+    ----------
+    n : int or iterable container of nodes (default = 0)
+        If n is an integer, nodes are from `range(n)`.
+        If n is a container of nodes, those nodes appear in the graph.
+    create_using : Graph Instance, Constructor or None
+        Indicator of type of graph to return.
+        If a Graph-type instance, then clear and use it.
+        If None, use the `default` constructor.
+        If a constructor, call it to create an empty graph.
+    default : Graph constructor (optional, default = nx.Graph)
+        The constructor to use if create_using is None.
+        If None, then nx.Graph is used.
+        This is used when passing an unknown `create_using` value
+        through your home-grown function to `empty_graph` and
+        you want a default constructor other than nx.Graph.
+
+    Examples
+    --------
+    >>> G = nx.empty_graph(10)
+    >>> G.number_of_nodes()
+    10
+    >>> G.number_of_edges()
+    0
+    >>> G = nx.empty_graph("ABC")
+    >>> G.number_of_nodes()
+    3
+    >>> sorted(G)
+    ['A', 'B', 'C']
+
+    Notes
+    -----
+    The variable create_using should be a Graph Constructor or a
+    "graph"-like object. Constructors, e.g. `nx.Graph` or `nx.MultiGraph`
+    will be used to create the returned graph. "graph"-like objects
+    will be cleared (nodes and edges will be removed) and refitted as
+    an empty "graph" with nodes specified in n. This capability
+    is useful for specifying the class-nature of the resulting empty
+    "graph" (i.e. Graph, DiGraph, MyWeirdGraphClass, etc.).
+
+    The variable create_using has three main uses:
+    Firstly, the variable create_using can be used to create an
+    empty digraph, multigraph, etc.  For example,
+
+    >>> n = 10
+    >>> G = nx.empty_graph(n, create_using=nx.DiGraph)
+
+    will create an empty digraph on n nodes.
+
+    Secondly, one can pass an existing graph (digraph, multigraph,
+    etc.) via create_using. For example, if G is an existing graph
+    (resp. digraph, multigraph, etc.), then empty_graph(n, create_using=G)
+    will empty G (i.e. delete all nodes and edges using G.clear())
+    and then add n nodes and zero edges, and return the modified graph.
+
+    Thirdly, when constructing your home-grown graph creation function
+    you can use empty_graph to construct the graph by passing a user
+    defined create_using to empty_graph. In this case, if you want the
+    default constructor to be other than nx.Graph, specify `default`.
+
+    >>> def mygraph(n, create_using=None):
+    ...     G = nx.empty_graph(n, create_using, nx.MultiGraph)
+    ...     G.add_edges_from([(0, 1), (0, 1)])
+    ...     return G
+    >>> G = mygraph(3)
+    >>> G.is_multigraph()
+    True
+    >>> G = mygraph(3, nx.Graph)
+    >>> G.is_multigraph()
+    False
+
+    See also create_empty_copy(G).
+
+    """
+    if create_using is None:
+        G = default()
+    elif isinstance(create_using, type):
+        G = create_using()
+    elif not hasattr(create_using, "adj"):
+        raise TypeError("create_using is not a valid NetworkX graph type or instance")
+    else:
+        # create_using is a NetworkX style Graph
+        create_using.clear()
+        G = create_using
+
+    _, nodes = n
+    G.add_nodes_from(nodes)
+    return G
+
+
+def ladder_graph(n, create_using=None):
+    """Returns the Ladder graph of length n.
+
+    This is two paths of n nodes, with
+    each pair connected by a single edge.
+
+    Node labels are the integers 0 to 2*n - 1.
+
+    """
+    G = empty_graph(2 * n, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+    G.add_edges_from(pairwise(range(n)))
+    G.add_edges_from(pairwise(range(n, 2 * n)))
+    G.add_edges_from((v, v + n) for v in range(n))
+    return G
+
+
+@nodes_or_number([0, 1])
+def lollipop_graph(m, n, create_using=None):
+    """Returns the Lollipop Graph; `K_m` connected to `P_n`.
+
+    This is the Barbell Graph without the right barbell.
+
+    Parameters
+    ----------
+    m, n : int or iterable container of nodes (default = 0)
+        If an integer, nodes are from `range(m)` and `range(m,m+n)`.
+        If a container of nodes, those nodes appear in the graph.
+        Warning: m and n are not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+
+        The nodes for m appear in the complete graph $K_m$ and the nodes
+        for n appear in the path $P_n$
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Notes
+    -----
+    The 2 subgraphs are joined via an edge (m-1, m).
+    If n=0, this is merely a complete graph.
+
+    (This graph is an extremal example in David Aldous and Jim
+    Fill's etext on Random Walks on Graphs.)
+
+    """
+    m, m_nodes = m
+    M = len(m_nodes)
+    if M < 2:
+        raise NetworkXError("Invalid description: m should indicate at least 2 nodes")
+
+    n, n_nodes = n
+    if isinstance(m, numbers.Integral) and isinstance(n, numbers.Integral):
+        n_nodes = list(range(M, M + n))
+    N = len(n_nodes)
+
+    # the ball
+    G = complete_graph(m_nodes, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+
+    # the stick
+    G.add_nodes_from(n_nodes)
+    if N > 1:
+        G.add_edges_from(pairwise(n_nodes))
+
+    if len(G) != M + N:
+        raise NetworkXError("Nodes must be distinct in containers m and n")
+
+    # connect ball to stick
+    if M > 0 and N > 0:
+        G.add_edge(m_nodes[-1], n_nodes[0])
+    return G
+
+
+def null_graph(create_using=None):
+    """Returns the Null graph with no nodes or edges.
+
+    See empty_graph for the use of create_using.
+
+    """
+    G = empty_graph(0, create_using)
+    return G
+
+
+@nodes_or_number(0)
+def path_graph(n, create_using=None):
+    """Returns the Path graph `P_n` of linearly connected nodes.
+
+    Parameters
+    ----------
+    n : int or iterable
+        If an integer, nodes are 0 to n - 1.
+        If an iterable of nodes, in the order they appear in the path.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    """
+    _, nodes = n
+    G = empty_graph(nodes, create_using)
+    G.add_edges_from(pairwise(nodes))
+    return G
+
+
+@nodes_or_number(0)
+def star_graph(n, create_using=None):
+    """Return the star graph
+
+    The star graph consists of one center node connected to n outer nodes.
+
+    Parameters
+    ----------
+    n : int or iterable
+        If an integer, node labels are 0 to n with center 0.
+        If an iterable of nodes, the center is the first.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Notes
+    -----
+    The graph has n+1 nodes for integer n.
+    So star_graph(3) is the same as star_graph(range(4)).
+    """
+    n, nodes = n
+    if isinstance(n, numbers.Integral):
+        nodes.append(n)  # there should be n+1 nodes
+    G = empty_graph(nodes, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+
+    if len(nodes) > 1:
+        hub, *spokes = nodes
+        G.add_edges_from((hub, node) for node in spokes)
+    return G
+
+
+def trivial_graph(create_using=None):
+    """Return the Trivial graph with one node (with label 0) and no edges."""
+    G = empty_graph(1, create_using)
+    return G
+
+
+def turan_graph(n, r):
+    r"""Return the Turan Graph
+
+    The Turan Graph is a complete multipartite graph on $n$ nodes
+    with $r$ disjoint subsets. That is, edges connect each node to
+    every node not in its subset.
+
+    Given $n$ and $r$, we create a complete multipartite graph with
+    $r-(n \mod r)$ partitions of size $n/r$, rounded down, and
+    $n \mod r$ partitions of size $n/r+1$, rounded down.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes.
+    r : int
+        The number of partitions.
+        Must be less than or equal to n.
+
+    Notes
+    -----
+    Must satisfy $1 <= r <= n$.
+    The graph has $(r-1)(n^2)/(2r)$ edges, rounded down.
+    """
+
+    if not 1 <= r <= n:
+        raise NetworkXError("Must satisfy 1 <= r <= n")
+
+    partitions = [n // r] * (r - (n % r)) + [n // r + 1] * (n % r)
+    G = complete_multipartite_graph(*partitions)
+    return G
+
+
+@nodes_or_number(0)
+def wheel_graph(n, create_using=None):
+    """Return the wheel graph
+
+    The wheel graph consists of a hub node connected to a cycle of (n-1) nodes.
+
+    Parameters
+    ----------
+    n : int or iterable
+        If an integer, node labels are 0 to n with center 0.
+        If an iterable of nodes, the center is the first.
+        Warning: n is not checked for duplicates and if present the
+        resulting graph may not be as desired. Make sure you have no duplicates.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Node labels are the integers 0 to n - 1.
+    """
+    _, nodes = n
+    G = empty_graph(nodes, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+
+    if len(nodes) > 1:
+        hub, *rim = nodes
+        G.add_edges_from((hub, node) for node in rim)
+        if len(rim) > 1:
+            G.add_edges_from(pairwise(rim, cyclic=True))
+    return G
+
+
+def complete_multipartite_graph(*subset_sizes):
+    """Returns the complete multipartite graph with the specified subset sizes.
+
+    Parameters
+    ----------
+    subset_sizes : tuple of integers or tuple of node iterables
+       The arguments can either all be integer number of nodes or they
+       can all be iterables of nodes. If integers, they represent the
+       number of nodes in each subset of the multipartite graph.
+       If iterables, each is used to create the nodes for that subset.
+       The length of subset_sizes is the number of subsets.
+
+    Returns
+    -------
+    G : NetworkX Graph
+       Returns the complete multipartite graph with the specified subsets.
+
+       For each node, the node attribute 'subset' is an integer
+       indicating which subset contains the node.
+
+    Examples
+    --------
+    Creating a complete tripartite graph, with subsets of one, two, and three
+    nodes, respectively.
+
+        >>> G = nx.complete_multipartite_graph(1, 2, 3)
+        >>> [G.nodes[u]["subset"] for u in G]
+        [0, 1, 1, 2, 2, 2]
+        >>> list(G.edges(0))
+        [(0, 1), (0, 2), (0, 3), (0, 4), (0, 5)]
+        >>> list(G.edges(2))
+        [(2, 0), (2, 3), (2, 4), (2, 5)]
+        >>> list(G.edges(4))
+        [(4, 0), (4, 1), (4, 2)]
+
+        >>> G = nx.complete_multipartite_graph("a", "bc", "def")
+        >>> [G.nodes[u]["subset"] for u in sorted(G)]
+        [0, 1, 1, 2, 2, 2]
+
+    Notes
+    -----
+    This function generalizes several other graph builder functions.
+
+    - If no subset sizes are given, this returns the null graph.
+    - If a single subset size `n` is given, this returns the empty graph on
+      `n` nodes.
+    - If two subset sizes `m` and `n` are given, this returns the complete
+      bipartite graph on `m + n` nodes.
+    - If subset sizes `1` and `n` are given, this returns the star graph on
+      `n + 1` nodes.
+
+    See also
+    --------
+    complete_bipartite_graph
+    """
+    # The complete multipartite graph is an undirected simple graph.
+    G = Graph()
+
+    if len(subset_sizes) == 0:
+        return G
+
+    # set up subsets of nodes
+    try:
+        extents = pairwise(itertools.accumulate((0,) + subset_sizes))
+        subsets = [range(start, end) for start, end in extents]
+    except TypeError:
+        subsets = subset_sizes
+
+    # add nodes with subset attribute
+    # while checking that ints are not mixed with iterables
+    try:
+        for i, subset in enumerate(subsets):
+            G.add_nodes_from(subset, subset=i)
+    except TypeError as err:
+        raise NetworkXError("Arguments must be all ints or all iterables") from err
+
+    # Across subsets, all nodes should be adjacent.
+    # We can use itertools.combinations() because undirected.
+    for subset1, subset2 in itertools.combinations(subsets, 2):
+        G.add_edges_from(itertools.product(subset1, subset2))
+    return G
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diff --git a/shared/package-files/09/09052e30ef3321739d7a14a841069fd57269a690cdb4cf4743c76693ec998c85/linalg_solve_triangular_cuda_dispatch.h b/shared/package-files/09/09052e30ef3321739d7a14a841069fd57269a690cdb4cf4743c76693ec998c85/linalg_solve_triangular_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..79be6ba3e2f32dc84ffbfa214fe7736e6a4c9ed7
--- /dev/null
+++ b/shared/package-files/09/09052e30ef3321739d7a14a841069fd57269a690cdb4cf4743c76693ec998c85/linalg_solve_triangular_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor linalg_solve_triangular(const at::Tensor & self, const at::Tensor & B, bool upper, bool left=true, bool unitriangular=false);
+TORCH_API at::Tensor & linalg_solve_triangular_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & B, bool upper, bool left=true, bool unitriangular=false);
+TORCH_API at::Tensor & linalg_solve_triangular_outf(const at::Tensor & self, const at::Tensor & B, bool upper, bool left, bool unitriangular, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/09/090588299152a6451ee57ec0176ec4a95c19e25d19ab2725ddb758815663dc7b/test_qft.py b/shared/package-files/09/090588299152a6451ee57ec0176ec4a95c19e25d19ab2725ddb758815663dc7b/test_qft.py
new file mode 100644
index 0000000000000000000000000000000000000000..2920097aa23927138aa0360f77b8068500c6cc09
--- /dev/null
+++ b/shared/package-files/09/090588299152a6451ee57ec0176ec4a95c19e25d19ab2725ddb758815663dc7b/test_qft.py
@@ -0,0 +1,50 @@
+from sympy.core.numbers import (I, pi)
+from sympy.core.symbol import Symbol
+from sympy.functions.elementary.exponential import exp
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.matrices.dense import Matrix
+
+from sympy.physics.quantum.qft import QFT, IQFT, RkGate
+from sympy.physics.quantum.gate import (ZGate, SwapGate, HadamardGate, CGate,
+                                        PhaseGate, TGate)
+from sympy.physics.quantum.qubit import Qubit
+from sympy.physics.quantum.qapply import qapply
+from sympy.physics.quantum.represent import represent
+
+
+def test_RkGate():
+    x = Symbol('x')
+    assert RkGate(1, x).k == x
+    assert RkGate(1, x).targets == (1,)
+    assert RkGate(1, 1) == ZGate(1)
+    assert RkGate(2, 2) == PhaseGate(2)
+    assert RkGate(3, 3) == TGate(3)
+
+    assert represent(
+        RkGate(0, x), nqubits=1) == Matrix([[1, 0], [0, exp(2*I*pi/2**x)]])
+
+
+def test_quantum_fourier():
+    assert QFT(0, 3).decompose() == \
+        SwapGate(0, 2)*HadamardGate(0)*CGate((0,), PhaseGate(1)) * \
+        HadamardGate(1)*CGate((0,), TGate(2))*CGate((1,), PhaseGate(2)) * \
+        HadamardGate(2)
+
+    assert IQFT(0, 3).decompose() == \
+        HadamardGate(2)*CGate((1,), RkGate(2, -2))*CGate((0,), RkGate(2, -3)) * \
+        HadamardGate(1)*CGate((0,), RkGate(1, -2))*HadamardGate(0)*SwapGate(0, 2)
+
+    assert represent(QFT(0, 3), nqubits=3) == \
+        Matrix([[exp(2*pi*I/8)**(i*j % 8)/sqrt(8) for i in range(8)] for j in range(8)])
+
+    assert QFT(0, 4).decompose()  # non-trivial decomposition
+    assert qapply(QFT(0, 3).decompose()*Qubit(0, 0, 0)).expand() == qapply(
+        HadamardGate(0)*HadamardGate(1)*HadamardGate(2)*Qubit(0, 0, 0)
+    ).expand()
+
+
+def test_qft_represent():
+    c = QFT(0, 3)
+    a = represent(c, nqubits=3)
+    b = represent(c.decompose(), nqubits=3)
+    assert a.evalf(n=10) == b.evalf(n=10)
diff --git a/shared/package-files/09/0906a3a9745218c604aeaca0c2961eb9fb7cc975c3477f490d344b80f4b6ae45/conda.py b/shared/package-files/09/0906a3a9745218c604aeaca0c2961eb9fb7cc975c3477f490d344b80f4b6ae45/conda.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb1ba86948312e48ff851f5acaf743229720d006
--- /dev/null
+++ b/shared/package-files/09/0906a3a9745218c604aeaca0c2961eb9fb7cc975c3477f490d344b80f4b6ae45/conda.py
@@ -0,0 +1,63 @@
+# Copyright (C) 2012 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+import tempfile
+from os.path import basename
+
+try:
+    from boltons.setutils import IndexedSet
+except ImportError:  # pragma: no cover
+    from conda._vendor.boltons.setutils import IndexedSet
+
+from conda.base.constants import UpdateModifier
+from conda.base.context import context
+from conda.common.constants import NULL
+from conda.exceptions import UnsatisfiableError
+from conda.models.channel import Channel, prioritize_channels
+
+from ..env import Environment
+
+
+def _solve(prefix, specs, args, env, *_, **kwargs):
+    # TODO: support all various ways this happens
+    # Including 'nodefaults' in the channels list disables the defaults
+    channel_urls = [chan for chan in env.channels if chan != "nodefaults"]
+
+    if "nodefaults" not in env.channels:
+        channel_urls.extend(context.channels)
+    _channel_priority_map = prioritize_channels(channel_urls)
+
+    channels = IndexedSet(Channel(url) for url in _channel_priority_map)
+    subdirs = IndexedSet(basename(url) for url in _channel_priority_map)
+
+    solver_backend = context.plugin_manager.get_cached_solver_backend()
+    solver = solver_backend(prefix, channels, subdirs, specs_to_add=specs)
+    return solver
+
+
+def dry_run(specs, args, env, *_, **kwargs):
+    solver = _solve(tempfile.mkdtemp(), specs, args, env, *_, **kwargs)
+    pkgs = solver.solve_final_state()
+    solved_env = Environment(
+        name=env.name, dependencies=[str(p) for p in pkgs], channels=env.channels
+    )
+    return solved_env
+
+
+def install(prefix, specs, args, env, *_, **kwargs):
+    solver = _solve(prefix, specs, args, env, *_, **kwargs)
+
+    try:
+        unlink_link_transaction = solver.solve_for_transaction(
+            prune=getattr(args, "prune", False),
+            update_modifier=UpdateModifier.FREEZE_INSTALLED,
+        )
+    except (UnsatisfiableError, SystemExit):
+        unlink_link_transaction = solver.solve_for_transaction(
+            prune=getattr(args, "prune", False), update_modifier=NULL
+        )
+
+    if unlink_link_transaction.nothing_to_do:
+        return None
+    unlink_link_transaction.download_and_extract()
+    unlink_link_transaction.execute()
+    return unlink_link_transaction._make_legacy_action_groups()[0]
diff --git a/shared/package-files/09/09076b739d5ba8bc591bf49dfeedb748f708c5f7d0b6081fa75b12f0524b8a88/connectionpool.pyi b/shared/package-files/09/09076b739d5ba8bc591bf49dfeedb748f708c5f7d0b6081fa75b12f0524b8a88/connectionpool.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..90832482d6d7e460f668f43c8ec3642120324285
--- /dev/null
+++ b/shared/package-files/09/09076b739d5ba8bc591bf49dfeedb748f708c5f7d0b6081fa75b12f0524b8a88/connectionpool.pyi
@@ -0,0 +1,121 @@
+from typing import Any
+
+from . import connection, exceptions, request, response
+from .connection import BaseSSLError as BaseSSLError, ConnectionError as ConnectionError, HTTPException as HTTPException
+from .packages import ssl_match_hostname
+from .util import connection as _connection, retry, timeout, url
+
+ClosedPoolError = exceptions.ClosedPoolError
+ProtocolError = exceptions.ProtocolError
+EmptyPoolError = exceptions.EmptyPoolError
+HostChangedError = exceptions.HostChangedError
+LocationValueError = exceptions.LocationValueError
+MaxRetryError = exceptions.MaxRetryError
+ProxyError = exceptions.ProxyError
+ReadTimeoutError = exceptions.ReadTimeoutError
+SSLError = exceptions.SSLError
+TimeoutError = exceptions.TimeoutError
+InsecureRequestWarning = exceptions.InsecureRequestWarning
+CertificateError = ssl_match_hostname.CertificateError
+port_by_scheme = connection.port_by_scheme
+DummyConnection = connection.DummyConnection
+HTTPConnection = connection.HTTPConnection
+HTTPSConnection = connection.HTTPSConnection
+VerifiedHTTPSConnection = connection.VerifiedHTTPSConnection
+RequestMethods = request.RequestMethods
+HTTPResponse = response.HTTPResponse
+is_connection_dropped = _connection.is_connection_dropped
+Retry = retry.Retry
+Timeout = timeout.Timeout
+get_host = url.get_host
+
+xrange: Any
+log: Any
+
+class ConnectionPool:
+    scheme: Any
+    QueueCls: Any
+    host: Any
+    port: Any
+    def __init__(self, host, port=...) -> None: ...
+    def __enter__(self): ...
+    def __exit__(self, exc_type, exc_val, exc_tb): ...
+    def close(self): ...
+
+class HTTPConnectionPool(ConnectionPool, RequestMethods):
+    scheme: Any
+    ConnectionCls: Any
+    strict: Any
+    timeout: Any
+    retries: Any
+    pool: Any
+    block: Any
+    proxy: Any
+    proxy_headers: Any
+    num_connections: Any
+    num_requests: Any
+    conn_kw: Any
+    def __init__(
+        self,
+        host,
+        port=...,
+        strict=...,
+        timeout=...,
+        maxsize=...,
+        block=...,
+        headers=...,
+        retries=...,
+        _proxy=...,
+        _proxy_headers=...,
+        **conn_kw,
+    ) -> None: ...
+    def close(self): ...
+    def is_same_host(self, url): ...
+    def urlopen(
+        self,
+        method,
+        url,
+        body=...,
+        headers=...,
+        retries=...,
+        redirect=...,
+        assert_same_host=...,
+        timeout=...,
+        pool_timeout=...,
+        release_conn=...,
+        **response_kw,
+    ): ...
+
+class HTTPSConnectionPool(HTTPConnectionPool):
+    scheme: Any
+    ConnectionCls: Any
+    key_file: Any
+    cert_file: Any
+    cert_reqs: Any
+    ca_certs: Any
+    ssl_version: Any
+    assert_hostname: Any
+    assert_fingerprint: Any
+    def __init__(
+        self,
+        host,
+        port=...,
+        strict=...,
+        timeout=...,
+        maxsize=...,
+        block=...,
+        headers=...,
+        retries=...,
+        _proxy=...,
+        _proxy_headers=...,
+        key_file=...,
+        cert_file=...,
+        cert_reqs=...,
+        ca_certs=...,
+        ssl_version=...,
+        assert_hostname=...,
+        assert_fingerprint=...,
+        **conn_kw,
+    ) -> None: ...
+
+def connection_from_url(url, **kw): ...
diff --git a/shared/package-files/09/090d80174870c9d7204c7c9c302162c99200539ed17194fe4d589dd7b488b15e/api.py b/shared/package-files/09/090d80174870c9d7204c7c9c302162c99200539ed17194fe4d589dd7b488b15e/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd3be5b3f8c42267c8a61421b7f0877a01b33d34
--- /dev/null
+++ b/shared/package-files/09/090d80174870c9d7204c7c9c302162c99200539ed17194fe4d589dd7b488b15e/api.py
@@ -0,0 +1,2 @@
+__all__ = ["eval"]
+from pandas.core.computation.eval import eval
diff --git a/shared/package-files/09/090eed401f903d4c43e999db4b64b31558e3bc20fbf9c8a095ec6475b6e7ef2f/__init__.py b/shared/package-files/09/090eed401f903d4c43e999db4b64b31558e3bc20fbf9c8a095ec6475b6e7ef2f/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ae889a7fdbc24935c0884e8bbcdf56bde8946460
--- /dev/null
+++ b/shared/package-files/09/090eed401f903d4c43e999db4b64b31558e3bc20fbf9c8a095ec6475b6e7ef2f/__init__.py
@@ -0,0 +1,93 @@
+"""
+Arithmetic operations for PandasObjects
+
+This is not a public API.
+"""
+from __future__ import annotations
+
+from pandas.core.ops.array_ops import (
+    arithmetic_op,
+    comp_method_OBJECT_ARRAY,
+    comparison_op,
+    fill_binop,
+    get_array_op,
+    logical_op,
+    maybe_prepare_scalar_for_op,
+)
+from pandas.core.ops.common import (
+    get_op_result_name,
+    unpack_zerodim_and_defer,
+)
+from pandas.core.ops.docstrings import make_flex_doc
+from pandas.core.ops.invalid import invalid_comparison
+from pandas.core.ops.mask_ops import (
+    kleene_and,
+    kleene_or,
+    kleene_xor,
+)
+from pandas.core.roperator import (
+    radd,
+    rand_,
+    rdiv,
+    rdivmod,
+    rfloordiv,
+    rmod,
+    rmul,
+    ror_,
+    rpow,
+    rsub,
+    rtruediv,
+    rxor,
+)
+
+# -----------------------------------------------------------------------------
+# constants
+ARITHMETIC_BINOPS: set[str] = {
+    "add",
+    "sub",
+    "mul",
+    "pow",
+    "mod",
+    "floordiv",
+    "truediv",
+    "divmod",
+    "radd",
+    "rsub",
+    "rmul",
+    "rpow",
+    "rmod",
+    "rfloordiv",
+    "rtruediv",
+    "rdivmod",
+}
+
+
+__all__ = [
+    "ARITHMETIC_BINOPS",
+    "arithmetic_op",
+    "comparison_op",
+    "comp_method_OBJECT_ARRAY",
+    "invalid_comparison",
+    "fill_binop",
+    "kleene_and",
+    "kleene_or",
+    "kleene_xor",
+    "logical_op",
+    "make_flex_doc",
+    "radd",
+    "rand_",
+    "rdiv",
+    "rdivmod",
+    "rfloordiv",
+    "rmod",
+    "rmul",
+    "ror_",
+    "rpow",
+    "rsub",
+    "rtruediv",
+    "rxor",
+    "unpack_zerodim_and_defer",
+    "get_op_result_name",
+    "maybe_prepare_scalar_for_op",
+    "get_array_op",
+]
diff --git a/shared/package-files/09/09140d274234ec4174f608f106ff3673ad60b48731e4ed725a89937cd4443064/resids_exact_c.csv b/shared/package-files/09/09140d274234ec4174f608f106ff3673ad60b48731e4ed725a89937cd4443064/resids_exact_c.csv
new file mode 100644
index 0000000000000000000000000000000000000000..9985d4feda0b1942d9b08b0e680f0b05789576bf
--- /dev/null
+++ b/shared/package-files/09/09140d274234ec4174f608f106ff3673ad60b48731e4ed725a89937cd4443064/resids_exact_c.csv
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diff --git a/shared/package-files/09/091440954d092f28032fba479f17c82d20c043a51b25f68a66b0a501d181d621/test_gruntz.py b/shared/package-files/09/091440954d092f28032fba479f17c82d20c043a51b25f68a66b0a501d181d621/test_gruntz.py
new file mode 100644
index 0000000000000000000000000000000000000000..afb63b5977908d719ae6358dfd8dc62fc4118bc6
--- /dev/null
+++ b/shared/package-files/09/091440954d092f28032fba479f17c82d20c043a51b25f68a66b0a501d181d621/test_gruntz.py
@@ -0,0 +1,476 @@
+from sympy.core import EulerGamma
+from sympy.core.numbers import (E, I, Integer, Rational, oo, pi)
+from sympy.core.singleton import S
+from sympy.core.symbol import Symbol
+from sympy.functions.elementary.exponential import (exp, log)
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import (acot, atan, cos, sin)
+from sympy.functions.elementary.complexes import sign as _sign
+from sympy.functions.special.error_functions import (Ei, erf)
+from sympy.functions.special.gamma_functions import (digamma, gamma, loggamma)
+from sympy.functions.special.zeta_functions import zeta
+from sympy.polys.polytools import cancel
+from sympy.functions.elementary.hyperbolic import cosh, coth, sinh, tanh
+from sympy.series.gruntz import compare, mrv, rewrite, mrv_leadterm, gruntz, \
+    sign
+from sympy.testing.pytest import XFAIL, skip, slow
+
+"""
+This test suite is testing the limit algorithm using the bottom up approach.
+See the documentation in limits2.py. The algorithm itself is highly recursive
+by nature, so "compare" is logically the lowest part of the algorithm, yet in
+some sense it's the most complex part, because it needs to calculate a limit
+to return the result.
+
+Nevertheless, the rest of the algorithm depends on compare working correctly.
+"""
+
+x = Symbol('x', real=True)
+m = Symbol('m', real=True)
+
+
+runslow = False
+
+
+def _sskip():
+    if not runslow:
+        skip("slow")
+
+
+@slow
+def test_gruntz_evaluation():
+    # Gruntz' thesis pp. 122 to 123
+    # 8.1
+    assert gruntz(exp(x)*(exp(1/x - exp(-x)) - exp(1/x)), x, oo) == -1
+    # 8.2
+    assert gruntz(exp(x)*(exp(1/x + exp(-x) + exp(-x**2))
+                  - exp(1/x - exp(-exp(x)))), x, oo) == 1
+    # 8.3
+    assert gruntz(exp(exp(x - exp(-x))/(1 - 1/x)) - exp(exp(x)), x, oo) is oo
+    # 8.5
+    assert gruntz(exp(exp(exp(x + exp(-x)))) / exp(exp(exp(x))), x, oo) is oo
+    # 8.6
+    assert gruntz(exp(exp(exp(x))) / exp(exp(exp(x - exp(-exp(x))))),
+                  x, oo) is oo
+    # 8.7
+    assert gruntz(exp(exp(exp(x))) / exp(exp(exp(x - exp(-exp(exp(x)))))),
+                  x, oo) == 1
+    # 8.8
+    assert gruntz(exp(exp(x)) / exp(exp(x - exp(-exp(exp(x))))), x, oo) == 1
+    # 8.9
+    assert gruntz(log(x)**2 * exp(sqrt(log(x))*(log(log(x)))**2
+                  * exp(sqrt(log(log(x))) * (log(log(log(x))))**3)) / sqrt(x),
+                  x, oo) == 0
+    # 8.10
+    assert gruntz((x*log(x)*(log(x*exp(x) - x**2))**2)
+                  / (log(log(x**2 + 2*exp(exp(3*x**3*log(x)))))), x, oo) == Rational(1, 3)
+    # 8.11
+    assert gruntz((exp(x*exp(-x)/(exp(-x) + exp(-2*x**2/(x + 1)))) - exp(x))/x,
+                  x, oo) == -exp(2)
+    # 8.12
+    assert gruntz((3**x + 5**x)**(1/x), x, oo) == 5
+    # 8.13
+    assert gruntz(x/log(x**(log(x**(log(2)/log(x))))), x, oo) is oo
+    # 8.14
+    assert gruntz(exp(exp(2*log(x**5 + x)*log(log(x))))
+                  / exp(exp(10*log(x)*log(log(x)))), x, oo) is oo
+    # 8.15
+    assert gruntz(exp(exp(Rational(5, 2)*x**Rational(-5, 7) + Rational(21, 8)*x**Rational(6, 11)
+                          + 2*x**(-8) + Rational(54, 17)*x**Rational(49, 45)))**8
+                  / log(log(-log(Rational(4, 3)*x**Rational(-5, 14))))**Rational(7, 6), x, oo) is oo
+    # 8.16
+    assert gruntz((exp(4*x*exp(-x)/(1/exp(x) + 1/exp(2*x**2/(x + 1)))) - exp(x))
+                  / exp(x)**4, x, oo) == 1
+    # 8.17
+    assert gruntz(exp(x*exp(-x)/(exp(-x) + exp(-2*x**2/(x + 1))))/exp(x), x, oo) \
+        == 1
+    # 8.19
+    assert gruntz(log(x)*(log(log(x) + log(log(x))) - log(log(x)))
+                  / (log(log(x) + log(log(log(x))))), x, oo) == 1
+    # 8.20
+    assert gruntz(exp((log(log(x + exp(log(x)*log(log(x))))))
+                  / (log(log(log(exp(x) + x + log(x)))))), x, oo) == E
+    # Another
+    assert gruntz(exp(exp(exp(x + exp(-x)))) / exp(exp(x)), x, oo) is oo
+
+
+def test_gruntz_evaluation_slow():
+    _sskip()
+    # 8.4
+    assert gruntz(exp(exp(exp(x)/(1 - 1/x)))
+                  - exp(exp(exp(x)/(1 - 1/x - log(x)**(-log(x))))), x, oo) is -oo
+    # 8.18
+    assert gruntz((exp(exp(-x/(1 + exp(-x))))*exp(-x/(1 + exp(-x/(1 + exp(-x)))))
+                   *exp(exp(-x + exp(-x/(1 + exp(-x))))))
+                  / (exp(-x/(1 + exp(-x))))**2 - exp(x) + x, x, oo) == 2
+
+
+@slow
+def test_gruntz_eval_special():
+    # Gruntz, p. 126
+    assert gruntz(exp(x)*(sin(1/x + exp(-x)) - sin(1/x + exp(-x**2))), x, oo) == 1
+    assert gruntz((erf(x - exp(-exp(x))) - erf(x)) * exp(exp(x)) * exp(x**2),
+                  x, oo) == -2/sqrt(pi)
+    assert gruntz(exp(exp(x)) * (exp(sin(1/x + exp(-exp(x)))) - exp(sin(1/x))),
+                  x, oo) == 1
+    assert gruntz(exp(x)*(gamma(x + exp(-x)) - gamma(x)), x, oo) is oo
+    assert gruntz(exp(exp(digamma(digamma(x))))/x, x, oo) == exp(Rational(-1, 2))
+    assert gruntz(exp(exp(digamma(log(x))))/x, x, oo) == exp(Rational(-1, 2))
+    assert gruntz(digamma(digamma(digamma(x))), x, oo) is oo
+    assert gruntz(loggamma(loggamma(x)), x, oo) is oo
+    assert gruntz(((gamma(x + 1/gamma(x)) - gamma(x))/log(x) - cos(1/x))
+                  * x*log(x), x, oo) == Rational(-1, 2)
+    assert gruntz(x * (gamma(x - 1/gamma(x)) - gamma(x) + log(x)), x, oo) \
+        == S.Half
+    assert gruntz((gamma(x + 1/gamma(x)) - gamma(x)) / log(x), x, oo) == 1
+
+
+def test_gruntz_eval_special_slow():
+    _sskip()
+    assert gruntz(gamma(x + 1)/sqrt(2*pi)
+                  - exp(-x)*(x**(x + S.Half) + x**(x - S.Half)/12), x, oo) is oo
+    assert gruntz(exp(exp(exp(digamma(digamma(digamma(x))))))/x, x, oo) == 0
+
+
+@XFAIL
+def test_grunts_eval_special_slow_sometimes_fail():
+    _sskip()
+    # XXX This sometimes fails!!!
+    assert gruntz(exp(gamma(x - exp(-x))*exp(1/x)) - exp(gamma(x)), x, oo) is oo
+
+
+def test_gruntz_Ei():
+    assert gruntz((Ei(x - exp(-exp(x))) - Ei(x)) *exp(-x)*exp(exp(x))*x, x, oo) == -1
+
+
+@XFAIL
+def test_gruntz_eval_special_fail():
+    # TODO zeta function series
+    assert gruntz(
+        exp((log(2) + 1)*x) * (zeta(x + exp(-x)) - zeta(x)), x, oo) == -log(2)
+
+    # TODO 8.35 - 8.37 (bessel, max-min)
+
+
+def test_gruntz_hyperbolic():
+    assert gruntz(cosh(x), x, oo) is oo
+    assert gruntz(cosh(x), x, -oo) is oo
+    assert gruntz(sinh(x), x, oo) is oo
+    assert gruntz(sinh(x), x, -oo) is -oo
+    assert gruntz(2*cosh(x)*exp(x), x, oo) is oo
+    assert gruntz(2*cosh(x)*exp(x), x, -oo) == 1
+    assert gruntz(2*sinh(x)*exp(x), x, oo) is oo
+    assert gruntz(2*sinh(x)*exp(x), x, -oo) == -1
+    assert gruntz(tanh(x), x, oo) == 1
+    assert gruntz(tanh(x), x, -oo) == -1
+    assert gruntz(coth(x), x, oo) == 1
+    assert gruntz(coth(x), x, -oo) == -1
+
+
+def test_compare1():
+    assert compare(2, x, x) == "<"
+    assert compare(x, exp(x), x) == "<"
+    assert compare(exp(x), exp(x**2), x) == "<"
+    assert compare(exp(x**2), exp(exp(x)), x) == "<"
+    assert compare(1, exp(exp(x)), x) == "<"
+
+    assert compare(x, 2, x) == ">"
+    assert compare(exp(x), x, x) == ">"
+    assert compare(exp(x**2), exp(x), x) == ">"
+    assert compare(exp(exp(x)), exp(x**2), x) == ">"
+    assert compare(exp(exp(x)), 1, x) == ">"
+
+    assert compare(2, 3, x) == "="
+    assert compare(3, -5, x) == "="
+    assert compare(2, -5, x) == "="
+
+    assert compare(x, x**2, x) == "="
+    assert compare(x**2, x**3, x) == "="
+    assert compare(x**3, 1/x, x) == "="
+    assert compare(1/x, x**m, x) == "="
+    assert compare(x**m, -x, x) == "="
+
+    assert compare(exp(x), exp(-x), x) == "="
+    assert compare(exp(-x), exp(2*x), x) == "="
+    assert compare(exp(2*x), exp(x)**2, x) == "="
+    assert compare(exp(x)**2, exp(x + exp(-x)), x) == "="
+    assert compare(exp(x), exp(x + exp(-x)), x) == "="
+
+    assert compare(exp(x**2), 1/exp(x**2), x) == "="
+
+
+def test_compare2():
+    assert compare(exp(x), x**5, x) == ">"
+    assert compare(exp(x**2), exp(x)**2, x) == ">"
+    assert compare(exp(x), exp(x + exp(-x)), x) == "="
+    assert compare(exp(x + exp(-x)), exp(x), x) == "="
+    assert compare(exp(x + exp(-x)), exp(-x), x) == "="
+    assert compare(exp(-x), x, x) == ">"
+    assert compare(x, exp(-x), x) == "<"
+    assert compare(exp(x + 1/x), x, x) == ">"
+    assert compare(exp(-exp(x)), exp(x), x) == ">"
+    assert compare(exp(exp(-exp(x)) + x), exp(-exp(x)), x) == "<"
+
+
+def test_compare3():
+    assert compare(exp(exp(x)), exp(x + exp(-exp(x))), x) == ">"
+
+
+def test_sign1():
+    assert sign(Rational(0), x) == 0
+    assert sign(Rational(3), x) == 1
+    assert sign(Rational(-5), x) == -1
+    assert sign(log(x), x) == 1
+    assert sign(exp(-x), x) == 1
+    assert sign(exp(x), x) == 1
+    assert sign(-exp(x), x) == -1
+    assert sign(3 - 1/x, x) == 1
+    assert sign(-3 - 1/x, x) == -1
+    assert sign(sin(1/x), x) == 1
+    assert sign((x**Integer(2)), x) == 1
+    assert sign(x**2, x) == 1
+    assert sign(x**5, x) == 1
+
+
+def test_sign2():
+    assert sign(x, x) == 1
+    assert sign(-x, x) == -1
+    y = Symbol("y", positive=True)
+    assert sign(y, x) == 1
+    assert sign(-y, x) == -1
+    assert sign(y*x, x) == 1
+    assert sign(-y*x, x) == -1
+
+
+def mmrv(a, b):
+    return set(mrv(a, b)[0].keys())
+
+
+def test_mrv1():
+    assert mmrv(x, x) == {x}
+    assert mmrv(x + 1/x, x) == {x}
+    assert mmrv(x**2, x) == {x}
+    assert mmrv(log(x), x) == {x}
+    assert mmrv(exp(x), x) == {exp(x)}
+    assert mmrv(exp(-x), x) == {exp(-x)}
+    assert mmrv(exp(x**2), x) == {exp(x**2)}
+    assert mmrv(-exp(1/x), x) == {x}
+    assert mmrv(exp(x + 1/x), x) == {exp(x + 1/x)}
+
+
+def test_mrv2a():
+    assert mmrv(exp(x + exp(-exp(x))), x) == {exp(-exp(x))}
+    assert mmrv(exp(x + exp(-x)), x) == {exp(x + exp(-x)), exp(-x)}
+    assert mmrv(exp(1/x + exp(-x)), x) == {exp(-x)}
+
+#sometimes infinite recursion due to log(exp(x**2)) not simplifying
+
+
+def test_mrv2b():
+    assert mmrv(exp(x + exp(-x**2)), x) == {exp(-x**2)}
+
+#sometimes infinite recursion due to log(exp(x**2)) not simplifying
+
+
+def test_mrv2c():
+    assert mmrv(
+        exp(-x + 1/x**2) - exp(x + 1/x), x) == {exp(x + 1/x), exp(1/x**2 - x)}
+
+#sometimes infinite recursion due to log(exp(x**2)) not simplifying
+
+
+def test_mrv3():
+    assert mmrv(exp(x**2) + x*exp(x) + log(x)**x/x, x) == {exp(x**2)}
+    assert mmrv(
+        exp(x)*(exp(1/x + exp(-x)) - exp(1/x)), x) == {exp(x), exp(-x)}
+    assert mmrv(log(
+        x**2 + 2*exp(exp(3*x**3*log(x)))), x) == {exp(exp(3*x**3*log(x)))}
+    assert mmrv(log(x - log(x))/log(x), x) == {x}
+    assert mmrv(
+        (exp(1/x - exp(-x)) - exp(1/x))*exp(x), x) == {exp(x), exp(-x)}
+    assert mmrv(
+        1/exp(-x + exp(-x)) - exp(x), x) == {exp(x), exp(-x), exp(x - exp(-x))}
+    assert mmrv(log(log(x*exp(x*exp(x)) + 1)), x) == {exp(x*exp(x))}
+    assert mmrv(exp(exp(log(log(x) + 1/x))), x) == {x}
+
+
+def test_mrv4():
+    ln = log
+    assert mmrv((ln(ln(x) + ln(ln(x))) - ln(ln(x)))/ln(ln(x) + ln(ln(ln(x))))*ln(x),
+            x) == {x}
+    assert mmrv(log(log(x*exp(x*exp(x)) + 1)) - exp(exp(log(log(x) + 1/x))), x) == \
+        {exp(x*exp(x))}
+
+
+def mrewrite(a, b, c):
+    return rewrite(a[1], a[0], b, c)
+
+
+def test_rewrite1():
+    e = exp(x)
+    assert mrewrite(mrv(e, x), x, m) == (1/m, -x)
+    e = exp(x**2)
+    assert mrewrite(mrv(e, x), x, m) == (1/m, -x**2)
+    e = exp(x + 1/x)
+    assert mrewrite(mrv(e, x), x, m) == (1/m, -x - 1/x)
+    e = 1/exp(-x + exp(-x)) - exp(x)
+    assert mrewrite(mrv(e, x), x, m) == (1/(m*exp(m)) - 1/m, -x)
+
+
+def test_rewrite2():
+    e = exp(x)*log(log(exp(x)))
+    assert mmrv(e, x) == {exp(x)}
+    assert mrewrite(mrv(e, x), x, m) == (1/m*log(x), -x)
+
+#sometimes infinite recursion due to log(exp(x**2)) not simplifying
+
+
+def test_rewrite3():
+    e = exp(-x + 1/x**2) - exp(x + 1/x)
+    #both of these are correct and should be equivalent:
+    assert mrewrite(mrv(e, x), x, m) in [(-1/m + m*exp(
+        1/x + 1/x**2), -x - 1/x), (m - 1/m*exp(1/x + x**(-2)), x**(-2) - x)]
+
+
+def test_mrv_leadterm1():
+    assert mrv_leadterm(-exp(1/x), x) == (-1, 0)
+    assert mrv_leadterm(1/exp(-x + exp(-x)) - exp(x), x) == (-1, 0)
+    assert mrv_leadterm(
+        (exp(1/x - exp(-x)) - exp(1/x))*exp(x), x) == (-exp(1/x), 0)
+
+
+def test_mrv_leadterm2():
+    #Gruntz: p51, 3.25
+    assert mrv_leadterm((log(exp(x) + x) - x)/log(exp(x) + log(x))*exp(x), x) == \
+        (1, 0)
+
+
+def test_mrv_leadterm3():
+    #Gruntz: p56, 3.27
+    assert mmrv(exp(-x + exp(-x)*exp(-x*log(x))), x) == {exp(-x - x*log(x))}
+    assert mrv_leadterm(exp(-x + exp(-x)*exp(-x*log(x))), x) == (exp(-x), 0)
+
+
+def test_limit1():
+    assert gruntz(x, x, oo) is oo
+    assert gruntz(x, x, -oo) is -oo
+    assert gruntz(-x, x, oo) is -oo
+    assert gruntz(x**2, x, -oo) is oo
+    assert gruntz(-x**2, x, oo) is -oo
+    assert gruntz(x*log(x), x, 0, dir="+") == 0
+    assert gruntz(1/x, x, oo) == 0
+    assert gruntz(exp(x), x, oo) is oo
+    assert gruntz(-exp(x), x, oo) is -oo
+    assert gruntz(exp(x)/x, x, oo) is oo
+    assert gruntz(1/x - exp(-x), x, oo) == 0
+    assert gruntz(x + 1/x, x, oo) is oo
+
+
+def test_limit2():
+    assert gruntz(x**x, x, 0, dir="+") == 1
+    assert gruntz((exp(x) - 1)/x, x, 0) == 1
+    assert gruntz(1 + 1/x, x, oo) == 1
+    assert gruntz(-exp(1/x), x, oo) == -1
+    assert gruntz(x + exp(-x), x, oo) is oo
+    assert gruntz(x + exp(-x**2), x, oo) is oo
+    assert gruntz(x + exp(-exp(x)), x, oo) is oo
+    assert gruntz(13 + 1/x - exp(-x), x, oo) == 13
+
+
+def test_limit3():
+    a = Symbol('a')
+    assert gruntz(x - log(1 + exp(x)), x, oo) == 0
+    assert gruntz(x - log(a + exp(x)), x, oo) == 0
+    assert gruntz(exp(x)/(1 + exp(x)), x, oo) == 1
+    assert gruntz(exp(x)/(a + exp(x)), x, oo) == 1
+
+
+def test_limit4():
+    #issue 3463
+    assert gruntz((3**x + 5**x)**(1/x), x, oo) == 5
+    #issue 3463
+    assert gruntz((3**(1/x) + 5**(1/x))**x, x, 0) == 5
+
+
+@XFAIL
+def test_MrvTestCase_page47_ex3_21():
+    h = exp(-x/(1 + exp(-x)))
+    expr = exp(h)*exp(-x/(1 + h))*exp(exp(-x + h))/h**2 - exp(x) + x
+    assert mmrv(expr, x) == {1/h, exp(-x), exp(x), exp(x - h), exp(x/(1 + h))}
+
+
+def test_gruntz_I():
+    y = Symbol("y")
+    assert gruntz(I*x, x, oo) == I*oo
+    assert gruntz(y*I*x, x, oo) == y*I*oo
+    assert gruntz(y*3*I*x, x, oo) == y*I*oo
+    assert gruntz(y*3*sin(I)*x, x, oo).simplify().rewrite(_sign) == _sign(y)*I*oo
+
+
+def test_issue_4814():
+    assert gruntz((x + 1)**(1/log(x + 1)), x, oo) == E
+
+
+def test_intractable():
+    assert gruntz(1/gamma(x), x, oo) == 0
+    assert gruntz(1/loggamma(x), x, oo) == 0
+    assert gruntz(gamma(x)/loggamma(x), x, oo) is oo
+    assert gruntz(exp(gamma(x))/gamma(x), x, oo) is oo
+    assert gruntz(gamma(x), x, 3) == 2
+    assert gruntz(gamma(Rational(1, 7) + 1/x), x, oo) == gamma(Rational(1, 7))
+    assert gruntz(log(x**x)/log(gamma(x)), x, oo) == 1
+    assert gruntz(log(gamma(gamma(x)))/exp(x), x, oo) is oo
+
+
+def test_aseries_trig():
+    assert cancel(gruntz(1/log(atan(x)), x, oo)
+           - 1/(log(pi) + log(S.Half))) == 0
+    assert gruntz(1/acot(x), x, -oo) is -oo
+
+
+def test_exp_log_series():
+    assert gruntz(x/log(log(x*exp(x))), x, oo) is oo
+
+
+def test_issue_3644():
+    assert gruntz(((x**7 + x + 1)/(2**x + x**2))**(-1/x), x, oo) == 2
+
+
+def test_issue_6843():
+    n = Symbol('n', integer=True, positive=True)
+    r = (n + 1)*x**(n + 1)/(x**(n + 1) - 1) - x/(x - 1)
+    assert gruntz(r, x, 1).simplify() == n/2
+
+
+def test_issue_4190():
+    assert gruntz(x - gamma(1/x), x, oo) == S.EulerGamma
+
+
+@XFAIL
+def test_issue_5172():
+    n = Symbol('n')
+    r = Symbol('r', positive=True)
+    c = Symbol('c')
+    p = Symbol('p', positive=True)
+    m = Symbol('m', negative=True)
+    expr = ((2*n*(n - r + 1)/(n + r*(n - r + 1)))**c + \
+        (r - 1)*(n*(n - r + 2)/(n + r*(n - r + 1)))**c - n)/(n**c - n)
+    expr = expr.subs(c, c + 1)
+    assert gruntz(expr.subs(c, m), n, oo) == 1
+    # fail:
+    assert gruntz(expr.subs(c, p), n, oo).simplify() == \
+        (2**(p + 1) + r - 1)/(r + 1)**(p + 1)
+
+
+def test_issue_4109():
+    assert gruntz(1/gamma(x), x, 0) == 0
+    assert gruntz(x*gamma(x), x, 0) == 1
+
+
+def test_issue_6682():
+    assert gruntz(exp(2*Ei(-x))/x**2, x, 0) == exp(2*EulerGamma)
+
+
+def test_issue_7096():
+    from sympy.functions import sign
+    assert gruntz(x**-pi, x, 0, dir='-') == oo*sign((-1)**(-pi))
diff --git a/shared/package-files/09/09156e525e6333899095fd62d57b27db39b52a58d24b45f312f369008da43d19/__init__.pyi b/shared/package-files/09/09156e525e6333899095fd62d57b27db39b52a58d24b45f312f369008da43d19/__init__.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..65f1e0e95b736a4692a0b9a6ad24264f7cfa5c56
--- /dev/null
+++ b/shared/package-files/09/09156e525e6333899095fd62d57b27db39b52a58d24b45f312f369008da43d19/__init__.pyi
@@ -0,0 +1,327 @@
+import sys
+import typing
+from typing import (
+    AbstractSet,
+    Any,
+    AsyncGenerator as AsyncGenerator,
+    AsyncIterable as AsyncIterable,
+    AsyncIterator as AsyncIterator,
+    Awaitable as Awaitable,
+    ByteString as ByteString,
+    Callable as Callable,
+    Collection as Collection,
+    Container as Container,
+    Coroutine as Coroutine,
+    Dict,
+    Generator as Generator,
+    Generic,
+    Hashable as Hashable,
+    ItemsView as ItemsView,
+    Iterable as Iterable,
+    Iterator as Iterator,
+    KeysView as KeysView,
+    List,
+    Mapping as Mapping,
+    MappingView as MappingView,
+    MutableMapping as MutableMapping,
+    MutableSequence as MutableSequence,
+    MutableSet as MutableSet,
+    Optional,
+    Reversible as Reversible,
+    Sequence as Sequence,
+    Sized as Sized,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    ValuesView as ValuesView,
+    overload,
+)
+
+Set = AbstractSet
+
+_S = TypeVar("_S")
+_T = TypeVar("_T")
+_KT = TypeVar("_KT")
+_VT = TypeVar("_VT")
+
+# namedtuple is special-cased in the type checker; the initializer is ignored.
+if sys.version_info >= (3, 7):
+    def namedtuple(
+        typename: str,
+        field_names: Union[str, Iterable[str]],
+        *,
+        rename: bool = ...,
+        module: Optional[str] = ...,
+        defaults: Optional[Iterable[Any]] = ...,
+    ) -> Type[Tuple[Any, ...]]: ...
+
+else:
+    def namedtuple(
+        typename: str,
+        field_names: Union[str, Iterable[str]],
+        *,
+        verbose: bool = ...,
+        rename: bool = ...,
+        module: Optional[str] = ...,
+    ) -> Type[Tuple[Any, ...]]: ...
+
+class UserDict(MutableMapping[_KT, _VT]):
+    data: Dict[_KT, _VT]
+    def __init__(self, __dict: Optional[Mapping[_KT, _VT]] = ..., **kwargs: _VT) -> None: ...
+    def __len__(self) -> int: ...
+    def __getitem__(self, key: _KT) -> _VT: ...
+    def __setitem__(self, key: _KT, item: _VT) -> None: ...
+    def __delitem__(self, key: _KT) -> None: ...
+    def __iter__(self) -> Iterator[_KT]: ...
+    def __contains__(self, key: object) -> bool: ...
+    def copy(self: _S) -> _S: ...
+    @classmethod
+    def fromkeys(cls: Type[_S], iterable: Iterable[_KT], value: Optional[_VT] = ...) -> _S: ...
+
+class UserList(MutableSequence[_T]):
+    data: List[_T]
+    def __init__(self, initlist: Optional[Iterable[_T]] = ...) -> None: ...
+    def __lt__(self, other: object) -> bool: ...
+    def __le__(self, other: object) -> bool: ...
+    def __gt__(self, other: object) -> bool: ...
+    def __ge__(self, other: object) -> bool: ...
+    def __contains__(self, item: object) -> bool: ...
+    def __len__(self) -> int: ...
+    @overload
+    def __getitem__(self, i: int) -> _T: ...
+    @overload
+    def __getitem__(self, i: slice) -> MutableSequence[_T]: ...
+    @overload
+    def __setitem__(self, i: int, o: _T) -> None: ...
+    @overload
+    def __setitem__(self, i: slice, o: Iterable[_T]) -> None: ...
+    def __delitem__(self, i: Union[int, slice]) -> None: ...
+    def __add__(self: _S, other: Iterable[_T]) -> _S: ...
+    def __iadd__(self: _S, other: Iterable[_T]) -> _S: ...
+    def __mul__(self: _S, n: int) -> _S: ...
+    def __imul__(self: _S, n: int) -> _S: ...
+    def append(self, item: _T) -> None: ...
+    def insert(self, i: int, item: _T) -> None: ...
+    def pop(self, i: int = ...) -> _T: ...
+    def remove(self, item: _T) -> None: ...
+    def clear(self) -> None: ...
+    def copy(self: _S) -> _S: ...
+    def count(self, item: _T) -> int: ...
+    def index(self, item: _T, *args: Any) -> int: ...
+    def reverse(self) -> None: ...
+    def sort(self, *args: Any, **kwds: Any) -> None: ...
+    def extend(self, other: Iterable[_T]) -> None: ...
+
+_UserStringT = TypeVar("_UserStringT", bound=UserString)
+
+class UserString(Sequence[str]):
+    data: str
+    def __init__(self, seq: object) -> None: ...
+    def __int__(self) -> int: ...
+    def __float__(self) -> float: ...
+    def __complex__(self) -> complex: ...
+    def __getnewargs__(self) -> Tuple[str]: ...
+    def __lt__(self, string: Union[str, UserString]) -> bool: ...
+    def __le__(self, string: Union[str, UserString]) -> bool: ...
+    def __gt__(self, string: Union[str, UserString]) -> bool: ...
+    def __ge__(self, string: Union[str, UserString]) -> bool: ...
+    def __contains__(self, char: object) -> bool: ...
+    def __len__(self) -> int: ...
+    # It should return a str to implement Sequence correctly, but it doesn't.
+    def __getitem__(self: _UserStringT, i: Union[int, slice]) -> _UserStringT: ...  # type: ignore
+    def __add__(self: _UserStringT, other: object) -> _UserStringT: ...
+    def __mul__(self: _UserStringT, n: int) -> _UserStringT: ...
+    def __mod__(self: _UserStringT, args: Any) -> _UserStringT: ...
+    def capitalize(self: _UserStringT) -> _UserStringT: ...
+    def casefold(self: _UserStringT) -> _UserStringT: ...
+    def center(self: _UserStringT, width: int, *args: Any) -> _UserStringT: ...
+    def count(self, sub: Union[str, UserString], start: int = ..., end: int = ...) -> int: ...
+    if sys.version_info >= (3, 8):
+        def encode(self: _UserStringT, encoding: Optional[str] = ..., errors: Optional[str] = ...) -> bytes: ...
+    else:
+        def encode(self: _UserStringT, encoding: Optional[str] = ..., errors: Optional[str] = ...) -> _UserStringT: ...
+    def endswith(self, suffix: Union[str, Tuple[str, ...]], start: int = ..., end: int = ...) -> bool: ...
+    def expandtabs(self: _UserStringT, tabsize: int = ...) -> _UserStringT: ...
+    def find(self, sub: Union[str, UserString], start: int = ..., end: int = ...) -> int: ...
+    def format(self, *args: Any, **kwds: Any) -> str: ...
+    def format_map(self, mapping: Mapping[str, Any]) -> str: ...
+    def index(self, sub: str, start: int = ..., end: int = ...) -> int: ...
+    def isalpha(self) -> bool: ...
+    def isalnum(self) -> bool: ...
+    def isdecimal(self) -> bool: ...
+    def isdigit(self) -> bool: ...
+    def isidentifier(self) -> bool: ...
+    def islower(self) -> bool: ...
+    def isnumeric(self) -> bool: ...
+    def isprintable(self) -> bool: ...
+    def isspace(self) -> bool: ...
+    def istitle(self) -> bool: ...
+    def isupper(self) -> bool: ...
+    def join(self, seq: Iterable[str]) -> str: ...
+    def ljust(self: _UserStringT, width: int, *args: Any) -> _UserStringT: ...
+    def lower(self: _UserStringT) -> _UserStringT: ...
+    def lstrip(self: _UserStringT, chars: Optional[str] = ...) -> _UserStringT: ...
+    @staticmethod
+    @overload
+    def maketrans(x: Union[Dict[int, _T], Dict[str, _T], Dict[Union[str, int], _T]]) -> Dict[int, _T]: ...
+    @staticmethod
+    @overload
+    def maketrans(x: str, y: str, z: str = ...) -> Dict[int, Union[int, None]]: ...
+    def partition(self, sep: str) -> Tuple[str, str, str]: ...
+    if sys.version_info >= (3, 9):
+        def removeprefix(self: _UserStringT, __prefix: Union[str, UserString]) -> _UserStringT: ...
+        def removesuffix(self: _UserStringT, __suffix: Union[str, UserString]) -> _UserStringT: ...
+    def replace(
+        self: _UserStringT, old: Union[str, UserString], new: Union[str, UserString], maxsplit: int = ...
+    ) -> _UserStringT: ...
+    def rfind(self, sub: Union[str, UserString], start: int = ..., end: int = ...) -> int: ...
+    def rindex(self, sub: Union[str, UserString], start: int = ..., end: int = ...) -> int: ...
+    def rjust(self: _UserStringT, width: int, *args: Any) -> _UserStringT: ...
+    def rpartition(self, sep: str) -> Tuple[str, str, str]: ...
+    def rstrip(self: _UserStringT, chars: Optional[str] = ...) -> _UserStringT: ...
+    def split(self, sep: Optional[str] = ..., maxsplit: int = ...) -> List[str]: ...
+    def rsplit(self, sep: Optional[str] = ..., maxsplit: int = ...) -> List[str]: ...
+    def splitlines(self, keepends: bool = ...) -> List[str]: ...
+    def startswith(self, prefix: Union[str, Tuple[str, ...]], start: int = ..., end: int = ...) -> bool: ...
+    def strip(self: _UserStringT, chars: Optional[str] = ...) -> _UserStringT: ...
+    def swapcase(self: _UserStringT) -> _UserStringT: ...
+    def title(self: _UserStringT) -> _UserStringT: ...
+    def translate(self: _UserStringT, *args: Any) -> _UserStringT: ...
+    def upper(self: _UserStringT) -> _UserStringT: ...
+    def zfill(self: _UserStringT, width: int) -> _UserStringT: ...
+
+class deque(MutableSequence[_T], Generic[_T]):
+    @property
+    def maxlen(self) -> Optional[int]: ...
+    def __init__(self, iterable: Iterable[_T] = ..., maxlen: Optional[int] = ...) -> None: ...
+    def append(self, x: _T) -> None: ...
+    def appendleft(self, x: _T) -> None: ...
+    def clear(self) -> None: ...
+    def copy(self) -> deque[_T]: ...
+    def count(self, x: _T) -> int: ...
+    def extend(self, iterable: Iterable[_T]) -> None: ...
+    def extendleft(self, iterable: Iterable[_T]) -> None: ...
+    def insert(self, i: int, x: _T) -> None: ...
+    def index(self, x: _T, start: int = ..., stop: int = ...) -> int: ...
+    def pop(self) -> _T: ...  # type: ignore
+    def popleft(self) -> _T: ...
+    def remove(self, value: _T) -> None: ...
+    def reverse(self) -> None: ...
+    def rotate(self, n: int = ...) -> None: ...
+    def __len__(self) -> int: ...
+    def __iter__(self) -> Iterator[_T]: ...
+    def __str__(self) -> str: ...
+    def __hash__(self) -> int: ...
+    # These methods of deque don't really take slices, but we need to
+    # define them as taking a slice to satisfy MutableSequence.
+    @overload
+    def __getitem__(self, index: int) -> _T: ...
+    @overload
+    def __getitem__(self, s: slice) -> MutableSequence[_T]: ...
+    @overload
+    def __setitem__(self, i: int, x: _T) -> None: ...
+    @overload
+    def __setitem__(self, s: slice, o: Iterable[_T]) -> None: ...
+    @overload
+    def __delitem__(self, i: int) -> None: ...
+    @overload
+    def __delitem__(self, s: slice) -> None: ...
+    def __contains__(self, o: object) -> bool: ...
+    def __reversed__(self) -> Iterator[_T]: ...
+    def __iadd__(self: _S, iterable: Iterable[_T]) -> _S: ...
+    def __add__(self, other: deque[_T]) -> deque[_T]: ...
+    def __mul__(self, other: int) -> deque[_T]: ...
+    def __imul__(self, other: int) -> None: ...
+
+class Counter(Dict[_T, int], Generic[_T]):
+    @overload
+    def __init__(self, __iterable: None = ..., **kwargs: int) -> None: ...
+    @overload
+    def __init__(self, __mapping: Mapping[_T, int]) -> None: ...
+    @overload
+    def __init__(self, __iterable: Iterable[_T]) -> None: ...
+    def copy(self: _S) -> _S: ...
+    def elements(self) -> Iterator[_T]: ...
+    def most_common(self, n: Optional[int] = ...) -> List[Tuple[_T, int]]: ...
+    @overload
+    def subtract(self, __iterable: None = ...) -> None: ...
+    @overload
+    def subtract(self, __mapping: Mapping[_T, int]) -> None: ...
+    @overload
+    def subtract(self, __iterable: Iterable[_T]) -> None: ...
+    # The Iterable[Tuple[...]] argument type is not actually desirable
+    # (the tuples will be added as keys, breaking type safety) but
+    # it's included so that the signature is compatible with
+    # Dict.update. Not sure if we should use '# type: ignore' instead
+    # and omit the type from the union.
+    @overload
+    def update(self, __m: Mapping[_T, int], **kwargs: int) -> None: ...
+    @overload
+    def update(self, __m: Union[Iterable[_T], Iterable[Tuple[_T, int]]], **kwargs: int) -> None: ...
+    @overload
+    def update(self, __m: None = ..., **kwargs: int) -> None: ...
+    def __add__(self, other: Counter[_T]) -> Counter[_T]: ...
+    def __sub__(self, other: Counter[_T]) -> Counter[_T]: ...
+    def __and__(self, other: Counter[_T]) -> Counter[_T]: ...
+    def __or__(self, other: Counter[_T]) -> Counter[_T]: ...  # type: ignore
+    def __pos__(self) -> Counter[_T]: ...
+    def __neg__(self) -> Counter[_T]: ...
+    def __iadd__(self, other: Counter[_T]) -> Counter[_T]: ...
+    def __isub__(self, other: Counter[_T]) -> Counter[_T]: ...
+    def __iand__(self, other: Counter[_T]) -> Counter[_T]: ...
+    def __ior__(self, other: Counter[_T]) -> Counter[_T]: ...  # type: ignore
+
+class _OrderedDictKeysView(KeysView[_KT], Reversible[_KT]):
+    def __reversed__(self) -> Iterator[_KT]: ...
+
+class _OrderedDictItemsView(ItemsView[_KT, _VT], Reversible[Tuple[_KT, _VT]]):
+    def __reversed__(self) -> Iterator[Tuple[_KT, _VT]]: ...
+
+class _OrderedDictValuesView(ValuesView[_VT], Reversible[_VT]):
+    def __reversed__(self) -> Iterator[_VT]: ...
+
+class OrderedDict(Dict[_KT, _VT], Reversible[_KT], Generic[_KT, _VT]):
+    def popitem(self, last: bool = ...) -> Tuple[_KT, _VT]: ...
+    def move_to_end(self, key: _KT, last: bool = ...) -> None: ...
+    def copy(self: _S) -> _S: ...
+    def __reversed__(self) -> Iterator[_KT]: ...
+    def keys(self) -> _OrderedDictKeysView[_KT]: ...
+    def items(self) -> _OrderedDictItemsView[_KT, _VT]: ...
+    def values(self) -> _OrderedDictValuesView[_VT]: ...
+
+class defaultdict(Dict[_KT, _VT], Generic[_KT, _VT]):
+    default_factory: Optional[Callable[[], _VT]]
+    @overload
+    def __init__(self, **kwargs: _VT) -> None: ...
+    @overload
+    def __init__(self, default_factory: Optional[Callable[[], _VT]]) -> None: ...
+    @overload
+    def __init__(self, default_factory: Optional[Callable[[], _VT]], **kwargs: _VT) -> None: ...
+    @overload
+    def __init__(self, default_factory: Optional[Callable[[], _VT]], map: Mapping[_KT, _VT]) -> None: ...
+    @overload
+    def __init__(self, default_factory: Optional[Callable[[], _VT]], map: Mapping[_KT, _VT], **kwargs: _VT) -> None: ...
+    @overload
+    def __init__(self, default_factory: Optional[Callable[[], _VT]], iterable: Iterable[Tuple[_KT, _VT]]) -> None: ...
+    @overload
+    def __init__(
+        self, default_factory: Optional[Callable[[], _VT]], iterable: Iterable[Tuple[_KT, _VT]], **kwargs: _VT
+    ) -> None: ...
+    def __missing__(self, key: _KT) -> _VT: ...
+    # TODO __reversed__
+    def copy(self: _S) -> _S: ...
+
+class ChainMap(MutableMapping[_KT, _VT], Generic[_KT, _VT]):
+    def __init__(self, *maps: Mapping[_KT, _VT]) -> None: ...
+    @property
+    def maps(self) -> List[Mapping[_KT, _VT]]: ...
+    def new_child(self, m: Mapping[_KT, _VT] = ...) -> typing.ChainMap[_KT, _VT]: ...
+    @property
+    def parents(self) -> typing.ChainMap[_KT, _VT]: ...
+    def __setitem__(self, k: _KT, v: _VT) -> None: ...
+    def __delitem__(self, v: _KT) -> None: ...
+    def __getitem__(self, k: _KT) -> _VT: ...
+    def __iter__(self) -> Iterator[_KT]: ...
+    def __len__(self) -> int: ...
+    def __missing__(self, key: _KT) -> _VT: ...  # undocumented
diff --git a/shared/package-files/09/09191205c0d364c1fa29cebecc12e9670ed851c0146c24b3ed11d87c0b212cd5/conda_auto_activate.bat b/shared/package-files/09/09191205c0d364c1fa29cebecc12e9670ed851c0146c24b3ed11d87c0b212cd5/conda_auto_activate.bat
new file mode 100644
index 0000000000000000000000000000000000000000..c00bc2eb8ee6fdbde81a69213ac933b51689f9c8
--- /dev/null
+++ b/shared/package-files/09/09191205c0d364c1fa29cebecc12e9670ed851c0146c24b3ed11d87c0b212cd5/conda_auto_activate.bat
@@ -0,0 +1,8 @@
+@REM Copyright (C) 2012 Anaconda, Inc
+@REM SPDX-License-Identifier: BSD-3-Clause
+@REM Helper script to conda_hook.bat.  This is a separate script so that the Anaconda Prompt
+@REM can enable this option while only conda_hook.bat can be used in AutoRun.
+
+@FOR /F "delims=" %%i IN ('@CALL "%CONDA_EXE%" config --show auto_activate_base') DO @SET "__conda_auto_activate_base=%%i"
+@IF NOT "x%__conda_auto_activate_base:True=%"=="x%__conda_auto_activate_base%" @CALL "%CONDA_BAT%" activate base
+@SET __conda_auto_activate_base=
diff --git a/shared/package-files/09/09198aa0ce140bd0efcb562bb812f96cbe0c18b5e5b30d3b5290c91974e4736d/_gpu_tree.py b/shared/package-files/09/09198aa0ce140bd0efcb562bb812f96cbe0c18b5e5b30d3b5290c91974e4736d/_gpu_tree.py
new file mode 100644
index 0000000000000000000000000000000000000000..80bce114749b0aeb652a51785f0729d06db4b7a5
--- /dev/null
+++ b/shared/package-files/09/09198aa0ce140bd0efcb562bb812f96cbe0c18b5e5b30d3b5290c91974e4736d/_gpu_tree.py
@@ -0,0 +1,178 @@
+"""GPU accelerated tree explanations"""
+
+import numpy as np
+
+from ..utils import assert_import, record_import_error
+from ._tree import (
+    TreeExplainer,
+    _xgboost_cat_unsupported,
+    feature_perturbation_codes,
+    output_transform_codes,
+)
+
+try:
+    from .. import _cext_gpu  # type: ignore
+except ImportError as e:
+    record_import_error("cext_gpu", "cuda extension was not built during install!", e)
+
+
+class GPUTreeExplainer(TreeExplainer):
+    """Experimental GPU accelerated version of TreeExplainer. Currently requires source build with
+    cuda available and 'CUDA_PATH' environment variable defined.
+
+    Examples
+    --------
+    See `GPUTree explainer examples `_
+
+    """
+
+    def shap_values(self, X, y=None, tree_limit=None, approximate=False, check_additivity=True, from_call=False):
+        """Estimate the SHAP values for a set of samples.
+
+        Parameters
+        ----------
+        X : numpy.array, pandas.DataFrame or catboost.Pool (for catboost)
+            A matrix of samples (# samples x # features) on which to explain the model's output.
+
+        y : numpy.array
+            An array of label values for each sample. Used when explaining loss functions.
+
+        tree_limit : None (default) or int
+            Limit the number of trees used by the model. By default None means no use the limit
+            of the
+            original model, and -1 means no limit.
+
+        approximate : bool
+            Not supported.
+
+        check_additivity : bool
+            Run a validation check that the sum of the SHAP values equals the output of the
+            model. This
+            check takes only a small amount of time, and will catch potential unforeseen errors.
+            Note that this check only runs right now when explaining the margin of the model.
+
+        Returns
+        -------
+        array or list
+            For models with a single output this returns a matrix of SHAP values
+            (# samples x # features). Each row sums to the difference between the model output
+            for that
+            sample and the expected value of the model output (which is stored in the expected_value
+            attribute of the explainer when it is constant). For models with vector outputs this
+            returns
+            a list of such matrices, one for each output.
+
+        """
+        assert not approximate, "approximate not supported"
+
+        X, y, X_missing, flat_output, tree_limit, check_additivity = self._validate_inputs(
+            X, y, tree_limit, check_additivity
+        )
+
+        model = self.model
+        _xgboost_cat_unsupported(model)
+        transform = model.get_transform()
+
+        # run the core algorithm using the C extension
+        assert_import("cext_gpu")
+        phi = np.zeros((X.shape[0], X.shape[1] + 1, self.model.num_outputs))
+        _cext_gpu.dense_tree_shap(
+            self.model.children_left,
+            self.model.children_right,
+            self.model.children_default,
+            self.model.features,
+            self.model.thresholds,
+            self.model.values,
+            self.model.node_sample_weight,
+            self.model.max_depth,
+            X,
+            X_missing,
+            y,
+            self.data,
+            self.data_missing,
+            tree_limit,
+            self.model.base_offset,
+            phi,
+            feature_perturbation_codes[self.feature_perturbation],
+            output_transform_codes[transform],
+            False,
+        )
+
+        out = self._get_shap_output(phi, flat_output)
+        if check_additivity and self.model.model_output == "raw":
+            self.assert_additivity(out, self.model.predict(X))
+
+        return out
+
+    def shap_interaction_values(self, X, y=None, tree_limit=None):
+        """Estimate the SHAP interaction values for a set of samples.
+
+        Parameters
+        ----------
+        X : numpy.array, pandas.DataFrame or catboost.Pool (for catboost)
+            A matrix of samples (# samples x # features) on which to explain the model's output.
+
+        y : numpy.array
+            An array of label values for each sample. Used when explaining loss functions (not
+            yet supported).
+
+        tree_limit : None (default) or int
+            Limit the number of trees used by the model. By default None means no use the limit
+            of the
+            original model, and -1 means no limit.
+
+        Returns
+        -------
+        array or list
+            For models with a single output this returns a tensor of SHAP values
+            (# samples x # features x # features). The matrix (# features x # features) for each
+            sample sums
+            to the difference between the model output for that sample and the expected value of
+            the model output
+            (which is stored in the expected_value attribute of the explainer). Each row of this
+            matrix sums to the
+            SHAP value for that feature for that sample. The diagonal entries of the matrix
+            represent the
+            "main effect" of that feature on the prediction and the symmetric off-diagonal
+            entries represent the
+            interaction effects between all pairs of features for that sample. For models with
+            vector outputs
+            this returns a list of tensors, one for each output.
+
+        """
+        if self.model.model_output != "raw":
+            raise ValueError('Only model_output = "raw" is supported for SHAP interaction values right now!')
+        if self.feature_perturbation == "interventional":
+            raise ValueError(
+                'feature_perturbation="interventional" is not yet supported for interaction values. '
+                'Use feature_perturbation="tree_path_dependent" instead.'
+            )
+        transform = "identity"
+
+        X, y, X_missing, flat_output, tree_limit, _ = self._validate_inputs(X, y, tree_limit, False)
+        # run the core algorithm using the C extension
+        assert_import("cext_gpu")
+        phi = np.zeros((X.shape[0], X.shape[1] + 1, X.shape[1] + 1, self.model.num_outputs))
+        _cext_gpu.dense_tree_shap(
+            self.model.children_left,
+            self.model.children_right,
+            self.model.children_default,
+            self.model.features,
+            self.model.thresholds,
+            self.model.values,
+            self.model.node_sample_weight,
+            self.model.max_depth,
+            X,
+            X_missing,
+            y,
+            self.data,
+            self.data_missing,
+            tree_limit,
+            self.model.base_offset,
+            phi,
+            feature_perturbation_codes[self.feature_perturbation],
+            output_transform_codes[transform],
+            True,
+        )
+
+        return self._get_shap_interactions_output(phi, flat_output)
diff --git a/shared/package-files/09/091b2b39205b4005e957d2669e456d1cfa7f71021fa4b2f5b4e03fdee1ba74cf/manni.py b/shared/package-files/09/091b2b39205b4005e957d2669e456d1cfa7f71021fa4b2f5b4e03fdee1ba74cf/manni.py
new file mode 100644
index 0000000000000000000000000000000000000000..769b5cb2ad3d99e979a5fb371a9e7b41cea9027b
--- /dev/null
+++ b/shared/package-files/09/091b2b39205b4005e957d2669e456d1cfa7f71021fa4b2f5b4e03fdee1ba74cf/manni.py
@@ -0,0 +1,74 @@
+"""
+    pygments.styles.manni
+    ~~~~~~~~~~~~~~~~~~~~~
+
+    A colorful style, inspired by the terminal highlighting style.
+
+    This is a port of the style used in the `php port`_ of pygments
+    by Manni. The style is called 'default' there.
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+from pygments.style import Style
+from pygments.token import Keyword, Name, Comment, String, Error, \
+     Number, Operator, Generic, Whitespace
+
+
+class ManniStyle(Style):
+    """
+    A colorful style, inspired by the terminal highlighting style.
+    """
+
+    background_color = '#f0f3f3'
+
+    styles = {
+        Whitespace:         '#bbbbbb',
+        Comment:            'italic #0099FF',
+        Comment.Preproc:    'noitalic #009999',
+        Comment.Special:    'bold',
+
+        Keyword:            'bold #006699',
+        Keyword.Pseudo:     'nobold',
+        Keyword.Type:       '#007788',
+
+        Operator:           '#555555',
+        Operator.Word:      'bold #000000',
+
+        Name.Builtin:       '#336666',
+        Name.Function:      '#CC00FF',
+        Name.Class:         'bold #00AA88',
+        Name.Namespace:     'bold #00CCFF',
+        Name.Exception:     'bold #CC0000',
+        Name.Variable:      '#003333',
+        Name.Constant:      '#336600',
+        Name.Label:         '#9999FF',
+        Name.Entity:        'bold #999999',
+        Name.Attribute:     '#330099',
+        Name.Tag:           'bold #330099',
+        Name.Decorator:     '#9999FF',
+
+        String:             '#CC3300',
+        String.Doc:         'italic',
+        String.Interpol:    '#AA0000',
+        String.Escape:      'bold #CC3300',
+        String.Regex:       '#33AAAA',
+        String.Symbol:      '#FFCC33',
+        String.Other:       '#CC3300',
+
+        Number:             '#FF6600',
+
+        Generic.Heading:    'bold #003300',
+        Generic.Subheading: 'bold #003300',
+        Generic.Deleted:    'border:#CC0000 bg:#FFCCCC',
+        Generic.Inserted:   'border:#00CC00 bg:#CCFFCC',
+        Generic.Error:      '#FF0000',
+        Generic.Emph:       'italic',
+        Generic.Strong:     'bold',
+        Generic.Prompt:     'bold #000099',
+        Generic.Output:     '#AAAAAA',
+        Generic.Traceback:  '#99CC66',
+
+        Error:              'bg:#FFAAAA #AA0000'
+    }
diff --git a/shared/package-files/09/091cc0c2a376ab8d2d5fcee31dda9eb881d45c339100cdc8c3c0b581864c0cc1/test_tuples.py b/shared/package-files/09/091cc0c2a376ab8d2d5fcee31dda9eb881d45c339100cdc8c3c0b581864c0cc1/test_tuples.py
new file mode 100644
index 0000000000000000000000000000000000000000..9991a1c8bfd030f27535d8ad936ddb896fdbf938
--- /dev/null
+++ b/shared/package-files/09/091cc0c2a376ab8d2d5fcee31dda9eb881d45c339100cdc8c3c0b581864c0cc1/test_tuples.py
@@ -0,0 +1,822 @@
+import collections
+import itertools
+
+import numpy as np
+
+from numba import njit, jit, typeof, literally
+from numba.core import types, errors, utils
+from numba.tests.support import TestCase, MemoryLeakMixin, tag
+import unittest
+
+
+Rect = collections.namedtuple('Rect', ('width', 'height'))
+
+Point = collections.namedtuple('Point', ('x', 'y', 'z'))
+
+Point2 = collections.namedtuple('Point2', ('x', 'y', 'z'))
+
+Empty = collections.namedtuple('Empty', ())
+
+def tuple_return_usecase(a, b):
+    return a, b
+
+def tuple_first(tup):
+    a, b = tup
+    return a
+
+def tuple_second(tup):
+    a, b = tup
+    return b
+
+def tuple_index(tup, idx):
+    return tup[idx]
+
+def tuple_index_static(tup):
+    # Note the negative index
+    return tup[-2]
+
+def tuple_slice2(tup):
+    return tup[1:-1]
+
+def tuple_slice3(tup):
+    return tup[1::2]
+
+def len_usecase(tup):
+    return len(tup)
+
+def add_usecase(a, b):
+    return a + b
+
+def eq_usecase(a, b):
+    return a == b
+
+def ne_usecase(a, b):
+    return a != b
+
+def gt_usecase(a, b):
+    return a > b
+
+def ge_usecase(a, b):
+    return a >= b
+
+def lt_usecase(a, b):
+    return a < b
+
+def le_usecase(a, b):
+    return a <= b
+
+def in_usecase(a, b):
+    return a in b
+
+def bool_usecase(tup):
+    return bool(tup), (3 if tup else 2)
+
+def getattr_usecase(tup):
+    return tup.z, tup.y, tup.x
+
+def make_point(a, b, c):
+    return Point(a, b, c)
+
+def make_point_kws(a, b, c):
+    return Point(z=c, y=b, x=a)
+
+def make_point_nrt(n):
+    r = Rect(list(range(n)), np.zeros(n + 1))
+    # This also exercises attribute access
+    p = Point(r, len(r.width), len(r.height))
+    return p
+
+def type_usecase(tup, *args):
+    return type(tup)(*args)
+
+def identity(tup):
+    return tup
+
+def index_method_usecase(tup, value):
+    return tup.index(value)
+
+def tuple_unpack_static_getitem_err():
+    # see issue3895, `c` is imprecise
+    a, b, c, d = [], [], [], 0.0
+    a.append(1)
+    b.append(1)
+    return
+
+
+class TestTupleLengthError(unittest.TestCase):
+
+    def test_tuple_length_error(self):
+        # issue 2195
+        # raise an error on tuples greater than 1000 in length
+        @njit
+        def eattuple(tup):
+            return len(tup)
+
+        with self.assertRaises(errors.UnsupportedError) as raises:
+            tup = tuple(range(1001))
+            eattuple(tup)
+
+        expected = "Tuple 'tup' length must be smaller than 1000"
+        self.assertIn(expected, str(raises.exception))
+
+class TestTupleTypeNotIterable(unittest.TestCase):
+    '''
+    issue 4369
+    raise an error if 'type' is not iterable
+    '''
+    def test_namedtuple_types_exception(self):
+        with self.assertRaises(errors.TypingError) as raises:
+            types.NamedTuple(types.uint32, 'p')
+        self.assertIn(
+            "Argument 'types' is not iterable",
+            str(raises.exception)
+        )
+
+    def test_tuple_types_exception(self):
+        with self.assertRaises(errors.TypingError) as raises:
+            types.Tuple((types.uint32))
+        self.assertIn(
+            "Argument 'types' is not iterable",
+            str(raises.exception)
+        )
+
+
+class TestTupleReturn(TestCase):
+
+    def test_array_tuple(self):
+        aryty = types.Array(types.float64, 1, 'C')
+        cfunc = njit((aryty, aryty))(tuple_return_usecase)
+        a = b = np.arange(5, dtype='float64')
+        ra, rb = cfunc(a, b)
+        self.assertPreciseEqual(ra, a)
+        self.assertPreciseEqual(rb, b)
+        del a, b
+        self.assertPreciseEqual(ra, rb)
+
+    def test_scalar_tuple(self):
+        scalarty = types.float32
+        cfunc = njit((scalarty, scalarty))(tuple_return_usecase)
+        a = b = 1
+        ra, rb = cfunc(a, b)
+        self.assertEqual(ra, a)
+        self.assertEqual(rb, b)
+
+    def test_hetero_tuple(self):
+        alltypes = []
+        allvalues = []
+
+        alltypes.append((types.int32, types.int64))
+        allvalues.append((1, 2))
+
+        alltypes.append((types.float32, types.float64))
+        allvalues.append((1.125, .25))
+
+        alltypes.append((types.int32, types.float64))
+        allvalues.append((1231, .5))
+
+        for (ta, tb), (a, b) in zip(alltypes, allvalues):
+            cfunc = njit((ta, tb))(tuple_return_usecase)
+            ra, rb = cfunc(a, b)
+            self.assertPreciseEqual((ra, rb), (a, b))
+
+
+class TestTuplePassing(TestCase):
+
+    def test_unituple(self):
+        tuple_type = types.UniTuple(types.int32, 2)
+        cf_first = njit((tuple_type,))(tuple_first)
+        cf_second = njit((tuple_type,))(tuple_second)
+        self.assertPreciseEqual(cf_first((4, 5)), 4)
+        self.assertPreciseEqual(cf_second((4, 5)), 5)
+
+    def test_hetero_tuple(self):
+        tuple_type = types.Tuple((types.int64, types.float32))
+        cf_first = njit((tuple_type,))(tuple_first)
+        cf_second = njit((tuple_type,))(tuple_second)
+        self.assertPreciseEqual(cf_first((2**61, 1.5)), 2**61)
+        self.assertPreciseEqual(cf_second((2**61, 1.5)), 1.5)
+
+    def test_size_mismatch(self):
+        # Issue #1638: tuple size should be checked when unboxing
+        tuple_type = types.UniTuple(types.int32, 2)
+        cfunc = njit((tuple_type,))(tuple_first)
+        entry_point = cfunc.overloads[cfunc.signatures[0]].entry_point
+        with self.assertRaises(ValueError) as raises:
+            entry_point((4, 5, 6))
+        self.assertEqual(str(raises.exception),
+                         ("size mismatch for tuple, "
+                          "expected 2 element(s) but got 3"))
+
+
+class TestOperations(TestCase):
+
+    def test_len(self):
+        pyfunc = len_usecase
+        cfunc = njit((types.Tuple((types.int64, types.float32)),))(pyfunc)
+        self.assertPreciseEqual(cfunc((4, 5)), 2)
+        cfunc = njit((types.UniTuple(types.int64, 3),))(pyfunc)
+        self.assertPreciseEqual(cfunc((4, 5, 6)), 3)
+
+    def test_index_literal(self):
+        # issue #6023, test non-static getitem with IntegerLiteral index
+        def pyfunc(tup, idx):
+            idx = literally(idx)
+            return tup[idx]
+        cfunc = njit(pyfunc)
+
+        tup = (4, 3.1, 'sss')
+        for i in range(len(tup)):
+            self.assertPreciseEqual(cfunc(tup, i), tup[i])
+
+    def test_index(self):
+        pyfunc = tuple_index
+        cfunc = njit((types.UniTuple(types.int64, 3), types.int64),)(pyfunc)
+        tup = (4, 3, 6)
+        for i in range(len(tup)):
+            self.assertPreciseEqual(cfunc(tup, i), tup[i])
+
+        # test negative indexing
+        for i in range(len(tup) + 1):
+            self.assertPreciseEqual(cfunc(tup, -i), tup[-i])
+
+        # oob indexes, +ve then -ve
+        with self.assertRaises(IndexError) as raises:
+            cfunc(tup, len(tup))
+        self.assertEqual("tuple index out of range", str(raises.exception))
+        with self.assertRaises(IndexError) as raises:
+            cfunc(tup, -(len(tup) + 1))
+        self.assertEqual("tuple index out of range", str(raises.exception))
+
+        # Test empty tuple, this is a bit unusual as `njit` will infer the empty
+        # tuple arg as a types.Tuple and not match the compiled signature, this
+        # is essentially because the test originally relied on
+        # `compile_isolated`.
+        args = (types.UniTuple(types.int64, 0), types.int64,)
+        cr = njit(args)(pyfunc).overloads[args]
+        with self.assertRaises(IndexError) as raises:
+            cr.entry_point((), 0)
+        self.assertEqual("tuple index out of range", str(raises.exception))
+
+        # test uintp indexing (because, e.g., parfor generates unsigned prange)
+        cfunc = njit((types.UniTuple(types.int64, 3), types.uintp,),)(pyfunc)
+        for i in range(len(tup)):
+            self.assertPreciseEqual(cfunc(tup, types.uintp(i)), tup[i])
+
+        # With a compile-time static index (the code generation path is
+        # different)
+        pyfunc = tuple_index_static
+        for typ in (types.UniTuple(types.int64, 4),
+                    types.Tuple((types.int64, types.int32, types.int64, types.int32))):
+            cfunc = njit((typ,))(pyfunc)
+            tup = (4, 3, 42, 6)
+            self.assertPreciseEqual(cfunc(tup), pyfunc(tup))
+
+        typ = types.UniTuple(types.int64, 1)
+        with self.assertTypingError():
+            njit((typ,))(pyfunc)
+
+        # test unpack, staticgetitem with imprecise type (issue #3895)
+        pyfunc = tuple_unpack_static_getitem_err
+        with self.assertTypingError() as raises:
+            njit((),)(pyfunc)
+        msg = ("Cannot infer the type of variable 'c', have imprecise type: "
+               "list(undefined).")
+        self.assertIn(msg, str(raises.exception))
+
+    def test_in(self):
+        pyfunc = in_usecase
+        cfunc = njit((types.int64, types.UniTuple(types.int64, 3),),)(pyfunc)
+        tup = (4, 1, 5)
+        for i in range(5):
+            self.assertPreciseEqual(cfunc(i, tup), pyfunc(i, tup))
+
+        # Test the empty case
+        cfunc = njit((types.int64, types.Tuple([]),),)(pyfunc)
+        self.assertPreciseEqual(cfunc(1, ()), pyfunc(1, ()))
+
+    def check_slice(self, pyfunc):
+        tup = (4, 5, 6, 7)
+        cfunc = njit((types.UniTuple(types.int64, 4),),)(pyfunc)
+        self.assertPreciseEqual(cfunc(tup), pyfunc(tup))
+        args = types.Tuple((types.int64, types.int32, types.int64, types.int32))
+        cfunc = njit((args,))(pyfunc)
+        self.assertPreciseEqual(cfunc(tup), pyfunc(tup))
+
+    def test_slice2(self):
+        self.check_slice(tuple_slice2)
+
+    def test_slice3(self):
+        self.check_slice(tuple_slice3)
+
+    def test_bool(self):
+        pyfunc = bool_usecase
+        cfunc = njit((types.Tuple((types.int64, types.int32)),),)(pyfunc)
+        args = ((4, 5),)
+        self.assertPreciseEqual(cfunc(*args), pyfunc(*args))
+        cfunc = njit((types.UniTuple(types.int64, 3),),)(pyfunc)
+        args = ((4, 5, 6),)
+        self.assertPreciseEqual(cfunc(*args), pyfunc(*args))
+        cfunc = njit((types.Tuple(()),),)(pyfunc)
+        self.assertPreciseEqual(cfunc(()), pyfunc(()))
+
+    def test_add(self):
+        pyfunc = add_usecase
+        samples = [(types.Tuple(()), ()),
+                   (types.UniTuple(types.int32, 0), ()),
+                   (types.UniTuple(types.int32, 1), (42,)),
+                   (types.Tuple((types.int64, types.float32)), (3, 4.5)),
+                   ]
+        for (ta, a), (tb, b) in itertools.product(samples, samples):
+            cfunc = njit((ta, tb),)(pyfunc)
+            expected = pyfunc(a, b)
+            got = cfunc(a, b)
+            self.assertPreciseEqual(got, expected, msg=(ta, tb))
+
+    def _test_compare(self, pyfunc):
+        def eq(pyfunc, cfunc, args):
+            self.assertIs(cfunc(*args), pyfunc(*args),
+                          "mismatch for arguments %s" % (args,))
+
+        # Same-sized tuples
+        argtypes = [types.Tuple((types.int64, types.float32)),
+                    types.UniTuple(types.int32, 2)]
+        for ta, tb in itertools.product(argtypes, argtypes):
+            cfunc = njit((ta, tb),)(pyfunc)
+            for args in [((4, 5), (4, 5)),
+                         ((4, 5), (4, 6)),
+                         ((4, 6), (4, 5)),
+                         ((4, 5), (5, 4))]:
+                eq(pyfunc, cfunc, args)
+        # Different-sized tuples
+        argtypes = [types.Tuple((types.int64, types.float32)),
+                    types.UniTuple(types.int32, 3)]
+        cfunc = njit(tuple(argtypes),)(pyfunc)
+        for args in [((4, 5), (4, 5, 6)),
+                     ((4, 5), (4, 4, 6)),
+                     ((4, 5), (4, 6, 7))]:
+            eq(pyfunc, cfunc, args)
+
+    def test_eq(self):
+        self._test_compare(eq_usecase)
+
+    def test_ne(self):
+        self._test_compare(ne_usecase)
+
+    def test_gt(self):
+        self._test_compare(gt_usecase)
+
+    def test_ge(self):
+        self._test_compare(ge_usecase)
+
+    def test_lt(self):
+        self._test_compare(lt_usecase)
+
+    def test_le(self):
+        self._test_compare(le_usecase)
+
+
+class TestNamedTuple(TestCase, MemoryLeakMixin):
+
+    def test_unpack(self):
+        def check(p):
+            for pyfunc in tuple_first, tuple_second:
+                cfunc = jit(nopython=True)(pyfunc)
+                self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check(Rect(4, 5))
+        # Heterogeneous
+        check(Rect(4, 5.5))
+
+    def test_len(self):
+        def check(p):
+            pyfunc = len_usecase
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check(Rect(4, 5))
+        check(Point(4, 5, 6))
+        # Heterogeneous
+        check(Rect(4, 5.5))
+        check(Point(4, 5.5, 6j))
+
+    def test_index(self):
+        pyfunc = tuple_index
+        cfunc = jit(nopython=True)(pyfunc)
+
+        p = Point(4, 5, 6)
+        for i in range(len(p)):
+            self.assertPreciseEqual(cfunc(p, i), pyfunc(p, i))
+
+        # test uintp indexing (because, e.g., parfor generates unsigned prange)
+        for i in range(len(p)):
+            self.assertPreciseEqual(cfunc(p, types.uintp(i)), pyfunc(p, i))
+
+    def test_bool(self):
+        def check(p):
+            pyfunc = bool_usecase
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check(Rect(4, 5))
+        # Heterogeneous
+        check(Rect(4, 5.5))
+        check(Empty())
+
+    def _test_compare(self, pyfunc):
+        def eq(pyfunc, cfunc, args):
+            self.assertIs(cfunc(*args), pyfunc(*args),
+                          "mismatch for arguments %s" % (args,))
+
+        cfunc = jit(nopython=True)(pyfunc)
+
+        # Same-sized named tuples
+        for a, b in [((4, 5), (4, 5)),
+                     ((4, 5), (4, 6)),
+                     ((4, 6), (4, 5)),
+                     ((4, 5), (5, 4))]:
+            eq(pyfunc, cfunc, (Rect(*a), Rect(*b)))
+
+        # Different-sized named tuples
+        for a, b in [((4, 5), (4, 5, 6)),
+                     ((4, 5), (4, 4, 6)),
+                     ((4, 5), (4, 6, 7))]:
+            eq(pyfunc, cfunc, (Rect(*a), Point(*b)))
+
+    def test_eq(self):
+        self._test_compare(eq_usecase)
+
+    def test_ne(self):
+        self._test_compare(ne_usecase)
+
+    def test_gt(self):
+        self._test_compare(gt_usecase)
+
+    def test_ge(self):
+        self._test_compare(ge_usecase)
+
+    def test_lt(self):
+        self._test_compare(lt_usecase)
+
+    def test_le(self):
+        self._test_compare(le_usecase)
+
+    def test_getattr(self):
+        pyfunc = getattr_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for args in (4, 5, 6), (4, 5.5, 6j):
+            p = Point(*args)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+    def test_construct(self):
+        def check(pyfunc):
+            cfunc = jit(nopython=True)(pyfunc)
+            for args in (4, 5, 6), (4, 5.5, 6j):
+                expected = pyfunc(*args)
+                got = cfunc(*args)
+                self.assertIs(type(got), type(expected))
+                self.assertPreciseEqual(got, expected)
+
+        check(make_point)
+        check(make_point_kws)
+
+    def test_type(self):
+        # Test the type() built-in on named tuples
+        pyfunc = type_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+
+        arg_tuples = [(4, 5, 6), (4, 5.5, 6j)]
+        for tup_args, args in itertools.product(arg_tuples, arg_tuples):
+            tup = Point(*tup_args)
+            expected = pyfunc(tup, *args)
+            got = cfunc(tup, *args)
+            self.assertIs(type(got), type(expected))
+            self.assertPreciseEqual(got, expected)
+
+    def test_literal_unification(self):
+        # Test for #3565.
+        @jit(nopython=True)
+        def Data1(value):
+            return Rect(value, -321)
+
+        @jit(nopython=True)
+        def call(i, j):
+            if j == 0:
+                # In the error, `result` is typed to `Rect(int, LiteralInt)`
+                # because of the `-321` literal.  This doesn't match the
+                # `result` type in the other branch.
+                result = Data1(i)
+            else:
+                # `result` is typed to be `Rect(int, int)`
+                result = Rect(i, j)
+            return result
+
+        r = call(123, 1321)
+        self.assertEqual(r, Rect(width=123, height=1321))
+        r = call(123, 0)
+        self.assertEqual(r, Rect(width=123, height=-321))
+
+    def test_string_literal_in_ctor(self):
+        # Test for issue #3813
+
+        @jit(nopython=True)
+        def foo():
+            return Rect(10, 'somestring')
+
+        r = foo()
+        self.assertEqual(r, Rect(width=10, height='somestring'))
+
+    def test_dispatcher_mistreat(self):
+        # Test for issue #5215 that mistreat namedtuple as tuples
+        @jit(nopython=True)
+        def foo(x):
+            return x
+
+        in1 = (1, 2,  3)
+        out1 = foo(in1)
+        self.assertEqual(in1, out1)
+
+        in2 = Point(1, 2, 3)
+        out2 = foo(in2)
+        self.assertEqual(in2, out2)
+
+        # Check the signatures
+        self.assertEqual(len(foo.nopython_signatures), 2)
+        self.assertEqual(foo.nopython_signatures[0].args[0], typeof(in1))
+        self.assertEqual(foo.nopython_signatures[1].args[0], typeof(in2))
+
+        # Differently named
+        in3 = Point2(1, 2, 3)
+        out3 = foo(in3)
+        self.assertEqual(in3, out3)
+        self.assertEqual(len(foo.nopython_signatures), 3)
+        self.assertEqual(foo.nopython_signatures[2].args[0], typeof(in3))
+
+
+class TestTupleNRT(TestCase, MemoryLeakMixin):
+    def test_tuple_add(self):
+        def pyfunc(x):
+            a = np.arange(3)
+            return (a,) + (x,)
+
+        cfunc = jit(nopython=True)(pyfunc)
+        x = 123
+        expect_a, expect_x = pyfunc(x)
+        got_a, got_x = cfunc(x)
+        np.testing.assert_equal(got_a, expect_a)
+        self.assertEqual(got_x, expect_x)
+
+
+class TestNamedTupleNRT(TestCase, MemoryLeakMixin):
+
+    def test_return(self):
+        # Check returning a namedtuple with a list inside it
+        pyfunc = make_point_nrt
+        cfunc = jit(nopython=True)(pyfunc)
+
+        for arg in (3, 0):
+            expected = pyfunc(arg)
+            got = cfunc(arg)
+            self.assertIs(type(got), type(expected))
+            self.assertPreciseEqual(got, expected)
+
+
+class TestConversions(TestCase):
+    """
+    Test implicit conversions between tuple types.
+    """
+
+    def check_conversion(self, fromty, toty, val):
+        pyfunc = identity
+        cfunc = njit(toty(fromty))(pyfunc)
+        res = cfunc(val)
+        self.assertEqual(res, val)
+
+    def test_conversions(self):
+        check = self.check_conversion
+        fromty = types.UniTuple(types.int32, 2)
+        check(fromty, types.UniTuple(types.float32, 2), (4, 5))
+        check(fromty, types.Tuple((types.float32, types.int16)), (4, 5))
+        aty = types.UniTuple(types.int32, 0)
+        bty = types.Tuple(())
+        check(aty, bty, ())
+        check(bty, aty, ())
+
+        with self.assertRaises(errors.TypingError) as raises:
+            check(fromty, types.Tuple((types.float32,)), (4, 5))
+        msg = "No conversion from UniTuple(int32 x 2) to UniTuple(float32 x 1)"
+        self.assertIn(msg, str(raises.exception))
+
+
+class TestMethods(TestCase):
+
+    def test_index(self):
+        pyfunc = index_method_usecase
+        cfunc = jit(nopython=True)(pyfunc)
+        self.assertEqual(cfunc((1, 2, 3), 2), 1)
+
+        with self.assertRaises(ValueError) as raises:
+            cfunc((1, 2, 3), 4)
+        msg = 'tuple.index(x): x not in tuple'
+        self.assertEqual(msg, str(raises.exception))
+
+
+class TestTupleBuild(TestCase):
+
+    def test_build_unpack(self):
+        def check(p):
+            pyfunc = lambda a: (1, *a)
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check((4, 5))
+        # Heterogeneous
+        check((4, 5.5))
+
+    def test_build_unpack_assign_like(self):
+        # see #6534
+        def check(p):
+            pyfunc = lambda a: (*a,)
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check((4, 5))
+        # Heterogeneous
+        check((4, 5.5))
+
+    def test_build_unpack_fail_on_list_assign_like(self):
+        # see #6534
+        def check(p):
+            pyfunc = lambda a: (*a,)
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        with self.assertRaises(errors.TypingError) as raises:
+            check([4, 5])
+
+        # Python 3.9 has a peephole rewrite due to large changes in tuple
+        # unpacking. It results in a tuple + list situation from the above
+        # so the error message reflects that. Catching this specific and
+        # seemingly rare sequence in the peephole rewrite is prohibitively
+        # hard. Should it be reported numerous times, revisit then.
+        msg1 = "No implementation of function"
+        self.assertIn(msg1, str(raises.exception))
+        msg2 = "tuple(reflected list(" # ignore the rest of reflected list
+                                        # part, it's repr is quite volatile.
+        self.assertIn(msg2, str(raises.exception))
+
+    def test_build_unpack_more(self):
+        def check(p):
+            pyfunc = lambda a: (1, *a, (1, 2), *a)
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check((4, 5))
+        # Heterogeneous
+        check((4, 5.5))
+
+    def test_build_unpack_call(self):
+        def check(p):
+            @jit
+            def inner(*args):
+                return args
+            pyfunc = lambda a: inner(1, *a)
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check((4, 5))
+        # Heterogeneous
+        check((4, 5.5))
+
+    def test_build_unpack_call_more(self):
+        def check(p):
+            @jit
+            def inner(*args):
+                return args
+            pyfunc = lambda a: inner(1, *a, *(1, 2), *a)
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        # Homogeneous
+        check((4, 5))
+        # Heterogeneous
+        check((4, 5.5))
+
+    def test_tuple_constructor(self):
+        def check(pyfunc, arg):
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(arg), pyfunc(arg))
+
+        # empty
+        check(lambda _: tuple(), ())
+        # Homogeneous
+        check(lambda a: tuple(a), (4, 5))
+        # Heterogeneous
+        check(lambda a: tuple(a), (4, 5.5))
+
+    def test_many_positional_args_are_one_build_tuple(self):
+        # CPython emits BUILD_LIST/LIST_APPEND/LIST_TO_TUPLE/CALL_FUNCTION_EX
+        # for calls with more than 30 arguments. Coalescing the appends keeps
+        # the IR (and the LLVM generated from it) linear in the argument count
+        # instead of quadratic.
+        from numba.core import compiler, ir
+
+        n_args = 35
+        glbls = {"g": jit(lambda *a: a)}
+        # variables, not constants: CPython builds a constant tuple and a single
+        # LIST_EXTEND for literal arguments, which is not the path under test
+        src = "def f(x):\n    return g(%s)\n" % ", ".join(["x"] * n_args)
+        exec(src, glbls)
+
+        func_ir = compiler.run_frontend(glbls["f"])
+        build_tuples = [
+            stmt.value
+            for blk in func_ir.blocks.values()
+            for stmt in blk.body
+            if isinstance(stmt, ir.Assign)
+            and isinstance(stmt.value, ir.Expr)
+            and stmt.value.op == "build_tuple"
+        ]
+        # all the appends land in a single build_tuple (the empty list the
+        # peephole starts from may survive as dead code)
+        self.assertEqual(
+            [len(bt.items) for bt in build_tuples if bt.items], [n_args]
+        )
+        binops = [
+            stmt
+            for blk in func_ir.blocks.values()
+            for stmt in blk.body
+            if isinstance(stmt, ir.Assign)
+            and isinstance(stmt.value, ir.Expr)
+            and stmt.value.op == "binop"
+        ]
+        self.assertFalse(binops)
+
+        self.assertPreciseEqual(glbls["f"](3), (3,) * n_args)
+
+    def test_many_positional_args_mixed_with_unpack(self):
+        # runs of appends are coalesced around the extends, order preserved
+        @jit
+        def inner(*args):
+            return args
+
+        n = 33
+        glbls = {"inner": inner}
+        src = (
+            "def f(t, x):\n    return inner(%s, *t, x, *t)\n"
+            % ", ".join(["x"] * n)
+        )
+        exec(src, glbls)
+        pyfunc = glbls["f"]
+        cfunc = jit(pyfunc)
+        for arg in ((4, 5), (4, 5.5)):
+            self.assertPreciseEqual(cfunc(arg, 1), pyfunc(arg, 1))
+
+    def test_unpack_with_predicate_fails(self):
+        # this fails as the list_to_tuple/list_extend peephole bytecode
+        # rewriting needed for Python 3.9+ cannot yet traverse the CFG.
+        @njit
+        def foo():
+            a = (1,)
+            b = (3,2,  4)
+            return (*(b if a[0] else (5, 6)),)
+
+        with self.assertRaises(errors.UnsupportedBytecodeError) as raises:
+            foo()
+        msg = "op_LIST_EXTEND at the start of a block"
+        self.assertIn(msg, str(raises.exception))
+
+    def test_build_unpack_with_calls_in_unpack(self):
+        def check(p):
+            def pyfunc(a):
+                z = [1, 2]
+                return (*a, z.append(3), z.extend(a), np.ones(3)), z
+
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        check((4, 5))
+
+    def test_build_unpack_complicated(self):
+        def check(p):
+            def pyfunc(a):
+                z = [1, 2]
+                return (*a, *(*a, a), *(a, (*(a, (1, 2), *(3,), *a),
+                        (a, 1, (2, 3), *a, 1), (1,))),
+                        *(z.append(4), z.extend(a))), z
+
+            cfunc = jit(nopython=True)(pyfunc)
+            self.assertPreciseEqual(cfunc(p), pyfunc(p))
+
+        check((10, 20))
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/shared/package-files/09/0920e485d9bcec5089cc33d267b9114aeaee2da5090853bafa0ae75c902884f8/_posixsubprocess.pyi b/shared/package-files/09/0920e485d9bcec5089cc33d267b9114aeaee2da5090853bafa0ae75c902884f8/_posixsubprocess.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..05209ba05b9bc7d47f9d4d358266c90c8f54844e
--- /dev/null
+++ b/shared/package-files/09/0920e485d9bcec5089cc33d267b9114aeaee2da5090853bafa0ae75c902884f8/_posixsubprocess.pyi
@@ -0,0 +1,24 @@
+# NOTE: These are incomplete!
+
+from typing import Callable, Sequence, Tuple
+
+def cloexec_pipe() -> Tuple[int, int]: ...
+def fork_exec(
+    args: Sequence[str],
+    executable_list: Sequence[bytes],
+    close_fds: bool,
+    fds_to_keep: Sequence[int],
+    cwd: str,
+    env_list: Sequence[bytes],
+    p2cread: int,
+    p2cwrite: int,
+    c2pred: int,
+    c2pwrite: int,
+    errread: int,
+    errwrite: int,
+    errpipe_read: int,
+    errpipe_write: int,
+    restore_signals: int,
+    start_new_session: int,
+    preexec_fn: Callable[[], None],
+) -> int: ...
diff --git a/shared/package-files/09/092508d08fd6e88f5f37e9dbeab1a0ccb8c63083b40ba3b03a6cd8cfe1c44195/literals.pyi b/shared/package-files/09/092508d08fd6e88f5f37e9dbeab1a0ccb8c63083b40ba3b03a6cd8cfe1c44195/literals.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..160d6fd76f76dfe0b04aee3d478f05f68a22a195
--- /dev/null
+++ b/shared/package-files/09/092508d08fd6e88f5f37e9dbeab1a0ccb8c63083b40ba3b03a6cd8cfe1c44195/literals.pyi
@@ -0,0 +1,7 @@
+from typing import Dict, Match, Text
+
+simple_escapes: Dict[Text, Text]
+
+def escape(m: Match[str]) -> Text: ...
+def evalString(s: Text) -> Text: ...
+def test() -> None: ...
diff --git a/shared/package-files/09/0926566f9f406178d1214f8cc796e166b1213dd7c05e0c5b461a8b8ac9e50bbe/COPYING b/shared/package-files/09/0926566f9f406178d1214f8cc796e166b1213dd7c05e0c5b461a8b8ac9e50bbe/COPYING
new file mode 100644
index 0000000000000000000000000000000000000000..e87667e1b8c178e53c6a7c6268ebc09ab4b0476c
--- /dev/null
+++ b/shared/package-files/09/0926566f9f406178d1214f8cc796e166b1213dd7c05e0c5b461a8b8ac9e50bbe/COPYING
@@ -0,0 +1,45 @@
+
+BSD Software License
+
+Pertains to ARPACK and P_ARPACK
+
+Copyright (c) 1996-2008 Rice University.
+Developed by D.C. Sorensen, R.B. Lehoucq, C. Yang, and K. Maschhoff.
+All rights reserved.
+
+Arpack has been renamed to arpack-ng.
+
+Copyright (c) 2001-2011 - Scilab Enterprises
+Updated by Allan Cornet, Sylvestre Ledru.
+
+Copyright (c) 2010 - Jordi Gutiérrez Hermoso (Octave patch)
+
+Copyright (c) 2007 - Sébastien Fabbro (gentoo patch)
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+- Redistributions of source code must retain the above copyright
+  notice, this list of conditions and the following disclaimer.
+
+- Redistributions in binary form must reproduce the above copyright
+  notice, this list of conditions and the following disclaimer listed
+  in this license in the documentation and/or other materials
+  provided with the distribution.
+
+- Neither the name of the copyright holders nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/shared/package-files/09/0927ec7cc34edb5670920cb2ff1542e46de27a2010746e1662f4276cf3569a24/longley.csv b/shared/package-files/09/0927ec7cc34edb5670920cb2ff1542e46de27a2010746e1662f4276cf3569a24/longley.csv
new file mode 100644
index 0000000000000000000000000000000000000000..0661f2682e6f31e60ef0483f198d9b72df9e3962
--- /dev/null
+++ b/shared/package-files/09/0927ec7cc34edb5670920cb2ff1542e46de27a2010746e1662f4276cf3569a24/longley.csv
@@ -0,0 +1,17 @@
+"Obs","TOTEMP","GNPDEFL","GNP","UNEMP","ARMED","POP","YEAR"
+1,60323,83,234289,2356,1590,107608,1947
+2,61122,88.5,259426,2325,1456,108632,1948
+3,60171,88.2,258054,3682,1616,109773,1949
+4,61187,89.5,284599,3351,1650,110929,1950
+5,63221,96.2,328975,2099,3099,112075,1951
+6,63639,98.1,346999,1932,3594,113270,1952
+7,64989,99,365385,1870,3547,115094,1953
+8,63761,100,363112,3578,3350,116219,1954
+9,66019,101.2,397469,2904,3048,117388,1955
+10,67857,104.6,419180,2822,2857,118734,1956
+11,68169,108.4,442769,2936,2798,120445,1957
+12,66513,110.8,444546,4681,2637,121950,1958
+13,68655,112.6,482704,3813,2552,123366,1959
+14,69564,114.2,502601,3931,2514,125368,1960
+15,69331,115.7,518173,4806,2572,127852,1961
+16,70551,116.9,554894,4007,2827,130081,1962
diff --git a/shared/package-files/09/09283c5fe86aebeea35bcbe48ba6957c415845419358e839f2c8a53355cd5db5/test_regression.py b/shared/package-files/09/09283c5fe86aebeea35bcbe48ba6957c415845419358e839f2c8a53355cd5db5/test_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..44916ba96293db19756b8222422e76945aa48ebb
--- /dev/null
+++ b/shared/package-files/09/09283c5fe86aebeea35bcbe48ba6957c415845419358e839f2c8a53355cd5db5/test_regression.py
@@ -0,0 +1,40 @@
+"""Regression tests for optimize.
+
+"""
+import numpy as np
+from numpy.testing import assert_almost_equal
+from pytest import raises as assert_raises
+
+import scipy.optimize
+
+
+class TestRegression:
+
+    def test_newton_x0_is_0(self):
+        # Regression test for gh-1601
+        tgt = 1
+        res = scipy.optimize.newton(lambda x: x - 1, 0)
+        assert_almost_equal(res, tgt)
+
+    def test_newton_integers(self):
+        # Regression test for gh-1741
+        root = scipy.optimize.newton(lambda x: x**2 - 1, x0=2,
+                                    fprime=lambda x: 2*x)
+        assert_almost_equal(root, 1.0)
+
+    def test_lmdif_errmsg(self):
+        # This shouldn't cause a crash on Python 3
+        class SomeError(Exception):
+            pass
+        counter = [0]
+
+        def func(x):
+            counter[0] += 1
+            if counter[0] < 3:
+                return x**2 - np.array([9, 10, 11])
+            else:
+                raise SomeError()
+        assert_raises(SomeError,
+                      scipy.optimize.leastsq,
+                      func, [1, 2, 3])
+
diff --git a/shared/package-files/09/0929b63f53e2dfb1d716ff4578585f8db35bcd6dc6884e41f9cf1fe0254771e7/VariableType.h b/shared/package-files/09/0929b63f53e2dfb1d716ff4578585f8db35bcd6dc6884e41f9cf1fe0254771e7/VariableType.h
new file mode 100644
index 0000000000000000000000000000000000000000..065812694cfe4e17623d5fa0464b8ea5f0199a6a
--- /dev/null
+++ b/shared/package-files/09/0929b63f53e2dfb1d716ff4578585f8db35bcd6dc6884e41f9cf1fe0254771e7/VariableType.h
@@ -0,0 +1,59 @@
+#pragma once
+
+// ${generated_comment}
+
+#include 
+#include 
+
+#include 
+
+#include 
+#include 
+
+#include  // for size_t
+#include  // for function
+#include  // for unique_ptr
+#include 
+#include 
+
+namespace at {
+  struct Quantizer;
+};
+
+namespace torch { namespace autograd {
+
+using Variable = at::Tensor;
+using at::Context;
+using at::Device;
+using at::Dimname;
+using at::DimnameList;
+using at::Generator;
+using at::IntArrayRef;
+using at::MemoryFormat;
+using at::QScheme;
+using at::Scalar;
+using at::ScalarType;
+using at::Storage;
+using at::Tensor;
+using at::TensorList;
+using at::TensorOptions;
+using at::Quantizer;
+// This is temporary typedef to enable Quantizer in aten native function API
+// we'll remove them when we are actually exposing Quantizer class
+// to frontend
+using ConstQuantizerPtr = const c10::intrusive_ptr&;
+using c10::optional;
+
+namespace VariableType {
+  TORCH_API std::vector allCUDATypes();
+  TORCH_API std::vector allXPUTypes();
+  TORCH_API std::vector allCPUTypes();
+  TORCH_API std::vector allPrivateUser1Types();
+
+  at::Tensor & unpack(Tensor & t, const char * name, int pos);
+  const at::Tensor & unpack(const Tensor & t, const char * name, int pos);
+  at::Tensor unpack_opt(const Tensor & t, const char * name, int pos);
+  std::vector unpack(const at::ITensorListRef& tl, const char *name, int pos);
+};
+
+}} // namespace torch::autograd
diff --git a/shared/package-files/09/0929d7f0f99fd683c29ddee3edb9f5fdfe7c1bd28736201b96f549e73ca437e0/wheel.py b/shared/package-files/09/0929d7f0f99fd683c29ddee3edb9f5fdfe7c1bd28736201b96f549e73ca437e0/wheel.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed578aa2500d8917d5d3ed1249526b48ad7ee996
--- /dev/null
+++ b/shared/package-files/09/0929d7f0f99fd683c29ddee3edb9f5fdfe7c1bd28736201b96f549e73ca437e0/wheel.py
@@ -0,0 +1,183 @@
+import logging
+import os
+import shutil
+from optparse import Values
+from typing import List
+
+from pip._internal.cache import WheelCache
+from pip._internal.cli import cmdoptions
+from pip._internal.cli.req_command import RequirementCommand, with_cleanup
+from pip._internal.cli.status_codes import SUCCESS
+from pip._internal.exceptions import CommandError
+from pip._internal.operations.build.build_tracker import get_build_tracker
+from pip._internal.req.req_install import (
+    InstallRequirement,
+    check_legacy_setup_py_options,
+)
+from pip._internal.utils.misc import ensure_dir, normalize_path
+from pip._internal.utils.temp_dir import TempDirectory
+from pip._internal.wheel_builder import build, should_build_for_wheel_command
+
+logger = logging.getLogger(__name__)
+
+
+class WheelCommand(RequirementCommand):
+    """
+    Build Wheel archives for your requirements and dependencies.
+
+    Wheel is a built-package format, and offers the advantage of not
+    recompiling your software during every install. For more details, see the
+    wheel docs: https://wheel.readthedocs.io/en/latest/
+
+    'pip wheel' uses the build system interface as described here:
+    https://pip.pypa.io/en/stable/reference/build-system/
+
+    """
+
+    usage = """
+      %prog [options]  ...
+      %prog [options] -r  ...
+      %prog [options] [-e]  ...
+      %prog [options] [-e]  ...
+      %prog [options]  ..."""
+
+    def add_options(self) -> None:
+        self.cmd_opts.add_option(
+            "-w",
+            "--wheel-dir",
+            dest="wheel_dir",
+            metavar="dir",
+            default=os.curdir,
+            help=(
+                "Build wheels into , where the default is the "
+                "current working directory."
+            ),
+        )
+        self.cmd_opts.add_option(cmdoptions.no_binary())
+        self.cmd_opts.add_option(cmdoptions.only_binary())
+        self.cmd_opts.add_option(cmdoptions.prefer_binary())
+        self.cmd_opts.add_option(cmdoptions.no_build_isolation())
+        self.cmd_opts.add_option(cmdoptions.use_pep517())
+        self.cmd_opts.add_option(cmdoptions.no_use_pep517())
+        self.cmd_opts.add_option(cmdoptions.check_build_deps())
+        self.cmd_opts.add_option(cmdoptions.constraints())
+        self.cmd_opts.add_option(cmdoptions.editable())
+        self.cmd_opts.add_option(cmdoptions.requirements())
+        self.cmd_opts.add_option(cmdoptions.src())
+        self.cmd_opts.add_option(cmdoptions.ignore_requires_python())
+        self.cmd_opts.add_option(cmdoptions.no_deps())
+        self.cmd_opts.add_option(cmdoptions.progress_bar())
+
+        self.cmd_opts.add_option(
+            "--no-verify",
+            dest="no_verify",
+            action="store_true",
+            default=False,
+            help="Don't verify if built wheel is valid.",
+        )
+
+        self.cmd_opts.add_option(cmdoptions.config_settings())
+        self.cmd_opts.add_option(cmdoptions.build_options())
+        self.cmd_opts.add_option(cmdoptions.global_options())
+
+        self.cmd_opts.add_option(
+            "--pre",
+            action="store_true",
+            default=False,
+            help=(
+                "Include pre-release and development versions. By default, "
+                "pip only finds stable versions."
+            ),
+        )
+
+        self.cmd_opts.add_option(cmdoptions.require_hashes())
+
+        index_opts = cmdoptions.make_option_group(
+            cmdoptions.index_group,
+            self.parser,
+        )
+
+        self.parser.insert_option_group(0, index_opts)
+        self.parser.insert_option_group(0, self.cmd_opts)
+
+    @with_cleanup
+    def run(self, options: Values, args: List[str]) -> int:
+        session = self.get_default_session(options)
+
+        finder = self._build_package_finder(options, session)
+
+        options.wheel_dir = normalize_path(options.wheel_dir)
+        ensure_dir(options.wheel_dir)
+
+        build_tracker = self.enter_context(get_build_tracker())
+
+        directory = TempDirectory(
+            delete=not options.no_clean,
+            kind="wheel",
+            globally_managed=True,
+        )
+
+        reqs = self.get_requirements(args, options, finder, session)
+        check_legacy_setup_py_options(options, reqs)
+
+        wheel_cache = WheelCache(options.cache_dir)
+
+        preparer = self.make_requirement_preparer(
+            temp_build_dir=directory,
+            options=options,
+            build_tracker=build_tracker,
+            session=session,
+            finder=finder,
+            download_dir=options.wheel_dir,
+            use_user_site=False,
+            verbosity=self.verbosity,
+        )
+
+        resolver = self.make_resolver(
+            preparer=preparer,
+            finder=finder,
+            options=options,
+            wheel_cache=wheel_cache,
+            ignore_requires_python=options.ignore_requires_python,
+            use_pep517=options.use_pep517,
+        )
+
+        self.trace_basic_info(finder)
+
+        requirement_set = resolver.resolve(reqs, check_supported_wheels=True)
+
+        reqs_to_build: List[InstallRequirement] = []
+        for req in requirement_set.requirements.values():
+            if req.is_wheel:
+                preparer.save_linked_requirement(req)
+            elif should_build_for_wheel_command(req):
+                reqs_to_build.append(req)
+
+        preparer.prepare_linked_requirements_more(requirement_set.requirements.values())
+        requirement_set.warn_legacy_versions_and_specifiers()
+
+        # build wheels
+        build_successes, build_failures = build(
+            reqs_to_build,
+            wheel_cache=wheel_cache,
+            verify=(not options.no_verify),
+            build_options=options.build_options or [],
+            global_options=options.global_options or [],
+        )
+        for req in build_successes:
+            assert req.link and req.link.is_wheel
+            assert req.local_file_path
+            # copy from cache to target directory
+            try:
+                shutil.copy(req.local_file_path, options.wheel_dir)
+            except OSError as e:
+                logger.warning(
+                    "Building wheel for %s failed: %s",
+                    req.name,
+                    e,
+                )
+                build_failures.append(req)
+        if len(build_failures) != 0:
+            raise CommandError("Failed to build one or more wheels")
+
+        return SUCCESS
diff --git a/shared/package-files/09/092ac789ee7dfcd8cd67d07619a1f328f36c333c66e4a87a1aec65624f334ee2/test_singularityfunctions.py b/shared/package-files/09/092ac789ee7dfcd8cd67d07619a1f328f36c333c66e4a87a1aec65624f334ee2/test_singularityfunctions.py
new file mode 100644
index 0000000000000000000000000000000000000000..587e5f104cbf095f851ec538601ca146377b51ae
--- /dev/null
+++ b/shared/package-files/09/092ac789ee7dfcd8cd67d07619a1f328f36c333c66e4a87a1aec65624f334ee2/test_singularityfunctions.py
@@ -0,0 +1,22 @@
+from sympy.integrals.singularityfunctions import singularityintegrate
+from sympy.core.function import Function
+from sympy.core.symbol import symbols
+from sympy.functions.special.singularity_functions import SingularityFunction
+
+x, a, n, y = symbols('x a n y')
+f = Function('f')
+
+
+def test_singularityintegrate():
+    assert singularityintegrate(x, x) is None
+    assert singularityintegrate(x + SingularityFunction(x, 9, 1), x) is None
+
+    assert 4*singularityintegrate(SingularityFunction(x, a, 3), x) == 4*SingularityFunction(x, a, 4)/4
+    assert singularityintegrate(5*SingularityFunction(x, 5, -2), x) == 5*SingularityFunction(x, 5, -1)
+    assert singularityintegrate(6*SingularityFunction(x, 5, -1), x) == 6*SingularityFunction(x, 5, 0)
+    assert singularityintegrate(x*SingularityFunction(x, 0, -1), x) == 0
+    assert singularityintegrate((x - 5)*SingularityFunction(x, 5, -1), x) == 0
+    assert singularityintegrate(SingularityFunction(x, 0, -1) * f(x), x) == f(0) * SingularityFunction(x, 0, 0)
+    assert singularityintegrate(SingularityFunction(x, 1, -1) * f(x), x) == f(1) * SingularityFunction(x, 1, 0)
+    assert singularityintegrate(y*SingularityFunction(x, 0, -1)**2, x) == \
+        y*SingularityFunction(0, 0, -1)*SingularityFunction(x, 0, 0)
diff --git a/shared/package-files/09/092c55c8d143ede58245b8d0e5c3b44bf4d4328707a86312614542073abeeaf9/logit_backward_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/09/092c55c8d143ede58245b8d0e5c3b44bf4d4328707a86312614542073abeeaf9/logit_backward_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..9cdebee676db088e052c2c9d2f4c66ef96189cb6
--- /dev/null
+++ b/shared/package-files/09/092c55c8d143ede58245b8d0e5c3b44bf4d4328707a86312614542073abeeaf9/logit_backward_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor logit_backward(const at::Tensor & grad_output, const at::Tensor & self, c10::optional eps=c10::nullopt);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/09/092e0bf966c8d99b962e06c6013b45e35fbe87488691fb84e9d2b90d86220b98/test_precedence.py b/shared/package-files/09/092e0bf966c8d99b962e06c6013b45e35fbe87488691fb84e9d2b90d86220b98/test_precedence.py
new file mode 100644
index 0000000000000000000000000000000000000000..372a5b0356b7a7473ecf595df45ae31c3bfaff71
--- /dev/null
+++ b/shared/package-files/09/092e0bf966c8d99b962e06c6013b45e35fbe87488691fb84e9d2b90d86220b98/test_precedence.py
@@ -0,0 +1,89 @@
+from sympy.concrete.products import Product
+from sympy.concrete.summations import Sum
+from sympy.core.function import Derivative
+from sympy.core.numbers import Integer, Rational, Float, oo
+from sympy.core.relational import Rel
+from sympy.core.symbol import symbols
+from sympy.functions import sin
+from sympy.integrals.integrals import Integral
+from sympy.series.order import Order
+
+from sympy.printing.precedence import precedence, PRECEDENCE
+
+x, y = symbols("x,y")
+
+
+def test_Add():
+    assert precedence(x + y) == PRECEDENCE["Add"]
+    assert precedence(x*y + 1) == PRECEDENCE["Add"]
+
+
+def test_Function():
+    assert precedence(sin(x)) == PRECEDENCE["Func"]
+
+def test_Derivative():
+    assert precedence(Derivative(x, y)) == PRECEDENCE["Atom"]
+
+def test_Integral():
+    assert precedence(Integral(x, y)) == PRECEDENCE["Atom"]
+
+
+def test_Mul():
+    assert precedence(x*y) == PRECEDENCE["Mul"]
+    assert precedence(-x*y) == PRECEDENCE["Add"]
+
+
+def test_Number():
+    assert precedence(Integer(0)) == PRECEDENCE["Atom"]
+    assert precedence(Integer(1)) == PRECEDENCE["Atom"]
+    assert precedence(Integer(-1)) == PRECEDENCE["Add"]
+    assert precedence(Integer(10)) == PRECEDENCE["Atom"]
+    assert precedence(Rational(5, 2)) == PRECEDENCE["Mul"]
+    assert precedence(Rational(-5, 2)) == PRECEDENCE["Add"]
+    assert precedence(Float(5)) == PRECEDENCE["Atom"]
+    assert precedence(Float(-5)) == PRECEDENCE["Add"]
+    assert precedence(oo) == PRECEDENCE["Atom"]
+    assert precedence(-oo) == PRECEDENCE["Add"]
+
+
+def test_Order():
+    assert precedence(Order(x)) == PRECEDENCE["Atom"]
+
+
+def test_Pow():
+    assert precedence(x**y) == PRECEDENCE["Pow"]
+    assert precedence(-x**y) == PRECEDENCE["Add"]
+    assert precedence(x**-y) == PRECEDENCE["Pow"]
+
+
+def test_Product():
+    assert precedence(Product(x, (x, y, y + 1))) == PRECEDENCE["Atom"]
+
+
+def test_Relational():
+    assert precedence(Rel(x + y, y, "<")) == PRECEDENCE["Relational"]
+
+
+def test_Sum():
+    assert precedence(Sum(x, (x, y, y + 1))) == PRECEDENCE["Atom"]
+
+
+def test_Symbol():
+    assert precedence(x) == PRECEDENCE["Atom"]
+
+
+def test_And_Or():
+    # precedence relations between logical operators, ...
+    assert precedence(x & y) > precedence(x | y)
+    assert precedence(~y) > precedence(x & y)
+    # ... and with other operators (cfr. other programming languages)
+    assert precedence(x + y) > precedence(x | y)
+    assert precedence(x + y) > precedence(x & y)
+    assert precedence(x*y) > precedence(x | y)
+    assert precedence(x*y) > precedence(x & y)
+    assert precedence(~y) > precedence(x*y)
+    assert precedence(~y) > precedence(x - y)
+    # double checks
+    assert precedence(x & y) == PRECEDENCE["And"]
+    assert precedence(x | y) == PRECEDENCE["Or"]
+    assert precedence(~y) == PRECEDENCE["Not"]
diff --git a/shared/package-files/09/092f0da64648f18f2dcb4345402ec665c525339a3cff501bbf519b1a5b6c6e40/matrix.h b/shared/package-files/09/092f0da64648f18f2dcb4345402ec665c525339a3cff501bbf519b1a5b6c6e40/matrix.h
new file mode 100644
index 0000000000000000000000000000000000000000..8d4342f81bb372105dbc5a3511bb2159da2e3129
--- /dev/null
+++ b/shared/package-files/09/092f0da64648f18f2dcb4345402ec665c525339a3cff501bbf519b1a5b6c6e40/matrix.h
@@ -0,0 +1,714 @@
+/*
+    pybind11/eigen/matrix.h: Transparent conversion for dense and sparse Eigen matrices
+
+    Copyright (c) 2016 Wenzel Jakob 
+
+    All rights reserved. Use of this source code is governed by a
+    BSD-style license that can be found in the LICENSE file.
+*/
+
+#pragma once
+
+#include "../numpy.h"
+#include "common.h"
+
+/* HINT: To suppress warnings originating from the Eigen headers, use -isystem.
+   See also:
+       https://stackoverflow.com/questions/2579576/i-dir-vs-isystem-dir
+       https://stackoverflow.com/questions/1741816/isystem-for-ms-visual-studio-c-compiler
+*/
+PYBIND11_WARNING_PUSH
+PYBIND11_WARNING_DISABLE_MSVC(5054) // https://github.com/pybind/pybind11/pull/3741
+//       C5054: operator '&': deprecated between enumerations of different types
+#if defined(__MINGW32__)
+PYBIND11_WARNING_DISABLE_GCC("-Wmaybe-uninitialized")
+#endif
+
+#include 
+#include 
+
+PYBIND11_WARNING_POP
+
+// Eigen prior to 3.2.7 doesn't have proper move constructors--but worse, some classes get implicit
+// move constructors that break things.  We could detect this an explicitly copy, but an extra copy
+// of matrices seems highly undesirable.
+static_assert(EIGEN_VERSION_AT_LEAST(3, 2, 7),
+              "Eigen matrix support in pybind11 requires Eigen >= 3.2.7");
+
+PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
+
+PYBIND11_WARNING_DISABLE_MSVC(4127)
+
+// Provide a convenience alias for easier pass-by-ref usage with fully dynamic strides:
+using EigenDStride = Eigen::Stride;
+template 
+using EigenDRef = Eigen::Ref;
+template 
+using EigenDMap = Eigen::Map;
+
+PYBIND11_NAMESPACE_BEGIN(detail)
+
+#if EIGEN_VERSION_AT_LEAST(3, 3, 0)
+using EigenIndex = Eigen::Index;
+template 
+using EigenMapSparseMatrix = Eigen::Map>;
+#else
+using EigenIndex = EIGEN_DEFAULT_DENSE_INDEX_TYPE;
+template 
+using EigenMapSparseMatrix = Eigen::MappedSparseMatrix;
+#endif
+
+// Matches Eigen::Map, Eigen::Ref, blocks, etc:
+template 
+using is_eigen_dense_map = all_of,
+                                  std::is_base_of, T>>;
+template 
+using is_eigen_mutable_map = std::is_base_of, T>;
+template 
+using is_eigen_dense_plain
+    = all_of>, is_template_base_of>;
+template 
+using is_eigen_sparse = is_template_base_of;
+// Test for objects inheriting from EigenBase that aren't captured by the above.  This
+// basically covers anything that can be assigned to a dense matrix but that don't have a typical
+// matrix data layout that can be copied from their .data().  For example, DiagonalMatrix and
+// SelfAdjointView fall into this category.
+template 
+using is_eigen_other
+    = all_of,
+             negation, is_eigen_dense_plain, is_eigen_sparse>>>;
+
+// Captures numpy/eigen conformability status (returned by EigenProps::conformable()):
+template 
+struct EigenConformable {
+    bool conformable = false;
+    EigenIndex rows = 0, cols = 0;
+    EigenDStride stride{0, 0};    // Only valid if negativestrides is false!
+    bool negativestrides = false; // If true, do not use stride!
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    EigenConformable(bool fits = false) : conformable{fits} {}
+    // Matrix type:
+    EigenConformable(EigenIndex r, EigenIndex c, EigenIndex rstride, EigenIndex cstride)
+        : conformable{true}, rows{r}, cols{c},
+          // TODO: when Eigen bug #747 is fixed, remove the tests for non-negativity.
+          // http://eigen.tuxfamily.org/bz/show_bug.cgi?id=747
+          stride{EigenRowMajor ? (rstride > 0 ? rstride : 0)
+                               : (cstride > 0 ? cstride : 0) /* outer stride */,
+                 EigenRowMajor ? (cstride > 0 ? cstride : 0)
+                               : (rstride > 0 ? rstride : 0) /* inner stride */},
+          negativestrides{rstride < 0 || cstride < 0} {}
+    // Vector type:
+    EigenConformable(EigenIndex r, EigenIndex c, EigenIndex stride)
+        : EigenConformable(r, c, r == 1 ? c * stride : stride, c == 1 ? r : r * stride) {}
+
+    template 
+    bool stride_compatible() const {
+        // To have compatible strides, we need (on both dimensions) one of fully dynamic strides,
+        // matching strides, or a dimension size of 1 (in which case the stride value is
+        // irrelevant). Alternatively, if any dimension size is 0, the strides are not relevant
+        // (and numpy ≥ 1.23 sets the strides to 0 in that case, so we need to check explicitly).
+        if (negativestrides) {
+            return false;
+        }
+        if (rows == 0 || cols == 0) {
+            return true;
+        }
+        return (props::inner_stride == Eigen::Dynamic || props::inner_stride == stride.inner()
+                || (EigenRowMajor ? cols : rows) == 1)
+               && (props::outer_stride == Eigen::Dynamic || props::outer_stride == stride.outer()
+                   || (EigenRowMajor ? rows : cols) == 1);
+    }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator bool() const { return conformable; }
+};
+
+template 
+struct eigen_extract_stride {
+    using type = Type;
+};
+template 
+struct eigen_extract_stride> {
+    using type = StrideType;
+};
+template 
+struct eigen_extract_stride> {
+    using type = StrideType;
+};
+
+// Helper struct for extracting information from an Eigen type
+template 
+struct EigenProps {
+    using Type = Type_;
+    using Scalar = typename Type::Scalar;
+    using StrideType = typename eigen_extract_stride::type;
+    static constexpr EigenIndex rows = Type::RowsAtCompileTime, cols = Type::ColsAtCompileTime,
+                                size = Type::SizeAtCompileTime;
+    static constexpr bool row_major = Type::IsRowMajor,
+                          vector
+                          = Type::IsVectorAtCompileTime, // At least one dimension has fixed size 1
+        fixed_rows = rows != Eigen::Dynamic, fixed_cols = cols != Eigen::Dynamic,
+                          fixed = size != Eigen::Dynamic, // Fully-fixed size
+        dynamic = !fixed_rows && !fixed_cols;             // Fully-dynamic size
+
+    template 
+    using if_zero = std::integral_constant;
+    static constexpr EigenIndex inner_stride
+        = if_zero::value,
+        outer_stride = if_zero < StrideType::OuterStrideAtCompileTime,
+        vector      ? size
+        : row_major ? cols
+                    : rows > ::value;
+    static constexpr bool dynamic_stride
+        = inner_stride == Eigen::Dynamic && outer_stride == Eigen::Dynamic;
+    static constexpr bool requires_row_major
+        = !dynamic_stride && !vector && (row_major ? inner_stride : outer_stride) == 1;
+    static constexpr bool requires_col_major
+        = !dynamic_stride && !vector && (row_major ? outer_stride : inner_stride) == 1;
+
+    // Takes an input array and determines whether we can make it fit into the Eigen type.  If
+    // the array is a vector, we attempt to fit it into either an Eigen 1xN or Nx1 vector
+    // (preferring the latter if it will fit in either, i.e. for a fully dynamic matrix type).
+    static EigenConformable conformable(const array &a) {
+        const auto dims = a.ndim();
+        if (dims < 1 || dims > 2) {
+            return false;
+        }
+
+        if (dims == 2) { // Matrix type: require exact match (or dynamic)
+
+            EigenIndex np_rows = a.shape(0), np_cols = a.shape(1),
+                       np_rstride = a.strides(0) / static_cast(sizeof(Scalar)),
+                       np_cstride = a.strides(1) / static_cast(sizeof(Scalar));
+            if ((fixed_rows && np_rows != rows) || (fixed_cols && np_cols != cols)) {
+                return false;
+            }
+
+            return {np_rows, np_cols, np_rstride, np_cstride};
+        }
+
+        // Otherwise we're storing an n-vector.  Only one of the strides will be used, but
+        // whichever is used, we want the (single) numpy stride value.
+        const EigenIndex n = a.shape(0),
+                         stride = a.strides(0) / static_cast(sizeof(Scalar));
+
+        if (vector) { // Eigen type is a compile-time vector
+            if (fixed && size != n) {
+                return false; // Vector size mismatch
+            }
+            return {rows == 1 ? 1 : n, cols == 1 ? 1 : n, stride};
+        }
+        if (fixed) {
+            // The type has a fixed size, but is not a vector: abort
+            return false;
+        }
+        if (fixed_cols) {
+            // Since this isn't a vector, cols must be != 1.  We allow this only if it exactly
+            // equals the number of elements (rows is Dynamic, and so 1 row is allowed).
+            if (cols != n) {
+                return false;
+            }
+            return {1, n, stride};
+        } // Otherwise it's either fully dynamic, or column dynamic; both become a column vector
+        if (fixed_rows && rows != n) {
+            return false;
+        }
+        return {n, 1, stride};
+    }
+
+    static constexpr bool show_writeable
+        = is_eigen_dense_map::value && is_eigen_mutable_map::value;
+    static constexpr bool show_order = is_eigen_dense_map::value;
+    static constexpr bool show_c_contiguous = show_order && requires_row_major;
+    static constexpr bool show_f_contiguous
+        = !show_c_contiguous && show_order && requires_col_major;
+
+    static constexpr auto descriptor
+        = const_name("numpy.ndarray[") + npy_format_descriptor::name + const_name("[")
+          + const_name(const_name<(size_t) rows>(), const_name("m")) + const_name(", ")
+          + const_name(const_name<(size_t) cols>(), const_name("n")) + const_name("]")
+          +
+          // For a reference type (e.g. Ref) we have other constraints that might need to
+          // be satisfied: writeable=True (for a mutable reference), and, depending on the map's
+          // stride options, possibly f_contiguous or c_contiguous.  We include them in the
+          // descriptor output to provide some hint as to why a TypeError is occurring (otherwise
+          // it can be confusing to see that a function accepts a 'numpy.ndarray[float64[3,2]]' and
+          // an error message that you *gave* a numpy.ndarray of the right type and dimensions.
+          const_name(", flags.writeable", "")
+          + const_name(", flags.c_contiguous", "")
+          + const_name(", flags.f_contiguous", "") + const_name("]");
+};
+
+// Casts an Eigen type to numpy array.  If given a base, the numpy array references the src data,
+// otherwise it'll make a copy.  writeable lets you turn off the writeable flag for the array.
+template 
+handle
+eigen_array_cast(typename props::Type const &src, handle base = handle(), bool writeable = true) {
+    constexpr ssize_t elem_size = sizeof(typename props::Scalar);
+    array a;
+    if (props::vector) {
+        a = array({src.size()}, {elem_size * src.innerStride()}, src.data(), base);
+    } else {
+        a = array({src.rows(), src.cols()},
+                  {elem_size * src.rowStride(), elem_size * src.colStride()},
+                  src.data(),
+                  base);
+    }
+
+    if (!writeable) {
+        array_proxy(a.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
+    }
+
+    return a.release();
+}
+
+// Takes an lvalue ref to some Eigen type and a (python) base object, creating a numpy array that
+// reference the Eigen object's data with `base` as the python-registered base class (if omitted,
+// the base will be set to None, and lifetime management is up to the caller).  The numpy array is
+// non-writeable if the given type is const.
+template 
+handle eigen_ref_array(Type &src, handle parent = none()) {
+    // none here is to get past array's should-we-copy detection, which currently always
+    // copies when there is no base.  Setting the base to None should be harmless.
+    return eigen_array_cast(src, parent, !std::is_const::value);
+}
+
+// Takes a pointer to some dense, plain Eigen type, builds a capsule around it, then returns a
+// numpy array that references the encapsulated data with a python-side reference to the capsule to
+// tie its destruction to that of any dependent python objects.  Const-ness is determined by
+// whether or not the Type of the pointer given is const.
+template ::value>>
+handle eigen_encapsulate(Type *src) {
+    capsule base(src, [](void *o) { delete static_cast(o); });
+    return eigen_ref_array(*src, base);
+}
+
+// Type caster for regular, dense matrix types (e.g. MatrixXd), but not maps/refs/etc. of dense
+// types.
+template 
+struct type_caster::value>> {
+    using Scalar = typename Type::Scalar;
+    static_assert(!std::is_pointer::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+    using props = EigenProps;
+
+    bool load(handle src, bool convert) {
+        // If we're in no-convert mode, only load if given an array of the correct type
+        if (!convert && !isinstance>(src)) {
+            return false;
+        }
+
+        // Coerce into an array, but don't do type conversion yet; the copy below handles it.
+        auto buf = array::ensure(src);
+
+        if (!buf) {
+            return false;
+        }
+
+        auto dims = buf.ndim();
+        if (dims < 1 || dims > 2) {
+            return false;
+        }
+
+        auto fits = props::conformable(buf);
+        if (!fits) {
+            return false;
+        }
+
+        // Allocate the new type, then build a numpy reference into it
+        value = Type(fits.rows, fits.cols);
+        auto ref = reinterpret_steal(eigen_ref_array(value));
+        if (dims == 1) {
+            ref = ref.squeeze();
+        } else if (ref.ndim() == 1) {
+            buf = buf.squeeze();
+        }
+
+        int result = detail::npy_api::get().PyArray_CopyInto_(ref.ptr(), buf.ptr());
+
+        if (result < 0) { // Copy failed!
+            PyErr_Clear();
+            return false;
+        }
+
+        return true;
+    }
+
+private:
+    // Cast implementation
+    template 
+    static handle cast_impl(CType *src, return_value_policy policy, handle parent) {
+        switch (policy) {
+            case return_value_policy::take_ownership:
+            case return_value_policy::automatic:
+                return eigen_encapsulate(src);
+            case return_value_policy::move:
+                return eigen_encapsulate(new CType(std::move(*src)));
+            case return_value_policy::copy:
+                return eigen_array_cast(*src);
+            case return_value_policy::reference:
+            case return_value_policy::automatic_reference:
+                return eigen_ref_array(*src);
+            case return_value_policy::reference_internal:
+                return eigen_ref_array(*src, parent);
+            default:
+                throw cast_error("unhandled return_value_policy: should not happen!");
+        };
+    }
+
+public:
+    // Normal returned non-reference, non-const value:
+    static handle cast(Type &&src, return_value_policy /* policy */, handle parent) {
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+    // If you return a non-reference const, we mark the numpy array readonly:
+    static handle cast(const Type &&src, return_value_policy /* policy */, handle parent) {
+        return cast_impl(&src, return_value_policy::move, parent);
+    }
+    // lvalue reference return; default (automatic) becomes copy
+    static handle cast(Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast_impl(&src, policy, parent);
+    }
+    // const lvalue reference return; default (automatic) becomes copy
+    static handle cast(const Type &src, return_value_policy policy, handle parent) {
+        if (policy == return_value_policy::automatic
+            || policy == return_value_policy::automatic_reference) {
+            policy = return_value_policy::copy;
+        }
+        return cast(&src, policy, parent);
+    }
+    // non-const pointer return
+    static handle cast(Type *src, return_value_policy policy, handle parent) {
+        return cast_impl(src, policy, parent);
+    }
+    // const pointer return
+    static handle cast(const Type *src, return_value_policy policy, handle parent) {
+        return cast_impl(src, policy, parent);
+    }
+
+    static constexpr auto name = props::descriptor;
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type *() { return &value; }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type &() { return value; }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type &&() && { return std::move(value); }
+    template 
+    using cast_op_type = movable_cast_op_type;
+
+private:
+    Type value;
+};
+
+// Base class for casting reference/map/block/etc. objects back to python.
+template 
+struct eigen_map_caster {
+    static_assert(!std::is_pointer::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+
+private:
+    using props = EigenProps;
+
+public:
+    // Directly referencing a ref/map's data is a bit dangerous (whatever the map/ref points to has
+    // to stay around), but we'll allow it under the assumption that you know what you're doing
+    // (and have an appropriate keep_alive in place).  We return a numpy array pointing directly at
+    // the ref's data (The numpy array ends up read-only if the ref was to a const matrix type.)
+    // Note that this means you need to ensure you don't destroy the object in some other way (e.g.
+    // with an appropriate keep_alive, or with a reference to a statically allocated matrix).
+    static handle cast(const MapType &src, return_value_policy policy, handle parent) {
+        switch (policy) {
+            case return_value_policy::copy:
+                return eigen_array_cast(src);
+            case return_value_policy::reference_internal:
+                return eigen_array_cast(src, parent, is_eigen_mutable_map::value);
+            case return_value_policy::reference:
+            case return_value_policy::automatic:
+            case return_value_policy::automatic_reference:
+                return eigen_array_cast(src, none(), is_eigen_mutable_map::value);
+            default:
+                // move, take_ownership don't make any sense for a ref/map:
+                pybind11_fail("Invalid return_value_policy for Eigen Map/Ref/Block type");
+        }
+    }
+
+    static constexpr auto name = props::descriptor;
+
+    // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
+    // types but not bound arguments).  We still provide them (with an explicitly delete) so that
+    // you end up here if you try anyway.
+    bool load(handle, bool) = delete;
+    operator MapType() = delete;
+    template 
+    using cast_op_type = MapType;
+};
+
+// We can return any map-like object (but can only load Refs, specialized next):
+template 
+struct type_caster::value>> : eigen_map_caster {};
+
+// Loader for Ref<...> arguments.  See the documentation for info on how to make this work without
+// copying (it requires some extra effort in many cases).
+template 
+struct type_caster<
+    Eigen::Ref,
+    enable_if_t>::value>>
+    : public eigen_map_caster> {
+private:
+    using Type = Eigen::Ref;
+    using props = EigenProps;
+    using Scalar = typename props::Scalar;
+    static_assert(!std::is_pointer::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+    using MapType = Eigen::Map;
+    using Array
+        = array_t;
+    static constexpr bool need_writeable = is_eigen_mutable_map::value;
+    // Delay construction (these have no default constructor)
+    std::unique_ptr map;
+    std::unique_ptr ref;
+    // Our array.  When possible, this is just a numpy array pointing to the source data, but
+    // sometimes we can't avoid copying (e.g. input is not a numpy array at all, has an
+    // incompatible layout, or is an array of a type that needs to be converted).  Using a numpy
+    // temporary (rather than an Eigen temporary) saves an extra copy when we need both type
+    // conversion and storage order conversion.  (Note that we refuse to use this temporary copy
+    // when loading an argument for a Ref with M non-const, i.e. a read-write reference).
+    Array copy_or_ref;
+
+public:
+    bool load(handle src, bool convert) {
+        // First check whether what we have is already an array of the right type.  If not, we
+        // can't avoid a copy (because the copy is also going to do type conversion).
+        bool need_copy = !isinstance(src);
+
+        EigenConformable fits;
+        if (!need_copy) {
+            // We don't need a converting copy, but we also need to check whether the strides are
+            // compatible with the Ref's stride requirements
+            auto aref = reinterpret_borrow(src);
+
+            if (aref && (!need_writeable || aref.writeable())) {
+                fits = props::conformable(aref);
+                if (!fits) {
+                    return false; // Incompatible dimensions
+                }
+                if (!fits.template stride_compatible()) {
+                    need_copy = true;
+                } else {
+                    copy_or_ref = std::move(aref);
+                }
+            } else {
+                need_copy = true;
+            }
+        }
+
+        if (need_copy) {
+            // We need to copy: If we need a mutable reference, or we're not supposed to convert
+            // (either because we're in the no-convert overload pass, or because we're explicitly
+            // instructed not to copy (via `py::arg().noconvert()`) we have to fail loading.
+            if (!convert || need_writeable) {
+                return false;
+            }
+
+            Array copy = Array::ensure(src);
+            if (!copy) {
+                return false;
+            }
+            fits = props::conformable(copy);
+            if (!fits || !fits.template stride_compatible()) {
+                return false;
+            }
+            copy_or_ref = std::move(copy);
+            loader_life_support::add_patient(copy_or_ref);
+        }
+
+        ref.reset();
+        map.reset(new MapType(data(copy_or_ref),
+                              fits.rows,
+                              fits.cols,
+                              make_stride(fits.stride.outer(), fits.stride.inner())));
+        ref.reset(new Type(*map));
+
+        return true;
+    }
+
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type *() { return ref.get(); }
+    // NOLINTNEXTLINE(google-explicit-constructor)
+    operator Type &() { return *ref; }
+    template 
+    using cast_op_type = pybind11::detail::cast_op_type<_T>;
+
+private:
+    template ::value, int> = 0>
+    Scalar *data(Array &a) {
+        return a.mutable_data();
+    }
+
+    template ::value, int> = 0>
+    const Scalar *data(Array &a) {
+        return a.data();
+    }
+
+    // Attempt to figure out a constructor of `Stride` that will work.
+    // If both strides are fixed, use a default constructor:
+    template 
+    using stride_ctor_default = bool_constant::value>;
+    // Otherwise, if there is a two-index constructor, assume it is (outer,inner) like
+    // Eigen::Stride, and use it:
+    template 
+    using stride_ctor_dual
+        = bool_constant::value
+                        && std::is_constructible::value>;
+    // Otherwise, if there is a one-index constructor, and just one of the strides is dynamic, use
+    // it (passing whichever stride is dynamic).
+    template 
+    using stride_ctor_outer
+        = bool_constant, stride_ctor_dual>::value
+                        && S::OuterStrideAtCompileTime == Eigen::Dynamic
+                        && S::InnerStrideAtCompileTime != Eigen::Dynamic
+                        && std::is_constructible::value>;
+    template 
+    using stride_ctor_inner
+        = bool_constant, stride_ctor_dual>::value
+                        && S::InnerStrideAtCompileTime == Eigen::Dynamic
+                        && S::OuterStrideAtCompileTime != Eigen::Dynamic
+                        && std::is_constructible::value>;
+
+    template ::value, int> = 0>
+    static S make_stride(EigenIndex, EigenIndex) {
+        return S();
+    }
+    template ::value, int> = 0>
+    static S make_stride(EigenIndex outer, EigenIndex inner) {
+        return S(outer, inner);
+    }
+    template ::value, int> = 0>
+    static S make_stride(EigenIndex outer, EigenIndex) {
+        return S(outer);
+    }
+    template ::value, int> = 0>
+    static S make_stride(EigenIndex, EigenIndex inner) {
+        return S(inner);
+    }
+};
+
+// type_caster for special matrix types (e.g. DiagonalMatrix), which are EigenBase, but not
+// EigenDense (i.e. they don't have a data(), at least not with the usual matrix layout).
+// load() is not supported, but we can cast them into the python domain by first copying to a
+// regular Eigen::Matrix, then casting that.
+template 
+struct type_caster::value>> {
+    static_assert(!std::is_pointer::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+
+protected:
+    using Matrix
+        = Eigen::Matrix;
+    using props = EigenProps;
+
+public:
+    static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
+        handle h = eigen_encapsulate(new Matrix(src));
+        return h;
+    }
+    static handle cast(const Type *src, return_value_policy policy, handle parent) {
+        return cast(*src, policy, parent);
+    }
+
+    static constexpr auto name = props::descriptor;
+
+    // Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
+    // types but not bound arguments).  We still provide them (with an explicitly delete) so that
+    // you end up here if you try anyway.
+    bool load(handle, bool) = delete;
+    operator Type() = delete;
+    template 
+    using cast_op_type = Type;
+};
+
+template 
+struct type_caster::value>> {
+    using Scalar = typename Type::Scalar;
+    static_assert(!std::is_pointer::value,
+                  PYBIND11_EIGEN_MESSAGE_POINTER_TYPES_ARE_NOT_SUPPORTED);
+    using StorageIndex = remove_reference_t().outerIndexPtr())>;
+    using Index = typename Type::Index;
+    static constexpr bool rowMajor = Type::IsRowMajor;
+
+    bool load(handle src, bool) {
+        if (!src) {
+            return false;
+        }
+
+        auto obj = reinterpret_borrow(src);
+        object sparse_module = module_::import("scipy.sparse");
+        object matrix_type = sparse_module.attr(rowMajor ? "csr_matrix" : "csc_matrix");
+
+        if (!type::handle_of(obj).is(matrix_type)) {
+            try {
+                obj = matrix_type(obj);
+            } catch (const error_already_set &) {
+                return false;
+            }
+        }
+
+        auto values = array_t((object) obj.attr("data"));
+        auto innerIndices = array_t((object) obj.attr("indices"));
+        auto outerIndices = array_t((object) obj.attr("indptr"));
+        auto shape = pybind11::tuple((pybind11::object) obj.attr("shape"));
+        auto nnz = obj.attr("nnz").cast();
+
+        if (!values || !innerIndices || !outerIndices) {
+            return false;
+        }
+
+        value = EigenMapSparseMatrix(shape[0].cast(),
+                                                   shape[1].cast(),
+                                                   std::move(nnz),
+                                                   outerIndices.mutable_data(),
+                                                   innerIndices.mutable_data(),
+                                                   values.mutable_data());
+
+        return true;
+    }
+
+    static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
+        const_cast(src).makeCompressed();
+
+        object matrix_type
+            = module_::import("scipy.sparse").attr(rowMajor ? "csr_matrix" : "csc_matrix");
+
+        array data(src.nonZeros(), src.valuePtr());
+        array outerIndices((rowMajor ? src.rows() : src.cols()) + 1, src.outerIndexPtr());
+        array innerIndices(src.nonZeros(), src.innerIndexPtr());
+
+        return matrix_type(pybind11::make_tuple(
+                               std::move(data), std::move(innerIndices), std::move(outerIndices)),
+                           pybind11::make_tuple(src.rows(), src.cols()))
+            .release();
+    }
+
+    PYBIND11_TYPE_CASTER(Type,
+                         const_name<(Type::IsRowMajor) != 0>("scipy.sparse.csr_matrix[",
+                                                             "scipy.sparse.csc_matrix[")
+                             + npy_format_descriptor::name + const_name("]"));
+};
+
+PYBIND11_NAMESPACE_END(detail)
+PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
diff --git a/shared/package-files/09/092fad4454f593d7bf2e5e1e746acade92bb346d06476ba527f162f843ae3208/mainmenu.py b/shared/package-files/09/092fad4454f593d7bf2e5e1e746acade92bb346d06476ba527f162f843ae3208/mainmenu.py
new file mode 100644
index 0000000000000000000000000000000000000000..91a32cebb513f9140bf5e8df709e90b9e1c996e8
--- /dev/null
+++ b/shared/package-files/09/092fad4454f593d7bf2e5e1e746acade92bb346d06476ba527f162f843ae3208/mainmenu.py
@@ -0,0 +1,126 @@
+"""Define the menu contents, hotkeys, and event bindings.
+
+There is additional configuration information in the EditorWindow class (and
+subclasses): the menus are created there based on the menu_specs (class)
+variable, and menus not created are silently skipped in the code here.  This
+makes it possible, for example, to define a Debug menu which is only present in
+the PythonShell window, and a Format menu which is only present in the Editor
+windows.
+
+"""
+from importlib.util import find_spec
+
+from idlelib.config import idleConf
+
+#   Warning: menudefs is altered in macosx.overrideRootMenu()
+#   after it is determined that an OS X Aqua Tk is in use,
+#   which cannot be done until after Tk() is first called.
+#   Do not alter the 'file', 'options', or 'help' cascades here
+#   without altering overrideRootMenu() as well.
+#       TODO: Make this more robust
+
+menudefs = [
+ # underscore prefixes character to underscore
+ ('file', [
+   ('_New File', '<>'),
+   ('_Open...', '<>'),
+   ('Open _Module...', '<>'),
+   ('Module _Browser', '<>'),
+   ('_Path Browser', '<>'),
+   None,
+   ('_Save', '<>'),
+   ('Save _As...', '<>'),
+   ('Save Cop_y As...', '<>'),
+   None,
+   ('Prin_t Window', '<>'),
+   None,
+   ('_Close Window', '<>'),
+   ('E_xit IDLE', '<>'),
+   ]),
+
+ ('edit', [
+   ('_Undo', '<>'),
+   ('_Redo', '<>'),
+   None,
+   ('Select _All', '<>'),
+   ('Cu_t', '<>'),
+   ('_Copy', '<>'),
+   ('_Paste', '<>'),
+   None,
+   ('_Find...', '<>'),
+   ('Find A_gain', '<>'),
+   ('Find _Selection', '<>'),
+   ('Find in Files...', '<>'),
+   ('R_eplace...', '<>'),
+   None,
+   ('Go to _Line', '<>'),
+   ('S_how Completions', '<>'),
+   ('E_xpand Word', '<>'),
+   ('Show C_all Tip', '<>'),
+   ('Show Surrounding P_arens', '<>'),
+   ]),
+
+ ('format', [
+   ('F_ormat Paragraph', '<>'),
+   ('_Indent Region', '<>'),
+   ('_Dedent Region', '<>'),
+   ('Comment _Out Region', '<>'),
+   ('U_ncomment Region', '<>'),
+   ('Tabify Region', '<>'),
+   ('Untabify Region', '<>'),
+   ('Toggle Tabs', '<>'),
+   ('New Indent Width', '<>'),
+   ('S_trip Trailing Whitespace', '<>'),
+   ]),
+
+ ('run', [
+   ('R_un Module', '<>'),
+   ('Run... _Customized', '<>'),
+   ('C_heck Module', '<>'),
+   ('Python Shell', '<>'),
+   ]),
+
+ ('shell', [
+   ('_View Last Restart', '<>'),
+   ('_Restart Shell', '<>'),
+   None,
+   ('_Previous History', '<>'),
+   ('_Next History', '<>'),
+   None,
+   ('_Interrupt Execution', '<>'),
+   ]),
+
+ ('debug', [
+   ('_Go to File/Line', '<>'),
+   ('!_Debugger', '<>'),
+   ('_Stack Viewer', '<>'),
+   ('!_Auto-open Stack Viewer', '<>'),
+   ]),
+
+ ('options', [
+   ('Configure _IDLE', '<>'),
+   None,
+   ('Show _Code Context', '<>'),
+   ('Show _Line Numbers', '<>'),
+   ('_Zoom Height', '<>'),
+   ]),
+
+ ('window', [
+   ]),
+
+ ('help', [
+   ('_About IDLE', '<>'),
+   None,
+   ('_IDLE Doc', '<>'),
+   ('Python _Docs', '<>'),
+   ]),
+]
+
+if find_spec('turtledemo'):
+    menudefs[-1][1].append(('Turtle Demo', '<>'))
+
+default_keydefs = idleConf.GetCurrentKeySet()
+
+if __name__ == '__main__':
+    from unittest import main
+    main('idlelib.idle_test.test_mainmenu', verbosity=2)
diff --git a/shared/package-files/09/0931c05cbbcf280d5eb97df9742e8049e99c8100ca6fb2445fb0b7535169fe4a/attrs.py b/shared/package-files/09/0931c05cbbcf280d5eb97df9742e8049e99c8100ca6fb2445fb0b7535169fe4a/attrs.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b08f3a43ddf63a97401cf3e67f81a32b5c693e8
--- /dev/null
+++ b/shared/package-files/09/0931c05cbbcf280d5eb97df9742e8049e99c8100ca6fb2445fb0b7535169fe4a/attrs.py
@@ -0,0 +1,179 @@
+# This file is part of Hypothesis, which may be found at
+# https://github.com/HypothesisWorks/hypothesis/
+#
+# Copyright the Hypothesis Authors.
+# Individual contributors are listed in AUTHORS.rst and the git log.
+#
+# This Source Code Form is subject to the terms of the Mozilla Public License,
+# v. 2.0. If a copy of the MPL was not distributed with this file, You can
+# obtain one at https://mozilla.org/MPL/2.0/.
+
+from functools import reduce
+from itertools import chain
+
+import attr
+
+from hypothesis import strategies as st
+from hypothesis.errors import ResolutionFailed
+from hypothesis.internal.compat import get_type_hints
+from hypothesis.strategies._internal.core import BuildsStrategy
+from hypothesis.strategies._internal.types import is_a_type, type_sorting_key
+from hypothesis.utils.conventions import infer
+
+
+def get_attribute_by_alias(fields, alias, *, target=None):
+    """
+    Get an attrs attribute by its alias, rather than its name (compare
+    getattr(fields, name)).
+
+    ``target`` is used only to provide a nicer error message, and can be safely
+    omitted.
+    """
+    # attrs supports defining an alias for a field, which is the name used when
+    # defining __init__. The init args are what we pull from when determining
+    # what parameters we need to supply to the class, so it's what we need to
+    # match against as well, rather than the class-level attribute name.
+    matched_fields = [f for f in fields if f.alias == alias]
+    if not matched_fields:
+        raise TypeError(
+            f"Unexpected keyword argument {alias} for attrs class"
+            f"{f' {target}' if target else ''}. Expected one of "
+            f"{[f.name for f in fields]}"
+        )
+    # alias is used as an arg in __init__, so it is guaranteed to be unique, if
+    # it exists.
+    assert len(matched_fields) == 1
+    return matched_fields[0]
+
+
+def from_attrs(target, args, kwargs, to_infer):
+    """An internal version of builds(), specialised for Attrs classes."""
+    fields = attr.fields(target)
+    kwargs = {k: v for k, v in kwargs.items() if v is not infer}
+    for name in to_infer:
+        attrib = get_attribute_by_alias(fields, name, target=target)
+        kwargs[name] = from_attrs_attribute(attrib, target)
+    # We might make this strategy more efficient if we added a layer here that
+    # retries drawing if validation fails, for improved composition.
+    # The treatment of timezones in datetimes() provides a precedent.
+    return BuildsStrategy(target, args, kwargs)
+
+
+def from_attrs_attribute(attrib, target):
+    """Infer a strategy from the metadata on an attr.Attribute object."""
+    # Try inferring from the default argument.  Note that this will only help if
+    # the user passed `...` to builds() for this attribute, but in that case
+    # we use it as the minimal example.
+    default = st.nothing()
+    if isinstance(attrib.default, attr.Factory):
+        if not attrib.default.takes_self:
+            default = st.builds(attrib.default.factory)
+    elif attrib.default is not attr.NOTHING:
+        default = st.just(attrib.default)
+
+    # Try inferring None, exact values, or type from attrs provided validators.
+    null = st.nothing()  # updated to none() on seeing an OptionalValidator
+    in_collections = []  # list of in_ validator collections to sample from
+    validator_types = set()  # type constraints to pass to types_to_strategy()
+    if attrib.validator is not None:
+        validator = attrib.validator
+        if isinstance(validator, attr.validators._OptionalValidator):
+            null = st.none()
+            validator = validator.validator
+        if isinstance(validator, attr.validators._AndValidator):
+            vs = validator._validators
+        else:
+            vs = [validator]
+        for v in vs:
+            if isinstance(v, attr.validators._InValidator):
+                if isinstance(v.options, str):
+                    in_collections.append(list(all_substrings(v.options)))
+                else:
+                    in_collections.append(v.options)
+            elif isinstance(v, attr.validators._InstanceOfValidator):
+                validator_types.add(v.type)
+
+    # This is the important line.  We compose the final strategy from various
+    # parts.  The default value, if any, is the minimal shrink, followed by
+    # None (again, if allowed).  We then prefer to sample from values passed
+    # to an in_ validator if available, but infer from a type otherwise.
+    # Pick one because (sampled_from((1, 2)) | from_type(int)) would usually
+    # fail validation by generating e.g. zero!
+    if in_collections:
+        sample = st.sampled_from(list(ordered_intersection(in_collections)))
+        strat = default | null | sample
+    else:
+        strat = default | null | types_to_strategy(attrib, validator_types)
+
+    # Better to give a meaningful error here than an opaque "could not draw"
+    # when we try to get a value but have lost track of where this was created.
+    if strat.is_empty:
+        raise ResolutionFailed(
+            "Cannot infer a strategy from the default, validator, type, or "
+            f"converter for attribute={attrib!r} of class={target!r}"
+        )
+    return strat
+
+
+def types_to_strategy(attrib, types):
+    """Find all the type metadata for this attribute, reconcile it, and infer a
+    strategy from the mess."""
+    # If we know types from the validator(s), that's sufficient.
+    if len(types) == 1:
+        (typ,) = types
+        if isinstance(typ, tuple):
+            return st.one_of(*map(st.from_type, typ))
+        return st.from_type(typ)
+    elif types:
+        # We have a list of tuples of types, and want to find a type
+        # (or tuple of types) that is a subclass of all of of them.
+        type_tuples = [k if isinstance(k, tuple) else (k,) for k in types]
+        # Flatten the list, filter types that would fail validation, and
+        # sort so that ordering is stable between runs and shrinks well.
+        allowed = [
+            t
+            for t in set(sum(type_tuples, ()))
+            if all(issubclass(t, tup) for tup in type_tuples)
+        ]
+        allowed.sort(key=type_sorting_key)
+        return st.one_of([st.from_type(t) for t in allowed])
+
+    # Otherwise, try the `type` attribute as a fallback, and finally try
+    # the type hints on a converter (desperate!) before giving up.
+    if is_a_type(getattr(attrib, "type", None)):
+        # The convoluted test is because variable annotations may be stored
+        # in string form; attrs doesn't evaluate them and we don't handle them.
+        # See PEP 526, PEP 563, and Hypothesis issue #1004 for details.
+        return st.from_type(attrib.type)
+
+    converter = getattr(attrib, "converter", None)
+    if isinstance(converter, type):
+        return st.from_type(converter)
+    elif callable(converter):
+        hints = get_type_hints(converter)
+        if "return" in hints:
+            return st.from_type(hints["return"])
+
+    return st.nothing()
+
+
+def ordered_intersection(in_):
+    """Set union of n sequences, ordered for reproducibility across runs."""
+    intersection = reduce(set.intersection, in_, set(in_[0]))
+    for x in chain.from_iterable(in_):
+        if x in intersection:
+            yield x
+            intersection.remove(x)
+
+
+def all_substrings(s):
+    """Generate all substrings of `s`, in order of length then occurrence.
+    Includes the empty string (first), and any duplicates that are present.
+
+    >>> list(all_substrings('010'))
+    ['', '0', '1', '0', '01', '10', '010']
+    """
+    yield s[:0]
+    for n, _ in enumerate(s):
+        for i in range(len(s) - n):
+            yield s[i : i + n + 1]
diff --git a/shared/package-files/09/0931d025bb82f990203caca6c6beb60e3b2c9671783f8e51650fdad7be3cd540/multipart.pyi b/shared/package-files/09/0931d025bb82f990203caca6c6beb60e3b2c9671783f8e51650fdad7be3cd540/multipart.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..eb5f662de05865e637770c8e354b7ff45c94ea52
--- /dev/null
+++ b/shared/package-files/09/0931d025bb82f990203caca6c6beb60e3b2c9671783f8e51650fdad7be3cd540/multipart.pyi
@@ -0,0 +1,17 @@
+from email.message import Message
+from email.mime.base import MIMEBase
+from email.policy import Policy
+from typing import Optional, Sequence, Tuple, Union
+
+_ParamsType = Union[str, None, Tuple[str, Optional[str], str]]
+
+class MIMEMultipart(MIMEBase):
+    def __init__(
+        self,
+        _subtype: str = ...,
+        boundary: Optional[str] = ...,
+        _subparts: Optional[Sequence[Message]] = ...,
+        *,
+        policy: Optional[Policy] = ...,
+        **_params: _ParamsType,
+    ) -> None: ...
diff --git a/shared/package-files/09/093273687bc10458a29ca269d8766c293d05589f107f92da53f75d3f47690b07/boxing.py b/shared/package-files/09/093273687bc10458a29ca269d8766c293d05589f107f92da53f75d3f47690b07/boxing.py
new file mode 100644
index 0000000000000000000000000000000000000000..999c7acf2b5174eb00eb0776c5c3d92d7a9ebb02
--- /dev/null
+++ b/shared/package-files/09/093273687bc10458a29ca269d8766c293d05589f107f92da53f75d3f47690b07/boxing.py
@@ -0,0 +1,275 @@
+"""
+Implement logic relating to wrapping (box) and unwrapping (unbox) instances
+of jitclasses for use inside the python interpreter.
+"""
+
+from functools import wraps, partial
+
+from llvmlite import ir
+
+from numba.core import types, cgutils
+from numba.core.decorators import njit
+from numba.core.pythonapi import box, unbox, NativeValue
+from numba.core.typing.typeof import typeof_impl
+from numba.experimental.jitclass import _box
+
+
+_getter_code_template = """
+def accessor(__numba_self_):
+    return __numba_self_.{0}
+"""
+
+_setter_code_template = """
+def mutator(__numba_self_, __numba_val):
+    __numba_self_.{0} = __numba_val
+"""
+
+_method_code_template = """
+def method(__numba_self_, *args):
+    return __numba_self_.{method}(*args)
+"""
+
+
+def _generate_property(field, template, fname):
+    """
+    Generate simple function that get/set a field of the instance
+    """
+    source = template.format(field)
+    glbls = {}
+    exec(source, glbls)
+    return njit(glbls[fname])
+
+
+_generate_getter = partial(_generate_property, template=_getter_code_template,
+                           fname='accessor')
+_generate_setter = partial(_generate_property, template=_setter_code_template,
+                           fname='mutator')
+
+
+def _generate_method(name, func):
+    """
+    Generate a wrapper for calling a method.  Note the wrapper will only
+    accept positional arguments.
+    """
+    source = _method_code_template.format(method=name)
+    glbls = {}
+    exec(source, glbls)
+    method = njit(glbls['method'])
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        return method(*args, **kwargs)
+
+    return wrapper
+
+
+_cache_specialized_box = {}
+
+
+def _specialize_box(typ):
+    """
+    Create a subclass of Box that is specialized to the jitclass.
+
+    This function caches the result to avoid code bloat.
+    """
+    # Check cache
+    if typ in _cache_specialized_box:
+        return _cache_specialized_box[typ]
+    dct = {'__slots__': (),
+           '_numba_type_': typ,
+           '__doc__': typ.class_type.class_doc,
+           }
+    # Inject attributes as class properties
+    for field in typ.struct:
+        getter = _generate_getter(field)
+        setter = _generate_setter(field)
+        dct[field] = property(getter, setter)
+    # Inject properties as class properties
+    for field, impdct in typ.jit_props.items():
+        getter = None
+        setter = None
+        if 'get' in impdct:
+            getter = _generate_getter(field)
+        if 'set' in impdct:
+            setter = _generate_setter(field)
+        # get docstring from either the fget or fset
+        imp = impdct.get('get') or impdct.get('set') or None
+        doc = getattr(imp, '__doc__', None)
+        dct[field] = property(getter, setter, doc=doc)
+    # Inject methods as class members
+    supported_dunders = {
+        "__abs__",
+        "__annotate_func__",
+        "__bool__",
+        "__complex__",
+        "__contains__",
+        "__float__",
+        "__getitem__",
+        "__hash__",
+        "__index__",
+        "__invert__",
+        "__int__",
+        "__len__",
+        "__setitem__",
+        "__str__",
+        "__eq__",
+        "__ne__",
+        "__ge__",
+        "__gt__",
+        "__le__",
+        "__lt__",
+        "__add__",
+        "__floordiv__",
+        "__lshift__",
+        "__matmul__",
+        "__mod__",
+        "__mul__",
+        "__neg__",
+        "__pos__",
+        "__pow__",
+        "__rshift__",
+        "__sub__",
+        "__truediv__",
+        "__and__",
+        "__or__",
+        "__xor__",
+        "__iadd__",
+        "__ifloordiv__",
+        "__ilshift__",
+        "__imatmul__",
+        "__imod__",
+        "__imul__",
+        "__ipow__",
+        "__irshift__",
+        "__isub__",
+        "__itruediv__",
+        "__iand__",
+        "__ior__",
+        "__ixor__",
+        "__radd__",
+        "__rfloordiv__",
+        "__rlshift__",
+        "__rmatmul__",
+        "__rmod__",
+        "__rmul__",
+        "__rpow__",
+        "__rrshift__",
+        "__rsub__",
+        "__rtruediv__",
+        "__rand__",
+        "__ror__",
+        "__rxor__",
+    }
+    for name, func in typ.methods.items():
+        if name == "__init__":
+            continue
+        if (
+            name.startswith("__")
+            and name.endswith("__")
+            and name not in supported_dunders
+        ):
+            raise TypeError(f"Method '{name}' is not supported.")
+        dct[name] = _generate_method(name, func)
+
+    # Inject static methods as class members
+    for name, func in typ.static_methods.items():
+        dct[name] = _generate_method(name, func)
+
+    # Create subclass
+    subcls = type(typ.classname, (_box.Box,), dct)
+    # Store to cache
+    _cache_specialized_box[typ] = subcls
+
+    # Pre-compile attribute getter.
+    # Note: This must be done after the "box" class is created because
+    #       compiling the getter requires the "box" class to be defined.
+    for k, v in dct.items():
+        if isinstance(v, property):
+            prop = getattr(subcls, k)
+            if prop.fget is not None:
+                fget = prop.fget
+                fast_fget = fget.compile((typ,))
+                fget.disable_compile()
+                setattr(subcls, k,
+                        property(fast_fget, prop.fset, prop.fdel,
+                                 doc=prop.__doc__))
+
+    return subcls
+
+
+###############################################################################
+# Implement box/unbox for call wrapper
+
+@box(types.ClassInstanceType)
+def _box_class_instance(typ, val, c):
+    meminfo, dataptr = cgutils.unpack_tuple(c.builder, val)
+
+    # Create Box instance
+    box_subclassed = _specialize_box(typ)
+    # Note: the ``box_subclassed`` is kept alive by the cache
+    voidptr_boxcls = c.context.add_dynamic_addr(
+        c.builder,
+        id(box_subclassed),
+        info="box_class_instance",
+    )
+    box_cls = c.builder.bitcast(voidptr_boxcls, c.pyapi.pyobj)
+
+    box = c.pyapi.call_function_objargs(box_cls, ())
+
+    # Initialize Box instance
+    llvoidptr = ir.IntType(8).as_pointer()
+    addr_meminfo = c.builder.bitcast(meminfo, llvoidptr)
+    addr_data = c.builder.bitcast(dataptr, llvoidptr)
+
+    def set_member(member_offset, value):
+        # Access member by byte offset
+        offset = c.context.get_constant(types.uintp, member_offset)
+        ptr = cgutils.pointer_add(c.builder, box, offset)
+        casted = c.builder.bitcast(ptr, llvoidptr.as_pointer())
+        c.builder.store(value, casted)
+
+    set_member(_box.box_meminfoptr_offset, addr_meminfo)
+    set_member(_box.box_dataptr_offset, addr_data)
+    return box
+
+
+@unbox(types.ClassInstanceType)
+def _unbox_class_instance(typ, val, c):
+    def access_member(member_offset):
+        # Access member by byte offset
+        offset = c.context.get_constant(types.uintp, member_offset)
+        llvoidptr = ir.IntType(8).as_pointer()
+        ptr = cgutils.pointer_add(c.builder, val, offset)
+        casted = c.builder.bitcast(ptr, llvoidptr.as_pointer())
+        return c.builder.load(casted)
+
+    struct_cls = cgutils.create_struct_proxy(typ)
+    inst = struct_cls(c.context, c.builder)
+
+    # load from Python object
+    ptr_meminfo = access_member(_box.box_meminfoptr_offset)
+    ptr_dataptr = access_member(_box.box_dataptr_offset)
+
+    # store to native structure
+    inst.meminfo = c.builder.bitcast(ptr_meminfo, inst.meminfo.type)
+    inst.data = c.builder.bitcast(ptr_dataptr, inst.data.type)
+
+    ret = inst._getvalue()
+
+    c.context.nrt.incref(c.builder, typ, ret)
+
+    return NativeValue(ret, is_error=c.pyapi.c_api_error())
+
+
+# Add a typeof_impl implementation for boxed jitclasses to short-circut the
+# various tests in typeof. This is needed for jitclasses which implement a
+# custom hash method. Without this, typeof_impl will return None, and one of the
+# later attempts to determine the type of the jitclass (before checking for
+# _numba_type_) will look up the object in a dictionary, triggering the hash
+# method. This will cause the dispatcher to determine the call signature of the
+# jit decorated obj.__hash__ method, which will call typeof(obj), and thus
+# infinite loop.
+# This implementation is here instead of in typeof.py to avoid circular imports.
+@typeof_impl.register(_box.Box)
+def _typeof_jitclass_box(val, c):
+    return getattr(type(val), "_numba_type_")
diff --git a/shared/package-files/09/093360564826db257d7356cef707a6f3afccf17e931b2b84b62b548c1cc6c017/setopt.py b/shared/package-files/09/093360564826db257d7356cef707a6f3afccf17e931b2b84b62b548c1cc6c017/setopt.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9a60751285785eecf91921ae9148930ebb427f8
--- /dev/null
+++ b/shared/package-files/09/093360564826db257d7356cef707a6f3afccf17e931b2b84b62b548c1cc6c017/setopt.py
@@ -0,0 +1,138 @@
+from distutils.util import convert_path
+from distutils import log
+from distutils.errors import DistutilsOptionError
+import distutils
+import os
+import configparser
+
+from setuptools import Command
+
+__all__ = ['config_file', 'edit_config', 'option_base', 'setopt']
+
+
+def config_file(kind="local"):
+    """Get the filename of the distutils, local, global, or per-user config
+
+    `kind` must be one of "local", "global", or "user"
+    """
+    if kind == 'local':
+        return 'setup.cfg'
+    if kind == 'global':
+        return os.path.join(os.path.dirname(distutils.__file__), 'distutils.cfg')
+    if kind == 'user':
+        dot = os.name == 'posix' and '.' or ''
+        return os.path.expanduser(convert_path("~/%spydistutils.cfg" % dot))
+    raise ValueError("config_file() type must be 'local', 'global', or 'user'", kind)
+
+
+def edit_config(filename, settings, dry_run=False):
+    """Edit a configuration file to include `settings`
+
+    `settings` is a dictionary of dictionaries or ``None`` values, keyed by
+    command/section name.  A ``None`` value means to delete the entire section,
+    while a dictionary lists settings to be changed or deleted in that section.
+    A setting of ``None`` means to delete that setting.
+    """
+    log.debug("Reading configuration from %s", filename)
+    opts = configparser.RawConfigParser()
+    opts.optionxform = lambda x: x
+    opts.read([filename])
+    for section, options in settings.items():
+        if options is None:
+            log.info("Deleting section [%s] from %s", section, filename)
+            opts.remove_section(section)
+        else:
+            if not opts.has_section(section):
+                log.debug("Adding new section [%s] to %s", section, filename)
+                opts.add_section(section)
+            for option, value in options.items():
+                if value is None:
+                    log.debug("Deleting %s.%s from %s", section, option, filename)
+                    opts.remove_option(section, option)
+                    if not opts.options(section):
+                        log.info(
+                            "Deleting empty [%s] section from %s", section, filename
+                        )
+                        opts.remove_section(section)
+                else:
+                    log.debug(
+                        "Setting %s.%s to %r in %s", section, option, value, filename
+                    )
+                    opts.set(section, option, value)
+
+    log.info("Writing %s", filename)
+    if not dry_run:
+        with open(filename, 'w') as f:
+            opts.write(f)
+
+
+class option_base(Command):
+    """Abstract base class for commands that mess with config files"""
+
+    user_options = [
+        ('global-config', 'g', "save options to the site-wide distutils.cfg file"),
+        ('user-config', 'u', "save options to the current user's pydistutils.cfg file"),
+        ('filename=', 'f', "configuration file to use (default=setup.cfg)"),
+    ]
+
+    boolean_options = [
+        'global-config',
+        'user-config',
+    ]
+
+    def initialize_options(self):
+        self.global_config = None
+        self.user_config = None
+        self.filename = None
+
+    def finalize_options(self):
+        filenames = []
+        if self.global_config:
+            filenames.append(config_file('global'))
+        if self.user_config:
+            filenames.append(config_file('user'))
+        if self.filename is not None:
+            filenames.append(self.filename)
+        if not filenames:
+            filenames.append(config_file('local'))
+        if len(filenames) > 1:
+            raise DistutilsOptionError(
+                "Must specify only one configuration file option", filenames
+            )
+        (self.filename,) = filenames
+
+
+class setopt(option_base):
+    """Save command-line options to a file"""
+
+    description = "set an option in setup.cfg or another config file"
+
+    user_options = [
+        ('command=', 'c', 'command to set an option for'),
+        ('option=', 'o', 'option to set'),
+        ('set-value=', 's', 'value of the option'),
+        ('remove', 'r', 'remove (unset) the value'),
+    ] + option_base.user_options
+
+    boolean_options = option_base.boolean_options + ['remove']
+
+    def initialize_options(self):
+        option_base.initialize_options(self)
+        self.command = None
+        self.option = None
+        self.set_value = None
+        self.remove = None
+
+    def finalize_options(self):
+        option_base.finalize_options(self)
+        if self.command is None or self.option is None:
+            raise DistutilsOptionError("Must specify --command *and* --option")
+        if self.set_value is None and not self.remove:
+            raise DistutilsOptionError("Must specify --set-value or --remove")
+
+    def run(self):
+        edit_config(
+            self.filename,
+            {self.command: {self.option.replace('-', '_'): self.set_value}},
+            self.dry_run,
+        )
diff --git a/shared/package-files/09/09349633e530eeef7399c8889f6253f97a12015319c55f27cba69d9186306692/results_quantile_regression.py b/shared/package-files/09/09349633e530eeef7399c8889f6253f97a12015319c55f27cba69d9186306692/results_quantile_regression.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca2fb3ec8babce6c3f036aa83a504a433e215c56
--- /dev/null
+++ b/shared/package-files/09/09349633e530eeef7399c8889f6253f97a12015319c55f27cba69d9186306692/results_quantile_regression.py
@@ -0,0 +1,638 @@
+"""Reference results for quantile regression tests."""
+
+import numpy as np
+
+from statsmodels.tools.tools import Bunch
+
+epanechnikov_hsheather_q75 = Bunch()
+epanechnikov_hsheather_q75.table = np.array([
+    [.6440143,  .0122001,   52.79,  0.000,    .6199777,   .6680508],
+    [62.39648,   13.5509,    4.60,  0.000,    35.69854,   89.09443]
+])
+epanechnikov_hsheather_q75.psrsquared = 0.6966
+epanechnikov_hsheather_q75.rank = 2
+epanechnikov_hsheather_q75.sparsity = 223.784434936344
+epanechnikov_hsheather_q75.bwidth = .1090401129546568
+# epanechnikov_hsheather_q75.kbwidth = 59.62067927472172  # Stata 12 results
+epanechnikov_hsheather_q75.kbwidth = 59.30  # TODO: why do we need lower tol?
+epanechnikov_hsheather_q75.df_m = 1
+epanechnikov_hsheather_q75.df_r = 233
+epanechnikov_hsheather_q75.f_r = .0044685860313942
+epanechnikov_hsheather_q75.N = 235
+epanechnikov_hsheather_q75.q_v = 745.2352905273438
+epanechnikov_hsheather_q75.q = .75
+epanechnikov_hsheather_q75.sum_rdev = 43036.06956481934
+epanechnikov_hsheather_q75.sum_adev = 13058.50008841318
+epanechnikov_hsheather_q75.convcode = 0
+
+
+biweight_bofinger = Bunch()
+biweight_bofinger.table = np.array([
+    [.5601805,  .0136491,  41.04,  0.000,  .533289,  .5870719],
+    [81.48233,  15.1604,  5.37,  0.000,  51.61335,  111.3513]
+ ])
+biweight_bofinger.psrsquared = 0.6206
+biweight_bofinger.rank = 2
+biweight_bofinger.sparsity = 216.8218989750115
+biweight_bofinger.bwidth = .2173486679767846
+biweight_bofinger.kbwidth = 91.50878448104551
+biweight_bofinger.df_m = 1
+biweight_bofinger.df_r = 233
+biweight_bofinger.f_r = .0046120802590851
+biweight_bofinger.N = 235
+biweight_bofinger.q_v = 582.541259765625
+biweight_bofinger.q = .5
+biweight_bofinger.sum_rdev = 46278.05667114258
+biweight_bofinger.sum_adev = 17559.93220318131
+biweight_bofinger.convcode = 0
+
+
+biweight_hsheather = Bunch()
+biweight_hsheather.table = np.array([
+    [.5601805,  .0128449,  43.61,  0.000,  .5348735,  .5854875],
+    [81.48233,  14.26713,  5.71,  0.000,  53.37326,  109.5914]
+ ])
+biweight_hsheather.psrsquared = 0.6206
+biweight_hsheather.rank = 2
+biweight_hsheather.sparsity = 204.0465407204423
+biweight_hsheather.bwidth = .1574393314202373
+biweight_hsheather.kbwidth = 64.53302151153288
+biweight_hsheather.df_m = 1
+biweight_hsheather.df_r = 233
+biweight_hsheather.f_r = .0049008427022052
+biweight_hsheather.N = 235
+biweight_hsheather.q_v = 582.541259765625
+biweight_hsheather.q = .5
+biweight_hsheather.sum_rdev = 46278.05667114258
+biweight_hsheather.sum_adev = 17559.93220318131
+biweight_hsheather.convcode = 0
+
+biweight_chamberlain = Bunch()
+biweight_chamberlain.table = np.array([
+    [.5601805,  .0114969,  48.72,  0.000,  .5375294,  .5828315],
+    [81.48233,  12.76983,  6.38,  0.000,  56.32325,  106.6414]
+])
+biweight_chamberlain.psrsquared = 0.6206
+biweight_chamberlain.rank = 2
+biweight_chamberlain.sparsity = 182.6322495257494
+biweight_chamberlain.bwidth = .063926976464458
+biweight_chamberlain.kbwidth = 25.61257055690209
+biweight_chamberlain.df_m = 1
+biweight_chamberlain.df_r = 233
+biweight_chamberlain.f_r = .005475484218131
+biweight_chamberlain.N = 235
+biweight_chamberlain.q_v = 582.541259765625
+biweight_chamberlain.q = .5
+biweight_chamberlain.sum_rdev = 46278.05667114258
+biweight_chamberlain.sum_adev = 17559.93220318131
+biweight_chamberlain.convcode = 0
+
+epanechnikov_bofinger = Bunch()
+epanechnikov_bofinger.table = np.array([
+    [.5601805,  .0209663,  26.72,  0.000,  .5188727,  .6014882],
+    [81.48233,  23.28774,  3.50,  0.001,  35.60088,  127.3638]
+    ])
+epanechnikov_bofinger.psrsquared = 0.6206
+epanechnikov_bofinger.rank = 2
+epanechnikov_bofinger.sparsity = 333.0579553401614
+epanechnikov_bofinger.bwidth = .2173486679767846
+epanechnikov_bofinger.kbwidth = 91.50878448104551
+epanechnikov_bofinger.df_m = 1
+epanechnikov_bofinger.df_r = 233
+epanechnikov_bofinger.f_r = .0030024804511235
+epanechnikov_bofinger.N = 235
+epanechnikov_bofinger.q_v = 582.541259765625
+epanechnikov_bofinger.q = .5
+epanechnikov_bofinger.sum_rdev = 46278.05667114258
+epanechnikov_bofinger.sum_adev = 17559.93220318131
+epanechnikov_bofinger.convcode = 0
+
+epanechnikov_hsheather = Bunch()
+epanechnikov_hsheather.table = np.array([
+    [.5601805,  .0170484,  32.86,  0.000,  .5265918,  .5937692],
+    [81.48233,  18.93605,  4.30,  0.000,  44.17457,  118.7901]
+    ])
+epanechnikov_hsheather.psrsquared = 0.6206
+epanechnikov_hsheather.rank = 2
+epanechnikov_hsheather.sparsity = 270.8207209067576
+epanechnikov_hsheather.bwidth = .1574393314202373
+epanechnikov_hsheather.kbwidth = 64.53302151153288
+epanechnikov_hsheather.df_m = 1
+epanechnikov_hsheather.df_r = 233
+epanechnikov_hsheather.f_r = .0036924796472434
+epanechnikov_hsheather.N = 235
+epanechnikov_hsheather.q_v = 582.541259765625
+epanechnikov_hsheather.q = .5
+epanechnikov_hsheather.sum_rdev = 46278.05667114258
+epanechnikov_hsheather.sum_adev = 17559.93220318131
+epanechnikov_hsheather.convcode = 0
+
+epanechnikov_chamberlain = Bunch()
+epanechnikov_chamberlain.table = np.array([
+    [.5601805,  .0130407,  42.96,  0.000,  .5344876,  .5858733],
+    [81.48233,  14.48467,  5.63,  0.000,  52.94468,  110.02]
+    ])
+epanechnikov_chamberlain.psrsquared = 0.6206
+epanechnikov_chamberlain.rank = 2
+epanechnikov_chamberlain.sparsity = 207.1576340635951
+epanechnikov_chamberlain.bwidth = .063926976464458
+epanechnikov_chamberlain.kbwidth = 25.61257055690209
+epanechnikov_chamberlain.df_m = 1
+epanechnikov_chamberlain.df_r = 233
+epanechnikov_chamberlain.f_r = .0048272418466269
+epanechnikov_chamberlain.N = 235
+epanechnikov_chamberlain.q_v = 582.541259765625
+epanechnikov_chamberlain.q = .5
+epanechnikov_chamberlain.sum_rdev = 46278.05667114258
+epanechnikov_chamberlain.sum_adev = 17559.93220318131
+epanechnikov_chamberlain.convcode = 0
+
+epan2_bofinger = Bunch()
+epan2_bofinger.table = np.array([
+    [.5601805,  .0143484,  39.04,  0.000,  .5319113,  .5884496],
+    [81.48233,  15.93709,  5.11,  0.000,  50.08313,  112.8815]
+    ])
+epan2_bofinger.psrsquared = 0.6206
+epan2_bofinger.rank = 2
+epan2_bofinger.sparsity = 227.9299402797656
+epan2_bofinger.bwidth = .2173486679767846
+epan2_bofinger.kbwidth = 91.50878448104551
+epan2_bofinger.df_m = 1
+epan2_bofinger.df_r = 233
+epan2_bofinger.f_r = .0043873130435281
+epan2_bofinger.N = 235
+epan2_bofinger.q_v = 582.541259765625
+epan2_bofinger.q = .5
+epan2_bofinger.sum_rdev = 46278.05667114258
+epan2_bofinger.sum_adev = 17559.93220318131
+epan2_bofinger.convcode = 0
+
+epan2_hsheather = Bunch()
+epan2_hsheather.table = np.array([
+    [.5601805,  .0131763,   42.51,  0.000,   .5342206,   .5861403],
+    [81.48233,  14.63518,    5.57,  0.000,   52.64815,   110.3165]
+    ])
+epan2_hsheather.psrsquared = 0.6206
+epan2_hsheather.rank = 2
+epan2_hsheather.sparsity = 209.3102085912557
+epan2_hsheather.bwidth = .1574393314202373
+epan2_hsheather.kbwidth = 64.53302151153288
+epan2_hsheather.df_m = 1
+epan2_hsheather.df_r = 233
+epan2_hsheather.f_r = .0047775978378236
+epan2_hsheather.N = 235
+epan2_hsheather.q_v = 582.541259765625
+epan2_hsheather.q = .5
+epan2_hsheather.sum_rdev = 46278.05667114258
+epan2_hsheather.sum_adev = 17559.93220318131
+epan2_hsheather.convcode = 0
+
+epan2_chamberlain = Bunch()
+epan2_chamberlain.table = np.array([
+    [.5601805,  .0117925,  47.50,  0.000,  .5369469,  .583414],
+    [81.48233,  13.0982,  6.22,  0.000,  55.67629,  107.2884]
+    ])
+epan2_chamberlain.psrsquared = 0.6206
+epan2_chamberlain.rank = 2
+epan2_chamberlain.sparsity = 187.3286437436797
+epan2_chamberlain.bwidth = .063926976464458
+epan2_chamberlain.kbwidth = 25.61257055690209
+epan2_chamberlain.df_m = 1
+epan2_chamberlain.df_r = 233
+epan2_chamberlain.f_r = .0053382119253919
+epan2_chamberlain.N = 235
+epan2_chamberlain.q_v = 582.541259765625
+epan2_chamberlain.q = .5
+epan2_chamberlain.sum_rdev = 46278.05667114258
+epan2_chamberlain.sum_adev = 17559.93220318131
+epan2_chamberlain.convcode = 0
+
+
+rectangle_bofinger = Bunch()
+rectangle_bofinger.table = np.array([
+    [.5601805,  .0158331,  35.38,  0.000,  .5289861,  .5913748],
+    [81.48233,  17.5862,  4.63,  0.000,  46.83404,  116.1306]
+    ])
+rectangle_bofinger.psrsquared = 0.6206
+rectangle_bofinger.rank = 2
+rectangle_bofinger.sparsity = 251.515372550242
+rectangle_bofinger.bwidth = .2173486679767846
+rectangle_bofinger.kbwidth = 91.50878448104551
+rectangle_bofinger.df_m = 1
+rectangle_bofinger.df_r = 233
+rectangle_bofinger.f_r = .0039759001203803
+rectangle_bofinger.N = 235
+rectangle_bofinger.q_v = 582.541259765625
+rectangle_bofinger.q = .5
+rectangle_bofinger.sum_rdev = 46278.05667114258
+rectangle_bofinger.sum_adev = 17559.93220318131
+rectangle_bofinger.convcode = 0
+
+rectangle_hsheather = Bunch()
+rectangle_hsheather.table = np.array([
+    [.5601805,  .0137362,  40.78,  0.000,  .5331174,  .5872435],
+    [81.48233,  15.25712,  5.34,  0.000,  51.42279,  111.5419]
+    ])
+rectangle_hsheather.psrsquared = 0.6206
+rectangle_hsheather.rank = 2
+rectangle_hsheather.sparsity = 218.2051806505069
+rectangle_hsheather.bwidth = .1574393314202373
+rectangle_hsheather.kbwidth = 64.53302151153288
+rectangle_hsheather.df_m = 1
+rectangle_hsheather.df_r = 233
+rectangle_hsheather.f_r = .004582842611797
+rectangle_hsheather.N = 235
+rectangle_hsheather.q_v = 582.541259765625
+rectangle_hsheather.q = .5
+rectangle_hsheather.sum_rdev = 46278.05667114258
+rectangle_hsheather.sum_adev = 17559.93220318131
+rectangle_hsheather.convcode = 0
+
+rectangle_chamberlain = Bunch()
+rectangle_chamberlain.table = np.array([
+    [.5601805,  .0118406,  47.31,  0.000,  .5368522,  .5835087],
+    [81.48233,  13.1516,  6.20,  0.000,  55.57108,  107.3936]
+    ])
+rectangle_chamberlain.psrsquared = 0.6206
+rectangle_chamberlain.rank = 2
+rectangle_chamberlain.sparsity = 188.0923150272497
+rectangle_chamberlain.bwidth = .063926976464458
+rectangle_chamberlain.kbwidth = 25.61257055690209
+rectangle_chamberlain.df_m = 1
+rectangle_chamberlain.df_r = 233
+rectangle_chamberlain.f_r = .0053165383171297
+rectangle_chamberlain.N = 235
+rectangle_chamberlain.q_v = 582.541259765625
+rectangle_chamberlain.q = .5
+rectangle_chamberlain.sum_rdev = 46278.05667114258
+rectangle_chamberlain.sum_adev = 17559.93220318131
+rectangle_chamberlain.convcode = 0
+
+triangle_bofinger = Bunch()
+triangle_bofinger.table = np.array([
+    [.5601805,  .0138712,  40.38,  0.000,  .5328515,  .5875094],
+    [81.48233,  15.40706,  5.29,  0.000,  51.12738,  111.8373]
+    ])
+triangle_bofinger.psrsquared = 0.6206
+triangle_bofinger.rank = 2
+triangle_bofinger.sparsity = 220.3495620604223
+triangle_bofinger.bwidth = .2173486679767846
+triangle_bofinger.kbwidth = 91.50878448104551
+triangle_bofinger.df_m = 1
+triangle_bofinger.df_r = 233
+triangle_bofinger.f_r = .0045382436463649
+triangle_bofinger.N = 235
+triangle_bofinger.q_v = 582.541259765625
+triangle_bofinger.q = .5
+triangle_bofinger.sum_rdev = 46278.05667114258
+triangle_bofinger.sum_adev = 17559.93220318131
+triangle_bofinger.convcode = 0
+
+triangle_hsheather = Bunch()
+triangle_hsheather.table = np.array([
+    [.5601805,  .0128874,  43.47,  0.000,  .5347898,  .5855711],
+    [81.48233,  14.31431,  5.69,  0.000,  53.2803,  109.6844]
+    ])
+triangle_hsheather.psrsquared = 0.6206
+triangle_hsheather.rank = 2
+triangle_hsheather.sparsity = 204.7212998199564
+triangle_hsheather.bwidth = .1574393314202373
+triangle_hsheather.kbwidth = 64.53302151153288
+triangle_hsheather.df_m = 1
+triangle_hsheather.df_r = 233
+triangle_hsheather.f_r = .004884689579831
+triangle_hsheather.N = 235
+triangle_hsheather.q_v = 582.541259765625
+triangle_hsheather.q = .5
+triangle_hsheather.sum_rdev = 46278.05667114258
+triangle_hsheather.sum_adev = 17559.93220318131
+triangle_hsheather.convcode = 0
+
+triangle_chamberlain = Bunch()
+triangle_chamberlain.table = np.array([
+    [.5601805,  .0115725,  48.41,  0.000,  .5373803,  .5829806],
+    [81.48233,  12.85389,  6.34,  0.000,  56.15764,  106.807]
+    ])
+triangle_chamberlain.psrsquared = 0.6206
+triangle_chamberlain.rank = 2
+triangle_chamberlain.sparsity = 183.8344452913298
+triangle_chamberlain.bwidth = .063926976464458
+triangle_chamberlain.kbwidth = 25.61257055690209
+triangle_chamberlain.df_m = 1
+triangle_chamberlain.df_r = 233
+triangle_chamberlain.f_r = .0054396769790083
+triangle_chamberlain.N = 235
+triangle_chamberlain.q_v = 582.541259765625
+triangle_chamberlain.q = .5
+triangle_chamberlain.sum_rdev = 46278.05667114258
+triangle_chamberlain.sum_adev = 17559.93220318131
+triangle_chamberlain.convcode = 0
+
+gaussian_bofinger = Bunch()
+gaussian_bofinger.table = np.array([
+    [.5601805,  .0197311,  28.39,  0.000,  .5213062,  .5990547],
+    [81.48233,  21.91582,  3.72,  0.000,  38.30383,  124.6608]
+    ])
+gaussian_bofinger.psrsquared = 0.6206
+gaussian_bofinger.rank = 2
+gaussian_bofinger.sparsity = 313.4370075776719
+gaussian_bofinger.bwidth = .2173486679767846
+gaussian_bofinger.kbwidth = 91.50878448104551
+gaussian_bofinger.df_m = 1
+gaussian_bofinger.df_r = 233
+gaussian_bofinger.f_r = .0031904337261521
+gaussian_bofinger.N = 235
+gaussian_bofinger.q_v = 582.541259765625
+gaussian_bofinger.q = .5
+gaussian_bofinger.sum_rdev = 46278.05667114258
+gaussian_bofinger.sum_adev = 17559.93220318131
+gaussian_bofinger.convcode = 0
+
+gaussian_hsheather = Bunch()
+gaussian_hsheather.table = np.array([
+    [.5601805,  .016532,  33.88,  0.000,  .5276092,  .5927518],
+    [81.48233,  18.36248,  4.44,  0.000,  45.30462,  117.66]
+    ])
+gaussian_hsheather.psrsquared = 0.6206
+gaussian_hsheather.rank = 2
+gaussian_hsheather.sparsity = 262.6175743002715
+gaussian_hsheather.bwidth = .1574393314202373
+gaussian_hsheather.kbwidth = 64.53302151153288
+gaussian_hsheather.df_m = 1
+gaussian_hsheather.df_r = 233
+gaussian_hsheather.f_r = .0038078182797341
+gaussian_hsheather.N = 235
+gaussian_hsheather.q_v = 582.541259765625
+gaussian_hsheather.q = .5
+gaussian_hsheather.sum_rdev = 46278.05667114258
+gaussian_hsheather.sum_adev = 17559.93220318131
+gaussian_hsheather.convcode = 0
+
+gaussian_chamberlain = Bunch()
+gaussian_chamberlain.table = np.array([
+    [.5601805,  .0128123,  43.72,  0.000,  .5349378,  .5854232],
+    [81.48233,  14.23088,  5.73,  0.000,  53.44468,  109.52]
+    ])
+gaussian_chamberlain.psrsquared = 0.6206
+gaussian_chamberlain.rank = 2
+gaussian_chamberlain.sparsity = 203.5280962791137
+gaussian_chamberlain.bwidth = .063926976464458
+gaussian_chamberlain.kbwidth = 25.61257055690209
+gaussian_chamberlain.df_m = 1
+gaussian_chamberlain.df_r = 233
+gaussian_chamberlain.f_r = .004913326554328
+gaussian_chamberlain.N = 235
+gaussian_chamberlain.q_v = 582.541259765625
+gaussian_chamberlain.q = .5
+gaussian_chamberlain.sum_rdev = 46278.05667114258
+gaussian_chamberlain.sum_adev = 17559.93220318131
+gaussian_chamberlain.convcode = 0
+
+cosine_bofinger = Bunch()
+cosine_bofinger.table = np.array([
+    [.5601805,  .0121011,  46.29,  0.000,  .536339,  .5840219],
+    [81.48233,  13.44092,  6.06,  0.000,  55.00106,  107.9636]
+    ])
+cosine_bofinger.psrsquared = 0.6206
+cosine_bofinger.rank = 2
+cosine_bofinger.sparsity = 192.2302014415605
+cosine_bofinger.bwidth = .2173486679767846
+cosine_bofinger.kbwidth = 91.50878448104551
+cosine_bofinger.df_m = 1
+cosine_bofinger.df_r = 233
+cosine_bofinger.f_r = .0052020961976883
+cosine_bofinger.N = 235
+cosine_bofinger.q_v = 582.541259765625
+cosine_bofinger.q = .5
+cosine_bofinger.sum_rdev = 46278.05667114258
+cosine_bofinger.sum_adev = 17559.93220318131
+cosine_bofinger.convcode = 0
+
+cosine_hsheather = Bunch()
+cosine_hsheather.table = np.array([
+    [.5601805,  .0116679,  48.01,  0.000,  .5371924,  .5831685],
+    [81.48233,  12.9598,  6.29,  0.000,  55.94897,  107.0157]
+])
+cosine_hsheather.psrsquared = 0.6206
+cosine_hsheather.rank = 2
+cosine_hsheather.sparsity = 185.349198428224
+cosine_hsheather.bwidth = .1574393314202373
+cosine_hsheather.kbwidth = 64.53302151153288
+cosine_hsheather.df_m = 1
+cosine_hsheather.df_r = 233
+cosine_hsheather.f_r = .0053952216059205
+cosine_hsheather.N = 235
+cosine_hsheather.q_v = 582.541259765625
+cosine_hsheather.q = .5
+cosine_hsheather.sum_rdev = 46278.05667114258
+cosine_hsheather.sum_adev = 17559.93220318131
+cosine_hsheather.convcode = 0
+
+cosine_chamberlain = Bunch()
+cosine_chamberlain.table = np.array([
+    [.5601805,  .0106479,  52.61,  0.000,  .539202,  .5811589],
+    [81.48233,  11.82688,  6.89,  0.000,  58.18104,  104.7836]
+])
+cosine_chamberlain.psrsquared = 0.6206
+cosine_chamberlain.rank = 2
+cosine_chamberlain.sparsity = 169.1463943762948
+cosine_chamberlain.bwidth = .063926976464458
+cosine_chamberlain.kbwidth = 25.61257055690209
+cosine_chamberlain.df_m = 1
+cosine_chamberlain.df_r = 233
+cosine_chamberlain.f_r = .0059120385254878
+cosine_chamberlain.N = 235
+cosine_chamberlain.q_v = 582.541259765625
+cosine_chamberlain.q = .5
+cosine_chamberlain.sum_rdev = 46278.05667114258
+cosine_chamberlain.sum_adev = 17559.93220318131
+cosine_chamberlain.convcode = 0
+
+parzen_bofinger = Bunch()
+parzen_bofinger.table = np.array([
+    [.5601805,  .012909,  43.39,  0.000,  .5347471,  .5856138],
+    [81.48233,  14.33838,  5.68,  0.000,  53.23289,  109.7318]
+])
+parzen_bofinger.psrsquared = 0.6206
+parzen_bofinger.rank = 2
+parzen_bofinger.sparsity = 205.0654663067616
+parzen_bofinger.bwidth = .2173486679767846
+parzen_bofinger.kbwidth = 91.50878448104551
+parzen_bofinger.df_m = 1
+parzen_bofinger.df_r = 233
+parzen_bofinger.f_r = .0048764914834762
+parzen_bofinger.N = 235
+parzen_bofinger.q_v = 582.541259765625
+parzen_bofinger.q = .5
+parzen_bofinger.sum_rdev = 46278.05667114258
+parzen_bofinger.sum_adev = 17559.93220318131
+parzen_bofinger.convcode = 0
+
+parzen_hsheather = Bunch()
+parzen_hsheather.table = np.array([
+    [.5601805,  .0122688,  45.66,  0.000,  .5360085,  .5843524],
+    [81.48233,  13.62723,  5.98,  0.000,  54.63401,  108.3307]
+])
+parzen_hsheather.psrsquared = 0.6206
+parzen_hsheather.rank = 2
+parzen_hsheather.sparsity = 194.8946558099188
+parzen_hsheather.bwidth = .1574393314202373
+parzen_hsheather.kbwidth = 64.53302151153288
+parzen_hsheather.df_m = 1
+parzen_hsheather.df_r = 233
+parzen_hsheather.f_r = .0051309770185556
+parzen_hsheather.N = 235
+parzen_hsheather.q_v = 582.541259765625
+parzen_hsheather.q = .5
+parzen_hsheather.sum_rdev = 46278.05667114258
+parzen_hsheather.sum_adev = 17559.93220318131
+parzen_hsheather.convcode = 0
+
+parzen_chamberlain = Bunch()
+parzen_chamberlain.table = np.array([
+    [.5601805,  .0110507,  50.69,  0.000,  .5384084,  .5819526],
+    [81.48233,  12.2743,  6.64,  0.000,  57.29954,  105.6651]
+])
+parzen_chamberlain.psrsquared = 0.6206
+parzen_chamberlain.rank = 2
+parzen_chamberlain.sparsity = 175.5452813763412
+parzen_chamberlain.bwidth = .063926976464458
+parzen_chamberlain.kbwidth = 25.61257055690209
+parzen_chamberlain.df_m = 1
+parzen_chamberlain.df_r = 233
+parzen_chamberlain.f_r = .0056965359146063
+parzen_chamberlain.N = 235
+parzen_chamberlain.q_v = 582.541259765625
+parzen_chamberlain.q = .5
+parzen_chamberlain.sum_rdev = 46278.05667114258
+parzen_chamberlain.sum_adev = 17559.93220318131
+parzen_chamberlain.convcode = 0
+
+Rquantreg = Bunch()
+Rquantreg.fittedvalues = np.array([
+    278.946531823426, 327.662259651587, 472.195784028597, 366.902127539958,
+    411.817682123087, 490.131199885949, 443.36524597881, 503.536477958636,
+    636.406081281679, 709.736288922034, 312.165058899648, 357.917286612496,
+    427.907157504212, 333.474578745265, 396.777813086185, 447.125068738706,
+    325.117049130677, 349.771067249961, 481.598886608367, 306.106158691415,
+    388.420502955027, 511.05437589194, 313.836609745169, 372.960145596262,
+    485.358358918327, 284.379882747628, 346.21761302202, 470.314386890694,
+    292.735362869831, 345.174109237497, 431.875199165716, 312.003504742171,
+    396.809344806674, 474.141604734191, 463.93526593027, 430.280150030025,
+    453.602705891221, 579.151509166254, 320.586493222875, 379.637682965454,
+    261.63071606774, 452.262394881918, 560.558633285135, 361.453261675451,
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+    934.235038902725, 699.263962186247, 403.470311834164, 431.875199165716,
+    610.619729234566, 592.662336978523, 365.021855935831, 494.3226340628,
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+    387.548077543462, 455.939284414524, 461.007422390708, 469.504909066202,
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+    913.373865558921, 596.066409235456, 562.142375003233, 844.328711460744,
+    517.593907165227, 463.853902738299, 1087.39298812569, 583.363256443676,
+    601.337926899021, 720.657238588974, 386.421327566452, 493.072243052398,
+    493.072243052398, 493.072243052398, 487.416418391789, 625.768736956952,
+    717.442211022949, 649.545102325439, 315.764483006998, 387.279123698926,
+    725.770230770932, 534.438688677752, 265.753297513836, 265.753297513836,
+    275.266770483504, 444.714192650306, 357.226955834115, 466.694950404995,
+    521.878226495306, 514.492832530541, 401.82351113515, 308.781536524607,
+    410.920025494164, 506.942199600954, 426.771590593594, 444.329655290455,
+    567.951437149727, 314.044484679614, 341.637888287124, 390.011222091572,
+    371.617953454683, 491.650387255751, 309.86707973756, 339.215312155678,
+    401.313514123063, 274.198577993648, 366.737853359169, 602.621623875386,
+    397.5527766612, 431.743315417498, 502.103203876367, 758.493699184236,
+    354.616793574895, 495.299694296035, 445.994704272822, 461.179388784645,
+    492.398877810457, 300.177654706392, 351.604018662754, 397.001476569931,
+    443.325261537312, 495.754449130328, 597.467889923833, 495.384438403965,
+    563.913157758648, 890.789267794131, 326.949960612346, 296.399786939819,
+    336.191062827908, 406.145865402583, 678.81933687482, 997.557647439337,
+    365.6649642219, 415.242904378452, 543.581822640472, 310.924419326878,
+    519.951702825405, 751.022676264054, 422.151172626615, 604.684498888169,
+    836.51478813311, 277.051441764913, 287.126391607546, 327.57744312456,
+    343.712072517474, 408.684566875422, 535.079861284096
+])
+
+Rquantreg.residuals = np.array([
+    -23.1071072288498, -16.7035925924416, 13.4842301424879,
+    36.0952280042049, 83.7430928106889, 143.666615246444, 187.391321726407,
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+    -18.5990635840888, 32.0472277998125, 187.478653019356,
+    131.972664872163, 54.6380484088667, 592.550594160211, 93.7637141660844,
+    60.0193510971532, 93.5633996863289, -46.5847395899585,
+    25.0901440883857, 35.3349035566588, 38.0569510057356, 46.1382548463761,
+    -21.4915275364559, -274.821124029843, 23.7637774954742,
+    56.1174237129451, 137.267370647069, 194.004180243594, 257.734838779675,
+    151.279738459292, 175.792712085095, 5.37066578891586, 161.87201799729,
+    301.825301306357, 337.590468355399, 141.753670144823, 248.534867350933,
+    138.672153434971, 191.302922030722, 169.266111420092, 337.411668163301,
+    247.595365061674, 324.241547334534, 441.345158140916,
+    -2.96336484193375, 128.045086149367, 128.045086149367,
+    128.045086149367, 61.183812841238, 119.466557499153, 120.358284525061,
+    145.795139311381, 102.833084929086, 121.51832618868, 157.507780852421,
+    208.08888035649, -23.4330955930956, -23.4330955930956,
+    -9.26578861762408, 170.044609958764, 28.0914420512634,
+    48.9250186205624, 186.600476930648, 219.742798279756, 31.1774726408891,
+    18.6372355131512, 18.1199081442213, 112.698628091063,
+    -25.9726125403696, 176.470984835994, 252.045003846191,
+    46.8335329758523, 54.1229163256514, 51.9888300300891, 32.4204776768067,
+    179.14892185478, -12.2969284011601, 14.2728509760057,
+    -17.3759292437432, 10.6022252751131, 64.362111397076, 198.730134737533,
+    50.898482335236, 146.167755331756, 68.4178084237137, 106.826836694182,
+    89.9409707510827, 185.120132493866, 130.283190180665, 170.618786347098,
+    116.24297250849, 0.822265604206791, 26.3943953963694, 0,
+    145.19420242141, 186.007125906439, 209.892380176326, 201.416658681816,
+    247.283084092749, 414.930873548859, 115.050091509315, 57.2015105045924,
+    131.809734683313, 120.611481397197, 211.419693391706, 321.245634797383,
+    -34.6644264712713, 1.15862100546684, 53.2587319600932,
+    97.5747986822971, 255.069199707627, 387.139369759176, 63.3685935821613,
+    168.076643464964, 157.44822913826, 28.387531965303, 19.3926869633912,
+    -28.3781151570223, 124.288724993746, 113.917339005042, 215.240302135105
+])
diff --git a/shared/package-files/09/0934de8f9f68aabdbae28e8c9fab75905a3df484614bb04be50f77c359b049e2/_functional_assert_scalar_ops.h b/shared/package-files/09/0934de8f9f68aabdbae28e8c9fab75905a3df484614bb04be50f77c359b049e2/_functional_assert_scalar_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..bdddd008470438109129901215c59c738d9de051
--- /dev/null
+++ b/shared/package-files/09/0934de8f9f68aabdbae28e8c9fab75905a3df484614bb04be50f77c359b049e2/_functional_assert_scalar_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _functional_assert_scalar {
+  using schema = at::Tensor (const at::Scalar &, c10::string_view, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_functional_assert_scalar")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_functional_assert_scalar(Scalar self, str assert_msg, Tensor dep_token) -> Tensor")
+  static at::Tensor call(const at::Scalar & self, c10::string_view assert_msg, const at::Tensor & dep_token);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & self, c10::string_view assert_msg, const at::Tensor & dep_token);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/09/0937a7157f289e976861b7b61f621ee424d94363dcac53dbfc45dec89a4635d8/mutator.py b/shared/package-files/09/0937a7157f289e976861b7b61f621ee424d94363dcac53dbfc45dec89a4635d8/mutator.py
new file mode 100644
index 0000000000000000000000000000000000000000..082750f56d4644b63e06025cf07085467812af9c
--- /dev/null
+++ b/shared/package-files/09/0937a7157f289e976861b7b61f621ee424d94363dcac53dbfc45dec89a4635d8/mutator.py
@@ -0,0 +1,528 @@
+"""
+Instantiate a variation font.  Run, eg:
+
+.. code-block:: sh
+
+    $ fonttools varLib.mutator ./NotoSansArabic-VF.ttf wght=140 wdth=85
+
+.. warning::
+   ``fontTools.varLib.mutator`` is deprecated in favor of :mod:`fontTools.varLib.instancer`
+   which provides equivalent full instancing and also supports partial instancing.
+   Please migrate CLI usage to ``fonttools varLib.instancer`` and API usage to
+   :func:`fontTools.varLib.instancer.instantiateVariableFont`.
+"""
+
+from fontTools.misc.fixedTools import floatToFixedToFloat, floatToFixed
+from fontTools.misc.loggingTools import deprecateFunction
+from fontTools.misc.roundTools import otRound
+from fontTools.pens.boundsPen import BoundsPen
+from fontTools.ttLib import TTFont, newTable
+from fontTools.ttLib.tables import ttProgram
+from fontTools.ttLib.tables._g_l_y_f import (
+    GlyphCoordinates,
+    flagOverlapSimple,
+    OVERLAP_COMPOUND,
+)
+from fontTools.varLib.models import (
+    supportScalar,
+    normalizeLocation,
+    piecewiseLinearMap,
+)
+from fontTools.varLib.merger import MutatorMerger
+from fontTools.varLib.varStore import VarStoreInstancer
+from fontTools.varLib.mvar import MVAR_ENTRIES
+from fontTools.varLib.iup import iup_delta
+import fontTools.subset.cff
+import os.path
+import logging
+from io import BytesIO
+
+log = logging.getLogger("fontTools.varlib.mutator")
+
+# map 'wdth' axis (1..200) to OS/2.usWidthClass (1..9), rounding to closest
+OS2_WIDTH_CLASS_VALUES = {}
+percents = [50.0, 62.5, 75.0, 87.5, 100.0, 112.5, 125.0, 150.0, 200.0]
+for i, (prev, curr) in enumerate(zip(percents[:-1], percents[1:]), start=1):
+    half = (prev + curr) / 2
+    OS2_WIDTH_CLASS_VALUES[half] = i
+
+
+def interpolate_cff2_PrivateDict(topDict, interpolateFromDeltas):
+    pd_blend_lists = (
+        "BlueValues",
+        "OtherBlues",
+        "FamilyBlues",
+        "FamilyOtherBlues",
+        "StemSnapH",
+        "StemSnapV",
+    )
+    pd_blend_values = ("BlueScale", "BlueShift", "BlueFuzz", "StdHW", "StdVW")
+    for fontDict in topDict.FDArray:
+        pd = fontDict.Private
+        vsindex = pd.vsindex if (hasattr(pd, "vsindex")) else 0
+        for key, value in pd.rawDict.items():
+            if (key in pd_blend_values) and isinstance(value, list):
+                delta = interpolateFromDeltas(vsindex, value[1:])
+                pd.rawDict[key] = otRound(value[0] + delta)
+            elif (key in pd_blend_lists) and isinstance(value[0], list):
+                """If any argument in a BlueValues list is a blend list,
+                then they all are. The first value of each list is an
+                absolute value. The delta tuples are calculated from
+                relative master values, hence we need to append all the
+                deltas to date to each successive absolute value."""
+                delta = 0
+                for i, val_list in enumerate(value):
+                    delta += otRound(interpolateFromDeltas(vsindex, val_list[1:]))
+                    value[i] = val_list[0] + delta
+
+
+def interpolate_cff2_charstrings(topDict, interpolateFromDeltas, glyphOrder):
+    charstrings = topDict.CharStrings
+    for gname in glyphOrder:
+        # Interpolate charstring
+        # e.g replace blend op args with regular args,
+        # and use and discard vsindex op.
+        charstring = charstrings[gname]
+        new_program = []
+        vsindex = 0
+        last_i = 0
+        for i, token in enumerate(charstring.program):
+            if token == "vsindex":
+                vsindex = charstring.program[i - 1]
+                if last_i != 0:
+                    new_program.extend(charstring.program[last_i : i - 1])
+                last_i = i + 1
+            elif token == "blend":
+                num_regions = charstring.getNumRegions(vsindex)
+                numMasters = 1 + num_regions
+                num_args = charstring.program[i - 1]
+                # The program list starting at program[i] is now:
+                # ..args for following operations
+                # num_args values  from the default font
+                # num_args tuples, each with numMasters-1 delta values
+                # num_blend_args
+                # 'blend'
+                argi = i - (num_args * numMasters + 1)
+                end_args = tuplei = argi + num_args
+                while argi < end_args:
+                    next_ti = tuplei + num_regions
+                    deltas = charstring.program[tuplei:next_ti]
+                    delta = interpolateFromDeltas(vsindex, deltas)
+                    charstring.program[argi] += otRound(delta)
+                    tuplei = next_ti
+                    argi += 1
+                new_program.extend(charstring.program[last_i:end_args])
+                last_i = i + 1
+        if last_i != 0:
+            new_program.extend(charstring.program[last_i:])
+            charstring.program = new_program
+
+
+def interpolate_cff2_metrics(varfont, topDict, glyphOrder, loc):
+    """Unlike TrueType glyphs, neither advance width nor bounding box
+    info is stored in a CFF2 charstring. The width data exists only in
+    the hmtx and HVAR tables. Since LSB data cannot be interpolated
+    reliably from the master LSB values in the hmtx table, we traverse
+    the charstring to determine the actual bound box."""
+
+    charstrings = topDict.CharStrings
+    boundsPen = BoundsPen(glyphOrder)
+    hmtx = varfont["hmtx"]
+    hvar_table = None
+    if "HVAR" in varfont:
+        hvar_table = varfont["HVAR"].table
+        fvar = varfont["fvar"]
+        varStoreInstancer = VarStoreInstancer(hvar_table.VarStore, fvar.axes, loc)
+
+    for gid, gname in enumerate(glyphOrder):
+        entry = list(hmtx[gname])
+        # get width delta.
+        if hvar_table:
+            if hvar_table.AdvWidthMap:
+                width_idx = hvar_table.AdvWidthMap.mapping[gname]
+            else:
+                width_idx = gid
+            width_delta = otRound(varStoreInstancer[width_idx])
+        else:
+            width_delta = 0
+
+        # get LSB.
+        boundsPen.init()
+        charstring = charstrings[gname]
+        charstring.draw(boundsPen)
+        if boundsPen.bounds is None:
+            # Happens with non-marking glyphs
+            lsb_delta = 0
+        else:
+            lsb = otRound(boundsPen.bounds[0])
+            lsb_delta = entry[1] - lsb
+
+        if lsb_delta or width_delta:
+            if width_delta:
+                entry[0] = max(0, entry[0] + width_delta)
+            if lsb_delta:
+                entry[1] = lsb
+            hmtx[gname] = tuple(entry)
+
+
+@deprecateFunction(
+    "use fontTools.varLib.instancer.instantiateVariableFont instead "
+    "for either full or partial instancing",
+)
+def instantiateVariableFont(varfont, location, inplace=False, overlap=True):
+    """Generate a static instance from a variable TTFont and a dictionary
+    defining the desired location along the variable font's axes.
+    The location values must be specified as user-space coordinates, e.g.:
+
+    .. code-block::
+
+        {'wght': 400, 'wdth': 100}
+
+    By default, a new TTFont object is returned. If ``inplace`` is True, the
+    input varfont is modified and reduced to a static font.
+
+    When the overlap parameter is defined as True,
+    OVERLAP_SIMPLE and OVERLAP_COMPOUND bits are set to 1.  See
+    https://docs.microsoft.com/en-us/typography/opentype/spec/glyf
+    """
+    if not inplace:
+        # make a copy to leave input varfont unmodified
+        stream = BytesIO()
+        varfont.save(stream)
+        stream.seek(0)
+        varfont = TTFont(stream)
+
+    fvar = varfont["fvar"]
+    axes = {a.axisTag: (a.minValue, a.defaultValue, a.maxValue) for a in fvar.axes}
+    loc = normalizeLocation(location, axes)
+    if "avar" in varfont:
+        maps = varfont["avar"].segments
+        loc = {k: piecewiseLinearMap(v, maps[k]) for k, v in loc.items()}
+    # Quantize to F2Dot14, to avoid surprise interpolations.
+    loc = {k: floatToFixedToFloat(v, 14) for k, v in loc.items()}
+    # Location is normalized now
+    log.info("Normalized location: %s", loc)
+
+    if "gvar" in varfont:
+        log.info("Mutating glyf/gvar tables")
+        gvar = varfont["gvar"]
+        glyf = varfont["glyf"]
+        hMetrics = varfont["hmtx"].metrics
+        vMetrics = getattr(varfont.get("vmtx"), "metrics", None)
+        # get list of glyph names in gvar sorted by component depth
+        glyphnames = sorted(
+            gvar.variations.keys(),
+            key=lambda name: (
+                (
+                    glyf[name].getCompositeMaxpValues(glyf).maxComponentDepth
+                    if glyf[name].isComposite()
+                    else 0
+                ),
+                name,
+            ),
+        )
+        for glyphname in glyphnames:
+            variations = gvar.variations[glyphname]
+            coordinates, _ = glyf._getCoordinatesAndControls(
+                glyphname, hMetrics, vMetrics
+            )
+            origCoords, endPts = None, None
+            for var in variations:
+                scalar = supportScalar(loc, var.axes)
+                if not scalar:
+                    continue
+                delta = var.coordinates
+                if None in delta:
+                    if origCoords is None:
+                        origCoords, g = glyf._getCoordinatesAndControls(
+                            glyphname, hMetrics, vMetrics
+                        )
+                    delta = iup_delta(delta, origCoords, g.endPts)
+                coordinates += GlyphCoordinates(delta) * scalar
+            glyf._setCoordinates(glyphname, coordinates, hMetrics, vMetrics)
+    else:
+        glyf = None
+
+    if "DSIG" in varfont:
+        del varfont["DSIG"]
+
+    if "cvar" in varfont:
+        log.info("Mutating cvt/cvar tables")
+        cvar = varfont["cvar"]
+        cvt = varfont["cvt "]
+        deltas = {}
+        for var in cvar.variations:
+            scalar = supportScalar(loc, var.axes)
+            if not scalar:
+                continue
+            for i, c in enumerate(var.coordinates):
+                if c is not None:
+                    deltas[i] = deltas.get(i, 0) + scalar * c
+        for i, delta in deltas.items():
+            cvt[i] += otRound(delta)
+
+    if "CFF2" in varfont:
+        log.info("Mutating CFF2 table")
+        glyphOrder = varfont.getGlyphOrder()
+        CFF2 = varfont["CFF2"]
+        topDict = CFF2.cff.topDictIndex[0]
+        vsInstancer = VarStoreInstancer(topDict.VarStore.otVarStore, fvar.axes, loc)
+        interpolateFromDeltas = vsInstancer.interpolateFromDeltas
+        interpolate_cff2_PrivateDict(topDict, interpolateFromDeltas)
+        CFF2.desubroutinize()
+        interpolate_cff2_charstrings(topDict, interpolateFromDeltas, glyphOrder)
+        interpolate_cff2_metrics(varfont, topDict, glyphOrder, loc)
+        del topDict.rawDict["VarStore"]
+        del topDict.VarStore
+
+    if "MVAR" in varfont:
+        log.info("Mutating MVAR table")
+        mvar = varfont["MVAR"].table
+        varStoreInstancer = VarStoreInstancer(mvar.VarStore, fvar.axes, loc)
+        records = mvar.ValueRecord
+        for rec in records:
+            mvarTag = rec.ValueTag
+            if mvarTag not in MVAR_ENTRIES:
+                continue
+            tableTag, itemName = MVAR_ENTRIES[mvarTag]
+            delta = otRound(varStoreInstancer[rec.VarIdx])
+            if not delta:
+                continue
+            setattr(
+                varfont[tableTag],
+                itemName,
+                getattr(varfont[tableTag], itemName) + delta,
+            )
+
+    log.info("Mutating FeatureVariations")
+    for tableTag in "GSUB", "GPOS":
+        if not tableTag in varfont:
+            continue
+        table = varfont[tableTag].table
+        if not getattr(table, "FeatureVariations", None):
+            continue
+        variations = table.FeatureVariations
+        for record in variations.FeatureVariationRecord:
+            applies = True
+            for condition in record.ConditionSet.ConditionTable:
+                if condition.Format == 1:
+                    axisIdx = condition.AxisIndex
+                    axisTag = fvar.axes[axisIdx].axisTag
+                    Min = condition.FilterRangeMinValue
+                    Max = condition.FilterRangeMaxValue
+                    v = loc[axisTag]
+                    if not (Min <= v <= Max):
+                        applies = False
+                else:
+                    applies = False
+                if not applies:
+                    break
+
+            if applies:
+                assert record.FeatureTableSubstitution.Version == 0x00010000
+                for rec in record.FeatureTableSubstitution.SubstitutionRecord:
+                    table.FeatureList.FeatureRecord[rec.FeatureIndex].Feature = (
+                        rec.Feature
+                    )
+                break
+        del table.FeatureVariations
+
+    if "GDEF" in varfont and varfont["GDEF"].table.Version >= 0x00010003:
+        log.info("Mutating GDEF/GPOS/GSUB tables")
+        gdef = varfont["GDEF"].table
+        instancer = VarStoreInstancer(gdef.VarStore, fvar.axes, loc)
+
+        merger = MutatorMerger(varfont, instancer)
+        merger.mergeTables(varfont, [varfont], ["GDEF", "GPOS"])
+
+        # Downgrade GDEF.
+        del gdef.VarStore
+        gdef.Version = 0x00010002
+        if gdef.MarkGlyphSetsDef is None:
+            del gdef.MarkGlyphSetsDef
+            gdef.Version = 0x00010000
+
+        if not (
+            gdef.LigCaretList
+            or gdef.MarkAttachClassDef
+            or gdef.GlyphClassDef
+            or gdef.AttachList
+            or (gdef.Version >= 0x00010002 and gdef.MarkGlyphSetsDef)
+        ):
+            del varfont["GDEF"]
+
+    addidef = False
+    if glyf:
+        for glyph in glyf.glyphs.values():
+            if hasattr(glyph, "program"):
+                instructions = glyph.program.getAssembly()
+                # If GETVARIATION opcode is used in bytecode of any glyph add IDEF
+                addidef = any(op.startswith("GETVARIATION") for op in instructions)
+                if addidef:
+                    break
+        if overlap:
+            for glyph_name in glyf.keys():
+                glyph = glyf[glyph_name]
+                # Set OVERLAP_COMPOUND bit for compound glyphs
+                if glyph.isComposite():
+                    glyph.components[0].flags |= OVERLAP_COMPOUND
+                # Set OVERLAP_SIMPLE bit for simple glyphs
+                elif glyph.numberOfContours > 0:
+                    glyph.flags[0] |= flagOverlapSimple
+    if addidef:
+        log.info("Adding IDEF to fpgm table for GETVARIATION opcode")
+        asm = []
+        if "fpgm" in varfont:
+            fpgm = varfont["fpgm"]
+            asm = fpgm.program.getAssembly()
+        else:
+            fpgm = newTable("fpgm")
+            fpgm.program = ttProgram.Program()
+            varfont["fpgm"] = fpgm
+        asm.append("PUSHB[000] 145")
+        asm.append("IDEF[ ]")
+        args = [str(len(loc))]
+        for a in fvar.axes:
+            args.append(str(floatToFixed(loc[a.axisTag], 14)))
+        asm.append("NPUSHW[ ] " + " ".join(args))
+        asm.append("ENDF[ ]")
+        fpgm.program.fromAssembly(asm)
+
+        # Change maxp attributes as IDEF is added
+        if "maxp" in varfont:
+            maxp = varfont["maxp"]
+            setattr(
+                maxp, "maxInstructionDefs", 1 + getattr(maxp, "maxInstructionDefs", 0)
+            )
+            setattr(
+                maxp,
+                "maxStackElements",
+                max(len(loc), getattr(maxp, "maxStackElements", 0)),
+            )
+
+    if "name" in varfont:
+        log.info("Pruning name table")
+        exclude = {a.axisNameID for a in fvar.axes}
+        for i in fvar.instances:
+            exclude.add(i.subfamilyNameID)
+            exclude.add(i.postscriptNameID)
+        if "ltag" in varfont:
+            # Drop the whole 'ltag' table if all its language tags are referenced by
+            # name records to be pruned.
+            # TODO: prune unused ltag tags and re-enumerate langIDs accordingly
+            excludedUnicodeLangIDs = [
+                n.langID
+                for n in varfont["name"].names
+                if n.nameID in exclude and n.platformID == 0 and n.langID != 0xFFFF
+            ]
+            if set(excludedUnicodeLangIDs) == set(range(len((varfont["ltag"].tags)))):
+                del varfont["ltag"]
+        varfont["name"].names[:] = [
+            n
+            for n in varfont["name"].names
+            if n.nameID < 256 or n.nameID not in exclude
+        ]
+
+    if "wght" in location and "OS/2" in varfont:
+        varfont["OS/2"].usWeightClass = otRound(max(1, min(location["wght"], 1000)))
+    if "wdth" in location:
+        wdth = location["wdth"]
+        for percent, widthClass in sorted(OS2_WIDTH_CLASS_VALUES.items()):
+            if wdth < percent:
+                varfont["OS/2"].usWidthClass = widthClass
+                break
+        else:
+            varfont["OS/2"].usWidthClass = 9
+    if "slnt" in location and "post" in varfont:
+        varfont["post"].italicAngle = max(-90, min(location["slnt"], 90))
+
+    log.info("Removing variable tables")
+    for tag in ("avar", "cvar", "fvar", "gvar", "HVAR", "MVAR", "VVAR", "STAT"):
+        if tag in varfont:
+            del varfont[tag]
+
+    return varfont
+
+
+def main(args=None):
+    """Instantiate a variation font"""
+    from fontTools import configLogger
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        "fonttools varLib.mutator", description="Instantiate a variable font"
+    )
+    parser.add_argument("input", metavar="INPUT.ttf", help="Input variable TTF file.")
+    parser.add_argument(
+        "locargs",
+        metavar="AXIS=LOC",
+        nargs="*",
+        help="List of space separated locations. A location consist in "
+        "the name of a variation axis, followed by '=' and a number. E.g.: "
+        " wght=700 wdth=80. The default is the location of the base master.",
+    )
+    parser.add_argument(
+        "-o",
+        "--output",
+        metavar="OUTPUT.ttf",
+        default=None,
+        help="Output instance TTF file (default: INPUT-instance.ttf).",
+    )
+    parser.add_argument(
+        "--no-recalc-timestamp",
+        dest="recalc_timestamp",
+        action="store_false",
+        help="Don't set the output font's timestamp to the current time.",
+    )
+    logging_group = parser.add_mutually_exclusive_group(required=False)
+    logging_group.add_argument(
+        "-v", "--verbose", action="store_true", help="Run more verbosely."
+    )
+    logging_group.add_argument(
+        "-q", "--quiet", action="store_true", help="Turn verbosity off."
+    )
+    parser.add_argument(
+        "--no-overlap",
+        dest="overlap",
+        action="store_false",
+        help="Don't set OVERLAP_SIMPLE/OVERLAP_COMPOUND glyf flags.",
+    )
+    options = parser.parse_args(args)
+
+    varfilename = options.input
+    outfile = (
+        os.path.splitext(varfilename)[0] + "-instance.ttf"
+        if not options.output
+        else options.output
+    )
+    configLogger(
+        level=("DEBUG" if options.verbose else "ERROR" if options.quiet else "INFO")
+    )
+
+    loc = {}
+    for arg in options.locargs:
+        try:
+            tag, val = arg.split("=")
+            assert len(tag) <= 4
+            loc[tag.ljust(4)] = float(val)
+        except (ValueError, AssertionError):
+            parser.error("invalid location argument format: %r" % arg)
+    log.info("Location: %s", loc)
+
+    log.info("Loading variable font")
+    varfont = TTFont(varfilename, recalcTimestamp=options.recalc_timestamp)
+
+    instantiateVariableFont(varfont, loc, inplace=True, overlap=options.overlap)
+
+    log.info("Saving instance font %s", outfile)
+    varfont.save(outfile)
+
+
+if __name__ == "__main__":
+    import sys
+
+    if len(sys.argv) > 1:
+        sys.exit(main())
+    import doctest
+
+    sys.exit(doctest.testmod().failed)
diff --git a/shared/package-files/09/0937eba3aba4585c86ec30d86450b6b80de9333c6be40fdab150f0b3c639f81b/index_copy_cpu_dispatch.h b/shared/package-files/09/0937eba3aba4585c86ec30d86450b6b80de9333c6be40fdab150f0b3c639f81b/index_copy_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..587861ac0f1f9aad782d6f3abae6bf70ea60e1c8
--- /dev/null
+++ b/shared/package-files/09/0937eba3aba4585c86ec30d86450b6b80de9333c6be40fdab150f0b3c639f81b/index_copy_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor index_copy(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source);
+TORCH_API at::Tensor & index_copy_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source);
+TORCH_API at::Tensor & index_copy_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, at::Tensor & out);
+TORCH_API at::Tensor & index_copy_(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/09/0938f82cbc2dbac239d47929cc3d093e884fd5f7cdc68800545cfc58bb961733/test_elementwise_functions.py b/shared/package-files/09/0938f82cbc2dbac239d47929cc3d093e884fd5f7cdc68800545cfc58bb961733/test_elementwise_functions.py
new file mode 100644
index 0000000000000000000000000000000000000000..1228d0af2e6a6a8ae504297c7562d3beb5ba9516
--- /dev/null
+++ b/shared/package-files/09/0938f82cbc2dbac239d47929cc3d093e884fd5f7cdc68800545cfc58bb961733/test_elementwise_functions.py
@@ -0,0 +1,114 @@
+from inspect import getfullargspec
+
+from numpy.testing import assert_raises
+
+from .. import asarray, _elementwise_functions
+from .._elementwise_functions import bitwise_left_shift, bitwise_right_shift
+from .._dtypes import (
+    _dtype_categories,
+    _boolean_dtypes,
+    _floating_dtypes,
+    _integer_dtypes,
+)
+
+
+def nargs(func):
+    return len(getfullargspec(func).args)
+
+
+def test_function_types():
+    # Test that every function accepts only the required input types. We only
+    # test the negative cases here (error). The positive cases are tested in
+    # the array API test suite.
+
+    elementwise_function_input_types = {
+        "abs": "numeric",
+        "acos": "floating-point",
+        "acosh": "floating-point",
+        "add": "numeric",
+        "asin": "floating-point",
+        "asinh": "floating-point",
+        "atan": "floating-point",
+        "atan2": "real floating-point",
+        "atanh": "floating-point",
+        "bitwise_and": "integer or boolean",
+        "bitwise_invert": "integer or boolean",
+        "bitwise_left_shift": "integer",
+        "bitwise_or": "integer or boolean",
+        "bitwise_right_shift": "integer",
+        "bitwise_xor": "integer or boolean",
+        "ceil": "real numeric",
+        "conj": "complex floating-point",
+        "cos": "floating-point",
+        "cosh": "floating-point",
+        "divide": "floating-point",
+        "equal": "all",
+        "exp": "floating-point",
+        "expm1": "floating-point",
+        "floor": "real numeric",
+        "floor_divide": "real numeric",
+        "greater": "real numeric",
+        "greater_equal": "real numeric",
+        "imag": "complex floating-point",
+        "isfinite": "numeric",
+        "isinf": "numeric",
+        "isnan": "numeric",
+        "less": "real numeric",
+        "less_equal": "real numeric",
+        "log": "floating-point",
+        "logaddexp": "real floating-point",
+        "log10": "floating-point",
+        "log1p": "floating-point",
+        "log2": "floating-point",
+        "logical_and": "boolean",
+        "logical_not": "boolean",
+        "logical_or": "boolean",
+        "logical_xor": "boolean",
+        "multiply": "numeric",
+        "negative": "numeric",
+        "not_equal": "all",
+        "positive": "numeric",
+        "pow": "numeric",
+        "real": "complex floating-point",
+        "remainder": "real numeric",
+        "round": "numeric",
+        "sign": "numeric",
+        "sin": "floating-point",
+        "sinh": "floating-point",
+        "sqrt": "floating-point",
+        "square": "numeric",
+        "subtract": "numeric",
+        "tan": "floating-point",
+        "tanh": "floating-point",
+        "trunc": "real numeric",
+    }
+
+    def _array_vals():
+        for d in _integer_dtypes:
+            yield asarray(1, dtype=d)
+        for d in _boolean_dtypes:
+            yield asarray(False, dtype=d)
+        for d in _floating_dtypes:
+            yield asarray(1.0, dtype=d)
+
+    for x in _array_vals():
+        for func_name, types in elementwise_function_input_types.items():
+            dtypes = _dtype_categories[types]
+            func = getattr(_elementwise_functions, func_name)
+            if nargs(func) == 2:
+                for y in _array_vals():
+                    if x.dtype not in dtypes or y.dtype not in dtypes:
+                        assert_raises(TypeError, lambda: func(x, y))
+            else:
+                if x.dtype not in dtypes:
+                    assert_raises(TypeError, lambda: func(x))
+
+
+def test_bitwise_shift_error():
+    # bitwise shift functions should raise when the second argument is negative
+    assert_raises(
+        ValueError, lambda: bitwise_left_shift(asarray([1, 1]), asarray([1, -1]))
+    )
+    assert_raises(
+        ValueError, lambda: bitwise_right_shift(asarray([1, 1]), asarray([1, -1]))
+    )
diff --git a/shared/package-files/09/0939d6eba1e1f58eade0f604c8bb8d08b3588dff3d718a67cca19d3221e5e1df/linalg_lstsq_ops.h b/shared/package-files/09/0939d6eba1e1f58eade0f604c8bb8d08b3588dff3d718a67cca19d3221e5e1df/linalg_lstsq_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..1a69ee8bb5e223ce3c801ccef4aaf147aa9e3d1c
--- /dev/null
+++ b/shared/package-files/09/0939d6eba1e1f58eade0f604c8bb8d08b3588dff3d718a67cca19d3221e5e1df/linalg_lstsq_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API linalg_lstsq {
+  using schema = ::std::tuple (const at::Tensor &, const at::Tensor &, c10::optional, c10::optional);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::linalg_lstsq")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "linalg_lstsq(Tensor self, Tensor b, float? rcond=None, *, str? driver=None) -> (Tensor solution, Tensor residuals, Tensor rank, Tensor singular_values)")
+  static ::std::tuple call(const at::Tensor & self, const at::Tensor & b, c10::optional rcond, c10::optional driver);
+  static ::std::tuple redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & b, c10::optional rcond, c10::optional driver);
+};
+
+struct TORCH_API linalg_lstsq_out {
+  using schema = ::std::tuple (const at::Tensor &, const at::Tensor &, c10::optional, c10::optional, at::Tensor &, at::Tensor &, at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::linalg_lstsq")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "linalg_lstsq.out(Tensor self, Tensor b, float? rcond=None, *, str? driver=None, Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values) -> (Tensor(a!) solution, Tensor(b!) residuals, Tensor(c!) rank, Tensor(d!) singular_values)")
+  static ::std::tuple call(const at::Tensor & self, const at::Tensor & b, c10::optional rcond, c10::optional driver, at::Tensor & solution, at::Tensor & residuals, at::Tensor & rank, at::Tensor & singular_values);
+  static ::std::tuple redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & b, c10::optional rcond, c10::optional driver, at::Tensor & solution, at::Tensor & residuals, at::Tensor & rank, at::Tensor & singular_values);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/09/093c08b8b6e09f425fa7baca10f6461bf168d35abda14ebb329c38f12202525b/cuda_buffer.h b/shared/package-files/09/093c08b8b6e09f425fa7baca10f6461bf168d35abda14ebb329c38f12202525b/cuda_buffer.h
new file mode 100644
index 0000000000000000000000000000000000000000..e772304b3ffdd97a39026f018d40ed7877f39e60
--- /dev/null
+++ b/shared/package-files/09/093c08b8b6e09f425fa7baca10f6461bf168d35abda14ebb329c38f12202525b/cuda_buffer.h
@@ -0,0 +1,24 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include 
+
+#include 
+
+namespace tensorpipe {
+
+struct CudaBuffer {
+  void* ptr{nullptr};
+  cudaStream_t stream{cudaStreamDefault};
+
+  Device getDevice() const;
+};
+
+} // namespace tensorpipe
diff --git a/shared/package-files/09/093ece02a65662a114c92cc51ee6823dda612ef2bf0e9d5509b2d5d042dba61d/_foreach_zero.h b/shared/package-files/09/093ece02a65662a114c92cc51ee6823dda612ef2bf0e9d5509b2d5d042dba61d/_foreach_zero.h
new file mode 100644
index 0000000000000000000000000000000000000000..2b9dac4e6fddc4497d3ca5d8c37727ed9a4826e0
--- /dev/null
+++ b/shared/package-files/09/093ece02a65662a114c92cc51ee6823dda612ef2bf0e9d5509b2d5d042dba61d/_foreach_zero.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::_foreach_zero_(Tensor(a!)[] self) -> ()
+inline void _foreach_zero_(at::TensorList self) {
+    return at::_ops::_foreach_zero_::call(self);
+}
+
+// aten::_foreach_zero.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
+inline void _foreach_zero_out(at::TensorList out, at::TensorList self) {
+    return at::_ops::_foreach_zero_out::call(self, out);
+}
+// aten::_foreach_zero.out(Tensor[] self, *, Tensor(a!)[] out) -> ()
+inline void _foreach_zero_outf(at::TensorList self, at::TensorList out) {
+    return at::_ops::_foreach_zero_out::call(self, out);
+}
+
+// aten::_foreach_zero(Tensor[] self) -> Tensor[] self_out
+inline ::std::vector _foreach_zero(at::TensorList self) {
+    return at::_ops::_foreach_zero::call(self);
+}
+
+}
diff --git a/shared/package-files/09/0940b70196c6f88b6301ea2161020e4f9ecde429edc5d87e07b055fe48d16c64/d410-7b b/shared/package-files/09/0940b70196c6f88b6301ea2161020e4f9ecde429edc5d87e07b055fe48d16c64/d410-7b
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diff --git a/shared/package-files/09/094104114df3261ea99a3fa0c2ceda87286c1c94f13b45bd30aada5de160f3d0/test_complex.py b/shared/package-files/09/094104114df3261ea99a3fa0c2ceda87286c1c94f13b45bd30aada5de160f3d0/test_complex.py
new file mode 100644
index 0000000000000000000000000000000000000000..c5cac5a5caf090d85d7284103459c6f03d3d41ce
--- /dev/null
+++ b/shared/package-files/09/094104114df3261ea99a3fa0c2ceda87286c1c94f13b45bd30aada5de160f3d0/test_complex.py
@@ -0,0 +1,195 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Series,
+)
+import pandas._testing as tm
+from pandas.tests.io.pytables.common import ensure_clean_store
+
+from pandas.io.pytables import read_hdf
+
+
+def test_complex_fixed(tmp_path, setup_path):
+    df = DataFrame(
+        np.random.default_rng(2).random((4, 5)).astype(np.complex64),
+        index=list("abcd"),
+        columns=list("ABCDE"),
+    )
+
+    path = tmp_path / setup_path
+    df.to_hdf(path, key="df")
+    reread = read_hdf(path, "df")
+    tm.assert_frame_equal(df, reread)
+
+    df = DataFrame(
+        np.random.default_rng(2).random((4, 5)).astype(np.complex128),
+        index=list("abcd"),
+        columns=list("ABCDE"),
+    )
+    path = tmp_path / setup_path
+    df.to_hdf(path, key="df")
+    reread = read_hdf(path, "df")
+    tm.assert_frame_equal(df, reread)
+
+
+def test_complex_table(tmp_path, setup_path):
+    df = DataFrame(
+        np.random.default_rng(2).random((4, 5)).astype(np.complex64),
+        index=list("abcd"),
+        columns=list("ABCDE"),
+    )
+
+    path = tmp_path / setup_path
+    df.to_hdf(path, key="df", format="table")
+    reread = read_hdf(path, key="df")
+    tm.assert_frame_equal(df, reread)
+
+    df = DataFrame(
+        np.random.default_rng(2).random((4, 5)).astype(np.complex128),
+        index=list("abcd"),
+        columns=list("ABCDE"),
+    )
+
+    path = tmp_path / setup_path
+    df.to_hdf(path, key="df", format="table", mode="w")
+    reread = read_hdf(path, "df")
+    tm.assert_frame_equal(df, reread)
+
+
+def test_complex_mixed_fixed(tmp_path, setup_path):
+    complex64 = np.array(
+        [1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j], dtype=np.complex64
+    )
+    complex128 = np.array(
+        [1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j], dtype=np.complex128
+    )
+    df = DataFrame(
+        {
+            "A": [1, 2, 3, 4],
+            "B": ["a", "b", "c", "d"],
+            "C": complex64,
+            "D": complex128,
+            "E": [1.0, 2.0, 3.0, 4.0],
+        },
+        index=list("abcd"),
+    )
+    path = tmp_path / setup_path
+    df.to_hdf(path, key="df")
+    reread = read_hdf(path, "df")
+    tm.assert_frame_equal(df, reread)
+
+
+def test_complex_mixed_table(tmp_path, setup_path):
+    complex64 = np.array(
+        [1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j], dtype=np.complex64
+    )
+    complex128 = np.array(
+        [1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j], dtype=np.complex128
+    )
+    df = DataFrame(
+        {
+            "A": [1, 2, 3, 4],
+            "B": ["a", "b", "c", "d"],
+            "C": complex64,
+            "D": complex128,
+            "E": [1.0, 2.0, 3.0, 4.0],
+        },
+        index=list("abcd"),
+    )
+
+    with ensure_clean_store(setup_path) as store:
+        store.append("df", df, data_columns=["A", "B"])
+        result = store.select("df", where="A>2")
+        tm.assert_frame_equal(df.loc[df.A > 2], result)
+
+    path = tmp_path / setup_path
+    df.to_hdf(path, key="df", format="table")
+    reread = read_hdf(path, "df")
+    tm.assert_frame_equal(df, reread)
+
+
+def test_complex_across_dimensions_fixed(tmp_path, setup_path):
+    complex128 = np.array([1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j])
+    s = Series(complex128, index=list("abcd"))
+    df = DataFrame({"A": s, "B": s})
+
+    objs = [s, df]
+    comps = [tm.assert_series_equal, tm.assert_frame_equal]
+    for obj, comp in zip(objs, comps):
+        path = tmp_path / setup_path
+        obj.to_hdf(path, key="obj", format="fixed")
+        reread = read_hdf(path, "obj")
+        comp(obj, reread)
+
+
+def test_complex_across_dimensions(tmp_path, setup_path):
+    complex128 = np.array([1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j])
+    s = Series(complex128, index=list("abcd"))
+    df = DataFrame({"A": s, "B": s})
+
+    path = tmp_path / setup_path
+    df.to_hdf(path, key="obj", format="table")
+    reread = read_hdf(path, "obj")
+    tm.assert_frame_equal(df, reread)
+
+
+def test_complex_indexing_error(setup_path):
+    complex128 = np.array(
+        [1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j], dtype=np.complex128
+    )
+    df = DataFrame(
+        {"A": [1, 2, 3, 4], "B": ["a", "b", "c", "d"], "C": complex128},
+        index=list("abcd"),
+    )
+
+    msg = (
+        "Columns containing complex values can be stored "
+        "but cannot be indexed when using table format. "
+        "Either use fixed format, set index=False, "
+        "or do not include the columns containing complex "
+        "values to data_columns when initializing the table."
+    )
+
+    with ensure_clean_store(setup_path) as store:
+        with pytest.raises(TypeError, match=msg):
+            store.append("df", df, data_columns=["C"])
+
+
+def test_complex_series_error(tmp_path, setup_path):
+    complex128 = np.array([1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j, 1.0 + 1.0j])
+    s = Series(complex128, index=list("abcd"))
+
+    msg = (
+        "Columns containing complex values can be stored "
+        "but cannot be indexed when using table format. "
+        "Either use fixed format, set index=False, "
+        "or do not include the columns containing complex "
+        "values to data_columns when initializing the table."
+    )
+
+    path = tmp_path / setup_path
+    with pytest.raises(TypeError, match=msg):
+        s.to_hdf(path, key="obj", format="t")
+
+    path = tmp_path / setup_path
+    s.to_hdf(path, key="obj", format="t", index=False)
+    reread = read_hdf(path, "obj")
+    tm.assert_series_equal(s, reread)
+
+
+def test_complex_append(setup_path):
+    df = DataFrame(
+        {
+            "a": np.random.default_rng(2).standard_normal(100).astype(np.complex128),
+            "b": np.random.default_rng(2).standard_normal(100),
+        }
+    )
+
+    with ensure_clean_store(setup_path) as store:
+        store.append("df", df, data_columns=["b"])
+        store.append("df", df)
+        result = store.select("df")
+        tm.assert_frame_equal(pd.concat([df, df], axis=0), result)
diff --git a/shared/package-files/09/09418309b09cb25002cb7360abece88a0f499cee321d3c3720f70acb4167469c/test_ndim_array_conversions.py b/shared/package-files/09/09418309b09cb25002cb7360abece88a0f499cee321d3c3720f70acb4167469c/test_ndim_array_conversions.py
new file mode 100644
index 0000000000000000000000000000000000000000..f43260ccc636ac461ba0c06dbfcf3fe3a8d5338d
--- /dev/null
+++ b/shared/package-files/09/09418309b09cb25002cb7360abece88a0f499cee321d3c3720f70acb4167469c/test_ndim_array_conversions.py
@@ -0,0 +1,22 @@
+from sympy.tensor.array import (ImmutableDenseNDimArray,
+        ImmutableSparseNDimArray, MutableDenseNDimArray, MutableSparseNDimArray)
+from sympy.abc import x, y, z
+
+
+def test_NDim_array_conv():
+    MD = MutableDenseNDimArray([x, y, z])
+    MS = MutableSparseNDimArray([x, y, z])
+    ID = ImmutableDenseNDimArray([x, y, z])
+    IS = ImmutableSparseNDimArray([x, y, z])
+
+    assert MD.as_immutable() == ID
+    assert MD.as_mutable() == MD
+
+    assert MS.as_immutable() == IS
+    assert MS.as_mutable() == MS
+
+    assert ID.as_immutable() == ID
+    assert ID.as_mutable() == MD
+
+    assert IS.as_immutable() == IS
+    assert IS.as_mutable() == MS
diff --git a/shared/package-files/09/0941ad930214f4205ad12971d7799b68cebbfde734a7eba765dbbc6786183377/testcellnest_7.1_GLNX86.mat b/shared/package-files/09/0941ad930214f4205ad12971d7799b68cebbfde734a7eba765dbbc6786183377/testcellnest_7.1_GLNX86.mat
new file mode 100644
index 0000000000000000000000000000000000000000..b3b086cc31dce2de1e300a1d018b0bf5661b69f3
Binary files /dev/null and b/shared/package-files/09/0941ad930214f4205ad12971d7799b68cebbfde734a7eba765dbbc6786183377/testcellnest_7.1_GLNX86.mat differ
diff --git a/shared/package-files/09/0941b2eb518c2c83dcc1251a0ac62afb0142fdcff081611a738c41bb3a580dea/heart.csv b/shared/package-files/09/0941b2eb518c2c83dcc1251a0ac62afb0142fdcff081611a738c41bb3a580dea/heart.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4a1cd78823f294e3bf791774419722daafd15b79
--- /dev/null
+++ b/shared/package-files/09/0941b2eb518c2c83dcc1251a0ac62afb0142fdcff081611a738c41bb3a580dea/heart.csv
@@ -0,0 +1,70 @@
+survival,censors,age
+15.000000,1.000000,54.300000
+3.000000,1.000000,40.400000
+624.000000,1.000000,51.000000
+46.000000,1.000000,42.500000
+127.000000,1.000000,48.000000
+64.000000,1.000000,54.600000
+1350.000000,1.000000,54.100000
+280.000000,1.000000,49.500000
+23.000000,1.000000,56.900000
+10.000000,1.000000,55.300000
+1024.000000,1.000000,43.400000
+39.000000,1.000000,42.800000
+730.000000,1.000000,58.400000
+136.000000,1.000000,52.000000
+1775.000000,0.000000,33.300000
+1.000000,1.000000,54.200000
+836.000000,1.000000,45.000000
+60.000000,1.000000,64.500000
+1536.000000,0.000000,49.000000
+1549.000000,0.000000,40.600000
+54.000000,1.000000,49.000000
+47.000000,1.000000,61.500000
+1.000000,1.000000,41.500000
+51.000000,1.000000,50.500000
+1367.000000,0.000000,48.600000
+1264.000000,0.000000,45.500000
+44.000000,1.000000,36.200000
+994.000000,1.000000,48.600000
+51.000000,1.000000,47.200000
+1106.000000,0.000000,36.800000
+897.000000,1.000000,46.100000
+253.000000,1.000000,48.800000
+147.000000,1.000000,47.500000
+51.000000,1.000000,52.500000
+875.000000,0.000000,38.900000
+322.000000,1.000000,48.100000
+838.000000,0.000000,41.600000
+65.000000,1.000000,49.100000
+815.000000,0.000000,32.700000
+551.000000,1.000000,48.900000
+66.000000,1.000000,51.300000
+228.000000,1.000000,19.700000
+65.000000,1.000000,45.200000
+660.000000,0.000000,48.000000
+25.000000,1.000000,53.000000
+589.000000,0.000000,47.500000
+592.000000,0.000000,26.700000
+63.000000,1.000000,56.400000
+12.000000,1.000000,29.200000
+499.000000,0.000000,52.200000
+305.000000,0.000000,49.300000
+29.000000,1.000000,54.000000
+456.000000,0.000000,46.500000
+439.000000,0.000000,52.900000
+48.000000,1.000000,53.400000
+297.000000,1.000000,42.800000
+389.000000,0.000000,48.900000
+50.000000,1.000000,46.400000
+339.000000,0.000000,54.400000
+68.000000,1.000000,51.400000
+26.000000,1.000000,52.500000
+30.000000,0.000000,45.800000
+237.000000,0.000000,47.800000
+161.000000,1.000000,43.800000
+14.000000,1.000000,40.300000
+167.000000,0.000000,26.700000
+110.000000,0.000000,23.700000
+13.000000,0.000000,28.900000
+1.000000,0.000000,35.200000
diff --git a/shared/package-files/09/094253f85a16114892089ec19df04567fffbe2c848b8c6c7481850a706d58271/_available_if.py b/shared/package-files/09/094253f85a16114892089ec19df04567fffbe2c848b8c6c7481850a706d58271/_available_if.py
new file mode 100644
index 0000000000000000000000000000000000000000..91dee2641f20cbc8fa3d131188c9173037081db3
--- /dev/null
+++ b/shared/package-files/09/094253f85a16114892089ec19df04567fffbe2c848b8c6c7481850a706d58271/_available_if.py
@@ -0,0 +1,96 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from functools import update_wrapper, wraps
+from types import MethodType
+
+
+class _AvailableIfDescriptor:
+    """Implements a conditional property using the descriptor protocol.
+
+    Using this class to create a decorator will raise an ``AttributeError``
+    if check(self) returns a falsey value. Note that if check raises an error
+    this will also result in hasattr returning false.
+
+    See https://docs.python.org/3/howto/descriptor.html for an explanation of
+    descriptors.
+    """
+
+    def __init__(self, fn, check, attribute_name):
+        self.fn = fn
+        self.check = check
+        self.attribute_name = attribute_name
+
+        # update the docstring of the descriptor
+        update_wrapper(self, fn)
+
+    def _check(self, obj, owner):
+        attr_err_msg = (
+            f"This {owner.__name__!r} has no attribute {self.attribute_name!r}"
+        )
+        try:
+            check_result = self.check(obj)
+        except Exception as e:
+            raise AttributeError(attr_err_msg) from e
+
+        if not check_result:
+            raise AttributeError(attr_err_msg)
+
+    def __get__(self, obj, owner=None):
+        if obj is not None:
+            # delegate only on instances, not the classes.
+            # this is to allow access to the docstrings.
+            self._check(obj, owner=owner)
+            out = MethodType(self.fn, obj)
+
+        else:
+            # This makes it possible to use the decorated method as an unbound method,
+            # for instance when monkeypatching.
+            @wraps(self.fn)
+            def out(*args, **kwargs):
+                self._check(args[0], owner=owner)
+                return self.fn(*args, **kwargs)
+
+        return out
+
+
+def available_if(check):
+    """An attribute that is available only if check returns a truthy value.
+
+    Parameters
+    ----------
+    check : callable
+        When passed the object with the decorated method, this should return
+        a truthy value if the attribute is available, and either return False
+        or raise an AttributeError if not available.
+
+    Returns
+    -------
+    callable
+        Callable makes the decorated method available if `check` returns
+        a truthy value, otherwise the decorated method is unavailable.
+
+    Examples
+    --------
+    >>> from sklearn.utils.metaestimators import available_if
+    >>> class HelloIfEven:
+    ...    def __init__(self, x):
+    ...        self.x = x
+    ...
+    ...    def _x_is_even(self):
+    ...        return self.x % 2 == 0
+    ...
+    ...    @available_if(_x_is_even)
+    ...    def say_hello(self):
+    ...        print("Hello")
+    ...
+    >>> obj = HelloIfEven(1)
+    >>> hasattr(obj, "say_hello")
+    False
+    >>> obj.x = 2
+    >>> hasattr(obj, "say_hello")
+    True
+    >>> obj.say_hello()
+    Hello
+    """
+    return lambda fn: _AvailableIfDescriptor(fn, check, attribute_name=fn.__name__)
diff --git a/shared/package-files/09/0949d3c7e98a2fe6229db51faefc3c58858bc2eaaee0fb52559f4cda6c524af6/nis.pyi b/shared/package-files/09/0949d3c7e98a2fe6229db51faefc3c58858bc2eaaee0fb52559f4cda6c524af6/nis.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..bc6c2bc072566ebc67a93da7e109650fec648160
--- /dev/null
+++ b/shared/package-files/09/0949d3c7e98a2fe6229db51faefc3c58858bc2eaaee0fb52559f4cda6c524af6/nis.pyi
@@ -0,0 +1,9 @@
+import sys
+from typing import Dict, List
+
+if sys.platform != "win32":
+    def cat(map: str, domain: str = ...) -> Dict[str, str]: ...
+    def get_default_domain() -> str: ...
+    def maps(domain: str = ...) -> List[str]: ...
+    def match(key: str, map: str, domain: str = ...) -> str: ...
+    class error(Exception): ...
diff --git a/shared/package-files/09/0949f15ecc3fdff6bcc666a3f823bb673b19619179a8f9bc3a0544a8e943a2a6/sub_meta.h b/shared/package-files/09/0949f15ecc3fdff6bcc666a3f823bb673b19619179a8f9bc3a0544a8e943a2a6/sub_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..c38c77a7466ea23882bf22a7ab0dc92b9f295cde
--- /dev/null
+++ b/shared/package-files/09/0949f15ecc3fdff6bcc666a3f823bb673b19619179a8f9bc3a0544a8e943a2a6/sub_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_sub_Tensor : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self, const at::Tensor & other, const at::Scalar & alpha);
+};
+
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/09/094a35ae4fc294c731108a1b6ff5f18515998b5219fbe1641dacf2918c3c6ba4/__init__.py b/shared/package-files/09/094a35ae4fc294c731108a1b6ff5f18515998b5219fbe1641dacf2918c3c6ba4/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6a73e7b7814175c9e19977fc2ebff2367fc1dca
--- /dev/null
+++ b/shared/package-files/09/094a35ae4fc294c731108a1b6ff5f18515998b5219fbe1641dacf2918c3c6ba4/__init__.py
@@ -0,0 +1,667 @@
+"""
+.. _statsrefmanual:
+
+==========================================
+Statistical functions (:mod:`scipy.stats`)
+==========================================
+
+.. currentmodule:: scipy.stats
+
+This module contains a large number of probability distributions,
+summary and frequency statistics, correlation functions and statistical
+tests, masked statistics, kernel density estimation, quasi-Monte Carlo
+functionality, and more.
+
+Statistics is a very large area, and there are topics that are out of scope
+for SciPy and are covered by other packages. Some of the most important ones
+are:
+
+- `statsmodels `__:
+  regression, linear models, time series analysis, extensions to topics
+  also covered by ``scipy.stats``.
+- `Pandas `__: tabular data, time series
+  functionality, interfaces to other statistical languages.
+- `PyMC `__: Bayesian statistical
+  modeling, probabilistic machine learning.
+- `scikit-learn `__: classification, regression,
+  model selection.
+- `Seaborn `__: statistical data visualization.
+- `rpy2 `__: Python to R bridge.
+
+
+Probability distributions
+=========================
+
+Each univariate distribution is an instance of a subclass of `rv_continuous`
+(`rv_discrete` for discrete distributions):
+
+.. autosummary::
+   :toctree: generated/
+
+   rv_continuous
+   rv_discrete
+   rv_histogram
+
+Continuous distributions
+------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   alpha             -- Alpha
+   anglit            -- Anglit
+   arcsine           -- Arcsine
+   argus             -- Argus
+   beta              -- Beta
+   betaprime         -- Beta Prime
+   bradford          -- Bradford
+   burr              -- Burr (Type III)
+   burr12            -- Burr (Type XII)
+   cauchy            -- Cauchy
+   chi               -- Chi
+   chi2              -- Chi-squared
+   cosine            -- Cosine
+   crystalball       -- Crystalball
+   dgamma            -- Double Gamma
+   dpareto_lognorm   -- Double Pareto Lognormal
+   dweibull          -- Double Weibull
+   erlang            -- Erlang
+   expon             -- Exponential
+   exponnorm         -- Exponentially Modified Normal
+   exponweib         -- Exponentiated Weibull
+   exponpow          -- Exponential Power
+   f                 -- F (Snecdor F)
+   fatiguelife       -- Fatigue Life (Birnbaum-Saunders)
+   fisk              -- Fisk
+   foldcauchy        -- Folded Cauchy
+   foldnorm          -- Folded Normal
+   genlogistic       -- Generalized Logistic
+   gennorm           -- Generalized normal
+   genpareto         -- Generalized Pareto
+   genexpon          -- Generalized Exponential
+   genextreme        -- Generalized Extreme Value
+   gausshyper        -- Gauss Hypergeometric
+   gamma             -- Gamma
+   gengamma          -- Generalized gamma
+   genhalflogistic   -- Generalized Half Logistic
+   genhyperbolic     -- Generalized Hyperbolic
+   geninvgauss       -- Generalized Inverse Gaussian
+   gibrat            -- Gibrat
+   gompertz          -- Gompertz (Truncated Gumbel)
+   gumbel_r          -- Right Sided Gumbel, Log-Weibull, Fisher-Tippett, Extreme Value Type I
+   gumbel_l          -- Left Sided Gumbel, etc.
+   halfcauchy        -- Half Cauchy
+   halflogistic      -- Half Logistic
+   halfnorm          -- Half Normal
+   halfgennorm       -- Generalized Half Normal
+   hypsecant         -- Hyperbolic Secant
+   invgamma          -- Inverse Gamma
+   invgauss          -- Inverse Gaussian
+   invweibull        -- Inverse Weibull
+   irwinhall         -- Irwin-Hall
+   jf_skew_t         -- Jones and Faddy Skew-T
+   johnsonsb         -- Johnson SB
+   johnsonsu         -- Johnson SU
+   kappa4            -- Kappa 4 parameter
+   kappa3            -- Kappa 3 parameter
+   ksone             -- Distribution of Kolmogorov-Smirnov one-sided test statistic
+   kstwo             -- Distribution of Kolmogorov-Smirnov two-sided test statistic
+   kstwobign         -- Limiting Distribution of scaled Kolmogorov-Smirnov two-sided test statistic.
+   landau            -- Landau
+   laplace           -- Laplace
+   laplace_asymmetric    -- Asymmetric Laplace
+   levy              -- Levy
+   levy_l
+   levy_stable
+   logistic          -- Logistic
+   loggamma          -- Log-Gamma
+   loglaplace        -- Log-Laplace (Log Double Exponential)
+   lognorm           -- Log-Normal
+   loguniform        -- Log-Uniform
+   lomax             -- Lomax (Pareto of the second kind)
+   maxwell           -- Maxwell
+   mielke            -- Mielke's Beta-Kappa
+   moyal             -- Moyal
+   nakagami          -- Nakagami
+   ncx2              -- Non-central chi-squared
+   ncf               -- Non-central F
+   nct               -- Non-central Student's T
+   norm              -- Normal (Gaussian)
+   norminvgauss      -- Normal Inverse Gaussian
+   pareto            -- Pareto
+   pearson3          -- Pearson type III
+   powerlaw          -- Power-function
+   powerlognorm      -- Power log normal
+   powernorm         -- Power normal
+   rdist             -- R-distribution
+   rayleigh          -- Rayleigh
+   rel_breitwigner   -- Relativistic Breit-Wigner
+   rice              -- Rice
+   recipinvgauss     -- Reciprocal Inverse Gaussian
+   semicircular      -- Semicircular
+   skewcauchy        -- Skew Cauchy
+   skewnorm          -- Skew normal
+   studentized_range    -- Studentized Range
+   t                 -- Student's T
+   trapezoid         -- Trapezoidal
+   triang            -- Triangular
+   truncexpon        -- Truncated Exponential
+   truncnorm         -- Truncated Normal
+   truncpareto       -- Truncated Pareto
+   truncweibull_min  -- Truncated minimum Weibull distribution
+   tukeylambda       -- Tukey-Lambda
+   uniform           -- Uniform
+   vonmises          -- Von-Mises (Circular)
+   vonmises_line     -- Von-Mises (Line)
+   wald              -- Wald
+   weibull_min       -- Minimum Weibull (see Frechet)
+   weibull_max       -- Maximum Weibull (see Frechet)
+   wrapcauchy        -- Wrapped Cauchy
+
+The ``fit`` method of the univariate continuous distributions uses
+maximum likelihood estimation to fit the distribution to a data set.
+The ``fit`` method can accept regular data or *censored data*.
+Censored data is represented with instances of the `CensoredData`
+class.
+
+.. autosummary::
+   :toctree: generated/
+
+   CensoredData
+
+
+Multivariate distributions
+--------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   multivariate_normal    -- Multivariate normal distribution
+   matrix_normal          -- Matrix normal distribution
+   dirichlet              -- Dirichlet
+   dirichlet_multinomial  -- Dirichlet multinomial distribution
+   wishart                -- Wishart
+   invwishart             -- Inverse Wishart
+   multinomial            -- Multinomial distribution
+   special_ortho_group    -- SO(N) group
+   ortho_group            -- O(N) group
+   unitary_group          -- U(N) group
+   random_correlation     -- random correlation matrices
+   multivariate_t         -- Multivariate t-distribution
+   multivariate_hypergeom -- Multivariate hypergeometric distribution
+   normal_inverse_gamma   -- Normal-inverse-gamma distribution
+   random_table           -- Distribution of random tables with given marginals
+   uniform_direction      -- Uniform distribution on S(N-1)
+   vonmises_fisher        -- Von Mises-Fisher distribution
+
+`scipy.stats.multivariate_normal` methods accept instances
+of the following class to represent the covariance.
+
+.. autosummary::
+   :toctree: generated/
+
+   Covariance             -- Representation of a covariance matrix
+
+
+Discrete distributions
+----------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   bernoulli                -- Bernoulli
+   betabinom                -- Beta-Binomial
+   betanbinom               -- Beta-Negative Binomial
+   binom                    -- Binomial
+   boltzmann                -- Boltzmann (Truncated Discrete Exponential)
+   dlaplace                 -- Discrete Laplacian
+   geom                     -- Geometric
+   hypergeom                -- Hypergeometric
+   logser                   -- Logarithmic (Log-Series, Series)
+   nbinom                   -- Negative Binomial
+   nchypergeom_fisher       -- Fisher's Noncentral Hypergeometric
+   nchypergeom_wallenius    -- Wallenius's Noncentral Hypergeometric
+   nhypergeom               -- Negative Hypergeometric
+   planck                   -- Planck (Discrete Exponential)
+   poisson                  -- Poisson
+   poisson_binom            -- Poisson Binomial
+   randint                  -- Discrete Uniform
+   skellam                  -- Skellam
+   yulesimon                -- Yule-Simon
+   zipf                     -- Zipf (Zeta)
+   zipfian                  -- Zipfian
+
+
+An overview of statistical functions is given below.  Many of these functions
+have a similar version in `scipy.stats.mstats` which work for masked arrays.
+
+Summary statistics
+==================
+
+.. autosummary::
+   :toctree: generated/
+
+   describe          -- Descriptive statistics
+   gmean             -- Geometric mean
+   hmean             -- Harmonic mean
+   pmean             -- Power mean
+   kurtosis          -- Fisher or Pearson kurtosis
+   mode              -- Modal value
+   moment            -- Central moment
+   lmoment
+   expectile         -- Expectile
+   skew              -- Skewness
+   kstat             --
+   kstatvar          --
+   tmean             -- Truncated arithmetic mean
+   tvar              -- Truncated variance
+   tmin              --
+   tmax              --
+   tstd              --
+   tsem              --
+   variation         -- Coefficient of variation
+   find_repeats
+   rankdata
+   tiecorrect
+   trim_mean
+   gstd              -- Geometric Standard Deviation
+   iqr
+   sem
+   bayes_mvs
+   mvsdist
+   entropy
+   differential_entropy
+   median_abs_deviation
+
+Frequency statistics
+====================
+
+.. autosummary::
+   :toctree: generated/
+
+   cumfreq
+   percentileofscore
+   scoreatpercentile
+   relfreq
+
+.. autosummary::
+   :toctree: generated/
+
+   binned_statistic     -- Compute a binned statistic for a set of data.
+   binned_statistic_2d  -- Compute a 2-D binned statistic for a set of data.
+   binned_statistic_dd  -- Compute a d-D binned statistic for a set of data.
+
+.. _hypotests:
+
+Hypothesis Tests and related functions
+======================================
+SciPy has many functions for performing hypothesis tests that return a
+test statistic and a p-value, and several of them return confidence intervals
+and/or other related information.
+
+The headings below are based on common uses of the functions within, but due to
+the wide variety of statistical procedures, any attempt at coarse-grained
+categorization will be imperfect. Also, note that tests within the same heading
+are not interchangeable in general (e.g. many have different distributional
+assumptions).
+
+One Sample Tests / Paired Sample Tests
+--------------------------------------
+One sample tests are typically used to assess whether a single sample was
+drawn from a specified distribution or a distribution with specified properties
+(e.g. zero mean).
+
+.. autosummary::
+   :toctree: generated/
+
+   ttest_1samp
+   binomtest
+   quantile_test
+   skewtest
+   kurtosistest
+   normaltest
+   jarque_bera
+   shapiro
+   anderson
+   cramervonmises
+   ks_1samp
+   goodness_of_fit
+   chisquare
+   power_divergence
+
+Paired sample tests are often used to assess whether two samples were drawn
+from the same distribution; they differ from the independent sample tests below
+in that each observation in one sample is treated as paired with a
+closely-related observation in the other sample (e.g. when environmental
+factors are controlled between observations within a pair but not among pairs).
+They can also be interpreted or used as one-sample tests (e.g. tests on the
+mean or median of *differences* between paired observations).
+
+.. autosummary::
+   :toctree: generated/
+
+   ttest_rel
+   wilcoxon
+
+Association/Correlation Tests
+-----------------------------
+
+These tests are often used to assess whether there is a relationship (e.g.
+linear) between paired observations in multiple samples or among the
+coordinates of multivariate observations.
+
+.. autosummary::
+   :toctree: generated/
+
+   linregress
+   pearsonr
+   spearmanr
+   pointbiserialr
+   kendalltau
+   chatterjeexi
+   weightedtau
+   somersd
+   siegelslopes
+   theilslopes
+   page_trend_test
+   multiscale_graphcorr
+
+These association tests and are to work with samples in the form of contingency
+tables. Supporting functions are available in `scipy.stats.contingency`.
+
+.. autosummary::
+   :toctree: generated/
+
+   chi2_contingency
+   fisher_exact
+   barnard_exact
+   boschloo_exact
+
+Independent Sample Tests
+------------------------
+Independent sample tests are typically used to assess whether multiple samples
+were independently drawn from the same distribution or different distributions
+with a shared property (e.g. equal means).
+
+Some tests are specifically for comparing two samples.
+
+.. autosummary::
+   :toctree: generated/
+
+   ttest_ind_from_stats
+   poisson_means_test
+   ttest_ind
+   mannwhitneyu
+   bws_test
+   ranksums
+   brunnermunzel
+   mood
+   ansari
+   cramervonmises_2samp
+   epps_singleton_2samp
+   ks_2samp
+   kstest
+
+Others are generalized to multiple samples.
+
+.. autosummary::
+   :toctree: generated/
+
+   f_oneway
+   tukey_hsd
+   dunnett
+   kruskal
+   alexandergovern
+   fligner
+   levene
+   bartlett
+   median_test
+   friedmanchisquare
+   anderson_ksamp
+
+Resampling and Monte Carlo Methods
+----------------------------------
+The following functions can reproduce the p-value and confidence interval
+results of most of the functions above, and often produce accurate results in a
+wider variety of conditions. They can also be used to perform hypothesis tests
+and generate confidence intervals for custom statistics. This flexibility comes
+at the cost of greater computational requirements and stochastic results.
+
+.. autosummary::
+   :toctree: generated/
+
+   monte_carlo_test
+   permutation_test
+   bootstrap
+   power
+
+Instances of the following object can be passed into some hypothesis test
+functions to perform a resampling or Monte Carlo version of the hypothesis
+test.
+
+.. autosummary::
+   :toctree: generated/
+
+   MonteCarloMethod
+   PermutationMethod
+   BootstrapMethod
+
+Multiple Hypothesis Testing and Meta-Analysis
+---------------------------------------------
+These functions are for assessing the results of individual tests as a whole.
+Functions for performing specific multiple hypothesis tests (e.g. post hoc
+tests) are listed above.
+
+.. autosummary::
+   :toctree: generated/
+
+   combine_pvalues
+   false_discovery_control
+
+
+The following functions are related to the tests above but do not belong in the
+above categories.
+
+Random Variables
+================
+
+.. autosummary::
+   :toctree: generated/
+
+   make_distribution
+   Normal
+   Uniform
+   Mixture
+   order_statistic
+   truncate
+   abs
+   exp
+   log
+
+Quasi-Monte Carlo
+=================
+
+.. toctree::
+   :maxdepth: 4
+
+   stats.qmc
+
+Contingency Tables
+==================
+
+.. toctree::
+   :maxdepth: 4
+
+   stats.contingency
+
+Masked statistics functions
+===========================
+
+.. toctree::
+
+   stats.mstats
+
+
+Other statistical functionality
+===============================
+
+Transformations
+---------------
+
+.. autosummary::
+   :toctree: generated/
+
+   boxcox
+   boxcox_normmax
+   boxcox_llf
+   yeojohnson
+   yeojohnson_normmax
+   yeojohnson_llf
+   obrientransform
+   sigmaclip
+   trimboth
+   trim1
+   zmap
+   zscore
+   gzscore
+
+Statistical distances
+---------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   wasserstein_distance
+   wasserstein_distance_nd
+   energy_distance
+
+Sampling
+--------
+
+.. toctree::
+   :maxdepth: 4
+
+   stats.sampling
+
+Fitting / Survival Analysis
+---------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   fit
+   ecdf
+   logrank
+
+Directional statistical functions
+---------------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   directional_stats
+   circmean
+   circvar
+   circstd
+
+Sensitivity Analysis
+--------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   sobol_indices
+
+Plot-tests
+----------
+
+.. autosummary::
+   :toctree: generated/
+
+   ppcc_max
+   ppcc_plot
+   probplot
+   boxcox_normplot
+   yeojohnson_normplot
+
+Univariate and multivariate kernel density estimation
+-----------------------------------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   gaussian_kde
+
+Warnings / Errors used in :mod:`scipy.stats`
+--------------------------------------------
+
+.. autosummary::
+   :toctree: generated/
+
+   DegenerateDataWarning
+   ConstantInputWarning
+   NearConstantInputWarning
+   FitError
+
+Result classes used in :mod:`scipy.stats`
+-----------------------------------------
+
+.. warning::
+
+    These classes are private, but they are included here because instances
+    of them are returned by other statistical functions. User import and
+    instantiation is not supported.
+
+.. toctree::
+   :maxdepth: 2
+
+   stats._result_classes
+
+"""  # noqa: E501
+
+from ._warnings_errors import (ConstantInputWarning, NearConstantInputWarning,
+                               DegenerateDataWarning, FitError)
+from ._stats_py import *
+from ._variation import variation
+from .distributions import *
+from ._morestats import *
+from ._multicomp import *
+from ._binomtest import binomtest
+from ._binned_statistic import *
+from ._kde import gaussian_kde
+from . import mstats
+from . import qmc
+from ._multivariate import *
+from . import contingency
+from .contingency import chi2_contingency
+from ._censored_data import CensoredData
+from ._resampling import (bootstrap, monte_carlo_test, permutation_test, power,
+                          MonteCarloMethod, PermutationMethod, BootstrapMethod)
+from ._entropy import *
+from ._hypotests import *
+from ._page_trend_test import page_trend_test
+from ._mannwhitneyu import mannwhitneyu
+from ._bws_test import bws_test
+from ._fit import fit, goodness_of_fit
+from ._covariance import Covariance
+from ._sensitivity_analysis import *
+from ._survival import *
+from ._distribution_infrastructure import (
+    make_distribution, Mixture, order_statistic, truncate, exp, log, abs
+)
+from ._new_distributions import Normal, Uniform
+from ._mgc import multiscale_graphcorr
+from ._correlation import chatterjeexi
+
+
+# Deprecated namespaces, to be removed in v2.0.0
+from . import (
+    biasedurn, kde, morestats, mstats_basic, mstats_extras, mvn, stats
+)
+
+
+__all__ = [s for s in dir() if not s.startswith("_")]  # Remove dunders.
+
+from scipy._lib._testutils import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/shared/package-files/09/094aae3095028060a3795524dd04b1f6f55c663987289b20548dd58eab62d599/datastructures.pyi b/shared/package-files/09/094aae3095028060a3795524dd04b1f6f55c663987289b20548dd58eab62d599/datastructures.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..7b5f7b2d182a86676ef63a639789ad5293c52f11
--- /dev/null
+++ b/shared/package-files/09/094aae3095028060a3795524dd04b1f6f55c663987289b20548dd58eab62d599/datastructures.pyi
@@ -0,0 +1,75 @@
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Mapping,
+    MutableMapping,
+    MutableSet,
+    Tuple,
+    TypeVar,
+    Union,
+    overload,
+    Iterator,
+    Optional,
+)
+
+from typing_extensions import Literal
+
+_K = TypeVar("_K")
+_V = TypeVar("_V")
+
+class OrderedSet(MutableSet[_K]):
+    dict: Dict[_K, None] = ...
+    def __init__(self, iterable: Optional[Iterable[_K]] = ...) -> None: ...
+    def __contains__(self, item: object) -> bool: ...
+    def __iter__(self) -> Iterator[_K]: ...
+    def __len__(self) -> int: ...
+    def add(self, x: _K) -> None: ...
+    def discard(self, item: _K) -> None: ...
+
+class MultiValueDictKeyError(KeyError): ...
+
+_D = TypeVar("_D", bound="MultiValueDict")
+
+class MultiValueDict(MutableMapping[_K, _V]):
+    @overload
+    def __init__(self, key_to_list_mapping: Mapping[_K, Optional[List[_V]]] = ...) -> None: ...
+    @overload
+    def __init__(self, key_to_list_mapping: Iterable[Tuple[_K, List[_V]]] = ...) -> None: ...
+    def getlist(self, key: _K, default: Any = ...) -> List[_V]: ...
+    def setlist(self, key: _K, list_: List[_V]) -> None: ...
+    def setlistdefault(self, key: _K, default_list: Optional[List[_V]] = ...) -> List[_V]: ...
+    def appendlist(self, key: _K, value: _V) -> None: ...
+    def lists(self) -> Iterable[Tuple[_K, List[_V]]]: ...
+    def dict(self) -> Dict[_K, Union[_V, List[_V]]]: ...
+    def copy(self: _D) -> _D: ...
+    # These overrides are needed to convince mypy that this isn't an abstract class
+    def __delitem__(self, item: _K) -> None: ...
+    def __getitem__(self, item: _K) -> Union[_V, Literal[[]]]: ...  # type: ignore
+    def __setitem__(self, k: _K, v: Union[_V, List[_V]]) -> None: ...
+    def __len__(self) -> int: ...
+    def __iter__(self) -> Iterator[_K]: ...
+
+class ImmutableList(Tuple[_V, ...]):
+    warning: str = ...
+    def __init__(self, *args: Any, warning: str = ..., **kwargs: Any) -> None: ...
+    def complain(self, *wargs: Any, **kwargs: Any) -> None: ...
+
+class DictWrapper(Dict[str, _V]):
+    func: Callable[[_V], _V] = ...
+    prefix: str = ...
+    @overload
+    def __init__(self, data: Mapping[str, _V], func: Callable[[_V], _V], prefix: str) -> None: ...
+    @overload
+    def __init__(self, data: Iterable[Tuple[str, _V]], func: Callable[[_V], _V], prefix: str) -> None: ...
+
+_T = TypeVar("_T", bound="CaseInsensitiveMapping")
+
+class CaseInsensitiveMapping(Mapping):
+    def __init__(self, data: Any) -> None: ...
+    def __getitem__(self, key: str) -> Any: ...
+    def __len__(self) -> int: ...
+    def __iter__(self) -> Iterator[str]: ...
+    def copy(self: _T) -> _T: ...
diff --git a/shared/package-files/09/094b38e25f20ffa9188bec8b752b8f21f13a6d716b20a2ac061f8a531dfa6249/CPUGeneratorImpl.h b/shared/package-files/09/094b38e25f20ffa9188bec8b752b8f21f13a6d716b20a2ac061f8a531dfa6249/CPUGeneratorImpl.h
new file mode 100644
index 0000000000000000000000000000000000000000..f74c42f44fda584982bc9aa1e4b5d3f8bd4db3b7
--- /dev/null
+++ b/shared/package-files/09/094b38e25f20ffa9188bec8b752b8f21f13a6d716b20a2ac061f8a531dfa6249/CPUGeneratorImpl.h
@@ -0,0 +1,49 @@
+#pragma once
+
+#include 
+#include 
+#include 
+#include 
+
+namespace at {
+
+struct TORCH_API CPUGeneratorImpl : public c10::GeneratorImpl {
+  // Constructors
+  CPUGeneratorImpl(uint64_t seed_in = default_rng_seed_val);
+  ~CPUGeneratorImpl() override = default;
+
+  // CPUGeneratorImpl methods
+  std::shared_ptr clone() const;
+  void set_current_seed(uint64_t seed) override;
+  void set_offset(uint64_t offset) override;
+  uint64_t get_offset() const override;
+  uint64_t current_seed() const override;
+  uint64_t seed() override;
+  void set_state(const c10::TensorImpl& new_state) override;
+  c10::intrusive_ptr get_state() const override;
+  static c10::DeviceType device_type();
+  uint32_t random();
+  uint64_t random64();
+  c10::optional next_float_normal_sample();
+  c10::optional next_double_normal_sample();
+  void set_next_float_normal_sample(c10::optional randn);
+  void set_next_double_normal_sample(c10::optional randn);
+  at::mt19937 engine();
+  void set_engine(at::mt19937 engine);
+
+ private:
+  CPUGeneratorImpl* clone_impl() const override;
+  at::mt19937 engine_;
+  c10::optional next_float_normal_sample_;
+  c10::optional next_double_normal_sample_;
+};
+
+namespace detail {
+
+TORCH_API const Generator& getDefaultCPUGenerator();
+TORCH_API Generator
+createCPUGenerator(uint64_t seed_val = default_rng_seed_val);
+
+} // namespace detail
+
+} // namespace at
diff --git a/shared/package-files/09/094e096eec7941afc484f3edf508e35f830a4f144c7e2486480b263fab4e3ba8/adaptive_max_pool2d_backward_native.h b/shared/package-files/09/094e096eec7941afc484f3edf508e35f830a4f144c7e2486480b263fab4e3ba8/adaptive_max_pool2d_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..b4516bf8904e1553e9cce2b806919e35ee9343ba
--- /dev/null
+++ b/shared/package-files/09/094e096eec7941afc484f3edf508e35f830a4f144c7e2486480b263fab4e3ba8/adaptive_max_pool2d_backward_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+namespace at {
+namespace native {
+struct TORCH_API structured_adaptive_max_pool2d_backward_out_cpu : public at::meta::structured_adaptive_max_pool2d_backward {
+void impl(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, const at::Tensor & grad_input);
+};
+struct TORCH_API structured_adaptive_max_pool2d_backward_out_cuda : public at::meta::structured_adaptive_max_pool2d_backward {
+void impl(const at::Tensor & grad_output, const at::Tensor & self, const at::Tensor & indices, const at::Tensor & grad_input);
+};
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/09/095280f515d28efe19ca0a831cc885f87da1521dd01d0be9d466580b73008641/ieee.h b/shared/package-files/09/095280f515d28efe19ca0a831cc885f87da1521dd01d0be9d466580b73008641/ieee.h
new file mode 100644
index 0000000000000000000000000000000000000000..4cedc0bee04e6470ce02d27b9390068e0ec0fcc1
--- /dev/null
+++ b/shared/package-files/09/095280f515d28efe19ca0a831cc885f87da1521dd01d0be9d466580b73008641/ieee.h
@@ -0,0 +1,449 @@
+// Copyright 2012 the V8 project authors. All rights reserved.
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions are
+// met:
+//
+//     * Redistributions of source code must retain the above copyright
+//       notice, this list of conditions and the following disclaimer.
+//     * Redistributions in binary form must reproduce the above
+//       copyright notice, this list of conditions and the following
+//       disclaimer in the documentation and/or other materials provided
+//       with the distribution.
+//     * Neither the name of Google Inc. nor the names of its
+//       contributors may be used to endorse or promote products derived
+//       from this software without specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+// "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+// LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+// A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+// OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+// LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+// THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+#ifndef DOUBLE_CONVERSION_DOUBLE_H_
+#define DOUBLE_CONVERSION_DOUBLE_H_
+
+#include "diy-fp.h"
+
+namespace arrow_vendored {
+namespace double_conversion {
+
+// We assume that doubles and uint64_t have the same endianness.
+static uint64_t double_to_uint64(double d) { return BitCast(d); }
+static double uint64_to_double(uint64_t d64) { return BitCast(d64); }
+static uint32_t float_to_uint32(float f) { return BitCast(f); }
+static float uint32_to_float(uint32_t d32) { return BitCast(d32); }
+
+// Helper functions for doubles.
+class Double {
+ public:
+  static const uint64_t kSignMask = DOUBLE_CONVERSION_UINT64_2PART_C(0x80000000, 00000000);
+  static const uint64_t kExponentMask = DOUBLE_CONVERSION_UINT64_2PART_C(0x7FF00000, 00000000);
+  static const uint64_t kSignificandMask = DOUBLE_CONVERSION_UINT64_2PART_C(0x000FFFFF, FFFFFFFF);
+  static const uint64_t kHiddenBit = DOUBLE_CONVERSION_UINT64_2PART_C(0x00100000, 00000000);
+  static const uint64_t kQuietNanBit = DOUBLE_CONVERSION_UINT64_2PART_C(0x00080000, 00000000);
+  static const int kPhysicalSignificandSize = 52;  // Excludes the hidden bit.
+  static const int kSignificandSize = 53;
+  static const int kExponentBias = 0x3FF + kPhysicalSignificandSize;
+  static const int kMaxExponent = 0x7FF - kExponentBias;
+
+  Double() : d64_(0) {}
+  explicit Double(double d) : d64_(double_to_uint64(d)) {}
+  explicit Double(uint64_t d64) : d64_(d64) {}
+  explicit Double(DiyFp diy_fp)
+    : d64_(DiyFpToUint64(diy_fp)) {}
+
+  // The value encoded by this Double must be greater or equal to +0.0.
+  // It must not be special (infinity, or NaN).
+  DiyFp AsDiyFp() const {
+    DOUBLE_CONVERSION_ASSERT(Sign() > 0);
+    DOUBLE_CONVERSION_ASSERT(!IsSpecial());
+    return DiyFp(Significand(), Exponent());
+  }
+
+  // The value encoded by this Double must be strictly greater than 0.
+  DiyFp AsNormalizedDiyFp() const {
+    DOUBLE_CONVERSION_ASSERT(value() > 0.0);
+    uint64_t f = Significand();
+    int e = Exponent();
+
+    // The current double could be a denormal.
+    while ((f & kHiddenBit) == 0) {
+      f <<= 1;
+      e--;
+    }
+    // Do the final shifts in one go.
+    f <<= DiyFp::kSignificandSize - kSignificandSize;
+    e -= DiyFp::kSignificandSize - kSignificandSize;
+    return DiyFp(f, e);
+  }
+
+  // Returns the double's bit as uint64.
+  uint64_t AsUint64() const {
+    return d64_;
+  }
+
+  // Returns the next greater double. Returns +infinity on input +infinity.
+  double NextDouble() const {
+    if (d64_ == kInfinity) return Double(kInfinity).value();
+    if (Sign() < 0 && Significand() == 0) {
+      // -0.0
+      return 0.0;
+    }
+    if (Sign() < 0) {
+      return Double(d64_ - 1).value();
+    } else {
+      return Double(d64_ + 1).value();
+    }
+  }
+
+  double PreviousDouble() const {
+    if (d64_ == (kInfinity | kSignMask)) return -Infinity();
+    if (Sign() < 0) {
+      return Double(d64_ + 1).value();
+    } else {
+      if (Significand() == 0) return -0.0;
+      return Double(d64_ - 1).value();
+    }
+  }
+
+  int Exponent() const {
+    if (IsDenormal()) return kDenormalExponent;
+
+    uint64_t d64 = AsUint64();
+    int biased_e =
+        static_cast((d64 & kExponentMask) >> kPhysicalSignificandSize);
+    return biased_e - kExponentBias;
+  }
+
+  uint64_t Significand() const {
+    uint64_t d64 = AsUint64();
+    uint64_t significand = d64 & kSignificandMask;
+    if (!IsDenormal()) {
+      return significand + kHiddenBit;
+    } else {
+      return significand;
+    }
+  }
+
+  // Returns true if the double is a denormal.
+  bool IsDenormal() const {
+    uint64_t d64 = AsUint64();
+    return (d64 & kExponentMask) == 0;
+  }
+
+  // We consider denormals not to be special.
+  // Hence only Infinity and NaN are special.
+  bool IsSpecial() const {
+    uint64_t d64 = AsUint64();
+    return (d64 & kExponentMask) == kExponentMask;
+  }
+
+  bool IsNan() const {
+    uint64_t d64 = AsUint64();
+    return ((d64 & kExponentMask) == kExponentMask) &&
+        ((d64 & kSignificandMask) != 0);
+  }
+
+  bool IsQuietNan() const {
+#if (defined(__mips__) && !defined(__mips_nan2008)) || defined(__hppa__)
+    return IsNan() && ((AsUint64() & kQuietNanBit) == 0);
+#else
+    return IsNan() && ((AsUint64() & kQuietNanBit) != 0);
+#endif
+  }
+
+  bool IsSignalingNan() const {
+#if (defined(__mips__) && !defined(__mips_nan2008)) || defined(__hppa__)
+    return IsNan() && ((AsUint64() & kQuietNanBit) != 0);
+#else
+    return IsNan() && ((AsUint64() & kQuietNanBit) == 0);
+#endif
+  }
+
+
+  bool IsInfinite() const {
+    uint64_t d64 = AsUint64();
+    return ((d64 & kExponentMask) == kExponentMask) &&
+        ((d64 & kSignificandMask) == 0);
+  }
+
+  int Sign() const {
+    uint64_t d64 = AsUint64();
+    return (d64 & kSignMask) == 0? 1: -1;
+  }
+
+  // Precondition: the value encoded by this Double must be greater or equal
+  // than +0.0.
+  DiyFp UpperBoundary() const {
+    DOUBLE_CONVERSION_ASSERT(Sign() > 0);
+    return DiyFp(Significand() * 2 + 1, Exponent() - 1);
+  }
+
+  // Computes the two boundaries of this.
+  // The bigger boundary (m_plus) is normalized. The lower boundary has the same
+  // exponent as m_plus.
+  // Precondition: the value encoded by this Double must be greater than 0.
+  void NormalizedBoundaries(DiyFp* out_m_minus, DiyFp* out_m_plus) const {
+    DOUBLE_CONVERSION_ASSERT(value() > 0.0);
+    DiyFp v = this->AsDiyFp();
+    DiyFp m_plus = DiyFp::Normalize(DiyFp((v.f() << 1) + 1, v.e() - 1));
+    DiyFp m_minus;
+    if (LowerBoundaryIsCloser()) {
+      m_minus = DiyFp((v.f() << 2) - 1, v.e() - 2);
+    } else {
+      m_minus = DiyFp((v.f() << 1) - 1, v.e() - 1);
+    }
+    m_minus.set_f(m_minus.f() << (m_minus.e() - m_plus.e()));
+    m_minus.set_e(m_plus.e());
+    *out_m_plus = m_plus;
+    *out_m_minus = m_minus;
+  }
+
+  bool LowerBoundaryIsCloser() const {
+    // The boundary is closer if the significand is of the form f == 2^p-1 then
+    // the lower boundary is closer.
+    // Think of v = 1000e10 and v- = 9999e9.
+    // Then the boundary (== (v - v-)/2) is not just at a distance of 1e9 but
+    // at a distance of 1e8.
+    // The only exception is for the smallest normal: the largest denormal is
+    // at the same distance as its successor.
+    // Note: denormals have the same exponent as the smallest normals.
+    bool physical_significand_is_zero = ((AsUint64() & kSignificandMask) == 0);
+    return physical_significand_is_zero && (Exponent() != kDenormalExponent);
+  }
+
+  double value() const { return uint64_to_double(d64_); }
+
+  // Returns the significand size for a given order of magnitude.
+  // If v = f*2^e with 2^p-1 <= f <= 2^p then p+e is v's order of magnitude.
+  // This function returns the number of significant binary digits v will have
+  // once it's encoded into a double. In almost all cases this is equal to
+  // kSignificandSize. The only exceptions are denormals. They start with
+  // leading zeroes and their effective significand-size is hence smaller.
+  static int SignificandSizeForOrderOfMagnitude(int order) {
+    if (order >= (kDenormalExponent + kSignificandSize)) {
+      return kSignificandSize;
+    }
+    if (order <= kDenormalExponent) return 0;
+    return order - kDenormalExponent;
+  }
+
+  static double Infinity() {
+    return Double(kInfinity).value();
+  }
+
+  static double NaN() {
+    return Double(kNaN).value();
+  }
+
+ private:
+  static const int kDenormalExponent = -kExponentBias + 1;
+  static const uint64_t kInfinity = DOUBLE_CONVERSION_UINT64_2PART_C(0x7FF00000, 00000000);
+#if (defined(__mips__) && !defined(__mips_nan2008)) || defined(__hppa__)
+  static const uint64_t kNaN = DOUBLE_CONVERSION_UINT64_2PART_C(0x7FF7FFFF, FFFFFFFF);
+#else
+  static const uint64_t kNaN = DOUBLE_CONVERSION_UINT64_2PART_C(0x7FF80000, 00000000);
+#endif
+
+
+  const uint64_t d64_;
+
+  static uint64_t DiyFpToUint64(DiyFp diy_fp) {
+    uint64_t significand = diy_fp.f();
+    int exponent = diy_fp.e();
+    while (significand > kHiddenBit + kSignificandMask) {
+      significand >>= 1;
+      exponent++;
+    }
+    if (exponent >= kMaxExponent) {
+      return kInfinity;
+    }
+    if (exponent < kDenormalExponent) {
+      return 0;
+    }
+    while (exponent > kDenormalExponent && (significand & kHiddenBit) == 0) {
+      significand <<= 1;
+      exponent--;
+    }
+    uint64_t biased_exponent;
+    if (exponent == kDenormalExponent && (significand & kHiddenBit) == 0) {
+      biased_exponent = 0;
+    } else {
+      biased_exponent = static_cast(exponent + kExponentBias);
+    }
+    return (significand & kSignificandMask) |
+        (biased_exponent << kPhysicalSignificandSize);
+  }
+
+  DOUBLE_CONVERSION_DISALLOW_COPY_AND_ASSIGN(Double);
+};
+
+class Single {
+ public:
+  static const uint32_t kSignMask = 0x80000000;
+  static const uint32_t kExponentMask = 0x7F800000;
+  static const uint32_t kSignificandMask = 0x007FFFFF;
+  static const uint32_t kHiddenBit = 0x00800000;
+  static const uint32_t kQuietNanBit = 0x00400000;
+  static const int kPhysicalSignificandSize = 23;  // Excludes the hidden bit.
+  static const int kSignificandSize = 24;
+
+  Single() : d32_(0) {}
+  explicit Single(float f) : d32_(float_to_uint32(f)) {}
+  explicit Single(uint32_t d32) : d32_(d32) {}
+
+  // The value encoded by this Single must be greater or equal to +0.0.
+  // It must not be special (infinity, or NaN).
+  DiyFp AsDiyFp() const {
+    DOUBLE_CONVERSION_ASSERT(Sign() > 0);
+    DOUBLE_CONVERSION_ASSERT(!IsSpecial());
+    return DiyFp(Significand(), Exponent());
+  }
+
+  // Returns the single's bit as uint64.
+  uint32_t AsUint32() const {
+    return d32_;
+  }
+
+  int Exponent() const {
+    if (IsDenormal()) return kDenormalExponent;
+
+    uint32_t d32 = AsUint32();
+    int biased_e =
+        static_cast((d32 & kExponentMask) >> kPhysicalSignificandSize);
+    return biased_e - kExponentBias;
+  }
+
+  uint32_t Significand() const {
+    uint32_t d32 = AsUint32();
+    uint32_t significand = d32 & kSignificandMask;
+    if (!IsDenormal()) {
+      return significand + kHiddenBit;
+    } else {
+      return significand;
+    }
+  }
+
+  // Returns true if the single is a denormal.
+  bool IsDenormal() const {
+    uint32_t d32 = AsUint32();
+    return (d32 & kExponentMask) == 0;
+  }
+
+  // We consider denormals not to be special.
+  // Hence only Infinity and NaN are special.
+  bool IsSpecial() const {
+    uint32_t d32 = AsUint32();
+    return (d32 & kExponentMask) == kExponentMask;
+  }
+
+  bool IsNan() const {
+    uint32_t d32 = AsUint32();
+    return ((d32 & kExponentMask) == kExponentMask) &&
+        ((d32 & kSignificandMask) != 0);
+  }
+
+  bool IsQuietNan() const {
+#if (defined(__mips__) && !defined(__mips_nan2008)) || defined(__hppa__)
+    return IsNan() && ((AsUint32() & kQuietNanBit) == 0);
+#else
+    return IsNan() && ((AsUint32() & kQuietNanBit) != 0);
+#endif
+  }
+
+  bool IsSignalingNan() const {
+#if (defined(__mips__) && !defined(__mips_nan2008)) || defined(__hppa__)
+    return IsNan() && ((AsUint32() & kQuietNanBit) != 0);
+#else
+    return IsNan() && ((AsUint32() & kQuietNanBit) == 0);
+#endif
+  }
+
+
+  bool IsInfinite() const {
+    uint32_t d32 = AsUint32();
+    return ((d32 & kExponentMask) == kExponentMask) &&
+        ((d32 & kSignificandMask) == 0);
+  }
+
+  int Sign() const {
+    uint32_t d32 = AsUint32();
+    return (d32 & kSignMask) == 0? 1: -1;
+  }
+
+  // Computes the two boundaries of this.
+  // The bigger boundary (m_plus) is normalized. The lower boundary has the same
+  // exponent as m_plus.
+  // Precondition: the value encoded by this Single must be greater than 0.
+  void NormalizedBoundaries(DiyFp* out_m_minus, DiyFp* out_m_plus) const {
+    DOUBLE_CONVERSION_ASSERT(value() > 0.0);
+    DiyFp v = this->AsDiyFp();
+    DiyFp m_plus = DiyFp::Normalize(DiyFp((v.f() << 1) + 1, v.e() - 1));
+    DiyFp m_minus;
+    if (LowerBoundaryIsCloser()) {
+      m_minus = DiyFp((v.f() << 2) - 1, v.e() - 2);
+    } else {
+      m_minus = DiyFp((v.f() << 1) - 1, v.e() - 1);
+    }
+    m_minus.set_f(m_minus.f() << (m_minus.e() - m_plus.e()));
+    m_minus.set_e(m_plus.e());
+    *out_m_plus = m_plus;
+    *out_m_minus = m_minus;
+  }
+
+  // Precondition: the value encoded by this Single must be greater or equal
+  // than +0.0.
+  DiyFp UpperBoundary() const {
+    DOUBLE_CONVERSION_ASSERT(Sign() > 0);
+    return DiyFp(Significand() * 2 + 1, Exponent() - 1);
+  }
+
+  bool LowerBoundaryIsCloser() const {
+    // The boundary is closer if the significand is of the form f == 2^p-1 then
+    // the lower boundary is closer.
+    // Think of v = 1000e10 and v- = 9999e9.
+    // Then the boundary (== (v - v-)/2) is not just at a distance of 1e9 but
+    // at a distance of 1e8.
+    // The only exception is for the smallest normal: the largest denormal is
+    // at the same distance as its successor.
+    // Note: denormals have the same exponent as the smallest normals.
+    bool physical_significand_is_zero = ((AsUint32() & kSignificandMask) == 0);
+    return physical_significand_is_zero && (Exponent() != kDenormalExponent);
+  }
+
+  float value() const { return uint32_to_float(d32_); }
+
+  static float Infinity() {
+    return Single(kInfinity).value();
+  }
+
+  static float NaN() {
+    return Single(kNaN).value();
+  }
+
+ private:
+  static const int kExponentBias = 0x7F + kPhysicalSignificandSize;
+  static const int kDenormalExponent = -kExponentBias + 1;
+  static const int kMaxExponent = 0xFF - kExponentBias;
+  static const uint32_t kInfinity = 0x7F800000;
+#if (defined(__mips__) && !defined(__mips_nan2008)) || defined(__hppa__)
+  static const uint32_t kNaN = 0x7FBFFFFF;
+#else
+  static const uint32_t kNaN = 0x7FC00000;
+#endif
+
+  const uint32_t d32_;
+
+  DOUBLE_CONVERSION_DISALLOW_COPY_AND_ASSIGN(Single);
+};
+
+}  // namespace double_conversion
+}  // namespace arrow_vendored
+
+#endif  // DOUBLE_CONVERSION_DOUBLE_H_
diff --git a/shared/package-files/09/0953670e900322628d9ff059a8282a02a9dbab27cd59f4f643348dccb16000d3/direct_url.json b/shared/package-files/09/0953670e900322628d9ff059a8282a02a9dbab27cd59f4f643348dccb16000d3/direct_url.json
new file mode 100644
index 0000000000000000000000000000000000000000..f5b7f6611c28533851f41b16ca0eec532095741a
--- /dev/null
+++ b/shared/package-files/09/0953670e900322628d9ff059a8282a02a9dbab27cd59f4f643348dccb16000d3/direct_url.json
@@ -0,0 +1 @@
+{"dir_info": {}, "url": "file:///opt/conda/conda-bld/decorator_1643638310831/work"}
\ No newline at end of file
diff --git a/shared/package-files/09/0954b2d9a301d94f4348024606a71bbcb2fa24d3cd3709f5bc8bca605039785d/Kuala_Lumpur b/shared/package-files/09/0954b2d9a301d94f4348024606a71bbcb2fa24d3cd3709f5bc8bca605039785d/Kuala_Lumpur
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diff --git a/shared/package-files/09/095509071c1ecb629e0117da1ae5466109f289595b3acd162ed103a54177ccb9/_embedding_bag_per_sample_weights_backward_ops.h b/shared/package-files/09/095509071c1ecb629e0117da1ae5466109f289595b3acd162ed103a54177ccb9/_embedding_bag_per_sample_weights_backward_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..becb3e4bd64d377ce246bd2a41a1e822c3e30656
--- /dev/null
+++ b/shared/package-files/09/095509071c1ecb629e0117da1ae5466109f289595b3acd162ed103a54177ccb9/_embedding_bag_per_sample_weights_backward_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _embedding_bag_per_sample_weights_backward {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_embedding_bag_per_sample_weights_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_embedding_bag_per_sample_weights_backward(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1) -> Tensor")
+  static at::Tensor call(const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx);
+};
+
+struct TORCH_API _embedding_bag_per_sample_weights_backward_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, const at::Tensor &, int64_t, int64_t, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_embedding_bag_per_sample_weights_backward")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_embedding_bag_per_sample_weights_backward.out(Tensor grad, Tensor weight, Tensor indices, Tensor offsets, Tensor offset2bag, int mode, int padding_idx=-1, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & grad, const at::Tensor & weight, const at::Tensor & indices, const at::Tensor & offsets, const at::Tensor & offset2bag, int64_t mode, int64_t padding_idx, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/09/0956b0ec3d3ee83391820efc43a6f7e5234a9f4e4f94ac1626ea973ae53ea9d6/test_scipy.py b/shared/package-files/09/0956b0ec3d3ee83391820efc43a6f7e5234a9f4e4f94ac1626ea973ae53ea9d6/test_scipy.py
new file mode 100644
index 0000000000000000000000000000000000000000..3746d1a311eb68bb1af16e18ab152c7236b42bb5
--- /dev/null
+++ b/shared/package-files/09/0956b0ec3d3ee83391820efc43a6f7e5234a9f4e4f94ac1626ea973ae53ea9d6/test_scipy.py
@@ -0,0 +1,35 @@
+# This testfile tests SymPy <-> SciPy compatibility
+
+# Don't test any SymPy features here. Just pure interaction with SciPy.
+# Always write regular SymPy tests for anything, that can be tested in pure
+# Python (without scipy). Here we test everything, that a user may need when
+# using SymPy with SciPy
+
+from sympy.external import import_module
+
+scipy = import_module('scipy')
+if not scipy:
+    #bin/test will not execute any tests now
+    disabled = True
+
+from sympy.functions.special.bessel import jn_zeros
+
+
+def eq(a, b, tol=1e-6):
+    for x, y in zip(a, b):
+        if not (abs(x - y) < tol):
+            return False
+    return True
+
+
+def test_jn_zeros():
+    assert eq(jn_zeros(0, 4, method="scipy"),
+            [3.141592, 6.283185, 9.424777, 12.566370])
+    assert eq(jn_zeros(1, 4, method="scipy"),
+            [4.493409, 7.725251, 10.904121, 14.066193])
+    assert eq(jn_zeros(2, 4, method="scipy"),
+            [5.763459, 9.095011, 12.322940, 15.514603])
+    assert eq(jn_zeros(3, 4, method="scipy"),
+            [6.987932, 10.417118, 13.698023, 16.923621])
+    assert eq(jn_zeros(4, 4, method="scipy"),
+            [8.182561, 11.704907, 15.039664, 18.301255])
diff --git a/shared/package-files/09/09580ed397fbc398a6654b7bf3d8bfcc96bc4d8ca0346f9542c6b86108686909/vt200-8 b/shared/package-files/09/09580ed397fbc398a6654b7bf3d8bfcc96bc4d8ca0346f9542c6b86108686909/vt200-8
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diff --git a/shared/package-files/09/0958eb7910661af17718c1c21698e8de7bac9fc6ea90b6e3cca6bec58ab741ee/_utils.py b/shared/package-files/09/0958eb7910661af17718c1c21698e8de7bac9fc6ea90b6e3cca6bec58ab741ee/_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2228691e29ea25b64facd841d84648c098dd39ab
--- /dev/null
+++ b/shared/package-files/09/0958eb7910661af17718c1c21698e8de7bac9fc6ea90b6e3cca6bec58ab741ee/_utils.py
@@ -0,0 +1,12 @@
+from warnings import warn
+
+from .std import TqdmDeprecationWarning
+from .utils import (  # NOQA, pylint: disable=unused-import
+    CUR_OS, IS_NIX, IS_WIN, RE_ANSI, Comparable, FormatReplace, SimpleTextIOWrapper,
+    _basestring, _environ_cols_wrapper, _is_ascii, _is_utf, _range, _screen_shape_linux,
+    _screen_shape_tput, _screen_shape_windows, _screen_shape_wrapper, _supports_unicode,
+    _term_move_up, _unich, _unicode, colorama)
+
+warn("This function will be removed in tqdm==5.0.0\n"
+     "Please use `tqdm.utils.*` instead of `tqdm._utils.*`",
+     TqdmDeprecationWarning, stacklevel=2)
diff --git a/shared/package-files/09/0959181e9fd4e23c582914474ae6797d1128a78b498e765c434d8329f39b9110/bad_miuint32.mat b/shared/package-files/09/0959181e9fd4e23c582914474ae6797d1128a78b498e765c434d8329f39b9110/bad_miuint32.mat
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index 0000000000000000000000000000000000000000..c9ab357ec85972cf0014752a1e0ccb08ff284af9
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diff --git a/shared/package-files/09/095999abb5bfc856893703e933d53591adc5ed8e60dce9c569f2a53ea9873439/vander.h b/shared/package-files/09/095999abb5bfc856893703e933d53591adc5ed8e60dce9c569f2a53ea9873439/vander.h
new file mode 100644
index 0000000000000000000000000000000000000000..a9ad0421b69082c667156284190d21a6a83a1a24
--- /dev/null
+++ b/shared/package-files/09/095999abb5bfc856893703e933d53591adc5ed8e60dce9c569f2a53ea9873439/vander.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::vander(Tensor x, int? N=None, bool increasing=False) -> Tensor
+inline at::Tensor vander(const at::Tensor & x, c10::optional N=c10::nullopt, bool increasing=false) {
+    return at::_ops::vander::call(x, N, increasing);
+}
+
+}
diff --git a/shared/package-files/09/095dd59f39dea8b6f09615631ccecedb30e347d1db484ee64785fa3dbcbef33c/linalg_ldl_solve_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/09/095dd59f39dea8b6f09615631ccecedb30e347d1db484ee64785fa3dbcbef33c/linalg_ldl_solve_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d46caca8c21fed8a90c5c3664974d3f1b010e7c4
--- /dev/null
+++ b/shared/package-files/09/095dd59f39dea8b6f09615631ccecedb30e347d1db484ee64785fa3dbcbef33c/linalg_ldl_solve_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor linalg_ldl_solve(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian=false);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/09/095eb70dfa92e3a49654e241a545ffc7c900dc9e727af8d0debdfa181755cfcf/xlogy_meta.h b/shared/package-files/09/095eb70dfa92e3a49654e241a545ffc7c900dc9e727af8d0debdfa181755cfcf/xlogy_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..1634701593160305d66923be4374989a798bc003
--- /dev/null
+++ b/shared/package-files/09/095eb70dfa92e3a49654e241a545ffc7c900dc9e727af8d0debdfa181755cfcf/xlogy_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_xlogy_Tensor : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & self, const at::Tensor & other);
+};
+
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/09/095f121f7cd5cc05295e97a550cd54f18d37a5c464f67a20c17a35308c1fe4ce/special_chebyshev_polynomial_v_ops.h b/shared/package-files/09/095f121f7cd5cc05295e97a550cd54f18d37a5c464f67a20c17a35308c1fe4ce/special_chebyshev_polynomial_v_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..8e976d80c3f737a868d9c4aeffbb281e4444fd7e
--- /dev/null
+++ b/shared/package-files/09/095f121f7cd5cc05295e97a550cd54f18d37a5c464f67a20c17a35308c1fe4ce/special_chebyshev_polynomial_v_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_chebyshev_polynomial_v {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_chebyshev_polynomial_v")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_chebyshev_polynomial_v(Tensor x, Tensor n) -> Tensor")
+  static at::Tensor call(const at::Tensor & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_chebyshev_polynomial_v_x_scalar {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_chebyshev_polynomial_v")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "x_scalar")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_chebyshev_polynomial_v.x_scalar(Scalar x, Tensor n) -> Tensor")
+  static at::Tensor call(const at::Scalar & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_chebyshev_polynomial_v_n_scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_chebyshev_polynomial_v")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "n_scalar")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_chebyshev_polynomial_v.n_scalar(Tensor x, Scalar n) -> Tensor")
+  static at::Tensor call(const at::Tensor & x, const at::Scalar & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n);
+};
+
+struct TORCH_API special_chebyshev_polynomial_v_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_chebyshev_polynomial_v")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_chebyshev_polynomial_v.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_chebyshev_polynomial_v_x_scalar_out {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_chebyshev_polynomial_v")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "x_scalar_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_chebyshev_polynomial_v.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_chebyshev_polynomial_v_n_scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_chebyshev_polynomial_v")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "n_scalar_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_chebyshev_polynomial_v.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/09/095f7f37c150e6915d06e745c51a9d60c9b1da51810685c4156fac3cc5c9abd6/tvi912b+2p b/shared/package-files/09/095f7f37c150e6915d06e745c51a9d60c9b1da51810685c4156fac3cc5c9abd6/tvi912b+2p
new file mode 100644
index 0000000000000000000000000000000000000000..fd6f79ad121e799e453a91b90fe3be1eff8eaf6d
Binary files /dev/null and b/shared/package-files/09/095f7f37c150e6915d06e745c51a9d60c9b1da51810685c4156fac3cc5c9abd6/tvi912b+2p differ
diff --git a/shared/package-files/09/09629d990753e2081a47f778f2828aaf8274b0a54253c37c4382a4c76561655b/fbgemm_pack_gemm_matrix_fp16_native.h b/shared/package-files/09/09629d990753e2081a47f778f2828aaf8274b0a54253c37c4382a4c76561655b/fbgemm_pack_gemm_matrix_fp16_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..c19a1f167253eff6b67729a41e39ee7da68fecab
--- /dev/null
+++ b/shared/package-files/09/09629d990753e2081a47f778f2828aaf8274b0a54253c37c4382a4c76561655b/fbgemm_pack_gemm_matrix_fp16_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor fbgemm_pack_gemm_matrix_fp16(const at::Tensor & input);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/09/0963b64ceb8480ce87d74d451c9a7fccad8062a00b55bf2b659b207d047c4cab/_cudnn_init_dropout_state_compositeexplicitautograd_dispatch.h b/shared/package-files/09/0963b64ceb8480ce87d74d451c9a7fccad8062a00b55bf2b659b207d047c4cab/_cudnn_init_dropout_state_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..d82246bf2894f7a4638ce7bd988f5c6e77f34704
--- /dev/null
+++ b/shared/package-files/09/0963b64ceb8480ce87d74d451c9a7fccad8062a00b55bf2b659b207d047c4cab/_cudnn_init_dropout_state_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & _cudnn_init_dropout_state_out(at::Tensor & out, double dropout, bool train, int64_t dropout_seed);
+TORCH_API at::Tensor & _cudnn_init_dropout_state_outf(double dropout, bool train, int64_t dropout_seed, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/09/09651e52ccc5404eefd3f1376c0d2a9a11fde1936d7db9017bcc7a12dd08da09/type_as_compositeimplicitautograd_dispatch.h b/shared/package-files/09/09651e52ccc5404eefd3f1376c0d2a9a11fde1936d7db9017bcc7a12dd08da09/type_as_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7c3f58b82ca0ad2b82bb4589277ab1d4711729c7
--- /dev/null
+++ b/shared/package-files/09/09651e52ccc5404eefd3f1376c0d2a9a11fde1936d7db9017bcc7a12dd08da09/type_as_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor type_as(const at::Tensor & self, const at::Tensor & other);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/09/09660ec4ef8c338222af58140a0917f526d30290ed9749705a9ad604640720e0/test_collapsed.py b/shared/package-files/09/09660ec4ef8c338222af58140a0917f526d30290ed9749705a9ad604640720e0/test_collapsed.py
new file mode 100644
index 0000000000000000000000000000000000000000..e2e30462968258dfe608ae0a73604a71a312cf85
--- /dev/null
+++ b/shared/package-files/09/09660ec4ef8c338222af58140a0917f526d30290ed9749705a9ad604640720e0/test_collapsed.py
@@ -0,0 +1,739 @@
+"""
+Tests for collapsed observation vector
+
+These tests cannot be run for the Clark 1989 model since the dimension of
+observations (2) is smaller than the number of states (6).
+
+Author: Chad Fulton
+License: Simplified-BSD
+"""
+from pathlib import Path
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+import pandas as pd
+import pytest
+
+from statsmodels import datasets
+from statsmodels.tsa.statespace import dynamic_factor
+from statsmodels.tsa.statespace.kalman_filter import FILTER_UNIVARIATE
+from statsmodels.tsa.statespace.kalman_smoother import (
+    SMOOTH_ALTERNATIVE,
+    SMOOTH_CLASSICAL,
+    SMOOTH_UNIVARIATE,
+)
+from statsmodels.tsa.statespace.mlemodel import MLEModel
+from statsmodels.tsa.statespace.tests.results import results_kalman_filter
+
+current_path = Path(__file__).resolve().parent
+
+
+class Trivariate:
+    """
+    Tests collapsing three-dimensional observation data to two-dimensional
+    """
+
+    @classmethod
+    def setup_class(cls, dtype=float, alternate_timing=False, **kwargs):
+        cls.results = results_kalman_filter.uc_bi
+
+        # GDP and Unemployment, Quarterly, 1948.1 - 1995.3
+        data = pd.DataFrame(
+            cls.results["data"],
+            index=pd.date_range("1947-01-01", "1995-07-01", freq="QS"),
+            columns=["GDP", "UNEMP"],
+        )[4:]
+        data["GDP"] = np.log(data["GDP"])
+        data["UNEMP"] = data["UNEMP"] / 100
+        data["X"] = np.exp(data["GDP"]) * data["UNEMP"]
+
+        k_states = 2
+        cls.mlemodel = MLEModel(data, k_states=k_states, **kwargs)
+        cls.model = cls.mlemodel.ssm
+        if alternate_timing:
+            cls.model.timing_init_filtered = True
+
+        # Statespace representation
+        cls.model["selection"] = np.eye(cls.model.k_states)
+
+        # Update matrices with test parameters
+        cls.model["design"] = np.array([[0.5, 0.2], [0, 0.8], [1, -0.5]])
+        cls.model["transition"] = np.array([[0.4, 0.5], [1, 0]])
+        cls.model["obs_cov"] = np.diag([0.2, 1.1, 0.5])
+        cls.model["state_cov"] = np.diag([2.0, 1])
+
+        # Initialization
+        cls.model.initialize_approximate_diffuse()
+
+    def test_using_collapsed(self):
+        # Test to make sure the results_b actually used a collapsed Kalman
+        # filtering approach (i.e., that the flag being set actually caused the
+        # filter to not use the conventional filter)
+
+        assert not self.results_a.filter_collapsed
+        assert self.results_b.filter_collapsed
+
+        assert self.results_a.collapsed_forecasts is None
+        assert self.results_b.collapsed_forecasts is not None
+
+        assert_equal(self.results_a.forecasts.shape[0], 3)
+        assert_equal(self.results_b.collapsed_forecasts.shape[0], 2)
+
+    def test_forecasts(self):
+        assert_allclose(
+            self.results_a.forecasts[0, :],
+            self.results_b.forecasts[0, :],
+        )
+
+    def test_forecasts_error(self):
+        assert_allclose(
+            self.results_a.forecasts_error[0, :], self.results_b.forecasts_error[0, :]
+        )
+
+    def test_forecasts_error_cov(self):
+        assert_allclose(
+            self.results_a.forecasts_error_cov[0, 0, :],
+            self.results_b.forecasts_error_cov[0, 0, :],
+        )
+
+    def test_filtered_state(self):
+        assert_allclose(self.results_a.filtered_state, self.results_b.filtered_state)
+
+    def test_filtered_state_cov(self):
+        assert_allclose(
+            self.results_a.filtered_state_cov, self.results_b.filtered_state_cov
+        )
+
+    def test_predicted_state(self):
+        assert_allclose(self.results_a.predicted_state, self.results_b.predicted_state)
+
+    def test_predicted_state_cov(self):
+        assert_allclose(
+            self.results_a.predicted_state_cov, self.results_b.predicted_state_cov
+        )
+
+    def test_loglike(self):
+        assert_allclose(self.results_a.llf_obs, self.results_b.llf_obs)
+
+    def test_smoothed_states(self):
+        assert_allclose(self.results_a.smoothed_state, self.results_b.smoothed_state)
+
+    def test_smoothed_states_cov(self):
+        assert_allclose(
+            self.results_a.smoothed_state_cov,
+            self.results_b.smoothed_state_cov,
+            atol=1e-4,
+        )
+
+    def test_smoothed_states_autocov(self):
+        assert_allclose(
+            self.results_a.smoothed_state_autocov, self.results_b.smoothed_state_autocov
+        )
+
+    # Skipped because "measurement" refers to different things; even different
+    # dimensions
+    @pytest.mark.skip
+    def test_smoothed_measurement_disturbance(self):
+        assert_allclose(
+            self.results_a.smoothed_measurement_disturbance,
+            self.results_b.smoothed_measurement_disturbance,
+        )
+
+    # Skipped because "measurement" refers to different things; even different
+    # dimensions
+    @pytest.mark.skip
+    def test_smoothed_measurement_disturbance_cov(self):
+        assert_allclose(
+            self.results_a.smoothed_measurement_disturbance_cov,
+            self.results_b.smoothed_measurement_disturbance_cov,
+        )
+
+    def test_smoothed_state_disturbance(self):
+        assert_allclose(
+            self.results_a.smoothed_state_disturbance,
+            self.results_b.smoothed_state_disturbance,
+        )
+
+    def test_smoothed_state_disturbance_cov(self):
+        assert_allclose(
+            self.results_a.smoothed_state_disturbance_cov,
+            self.results_b.smoothed_state_disturbance_cov,
+        )
+
+    def test_simulation_smoothed_state(self):
+        assert_allclose(self.sim_a.simulated_state, self.sim_a.simulated_state)
+
+    def test_simulation_smoothed_measurement_disturbance(self):
+        assert_allclose(
+            self.sim_a.simulated_measurement_disturbance,
+            self.sim_a.simulated_measurement_disturbance,
+        )
+
+    def test_simulation_smoothed_state_disturbance(self):
+        assert_allclose(
+            self.sim_a.simulated_state_disturbance,
+            self.sim_a.simulated_state_disturbance,
+        )
+
+
+class TestTrivariateConventional(Trivariate):
+
+    @classmethod
+    def setup_class(cls, dtype=float, **kwargs):
+        super().setup_class(dtype, **kwargs)
+
+        nobs = cls.model.nobs
+        k_endog = cls.model.k_endog
+        k_posdef = cls.model.k_posdef
+
+        # Collapsed filtering, smoothing, and simulation smoothing
+        cls.model.filter_conventional = True
+        cls.model.filter_collapsed = True
+        cls.results_b = cls.model.smooth()
+        cls.sim_b = cls.model.simulation_smoother(
+            measurement_disturbance_variates=np.zeros(nobs * k_endog),
+            state_disturbance_variates=np.zeros(nobs * k_posdef),
+            initial_state_variates=np.zeros(cls.model.k_states),
+        )
+
+        # Conventional filtering, smoothing, and simulation smoothing
+        cls.model.filter_collapsed = False
+        cls.results_a = cls.model.smooth()
+        cls.sim_a = cls.model.simulation_smoother(
+            measurement_disturbance_variates=np.zeros(nobs * k_endog),
+            state_disturbance_variates=np.zeros(nobs * k_posdef),
+            initial_state_variates=np.zeros(cls.model.k_states),
+        )
+
+
+class TestTrivariateConventionalAlternate(TestTrivariateConventional):
+    @classmethod
+    def setup_class(cls, *args, **kwargs):
+        super().setup_class(*args, alternate_timing=True, **kwargs)
+
+    def test_using_alterate(self):
+        assert self.model._kalman_filter.filter_timing == 1
+
+
+class TestTrivariateConventionalPartialMissing(Trivariate):
+    @classmethod
+    def setup_class(cls, dtype=float, **kwargs):
+        super().setup_class(dtype, **kwargs)
+        nobs = cls.model.nobs
+        k_endog = cls.model.k_endog
+        k_posdef = cls.model.k_posdef
+
+        # Set partially missing data
+        cls.model.endog[:2, 10:180] = np.nan
+
+        # Collapsed filtering, smoothing, and simulation smoothing
+        cls.model.filter_conventional = True
+        cls.model.filter_collapsed = True
+        cls.results_b = cls.model.smooth()
+        cls.sim_b = cls.model.simulation_smoother(
+            measurement_disturbance_variates=np.zeros(nobs * k_endog),
+            state_disturbance_variates=np.zeros(nobs * k_posdef),
+            initial_state_variates=np.zeros(cls.model.k_states),
+        )
+
+        # Conventional filtering, smoothing, and simulation smoothing
+        cls.model.filter_collapsed = False
+        cls.results_a = cls.model.smooth()
+        cls.sim_a = cls.model.simulation_smoother(
+            measurement_disturbance_variates=np.zeros(nobs * k_endog),
+            state_disturbance_variates=np.zeros(nobs * k_posdef),
+            initial_state_variates=np.zeros(cls.model.k_states),
+        )
+
+
+class TestTrivariateConventionalPartialMissingAlternate(
+    TestTrivariateConventionalPartialMissing
+):
+    @classmethod
+    def setup_class(cls, *args, **kwargs):
+        super().setup_class(*args, alternate_timing=True, **kwargs)
+
+    def test_using_alterate(self):
+        assert self.model._kalman_filter.filter_timing == 1
+
+
+class TestTrivariateConventionalAllMissing(Trivariate):
+    @classmethod
+    def setup_class(cls, dtype=float, **kwargs):
+        super().setup_class(dtype, **kwargs)
+        nobs = cls.model.nobs
+        k_endog = cls.model.k_endog
+        k_posdef = cls.model.k_posdef
+
+        # Set partially missing data
+        cls.model.endog[:, 10:180] = np.nan
+
+        # Collapsed filtering, smoothing, and simulation smoothing
+        cls.model.filter_conventional = True
+        cls.model.filter_collapsed = True
+        cls.results_b = cls.model.smooth()
+        cls.sim_b = cls.model.simulation_smoother(
+            measurement_disturbance_variates=np.zeros(nobs * k_endog),
+            state_disturbance_variates=np.zeros(nobs * k_posdef),
+            initial_state_variates=np.zeros(cls.model.k_states),
+        )
+
+        # Conventional filtering, smoothing, and simulation smoothing
+        cls.model.filter_collapsed = False
+        cls.results_a = cls.model.smooth()
+        cls.sim_a = cls.model.simulation_smoother(
+            measurement_disturbance_variates=np.zeros(nobs * k_endog),
+            state_disturbance_variates=np.zeros(nobs * k_posdef),
+            initial_state_variates=np.zeros(cls.model.k_states),
+        )
+
+
+class TestTrivariateConventionalAllMissingAlternate(
+    TestTrivariateConventionalAllMissing
+):
+    @classmethod
+    def setup_class(cls, *args, **kwargs):
+        super().setup_class(*args, alternate_timing=True, **kwargs)
+
+    def test_using_alterate(self):
+        assert self.model._kalman_filter.filter_timing == 1
+
+
+class TestTrivariateUnivariate(Trivariate):
+    @classmethod
+    def setup_class(cls, dtype=float, **kwargs):
+        super().setup_class(dtype, **kwargs)
+        nobs = cls.model.nobs
+        k_endog = cls.model.k_endog
+        k_posdef = cls.model.k_posdef
+
+        # Collapsed filtering, smoothing, and simulation smoothing
+        cls.model.filter_univariate = True
+        cls.model.filter_collapsed = True
+        cls.results_b = cls.model.smooth()
+        cls.sim_b = cls.model.simulation_smoother(
+            measurement_disturbance_variates=np.zeros(nobs * k_endog),
+            state_disturbance_variates=np.zeros(nobs * k_posdef),
+            initial_state_variates=np.zeros(cls.model.k_states),
+        )
+
+        # Univariate filtering, smoothing, and simulation smoothing
+        cls.model.filter_collapsed = False
+        cls.results_a = cls.model.smooth()
+        cls.sim_a = cls.model.simulation_smoother(
+            measurement_disturbance_variates=np.zeros(nobs * k_endog),
+            state_disturbance_variates=np.zeros(nobs * k_posdef),
+            initial_state_variates=np.zeros(cls.model.k_states),
+        )
+
+
+class TestTrivariateUnivariateAlternate(TestTrivariateUnivariate):
+    @classmethod
+    def setup_class(cls, *args, **kwargs):
+        super().setup_class(*args, alternate_timing=True, **kwargs)
+
+    def test_using_alterate(self):
+        assert self.model._kalman_filter.filter_timing == 1
+
+
+class TestTrivariateUnivariatePartialMissing(Trivariate):
+    @classmethod
+    def setup_class(cls, dtype=float, **kwargs):
+        super().setup_class(dtype, **kwargs)
+        nobs = cls.model.nobs
+        k_endog = cls.model.k_endog
+        k_posdef = cls.model.k_posdef
+
+        # Set partially missing data
+        cls.model.endog[:2, 10:180] = np.nan
+
+        # Collapsed filtering, smoothing, and simulation smoothing
+        cls.model.filter_univariate = True
+        cls.model.filter_collapsed = True
+        cls.results_b = cls.model.smooth()
+        cls.sim_b = cls.model.simulation_smoother(
+            measurement_disturbance_variates=np.zeros(nobs * k_endog),
+            state_disturbance_variates=np.zeros(nobs * k_posdef),
+            initial_state_variates=np.zeros(cls.model.k_states),
+        )
+
+        # Univariate filtering, smoothing, and simulation smoothing
+        cls.model.filter_collapsed = False
+        cls.results_a = cls.model.smooth()
+        cls.sim_a = cls.model.simulation_smoother(
+            measurement_disturbance_variates=np.zeros(nobs * k_endog),
+            state_disturbance_variates=np.zeros(nobs * k_posdef),
+            initial_state_variates=np.zeros(cls.model.k_states),
+        )
+
+
+class TestTrivariateUnivariatePartialMissingAlternate(
+    TestTrivariateUnivariatePartialMissing
+):
+    @classmethod
+    def setup_class(cls, *args, **kwargs):
+        super().setup_class(*args, alternate_timing=True, **kwargs)
+
+    def test_using_alterate(self):
+        assert self.model._kalman_filter.filter_timing == 1
+
+
+class TestTrivariateUnivariateAllMissing(Trivariate):
+    @classmethod
+    def setup_class(cls, dtype=float, **kwargs):
+        super().setup_class(dtype, **kwargs)
+        nobs = cls.model.nobs
+        k_endog = cls.model.k_endog
+        k_posdef = cls.model.k_posdef
+
+        # Set partially missing data
+        cls.model.endog[:, 10:180] = np.nan
+
+        # Univariate filtering, smoothing, and simulation smoothing
+        cls.model.filter_univariate = True
+        cls.model.filter_collapsed = True
+        cls.results_b = cls.model.smooth()
+        cls.sim_b = cls.model.simulation_smoother(
+            measurement_disturbance_variates=np.zeros(nobs * k_endog),
+            state_disturbance_variates=np.zeros(nobs * k_posdef),
+            initial_state_variates=np.zeros(cls.model.k_states),
+        )
+
+        # Conventional filtering, smoothing, and simulation smoothing
+        cls.model.filter_collapsed = False
+        cls.results_a = cls.model.smooth()
+        cls.sim_a = cls.model.simulation_smoother(
+            measurement_disturbance_variates=np.zeros(nobs * k_endog),
+            state_disturbance_variates=np.zeros(nobs * k_posdef),
+            initial_state_variates=np.zeros(cls.model.k_states),
+        )
+
+
+class TestTrivariateUnivariateAllMissingAlternate(TestTrivariateUnivariateAllMissing):
+    @classmethod
+    def setup_class(cls, *args, **kwargs):
+        super().setup_class(*args, alternate_timing=True, **kwargs)
+
+    def test_using_alterate(self):
+        assert self.model._kalman_filter.filter_timing == 1
+
+
+class TestDFM:
+    @classmethod
+    def create_model(cls, obs, **kwargs):
+        # Create the model with typical state space
+        mod = MLEModel(obs, k_states=2, k_posdef=2, **kwargs)
+        mod["design"] = np.array(
+            [
+                [-32.47143586, 17.33779024],
+                [-7.40264169, 1.69279859],
+                [-209.04702853, 125.2879374],
+            ]
+        )
+        mod["obs_cov"] = np.diag(np.array([0.0622668, 1.95666886, 58.37473642]))
+        mod["transition"] = np.array(
+            [[0.29935707, 0.33289005], [-0.7639868, 1.2844237]]
+        )
+        mod["selection"] = np.eye(2)
+        mod["state_cov"] = np.array([[1.2, -0.25], [-0.25, 1.1]])
+        mod.initialize_approximate_diffuse(1e6)
+        return mod
+
+    @classmethod
+    def collapse(cls, obs, **kwargs):
+        mod = cls.create_model(obs, **kwargs)
+        mod.smooth([], return_ssm=True)
+
+        _ss = mod.ssm._statespace
+        out = np.zeros((mod.nobs, mod.k_states))
+        for t in range(mod.nobs):
+            _ss.seek(t, mod.ssm.filter_univariate, 1)
+            out[t] = np.array(_ss.collapse_obs)
+        return out
+
+    @classmethod
+    def setup_class(cls, which="mixed", *args, **kwargs):
+        # Data
+        dta = datasets.macrodata.load_pandas().data
+        dta.index = pd.date_range(start="1959-01-01", end="2009-7-01", freq="QS")
+        levels = dta[["realgdp", "realcons", "realinv"]]
+        obs = np.log(levels).diff().iloc[1:] * 400
+
+        if which == "all":
+            obs.iloc[:50, :] = np.nan
+            obs.iloc[119:130, :] = np.nan
+        elif which == "partial":
+            obs.iloc[0:50, 0] = np.nan
+            obs.iloc[119:130, 0] = np.nan
+        elif which == "mixed":
+            obs.iloc[0:50, 0] = np.nan
+            obs.iloc[19:70, 1] = np.nan
+            obs.iloc[39:90, 2] = np.nan
+            obs.iloc[119:130, 0] = np.nan
+            obs.iloc[119:130, 2] = np.nan
+
+        mod = cls.create_model(obs, **kwargs)
+        cls.model = mod.ssm
+
+        nobs = cls.model.nobs
+        k_endog = cls.model.k_endog
+        k_posdef = cls.model.k_posdef
+
+        rs = np.random.RandomState(1234)
+        mdv = rs.normal(size=nobs * k_endog)
+        sdv = rs.normal(size=nobs * k_posdef)
+        isv = rs.normal(size=cls.model.k_states)
+
+        # Collapsed filtering, smoothing, and simulation smoothing
+        cls.model.filter_collapsed = True
+        cls.results_b = cls.model.smooth()
+        cls.sim_b = cls.model.simulation_smoother()
+        cls.sim_b.simulate(
+            measurement_disturbance_variates=mdv,
+            state_disturbance_variates=sdv,
+            initial_state_variates=isv,
+        )
+
+        # Conventional filtering, smoothing, and simulation smoothing
+        cls.model.filter_collapsed = False
+        cls.results_a = cls.model.smooth()
+        cls.sim_a = cls.model.simulation_smoother()
+        cls.sim_a.simulate(
+            measurement_disturbance_variates=mdv,
+            state_disturbance_variates=sdv,
+            initial_state_variates=isv,
+        )
+
+        # Create the model with augmented state space
+        kwargs.pop("filter_collapsed", None)
+        mod = MLEModel(obs, k_states=4, k_posdef=2, **kwargs)
+        mod["design", :3, :2] = np.array(
+            [
+                [-32.47143586, 17.33779024],
+                [-7.40264169, 1.69279859],
+                [-209.04702853, 125.2879374],
+            ]
+        )
+        mod["obs_cov"] = np.diag(np.array([0.0622668, 1.95666886, 58.37473642]))
+        mod["transition", :2, :2] = np.array(
+            [[0.29935707, 0.33289005], [-0.7639868, 1.2844237]]
+        )
+        mod["transition", 2:, :2] = np.eye(2)
+        mod["selection", :2, :2] = np.eye(2)
+        mod["state_cov"] = np.array([[1.2, -0.25], [-0.25, 1.1]])
+
+        mod.initialize_approximate_diffuse(1e6)
+        cls.augmented_model = mod.ssm
+        cls.augmented_results = mod.ssm.smooth()
+
+    def test_using_collapsed(self):
+        # Test to make sure the results_b actually used a collapsed Kalman
+        # filtering approach (i.e., that the flag being set actually caused the
+        # filter to not use the conventional filter)
+
+        assert not self.results_a.filter_collapsed
+        assert self.results_b.filter_collapsed
+
+        assert self.results_a.collapsed_forecasts is None
+        assert self.results_b.collapsed_forecasts is not None
+
+        assert_equal(self.results_a.forecasts.shape[0], 3)
+        assert_equal(self.results_b.collapsed_forecasts.shape[0], 2)
+
+    def test_forecasts(self):
+        assert_allclose(
+            self.results_a.forecasts[0, :],
+            self.results_b.forecasts[0, :],
+        )
+
+    def test_forecasts_error(self):
+        assert_allclose(
+            self.results_a.forecasts_error[0, :], self.results_b.forecasts_error[0, :]
+        )
+
+    def test_forecasts_error_cov(self):
+        assert_allclose(
+            self.results_a.forecasts_error_cov[0, 0, :],
+            self.results_b.forecasts_error_cov[0, 0, :],
+        )
+
+    def test_filtered_state(self):
+        assert_allclose(self.results_a.filtered_state, self.results_b.filtered_state)
+
+    def test_filtered_state_cov(self):
+        assert_allclose(
+            self.results_a.filtered_state_cov, self.results_b.filtered_state_cov
+        )
+
+    def test_predicted_state(self):
+        assert_allclose(self.results_a.predicted_state, self.results_b.predicted_state)
+
+    def test_predicted_state_cov(self):
+        assert_allclose(
+            self.results_a.predicted_state_cov, self.results_b.predicted_state_cov
+        )
+
+    def test_loglike(self):
+        assert_allclose(self.results_a.llf_obs, self.results_b.llf_obs)
+
+    def test_smoothed_states(self):
+        assert_allclose(self.results_a.smoothed_state, self.results_b.smoothed_state)
+
+    def test_smoothed_states_cov(self):
+        assert_allclose(
+            self.results_a.smoothed_state_cov,
+            self.results_b.smoothed_state_cov,
+            atol=1e-4,
+        )
+
+    def test_smoothed_states_autocov(self):
+        # Cov(\alpha_{t+1}, \alpha_t)
+        # Test collapsed against non-collapsed
+        assert_allclose(
+            self.results_a.smoothed_state_autocov, self.results_b.smoothed_state_autocov
+        )
+
+        # Directly test using the augmented model
+        # Initialization makes these two methods slightly different for the
+        # first few observations
+        assert_allclose(
+            self.results_a.smoothed_state_autocov[:, :, 0:5],
+            self.augmented_results.smoothed_state_cov[:2, 2:, 1:6],
+            atol=1e-4,
+        )
+        assert_allclose(
+            self.results_a.smoothed_state_autocov[:, :, 5:-1],
+            self.augmented_results.smoothed_state_cov[:2, 2:, 6:],
+            atol=1e-7,
+        )
+
+    # Skipped because "measurement" refers to different things; even different
+    # dimensions
+    @pytest.mark.skip
+    def test_smoothed_measurement_disturbance(self):
+        assert_allclose(
+            self.results_a.smoothed_measurement_disturbance,
+            self.results_b.smoothed_measurement_disturbance,
+        )
+
+    # Skipped because "measurement" refers to different things; even different
+    # dimensions
+    @pytest.mark.skip
+    def test_smoothed_measurement_disturbance_cov(self):
+        assert_allclose(
+            self.results_a.smoothed_measurement_disturbance_cov,
+            self.results_b.smoothed_measurement_disturbance_cov,
+        )
+
+    def test_smoothed_state_disturbance(self):
+        assert_allclose(
+            self.results_a.smoothed_state_disturbance,
+            self.results_b.smoothed_state_disturbance,
+        )
+
+    def test_smoothed_state_disturbance_cov(self):
+        assert_allclose(
+            self.results_a.smoothed_state_disturbance_cov,
+            self.results_b.smoothed_state_disturbance_cov,
+        )
+
+    def test_simulation_smoothed_state(self):
+        assert_allclose(self.sim_a.simulated_state, self.sim_b.simulated_state)
+
+    # Skipped because "measurement" refers to different things; even different
+    # dimensions
+    @pytest.mark.skip
+    def test_simulation_smoothed_measurement_disturbance(self):
+        assert_allclose(
+            self.sim_a.simulated_measurement_disturbance,
+            self.sim_b.simulated_measurement_disturbance,
+        )
+
+    def test_simulation_smoothed_state_disturbance(self):
+        assert_allclose(
+            self.sim_a.simulated_state_disturbance,
+            self.sim_b.simulated_state_disturbance,
+        )
+
+
+class TestDFMClassicalSmoothing(TestDFM):
+    @classmethod
+    def setup_class(cls, *args, **kwargs):
+        super().setup_class(*args, smooth_method=SMOOTH_CLASSICAL, **kwargs)
+
+    def test_smooth_method(self):
+        assert_equal(self.model.smooth_method, SMOOTH_CLASSICAL)
+        assert_equal(self.model._kalman_smoother.smooth_method, SMOOTH_CLASSICAL)
+        assert_equal(self.model._kalman_smoother._smooth_method, SMOOTH_CLASSICAL)
+
+
+class TestDFMUnivariateSmoothing(TestDFM):
+    @classmethod
+    def setup_class(cls, *args, **kwargs):
+        super().setup_class(*args, filter_method=FILTER_UNIVARIATE, **kwargs)
+
+    def test_smooth_method(self):
+        assert_equal(self.model.smooth_method, 0)
+        assert_equal(self.model._kalman_smoother.smooth_method, 0)
+        assert_equal(self.model._kalman_smoother._smooth_method, SMOOTH_UNIVARIATE)
+
+
+class TestDFMAlternativeSmoothing(TestDFM):
+    @classmethod
+    def setup_class(cls, *args, **kwargs):
+        super().setup_class(smooth_method=SMOOTH_ALTERNATIVE, **kwargs)
+
+    def test_smooth_method(self):
+        assert_equal(self.model.smooth_method, SMOOTH_ALTERNATIVE)
+        assert_equal(self.model._kalman_smoother.smooth_method, SMOOTH_ALTERNATIVE)
+        assert_equal(self.model._kalman_smoother._smooth_method, SMOOTH_ALTERNATIVE)
+
+
+class TestDFMMeasurementDisturbance(TestDFM):
+    @classmethod
+    def setup_class(cls, *args, **kwargs):
+        super().setup_class(smooth_method=SMOOTH_CLASSICAL, which="none", **kwargs)
+
+    def test_smoothed_state_disturbance(self):
+        assert_allclose(
+            self.results_a.smoothed_state_disturbance,
+            self.results_b.smoothed_state_disturbance,
+            atol=1e-7,
+        )
+
+    def test_smoothed_measurement_disturbance(self):
+        assert_allclose(
+            self.collapse(self.results_a.smoothed_measurement_disturbance.T).T,
+            self.results_b.smoothed_measurement_disturbance,
+            atol=1e-7,
+        )
+
+    def test_simulation_smoothed_measurement_disturbance(self):
+        assert_allclose(
+            self.collapse(self.sim_a.simulated_measurement_disturbance.T),
+            self.sim_b.simulated_measurement_disturbance.T,
+            atol=1e-7,
+        )
+
+    def test_simulation_smoothed_state_disturbance(self):
+        assert_allclose(
+            self.sim_a.simulated_state_disturbance,
+            self.sim_b.simulated_state_disturbance,
+            atol=1e-7,
+        )
+
+
+def test_dfm_missing():
+    # This test is not captured by the TestTrivariate and TestDFM tests above
+    # because it has k_states = 1
+    rs = np.random.RandomState(328392)
+    endog = rs.normal(size=(100, 3))
+    endog[0, :1] = np.nan
+
+    mod = dynamic_factor.DynamicFactor(endog, k_factors=1, factor_order=1)
+    mod.ssm.filter_collapsed = True
+    res = mod.smooth(mod.start_params)
+    mod.ssm.filter_collapsed = False
+    res2 = mod.smooth(mod.start_params)
+
+    assert_allclose(res.llf, res2.llf)
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diff --git a/shared/package-files/09/096af33c9365a2c8e1c52717cc81d8650c028ec2e7900800788818ad8f7681b7/solvers.py b/shared/package-files/09/096af33c9365a2c8e1c52717cc81d8650c028ec2e7900800788818ad8f7681b7/solvers.py
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+++ b/shared/package-files/09/096af33c9365a2c8e1c52717cc81d8650c028ec2e7900800788818ad8f7681b7/solvers.py
@@ -0,0 +1,435 @@
+"""Low-level linear systems solver. """
+
+
+from sympy.utilities.exceptions import sympy_deprecation_warning
+from sympy.utilities.iterables import connected_components
+
+from sympy.core.sympify import sympify
+from sympy.core.numbers import Integer, Rational
+from sympy.matrices.dense import MutableDenseMatrix
+from sympy.polys.domains import ZZ, QQ
+
+from sympy.polys.domains import EX
+from sympy.polys.rings import sring
+from sympy.polys.polyerrors import NotInvertible
+from sympy.polys.domainmatrix import DomainMatrix
+
+
+class PolyNonlinearError(Exception):
+    """Raised by solve_lin_sys for nonlinear equations"""
+    pass
+
+
+class RawMatrix(MutableDenseMatrix):
+    """
+    .. deprecated:: 1.9
+
+       This class fundamentally is broken by design. Use ``DomainMatrix`` if
+       you want a matrix over the polys domains or ``Matrix`` for a matrix
+       with ``Expr`` elements. The ``RawMatrix`` class will be removed/broken
+       in future in order to reestablish the invariant that the elements of a
+       Matrix should be of type ``Expr``.
+
+    """
+    _sympify = staticmethod(lambda x: x)  # type: ignore
+
+    def __init__(self, *args, **kwargs):
+        sympy_deprecation_warning(
+            """
+            The RawMatrix class is deprecated. Use either DomainMatrix or
+            Matrix instead.
+            """,
+            deprecated_since_version="1.9",
+            active_deprecations_target="deprecated-rawmatrix",
+        )
+
+        domain = ZZ
+        for i in range(self.rows):
+            for j in range(self.cols):
+                val = self[i,j]
+                if getattr(val, 'is_Poly', False):
+                    K = val.domain[val.gens]
+                    val_sympy = val.as_expr()
+                elif hasattr(val, 'parent'):
+                    K = val.parent()
+                    val_sympy = K.to_sympy(val)
+                elif isinstance(val, (int, Integer)):
+                    K = ZZ
+                    val_sympy = sympify(val)
+                elif isinstance(val, Rational):
+                    K = QQ
+                    val_sympy = val
+                else:
+                    for K in ZZ, QQ:
+                        if K.of_type(val):
+                            val_sympy = K.to_sympy(val)
+                            break
+                    else:
+                        raise TypeError
+                domain = domain.unify(K)
+                self[i,j] = val_sympy
+        self.ring = domain
+
+
+def eqs_to_matrix(eqs_coeffs, eqs_rhs, gens, domain):
+    """Get matrix from linear equations in dict format.
+
+    Explanation
+    ===========
+
+    Get the matrix representation of a system of linear equations represented
+    as dicts with low-level DomainElement coefficients. This is an
+    *internal* function that is used by solve_lin_sys.
+
+    Parameters
+    ==========
+
+    eqs_coeffs: list[dict[Symbol, DomainElement]]
+        The left hand sides of the equations as dicts mapping from symbols to
+        coefficients where the coefficients are instances of
+        DomainElement.
+    eqs_rhs: list[DomainElements]
+        The right hand sides of the equations as instances of
+        DomainElement.
+    gens: list[Symbol]
+        The unknowns in the system of equations.
+    domain: Domain
+        The domain for coefficients of both lhs and rhs.
+
+    Returns
+    =======
+
+    The augmented matrix representation of the system as a DomainMatrix.
+
+    Examples
+    ========
+
+    >>> from sympy import symbols, ZZ
+    >>> from sympy.polys.solvers import eqs_to_matrix
+    >>> x, y = symbols('x, y')
+    >>> eqs_coeff = [{x:ZZ(1), y:ZZ(1)}, {x:ZZ(1), y:ZZ(-1)}]
+    >>> eqs_rhs = [ZZ(0), ZZ(-1)]
+    >>> eqs_to_matrix(eqs_coeff, eqs_rhs, [x, y], ZZ)
+    DomainMatrix([[1, 1, 0], [1, -1, 1]], (2, 3), ZZ)
+
+    See also
+    ========
+
+    solve_lin_sys: Uses :func:`~eqs_to_matrix` internally
+    """
+    sym2index = {x: n for n, x in enumerate(gens)}
+    nrows = len(eqs_coeffs)
+    ncols = len(gens) + 1
+    rows = [[domain.zero] * ncols for _ in range(nrows)]
+    for row, eq_coeff, eq_rhs in zip(rows, eqs_coeffs, eqs_rhs):
+        for sym, coeff in eq_coeff.items():
+            row[sym2index[sym]] = domain.convert(coeff)
+        row[-1] = -domain.convert(eq_rhs)
+
+    return DomainMatrix(rows, (nrows, ncols), domain)
+
+
+def sympy_eqs_to_ring(eqs, symbols):
+    """Convert a system of equations from Expr to a PolyRing
+
+    Explanation
+    ===========
+
+    High-level functions like ``solve`` expect Expr as inputs but can use
+    ``solve_lin_sys`` internally. This function converts equations from
+    ``Expr`` to the low-level poly types used by the ``solve_lin_sys``
+    function.
+
+    Parameters
+    ==========
+
+    eqs: List of Expr
+        A list of equations as Expr instances
+    symbols: List of Symbol
+        A list of the symbols that are the unknowns in the system of
+        equations.
+
+    Returns
+    =======
+
+    Tuple[List[PolyElement], Ring]: The equations as PolyElement instances
+    and the ring of polynomials within which each equation is represented.
+
+    Examples
+    ========
+
+    >>> from sympy import symbols
+    >>> from sympy.polys.solvers import sympy_eqs_to_ring
+    >>> a, x, y = symbols('a, x, y')
+    >>> eqs = [x-y, x+a*y]
+    >>> eqs_ring, ring = sympy_eqs_to_ring(eqs, [x, y])
+    >>> eqs_ring
+    [x - y, x + a*y]
+    >>> type(eqs_ring[0])
+    
+    >>> ring
+    ZZ(a)[x,y]
+
+    With the equations in this form they can be passed to ``solve_lin_sys``:
+
+    >>> from sympy.polys.solvers import solve_lin_sys
+    >>> solve_lin_sys(eqs_ring, ring)
+    {y: 0, x: 0}
+    """
+    try:
+        K, eqs_K = sring(eqs, symbols, field=True, extension=True)
+    except NotInvertible:
+        # https://github.com/sympy/sympy/issues/18874
+        K, eqs_K = sring(eqs, symbols, domain=EX)
+    return eqs_K, K.to_domain()
+
+
+def solve_lin_sys(eqs, ring, _raw=True):
+    """Solve a system of linear equations from a PolynomialRing
+
+    Explanation
+    ===========
+
+    Solves a system of linear equations given as PolyElement instances of a
+    PolynomialRing. The basic arithmetic is carried out using instance of
+    DomainElement which is more efficient than :class:`~sympy.core.expr.Expr`
+    for the most common inputs.
+
+    While this is a public function it is intended primarily for internal use
+    so its interface is not necessarily convenient. Users are suggested to use
+    the :func:`sympy.solvers.solveset.linsolve` function (which uses this
+    function internally) instead.
+
+    Parameters
+    ==========
+
+    eqs: list[PolyElement]
+        The linear equations to be solved as elements of a
+        PolynomialRing (assumed equal to zero).
+    ring: PolynomialRing
+        The polynomial ring from which eqs are drawn. The generators of this
+        ring are the unknowns to be solved for and the domain of the ring is
+        the domain of the coefficients of the system of equations.
+    _raw: bool
+        If *_raw* is False, the keys and values in the returned dictionary
+        will be of type Expr (and the unit of the field will be removed from
+        the keys) otherwise the low-level polys types will be returned, e.g.
+        PolyElement: PythonRational.
+
+    Returns
+    =======
+
+    ``None`` if the system has no solution.
+
+    dict[Symbol, Expr] if _raw=False
+
+    dict[Symbol, DomainElement] if _raw=True.
+
+    Examples
+    ========
+
+    >>> from sympy import symbols
+    >>> from sympy.polys.solvers import solve_lin_sys, sympy_eqs_to_ring
+    >>> x, y = symbols('x, y')
+    >>> eqs = [x - y, x + y - 2]
+    >>> eqs_ring, ring = sympy_eqs_to_ring(eqs, [x, y])
+    >>> solve_lin_sys(eqs_ring, ring)
+    {y: 1, x: 1}
+
+    Passing ``_raw=False`` returns the same result except that the keys are
+    ``Expr`` rather than low-level poly types.
+
+    >>> solve_lin_sys(eqs_ring, ring, _raw=False)
+    {x: 1, y: 1}
+
+    See also
+    ========
+
+    sympy_eqs_to_ring: prepares the inputs to ``solve_lin_sys``.
+    linsolve: ``linsolve`` uses ``solve_lin_sys`` internally.
+    sympy.solvers.solvers.solve: ``solve`` uses ``solve_lin_sys`` internally.
+    """
+    as_expr = not _raw
+
+    assert ring.domain.is_Field
+
+    eqs_dict = [dict(eq) for eq in eqs]
+
+    one_monom = ring.one.monoms()[0]
+    zero = ring.domain.zero
+
+    eqs_rhs = []
+    eqs_coeffs = []
+    for eq_dict in eqs_dict:
+        eq_rhs = eq_dict.pop(one_monom, zero)
+        eq_coeffs = {}
+        for monom, coeff in eq_dict.items():
+            if sum(monom) != 1:
+                msg = "Nonlinear term encountered in solve_lin_sys"
+                raise PolyNonlinearError(msg)
+            eq_coeffs[ring.gens[monom.index(1)]] = coeff
+        if not eq_coeffs:
+            if not eq_rhs:
+                continue
+            else:
+                return None
+        eqs_rhs.append(eq_rhs)
+        eqs_coeffs.append(eq_coeffs)
+
+    result = _solve_lin_sys(eqs_coeffs, eqs_rhs, ring)
+
+    if result is not None and as_expr:
+
+        def to_sympy(x):
+            as_expr = getattr(x, 'as_expr', None)
+            if as_expr:
+                return as_expr()
+            else:
+                return ring.domain.to_sympy(x)
+
+        tresult = {to_sympy(sym): to_sympy(val) for sym, val in result.items()}
+
+        # Remove 1.0x
+        result = {}
+        for k, v in tresult.items():
+            if k.is_Mul:
+                c, s = k.as_coeff_Mul()
+                result[s] = v/c
+            else:
+                result[k] = v
+
+    return result
+
+
+def _solve_lin_sys(eqs_coeffs, eqs_rhs, ring):
+    """Solve a linear system from dict of PolynomialRing coefficients
+
+    Explanation
+    ===========
+
+    This is an **internal** function used by :func:`solve_lin_sys` after the
+    equations have been preprocessed. The role of this function is to split
+    the system into connected components and pass those to
+    :func:`_solve_lin_sys_component`.
+
+    Examples
+    ========
+
+    Setup a system for $x-y=0$ and $x+y=2$ and solve:
+
+    >>> from sympy import symbols, sring
+    >>> from sympy.polys.solvers import _solve_lin_sys
+    >>> x, y = symbols('x, y')
+    >>> R, (xr, yr) = sring([x, y], [x, y])
+    >>> eqs = [{xr:R.one, yr:-R.one}, {xr:R.one, yr:R.one}]
+    >>> eqs_rhs = [R.zero, -2*R.one]
+    >>> _solve_lin_sys(eqs, eqs_rhs, R)
+    {y: 1, x: 1}
+
+    See also
+    ========
+
+    solve_lin_sys: This function is used internally by :func:`solve_lin_sys`.
+    """
+    V = ring.gens
+    E = []
+    for eq_coeffs in eqs_coeffs:
+        syms = list(eq_coeffs)
+        E.extend(zip(syms[:-1], syms[1:]))
+    G = V, E
+
+    components = connected_components(G)
+
+    sym2comp = {}
+    for n, component in enumerate(components):
+        for sym in component:
+            sym2comp[sym] = n
+
+    subsystems = [([], []) for _ in range(len(components))]
+    for eq_coeff, eq_rhs in zip(eqs_coeffs, eqs_rhs):
+        sym = next(iter(eq_coeff), None)
+        sub_coeff, sub_rhs = subsystems[sym2comp[sym]]
+        sub_coeff.append(eq_coeff)
+        sub_rhs.append(eq_rhs)
+
+    sol = {}
+    for subsystem in subsystems:
+        subsol = _solve_lin_sys_component(subsystem[0], subsystem[1], ring)
+        if subsol is None:
+            return None
+        sol.update(subsol)
+
+    return sol
+
+
+def _solve_lin_sys_component(eqs_coeffs, eqs_rhs, ring):
+    """Solve a linear system from dict of PolynomialRing coefficients
+
+    Explanation
+    ===========
+
+    This is an **internal** function used by :func:`solve_lin_sys` after the
+    equations have been preprocessed. After :func:`_solve_lin_sys` splits the
+    system into connected components this function is called for each
+    component. The system of equations is solved using Gauss-Jordan
+    elimination with division followed by back-substitution.
+
+    Examples
+    ========
+
+    Setup a system for $x-y=0$ and $x+y=2$ and solve:
+
+    >>> from sympy import symbols, sring
+    >>> from sympy.polys.solvers import _solve_lin_sys_component
+    >>> x, y = symbols('x, y')
+    >>> R, (xr, yr) = sring([x, y], [x, y])
+    >>> eqs = [{xr:R.one, yr:-R.one}, {xr:R.one, yr:R.one}]
+    >>> eqs_rhs = [R.zero, -2*R.one]
+    >>> _solve_lin_sys_component(eqs, eqs_rhs, R)
+    {y: 1, x: 1}
+
+    See also
+    ========
+
+    solve_lin_sys: This function is used internally by :func:`solve_lin_sys`.
+    """
+
+    # transform from equations to matrix form
+    matrix = eqs_to_matrix(eqs_coeffs, eqs_rhs, ring.gens, ring.domain)
+
+    # convert to a field for rref
+    if not matrix.domain.is_Field:
+        matrix = matrix.to_field()
+
+    # solve by row-reduction
+    echelon, pivots = matrix.rref()
+
+    # construct the returnable form of the solutions
+    keys = ring.gens
+
+    if pivots and pivots[-1] == len(keys):
+        return None
+
+    if len(pivots) == len(keys):
+        sol = []
+        for s in [row[-1] for row in echelon.rep.to_ddm()]:
+            a = s
+            sol.append(a)
+        sols = dict(zip(keys, sol))
+    else:
+        sols = {}
+        g = ring.gens
+        # Extract ground domain coefficients and convert to the ring:
+        if hasattr(ring, 'ring'):
+            convert = ring.ring.ground_new
+        else:
+            convert = ring.ground_new
+        echelon = echelon.rep.to_ddm()
+        vals_set = {v for row in echelon for v in row}
+        vals_map = {v: convert(v) for v in vals_set}
+        echelon = [[vals_map[eij] for eij in ei] for ei in echelon]
+        for i, p in enumerate(pivots):
+            v = echelon[i][-1] - sum(echelon[i][j]*g[j] for j in range(p+1, len(g)) if echelon[i][j])
+            sols[keys[p]] = v
+
+    return sols
diff --git a/shared/package-files/09/096ba81c1aaa3dd17d02a21843bfddb40f3b6b540e62541b69134a8bf58c1480/test_astype.py b/shared/package-files/09/096ba81c1aaa3dd17d02a21843bfddb40f3b6b540e62541b69134a8bf58c1480/test_astype.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c2672218f273c6ff39ae0a0b9c86f21879e45f3
--- /dev/null
+++ b/shared/package-files/09/096ba81c1aaa3dd17d02a21843bfddb40f3b6b540e62541b69134a8bf58c1480/test_astype.py
@@ -0,0 +1,59 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+import pandas._testing as tm
+
+
+def test_astype(using_infer_string):
+    # with missing values
+    arr = pd.array([True, False, None], dtype="boolean")
+
+    with pytest.raises(ValueError, match="cannot convert NA to integer"):
+        arr.astype("int64")
+
+    with pytest.raises(ValueError, match="cannot convert float NaN to"):
+        arr.astype("bool")
+
+    result = arr.astype("float64")
+    expected = np.array([1, 0, np.nan], dtype="float64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.astype("str")
+    if using_infer_string:
+        expected = pd.array(
+            ["True", "False", None], dtype=pd.StringDtype(na_value=np.nan)
+        )
+        tm.assert_extension_array_equal(result, expected)
+    else:
+        expected = np.array(["True", "False", ""], dtype=f"{tm.ENDIAN}U5")
+        tm.assert_numpy_array_equal(result, expected)
+
+    # no missing values
+    arr = pd.array([True, False, True], dtype="boolean")
+    result = arr.astype("int64")
+    expected = np.array([1, 0, 1], dtype="int64")
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.astype("bool")
+    expected = np.array([True, False, True], dtype="bool")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_astype_to_boolean_array():
+    # astype to BooleanArray
+    arr = pd.array([True, False, None], dtype="boolean")
+
+    result = arr.astype("boolean")
+    tm.assert_extension_array_equal(result, arr)
+    result = arr.astype(pd.BooleanDtype())
+    tm.assert_extension_array_equal(result, arr)
+
+
+def test_astype_to_integer_array():
+    # astype to IntegerArray
+    arr = pd.array([True, False, None], dtype="boolean")
+
+    result = arr.astype("Int64")
+    expected = pd.array([1, 0, None], dtype="Int64")
+    tm.assert_extension_array_equal(result, expected)
diff --git a/shared/package-files/09/096e3387c4666e870875abb77101e69b8fe1b1c36f4b3d5e11d7627222de23a0/__init__.py b/shared/package-files/09/096e3387c4666e870875abb77101e69b8fe1b1c36f4b3d5e11d7627222de23a0/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b32751d6e2493ab6a81f5a7f91a572553201f466
--- /dev/null
+++ b/shared/package-files/09/096e3387c4666e870875abb77101e69b8fe1b1c36f4b3d5e11d7627222de23a0/__init__.py
@@ -0,0 +1,3 @@
+from .rearrange import rearrange
+
+__all__ = ["rearrange"]
diff --git a/shared/package-files/09/097216cf659e1afde4ca842c3010ce3b49b7c85f4cfe92b5122857974d70d1bc/matplotlibrc b/shared/package-files/09/097216cf659e1afde4ca842c3010ce3b49b7c85f4cfe92b5122857974d70d1bc/matplotlibrc
new file mode 100644
index 0000000000000000000000000000000000000000..df4f4afadf96bfb30c5eb62f2b9301994cc395fd
--- /dev/null
+++ b/shared/package-files/09/097216cf659e1afde4ca842c3010ce3b49b7c85f4cfe92b5122857974d70d1bc/matplotlibrc
@@ -0,0 +1,813 @@
+#### MATPLOTLIBRC FORMAT
+
+## NOTE FOR END USERS: DO NOT EDIT THIS FILE!
+##
+## This is a sample Matplotlib configuration file - you can find a copy
+## of it on your system in site-packages/matplotlib/mpl-data/matplotlibrc
+## (relative to your Python installation location).
+## DO NOT EDIT IT!
+##
+## If you wish to change your default style, copy this file to one of the
+## following locations:
+##     Unix/Linux:
+##         $HOME/.config/matplotlib/matplotlibrc OR
+##         $XDG_CONFIG_HOME/matplotlib/matplotlibrc (if $XDG_CONFIG_HOME is set)
+##     Other platforms:
+##         $HOME/.matplotlib/matplotlibrc
+## and edit that copy.
+##
+## See https://matplotlib.org/stable/users/explain/customizing.html#customizing-with-matplotlibrc-files
+## for more details on the paths which are checked for the configuration file.
+##
+## Blank lines, or lines starting with a comment symbol, are ignored, as are
+## trailing comments.  Other lines must have the format:
+##     key: val  # optional comment
+##
+## Formatting: Use PEP8-like style (as enforced in the rest of the codebase).
+## All lines start with an additional '#', so that removing all leading '#'s
+## yields a valid style file.
+##
+## Colors: for the color values below, you can either use
+##     - a Matplotlib color string, such as r, k, or b
+##     - an RGB tuple, such as (1.0, 0.5, 0.0)
+##     - a double-quoted hex string, such as "#ff00ff".
+##       The unquoted string ff00ff is also supported for backward
+##       compatibility, but is discouraged.
+##     - a scalar grayscale intensity such as 0.75
+##     - a legal html color name, e.g., red, blue, darkslategray
+##
+## String values may optionally be enclosed in double quotes, which allows
+## using the comment character # in the string.
+##
+## This file (and other style files) must be encoded as utf-8.
+##
+## Matplotlib configuration are currently divided into following parts:
+##     - BACKENDS
+##     - LINES
+##     - PATCHES
+##     - HATCHES
+##     - BOXPLOT
+##     - FONT
+##     - TEXT
+##     - LaTeX
+##     - AXES
+##     - DATES
+##     - TICKS
+##     - GRIDS
+##     - LEGEND
+##     - FIGURE
+##     - IMAGES
+##     - CONTOUR PLOTS
+##     - ERRORBAR PLOTS
+##     - HISTOGRAM PLOTS
+##     - SCATTER PLOTS
+##     - AGG RENDERING
+##     - PATHS
+##     - SAVING FIGURES
+##     - INTERACTIVE KEYMAPS
+##     - ANIMATION
+
+##### CONFIGURATION BEGINS HERE
+
+
+## ***************************************************************************
+## * BACKENDS                                                                *
+## ***************************************************************************
+## The default backend.  If you omit this parameter, the first working
+## backend from the following list is used:
+##     MacOSX QtAgg Gtk4Agg Gtk3Agg TkAgg WxAgg Agg
+## Other choices include:
+##     QtCairo GTK4Cairo GTK3Cairo TkCairo WxCairo Cairo
+##     Qt5Agg Qt5Cairo Wx  # deprecated.
+##     PS PDF SVG Template
+## You can also deploy your own backend outside of Matplotlib by referring to
+## the module name (which must be in the PYTHONPATH) as 'module://my_backend'.
+##backend: Agg
+
+## The port to use for the web server in the WebAgg backend.
+#webagg.port: 8988
+
+## The address on which the WebAgg web server should be reachable
+#webagg.address: 127.0.0.1
+
+## If webagg.port is unavailable, a number of other random ports will
+## be tried until one that is available is found.
+#webagg.port_retries: 50
+
+## When True, open the web browser to the plot that is shown
+#webagg.open_in_browser: True
+
+## If you are running pyplot inside a GUI and your backend choice
+## conflicts, we will automatically try to find a compatible one for
+## you if backend_fallback is True
+#backend_fallback: True
+
+#interactive: False
+#figure.hooks:          # list of dotted.module.name:dotted.callable.name
+#toolbar:     toolbar2  # {None, toolbar2, toolmanager}
+#timezone:    UTC       # a pytz timezone string, e.g., US/Central or Europe/Paris
+
+
+## ***************************************************************************
+## * LINES                                                                   *
+## ***************************************************************************
+## See https://matplotlib.org/stable/api/artist_api.html#module-matplotlib.lines
+## for more information on line properties.
+#lines.linewidth: 1.5               # line width in points
+#lines.linestyle: -                 # solid line
+#lines.color:     C0                # has no affect on plot(); see axes.prop_cycle
+#lines.marker:          None        # the default marker
+#lines.markerfacecolor: auto        # the default marker face color
+#lines.markeredgecolor: auto        # the default marker edge color
+#lines.markeredgewidth: 1.0         # the line width around the marker symbol
+#lines.markersize:      6           # marker size, in points
+#lines.dash_joinstyle:  round       # {miter, round, bevel}
+#lines.dash_capstyle:   butt        # {butt, round, projecting}
+#lines.solid_joinstyle: round       # {miter, round, bevel}
+#lines.solid_capstyle:  projecting  # {butt, round, projecting}
+#lines.antialiased: True            # render lines in antialiased (no jaggies)
+
+## The three standard dash patterns.  These are scaled by the linewidth.
+#lines.dashed_pattern: 3.7, 1.6
+#lines.dashdot_pattern: 6.4, 1.6, 1, 1.6
+#lines.dotted_pattern: 1, 1.65
+#lines.scale_dashes: True
+
+#markers.fillstyle: full  # {full, left, right, bottom, top, none}
+
+#pcolor.shading: auto
+#pcolormesh.snap: True  # Whether to snap the mesh to pixel boundaries. This is
+                        # provided solely to allow old test images to remain
+                        # unchanged. Set to False to obtain the previous behavior.
+
+## ***************************************************************************
+## * PATCHES                                                                 *
+## ***************************************************************************
+## Patches are graphical objects that fill 2D space, like polygons or circles.
+## See https://matplotlib.org/stable/api/artist_api.html#module-matplotlib.patches
+## for more information on patch properties.
+#patch.linewidth:       1.0    # edge width in points.
+#patch.facecolor:       C0
+#patch.edgecolor:       black  # By default, Patches and Collections do not draw edges.
+                               # This value is only used if facecolor is "none"
+                               # (an Artist without facecolor and edgecolor would be
+                               # invisible)  or if patch.force_edgecolor is True.
+#patch.force_edgecolor: False  # By default, Patches and Collections do not draw edges.
+                               # Set this to True to draw edges with patch.edgedcolor
+                               # as the default edgecolor.
+                               # This is mainly relevant for styles.
+#patch.antialiased:     True   # render patches in antialiased (no jaggies)
+
+
+## ***************************************************************************
+## * HATCHES                                                                 *
+## ***************************************************************************
+#hatch.color:     black
+#hatch.linewidth: 1.0
+
+
+## ***************************************************************************
+## * BOXPLOT                                                                 *
+## ***************************************************************************
+#boxplot.notch:       False
+#boxplot.vertical:    True
+#boxplot.whiskers:    1.5
+#boxplot.bootstrap:   None
+#boxplot.patchartist: False
+#boxplot.showmeans:   False
+#boxplot.showcaps:    True
+#boxplot.showbox:     True
+#boxplot.showfliers:  True
+#boxplot.meanline:    False
+
+#boxplot.flierprops.color:           black
+#boxplot.flierprops.marker:          o
+#boxplot.flierprops.markerfacecolor: none
+#boxplot.flierprops.markeredgecolor: black
+#boxplot.flierprops.markeredgewidth: 1.0
+#boxplot.flierprops.markersize:      6
+#boxplot.flierprops.linestyle:       none
+#boxplot.flierprops.linewidth:       1.0
+
+#boxplot.boxprops.color:     black
+#boxplot.boxprops.linewidth: 1.0
+#boxplot.boxprops.linestyle: -
+
+#boxplot.whiskerprops.color:     black
+#boxplot.whiskerprops.linewidth: 1.0
+#boxplot.whiskerprops.linestyle: -
+
+#boxplot.capprops.color:     black
+#boxplot.capprops.linewidth: 1.0
+#boxplot.capprops.linestyle: -
+
+#boxplot.medianprops.color:     C1
+#boxplot.medianprops.linewidth: 1.0
+#boxplot.medianprops.linestyle: -
+
+#boxplot.meanprops.color:           C2
+#boxplot.meanprops.marker:          ^
+#boxplot.meanprops.markerfacecolor: C2
+#boxplot.meanprops.markeredgecolor: C2
+#boxplot.meanprops.markersize:       6
+#boxplot.meanprops.linestyle:       --
+#boxplot.meanprops.linewidth:       1.0
+
+
+## ***************************************************************************
+## * FONT                                                                    *
+## ***************************************************************************
+## The font properties used by `text.Text`.
+## See https://matplotlib.org/stable/api/font_manager_api.html for more information
+## on font properties.  The 6 font properties used for font matching are
+## given below with their default values.
+##
+## The font.family property can take either a single or multiple entries of any
+## combination of concrete font names (not supported when rendering text with
+## usetex) or the following five generic values:
+##     - 'serif' (e.g., Times),
+##     - 'sans-serif' (e.g., Helvetica),
+##     - 'cursive' (e.g., Zapf-Chancery),
+##     - 'fantasy' (e.g., Western), and
+##     - 'monospace' (e.g., Courier).
+## Each of these values has a corresponding default list of font names
+## (font.serif, etc.); the first available font in the list is used.  Note that
+## for font.serif, font.sans-serif, and font.monospace, the first element of
+## the list (a DejaVu font) will always be used because DejaVu is shipped with
+## Matplotlib and is thus guaranteed to be available; the other entries are
+## left as examples of other possible values.
+##
+## The font.style property has three values: normal (or roman), italic
+## or oblique.  The oblique style will be used for italic, if it is not
+## present.
+##
+## The font.variant property has two values: normal or small-caps.  For
+## TrueType fonts, which are scalable fonts, small-caps is equivalent
+## to using a font size of 'smaller', or about 83 % of the current font
+## size.
+##
+## The font.weight property has effectively 13 values: normal, bold,
+## bolder, lighter, 100, 200, 300, ..., 900.  Normal is the same as
+## 400, and bold is 700.  bolder and lighter are relative values with
+## respect to the current weight.
+##
+## The font.stretch property has 11 values: ultra-condensed,
+## extra-condensed, condensed, semi-condensed, normal, semi-expanded,
+## expanded, extra-expanded, ultra-expanded, wider, and narrower.  This
+## property is not currently implemented.
+##
+## The font.size property is the default font size for text, given in points.
+## 10 pt is the standard value.
+##
+## Note that font.size controls default text sizes.  To configure
+## special text sizes tick labels, axes, labels, title, etc., see the rc
+## settings for axes and ticks.  Special text sizes can be defined
+## relative to font.size, using the following values: xx-small, x-small,
+## small, medium, large, x-large, xx-large, larger, or smaller
+
+#font.family:  sans-serif
+#font.style:   normal
+#font.variant: normal
+#font.weight:  normal
+#font.stretch: normal
+#font.size:    10.0
+
+#font.serif:      DejaVu Serif, Bitstream Vera Serif, Computer Modern Roman, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif
+#font.sans-serif: DejaVu Sans, Bitstream Vera Sans, Computer Modern Sans Serif, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif
+#font.cursive:    Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, Comic Neue, Comic Sans MS, cursive
+#font.fantasy:    Chicago, Charcoal, Impact, Western, xkcd script, fantasy
+#font.monospace:  DejaVu Sans Mono, Bitstream Vera Sans Mono, Computer Modern Typewriter, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace
+
+
+## ***************************************************************************
+## * TEXT                                                                    *
+## ***************************************************************************
+## The text properties used by `text.Text`.
+## See https://matplotlib.org/stable/api/artist_api.html#module-matplotlib.text
+## for more information on text properties
+#text.color: black
+
+## FreeType hinting flag ("foo" corresponds to FT_LOAD_FOO); may be one of the
+## following (Proprietary Matplotlib-specific synonyms are given in parentheses,
+## but their use is discouraged):
+## - default: Use the font's native hinter if possible, else FreeType's auto-hinter.
+##            ("either" is a synonym).
+## - no_autohint: Use the font's native hinter if possible, else don't hint.
+##                ("native" is a synonym.)
+## - force_autohint: Use FreeType's auto-hinter.  ("auto" is a synonym.)
+## - no_hinting: Disable hinting.  ("none" is a synonym.)
+#text.hinting: force_autohint
+
+#text.hinting_factor: 8  # Specifies the amount of softness for hinting in the
+                         # horizontal direction.  A value of 1 will hint to full
+                         # pixels.  A value of 2 will hint to half pixels etc.
+#text.kerning_factor: 0  # Specifies the scaling factor for kerning values.  This
+                         # is provided solely to allow old test images to remain
+                         # unchanged.  Set to 6 to obtain previous behavior.
+                         # Values  other than 0 or 6 have no defined meaning.
+#text.antialiased: True  # If True (default), the text will be antialiased.
+                         # This only affects raster outputs.
+#text.parse_math: True  # Use mathtext if there is an even number of unescaped
+                        # dollar signs.
+
+
+## ***************************************************************************
+## * LaTeX                                                                   *
+## ***************************************************************************
+## For more information on LaTeX properties, see
+## https://matplotlib.org/stable/users/explain/text/usetex.html
+#text.usetex: False  # use latex for all text handling. The following fonts
+                     # are supported through the usual rc parameter settings:
+                     # new century schoolbook, bookman, times, palatino,
+                     # zapf chancery, charter, serif, sans-serif, helvetica,
+                     # avant garde, courier, monospace, computer modern roman,
+                     # computer modern sans serif, computer modern typewriter
+#text.latex.preamble:   # IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX FAILURES
+                        # AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT ASK FOR HELP
+                        # IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT IT TO.
+                        # text.latex.preamble is a single line of LaTeX code that
+                        # will be passed on to the LaTeX system. It may contain
+                        # any code that is valid for the LaTeX "preamble", i.e.
+                        # between the "\documentclass" and "\begin{document}"
+                        # statements.
+                        # Note that it has to be put on a single line, which may
+                        # become quite long.
+                        # The following packages are always loaded with usetex,
+                        # so beware of package collisions:
+                        #   geometry, inputenc, type1cm.
+                        # PostScript (PSNFSS) font packages may also be
+                        # loaded, depending on your font settings.
+
+## The following settings allow you to select the fonts in math mode.
+#mathtext.fontset: dejavusans  # Should be 'dejavusans' (default),
+                               # 'dejavuserif', 'cm' (Computer Modern), 'stix',
+                               # 'stixsans' or 'custom'
+## "mathtext.fontset: custom" is defined by the mathtext.bf, .cal, .it, ...
+## settings which map a TeX font name to a fontconfig font pattern.  (These
+## settings are not used for other font sets.)
+#mathtext.bf:  sans:bold
+#mathtext.bfit: sans:italic:bold
+#mathtext.cal: cursive
+#mathtext.it:  sans:italic
+#mathtext.rm:  sans
+#mathtext.sf:  sans
+#mathtext.tt:  monospace
+#mathtext.fallback: cm  # Select fallback font from ['cm' (Computer Modern), 'stix'
+                        # 'stixsans'] when a symbol cannot be found in one of the
+                        # custom math fonts. Select 'None' to not perform fallback
+                        # and replace the missing character by a dummy symbol.
+#mathtext.default: it  # The default font to use for math.
+                       # Can be any of the LaTeX font names, including
+                       # the special name "regular" for the same font
+                       # used in regular text.
+
+
+## ***************************************************************************
+## * AXES                                                                    *
+## ***************************************************************************
+## Following are default face and edge colors, default tick sizes,
+## default font sizes for tick labels, and so on.  See
+## https://matplotlib.org/stable/api/axes_api.html#module-matplotlib.axes
+#axes.facecolor:     white   # axes background color
+#axes.edgecolor:     black   # axes edge color
+#axes.linewidth:     0.8     # edge line width
+#axes.grid:          False   # display grid or not
+#axes.grid.axis:     both    # which axis the grid should apply to
+#axes.grid.which:    major   # grid lines at {major, minor, both} ticks
+#axes.titlelocation: center  # alignment of the title: {left, right, center}
+#axes.titlesize:     large   # font size of the axes title
+#axes.titleweight:   normal  # font weight of title
+#axes.titlecolor:    auto    # color of the axes title, auto falls back to
+                             # text.color as default value
+#axes.titley:        None    # position title (axes relative units).  None implies auto
+#axes.titlepad:      6.0     # pad between axes and title in points
+#axes.labelsize:     medium  # font size of the x and y labels
+#axes.labelpad:      4.0     # space between label and axis
+#axes.labelweight:   normal  # weight of the x and y labels
+#axes.labelcolor:    black
+#axes.axisbelow:     line    # draw axis gridlines and ticks:
+                             #     - below patches (True)
+                             #     - above patches but below lines ('line')
+                             #     - above all (False)
+
+#axes.formatter.limits: -5, 6  # use scientific notation if log10
+                               # of the axis range is smaller than the
+                               # first or larger than the second
+#axes.formatter.use_locale: False  # When True, format tick labels
+                                   # according to the user's locale.
+                                   # For example, use ',' as a decimal
+                                   # separator in the fr_FR locale.
+#axes.formatter.use_mathtext: False  # When True, use mathtext for scientific
+                                     # notation.
+#axes.formatter.min_exponent: 0  # minimum exponent to format in scientific notation
+#axes.formatter.useoffset: True  # If True, the tick label formatter
+                                 # will default to labeling ticks relative
+                                 # to an offset when the data range is
+                                 # small compared to the minimum absolute
+                                 # value of the data.
+#axes.formatter.offset_threshold: 4  # When useoffset is True, the offset
+                                     # will be used when it can remove
+                                     # at least this number of significant
+                                     # digits from tick labels.
+
+#axes.spines.left:   True  # display axis spines
+#axes.spines.bottom: True
+#axes.spines.top:    True
+#axes.spines.right:  True
+
+#axes.unicode_minus: True  # use Unicode for the minus symbol rather than hyphen.  See
+                           # https://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes
+#axes.prop_cycle: cycler('color', ['1f77b4', 'ff7f0e', '2ca02c', 'd62728', '9467bd', '8c564b', 'e377c2', '7f7f7f', 'bcbd22', '17becf'])
+                  # color cycle for plot lines as list of string color specs:
+                  # single letter, long name, or web-style hex
+                  # As opposed to all other parameters in this file, the color
+                  # values must be enclosed in quotes for this parameter,
+                  # e.g. '1f77b4', instead of 1f77b4.
+                  # See also https://matplotlib.org/stable/users/explain/artists/color_cycle.html
+                  # for more details on prop_cycle usage.
+#axes.xmargin:   .05  # x margin.  See `axes.Axes.margins`
+#axes.ymargin:   .05  # y margin.  See `axes.Axes.margins`
+#axes.zmargin:   .05  # z margin.  See `axes.Axes.margins`
+#axes.autolimit_mode: data  # If "data", use axes.xmargin and axes.ymargin as is.
+                            # If "round_numbers", after application of margins, axis
+                            # limits are further expanded to the nearest "round" number.
+#polaraxes.grid:    True   # display grid on polar axes
+#axes3d.grid:       True   # display grid on 3D axes
+#axes3d.automargin: False  # automatically add margin when manually setting 3D axis limits
+
+#axes3d.xaxis.panecolor:    (0.95, 0.95, 0.95, 0.5)  # background pane on 3D axes
+#axes3d.yaxis.panecolor:    (0.90, 0.90, 0.90, 0.5)  # background pane on 3D axes
+#axes3d.zaxis.panecolor:    (0.925, 0.925, 0.925, 0.5)  # background pane on 3D axes
+
+#axes3d.mouserotationstyle: arcball  # {azel, trackball, sphere, arcball}
+                            # See also https://matplotlib.org/stable/api/toolkits/mplot3d/view_angles.html#rotation-with-mouse
+#axes3d.trackballsize: 0.667  # trackball diameter, in units of the Axes bbox
+#axes3d.trackballborder: 0.2  # trackball border width, in units of the Axes bbox (only for 'sphere' and 'arcball' style)
+
+## ***************************************************************************
+## * AXIS                                                                    *
+## ***************************************************************************
+#xaxis.labellocation: center  # alignment of the xaxis label: {left, right, center}
+#yaxis.labellocation: center  # alignment of the yaxis label: {bottom, top, center}
+
+
+## ***************************************************************************
+## * DATES                                                                   *
+## ***************************************************************************
+## These control the default format strings used in AutoDateFormatter.
+## Any valid format datetime format string can be used (see the python
+## `datetime` for details).  For example, by using:
+##     - '%x' will use the locale date representation
+##     - '%X' will use the locale time representation
+##     - '%c' will use the full locale datetime representation
+## These values map to the scales:
+##     {'year': 365, 'month': 30, 'day': 1, 'hour': 1/24, 'minute': 1 / (24 * 60)}
+
+#date.autoformatter.year:        %Y
+#date.autoformatter.month:       %Y-%m
+#date.autoformatter.day:         %Y-%m-%d
+#date.autoformatter.hour:        %m-%d %H
+#date.autoformatter.minute:      %d %H:%M
+#date.autoformatter.second:      %H:%M:%S
+#date.autoformatter.microsecond: %M:%S.%f
+## The reference date for Matplotlib's internal date representation
+## See https://matplotlib.org/stable/gallery/ticks/date_precision_and_epochs.html
+#date.epoch: 1970-01-01T00:00:00
+## 'auto', 'concise':
+#date.converter:                  auto
+## For auto converter whether to use interval_multiples:
+#date.interval_multiples:         True
+
+## ***************************************************************************
+## * TICKS                                                                   *
+## ***************************************************************************
+## See https://matplotlib.org/stable/api/axis_api.html#matplotlib.axis.Tick
+#xtick.top:           False   # draw ticks on the top side
+#xtick.bottom:        True    # draw ticks on the bottom side
+#xtick.labeltop:      False   # draw label on the top
+#xtick.labelbottom:   True    # draw label on the bottom
+#xtick.major.size:    3.5     # major tick size in points
+#xtick.minor.size:    2       # minor tick size in points
+#xtick.major.width:   0.8     # major tick width in points
+#xtick.minor.width:   0.6     # minor tick width in points
+#xtick.major.pad:     3.5     # distance to major tick label in points
+#xtick.minor.pad:     3.4     # distance to the minor tick label in points
+#xtick.color:         black   # color of the ticks
+#xtick.labelcolor:    inherit # color of the tick labels or inherit from xtick.color
+#xtick.labelsize:     medium  # font size of the tick labels
+#xtick.direction:     out     # direction: {in, out, inout}
+#xtick.minor.visible: False   # visibility of minor ticks on x-axis
+#xtick.major.top:     True    # draw x axis top major ticks
+#xtick.major.bottom:  True    # draw x axis bottom major ticks
+#xtick.minor.top:     True    # draw x axis top minor ticks
+#xtick.minor.bottom:  True    # draw x axis bottom minor ticks
+#xtick.minor.ndivs:   auto    # number of minor ticks between the major ticks on x-axis
+#xtick.alignment:     center  # alignment of xticks
+
+#ytick.left:          True    # draw ticks on the left side
+#ytick.right:         False   # draw ticks on the right side
+#ytick.labelleft:     True    # draw tick labels on the left side
+#ytick.labelright:    False   # draw tick labels on the right side
+#ytick.major.size:    3.5     # major tick size in points
+#ytick.minor.size:    2       # minor tick size in points
+#ytick.major.width:   0.8     # major tick width in points
+#ytick.minor.width:   0.6     # minor tick width in points
+#ytick.major.pad:     3.5     # distance to major tick label in points
+#ytick.minor.pad:     3.4     # distance to the minor tick label in points
+#ytick.color:         black   # color of the ticks
+#ytick.labelcolor:    inherit # color of the tick labels or inherit from ytick.color
+#ytick.labelsize:     medium  # font size of the tick labels
+#ytick.direction:     out     # direction: {in, out, inout}
+#ytick.minor.visible: False   # visibility of minor ticks on y-axis
+#ytick.major.left:    True    # draw y axis left major ticks
+#ytick.major.right:   True    # draw y axis right major ticks
+#ytick.minor.left:    True    # draw y axis left minor ticks
+#ytick.minor.right:   True    # draw y axis right minor ticks
+#ytick.minor.ndivs:   auto    # number of minor ticks between the major ticks on y-axis
+#ytick.alignment:     center_baseline  # alignment of yticks
+
+
+## ***************************************************************************
+## * GRIDS                                                                   *
+## ***************************************************************************
+#grid.color:     "#b0b0b0"  # grid color
+#grid.linestyle: -          # solid
+#grid.linewidth: 0.8        # in points
+#grid.alpha:     1.0        # transparency, between 0.0 and 1.0
+
+
+## ***************************************************************************
+## * LEGEND                                                                  *
+## ***************************************************************************
+#legend.loc:           best
+#legend.frameon:       True     # if True, draw the legend on a background patch
+#legend.framealpha:    0.8      # legend patch transparency
+#legend.facecolor:     inherit  # inherit from axes.facecolor; or color spec
+#legend.edgecolor:     0.8      # background patch boundary color
+#legend.fancybox:      True     # if True, use a rounded box for the
+                                # legend background, else a rectangle
+#legend.shadow:        False    # if True, give background a shadow effect
+#legend.numpoints:     1        # the number of marker points in the legend line
+#legend.scatterpoints: 1        # number of scatter points
+#legend.markerscale:   1.0      # the relative size of legend markers vs. original
+#legend.fontsize:      medium
+#legend.labelcolor:    None
+#legend.title_fontsize: None    # None sets to the same as the default axes.
+
+## Dimensions as fraction of font size:
+#legend.borderpad:     0.4  # border whitespace
+#legend.labelspacing:  0.5  # the vertical space between the legend entries
+#legend.handlelength:  2.0  # the length of the legend lines
+#legend.handleheight:  0.7  # the height of the legend handle
+#legend.handletextpad: 0.8  # the space between the legend line and legend text
+#legend.borderaxespad: 0.5  # the border between the axes and legend edge
+#legend.columnspacing: 2.0  # column separation
+
+
+## ***************************************************************************
+## * FIGURE                                                                  *
+## ***************************************************************************
+## See https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure
+#figure.titlesize:   large     # size of the figure title (``Figure.suptitle()``)
+#figure.titleweight: normal    # weight of the figure title
+#figure.labelsize:   large     # size of the figure label (``Figure.sup[x|y]label()``)
+#figure.labelweight: normal    # weight of the figure label
+#figure.figsize:     6.4, 4.8  # figure size in inches
+#figure.dpi:         100       # figure dots per inch
+#figure.facecolor:   white     # figure face color
+#figure.edgecolor:   white     # figure edge color
+#figure.frameon:     True      # enable figure frame
+#figure.max_open_warning: 20   # The maximum number of figures to open through
+                               # the pyplot interface before emitting a warning.
+                               # If less than one this feature is disabled.
+#figure.raise_window : True    # Raise the GUI window to front when show() is called.
+
+## The figure subplot parameters.  All dimensions are a fraction of the figure width and height.
+#figure.subplot.left:   0.125  # the left side of the subplots of the figure
+#figure.subplot.right:  0.9    # the right side of the subplots of the figure
+#figure.subplot.bottom: 0.11   # the bottom of the subplots of the figure
+#figure.subplot.top:    0.88   # the top of the subplots of the figure
+#figure.subplot.wspace: 0.2    # the amount of width reserved for space between subplots,
+                               # expressed as a fraction of the average axis width
+#figure.subplot.hspace: 0.2    # the amount of height reserved for space between subplots,
+                               # expressed as a fraction of the average axis height
+
+## Figure layout
+#figure.autolayout: False  # When True, automatically adjust subplot
+                           # parameters to make the plot fit the figure
+                           # using `tight_layout`
+#figure.constrained_layout.use: False  # When True, automatically make plot
+                                       # elements fit on the figure. (Not
+                                       # compatible with `autolayout`, above).
+## Padding (in inches) around axes; defaults to 3/72 inches, i.e. 3 points.
+#figure.constrained_layout.h_pad:  0.04167
+#figure.constrained_layout.w_pad:  0.04167
+## Spacing between subplots, relative to the subplot sizes.  Much smaller than for
+## tight_layout (figure.subplot.hspace, figure.subplot.wspace) as constrained_layout
+## already takes surrounding texts (titles, labels, # ticklabels) into account.
+#figure.constrained_layout.hspace: 0.02
+#figure.constrained_layout.wspace: 0.02
+
+
+## ***************************************************************************
+## * IMAGES                                                                  *
+## ***************************************************************************
+#image.aspect:          equal        # {equal, auto} or a number
+#image.interpolation:   auto         # see help(imshow) for options
+#image.interpolation_stage: auto     # see help(imshow) for options
+#image.cmap:            viridis      # A colormap name (plasma, magma, etc.)
+#image.lut:             256          # the size of the colormap lookup table
+#image.origin:          upper        # {lower, upper}
+#image.resample:        True
+#image.composite_image: True  # When True, all the images on a set of axes are
+                              # combined into a single composite image before
+                              # saving a figure as a vector graphics file,
+                              # such as a PDF.
+
+
+## ***************************************************************************
+## * CONTOUR PLOTS                                                           *
+## ***************************************************************************
+#contour.negative_linestyle: dashed  # string or on-off ink sequence
+#contour.corner_mask:        True    # {True, False}
+#contour.linewidth:          None    # {float, None} Size of the contour line
+                                     # widths. If set to None, it falls back to
+                                     # `line.linewidth`.
+#contour.algorithm:          mpl2014 # {mpl2005, mpl2014, serial, threaded}
+
+
+## ***************************************************************************
+## * ERRORBAR PLOTS                                                          *
+## ***************************************************************************
+#errorbar.capsize: 0  # length of end cap on error bars in pixels
+
+
+## ***************************************************************************
+## * HISTOGRAM PLOTS                                                         *
+## ***************************************************************************
+#hist.bins: 10  # The default number of histogram bins or 'auto'.
+
+
+## ***************************************************************************
+## * SCATTER PLOTS                                                           *
+## ***************************************************************************
+#scatter.marker: o         # The default marker type for scatter plots.
+#scatter.edgecolors: face  # The default edge colors for scatter plots.
+
+
+## ***************************************************************************
+## * AGG RENDERING                                                           *
+## ***************************************************************************
+## Warning: experimental, 2008/10/10
+#agg.path.chunksize: 0  # 0 to disable; values in the range
+                        # 10000 to 100000 can improve speed slightly
+                        # and prevent an Agg rendering failure
+                        # when plotting very large data sets,
+                        # especially if they are very gappy.
+                        # It may cause minor artifacts, though.
+                        # A value of 20000 is probably a good
+                        # starting point.
+
+
+## ***************************************************************************
+## * PATHS                                                                   *
+## ***************************************************************************
+#path.simplify: True  # When True, simplify paths by removing "invisible"
+                      # points to reduce file size and increase rendering
+                      # speed
+#path.simplify_threshold: 0.111111111111  # The threshold of similarity below
+                                          # which vertices will be removed in
+                                          # the simplification process.
+#path.snap: True  # When True, rectilinear axis-aligned paths will be snapped
+                  # to the nearest pixel when certain criteria are met.
+                  # When False, paths will never be snapped.
+#path.sketch: None  # May be None, or a tuple of the form:
+                    #  path.sketch: (scale, length, randomness)
+                    # - *scale* is the amplitude of the wiggle
+                    #    perpendicular to the line (in pixels).
+                    # - *length* is the length of the wiggle along the
+                    #    line (in pixels).
+                    # - *randomness* is the factor by which the length is
+                    #    randomly scaled.
+#path.effects:
+
+
+## ***************************************************************************
+## * SAVING FIGURES                                                          *
+## ***************************************************************************
+## The default savefig parameters can be different from the display parameters
+## e.g., you may want a higher resolution, or to make the figure
+## background white
+#savefig.dpi:       figure      # figure dots per inch or 'figure'
+#savefig.facecolor: auto        # figure face color when saving
+#savefig.edgecolor: auto        # figure edge color when saving
+#savefig.format:    png         # {png, ps, pdf, svg}
+#savefig.bbox:      standard    # {tight, standard}
+                                # 'tight' is incompatible with generating frames
+                                # for animation
+#savefig.pad_inches:  0.1       # padding to be used, when bbox is set to 'tight'
+#savefig.directory:   ~         # default directory in savefig dialog, gets updated after
+                                # interactive saves, unless set to the empty string (i.e.
+                                # the current directory); use '.' to start at the current
+                                # directory but update after interactive saves
+#savefig.transparent: False     # whether figures are saved with a transparent
+                                # background by default
+#savefig.orientation: portrait  # orientation of saved figure, for PostScript output only
+
+### macosx backend params
+#macosx.window_mode : system   # How to open new figures (system, tab, window)
+                               # system uses the MacOS system preferences
+
+### tk backend params
+#tk.window_focus:   False  # Maintain shell focus for TkAgg
+
+### ps backend params
+#ps.papersize:      letter  # {figure, letter, legal, ledger, A0-A10, B0-B10}
+#ps.useafm:         False   # use AFM fonts, results in small files
+#ps.usedistiller:   False   # {ghostscript, xpdf, None}
+                            # Experimental: may produce smaller files.
+                            # xpdf intended for production of publication quality files,
+                            # but requires ghostscript, xpdf and ps2eps
+#ps.distiller.res:  6000    # dpi
+#ps.fonttype:       3       # Output Type 3 (Type3) or Type 42 (TrueType)
+
+### PDF backend params
+#pdf.compression:    6  # integer from 0 to 9
+                        # 0 disables compression (good for debugging)
+#pdf.fonttype:       3  # Output Type 3 (Type3) or Type 42 (TrueType)
+#pdf.use14corefonts: False
+#pdf.inheritcolor:   False
+
+### SVG backend params
+#svg.image_inline: True  # Write raster image data directly into the SVG file
+#svg.fonttype: path      # How to handle SVG fonts:
+                         #     path: Embed characters as paths -- supported
+                         #           by most SVG renderers
+                         #     None: Assume fonts are installed on the
+                         #           machine where the SVG will be viewed.
+#svg.hashsalt: None      # If not None, use this string as hash salt instead of uuid4
+#svg.id: None            # If not None, use this string as the value for the `id`
+                         # attribute in the top  tag
+
+### pgf parameter
+## See https://matplotlib.org/stable/tutorials/text/pgf.html for more information.
+#pgf.rcfonts: True
+#pgf.preamble:  # See text.latex.preamble for documentation
+#pgf.texsystem: xelatex
+
+### docstring params
+#docstring.hardcopy: False  # set this when you want to generate hardcopy docstring
+
+
+## ***************************************************************************
+## * INTERACTIVE KEYMAPS                                                     *
+## ***************************************************************************
+## Event keys to interact with figures/plots via keyboard.
+## See https://matplotlib.org/stable/users/explain/interactive.html for more
+## details on interactive navigation.  Customize these settings according to
+## your needs. Leave the field(s) empty if you don't need a key-map. (i.e.,
+## fullscreen : '')
+#keymap.fullscreen: f, ctrl+f   # toggling
+#keymap.home: h, r, home        # home or reset mnemonic
+#keymap.back: left, c, backspace, MouseButton.BACK  # forward / backward keys
+#keymap.forward: right, v, MouseButton.FORWARD      # for quick navigation
+#keymap.pan: p                  # pan mnemonic
+#keymap.zoom: o                 # zoom mnemonic
+#keymap.save: s, ctrl+s         # saving current figure
+#keymap.help: f1                # display help about active tools
+#keymap.quit: ctrl+w, cmd+w, q  # close the current figure
+#keymap.quit_all:               # close all figures
+#keymap.grid: g                 # switching on/off major grids in current axes
+#keymap.grid_minor: G           # switching on/off minor grids in current axes
+#keymap.yscale: l               # toggle scaling of y-axes ('log'/'linear')
+#keymap.xscale: k, L            # toggle scaling of x-axes ('log'/'linear')
+#keymap.copy: ctrl+c, cmd+c     # copy figure to clipboard
+
+
+## ***************************************************************************
+## * ANIMATION                                                               *
+## ***************************************************************************
+#animation.html: none  # How to display the animation as HTML in
+                       # the IPython notebook:
+                       #     - 'html5' uses HTML5 video tag
+                       #     - 'jshtml' creates a JavaScript animation
+#animation.writer:  ffmpeg        # MovieWriter 'backend' to use
+#animation.codec:   h264          # Codec to use for writing movie
+#animation.bitrate: -1            # Controls size/quality trade-off for movie.
+                                  # -1 implies let utility auto-determine
+#animation.frame_format: png      # Controls frame format used by temp files
+
+## Path to ffmpeg binary.  Unqualified paths are resolved by subprocess.Popen.
+#animation.ffmpeg_path:  ffmpeg
+## Additional arguments to pass to ffmpeg.
+#animation.ffmpeg_args:
+
+## Path to ImageMagick's convert binary.  Unqualified paths are resolved by
+## subprocess.Popen, except that on Windows, we look up an install of
+## ImageMagick in the registry (as convert is also the name of a system tool).
+#animation.convert_path: convert
+## Additional arguments to pass to convert.
+#animation.convert_args: -layers, OptimizePlus
+#
+#animation.embed_limit:  20.0     # Limit, in MB, of size of base64 encoded
+                                  # animation in HTML (i.e. IPython notebook)
diff --git a/shared/package-files/09/0975621e605521a74de035ab49d753f653753b559a363a71bdacfe294083f02d/log.pyi b/shared/package-files/09/0975621e605521a74de035ab49d753f653753b559a363a71bdacfe294083f02d/log.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..549b569e7356d5a76d5d7a547efe434ccbaba1be
--- /dev/null
+++ b/shared/package-files/09/0975621e605521a74de035ab49d753f653753b559a363a71bdacfe294083f02d/log.pyi
@@ -0,0 +1,25 @@
+from typing import Any
+
+DEBUG: int
+INFO: int
+WARN: int
+ERROR: int
+FATAL: int
+
+class Log:
+    def __init__(self, threshold: int = ...) -> None: ...
+    def log(self, level: int, msg: str, *args: Any) -> None: ...
+    def debug(self, msg: str, *args: Any) -> None: ...
+    def info(self, msg: str, *args: Any) -> None: ...
+    def warn(self, msg: str, *args: Any) -> None: ...
+    def error(self, msg: str, *args: Any) -> None: ...
+    def fatal(self, msg: str, *args: Any) -> None: ...
+
+def log(level: int, msg: str, *args: Any) -> None: ...
+def debug(msg: str, *args: Any) -> None: ...
+def info(msg: str, *args: Any) -> None: ...
+def warn(msg: str, *args: Any) -> None: ...
+def error(msg: str, *args: Any) -> None: ...
+def fatal(msg: str, *args: Any) -> None: ...
+def set_threshold(level: int) -> int: ...
+def set_verbosity(v: int) -> None: ...
diff --git a/shared/package-files/09/09765b2732153a1295f082dcc6ecb76d3bdc69c0ae7806f24319af0246cf92b9/dask.py b/shared/package-files/09/09765b2732153a1295f082dcc6ecb76d3bdc69c0ae7806f24319af0246cf92b9/dask.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e1276463db6866665e6a0fe114efc247971b57e
--- /dev/null
+++ b/shared/package-files/09/09765b2732153a1295f082dcc6ecb76d3bdc69c0ae7806f24319af0246cf92b9/dask.py
@@ -0,0 +1,152 @@
+import dask
+from distributed.client import Client, _get_global_client
+from distributed.worker import Worker
+
+from fsspec import filesystem
+from fsspec.spec import AbstractBufferedFile, AbstractFileSystem
+from fsspec.utils import infer_storage_options
+
+
+def _get_client(client):
+    if client is None:
+        return _get_global_client()
+    elif isinstance(client, Client):
+        return client
+    else:
+        # e.g., connection string
+        return Client(client)
+
+
+def _in_worker():
+    return bool(Worker._instances)
+
+
+class DaskWorkerFileSystem(AbstractFileSystem):
+    """View files accessible to a worker as any other remote file-system
+
+    When instances are run on the worker, uses the real filesystem. When
+    run on the client, they call the worker to provide information or data.
+
+    **Warning** this implementation is experimental, and read-only for now.
+    """
+
+    def __init__(
+        self, target_protocol=None, target_options=None, fs=None, client=None, **kwargs
+    ):
+        super().__init__(**kwargs)
+        if not (fs is None) ^ (target_protocol is None):
+            raise ValueError(
+                "Please provide one of filesystem instance (fs) or"
+                " target_protocol, not both"
+            )
+        self.target_protocol = target_protocol
+        self.target_options = target_options
+        self.worker = None
+        self.client = client
+        self.fs = fs
+        self._determine_worker()
+
+    @staticmethod
+    def _get_kwargs_from_urls(path):
+        so = infer_storage_options(path)
+        if "host" in so and "port" in so:
+            return {"client": f"{so['host']}:{so['port']}"}
+        else:
+            return {}
+
+    def _determine_worker(self):
+        if _in_worker():
+            self.worker = True
+            if self.fs is None:
+                self.fs = filesystem(
+                    self.target_protocol, **(self.target_options or {})
+                )
+        else:
+            self.worker = False
+            self.client = _get_client(self.client)
+            self.rfs = dask.delayed(self)
+
+    def mkdir(self, *args, **kwargs):
+        if self.worker:
+            self.fs.mkdir(*args, **kwargs)
+        else:
+            self.rfs.mkdir(*args, **kwargs).compute()
+
+    def rm(self, *args, **kwargs):
+        if self.worker:
+            self.fs.rm(*args, **kwargs)
+        else:
+            self.rfs.rm(*args, **kwargs).compute()
+
+    def copy(self, *args, **kwargs):
+        if self.worker:
+            self.fs.copy(*args, **kwargs)
+        else:
+            self.rfs.copy(*args, **kwargs).compute()
+
+    def mv(self, *args, **kwargs):
+        if self.worker:
+            self.fs.mv(*args, **kwargs)
+        else:
+            self.rfs.mv(*args, **kwargs).compute()
+
+    def ls(self, *args, **kwargs):
+        if self.worker:
+            return self.fs.ls(*args, **kwargs)
+        else:
+            return self.rfs.ls(*args, **kwargs).compute()
+
+    def _open(
+        self,
+        path,
+        mode="rb",
+        block_size=None,
+        autocommit=True,
+        cache_options=None,
+        **kwargs,
+    ):
+        if self.worker:
+            return self.fs._open(
+                path,
+                mode=mode,
+                block_size=block_size,
+                autocommit=autocommit,
+                cache_options=cache_options,
+                **kwargs,
+            )
+        else:
+            return DaskFile(
+                fs=self,
+                path=path,
+                mode=mode,
+                block_size=block_size,
+                autocommit=autocommit,
+                cache_options=cache_options,
+                **kwargs,
+            )
+
+    def fetch_range(self, path, mode, start, end):
+        if self.worker:
+            with self._open(path, mode) as f:
+                f.seek(start)
+                return f.read(end - start)
+        else:
+            return self.rfs.fetch_range(path, mode, start, end).compute()
+
+
+class DaskFile(AbstractBufferedFile):
+    def __init__(self, mode="rb", **kwargs):
+        if mode != "rb":
+            raise ValueError('Remote dask files can only be opened in "rb" mode')
+        super().__init__(**kwargs)
+
+    def _upload_chunk(self, final=False):
+        pass
+
+    def _initiate_upload(self):
+        """Create remote file/upload"""
+        pass
+
+    def _fetch_range(self, start, end):
+        """Get the specified set of bytes from remote"""
+        return self.fs.fetch_range(self.path, self.mode, start, end)
diff --git a/shared/package-files/09/0976957e2bf4b5670334f93183c8fbb3505efad787930315b9864b66d3156ac3/constraints.pyi b/shared/package-files/09/0976957e2bf4b5670334f93183c8fbb3505efad787930315b9864b66d3156ac3/constraints.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..dc163ab30bb0b46d40640e857a37a0c27766c7ce
--- /dev/null
+++ b/shared/package-files/09/0976957e2bf4b5670334f93183c8fbb3505efad787930315b9864b66d3156ac3/constraints.pyi
@@ -0,0 +1,27 @@
+from typing import Any, Optional, Sequence, Tuple, Type, TypeVar
+
+from django.db.backends.base.schema import BaseDatabaseSchemaEditor
+from django.db.models.base import Model
+from django.db.models.query_utils import Q
+
+_T = TypeVar("_T", bound="BaseConstraint")
+
+class BaseConstraint:
+    name: str
+    def __init__(self, name: str) -> None: ...
+    def constraint_sql(
+        self, model: Optional[Type[Model]], schema_editor: Optional[BaseDatabaseSchemaEditor]
+    ) -> str: ...
+    def create_sql(self, model: Optional[Type[Model]], schema_editor: Optional[BaseDatabaseSchemaEditor]) -> str: ...
+    def remove_sql(self, model: Optional[Type[Model]], schema_editor: Optional[BaseDatabaseSchemaEditor]) -> str: ...
+    def deconstruct(self) -> Any: ...
+    def clone(self: _T) -> _T: ...
+
+class CheckConstraint(BaseConstraint):
+    check: Q
+    def __init__(self, *, check: Q, name: str) -> None: ...
+
+class UniqueConstraint(BaseConstraint):
+    fields: Tuple[str]
+    condition: Optional[Q]
+    def __init__(self, *, fields: Sequence[str], name: str, condition: Optional[Q] = ...): ...
diff --git a/shared/package-files/09/0976b5d05430d908fa32a26e682f059b8b25b288a92858c2229605e9ce482005/test_mem_policy.py b/shared/package-files/09/0976b5d05430d908fa32a26e682f059b8b25b288a92858c2229605e9ce482005/test_mem_policy.py
new file mode 100644
index 0000000000000000000000000000000000000000..a381fa1d8905bf922d607643514cb30a4c645e0e
--- /dev/null
+++ b/shared/package-files/09/0976b5d05430d908fa32a26e682f059b8b25b288a92858c2229605e9ce482005/test_mem_policy.py
@@ -0,0 +1,443 @@
+import asyncio
+import gc
+import os
+import pytest
+import numpy as np
+import threading
+import warnings
+from numpy.testing import extbuild, assert_warns, IS_WASM
+import sys
+
+
+# FIXME: numpy.testing.extbuild uses `numpy.distutils`, so this won't work on
+# Python 3.12 and up. It's an internal test utility, so for now we just skip
+# these tests.
+
+
+@pytest.fixture
+def get_module(tmp_path):
+    """ Add a memory policy that returns a false pointer 64 bytes into the
+    actual allocation, and fill the prefix with some text. Then check at each
+    memory manipulation that the prefix exists, to make sure all alloc/realloc/
+    free/calloc go via the functions here.
+    """
+    if sys.platform.startswith('cygwin'):
+        pytest.skip('link fails on cygwin')
+    if IS_WASM:
+        pytest.skip("Can't build module inside Wasm")
+    functions = [
+        ("get_default_policy", "METH_NOARGS", """
+             Py_INCREF(PyDataMem_DefaultHandler);
+             return PyDataMem_DefaultHandler;
+         """),
+        ("set_secret_data_policy", "METH_NOARGS", """
+             PyObject *secret_data =
+                 PyCapsule_New(&secret_data_handler, "mem_handler", NULL);
+             if (secret_data == NULL) {
+                 return NULL;
+             }
+             PyObject *old = PyDataMem_SetHandler(secret_data);
+             Py_DECREF(secret_data);
+             return old;
+         """),
+        ("set_old_policy", "METH_O", """
+             PyObject *old;
+             if (args != NULL && PyCapsule_CheckExact(args)) {
+                 old = PyDataMem_SetHandler(args);
+             }
+             else {
+                 old = PyDataMem_SetHandler(NULL);
+             }
+             return old;
+         """),
+        ("get_array", "METH_NOARGS", """
+            char *buf = (char *)malloc(20);
+            npy_intp dims[1];
+            dims[0] = 20;
+            PyArray_Descr *descr =  PyArray_DescrNewFromType(NPY_UINT8);
+            return PyArray_NewFromDescr(&PyArray_Type, descr, 1, dims, NULL,
+                                        buf, NPY_ARRAY_WRITEABLE, NULL);
+         """),
+        ("set_own", "METH_O", """
+            if (!PyArray_Check(args)) {
+                PyErr_SetString(PyExc_ValueError,
+                             "need an ndarray");
+                return NULL;
+            }
+            PyArray_ENABLEFLAGS((PyArrayObject*)args, NPY_ARRAY_OWNDATA);
+            // Maybe try this too?
+            // PyArray_BASE(PyArrayObject *)args) = NULL;
+            Py_RETURN_NONE;
+         """),
+        ("get_array_with_base", "METH_NOARGS", """
+            char *buf = (char *)malloc(20);
+            npy_intp dims[1];
+            dims[0] = 20;
+            PyArray_Descr *descr =  PyArray_DescrNewFromType(NPY_UINT8);
+            PyObject *arr = PyArray_NewFromDescr(&PyArray_Type, descr, 1, dims,
+                                                 NULL, buf,
+                                                 NPY_ARRAY_WRITEABLE, NULL);
+            if (arr == NULL) return NULL;
+            PyObject *obj = PyCapsule_New(buf, "buf capsule",
+                                          (PyCapsule_Destructor)&warn_on_free);
+            if (obj == NULL) {
+                Py_DECREF(arr);
+                return NULL;
+            }
+            if (PyArray_SetBaseObject((PyArrayObject *)arr, obj) < 0) {
+                Py_DECREF(arr);
+                Py_DECREF(obj);
+                return NULL;
+            }
+            return arr;
+
+         """),
+    ]
+    prologue = '''
+        #define NPY_TARGET_VERSION NPY_1_22_API_VERSION
+        #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
+        #include 
+        /*
+         * This struct allows the dynamic configuration of the allocator funcs
+         * of the `secret_data_allocator`. It is provided here for
+         * demonstration purposes, as a valid `ctx` use-case scenario.
+         */
+        typedef struct {
+            void *(*malloc)(size_t);
+            void *(*calloc)(size_t, size_t);
+            void *(*realloc)(void *, size_t);
+            void (*free)(void *);
+        } SecretDataAllocatorFuncs;
+
+        NPY_NO_EXPORT void *
+        shift_alloc(void *ctx, size_t sz) {
+            SecretDataAllocatorFuncs *funcs = (SecretDataAllocatorFuncs *)ctx;
+            char *real = (char *)funcs->malloc(sz + 64);
+            if (real == NULL) {
+                return NULL;
+            }
+            snprintf(real, 64, "originally allocated %ld", (unsigned long)sz);
+            return (void *)(real + 64);
+        }
+        NPY_NO_EXPORT void *
+        shift_zero(void *ctx, size_t sz, size_t cnt) {
+            SecretDataAllocatorFuncs *funcs = (SecretDataAllocatorFuncs *)ctx;
+            char *real = (char *)funcs->calloc(sz + 64, cnt);
+            if (real == NULL) {
+                return NULL;
+            }
+            snprintf(real, 64, "originally allocated %ld via zero",
+                     (unsigned long)sz);
+            return (void *)(real + 64);
+        }
+        NPY_NO_EXPORT void
+        shift_free(void *ctx, void * p, npy_uintp sz) {
+            SecretDataAllocatorFuncs *funcs = (SecretDataAllocatorFuncs *)ctx;
+            if (p == NULL) {
+                return ;
+            }
+            char *real = (char *)p - 64;
+            if (strncmp(real, "originally allocated", 20) != 0) {
+                fprintf(stdout, "uh-oh, unmatched shift_free, "
+                        "no appropriate prefix\\n");
+                /* Make C runtime crash by calling free on the wrong address */
+                funcs->free((char *)p + 10);
+                /* funcs->free(real); */
+            }
+            else {
+                npy_uintp i = (npy_uintp)atoi(real +20);
+                if (i != sz) {
+                    fprintf(stderr, "uh-oh, unmatched shift_free"
+                            "(ptr, %ld) but allocated %ld\\n", sz, i);
+                    /* This happens in some places, only print */
+                    funcs->free(real);
+                }
+                else {
+                    funcs->free(real);
+                }
+            }
+        }
+        NPY_NO_EXPORT void *
+        shift_realloc(void *ctx, void * p, npy_uintp sz) {
+            SecretDataAllocatorFuncs *funcs = (SecretDataAllocatorFuncs *)ctx;
+            if (p != NULL) {
+                char *real = (char *)p - 64;
+                if (strncmp(real, "originally allocated", 20) != 0) {
+                    fprintf(stdout, "uh-oh, unmatched shift_realloc\\n");
+                    return realloc(p, sz);
+                }
+                return (void *)((char *)funcs->realloc(real, sz + 64) + 64);
+            }
+            else {
+                char *real = (char *)funcs->realloc(p, sz + 64);
+                if (real == NULL) {
+                    return NULL;
+                }
+                snprintf(real, 64, "originally allocated "
+                         "%ld  via realloc", (unsigned long)sz);
+                return (void *)(real + 64);
+            }
+        }
+        /* As an example, we use the standard {m|c|re}alloc/free funcs. */
+        static SecretDataAllocatorFuncs secret_data_handler_ctx = {
+            malloc,
+            calloc,
+            realloc,
+            free
+        };
+        static PyDataMem_Handler secret_data_handler = {
+            "secret_data_allocator",
+            1,
+            {
+                &secret_data_handler_ctx, /* ctx */
+                shift_alloc,              /* malloc */
+                shift_zero,               /* calloc */
+                shift_realloc,            /* realloc */
+                shift_free                /* free */
+            }
+        };
+        void warn_on_free(void *capsule) {
+            PyErr_WarnEx(PyExc_UserWarning, "in warn_on_free", 1);
+            void * obj = PyCapsule_GetPointer(capsule,
+                                              PyCapsule_GetName(capsule));
+            free(obj);
+        };
+        '''
+    more_init = "import_array();"
+    try:
+        import mem_policy
+        return mem_policy
+    except ImportError:
+        pass
+    # if it does not exist, build and load it
+    return extbuild.build_and_import_extension('mem_policy',
+                                               functions,
+                                               prologue=prologue,
+                                               include_dirs=[np.get_include()],
+                                               build_dir=tmp_path,
+                                               more_init=more_init)
+
+
+@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
+def test_set_policy(get_module):
+
+    get_handler_name = np.core.multiarray.get_handler_name
+    get_handler_version = np.core.multiarray.get_handler_version
+    orig_policy_name = get_handler_name()
+
+    a = np.arange(10).reshape((2, 5))  # a doesn't own its own data
+    assert get_handler_name(a) is None
+    assert get_handler_version(a) is None
+    assert get_handler_name(a.base) == orig_policy_name
+    assert get_handler_version(a.base) == 1
+
+    orig_policy = get_module.set_secret_data_policy()
+
+    b = np.arange(10).reshape((2, 5))  # b doesn't own its own data
+    assert get_handler_name(b) is None
+    assert get_handler_version(b) is None
+    assert get_handler_name(b.base) == 'secret_data_allocator'
+    assert get_handler_version(b.base) == 1
+
+    if orig_policy_name == 'default_allocator':
+        get_module.set_old_policy(None)  # tests PyDataMem_SetHandler(NULL)
+        assert get_handler_name() == 'default_allocator'
+    else:
+        get_module.set_old_policy(orig_policy)
+        assert get_handler_name() == orig_policy_name
+
+
+@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
+def test_default_policy_singleton(get_module):
+    get_handler_name = np.core.multiarray.get_handler_name
+
+    # set the policy to default
+    orig_policy = get_module.set_old_policy(None)
+
+    assert get_handler_name() == 'default_allocator'
+
+    # re-set the policy to default
+    def_policy_1 = get_module.set_old_policy(None)
+
+    assert get_handler_name() == 'default_allocator'
+
+    # set the policy to original
+    def_policy_2 = get_module.set_old_policy(orig_policy)
+
+    # since default policy is a singleton,
+    # these should be the same object
+    assert def_policy_1 is def_policy_2 is get_module.get_default_policy()
+
+
+@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
+def test_policy_propagation(get_module):
+    # The memory policy goes hand-in-hand with flags.owndata
+
+    class MyArr(np.ndarray):
+        pass
+
+    get_handler_name = np.core.multiarray.get_handler_name
+    orig_policy_name = get_handler_name()
+    a = np.arange(10).view(MyArr).reshape((2, 5))
+    assert get_handler_name(a) is None
+    assert a.flags.owndata is False
+
+    assert get_handler_name(a.base) is None
+    assert a.base.flags.owndata is False
+
+    assert get_handler_name(a.base.base) == orig_policy_name
+    assert a.base.base.flags.owndata is True
+
+
+async def concurrent_context1(get_module, orig_policy_name, event):
+    if orig_policy_name == 'default_allocator':
+        get_module.set_secret_data_policy()
+        assert np.core.multiarray.get_handler_name() == 'secret_data_allocator'
+    else:
+        get_module.set_old_policy(None)
+        assert np.core.multiarray.get_handler_name() == 'default_allocator'
+    event.set()
+
+
+async def concurrent_context2(get_module, orig_policy_name, event):
+    await event.wait()
+    # the policy is not affected by changes in parallel contexts
+    assert np.core.multiarray.get_handler_name() == orig_policy_name
+    # change policy in the child context
+    if orig_policy_name == 'default_allocator':
+        get_module.set_secret_data_policy()
+        assert np.core.multiarray.get_handler_name() == 'secret_data_allocator'
+    else:
+        get_module.set_old_policy(None)
+        assert np.core.multiarray.get_handler_name() == 'default_allocator'
+
+
+async def async_test_context_locality(get_module):
+    orig_policy_name = np.core.multiarray.get_handler_name()
+
+    event = asyncio.Event()
+    # the child contexts inherit the parent policy
+    concurrent_task1 = asyncio.create_task(
+        concurrent_context1(get_module, orig_policy_name, event))
+    concurrent_task2 = asyncio.create_task(
+        concurrent_context2(get_module, orig_policy_name, event))
+    await concurrent_task1
+    await concurrent_task2
+
+    # the parent context is not affected by child policy changes
+    assert np.core.multiarray.get_handler_name() == orig_policy_name
+
+
+@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
+def test_context_locality(get_module):
+    if (sys.implementation.name == 'pypy'
+            and sys.pypy_version_info[:3] < (7, 3, 6)):
+        pytest.skip('no context-locality support in PyPy < 7.3.6')
+    asyncio.run(async_test_context_locality(get_module))
+
+
+def concurrent_thread1(get_module, event):
+    get_module.set_secret_data_policy()
+    assert np.core.multiarray.get_handler_name() == 'secret_data_allocator'
+    event.set()
+
+
+def concurrent_thread2(get_module, event):
+    event.wait()
+    # the policy is not affected by changes in parallel threads
+    assert np.core.multiarray.get_handler_name() == 'default_allocator'
+    # change policy in the child thread
+    get_module.set_secret_data_policy()
+
+
+@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
+def test_thread_locality(get_module):
+    orig_policy_name = np.core.multiarray.get_handler_name()
+
+    event = threading.Event()
+    # the child threads do not inherit the parent policy
+    concurrent_task1 = threading.Thread(target=concurrent_thread1,
+                                        args=(get_module, event))
+    concurrent_task2 = threading.Thread(target=concurrent_thread2,
+                                        args=(get_module, event))
+    concurrent_task1.start()
+    concurrent_task2.start()
+    concurrent_task1.join()
+    concurrent_task2.join()
+
+    # the parent thread is not affected by child policy changes
+    assert np.core.multiarray.get_handler_name() == orig_policy_name
+
+
+@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
+@pytest.mark.skip(reason="too slow, see gh-23975")
+def test_new_policy(get_module):
+    a = np.arange(10)
+    orig_policy_name = np.core.multiarray.get_handler_name(a)
+
+    orig_policy = get_module.set_secret_data_policy()
+
+    b = np.arange(10)
+    assert np.core.multiarray.get_handler_name(b) == 'secret_data_allocator'
+
+    # test array manipulation. This is slow
+    if orig_policy_name == 'default_allocator':
+        # when the np.core.test tests recurse into this test, the
+        # policy will be set so this "if" will be false, preventing
+        # infinite recursion
+        #
+        # if needed, debug this by
+        # - running tests with -- -s (to not capture stdout/stderr
+        # - setting verbose=2
+        # - setting extra_argv=['-vv'] here
+        assert np.core.test('full', verbose=1, extra_argv=[])
+        # also try the ma tests, the pickling test is quite tricky
+        assert np.ma.test('full', verbose=1, extra_argv=[])
+
+    get_module.set_old_policy(orig_policy)
+
+    c = np.arange(10)
+    assert np.core.multiarray.get_handler_name(c) == orig_policy_name
+
+
+@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
+@pytest.mark.xfail(sys.implementation.name == "pypy",
+                   reason=("bad interaction between getenv and "
+                           "os.environ inside pytest"))
+@pytest.mark.parametrize("policy", ["0", "1", None])
+def test_switch_owner(get_module, policy):
+    a = get_module.get_array()
+    assert np.core.multiarray.get_handler_name(a) is None
+    get_module.set_own(a)
+
+    if policy is None:
+        # See what we expect to be set based on the env variable
+        policy = os.getenv("NUMPY_WARN_IF_NO_MEM_POLICY", "0") == "1"
+        oldval = None
+    else:
+        policy = policy == "1"
+        oldval = np.core._multiarray_umath._set_numpy_warn_if_no_mem_policy(
+            policy)
+    try:
+        # The policy should be NULL, so we have to assume we can call
+        # "free".  A warning is given if the policy == "1"
+        if policy:
+            with assert_warns(RuntimeWarning) as w:
+                del a
+                gc.collect()
+        else:
+            del a
+            gc.collect()
+
+    finally:
+        if oldval is not None:
+            np.core._multiarray_umath._set_numpy_warn_if_no_mem_policy(oldval)
+
+
+@pytest.mark.skipif(sys.version_info >= (3, 12), reason="no numpy.distutils")
+def test_owner_is_base(get_module):
+    a = get_module.get_array_with_base()
+    with pytest.warns(UserWarning, match='warn_on_free'):
+        del a
+        gc.collect()
+        gc.collect()
diff --git a/shared/package-files/09/0979373c62bd58be6dfb927e1b65f94e5fa76fa95c12b4c14b9a148f64ade9a5/move.pdf b/shared/package-files/09/0979373c62bd58be6dfb927e1b65f94e5fa76fa95c12b4c14b9a148f64ade9a5/move.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..d883d9224a6e61436205a18182f5e66708312976
Binary files /dev/null and b/shared/package-files/09/0979373c62bd58be6dfb927e1b65f94e5fa76fa95c12b4c14b9a148f64ade9a5/move.pdf differ
diff --git a/shared/package-files/09/097ac7fd806967e1c89871eba9722142dfef7afa783f93570a5a7b86714c54b3/__init__.py b/shared/package-files/09/097ac7fd806967e1c89871eba9722142dfef7afa783f93570a5a7b86714c54b3/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2140970015d099763797d60a98a3a3b6f4b5f219
--- /dev/null
+++ b/shared/package-files/09/097ac7fd806967e1c89871eba9722142dfef7afa783f93570a5a7b86714c54b3/__init__.py
@@ -0,0 +1,14 @@
+"""
+Module containing private utility functions
+===========================================
+
+The ``scipy._lib`` namespace is empty (for now). Tests for all
+utilities in submodules of ``_lib`` can be run with::
+
+    from scipy import _lib
+    _lib.test()
+
+"""
+from scipy._lib._testutils import PytestTester
+test = PytestTester(__name__)
+del PytestTester
diff --git a/shared/package-files/09/097c9d03bc92bc447abf0e7caa9fd98a10a483a632c726ac49b0424ab6e7dace/METADATA b/shared/package-files/09/097c9d03bc92bc447abf0e7caa9fd98a10a483a632c726ac49b0424ab6e7dace/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..d0af2537a11dcfc32ad43c065f9ce472e7ee517a
--- /dev/null
+++ b/shared/package-files/09/097c9d03bc92bc447abf0e7caa9fd98a10a483a632c726ac49b0424ab6e7dace/METADATA
@@ -0,0 +1,88 @@
+Metadata-Version: 2.1
+Name: duckdb
+Version: 1.4.4
+Summary: DuckDB in-process database
+Keywords: DuckDB,Database,SQL,OLAP
+Author: DuckDB Foundation
+Maintainer: DuckDB Foundation
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Classifier: Topic :: Database
+Classifier: Topic :: Database :: Database Engines/Servers
+Classifier: Topic :: Scientific/Engineering
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Information Technology
+Classifier: Intended Audience :: Science/Research
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Programming Language :: C++
+Project-URL: Documentation, https://duckdb.org/docs/stable/clients/python/overview
+Project-URL: Source, https://github.com/duckdb/duckdb-python
+Project-URL: Issues, https://github.com/duckdb/duckdb-python/issues
+Project-URL: Changelog, https://github.com/duckdb/duckdb-python/releases
+Requires-Python: >=3.9.0
+Provides-Extra: all
+Requires-Dist: ipython; extra == "all"
+Requires-Dist: fsspec; extra == "all"
+Requires-Dist: numpy; extra == "all"
+Requires-Dist: pandas; extra == "all"
+Requires-Dist: pyarrow; extra == "all"
+Requires-Dist: adbc-driver-manager; extra == "all"
+Description-Content-Type: text/markdown
+
+

+  
+    
+    
+    DuckDB logo
+  
+

+

+

+  Discord
+  PyPI Latest Release
+

+

+

+  DuckDB.org
+  |
+  User Guide (Python)
+  -
+  API Docs (Python)
+

+
+# DuckDB: A Fast, In-Process, Portable, Open Source, Analytical Database System
+
+* **Simple**: DuckDB is easy to install and deploy. It has zero external dependencies and runs in-process in its host application or as a single binary.
+* **Portable**: DuckDB runs on Linux, macOS, Windows, Android, iOS and all popular hardware architectures. It has idiomatic client APIs for major programming languages.
+* **Feature-rich**: DuckDB offers a rich SQL dialect. It can read and write file formats such as CSV, Parquet, and JSON, to and from the local file system and remote endpoints such as S3 buckets.
+* **Fast**: DuckDB runs analytical queries at blazing speed thanks to its columnar engine, which supports parallel execution and can process larger-than-memory workloads.
+* **Extensible**: DuckDB is extensible by third-party features such as new data types, functions, file formats and new SQL syntax. User contributions are available as community extensions.
+* **Free**: DuckDB and its core extensions are open-source under the permissive MIT License. The intellectual property of the project is held by the DuckDB Foundation.
+
+## Installation
+
+Install the latest release of DuckDB directly from [PyPI](https://pypi.org/project/duckdb/):
+
+```bash
+pip install duckdb
+```
+
+Install with all optional dependencies:
+
+```bash
+pip install 'duckdb[all]'
+```
+
+## Contributing
+
+See the [CONTRIBUTING.md](CONTRIBUTING.md) for instructions on how to set up a development environment.
diff --git a/shared/package-files/09/09883943e9dcb5c2e23f8baed6df41baf0d35b7e9877821c2208067c459edbbd/case.py b/shared/package-files/09/09883943e9dcb5c2e23f8baed6df41baf0d35b7e9877821c2208067c459edbbd/case.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c4c03572e3ab3c0c7ed9ff9f816ceac3b725051
--- /dev/null
+++ b/shared/package-files/09/09883943e9dcb5c2e23f8baed6df41baf0d35b7e9877821c2208067c459edbbd/case.py
@@ -0,0 +1,188 @@
+import inspect
+import re
+import string
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any, Dict, List, Optional, Set, Tuple, Union
+from types import ModuleType
+
+import torch
+
+_TAGS: Dict[str, Dict[str, Any]] = {
+    "torch": {
+        "cond": {},
+        "dynamic-shape": {},
+        "escape-hatch": {},
+        "map": {},
+        "dynamic-value": {},
+        "operator": {},
+        "mutation": {},
+    },
+    "python": {
+        "assert": {},
+        "builtin": {},
+        "closure": {},
+        "context-manager": {},
+        "control-flow": {},
+        "data-structure": {},
+        "standard-library": {},
+        "object-model": {},
+    },
+}
+
+
+class SupportLevel(Enum):
+    """
+    Indicates at what stage the feature
+    used in the example is handled in export.
+    """
+
+    SUPPORTED = 1
+    NOT_SUPPORTED_YET = 0
+
+
+class ExportArgs:
+    __slots__ = ("args", "kwargs")
+
+    def __init__(self, *args, **kwargs):
+        self.args = args
+        self.kwargs = kwargs
+
+
+InputsType = Union[Tuple[Any, ...], ExportArgs]
+
+
+def check_inputs_type(x):
+    if not isinstance(x, (ExportArgs, tuple)):
+        raise ValueError(
+            f"Expecting inputs type to be either a tuple, or ExportArgs, got: {type(x)}"
+        )
+
+
+def _validate_tag(tag: str):
+    parts = tag.split(".")
+    t = _TAGS
+    for part in parts:
+        assert set(part) <= set(
+            string.ascii_lowercase + "-"
+        ), f"Tag contains invalid characters: {part}"
+        if part in t:
+            t = t[part]
+        else:
+            raise ValueError(f"Tag {tag} is not found in registered tags.")
+
+
+@dataclass(frozen=True)
+class ExportCase:
+    example_inputs: InputsType
+    description: str  # A description of the use case.
+    model: torch.nn.Module
+    name: str
+    extra_inputs: Optional[InputsType] = None  # For testing graph generalization.
+    # Tags associated with the use case. (e.g dynamic-shape, escape-hatch)
+    tags: Set[str] = field(default_factory=set)
+    support_level: SupportLevel = SupportLevel.SUPPORTED
+    dynamic_shapes: Optional[Dict[str, Any]] = None
+
+    def __post_init__(self):
+        check_inputs_type(self.example_inputs)
+        if self.extra_inputs is not None:
+            check_inputs_type(self.extra_inputs)
+
+        for tag in self.tags:
+            _validate_tag(tag)
+
+        if not isinstance(self.description, str) or len(self.description) == 0:
+            raise ValueError(f'Invalid description: "{self.description}"')
+
+
+_EXAMPLE_CASES: Dict[str, ExportCase] = {}
+_MODULES: Set[ModuleType] = set()
+_EXAMPLE_CONFLICT_CASES: Dict[str, List[ExportCase]] = {}
+_EXAMPLE_REWRITE_CASES: Dict[str, List[ExportCase]] = {}
+
+
+def register_db_case(case: ExportCase) -> None:
+    """
+    Registers a user provided ExportCase into example bank.
+    """
+    if case.name in _EXAMPLE_CASES:
+        if case.name not in _EXAMPLE_CONFLICT_CASES:
+            _EXAMPLE_CONFLICT_CASES[case.name] = [_EXAMPLE_CASES[case.name]]
+        _EXAMPLE_CONFLICT_CASES[case.name].append(case)
+        return
+
+    _EXAMPLE_CASES[case.name] = case
+
+
+def to_snake_case(name):
+    name = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", name)
+    return re.sub("([a-z0-9])([A-Z])", r"\1_\2", name).lower()
+
+
+def _make_export_case(m, name, configs):
+    if not issubclass(m, torch.nn.Module):
+        raise TypeError("Export case class should be a torch.nn.Module.")
+    m = m()
+
+    if "description" not in configs:
+        # Fallback to docstring if description is missing.
+        assert (
+            m.__doc__ is not None
+        ), f"Could not find description or docstring for export case: {m}"
+        configs = {**configs, "description": m.__doc__}
+    return ExportCase(**{**configs, "model": m, "name": name})
+
+
+def export_case(**kwargs):
+    """
+    Decorator for registering a user provided case into example bank.
+    """
+
+    def wrapper(m):
+        configs = kwargs
+        module = inspect.getmodule(m)
+        if module in _MODULES:
+            raise RuntimeError("export_case should only be used once per example file.")
+
+        assert module is not None
+        _MODULES.add(module)
+        normalized_name = to_snake_case(m.__name__)
+        module_name = module.__name__.split(".")[-1]
+        if module_name != normalized_name:
+            raise RuntimeError(
+                f'Module name "{module.__name__}" is inconsistent with exported program '
+                + f'name "{m.__name__}". Please rename the module to "{normalized_name}".'
+            )
+
+        case = _make_export_case(m, module_name, configs)
+        register_db_case(case)
+        return case
+
+    return wrapper
+
+
+def export_rewrite_case(**kwargs):
+    def wrapper(m):
+        configs = kwargs
+
+        parent = configs.pop("parent")
+        assert isinstance(parent, ExportCase)
+        key = parent.name
+        if key not in _EXAMPLE_REWRITE_CASES:
+            _EXAMPLE_REWRITE_CASES[key] = []
+
+        configs["example_inputs"] = parent.example_inputs
+        case = _make_export_case(m, to_snake_case(m.__name__), configs)
+        _EXAMPLE_REWRITE_CASES[key].append(case)
+        return case
+
+    return wrapper
+
+
+def normalize_inputs(x: InputsType) -> ExportArgs:
+    if isinstance(x, tuple):
+        return ExportArgs(*x)
+
+    assert isinstance(x, ExportArgs)
+    return x
diff --git a/shared/package-files/09/0988a21e41eb43d425734266b59bd405650fc5e5090e36b1cf005bae24adc68f/dispatcher.pyi b/shared/package-files/09/0988a21e41eb43d425734266b59bd405650fc5e5090e36b1cf005bae24adc68f/dispatcher.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..60ea30e0c8b3e73c4e98f03005cbf83bbf3e64a4
--- /dev/null
+++ b/shared/package-files/09/0988a21e41eb43d425734266b59bd405650fc5e5090e36b1cf005bae24adc68f/dispatcher.pyi
@@ -0,0 +1,8 @@
+from _typeshed.wsgi import StartResponse, WSGIApplication, WSGIEnvironment
+from typing import Iterable, Mapping, Optional, Text
+
+class DispatcherMiddleware(object):
+    app: WSGIApplication
+    mounts: Mapping[Text, WSGIApplication]
+    def __init__(self, app: WSGIApplication, mounts: Optional[Mapping[Text, WSGIApplication]] = ...) -> None: ...
+    def __call__(self, environ: WSGIEnvironment, start_response: StartResponse) -> Iterable[bytes]: ...
diff --git a/shared/package-files/09/0988a26c4f1a5fb3109c16ad97fff993f3d673ff6f1d489fc12fac159cf32a4d/umath-validation-set-sinh.csv b/shared/package-files/09/0988a26c4f1a5fb3109c16ad97fff993f3d673ff6f1d489fc12fac159cf32a4d/umath-validation-set-sinh.csv
new file mode 100644
index 0000000000000000000000000000000000000000..5888c91c20db96c95930381bdff8ff83563b63c2
--- /dev/null
+++ b/shared/package-files/09/0988a26c4f1a5fb3109c16ad97fff993f3d673ff6f1d489fc12fac159cf32a4d/umath-validation-set-sinh.csv
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diff --git a/shared/package-files/09/098e43e53ec992a1459c875eebeb40f9a739daf901dfa9345cb37bc274e81109/special_shifted_chebyshev_polynomial_t_ops.h b/shared/package-files/09/098e43e53ec992a1459c875eebeb40f9a739daf901dfa9345cb37bc274e81109/special_shifted_chebyshev_polynomial_t_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..4982c878a4da0da5a71b6a7602d5b4c557e9521f
--- /dev/null
+++ b/shared/package-files/09/098e43e53ec992a1459c875eebeb40f9a739daf901dfa9345cb37bc274e81109/special_shifted_chebyshev_polynomial_t_ops.h
@@ -0,0 +1,83 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API special_shifted_chebyshev_polynomial_t {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_shifted_chebyshev_polynomial_t")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_shifted_chebyshev_polynomial_t(Tensor x, Tensor n) -> Tensor")
+  static at::Tensor call(const at::Tensor & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_t_x_scalar {
+  using schema = at::Tensor (const at::Scalar &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_shifted_chebyshev_polynomial_t")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "x_scalar")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_shifted_chebyshev_polynomial_t.x_scalar(Scalar x, Tensor n) -> Tensor")
+  static at::Tensor call(const at::Scalar & x, const at::Tensor & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_t_n_scalar {
+  using schema = at::Tensor (const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_shifted_chebyshev_polynomial_t")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "n_scalar")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_shifted_chebyshev_polynomial_t.n_scalar(Tensor x, Scalar n) -> Tensor")
+  static at::Tensor call(const at::Tensor & x, const at::Scalar & n);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_t_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_shifted_chebyshev_polynomial_t")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_shifted_chebyshev_polynomial_t.out(Tensor x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_t_x_scalar_out {
+  using schema = at::Tensor & (const at::Scalar &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_shifted_chebyshev_polynomial_t")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "x_scalar_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_shifted_chebyshev_polynomial_t.x_scalar_out(Scalar x, Tensor n, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Scalar & x, const at::Tensor & n, at::Tensor & out);
+};
+
+struct TORCH_API special_shifted_chebyshev_polynomial_t_n_scalar_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::special_shifted_chebyshev_polynomial_t")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "n_scalar_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "special_shifted_chebyshev_polynomial_t.n_scalar_out(Tensor x, Scalar n, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & x, const at::Scalar & n, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/09/098fb5f7bbd1333f4eab1a4e4cc1098f9655832783a4dcbc507bf5c5c52a02a8/quantized_batch_norm_compositeexplicitautograd_dispatch.h b/shared/package-files/09/098fb5f7bbd1333f4eab1a4e4cc1098f9655832783a4dcbc507bf5c5c52a02a8/quantized_batch_norm_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..26e66dfbb9e5999ecc9011acd3c2dc19edff5426
--- /dev/null
+++ b/shared/package-files/09/098fb5f7bbd1333f4eab1a4e4cc1098f9655832783a4dcbc507bf5c5c52a02a8/quantized_batch_norm_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & quantized_batch_norm_out(at::Tensor & out, const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point);
+TORCH_API at::Tensor & quantized_batch_norm_outf(const at::Tensor & input, const c10::optional & weight, const c10::optional & bias, const at::Tensor & mean, const at::Tensor & var, double eps, double output_scale, int64_t output_zero_point, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/09/0990f00cfb1d10b2ad76bece5a6705c86a9ec6bdf751c7e7bff454f79549c484/series.py b/shared/package-files/09/0990f00cfb1d10b2ad76bece5a6705c86a9ec6bdf751c7e7bff454f79549c484/series.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c55d3c8d2eda434131a6f991f023914eea93d9c
--- /dev/null
+++ b/shared/package-files/09/0990f00cfb1d10b2ad76bece5a6705c86a9ec6bdf751c7e7bff454f79549c484/series.py
@@ -0,0 +1,41 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, NoReturn
+
+from narwhals._ibis.utils import native_to_narwhals_dtype
+from narwhals.dependencies import get_ibis
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+    from typing_extensions import Self
+
+    from narwhals._utils import Version
+    from narwhals.dtypes import DType
+
+
+class IbisInterchangeSeries:
+    def __init__(self, df: Any, version: Version) -> None:
+        self._native_series = df
+        self._version = version
+
+    def __narwhals_series__(self) -> Self:
+        return self
+
+    def __native_namespace__(self) -> ModuleType:
+        return get_ibis()
+
+    @property
+    def dtype(self) -> DType:
+        return native_to_narwhals_dtype(
+            self._native_series.schema().types[0], self._version
+        )
+
+    def __getattr__(self, attr: str) -> NoReturn:
+        msg = (
+            f"Attribute {attr} is not supported for interchange-level dataframes.\n\n"
+            "If you would like to see this kind of object better supported in "
+            "Narwhals, please open a feature request "
+            "at https://github.com/narwhals-dev/narwhals/issues."
+        )
+        raise NotImplementedError(msg)
diff --git a/shared/package-files/09/099649b613a396a58120d8407db81e32b49ad5f90db47f9efe61530725d3fd0b/linalg_householder_product_cuda_dispatch.h b/shared/package-files/09/099649b613a396a58120d8407db81e32b49ad5f90db47f9efe61530725d3fd0b/linalg_householder_product_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..301705e7121f4ad6dea7871e597ac691557f1346
--- /dev/null
+++ b/shared/package-files/09/099649b613a396a58120d8407db81e32b49ad5f90db47f9efe61530725d3fd0b/linalg_householder_product_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor linalg_householder_product(const at::Tensor & input, const at::Tensor & tau);
+TORCH_API at::Tensor & linalg_householder_product_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & tau);
+TORCH_API at::Tensor & linalg_householder_product_outf(const at::Tensor & input, const at::Tensor & tau, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/09/099d0630427a95408d0b158863e97b592caed0ef330d211bbfd44815719869ee/macrodata_jmulti_ct_granger_causality_realcons_realinv.txt b/shared/package-files/09/099d0630427a95408d0b158863e97b592caed0ef330d211bbfd44815719869ee/macrodata_jmulti_ct_granger_causality_realcons_realinv.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ea087a462dbd84fe7d6af65c8da43b28e3e35770
--- /dev/null
+++ b/shared/package-files/09/099d0630427a95408d0b158863e97b592caed0ef330d211bbfd44815719869ee/macrodata_jmulti_ct_granger_causality_realcons_realinv.txt
@@ -0,0 +1,13 @@
+*** Sat, 3 Sep 2016 22:32:25 ***
+TEST FOR GRANGER-CAUSALITY:
+H0: "realcons, realinv" do not Granger-cause "realgdp"
+
+Test statistic l = 10.1522
+pval-F( l; 8, 555) = 0.0000 
+
+TEST FOR INSTANTANEOUS CAUSALITY:
+H0: No instantaneous causality between "realcons, realinv" and "realgdp"
+
+Test statistic: c = 86.2497
+pval-Chi( c; 2) = 0.0000 
+
diff --git a/shared/package-files/09/099d6782eee867b16d7686dd538cd1a767211a076a672deb4b15c2c097bafa57/conv_transpose2d_native.h b/shared/package-files/09/099d6782eee867b16d7686dd538cd1a767211a076a672deb4b15c2c097bafa57/conv_transpose2d_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..da649fac0e15daac467a12908ca91f4824bfc6d0
--- /dev/null
+++ b/shared/package-files/09/099d6782eee867b16d7686dd538cd1a767211a076a672deb4b15c2c097bafa57/conv_transpose2d_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor conv_transpose2d_symint(const at::Tensor & input, const at::Tensor & weight, const c10::optional & bias={}, c10::SymIntArrayRef stride=c10::SymInt(1), c10::SymIntArrayRef padding=c10::SymInt(0), c10::SymIntArrayRef output_padding=c10::SymInt(0), c10::SymInt groups=1, c10::SymIntArrayRef dilation=c10::SymInt(1));
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/09/099d8fa2d59af3da62674056aafb5d0c3347bab31b391388951e9ac8af4618bd/true_divide.h b/shared/package-files/09/099d8fa2d59af3da62674056aafb5d0c3347bab31b391388951e9ac8af4618bd/true_divide.h
new file mode 100644
index 0000000000000000000000000000000000000000..70e3eab39562e983c073bbe74c6359fe8617f367
--- /dev/null
+++ b/shared/package-files/09/099d8fa2d59af3da62674056aafb5d0c3347bab31b391388951e9ac8af4618bd/true_divide.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::true_divide.Tensor(Tensor self, Tensor other) -> Tensor
+inline at::Tensor true_divide(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::true_divide_Tensor::call(self, other);
+}
+
+// aten::true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & true_divide_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::true_divide_out::call(self, other, out);
+}
+// aten::true_divide.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & true_divide_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::true_divide_out::call(self, other, out);
+}
+
+// aten::true_divide.Scalar(Tensor self, Scalar other) -> Tensor
+inline at::Tensor true_divide(const at::Tensor & self, const at::Scalar & other) {
+    return at::_ops::true_divide_Scalar::call(self, other);
+}
+
+}
diff --git a/shared/package-files/09/099dba0f2f7558e52a870d57ef79aa2c65fe5875b304121e04282ba7a28ae19a/test_edge_kcomponents.py b/shared/package-files/09/099dba0f2f7558e52a870d57ef79aa2c65fe5875b304121e04282ba7a28ae19a/test_edge_kcomponents.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a1f681ab3da3f1f965ecbbf8dcf84eb49a512b9
--- /dev/null
+++ b/shared/package-files/09/099dba0f2f7558e52a870d57ef79aa2c65fe5875b304121e04282ba7a28ae19a/test_edge_kcomponents.py
@@ -0,0 +1,488 @@
+import itertools as it
+
+import pytest
+
+import networkx as nx
+from networkx.algorithms.connectivity import EdgeComponentAuxGraph, bridge_components
+from networkx.algorithms.connectivity.edge_kcomponents import general_k_edge_subgraphs
+from networkx.utils import pairwise
+
+# ----------------
+# Helper functions
+# ----------------
+
+
+def fset(list_of_sets):
+    """allows == to be used for list of sets"""
+    return set(map(frozenset, list_of_sets))
+
+
+def _assert_subgraph_edge_connectivity(G, ccs_subgraph, k):
+    """
+    tests properties of k-edge-connected subgraphs
+
+    the actual edge connectivity should be no less than k unless the cc is a
+    single node.
+    """
+    for cc in ccs_subgraph:
+        C = G.subgraph(cc)
+        if len(cc) > 1:
+            connectivity = nx.edge_connectivity(C)
+            assert connectivity >= k
+
+
+def _memo_connectivity(G, u, v, memo):
+    edge = (u, v)
+    if edge in memo:
+        return memo[edge]
+    if not G.is_directed():
+        redge = (v, u)
+        if redge in memo:
+            return memo[redge]
+    memo[edge] = nx.edge_connectivity(G, *edge)
+    return memo[edge]
+
+
+def _all_pairs_connectivity(G, cc, k, memo):
+    # Brute force check
+    for u, v in it.combinations(cc, 2):
+        # Use a memoization dict to save on computation
+        connectivity = _memo_connectivity(G, u, v, memo)
+        if G.is_directed():
+            connectivity = min(connectivity, _memo_connectivity(G, v, u, memo))
+        assert connectivity >= k
+
+
+def _assert_local_cc_edge_connectivity(G, ccs_local, k, memo):
+    """
+    tests properties of k-edge-connected components
+
+    the local edge connectivity between each pair of nodes in the original
+    graph should be no less than k unless the cc is a single node.
+    """
+    for cc in ccs_local:
+        if len(cc) > 1:
+            # Strategy for testing a bit faster: If the subgraph has high edge
+            # connectivity then it must have local connectivity
+            C = G.subgraph(cc)
+            connectivity = nx.edge_connectivity(C)
+            if connectivity < k:
+                # Otherwise do the brute force (with memoization) check
+                _all_pairs_connectivity(G, cc, k, memo)
+
+
+# Helper function
+def _check_edge_connectivity(G):
+    """
+    Helper - generates all k-edge-components using the aux graph.  Checks the
+    both local and subgraph edge connectivity of each cc. Also checks that
+    alternate methods of computing the k-edge-ccs generate the same result.
+    """
+    # Construct the auxiliary graph that can be used to make each k-cc or k-sub
+    aux_graph = EdgeComponentAuxGraph.construct(G)
+
+    # memoize the local connectivity in this graph
+    memo = {}
+
+    for k in it.count(1):
+        # Test "local" k-edge-components and k-edge-subgraphs
+        ccs_local = fset(aux_graph.k_edge_components(k))
+        ccs_subgraph = fset(aux_graph.k_edge_subgraphs(k))
+
+        # Check connectivity properties that should be guaranteed by the
+        # algorithms.
+        _assert_local_cc_edge_connectivity(G, ccs_local, k, memo)
+        _assert_subgraph_edge_connectivity(G, ccs_subgraph, k)
+
+        if k == 1 or k == 2 and not G.is_directed():
+            assert (
+                ccs_local == ccs_subgraph
+            ), "Subgraphs and components should be the same when k == 1 or (k == 2 and not G.directed())"
+
+        if G.is_directed():
+            # Test special case methods are the same as the aux graph
+            if k == 1:
+                alt_sccs = fset(nx.strongly_connected_components(G))
+                assert alt_sccs == ccs_local, "k=1 failed alt"
+                assert alt_sccs == ccs_subgraph, "k=1 failed alt"
+        else:
+            # Test special case methods are the same as the aux graph
+            if k == 1:
+                alt_ccs = fset(nx.connected_components(G))
+                assert alt_ccs == ccs_local, "k=1 failed alt"
+                assert alt_ccs == ccs_subgraph, "k=1 failed alt"
+            elif k == 2:
+                alt_bridge_ccs = fset(bridge_components(G))
+                assert alt_bridge_ccs == ccs_local, "k=2 failed alt"
+                assert alt_bridge_ccs == ccs_subgraph, "k=2 failed alt"
+            # if new methods for k == 3 or k == 4 are implemented add them here
+
+        # Check the general subgraph method works by itself
+        alt_subgraph_ccs = fset(
+            [set(C.nodes()) for C in general_k_edge_subgraphs(G, k=k)]
+        )
+        assert alt_subgraph_ccs == ccs_subgraph, "alt subgraph method failed"
+
+        # Stop once k is larger than all special case methods
+        # and we cannot break down ccs any further.
+        if k > 2 and all(len(cc) == 1 for cc in ccs_local):
+            break
+
+
+# ----------------
+# Misc tests
+# ----------------
+
+
+def test_zero_k_exception():
+    G = nx.Graph()
+    # functions that return generators error immediately
+    pytest.raises(ValueError, nx.k_edge_components, G, k=0)
+    pytest.raises(ValueError, nx.k_edge_subgraphs, G, k=0)
+
+    # actual generators only error when you get the first item
+    aux_graph = EdgeComponentAuxGraph.construct(G)
+    pytest.raises(ValueError, list, aux_graph.k_edge_components(k=0))
+    pytest.raises(ValueError, list, aux_graph.k_edge_subgraphs(k=0))
+
+    pytest.raises(ValueError, list, general_k_edge_subgraphs(G, k=0))
+
+
+def test_empty_input():
+    G = nx.Graph()
+    assert [] == list(nx.k_edge_components(G, k=5))
+    assert [] == list(nx.k_edge_subgraphs(G, k=5))
+
+    G = nx.DiGraph()
+    assert [] == list(nx.k_edge_components(G, k=5))
+    assert [] == list(nx.k_edge_subgraphs(G, k=5))
+
+
+def test_not_implemented():
+    G = nx.MultiGraph()
+    pytest.raises(nx.NetworkXNotImplemented, EdgeComponentAuxGraph.construct, G)
+    pytest.raises(nx.NetworkXNotImplemented, nx.k_edge_components, G, k=2)
+    pytest.raises(nx.NetworkXNotImplemented, nx.k_edge_subgraphs, G, k=2)
+    with pytest.raises(nx.NetworkXNotImplemented):
+        next(bridge_components(G))
+    with pytest.raises(nx.NetworkXNotImplemented):
+        next(bridge_components(nx.DiGraph()))
+
+
+def test_general_k_edge_subgraph_quick_return():
+    # tests quick return optimization
+    G = nx.Graph()
+    G.add_node(0)
+    subgraphs = list(general_k_edge_subgraphs(G, k=1))
+    assert len(subgraphs) == 1
+    for subgraph in subgraphs:
+        assert subgraph.number_of_nodes() == 1
+
+    G.add_node(1)
+    subgraphs = list(general_k_edge_subgraphs(G, k=1))
+    assert len(subgraphs) == 2
+    for subgraph in subgraphs:
+        assert subgraph.number_of_nodes() == 1
+
+
+# ----------------
+# Undirected tests
+# ----------------
+
+
+def test_random_gnp():
+    # seeds = [1550709854, 1309423156, 4208992358, 2785630813, 1915069929]
+    seeds = [12, 13]
+
+    for seed in seeds:
+        G = nx.gnp_random_graph(20, 0.2, seed=seed)
+        _check_edge_connectivity(G)
+
+
+def test_configuration():
+    # seeds = [2718183590, 2470619828, 1694705158, 3001036531, 2401251497]
+    seeds = [14, 15]
+    for seed in seeds:
+        deg_seq = nx.random_powerlaw_tree_sequence(20, seed=seed, tries=5000)
+        G = nx.Graph(nx.configuration_model(deg_seq, seed=seed))
+        G.remove_edges_from(nx.selfloop_edges(G))
+        _check_edge_connectivity(G)
+
+
+def test_shell():
+    # seeds = [2057382236, 3331169846, 1840105863, 476020778, 2247498425]
+    seeds = [20]
+    for seed in seeds:
+        constructor = [(12, 70, 0.8), (15, 40, 0.6)]
+        G = nx.random_shell_graph(constructor, seed=seed)
+        _check_edge_connectivity(G)
+
+
+def test_karate():
+    G = nx.karate_club_graph()
+    _check_edge_connectivity(G)
+
+
+def test_tarjan_bridge():
+    # graph from tarjan paper
+    # RE Tarjan - "A note on finding the bridges of a graph"
+    # Information Processing Letters, 1974 - Elsevier
+    # doi:10.1016/0020-0190(74)90003-9.
+    # define 2-connected components and bridges
+    ccs = [
+        (1, 2, 4, 3, 1, 4),
+        (5, 6, 7, 5),
+        (8, 9, 10, 8),
+        (17, 18, 16, 15, 17),
+        (11, 12, 14, 13, 11, 14),
+    ]
+    bridges = [(4, 8), (3, 5), (3, 17)]
+    G = nx.Graph(it.chain(*(pairwise(path) for path in ccs + bridges)))
+    _check_edge_connectivity(G)
+
+
+def test_bridge_cc():
+    # define 2-connected components and bridges
+    cc2 = [(1, 2, 4, 3, 1, 4), (8, 9, 10, 8), (11, 12, 13, 11)]
+    bridges = [(4, 8), (3, 5), (20, 21), (22, 23, 24)]
+    G = nx.Graph(it.chain(*(pairwise(path) for path in cc2 + bridges)))
+    bridge_ccs = fset(bridge_components(G))
+    target_ccs = fset(
+        [{1, 2, 3, 4}, {5}, {8, 9, 10}, {11, 12, 13}, {20}, {21}, {22}, {23}, {24}]
+    )
+    assert bridge_ccs == target_ccs
+    _check_edge_connectivity(G)
+
+
+def test_undirected_aux_graph():
+    # Graph similar to the one in
+    # http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136264
+    a, b, c, d, e, f, g, h, i = "abcdefghi"
+    paths = [
+        (a, d, b, f, c),
+        (a, e, b),
+        (a, e, b, c, g, b, a),
+        (c, b),
+        (f, g, f),
+        (h, i),
+    ]
+    G = nx.Graph(it.chain(*[pairwise(path) for path in paths]))
+    aux_graph = EdgeComponentAuxGraph.construct(G)
+
+    components_1 = fset(aux_graph.k_edge_subgraphs(k=1))
+    target_1 = fset([{a, b, c, d, e, f, g}, {h, i}])
+    assert target_1 == components_1
+
+    # Check that the undirected case for k=1 agrees with CCs
+    alt_1 = fset(nx.k_edge_subgraphs(G, k=1))
+    assert alt_1 == components_1
+
+    components_2 = fset(aux_graph.k_edge_subgraphs(k=2))
+    target_2 = fset([{a, b, c, d, e, f, g}, {h}, {i}])
+    assert target_2 == components_2
+
+    # Check that the undirected case for k=2 agrees with bridge components
+    alt_2 = fset(nx.k_edge_subgraphs(G, k=2))
+    assert alt_2 == components_2
+
+    components_3 = fset(aux_graph.k_edge_subgraphs(k=3))
+    target_3 = fset([{a}, {b, c, f, g}, {d}, {e}, {h}, {i}])
+    assert target_3 == components_3
+
+    components_4 = fset(aux_graph.k_edge_subgraphs(k=4))
+    target_4 = fset([{a}, {b}, {c}, {d}, {e}, {f}, {g}, {h}, {i}])
+    assert target_4 == components_4
+
+    _check_edge_connectivity(G)
+
+
+def test_local_subgraph_difference():
+    paths = [
+        (11, 12, 13, 14, 11, 13, 14, 12),  # first 4-clique
+        (21, 22, 23, 24, 21, 23, 24, 22),  # second 4-clique
+        # paths connecting each node of the 4 cliques
+        (11, 101, 21),
+        (12, 102, 22),
+        (13, 103, 23),
+        (14, 104, 24),
+    ]
+    G = nx.Graph(it.chain(*[pairwise(path) for path in paths]))
+    aux_graph = EdgeComponentAuxGraph.construct(G)
+
+    # Each clique is returned separately in k-edge-subgraphs
+    subgraph_ccs = fset(aux_graph.k_edge_subgraphs(3))
+    subgraph_target = fset(
+        [{101}, {102}, {103}, {104}, {21, 22, 23, 24}, {11, 12, 13, 14}]
+    )
+    assert subgraph_ccs == subgraph_target
+
+    # But in k-edge-ccs they are returned together
+    # because they are locally 3-edge-connected
+    local_ccs = fset(aux_graph.k_edge_components(3))
+    local_target = fset([{101}, {102}, {103}, {104}, {11, 12, 13, 14, 21, 22, 23, 24}])
+    assert local_ccs == local_target
+
+
+def test_local_subgraph_difference_directed():
+    dipaths = [(1, 2, 3, 4, 1), (1, 3, 1)]
+    G = nx.DiGraph(it.chain(*[pairwise(path) for path in dipaths]))
+
+    assert fset(nx.k_edge_components(G, k=1)) == fset(nx.k_edge_subgraphs(G, k=1))
+
+    # Unlike undirected graphs, when k=2, for directed graphs there is a case
+    # where the k-edge-ccs are not the same as the k-edge-subgraphs.
+    # (in directed graphs ccs and subgraphs are the same when k=2)
+    assert fset(nx.k_edge_components(G, k=2)) != fset(nx.k_edge_subgraphs(G, k=2))
+
+    assert fset(nx.k_edge_components(G, k=3)) == fset(nx.k_edge_subgraphs(G, k=3))
+
+    _check_edge_connectivity(G)
+
+
+def test_triangles():
+    paths = [
+        (11, 12, 13, 11),  # first 3-clique
+        (21, 22, 23, 21),  # second 3-clique
+        (11, 21),  # connected by an edge
+    ]
+    G = nx.Graph(it.chain(*[pairwise(path) for path in paths]))
+
+    # subgraph and ccs are the same in all cases here
+    assert fset(nx.k_edge_components(G, k=1)) == fset(nx.k_edge_subgraphs(G, k=1))
+
+    assert fset(nx.k_edge_components(G, k=2)) == fset(nx.k_edge_subgraphs(G, k=2))
+
+    assert fset(nx.k_edge_components(G, k=3)) == fset(nx.k_edge_subgraphs(G, k=3))
+
+    _check_edge_connectivity(G)
+
+
+def test_four_clique():
+    paths = [
+        (11, 12, 13, 14, 11, 13, 14, 12),  # first 4-clique
+        (21, 22, 23, 24, 21, 23, 24, 22),  # second 4-clique
+        # paths connecting the 4 cliques such that they are
+        # 3-connected in G, but not in the subgraph.
+        # Case where the nodes bridging them do not have degree less than 3.
+        (100, 13),
+        (12, 100, 22),
+        (13, 200, 23),
+        (14, 300, 24),
+    ]
+    G = nx.Graph(it.chain(*[pairwise(path) for path in paths]))
+
+    # The subgraphs and ccs are different for k=3
+    local_ccs = fset(nx.k_edge_components(G, k=3))
+    subgraphs = fset(nx.k_edge_subgraphs(G, k=3))
+    assert local_ccs != subgraphs
+
+    # The cliques ares in the same cc
+    clique1 = frozenset(paths[0])
+    clique2 = frozenset(paths[1])
+    assert clique1.union(clique2).union({100}) in local_ccs
+
+    # but different subgraphs
+    assert clique1 in subgraphs
+    assert clique2 in subgraphs
+
+    assert G.degree(100) == 3
+
+    _check_edge_connectivity(G)
+
+
+def test_five_clique():
+    # Make a graph that can be disconnected less than 4 edges, but no node has
+    # degree less than 4.
+    G = nx.disjoint_union(nx.complete_graph(5), nx.complete_graph(5))
+    paths = [
+        # add aux-connections
+        (1, 100, 6),
+        (2, 100, 7),
+        (3, 200, 8),
+        (4, 200, 100),
+    ]
+    G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))
+    assert min(dict(nx.degree(G)).values()) == 4
+
+    # For k=3 they are the same
+    assert fset(nx.k_edge_components(G, k=3)) == fset(nx.k_edge_subgraphs(G, k=3))
+
+    # For k=4 they are the different
+    # the aux nodes are in the same CC as clique 1 but no the same subgraph
+    assert fset(nx.k_edge_components(G, k=4)) != fset(nx.k_edge_subgraphs(G, k=4))
+
+    # For k=5 they are not the same
+    assert fset(nx.k_edge_components(G, k=5)) != fset(nx.k_edge_subgraphs(G, k=5))
+
+    # For k=6 they are the same
+    assert fset(nx.k_edge_components(G, k=6)) == fset(nx.k_edge_subgraphs(G, k=6))
+    _check_edge_connectivity(G)
+
+
+# ----------------
+# Undirected tests
+# ----------------
+
+
+def test_directed_aux_graph():
+    # Graph similar to the one in
+    # http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136264
+    a, b, c, d, e, f, g, h, i = "abcdefghi"
+    dipaths = [
+        (a, d, b, f, c),
+        (a, e, b),
+        (a, e, b, c, g, b, a),
+        (c, b),
+        (f, g, f),
+        (h, i),
+    ]
+    G = nx.DiGraph(it.chain(*[pairwise(path) for path in dipaths]))
+    aux_graph = EdgeComponentAuxGraph.construct(G)
+
+    components_1 = fset(aux_graph.k_edge_subgraphs(k=1))
+    target_1 = fset([{a, b, c, d, e, f, g}, {h}, {i}])
+    assert target_1 == components_1
+
+    # Check that the directed case for k=1 agrees with SCCs
+    alt_1 = fset(nx.strongly_connected_components(G))
+    assert alt_1 == components_1
+
+    components_2 = fset(aux_graph.k_edge_subgraphs(k=2))
+    target_2 = fset([{i}, {e}, {d}, {b, c, f, g}, {h}, {a}])
+    assert target_2 == components_2
+
+    components_3 = fset(aux_graph.k_edge_subgraphs(k=3))
+    target_3 = fset([{a}, {b}, {c}, {d}, {e}, {f}, {g}, {h}, {i}])
+    assert target_3 == components_3
+
+
+def test_random_gnp_directed():
+    # seeds = [3894723670, 500186844, 267231174, 2181982262, 1116750056]
+    seeds = [21]
+    for seed in seeds:
+        G = nx.gnp_random_graph(20, 0.2, directed=True, seed=seed)
+        _check_edge_connectivity(G)
+
+
+def test_configuration_directed():
+    # seeds = [671221681, 2403749451, 124433910, 672335939, 1193127215]
+    seeds = [67]
+    for seed in seeds:
+        deg_seq = nx.random_powerlaw_tree_sequence(20, seed=seed, tries=5000)
+        G = nx.DiGraph(nx.configuration_model(deg_seq, seed=seed))
+        G.remove_edges_from(nx.selfloop_edges(G))
+        _check_edge_connectivity(G)
+
+
+def test_shell_directed():
+    # seeds = [3134027055, 4079264063, 1350769518, 1405643020, 530038094]
+    seeds = [31]
+    for seed in seeds:
+        constructor = [(12, 70, 0.8), (15, 40, 0.6)]
+        G = nx.random_shell_graph(constructor, seed=seed).to_directed()
+        _check_edge_connectivity(G)
+
+
+def test_karate_directed():
+    G = nx.karate_club_graph().to_directed()
+    _check_edge_connectivity(G)
diff --git a/shared/package-files/09/09a1fb9a75931bb15c9c97b6bd737fd330db31a770e8d0aa2be249d879838caf/foo_deps.f90 b/shared/package-files/09/09a1fb9a75931bb15c9c97b6bd737fd330db31a770e8d0aa2be249d879838caf/foo_deps.f90
new file mode 100644
index 0000000000000000000000000000000000000000..e327b25c81986b2191fc740991ca9e907b5b0fb6
--- /dev/null
+++ b/shared/package-files/09/09a1fb9a75931bb15c9c97b6bd737fd330db31a770e8d0aa2be249d879838caf/foo_deps.f90
@@ -0,0 +1,6 @@
+module foo
+  type bar
+    character(len = 4) :: text
+  end type bar
+  type(bar), parameter :: abar = bar('abar')
+end module foo
diff --git a/shared/package-files/09/09a29024766fa88961c72433beb0e91364145ff34c9d607ce8c20ce7b8f2f1a2/test_cython.py b/shared/package-files/09/09a29024766fa88961c72433beb0e91364145ff34c9d607ce8c20ce7b8f2f1a2/test_cython.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d04af3801dbed076473e2563c1510cf15151311
--- /dev/null
+++ b/shared/package-files/09/09a29024766fa88961c72433beb0e91364145ff34c9d607ce8c20ce7b8f2f1a2/test_cython.py
@@ -0,0 +1,437 @@
+"""
+test cython .agg behavior
+"""
+
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import (
+    is_float_dtype,
+    is_integer_dtype,
+)
+
+import pandas as pd
+from pandas import (
+    DataFrame,
+    Index,
+    NaT,
+    Series,
+    Timedelta,
+    Timestamp,
+    bdate_range,
+)
+import pandas._testing as tm
+import pandas.core.common as com
+
+
+@pytest.mark.parametrize(
+    "op_name",
+    [
+        "count",
+        "sum",
+        "std",
+        "var",
+        "sem",
+        "mean",
+        pytest.param(
+            "median",
+            # ignore mean of empty slice
+            # and all-NaN
+            marks=[pytest.mark.filterwarnings("ignore::RuntimeWarning")],
+        ),
+        "prod",
+        "min",
+        "max",
+    ],
+)
+def test_cythonized_aggers(op_name):
+    data = {
+        "A": [0, 0, 0, 0, 1, 1, 1, 1, 1, 1.0, np.nan, np.nan],
+        "B": ["A", "B"] * 6,
+        "C": np.random.default_rng(2).standard_normal(12),
+    }
+    df = DataFrame(data)
+    df.loc[2:10:2, "C"] = np.nan
+
+    op = lambda x: getattr(x, op_name)()
+
+    # single column
+    grouped = df.drop(["B"], axis=1).groupby("A")
+    exp = {cat: op(group["C"]) for cat, group in grouped}
+    exp = DataFrame({"C": exp})
+    exp.index.name = "A"
+    result = op(grouped)
+    tm.assert_frame_equal(result, exp)
+
+    # multiple columns
+    grouped = df.groupby(["A", "B"])
+    expd = {}
+    for (cat1, cat2), group in grouped:
+        expd.setdefault(cat1, {})[cat2] = op(group["C"])
+    exp = DataFrame(expd).T.stack(future_stack=True)
+    exp.index.names = ["A", "B"]
+    exp.name = "C"
+
+    result = op(grouped)["C"]
+    if op_name in ["sum", "prod"]:
+        tm.assert_series_equal(result, exp)
+
+
+def test_cython_agg_boolean():
+    frame = DataFrame(
+        {
+            "a": np.random.default_rng(2).integers(0, 5, 50),
+            "b": np.random.default_rng(2).integers(0, 2, 50).astype("bool"),
+        }
+    )
+    result = frame.groupby("a")["b"].mean()
+    msg = "using SeriesGroupBy.mean"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        # GH#53425
+        expected = frame.groupby("a")["b"].agg(np.mean)
+
+    tm.assert_series_equal(result, expected)
+
+
+def test_cython_agg_nothing_to_agg():
+    frame = DataFrame(
+        {"a": np.random.default_rng(2).integers(0, 5, 50), "b": ["foo", "bar"] * 25}
+    )
+
+    msg = "Cannot use numeric_only=True with SeriesGroupBy.mean and non-numeric dtypes"
+    with pytest.raises(TypeError, match=msg):
+        frame.groupby("a")["b"].mean(numeric_only=True)
+
+    frame = DataFrame(
+        {"a": np.random.default_rng(2).integers(0, 5, 50), "b": ["foo", "bar"] * 25}
+    )
+
+    result = frame[["b"]].groupby(frame["a"]).mean(numeric_only=True)
+    expected = DataFrame(
+        [],
+        index=frame["a"].sort_values().drop_duplicates(),
+        columns=Index([], dtype="str"),
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+def test_cython_agg_nothing_to_agg_with_dates():
+    frame = DataFrame(
+        {
+            "a": np.random.default_rng(2).integers(0, 5, 50),
+            "b": ["foo", "bar"] * 25,
+            "dates": pd.date_range("now", periods=50, freq="min"),
+        }
+    )
+    msg = "Cannot use numeric_only=True with SeriesGroupBy.mean and non-numeric dtypes"
+    with pytest.raises(TypeError, match=msg):
+        frame.groupby("b").dates.mean(numeric_only=True)
+
+
+def test_cython_agg_frame_columns():
+    # #2113
+    df = DataFrame({"x": [1, 2, 3], "y": [3, 4, 5]})
+
+    msg = "DataFrame.groupby with axis=1 is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df.groupby(level=0, axis="columns").mean()
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df.groupby(level=0, axis="columns").mean()
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df.groupby(level=0, axis="columns").mean()
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        df.groupby(level=0, axis="columns").mean()
+
+
+def test_cython_agg_return_dict():
+    # GH 16741
+    df = DataFrame(
+        {
+            "A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
+            "B": ["one", "one", "two", "three", "two", "two", "one", "three"],
+            "C": np.random.default_rng(2).standard_normal(8),
+            "D": np.random.default_rng(2).standard_normal(8),
+        }
+    )
+
+    ts = df.groupby("A")["B"].agg(lambda x: x.value_counts().to_dict())
+    expected = Series(
+        [{"two": 1, "one": 1, "three": 1}, {"two": 2, "one": 2, "three": 1}],
+        index=Index(["bar", "foo"], name="A"),
+        name="B",
+    )
+    tm.assert_series_equal(ts, expected)
+
+
+def test_cython_fail_agg():
+    dr = bdate_range("1/1/2000", periods=50)
+    ts = Series(["A", "B", "C", "D", "E"] * 10, dtype=object, index=dr)
+
+    grouped = ts.groupby(lambda x: x.month)
+    summed = grouped.sum()
+    msg = "using SeriesGroupBy.sum"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        # GH#53425
+        expected = grouped.agg(np.sum).astype(object)
+    tm.assert_series_equal(summed, expected)
+
+
+@pytest.mark.parametrize(
+    "op, targop",
+    [
+        ("mean", np.mean),
+        ("median", np.median),
+        ("var", np.var),
+        ("sum", np.sum),
+        ("prod", np.prod),
+        ("min", np.min),
+        ("max", np.max),
+        ("first", lambda x: x.iloc[0]),
+        ("last", lambda x: x.iloc[-1]),
+    ],
+)
+def test__cython_agg_general(op, targop):
+    df = DataFrame(np.random.default_rng(2).standard_normal(1000))
+    labels = np.random.default_rng(2).integers(0, 50, size=1000).astype(float)
+
+    result = df.groupby(labels)._cython_agg_general(op, alt=None, numeric_only=True)
+    warn = FutureWarning if targop in com._cython_table else None
+    msg = f"using DataFrameGroupBy.{op}"
+    with tm.assert_produces_warning(warn, match=msg):
+        # GH#53425
+        expected = df.groupby(labels).agg(targop)
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "op, targop",
+    [
+        ("mean", np.mean),
+        ("median", lambda x: np.median(x) if len(x) > 0 else np.nan),
+        ("var", lambda x: np.var(x, ddof=1)),
+        ("min", np.min),
+        ("max", np.max),
+    ],
+)
+def test_cython_agg_empty_buckets(op, targop, observed):
+    df = DataFrame([11, 12, 13])
+    grps = range(0, 55, 5)
+
+    # calling _cython_agg_general directly, instead of via the user API
+    # which sets different values for min_count, so do that here.
+    g = df.groupby(pd.cut(df[0], grps), observed=observed)
+    result = g._cython_agg_general(op, alt=None, numeric_only=True)
+
+    g = df.groupby(pd.cut(df[0], grps), observed=observed)
+    expected = g.agg(lambda x: targop(x))
+    tm.assert_frame_equal(result, expected)
+
+
+def test_cython_agg_empty_buckets_nanops(observed):
+    # GH-18869 can't call nanops on empty groups, so hardcode expected
+    # for these
+    df = DataFrame([11, 12, 13], columns=["a"])
+    grps = np.arange(0, 25, 5, dtype=int)
+    # add / sum
+    result = df.groupby(pd.cut(df["a"], grps), observed=observed)._cython_agg_general(
+        "sum", alt=None, numeric_only=True
+    )
+    intervals = pd.interval_range(0, 20, freq=5)
+    expected = DataFrame(
+        {"a": [0, 0, 36, 0]},
+        index=pd.CategoricalIndex(intervals, name="a", ordered=True),
+    )
+    if observed:
+        expected = expected[expected.a != 0]
+
+    tm.assert_frame_equal(result, expected)
+
+    # prod
+    result = df.groupby(pd.cut(df["a"], grps), observed=observed)._cython_agg_general(
+        "prod", alt=None, numeric_only=True
+    )
+    expected = DataFrame(
+        {"a": [1, 1, 1716, 1]},
+        index=pd.CategoricalIndex(intervals, name="a", ordered=True),
+    )
+    if observed:
+        expected = expected[expected.a != 1]
+
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("op", ["first", "last", "max", "min"])
+@pytest.mark.parametrize(
+    "data", [Timestamp("2016-10-14 21:00:44.557"), Timedelta("17088 days 21:00:44.557")]
+)
+def test_cython_with_timestamp_and_nat(op, data):
+    # https://github.com/pandas-dev/pandas/issues/19526
+    df = DataFrame({"a": [0, 1], "b": [data, NaT]})
+    index = Index([0, 1], name="a")
+
+    # We will group by a and test the cython aggregations
+    expected = DataFrame({"b": [data, NaT]}, index=index)
+
+    result = df.groupby("a").aggregate(op)
+    tm.assert_frame_equal(expected, result)
+
+
+@pytest.mark.parametrize(
+    "agg",
+    [
+        "min",
+        "max",
+        "count",
+        "sum",
+        "prod",
+        "var",
+        "mean",
+        "median",
+        "ohlc",
+        "cumprod",
+        "cumsum",
+        "shift",
+        "any",
+        "all",
+        "quantile",
+        "first",
+        "last",
+        "rank",
+        "cummin",
+        "cummax",
+    ],
+)
+def test_read_only_buffer_source_agg(agg):
+    # https://github.com/pandas-dev/pandas/issues/36014
+    df = DataFrame(
+        {
+            "sepal_length": [5.1, 4.9, 4.7, 4.6, 5.0],
+            "species": ["setosa", "setosa", "setosa", "setosa", "setosa"],
+        }
+    )
+    df._mgr.arrays[0].flags.writeable = False
+
+    result = df.groupby(["species"]).agg({"sepal_length": agg})
+    expected = df.copy().groupby(["species"]).agg({"sepal_length": agg})
+
+    tm.assert_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "op_name",
+    [
+        "count",
+        "sum",
+        "std",
+        "var",
+        "sem",
+        "mean",
+        "median",
+        "prod",
+        "min",
+        "max",
+    ],
+)
+def test_cython_agg_nullable_int(op_name):
+    # ensure that the cython-based aggregations don't fail for nullable dtype
+    # (eg https://github.com/pandas-dev/pandas/issues/37415)
+    df = DataFrame(
+        {
+            "A": ["A", "B"] * 5,
+            "B": pd.array([1, 2, 3, 4, 5, 6, 7, 8, 9, pd.NA], dtype="Int64"),
+        }
+    )
+    result = getattr(df.groupby("A")["B"], op_name)()
+    df2 = df.assign(B=df["B"].astype("float64"))
+    expected = getattr(df2.groupby("A")["B"], op_name)()
+    if op_name in ("mean", "median"):
+        convert_integer = False
+    else:
+        convert_integer = True
+    expected = expected.convert_dtypes(convert_integer=convert_integer)
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize("dtype", ["Int64", "Float64", "boolean"])
+def test_count_masked_returns_masked_dtype(dtype):
+    df = DataFrame(
+        {
+            "A": [1, 1],
+            "B": pd.array([1, pd.NA], dtype=dtype),
+            "C": pd.array([1, 1], dtype=dtype),
+        }
+    )
+    result = df.groupby("A").count()
+    expected = DataFrame(
+        [[1, 2]], index=Index([1], name="A"), columns=["B", "C"], dtype="Int64"
+    )
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("with_na", [True, False])
+@pytest.mark.parametrize(
+    "op_name, action",
+    [
+        # ("count", "always_int"),
+        ("sum", "large_int"),
+        # ("std", "always_float"),
+        ("var", "always_float"),
+        # ("sem", "always_float"),
+        ("mean", "always_float"),
+        ("median", "always_float"),
+        ("prod", "large_int"),
+        ("min", "preserve"),
+        ("max", "preserve"),
+        ("first", "preserve"),
+        ("last", "preserve"),
+    ],
+)
+@pytest.mark.parametrize(
+    "data",
+    [
+        pd.array([1, 2, 3, 4], dtype="Int64"),
+        pd.array([1, 2, 3, 4], dtype="Int8"),
+        pd.array([0.1, 0.2, 0.3, 0.4], dtype="Float32"),
+        pd.array([0.1, 0.2, 0.3, 0.4], dtype="Float64"),
+        pd.array([True, True, False, False], dtype="boolean"),
+    ],
+)
+def test_cython_agg_EA_known_dtypes(data, op_name, action, with_na):
+    if with_na:
+        data[3] = pd.NA
+
+    df = DataFrame({"key": ["a", "a", "b", "b"], "col": data})
+    grouped = df.groupby("key")
+
+    if action == "always_int":
+        # always Int64
+        expected_dtype = pd.Int64Dtype()
+    elif action == "large_int":
+        # for any int/bool use Int64, for float preserve dtype
+        if is_float_dtype(data.dtype):
+            expected_dtype = data.dtype
+        elif is_integer_dtype(data.dtype):
+            # match the numpy dtype we'd get with the non-nullable analogue
+            expected_dtype = data.dtype
+        else:
+            expected_dtype = pd.Int64Dtype()
+    elif action == "always_float":
+        # for any int/bool use Float64, for float preserve dtype
+        if is_float_dtype(data.dtype):
+            expected_dtype = data.dtype
+        else:
+            expected_dtype = pd.Float64Dtype()
+    elif action == "preserve":
+        expected_dtype = data.dtype
+
+    result = getattr(grouped, op_name)()
+    assert result["col"].dtype == expected_dtype
+
+    result = grouped.aggregate(op_name)
+    assert result["col"].dtype == expected_dtype
+
+    result = getattr(grouped["col"], op_name)()
+    assert result.dtype == expected_dtype
+
+    result = grouped["col"].aggregate(op_name)
+    assert result.dtype == expected_dtype
diff --git a/shared/package-files/09/09a2dc3e449808cf89e37b759f0d82726b283540449fc2eba3a1b6423f5c5053/logical_xor.h b/shared/package-files/09/09a2dc3e449808cf89e37b759f0d82726b283540449fc2eba3a1b6423f5c5053/logical_xor.h
new file mode 100644
index 0000000000000000000000000000000000000000..eef31ac30cb51e11f90120e1340e8d32b5559385
--- /dev/null
+++ b/shared/package-files/09/09a2dc3e449808cf89e37b759f0d82726b283540449fc2eba3a1b6423f5c5053/logical_xor.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::logical_xor(Tensor self, Tensor other) -> Tensor
+inline at::Tensor logical_xor(const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::logical_xor::call(self, other);
+}
+
+// aten::logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & logical_xor_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other) {
+    return at::_ops::logical_xor_out::call(self, other, out);
+}
+// aten::logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & logical_xor_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out) {
+    return at::_ops::logical_xor_out::call(self, other, out);
+}
+
+}
diff --git a/shared/package-files/09/09a6854b80d56ea86a52276203e70cbd33fa0d1eaa205984533ba2312e470de7/exceptions.py b/shared/package-files/09/09a6854b80d56ea86a52276203e70cbd33fa0d1eaa205984533ba2312e470de7/exceptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..7db5a2ff0435f4327208860aaf0dfaa2e101a250
--- /dev/null
+++ b/shared/package-files/09/09a6854b80d56ea86a52276203e70cbd33fa0d1eaa205984533ba2312e470de7/exceptions.py
@@ -0,0 +1,249 @@
+"""Custom warnings and errors used across scikit-learn."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+__all__ = [
+    "ConvergenceWarning",
+    "DataConversionWarning",
+    "DataDimensionalityWarning",
+    "EfficiencyWarning",
+    "EstimatorCheckFailedWarning",
+    "FitFailedWarning",
+    "NotFittedError",
+    "PositiveSpectrumWarning",
+    "SkipTestWarning",
+    "UndefinedMetricWarning",
+    "UnsetMetadataPassedError",
+]
+
+
+class UnsetMetadataPassedError(ValueError):
+    """Exception class to raise if a metadata is passed which is not explicitly \
+        requested (metadata=True) or not requested (metadata=False).
+
+    .. versionadded:: 1.3
+
+    Parameters
+    ----------
+    message : str
+        The message
+
+    unrequested_params : dict
+        A dictionary of parameters and their values which are provided but not
+        requested.
+
+    routed_params : dict
+        A dictionary of routed parameters.
+    """
+
+    def __init__(self, *, message, unrequested_params, routed_params):
+        super().__init__(message)
+        self.unrequested_params = unrequested_params
+        self.routed_params = routed_params
+
+
+class NotFittedError(ValueError, AttributeError):
+    """Exception class to raise if estimator is used before fitting.
+
+    This class inherits from both ValueError and AttributeError to help with
+    exception handling and backward compatibility.
+
+    Examples
+    --------
+    >>> from sklearn.svm import LinearSVC
+    >>> from sklearn.exceptions import NotFittedError
+    >>> try:
+    ...     LinearSVC().predict([[1, 2], [2, 3], [3, 4]])
+    ... except NotFittedError as e:
+    ...     print(repr(e))
+    NotFittedError("This LinearSVC instance is not fitted yet. Call 'fit' with
+    appropriate arguments before using this estimator."...)
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.utils.validation.
+    """
+
+
+class ConvergenceWarning(UserWarning):
+    """Custom warning to capture convergence problems
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.utils.
+    """
+
+
+class DataConversionWarning(UserWarning):
+    """Warning used to notify implicit data conversions happening in the code.
+
+    This warning occurs when some input data needs to be converted or
+    interpreted in a way that may not match the user's expectations.
+
+    For example, this warning may occur when the user
+        - passes an integer array to a function which expects float input and
+          will convert the input
+        - requests a non-copying operation, but a copy is required to meet the
+          implementation's data-type expectations;
+        - passes an input whose shape can be interpreted ambiguously.
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.utils.validation.
+    """
+
+
+class DataDimensionalityWarning(UserWarning):
+    """Custom warning to notify potential issues with data dimensionality.
+
+    For example, in random projection, this warning is raised when the
+    number of components, which quantifies the dimensionality of the target
+    projection space, is higher than the number of features, which quantifies
+    the dimensionality of the original source space, to imply that the
+    dimensionality of the problem will not be reduced.
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.utils.
+    """
+
+
+class EfficiencyWarning(UserWarning):
+    """Warning used to notify the user of inefficient computation.
+
+    This warning notifies the user that the efficiency may not be optimal due
+    to some reason which may be included as a part of the warning message.
+    This may be subclassed into a more specific Warning class.
+
+    .. versionadded:: 0.18
+    """
+
+
+class FitFailedWarning(RuntimeWarning):
+    """Warning class used if there is an error while fitting the estimator.
+
+    This Warning is used in meta estimators GridSearchCV and RandomizedSearchCV
+    and the cross-validation helper function cross_val_score to warn when there
+    is an error while fitting the estimator.
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.cross_validation.
+    """
+
+
+class SkipTestWarning(UserWarning):
+    """Warning class used to notify the user of a test that was skipped.
+
+    For example, one of the estimator checks requires a pandas import.
+    If the pandas package cannot be imported, the test will be skipped rather
+    than register as a failure.
+    """
+
+
+class UndefinedMetricWarning(UserWarning):
+    """Warning used when the metric is invalid
+
+    .. versionchanged:: 0.18
+       Moved from sklearn.base.
+    """
+
+
+class PositiveSpectrumWarning(UserWarning):
+    """Warning raised when the eigenvalues of a PSD matrix have issues
+
+    This warning is typically raised by ``_check_psd_eigenvalues`` when the
+    eigenvalues of a positive semidefinite (PSD) matrix such as a gram matrix
+    (kernel) present significant negative eigenvalues, or bad conditioning i.e.
+    very small non-zero eigenvalues compared to the largest eigenvalue.
+
+    .. versionadded:: 0.22
+    """
+
+
+class InconsistentVersionWarning(UserWarning):
+    """Warning raised when an estimator is unpickled with an inconsistent version.
+
+    Parameters
+    ----------
+    estimator_name : str
+        Estimator name.
+
+    current_sklearn_version : str
+        Current scikit-learn version.
+
+    original_sklearn_version : str
+        Original scikit-learn version.
+    """
+
+    def __init__(
+        self, *, estimator_name, current_sklearn_version, original_sklearn_version
+    ):
+        self.estimator_name = estimator_name
+        self.current_sklearn_version = current_sklearn_version
+        self.original_sklearn_version = original_sklearn_version
+
+    def __str__(self):
+        return (
+            f"Trying to unpickle estimator {self.estimator_name} from version"
+            f" {self.original_sklearn_version} when "
+            f"using version {self.current_sklearn_version}. This might lead to breaking"
+            " code or "
+            "invalid results. Use at your own risk. "
+            "For more info please refer to:\n"
+            "https://scikit-learn.org/stable/model_persistence.html"
+            "#security-maintainability-limitations"
+        )
+
+
+class EstimatorCheckFailedWarning(UserWarning):
+    """Warning raised when an estimator check from the common tests fails.
+
+    Parameters
+    ----------
+    estimator : estimator object
+        Estimator instance for which the test failed.
+
+    check_name : str
+        Name of the check that failed.
+
+    exception : Exception
+        Exception raised by the failed check.
+
+    status : str
+        Status of the check.
+
+    expected_to_fail : bool
+        Whether the check was expected to fail.
+
+    expected_to_fail_reason : str
+        Reason for the expected failure.
+    """
+
+    def __init__(
+        self,
+        *,
+        estimator,
+        check_name: str,
+        exception: Exception,
+        status: str,
+        expected_to_fail: bool,
+        expected_to_fail_reason: str,
+    ):
+        self.estimator = estimator
+        self.check_name = check_name
+        self.exception = exception
+        self.status = status
+        self.expected_to_fail = expected_to_fail
+        self.expected_to_fail_reason = expected_to_fail_reason
+
+    def __repr__(self):
+        expected_to_fail_str = (
+            f"Expected to fail: {self.expected_to_fail_reason}"
+            if self.expected_to_fail
+            else "Not expected to fail"
+        )
+        return (
+            f"Test {self.check_name} failed for estimator {self.estimator!r}.\n"
+            f"Expected to fail reason: {expected_to_fail_str}\n"
+            f"Exception: {self.exception}"
+        )
+
+    def __str__(self):
+        return self.__repr__()
diff --git a/shared/package-files/09/09a6c6e71079ec531dc2637569ca5532fc8eb9c54fc4275174948ada0bbb81be/avarPlanner.py b/shared/package-files/09/09a6c6e71079ec531dc2637569ca5532fc8eb9c54fc4275174948ada0bbb81be/avarPlanner.py
new file mode 100644
index 0000000000000000000000000000000000000000..1fc63b1c1b32cc04151c06708d28cb3d75f0b978
--- /dev/null
+++ b/shared/package-files/09/09a6c6e71079ec531dc2637569ca5532fc8eb9c54fc4275174948ada0bbb81be/avarPlanner.py
@@ -0,0 +1,8 @@
+def main(args=None):
+    from .avar.plan import main
+
+    main(args)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/shared/package-files/09/09aa8697b54e7ed7deac8bfb1288c23876d55d491ff02d9cca0761c6add1b72d/macrodata_jmulti_c_granger_causality_realgdp_realinv.txt b/shared/package-files/09/09aa8697b54e7ed7deac8bfb1288c23876d55d491ff02d9cca0761c6add1b72d/macrodata_jmulti_c_granger_causality_realgdp_realinv.txt
new file mode 100644
index 0000000000000000000000000000000000000000..d2a1f34f7912cc227c93e68619d2ebf7cb8d6ccc
--- /dev/null
+++ b/shared/package-files/09/09aa8697b54e7ed7deac8bfb1288c23876d55d491ff02d9cca0761c6add1b72d/macrodata_jmulti_c_granger_causality_realgdp_realinv.txt
@@ -0,0 +1,13 @@
+*** Sat, 3 Sep 2016 22:24:28 ***
+TEST FOR GRANGER-CAUSALITY:
+H0: "realgdp, realinv" do not Granger-cause "realcons"
+
+Test statistic l = 3.4756
+pval-F( l; 8, 558) = 0.0006 
+
+TEST FOR INSTANTANEOUS CAUSALITY:
+H0: No instantaneous causality between "realgdp, realinv" and "realcons"
+
+Test statistic: c = 71.2453
+pval-Chi( c; 2) = 0.0000 
+
diff --git a/shared/package-files/09/09ab01de638c9e229b362043254f928f2205c4d08a21692014b12c6ce6254520/trigsimp.py b/shared/package-files/09/09ab01de638c9e229b362043254f928f2205c4d08a21692014b12c6ce6254520/trigsimp.py
new file mode 100644
index 0000000000000000000000000000000000000000..e055373b5b44114a692a63027e521b22836ee1f0
--- /dev/null
+++ b/shared/package-files/09/09ab01de638c9e229b362043254f928f2205c4d08a21692014b12c6ce6254520/trigsimp.py
@@ -0,0 +1,1252 @@
+from collections import defaultdict
+from functools import reduce
+
+from sympy.core import (sympify, Basic, S, Expr, factor_terms,
+                        Mul, Add, bottom_up)
+from sympy.core.cache import cacheit
+from sympy.core.function import (count_ops, _mexpand, FunctionClass, expand,
+                                 expand_mul, _coeff_isneg, Derivative)
+from sympy.core.numbers import I, Integer, igcd
+from sympy.core.sorting import _nodes
+from sympy.core.symbol import Dummy, symbols, Wild
+from sympy.external.gmpy import SYMPY_INTS
+from sympy.functions import sin, cos, exp, cosh, tanh, sinh, tan, cot, coth
+from sympy.functions import atan2
+from sympy.functions.elementary.hyperbolic import HyperbolicFunction
+from sympy.functions.elementary.trigonometric import TrigonometricFunction
+from sympy.polys import Poly, factor, cancel, parallel_poly_from_expr
+from sympy.polys.domains import ZZ
+from sympy.polys.polyerrors import PolificationFailed
+from sympy.polys.polytools import groebner
+from sympy.simplify.cse_main import cse
+from sympy.strategies.core import identity
+from sympy.strategies.tree import greedy
+from sympy.utilities.iterables import iterable
+from sympy.utilities.misc import debug
+
+def trigsimp_groebner(expr, hints=[], quick=False, order="grlex",
+                      polynomial=False):
+    """
+    Simplify trigonometric expressions using a groebner basis algorithm.
+
+    Explanation
+    ===========
+
+    This routine takes a fraction involving trigonometric or hyperbolic
+    expressions, and tries to simplify it. The primary metric is the
+    total degree. Some attempts are made to choose the simplest possible
+    expression of the minimal degree, but this is non-rigorous, and also
+    very slow (see the ``quick=True`` option).
+
+    If ``polynomial`` is set to True, instead of simplifying numerator and
+    denominator together, this function just brings numerator and denominator
+    into a canonical form. This is much faster, but has potentially worse
+    results. However, if the input is a polynomial, then the result is
+    guaranteed to be an equivalent polynomial of minimal degree.
+
+    The most important option is hints. Its entries can be any of the
+    following:
+
+    - a natural number
+    - a function
+    - an iterable of the form (func, var1, var2, ...)
+    - anything else, interpreted as a generator
+
+    A number is used to indicate that the search space should be increased.
+    A function is used to indicate that said function is likely to occur in a
+    simplified expression.
+    An iterable is used indicate that func(var1 + var2 + ...) is likely to
+    occur in a simplified .
+    An additional generator also indicates that it is likely to occur.
+    (See examples below).
+
+    This routine carries out various computationally intensive algorithms.
+    The option ``quick=True`` can be used to suppress one particularly slow
+    step (at the expense of potentially more complicated results, but never at
+    the expense of increased total degree).
+
+    Examples
+    ========
+
+    >>> from sympy.abc import x, y
+    >>> from sympy import sin, tan, cos, sinh, cosh, tanh
+    >>> from sympy.simplify.trigsimp import trigsimp_groebner
+
+    Suppose you want to simplify ``sin(x)*cos(x)``. Naively, nothing happens:
+
+    >>> ex = sin(x)*cos(x)
+    >>> trigsimp_groebner(ex)
+    sin(x)*cos(x)
+
+    This is because ``trigsimp_groebner`` only looks for a simplification
+    involving just ``sin(x)`` and ``cos(x)``. You can tell it to also try
+    ``2*x`` by passing ``hints=[2]``:
+
+    >>> trigsimp_groebner(ex, hints=[2])
+    sin(2*x)/2
+    >>> trigsimp_groebner(sin(x)**2 - cos(x)**2, hints=[2])
+    -cos(2*x)
+
+    Increasing the search space this way can quickly become expensive. A much
+    faster way is to give a specific expression that is likely to occur:
+
+    >>> trigsimp_groebner(ex, hints=[sin(2*x)])
+    sin(2*x)/2
+
+    Hyperbolic expressions are similarly supported:
+
+    >>> trigsimp_groebner(sinh(2*x)/sinh(x))
+    2*cosh(x)
+
+    Note how no hints had to be passed, since the expression already involved
+    ``2*x``.
+
+    The tangent function is also supported. You can either pass ``tan`` in the
+    hints, to indicate that tan should be tried whenever cosine or sine are,
+    or you can pass a specific generator:
+
+    >>> trigsimp_groebner(sin(x)/cos(x), hints=[tan])
+    tan(x)
+    >>> trigsimp_groebner(sinh(x)/cosh(x), hints=[tanh(x)])
+    tanh(x)
+
+    Finally, you can use the iterable form to suggest that angle sum formulae
+    should be tried:
+
+    >>> ex = (tan(x) + tan(y))/(1 - tan(x)*tan(y))
+    >>> trigsimp_groebner(ex, hints=[(tan, x, y)])
+    tan(x + y)
+    """
+    # TODO
+    #  - preprocess by replacing everything by funcs we can handle
+    # - optionally use cot instead of tan
+    # - more intelligent hinting.
+    #     For example, if the ideal is small, and we have sin(x), sin(y),
+    #     add sin(x + y) automatically... ?
+    # - algebraic numbers ...
+    # - expressions of lowest degree are not distinguished properly
+    #   e.g. 1 - sin(x)**2
+    # - we could try to order the generators intelligently, so as to influence
+    #   which monomials appear in the quotient basis
+
+    # THEORY
+    # ------
+    # Ratsimpmodprime above can be used to "simplify" a rational function
+    # modulo a prime ideal. "Simplify" mainly means finding an equivalent
+    # expression of lower total degree.
+    #
+    # We intend to use this to simplify trigonometric functions. To do that,
+    # we need to decide (a) which ring to use, and (b) modulo which ideal to
+    # simplify. In practice, (a) means settling on a list of "generators"
+    # a, b, c, ..., such that the fraction we want to simplify is a rational
+    # function in a, b, c, ..., with coefficients in ZZ (integers).
+    # (2) means that we have to decide what relations to impose on the
+    # generators. There are two practical problems:
+    #   (1) The ideal has to be *prime* (a technical term).
+    #   (2) The relations have to be polynomials in the generators.
+    #
+    # We typically have two kinds of generators:
+    # - trigonometric expressions, like sin(x), cos(5*x), etc
+    # - "everything else", like gamma(x), pi, etc.
+    #
+    # Since this function is trigsimp, we will concentrate on what to do with
+    # trigonometric expressions. We can also simplify hyperbolic expressions,
+    # but the extensions should be clear.
+    #
+    # One crucial point is that all *other* generators really should behave
+    # like indeterminates. In particular if (say) "I" is one of them, then
+    # in fact I**2 + 1 = 0 and we may and will compute non-sensical
+    # expressions. However, we can work with a dummy and add the relation
+    # I**2 + 1 = 0 to our ideal, then substitute back in the end.
+    #
+    # Now regarding trigonometric generators. We split them into groups,
+    # according to the argument of the trigonometric functions. We want to
+    # organise this in such a way that most trigonometric identities apply in
+    # the same group. For example, given sin(x), cos(2*x) and cos(y), we would
+    # group as [sin(x), cos(2*x)] and [cos(y)].
+    #
+    # Our prime ideal will be built in three steps:
+    # (1) For each group, compute a "geometrically prime" ideal of relations.
+    #     Geometrically prime means that it generates a prime ideal in
+    #     CC[gens], not just ZZ[gens].
+    # (2) Take the union of all the generators of the ideals for all groups.
+    #     By the geometric primality condition, this is still prime.
+    # (3) Add further inter-group relations which preserve primality.
+    #
+    # Step (1) works as follows. We will isolate common factors in the
+    # argument, so that all our generators are of the form sin(n*x), cos(n*x)
+    # or tan(n*x), with n an integer. Suppose first there are no tan terms.
+    # The ideal [sin(x)**2 + cos(x)**2 - 1] is geometrically prime, since
+    # X**2 + Y**2 - 1 is irreducible over CC.
+    # Now, if we have a generator sin(n*x), than we can, using trig identities,
+    # express sin(n*x) as a polynomial in sin(x) and cos(x). We can add this
+    # relation to the ideal, preserving geometric primality, since the quotient
+    # ring is unchanged.
+    # Thus we have treated all sin and cos terms.
+    # For tan(n*x), we add a relation tan(n*x)*cos(n*x) - sin(n*x) = 0.
+    # (This requires of course that we already have relations for cos(n*x) and
+    # sin(n*x).) It is not obvious, but it seems that this preserves geometric
+    # primality.
+    # XXX A real proof would be nice. HELP!
+    #     Sketch that  is a prime ideal of
+    #     CC[S, C, T]:
+    #     - it suffices to show that the projective closure in CP**3 is
+    #       irreducible
+    #     - using the half-angle substitutions, we can express sin(x), tan(x),
+    #       cos(x) as rational functions in tan(x/2)
+    #     - from this, we get a rational map from CP**1 to our curve
+    #     - this is a morphism, hence the curve is prime
+    #
+    # Step (2) is trivial.
+    #
+    # Step (3) works by adding selected relations of the form
+    # sin(x + y) - sin(x)*cos(y) - sin(y)*cos(x), etc. Geometric primality is
+    # preserved by the same argument as before.
+
+    def parse_hints(hints):
+        """Split hints into (n, funcs, iterables, gens)."""
+        n = 1
+        funcs, iterables, gens = [], [], []
+        for e in hints:
+            if isinstance(e, (SYMPY_INTS, Integer)):
+                n = e
+            elif isinstance(e, FunctionClass):
+                funcs.append(e)
+            elif iterable(e):
+                iterables.append((e[0], e[1:]))
+                # XXX sin(x+2y)?
+                # Note: we go through polys so e.g.
+                # sin(-x) -> -sin(x) -> sin(x)
+                gens.extend(parallel_poly_from_expr(
+                    [e[0](x) for x in e[1:]] + [e[0](Add(*e[1:]))])[1].gens)
+            else:
+                gens.append(e)
+        return n, funcs, iterables, gens
+
+    def build_ideal(x, terms):
+        """
+        Build generators for our ideal. ``Terms`` is an iterable with elements of
+        the form (fn, coeff), indicating that we have a generator fn(coeff*x).
+
+        If any of the terms is trigonometric, sin(x) and cos(x) are guaranteed
+        to appear in terms. Similarly for hyperbolic functions. For tan(n*x),
+        sin(n*x) and cos(n*x) are guaranteed.
+        """
+        I = []
+        y = Dummy('y')
+        for fn, coeff in terms:
+            for c, s, t, rel in (
+                    [cos, sin, tan, cos(x)**2 + sin(x)**2 - 1],
+                    [cosh, sinh, tanh, cosh(x)**2 - sinh(x)**2 - 1]):
+                if coeff == 1 and fn in [c, s]:
+                    I.append(rel)
+                elif fn == t:
+                    I.append(t(coeff*x)*c(coeff*x) - s(coeff*x))
+                elif fn in [c, s]:
+                    cn = fn(coeff*y).expand(trig=True).subs(y, x)
+                    I.append(fn(coeff*x) - cn)
+        return list(set(I))
+
+    def analyse_gens(gens, hints):
+        """
+        Analyse the generators ``gens``, using the hints ``hints``.
+
+        The meaning of ``hints`` is described in the main docstring.
+        Return a new list of generators, and also the ideal we should
+        work with.
+        """
+        # First parse the hints
+        n, funcs, iterables, extragens = parse_hints(hints)
+        debug('n=%s   funcs: %s   iterables: %s    extragens: %s',
+              (funcs, iterables, extragens))
+
+        # We just add the extragens to gens and analyse them as before
+        gens = list(gens)
+        gens.extend(extragens)
+
+        # remove duplicates
+        funcs = list(set(funcs))
+        iterables = list(set(iterables))
+        gens = list(set(gens))
+
+        # all the functions we can do anything with
+        allfuncs = {sin, cos, tan, sinh, cosh, tanh}
+        # sin(3*x) -> ((3, x), sin)
+        trigterms = [(g.args[0].as_coeff_mul(), g.func) for g in gens
+                     if g.func in allfuncs]
+        # Our list of new generators - start with anything that we cannot
+        # work with (i.e. is not a trigonometric term)
+        freegens = [g for g in gens if g.func not in allfuncs]
+        newgens = []
+        trigdict = {}
+        for (coeff, var), fn in trigterms:
+            trigdict.setdefault(var, []).append((coeff, fn))
+        res = [] # the ideal
+
+        for key, val in trigdict.items():
+            # We have now assembeled a dictionary. Its keys are common
+            # arguments in trigonometric expressions, and values are lists of
+            # pairs (fn, coeff). x0, (fn, coeff) in trigdict means that we
+            # need to deal with fn(coeff*x0). We take the rational gcd of the
+            # coeffs, call it ``gcd``. We then use x = x0/gcd as "base symbol",
+            # all other arguments are integral multiples thereof.
+            # We will build an ideal which works with sin(x), cos(x).
+            # If hint tan is provided, also work with tan(x). Moreover, if
+            # n > 1, also work with sin(k*x) for k <= n, and similarly for cos
+            # (and tan if the hint is provided). Finally, any generators which
+            # the ideal does not work with but we need to accommodate (either
+            # because it was in expr or because it was provided as a hint)
+            # we also build into the ideal.
+            # This selection process is expressed in the list ``terms``.
+            # build_ideal then generates the actual relations in our ideal,
+            # from this list.
+            fns = [x[1] for x in val]
+            val = [x[0] for x in val]
+            gcd = reduce(igcd, val)
+            terms = [(fn, v/gcd) for (fn, v) in zip(fns, val)]
+            fs = set(funcs + fns)
+            for c, s, t in ([cos, sin, tan], [cosh, sinh, tanh]):
+                if any(x in fs for x in (c, s, t)):
+                    fs.add(c)
+                    fs.add(s)
+            for fn in fs:
+                for k in range(1, n + 1):
+                    terms.append((fn, k))
+            extra = []
+            for fn, v in terms:
+                if fn == tan:
+                    extra.append((sin, v))
+                    extra.append((cos, v))
+                if fn in [sin, cos] and tan in fs:
+                    extra.append((tan, v))
+                if fn == tanh:
+                    extra.append((sinh, v))
+                    extra.append((cosh, v))
+                if fn in [sinh, cosh] and tanh in fs:
+                    extra.append((tanh, v))
+            terms.extend(extra)
+            x = gcd*Mul(*key)
+            r = build_ideal(x, terms)
+            res.extend(r)
+            newgens.extend({fn(v*x) for fn, v in terms})
+
+        # Add generators for compound expressions from iterables
+        for fn, args in iterables:
+            if fn == tan:
+                # Tan expressions are recovered from sin and cos.
+                iterables.extend([(sin, args), (cos, args)])
+            elif fn == tanh:
+                # Tanh expressions are recovered from sihn and cosh.
+                iterables.extend([(sinh, args), (cosh, args)])
+            else:
+                dummys = symbols('d:%i' % len(args), cls=Dummy)
+                expr = fn( Add(*dummys)).expand(trig=True).subs(list(zip(dummys, args)))
+                res.append(fn(Add(*args)) - expr)
+
+        if myI in gens:
+            res.append(myI**2 + 1)
+            freegens.remove(myI)
+            newgens.append(myI)
+
+        return res, freegens, newgens
+
+    myI = Dummy('I')
+    expr = expr.subs(S.ImaginaryUnit, myI)
+    subs = [(myI, S.ImaginaryUnit)]
+
+    num, denom = cancel(expr).as_numer_denom()
+    try:
+        (pnum, pdenom), opt = parallel_poly_from_expr([num, denom])
+    except PolificationFailed:
+        return expr
+    debug('initial gens:', opt.gens)
+    ideal, freegens, gens = analyse_gens(opt.gens, hints)
+    debug('ideal:', ideal)
+    debug('new gens:', gens, " -- len", len(gens))
+    debug('free gens:', freegens, " -- len", len(gens))
+    # NOTE we force the domain to be ZZ to stop polys from injecting generators
+    #      (which is usually a sign of a bug in the way we build the ideal)
+    if not gens:
+        return expr
+    G = groebner(ideal, order=order, gens=gens, domain=ZZ)
+    debug('groebner basis:', list(G), " -- len", len(G))
+
+    # If our fraction is a polynomial in the free generators, simplify all
+    # coefficients separately:
+
+    from sympy.simplify.ratsimp import ratsimpmodprime
+
+    if freegens and pdenom.has_only_gens(*set(gens).intersection(pdenom.gens)):
+        num = Poly(num, gens=gens+freegens).eject(*gens)
+        res = []
+        for monom, coeff in num.terms():
+            ourgens = set(parallel_poly_from_expr([coeff, denom])[1].gens)
+            # We compute the transitive closure of all generators that can
+            # be reached from our generators through relations in the ideal.
+            changed = True
+            while changed:
+                changed = False
+                for p in ideal:
+                    p = Poly(p)
+                    if not ourgens.issuperset(p.gens) and \
+                       not p.has_only_gens(*set(p.gens).difference(ourgens)):
+                        changed = True
+                        ourgens.update(p.exclude().gens)
+            # NOTE preserve order!
+            realgens = [x for x in gens if x in ourgens]
+            # The generators of the ideal have now been (implicitly) split
+            # into two groups: those involving ourgens and those that don't.
+            # Since we took the transitive closure above, these two groups
+            # live in subgrings generated by a *disjoint* set of variables.
+            # Any sensible groebner basis algorithm will preserve this disjoint
+            # structure (i.e. the elements of the groebner basis can be split
+            # similarly), and and the two subsets of the groebner basis then
+            # form groebner bases by themselves. (For the smaller generating
+            # sets, of course.)
+            ourG = [g.as_expr() for g in G.polys if
+                    g.has_only_gens(*ourgens.intersection(g.gens))]
+            res.append(Mul(*[a**b for a, b in zip(freegens, monom)]) * \
+                       ratsimpmodprime(coeff/denom, ourG, order=order,
+                                       gens=realgens, quick=quick, domain=ZZ,
+                                       polynomial=polynomial).subs(subs))
+        return Add(*res)
+        # NOTE The following is simpler and has less assumptions on the
+        #      groebner basis algorithm. If the above turns out to be broken,
+        #      use this.
+        return Add(*[Mul(*[a**b for a, b in zip(freegens, monom)]) * \
+                     ratsimpmodprime(coeff/denom, list(G), order=order,
+                                     gens=gens, quick=quick, domain=ZZ)
+                     for monom, coeff in num.terms()])
+    else:
+        return ratsimpmodprime(
+            expr, list(G), order=order, gens=freegens+gens,
+            quick=quick, domain=ZZ, polynomial=polynomial).subs(subs)
+
+
+_trigs = (TrigonometricFunction, HyperbolicFunction)
+
+
+def _trigsimp_inverse(rv):
+
+    def check_args(x, y):
+        try:
+            return x.args[0] == y.args[0]
+        except IndexError:
+            return False
+
+    def f(rv):
+        # for simple functions
+        g = getattr(rv, 'inverse', None)
+        if (g is not None and isinstance(rv.args[0], g()) and
+                isinstance(g()(1), TrigonometricFunction)):
+            return rv.args[0].args[0]
+
+        # for atan2 simplifications, harder because atan2 has 2 args
+        if isinstance(rv, atan2):
+            y, x = rv.args
+            if _coeff_isneg(y):
+                return -f(atan2(-y, x))
+            elif _coeff_isneg(x):
+                return S.Pi - f(atan2(y, -x))
+
+            if check_args(x, y):
+                if isinstance(y, sin) and isinstance(x, cos):
+                    return x.args[0]
+                if isinstance(y, cos) and isinstance(x, sin):
+                    return S.Pi / 2 - x.args[0]
+
+        return rv
+
+    return bottom_up(rv, f)
+
+
+def trigsimp(expr, inverse=False, **opts):
+    """Returns a reduced expression by using known trig identities.
+
+    Parameters
+    ==========
+
+    inverse : bool, optional
+        If ``inverse=True``, it will be assumed that a composition of inverse
+        functions, such as sin and asin, can be cancelled in any order.
+        For example, ``asin(sin(x))`` will yield ``x`` without checking whether
+        x belongs to the set where this relation is true. The default is False.
+        Default : True
+
+    method : string, optional
+        Specifies the method to use. Valid choices are:
+
+        - ``'matching'``, default
+        - ``'groebner'``
+        - ``'combined'``
+        - ``'fu'``
+        - ``'old'``
+
+        If ``'matching'``, simplify the expression recursively by targeting
+        common patterns. If ``'groebner'``, apply an experimental groebner
+        basis algorithm. In this case further options are forwarded to
+        ``trigsimp_groebner``, please refer to
+        its docstring. If ``'combined'``, it first runs the groebner basis
+        algorithm with small default parameters, then runs the ``'matching'``
+        algorithm. If ``'fu'``, run the collection of trigonometric
+        transformations described by Fu, et al. (see the
+        :py:func:`~sympy.simplify.fu.fu` docstring). If ``'old'``, the original
+        SymPy trig simplification function is run.
+    opts :
+        Optional keyword arguments passed to the method. See each method's
+        function docstring for details.
+
+    Examples
+    ========
+
+    >>> from sympy import trigsimp, sin, cos, log
+    >>> from sympy.abc import x
+    >>> e = 2*sin(x)**2 + 2*cos(x)**2
+    >>> trigsimp(e)
+    2
+
+    Simplification occurs wherever trigonometric functions are located.
+
+    >>> trigsimp(log(e))
+    log(2)
+
+    Using ``method='groebner'`` (or ``method='combined'``) might lead to
+    greater simplification.
+
+    The old trigsimp routine can be accessed as with method ``method='old'``.
+
+    >>> from sympy import coth, tanh
+    >>> t = 3*tanh(x)**7 - 2/coth(x)**7
+    >>> trigsimp(t, method='old') == t
+    True
+    >>> trigsimp(t)
+    tanh(x)**7
+
+    """
+    from sympy.simplify.fu import fu
+
+    expr = sympify(expr)
+
+    _eval_trigsimp = getattr(expr, '_eval_trigsimp', None)
+    if _eval_trigsimp is not None:
+        return _eval_trigsimp(**opts)
+
+    old = opts.pop('old', False)
+    if not old:
+        opts.pop('deep', None)
+        opts.pop('recursive', None)
+        method = opts.pop('method', 'matching')
+    else:
+        method = 'old'
+
+    def groebnersimp(ex, **opts):
+        def traverse(e):
+            if e.is_Atom:
+                return e
+            args = [traverse(x) for x in e.args]
+            if e.is_Function or e.is_Pow:
+                args = [trigsimp_groebner(x, **opts) for x in args]
+            return e.func(*args)
+        new = traverse(ex)
+        if not isinstance(new, Expr):
+            return new
+        return trigsimp_groebner(new, **opts)
+
+    trigsimpfunc = {
+        'fu': (lambda x: fu(x, **opts)),
+        'matching': (lambda x: futrig(x)),
+        'groebner': (lambda x: groebnersimp(x, **opts)),
+        'combined': (lambda x: futrig(groebnersimp(x,
+                               polynomial=True, hints=[2, tan]))),
+        'old': lambda x: trigsimp_old(x, **opts),
+                   }[method]
+
+    expr_simplified = trigsimpfunc(expr)
+    if inverse:
+        expr_simplified = _trigsimp_inverse(expr_simplified)
+
+    return expr_simplified
+
+
+def exptrigsimp(expr):
+    """
+    Simplifies exponential / trigonometric / hyperbolic functions.
+
+    Examples
+    ========
+
+    >>> from sympy import exptrigsimp, exp, cosh, sinh
+    >>> from sympy.abc import z
+
+    >>> exptrigsimp(exp(z) + exp(-z))
+    2*cosh(z)
+    >>> exptrigsimp(cosh(z) - sinh(z))
+    exp(-z)
+    """
+    from sympy.simplify.fu import hyper_as_trig, TR2i
+
+    def exp_trig(e):
+        # select the better of e, and e rewritten in terms of exp or trig
+        # functions
+        choices = [e]
+        if e.has(*_trigs):
+            choices.append(e.rewrite(exp))
+        choices.append(e.rewrite(cos))
+        return min(*choices, key=count_ops)
+    newexpr = bottom_up(expr, exp_trig)
+
+    def f(rv):
+        if not rv.is_Mul:
+            return rv
+        commutative_part, noncommutative_part = rv.args_cnc()
+        # Since as_powers_dict loses order information,
+        # if there is more than one noncommutative factor,
+        # it should only be used to simplify the commutative part.
+        if (len(noncommutative_part) > 1):
+            return f(Mul(*commutative_part))*Mul(*noncommutative_part)
+        rvd = rv.as_powers_dict()
+        newd = rvd.copy()
+
+        def signlog(expr, sign=S.One):
+            if expr is S.Exp1:
+                return sign, S.One
+            elif isinstance(expr, exp) or (expr.is_Pow and expr.base == S.Exp1):
+                return sign, expr.exp
+            elif sign is S.One:
+                return signlog(-expr, sign=-S.One)
+            else:
+                return None, None
+
+        ee = rvd[S.Exp1]
+        for k in rvd:
+            if k.is_Add and len(k.args) == 2:
+                # k == c*(1 + sign*E**x)
+                c = k.args[0]
+                sign, x = signlog(k.args[1]/c)
+                if not x:
+                    continue
+                m = rvd[k]
+                newd[k] -= m
+                if ee == -x*m/2:
+                    # sinh and cosh
+                    newd[S.Exp1] -= ee
+                    ee = 0
+                    if sign == 1:
+                        newd[2*c*cosh(x/2)] += m
+                    else:
+                        newd[-2*c*sinh(x/2)] += m
+                elif newd[1 - sign*S.Exp1**x] == -m:
+                    # tanh
+                    del newd[1 - sign*S.Exp1**x]
+                    if sign == 1:
+                        newd[-c/tanh(x/2)] += m
+                    else:
+                        newd[-c*tanh(x/2)] += m
+                else:
+                    newd[1 + sign*S.Exp1**x] += m
+                    newd[c] += m
+
+        return Mul(*[k**newd[k] for k in newd])
+    newexpr = bottom_up(newexpr, f)
+
+    # sin/cos and sinh/cosh ratios to tan and tanh, respectively
+    if newexpr.has(HyperbolicFunction):
+        e, f = hyper_as_trig(newexpr)
+        newexpr = f(TR2i(e))
+    if newexpr.has(TrigonometricFunction):
+        newexpr = TR2i(newexpr)
+
+    # can we ever generate an I where there was none previously?
+    if not (newexpr.has(I) and not expr.has(I)):
+        expr = newexpr
+    return expr
+
+#-------------------- the old trigsimp routines ---------------------
+
+def trigsimp_old(expr, *, first=True, **opts):
+    """
+    Reduces expression by using known trig identities.
+
+    Notes
+    =====
+
+    deep:
+    - Apply trigsimp inside all objects with arguments
+
+    recursive:
+    - Use common subexpression elimination (cse()) and apply
+    trigsimp recursively (this is quite expensive if the
+    expression is large)
+
+    method:
+    - Determine the method to use. Valid choices are 'matching' (default),
+    'groebner', 'combined', 'fu' and 'futrig'. If 'matching', simplify the
+    expression recursively by pattern matching. If 'groebner', apply an
+    experimental groebner basis algorithm. In this case further options
+    are forwarded to ``trigsimp_groebner``, please refer to its docstring.
+    If 'combined', first run the groebner basis algorithm with small
+    default parameters, then run the 'matching' algorithm. 'fu' runs the
+    collection of trigonometric transformations described by Fu, et al.
+    (see the `fu` docstring) while `futrig` runs a subset of Fu-transforms
+    that mimic the behavior of `trigsimp`.
+
+    compare:
+    - show input and output from `trigsimp` and `futrig` when different,
+    but returns the `trigsimp` value.
+
+    Examples
+    ========
+
+    >>> from sympy import trigsimp, sin, cos, log, cot
+    >>> from sympy.abc import x
+    >>> e = 2*sin(x)**2 + 2*cos(x)**2
+    >>> trigsimp(e, old=True)
+    2
+    >>> trigsimp(log(e), old=True)
+    log(2*sin(x)**2 + 2*cos(x)**2)
+    >>> trigsimp(log(e), deep=True, old=True)
+    log(2)
+
+    Using `method="groebner"` (or `"combined"`) can sometimes lead to a lot
+    more simplification:
+
+    >>> e = (-sin(x) + 1)/cos(x) + cos(x)/(-sin(x) + 1)
+    >>> trigsimp(e, old=True)
+    (1 - sin(x))/cos(x) + cos(x)/(1 - sin(x))
+    >>> trigsimp(e, method="groebner", old=True)
+    2/cos(x)
+
+    >>> trigsimp(1/cot(x)**2, compare=True, old=True)
+          futrig: tan(x)**2
+    cot(x)**(-2)
+
+    """
+    old = expr
+    if first:
+        if not expr.has(*_trigs):
+            return expr
+
+        trigsyms = set().union(*[t.free_symbols for t in expr.atoms(*_trigs)])
+        if len(trigsyms) > 1:
+            from sympy.simplify.simplify import separatevars
+
+            d = separatevars(expr)
+            if d.is_Mul:
+                d = separatevars(d, dict=True) or d
+            if isinstance(d, dict):
+                expr = 1
+                for k, v in d.items():
+                    # remove hollow factoring
+                    was = v
+                    v = expand_mul(v)
+                    opts['first'] = False
+                    vnew = trigsimp(v, **opts)
+                    if vnew == v:
+                        vnew = was
+                    expr *= vnew
+                old = expr
+            else:
+                if d.is_Add:
+                    for s in trigsyms:
+                        r, e = expr.as_independent(s)
+                        if r:
+                            opts['first'] = False
+                            expr = r + trigsimp(e, **opts)
+                            if not expr.is_Add:
+                                break
+                    old = expr
+
+    recursive = opts.pop('recursive', False)
+    deep = opts.pop('deep', False)
+    method = opts.pop('method', 'matching')
+
+    def groebnersimp(ex, deep, **opts):
+        def traverse(e):
+            if e.is_Atom:
+                return e
+            args = [traverse(x) for x in e.args]
+            if e.is_Function or e.is_Pow:
+                args = [trigsimp_groebner(x, **opts) for x in args]
+            return e.func(*args)
+        if deep:
+            ex = traverse(ex)
+        return trigsimp_groebner(ex, **opts)
+
+    trigsimpfunc = {
+        'matching': (lambda x, d: _trigsimp(x, d)),
+        'groebner': (lambda x, d: groebnersimp(x, d, **opts)),
+        'combined': (lambda x, d: _trigsimp(groebnersimp(x,
+                                       d, polynomial=True, hints=[2, tan]),
+                                   d))
+                   }[method]
+
+    if recursive:
+        w, g = cse(expr)
+        g = trigsimpfunc(g[0], deep)
+
+        for sub in reversed(w):
+            g = g.subs(sub[0], sub[1])
+            g = trigsimpfunc(g, deep)
+        result = g
+    else:
+        result = trigsimpfunc(expr, deep)
+
+    if opts.get('compare', False):
+        f = futrig(old)
+        if f != result:
+            print('\tfutrig:', f)
+
+    return result
+
+
+def _dotrig(a, b):
+    """Helper to tell whether ``a`` and ``b`` have the same sorts
+    of symbols in them -- no need to test hyperbolic patterns against
+    expressions that have no hyperbolics in them."""
+    return a.func == b.func and (
+        a.has(TrigonometricFunction) and b.has(TrigonometricFunction) or
+        a.has(HyperbolicFunction) and b.has(HyperbolicFunction))
+
+
+_trigpat = None
+def _trigpats():
+    global _trigpat
+    a, b, c = symbols('a b c', cls=Wild)
+    d = Wild('d', commutative=False)
+
+    # for the simplifications like sinh/cosh -> tanh:
+    # DO NOT REORDER THE FIRST 14 since these are assumed to be in this
+    # order in _match_div_rewrite.
+    matchers_division = (
+        (a*sin(b)**c/cos(b)**c, a*tan(b)**c, sin(b), cos(b)),
+        (a*tan(b)**c*cos(b)**c, a*sin(b)**c, sin(b), cos(b)),
+        (a*cot(b)**c*sin(b)**c, a*cos(b)**c, sin(b), cos(b)),
+        (a*tan(b)**c/sin(b)**c, a/cos(b)**c, sin(b), cos(b)),
+        (a*cot(b)**c/cos(b)**c, a/sin(b)**c, sin(b), cos(b)),
+        (a*cot(b)**c*tan(b)**c, a, sin(b), cos(b)),
+        (a*(cos(b) + 1)**c*(cos(b) - 1)**c,
+            a*(-sin(b)**2)**c, cos(b) + 1, cos(b) - 1),
+        (a*(sin(b) + 1)**c*(sin(b) - 1)**c,
+            a*(-cos(b)**2)**c, sin(b) + 1, sin(b) - 1),
+
+        (a*sinh(b)**c/cosh(b)**c, a*tanh(b)**c, S.One, S.One),
+        (a*tanh(b)**c*cosh(b)**c, a*sinh(b)**c, S.One, S.One),
+        (a*coth(b)**c*sinh(b)**c, a*cosh(b)**c, S.One, S.One),
+        (a*tanh(b)**c/sinh(b)**c, a/cosh(b)**c, S.One, S.One),
+        (a*coth(b)**c/cosh(b)**c, a/sinh(b)**c, S.One, S.One),
+        (a*coth(b)**c*tanh(b)**c, a, S.One, S.One),
+
+        (c*(tanh(a) + tanh(b))/(1 + tanh(a)*tanh(b)),
+            tanh(a + b)*c, S.One, S.One),
+    )
+
+    matchers_add = (
+        (c*sin(a)*cos(b) + c*cos(a)*sin(b) + d, sin(a + b)*c + d),
+        (c*cos(a)*cos(b) - c*sin(a)*sin(b) + d, cos(a + b)*c + d),
+        (c*sin(a)*cos(b) - c*cos(a)*sin(b) + d, sin(a - b)*c + d),
+        (c*cos(a)*cos(b) + c*sin(a)*sin(b) + d, cos(a - b)*c + d),
+        (c*sinh(a)*cosh(b) + c*sinh(b)*cosh(a) + d, sinh(a + b)*c + d),
+        (c*cosh(a)*cosh(b) + c*sinh(a)*sinh(b) + d, cosh(a + b)*c + d),
+    )
+
+    # for cos(x)**2 + sin(x)**2 -> 1
+    matchers_identity = (
+        (a*sin(b)**2, a - a*cos(b)**2),
+        (a*tan(b)**2, a*(1/cos(b))**2 - a),
+        (a*cot(b)**2, a*(1/sin(b))**2 - a),
+        (a*sin(b + c), a*(sin(b)*cos(c) + sin(c)*cos(b))),
+        (a*cos(b + c), a*(cos(b)*cos(c) - sin(b)*sin(c))),
+        (a*tan(b + c), a*((tan(b) + tan(c))/(1 - tan(b)*tan(c)))),
+
+        (a*sinh(b)**2, a*cosh(b)**2 - a),
+        (a*tanh(b)**2, a - a*(1/cosh(b))**2),
+        (a*coth(b)**2, a + a*(1/sinh(b))**2),
+        (a*sinh(b + c), a*(sinh(b)*cosh(c) + sinh(c)*cosh(b))),
+        (a*cosh(b + c), a*(cosh(b)*cosh(c) + sinh(b)*sinh(c))),
+        (a*tanh(b + c), a*((tanh(b) + tanh(c))/(1 + tanh(b)*tanh(c)))),
+
+    )
+
+    # Reduce any lingering artifacts, such as sin(x)**2 changing
+    # to 1-cos(x)**2 when sin(x)**2 was "simpler"
+    artifacts = (
+        (a - a*cos(b)**2 + c, a*sin(b)**2 + c, cos),
+        (a - a*(1/cos(b))**2 + c, -a*tan(b)**2 + c, cos),
+        (a - a*(1/sin(b))**2 + c, -a*cot(b)**2 + c, sin),
+
+        (a - a*cosh(b)**2 + c, -a*sinh(b)**2 + c, cosh),
+        (a - a*(1/cosh(b))**2 + c, a*tanh(b)**2 + c, cosh),
+        (a + a*(1/sinh(b))**2 + c, a*coth(b)**2 + c, sinh),
+
+        # same as above but with noncommutative prefactor
+        (a*d - a*d*cos(b)**2 + c, a*d*sin(b)**2 + c, cos),
+        (a*d - a*d*(1/cos(b))**2 + c, -a*d*tan(b)**2 + c, cos),
+        (a*d - a*d*(1/sin(b))**2 + c, -a*d*cot(b)**2 + c, sin),
+
+        (a*d - a*d*cosh(b)**2 + c, -a*d*sinh(b)**2 + c, cosh),
+        (a*d - a*d*(1/cosh(b))**2 + c, a*d*tanh(b)**2 + c, cosh),
+        (a*d + a*d*(1/sinh(b))**2 + c, a*d*coth(b)**2 + c, sinh),
+    )
+
+    _trigpat = (a, b, c, d, matchers_division, matchers_add,
+        matchers_identity, artifacts)
+    return _trigpat
+
+
+def _replace_mul_fpowxgpow(expr, f, g, rexp, h, rexph):
+    """Helper for _match_div_rewrite.
+
+    Replace f(b_)**c_*g(b_)**(rexp(c_)) with h(b)**rexph(c) if f(b_)
+    and g(b_) are both positive or if c_ is an integer.
+    """
+    # assert expr.is_Mul and expr.is_commutative and f != g
+    fargs = defaultdict(int)
+    gargs = defaultdict(int)
+    args = []
+    for x in expr.args:
+        if x.is_Pow or x.func in (f, g):
+            b, e = x.as_base_exp()
+            if b.is_positive or e.is_integer:
+                if b.func == f:
+                    fargs[b.args[0]] += e
+                    continue
+                elif b.func == g:
+                    gargs[b.args[0]] += e
+                    continue
+        args.append(x)
+    common = set(fargs) & set(gargs)
+    hit = False
+    while common:
+        key = common.pop()
+        fe = fargs.pop(key)
+        ge = gargs.pop(key)
+        if fe == rexp(ge):
+            args.append(h(key)**rexph(fe))
+            hit = True
+        else:
+            fargs[key] = fe
+            gargs[key] = ge
+    if not hit:
+        return expr
+    while fargs:
+        key, e = fargs.popitem()
+        args.append(f(key)**e)
+    while gargs:
+        key, e = gargs.popitem()
+        args.append(g(key)**e)
+    return Mul(*args)
+
+
+_idn = lambda x: x
+_midn = lambda x: -x
+_one = lambda x: S.One
+
+def _match_div_rewrite(expr, i):
+    """helper for __trigsimp"""
+    if i == 0:
+        expr = _replace_mul_fpowxgpow(expr, sin, cos,
+            _midn, tan, _idn)
+    elif i == 1:
+        expr = _replace_mul_fpowxgpow(expr, tan, cos,
+            _idn, sin, _idn)
+    elif i == 2:
+        expr = _replace_mul_fpowxgpow(expr, cot, sin,
+            _idn, cos, _idn)
+    elif i == 3:
+        expr = _replace_mul_fpowxgpow(expr, tan, sin,
+            _midn, cos, _midn)
+    elif i == 4:
+        expr = _replace_mul_fpowxgpow(expr, cot, cos,
+            _midn, sin, _midn)
+    elif i == 5:
+        expr = _replace_mul_fpowxgpow(expr, cot, tan,
+            _idn, _one, _idn)
+    # i in (6, 7) is skipped
+    elif i == 8:
+        expr = _replace_mul_fpowxgpow(expr, sinh, cosh,
+            _midn, tanh, _idn)
+    elif i == 9:
+        expr = _replace_mul_fpowxgpow(expr, tanh, cosh,
+            _idn, sinh, _idn)
+    elif i == 10:
+        expr = _replace_mul_fpowxgpow(expr, coth, sinh,
+            _idn, cosh, _idn)
+    elif i == 11:
+        expr = _replace_mul_fpowxgpow(expr, tanh, sinh,
+            _midn, cosh, _midn)
+    elif i == 12:
+        expr = _replace_mul_fpowxgpow(expr, coth, cosh,
+            _midn, sinh, _midn)
+    elif i == 13:
+        expr = _replace_mul_fpowxgpow(expr, coth, tanh,
+            _idn, _one, _idn)
+    else:
+        return None
+    return expr
+
+
+def _trigsimp(expr, deep=False):
+    # protect the cache from non-trig patterns; we only allow
+    # trig patterns to enter the cache
+    if expr.has(*_trigs):
+        return __trigsimp(expr, deep)
+    return expr
+
+
+@cacheit
+def __trigsimp(expr, deep=False):
+    """recursive helper for trigsimp"""
+    from sympy.simplify.fu import TR10i
+
+    if _trigpat is None:
+        _trigpats()
+    a, b, c, d, matchers_division, matchers_add, \
+    matchers_identity, artifacts = _trigpat
+
+    if expr.is_Mul:
+        # do some simplifications like sin/cos -> tan:
+        if not expr.is_commutative:
+            com, nc = expr.args_cnc()
+            expr = _trigsimp(Mul._from_args(com), deep)*Mul._from_args(nc)
+        else:
+            for i, (pattern, simp, ok1, ok2) in enumerate(matchers_division):
+                if not _dotrig(expr, pattern):
+                    continue
+
+                newexpr = _match_div_rewrite(expr, i)
+                if newexpr is not None:
+                    if newexpr != expr:
+                        expr = newexpr
+                        break
+                    else:
+                        continue
+
+                # use SymPy matching instead
+                res = expr.match(pattern)
+                if res and res.get(c, 0):
+                    if not res[c].is_integer:
+                        ok = ok1.subs(res)
+                        if not ok.is_positive:
+                            continue
+                        ok = ok2.subs(res)
+                        if not ok.is_positive:
+                            continue
+                    # if "a" contains any of trig or hyperbolic funcs with
+                    # argument "b" then skip the simplification
+                    if any(w.args[0] == res[b] for w in res[a].atoms(
+                            TrigonometricFunction, HyperbolicFunction)):
+                        continue
+                    # simplify and finish:
+                    expr = simp.subs(res)
+                    break  # process below
+
+    if expr.is_Add:
+        args = []
+        for term in expr.args:
+            if not term.is_commutative:
+                com, nc = term.args_cnc()
+                nc = Mul._from_args(nc)
+                term = Mul._from_args(com)
+            else:
+                nc = S.One
+            term = _trigsimp(term, deep)
+            for pattern, result in matchers_identity:
+                res = term.match(pattern)
+                if res is not None:
+                    term = result.subs(res)
+                    break
+            args.append(term*nc)
+        if args != expr.args:
+            expr = Add(*args)
+            expr = min(expr, expand(expr), key=count_ops)
+        if expr.is_Add:
+            for pattern, result in matchers_add:
+                if not _dotrig(expr, pattern):
+                    continue
+                expr = TR10i(expr)
+                if expr.has(HyperbolicFunction):
+                    res = expr.match(pattern)
+                    # if "d" contains any trig or hyperbolic funcs with
+                    # argument "a" or "b" then skip the simplification;
+                    # this isn't perfect -- see tests
+                    if res is None or not (a in res and b in res) or any(
+                        w.args[0] in (res[a], res[b]) for w in res[d].atoms(
+                            TrigonometricFunction, HyperbolicFunction)):
+                        continue
+                    expr = result.subs(res)
+                    break
+
+        # Reduce any lingering artifacts, such as sin(x)**2 changing
+        # to 1 - cos(x)**2 when sin(x)**2 was "simpler"
+        for pattern, result, ex in artifacts:
+            if not _dotrig(expr, pattern):
+                continue
+            # Substitute a new wild that excludes some function(s)
+            # to help influence a better match. This is because
+            # sometimes, for example, 'a' would match sec(x)**2
+            a_t = Wild('a', exclude=[ex])
+            pattern = pattern.subs(a, a_t)
+            result = result.subs(a, a_t)
+
+            m = expr.match(pattern)
+            was = None
+            while m and was != expr:
+                was = expr
+                if m[a_t] == 0 or \
+                        -m[a_t] in m[c].args or m[a_t] + m[c] == 0:
+                    break
+                if d in m and m[a_t]*m[d] + m[c] == 0:
+                    break
+                expr = result.subs(m)
+                m = expr.match(pattern)
+                m.setdefault(c, S.Zero)
+
+    elif expr.is_Mul or expr.is_Pow or deep and expr.args:
+        expr = expr.func(*[_trigsimp(a, deep) for a in expr.args])
+
+    try:
+        if not expr.has(*_trigs):
+            raise TypeError
+        e = expr.atoms(exp)
+        new = expr.rewrite(exp, deep=deep)
+        if new == e:
+            raise TypeError
+        fnew = factor(new)
+        if fnew != new:
+            new = sorted([new, factor(new)], key=count_ops)[0]
+        # if all exp that were introduced disappeared then accept it
+        if not (new.atoms(exp) - e):
+            expr = new
+    except TypeError:
+        pass
+
+    return expr
+#------------------- end of old trigsimp routines --------------------
+
+
+def futrig(e, *, hyper=True, **kwargs):
+    """Return simplified ``e`` using Fu-like transformations.
+    This is not the "Fu" algorithm. This is called by default
+    from ``trigsimp``. By default, hyperbolics subexpressions
+    will be simplified, but this can be disabled by setting
+    ``hyper=False``.
+
+    Examples
+    ========
+
+    >>> from sympy import trigsimp, tan, sinh, tanh
+    >>> from sympy.simplify.trigsimp import futrig
+    >>> from sympy.abc import x
+    >>> trigsimp(1/tan(x)**2)
+    tan(x)**(-2)
+
+    >>> futrig(sinh(x)/tanh(x))
+    cosh(x)
+
+    """
+    from sympy.simplify.fu import hyper_as_trig
+
+    e = sympify(e)
+
+    if not isinstance(e, Basic):
+        return e
+
+    if not e.args:
+        return e
+
+    old = e
+    e = bottom_up(e, _futrig)
+
+    if hyper and e.has(HyperbolicFunction):
+        e, f = hyper_as_trig(e)
+        e = f(bottom_up(e, _futrig))
+
+    if e != old and e.is_Mul and e.args[0].is_Rational:
+        # redistribute leading coeff on 2-arg Add
+        e = Mul(*e.as_coeff_Mul())
+    return e
+
+
+def _futrig(e):
+    """Helper for futrig."""
+    from sympy.simplify.fu import (
+        TR1, TR2, TR3, TR2i, TR10, L, TR10i,
+        TR8, TR6, TR15, TR16, TR111, TR5, TRmorrie, TR11, _TR11, TR14, TR22,
+        TR12)
+
+    if not e.has(TrigonometricFunction):
+        return e
+
+    if e.is_Mul:
+        coeff, e = e.as_independent(TrigonometricFunction)
+    else:
+        coeff = None
+
+    Lops = lambda x: (L(x), x.count_ops(), _nodes(x), len(x.args), x.is_Add)
+    trigs = lambda x: x.has(TrigonometricFunction)
+
+    tree = [identity,
+        (
+        TR3,  # canonical angles
+        TR1,  # sec-csc -> cos-sin
+        TR12,  # expand tan of sum
+        lambda x: _eapply(factor, x, trigs),
+        TR2,  # tan-cot -> sin-cos
+        [identity, lambda x: _eapply(_mexpand, x, trigs)],
+        TR2i,  # sin-cos ratio -> tan
+        lambda x: _eapply(lambda i: factor(i.normal()), x, trigs),
+        TR14,  # factored identities
+        TR5,  # sin-pow -> cos_pow
+        TR10,  # sin-cos of sums -> sin-cos prod
+        TR11, _TR11, TR6, # reduce double angles and rewrite cos pows
+        lambda x: _eapply(factor, x, trigs),
+        TR14,  # factored powers of identities
+        [identity, lambda x: _eapply(_mexpand, x, trigs)],
+        TR10i,  # sin-cos products > sin-cos of sums
+        TRmorrie,
+        [identity, TR8],  # sin-cos products -> sin-cos of sums
+        [identity, lambda x: TR2i(TR2(x))],  # tan -> sin-cos -> tan
+        [
+            lambda x: _eapply(expand_mul, TR5(x), trigs),
+            lambda x: _eapply(
+                expand_mul, TR15(x), trigs)], # pos/neg powers of sin
+        [
+            lambda x:  _eapply(expand_mul, TR6(x), trigs),
+            lambda x:  _eapply(
+                expand_mul, TR16(x), trigs)], # pos/neg powers of cos
+        TR111,  # tan, sin, cos to neg power -> cot, csc, sec
+        [identity, TR2i],  # sin-cos ratio to tan
+        [identity, lambda x: _eapply(
+            expand_mul, TR22(x), trigs)],  # tan-cot to sec-csc
+        TR1, TR2, TR2i,
+        [identity, lambda x: _eapply(
+            factor_terms, TR12(x), trigs)],  # expand tan of sum
+        )]
+    e = greedy(tree, objective=Lops)(e)
+
+    if coeff is not None:
+        e = coeff * e
+
+    return e
+
+
+def _is_Expr(e):
+    """_eapply helper to tell whether ``e`` and all its args
+    are Exprs."""
+    if isinstance(e, Derivative):
+        return _is_Expr(e.expr)
+    if not isinstance(e, Expr):
+        return False
+    return all(_is_Expr(i) for i in e.args)
+
+
+def _eapply(func, e, cond=None):
+    """Apply ``func`` to ``e`` if all args are Exprs else only
+    apply it to those args that *are* Exprs."""
+    if not isinstance(e, Expr):
+        return e
+    if _is_Expr(e) or not e.args:
+        return func(e)
+    return e.func(*[
+        _eapply(func, ei) if (cond is None or cond(ei)) else ei
+        for ei in e.args])
diff --git a/shared/package-files/09/09ab274e90c9cbfd51a68af6760f7ee144894080e2fb7a239fbccbd6793cdfb5/smallworld.py b/shared/package-files/09/09ab274e90c9cbfd51a68af6760f7ee144894080e2fb7a239fbccbd6793cdfb5/smallworld.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b039e3a11596c001b5e3708cb805cf171508990
--- /dev/null
+++ b/shared/package-files/09/09ab274e90c9cbfd51a68af6760f7ee144894080e2fb7a239fbccbd6793cdfb5/smallworld.py
@@ -0,0 +1,399 @@
+"""Functions for estimating the small-world-ness of graphs.
+
+A small world network is characterized by a small average shortest path length,
+and a large clustering coefficient.
+
+Small-worldness is commonly measured with the coefficient sigma or omega.
+
+Both coefficients compare the average clustering coefficient and shortest path
+length of a given graph against the same quantities for an equivalent random
+or lattice graph.
+
+For more information, see the Wikipedia article on small-world network [1]_.
+
+.. [1] Small-world network:: https://en.wikipedia.org/wiki/Small-world_network
+
+"""
+import networkx as nx
+from networkx.utils import not_implemented_for, py_random_state
+
+__all__ = ["random_reference", "lattice_reference", "sigma", "omega"]
+
+
+@py_random_state(3)
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+def random_reference(G, niter=1, connectivity=True, seed=None):
+    """Compute a random graph by swapping edges of a given graph.
+
+    Parameters
+    ----------
+    G : graph
+        An undirected graph with 4 or more nodes.
+
+    niter : integer (optional, default=1)
+        An edge is rewired approximately `niter` times.
+
+    connectivity : boolean (optional, default=True)
+        When True, ensure connectivity for the randomized graph.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness`.
+
+    Returns
+    -------
+    G : graph
+        The randomized graph.
+
+    Raises
+    ------
+    NetworkXError
+        If there are fewer than 4 nodes or 2 edges in `G`
+
+    Notes
+    -----
+    The implementation is adapted from the algorithm by Maslov and Sneppen
+    (2002) [1]_.
+
+    References
+    ----------
+    .. [1] Maslov, Sergei, and Kim Sneppen.
+           "Specificity and stability in topology of protein networks."
+           Science 296.5569 (2002): 910-913.
+    """
+    if len(G) < 4:
+        raise nx.NetworkXError("Graph has fewer than four nodes.")
+    if len(G.edges) < 2:
+        raise nx.NetworkXError("Graph has fewer that 2 edges")
+
+    from networkx.utils import cumulative_distribution, discrete_sequence
+
+    local_conn = nx.connectivity.local_edge_connectivity
+
+    G = G.copy()
+    keys, degrees = zip(*G.degree())  # keys, degree
+    cdf = cumulative_distribution(degrees)  # cdf of degree
+    nnodes = len(G)
+    nedges = nx.number_of_edges(G)
+    niter = niter * nedges
+    ntries = int(nnodes * nedges / (nnodes * (nnodes - 1) / 2))
+    swapcount = 0
+
+    for i in range(niter):
+        n = 0
+        while n < ntries:
+            # pick two random edges without creating edge list
+            # choose source node indices from discrete distribution
+            (ai, ci) = discrete_sequence(2, cdistribution=cdf, seed=seed)
+            if ai == ci:
+                continue  # same source, skip
+            a = keys[ai]  # convert index to label
+            c = keys[ci]
+            # choose target uniformly from neighbors
+            b = seed.choice(list(G.neighbors(a)))
+            d = seed.choice(list(G.neighbors(c)))
+            if b in [a, c, d] or d in [a, b, c]:
+                continue  # all vertices should be different
+
+            # don't create parallel edges
+            if (d not in G[a]) and (b not in G[c]):
+                G.add_edge(a, d)
+                G.add_edge(c, b)
+                G.remove_edge(a, b)
+                G.remove_edge(c, d)
+
+                # Check if the graph is still connected
+                if connectivity and local_conn(G, a, b) == 0:
+                    # Not connected, revert the swap
+                    G.remove_edge(a, d)
+                    G.remove_edge(c, b)
+                    G.add_edge(a, b)
+                    G.add_edge(c, d)
+                else:
+                    swapcount += 1
+                    break
+            n += 1
+    return G
+
+
+@py_random_state(4)
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+def lattice_reference(G, niter=5, D=None, connectivity=True, seed=None):
+    """Latticize the given graph by swapping edges.
+
+    Parameters
+    ----------
+    G : graph
+        An undirected graph.
+
+    niter : integer (optional, default=1)
+        An edge is rewired approximately niter times.
+
+    D : numpy.array (optional, default=None)
+        Distance to the diagonal matrix.
+
+    connectivity : boolean (optional, default=True)
+        Ensure connectivity for the latticized graph when set to True.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness`.
+
+    Returns
+    -------
+    G : graph
+        The latticized graph.
+
+    Raises
+    ------
+    NetworkXError
+        If there are fewer than 4 nodes or 2 edges in `G`
+
+    Notes
+    -----
+    The implementation is adapted from the algorithm by Sporns et al. [1]_.
+    which is inspired from the original work by Maslov and Sneppen(2002) [2]_.
+
+    References
+    ----------
+    .. [1] Sporns, Olaf, and Jonathan D. Zwi.
+       "The small world of the cerebral cortex."
+       Neuroinformatics 2.2 (2004): 145-162.
+    .. [2] Maslov, Sergei, and Kim Sneppen.
+       "Specificity and stability in topology of protein networks."
+       Science 296.5569 (2002): 910-913.
+    """
+    import numpy as np
+
+    from networkx.utils import cumulative_distribution, discrete_sequence
+
+    local_conn = nx.connectivity.local_edge_connectivity
+
+    if len(G) < 4:
+        raise nx.NetworkXError("Graph has fewer than four nodes.")
+    if len(G.edges) < 2:
+        raise nx.NetworkXError("Graph has fewer that 2 edges")
+    # Instead of choosing uniformly at random from a generated edge list,
+    # this algorithm chooses nonuniformly from the set of nodes with
+    # probability weighted by degree.
+    G = G.copy()
+    keys, degrees = zip(*G.degree())  # keys, degree
+    cdf = cumulative_distribution(degrees)  # cdf of degree
+
+    nnodes = len(G)
+    nedges = nx.number_of_edges(G)
+    if D is None:
+        D = np.zeros((nnodes, nnodes))
+        un = np.arange(1, nnodes)
+        um = np.arange(nnodes - 1, 0, -1)
+        u = np.append((0,), np.where(un < um, un, um))
+
+        for v in range(int(np.ceil(nnodes / 2))):
+            D[nnodes - v - 1, :] = np.append(u[v + 1 :], u[: v + 1])
+            D[v, :] = D[nnodes - v - 1, :][::-1]
+
+    niter = niter * nedges
+    # maximal number of rewiring attempts per 'niter'
+    max_attempts = int(nnodes * nedges / (nnodes * (nnodes - 1) / 2))
+
+    for _ in range(niter):
+        n = 0
+        while n < max_attempts:
+            # pick two random edges without creating edge list
+            # choose source node indices from discrete distribution
+            (ai, ci) = discrete_sequence(2, cdistribution=cdf, seed=seed)
+            if ai == ci:
+                continue  # same source, skip
+            a = keys[ai]  # convert index to label
+            c = keys[ci]
+            # choose target uniformly from neighbors
+            b = seed.choice(list(G.neighbors(a)))
+            d = seed.choice(list(G.neighbors(c)))
+            bi = keys.index(b)
+            di = keys.index(d)
+
+            if b in [a, c, d] or d in [a, b, c]:
+                continue  # all vertices should be different
+
+            # don't create parallel edges
+            if (d not in G[a]) and (b not in G[c]):
+                if D[ai, bi] + D[ci, di] >= D[ai, ci] + D[bi, di]:
+                    # only swap if we get closer to the diagonal
+                    G.add_edge(a, d)
+                    G.add_edge(c, b)
+                    G.remove_edge(a, b)
+                    G.remove_edge(c, d)
+
+                    # Check if the graph is still connected
+                    if connectivity and local_conn(G, a, b) == 0:
+                        # Not connected, revert the swap
+                        G.remove_edge(a, d)
+                        G.remove_edge(c, b)
+                        G.add_edge(a, b)
+                        G.add_edge(c, d)
+                    else:
+                        break
+            n += 1
+
+    return G
+
+
+@py_random_state(3)
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+def sigma(G, niter=100, nrand=10, seed=None):
+    """Returns the small-world coefficient (sigma) of the given graph.
+
+    The small-world coefficient is defined as:
+    sigma = C/Cr / L/Lr
+    where C and L are respectively the average clustering coefficient and
+    average shortest path length of G. Cr and Lr are respectively the average
+    clustering coefficient and average shortest path length of an equivalent
+    random graph.
+
+    A graph is commonly classified as small-world if sigma>1.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+    niter : integer (optional, default=100)
+        Approximate number of rewiring per edge to compute the equivalent
+        random graph.
+    nrand : integer (optional, default=10)
+        Number of random graphs generated to compute the average clustering
+        coefficient (Cr) and average shortest path length (Lr).
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness`.
+
+    Returns
+    -------
+    sigma : float
+        The small-world coefficient of G.
+
+    Notes
+    -----
+    The implementation is adapted from Humphries et al. [1]_ [2]_.
+
+    References
+    ----------
+    .. [1] The brainstem reticular formation is a small-world, not scale-free,
+           network M. D. Humphries, K. Gurney and T. J. Prescott,
+           Proc. Roy. Soc. B 2006 273, 503-511, doi:10.1098/rspb.2005.3354.
+    .. [2] Humphries and Gurney (2008).
+           "Network 'Small-World-Ness': A Quantitative Method for Determining
+           Canonical Network Equivalence".
+           PLoS One. 3 (4). PMID 18446219. doi:10.1371/journal.pone.0002051.
+    """
+    import numpy as np
+
+    # Compute the mean clustering coefficient and average shortest path length
+    # for an equivalent random graph
+    randMetrics = {"C": [], "L": []}
+    for i in range(nrand):
+        Gr = random_reference(G, niter=niter, seed=seed)
+        randMetrics["C"].append(nx.transitivity(Gr))
+        randMetrics["L"].append(nx.average_shortest_path_length(Gr))
+
+    C = nx.transitivity(G)
+    L = nx.average_shortest_path_length(G)
+    Cr = np.mean(randMetrics["C"])
+    Lr = np.mean(randMetrics["L"])
+
+    sigma = (C / Cr) / (L / Lr)
+
+    return sigma
+
+
+@py_random_state(3)
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+def omega(G, niter=5, nrand=10, seed=None):
+    """Returns the small-world coefficient (omega) of a graph
+
+    The small-world coefficient of a graph G is:
+
+    omega = Lr/L - C/Cl
+
+    where C and L are respectively the average clustering coefficient and
+    average shortest path length of G. Lr is the average shortest path length
+    of an equivalent random graph and Cl is the average clustering coefficient
+    of an equivalent lattice graph.
+
+    The small-world coefficient (omega) measures how much G is like a lattice
+    or a random graph. Negative values mean G is similar to a lattice whereas
+    positive values mean G is a random graph.
+    Values close to 0 mean that G has small-world characteristics.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        An undirected graph.
+
+    niter: integer (optional, default=5)
+        Approximate number of rewiring per edge to compute the equivalent
+        random graph.
+
+    nrand: integer (optional, default=10)
+        Number of random graphs generated to compute the maximal clustering
+        coefficient (Cr) and average shortest path length (Lr).
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness`.
+
+
+    Returns
+    -------
+    omega : float
+        The small-world coefficient (omega)
+
+    Notes
+    -----
+    The implementation is adapted from the algorithm by Telesford et al. [1]_.
+
+    References
+    ----------
+    .. [1] Telesford, Joyce, Hayasaka, Burdette, and Laurienti (2011).
+           "The Ubiquity of Small-World Networks".
+           Brain Connectivity. 1 (0038): 367-75.  PMC 3604768. PMID 22432451.
+           doi:10.1089/brain.2011.0038.
+    """
+    import numpy as np
+
+    # Compute the mean clustering coefficient and average shortest path length
+    # for an equivalent random graph
+    randMetrics = {"C": [], "L": []}
+
+    # Calculate initial average clustering coefficient which potentially will
+    # get replaced by higher clustering coefficients from generated lattice
+    # reference graphs
+    Cl = nx.average_clustering(G)
+
+    niter_lattice_reference = niter
+    niter_random_reference = niter * 2
+
+    for _ in range(nrand):
+        # Generate random graph
+        Gr = random_reference(G, niter=niter_random_reference, seed=seed)
+        randMetrics["L"].append(nx.average_shortest_path_length(Gr))
+
+        # Generate lattice graph
+        Gl = lattice_reference(G, niter=niter_lattice_reference, seed=seed)
+
+        # Replace old clustering coefficient, if clustering is higher in
+        # generated lattice reference
+        Cl_temp = nx.average_clustering(Gl)
+        if Cl_temp > Cl:
+            Cl = Cl_temp
+
+    C = nx.average_clustering(G)
+    L = nx.average_shortest_path_length(G)
+    Lr = np.mean(randMetrics["L"])
+
+    omega = (Lr / L) - (C / Cl)
+
+    return omega
diff --git a/shared/package-files/09/09ad928f23703055d27245135bf2ea60590573909639a675ca2601d2d2b6b81f/lsqr.py b/shared/package-files/09/09ad928f23703055d27245135bf2ea60590573909639a675ca2601d2d2b6b81f/lsqr.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e490a0769e6d20303b1e5fdd111237b72d3abc9
--- /dev/null
+++ b/shared/package-files/09/09ad928f23703055d27245135bf2ea60590573909639a675ca2601d2d2b6b81f/lsqr.py
@@ -0,0 +1,589 @@
+"""Sparse Equations and Least Squares.
+
+The original Fortran code was written by C. C. Paige and M. A. Saunders as
+described in
+
+C. C. Paige and M. A. Saunders, LSQR: An algorithm for sparse linear
+equations and sparse least squares, TOMS 8(1), 43--71 (1982).
+
+C. C. Paige and M. A. Saunders, Algorithm 583; LSQR: Sparse linear
+equations and least-squares problems, TOMS 8(2), 195--209 (1982).
+
+It is licensed under the following BSD license:
+
+Copyright (c) 2006, Systems Optimization Laboratory
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are
+met:
+
+    * Redistributions of source code must retain the above copyright
+      notice, this list of conditions and the following disclaimer.
+
+    * Redistributions in binary form must reproduce the above
+      copyright notice, this list of conditions and the following
+      disclaimer in the documentation and/or other materials provided
+      with the distribution.
+
+    * Neither the name of Stanford University nor the names of its
+      contributors may be used to endorse or promote products derived
+      from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+The Fortran code was translated to Python for use in CVXOPT by Jeffery
+Kline with contributions by Mridul Aanjaneya and Bob Myhill.
+
+Adapted for SciPy by Stefan van der Walt.
+
+"""
+
+__all__ = ['lsqr']
+
+import numpy as np
+from math import sqrt
+from scipy.sparse.linalg._interface import aslinearoperator
+from scipy.sparse._sputils import convert_pydata_sparse_to_scipy
+
+eps = np.finfo(np.float64).eps
+
+
+def _sym_ortho(a, b):
+    """
+    Stable implementation of Givens rotation.
+
+    Notes
+    -----
+    The routine 'SymOrtho' was added for numerical stability. This is
+    recommended by S.-C. Choi in [1]_.  It removes the unpleasant potential of
+    ``1/eps`` in some important places (see, for example text following
+    "Compute the next plane rotation Qk" in minres.py).
+
+    References
+    ----------
+    .. [1] S.-C. Choi, "Iterative Methods for Singular Linear Equations
+           and Least-Squares Problems", Dissertation,
+           http://www.stanford.edu/group/SOL/dissertations/sou-cheng-choi-thesis.pdf
+
+    """
+    if b == 0:
+        return np.sign(a), 0, abs(a)
+    elif a == 0:
+        return 0, np.sign(b), abs(b)
+    elif abs(b) > abs(a):
+        tau = a / b
+        s = np.sign(b) / sqrt(1 + tau * tau)
+        c = s * tau
+        r = b / s
+    else:
+        tau = b / a
+        c = np.sign(a) / sqrt(1+tau*tau)
+        s = c * tau
+        r = a / c
+    return c, s, r
+
+
+def lsqr(A, b, damp=0.0, atol=1e-6, btol=1e-6, conlim=1e8,
+         iter_lim=None, show=False, calc_var=False, x0=None):
+    """Find the least-squares solution to a large, sparse, linear system
+    of equations.
+
+    The function solves ``Ax = b``  or  ``min ||Ax - b||^2`` or
+    ``min ||Ax - b||^2 + d^2 ||x - x0||^2``.
+
+    The matrix A may be square or rectangular (over-determined or
+    under-determined), and may have any rank.
+
+    ::
+
+      1. Unsymmetric equations --    solve  Ax = b
+
+      2. Linear least squares  --    solve  Ax = b
+                                     in the least-squares sense
+
+      3. Damped least squares  --    solve  (   A    )*x = (    b    )
+                                            ( damp*I )     ( damp*x0 )
+                                     in the least-squares sense
+
+    Parameters
+    ----------
+    A : {sparse array, ndarray, LinearOperator}
+        Representation of an m-by-n matrix.
+        Alternatively, ``A`` can be a linear operator which can
+        produce ``Ax`` and ``A^T x`` using, e.g.,
+        ``scipy.sparse.linalg.LinearOperator``.
+    b : array_like, shape (m,)
+        Right-hand side vector ``b``.
+    damp : float
+        Damping coefficient. Default is 0.
+    atol, btol : float, optional
+        Stopping tolerances. `lsqr` continues iterations until a
+        certain backward error estimate is smaller than some quantity
+        depending on atol and btol.  Let ``r = b - Ax`` be the
+        residual vector for the current approximate solution ``x``.
+        If ``Ax = b`` seems to be consistent, `lsqr` terminates
+        when ``norm(r) <= atol * norm(A) * norm(x) + btol * norm(b)``.
+        Otherwise, `lsqr` terminates when ``norm(A^H r) <=
+        atol * norm(A) * norm(r)``.  If both tolerances are 1.0e-6 (default),
+        the final ``norm(r)`` should be accurate to about 6
+        digits. (The final ``x`` will usually have fewer correct digits,
+        depending on ``cond(A)`` and the size of LAMBDA.)  If `atol`
+        or `btol` is None, a default value of 1.0e-6 will be used.
+        Ideally, they should be estimates of the relative error in the
+        entries of ``A`` and ``b`` respectively.  For example, if the entries
+        of ``A`` have 7 correct digits, set ``atol = 1e-7``. This prevents
+        the algorithm from doing unnecessary work beyond the
+        uncertainty of the input data.
+    conlim : float, optional
+        Another stopping tolerance.  lsqr terminates if an estimate of
+        ``cond(A)`` exceeds `conlim`.  For compatible systems ``Ax =
+        b``, `conlim` could be as large as 1.0e+12 (say).  For
+        least-squares problems, conlim should be less than 1.0e+8.
+        Maximum precision can be obtained by setting ``atol = btol =
+        conlim = zero``, but the number of iterations may then be
+        excessive. Default is 1e8.
+    iter_lim : int, optional
+        Explicit limitation on number of iterations (for safety).
+    show : bool, optional
+        Display an iteration log. Default is False.
+    calc_var : bool, optional
+        Whether to estimate diagonals of ``(A'A + damp^2*I)^{-1}``.
+    x0 : array_like, shape (n,), optional
+        Initial guess of x, if None zeros are used. Default is None.
+
+        .. versionadded:: 1.0.0
+
+    Returns
+    -------
+    x : ndarray of float
+        The final solution.
+    istop : int
+        Gives the reason for termination.
+        1 means x is an approximate solution to Ax = b.
+        2 means x approximately solves the least-squares problem.
+    itn : int
+        Iteration number upon termination.
+    r1norm : float
+        ``norm(r)``, where ``r = b - Ax``.
+    r2norm : float
+        ``sqrt( norm(r)^2  +  damp^2 * norm(x - x0)^2 )``.  Equal to `r1norm`
+        if ``damp == 0``.
+    anorm : float
+        Estimate of Frobenius norm of ``Abar = [[A]; [damp*I]]``.
+    acond : float
+        Estimate of ``cond(Abar)``.
+    arnorm : float
+        Estimate of ``norm(A'@r - damp^2*(x - x0))``.
+    xnorm : float
+        ``norm(x)``
+    var : ndarray of float
+        If ``calc_var`` is True, estimates all diagonals of
+        ``(A'A)^{-1}`` (if ``damp == 0``) or more generally ``(A'A +
+        damp^2*I)^{-1}``.  This is well defined if A has full column
+        rank or ``damp > 0``.  (Not sure what var means if ``rank(A)
+        < n`` and ``damp = 0.``)
+
+    Notes
+    -----
+    LSQR uses an iterative method to approximate the solution.  The
+    number of iterations required to reach a certain accuracy depends
+    strongly on the scaling of the problem.  Poor scaling of the rows
+    or columns of A should therefore be avoided where possible.
+
+    For example, in problem 1 the solution is unaltered by
+    row-scaling.  If a row of A is very small or large compared to
+    the other rows of A, the corresponding row of ( A  b ) should be
+    scaled up or down.
+
+    In problems 1 and 2, the solution x is easily recovered
+    following column-scaling.  Unless better information is known,
+    the nonzero columns of A should be scaled so that they all have
+    the same Euclidean norm (e.g., 1.0).
+
+    In problem 3, there is no freedom to re-scale if damp is
+    nonzero.  However, the value of damp should be assigned only
+    after attention has been paid to the scaling of A.
+
+    The parameter damp is intended to help regularize
+    ill-conditioned systems, by preventing the true solution from
+    being very large.  Another aid to regularization is provided by
+    the parameter acond, which may be used to terminate iterations
+    before the computed solution becomes very large.
+
+    If some initial estimate ``x0`` is known and if ``damp == 0``,
+    one could proceed as follows:
+
+      1. Compute a residual vector ``r0 = b - A@x0``.
+      2. Use LSQR to solve the system  ``A@dx = r0``.
+      3. Add the correction dx to obtain a final solution ``x = x0 + dx``.
+
+    This requires that ``x0`` be available before and after the call
+    to LSQR.  To judge the benefits, suppose LSQR takes k1 iterations
+    to solve A@x = b and k2 iterations to solve A@dx = r0.
+    If x0 is "good", norm(r0) will be smaller than norm(b).
+    If the same stopping tolerances atol and btol are used for each
+    system, k1 and k2 will be similar, but the final solution x0 + dx
+    should be more accurate.  The only way to reduce the total work
+    is to use a larger stopping tolerance for the second system.
+    If some value btol is suitable for A@x = b, the larger value
+    btol*norm(b)/norm(r0)  should be suitable for A@dx = r0.
+
+    Preconditioning is another way to reduce the number of iterations.
+    If it is possible to solve a related system ``M@x = b``
+    efficiently, where M approximates A in some helpful way (e.g. M -
+    A has low rank or its elements are small relative to those of A),
+    LSQR may converge more rapidly on the system ``A@M(inverse)@z =
+    b``, after which x can be recovered by solving M@x = z.
+
+    If A is symmetric, LSQR should not be used!
+
+    Alternatives are the symmetric conjugate-gradient method (cg)
+    and/or SYMMLQ.  SYMMLQ is an implementation of symmetric cg that
+    applies to any symmetric A and will converge more rapidly than
+    LSQR.  If A is positive definite, there are other implementations
+    of symmetric cg that require slightly less work per iteration than
+    SYMMLQ (but will take the same number of iterations).
+
+    References
+    ----------
+    .. [1] C. C. Paige and M. A. Saunders (1982a).
+           "LSQR: An algorithm for sparse linear equations and
+           sparse least squares", ACM TOMS 8(1), 43-71.
+    .. [2] C. C. Paige and M. A. Saunders (1982b).
+           "Algorithm 583.  LSQR: Sparse linear equations and least
+           squares problems", ACM TOMS 8(2), 195-209.
+    .. [3] M. A. Saunders (1995).  "Solution of sparse rectangular
+           systems using LSQR and CRAIG", BIT 35, 588-604.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.sparse import csc_array
+    >>> from scipy.sparse.linalg import lsqr
+    >>> A = csc_array([[1., 0.], [1., 1.], [0., 1.]], dtype=float)
+
+    The first example has the trivial solution ``[0, 0]``
+
+    >>> b = np.array([0., 0., 0.], dtype=float)
+    >>> x, istop, itn, normr = lsqr(A, b)[:4]
+    >>> istop
+    0
+    >>> x
+    array([ 0.,  0.])
+
+    The stopping code ``istop=0`` returned indicates that a vector of zeros was
+    found as a solution. The returned solution `x` indeed contains
+    ``[0., 0.]``. The next example has a non-trivial solution:
+
+    >>> b = np.array([1., 0., -1.], dtype=float)
+    >>> x, istop, itn, r1norm = lsqr(A, b)[:4]
+    >>> istop
+    1
+    >>> x
+    array([ 1., -1.])
+    >>> itn
+    1
+    >>> r1norm
+    4.440892098500627e-16
+
+    As indicated by ``istop=1``, `lsqr` found a solution obeying the tolerance
+    limits. The given solution ``[1., -1.]`` obviously solves the equation. The
+    remaining return values include information about the number of iterations
+    (`itn=1`) and the remaining difference of left and right side of the solved
+    equation.
+    The final example demonstrates the behavior in the case where there is no
+    solution for the equation:
+
+    >>> b = np.array([1., 0.01, -1.], dtype=float)
+    >>> x, istop, itn, r1norm = lsqr(A, b)[:4]
+    >>> istop
+    2
+    >>> x
+    array([ 1.00333333, -0.99666667])
+    >>> A.dot(x)-b
+    array([ 0.00333333, -0.00333333,  0.00333333])
+    >>> r1norm
+    0.005773502691896255
+
+    `istop` indicates that the system is inconsistent and thus `x` is rather an
+    approximate solution to the corresponding least-squares problem. `r1norm`
+    contains the norm of the minimal residual that was found.
+    """
+    A = convert_pydata_sparse_to_scipy(A)
+    A = aslinearoperator(A)
+    b = np.atleast_1d(b)
+    if b.ndim > 1:
+        b = b.squeeze()
+
+    m, n = A.shape
+    if iter_lim is None:
+        iter_lim = 2 * n
+    var = np.zeros(n)
+
+    msg = ('The exact solution is  x = 0                              ',
+           'Ax - b is small enough, given atol, btol                  ',
+           'The least-squares solution is good enough, given atol     ',
+           'The estimate of cond(Abar) has exceeded conlim            ',
+           'Ax - b is small enough for this machine                   ',
+           'The least-squares solution is good enough for this machine',
+           'Cond(Abar) seems to be too large for this machine         ',
+           'The iteration limit has been reached                      ')
+
+    if show:
+        print(' ')
+        print('LSQR            Least-squares solution of  Ax = b')
+        str1 = f'The matrix A has {m} rows and {n} columns'
+        str2 = f'damp = {damp:20.14e}   calc_var = {calc_var:8g}'
+        str3 = f'atol = {atol:8.2e}                 conlim = {conlim:8.2e}'
+        str4 = f'btol = {btol:8.2e}               iter_lim = {iter_lim:8g}'
+        print(str1)
+        print(str2)
+        print(str3)
+        print(str4)
+
+    itn = 0
+    istop = 0
+    ctol = 0
+    if conlim > 0:
+        ctol = 1/conlim
+    anorm = 0
+    acond = 0
+    dampsq = damp**2
+    ddnorm = 0
+    res2 = 0
+    xnorm = 0
+    xxnorm = 0
+    z = 0
+    cs2 = -1
+    sn2 = 0
+
+    # Set up the first vectors u and v for the bidiagonalization.
+    # These satisfy  beta*u = b - A@x,  alfa*v = A'@u.
+    u = b
+    bnorm = np.linalg.norm(b)
+
+    if x0 is None:
+        x = np.zeros(n)
+        beta = bnorm.copy()
+    else:
+        x = np.asarray(x0)
+        u = u - A.matvec(x)
+        beta = np.linalg.norm(u)
+
+    if beta > 0:
+        u = (1/beta) * u
+        v = A.rmatvec(u)
+        alfa = np.linalg.norm(v)
+    else:
+        v = x.copy()
+        alfa = 0
+
+    if alfa > 0:
+        v = (1/alfa) * v
+    w = v.copy()
+
+    rhobar = alfa
+    phibar = beta
+    rnorm = beta
+    r1norm = rnorm
+    r2norm = rnorm
+
+    # Reverse the order here from the original matlab code because
+    # there was an error on return when arnorm==0
+    arnorm = alfa * beta
+    if arnorm == 0:
+        if show:
+            print(msg[0])
+        return x, istop, itn, r1norm, r2norm, anorm, acond, arnorm, xnorm, var
+
+    head1 = '   Itn      x[0]       r1norm     r2norm '
+    head2 = ' Compatible    LS      Norm A   Cond A'
+
+    if show:
+        print(' ')
+        print(head1, head2)
+        test1 = 1
+        test2 = alfa / beta
+        str1 = f'{itn:6g} {x[0]:12.5e}'
+        str2 = f' {r1norm:10.3e} {r2norm:10.3e}'
+        str3 = f'  {test1:8.1e} {test2:8.1e}'
+        print(str1, str2, str3)
+
+    # Main iteration loop.
+    while itn < iter_lim:
+        itn = itn + 1
+        # Perform the next step of the bidiagonalization to obtain the
+        # next  beta, u, alfa, v. These satisfy the relations
+        #     beta*u  =  a@v   -  alfa*u,
+        #     alfa*v  =  A'@u  -  beta*v.
+        u = A.matvec(v) - alfa * u
+        beta = np.linalg.norm(u)
+
+        if beta > 0:
+            u = (1/beta) * u
+            anorm = sqrt(anorm**2 + alfa**2 + beta**2 + dampsq)
+            v = A.rmatvec(u) - beta * v
+            alfa = np.linalg.norm(v)
+            if alfa > 0:
+                v = (1 / alfa) * v
+
+        # Use a plane rotation to eliminate the damping parameter.
+        # This alters the diagonal (rhobar) of the lower-bidiagonal matrix.
+        if damp > 0:
+            rhobar1 = sqrt(rhobar**2 + dampsq)
+            cs1 = rhobar / rhobar1
+            sn1 = damp / rhobar1
+            psi = sn1 * phibar
+            phibar = cs1 * phibar
+        else:
+            # cs1 = 1 and sn1 = 0
+            rhobar1 = rhobar
+            psi = 0.
+
+        # Use a plane rotation to eliminate the subdiagonal element (beta)
+        # of the lower-bidiagonal matrix, giving an upper-bidiagonal matrix.
+        cs, sn, rho = _sym_ortho(rhobar1, beta)
+
+        theta = sn * alfa
+        rhobar = -cs * alfa
+        phi = cs * phibar
+        phibar = sn * phibar
+        tau = sn * phi
+
+        # Update x and w.
+        t1 = phi / rho
+        t2 = -theta / rho
+        dk = (1 / rho) * w
+
+        x = x + t1 * w
+        w = v + t2 * w
+        ddnorm = ddnorm + np.linalg.norm(dk)**2
+
+        if calc_var:
+            var = var + dk**2
+
+        # Use a plane rotation on the right to eliminate the
+        # super-diagonal element (theta) of the upper-bidiagonal matrix.
+        # Then use the result to estimate norm(x).
+        delta = sn2 * rho
+        gambar = -cs2 * rho
+        rhs = phi - delta * z
+        zbar = rhs / gambar
+        xnorm = sqrt(xxnorm + zbar**2)
+        gamma = sqrt(gambar**2 + theta**2)
+        cs2 = gambar / gamma
+        sn2 = theta / gamma
+        z = rhs / gamma
+        xxnorm = xxnorm + z**2
+
+        # Test for convergence.
+        # First, estimate the condition of the matrix  Abar,
+        # and the norms of  rbar  and  Abar'rbar.
+        acond = anorm * sqrt(ddnorm)
+        res1 = phibar**2
+        res2 = res2 + psi**2
+        rnorm = sqrt(res1 + res2)
+        arnorm = alfa * abs(tau)
+
+        # Distinguish between
+        #    r1norm = ||b - Ax|| and
+        #    r2norm = rnorm in current code
+        #           = sqrt(r1norm^2 + damp^2*||x - x0||^2).
+        #    Estimate r1norm from
+        #    r1norm = sqrt(r2norm^2 - damp^2*||x - x0||^2).
+        # Although there is cancellation, it might be accurate enough.
+        if damp > 0:
+            r1sq = rnorm**2 - dampsq * xxnorm
+            r1norm = sqrt(abs(r1sq))
+            if r1sq < 0:
+                r1norm = -r1norm
+        else:
+            r1norm = rnorm
+        r2norm = rnorm
+
+        # Now use these norms to estimate certain other quantities,
+        # some of which will be small near a solution.
+        test1 = rnorm / bnorm
+        test2 = arnorm / (anorm * rnorm + eps)
+        test3 = 1 / (acond + eps)
+        t1 = test1 / (1 + anorm * xnorm / bnorm)
+        rtol = btol + atol * anorm * xnorm / bnorm
+
+        # The following tests guard against extremely small values of
+        # atol, btol  or  ctol.  (The user may have set any or all of
+        # the parameters  atol, btol, conlim  to 0.)
+        # The effect is equivalent to the normal tests using
+        # atol = eps,  btol = eps,  conlim = 1/eps.
+        if itn >= iter_lim:
+            istop = 7
+        if 1 + test3 <= 1:
+            istop = 6
+        if 1 + test2 <= 1:
+            istop = 5
+        if 1 + t1 <= 1:
+            istop = 4
+
+        # Allow for tolerances set by the user.
+        if test3 <= ctol:
+            istop = 3
+        if test2 <= atol:
+            istop = 2
+        if test1 <= rtol:
+            istop = 1
+
+        if show:
+            # See if it is time to print something.
+            prnt = False
+            if n <= 40:
+                prnt = True
+            if itn <= 10:
+                prnt = True
+            if itn >= iter_lim-10:
+                prnt = True
+            # if itn%10 == 0: prnt = True
+            if test3 <= 2*ctol:
+                prnt = True
+            if test2 <= 10*atol:
+                prnt = True
+            if test1 <= 10*rtol:
+                prnt = True
+            if istop != 0:
+                prnt = True
+
+            if prnt:
+                str1 = f'{itn:6g} {x[0]:12.5e}'
+                str2 = f' {r1norm:10.3e} {r2norm:10.3e}'
+                str3 = f'  {test1:8.1e} {test2:8.1e}'
+                str4 = f' {anorm:8.1e} {acond:8.1e}'
+                print(str1, str2, str3, str4)
+
+        if istop != 0:
+            break
+
+    # End of iteration loop.
+    # Print the stopping condition.
+    if show:
+        print(' ')
+        print('LSQR finished')
+        print(msg[istop])
+        print(' ')
+        str1 = f'istop ={istop:8g}   r1norm ={r1norm:8.1e}'
+        str2 = f'anorm ={anorm:8.1e}   arnorm ={arnorm:8.1e}'
+        str3 = f'itn   ={itn:8g}   r2norm ={r2norm:8.1e}'
+        str4 = f'acond ={acond:8.1e}   xnorm  ={xnorm:8.1e}'
+        print(str1 + '   ' + str2)
+        print(str3 + '   ' + str4)
+        print(' ')
+
+    return x, istop, itn, r1norm, r2norm, anorm, acond, arnorm, xnorm, var
diff --git a/shared/package-files/09/09ae79d56ab20d64eb42b43884ec079767691d2d4549683f48acb3d5739b42b5/_waveforms.py b/shared/package-files/09/09ae79d56ab20d64eb42b43884ec079767691d2d4549683f48acb3d5739b42b5/_waveforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..a6be46cfd38674ee8c3ae89c9762461440c1e620
--- /dev/null
+++ b/shared/package-files/09/09ae79d56ab20d64eb42b43884ec079767691d2d4549683f48acb3d5739b42b5/_waveforms.py
@@ -0,0 +1,696 @@
+# Author: Travis Oliphant
+# 2003
+#
+# Feb. 2010: Updated by Warren Weckesser:
+#   Rewrote much of chirp()
+#   Added sweep_poly()
+import numpy as np
+from numpy import asarray, zeros, place, nan, mod, pi, extract, log, sqrt, \
+    exp, cos, sin, polyval, polyint
+
+
+__all__ = ['sawtooth', 'square', 'gausspulse', 'chirp', 'sweep_poly',
+           'unit_impulse']
+
+
+def sawtooth(t, width=1):
+    """
+    Return a periodic sawtooth or triangle waveform.
+
+    The sawtooth waveform has a period ``2*pi``, rises from -1 to 1 on the
+    interval 0 to ``width*2*pi``, then drops from 1 to -1 on the interval
+    ``width*2*pi`` to ``2*pi``. `width` must be in the interval [0, 1].
+
+    Note that this is not band-limited.  It produces an infinite number
+    of harmonics, which are aliased back and forth across the frequency
+    spectrum.
+
+    Parameters
+    ----------
+    t : array_like
+        Time.
+    width : array_like, optional
+        Width of the rising ramp as a proportion of the total cycle.
+        Default is 1, producing a rising ramp, while 0 produces a falling
+        ramp.  `width` = 0.5 produces a triangle wave.
+        If an array, causes wave shape to change over time, and must be the
+        same length as t.
+
+    Returns
+    -------
+    y : ndarray
+        Output array containing the sawtooth waveform.
+
+    Examples
+    --------
+    A 5 Hz waveform sampled at 500 Hz for 1 second:
+
+    >>> import numpy as np
+    >>> from scipy import signal
+    >>> import matplotlib.pyplot as plt
+    >>> t = np.linspace(0, 1, 500)
+    >>> plt.plot(t, signal.sawtooth(2 * np.pi * 5 * t))
+
+    """
+    t, w = asarray(t), asarray(width)
+    w = asarray(w + (t - t))
+    t = asarray(t + (w - w))
+    if t.dtype.char in ['fFdD']:
+        ytype = t.dtype.char
+    else:
+        ytype = 'd'
+    y = zeros(t.shape, ytype)
+
+    # width must be between 0 and 1 inclusive
+    mask1 = (w > 1) | (w < 0)
+    place(y, mask1, nan)
+
+    # take t modulo 2*pi
+    tmod = mod(t, 2 * pi)
+
+    # on the interval 0 to width*2*pi function is
+    #  tmod / (pi*w) - 1
+    mask2 = (1 - mask1) & (tmod < w * 2 * pi)
+    tsub = extract(mask2, tmod)
+    wsub = extract(mask2, w)
+    place(y, mask2, tsub / (pi * wsub) - 1)
+
+    # on the interval width*2*pi to 2*pi function is
+    #  (pi*(w+1)-tmod) / (pi*(1-w))
+
+    mask3 = (1 - mask1) & (1 - mask2)
+    tsub = extract(mask3, tmod)
+    wsub = extract(mask3, w)
+    place(y, mask3, (pi * (wsub + 1) - tsub) / (pi * (1 - wsub)))
+    return y
+
+
+def square(t, duty=0.5):
+    """
+    Return a periodic square-wave waveform.
+
+    The square wave has a period ``2*pi``, has value +1 from 0 to
+    ``2*pi*duty`` and -1 from ``2*pi*duty`` to ``2*pi``. `duty` must be in
+    the interval [0,1].
+
+    Note that this is not band-limited.  It produces an infinite number
+    of harmonics, which are aliased back and forth across the frequency
+    spectrum.
+
+    Parameters
+    ----------
+    t : array_like
+        The input time array.
+    duty : array_like, optional
+        Duty cycle.  Default is 0.5 (50% duty cycle).
+        If an array, causes wave shape to change over time, and must be the
+        same length as t.
+
+    Returns
+    -------
+    y : ndarray
+        Output array containing the square waveform.
+
+    Examples
+    --------
+    A 5 Hz waveform sampled at 500 Hz for 1 second:
+
+    >>> import numpy as np
+    >>> from scipy import signal
+    >>> import matplotlib.pyplot as plt
+    >>> t = np.linspace(0, 1, 500, endpoint=False)
+    >>> plt.plot(t, signal.square(2 * np.pi * 5 * t))
+    >>> plt.ylim(-2, 2)
+
+    A pulse-width modulated sine wave:
+
+    >>> plt.figure()
+    >>> sig = np.sin(2 * np.pi * t)
+    >>> pwm = signal.square(2 * np.pi * 30 * t, duty=(sig + 1)/2)
+    >>> plt.subplot(2, 1, 1)
+    >>> plt.plot(t, sig)
+    >>> plt.subplot(2, 1, 2)
+    >>> plt.plot(t, pwm)
+    >>> plt.ylim(-1.5, 1.5)
+
+    """
+    t, w = asarray(t), asarray(duty)
+    w = asarray(w + (t - t))
+    t = asarray(t + (w - w))
+    if t.dtype.char in ['fFdD']:
+        ytype = t.dtype.char
+    else:
+        ytype = 'd'
+
+    y = zeros(t.shape, ytype)
+
+    # width must be between 0 and 1 inclusive
+    mask1 = (w > 1) | (w < 0)
+    place(y, mask1, nan)
+
+    # on the interval 0 to duty*2*pi function is 1
+    tmod = mod(t, 2 * pi)
+    mask2 = (1 - mask1) & (tmod < w * 2 * pi)
+    place(y, mask2, 1)
+
+    # on the interval duty*2*pi to 2*pi function is
+    #  (pi*(w+1)-tmod) / (pi*(1-w))
+    mask3 = (1 - mask1) & (1 - mask2)
+    place(y, mask3, -1)
+    return y
+
+
+def gausspulse(t, fc=1000, bw=0.5, bwr=-6, tpr=-60, retquad=False,
+               retenv=False):
+    """
+    Return a Gaussian modulated sinusoid:
+
+        ``exp(-a t^2) exp(1j*2*pi*fc*t).``
+
+    If `retquad` is True, then return the real and imaginary parts
+    (in-phase and quadrature).
+    If `retenv` is True, then return the envelope (unmodulated signal).
+    Otherwise, return the real part of the modulated sinusoid.
+
+    Parameters
+    ----------
+    t : ndarray or the string 'cutoff'
+        Input array.
+    fc : float, optional
+        Center frequency (e.g. Hz).  Default is 1000.
+    bw : float, optional
+        Fractional bandwidth in frequency domain of pulse (e.g. Hz).
+        Default is 0.5.
+    bwr : float, optional
+        Reference level at which fractional bandwidth is calculated (dB).
+        Default is -6.
+    tpr : float, optional
+        If `t` is 'cutoff', then the function returns the cutoff
+        time for when the pulse amplitude falls below `tpr` (in dB).
+        Default is -60.
+    retquad : bool, optional
+        If True, return the quadrature (imaginary) as well as the real part
+        of the signal.  Default is False.
+    retenv : bool, optional
+        If True, return the envelope of the signal.  Default is False.
+
+    Returns
+    -------
+    yI : ndarray
+        Real part of signal.  Always returned.
+    yQ : ndarray
+        Imaginary part of signal.  Only returned if `retquad` is True.
+    yenv : ndarray
+        Envelope of signal.  Only returned if `retenv` is True.
+
+    Examples
+    --------
+    Plot real component, imaginary component, and envelope for a 5 Hz pulse,
+    sampled at 100 Hz for 2 seconds:
+
+    >>> import numpy as np
+    >>> from scipy import signal
+    >>> import matplotlib.pyplot as plt
+    >>> t = np.linspace(-1, 1, 2 * 100, endpoint=False)
+    >>> i, q, e = signal.gausspulse(t, fc=5, retquad=True, retenv=True)
+    >>> plt.plot(t, i, t, q, t, e, '--')
+
+    """
+    if fc < 0:
+        raise ValueError(f"Center frequency (fc={fc:.2f}) must be >=0.")
+    if bw <= 0:
+        raise ValueError(f"Fractional bandwidth (bw={bw:.2f}) must be > 0.")
+    if bwr >= 0:
+        raise ValueError(f"Reference level for bandwidth (bwr={bwr:.2f}) "
+                         "must be < 0 dB")
+
+    # exp(-a t^2) <->  sqrt(pi/a) exp(-pi^2/a * f^2)  = g(f)
+
+    ref = pow(10.0, bwr / 20.0)
+    # fdel = fc*bw/2:  g(fdel) = ref --- solve this for a
+    #
+    # pi^2/a * fc^2 * bw^2 /4=-log(ref)
+    a = -(pi * fc * bw) ** 2 / (4.0 * log(ref))
+
+    if isinstance(t, str):
+        if t == 'cutoff':  # compute cut_off point
+            #  Solve exp(-a tc**2) = tref  for tc
+            #   tc = sqrt(-log(tref) / a) where tref = 10^(tpr/20)
+            if tpr >= 0:
+                raise ValueError("Reference level for time cutoff must "
+                                 "be < 0 dB")
+            tref = pow(10.0, tpr / 20.0)
+            return sqrt(-log(tref) / a)
+        else:
+            raise ValueError("If `t` is a string, it must be 'cutoff'")
+
+    yenv = exp(-a * t * t)
+    yI = yenv * cos(2 * pi * fc * t)
+    yQ = yenv * sin(2 * pi * fc * t)
+    if not retquad and not retenv:
+        return yI
+    if not retquad and retenv:
+        return yI, yenv
+    if retquad and not retenv:
+        return yI, yQ
+    if retquad and retenv:
+        return yI, yQ, yenv
+
+
+def chirp(t, f0, t1, f1, method='linear', phi=0, vertex_zero=True, *,
+          complex=False):
+    r"""Frequency-swept cosine generator.
+
+    In the following, 'Hz' should be interpreted as 'cycles per unit';
+    there is no requirement here that the unit is one second.  The
+    important distinction is that the units of rotation are cycles, not
+    radians. Likewise, `t` could be a measurement of space instead of time.
+
+    Parameters
+    ----------
+    t : array_like
+        Times at which to evaluate the waveform.
+    f0 : float
+        Frequency (e.g. Hz) at time t=0.
+    t1 : float
+        Time at which `f1` is specified.
+    f1 : float
+        Frequency (e.g. Hz) of the waveform at time `t1`.
+    method : {'linear', 'quadratic', 'logarithmic', 'hyperbolic'}, optional
+        Kind of frequency sweep.  If not given, `linear` is assumed.  See
+        Notes below for more details.
+    phi : float, optional
+        Phase offset, in degrees. Default is 0.
+    vertex_zero : bool, optional
+        This parameter is only used when `method` is 'quadratic'.
+        It determines whether the vertex of the parabola that is the graph
+        of the frequency is at t=0 or t=t1.
+    complex : bool, optional
+        This parameter creates a complex-valued analytic signal instead of a
+        real-valued signal. It allows the use of complex baseband (in communications
+        domain). Default is False.
+
+        .. versionadded:: 1.15.0
+
+    Returns
+    -------
+    y : ndarray
+        A numpy array containing the signal evaluated at `t` with the requested
+        time-varying frequency.  More precisely, the function returns
+        ``exp(1j*phase + 1j*(pi/180)*phi) if complex else cos(phase + (pi/180)*phi)``
+        where `phase` is the integral (from 0 to `t`) of ``2*pi*f(t)``.
+        The instantaneous frequency ``f(t)`` is defined below.
+
+    See Also
+    --------
+    sweep_poly
+
+    Notes
+    -----
+    There are four possible options for the parameter `method`, which have a (long)
+    standard form and some allowed abbreviations. The formulas for the instantaneous
+    frequency :math:`f(t)` of the generated signal are as follows:
+
+    1. Parameter `method` in ``('linear', 'lin', 'li')``:
+
+       .. math::
+           f(t) = f_0 + \beta\, t           \quad\text{with}\quad
+           \beta = \frac{f_1 - f_0}{t_1}
+
+       Frequency :math:`f(t)` varies linearly over time with a constant rate
+       :math:`\beta`.
+
+    2. Parameter `method` in ``('quadratic', 'quad', 'q')``:
+
+       .. math::
+            f(t) =
+            \begin{cases}
+              f_0 + \beta\, t^2          & \text{if vertex_zero is True,}\\
+              f_1 + \beta\, (t_1 - t)^2  & \text{otherwise,}
+            \end{cases}
+            \quad\text{with}\quad
+            \beta = \frac{f_1 - f_0}{t_1^2}
+
+       The graph of the frequency f(t) is a parabola through :math:`(0, f_0)` and
+       :math:`(t_1, f_1)`.  By default, the vertex of the parabola is at
+       :math:`(0, f_0)`. If `vertex_zero` is ``False``, then the vertex is at
+       :math:`(t_1, f_1)`.
+       To use a more general quadratic function, or an arbitrary
+       polynomial, use the function `scipy.signal.sweep_poly`.
+
+    3. Parameter `method` in ``('logarithmic', 'log', 'lo')``:
+
+       .. math::
+            f(t) = f_0  \left(\frac{f_1}{f_0}\right)^{t/t_1}
+
+       :math:`f_0` and :math:`f_1` must be nonzero and have the same sign.
+       This signal is also known as a geometric or exponential chirp.
+
+    4. Parameter `method` in ``('hyperbolic', 'hyp')``:
+
+       .. math::
+              f(t) = \frac{\alpha}{\beta\, t + \gamma} \quad\text{with}\quad
+              \alpha = f_0 f_1 t_1, \ \beta = f_0 - f_1, \ \gamma = f_1 t_1
+
+       :math:`f_0` and :math:`f_1` must be nonzero.
+
+
+    Examples
+    --------
+    For the first example, a linear chirp ranging from 6 Hz to 1 Hz over 10 seconds is
+    plotted:
+
+    >>> import numpy as np
+    >>> from matplotlib.pyplot import tight_layout
+    >>> from scipy.signal import chirp, square, ShortTimeFFT
+    >>> from scipy.signal.windows import gaussian
+    >>> import matplotlib.pyplot as plt
+    ...
+    >>> N, T = 1000, 0.01  # number of samples and sampling interval for 10 s signal
+    >>> t = np.arange(N) * T  # timestamps
+    ...
+    >>> x_lin = chirp(t, f0=6, f1=1, t1=10, method='linear')
+    ...
+    >>> fg0, ax0 = plt.subplots()
+    >>> ax0.set_title(r"Linear Chirp from $f(0)=6\,$Hz to $f(10)=1\,$Hz")
+    >>> ax0.set(xlabel="Time $t$ in Seconds", ylabel=r"Amplitude $x_\text{lin}(t)$")
+    >>> ax0.plot(t, x_lin)
+    >>> plt.show()
+
+    The following four plots each show the short-time Fourier transform of a chirp
+    ranging from 45 Hz to 5 Hz with different values for the parameter `method`
+    (and `vertex_zero`):
+
+    >>> x_qu0 = chirp(t, f0=45, f1=5, t1=N*T, method='quadratic', vertex_zero=True)
+    >>> x_qu1 = chirp(t, f0=45, f1=5, t1=N*T, method='quadratic', vertex_zero=False)
+    >>> x_log = chirp(t, f0=45, f1=5, t1=N*T, method='logarithmic')
+    >>> x_hyp = chirp(t, f0=45, f1=5, t1=N*T, method='hyperbolic')
+    ...
+    >>> win = gaussian(50, std=12, sym=True)
+    >>> SFT = ShortTimeFFT(win, hop=2, fs=1/T, mfft=800, scale_to='magnitude')
+    >>> ts = ("'quadratic', vertex_zero=True", "'quadratic', vertex_zero=False",
+    ...       "'logarithmic'", "'hyperbolic'")
+    >>> fg1, ax1s = plt.subplots(2, 2, sharex='all', sharey='all',
+    ...                          figsize=(6, 5),  layout="constrained")
+    >>> for x_, ax_, t_ in zip([x_qu0, x_qu1, x_log, x_hyp], ax1s.ravel(), ts):
+    ...     aSx = abs(SFT.stft(x_))
+    ...     im_ = ax_.imshow(aSx, origin='lower', aspect='auto', extent=SFT.extent(N),
+    ...                      cmap='plasma')
+    ...     ax_.set_title(t_)
+    ...     if t_ == "'hyperbolic'":
+    ...         fg1.colorbar(im_, ax=ax1s, label='Magnitude $|S_z(t,f)|$')
+    >>> _ = fg1.supxlabel("Time $t$ in Seconds")  # `_ =` is needed to pass doctests
+    >>> _ = fg1.supylabel("Frequency $f$ in Hertz")
+    >>> plt.show()
+
+    Finally, the short-time Fourier transform of a complex-valued linear chirp
+    ranging from -30 Hz to 30 Hz is depicted:
+
+    >>> z_lin = chirp(t, f0=-30, f1=30, t1=N*T, method="linear", complex=True)
+    >>> SFT.fft_mode = 'centered'  # needed to work with complex signals
+    >>> aSz = abs(SFT.stft(z_lin))
+    ...
+    >>> fg2, ax2 = plt.subplots()
+    >>> ax2.set_title(r"Linear Chirp from $-30\,$Hz to $30\,$Hz")
+    >>> ax2.set(xlabel="Time $t$ in Seconds", ylabel="Frequency $f$ in Hertz")
+    >>> im2 = ax2.imshow(aSz, origin='lower', aspect='auto',
+    ...                  extent=SFT.extent(N), cmap='viridis')
+    >>> fg2.colorbar(im2, label='Magnitude $|S_z(t,f)|$')
+    >>> plt.show()
+
+    Note that using negative frequencies makes only sense with complex-valued signals.
+    Furthermore, the magnitude of the complex exponential function is one whereas the
+    magnitude of the real-valued cosine function is only 1/2.
+    """
+    # 'phase' is computed in _chirp_phase, to make testing easier.
+    phase = _chirp_phase(t, f0, t1, f1, method, vertex_zero) + np.deg2rad(phi)
+    return np.exp(1j*phase) if complex else np.cos(phase)
+
+
+def _chirp_phase(t, f0, t1, f1, method='linear', vertex_zero=True):
+    """
+    Calculate the phase used by `chirp` to generate its output.
+
+    See `chirp` for a description of the arguments.
+
+    """
+    t = asarray(t)
+    f0 = float(f0)
+    t1 = float(t1)
+    f1 = float(f1)
+    if method in ['linear', 'lin', 'li']:
+        beta = (f1 - f0) / t1
+        phase = 2 * pi * (f0 * t + 0.5 * beta * t * t)
+
+    elif method in ['quadratic', 'quad', 'q']:
+        beta = (f1 - f0) / (t1 ** 2)
+        if vertex_zero:
+            phase = 2 * pi * (f0 * t + beta * t ** 3 / 3)
+        else:
+            phase = 2 * pi * (f1 * t + beta * ((t1 - t) ** 3 - t1 ** 3) / 3)
+
+    elif method in ['logarithmic', 'log', 'lo']:
+        if f0 * f1 <= 0.0:
+            raise ValueError("For a logarithmic chirp, f0 and f1 must be "
+                             "nonzero and have the same sign.")
+        if f0 == f1:
+            phase = 2 * pi * f0 * t
+        else:
+            beta = t1 / log(f1 / f0)
+            phase = 2 * pi * beta * f0 * (pow(f1 / f0, t / t1) - 1.0)
+
+    elif method in ['hyperbolic', 'hyp']:
+        if f0 == 0 or f1 == 0:
+            raise ValueError("For a hyperbolic chirp, f0 and f1 must be "
+                             "nonzero.")
+        if f0 == f1:
+            # Degenerate case: constant frequency.
+            phase = 2 * pi * f0 * t
+        else:
+            # Singular point: the instantaneous frequency blows up
+            # when t == sing.
+            sing = -f1 * t1 / (f0 - f1)
+            phase = 2 * pi * (-sing * f0) * log(np.abs(1 - t/sing))
+
+    else:
+        raise ValueError("method must be 'linear', 'quadratic', 'logarithmic', "
+                         f"or 'hyperbolic', but a value of {method!r} was given.")
+
+    return phase
+
+
+def sweep_poly(t, poly, phi=0):
+    """
+    Frequency-swept cosine generator, with a time-dependent frequency.
+
+    This function generates a sinusoidal function whose instantaneous
+    frequency varies with time.  The frequency at time `t` is given by
+    the polynomial `poly`.
+
+    Parameters
+    ----------
+    t : ndarray
+        Times at which to evaluate the waveform.
+    poly : 1-D array_like or instance of numpy.poly1d
+        The desired frequency expressed as a polynomial.  If `poly` is
+        a list or ndarray of length n, then the elements of `poly` are
+        the coefficients of the polynomial, and the instantaneous
+        frequency is
+
+          ``f(t) = poly[0]*t**(n-1) + poly[1]*t**(n-2) + ... + poly[n-1]``
+
+        If `poly` is an instance of numpy.poly1d, then the
+        instantaneous frequency is
+
+          ``f(t) = poly(t)``
+
+    phi : float, optional
+        Phase offset, in degrees, Default: 0.
+
+    Returns
+    -------
+    sweep_poly : ndarray
+        A numpy array containing the signal evaluated at `t` with the
+        requested time-varying frequency.  More precisely, the function
+        returns ``cos(phase + (pi/180)*phi)``, where `phase` is the integral
+        (from 0 to t) of ``2 * pi * f(t)``; ``f(t)`` is defined above.
+
+    See Also
+    --------
+    chirp
+
+    Notes
+    -----
+    .. versionadded:: 0.8.0
+
+    If `poly` is a list or ndarray of length `n`, then the elements of
+    `poly` are the coefficients of the polynomial, and the instantaneous
+    frequency is:
+
+        ``f(t) = poly[0]*t**(n-1) + poly[1]*t**(n-2) + ... + poly[n-1]``
+
+    If `poly` is an instance of `numpy.poly1d`, then the instantaneous
+    frequency is:
+
+          ``f(t) = poly(t)``
+
+    Finally, the output `s` is:
+
+        ``cos(phase + (pi/180)*phi)``
+
+    where `phase` is the integral from 0 to `t` of ``2 * pi * f(t)``,
+    ``f(t)`` as defined above.
+
+    Examples
+    --------
+    Compute the waveform with instantaneous frequency::
+
+        f(t) = 0.025*t**3 - 0.36*t**2 + 1.25*t + 2
+
+    over the interval 0 <= t <= 10.
+
+    >>> import numpy as np
+    >>> from scipy.signal import sweep_poly
+    >>> p = np.poly1d([0.025, -0.36, 1.25, 2.0])
+    >>> t = np.linspace(0, 10, 5001)
+    >>> w = sweep_poly(t, p)
+
+    Plot it:
+
+    >>> import matplotlib.pyplot as plt
+    >>> plt.subplot(2, 1, 1)
+    >>> plt.plot(t, w)
+    >>> plt.title("Sweep Poly\\nwith frequency " +
+    ...           "$f(t) = 0.025t^3 - 0.36t^2 + 1.25t + 2$")
+    >>> plt.subplot(2, 1, 2)
+    >>> plt.plot(t, p(t), 'r', label='f(t)')
+    >>> plt.legend()
+    >>> plt.xlabel('t')
+    >>> plt.tight_layout()
+    >>> plt.show()
+
+    """
+    # 'phase' is computed in _sweep_poly_phase, to make testing easier.
+    phase = _sweep_poly_phase(t, poly)
+    # Convert to radians.
+    phi *= pi / 180
+    return cos(phase + phi)
+
+
+def _sweep_poly_phase(t, poly):
+    """
+    Calculate the phase used by sweep_poly to generate its output.
+
+    See `sweep_poly` for a description of the arguments.
+
+    """
+    # polyint handles lists, ndarrays and instances of poly1d automatically.
+    intpoly = polyint(poly)
+    phase = 2 * pi * polyval(intpoly, t)
+    return phase
+
+
+def unit_impulse(shape, idx=None, dtype=float):
+    r"""
+    Unit impulse signal (discrete delta function) or unit basis vector.
+
+    Parameters
+    ----------
+    shape : int or tuple of int
+        Number of samples in the output (1-D), or a tuple that represents the
+        shape of the output (N-D).
+    idx : None or int or tuple of int or 'mid', optional
+        Index at which the value is 1.  If None, defaults to the 0th element.
+        If ``idx='mid'``, the impulse will be centered at ``shape // 2`` in
+        all dimensions.  If an int, the impulse will be at `idx` in all
+        dimensions.
+    dtype : data-type, optional
+        The desired data-type for the array, e.g., ``numpy.int8``.  Default is
+        ``numpy.float64``.
+
+    Returns
+    -------
+    y : ndarray
+        Output array containing an impulse signal.
+
+    Notes
+    -----
+    In digital signal processing literature the unit impulse signal is often
+    represented by the Kronecker delta. [1]_ I.e., a signal :math:`u_k[n]`,
+    which is zero everywhere except being one at the :math:`k`-th sample,
+    can be expressed as
+
+    .. math::
+
+        u_k[n] = \delta[n-k] \equiv \delta_{n,k}\ .
+
+    Furthermore, the unit impulse is frequently interpreted as the discrete-time
+    version of the continuous-time Dirac distribution. [2]_
+
+    References
+    ----------
+    .. [1] "Kronecker delta", *Wikipedia*,
+           https://en.wikipedia.org/wiki/Kronecker_delta#Digital_signal_processing
+    .. [2] "Dirac delta function" *Wikipedia*,
+           https://en.wikipedia.org/wiki/Dirac_delta_function#Relationship_to_the_Kronecker_delta
+
+    .. versionadded:: 0.19.0
+
+    Examples
+    --------
+    An impulse at the 0th element (:math:`\\delta[n]`):
+
+    >>> from scipy import signal
+    >>> signal.unit_impulse(8)
+    array([ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])
+
+    Impulse offset by 2 samples (:math:`\\delta[n-2]`):
+
+    >>> signal.unit_impulse(7, 2)
+    array([ 0.,  0.,  1.,  0.,  0.,  0.,  0.])
+
+    2-dimensional impulse, centered:
+
+    >>> signal.unit_impulse((3, 3), 'mid')
+    array([[ 0.,  0.,  0.],
+           [ 0.,  1.,  0.],
+           [ 0.,  0.,  0.]])
+
+    Impulse at (2, 2), using broadcasting:
+
+    >>> signal.unit_impulse((4, 4), 2)
+    array([[ 0.,  0.,  0.,  0.],
+           [ 0.,  0.,  0.,  0.],
+           [ 0.,  0.,  1.,  0.],
+           [ 0.,  0.,  0.,  0.]])
+
+    Plot the impulse response of a 4th-order Butterworth lowpass filter:
+
+    >>> imp = signal.unit_impulse(100, 'mid')
+    >>> b, a = signal.butter(4, 0.2)
+    >>> response = signal.lfilter(b, a, imp)
+
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> plt.plot(np.arange(-50, 50), imp)
+    >>> plt.plot(np.arange(-50, 50), response)
+    >>> plt.margins(0.1, 0.1)
+    >>> plt.xlabel('Time [samples]')
+    >>> plt.ylabel('Amplitude')
+    >>> plt.grid(True)
+    >>> plt.show()
+
+    """
+    out = zeros(shape, dtype)
+
+    shape = np.atleast_1d(shape)
+
+    if idx is None:
+        idx = (0,) * len(shape)
+    elif idx == 'mid':
+        idx = tuple(shape // 2)
+    elif not hasattr(idx, "__iter__"):
+        idx = (idx,) * len(shape)
+
+    out[idx] = 1
+    return out
diff --git a/shared/package-files/09/09b111e04e9c93f0accf75ab99213ced0c2b958d2bc3a831f00f6f387471dde9/RegisterBackendSelect.cpp b/shared/package-files/09/09b111e04e9c93f0accf75ab99213ced0c2b958d2bc3a831f00f6f387471dde9/RegisterBackendSelect.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..dcb5986ab69eda7c0dc4930603ec3592e5ee0172
--- /dev/null
+++ b/shared/package-files/09/09b111e04e9c93f0accf75ab99213ced0c2b958d2bc3a831f00f6f387471dde9/RegisterBackendSelect.cpp
@@ -0,0 +1,54 @@
+// We register ops with a higher priority dispatch key (BackendSelect) than the usual backend-specific keys (e.g. CPU)
+// which makes calls to the factory functions dispatch to here.
+// We then 'manually' compute a lower-priority to re-dispatch to (e.g. CPU) to get to the eventually correct backend.
+// ${generated_comment}
+
+#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
+#include 
+#include 
+#include 
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include 
+#else
+#include 
+#include 
+
+${ops_headers}
+#endif
+
+namespace at {
+
+namespace {
+
+${backend_select_method_definitions}
+
+bool is_pinned(const Tensor& self, c10::optional device) {
+  // Only CPU tensors can be pinned
+  if (!self.is_cpu()) {
+    return false;
+  }
+  // TODO: fetch scalar type from Tensor? But it doesn't really matter...
+  DispatchKeySet _dk = c10::DispatchKeySet(c10::computeDispatchKey(c10::nullopt, self.layout(), device.value_or(at::kCUDA)));
+  return at::_ops::is_pinned::redispatch(_dk, self, device);
+}
+
+at::Tensor _pin_memory(const Tensor& self, c10::optional device) {
+  TORCH_CHECK(self.device().is_cpu(), "cannot pin '", self.toString(), "' only dense CPU tensors can be pinned");
+  DispatchKeySet _dk = c10::DispatchKeySet(c10::computeDispatchKey(c10::nullopt, self.layout(), device.value_or(at::kCUDA)));
+  if (self.is_nested()) {
+    constexpr auto nested_key_set = c10::DispatchKeySet(
+        {c10::DispatchKey::NestedTensor, c10::DispatchKey::AutogradNestedTensor});
+    _dk = _dk.add(self.key_set() & nested_key_set);
+  }
+  return at::_ops::_pin_memory::redispatch(_dk, self, device);
+}
+
+TORCH_LIBRARY_IMPL(aten, BackendSelect, m) {
+  ${backend_select_function_registrations};
+  m.impl(TORCH_SELECTIVE_NAME("aten::is_pinned"), TORCH_FN(is_pinned));
+  m.impl(TORCH_SELECTIVE_NAME("aten::_pin_memory"), TORCH_FN(_pin_memory));
+}
+
+} // namespace
+} // at
diff --git a/shared/package-files/09/09b19e721560cd12246402d44e53c1ede5194b8398ef60822f02cab86ccf8d1f/METADATA b/shared/package-files/09/09b19e721560cd12246402d44e53c1ede5194b8398ef60822f02cab86ccf8d1f/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..4e47f1317cccccf9d38be38065fd2fa3cdb3c5dd
--- /dev/null
+++ b/shared/package-files/09/09b19e721560cd12246402d44e53c1ede5194b8398ef60822f02cab86ccf8d1f/METADATA
@@ -0,0 +1,645 @@
+Metadata-Version: 2.5
+Name: threadpoolctl
+Version: 3.7.0
+Summary: threadpoolctl
+Author-email: Thomas Moreau 
+Requires-Python: >=3.9
+Description-Content-Type: text/markdown
+License-Expression: BSD-3-Clause
+Classifier: Intended Audience :: Developers
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+License-File: LICENSE
+Project-URL: homepage, https://github.com/joblib/threadpoolctl
+Import-Name: threadpoolctl
+
+# Thread-pool Controls [![Build Status](https://github.com/joblib/threadpoolctl/actions/workflows/test.yml/badge.svg?branch=master)](https://github.com/joblib/threadpoolctl/actions?query=branch%3Amaster) [![codecov](https://codecov.io/gh/joblib/threadpoolctl/branch/master/graph/badge.svg)](https://codecov.io/gh/joblib/threadpoolctl)
+
+Python helpers to limit the number of threads used in the
+threadpool-backed of common native libraries used for scientific
+computing and data science (e.g. BLAS and OpenMP).
+
+Fine control of the underlying thread-pool size can be useful in
+workloads that involve nested parallelism so as to mitigate
+oversubscription issues.
+
+Note that "limiting the number of threads" in practice involves a variety of
+potential semantics depending on the underlying third-party library; see the
+section on [semantics](#semantics) below.
+
+## Installation
+
+- For users, install the last published version from PyPI:
+
+  ```bash
+  pip install threadpoolctl
+  ```
+
+- For contributors, install from the source repository in developer
+  mode:
+
+  ```bash
+  pip install -r dev-requirements.txt
+  flit install --symlink
+  ```
+
+  then you run the tests with pytest:
+
+  ```bash
+  pytest
+  ```
+
+## Usage: Introspection and debugging
+
+### Command Line Interface
+
+Get a JSON description of thread-pools initialized when importing python
+packages such as numpy or scipy for instance:
+
+```
+python -m threadpoolctl -i numpy scipy.linalg
+[
+  {
+    "filepath": "/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so",
+    "prefix": "libmkl_rt",
+    "user_api": "blas",
+    "internal_api": "mkl",
+    "version": "2019.0.4",
+    "num_threads": 2,
+    "threading_layer": "intel"
+  },
+  {
+    "filepath": "/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so",
+    "prefix": "libiomp",
+    "user_api": "openmp",
+    "internal_api": "openmp",
+    "version": null,
+    "num_threads": 4
+  }
+]
+```
+
+The JSON information is written on STDOUT. If some of the packages are missing,
+a warning message is displayed on STDERR.
+
+### Python Runtime Programmatic Introspection
+
+Introspect the current state of the threadpool-enabled runtime libraries
+that are loaded when importing Python packages:
+
+```python
+>>> from threadpoolctl import threadpool_info
+>>> from pprint import pprint
+>>> pprint(threadpool_info())
+[]
+
+>>> import numpy
+>>> pprint(threadpool_info())
+[{'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so',
+  'internal_api': 'mkl',
+  'num_threads': 2,
+  'prefix': 'libmkl_rt',
+  'threading_layer': 'intel',
+  'user_api': 'blas',
+  'version': '2019.0.4'},
+ {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libiomp',
+  'user_api': 'openmp',
+  'version': None}]
+
+>>> import xgboost
+>>> pprint(threadpool_info())
+[{'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libmkl_rt.so',
+  'internal_api': 'mkl',
+  'num_threads': 2,
+  'prefix': 'libmkl_rt',
+  'threading_layer': 'intel',
+  'user_api': 'blas',
+  'version': '2019.0.4'},
+ {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libiomp5.so',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libiomp',
+  'user_api': 'openmp',
+  'version': None},
+ {'filepath': '/home/ogrisel/miniconda3/envs/tmp/lib/libgomp.so.1.0.0',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libgomp',
+  'user_api': 'openmp',
+  'version': None}]
+```
+
+In the above example, `numpy` was installed from the default anaconda channel and comes
+with MKL and its Intel OpenMP (`libiomp5`) implementation while `xgboost` was installed
+from pypi.org and links against GNU OpenMP (`libgomp`) so both OpenMP runtimes are
+loaded in the same Python program.
+
+The state of these libraries is also accessible through the object oriented API:
+
+```python
+>>> from threadpoolctl import ThreadpoolController, threadpool_info
+>>> from pprint import pprint
+>>> import numpy
+>>> controller = ThreadpoolController()
+>>> pprint(controller.info())
+[{'architecture': 'Haswell',
+  'filepath': '/home/jeremie/miniconda/envs/dev/lib/libopenblasp-r0.3.17.so',
+  'internal_api': 'openblas',
+  'num_threads': 4,
+  'prefix': 'libopenblas',
+  'threading_layer': 'pthreads',
+  'user_api': 'blas',
+  'version': '0.3.17'}]
+
+>>> controller.info() == threadpool_info()
+True
+```
+
+## Usage when not using Python threads: Restricting Controlled Library Thread Pool Sizes
+
+There are two scenarios in which you might want to use `threadpoolctl`; each
+requires you to use different APIs.
+
+1. You do not expect to use any Python threads, so all the work will be started
+   directly from the main thread in the process. This is a simple case
+   where we can globally set thread limits.
+2. You will be parallelizing work using a Python thread pool, and your goal is
+   therefore to limit controlled libraries' thread pool sizes when
+   concurrently called from Python threads. This case is a bit more
+   complex to handle properly and requires a bit more verbose code.
+
+This section will cover the former case, and the latter is covered in the next
+usage section.
+
+### Setting the Maximum Size of Thread-Pools
+
+Control the number of threads used by the underlying runtime libraries
+in specific sections of your Python program:
+
+```python
+>>> from threadpoolctl import threadpool_limits
+>>> import numpy as np
+
+>>> with threadpool_limits(limits=1, user_api='blas'):
+...     # In this block, calls to blas implementation (like openblas or MKL)
+...     # will be limited to use only one thread. They can thus be used jointly
+...     # with thread-parallelism.
+...     a = np.random.randn(1000, 1000)
+...     a_squared = a @ a
+```
+
+The threadpools can also be controlled via the object oriented API, which is especially
+useful to avoid searching through all the loaded shared libraries each time. It will
+however not act on libraries loaded after the instantiation of the
+`ThreadpoolController`:
+
+```python
+>>> from threadpoolctl import ThreadpoolController
+>>> import numpy as np
+>>> controller = ThreadpoolController()
+
+>>> with controller.limit(limits=1, user_api='blas'):
+...     a = np.random.randn(1000, 1000)
+...     a_squared = a @ a
+```
+
+### Restricting the Limits to the Scope of a Function
+
+`threadpool_limits` and `ThreadpoolController` can also be used as decorators to set
+the maximum number of threads used by the supported libraries at a function level. The
+decorators are accessible through their `wrap` method.
+
+```python
+>>> from threadpoolctl import ThreadpoolController, threadpool_limits
+>>> import numpy as np
+>>> controller = ThreadpoolController()
+
+>>> @controller.wrap(limits=1, user_api='blas')
+... # or @threadpool_limits.wrap(limits=1, user_api='blas')
+... def my_func():
+...     # Inside this function, calls to blas implementation (like openblas or MKL)
+...     # will be limited to use only one thread.
+...     a = np.random.randn(1000, 1000)
+...     a_squared = a @ a
+...
+```
+
+## Usage for Python threads: Restricting Controlled Library Thread Pool Sizes
+
+This section covers APIs to use when you will be using Python thread pools to parallelize work.
+The usage is more complicated than one might expect because the underlying APIs have a variety of different semantics, as [explained later in the docs](#semantics).
+
+### Setting the Maximum Size of Thread-Pools, When Python Thread Pools Are Used
+
+Limiting thread pool size in controlled libraries requires a two-step process.
+**Importantly, each Python worker thread must also call a method to limit
+controlled libraries in that thread.** With Python's
+`concurrent.futures.ThreadPoolExecutor`, you can do so by passing in an
+initializer function that will get called on thread startup.
+
+```python
+from threadpoolctl import threadpool_limits
+from concurrent.futures import ThreadPoolExecutor
+
+# This top-level limiter doesn't actually change the limits initially; it is
+# there to ensure the limits are reset _after_ the Python thread pool is done.
+# This is necessary because some underlying limiting APIs operate on a
+# process-wide basis.
+with threadpool_limits():
+    # Make sure each Python worker thread also calls threadpool_limits(). If
+    # you're using another thread pool class, you will need to do so some other
+    # way.
+    with ThreadPoolExecutor(4, initializer=lambda: threadpool_limits(limits=1)) as pool:
+        # ... run some BLAS-using code in the thread pool ...
+        pool.map(somefunc, someargs)
+```
+
+Whenever `threadpool_limits` is called, it needs to do some work (inspecting and getting access to third-party shared libraries) that can take some time.
+To prevent the performance cost of doing this work every time, you can reuse a
+`ThreadpoolController` object:
+
+```python
+from threadpoolctl import ThreadpoolController
+
+# This won't have any side-effects:
+CONTROLLER = ThreadpoolController()
+
+with (
+    CONTROLLER.limit(),
+    ThreadPoolExecutor(4, initializer=lambda: CONTROLLER.limit(limits=1)) as pool,
+):
+    # ... run some BLAS-using code in the thread pool ...
+    pool.map(somefunc, someargs)
+
+# Later...
+with (
+    CONTROLLER.limit(),
+    ThreadPoolExecutor(4, initializer=lambda: CONTROLLER.limit(limits=2)) as pool,
+):
+    # ... run some BLAS-using code in the thread pool ...
+    pool.map(somefunc, someargs)
+```
+
+You can also operate without a context manager:
+
+```python
+from threadpoolctl import ThreadpoolController
+
+CONTROLLER = ThreadpoolController()
+try:
+    limiter = CONTROLLER.limit()
+    with ThreadPoolExecutor(
+            4, initializer=lambda: CONTROLLER.limit(limits=1)) as pool:
+        # ... run some BLAS-using code in the thread pool ...
+        pool.map(somefunc, someargs)
+finally:
+    limiter.restore_original_limits()
+
+```
+
+### Switching Back And Forth Between Main Thread and Python Threads
+
+Unfortunately not all controlled libraries providing limiting APIs that are
+thread-specific, as detailed in the section on [semantics](#semantics) below.
+Limiting some libraries' thread pool sizes can therefore impact the whole
+process. This makes switching back and forth between running code that
+uses these libraries in Python threads and running it in the main thread a bit
+more complex: you need to set the limits each time you switch back and forth.
+
+Let's say your computer has 4 cores, and you're using some OpenMP API.
+
+```python
+POOL = ThreadPoolExecutor(4)
+CONTROLLER = ThreadpoolController()
+
+# 1. Run some work in a Python thread pool (OpenMP effectively disabled).
+with CONTROLLER.limit(limits=1):
+
+    def limit_then_do_work(*args, **kwargs):
+        # Set a limit on OpenMP in the current thread:
+        CONTROLLER.limit(limits=1)
+        # Do the actual work:
+        return do_real_work_with_openmp(*args, **kwargs)
+
+    results = POOL.map(limit_then_do_work, args)
+
+
+# 2. Run some work with 4-threads OpenMP parallelism:
+with CONTROLLER.limit(limits=4):
+    results2 = do_more_work_with_openmp(results)
+
+
+# 3. Nest some OpenMP parallelism under Python-level parallelism:
+with CONTROLLER.limit(limits=2):
+
+    def limit_then_do_work2(*args, **kwargs):
+        CONTROLLER.limit(limits=2)
+        return do_even_more_real_work_with_openmp(*args, **kwargs)
+
+    results3 = POOL.map(limit_then_do_work2, results2)
+```
+
+## Usage: Additional APIs and details
+
+### Switching the FlexiBLAS backend
+
+`FlexiBLAS` is a BLAS wrapper for which the BLAS backend can be switched at runtime.
+`threadpoolctl` exposes python bindings for this feature. Here's an example but note
+that this part of the API is experimental and subject to change without deprecation:
+
+```python
+>>> from threadpoolctl import ThreadpoolController
+>>> import numpy as np
+>>> controller = ThreadpoolController()
+
+>>> controller.info()
+[{'user_api': 'blas',
+  'internal_api': 'flexiblas',
+  'num_threads': 1,
+  'prefix': 'libflexiblas',
+  'filepath': '/usr/local/lib/libflexiblas.so.3.3',
+  'version': '3.3.1',
+  'available_backends': ['NETLIB', 'OPENBLASPTHREAD', 'ATLAS'],
+  'loaded_backends': ['NETLIB'],
+  'current_backend': 'NETLIB'}]
+
+# Retrieve the flexiblas controller
+>>> flexiblas_ct = controller.select(internal_api="flexiblas").lib_controllers[0]
+
+# Switch the backend with one predefined at build time (listed in "available_backends")
+>>> flexiblas_ct.switch_backend("OPENBLASPTHREAD")
+>>> controller.info()
+[{'user_api': 'blas',
+  'internal_api': 'flexiblas',
+  'num_threads': 4,
+  'prefix': 'libflexiblas',
+  'filepath': '/usr/local/lib/libflexiblas.so.3.3',
+  'version': '3.3.1',
+  'available_backends': ['NETLIB', 'OPENBLASPTHREAD', 'ATLAS'],
+  'loaded_backends': ['NETLIB', 'OPENBLASPTHREAD'],
+  'current_backend': 'OPENBLASPTHREAD'},
+ {'user_api': 'blas',
+  'internal_api': 'openblas',
+  'num_threads': 4,
+  'prefix': 'libopenblas',
+  'filepath': '/usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so',
+  'version': '0.3.8',
+  'threading_layer': 'pthreads',
+  'architecture': 'Haswell'}]
+
+# It's also possible to directly give the path to a shared library
+>>> flexiblas_controller.switch_backend("/home/jeremie/miniforge/envs/flexiblas_threadpoolctl/lib/libmkl_rt.so")
+>>> controller.info()
+[{'user_api': 'blas',
+  'internal_api': 'flexiblas',
+  'num_threads': 2,
+  'prefix': 'libflexiblas',
+  'filepath': '/usr/local/lib/libflexiblas.so.3.3',
+  'version': '3.3.1',
+  'available_backends': ['NETLIB', 'OPENBLASPTHREAD', 'ATLAS'],
+  'loaded_backends': ['NETLIB',
+   'OPENBLASPTHREAD',
+   '/home/jeremie/miniforge/envs/flexiblas_threadpoolctl/lib/libmkl_rt.so'],
+  'current_backend': '/home/jeremie/miniforge/envs/flexiblas_threadpoolctl/lib/libmkl_rt.so'},
+ {'user_api': 'openmp',
+  'internal_api': 'openmp',
+  'num_threads': 4,
+  'prefix': 'libomp',
+  'filepath': '/home/jeremie/miniforge/envs/flexiblas_threadpoolctl/lib/libomp.so',
+  'version': None},
+ {'user_api': 'blas',
+  'internal_api': 'openblas',
+  'num_threads': 4,
+  'prefix': 'libopenblas',
+  'filepath': '/usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so',
+  'version': '0.3.8',
+  'threading_layer': 'pthreads',
+  'architecture': 'Haswell'},
+ {'user_api': 'blas',
+  'internal_api': 'mkl',
+  'num_threads': 2,
+  'prefix': 'libmkl_rt',
+  'filepath': '/home/jeremie/miniforge/envs/flexiblas_threadpoolctl/lib/libmkl_rt.so.2',
+  'version': '2024.0-Product',
+  'threading_layer': 'gnu'}]
+```
+
+You can observe that the previously linked OpenBLAS shared object stays loaded by
+the Python program indefinitely, but FlexiBLAS itself no longer delegates BLAS calls
+to OpenBLAS as indicated by the `current_backend` attribute.
+
+### Writing a custom library controller
+
+Currently, `threadpoolctl` has support for `OpenMP` and the main `BLAS` libraries.
+However it can also be used to control the threadpool of other native libraries,
+provided that they expose an API to get and set the limit on the number of threads.
+For that, one must implement a controller for this library and register it to
+`threadpoolctl`.
+
+A custom controller must be a subclass of the `LibController` class and implement
+the attributes and methods described in the docstring of `LibController`. Then this
+new controller class must be registered using the `threadpoolctl.register` function.
+An complete example can be found [here](
+  https://github.com/joblib/threadpoolctl/blob/master/tests/_pyMylib/__init__.py).
+
+### Sequential BLAS within OpenMP parallel region
+
+When one wants to have sequential BLAS calls within an OpenMP parallel region, it's
+safer to set `limits="sequential_blas_under_openmp"` since setting `limits=1` and
+`user_api="blas"` might not lead to the expected behavior in some configurations
+(e.g. OpenBLAS with the OpenMP threading layer
+https://github.com/xianyi/OpenBLAS/issues/2985).
+
+### Known Limitations
+
+- `threadpool_limits` can fail to limit the number of inner threads when nesting
+  parallel loops managed by distinct OpenMP runtime implementations (for instance
+  libgomp from GCC and libomp from clang/llvm or libiomp from ICC).
+
+  See the `test_openmp_nesting` function in [tests/test_threadpoolctl.py](
+  https://github.com/joblib/threadpoolctl/blob/master/tests/test_threadpoolctl.py)
+  for an example. More information can be found at:
+  https://github.com/jeremiedbb/Nested_OpenMP
+
+  Note however that this problem does not happen when `threadpool_limits` is
+  used to limit the number of threads used internally by BLAS calls that are
+  themselves nested under OpenMP parallel loops. `threadpool_limits` works as
+  expected, even if the inner BLAS implementation relies on a distinct OpenMP
+  implementation.
+
+- Using Intel OpenMP (ICC) and LLVM OpenMP (clang) in the same Python program
+  under Linux is known to cause problems. See the following guide for more details
+  and workarounds:
+  https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md
+
+- Setting the maximum number of threads of the OpenMP and BLAS libraries has
+  inconsistent scope and semantics (thread-local vs process-wide) depending on
+  the underlying library. For more details see
+  https://github.com/joblib/threadpoolctl/issues/208
+
+  For example, if you're using OpenMP with libgomp (gcc) or libomp (clang), the
+  setting is thread-local and sets how many OpenMP threads will be started in
+  the current thread. On the other hand, with OpenBLAS with pthreads backend or
+  on Windows, the setting is process-wide and impacts the size of a process-wide
+  thread pool shared across all threads in the process.
+
+## 
 Semantics of thread limiting 

+
+Setting the number of threads may seem like a simple operation, but in practice
+it can do quite different things depending on the underlying third-party library
+being limited.
+
+### Kind of thread pool
+
+* In some libraries, there is a shared process-wide thread pool. Setting the
+  number of threads changes the size of this shared pool.
+* In other libraries, there is a thread pool per calling thread. So each Python
+  thread gets its own personal thread pool from the third-party library.
+
+To give a concrete example: if you have 10 cores, and you're using OpenBLAS with
+the `pthreads` backend on Linux, by default there is a single 10-worker pool of
+threads created by OpenBLAS. If you start 10 Python threads, each calling BLAS
+routines, all those Python threads will feed into that single 10-thread pool.
+In total, you will have 20 threads running (10 Python, 10 OpenBLAS).
+
+On the other hand, if you use MKL, each Python thread gets its own individual
+pool of worker threads from MKL, so if each Python threads calls a BLAS routine
+in MKL, you will get 10×10 = 100 MKL threads!
+
+### Scope of limiting
+
+If you have a third-party library that creates a thread pool per calling thread,
+there is another question about what limiting the number of threads means: which
+threads are affected by the limiting API?
+
+* **All threads in the process:** In this case, setting the limits changes it
+  for all threads in the process. So if you limit from the main Python thread,
+  all other Python threads will have the same limit.
+* **Current thread only:** Only the current thread's thread pool size will be
+  limited.
+
+MKL for example has two APIs, covering both cases,
+[`mkl_set_num_threads()`](https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2026-0/mkl-set-num-threads.html)
+and [`mkl_set_num_threads_local()`](https://www.intel.com/content/www/us/en/docs/onemkl/developer-reference-c/2026-0/mkl-set-num-threads-local.html) respectively.
+
+### `threadpoolctl`'s policy for thread limiting
+
+When there is a choice between different APIs, `threadpoolctl` will prefer APIs
+that:
+
+1. Have a per-thread worker pool (so each Python thread gets its own pool from
+   the underlying third-party library)
+2. Only affect the current thread.
+
+Current libraries where `threadpoolctl` explicitly makes this choice are:
+
+* MKL, for all threading backends.
+* OpenBLAS (v0.3.34 or later) when using the OpenMP backend, on Linux and macOS.
+
+When using OpenMP, this is also the default on Linux and macOS. On Windows
+OpenMP has a per-thread worker pool but the limiting API affects all threads in
+the process, not just the current one.
+
+### Checking the behavior of your installed libraries
+
+When you run `python -m threadpoolctl` it will include the scope of the API
+limit in the `"thread_limit_scope"` field, with the value of `"process"`
+indicating the API affects the whole process and `"current_thread"` indicating a
+per-thread thread pool. Information about the kind of limiting in the former
+case (shared process-wide pool, or per-thread pool) is not included.
+
+Here is example output for OpenBLAS with pthreads:
+
+```shell-session
+$ python -m threadpoolctl -i numpy
+[
+  {
+    "user_api": "blas",
+    "internal_api": "openblas",
+    "num_threads": 12,
+    "prefix": "libscipy_openblas",
+    "version": "0.3.33.112.0",
+    "threading_layer": "pthreads",
+    "architecture": "Haswell",
+    "thread_limit_scope": "process"
+  }
+]
+```
+
+You can also use another script to empirically determine both the kind and scope of the API. From a checkout of the [`threadpoolctl` GitHub repo](https://github.com/joblib/threadpoolctl):
+
+```shell-session
+$ python -m tests.empirical_scope_observation blas
+== Observed behavior: blas ==
+10x12 Python threads each running a parallel workload with a 2 thread limit.
+Maximum number of observed threads (includes Python and blas threads): 53
+Presumed API scope: process-wide shared thread pool
+```
+
+You can also call this script with an `openmp` argument instead of `blas`.
+
+## Maintainers
+
+To make a release:
+
+- Create a PR to bump the version number (`__version__`) in `threadpoolctl.py` and
+  update the release date in `CHANGES.md`.
+
+- Merge the PR and check that the `Publish threadpoolctl distribution to TestPyPI` job
+  of the `publish-to-pypi.yml` workflow successfully uploaded the wheel and source
+  distribution to Test PyPI.
+
+- If everything is fine create a tag for the release and push it to github:
+
+  ```bash
+  git tag -a X.Y.Z
+  git push git@github.com:joblib/threadpoolctl.git X.Y.Z
+  ```
+
+- Check that the `Publish threadpoolctl distribution to PyPI` job of the
+  `publish-to-pypi.yml` workflow successfully uploaded the wheel and source distribution
+  to PyPI this time.
+
+- Create a PR for the release on the [conda-forge feedstock](https://github.com/conda-forge/threadpoolctl-feedstock) (or wait for the bot to make it).
+
+- Publish the release on github.
+
+If for some reason the steps above can't be achieved and a munual upload of the wheel
+and source distribution is needed:
+
+- Build the distribution archives:
+
+  ```bash
+  pip install flit
+  flit build
+  ```
+
+- Upload the wheels and source distribution to PyPI using flit. Since PyPI doesn't
+  allow password authentication anymore, the username needs to be changed to the
+  generic name `__token__`:
+
+  ```bash
+  FLIT_USERNAME=__token__ flit publish
+  ```
+
+  and a PyPI token has to be passed in place of the password.
+
+### Credits
+
+The initial dynamic library introspection code was written by @anton-malakhov
+for the smp package available at https://github.com/IntelPython/smp .
+
+threadpoolctl extends this for other operating systems. Contrary to smp,
+threadpoolctl does not attempt to limit the size of Python multiprocessing
+pools (threads or processes) or set operating system-level CPU affinity
+constraints: threadpoolctl only interacts with native libraries via their
+public runtime APIs.
+
diff --git a/shared/package-files/09/09b2c20d8079e01f98a821a0101b032e40b4dd25beb342a2b5886c6c837827cf/harmonic.py b/shared/package-files/09/09b2c20d8079e01f98a821a0101b032e40b4dd25beb342a2b5886c6c837827cf/harmonic.py
new file mode 100644
index 0000000000000000000000000000000000000000..96e995d9649143f9255dca57761b4ee23fca0119
--- /dev/null
+++ b/shared/package-files/09/09b2c20d8079e01f98a821a0101b032e40b4dd25beb342a2b5886c6c837827cf/harmonic.py
@@ -0,0 +1,79 @@
+"""Functions for computing the harmonic centrality of a graph."""
+from functools import partial
+
+import networkx as nx
+
+__all__ = ["harmonic_centrality"]
+
+
+def harmonic_centrality(G, nbunch=None, distance=None, sources=None):
+    r"""Compute harmonic centrality for nodes.
+
+    Harmonic centrality [1]_ of a node `u` is the sum of the reciprocal
+    of the shortest path distances from all other nodes to `u`
+
+    .. math::
+
+        C(u) = \sum_{v \neq u} \frac{1}{d(v, u)}
+
+    where `d(v, u)` is the shortest-path distance between `v` and `u`.
+
+    If `sources` is given as an argument, the returned harmonic centrality
+    values are calculated as the sum of the reciprocals of the shortest
+    path distances from the nodes specified in `sources` to `u` instead
+    of from all nodes to `u`.
+
+    Notice that higher values indicate higher centrality.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX graph
+
+    nbunch : container (default: all nodes in G)
+      Container of nodes for which harmonic centrality values are calculated.
+
+    sources : container (default: all nodes in G)
+      Container of nodes `v` over which reciprocal distances are computed.
+      Nodes not in `G` are silently ignored.
+
+    distance : edge attribute key, optional (default=None)
+      Use the specified edge attribute as the edge distance in shortest
+      path calculations.  If `None`, then each edge will have distance equal to 1.
+
+    Returns
+    -------
+    nodes : dictionary
+      Dictionary of nodes with harmonic centrality as the value.
+
+    See Also
+    --------
+    betweenness_centrality, load_centrality, eigenvector_centrality,
+    degree_centrality, closeness_centrality
+
+    Notes
+    -----
+    If the 'distance' keyword is set to an edge attribute key then the
+    shortest-path length will be computed using Dijkstra's algorithm with
+    that edge attribute as the edge weight.
+
+    References
+    ----------
+    .. [1] Boldi, Paolo, and Sebastiano Vigna. "Axioms for centrality."
+           Internet Mathematics 10.3-4 (2014): 222-262.
+    """
+
+    nbunch = set(G.nbunch_iter(nbunch)) if nbunch is not None else set(G.nodes)
+    sources = set(G.nbunch_iter(sources)) if sources is not None else G.nodes
+
+    spl = partial(nx.shortest_path_length, G, weight=distance)
+    centrality = {u: 0 for u in nbunch}
+    for v in sources:
+        dist = spl(v)
+        for u in nbunch.intersection(dist):
+            d = dist[u]
+            if d == 0:  # handle u == v and edges with 0 weight
+                continue
+            centrality[u] += 1 / d
+
+    return centrality
diff --git a/shared/package-files/09/09b4392f83f2a63fbb7f1c0ed1730bd45b9210b73dbfec667309fb39c0c5cf4e/test_colors.py b/shared/package-files/09/09b4392f83f2a63fbb7f1c0ed1730bd45b9210b73dbfec667309fb39c0c5cf4e/test_colors.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ca9c0f77858641610bdbee01502d6783bb65141
--- /dev/null
+++ b/shared/package-files/09/09b4392f83f2a63fbb7f1c0ed1730bd45b9210b73dbfec667309fb39c0c5cf4e/test_colors.py
@@ -0,0 +1,1747 @@
+import copy
+import itertools
+import unittest.mock
+
+from io import BytesIO
+import numpy as np
+from PIL import Image
+import pytest
+import base64
+
+from numpy.testing import assert_array_equal, assert_array_almost_equal
+
+from matplotlib import cbook, cm
+import matplotlib
+import matplotlib as mpl
+import matplotlib.colors as mcolors
+import matplotlib.colorbar as mcolorbar
+import matplotlib.colorizer as mcolorizer
+import matplotlib.pyplot as plt
+import matplotlib.scale as mscale
+from matplotlib.rcsetup import cycler
+from matplotlib.testing.decorators import image_comparison, check_figures_equal
+from matplotlib.colors import is_color_like, to_rgba_array
+
+
+@pytest.mark.parametrize('N, result', [
+    (5, [1, .6, .2, .1, 0]),
+    (2, [1, 0]),
+    (1, [0]),
+])
+def test_create_lookup_table(N, result):
+    data = [(0.0, 1.0, 1.0), (0.5, 0.2, 0.2), (1.0, 0.0, 0.0)]
+    assert_array_almost_equal(mcolors._create_lookup_table(N, data), result)
+
+
+@pytest.mark.parametrize("dtype", [np.uint8, int, np.float16, float])
+def test_index_dtype(dtype):
+    # We use subtraction in the indexing, so need to verify that uint8 works
+    cm = mpl.colormaps["viridis"]
+    assert_array_equal(cm(dtype(0)), cm(0))
+
+
+def test_resampled():
+    """
+    GitHub issue #6025 pointed to incorrect ListedColormap.resampled;
+    here we test the method for LinearSegmentedColormap as well.
+    """
+    n = 101
+    colorlist = np.empty((n, 4), float)
+    colorlist[:, 0] = np.linspace(0, 1, n)
+    colorlist[:, 1] = 0.2
+    colorlist[:, 2] = np.linspace(1, 0, n)
+    colorlist[:, 3] = 0.7
+    lsc = mcolors.LinearSegmentedColormap.from_list('lsc', colorlist)
+    lc = mcolors.ListedColormap(colorlist)
+    # Set some bad values for testing too
+    for cmap in [lsc, lc]:
+        cmap.set_under('r')
+        cmap.set_over('g')
+        cmap.set_bad('b')
+    lsc3 = lsc.resampled(3)
+    lc3 = lc.resampled(3)
+    expected = np.array([[0.0, 0.2, 1.0, 0.7],
+                         [0.5, 0.2, 0.5, 0.7],
+                         [1.0, 0.2, 0.0, 0.7]], float)
+    assert_array_almost_equal(lsc3([0, 0.5, 1]), expected)
+    assert_array_almost_equal(lc3([0, 0.5, 1]), expected)
+    # Test over/under was copied properly
+    assert_array_almost_equal(lsc(np.inf), lsc3(np.inf))
+    assert_array_almost_equal(lsc(-np.inf), lsc3(-np.inf))
+    assert_array_almost_equal(lsc(np.nan), lsc3(np.nan))
+    assert_array_almost_equal(lc(np.inf), lc3(np.inf))
+    assert_array_almost_equal(lc(-np.inf), lc3(-np.inf))
+    assert_array_almost_equal(lc(np.nan), lc3(np.nan))
+
+
+def test_colormaps_get_cmap():
+    cr = mpl.colormaps
+
+    # check str, and Colormap pass
+    assert cr.get_cmap('plasma') == cr["plasma"]
+    assert cr.get_cmap(cr["magma"]) == cr["magma"]
+
+    # check default
+    assert cr.get_cmap(None) == cr[mpl.rcParams['image.cmap']]
+
+    # check ValueError on bad name
+    bad_cmap = 'AardvarksAreAwkward'
+    with pytest.raises(ValueError, match=bad_cmap):
+        cr.get_cmap(bad_cmap)
+
+    # check TypeError on bad type
+    with pytest.raises(TypeError, match='object'):
+        cr.get_cmap(object())
+
+
+def test_double_register_builtin_cmap():
+    name = "viridis"
+    match = f"Re-registering the builtin cmap {name!r}."
+    with pytest.raises(ValueError, match=match):
+        matplotlib.colormaps.register(mpl.colormaps[name], name=name, force=True)
+
+
+def test_colormap_copy():
+    cmap = plt.cm.Reds
+    copied_cmap = copy.copy(cmap)
+    with np.errstate(invalid='ignore'):
+        ret1 = copied_cmap([-1, 0, .5, 1, np.nan, np.inf])
+    cmap2 = copy.copy(copied_cmap)
+    cmap2.set_bad('g')
+    with np.errstate(invalid='ignore'):
+        ret2 = copied_cmap([-1, 0, .5, 1, np.nan, np.inf])
+    assert_array_equal(ret1, ret2)
+    # again with the .copy method:
+    cmap = plt.cm.Reds
+    copied_cmap = cmap.copy()
+    with np.errstate(invalid='ignore'):
+        ret1 = copied_cmap([-1, 0, .5, 1, np.nan, np.inf])
+    cmap2 = copy.copy(copied_cmap)
+    cmap2.set_bad('g')
+    with np.errstate(invalid='ignore'):
+        ret2 = copied_cmap([-1, 0, .5, 1, np.nan, np.inf])
+    assert_array_equal(ret1, ret2)
+
+
+def test_colormap_equals():
+    cmap = mpl.colormaps["plasma"]
+    cm_copy = cmap.copy()
+    # different object id's
+    assert cm_copy is not cmap
+    # But the same data should be equal
+    assert cm_copy == cmap
+    # Change the copy
+    cm_copy.set_bad('y')
+    assert cm_copy != cmap
+    # Make sure we can compare different sizes without failure
+    cm_copy._lut = cm_copy._lut[:10, :]
+    assert cm_copy != cmap
+    # Test different names are equal if the lookup table is the same
+    cm_copy = cmap.copy()
+    cm_copy.name = "Test"
+    assert cm_copy == cmap
+    # Test colorbar extends
+    cm_copy = cmap.copy()
+    cm_copy.colorbar_extend = not cmap.colorbar_extend
+    assert cm_copy != cmap
+
+
+def test_colormap_endian():
+    """
+    GitHub issue #1005: a bug in putmask caused erroneous
+    mapping of 1.0 when input from a non-native-byteorder
+    array.
+    """
+    cmap = mpl.colormaps["jet"]
+    # Test under, over, and invalid along with values 0 and 1.
+    a = [-0.5, 0, 0.5, 1, 1.5, np.nan]
+    for dt in ["f2", "f4", "f8"]:
+        anative = np.ma.masked_invalid(np.array(a, dtype=dt))
+        aforeign = anative.byteswap().view(anative.dtype.newbyteorder())
+        assert_array_equal(cmap(anative), cmap(aforeign))
+
+
+def test_colormap_invalid():
+    """
+    GitHub issue #9892: Handling of nan's were getting mapped to under
+    rather than bad. This tests to make sure all invalid values
+    (-inf, nan, inf) are mapped respectively to (under, bad, over).
+    """
+    cmap = mpl.colormaps["plasma"]
+    x = np.array([-np.inf, -1, 0, np.nan, .7, 2, np.inf])
+
+    expected = np.array([[0.050383, 0.029803, 0.527975, 1.],
+                         [0.050383, 0.029803, 0.527975, 1.],
+                         [0.050383, 0.029803, 0.527975, 1.],
+                         [0.,       0.,       0.,       0.],
+                         [0.949217, 0.517763, 0.295662, 1.],
+                         [0.940015, 0.975158, 0.131326, 1.],
+                         [0.940015, 0.975158, 0.131326, 1.]])
+    assert_array_equal(cmap(x), expected)
+
+    # Test masked representation (-inf, inf) are now masked
+    expected = np.array([[0.,       0.,       0.,       0.],
+                         [0.050383, 0.029803, 0.527975, 1.],
+                         [0.050383, 0.029803, 0.527975, 1.],
+                         [0.,       0.,       0.,       0.],
+                         [0.949217, 0.517763, 0.295662, 1.],
+                         [0.940015, 0.975158, 0.131326, 1.],
+                         [0.,       0.,       0.,       0.]])
+    assert_array_equal(cmap(np.ma.masked_invalid(x)), expected)
+
+    # Test scalar representations
+    assert_array_equal(cmap(-np.inf), cmap(0))
+    assert_array_equal(cmap(np.inf), cmap(1.0))
+    assert_array_equal(cmap(np.nan), [0., 0., 0., 0.])
+
+
+def test_colormap_return_types():
+    """
+    Make sure that tuples are returned for scalar input and
+    that the proper shapes are returned for ndarrays.
+    """
+    cmap = mpl.colormaps["plasma"]
+    # Test return types and shapes
+    # scalar input needs to return a tuple of length 4
+    assert isinstance(cmap(0.5), tuple)
+    assert len(cmap(0.5)) == 4
+
+    # input array returns an ndarray of shape x.shape + (4,)
+    x = np.ones(4)
+    assert cmap(x).shape == x.shape + (4,)
+
+    # multi-dimensional array input
+    x2d = np.zeros((2, 2))
+    assert cmap(x2d).shape == x2d.shape + (4,)
+
+
+def test_BoundaryNorm():
+    """
+    GitHub issue #1258: interpolation was failing with numpy
+    1.7 pre-release.
+    """
+
+    boundaries = [0, 1.1, 2.2]
+    vals = [-1, 0, 1, 2, 2.2, 4]
+
+    # Without interpolation
+    expected = [-1, 0, 0, 1, 2, 2]
+    ncolors = len(boundaries) - 1
+    bn = mcolors.BoundaryNorm(boundaries, ncolors)
+    assert_array_equal(bn(vals), expected)
+
+    # ncolors != len(boundaries) - 1 triggers interpolation
+    expected = [-1, 0, 0, 2, 3, 3]
+    ncolors = len(boundaries)
+    bn = mcolors.BoundaryNorm(boundaries, ncolors)
+    assert_array_equal(bn(vals), expected)
+
+    # with a single region and interpolation
+    expected = [-1, 1, 1, 1, 3, 3]
+    bn = mcolors.BoundaryNorm([0, 2.2], ncolors)
+    assert_array_equal(bn(vals), expected)
+
+    # more boundaries for a third color
+    boundaries = [0, 1, 2, 3]
+    vals = [-1, 0.1, 1.1, 2.2, 4]
+    ncolors = 5
+    expected = [-1, 0, 2, 4, 5]
+    bn = mcolors.BoundaryNorm(boundaries, ncolors)
+    assert_array_equal(bn(vals), expected)
+
+    # a scalar as input should not trigger an error and should return a scalar
+    boundaries = [0, 1, 2]
+    vals = [-1, 0.1, 1.1, 2.2]
+    bn = mcolors.BoundaryNorm(boundaries, 2)
+    expected = [-1, 0, 1, 2]
+    for v, ex in zip(vals, expected):
+        ret = bn(v)
+        assert isinstance(ret, int)
+        assert_array_equal(ret, ex)
+        assert_array_equal(bn([v]), ex)
+
+    # same with interp
+    bn = mcolors.BoundaryNorm(boundaries, 3)
+    expected = [-1, 0, 2, 3]
+    for v, ex in zip(vals, expected):
+        ret = bn(v)
+        assert isinstance(ret, int)
+        assert_array_equal(ret, ex)
+        assert_array_equal(bn([v]), ex)
+
+    # Clipping
+    bn = mcolors.BoundaryNorm(boundaries, 3, clip=True)
+    expected = [0, 0, 2, 2]
+    for v, ex in zip(vals, expected):
+        ret = bn(v)
+        assert isinstance(ret, int)
+        assert_array_equal(ret, ex)
+        assert_array_equal(bn([v]), ex)
+
+    # Masked arrays
+    boundaries = [0, 1.1, 2.2]
+    vals = np.ma.masked_invalid([-1., np.nan, 0, 1.4, 9])
+
+    # Without interpolation
+    ncolors = len(boundaries) - 1
+    bn = mcolors.BoundaryNorm(boundaries, ncolors)
+    expected = np.ma.masked_array([-1, -99, 0, 1, 2], mask=[0, 1, 0, 0, 0])
+    assert_array_equal(bn(vals), expected)
+
+    # With interpolation
+    bn = mcolors.BoundaryNorm(boundaries, len(boundaries))
+    expected = np.ma.masked_array([-1, -99, 0, 2, 3], mask=[0, 1, 0, 0, 0])
+    assert_array_equal(bn(vals), expected)
+
+    # Non-trivial masked arrays
+    vals = np.ma.masked_invalid([np.inf, np.nan])
+    assert np.all(bn(vals).mask)
+    vals = np.ma.masked_invalid([np.inf])
+    assert np.all(bn(vals).mask)
+
+    # Incompatible extend and clip
+    with pytest.raises(ValueError, match="not compatible"):
+        mcolors.BoundaryNorm(np.arange(4), 5, extend='both', clip=True)
+
+    # Too small ncolors argument
+    with pytest.raises(ValueError, match="ncolors must equal or exceed"):
+        mcolors.BoundaryNorm(np.arange(4), 2)
+
+    with pytest.raises(ValueError, match="ncolors must equal or exceed"):
+        mcolors.BoundaryNorm(np.arange(4), 3, extend='min')
+
+    with pytest.raises(ValueError, match="ncolors must equal or exceed"):
+        mcolors.BoundaryNorm(np.arange(4), 4, extend='both')
+
+    # Testing extend keyword, with interpolation (large cmap)
+    bounds = [1, 2, 3]
+    cmap = mpl.colormaps['viridis']
+    mynorm = mcolors.BoundaryNorm(bounds, cmap.N, extend='both')
+    refnorm = mcolors.BoundaryNorm([0] + bounds + [4], cmap.N)
+    x = np.random.randn(100) * 10 + 2
+    ref = refnorm(x)
+    ref[ref == 0] = -1
+    ref[ref == cmap.N - 1] = cmap.N
+    assert_array_equal(mynorm(x), ref)
+
+    # Without interpolation
+    cmref = mcolors.ListedColormap(['blue', 'red'])
+    cmref.set_over('black')
+    cmref.set_under('white')
+    cmshould = mcolors.ListedColormap(['white', 'blue', 'red', 'black'])
+
+    assert mcolors.same_color(cmref.get_over(), 'black')
+    assert mcolors.same_color(cmref.get_under(), 'white')
+
+    refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
+    mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='both')
+    assert mynorm.vmin == refnorm.vmin
+    assert mynorm.vmax == refnorm.vmax
+
+    assert mynorm(bounds[0] - 0.1) == -1  # under
+    assert mynorm(bounds[0] + 0.1) == 1   # first bin -> second color
+    assert mynorm(bounds[-1] - 0.1) == cmshould.N - 2  # next-to-last color
+    assert mynorm(bounds[-1] + 0.1) == cmshould.N  # over
+
+    x = [-1, 1.2, 2.3, 9.6]
+    assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2, 3]))
+    x = np.random.randn(100) * 10 + 2
+    assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))
+
+    # Just min
+    cmref = mcolors.ListedColormap(['blue', 'red'])
+    cmref.set_under('white')
+    cmshould = mcolors.ListedColormap(['white', 'blue', 'red'])
+
+    assert mcolors.same_color(cmref.get_under(), 'white')
+
+    assert cmref.N == 2
+    assert cmshould.N == 3
+    refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
+    mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='min')
+    assert mynorm.vmin == refnorm.vmin
+    assert mynorm.vmax == refnorm.vmax
+    x = [-1, 1.2, 2.3]
+    assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2]))
+    x = np.random.randn(100) * 10 + 2
+    assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))
+
+    # Just max
+    cmref = mcolors.ListedColormap(['blue', 'red'])
+    cmref.set_over('black')
+    cmshould = mcolors.ListedColormap(['blue', 'red', 'black'])
+
+    assert mcolors.same_color(cmref.get_over(), 'black')
+
+    assert cmref.N == 2
+    assert cmshould.N == 3
+    refnorm = mcolors.BoundaryNorm(bounds, cmref.N)
+    mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='max')
+    assert mynorm.vmin == refnorm.vmin
+    assert mynorm.vmax == refnorm.vmax
+    x = [1.2, 2.3, 4]
+    assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2]))
+    x = np.random.randn(100) * 10 + 2
+    assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))
+
+
+def test_CenteredNorm():
+    np.random.seed(0)
+
+    # Assert equivalence to symmetrical Normalize.
+    x = np.random.normal(size=100)
+    x_maxabs = np.max(np.abs(x))
+    norm_ref = mcolors.Normalize(vmin=-x_maxabs, vmax=x_maxabs)
+    norm = mcolors.CenteredNorm()
+    assert_array_almost_equal(norm_ref(x), norm(x))
+
+    # Check that vcenter is in the center of vmin and vmax
+    # when vcenter is set.
+    vcenter = int(np.random.normal(scale=50))
+    norm = mcolors.CenteredNorm(vcenter=vcenter)
+    norm.autoscale_None([1, 2])
+    assert norm.vmax + norm.vmin == 2 * vcenter
+
+    # Check that halfrange can be set without setting vcenter and that it is
+    # not reset through autoscale_None.
+    norm = mcolors.CenteredNorm(halfrange=1.0)
+    norm.autoscale_None([1, 3000])
+    assert norm.halfrange == 1.0
+
+    # Check that halfrange input works correctly.
+    x = np.random.normal(size=10)
+    norm = mcolors.CenteredNorm(vcenter=0.5, halfrange=0.5)
+    assert_array_almost_equal(x, norm(x))
+    norm = mcolors.CenteredNorm(vcenter=1, halfrange=1)
+    assert_array_almost_equal(x, 2 * norm(x))
+
+    # Check that halfrange input works correctly and use setters.
+    norm = mcolors.CenteredNorm()
+    norm.vcenter = 2
+    norm.halfrange = 2
+    assert_array_almost_equal(x, 4 * norm(x))
+
+    # Check that prior to adding data, setting halfrange first has same effect.
+    norm = mcolors.CenteredNorm()
+    norm.halfrange = 2
+    norm.vcenter = 2
+    assert_array_almost_equal(x, 4 * norm(x))
+
+    # Check that manual change of vcenter adjusts halfrange accordingly.
+    norm = mcolors.CenteredNorm()
+    assert norm.vcenter == 0
+    # add data
+    norm(np.linspace(-1.0, 0.0, 10))
+    assert norm.vmax == 1.0
+    assert norm.halfrange == 1.0
+    # set vcenter to 1, which should move the center but leave the
+    # halfrange unchanged
+    norm.vcenter = 1
+    assert norm.vmin == 0
+    assert norm.vmax == 2
+    assert norm.halfrange == 1
+
+    # Check setting vmin directly updates the halfrange and vmax, but
+    # leaves vcenter alone
+    norm.vmin = -1
+    assert norm.halfrange == 2
+    assert norm.vmax == 3
+    assert norm.vcenter == 1
+
+    # also check vmax updates
+    norm.vmax = 2
+    assert norm.halfrange == 1
+    assert norm.vmin == 0
+    assert norm.vcenter == 1
+
+
+@pytest.mark.parametrize("vmin,vmax", [[-1, 2], [3, 1]])
+def test_lognorm_invalid(vmin, vmax):
+    # Check that invalid limits in LogNorm error
+    norm = mcolors.LogNorm(vmin=vmin, vmax=vmax)
+    with pytest.raises(ValueError):
+        norm(1)
+    with pytest.raises(ValueError):
+        norm.inverse(1)
+
+
+def test_LogNorm():
+    """
+    LogNorm ignored clip, now it has the same
+    behavior as Normalize, e.g., values > vmax are bigger than 1
+    without clip, with clip they are 1.
+    """
+    ln = mcolors.LogNorm(clip=True, vmax=5)
+    assert_array_equal(ln([1, 6]), [0, 1.0])
+
+
+def test_LogNorm_inverse():
+    """
+    Test that lists work, and that the inverse works
+    """
+    norm = mcolors.LogNorm(vmin=0.1, vmax=10)
+    assert_array_almost_equal(norm([0.5, 0.4]), [0.349485, 0.30103])
+    assert_array_almost_equal([0.5, 0.4], norm.inverse([0.349485, 0.30103]))
+    assert_array_almost_equal(norm(0.4), [0.30103])
+    assert_array_almost_equal([0.4], norm.inverse([0.30103]))
+
+
+def test_PowerNorm():
+    # Check an exponent of 1 gives same results as a normal linear
+    # normalization. Also implicitly checks that vmin/vmax are
+    # automatically initialized from first array input.
+    a = np.array([0, 0.5, 1, 1.5], dtype=float)
+    pnorm = mcolors.PowerNorm(1)
+    norm = mcolors.Normalize()
+    assert_array_almost_equal(norm(a), pnorm(a))
+
+    a = np.array([-0.5, 0, 2, 4, 8], dtype=float)
+    expected = [-1/16, 0, 1/16, 1/4, 1]
+    pnorm = mcolors.PowerNorm(2, vmin=0, vmax=8)
+    assert_array_almost_equal(pnorm(a), expected)
+    assert pnorm(a[0]) == expected[0]
+    assert pnorm(a[2]) == expected[2]
+    # Check inverse
+    a_roundtrip = pnorm.inverse(pnorm(a))
+    assert_array_almost_equal(a, a_roundtrip)
+    # PowerNorm inverse adds a mask, so check that is correct too
+    assert_array_equal(a_roundtrip.mask, np.zeros(a.shape, dtype=bool))
+
+    # Clip = True
+    a = np.array([-0.5, 0, 1, 8, 16], dtype=float)
+    expected = [0, 0, 0, 1, 1]
+    # Clip = True when creating the norm
+    pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=True)
+    assert_array_almost_equal(pnorm(a), expected)
+    assert pnorm(a[0]) == expected[0]
+    assert pnorm(a[-1]) == expected[-1]
+    # Clip = True at call time
+    pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=False)
+    assert_array_almost_equal(pnorm(a, clip=True), expected)
+    assert pnorm(a[0], clip=True) == expected[0]
+    assert pnorm(a[-1], clip=True) == expected[-1]
+
+    # Check clip=True preserves masked values
+    a = np.ma.array([5, 2], mask=[True, False])
+    out = pnorm(a, clip=True)
+    assert_array_equal(out.mask, [True, False])
+
+
+def test_PowerNorm_translation_invariance():
+    a = np.array([0, 1/2, 1], dtype=float)
+    expected = [0, 1/8, 1]
+    pnorm = mcolors.PowerNorm(vmin=0, vmax=1, gamma=3)
+    assert_array_almost_equal(pnorm(a), expected)
+    pnorm = mcolors.PowerNorm(vmin=-2, vmax=-1, gamma=3)
+    assert_array_almost_equal(pnorm(a - 2), expected)
+
+
+def test_powernorm_cbar_limits():
+    fig, ax = plt.subplots()
+    vmin, vmax = 300, 1000
+    data = np.arange(10*10).reshape(10, 10) + vmin
+    im = ax.imshow(data, norm=mcolors.PowerNorm(gamma=0.2, vmin=vmin, vmax=vmax))
+    cbar = fig.colorbar(im)
+    assert cbar.ax.get_ylim() == (vmin, vmax)
+
+
+def test_Normalize():
+    norm = mcolors.Normalize()
+    vals = np.arange(-10, 10, 1, dtype=float)
+    _inverse_tester(norm, vals)
+    _scalar_tester(norm, vals)
+    _mask_tester(norm, vals)
+
+    # Handle integer input correctly (don't overflow when computing max-min,
+    # i.e. 127-(-128) here).
+    vals = np.array([-128, 127], dtype=np.int8)
+    norm = mcolors.Normalize(vals.min(), vals.max())
+    assert_array_equal(norm(vals), [0, 1])
+
+    # Don't lose precision on longdoubles (float128 on Linux):
+    # for array inputs...
+    vals = np.array([1.2345678901, 9.8765432109], dtype=np.longdouble)
+    norm = mcolors.Normalize(vals[0], vals[1])
+    assert norm(vals).dtype == np.longdouble
+    assert_array_equal(norm(vals), [0, 1])
+    # and for scalar ones.
+    eps = np.finfo(np.longdouble).resolution
+    norm = plt.Normalize(1, 1 + 100 * eps)
+    # This returns exactly 0.5 when longdouble is extended precision (80-bit),
+    # but only a value close to it when it is quadruple precision (128-bit).
+    assert_array_almost_equal(norm(1 + 50 * eps), 0.5, decimal=3)
+
+
+def test_FuncNorm():
+    def forward(x):
+        return (x**2)
+    def inverse(x):
+        return np.sqrt(x)
+
+    norm = mcolors.FuncNorm((forward, inverse), vmin=0, vmax=10)
+    expected = np.array([0, 0.25, 1])
+    input = np.array([0, 5, 10])
+    assert_array_almost_equal(norm(input), expected)
+    assert_array_almost_equal(norm.inverse(expected), input)
+
+    def forward(x):
+        return np.log10(x)
+    def inverse(x):
+        return 10**x
+    norm = mcolors.FuncNorm((forward, inverse), vmin=0.1, vmax=10)
+    lognorm = mcolors.LogNorm(vmin=0.1, vmax=10)
+    assert_array_almost_equal(norm([0.2, 5, 10]), lognorm([0.2, 5, 10]))
+    assert_array_almost_equal(norm.inverse([0.2, 5, 10]),
+                              lognorm.inverse([0.2, 5, 10]))
+
+
+def test_TwoSlopeNorm_autoscale():
+    norm = mcolors.TwoSlopeNorm(vcenter=20)
+    norm.autoscale([10, 20, 30, 40])
+    assert norm.vmin == 10.
+    assert norm.vmax == 40.
+
+
+def test_TwoSlopeNorm_autoscale_None_vmin():
+    norm = mcolors.TwoSlopeNorm(2, vmin=0, vmax=None)
+    norm.autoscale_None([1, 2, 3, 4, 5])
+    assert norm(5) == 1
+    assert norm.vmax == 5
+
+
+def test_TwoSlopeNorm_autoscale_None_vmax():
+    norm = mcolors.TwoSlopeNorm(2, vmin=None, vmax=10)
+    norm.autoscale_None([1, 2, 3, 4, 5])
+    assert norm(1) == 0
+    assert norm.vmin == 1
+
+
+def test_TwoSlopeNorm_scale():
+    norm = mcolors.TwoSlopeNorm(2)
+    assert norm.scaled() is False
+    norm([1, 2, 3, 4])
+    assert norm.scaled() is True
+
+
+def test_TwoSlopeNorm_scaleout_center():
+    # test the vmin never goes above vcenter
+    norm = mcolors.TwoSlopeNorm(vcenter=0)
+    norm([0, 1, 2, 3, 5])
+    assert norm.vmin == -5
+    assert norm.vmax == 5
+
+
+def test_TwoSlopeNorm_scaleout_center_max():
+    # test the vmax never goes below vcenter
+    norm = mcolors.TwoSlopeNorm(vcenter=0)
+    norm([0, -1, -2, -3, -5])
+    assert norm.vmax == 5
+    assert norm.vmin == -5
+
+
+def test_TwoSlopeNorm_Even():
+    norm = mcolors.TwoSlopeNorm(vmin=-1, vcenter=0, vmax=4)
+    vals = np.array([-1.0, -0.5, 0.0, 1.0, 2.0, 3.0, 4.0])
+    expected = np.array([0.0, 0.25, 0.5, 0.625, 0.75, 0.875, 1.0])
+    assert_array_equal(norm(vals), expected)
+
+
+def test_TwoSlopeNorm_Odd():
+    norm = mcolors.TwoSlopeNorm(vmin=-2, vcenter=0, vmax=5)
+    vals = np.array([-2.0, -1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0])
+    expected = np.array([0.0, 0.25, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
+    assert_array_equal(norm(vals), expected)
+
+
+def test_TwoSlopeNorm_VminEqualsVcenter():
+    with pytest.raises(ValueError):
+        mcolors.TwoSlopeNorm(vmin=-2, vcenter=-2, vmax=2)
+
+
+def test_TwoSlopeNorm_VmaxEqualsVcenter():
+    with pytest.raises(ValueError):
+        mcolors.TwoSlopeNorm(vmin=-2, vcenter=2, vmax=2)
+
+
+def test_TwoSlopeNorm_VminGTVcenter():
+    with pytest.raises(ValueError):
+        mcolors.TwoSlopeNorm(vmin=10, vcenter=0, vmax=20)
+
+
+def test_TwoSlopeNorm_TwoSlopeNorm_VminGTVmax():
+    with pytest.raises(ValueError):
+        mcolors.TwoSlopeNorm(vmin=10, vcenter=0, vmax=5)
+
+
+def test_TwoSlopeNorm_VcenterGTVmax():
+    with pytest.raises(ValueError):
+        mcolors.TwoSlopeNorm(vmin=10, vcenter=25, vmax=20)
+
+
+def test_TwoSlopeNorm_premature_scaling():
+    norm = mcolors.TwoSlopeNorm(vcenter=2)
+    with pytest.raises(ValueError):
+        norm.inverse(np.array([0.1, 0.5, 0.9]))
+
+
+def test_SymLogNorm():
+    """
+    Test SymLogNorm behavior
+    """
+    norm = mcolors.SymLogNorm(3, vmax=5, linscale=1.2, base=np.e)
+    vals = np.array([-30, -1, 2, 6], dtype=float)
+    normed_vals = norm(vals)
+    expected = [0., 0.53980074, 0.826991, 1.02758204]
+    assert_array_almost_equal(normed_vals, expected)
+    _inverse_tester(norm, vals)
+    _scalar_tester(norm, vals)
+    _mask_tester(norm, vals)
+
+    # Ensure that specifying vmin returns the same result as above
+    norm = mcolors.SymLogNorm(3, vmin=-30, vmax=5, linscale=1.2, base=np.e)
+    normed_vals = norm(vals)
+    assert_array_almost_equal(normed_vals, expected)
+
+    # test something more easily checked.
+    norm = mcolors.SymLogNorm(1, vmin=-np.e**3, vmax=np.e**3, base=np.e)
+    nn = norm([-np.e**3, -np.e**2, -np.e**1, -1,
+              0, 1, np.e**1, np.e**2, np.e**3])
+    xx = np.array([0., 0.109123, 0.218246, 0.32737, 0.5, 0.67263,
+                   0.781754, 0.890877, 1.])
+    assert_array_almost_equal(nn, xx)
+    norm = mcolors.SymLogNorm(1, vmin=-10**3, vmax=10**3, base=10)
+    nn = norm([-10**3, -10**2, -10**1, -1,
+              0, 1, 10**1, 10**2, 10**3])
+    xx = np.array([0., 0.121622, 0.243243, 0.364865, 0.5, 0.635135,
+                   0.756757, 0.878378, 1.])
+    assert_array_almost_equal(nn, xx)
+
+
+def test_SymLogNorm_colorbar():
+    """
+    Test un-called SymLogNorm in a colorbar.
+    """
+    norm = mcolors.SymLogNorm(0.1, vmin=-1, vmax=1, linscale=1, base=np.e)
+    fig = plt.figure()
+    mcolorbar.ColorbarBase(fig.add_subplot(), norm=norm)
+    plt.close(fig)
+
+
+def test_SymLogNorm_single_zero():
+    """
+    Test SymLogNorm to ensure it is not adding sub-ticks to zero label
+    """
+    fig = plt.figure()
+    norm = mcolors.SymLogNorm(1e-5, vmin=-1, vmax=1, base=np.e)
+    cbar = mcolorbar.ColorbarBase(fig.add_subplot(), norm=norm)
+    ticks = cbar.get_ticks()
+    assert np.count_nonzero(ticks == 0) <= 1
+    plt.close(fig)
+
+
+class TestAsinhNorm:
+    """
+    Tests for `~.colors.AsinhNorm`
+    """
+
+    def test_init(self):
+        norm0 = mcolors.AsinhNorm()
+        assert norm0.linear_width == 1
+
+        norm5 = mcolors.AsinhNorm(linear_width=5)
+        assert norm5.linear_width == 5
+
+    def test_norm(self):
+        norm = mcolors.AsinhNorm(2, vmin=-4, vmax=4)
+        vals = np.arange(-3.5, 3.5, 10)
+        normed_vals = norm(vals)
+        asinh2 = np.arcsinh(2)
+
+        expected = (2 * np.arcsinh(vals / 2) + 2 * asinh2) / (4 * asinh2)
+        assert_array_almost_equal(normed_vals, expected)
+
+
+def _inverse_tester(norm_instance, vals):
+    """
+    Checks if the inverse of the given normalization is working.
+    """
+    assert_array_almost_equal(norm_instance.inverse(norm_instance(vals)), vals)
+
+
+def _scalar_tester(norm_instance, vals):
+    """
+    Checks if scalars and arrays are handled the same way.
+    Tests only for float.
+    """
+    scalar_result = [norm_instance(float(v)) for v in vals]
+    assert_array_almost_equal(scalar_result, norm_instance(vals))
+
+
+def _mask_tester(norm_instance, vals):
+    """
+    Checks mask handling
+    """
+    masked_array = np.ma.array(vals)
+    masked_array[0] = np.ma.masked
+    assert_array_equal(masked_array.mask, norm_instance(masked_array).mask)
+
+
+@image_comparison(['levels_and_colors.png'])
+def test_cmap_and_norm_from_levels_and_colors():
+    # Remove this line when this test image is regenerated.
+    plt.rcParams['pcolormesh.snap'] = False
+
+    data = np.linspace(-2, 4, 49).reshape(7, 7)
+    levels = [-1, 2, 2.5, 3]
+    colors = ['red', 'green', 'blue', 'yellow', 'black']
+    extend = 'both'
+    cmap, norm = mcolors.from_levels_and_colors(levels, colors, extend=extend)
+
+    ax = plt.axes()
+    m = plt.pcolormesh(data, cmap=cmap, norm=norm)
+    plt.colorbar(m)
+
+    # Hide the axes labels (but not the colorbar ones, as they are useful)
+    ax.tick_params(labelleft=False, labelbottom=False)
+
+
+@image_comparison(baseline_images=['boundarynorm_and_colorbar'],
+                  extensions=['png'], tol=1.0)
+def test_boundarynorm_and_colorbarbase():
+    # Remove this line when this test image is regenerated.
+    plt.rcParams['pcolormesh.snap'] = False
+
+    # Make a figure and axes with dimensions as desired.
+    fig = plt.figure()
+    ax1 = fig.add_axes([0.05, 0.80, 0.9, 0.15])
+    ax2 = fig.add_axes([0.05, 0.475, 0.9, 0.15])
+    ax3 = fig.add_axes([0.05, 0.15, 0.9, 0.15])
+
+    # Set the colormap and bounds
+    bounds = [-1, 2, 5, 7, 12, 15]
+    cmap = mpl.colormaps['viridis']
+
+    # Default behavior
+    norm = mcolors.BoundaryNorm(bounds, cmap.N)
+    cb1 = mcolorbar.ColorbarBase(ax1, cmap=cmap, norm=norm, extend='both',
+                                 orientation='horizontal', spacing='uniform')
+    # New behavior
+    norm = mcolors.BoundaryNorm(bounds, cmap.N, extend='both')
+    cb2 = mcolorbar.ColorbarBase(ax2, cmap=cmap, norm=norm,
+                                 orientation='horizontal')
+
+    # User can still force to any extend='' if really needed
+    norm = mcolors.BoundaryNorm(bounds, cmap.N, extend='both')
+    cb3 = mcolorbar.ColorbarBase(ax3, cmap=cmap, norm=norm,
+                                 extend='neither', orientation='horizontal')
+
+
+def test_cmap_and_norm_from_levels_and_colors2():
+    levels = [-1, 2, 2.5, 3]
+    colors = ['red', (0, 1, 0), 'blue', (0.5, 0.5, 0.5), (0.0, 0.0, 0.0, 1.0)]
+    clr = mcolors.to_rgba_array(colors)
+    bad = (0.1, 0.1, 0.1, 0.1)
+    no_color = (0.0, 0.0, 0.0, 0.0)
+    masked_value = 'masked_value'
+
+    # Define the test values which are of interest.
+    # Note: levels are lev[i] <= v < lev[i+1]
+    tests = [('both', None, {-2: clr[0],
+                             -1: clr[1],
+                             2: clr[2],
+                             2.25: clr[2],
+                             3: clr[4],
+                             3.5: clr[4],
+                             masked_value: bad}),
+
+             ('min', -1, {-2: clr[0],
+                          -1: clr[1],
+                          2: clr[2],
+                          2.25: clr[2],
+                          3: no_color,
+                          3.5: no_color,
+                          masked_value: bad}),
+
+             ('max', -1, {-2: no_color,
+                          -1: clr[0],
+                          2: clr[1],
+                          2.25: clr[1],
+                          3: clr[3],
+                          3.5: clr[3],
+                          masked_value: bad}),
+
+             ('neither', -2, {-2: no_color,
+                              -1: clr[0],
+                              2: clr[1],
+                              2.25: clr[1],
+                              3: no_color,
+                              3.5: no_color,
+                              masked_value: bad}),
+             ]
+
+    for extend, i1, cases in tests:
+        cmap, norm = mcolors.from_levels_and_colors(levels, colors[0:i1],
+                                                    extend=extend)
+        cmap.set_bad(bad)
+        for d_val, expected_color in cases.items():
+            if d_val == masked_value:
+                d_val = np.ma.array([1], mask=True)
+            else:
+                d_val = [d_val]
+            assert_array_equal(expected_color, cmap(norm(d_val))[0],
+                               f'With extend={extend!r} and data '
+                               f'value={d_val!r}')
+
+    with pytest.raises(ValueError):
+        mcolors.from_levels_and_colors(levels, colors)
+
+
+def test_rgb_hsv_round_trip():
+    for a_shape in [(500, 500, 3), (500, 3), (1, 3), (3,)]:
+        np.random.seed(0)
+        tt = np.random.random(a_shape)
+        assert_array_almost_equal(
+            tt, mcolors.hsv_to_rgb(mcolors.rgb_to_hsv(tt)))
+        assert_array_almost_equal(
+            tt, mcolors.rgb_to_hsv(mcolors.hsv_to_rgb(tt)))
+
+
+def test_autoscale_masked():
+    # Test for #2336. Previously fully masked data would trigger a ValueError.
+    data = np.ma.masked_all((12, 20))
+    plt.pcolor(data)
+    plt.draw()
+
+
+@image_comparison(['light_source_shading_topo.png'])
+def test_light_source_topo_surface():
+    """Shades a DEM using different v.e.'s and blend modes."""
+    dem = cbook.get_sample_data('jacksboro_fault_dem.npz')
+    elev = dem['elevation']
+    dx, dy = dem['dx'], dem['dy']
+    # Get the true cellsize in meters for accurate vertical exaggeration
+    # Convert from decimal degrees to meters
+    dx = 111320.0 * dx * np.cos(dem['ymin'])
+    dy = 111320.0 * dy
+
+    ls = mcolors.LightSource(315, 45)
+    cmap = cm.gist_earth
+
+    fig, axs = plt.subplots(nrows=3, ncols=3)
+    for row, mode in zip(axs, ['hsv', 'overlay', 'soft']):
+        for ax, ve in zip(row, [0.1, 1, 10]):
+            rgb = ls.shade(elev, cmap, vert_exag=ve, dx=dx, dy=dy,
+                           blend_mode=mode)
+            ax.imshow(rgb)
+            ax.set(xticks=[], yticks=[])
+
+
+def test_light_source_shading_default():
+    """
+    Array comparison test for the default "hsv" blend mode. Ensure the
+    default result doesn't change without warning.
+    """
+    y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j]
+    z = 10 * np.cos(x**2 + y**2)
+
+    cmap = plt.cm.copper
+    ls = mcolors.LightSource(315, 45)
+    rgb = ls.shade(z, cmap)
+
+    # Result stored transposed and rounded for more compact display...
+    expect = np.array(
+        [[[0.00, 0.45, 0.90, 0.90, 0.82, 0.62, 0.28, 0.00],
+          [0.45, 0.94, 0.99, 1.00, 1.00, 0.96, 0.65, 0.17],
+          [0.90, 0.99, 1.00, 1.00, 1.00, 1.00, 0.94, 0.35],
+          [0.90, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 0.49],
+          [0.82, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 0.41],
+          [0.62, 0.96, 1.00, 1.00, 1.00, 1.00, 0.90, 0.07],
+          [0.28, 0.65, 0.94, 1.00, 1.00, 0.90, 0.35, 0.01],
+          [0.00, 0.17, 0.35, 0.49, 0.41, 0.07, 0.01, 0.00]],
+
+         [[0.00, 0.28, 0.59, 0.72, 0.62, 0.40, 0.18, 0.00],
+          [0.28, 0.78, 0.93, 0.92, 0.83, 0.66, 0.39, 0.11],
+          [0.59, 0.93, 0.99, 1.00, 0.92, 0.75, 0.50, 0.21],
+          [0.72, 0.92, 1.00, 0.99, 0.93, 0.76, 0.51, 0.18],
+          [0.62, 0.83, 0.92, 0.93, 0.87, 0.68, 0.42, 0.08],
+          [0.40, 0.66, 0.75, 0.76, 0.68, 0.52, 0.23, 0.02],
+          [0.18, 0.39, 0.50, 0.51, 0.42, 0.23, 0.00, 0.00],
+          [0.00, 0.11, 0.21, 0.18, 0.08, 0.02, 0.00, 0.00]],
+
+         [[0.00, 0.18, 0.38, 0.46, 0.39, 0.26, 0.11, 0.00],
+          [0.18, 0.50, 0.70, 0.75, 0.64, 0.44, 0.25, 0.07],
+          [0.38, 0.70, 0.91, 0.98, 0.81, 0.51, 0.29, 0.13],
+          [0.46, 0.75, 0.98, 0.96, 0.84, 0.48, 0.22, 0.12],
+          [0.39, 0.64, 0.81, 0.84, 0.71, 0.31, 0.11, 0.05],
+          [0.26, 0.44, 0.51, 0.48, 0.31, 0.10, 0.03, 0.01],
+          [0.11, 0.25, 0.29, 0.22, 0.11, 0.03, 0.00, 0.00],
+          [0.00, 0.07, 0.13, 0.12, 0.05, 0.01, 0.00, 0.00]],
+
+         [[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00]]
+         ]).T
+
+    assert_array_almost_equal(rgb, expect, decimal=2)
+
+
+def test_light_source_shading_empty_mask():
+    y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j]
+    z0 = 10 * np.cos(x**2 + y**2)
+    z1 = np.ma.array(z0)
+
+    cmap = plt.cm.copper
+    ls = mcolors.LightSource(315, 45)
+    rgb0 = ls.shade(z0, cmap)
+    rgb1 = ls.shade(z1, cmap)
+
+    assert_array_almost_equal(rgb0, rgb1)
+
+
+# Numpy 1.9.1 fixed a bug in masked arrays which resulted in
+# additional elements being masked when calculating the gradient thus
+# the output is different with earlier numpy versions.
+def test_light_source_masked_shading():
+    """
+    Array comparison test for a surface with a masked portion. Ensures that
+    we don't wind up with "fringes" of odd colors around masked regions.
+    """
+    y, x = np.mgrid[-1.2:1.2:8j, -1.2:1.2:8j]
+    z = 10 * np.cos(x**2 + y**2)
+
+    z = np.ma.masked_greater(z, 9.9)
+
+    cmap = plt.cm.copper
+    ls = mcolors.LightSource(315, 45)
+    rgb = ls.shade(z, cmap)
+
+    # Result stored transposed and rounded for more compact display...
+    expect = np.array(
+        [[[0.00, 0.46, 0.91, 0.91, 0.84, 0.64, 0.29, 0.00],
+          [0.46, 0.96, 1.00, 1.00, 1.00, 0.97, 0.67, 0.18],
+          [0.91, 1.00, 1.00, 1.00, 1.00, 1.00, 0.96, 0.36],
+          [0.91, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 0.51],
+          [0.84, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 0.44],
+          [0.64, 0.97, 1.00, 1.00, 1.00, 1.00, 0.94, 0.09],
+          [0.29, 0.67, 0.96, 1.00, 1.00, 0.94, 0.38, 0.01],
+          [0.00, 0.18, 0.36, 0.51, 0.44, 0.09, 0.01, 0.00]],
+
+         [[0.00, 0.29, 0.61, 0.75, 0.64, 0.41, 0.18, 0.00],
+          [0.29, 0.81, 0.95, 0.93, 0.85, 0.68, 0.40, 0.11],
+          [0.61, 0.95, 1.00, 0.78, 0.78, 0.77, 0.52, 0.22],
+          [0.75, 0.93, 0.78, 0.00, 0.00, 0.78, 0.54, 0.19],
+          [0.64, 0.85, 0.78, 0.00, 0.00, 0.78, 0.45, 0.08],
+          [0.41, 0.68, 0.77, 0.78, 0.78, 0.55, 0.25, 0.02],
+          [0.18, 0.40, 0.52, 0.54, 0.45, 0.25, 0.00, 0.00],
+          [0.00, 0.11, 0.22, 0.19, 0.08, 0.02, 0.00, 0.00]],
+
+         [[0.00, 0.19, 0.39, 0.48, 0.41, 0.26, 0.12, 0.00],
+          [0.19, 0.52, 0.73, 0.78, 0.66, 0.46, 0.26, 0.07],
+          [0.39, 0.73, 0.95, 0.50, 0.50, 0.53, 0.30, 0.14],
+          [0.48, 0.78, 0.50, 0.00, 0.00, 0.50, 0.23, 0.12],
+          [0.41, 0.66, 0.50, 0.00, 0.00, 0.50, 0.11, 0.05],
+          [0.26, 0.46, 0.53, 0.50, 0.50, 0.11, 0.03, 0.01],
+          [0.12, 0.26, 0.30, 0.23, 0.11, 0.03, 0.00, 0.00],
+          [0.00, 0.07, 0.14, 0.12, 0.05, 0.01, 0.00, 0.00]],
+
+         [[1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 0.00, 0.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00],
+          [1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00]],
+         ]).T
+
+    assert_array_almost_equal(rgb, expect, decimal=2)
+
+
+def test_light_source_hillshading():
+    """
+    Compare the current hillshading method against one that should be
+    mathematically equivalent. Illuminates a cone from a range of angles.
+    """
+
+    def alternative_hillshade(azimuth, elev, z):
+        illum = _sph2cart(*_azimuth2math(azimuth, elev))
+        illum = np.array(illum)
+
+        dy, dx = np.gradient(-z)
+        dy = -dy
+        dz = np.ones_like(dy)
+        normals = np.dstack([dx, dy, dz])
+        normals /= np.linalg.norm(normals, axis=2)[..., None]
+
+        intensity = np.tensordot(normals, illum, axes=(2, 0))
+        intensity -= intensity.min()
+        intensity /= np.ptp(intensity)
+        return intensity
+
+    y, x = np.mgrid[5:0:-1, :5]
+    z = -np.hypot(x - x.mean(), y - y.mean())
+
+    for az, elev in itertools.product(range(0, 390, 30), range(0, 105, 15)):
+        ls = mcolors.LightSource(az, elev)
+        h1 = ls.hillshade(z)
+        h2 = alternative_hillshade(az, elev, z)
+        assert_array_almost_equal(h1, h2)
+
+
+def test_light_source_planar_hillshading():
+    """
+    Ensure that the illumination intensity is correct for planar surfaces.
+    """
+
+    def plane(azimuth, elevation, x, y):
+        """
+        Create a plane whose normal vector is at the given azimuth and
+        elevation.
+        """
+        theta, phi = _azimuth2math(azimuth, elevation)
+        a, b, c = _sph2cart(theta, phi)
+        z = -(a*x + b*y) / c
+        return z
+
+    def angled_plane(azimuth, elevation, angle, x, y):
+        """
+        Create a plane whose normal vector is at an angle from the given
+        azimuth and elevation.
+        """
+        elevation = elevation + angle
+        if elevation > 90:
+            azimuth = (azimuth + 180) % 360
+            elevation = (90 - elevation) % 90
+        return plane(azimuth, elevation, x, y)
+
+    y, x = np.mgrid[5:0:-1, :5]
+    for az, elev in itertools.product(range(0, 390, 30), range(0, 105, 15)):
+        ls = mcolors.LightSource(az, elev)
+
+        # Make a plane at a range of angles to the illumination
+        for angle in range(0, 105, 15):
+            z = angled_plane(az, elev, angle, x, y)
+            h = ls.hillshade(z)
+            assert_array_almost_equal(h, np.cos(np.radians(angle)))
+
+
+def test_color_names():
+    assert mcolors.to_hex("blue") == "#0000ff"
+    assert mcolors.to_hex("xkcd:blue") == "#0343df"
+    assert mcolors.to_hex("tab:blue") == "#1f77b4"
+
+
+def _sph2cart(theta, phi):
+    x = np.cos(theta) * np.sin(phi)
+    y = np.sin(theta) * np.sin(phi)
+    z = np.cos(phi)
+    return x, y, z
+
+
+def _azimuth2math(azimuth, elevation):
+    """
+    Convert from clockwise-from-north and up-from-horizontal to mathematical
+    conventions.
+    """
+    theta = np.radians((90 - azimuth) % 360)
+    phi = np.radians(90 - elevation)
+    return theta, phi
+
+
+def test_pandas_iterable(pd):
+    # Using a list or series yields equivalent
+    # colormaps, i.e the series isn't seen as
+    # a single color
+    lst = ['red', 'blue', 'green']
+    s = pd.Series(lst)
+    cm1 = mcolors.ListedColormap(lst, N=5)
+    cm2 = mcolors.ListedColormap(s, N=5)
+    assert_array_equal(cm1.colors, cm2.colors)
+
+
+@pytest.mark.parametrize('name', sorted(mpl.colormaps()))
+def test_colormap_reversing(name):
+    """
+    Check the generated _lut data of a colormap and corresponding reversed
+    colormap if they are almost the same.
+    """
+    cmap = mpl.colormaps[name]
+    cmap_r = cmap.reversed()
+    if not cmap_r._isinit:
+        cmap._init()
+        cmap_r._init()
+    assert_array_almost_equal(cmap._lut[:-3], cmap_r._lut[-4::-1])
+    # Test the bad, over, under values too
+    assert_array_almost_equal(cmap(-np.inf), cmap_r(np.inf))
+    assert_array_almost_equal(cmap(np.inf), cmap_r(-np.inf))
+    assert_array_almost_equal(cmap(np.nan), cmap_r(np.nan))
+
+
+def test_has_alpha_channel():
+    assert mcolors._has_alpha_channel((0, 0, 0, 0))
+    assert mcolors._has_alpha_channel([1, 1, 1, 1])
+    assert not mcolors._has_alpha_channel('blue')  # 4-char string!
+    assert not mcolors._has_alpha_channel('0.25')
+    assert not mcolors._has_alpha_channel('r')
+    assert not mcolors._has_alpha_channel((1, 0, 0))
+
+
+def test_cn():
+    matplotlib.rcParams['axes.prop_cycle'] = cycler('color',
+                                                    ['blue', 'r'])
+    assert mcolors.to_hex("C0") == '#0000ff'
+    assert mcolors.to_hex("C1") == '#ff0000'
+
+    matplotlib.rcParams['axes.prop_cycle'] = cycler('color',
+                                                    ['xkcd:blue', 'r'])
+    assert mcolors.to_hex("C0") == '#0343df'
+    assert mcolors.to_hex("C1") == '#ff0000'
+    assert mcolors.to_hex("C10") == '#0343df'
+    assert mcolors.to_hex("C11") == '#ff0000'
+
+    matplotlib.rcParams['axes.prop_cycle'] = cycler('color', ['8e4585', 'r'])
+
+    assert mcolors.to_hex("C0") == '#8e4585'
+    # if '8e4585' gets parsed as a float before it gets detected as a hex
+    # colour it will be interpreted as a very large number.
+    # this mustn't happen.
+    assert mcolors.to_rgb("C0")[0] != np.inf
+
+
+def test_conversions():
+    # to_rgba_array("none") returns a (0, 4) array.
+    assert_array_equal(mcolors.to_rgba_array("none"), np.zeros((0, 4)))
+    assert_array_equal(mcolors.to_rgba_array([]), np.zeros((0, 4)))
+    # a list of grayscale levels, not a single color.
+    assert_array_equal(
+        mcolors.to_rgba_array([".2", ".5", ".8"]),
+        np.vstack([mcolors.to_rgba(c) for c in [".2", ".5", ".8"]]))
+    # alpha is properly set.
+    assert mcolors.to_rgba((1, 1, 1), .5) == (1, 1, 1, .5)
+    assert mcolors.to_rgba(".1", .5) == (.1, .1, .1, .5)
+    # builtin round differs between py2 and py3.
+    assert mcolors.to_hex((.7, .7, .7)) == "#b2b2b2"
+    # hex roundtrip.
+    hex_color = "#1234abcd"
+    assert mcolors.to_hex(mcolors.to_rgba(hex_color), keep_alpha=True) == \
+        hex_color
+
+
+def test_conversions_masked():
+    x1 = np.ma.array(['k', 'b'], mask=[True, False])
+    x2 = np.ma.array([[0, 0, 0, 1], [0, 0, 1, 1]])
+    x2[0] = np.ma.masked
+    assert mcolors.to_rgba(x1[0]) == (0, 0, 0, 0)
+    assert_array_equal(mcolors.to_rgba_array(x1),
+                       [[0, 0, 0, 0], [0, 0, 1, 1]])
+    assert_array_equal(mcolors.to_rgba_array(x2), mcolors.to_rgba_array(x1))
+
+
+def test_to_rgba_array_single_str():
+    # single color name is valid
+    assert_array_equal(mcolors.to_rgba_array("red"), [(1, 0, 0, 1)])
+
+    # single char color sequence is invalid
+    with pytest.raises(ValueError,
+                       match="'rgb' is not a valid color value."):
+        array = mcolors.to_rgba_array("rgb")
+
+
+def test_to_rgba_array_2tuple_str():
+    expected = np.array([[0, 0, 0, 1], [1, 1, 1, 1]])
+    assert_array_equal(mcolors.to_rgba_array(("k", "w")), expected)
+
+
+def test_to_rgba_array_alpha_array():
+    with pytest.raises(ValueError, match="The number of colors must match"):
+        mcolors.to_rgba_array(np.ones((5, 3), float), alpha=np.ones((2,)))
+    alpha = [0.5, 0.6]
+    c = mcolors.to_rgba_array(np.ones((2, 3), float), alpha=alpha)
+    assert_array_equal(c[:, 3], alpha)
+    c = mcolors.to_rgba_array(['r', 'g'], alpha=alpha)
+    assert_array_equal(c[:, 3], alpha)
+
+
+def test_to_rgba_array_accepts_color_alpha_tuple():
+    assert_array_equal(
+        mcolors.to_rgba_array(('black', 0.9)),
+        [[0, 0, 0, 0.9]])
+
+
+def test_to_rgba_array_explicit_alpha_overrides_tuple_alpha():
+    assert_array_equal(
+        mcolors.to_rgba_array(('black', 0.9), alpha=0.5),
+        [[0, 0, 0, 0.5]])
+
+
+def test_to_rgba_array_accepts_color_alpha_tuple_with_multiple_colors():
+    color_array = np.array([[1., 1., 1., 1.], [0., 0., 1., 0.]])
+    assert_array_equal(
+        mcolors.to_rgba_array((color_array, 0.2)),
+        [[1., 1., 1., 0.2], [0., 0., 1., 0.2]])
+
+    color_sequence = [[1., 1., 1., 1.], [0., 0., 1., 0.]]
+    assert_array_equal(
+        mcolors.to_rgba_array((color_sequence, 0.4)),
+        [[1., 1., 1., 0.4], [0., 0., 1., 0.4]])
+
+
+def test_to_rgba_array_error_with_color_invalid_alpha_tuple():
+    with pytest.raises(ValueError, match="'alpha' must be between 0 and 1,"):
+        mcolors.to_rgba_array(('black', 2.0))
+
+
+@pytest.mark.parametrize('rgba_alpha',
+                         [('white', 0.5), ('#ffffff', 0.5), ('#ffffff00', 0.5),
+                          ((1.0, 1.0, 1.0, 1.0), 0.5)])
+def test_to_rgba_accepts_color_alpha_tuple(rgba_alpha):
+    assert mcolors.to_rgba(rgba_alpha) == (1, 1, 1, 0.5)
+
+
+def test_to_rgba_explicit_alpha_overrides_tuple_alpha():
+    assert mcolors.to_rgba(('red', 0.1), alpha=0.9) == (1, 0, 0, 0.9)
+
+
+def test_to_rgba_error_with_color_invalid_alpha_tuple():
+    with pytest.raises(ValueError, match="'alpha' must be between 0 and 1"):
+        mcolors.to_rgba(('blue', 2.0))
+
+
+@pytest.mark.parametrize("bytes", (True, False))
+def test_scalarmappable_to_rgba(bytes):
+    sm = cm.ScalarMappable()
+    alpha_1 = 255 if bytes else 1
+
+    # uint8 RGBA
+    x = np.ones((2, 3, 4), dtype=np.uint8)
+    expected = x.copy() if bytes else x.astype(np.float32)/255
+    np.testing.assert_almost_equal(sm.to_rgba(x, bytes=bytes), expected)
+    # uint8 RGB
+    expected[..., 3] = alpha_1
+    np.testing.assert_almost_equal(sm.to_rgba(x[..., :3], bytes=bytes), expected)
+    # uint8 masked RGBA
+    xm = np.ma.masked_array(x, mask=np.zeros_like(x))
+    xm.mask[0, 0, 0] = True
+    expected = x.copy() if bytes else x.astype(np.float32)/255
+    expected[0, 0, 3] = 0
+    np.testing.assert_almost_equal(sm.to_rgba(xm, bytes=bytes), expected)
+    # uint8 masked RGB
+    expected[..., 3] = alpha_1
+    expected[0, 0, 3] = 0
+    np.testing.assert_almost_equal(sm.to_rgba(xm[..., :3], bytes=bytes), expected)
+
+    # float RGBA
+    x = np.ones((2, 3, 4), dtype=float) * 0.5
+    expected = (x * 255).astype(np.uint8) if bytes else x.copy()
+    np.testing.assert_almost_equal(sm.to_rgba(x, bytes=bytes), expected)
+    # float RGB
+    expected[..., 3] = alpha_1
+    np.testing.assert_almost_equal(sm.to_rgba(x[..., :3], bytes=bytes), expected)
+    # float masked RGBA
+    xm = np.ma.masked_array(x, mask=np.zeros_like(x))
+    xm.mask[0, 0, 0] = True
+    expected = (x * 255).astype(np.uint8) if bytes else x.copy()
+    expected[0, 0, 3] = 0
+    np.testing.assert_almost_equal(sm.to_rgba(xm, bytes=bytes), expected)
+    # float masked RGB
+    expected[..., 3] = alpha_1
+    expected[0, 0, 3] = 0
+    np.testing.assert_almost_equal(sm.to_rgba(xm[..., :3], bytes=bytes), expected)
+
+
+@pytest.mark.parametrize("bytes", (True, False))
+def test_scalarmappable_nan_to_rgba(bytes):
+    sm = cm.ScalarMappable()
+
+    # RGBA
+    x = np.ones((2, 3, 4), dtype=float) * 0.5
+    x[0, 0, 0] = np.nan
+    expected = x.copy()
+    expected[0, 0, :] = 0
+    if bytes:
+        expected = (expected * 255).astype(np.uint8)
+    np.testing.assert_almost_equal(sm.to_rgba(x, bytes=bytes), expected)
+    assert np.any(np.isnan(x))  # Input array should not be changed
+
+    # RGB
+    expected[..., 3] = 255 if bytes else 1
+    expected[0, 0, 3] = 0
+    np.testing.assert_almost_equal(sm.to_rgba(x[..., :3], bytes=bytes), expected)
+    assert np.any(np.isnan(x))  # Input array should not be changed
+
+    # Out-of-range fail
+    x[1, 0, 0] = 42
+    with pytest.raises(ValueError, match='0..1 range'):
+        sm.to_rgba(x[..., :3], bytes=bytes)
+
+
+def test_failed_conversions():
+    with pytest.raises(ValueError):
+        mcolors.to_rgba('5')
+    with pytest.raises(ValueError):
+        mcolors.to_rgba('-1')
+    with pytest.raises(ValueError):
+        mcolors.to_rgba('nan')
+    with pytest.raises(ValueError):
+        mcolors.to_rgba('unknown_color')
+    with pytest.raises(ValueError):
+        # Gray must be a string to distinguish 3-4 grays from RGB or RGBA.
+        mcolors.to_rgba(0.4)
+
+
+def test_grey_gray():
+    color_mapping = mcolors._colors_full_map
+    for k in color_mapping.keys():
+        if 'grey' in k:
+            assert color_mapping[k] == color_mapping[k.replace('grey', 'gray')]
+        if 'gray' in k:
+            assert color_mapping[k] == color_mapping[k.replace('gray', 'grey')]
+
+
+def test_tableau_order():
+    dflt_cycle = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728',
+                  '#9467bd', '#8c564b', '#e377c2', '#7f7f7f',
+                  '#bcbd22', '#17becf']
+
+    assert list(mcolors.TABLEAU_COLORS.values()) == dflt_cycle
+
+
+def test_ndarray_subclass_norm():
+    # Emulate an ndarray subclass that handles units
+    # which objects when adding or subtracting with other
+    # arrays. See #6622 and #8696
+    class MyArray(np.ndarray):
+        def __isub__(self, other):  # type: ignore[misc]
+            raise RuntimeError
+
+        def __add__(self, other):
+            raise RuntimeError
+
+    data = np.arange(-10, 10, 1, dtype=float).reshape((10, 2))
+    mydata = data.view(MyArray)
+
+    for norm in [mcolors.Normalize(), mcolors.LogNorm(),
+                 mcolors.SymLogNorm(3, vmax=5, linscale=1, base=np.e),
+                 mcolors.Normalize(vmin=mydata.min(), vmax=mydata.max()),
+                 mcolors.SymLogNorm(3, vmin=mydata.min(), vmax=mydata.max(),
+                                    base=np.e),
+                 mcolors.PowerNorm(1)]:
+        assert_array_equal(norm(mydata), norm(data))
+        fig, ax = plt.subplots()
+        ax.imshow(mydata, norm=norm)
+        fig.canvas.draw()  # Check that no warning is emitted.
+
+
+def test_same_color():
+    assert mcolors.same_color('k', (0, 0, 0))
+    assert not mcolors.same_color('w', (1, 1, 0))
+    assert mcolors.same_color(['red', 'blue'], ['r', 'b'])
+    assert mcolors.same_color('none', 'none')
+    assert not mcolors.same_color('none', 'red')
+    with pytest.raises(ValueError):
+        mcolors.same_color(['r', 'g', 'b'], ['r'])
+    with pytest.raises(ValueError):
+        mcolors.same_color(['red', 'green'], 'none')
+
+
+def test_hex_shorthand_notation():
+    assert mcolors.same_color("#123", "#112233")
+    assert mcolors.same_color("#123a", "#112233aa")
+
+
+def test_repr_png():
+    cmap = mpl.colormaps['viridis']
+    png = cmap._repr_png_()
+    assert len(png) > 0
+    img = Image.open(BytesIO(png))
+    assert img.width > 0
+    assert img.height > 0
+    assert 'Title' in img.text
+    assert 'Description' in img.text
+    assert 'Author' in img.text
+    assert 'Software' in img.text
+
+
+def test_repr_html():
+    cmap = mpl.colormaps['viridis']
+    html = cmap._repr_html_()
+    assert len(html) > 0
+    png = cmap._repr_png_()
+    assert base64.b64encode(png).decode('ascii') in html
+    assert cmap.name in html
+    assert html.startswith('')
+
+
+def test_get_under_over_bad():
+    cmap = mpl.colormaps['viridis']
+    assert_array_equal(cmap.get_under(), cmap(-np.inf))
+    assert_array_equal(cmap.get_over(), cmap(np.inf))
+    assert_array_equal(cmap.get_bad(), cmap(np.nan))
+
+
+@pytest.mark.parametrize('kind', ('over', 'under', 'bad'))
+def test_non_mutable_get_values(kind):
+    cmap = copy.copy(mpl.colormaps['viridis'])
+    init_value = getattr(cmap, f'get_{kind}')()
+    getattr(cmap, f'set_{kind}')('k')
+    black_value = getattr(cmap, f'get_{kind}')()
+    assert np.all(black_value == [0, 0, 0, 1])
+    assert not np.all(init_value == black_value)
+
+
+def test_colormap_alpha_array():
+    cmap = mpl.colormaps['viridis']
+    vals = [-1, 0.5, 2]  # under, valid, over
+    with pytest.raises(ValueError, match="alpha is array-like but"):
+        cmap(vals, alpha=[1, 1, 1, 1])
+    alpha = np.array([0.1, 0.2, 0.3])
+    c = cmap(vals, alpha=alpha)
+    assert_array_equal(c[:, -1], alpha)
+    c = cmap(vals, alpha=alpha, bytes=True)
+    assert_array_equal(c[:, -1], (alpha * 255).astype(np.uint8))
+
+
+def test_colormap_bad_data_with_alpha():
+    cmap = mpl.colormaps['viridis']
+    c = cmap(np.nan, alpha=0.5)
+    assert c == (0, 0, 0, 0)
+    c = cmap([0.5, np.nan], alpha=0.5)
+    assert_array_equal(c[1], (0, 0, 0, 0))
+    c = cmap([0.5, np.nan], alpha=[0.1, 0.2])
+    assert_array_equal(c[1], (0, 0, 0, 0))
+    c = cmap([[np.nan, 0.5], [0, 0]], alpha=0.5)
+    assert_array_equal(c[0, 0], (0, 0, 0, 0))
+    c = cmap([[np.nan, 0.5], [0, 0]], alpha=np.full((2, 2), 0.5))
+    assert_array_equal(c[0, 0], (0, 0, 0, 0))
+
+
+def test_2d_to_rgba():
+    color = np.array([0.1, 0.2, 0.3])
+    rgba_1d = mcolors.to_rgba(color.reshape(-1))
+    rgba_2d = mcolors.to_rgba(color.reshape((1, -1)))
+    assert rgba_1d == rgba_2d
+
+
+def test_set_dict_to_rgba():
+    # downstream libraries do this...
+    # note we can't test this because it is not well-ordered
+    # so just smoketest:
+    colors = {(0, .5, 1), (1, .2, .5), (.4, 1, .2)}
+    res = mcolors.to_rgba_array(colors)
+    palette = {"red": (1, 0, 0), "green": (0, 1, 0), "blue": (0, 0, 1)}
+    res = mcolors.to_rgba_array(palette.values())
+    exp = np.eye(3)
+    np.testing.assert_array_almost_equal(res[:, :-1], exp)
+
+
+def test_norm_deepcopy():
+    norm = mcolors.LogNorm()
+    norm.vmin = 0.0002
+    norm2 = copy.deepcopy(norm)
+    assert norm2.vmin == norm.vmin
+    assert isinstance(norm2._scale, mscale.LogScale)
+    norm = mcolors.Normalize()
+    norm.vmin = 0.0002
+    norm2 = copy.deepcopy(norm)
+    assert norm2._scale is None
+    assert norm2.vmin == norm.vmin
+
+
+def test_set_clim_emits_single_callback():
+    data = np.array([[1, 2], [3, 4]])
+    fig, ax = plt.subplots()
+    image = ax.imshow(data, cmap='viridis')
+
+    callback = unittest.mock.Mock()
+    image.norm.callbacks.connect('changed', callback)
+
+    callback.assert_not_called()
+
+    # Call set_clim() to update the limits
+    image.set_clim(1, 5)
+
+    # Assert that only one "changed" callback is sent after calling set_clim()
+    callback.assert_called_once()
+
+
+def test_norm_callback():
+    increment = unittest.mock.Mock(return_value=None)
+
+    norm = mcolors.Normalize()
+    norm.callbacks.connect('changed', increment)
+    # Haven't updated anything, so call count should be 0
+    assert increment.call_count == 0
+
+    # Now change vmin and vmax to test callbacks
+    norm.vmin = 1
+    assert increment.call_count == 1
+    norm.vmax = 5
+    assert increment.call_count == 2
+    # callback shouldn't be called if setting to the same value
+    norm.vmin = 1
+    assert increment.call_count == 2
+    norm.vmax = 5
+    assert increment.call_count == 2
+
+    # We only want autoscale() calls to send out one update signal
+    increment.reset_mock()
+    norm.autoscale([0, 1, 2])
+    assert increment.call_count == 1
+
+
+def test_scalarmappable_norm_update():
+    norm = mcolors.Normalize()
+    sm = matplotlib.cm.ScalarMappable(norm=norm, cmap='plasma')
+    # sm doesn't have a stale attribute at first, set it to False
+    sm.stale = False
+    # The mappable should be stale after updating vmin/vmax
+    norm.vmin = 5
+    assert sm.stale
+    sm.stale = False
+    norm.vmax = 5
+    assert sm.stale
+    sm.stale = False
+    norm.clip = True
+    assert sm.stale
+    # change to the CenteredNorm and TwoSlopeNorm to test those
+    # Also make sure that updating the norm directly and with
+    # set_norm both update the Norm callback
+    norm = mcolors.CenteredNorm()
+    sm.norm = norm
+    sm.stale = False
+    norm.vcenter = 1
+    assert sm.stale
+    norm = mcolors.TwoSlopeNorm(vcenter=0, vmin=-1, vmax=1)
+    sm.set_norm(norm)
+    sm.stale = False
+    norm.vcenter = 1
+    assert sm.stale
+
+
+@check_figures_equal(extensions=['png'])
+def test_norm_update_figs(fig_test, fig_ref):
+    ax_ref = fig_ref.add_subplot()
+    ax_test = fig_test.add_subplot()
+
+    z = np.arange(100).reshape((10, 10))
+    ax_ref.imshow(z, norm=mcolors.Normalize(10, 90))
+
+    # Create the norm beforehand with different limits and then update
+    # after adding to the plot
+    norm = mcolors.Normalize(0, 1)
+    ax_test.imshow(z, norm=norm)
+    # Force initial draw to make sure it isn't already stale
+    fig_test.canvas.draw()
+    norm.vmin, norm.vmax = 10, 90
+
+
+def test_make_norm_from_scale_name():
+    logitnorm = mcolors.make_norm_from_scale(
+        mscale.LogitScale, mcolors.Normalize)
+    assert logitnorm.__name__ == logitnorm.__qualname__ == "LogitScaleNorm"
+
+
+def test_color_sequences():
+    # basic access
+    assert plt.color_sequences is matplotlib.color_sequences  # same registry
+    assert list(plt.color_sequences) == [
+        'tab10', 'tab20', 'tab20b', 'tab20c', 'Pastel1', 'Pastel2', 'Paired',
+        'Accent', 'Dark2', 'Set1', 'Set2', 'Set3', 'petroff10']
+    assert len(plt.color_sequences['tab10']) == 10
+    assert len(plt.color_sequences['tab20']) == 20
+
+    tab_colors = [
+        'tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple',
+        'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']
+    for seq_color, tab_color in zip(plt.color_sequences['tab10'], tab_colors):
+        assert mcolors.same_color(seq_color, tab_color)
+
+    # registering
+    with pytest.raises(ValueError, match="reserved name"):
+        plt.color_sequences.register('tab10', ['r', 'g', 'b'])
+    with pytest.raises(ValueError, match="not a valid color specification"):
+        plt.color_sequences.register('invalid', ['not a color'])
+
+    rgb_colors = ['r', 'g', 'b']
+    plt.color_sequences.register('rgb', rgb_colors)
+    assert plt.color_sequences['rgb'] == ['r', 'g', 'b']
+    # should not affect the registered sequence because input is copied
+    rgb_colors.append('c')
+    assert plt.color_sequences['rgb'] == ['r', 'g', 'b']
+    # should not affect the registered sequence because returned list is a copy
+    plt.color_sequences['rgb'].append('c')
+    assert plt.color_sequences['rgb'] == ['r', 'g', 'b']
+
+    # unregister
+    plt.color_sequences.unregister('rgb')
+    with pytest.raises(KeyError):
+        plt.color_sequences['rgb']  # rgb is gone
+    plt.color_sequences.unregister('rgb')  # multiple unregisters are ok
+    with pytest.raises(ValueError, match="Cannot unregister builtin"):
+        plt.color_sequences.unregister('tab10')
+
+
+def test_cm_set_cmap_error():
+    sm = cm.ScalarMappable()
+    # Pick a name we are pretty sure will never be a colormap name
+    bad_cmap = 'AardvarksAreAwkward'
+    with pytest.raises(ValueError, match=bad_cmap):
+        sm.set_cmap(bad_cmap)
+
+
+def test_set_cmap_mismatched_name():
+    cmap = matplotlib.colormaps["viridis"].with_extremes(over='r')
+    # register it with different names
+    cmap.name = "test-cmap"
+    matplotlib.colormaps.register(name='wrong-cmap', cmap=cmap)
+
+    plt.set_cmap("wrong-cmap")
+    cmap_returned = plt.get_cmap("wrong-cmap")
+    assert cmap_returned == cmap
+    assert cmap_returned.name == "wrong-cmap"
+
+
+def test_cmap_alias_names():
+    assert matplotlib.colormaps["gray"].name == "gray"  # original
+    assert matplotlib.colormaps["grey"].name == "grey"  # alias
+
+
+def test_to_rgba_array_none_color_with_alpha_param():
+    # effective alpha for color "none" must always be 0 to achieve a vanishing color
+    # even explicit alpha must be ignored
+    c = ["blue", "none"]
+    alpha = [1, 1]
+    assert_array_equal(
+        to_rgba_array(c, alpha), [[0., 0., 1., 1.], [0., 0., 0., 0.]]
+    )
+
+
+@pytest.mark.parametrize('input, expected',
+                         [('red', True),
+                          (('red', 0.5), True),
+                          (('red', 2), False),
+                          (['red', 0.5], False),
+                          (('red', 'blue'), False),
+                          (['red', 'blue'], False),
+                          ('C3', True),
+                          (('C3', 0.5), True)])
+def test_is_color_like(input, expected):
+    assert is_color_like(input) is expected
+
+
+def test_colorizer_vmin_vmax():
+    ca = mcolorizer.Colorizer()
+    assert ca.vmin is None
+    assert ca.vmax is None
+    ca.vmin = 1
+    ca.vmax = 3
+    assert ca.vmin == 1.0
+    assert ca.vmax == 3.0
+    assert ca.norm.vmin == 1.0
+    assert ca.norm.vmax == 3.0
diff --git a/shared/package-files/09/09b569c8f8b77a71807d73e1e51d291a76384f407c3af409250af3656f6a4975/error.py b/shared/package-files/09/09b569c8f8b77a71807d73e1e51d291a76384f407c3af409250af3656f6a4975/error.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fc55092fda9515c953407b44317b678fe85f74f
--- /dev/null
+++ b/shared/package-files/09/09b569c8f8b77a71807d73e1e51d291a76384f407c3af409250af3656f6a4975/error.py
@@ -0,0 +1,56 @@
+from enum import Enum
+
+
+class ExportErrorType(Enum):
+    # User providing invalid inputs to either tracer, or other public facing APIs
+    INVALID_INPUT_TYPE = 1
+
+    # User returning values from their models that we don’t support.
+    INVALID_OUTPUT_TYPE = 2
+
+    # Generated IR does not conform to Export IR Specification.
+    VIOLATION_OF_SPEC = 3
+
+    # User’s code contains types and functionalities we don’t support.
+    NOT_SUPPORTED = 4
+
+    # User's code didn't provide necessary details for us to successfully trace and export.
+    # For example, we use a lot of decorators and ask users to annotate their model.
+    MISSING_PROPERTY = 5
+
+    # User is using an API without proper initialization step.
+    UNINITIALIZED = 6
+
+
+def internal_assert(pred: bool, assert_msg: str) -> None:
+    """
+    This is exir's custom assert method. It internally just throws InternalError.
+    Note that the sole purpose is to throw our own error while maintaining similar syntax
+    as python assert.
+    """
+
+    if not pred:
+        raise InternalError(assert_msg)
+
+
+class InternalError(Exception):
+    """
+    Raised when an internal invariance is violated in EXIR stack.
+    Should hint users to report a bug to dev and expose the original
+    error message.
+    """
+
+    def __init__(self, message: str) -> None:
+        super().__init__(message)
+
+
+class ExportError(Exception):
+    """
+    This type of exception is raised for errors that are directly caused by the user
+    code. In general, user errors happen during model authoring, tracing, using our public
+    facing APIs, and writing graph passes.
+    """
+
+    def __init__(self, error_code: ExportErrorType, message: str) -> None:
+        prefix = f"[{error_code}]: "
+        super().__init__(prefix + message)
diff --git a/shared/package-files/09/09b9183f61e2f4955bed63b91e2084d787f03a7d7a7d3dcb7b91df577d1ca424/adjoint.py b/shared/package-files/09/09b9183f61e2f4955bed63b91e2084d787f03a7d7a7d3dcb7b91df577d1ca424/adjoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf626ecc9ff093e8a9b3b2a00730bebeca44cb3c
--- /dev/null
+++ b/shared/package-files/09/09b9183f61e2f4955bed63b91e2084d787f03a7d7a7d3dcb7b91df577d1ca424/adjoint.py
@@ -0,0 +1,61 @@
+from sympy.core import Basic
+from sympy.functions import adjoint, conjugate
+from sympy.matrices.expressions.transpose import transpose
+from sympy.matrices.expressions.matexpr import MatrixExpr
+
+
+class Adjoint(MatrixExpr):
+    """
+    The Hermitian adjoint of a matrix expression.
+
+    This is a symbolic object that simply stores its argument without
+    evaluating it. To actually compute the adjoint, use the ``adjoint()``
+    function.
+
+    Examples
+    ========
+
+    >>> from sympy import MatrixSymbol, Adjoint, adjoint
+    >>> A = MatrixSymbol('A', 3, 5)
+    >>> B = MatrixSymbol('B', 5, 3)
+    >>> Adjoint(A*B)
+    Adjoint(A*B)
+    >>> adjoint(A*B)
+    Adjoint(B)*Adjoint(A)
+    >>> adjoint(A*B) == Adjoint(A*B)
+    False
+    >>> adjoint(A*B) == Adjoint(A*B).doit()
+    True
+    """
+    is_Adjoint = True
+
+    def doit(self, **hints):
+        arg = self.arg
+        if hints.get('deep', True) and isinstance(arg, Basic):
+            return adjoint(arg.doit(**hints))
+        else:
+            return adjoint(self.arg)
+
+    @property
+    def arg(self):
+        return self.args[0]
+
+    @property
+    def shape(self):
+        return self.arg.shape[::-1]
+
+    def _entry(self, i, j, **kwargs):
+        return conjugate(self.arg._entry(j, i, **kwargs))
+
+    def _eval_adjoint(self):
+        return self.arg
+
+    def _eval_conjugate(self):
+        return transpose(self.arg)
+
+    def _eval_trace(self):
+        from sympy.matrices.expressions.trace import Trace
+        return conjugate(Trace(self.arg))
+
+    def _eval_transpose(self):
+        return conjugate(self.arg)
diff --git a/shared/package-files/09/09b97c278c63e8fd90b9ebf0466e73e271a0eed491512f18c7e9422ca4f8a3a5/_deploy.py b/shared/package-files/09/09b97c278c63e8fd90b9ebf0466e73e271a0eed491512f18c7e9422ca4f8a3a5/_deploy.py
new file mode 100644
index 0000000000000000000000000000000000000000..35e8d497694016a8f2e5fb75c4259f6c46e014d1
--- /dev/null
+++ b/shared/package-files/09/09b97c278c63e8fd90b9ebf0466e73e271a0eed491512f18c7e9422ca4f8a3a5/_deploy.py
@@ -0,0 +1,105 @@
+import io
+
+import torch
+from torch.package import Importer, OrderedImporter, PackageImporter, sys_importer
+from torch.package._package_pickler import create_pickler
+from torch.package._package_unpickler import PackageUnpickler
+from torch.serialization import _maybe_decode_ascii
+
+
+def _save_storages(importer, obj):
+    serialized_storages = []
+    serialized_dtypes = []
+
+    importer = importer if isinstance(importer, torch.package.PackageImporter) else None
+    importers: Importer
+    if importer is not None:
+        importers = OrderedImporter(importer, sys_importer)
+    else:
+        importers = sys_importer
+
+    def persistent_id(obj):
+        if torch.is_storage(obj) or isinstance(obj, torch.storage.TypedStorage):
+            if isinstance(obj, torch.storage.TypedStorage):
+                # TODO: Once we decide to break serialization FC, we can
+                # remove this case
+                storage = obj._untyped_storage
+                dtype = obj.dtype
+            else:
+                storage = obj
+                dtype = torch.uint8
+
+            serialized_storages.append(obj)
+            serialized_dtypes.append(dtype)
+            return ("storage", len(serialized_storages) - 1)
+
+        if hasattr(obj, "__reduce_deploy__"):
+            if _serialized_reduces.get(id(obj)) is None:
+                _serialized_reduces[id(obj)] = (
+                    "reduce_deploy",
+                    id(obj),
+                    *obj.__reduce_deploy__(importers),
+                )
+            return _serialized_reduces[id(obj)]
+
+        return None
+
+    # Write the pickle data for `obj`
+    data_buf = io.BytesIO()
+    pickler = create_pickler(data_buf, importers)
+    pickler.persistent_id = persistent_id
+    pickler.dump(obj)
+    data_value = data_buf.getvalue()
+    return (
+        data_value,
+        serialized_storages,
+        serialized_dtypes,
+        importer.zip_reader if importer else None,
+    )
+
+
+def _load_storages(id, zip_reader, obj_bytes, serialized_storages, serialized_dtypes):
+    def persistent_load(saved_id):
+        assert isinstance(saved_id, tuple)
+        typename = _maybe_decode_ascii(saved_id[0])
+        data = saved_id[1:]
+
+        if typename == "storage":
+            # TODO: Once we decide to break serialization FC, we can
+            # stop wrapping with TypedStorage
+            storage = serialized_storages[data[0]]
+            dtype = serialized_dtypes[data[0]]
+            return torch.storage.TypedStorage(
+                wrap_storage=storage.untyped(), dtype=dtype
+            )
+
+        if typename == "reduce_deploy":
+            reduce_id, func, args = data
+            if reduce_id not in _loaded_reduces:
+                _loaded_reduces[reduce_id] = func(_raw_packages[zip_reader], *args)
+            return _loaded_reduces[reduce_id]
+
+        return None
+
+    importer: Importer
+    if zip_reader is not None:
+        importer = OrderedImporter(_get_package(zip_reader), sys_importer)
+    else:
+        importer = sys_importer
+
+    unpickler = PackageUnpickler(importer, io.BytesIO(obj_bytes))
+    unpickler.persistent_load = persistent_load  # type: ignore[method-assign]
+    result = _deploy_objects[id] = unpickler.load()
+    return result
+
+
+def _get_package(zip_reader):
+    if zip_reader not in _raw_packages:
+        _raw_packages[zip_reader] = PackageImporter(zip_reader)
+    return _raw_packages[zip_reader]
+
+
+_raw_packages: dict = {}
+_deploy_objects: dict = {}
+_serialized_reduces: dict = {}
+_loaded_reduces: dict = {}
diff --git a/shared/package-files/09/09bab9eaed45243f28a7caf5917e06f7a3c239bd8bc3bc5d18c61972a5fb6a39/interned_strings_class.h b/shared/package-files/09/09bab9eaed45243f28a7caf5917e06f7a3c239bd8bc3bc5d18c61972a5fb6a39/interned_strings_class.h
new file mode 100644
index 0000000000000000000000000000000000000000..6e57332b99f97161437e3c51f895b1f10fe6be7b
--- /dev/null
+++ b/shared/package-files/09/09bab9eaed45243f28a7caf5917e06f7a3c239bd8bc3bc5d18c61972a5fb6a39/interned_strings_class.h
@@ -0,0 +1,34 @@
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+namespace c10 {
+
+struct TORCH_API InternedStrings {
+  InternedStrings();
+  Symbol symbol(const std::string& s);
+  std::pair string(Symbol sym);
+  Symbol ns(Symbol sym);
+
+ private:
+  // prereq - holding mutex_
+  Symbol _symbol(const std::string& s);
+  std::pair customString(Symbol sym);
+  std::unordered_map string_to_sym_;
+
+  struct SymbolInfo {
+    Symbol ns;
+    std::string qual_name;
+    std::string unqual_name;
+  };
+  std::vector sym_to_info_;
+
+  std::mutex mutex_;
+};
+
+} // namespace c10
diff --git a/shared/package-files/09/09be6156225af5286c5daef4ceddc418561b58d177099c87a4ca355d85cf46c0/version.py b/shared/package-files/09/09be6156225af5286c5daef4ceddc418561b58d177099c87a4ca355d85cf46c0/version.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ebbaf2bf2ca3f56c2e6969ff5e6afc553694858
--- /dev/null
+++ b/shared/package-files/09/09be6156225af5286c5daef4ceddc418561b58d177099c87a4ca355d85cf46c0/version.py
@@ -0,0 +1,51 @@
+# Copyright 2022-2024 MetaOPT Team. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""OpTree: Optimized PyTree Utilities."""
+
+__version__ = '0.11.0'
+__license__ = 'Apache License, Version 2.0'
+__author__ = 'OpTree Contributors'
+__release__ = True
+
+if not __release__:
+    import os
+    import subprocess
+
+    try:
+        prefix, sep, suffix = (
+            subprocess.check_output(
+                ['git', 'describe', '--abbrev=7'],  # noqa: S603,S607
+                cwd=os.path.dirname(os.path.abspath(__file__)),
+                stderr=subprocess.DEVNULL,
+                text=True,
+            )
+            .strip()
+            .lstrip('v')
+            .replace('-', '.dev', 1)
+            .replace('-', '+', 1)
+            .partition('.dev')
+        )
+        if sep:
+            version_prefix, dot, version_tail = prefix.rpartition('.')
+            prefix = f'{version_prefix}{dot}{int(version_tail) + 1}'
+            __version__ = sep.join((prefix, suffix))
+            del version_prefix, dot, version_tail
+        else:
+            __version__ = prefix
+        del prefix, sep, suffix
+    except (OSError, subprocess.CalledProcessError):
+        pass
+
+    del os, subprocess
diff --git a/shared/package-files/09/09c2abe07aecc24ba92e66c976b24b4ccfb3f5780b79f432bfce3aee3f55d33b/_p_r_e_p.py b/shared/package-files/09/09c2abe07aecc24ba92e66c976b24b4ccfb3f5780b79f432bfce3aee3f55d33b/_p_r_e_p.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf4a5dd24f27ab08082cf9976bc2f5a9e1a967a5
--- /dev/null
+++ b/shared/package-files/09/09c2abe07aecc24ba92e66c976b24b4ccfb3f5780b79f432bfce3aee3f55d33b/_p_r_e_p.py
@@ -0,0 +1,16 @@
+from fontTools import ttLib
+
+superclass = ttLib.getTableClass("fpgm")
+
+
+class table__p_r_e_p(superclass):
+    """Control Value Program table
+
+    The ``prep`` table contains TrueType instructions that can makee font-wide
+    alterations to the Control Value Table. It may potentially be executed
+    before any glyph is processed.
+
+    See also https://learn.microsoft.com/en-us/typography/opentype/spec/prep
+    """
+
+    pass
diff --git a/shared/package-files/09/09c3c256ac4a3e2e63774936f8434ee25cff907648211b52a55734b08ac9d8ca/_reshape_alias_ops.h b/shared/package-files/09/09c3c256ac4a3e2e63774936f8434ee25cff907648211b52a55734b08ac9d8ca/_reshape_alias_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..2b1d0e0e22698dae8fdf34caf80137660bfcbd06
--- /dev/null
+++ b/shared/package-files/09/09c3c256ac4a3e2e63774936f8434ee25cff907648211b52a55734b08ac9d8ca/_reshape_alias_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _reshape_alias {
+  using schema = at::Tensor (const at::Tensor &, c10::SymIntArrayRef, c10::SymIntArrayRef);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_reshape_alias")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_reshape_alias(Tensor(a) self, SymInt[] size, SymInt[] stride) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::SymIntArrayRef size, c10::SymIntArrayRef stride);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/09/09c52754cc3def294ed5a2feb4c9cbe4c7f4922b23ca7519b1b32c3215eefc59/_foreach_norm_ops.h b/shared/package-files/09/09c52754cc3def294ed5a2feb4c9cbe4c7f4922b23ca7519b1b32c3215eefc59/_foreach_norm_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..a7f8ece3c3fced9110d63a4030081c6283958d68
--- /dev/null
+++ b/shared/package-files/09/09c52754cc3def294ed5a2feb4c9cbe4c7f4922b23ca7519b1b32c3215eefc59/_foreach_norm_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _foreach_norm_Scalar {
+  using schema = ::std::vector (at::TensorList, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_norm")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Scalar")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_norm.Scalar(Tensor[] self, Scalar ord=2) -> Tensor[]")
+  static ::std::vector call(at::TensorList self, const at::Scalar & ord);
+  static ::std::vector redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & ord);
+};
+
+struct TORCH_API _foreach_norm_Scalar_out {
+  using schema = void (at::TensorList, const at::Scalar &, at::TensorList);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_foreach_norm")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "Scalar_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_foreach_norm.Scalar_out(Tensor[] self, Scalar ord=2, *, Tensor(a!)[] out) -> ()")
+  static void call(at::TensorList self, const at::Scalar & ord, at::TensorList out);
+  static void redispatch(c10::DispatchKeySet dispatchKeySet, at::TensorList self, const at::Scalar & ord, at::TensorList out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/09/09c9406b50245def117e1523e1a20fa62d4912e5c73f510726f2c19c42d3b010/cumsum_cuda_dispatch.h b/shared/package-files/09/09c9406b50245def117e1523e1a20fa62d4912e5c73f510726f2c19c42d3b010/cumsum_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a29db0a4b974b167b9c72405c9045e9f5516af9d
--- /dev/null
+++ b/shared/package-files/09/09c9406b50245def117e1523e1a20fa62d4912e5c73f510726f2c19c42d3b010/cumsum_cuda_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor cumsum(const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt);
+TORCH_API at::Tensor & cumsum_out(at::Tensor & out, const at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt);
+TORCH_API at::Tensor & cumsum_outf(const at::Tensor & self, int64_t dim, c10::optional dtype, at::Tensor & out);
+TORCH_API at::Tensor & cumsum_(at::Tensor & self, int64_t dim, c10::optional dtype=c10::nullopt);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/09/09cc0babb5cde11886b653a65808b3a7713428d836ba2c2dec01dfb4cc7538fc/bounds_overlap.h b/shared/package-files/09/09cc0babb5cde11886b653a65808b3a7713428d836ba2c2dec01dfb4cc7538fc/bounds_overlap.h
new file mode 100644
index 0000000000000000000000000000000000000000..a602e2938f293bdb6931b7278e72fc65b9142401
--- /dev/null
+++ b/shared/package-files/09/09cc0babb5cde11886b653a65808b3a7713428d836ba2c2dec01dfb4cc7538fc/bounds_overlap.h
@@ -0,0 +1,128 @@
+#pragma once
+
+#include 
+#include 
+
+#include 
+#include 
+#include 
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+namespace analysis {
+
+// A simple class containing the start and end of a range in a single dimension.
+struct TORCH_API Bound {
+  ExprPtr start{nullptr};
+  ExprPtr end{nullptr};
+
+  // This stores whether or not the start and end of this Bound have previously
+  // been swapped. This occurs when the bound is in a loop with a negative
+  // stride.
+  bool swapped{false};
+
+  Bound() = default;
+  Bound(ExprPtr s, ExprPtr e) : start(std::move(s)), end(std::move(e)) {}
+
+  void print() const;
+  bool equals(const Bound& other) const;
+
+  // The comparison operators are conservative. If the compare operator returns
+  // true, it means that all the elements satisfy the logical expression. But
+  // the false does not mean the opposite comparison is satisfied. It could be
+  // but not always.
+  bool operator==(const Bound& other) const;
+  bool operator!=(const Bound& other) const;
+  bool operator<(const Bound& other) const;
+  bool operator<=(const Bound& other) const;
+  bool operator>(const Bound& other) const;
+  bool operator>=(const Bound& other) const;
+
+  void swap() {
+    std::swap(start, end);
+    swapped = !swapped;
+  }
+};
+
+struct BoundHash {
+  size_t operator()(const Bound& b) const {
+    return std::hash()(b.start) ^ std::hash()(b.end);
+  }
+};
+
+// The type of overlap found. Each condition is true only if none of the
+// previous conditions hold.
+//     ContainedOrEqual: All elements in the Bound A are in the Bound B (this
+//                       includes the case where the bounds are equal).
+//     Contains: All elements in the Bound B are in the Bound B.
+//     PartialOverlap: Any elements in the Bound B are in the Bound A.
+//     NoOverlap: No elements in the Bound A are in the bound B.
+enum class OverlapKind {
+  ContainedOrEqual,
+  Contains,
+  PartialOverlap,
+  NoOverlap
+};
+
+// The Bound comparison result.
+//     True: Every Bound element always satisfies the given comparison operator
+//     False: Every Bound element always does NOT satisfy the given comparison
+//     operator
+//     NotDetermined: Some elements satisfy the given comparison operator and
+//     some elements not
+enum class CmpEvalResult { True, False, NotDetermined };
+
+// Returns the kind of overlap between Bound A and Bound A in a single
+// dimension.
+OverlapKind TORCH_API boundOverlap(Bound A, Bound B);
+
+// The comparison is conservative and the compare result is deterministic.
+// It means that every element of the Bound to be compared needs to satisfy
+// the given comparison operator.
+CmpEvalResult TORCH_API compareBound(
+    const Bound& a,
+    const Bound& b,
+    const CompareSelectOperation& cmp_op);
+
+// A multi dimensional bound representing the bound of a set of indices.
+using IndexBounds = std::vector;
+
+// Returns true if two IndexBounds are equivalent.
+bool TORCH_API indexBoundsEquals(const IndexBounds& A, const IndexBounds& B);
+
+// Flattens a multi dimensional bound to a single dimension. The IndexBounds "a"
+// *must* encapsulate the entire range of the buffer.
+Bound TORCH_API flattenBounds(const IndexBounds& a);
+
+// Determines the kind of overlap in X dimensions.
+OverlapKind TORCH_API overlaps(const IndexBounds& a, const IndexBounds& b);
+
+// Returns the Bound slices created by subtracing bound B from bound A.
+// Multiple Bounds can be returned in the case where B slices A into two
+// distinct regions with no overlap.
+//
+// For example:
+//    subtractBound((0, 10), (2, 4)) => [(0, 1), (5, 10)]
+//       bound A: (0, 10)
+//       bound B: (2, 4)
+//       If we remove slice (2, 4) from the slice (0, 10), we will be left
+//       with 2 slices, one at the start (0, 1), and one at the end (5, 10).
+//       So, the result of this subtraction is [(0, 1), (5, 10)].
+//
+// Note: this doesn't use IndexBounds because the Bounds returned do not
+// represent multiple different dimensions.
+std::vector TORCH_API subtractBound(Bound a, Bound b);
+
+// Returns the bound slices created by subtracting the IndexBounds B from A.
+std::vector TORCH_API subtractIndicesBounds(
+    const IndexBounds& A,
+    const IndexBounds& B,
+    OverlapKind overlap);
+std::vector TORCH_API
+subtractIndicesBounds(const IndexBounds& A, const IndexBounds& B);
+
+} // namespace analysis
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
diff --git a/shared/package-files/09/09cc1f52d22450e8060655d736995013a760d94fd404a747e806a344e3c9e6ec/conftest.py b/shared/package-files/09/09cc1f52d22450e8060655d736995013a760d94fd404a747e806a344e3c9e6ec/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..2961e7f02f3f9081f9918f090233281e18db2c2e
--- /dev/null
+++ b/shared/package-files/09/09cc1f52d22450e8060655d736995013a760d94fd404a747e806a344e3c9e6ec/conftest.py
@@ -0,0 +1,178 @@
+import pytest
+import sys
+import matplotlib
+from matplotlib import _api
+
+
+def pytest_configure(config):
+    # config is initialized here rather than in pytest.ini so that `pytest
+    # --pyargs matplotlib` (which would not find pytest.ini) works.  The only
+    # entries in pytest.ini set minversion (which is checked earlier),
+    # testpaths/python_files, as they are required to properly find the tests
+    for key, value in [
+        ("markers", "flaky: (Provided by pytest-rerunfailures.)"),
+        ("markers", "timeout: (Provided by pytest-timeout.)"),
+        ("markers", "backend: Set alternate Matplotlib backend temporarily."),
+        ("markers", "baseline_images: Compare output against references."),
+        ("markers", "pytz: Tests that require pytz to be installed."),
+        ("filterwarnings", "error"),
+        ("filterwarnings",
+         "ignore:.*The py23 module has been deprecated:DeprecationWarning"),
+        ("filterwarnings",
+         r"ignore:DynamicImporter.find_spec\(\) not found; "
+         r"falling back to find_module\(\):ImportWarning"),
+    ]:
+        config.addinivalue_line(key, value)
+
+    matplotlib.use('agg', force=True)
+    matplotlib._called_from_pytest = True
+    matplotlib._init_tests()
+
+
+def pytest_unconfigure(config):
+    matplotlib._called_from_pytest = False
+
+
+@pytest.fixture(autouse=True)
+def mpl_test_settings(request):
+    from matplotlib.testing.decorators import _cleanup_cm
+
+    with _cleanup_cm():
+
+        backend = None
+        backend_marker = request.node.get_closest_marker('backend')
+        prev_backend = matplotlib.get_backend()
+        if backend_marker is not None:
+            assert len(backend_marker.args) == 1, \
+                "Marker 'backend' must specify 1 backend."
+            backend, = backend_marker.args
+            skip_on_importerror = backend_marker.kwargs.get(
+                'skip_on_importerror', False)
+
+            # special case Qt backend importing to avoid conflicts
+            if backend.lower().startswith('qt5'):
+                if any(sys.modules.get(k) for k in ('PyQt4', 'PySide')):
+                    pytest.skip('Qt4 binding already imported')
+
+        matplotlib.testing.setup()
+        with _api.suppress_matplotlib_deprecation_warning():
+            if backend is not None:
+                # This import must come after setup() so it doesn't load the
+                # default backend prematurely.
+                import matplotlib.pyplot as plt
+                try:
+                    plt.switch_backend(backend)
+                except ImportError as exc:
+                    # Should only occur for the cairo backend tests, if neither
+                    # pycairo nor cairocffi are installed.
+                    if 'cairo' in backend.lower() or skip_on_importerror:
+                        pytest.skip("Failed to switch to backend "
+                                    f"{backend} ({exc}).")
+                    else:
+                        raise
+            # Default of cleanup and image_comparison too.
+            matplotlib.style.use(["classic", "_classic_test_patch"])
+        try:
+            yield
+        finally:
+            if backend is not None:
+                plt.close("all")
+                matplotlib.use(prev_backend)
+
+
+@pytest.fixture
+def pd():
+    """
+    Fixture to import and configure pandas. Using this fixture, the test is skipped when
+    pandas is not installed. Use this fixture instead of importing pandas in test files.
+
+    Examples
+    --------
+    Request the pandas fixture by passing in ``pd`` as an argument to the test ::
+
+        def test_matshow_pandas(pd):
+
+            df = pd.DataFrame({'x':[1,2,3], 'y':[4,5,6]})
+            im = plt.figure().subplots().matshow(df)
+            np.testing.assert_array_equal(im.get_array(), df)
+    """
+    pd = pytest.importorskip('pandas')
+    try:
+        from pandas.plotting import (
+            deregister_matplotlib_converters as deregister)
+        deregister()
+    except ImportError:
+        pass
+    return pd
+
+
+@pytest.fixture
+def xr():
+    """
+    Fixture to import xarray so that the test is skipped when xarray is not installed.
+    Use this fixture instead of importing xrray in test files.
+
+    Examples
+    --------
+    Request the xarray fixture by passing in ``xr`` as an argument to the test ::
+
+        def test_imshow_xarray(xr):
+
+            ds = xr.DataArray(np.random.randn(2, 3))
+            im = plt.figure().subplots().imshow(ds)
+            np.testing.assert_array_equal(im.get_array(), ds)
+    """
+
+    xr = pytest.importorskip('xarray')
+    return xr
+
+
+@pytest.fixture
+def text_placeholders(monkeypatch):
+    """
+    Replace texts with placeholder rectangles.
+
+    The rectangle size only depends on the font size and the number of characters. It is
+    thus insensitive to font properties and rendering details. This should be used for
+    tests that depend on text geometries but not the actual text rendering, e.g. layout
+    tests.
+    """
+    from matplotlib.patches import Rectangle
+
+    def patched_get_text_metrics_with_cache(renderer, text, fontprop, ismath, dpi):
+        """
+        Replace ``_get_text_metrics_with_cache`` with fixed results.
+
+        The usual ``renderer.get_text_width_height_descent`` would depend on font
+        metrics; instead the fixed results are based on font size and the length of the
+        string only.
+        """
+        # While get_window_extent returns pixels and font size is in points, font size
+        # includes ascenders and descenders. Leaving out this factor and setting
+        # descent=0 ends up with a box that is relatively close to DejaVu Sans.
+        height = fontprop.get_size()
+        width = len(text) * height / 1.618  # Golden ratio for character size.
+        descent = 0
+        return width, height, descent
+
+    def patched_text_draw(self, renderer):
+        """
+        Replace ``Text.draw`` with a fixed bounding box Rectangle.
+
+        The bounding box corresponds to ``Text.get_window_extent``, which ultimately
+        depends on the above patched ``_get_text_metrics_with_cache``.
+        """
+        if renderer is not None:
+            self._renderer = renderer
+        if not self.get_visible():
+            return
+        if self.get_text() == '':
+            return
+        bbox = self.get_window_extent()
+        rect = Rectangle(bbox.p0, bbox.width, bbox.height,
+                         facecolor=self.get_color(), edgecolor='none')
+        rect.draw(renderer)
+
+    monkeypatch.setattr('matplotlib.text._get_text_metrics_with_cache',
+                        patched_get_text_metrics_with_cache)
+    monkeypatch.setattr('matplotlib.text.Text.draw', patched_text_draw)
diff --git a/shared/package-files/09/09d0eb84fd426d29ff520ddaf6eb0f015195f0354991e808cdb9445cac38f246/variable.py b/shared/package-files/09/09d0eb84fd426d29ff520ddaf6eb0f015195f0354991e808cdb9445cac38f246/variable.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f7efda3328b0fd3dfbbc452347100bf3db8d506
--- /dev/null
+++ b/shared/package-files/09/09d0eb84fd426d29ff520ddaf6eb0f015195f0354991e808cdb9445cac38f246/variable.py
@@ -0,0 +1,85 @@
+from contextlib import contextmanager
+from .utils import hashable
+from .dispatch import dispatch
+
+_global_logic_variables = set()  # type: ignore[var-annotated]
+_glv = _global_logic_variables
+
+
+class Var:
+    """ Logic Variable """
+
+    _id = 1
+
+    def __new__(cls, *token):
+        if len(token) == 0:
+            token = f"_{Var._id}"  # type: ignore[assignment]
+            Var._id += 1
+        elif len(token) == 1:
+            token = token[0]
+
+        obj = object.__new__(cls)
+        obj.token = token  # type: ignore[attr-defined]
+        return obj
+
+    def __str__(self):
+        return "~" + str(self.token)  # type: ignore[attr-defined]
+    __repr__ = __str__
+
+    def __eq__(self, other):
+        return type(self) == type(other) and self.token == other.token  # type: ignore[attr-defined]
+
+    def __hash__(self):
+        return hash((type(self), self.token))  # type: ignore[attr-defined]
+
+
+def var():
+    return lambda *args: Var(*args)
+
+
+def vars():
+    return lambda n: [var() for i in range(n)]
+
+
+@dispatch(Var)
+def isvar(v):
+    return True
+
+isvar
+
+
+@dispatch(object)  # type: ignore[no-redef]
+def isvar(o):
+    return not not _glv and hashable(o) and o in _glv
+
+
+@contextmanager
+def variables(*variables):
+    """
+    Context manager for logic variables
+
+    Example:
+        >>> # xdoctest: +SKIP("undefined vars")
+        >>> from __future__ import with_statement
+        >>> with variables(1):
+        ...     print(isvar(1))
+        True
+        >>> print(isvar(1))
+        False
+        >>> # Normal approach
+        >>> from unification import unify
+        >>> x = var('x')
+        >>> unify(x, 1)
+        {~x: 1}
+        >>> # Context Manager approach
+        >>> with variables('x'):
+        ...     print(unify('x', 1))
+        {'x': 1}
+    """
+    old_global_logic_variables = _global_logic_variables.copy()
+    _global_logic_variables.update(set(variables))
+    try:
+        yield
+    finally:
+        _global_logic_variables.clear()
+        _global_logic_variables.update(old_global_logic_variables)
diff --git a/shared/package-files/09/09d593953d544aa6feb70ff7b408ff0910b4556bf8b48c9e0c9af79f60aae834/wrappers.py b/shared/package-files/09/09d593953d544aa6feb70ff7b408ff0910b4556bf8b48c9e0c9af79f60aae834/wrappers.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef37c40770aeed73253b6e0709ad5c4f5a8de352
--- /dev/null
+++ b/shared/package-files/09/09d593953d544aa6feb70ff7b408ff0910b4556bf8b48c9e0c9af79f60aae834/wrappers.py
@@ -0,0 +1,743 @@
+from collections import namedtuple
+
+import numpy as np
+
+from llvmlite.ir import Constant, IRBuilder
+from llvmlite import ir
+
+from numba.core import types, cgutils
+from numba.core.compiler_lock import global_compiler_lock
+from numba.core.caching import make_library_cache, NullCache
+
+
+_wrapper_info = namedtuple('_wrapper_info', ['library', 'env', 'name'])
+
+
+def _build_ufunc_loop_body(load, store, context, func, builder, arrays, out,
+                           offsets, store_offset, signature, pyapi, env):
+    elems = load()
+
+    # Compute
+    status, retval = context.call_conv.call_function(builder, func,
+                                                     signature.return_type,
+                                                     signature.args, elems)
+
+    # Store
+    with builder.if_else(status.is_ok, likely=True) as (if_ok, if_error):
+        with if_ok:
+            store(retval)
+        with if_error:
+            gil = pyapi.gil_ensure()
+            context.call_conv.raise_error(builder, pyapi, status)
+            pyapi.gil_release(gil)
+
+    # increment indices
+    for off, ary in zip(offsets, arrays):
+        builder.store(builder.add(builder.load(off), ary.step), off)
+
+    builder.store(builder.add(builder.load(store_offset), out.step),
+                  store_offset)
+
+    return status.code
+
+
+def _build_ufunc_loop_body_objmode(load, store, context, func, builder,
+                                   arrays, out, offsets, store_offset,
+                                   signature, env, pyapi):
+    elems = load()
+
+    # Compute
+    _objargs = [types.pyobject] * len(signature.args)
+    # We need to push the error indicator to avoid it messing with
+    # the ufunc's execution.  We restore it unless the ufunc raised
+    # a new error.
+    with pyapi.err_push(keep_new=True):
+        status, retval = context.call_conv.call_function(builder, func,
+                                                         types.pyobject,
+                                                         _objargs, elems)
+        # Release owned reference to arguments
+        for elem in elems:
+            pyapi.decref(elem)
+    # NOTE: if an error occurred, it will be caught by the Numpy machinery
+
+    # Store
+    store(retval)
+
+    # increment indices
+    for off, ary in zip(offsets, arrays):
+        builder.store(builder.add(builder.load(off), ary.step), off)
+
+    builder.store(builder.add(builder.load(store_offset), out.step),
+                  store_offset)
+
+    return status.code
+
+
+def build_slow_loop_body(context, func, builder, arrays, out, offsets,
+                         store_offset, signature, pyapi, env):
+    def load():
+        elems = [ary.load_direct(builder.load(off))
+                 for off, ary in zip(offsets, arrays)]
+        return elems
+
+    def store(retval):
+        out.store_direct(retval, builder.load(store_offset))
+
+    return _build_ufunc_loop_body(load, store, context, func, builder, arrays,
+                                  out, offsets, store_offset, signature, pyapi,
+                                  env=env)
+
+
+def build_obj_loop_body(context, func, builder, arrays, out, offsets,
+                        store_offset, signature, pyapi, envptr, env):
+    env_body = context.get_env_body(builder, envptr)
+    env_manager = pyapi.get_env_manager(env, env_body, envptr)
+
+    def load():
+        # Load
+        elems = [ary.load_direct(builder.load(off))
+                 for off, ary in zip(offsets, arrays)]
+        # Box
+        elems = [pyapi.from_native_value(t, v, env_manager)
+                 for v, t in zip(elems, signature.args)]
+        return elems
+
+    def store(retval):
+        is_ok = cgutils.is_not_null(builder, retval)
+        # If an error is raised by the object mode ufunc, it will
+        # simply get caught by the Numpy ufunc machinery.
+        with builder.if_then(is_ok, likely=True):
+            # Unbox
+            native = pyapi.to_native_value(signature.return_type, retval)
+            assert native.cleanup is None
+            # Store
+            out.store_direct(native.value, builder.load(store_offset))
+            # Release owned reference
+            pyapi.decref(retval)
+
+    return _build_ufunc_loop_body_objmode(load, store, context, func, builder,
+                                          arrays, out, offsets, store_offset,
+                                          signature, envptr, pyapi)
+
+
+def build_fast_loop_body(context, func, builder, arrays, out, offsets,
+                         store_offset, signature, ind, pyapi, env):
+    def load():
+        elems = [ary.load_aligned(ind)
+                 for ary in arrays]
+        return elems
+
+    def store(retval):
+        out.store_aligned(retval, ind)
+
+    return _build_ufunc_loop_body(load, store, context, func, builder, arrays,
+                                  out, offsets, store_offset, signature, pyapi,
+                                  env=env)
+
+
+def build_ufunc_wrapper(library, context, fname, signature, objmode, cres):
+    """
+    Wrap the scalar function with a loop that iterates over the arguments
+
+    Returns
+    -------
+    (library, env, name)
+    """
+    assert isinstance(fname, str)
+    byte_t = ir.IntType(8)
+    byte_ptr_t = ir.PointerType(byte_t)
+    byte_ptr_ptr_t = ir.PointerType(byte_ptr_t)
+    intp_t = context.get_value_type(types.intp)
+    intp_ptr_t = ir.PointerType(intp_t)
+
+    fnty = ir.FunctionType(ir.VoidType(), [byte_ptr_ptr_t, intp_ptr_t,
+                                           intp_ptr_t, byte_ptr_t])
+
+    wrapperlib = context.codegen().create_library('ufunc_wrapper')
+    wrapper_module = wrapperlib.create_ir_module('')
+    if objmode:
+        func_type = context.call_conv.get_function_type(
+            types.pyobject, [types.pyobject] * len(signature.args))
+    else:
+        func_type = context.call_conv.get_function_type(
+            signature.return_type, signature.args)
+
+    func = ir.Function(wrapper_module, func_type, name=fname)
+    func.attributes.add("alwaysinline")
+
+    wrapper = ir.Function(wrapper_module, fnty, "__ufunc__." + func.name)
+    arg_args, arg_dims, arg_steps, arg_data = wrapper.args
+    arg_args.name = "args"
+    arg_dims.name = "dims"
+    arg_steps.name = "steps"
+    arg_data.name = "data"
+
+    builder = IRBuilder(wrapper.append_basic_block("entry"))
+
+    # Prepare Environment
+    envname = context.get_env_name(cres.fndesc)
+    env = cres.environment
+    envptr = builder.load(context.declare_env_global(builder.module, envname))
+
+    # Emit loop
+    loopcount = builder.load(arg_dims, name="loopcount")
+
+    # Prepare inputs
+    arrays = []
+    for i, typ in enumerate(signature.args):
+        arrays.append(UArrayArg(context, builder, arg_args, arg_steps, i, typ))
+
+    # Prepare output
+    out = UArrayArg(context, builder, arg_args, arg_steps, len(arrays),
+                    signature.return_type)
+
+    # Setup indices
+    offsets = []
+    zero = context.get_constant(types.intp, 0)
+    for _ in arrays:
+        p = cgutils.alloca_once(builder, intp_t)
+        offsets.append(p)
+        builder.store(zero, p)
+
+    store_offset = cgutils.alloca_once(builder, intp_t)
+    builder.store(zero, store_offset)
+
+    unit_strided = cgutils.true_bit
+    for ary in arrays:
+        unit_strided = builder.and_(unit_strided, ary.is_unit_strided)
+
+    pyapi = context.get_python_api(builder)
+    if objmode:
+        # General loop
+        gil = pyapi.gil_ensure()
+        with cgutils.for_range(builder, loopcount, intp=intp_t):
+            build_obj_loop_body(
+                context, func, builder, arrays, out, offsets,
+                store_offset, signature, pyapi, envptr, env,
+            )
+        pyapi.gil_release(gil)
+        builder.ret_void()
+
+    else:
+        with builder.if_else(unit_strided) as (is_unit_strided, is_strided):
+            with is_unit_strided:
+                with cgutils.for_range(builder, loopcount, intp=intp_t) as loop:
+                    build_fast_loop_body(
+                        context, func, builder, arrays, out, offsets,
+                        store_offset, signature, loop.index, pyapi,
+                        env=envptr,
+                    )
+
+            with is_strided:
+                # General loop
+                with cgutils.for_range(builder, loopcount, intp=intp_t):
+                    build_slow_loop_body(
+                        context, func, builder, arrays, out, offsets,
+                        store_offset, signature, pyapi,
+                        env=envptr,
+                    )
+
+        builder.ret_void()
+    del builder
+
+    # Link and finalize
+    wrapperlib.add_ir_module(wrapper_module)
+    wrapperlib.add_linking_library(library)
+    return _wrapper_info(library=wrapperlib, env=env, name=wrapper.name)
+
+
+class UArrayArg(object):
+    def __init__(self, context, builder, args, steps, i, fe_type):
+        self.context = context
+        self.builder = builder
+        self.fe_type = fe_type
+        offset = self.context.get_constant(types.intp, i)
+        offseted_args = self.builder.load(builder.gep(args, [offset]))
+        data_type = context.get_data_type(fe_type)
+        self.dataptr = self.builder.bitcast(offseted_args,
+                                            data_type.as_pointer())
+        sizeof = self.context.get_abi_sizeof(data_type)
+        self.abisize = self.context.get_constant(types.intp, sizeof)
+        offseted_step = self.builder.gep(steps, [offset])
+        self.step = self.builder.load(offseted_step)
+        self.is_unit_strided = builder.icmp_unsigned('==',
+                                                     self.abisize, self.step)
+        self.builder = builder
+
+    def load_direct(self, byteoffset):
+        """
+        Generic load from the given *byteoffset*.  load_aligned() is
+        preferred if possible.
+        """
+        ptr = cgutils.pointer_add(self.builder, self.dataptr, byteoffset)
+        return self.context.unpack_value(self.builder, self.fe_type, ptr)
+
+    def load_aligned(self, ind):
+        # Using gep() instead of explicit pointer addition helps LLVM
+        # vectorize the loop.
+        ptr = self.builder.gep(self.dataptr, [ind])
+        return self.context.unpack_value(self.builder, self.fe_type, ptr)
+
+    def store_direct(self, value, byteoffset):
+        ptr = cgutils.pointer_add(self.builder, self.dataptr, byteoffset)
+        self.context.pack_value(self.builder, self.fe_type, value, ptr)
+
+    def store_aligned(self, value, ind):
+        ptr = self.builder.gep(self.dataptr, [ind])
+        self.context.pack_value(self.builder, self.fe_type, value, ptr)
+
+
+GufWrapperCache = make_library_cache('guf')
+
+
+class _GufuncWrapper(object):
+    def __init__(self, py_func, cres, sin, sout, cache, is_parfors):
+        """
+        The *is_parfors* argument is a boolean that indicates if the GUfunc
+        being built is to be used as a ParFors kernel. If True, it disables
+        the caching on the wrapper as a separate unit because it will be linked
+        into the caller function and cached along with it.
+        """
+        self.py_func = py_func
+        self.cres = cres
+        self.sin = sin
+        self.sout = sout
+        self.is_objectmode = self.signature.return_type == types.pyobject
+        self.cache = (GufWrapperCache(py_func=self.py_func)
+                      if cache else NullCache())
+        self.is_parfors = bool(is_parfors)
+
+    @property
+    def library(self):
+        return self.cres.library
+
+    @property
+    def context(self):
+        return self.cres.target_context
+
+    @property
+    def call_conv(self):
+        return self.context.call_conv
+
+    @property
+    def signature(self):
+        return self.cres.signature
+
+    @property
+    def fndesc(self):
+        return self.cres.fndesc
+
+    @property
+    def env(self):
+        return self.cres.environment
+
+    def _wrapper_function_type(self):
+        byte_t = ir.IntType(8)
+        byte_ptr_t = ir.PointerType(byte_t)
+        byte_ptr_ptr_t = ir.PointerType(byte_ptr_t)
+        intp_t = self.context.get_value_type(types.intp)
+        intp_ptr_t = ir.PointerType(intp_t)
+
+        fnty = ir.FunctionType(ir.VoidType(), [byte_ptr_ptr_t, intp_ptr_t,
+                                               intp_ptr_t, byte_ptr_t])
+        return fnty
+
+    def _build_wrapper(self, library, name):
+        """
+        The LLVM IRBuilder code to create the gufunc wrapper.
+        The *library* arg is the CodeLibrary to which the wrapper should
+        be added.  The *name* arg is the name of the wrapper function being
+        created.
+        """
+        intp_t = self.context.get_value_type(types.intp)
+        fnty = self._wrapper_function_type()
+
+        wrapper_module = library.create_ir_module('_gufunc_wrapper')
+        func_type = self.call_conv.get_function_type(self.fndesc.restype,
+                                                     self.fndesc.argtypes)
+        fname = self.fndesc.llvm_func_name
+        func = ir.Function(wrapper_module, func_type, name=fname)
+
+        func.attributes.add("alwaysinline")
+        wrapper = ir.Function(wrapper_module, fnty, name)
+        # The use of weak_odr linkage avoids the function being dropped due
+        # to the order in which the wrappers and the user function are linked.
+        wrapper.linkage = 'weak_odr'
+        arg_args, arg_dims, arg_steps, arg_data = wrapper.args
+        arg_args.name = "args"
+        arg_dims.name = "dims"
+        arg_steps.name = "steps"
+        arg_data.name = "data"
+
+        builder = IRBuilder(wrapper.append_basic_block("entry"))
+        loopcount = builder.load(arg_dims, name="loopcount")
+        pyapi = self.context.get_python_api(builder)
+
+        # Unpack shapes
+        unique_syms = set()
+        for grp in (self.sin, self.sout):
+            for syms in grp:
+                unique_syms |= set(syms)
+
+        sym_map = {}
+        for syms in self.sin:
+            for s in syms:
+                if s not in sym_map:
+                    sym_map[s] = len(sym_map)
+
+        sym_dim = {}
+        for s, i in sym_map.items():
+            sym_dim[s] = builder.load(builder.gep(arg_dims,
+                                                  [self.context.get_constant(
+                                                      types.intp,
+                                                      i + 1)]))
+
+        # Prepare inputs
+        arrays = []
+        step_offset = len(self.sin) + len(self.sout)
+        for i, (typ, sym) in enumerate(zip(self.signature.args,
+                                           self.sin + self.sout)):
+            ary = GUArrayArg(self.context, builder, arg_args,
+                             arg_steps, i, step_offset, typ, sym, sym_dim)
+            step_offset += len(sym)
+            arrays.append(ary)
+
+        bbreturn = builder.append_basic_block('.return')
+
+        # Prologue
+        self.gen_prologue(builder, pyapi)
+
+        # Loop
+        with cgutils.for_range(builder, loopcount, intp=intp_t) as loop:
+            args = [a.get_array_at_offset(loop.index) for a in arrays]
+            innercall, error = self.gen_loop_body(builder, pyapi, func, args)
+            # If error, escape
+            cgutils.cbranch_or_continue(builder, error, bbreturn)
+
+        builder.branch(bbreturn)
+        builder.position_at_end(bbreturn)
+
+        # Epilogue
+        self.gen_epilogue(builder, pyapi)
+
+        builder.ret_void()
+
+        # Link
+        library.add_ir_module(wrapper_module)
+        library.add_linking_library(self.library)
+
+    def _compile_wrapper(self, wrapper_name):
+        # Gufunc created by Parfors?
+        if self.is_parfors:
+            # No wrapper caching for parfors
+            wrapperlib = self.context.codegen().create_library(str(self))
+            # Build wrapper
+            self._build_wrapper(wrapperlib, wrapper_name)
+        # Non-parfors?
+        else:
+            # Use cache and compiler in a critical section
+            wrapperlib = self.cache.load_overload(
+                self.cres.signature, self.cres.target_context,
+            )
+            if wrapperlib is None:
+                # Create library and enable caching
+                wrapperlib = self.context.codegen().create_library(str(self))
+                wrapperlib.enable_object_caching()
+                # Build wrapper
+                self._build_wrapper(wrapperlib, wrapper_name)
+                # Cache
+                self.cache.save_overload(self.cres.signature, wrapperlib)
+
+        return wrapperlib
+
+    @global_compiler_lock
+    def build(self):
+        wrapper_name = "__gufunc__." + self.fndesc.mangled_name
+        wrapperlib = self._compile_wrapper(wrapper_name)
+        return _wrapper_info(
+            library=wrapperlib, env=self.env, name=wrapper_name,
+        )
+
+    def gen_loop_body(self, builder, pyapi, func, args):
+        status, retval = self.call_conv.call_function(
+            builder, func, self.signature.return_type, self.signature.args,
+            args)
+
+        with builder.if_then(status.is_error, likely=False):
+            gil = pyapi.gil_ensure()
+            self.context.call_conv.raise_error(builder, pyapi, status)
+            pyapi.gil_release(gil)
+
+        return status.code, status.is_error
+
+    def gen_prologue(self, builder, pyapi):
+        pass        # Do nothing
+
+    def gen_epilogue(self, builder, pyapi):
+        pass        # Do nothing
+
+
+class _GufuncObjectWrapper(_GufuncWrapper):
+    def gen_loop_body(self, builder, pyapi, func, args):
+        innercall, error = _prepare_call_to_object_mode(self.context,
+                                                        builder, pyapi, func,
+                                                        self.signature,
+                                                        args)
+        return innercall, error
+
+    def gen_prologue(self, builder, pyapi):
+        # Acquire the GIL
+        self.gil = pyapi.gil_ensure()
+
+    def gen_epilogue(self, builder, pyapi):
+        # Release GIL
+        pyapi.gil_release(self.gil)
+
+
+def build_gufunc_wrapper(py_func, cres, sin, sout, cache, is_parfors):
+    signature = cres.signature
+    wrapcls = (_GufuncObjectWrapper
+               if signature.return_type == types.pyobject
+               else _GufuncWrapper)
+    return wrapcls(
+        py_func, cres, sin, sout, cache, is_parfors=is_parfors,
+    ).build()
+
+
+def _prepare_call_to_object_mode(context, builder, pyapi, func,
+                                 signature, args):
+    mod = builder.module
+
+    bb_core_return = builder.append_basic_block('ufunc.core.return')
+
+    # Call to
+    # PyObject* ndarray_new(int nd,
+    #       npy_intp *dims,   /* shape */
+    #       npy_intp *strides,
+    #       void* data,
+    #       int type_num,
+    #       int itemsize)
+
+    ll_int = context.get_value_type(types.int32)
+    ll_intp = context.get_value_type(types.intp)
+    ll_intp_ptr = ir.PointerType(ll_intp)
+    ll_voidptr = context.get_value_type(types.voidptr)
+    ll_pyobj = context.get_value_type(types.pyobject)
+    fnty = ir.FunctionType(ll_pyobj, [ll_int, ll_intp_ptr,
+                                      ll_intp_ptr, ll_voidptr,
+                                      ll_int, ll_int])
+
+    fn_array_new = cgutils.get_or_insert_function(mod, fnty,
+                                                  "numba_ndarray_new")
+
+    # Convert each llarray into pyobject
+    error_pointer = cgutils.alloca_once(builder, ir.IntType(1), name='error')
+    builder.store(cgutils.true_bit, error_pointer)
+
+    # The PyObject* arguments to the kernel function
+    object_args = []
+    object_pointers = []
+
+    for i, (arg, argty) in enumerate(zip(args, signature.args)):
+        # Allocate NULL-initialized slot for this argument
+        objptr = cgutils.alloca_once(builder, ll_pyobj, zfill=True)
+        object_pointers.append(objptr)
+
+        if isinstance(argty, types.Array):
+            # Special case arrays: we don't need full-blown NRT reflection
+            # since the argument will be gone at the end of the kernel
+            arycls = context.make_array(argty)
+            array = arycls(context, builder, value=arg)
+
+            zero = Constant(ll_int, 0)
+
+            # Extract members of the llarray
+            nd = Constant(ll_int, argty.ndim)
+            dims = builder.gep(array._get_ptr_by_name('shape'), [zero, zero])
+            strides = builder.gep(array._get_ptr_by_name('strides'),
+                                  [zero, zero])
+            data = builder.bitcast(array.data, ll_voidptr)
+            dtype = np.dtype(str(argty.dtype))
+
+            # Prepare other info for reconstruction of the PyArray
+            type_num = Constant(ll_int, dtype.num)
+            itemsize = Constant(ll_int, dtype.itemsize)
+
+            # Call helper to reconstruct PyArray objects
+            obj = builder.call(fn_array_new, [nd, dims, strides, data,
+                                              type_num, itemsize])
+        else:
+            # Other argument types => use generic boxing
+            obj = pyapi.from_native_value(argty, arg)
+
+        builder.store(obj, objptr)
+        object_args.append(obj)
+
+        obj_is_null = cgutils.is_null(builder, obj)
+        builder.store(obj_is_null, error_pointer)
+        cgutils.cbranch_or_continue(builder, obj_is_null, bb_core_return)
+
+    # Call ufunc core function
+    object_sig = [types.pyobject] * len(object_args)
+
+    status, retval = context.call_conv.call_function(
+        builder, func, types.pyobject, object_sig,
+        object_args)
+    builder.store(status.is_error, error_pointer)
+
+    # Release returned object
+    pyapi.decref(retval)
+
+    builder.branch(bb_core_return)
+    # At return block
+    builder.position_at_end(bb_core_return)
+
+    # Release argument objects
+    for objptr in object_pointers:
+        pyapi.decref(builder.load(objptr))
+
+    innercall = status.code
+    return innercall, builder.load(error_pointer)
+
+
+class GUArrayArg(object):
+    def __init__(self, context, builder, args, steps, i, step_offset,
+                 typ, syms, sym_dim):
+
+        self.context = context
+        self.builder = builder
+
+        offset = context.get_constant(types.intp, i)
+
+        data = builder.load(builder.gep(args, [offset], name="data.ptr"),
+                            name="data")
+        self.data = data
+
+        core_step_ptr = builder.gep(steps, [offset], name="core.step.ptr")
+        core_step = builder.load(core_step_ptr)
+
+        if isinstance(typ, types.Array):
+            as_scalar = not syms
+
+            # number of symbol in the shape spec should match the dimension
+            # of the array type.
+            if len(syms) != typ.ndim:
+                if len(syms) == 0 and typ.ndim == 1:
+                    # This is an exception for handling scalar argument.
+                    # The type can be 1D array for scalar.
+                    # In the future, we may deprecate this exception.
+                    pass
+                else:
+                    raise TypeError("type and shape signature mismatch for arg "
+                                    "#{0}".format(i + 1))
+
+            ndim = typ.ndim
+            shape = [sym_dim[s] for s in syms]
+            strides = []
+
+            for j in range(ndim):
+                stepptr = builder.gep(steps,
+                                      [context.get_constant(types.intp,
+                                                            step_offset + j)],
+                                      name="step.ptr")
+                step = builder.load(stepptr)
+                strides.append(step)
+
+            ldcls = (_ArrayAsScalarArgLoader
+                     if as_scalar
+                     else _ArrayArgLoader)
+
+            self._loader = ldcls(dtype=typ.dtype,
+                                 ndim=ndim,
+                                 core_step=core_step,
+                                 as_scalar=as_scalar,
+                                 shape=shape,
+                                 strides=strides)
+        else:
+            # If typ is not an array
+            if syms:
+                raise TypeError("scalar type {0} given for non scalar "
+                                "argument #{1}".format(typ, i + 1))
+            self._loader = _ScalarArgLoader(dtype=typ, stride=core_step)
+
+    def get_array_at_offset(self, ind):
+        return self._loader.load(context=self.context, builder=self.builder,
+                                 data=self.data, ind=ind)
+
+
+class _ScalarArgLoader(object):
+    """
+    Handle GFunc argument loading where a scalar type is used in the core
+    function.
+    Note: It still has a stride because the input to the gufunc can be an array
+          for this argument.
+    """
+
+    def __init__(self, dtype, stride):
+        self.dtype = dtype
+        self.stride = stride
+
+    def load(self, context, builder, data, ind):
+        # Load at base + ind * stride
+        data = builder.gep(data, [builder.mul(ind, self.stride)])
+        dptr = builder.bitcast(data,
+                               context.get_data_type(self.dtype).as_pointer())
+        return builder.load(dptr)
+
+
+class _ArrayArgLoader(object):
+    """
+    Handle GUFunc argument loading where an array is expected.
+    """
+
+    def __init__(self, dtype, ndim, core_step, as_scalar, shape, strides):
+        self.dtype = dtype
+        self.ndim = ndim
+        self.core_step = core_step
+        self.as_scalar = as_scalar
+        self.shape = shape
+        self.strides = strides
+
+    def load(self, context, builder, data, ind):
+        arytyp = types.Array(dtype=self.dtype, ndim=self.ndim, layout="A")
+        arycls = context.make_array(arytyp)
+
+        array = arycls(context, builder)
+        offseted_data = cgutils.pointer_add(builder,
+                                            data,
+                                            builder.mul(self.core_step,
+                                                        ind))
+
+        shape, strides = self._shape_and_strides(context, builder)
+
+        itemsize = context.get_abi_sizeof(context.get_data_type(self.dtype))
+        context.populate_array(array,
+                               data=builder.bitcast(offseted_data,
+                                                    array.data.type),
+                               shape=shape,
+                               strides=strides,
+                               itemsize=context.get_constant(types.intp,
+                                                             itemsize),
+                               meminfo=None)
+
+        return array._getvalue()
+
+    def _shape_and_strides(self, context, builder):
+        shape = cgutils.pack_array(builder, self.shape)
+        strides = cgutils.pack_array(builder, self.strides)
+        return shape, strides
+
+
+class _ArrayAsScalarArgLoader(_ArrayArgLoader):
+    """
+    Handle GUFunc argument loading where the shape signature specifies
+    a scalar "()" but a 1D array is used for the type of the core function.
+    """
+
+    def _shape_and_strides(self, context, builder):
+        # Set shape and strides for a 1D size 1 array
+        one = context.get_constant(types.intp, 1)
+        zero = context.get_constant(types.intp, 0)
+        shape = cgutils.pack_array(builder, [one])
+        strides = cgutils.pack_array(builder, [zero])
+        return shape, strides
diff --git a/shared/package-files/09/09dad132590b35317ced7f3de872b76b1ed715977ecf8ce157b765c018eedd56/tree_matcher.py b/shared/package-files/09/09dad132590b35317ced7f3de872b76b1ed715977ecf8ce157b765c018eedd56/tree_matcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a15328352719a4a49362b523d7dd7a586bccbc5
--- /dev/null
+++ b/shared/package-files/09/09dad132590b35317ced7f3de872b76b1ed715977ecf8ce157b765c018eedd56/tree_matcher.py
@@ -0,0 +1,186 @@
+"""Tree matcher based on Lark grammar"""
+
+import re
+from collections import defaultdict
+
+from . import Tree, Token
+from .common import ParserConf
+from .parsers import earley
+from .grammar import Rule, Terminal, NonTerminal
+
+
+def is_discarded_terminal(t):
+    return t.is_term and t.filter_out
+
+
+class _MakeTreeMatch:
+    def __init__(self, name, expansion):
+        self.name = name
+        self.expansion = expansion
+
+    def __call__(self, args):
+        t = Tree(self.name, args)
+        t.meta.match_tree = True
+        t.meta.orig_expansion = self.expansion
+        return t
+
+
+def _best_from_group(seq, group_key, cmp_key):
+    d = {}
+    for item in seq:
+        key = group_key(item)
+        if key in d:
+            v1 = cmp_key(item)
+            v2 = cmp_key(d[key])
+            if v2 > v1:
+                d[key] = item
+        else:
+            d[key] = item
+    return list(d.values())
+
+
+def _best_rules_from_group(rules):
+    rules = _best_from_group(rules, lambda r: r, lambda r: -len(r.expansion))
+    rules.sort(key=lambda r: len(r.expansion))
+    return rules
+
+
+def _match(term, token):
+    if isinstance(token, Tree):
+        name, _args = parse_rulename(term.name)
+        return token.data == name
+    elif isinstance(token, Token):
+        return term == Terminal(token.type)
+    assert False, (term, token)
+
+
+def make_recons_rule(origin, expansion, old_expansion):
+    return Rule(origin, expansion, alias=_MakeTreeMatch(origin.name, old_expansion))
+
+
+def make_recons_rule_to_term(origin, term):
+    return make_recons_rule(origin, [Terminal(term.name)], [term])
+
+
+def parse_rulename(s):
+    "Parse rule names that may contain a template syntax (like rule{a, b, ...})"
+    name, args_str = re.match(r'(\w+)(?:{(.+)})?', s).groups()
+    args = args_str and [a.strip() for a in args_str.split(',')]
+    return name, args
+
+
+
+class ChildrenLexer:
+    def __init__(self, children):
+        self.children = children
+
+    def lex(self, parser_state):
+        return self.children
+
+class TreeMatcher:
+    """Match the elements of a tree node, based on an ontology
+    provided by a Lark grammar.
+
+    Supports templates and inlined rules (`rule{a, b,..}` and `_rule`)
+
+    Initialize with an instance of Lark.
+    """
+
+    def __init__(self, parser):
+        # XXX TODO calling compile twice returns different results!
+        assert not parser.options.maybe_placeholders
+        # XXX TODO: we just ignore the potential existence of a postlexer
+        self.tokens, rules, _extra = parser.grammar.compile(parser.options.start, set())
+
+        self.rules_for_root = defaultdict(list)
+
+        self.rules = list(self._build_recons_rules(rules))
+        self.rules.reverse()
+
+        # Choose the best rule from each group of {rule => [rule.alias]}, since we only really need one derivation.
+        self.rules = _best_rules_from_group(self.rules)
+
+        self.parser = parser
+        self._parser_cache = {}
+
+    def _build_recons_rules(self, rules):
+        "Convert tree-parsing/construction rules to tree-matching rules"
+        expand1s = {r.origin for r in rules if r.options.expand1}
+
+        aliases = defaultdict(list)
+        for r in rules:
+            if r.alias:
+                aliases[r.origin].append(r.alias)
+
+        rule_names = {r.origin for r in rules}
+        nonterminals = {sym for sym in rule_names
+                        if sym.name.startswith('_') or sym in expand1s or sym in aliases}
+
+        seen = set()
+        for r in rules:
+            recons_exp = [sym if sym in nonterminals else Terminal(sym.name)
+                          for sym in r.expansion if not is_discarded_terminal(sym)]
+
+            # Skip self-recursive constructs
+            if recons_exp == [r.origin] and r.alias is None:
+                continue
+
+            sym = NonTerminal(r.alias) if r.alias else r.origin
+            rule = make_recons_rule(sym, recons_exp, r.expansion)
+
+            if sym in expand1s and len(recons_exp) != 1:
+                self.rules_for_root[sym.name].append(rule)
+
+                if sym.name not in seen:
+                    yield make_recons_rule_to_term(sym, sym)
+                    seen.add(sym.name)
+            else:
+                if sym.name.startswith('_') or sym in expand1s:
+                    yield rule
+                else:
+                    self.rules_for_root[sym.name].append(rule)
+
+        for origin, rule_aliases in aliases.items():
+            for alias in rule_aliases:
+                yield make_recons_rule_to_term(origin, NonTerminal(alias))
+            yield make_recons_rule_to_term(origin, origin)
+
+    def match_tree(self, tree, rulename):
+        """Match the elements of `tree` to the symbols of rule `rulename`.
+
+        Parameters:
+            tree (Tree): the tree node to match
+            rulename (str): The expected full rule name (including template args)
+
+        Returns:
+            Tree: an unreduced tree that matches `rulename`
+
+        Raises:
+            UnexpectedToken: If no match was found.
+
+        Note:
+            It's the callers' responsibility match the tree recursively.
+        """
+        if rulename:
+            # validate
+            name, _args = parse_rulename(rulename)
+            assert tree.data == name
+        else:
+            rulename = tree.data
+
+        # TODO: ambiguity?
+        try:
+            parser = self._parser_cache[rulename]
+        except KeyError:
+            rules = self.rules + _best_rules_from_group(self.rules_for_root[rulename])
+
+            # TODO pass callbacks through dict, instead of alias?
+            callbacks = {rule: rule.alias for rule in rules}
+            conf = ParserConf(rules, callbacks, [rulename])
+            parser = earley.Parser(self.parser.lexer_conf, conf, _match, resolve_ambiguity=True)
+            self._parser_cache[rulename] = parser
+
+        # find a full derivation
+        unreduced_tree = parser.parse(ChildrenLexer(tree.children), rulename)
+        assert unreduced_tree.data == rulename
+        return unreduced_tree
diff --git a/shared/package-files/09/09dd57c5419a6d199b82e9b3f5ad16e4f9242e94f101fdb648d13d394fe4e6a4/_adaptive_avg_pool3d_backward_cpu_dispatch.h b/shared/package-files/09/09dd57c5419a6d199b82e9b3f5ad16e4f9242e94f101fdb648d13d394fe4e6a4/_adaptive_avg_pool3d_backward_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..e15c2c744f1480f999ec4a231da2d25354dffbf2
--- /dev/null
+++ b/shared/package-files/09/09dd57c5419a6d199b82e9b3f5ad16e4f9242e94f101fdb648d13d394fe4e6a4/_adaptive_avg_pool3d_backward_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor _adaptive_avg_pool3d_backward(const at::Tensor & grad_output, const at::Tensor & self);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/09/09df979ff1d267e2e4e590435fcab8f4e40a4af9d2d3f938af4fad6fa1bc2d6e/fmt-config.cmake b/shared/package-files/09/09df979ff1d267e2e4e590435fcab8f4e40a4af9d2d3f938af4fad6fa1bc2d6e/fmt-config.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..c1b9de53150a3432280f1a14603077d7993eef78
--- /dev/null
+++ b/shared/package-files/09/09df979ff1d267e2e4e590435fcab8f4e40a4af9d2d3f938af4fad6fa1bc2d6e/fmt-config.cmake
@@ -0,0 +1,31 @@
+
+####### Expanded from @PACKAGE_INIT@ by configure_package_config_file() #######
+####### Any changes to this file will be overwritten by the next CMake run ####
+####### The input file was fmt-config.cmake.in                            ########
+
+get_filename_component(PACKAGE_PREFIX_DIR "${CMAKE_CURRENT_LIST_DIR}/../../../" ABSOLUTE)
+
+macro(set_and_check _var _file)
+  set(${_var} "${_file}")
+  if(NOT EXISTS "${_file}")
+    message(FATAL_ERROR "File or directory ${_file} referenced by variable ${_var} does not exist !")
+  endif()
+endmacro()
+
+macro(check_required_components _NAME)
+  foreach(comp ${${_NAME}_FIND_COMPONENTS})
+    if(NOT ${_NAME}_${comp}_FOUND)
+      if(${_NAME}_FIND_REQUIRED_${comp})
+        set(${_NAME}_FOUND FALSE)
+      endif()
+    endif()
+  endforeach()
+endmacro()
+
+####################################################################################
+
+if (NOT TARGET fmt::fmt)
+  include(${CMAKE_CURRENT_LIST_DIR}/fmt-targets.cmake)
+endif ()
+
+check_required_components(fmt)
diff --git a/shared/package-files/09/09dfa8272613416b4e2121ea9fdf9709a25e1abf144e4c17c81a975a6fd771c9/LICENSE b/shared/package-files/09/09dfa8272613416b4e2121ea9fdf9709a25e1abf144e4c17c81a975a6fd771c9/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..bb7dfabecb7c07ab2f0fa0d46c6c87a5f64cb4f4
--- /dev/null
+++ b/shared/package-files/09/09dfa8272613416b4e2121ea9fdf9709a25e1abf144e4c17c81a975a6fd771c9/LICENSE
@@ -0,0 +1,29 @@
+BSD License
+
+Copyright (c) 2019, Anaconda, Inc.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice, this
+  list of conditions and the following disclaimer in the documentation and/or
+  other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from this
+  software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
+IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
+INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
+BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
+OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/shared/package-files/09/09e13defc20c8b4616ce758a8c84f547c22b3f82a16744bbbac3d4beb79281c0/help.pdf b/shared/package-files/09/09e13defc20c8b4616ce758a8c84f547c22b3f82a16744bbbac3d4beb79281c0/help.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..38178d05b2725addadec9f4c005a1a764f1096d1
--- /dev/null
+++ b/shared/package-files/09/09e13defc20c8b4616ce758a8c84f547c22b3f82a16744bbbac3d4beb79281c0/help.pdf
@@ -0,0 +1,68 @@
+%PDF-1.4
+%† Ǽ
+1 0 obj
+<< /Pages 2 0 R /Type /Catalog >>
+endobj
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+<< /ExtGState 4 0 R /Font 3 0 R /Pattern 5 0 R
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+/XObject 7 0 R >>
+endobj
+10 0 obj
+<< /Annots [ ] /Contents 9 0 R
+/Group << /CS /DeviceRGB /S /Transparency /Type /Group >>
+/MediaBox [ 0 0 72 72 ] /Parent 2 0 R /Resources 8 0 R /Type /Page >>
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/É|éÑ×ý¥Œ—ÀR*Jb\›nÜ(—¢½âÁ5sX„Z’;áGL½	›è’÷j;Ý»%e2éÆz@ ÖÁ÷'´´\ÒA
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+<< /CreationDate (D:20170812144045-04'00')
+/Creator (matplotlib 2.0.2.post4623.dev0+gcb3aea0db, http://matplotlib.org)
+/Producer (matplotlib pdf backend 2.0.2.post4623.dev0+gcb3aea0db) >>
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+<< /Info 12 0 R /Root 1 0 R /Size 13 >>
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+1474
+%%EOF
diff --git a/shared/package-files/09/09e2811899e5eaeb24bef8e40145eb255c5b1c9ae94bf07fa727c57632f70917/git.py b/shared/package-files/09/09e2811899e5eaeb24bef8e40145eb255c5b1c9ae94bf07fa727c57632f70917/git.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c242cf89565401eda7a863b2e2829a3b4745d36
--- /dev/null
+++ b/shared/package-files/09/09e2811899e5eaeb24bef8e40145eb255c5b1c9ae94bf07fa727c57632f70917/git.py
@@ -0,0 +1,526 @@
+import logging
+import os.path
+import pathlib
+import re
+import urllib.parse
+import urllib.request
+from typing import List, Optional, Tuple
+
+from pip._internal.exceptions import BadCommand, InstallationError
+from pip._internal.utils.misc import HiddenText, display_path, hide_url
+from pip._internal.utils.subprocess import make_command
+from pip._internal.vcs.versioncontrol import (
+    AuthInfo,
+    RemoteNotFoundError,
+    RemoteNotValidError,
+    RevOptions,
+    VersionControl,
+    find_path_to_project_root_from_repo_root,
+    vcs,
+)
+
+urlsplit = urllib.parse.urlsplit
+urlunsplit = urllib.parse.urlunsplit
+
+
+logger = logging.getLogger(__name__)
+
+
+GIT_VERSION_REGEX = re.compile(
+    r"^git version "  # Prefix.
+    r"(\d+)"  # Major.
+    r"\.(\d+)"  # Dot, minor.
+    r"(?:\.(\d+))?"  # Optional dot, patch.
+    r".*$"  # Suffix, including any pre- and post-release segments we don't care about.
+)
+
+HASH_REGEX = re.compile("^[a-fA-F0-9]{40}$")
+
+# SCP (Secure copy protocol) shorthand. e.g. 'git@example.com:foo/bar.git'
+SCP_REGEX = re.compile(
+    r"""^
+    # Optional user, e.g. 'git@'
+    (\w+@)?
+    # Server, e.g. 'github.com'.
+    ([^/:]+):
+    # The server-side path. e.g. 'user/project.git'. Must start with an
+    # alphanumeric character so as not to be confusable with a Windows paths
+    # like 'C:/foo/bar' or 'C:\foo\bar'.
+    (\w[^:]*)
+    $""",
+    re.VERBOSE,
+)
+
+
+def looks_like_hash(sha: str) -> bool:
+    return bool(HASH_REGEX.match(sha))
+
+
+class Git(VersionControl):
+    name = "git"
+    dirname = ".git"
+    repo_name = "clone"
+    schemes = (
+        "git+http",
+        "git+https",
+        "git+ssh",
+        "git+git",
+        "git+file",
+    )
+    # Prevent the user's environment variables from interfering with pip:
+    # https://github.com/pypa/pip/issues/1130
+    unset_environ = ("GIT_DIR", "GIT_WORK_TREE")
+    default_arg_rev = "HEAD"
+
+    @staticmethod
+    def get_base_rev_args(rev: str) -> List[str]:
+        return [rev]
+
+    def is_immutable_rev_checkout(self, url: str, dest: str) -> bool:
+        _, rev_options = self.get_url_rev_options(hide_url(url))
+        if not rev_options.rev:
+            return False
+        if not self.is_commit_id_equal(dest, rev_options.rev):
+            # the current commit is different from rev,
+            # which means rev was something else than a commit hash
+            return False
+        # return False in the rare case rev is both a commit hash
+        # and a tag or a branch; we don't want to cache in that case
+        # because that branch/tag could point to something else in the future
+        is_tag_or_branch = bool(self.get_revision_sha(dest, rev_options.rev)[0])
+        return not is_tag_or_branch
+
+    def get_git_version(self) -> Tuple[int, ...]:
+        version = self.run_command(
+            ["version"],
+            command_desc="git version",
+            show_stdout=False,
+            stdout_only=True,
+        )
+        match = GIT_VERSION_REGEX.match(version)
+        if not match:
+            logger.warning("Can't parse git version: %s", version)
+            return ()
+        return (int(match.group(1)), int(match.group(2)))
+
+    @classmethod
+    def get_current_branch(cls, location: str) -> Optional[str]:
+        """
+        Return the current branch, or None if HEAD isn't at a branch
+        (e.g. detached HEAD).
+        """
+        # git-symbolic-ref exits with empty stdout if "HEAD" is a detached
+        # HEAD rather than a symbolic ref.  In addition, the -q causes the
+        # command to exit with status code 1 instead of 128 in this case
+        # and to suppress the message to stderr.
+        args = ["symbolic-ref", "-q", "HEAD"]
+        output = cls.run_command(
+            args,
+            extra_ok_returncodes=(1,),
+            show_stdout=False,
+            stdout_only=True,
+            cwd=location,
+        )
+        ref = output.strip()
+
+        if ref.startswith("refs/heads/"):
+            return ref[len("refs/heads/") :]
+
+        return None
+
+    @classmethod
+    def get_revision_sha(cls, dest: str, rev: str) -> Tuple[Optional[str], bool]:
+        """
+        Return (sha_or_none, is_branch), where sha_or_none is a commit hash
+        if the revision names a remote branch or tag, otherwise None.
+
+        Args:
+          dest: the repository directory.
+          rev: the revision name.
+        """
+        # Pass rev to pre-filter the list.
+        output = cls.run_command(
+            ["show-ref", rev],
+            cwd=dest,
+            show_stdout=False,
+            stdout_only=True,
+            on_returncode="ignore",
+        )
+        refs = {}
+        # NOTE: We do not use splitlines here since that would split on other
+        #       unicode separators, which can be maliciously used to install a
+        #       different revision.
+        for line in output.strip().split("\n"):
+            line = line.rstrip("\r")
+            if not line:
+                continue
+            try:
+                ref_sha, ref_name = line.split(" ", maxsplit=2)
+            except ValueError:
+                # Include the offending line to simplify troubleshooting if
+                # this error ever occurs.
+                raise ValueError(f"unexpected show-ref line: {line!r}")
+
+            refs[ref_name] = ref_sha
+
+        branch_ref = f"refs/remotes/origin/{rev}"
+        tag_ref = f"refs/tags/{rev}"
+
+        sha = refs.get(branch_ref)
+        if sha is not None:
+            return (sha, True)
+
+        sha = refs.get(tag_ref)
+
+        return (sha, False)
+
+    @classmethod
+    def _should_fetch(cls, dest: str, rev: str) -> bool:
+        """
+        Return true if rev is a ref or is a commit that we don't have locally.
+
+        Branches and tags are not considered in this method because they are
+        assumed to be always available locally (which is a normal outcome of
+        ``git clone`` and ``git fetch --tags``).
+        """
+        if rev.startswith("refs/"):
+            # Always fetch remote refs.
+            return True
+
+        if not looks_like_hash(rev):
+            # Git fetch would fail with abbreviated commits.
+            return False
+
+        if cls.has_commit(dest, rev):
+            # Don't fetch if we have the commit locally.
+            return False
+
+        return True
+
+    @classmethod
+    def resolve_revision(
+        cls, dest: str, url: HiddenText, rev_options: RevOptions
+    ) -> RevOptions:
+        """
+        Resolve a revision to a new RevOptions object with the SHA1 of the
+        branch, tag, or ref if found.
+
+        Args:
+          rev_options: a RevOptions object.
+        """
+        rev = rev_options.arg_rev
+        # The arg_rev property's implementation for Git ensures that the
+        # rev return value is always non-None.
+        assert rev is not None
+
+        sha, is_branch = cls.get_revision_sha(dest, rev)
+
+        if sha is not None:
+            rev_options = rev_options.make_new(sha)
+            rev_options.branch_name = rev if is_branch else None
+
+            return rev_options
+
+        # Do not show a warning for the common case of something that has
+        # the form of a Git commit hash.
+        if not looks_like_hash(rev):
+            logger.warning(
+                "Did not find branch or tag '%s', assuming revision or ref.",
+                rev,
+            )
+
+        if not cls._should_fetch(dest, rev):
+            return rev_options
+
+        # fetch the requested revision
+        cls.run_command(
+            make_command("fetch", "-q", url, rev_options.to_args()),
+            cwd=dest,
+        )
+        # Change the revision to the SHA of the ref we fetched
+        sha = cls.get_revision(dest, rev="FETCH_HEAD")
+        rev_options = rev_options.make_new(sha)
+
+        return rev_options
+
+    @classmethod
+    def is_commit_id_equal(cls, dest: str, name: Optional[str]) -> bool:
+        """
+        Return whether the current commit hash equals the given name.
+
+        Args:
+          dest: the repository directory.
+          name: a string name.
+        """
+        if not name:
+            # Then avoid an unnecessary subprocess call.
+            return False
+
+        return cls.get_revision(dest) == name
+
+    def fetch_new(
+        self, dest: str, url: HiddenText, rev_options: RevOptions, verbosity: int
+    ) -> None:
+        rev_display = rev_options.to_display()
+        logger.info("Cloning %s%s to %s", url, rev_display, display_path(dest))
+        if verbosity <= 0:
+            flags: Tuple[str, ...] = ("--quiet",)
+        elif verbosity == 1:
+            flags = ()
+        else:
+            flags = ("--verbose", "--progress")
+        if self.get_git_version() >= (2, 17):
+            # Git added support for partial clone in 2.17
+            # https://git-scm.com/docs/partial-clone
+            # Speeds up cloning by functioning without a complete copy of repository
+            self.run_command(
+                make_command(
+                    "clone",
+                    "--filter=blob:none",
+                    *flags,
+                    url,
+                    dest,
+                )
+            )
+        else:
+            self.run_command(make_command("clone", *flags, url, dest))
+
+        if rev_options.rev:
+            # Then a specific revision was requested.
+            rev_options = self.resolve_revision(dest, url, rev_options)
+            branch_name = getattr(rev_options, "branch_name", None)
+            logger.debug("Rev options %s, branch_name %s", rev_options, branch_name)
+            if branch_name is None:
+                # Only do a checkout if the current commit id doesn't match
+                # the requested revision.
+                if not self.is_commit_id_equal(dest, rev_options.rev):
+                    cmd_args = make_command(
+                        "checkout",
+                        "-q",
+                        rev_options.to_args(),
+                    )
+                    self.run_command(cmd_args, cwd=dest)
+            elif self.get_current_branch(dest) != branch_name:
+                # Then a specific branch was requested, and that branch
+                # is not yet checked out.
+                track_branch = f"origin/{branch_name}"
+                cmd_args = [
+                    "checkout",
+                    "-b",
+                    branch_name,
+                    "--track",
+                    track_branch,
+                ]
+                self.run_command(cmd_args, cwd=dest)
+        else:
+            sha = self.get_revision(dest)
+            rev_options = rev_options.make_new(sha)
+
+        logger.info("Resolved %s to commit %s", url, rev_options.rev)
+
+        #: repo may contain submodules
+        self.update_submodules(dest)
+
+    def switch(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
+        self.run_command(
+            make_command("config", "remote.origin.url", url),
+            cwd=dest,
+        )
+        cmd_args = make_command("checkout", "-q", rev_options.to_args())
+        self.run_command(cmd_args, cwd=dest)
+
+        self.update_submodules(dest)
+
+    def update(self, dest: str, url: HiddenText, rev_options: RevOptions) -> None:
+        # First fetch changes from the default remote
+        if self.get_git_version() >= (1, 9):
+            # fetch tags in addition to everything else
+            self.run_command(["fetch", "-q", "--tags"], cwd=dest)
+        else:
+            self.run_command(["fetch", "-q"], cwd=dest)
+        # Then reset to wanted revision (maybe even origin/master)
+        rev_options = self.resolve_revision(dest, url, rev_options)
+        cmd_args = make_command("reset", "--hard", "-q", rev_options.to_args())
+        self.run_command(cmd_args, cwd=dest)
+        #: update submodules
+        self.update_submodules(dest)
+
+    @classmethod
+    def get_remote_url(cls, location: str) -> str:
+        """
+        Return URL of the first remote encountered.
+
+        Raises RemoteNotFoundError if the repository does not have a remote
+        url configured.
+        """
+        # We need to pass 1 for extra_ok_returncodes since the command
+        # exits with return code 1 if there are no matching lines.
+        stdout = cls.run_command(
+            ["config", "--get-regexp", r"remote\..*\.url"],
+            extra_ok_returncodes=(1,),
+            show_stdout=False,
+            stdout_only=True,
+            cwd=location,
+        )
+        remotes = stdout.splitlines()
+        try:
+            found_remote = remotes[0]
+        except IndexError:
+            raise RemoteNotFoundError
+
+        for remote in remotes:
+            if remote.startswith("remote.origin.url "):
+                found_remote = remote
+                break
+        url = found_remote.split(" ")[1]
+        return cls._git_remote_to_pip_url(url.strip())
+
+    @staticmethod
+    def _git_remote_to_pip_url(url: str) -> str:
+        """
+        Convert a remote url from what git uses to what pip accepts.
+
+        There are 3 legal forms **url** may take:
+
+            1. A fully qualified url: ssh://git@example.com/foo/bar.git
+            2. A local project.git folder: /path/to/bare/repository.git
+            3. SCP shorthand for form 1: git@example.com:foo/bar.git
+
+        Form 1 is output as-is. Form 2 must be converted to URI and form 3 must
+        be converted to form 1.
+
+        See the corresponding test test_git_remote_url_to_pip() for examples of
+        sample inputs/outputs.
+        """
+        if re.match(r"\w+://", url):
+            # This is already valid. Pass it though as-is.
+            return url
+        if os.path.exists(url):
+            # A local bare remote (git clone --mirror).
+            # Needs a file:// prefix.
+            return pathlib.PurePath(url).as_uri()
+        scp_match = SCP_REGEX.match(url)
+        if scp_match:
+            # Add an ssh:// prefix and replace the ':' with a '/'.
+            return scp_match.expand(r"ssh://\1\2/\3")
+        # Otherwise, bail out.
+        raise RemoteNotValidError(url)
+
+    @classmethod
+    def has_commit(cls, location: str, rev: str) -> bool:
+        """
+        Check if rev is a commit that is available in the local repository.
+        """
+        try:
+            cls.run_command(
+                ["rev-parse", "-q", "--verify", "sha^" + rev],
+                cwd=location,
+                log_failed_cmd=False,
+            )
+        except InstallationError:
+            return False
+        else:
+            return True
+
+    @classmethod
+    def get_revision(cls, location: str, rev: Optional[str] = None) -> str:
+        if rev is None:
+            rev = "HEAD"
+        current_rev = cls.run_command(
+            ["rev-parse", rev],
+            show_stdout=False,
+            stdout_only=True,
+            cwd=location,
+        )
+        return current_rev.strip()
+
+    @classmethod
+    def get_subdirectory(cls, location: str) -> Optional[str]:
+        """
+        Return the path to Python project root, relative to the repo root.
+        Return None if the project root is in the repo root.
+        """
+        # find the repo root
+        git_dir = cls.run_command(
+            ["rev-parse", "--git-dir"],
+            show_stdout=False,
+            stdout_only=True,
+            cwd=location,
+        ).strip()
+        if not os.path.isabs(git_dir):
+            git_dir = os.path.join(location, git_dir)
+        repo_root = os.path.abspath(os.path.join(git_dir, ".."))
+        return find_path_to_project_root_from_repo_root(location, repo_root)
+
+    @classmethod
+    def get_url_rev_and_auth(cls, url: str) -> Tuple[str, Optional[str], AuthInfo]:
+        """
+        Prefixes stub URLs like 'user@hostname:user/repo.git' with 'ssh://'.
+        That's required because although they use SSH they sometimes don't
+        work with a ssh:// scheme (e.g. GitHub). But we need a scheme for
+        parsing. Hence we remove it again afterwards and return it as a stub.
+        """
+        # Works around an apparent Git bug
+        # (see https://article.gmane.org/gmane.comp.version-control.git/146500)
+        scheme, netloc, path, query, fragment = urlsplit(url)
+        if scheme.endswith("file"):
+            initial_slashes = path[: -len(path.lstrip("/"))]
+            newpath = initial_slashes + urllib.request.url2pathname(path).replace(
+                "\\", "/"
+            ).lstrip("/")
+            after_plus = scheme.find("+") + 1
+            url = scheme[:after_plus] + urlunsplit(
+                (scheme[after_plus:], netloc, newpath, query, fragment),
+            )
+
+        if "://" not in url:
+            assert "file:" not in url
+            url = url.replace("git+", "git+ssh://")
+            url, rev, user_pass = super().get_url_rev_and_auth(url)
+            url = url.replace("ssh://", "")
+        else:
+            url, rev, user_pass = super().get_url_rev_and_auth(url)
+
+        return url, rev, user_pass
+
+    @classmethod
+    def update_submodules(cls, location: str) -> None:
+        if not os.path.exists(os.path.join(location, ".gitmodules")):
+            return
+        cls.run_command(
+            ["submodule", "update", "--init", "--recursive", "-q"],
+            cwd=location,
+        )
+
+    @classmethod
+    def get_repository_root(cls, location: str) -> Optional[str]:
+        loc = super().get_repository_root(location)
+        if loc:
+            return loc
+        try:
+            r = cls.run_command(
+                ["rev-parse", "--show-toplevel"],
+                cwd=location,
+                show_stdout=False,
+                stdout_only=True,
+                on_returncode="raise",
+                log_failed_cmd=False,
+            )
+        except BadCommand:
+            logger.debug(
+                "could not determine if %s is under git control "
+                "because git is not available",
+                location,
+            )
+            return None
+        except InstallationError:
+            return None
+        return os.path.normpath(r.rstrip("\r\n"))
+
+    @staticmethod
+    def should_add_vcs_url_prefix(repo_url: str) -> bool:
+        """In either https or ssh form, requirements must be prefixed with git+."""
+        return True
+
+
+vcs.register(Git)
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diff --git a/shared/package-files/09/09e8a9bcec8067104652c168685ab0931e7868f9c8284b66f5ae6edae5f1130b/LICENSE b/shared/package-files/09/09e8a9bcec8067104652c168685ab0931e7868f9c8284b66f5ae6edae5f1130b/LICENSE
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--- /dev/null
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@@ -0,0 +1,175 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
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+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
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+
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+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
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+      Work and such Derivative Works in Source or Object form.
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+      (except as stated in this section) patent license to make, have made,
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+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
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+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
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+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
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+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
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+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
diff --git a/shared/package-files/09/09ece5ead59ff9af213041147c9a9823710f7fab124b7911cf41a23ae1484386/ecl.py b/shared/package-files/09/09ece5ead59ff9af213041147c9a9823710f7fab124b7911cf41a23ae1484386/ecl.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e93e0cb52e54f62868e3d5488d8058e1a03ce8b
--- /dev/null
+++ b/shared/package-files/09/09ece5ead59ff9af213041147c9a9823710f7fab124b7911cf41a23ae1484386/ecl.py
@@ -0,0 +1,145 @@
+"""
+    pygments.lexers.ecl
+    ~~~~~~~~~~~~~~~~~~~
+
+    Lexers for the ECL language.
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+import re
+
+from pygments.lexer import RegexLexer, include, bygroups, words
+from pygments.token import Comment, Operator, Keyword, Name, String, \
+    Number, Punctuation, Whitespace
+
+__all__ = ['ECLLexer']
+
+
+class ECLLexer(RegexLexer):
+    """
+    Lexer for the declarative big-data ECL language.
+
+    .. versionadded:: 1.5
+    """
+
+    name = 'ECL'
+    url = 'https://hpccsystems.com/training/documentation/ecl-language-reference/html'
+    aliases = ['ecl']
+    filenames = ['*.ecl']
+    mimetypes = ['application/x-ecl']
+
+    flags = re.IGNORECASE | re.MULTILINE
+
+    tokens = {
+        'root': [
+            include('whitespace'),
+            include('statements'),
+        ],
+        'whitespace': [
+            (r'\s+', Whitespace),
+            (r'\/\/.*', Comment.Single),
+            (r'/(\\\n)?\*(.|\n)*?\*(\\\n)?/', Comment.Multiline),
+        ],
+        'statements': [
+            include('types'),
+            include('keywords'),
+            include('functions'),
+            include('hash'),
+            (r'"', String, 'string'),
+            (r'\'', String, 'string'),
+            (r'(\d+\.\d*|\.\d+|\d+)e[+-]?\d+[lu]*', Number.Float),
+            (r'(\d+\.\d*|\.\d+|\d+f)f?', Number.Float),
+            (r'0x[0-9a-f]+[lu]*', Number.Hex),
+            (r'0[0-7]+[lu]*', Number.Oct),
+            (r'\d+[lu]*', Number.Integer),
+            (r'[~!%^&*+=|?:<>/-]+', Operator),
+            (r'[{}()\[\],.;]', Punctuation),
+            (r'[a-z_]\w*', Name),
+        ],
+        'hash': [
+            (r'^#.*$', Comment.Preproc),
+        ],
+        'types': [
+            (r'(RECORD|END)\D', Keyword.Declaration),
+            (r'((?:ASCII|BIG_ENDIAN|BOOLEAN|DATA|DECIMAL|EBCDIC|INTEGER|PATTERN|'
+             r'QSTRING|REAL|RECORD|RULE|SET OF|STRING|TOKEN|UDECIMAL|UNICODE|'
+             r'UNSIGNED|VARSTRING|VARUNICODE)\d*)(\s+)',
+             bygroups(Keyword.Type, Whitespace)),
+        ],
+        'keywords': [
+            (words((
+                'APPLY', 'ASSERT', 'BUILD', 'BUILDINDEX', 'EVALUATE', 'FAIL',
+                'KEYDIFF', 'KEYPATCH', 'LOADXML', 'NOTHOR', 'NOTIFY', 'OUTPUT',
+                'PARALLEL', 'SEQUENTIAL', 'SOAPCALL', 'CHECKPOINT', 'DEPRECATED',
+                'FAILCODE', 'FAILMESSAGE', 'FAILURE', 'GLOBAL', 'INDEPENDENT',
+                'ONWARNING', 'PERSIST', 'PRIORITY', 'RECOVERY', 'STORED', 'SUCCESS',
+                'WAIT', 'WHEN'), suffix=r'\b'),
+             Keyword.Reserved),
+            # These are classed differently, check later
+            (words((
+                'ALL', 'AND', 'ANY', 'AS', 'ATMOST', 'BEFORE', 'BEGINC++', 'BEST',
+                'BETWEEN', 'CASE', 'CONST', 'COUNTER', 'CSV', 'DESCEND', 'ENCRYPT',
+                'ENDC++', 'ENDMACRO', 'EXCEPT', 'EXCLUSIVE', 'EXPIRE', 'EXPORT',
+                'EXTEND', 'FALSE', 'FEW', 'FIRST', 'FLAT', 'FULL', 'FUNCTION',
+                'GROUP', 'HEADER', 'HEADING', 'HOLE', 'IFBLOCK', 'IMPORT', 'IN',
+                'JOINED', 'KEEP', 'KEYED', 'LAST', 'LEFT', 'LIMIT', 'LOAD', 'LOCAL',
+                'LOCALE', 'LOOKUP', 'MACRO', 'MANY', 'MAXCOUNT', 'MAXLENGTH',
+                'MIN SKEW', 'MODULE', 'INTERFACE', 'NAMED', 'NOCASE', 'NOROOT',
+                'NOSCAN', 'NOSORT', 'NOT', 'OF', 'ONLY', 'OPT', 'OR', 'OUTER',
+                'OVERWRITE', 'PACKED', 'PARTITION', 'PENALTY', 'PHYSICALLENGTH',
+                'PIPE', 'QUOTE', 'RELATIONSHIP', 'REPEAT', 'RETURN', 'RIGHT',
+                'SCAN', 'SELF', 'SEPARATOR', 'SERVICE', 'SHARED', 'SKEW', 'SKIP',
+                'SQL', 'STORE', 'TERMINATOR', 'THOR', 'THRESHOLD', 'TOKEN',
+                'TRANSFORM', 'TRIM', 'TRUE', 'TYPE', 'UNICODEORDER', 'UNSORTED',
+                'VALIDATE', 'VIRTUAL', 'WHOLE', 'WILD', 'WITHIN', 'XML', 'XPATH',
+                '__COMPRESSED__'), suffix=r'\b'),
+             Keyword.Reserved),
+        ],
+        'functions': [
+            (words((
+                'ABS', 'ACOS', 'ALLNODES', 'ASCII', 'ASIN', 'ASSTRING', 'ATAN',
+                'ATAN2', 'AVE', 'CASE', 'CHOOSE', 'CHOOSEN', 'CHOOSESETS',
+                'CLUSTERSIZE', 'COMBINE', 'CORRELATION', 'COS', 'COSH', 'COUNT',
+                'COVARIANCE', 'CRON', 'DATASET', 'DEDUP', 'DEFINE', 'DENORMALIZE',
+                'DISTRIBUTE', 'DISTRIBUTED', 'DISTRIBUTION', 'EBCDIC', 'ENTH',
+                'ERROR', 'EVALUATE', 'EVENT', 'EVENTEXTRA', 'EVENTNAME', 'EXISTS',
+                'EXP', 'FAILCODE', 'FAILMESSAGE', 'FETCH', 'FROMUNICODE',
+                'GETISVALID', 'GLOBAL', 'GRAPH', 'GROUP', 'HASH', 'HASH32',
+                'HASH64', 'HASHCRC', 'HASHMD5', 'HAVING', 'IF', 'INDEX',
+                'INTFORMAT', 'ISVALID', 'ITERATE', 'JOIN', 'KEYUNICODE', 'LENGTH',
+                'LIBRARY', 'LIMIT', 'LN', 'LOCAL', 'LOG', 'LOOP', 'MAP', 'MATCHED',
+                'MATCHLENGTH', 'MATCHPOSITION', 'MATCHTEXT', 'MATCHUNICODE', 'MAX',
+                'MERGE', 'MERGEJOIN', 'MIN', 'NOLOCAL', 'NONEMPTY', 'NORMALIZE',
+                'PARSE', 'PIPE', 'POWER', 'PRELOAD', 'PROCESS', 'PROJECT', 'PULL',
+                'RANDOM', 'RANGE', 'RANK', 'RANKED', 'REALFORMAT', 'RECORDOF',
+                'REGEXFIND', 'REGEXREPLACE', 'REGROUP', 'REJECTED', 'ROLLUP',
+                'ROUND', 'ROUNDUP', 'ROW', 'ROWDIFF', 'SAMPLE', 'SET', 'SIN',
+                'SINH', 'SIZEOF', 'SOAPCALL', 'SORT', 'SORTED', 'SQRT', 'STEPPED',
+                'STORED', 'SUM', 'TABLE', 'TAN', 'TANH', 'THISNODE', 'TOPN',
+                'TOUNICODE', 'TRANSFER', 'TRIM', 'TRUNCATE', 'TYPEOF', 'UNGROUP',
+                'UNICODEORDER', 'VARIANCE', 'WHICH', 'WORKUNIT', 'XMLDECODE',
+                'XMLENCODE', 'XMLTEXT', 'XMLUNICODE'), suffix=r'\b'),
+             Name.Function),
+        ],
+        'string': [
+            (r'"', String, '#pop'),
+            (r'\'', String, '#pop'),
+            (r'[^"\']+', String),
+        ],
+    }
+
+    def analyse_text(text):
+        """This is very difficult to guess relative to other business languages.
+        -> in conjunction with BEGIN/END seems relatively rare though."""
+        result = 0
+
+        if '->' in text:
+            result += 0.01
+        if 'BEGIN' in text:
+            result += 0.01
+        if 'END' in text:
+            result += 0.01
+
+        return result
diff --git a/shared/package-files/09/09ecf13a252a222931a80bf875fe46e6bb368ea98630859855f6d8b7686f5198/poplib.pyi b/shared/package-files/09/09ecf13a252a222931a80bf875fe46e6bb368ea98630859855f6d8b7686f5198/poplib.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2c08f3586c0f6d8343726647ee9278868e3b1137
--- /dev/null
+++ b/shared/package-files/09/09ecf13a252a222931a80bf875fe46e6bb368ea98630859855f6d8b7686f5198/poplib.pyi
@@ -0,0 +1,73 @@
+import socket
+import ssl
+import sys
+from typing import Any, BinaryIO, Dict, List, Optional, Pattern, Text, Tuple, overload
+
+_LongResp = Tuple[bytes, List[bytes], int]
+
+class error_proto(Exception): ...
+
+POP3_PORT: int
+POP3_SSL_PORT: int
+CR: bytes
+LF: bytes
+CRLF: bytes
+
+class POP3:
+    if sys.version_info >= (3, 0):
+        encoding: Text
+
+    host: Text
+    port: int
+    sock: socket.socket
+    file: BinaryIO
+    welcome: bytes
+    def __init__(self, host: Text, port: int = ..., timeout: float = ...) -> None: ...
+    def getwelcome(self) -> bytes: ...
+    def set_debuglevel(self, level: int) -> None: ...
+    def user(self, user: Text) -> bytes: ...
+    def pass_(self, pswd: Text) -> bytes: ...
+    def stat(self) -> Tuple[int, int]: ...
+    def list(self, which: Optional[Any] = ...) -> _LongResp: ...
+    def retr(self, which: Any) -> _LongResp: ...
+    def dele(self, which: Any) -> bytes: ...
+    def noop(self) -> bytes: ...
+    def rset(self) -> bytes: ...
+    def quit(self) -> bytes: ...
+    def close(self) -> None: ...
+    def rpop(self, user: Text) -> bytes: ...
+    timestamp: Pattern[Text]
+
+    if sys.version_info < (3, 0):
+        def apop(self, user: Text, secret: Text) -> bytes: ...
+    else:
+        def apop(self, user: Text, password: Text) -> bytes: ...
+    def top(self, which: Any, howmuch: int) -> _LongResp: ...
+    @overload
+    def uidl(self) -> _LongResp: ...
+    @overload
+    def uidl(self, which: Any) -> bytes: ...
+    if sys.version_info >= (3, 5):
+        def utf8(self) -> bytes: ...
+    if sys.version_info >= (3, 4):
+        def capa(self) -> Dict[Text, List[Text]]: ...
+        def stls(self, context: Optional[ssl.SSLContext] = ...) -> bytes: ...
+
+class POP3_SSL(POP3):
+    if sys.version_info >= (3, 0):
+        def __init__(
+            self,
+            host: Text,
+            port: int = ...,
+            keyfile: Optional[Text] = ...,
+            certfile: Optional[Text] = ...,
+            timeout: float = ...,
+            context: Optional[ssl.SSLContext] = ...,
+        ) -> None: ...
+    else:
+        def __init__(
+            self, host: Text, port: int = ..., keyfile: Optional[Text] = ..., certfile: Optional[Text] = ..., timeout: float = ...
+        ) -> None: ...
+    if sys.version_info >= (3, 4):
+        # "context" is actually the last argument, but that breaks LSP and it doesn't really matter because all the arguments are ignored
+        def stls(self, context: Any = ..., keyfile: Any = ..., certfile: Any = ...) -> bytes: ...
diff --git a/shared/package-files/09/09ee2584d09faf3965796386b3bbc698b5f7c2e97bb8b20ead5c4723fb9fb1b5/FliImagePlugin.py b/shared/package-files/09/09ee2584d09faf3965796386b3bbc698b5f7c2e97bb8b20ead5c4723fb9fb1b5/FliImagePlugin.py
new file mode 100644
index 0000000000000000000000000000000000000000..9769761fc15021fd8ebfd9b5b4c50e729f545679
--- /dev/null
+++ b/shared/package-files/09/09ee2584d09faf3965796386b3bbc698b5f7c2e97bb8b20ead5c4723fb9fb1b5/FliImagePlugin.py
@@ -0,0 +1,173 @@
+#
+# The Python Imaging Library.
+# $Id$
+#
+# FLI/FLC file handling.
+#
+# History:
+#       95-09-01 fl     Created
+#       97-01-03 fl     Fixed parser, setup decoder tile
+#       98-07-15 fl     Renamed offset attribute to avoid name clash
+#
+# Copyright (c) Secret Labs AB 1997-98.
+# Copyright (c) Fredrik Lundh 1995-97.
+#
+# See the README file for information on usage and redistribution.
+#
+from __future__ import annotations
+
+import os
+
+from . import Image, ImageFile, ImagePalette
+from ._binary import i16le as i16
+from ._binary import i32le as i32
+from ._binary import o8
+
+#
+# decoder
+
+
+def _accept(prefix):
+    return (
+        len(prefix) >= 6
+        and i16(prefix, 4) in [0xAF11, 0xAF12]
+        and i16(prefix, 14) in [0, 3]  # flags
+    )
+
+
+##
+# Image plugin for the FLI/FLC animation format.  Use the seek
+# method to load individual frames.
+
+
+class FliImageFile(ImageFile.ImageFile):
+    format = "FLI"
+    format_description = "Autodesk FLI/FLC Animation"
+    _close_exclusive_fp_after_loading = False
+
+    def _open(self):
+        # HEAD
+        s = self.fp.read(128)
+        if not (_accept(s) and s[20:22] == b"\x00\x00"):
+            msg = "not an FLI/FLC file"
+            raise SyntaxError(msg)
+
+        # frames
+        self.n_frames = i16(s, 6)
+        self.is_animated = self.n_frames > 1
+
+        # image characteristics
+        self._mode = "P"
+        self._size = i16(s, 8), i16(s, 10)
+
+        # animation speed
+        duration = i32(s, 16)
+        magic = i16(s, 4)
+        if magic == 0xAF11:
+            duration = (duration * 1000) // 70
+        self.info["duration"] = duration
+
+        # look for palette
+        palette = [(a, a, a) for a in range(256)]
+
+        s = self.fp.read(16)
+
+        self.__offset = 128
+
+        if i16(s, 4) == 0xF100:
+            # prefix chunk; ignore it
+            self.__offset = self.__offset + i32(s)
+            s = self.fp.read(16)
+
+        if i16(s, 4) == 0xF1FA:
+            # look for palette chunk
+            number_of_subchunks = i16(s, 6)
+            chunk_size = None
+            for _ in range(number_of_subchunks):
+                if chunk_size is not None:
+                    self.fp.seek(chunk_size - 6, os.SEEK_CUR)
+                s = self.fp.read(6)
+                chunk_type = i16(s, 4)
+                if chunk_type in (4, 11):
+                    self._palette(palette, 2 if chunk_type == 11 else 0)
+                    break
+                chunk_size = i32(s)
+                if not chunk_size:
+                    break
+
+        palette = [o8(r) + o8(g) + o8(b) for (r, g, b) in palette]
+        self.palette = ImagePalette.raw("RGB", b"".join(palette))
+
+        # set things up to decode first frame
+        self.__frame = -1
+        self._fp = self.fp
+        self.__rewind = self.fp.tell()
+        self.seek(0)
+
+    def _palette(self, palette, shift):
+        # load palette
+
+        i = 0
+        for e in range(i16(self.fp.read(2))):
+            s = self.fp.read(2)
+            i = i + s[0]
+            n = s[1]
+            if n == 0:
+                n = 256
+            s = self.fp.read(n * 3)
+            for n in range(0, len(s), 3):
+                r = s[n] << shift
+                g = s[n + 1] << shift
+                b = s[n + 2] << shift
+                palette[i] = (r, g, b)
+                i += 1
+
+    def seek(self, frame):
+        if not self._seek_check(frame):
+            return
+        if frame < self.__frame:
+            self._seek(0)
+
+        for f in range(self.__frame + 1, frame + 1):
+            self._seek(f)
+
+    def _seek(self, frame):
+        if frame == 0:
+            self.__frame = -1
+            self._fp.seek(self.__rewind)
+            self.__offset = 128
+        else:
+            # ensure that the previous frame was loaded
+            self.load()
+
+        if frame != self.__frame + 1:
+            msg = f"cannot seek to frame {frame}"
+            raise ValueError(msg)
+        self.__frame = frame
+
+        # move to next frame
+        self.fp = self._fp
+        self.fp.seek(self.__offset)
+
+        s = self.fp.read(4)
+        if not s:
+            msg = "missing frame size"
+            raise EOFError(msg)
+
+        framesize = i32(s)
+
+        self.decodermaxblock = framesize
+        self.tile = [("fli", (0, 0) + self.size, self.__offset, None)]
+
+        self.__offset += framesize
+
+    def tell(self):
+        return self.__frame
+
+
+#
+# registry
+
+Image.register_open(FliImageFile.format, FliImageFile, _accept)
+
+Image.register_extensions(FliImageFile.format, [".fli", ".flc"])
diff --git a/shared/package-files/09/09ee455f1f2c3056db31f50d8443d5dd02bb2233db9447f5a95a3a963af549e8/test_DFM.m b/shared/package-files/09/09ee455f1f2c3056db31f50d8443d5dd02bb2233db9447f5a95a3a963af549e8/test_DFM.m
new file mode 100644
index 0000000000000000000000000000000000000000..a633d5202005292bf5cf96026392b62e220fb59a
--- /dev/null
+++ b/shared/package-files/09/09ee455f1f2c3056db31f50d8443d5dd02bb2233db9447f5a95a3a963af549e8/test_DFM.m
@@ -0,0 +1,157 @@
+%%% Test for Dynamic factor model (DFM) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% This script produces results for a variety of dynamic factor models (DFM)
+% using a panel of monthly and quarterly series that can be used for tests.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+%% Clear workspace and set paths.
+close all; clear; clc;
+addpath('functions');
+
+
+%% User inputs.
+vintage = '2016-06-29'; % vintage dataset to use for estimation
+country = 'US';         % United States macroeconomic data
+sample_start  = datenum('2000-01-01','yyyy-mm-dd'); % estimation sample
+
+
+%% Load model specification and dataset.
+% Load model specification structure `Spec`
+Spec = load_spec('test_spec.xls');
+% Load data
+datafile = fullfile('data',country,[vintage '.xls']);
+[X,Time,Z] = load_data(datafile,Spec,sample_start);
+
+% Basic settings
+threshold = 1e-4; % Set to 1e-5 for more robust estimates
+format long
+
+%% Test 1: p=1, k=1, max_iter=1
+Spec.p = 1;
+Spec.k = 1;
+max_iter = 1;
+
+Res = dfm(X,Spec,threshold,max_iter);
+Res = rmfield(Res, {'X_sm', 'Z', 'Vsmooth', 'VVsmooth'});
+
+kp = Spec.k * Spec.p;
+Lambda = Res.C(1:end, 1:Spec.k)';
+f_A = Res.A(1:Spec.k, 1:kp)';
+f_Q = Res.Q(1:Spec.k, 1:Spec.k);
+
+Ad = diag(Res.A);
+Qd = diag(Res.Q);
+
+idio = Spec.k * max(5, Spec.p) + 1;
+params = [Lambda(:)' f_A(:)' nonzeros(chol(f_Q)')' ...
+          Ad(idio:idio+3)' Ad(idio+3+5) Qd(idio:idio+3)' Qd(idio+3+5)]';
+save('test_dfm_111.mat','Res','Spec','params');
+
+
+%% Test 2: p=1, k=1, max_iter=2
+Spec.p = 1;
+Spec.k = 1;
+max_iter = 2;
+
+Res = dfm(X,Spec,threshold,max_iter);
+Res = rmfield(Res, {'X_sm', 'Z', 'Vsmooth', 'VVsmooth'});
+
+kp = Spec.k * Spec.p;
+Lambda = Res.C(1:end, 1:Spec.k)';
+f_A = Res.A(1:Spec.k, 1:kp)';
+f_Q = Res.Q(1:Spec.k, 1:Spec.k);
+
+Ad = diag(Res.A);
+Qd = diag(Res.Q);
+
+idio = Spec.k * max(5, Spec.p) + 1;
+params = [Lambda(:)' f_A(:)' nonzeros(chol(f_Q)')' ...
+          Ad(idio:idio+3)' Ad(idio+3+5) Qd(idio:idio+3)' Qd(idio+3+5)]';
+save('test_dfm_112.mat','Res','Spec','params');
+
+
+%% Test 3: p=1, k=1, max_iter=5000
+Spec.p = 1;
+Spec.k = 1;
+max_iter = 5000;
+
+Res = dfm(X,Spec,threshold,max_iter);
+Res = rmfield(Res, {'X_sm', 'Z', 'Vsmooth', 'VVsmooth'});
+
+kp = Spec.k * Spec.p;
+Lambda = Res.C(1:end, 1:Spec.k)';
+f_A = Res.A(1:Spec.k, 1:kp)';
+f_Q = Res.Q(1:Spec.k, 1:Spec.k);
+
+Ad = diag(Res.A);
+Qd = diag(Res.Q);
+
+idio = Spec.k * max(5, Spec.p) + 1;
+params = [Lambda(:)' f_A(:)' nonzeros(chol(f_Q)')' ...
+          Ad(idio:idio+3)' Ad(idio+3+5) Qd(idio:idio+3)' Qd(idio+3+5)]';
+save('test_dfm_11F.mat','Res','Spec','params');
+
+%% Test 4: p=2, k=2, max_iter=1
+Spec.p = 2;
+Spec.k = 2;
+max_iter = 1;
+
+Res = dfm(X,Spec,threshold,max_iter);
+Res = rmfield(Res, {'X_sm', 'Z', 'Vsmooth', 'VVsmooth'});
+
+kp = Spec.k * Spec.p;
+Lambda = Res.C(1:end, 1:Spec.k)';
+f_A = Res.A(1:Spec.k, 1:kp)';
+f_Q = Res.Q(1:Spec.k, 1:Spec.k);
+
+Ad = diag(Res.A);
+Qd = diag(Res.Q);
+
+idio = Spec.k * max(5, Spec.p) + 1;
+params = [Lambda(:)' f_A(:)' nonzeros(chol(f_Q)')' ...
+          Ad(idio:idio+3)' Ad(idio+3+5) Qd(idio:idio+3)' Qd(idio+3+5)]';
+save('test_dfm_221.mat','Res','Spec','params');
+
+
+%% Test 5: p=2, k=2, max_iter=2
+Spec.p = 2;
+Spec.k = 2;
+max_iter = 2;
+
+Res = dfm(X,Spec,threshold,max_iter);
+Res = rmfield(Res, {'X_sm', 'Z', 'Vsmooth', 'VVsmooth'});
+
+kp = Spec.k * Spec.p;
+Lambda = Res.C(1:end, 1:Spec.k)';
+f_A = Res.A(1:Spec.k, 1:kp)';
+f_Q = Res.Q(1:Spec.k, 1:Spec.k);
+
+Ad = diag(Res.A);
+Qd = diag(Res.Q);
+
+idio = Spec.k * max(5, Spec.p) + 1;
+params = [Lambda(:)' f_A(:)' nonzeros(chol(f_Q)')' ...
+          Ad(idio:idio+3)' Ad(idio+3+5) Qd(idio:idio+3)' Qd(idio+3+5)]';
+save('test_dfm_222.mat','Res','Spec','params');
+
+
+%% Test 6: p=2, k=2, max_iter=5000
+Spec.p = 2;
+Spec.k = 2;
+max_iter = 5000;
+
+Res = dfm(X,Spec,threshold,max_iter);
+Res = rmfield(Res, {'X_sm', 'Z', 'Vsmooth', 'VVsmooth'});
+
+kp = Spec.k * Spec.p;
+Lambda = Res.C(1:end, 1:Spec.k)';
+f_A = Res.A(1:Spec.k, 1:kp)';
+f_Q = Res.Q(1:Spec.k, 1:Spec.k);
+
+Ad = diag(Res.A);
+Qd = diag(Res.Q);
+
+idio = Spec.k * max(5, Spec.p) + 1;
+params = [Lambda(:)' f_A(:)' nonzeros(chol(f_Q)')' ...
+          Ad(idio:idio+3)' Ad(idio+3+5) Qd(idio:idio+3)' Qd(idio+3+5)]';
+save('test_dfm_22F.mat','Res','Spec','params');
diff --git a/shared/package-files/09/09eebda99ae92a5645dce5b3fd7d4c8466a980b0516bc95e995ca5b413db44c8/contingency_table_r_results.csv b/shared/package-files/09/09eebda99ae92a5645dce5b3fd7d4c8466a980b0516bc95e995ca5b413db44c8/contingency_table_r_results.csv
new file mode 100644
index 0000000000000000000000000000000000000000..cd94963ee1284fda1de9c24be64122d37e784621
--- /dev/null
+++ b/shared/package-files/09/09eebda99ae92a5645dce5b3fd7d4c8466a980b0516bc95e995ca5b413db44c8/contingency_table_r_results.csv
@@ -0,0 +1,4 @@
+"homog_stat","homog_df","homog_binom_p","homog_cont_p","lbl_stat","lbl_expval","lbl_var","lbl_chi2","lbl_pvalue","lbl2_stat","lbl2_expval","lbl2_var","lbl2_chi2","lbl2_pvalue","bowker_stat","bowker_df","bowker_pvalue"
+0.470588235294118,1,0.60759136127308,0.606905427217951,215,210.136363636364,5.44848959817612,4.34156260215759,0.0371927738063454,369,354.409090909091,49.0364063835851,4.34156260215755,0.0371927738063464,0.470588235294118,1,0.492716677227088
+271.922246968262,3,0,0,1760,1637.12,318.387012391739,47.4249696511547,5.71498404156046e-12,6006,5384.96,8083.23776042639,47.7148752803301,4.9293902293357e-12,297.610989010989,6,2.65851858368065e-61
+13.7647058823529,2,0,0,661,608.890625,45.1014721487451,60.2061714068945,8.54871728961371e-15,1761,1544.046875,804.735991710752,58.4895654377337,2.04281036531029e-14,13.7692307692308,3,0.00323670650323545
diff --git a/shared/package-files/09/09f1c8c9e941af3e584d59641ea9b87d83c0cb0fd007eb5ef391a7e2643c1a46/LICENSE b/shared/package-files/09/09f1c8c9e941af3e584d59641ea9b87d83c0cb0fd007eb5ef391a7e2643c1a46/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..0a523bece3e50519653c4d7a38399baa487fefa1
--- /dev/null
+++ b/shared/package-files/09/09f1c8c9e941af3e584d59641ea9b87d83c0cb0fd007eb5ef391a7e2643c1a46/LICENSE
@@ -0,0 +1,19 @@
+Copyright (c) 2012 Erik Rose
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/shared/package-files/09/09f3daef73550479f75516bdc344aaa307b880187812773a67c09c6fd53f8776/mkldnn_rnn_layer_backward_native.h b/shared/package-files/09/09f3daef73550479f75516bdc344aaa307b880187812773a67c09c6fd53f8776/mkldnn_rnn_layer_backward_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..cdc16f33d0f151d6aae9b9eedd8dfff0d4a0ddc7
--- /dev/null
+++ b/shared/package-files/09/09f3daef73550479f75516bdc344aaa307b880187812773a67c09c6fd53f8776/mkldnn_rnn_layer_backward_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple mkldnn_rnn_layer_backward_out(const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace, at::Tensor & out0, at::Tensor & out1, at::Tensor & out2, at::Tensor & out3, at::Tensor & out4, at::Tensor & out5, at::Tensor & out6);
+TORCH_API ::std::tuple mkldnn_rnn_layer_backward(const at::Tensor & input, const at::Tensor & weight1, const at::Tensor & weight2, const at::Tensor & weight3, const at::Tensor & weight4, const at::Tensor & hx_, const at::Tensor & cx_tmp, const at::Tensor & output, const at::Tensor & hy_, const at::Tensor & cy_, const c10::optional & grad_output, const c10::optional & grad_hy, const c10::optional & grad_cy, bool reverse, int64_t mode, int64_t hidden_size, int64_t num_layers, bool has_biases, bool train, bool bidirectional, at::IntArrayRef batch_sizes, bool batch_first, const at::Tensor & workspace);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/09/09f5b46e25aeb1d3ad302303f0a17f8119a472ed39012ec2589049e4a7a8bd7c/index_add_ops.h b/shared/package-files/09/09f5b46e25aeb1d3ad302303f0a17f8119a472ed39012ec2589049e4a7a8bd7c/index_add_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..748abba02340adc8369eb4136e2affc9ea6cb50a
--- /dev/null
+++ b/shared/package-files/09/09f5b46e25aeb1d3ad302303f0a17f8119a472ed39012ec2589049e4a7a8bd7c/index_add_ops.h
@@ -0,0 +1,61 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API index_add_out {
+  using schema = at::Tensor & (const at::Tensor &, int64_t, const at::Tensor &, const at::Tensor &, const at::Scalar &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::index_add")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "index_add.out(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha, at::Tensor & out);
+};
+
+struct TORCH_API index_add_ {
+  using schema = at::Tensor & (at::Tensor &, int64_t, const at::Tensor &, const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::index_add_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "index_add_(Tensor(a!) self, int dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha);
+};
+
+struct TORCH_API index_add {
+  using schema = at::Tensor (const at::Tensor &, int64_t, const at::Tensor &, const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::index_add")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "index_add(Tensor self, int dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha);
+};
+
+struct TORCH_API index_add_dimname {
+  using schema = at::Tensor (const at::Tensor &, at::Dimname, const at::Tensor &, const at::Tensor &, const at::Scalar &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::index_add")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "dimname")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "index_add.dimname(Tensor self, Dimname dim, Tensor index, Tensor source, *, Scalar alpha=1) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & source, const at::Scalar & alpha);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/09/09fcd46be906cbf5b9e20bc149432ec857c458d6cf22fbbf10bf620af483a708/xterm+app b/shared/package-files/09/09fcd46be906cbf5b9e20bc149432ec857c458d6cf22fbbf10bf620af483a708/xterm+app
new file mode 100644
index 0000000000000000000000000000000000000000..0df7c394e41e7869148d19aae00502a58613c86d
Binary files /dev/null and b/shared/package-files/09/09fcd46be906cbf5b9e20bc149432ec857c458d6cf22fbbf10bf620af483a708/xterm+app differ
diff --git a/shared/package-files/09/09fdbf97b14258d8c89328be5d4d4c686ce62e77f627b34ad9267fd743bef1dd/label_results.txt b/shared/package-files/09/09fdbf97b14258d8c89328be5d4d4c686ce62e77f627b34ad9267fd743bef1dd/label_results.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c239b0369c9df3e06df9a2fbf048faec2f84941f
--- /dev/null
+++ b/shared/package-files/09/09fdbf97b14258d8c89328be5d4d4c686ce62e77f627b34ad9267fd743bef1dd/label_results.txt
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diff --git a/shared/package-files/09/09fe6da94c357ba377aad36ce1eb75610c7ddd87a5c3bf06d35d53ae426c0092/to_dense_backward.h b/shared/package-files/09/09fe6da94c357ba377aad36ce1eb75610c7ddd87a5c3bf06d35d53ae426c0092/to_dense_backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..60ae7d2ec94d682a55c5b9a37270592fc184567c
--- /dev/null
+++ b/shared/package-files/09/09fe6da94c357ba377aad36ce1eb75610c7ddd87a5c3bf06d35d53ae426c0092/to_dense_backward.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::to_dense_backward(Tensor grad, Tensor input, bool? masked_grad=None) -> Tensor
+inline at::Tensor to_dense_backward(const at::Tensor & grad, const at::Tensor & input, c10::optional masked_grad=c10::nullopt) {
+    return at::_ops::to_dense_backward::call(grad, input, masked_grad);
+}
+
+}
diff --git a/shared/package-files/3d/3d0263bfe9ee4f23b244bdcaff73acbe7a1957ebf82743b6ae42693adc48d721/METADATA b/shared/package-files/3d/3d0263bfe9ee4f23b244bdcaff73acbe7a1957ebf82743b6ae42693adc48d721/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..1acd93fe4f5e1e41580a515fa2a1340646abeff8
--- /dev/null
+++ b/shared/package-files/3d/3d0263bfe9ee4f23b244bdcaff73acbe7a1957ebf82743b6ae42693adc48d721/METADATA
@@ -0,0 +1,187 @@
+Metadata-Version: 2.4
+Name: cloudpickle
+Version: 3.1.2
+Summary: Pickler class to extend the standard pickle.Pickler functionality
+Home-page: https://github.com/cloudpipe/cloudpickle
+Author: The cloudpickle developer team
+Author-email: cloudpipe@googlegroups.com
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown
+License: BSD-3-Clause
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: MacOS :: MacOS X
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: System :: Distributed Computing
+License-File: LICENSE
+
+# cloudpickle
+
+[![Automated Tests](https://github.com/cloudpipe/cloudpickle/workflows/Automated%20Tests/badge.svg?branch=master&event=push)](https://github.com/cloudpipe/cloudpickle/actions)
+[![codecov.io](https://codecov.io/github/cloudpipe/cloudpickle/coverage.svg?branch=master)](https://codecov.io/github/cloudpipe/cloudpickle?branch=master)
+
+`cloudpickle` makes it possible to serialize Python constructs not supported
+by the default `pickle` module from the Python standard library.
+
+`cloudpickle` is especially useful for **cluster computing** where Python
+code is shipped over the network to execute on remote hosts, possibly close
+to the data.
+
+Among other things, `cloudpickle` supports pickling for **lambda functions**
+along with **functions and classes defined interactively** in the
+`__main__` module (for instance in a script, a shell or a Jupyter notebook).
+
+Cloudpickle can only be used to send objects between the **exact same version
+of Python**.
+
+Using `cloudpickle` for **long-term object storage is not supported and
+strongly discouraged.**
+
+**Security notice**: one should **only load pickle data from trusted sources** as
+otherwise `pickle.load` can lead to arbitrary code execution resulting in a critical
+security vulnerability.
+
+
+Installation
+------------
+
+The latest release of `cloudpickle` is available from
+[pypi](https://pypi.python.org/pypi/cloudpickle):
+
+    pip install cloudpickle
+
+
+Examples
+--------
+
+Pickling a lambda expression:
+
+```python
+>>> import cloudpickle
+>>> squared = lambda x: x ** 2
+>>> pickled_lambda = cloudpickle.dumps(squared)
+
+>>> import pickle
+>>> new_squared = pickle.loads(pickled_lambda)
+>>> new_squared(2)
+4
+```
+
+Pickling a function interactively defined in a Python shell session
+(in the `__main__` module):
+
+```python
+>>> CONSTANT = 42
+>>> def my_function(data: int) -> int:
+...     return data + CONSTANT
+...
+>>> pickled_function = cloudpickle.dumps(my_function)
+>>> depickled_function = pickle.loads(pickled_function)
+>>> depickled_function
+ int>
+>>> depickled_function(43)
+85
+```
+
+
+Overriding pickle's serialization mechanism for importable constructs:
+----------------------------------------------------------------------
+
+An important difference between `cloudpickle` and `pickle` is that
+`cloudpickle` can serialize a function or class **by value**, whereas `pickle`
+can only serialize it **by reference**. Serialization by reference treats
+functions and classes as attributes of modules, and pickles them through
+instructions that trigger the import of their module at load time.
+Serialization by reference is thus limited in that it assumes that the module
+containing the function or class is available/importable in the unpickling
+environment. This assumption breaks when pickling constructs defined in an
+interactive session, a case that is automatically detected by `cloudpickle`,
+that pickles such constructs **by value**.
+
+Another case where the importability assumption is expected to break is when
+developing a module in a distributed execution environment: the worker
+processes may not have access to the said module, for example if they live on a
+different machine than the process in which the module is being developed. By
+itself, `cloudpickle` cannot detect such "locally importable" modules and
+switch to serialization by value; instead, it relies on its default mode, which
+is serialization by reference. However, since `cloudpickle 2.0.0`, one can
+explicitly specify modules for which serialization by value should be used,
+using the
+`register_pickle_by_value(module)`/`/unregister_pickle_by_value(module)` API:
+
+```python
+>>> import cloudpickle
+>>> import my_module
+>>> cloudpickle.register_pickle_by_value(my_module)
+>>> cloudpickle.dumps(my_module.my_function)  # my_function is pickled by value
+>>> cloudpickle.unregister_pickle_by_value(my_module)
+>>> cloudpickle.dumps(my_module.my_function)  # my_function is pickled by reference
+```
+
+Using this API, there is no need to re-install the new version of the module on
+all the worker nodes nor to restart the workers: restarting the client Python
+process with the new source code is enough.
+
+Note that this feature is still **experimental**, and may fail in the following
+situations:
+
+- If the body of a function/class pickled by value contains an `import` statement:
+  ```python
+  >>> def f():
+  >>> ... from another_module import g
+  >>> ... # calling f in the unpickling environment may fail if another_module
+  >>> ... # is unavailable
+  >>> ... return g() + 1
+  ```
+
+- If a function pickled by reference uses a function pickled by value during its execution.
+
+
+Running the tests
+-----------------
+
+- With `tox`, to test run the tests for all the supported versions of
+  Python and PyPy:
+
+      pip install tox
+      tox
+
+  or alternatively for a specific environment:
+
+      tox -e py312
+
+
+- With `pytest` to only run the tests for your current version of
+  Python:
+
+      pip install -r dev-requirements.txt
+      PYTHONPATH='.:tests' pytest
+
+History
+-------
+
+`cloudpickle` was initially developed by [picloud.com](http://web.archive.org/web/20140721022102/http://blog.picloud.com/2013/11/17/picloud-has-joined-dropbox/) and shipped as part of
+the client SDK.
+
+A copy of `cloudpickle.py` was included as part of PySpark, the Python
+interface to [Apache Spark](https://spark.apache.org/). Davies Liu, Josh
+Rosen, Thom Neale and other Apache Spark developers improved it significantly,
+most notably to add support for PyPy and Python 3.
+
+The aim of the `cloudpickle` project is to make that work available to a wider
+audience outside of the Spark ecosystem and to make it easier to improve it
+further notably with the help of a dedicated non-regression test suite.
+
diff --git a/shared/package-files/3d/3d033c72b51e502e9ecdf1b46aeacdfbb4e0dcecaa5476ec661d2b5157777678/group_by.py b/shared/package-files/3d/3d033c72b51e502e9ecdf1b46aeacdfbb4e0dcecaa5476ec661d2b5157777678/group_by.py
new file mode 100644
index 0000000000000000000000000000000000000000..84d39c2a52b651cd149010c36be4d2e224538014
--- /dev/null
+++ b/shared/package-files/3d/3d033c72b51e502e9ecdf1b46aeacdfbb4e0dcecaa5476ec661d2b5157777678/group_by.py
@@ -0,0 +1,182 @@
+from __future__ import annotations
+
+from itertools import chain
+from typing import TYPE_CHECKING, Any, ClassVar, Protocol, TypeVar
+
+from narwhals._compliant.typing import (
+    CompliantDataFrameT,
+    CompliantDataFrameT_co,
+    CompliantExprT_contra,
+    CompliantFrameT,
+    CompliantFrameT_co,
+    DepthTrackingExprAny,
+    DepthTrackingExprT_contra,
+    EagerExprT_contra,
+    ImplExprT_contra,
+    NarwhalsAggregation,
+)
+from narwhals._utils import is_sequence_of
+
+if TYPE_CHECKING:
+    from collections.abc import Callable, Iterable, Iterator, Mapping, Sequence
+
+    from narwhals._compliant.expr import ImplExpr
+
+
+__all__ = ["CompliantGroupBy", "DepthTrackingGroupBy", "EagerGroupBy"]
+
+NativeAggregationT_co = TypeVar(
+    "NativeAggregationT_co", bound="str | Callable[..., Any]", covariant=True
+)
+
+
+def _evaluate_aliases(
+    frame: CompliantFrameT, exprs: Iterable[ImplExpr[CompliantFrameT, Any]], /
+) -> list[str]:
+    it = (expr._evaluate_aliases(frame) for expr in exprs)
+    return list(chain.from_iterable(it))
+
+
+class CompliantGroupBy(Protocol[CompliantFrameT_co, CompliantExprT_contra]):
+    _compliant_frame: Any
+
+    @property
+    def compliant(self) -> CompliantFrameT_co:
+        return self._compliant_frame  # type: ignore[no-any-return]
+
+    def __init__(
+        self,
+        compliant_frame: CompliantFrameT_co,
+        keys: Sequence[CompliantExprT_contra] | Sequence[str],
+        /,
+        *,
+        drop_null_keys: bool,
+    ) -> None: ...
+
+    def agg(self, *exprs: CompliantExprT_contra) -> CompliantFrameT_co: ...
+
+
+class DataFrameGroupBy(
+    CompliantGroupBy[CompliantDataFrameT_co, CompliantExprT_contra],
+    Protocol[CompliantDataFrameT_co, CompliantExprT_contra],
+):
+    def __iter__(self) -> Iterator[tuple[Any, CompliantDataFrameT_co]]: ...
+
+
+class ParseKeysGroupBy(
+    CompliantGroupBy[CompliantFrameT, ImplExprT_contra],
+    Protocol[CompliantFrameT, ImplExprT_contra],
+):
+    def _parse_keys(
+        self,
+        compliant_frame: CompliantFrameT,
+        keys: Sequence[ImplExprT_contra] | Sequence[str],
+    ) -> tuple[CompliantFrameT, list[str], list[str]]:
+        if is_sequence_of(keys, str):
+            keys_str = list(keys)
+            return compliant_frame, keys_str, keys_str.copy()
+        return self._parse_expr_keys(compliant_frame, keys=keys)
+
+    @staticmethod
+    def _parse_expr_keys(
+        compliant_frame: CompliantFrameT, keys: Sequence[ImplExprT_contra]
+    ) -> tuple[CompliantFrameT, list[str], list[str]]:
+        """Parses key expressions to set up `.agg` operation with correct information.
+
+        Since keys are expressions, it's possible to alias any such key to match
+        other dataframe column names.
+
+        In order to match polars behavior and not overwrite columns when evaluating keys:
+
+        - We evaluate what the output key names should be, in order to remap temporary column
+            names to the expected ones, and to exclude those from unnamed expressions in
+            `.agg(...)` context (see https://github.com/narwhals-dev/narwhals/pull/2325#issuecomment-2800004520)
+        - Create temporary names for evaluated key expressions that are guaranteed to have
+            no overlap with any existing column name.
+        - Add these temporary columns to the compliant dataframe.
+        """
+        tmp_name_length = max(len(str(c)) for c in compliant_frame.columns) + 1
+
+        def _temporary_name(key: str) -> str:
+            # 5 is the length of `__tmp`
+            key_str = str(key)  # pandas allows non-string column names :sob:
+            return f"_{key_str}_tmp{'_' * (tmp_name_length - len(key_str) - 5)}"
+
+        keys_aliases = [expr._evaluate_aliases(compliant_frame) for expr in keys]
+        safe_keys = [
+            # multi-output expression cannot have duplicate names, hence it's safe to suffix
+            key.name.map(_temporary_name)
+            if (metadata := key._metadata) and metadata.expansion_kind.is_multi_output()
+            # otherwise it's single named and we can use Expr.alias
+            else key.alias(_temporary_name(new_names[0]))
+            for key, new_names in zip(keys, keys_aliases, strict=True)
+        ]
+        return (
+            compliant_frame.with_columns(*safe_keys),
+            _evaluate_aliases(compliant_frame, safe_keys),
+            list(chain.from_iterable(keys_aliases)),
+        )
+
+
+class DepthTrackingGroupBy(
+    ParseKeysGroupBy[CompliantFrameT, DepthTrackingExprT_contra],
+    Protocol[CompliantFrameT, DepthTrackingExprT_contra, NativeAggregationT_co],
+):
+    """`CompliantGroupBy` variant, deals with `Eager` and other backends that utilize `CompliantExpr._depth`."""
+
+    _REMAP_AGGS: ClassVar[Mapping[NarwhalsAggregation, Any]]
+    """Mapping from `narwhals` to native representation.
+
+    Note:
+    - `Dask` *may* return a `Callable` instead of a `str` referring to one.
+    """
+
+    def _ensure_all_simple(self, exprs: Sequence[DepthTrackingExprT_contra]) -> None:
+        for expr in exprs:
+            if not self._is_simple(expr):
+                name = self.compliant._implementation.name.lower()
+                msg = (
+                    f"Non-trivial complex aggregation found.\n\n"
+                    f"Hint: you were probably trying to apply a non-elementary aggregation with a"
+                    f"{name!r} table.\n"
+                    "Please rewrite your query such that group-by aggregations "
+                    "are elementary. For example, instead of:\n\n"
+                    "    df.group_by('a').agg(nw.col('b').round(2).mean())\n\n"
+                    "use:\n\n"
+                    "    df.with_columns(nw.col('b').round(2)).group_by('a').agg(nw.col('b').mean())\n\n"
+                )
+                raise ValueError(msg)
+
+    @classmethod
+    def _is_simple(cls, expr: DepthTrackingExprAny, /) -> bool:
+        """Return `True` is we can efficiently use `expr` in a native `group_by` context."""
+        return expr._is_elementary() and cls._leaf_name(expr) in cls._REMAP_AGGS
+
+    @classmethod
+    def _remap_expr_name(
+        cls, name: NarwhalsAggregation | Any, /
+    ) -> NativeAggregationT_co:
+        """Replace `name`, with some native representation.
+
+        Arguments:
+            name: Name of a `nw.Expr` aggregation method.
+        """
+        result: NativeAggregationT_co = cls._REMAP_AGGS.get(name, name)
+        return result
+
+    @classmethod
+    def _leaf_name(cls, expr: DepthTrackingExprAny, /) -> NarwhalsAggregation | Any:
+        """Return the last function name in the chain defined by `expr`."""
+        return next(expr._metadata.op_nodes_reversed()).name
+
+    @classmethod
+    def _kwargs(cls, expr: DepthTrackingExprAny, /) -> dict[str, Any]:
+        """Return the last function kwargs in the chain defined by `expr`."""
+        return next(expr._metadata.op_nodes_reversed()).kwargs
+
+
+class EagerGroupBy(
+    DepthTrackingGroupBy[CompliantDataFrameT, EagerExprT_contra, NativeAggregationT_co],
+    DataFrameGroupBy[CompliantDataFrameT, EagerExprT_contra],
+    Protocol[CompliantDataFrameT, EagerExprT_contra, NativeAggregationT_co],
+): ...
diff --git a/shared/package-files/3d/3d04357524939558be35397c42fd082b06004f29c9b8bcf5e20e9bdbb0b41beb/cStringIO.pyi b/shared/package-files/3d/3d04357524939558be35397c42fd082b06004f29c9b8bcf5e20e9bdbb0b41beb/cStringIO.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..603ce3f2403f33cecfedcc8467f8677578fe12da
--- /dev/null
+++ b/shared/package-files/3d/3d04357524939558be35397c42fd082b06004f29c9b8bcf5e20e9bdbb0b41beb/cStringIO.pyi
@@ -0,0 +1,48 @@
+from abc import ABCMeta
+from types import TracebackType
+from typing import IO, Iterable, Iterator, List, Optional, Union, overload
+
+# This class isn't actually abstract, but you can't instantiate it
+# directly, so we might as well treat it as abstract in the stub.
+class InputType(IO[str], Iterator[str], metaclass=ABCMeta):
+    def getvalue(self) -> str: ...
+    def close(self) -> None: ...
+    @property
+    def closed(self) -> bool: ...
+    def flush(self) -> None: ...
+    def isatty(self) -> bool: ...
+    def read(self, size: int = ...) -> str: ...
+    def readline(self, size: int = ...) -> str: ...
+    def readlines(self, hint: int = ...) -> List[str]: ...
+    def seek(self, offset: int, whence: int = ...) -> int: ...
+    def tell(self) -> int: ...
+    def truncate(self, size: Optional[int] = ...) -> int: ...
+    def __iter__(self) -> InputType: ...
+    def next(self) -> str: ...
+    def reset(self) -> None: ...
+
+class OutputType(IO[str], Iterator[str], metaclass=ABCMeta):
+    @property
+    def softspace(self) -> int: ...
+    def getvalue(self) -> str: ...
+    def close(self) -> None: ...
+    @property
+    def closed(self) -> bool: ...
+    def flush(self) -> None: ...
+    def isatty(self) -> bool: ...
+    def read(self, size: int = ...) -> str: ...
+    def readline(self, size: int = ...) -> str: ...
+    def readlines(self, hint: int = ...) -> List[str]: ...
+    def seek(self, offset: int, whence: int = ...) -> int: ...
+    def tell(self) -> int: ...
+    def truncate(self, size: Optional[int] = ...) -> int: ...
+    def __iter__(self) -> OutputType: ...
+    def next(self) -> str: ...
+    def reset(self) -> None: ...
+    def write(self, b: Union[str, unicode]) -> int: ...
+    def writelines(self, lines: Iterable[Union[str, unicode]]) -> None: ...
+
+@overload
+def StringIO() -> OutputType: ...
+@overload
+def StringIO(s: str) -> InputType: ...
diff --git a/shared/package-files/3d/3d047c6afccc32f5122d5f7a58bbf727895eb89a4a114068a3bbc3ccfe8cf38f/urllib_parse.pyi b/shared/package-files/3d/3d047c6afccc32f5122d5f7a58bbf727895eb89a4a114068a3bbc3ccfe8cf38f/urllib_parse.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..b557bbbb645f50d1615fb814322f683a5239d5ab
--- /dev/null
+++ b/shared/package-files/3d/3d047c6afccc32f5122d5f7a58bbf727895eb89a4a114068a3bbc3ccfe8cf38f/urllib_parse.pyi
@@ -0,0 +1 @@
+from urllib.parse import *
diff --git a/shared/package-files/3d/3d06ecdbddd954cedc1cd97d5ac4b28e949a527142c68ffbc4553d5a41f78301/sparse6.py b/shared/package-files/3d/3d06ecdbddd954cedc1cd97d5ac4b28e949a527142c68ffbc4553d5a41f78301/sparse6.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b9e81475b56e089521903bebe482245e0a4cff4
--- /dev/null
+++ b/shared/package-files/3d/3d06ecdbddd954cedc1cd97d5ac4b28e949a527142c68ffbc4553d5a41f78301/sparse6.py
@@ -0,0 +1,374 @@
+# Original author: D. Eppstein, UC Irvine, August 12, 2003.
+# The original code at https://www.ics.uci.edu/~eppstein/PADS/ is public domain.
+"""Functions for reading and writing graphs in the *sparse6* format.
+
+The *sparse6* file format is a space-efficient format for large sparse
+graphs. For small graphs or large dense graphs, use the *graph6* file
+format.
+
+For more information, see the `sparse6`_ homepage.
+
+.. _sparse6: https://users.cecs.anu.edu.au/~bdm/data/formats.html
+
+"""
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.readwrite.graph6 import data_to_n, n_to_data
+from networkx.utils import not_implemented_for, open_file
+
+__all__ = ["from_sparse6_bytes", "read_sparse6", "to_sparse6_bytes", "write_sparse6"]
+
+
+def _generate_sparse6_bytes(G, nodes, header):
+    """Yield bytes in the sparse6 encoding of a graph.
+
+    `G` is an undirected simple graph. `nodes` is the list of nodes for
+    which the node-induced subgraph will be encoded; if `nodes` is the
+    list of all nodes in the graph, the entire graph will be
+    encoded. `header` is a Boolean that specifies whether to generate
+    the header ``b'>>sparse6<<'`` before the remaining data.
+
+    This function generates `bytes` objects in the following order:
+
+    1. the header (if requested),
+    2. the encoding of the number of nodes,
+    3. each character, one-at-a-time, in the encoding of the requested
+       node-induced subgraph,
+    4. a newline character.
+
+    This function raises :exc:`ValueError` if the graph is too large for
+    the graph6 format (that is, greater than ``2 ** 36`` nodes).
+
+    """
+    n = len(G)
+    if n >= 2**36:
+        raise ValueError(
+            "sparse6 is only defined if number of nodes is less " "than 2 ** 36"
+        )
+    if header:
+        yield b">>sparse6<<"
+    yield b":"
+    for d in n_to_data(n):
+        yield str.encode(chr(d + 63))
+
+    k = 1
+    while 1 << k < n:
+        k += 1
+
+    def enc(x):
+        """Big endian k-bit encoding of x"""
+        return [1 if (x & 1 << (k - 1 - i)) else 0 for i in range(k)]
+
+    edges = sorted((max(u, v), min(u, v)) for u, v in G.edges())
+    bits = []
+    curv = 0
+    for v, u in edges:
+        if v == curv:  # current vertex edge
+            bits.append(0)
+            bits.extend(enc(u))
+        elif v == curv + 1:  # next vertex edge
+            curv += 1
+            bits.append(1)
+            bits.extend(enc(u))
+        else:  # skip to vertex v and then add edge to u
+            curv = v
+            bits.append(1)
+            bits.extend(enc(v))
+            bits.append(0)
+            bits.extend(enc(u))
+    if k < 6 and n == (1 << k) and ((-len(bits)) % 6) >= k and curv < (n - 1):
+        # Padding special case: small k, n=2^k,
+        # more than k bits of padding needed,
+        # current vertex is not (n-1) --
+        # appending 1111... would add a loop on (n-1)
+        bits.append(0)
+        bits.extend([1] * ((-len(bits)) % 6))
+    else:
+        bits.extend([1] * ((-len(bits)) % 6))
+
+    data = [
+        (bits[i + 0] << 5)
+        + (bits[i + 1] << 4)
+        + (bits[i + 2] << 3)
+        + (bits[i + 3] << 2)
+        + (bits[i + 4] << 1)
+        + (bits[i + 5] << 0)
+        for i in range(0, len(bits), 6)
+    ]
+
+    for d in data:
+        yield str.encode(chr(d + 63))
+    yield b"\n"
+
+
+def from_sparse6_bytes(string):
+    """Read an undirected graph in sparse6 format from string.
+
+    Parameters
+    ----------
+    string : string
+       Data in sparse6 format
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If the string is unable to be parsed in sparse6 format
+
+    Examples
+    --------
+    >>> G = nx.from_sparse6_bytes(b":A_")
+    >>> sorted(G.edges())
+    [(0, 1), (0, 1), (0, 1)]
+
+    See Also
+    --------
+    read_sparse6, write_sparse6
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           
+
+    """
+    if string.startswith(b">>sparse6<<"):
+        string = string[11:]
+    if not string.startswith(b":"):
+        raise NetworkXError("Expected leading colon in sparse6")
+
+    chars = [c - 63 for c in string[1:]]
+    n, data = data_to_n(chars)
+    k = 1
+    while 1 << k < n:
+        k += 1
+
+    def parseData():
+        """Returns stream of pairs b[i], x[i] for sparse6 format."""
+        chunks = iter(data)
+        d = None  # partial data word
+        dLen = 0  # how many unparsed bits are left in d
+
+        while 1:
+            if dLen < 1:
+                try:
+                    d = next(chunks)
+                except StopIteration:
+                    return
+                dLen = 6
+            dLen -= 1
+            b = (d >> dLen) & 1  # grab top remaining bit
+
+            x = d & ((1 << dLen) - 1)  # partially built up value of x
+            xLen = dLen  # how many bits included so far in x
+            while xLen < k:  # now grab full chunks until we have enough
+                try:
+                    d = next(chunks)
+                except StopIteration:
+                    return
+                dLen = 6
+                x = (x << 6) + d
+                xLen += 6
+            x = x >> (xLen - k)  # shift back the extra bits
+            dLen = xLen - k
+            yield b, x
+
+    v = 0
+
+    G = nx.MultiGraph()
+    G.add_nodes_from(range(n))
+
+    multigraph = False
+    for b, x in parseData():
+        if b == 1:
+            v += 1
+        # padding with ones can cause overlarge number here
+        if x >= n or v >= n:
+            break
+        elif x > v:
+            v = x
+        else:
+            if G.has_edge(x, v):
+                multigraph = True
+            G.add_edge(x, v)
+    if not multigraph:
+        G = nx.Graph(G)
+    return G
+
+
+def to_sparse6_bytes(G, nodes=None, header=True):
+    """Convert an undirected graph to bytes in sparse6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by ``G.nodes()`` is used.
+
+    header: bool
+       If True add '>>sparse6<<' bytes to head of data.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the graph is directed.
+
+    ValueError
+        If the graph has at least ``2 ** 36`` nodes; the sparse6 format
+        is only defined for graphs of order less than ``2 ** 36``.
+
+    Examples
+    --------
+    >>> nx.to_sparse6_bytes(nx.path_graph(2))
+    b'>>sparse6<<:An\\n'
+
+    See Also
+    --------
+    to_sparse6_bytes, read_sparse6, write_sparse6_bytes
+
+    Notes
+    -----
+    The returned bytes end with a newline character.
+
+    The format does not support edge or node labels.
+
+    References
+    ----------
+    .. [1] Graph6 specification
+           
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    G = nx.convert_node_labels_to_integers(G, ordering="sorted")
+    return b"".join(_generate_sparse6_bytes(G, nodes, header))
+
+
+@open_file(0, mode="rb")
+def read_sparse6(path):
+    """Read an undirected graph in sparse6 format from path.
+
+    Parameters
+    ----------
+    path : file or string
+       File or filename to write.
+
+    Returns
+    -------
+    G : Graph/Multigraph or list of Graphs/MultiGraphs
+       If the file contains multiple lines then a list of graphs is returned
+
+    Raises
+    ------
+    NetworkXError
+        If the string is unable to be parsed in sparse6 format
+
+    Examples
+    --------
+    You can read a sparse6 file by giving the path to the file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     _ = f.write(b">>sparse6<<:An\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_sparse6(f.name)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    You can also read a sparse6 file by giving an open file-like object::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     _ = f.write(b">>sparse6<<:An\\n")
+        ...     _ = f.seek(0)
+        ...     G = nx.read_sparse6(f)
+        >>> list(G.edges())
+        [(0, 1)]
+
+    See Also
+    --------
+    read_sparse6, from_sparse6_bytes
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           
+
+    """
+    glist = []
+    for line in path:
+        line = line.strip()
+        if not len(line):
+            continue
+        glist.append(from_sparse6_bytes(line))
+    if len(glist) == 1:
+        return glist[0]
+    else:
+        return glist
+
+
+@not_implemented_for("directed")
+@open_file(1, mode="wb")
+def write_sparse6(G, path, nodes=None, header=True):
+    """Write graph G to given path in sparse6 format.
+
+    Parameters
+    ----------
+    G : Graph (undirected)
+
+    path : file or string
+       File or filename to write
+
+    nodes: list or iterable
+       Nodes are labeled 0...n-1 in the order provided.  If None the ordering
+       given by G.nodes() is used.
+
+    header: bool
+       If True add '>>sparse6<<' string to head of data
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is directed
+
+    Examples
+    --------
+    You can write a sparse6 file by giving the path to the file::
+
+        >>> import tempfile
+        >>> with tempfile.NamedTemporaryFile(delete=False) as f:
+        ...     nx.write_sparse6(nx.path_graph(2), f.name)
+        ...     print(f.read())
+        b'>>sparse6<<:An\\n'
+
+    You can also write a sparse6 file by giving an open file-like object::
+
+        >>> with tempfile.NamedTemporaryFile() as f:
+        ...     nx.write_sparse6(nx.path_graph(2), f)
+        ...     _ = f.seek(0)
+        ...     print(f.read())
+        b'>>sparse6<<:An\\n'
+
+    See Also
+    --------
+    read_sparse6, from_sparse6_bytes
+
+    Notes
+    -----
+    The format does not support edge or node labels.
+
+    References
+    ----------
+    .. [1] Sparse6 specification
+           
+
+    """
+    if nodes is not None:
+        G = G.subgraph(nodes)
+    G = nx.convert_node_labels_to_integers(G, ordering="sorted")
+    for b in _generate_sparse6_bytes(G, nodes, header):
+        path.write(b)
diff --git a/shared/package-files/3d/3d0771fbd820fb5a49720d412ed844f4afc68f09459d3718f5315e0e9c9a2af1/tools.py b/shared/package-files/3d/3d0771fbd820fb5a49720d412ed844f4afc68f09459d3718f5315e0e9c9a2af1/tools.py
new file mode 100644
index 0000000000000000000000000000000000000000..848d8b26dbf64bee02358f83e2c92d155b4a051f
--- /dev/null
+++ b/shared/package-files/3d/3d0771fbd820fb5a49720d412ed844f4afc68f09459d3718f5315e0e9c9a2af1/tools.py
@@ -0,0 +1,569 @@
+"""
+Created on Thu Feb 11 09:19:30 2021
+
+Author: Josef Perktold
+License: BSD-3
+
+"""
+
+import warnings
+
+import numpy as np
+from scipy import interpolate, stats
+
+from statsmodels.tools.rng_qrng import check_random_state
+from statsmodels.tools.validation import string_like
+
+# helper functions to work on a grid of cdf and pdf, histogram
+
+
+class _Grid:
+    """Create Grid values and indices, grid in [0, 1]^d
+
+    This class creates a regular grid in a d dimensional hyper cube.
+
+    Intended for internal use, implementation might change without warning.
+
+
+    Parameters
+    ----------
+    k_grid : sequence of int
+        Number of elements for axes, this defines k_grid - 1 equal sized
+        intervals of [0, 1] for each axis.
+    eps : float, optional
+        If eps is not zero, then x values will be clipped to [eps, 1 - eps],
+        i.e., to the interior of the unit interval or hyper cube.
+
+    Attributes
+    ----------
+    k_grid : sequence of int
+        Number of grid points along each axis.
+    x_marginal : list of ndarray
+        List of 1-dimensional marginal grid values, one array per axis.
+    idx_flat : ndarray
+        Integer array with indices.
+    x_flat : ndarray
+        Flattened grid values, rows are grid points, columns represent
+        variables or axis. ``x_flat`` is currently also 2-dim in the
+        univariate 1-dim grid case.
+
+    """
+
+    def __init__(self, k_grid, eps=0):
+        self.k_grid = k_grid
+
+        x_marginal = [np.arange(ki) / (ki - 1) for ki in k_grid]
+
+        idx_flat = np.column_stack(
+            np.unravel_index(np.arange(np.prod(k_grid)), k_grid)
+        ).astype(float)
+        x_flat = idx_flat / idx_flat.max(0)
+        if eps != 0:
+            x_marginal = [np.clip(xi, eps, 1 - eps) for xi in x_marginal]
+            x_flat = np.clip(x_flat, eps, 1 - eps)
+
+        self.x_marginal = x_marginal
+        self.idx_flat = idx_flat
+        self.x_flat = x_flat
+
+
+def prob2cdf_grid(probs):
+    """Cumulative probabilities from cell probabilities on a grid
+
+    Parameters
+    ----------
+    probs : array_like
+        Rectangular grid of cell probabilities.
+
+    Returns
+    -------
+    cdf : ndarray
+        Grid of cumulative probabilities with same shape as probs.
+    """
+    cdf = np.asarray(probs).copy()
+    k = cdf.ndim
+    for i in range(k):
+        cdf = cdf.cumsum(axis=i)
+
+    return cdf
+
+
+def cdf2prob_grid(cdf, prepend=0):
+    """Cell probabilities from cumulative probabilities on a grid.
+
+    Parameters
+    ----------
+    cdf : array_like
+        Grid of cumulative probabilities with same shape as probs.
+    prepend : int, float, or None, optional
+        Value to prepend to the difference along each axis before taking
+        ``np.diff``. If None, then no value is prepended (i.e., the length
+        along each axis decreases by 1 relative to ``cdf``).
+
+    Returns
+    -------
+    probs : ndarray
+        Rectangular grid of cell probabilities.
+
+    """
+    if prepend is None:
+        prepend = np._NoValue
+    prob = np.asarray(cdf).copy()
+    k = prob.ndim
+    for i in range(k):
+        prob = np.diff(prob, prepend=prepend, axis=i)
+
+    return prob
+
+
+def average_grid(values, coords=None, _method="slicing"):
+    """Compute average for each cell in grid using endpoints
+
+    Parameters
+    ----------
+    values : ndarray
+        Values on a grid that will average over corner points of each cell.
+    coords : None or list of array_like, optional
+        Grid coordinates for each axis used to compute the volume of cell.
+        If None, then averaged values are not rescaled.
+    _method : {"slicing", "convolve"}, optional
+        Grid averaging is implemented using numpy "slicing" or using
+        scipy.signal "convolve".
+
+    Returns
+    -------
+    ndarray
+        Grid with averaged cell values.
+    """
+    _method = string_like(
+        _method, "_method", options=("slicing", "convolve"), lower=False
+    )
+    k_dim = values.ndim
+    if _method == "slicing":
+        p = values.copy()
+
+        for d in range(k_dim):
+            # average (p[:-1] + p[1:]) / 2 over each axis
+            sl1 = [slice(None, None, None)] * k_dim
+            sl2 = [slice(None, None, None)] * k_dim
+            sl1[d] = slice(None, -1, None)
+            sl2[d] = slice(1, None, None)
+            sl1 = tuple(sl1)
+            sl2 = tuple(sl2)
+
+            p = (p[sl1] + p[sl2]) / 2
+
+    else:  # _method == "convolve"
+        from scipy import signal
+
+        p = signal.convolve(values, 0.5**k_dim * np.ones([2] * k_dim), mode="valid")
+
+    if coords is not None:
+        dx = np.array(1)
+        for d in range(k_dim):
+            dx = dx[..., None] * np.diff(coords[d])
+
+        p = p * dx
+
+    return p
+
+
+def nearest_matrix_margins(mat, maxiter=100, tol=1e-8):
+    """nearest matrix with uniform margins
+
+    Parameters
+    ----------
+    mat : array_like
+        Matrix that will be converted to have uniform margins.
+    maxiter : int, optional
+        Maximum number of iterations.
+    tol : float, optional
+        Tolerance for convergence, defined for difference between largest and
+        smallest margin in each dimension.
+
+    Returns
+    -------
+    ndarray
+        Nearest matrix with uniform margins.
+
+    Notes
+    -----
+    This function is intended for internal use and will be generalized in
+    future. API will change.
+
+    changed in 0.14 to support k_dim > 2.
+
+
+    """
+    pc = np.asarray(mat)
+    converged = False
+
+    for _ in range(maxiter):
+        pc0 = pc.copy()
+        for ax in range(pc.ndim):
+            axs = tuple(i for i in range(pc.ndim) if not i == ax)
+            pc0 /= pc.sum(axis=axs, keepdims=True)
+        pc = pc0
+        pc /= pc.sum()
+
+        # check convergence
+        mptps = []
+        for ax in range(pc.ndim):
+            axs = tuple(i for i in range(pc.ndim) if not i == ax)
+            marg = pc.sum(axis=axs, keepdims=False)
+            mptps.append(np.ptp(marg))
+        if max(mptps) < tol:
+            converged = True
+            break
+
+    if not converged:
+        from statsmodels.tools.sm_exceptions import ConvergenceWarning
+
+        warnings.warn(
+            "Iterations did not converge, maxiter reached",
+            ConvergenceWarning,
+            stacklevel=2,
+        )
+    return pc
+
+
+def _rankdata_no_ties(x):
+    """rankdata without ties for 2-d array
+
+    This is a simplified version for ranking data if there are no ties.
+    Works vectorized across columns.
+
+    Parameters
+    ----------
+    x : ndarray
+        2-D array with observations in rows and variables in columns.
+
+    Returns
+    -------
+    ndarray
+        Ranks of `x`, computed separately for each column.
+
+    See Also
+    --------
+    scipy.stats.rankdata
+
+    """
+    nobs, k_vars = x.shape
+    ranks = np.ones((nobs, k_vars))
+    sidx = np.argsort(x, axis=0)
+    ranks[sidx, np.arange(k_vars)] = np.arange(1, nobs + 1)[:, None]
+    return ranks
+
+
+def frequencies_fromdata(data, k_bins, use_ranks=True):
+    """count of observations in bins (histogram)
+
+    currently only for bivariate data
+
+    Parameters
+    ----------
+    data : array_like
+        Bivariate data with observations in rows and two columns. Binning is
+        in unit rectangle [0, 1]^2. If use_rank is False, then data should be
+        in unit interval.
+    k_bins : int
+        Number of bins along each dimension in the histogram
+    use_ranks : bool, optional
+        If use_rank is True, then data will be converted to ranks without
+        tie handling.
+
+    Returns
+    -------
+    bin counts : ndarray
+        Frequencies are the number of observations in a given bin.
+        Bin counts are a 2-dim array with k_bins rows and k_bins columns.
+
+    Notes
+    -----
+    This function is intended for internal use and will be generalized in
+    future. API will change.
+    """
+    data = np.asarray(data)
+    k_dim = data.shape[-1]
+    k = k_bins + 1
+    g2 = _Grid([k] * k_dim, eps=0)
+    if use_ranks:
+        data = _rankdata_no_ties(data) / (data.shape[0] + 1)
+        # alternatives: scipy handles ties, but uses np.apply_along_axis
+        # rvs = stats.rankdata(rvs, axis=0) / (rvs.shape[0] + 1)
+        # rvs = (np.argsort(np.argsort(rvs, axis=0), axis=0) + 1
+        #                              ) / (rvs.shape[0] + 1)
+    freqr, _ = np.histogramdd(data, bins=g2.x_marginal)
+    return freqr
+
+
+def approx_copula_pdf(copula, k_bins=10, force_uniform=True, use_pdf=False, rng=None):
+    """Histogram probabilities as approximation to a copula density.
+
+    Parameters
+    ----------
+    copula : instance
+        Instance of a copula class. Only the ``pdf`` method is used.
+    k_bins : int, optional
+        Number of bins along each dimension in the approximating histogram.
+    force_uniform : bool, optional
+        If true, then the pdf grid will be adjusted to have uniform margins
+        using `nearest_matrix_margin`.
+        If false, then no adjustment is done and the margins may not be exactly
+        uniform.
+    use_pdf : bool, optional
+        If false, then the grid cell probabilities will be computed from the
+        copula cdf.
+        If true, then the density, ``pdf``, is used and cell probabilities
+        are approximated by averaging the pdf of the cell corners. This is
+        only useful if the cdf is not available.
+    rng : int, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional
+        The source of the random variables to use in cdf calculation, if
+        needed. If `rng` is None, a new ``Generator`` is created using
+        fresh entropy from the operating system. If `rng` is an int or
+        array of ints, a new ``Generator`` is created, seeded with `rng`.
+        If `rng` is already a ``Generator`` or ``RandomState`` instance,
+        that instance is used.
+
+    Returns
+    -------
+    ndarray
+        Probability that random variable falls in given bin. This corresponds
+        to a discrete distribution, and is not scaled to bin size to form a
+        piecewise uniform, histogram density.
+        Bin probabilities are a k-dim array with k_bins segments in each
+        dimension.
+
+    Notes
+    -----
+    This function is intended for internal use and will be generalized in
+    future. API will change.
+    """
+    k_dim = copula.k_dim
+    k = k_bins + 1
+    ks = tuple([k] * k_dim)
+
+    if use_pdf:
+        g = _Grid([k] * k_dim, eps=0.1 / k_bins)
+        pdfg = copula.pdf(g.x_flat).reshape(*ks)
+        # correct for bin size
+        pdfg *= 1 / k**k_dim
+        ag = average_grid(pdfg)
+        if force_uniform:
+            pdf_grid = nearest_matrix_margins(ag, maxiter=100, tol=1e-8)
+        else:
+            pdf_grid = ag / ag.sum()
+    else:
+        g = _Grid([k] * k_dim, eps=1e-6)
+        rng = check_random_state(rng)
+        try:
+            # This is a hack because some copula CDFs are approximate and use
+            # random variates in their calculation, while most do not.
+            cdfg = copula.cdf(g.x_flat, rng=rng).reshape(*ks)
+        except TypeError:
+            cdfg = copula.cdf(g.x_flat).reshape(*ks)
+        # correct for bin size
+        pdf_grid = cdf2prob_grid(cdfg, prepend=None)
+        # TODO: check boundary approximation, eg. undefined at zero
+        # for now just normalize
+        pdf_grid /= pdf_grid.sum()
+
+    return pdf_grid
+
+
+# functions to evaluate bernstein polynomials
+
+
+def _eval_bernstein_1d(x, fvals, method="binom"):
+    """Evaluate 1-dimensional bernstein polynomial given grid of values.
+
+    experimental, comparing methods
+
+    Parameters
+    ----------
+    x : array_like
+        Values at which to evaluate the Bernstein polynomial.
+    fvals : ndarray
+        Grid values of coefficients for Bernstein polynomial basis in the
+        weighted sum.
+    method : {"binom", "beta", "bpoly"}, optional
+        Method to construct Bernstein polynomial basis, used for comparison
+        of parameterizations.
+
+        - "binom" uses pmf of Binomial distribution
+        - "beta" uses pdf of Beta distribution
+        - "bpoly" uses one interval in scipy.interpolate.BPoly
+
+    Returns
+    -------
+    ndarray
+        Bernstein polynomial at evaluation points, weighted sum of Bernstein
+        polynomial basis.
+    """
+    method = string_like(method, "method", options=("binom", "beta", "bpoly"))
+    k_terms = fvals.shape[-1]
+    xx = np.asarray(x)
+    k = np.arange(k_terms).astype(float)
+    n = k_terms - 1.0
+
+    if method == "binom":
+        # Divide by 0 RuntimeWarning here
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", RuntimeWarning)
+            poly_base = stats.binom.pmf(k, n, xx[..., None])
+        bp_values = (fvals * poly_base).sum(-1)
+    elif method == "bpoly":
+        bpb = interpolate.BPoly(fvals[:, None], [0.0, 1])
+        bp_values = bpb(x)
+    else:  # method == "beta"
+        # Divide by 0 RuntimeWarning here
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", RuntimeWarning)
+            poly_base = stats.beta.pdf(xx[..., None], k + 1, n - k + 1) / (n + 1)
+        bp_values = (fvals * poly_base).sum(-1)
+
+    return bp_values
+
+
+def _eval_bernstein_2d(x, fvals):
+    """Evaluate 2-dimensional bernstein polynomial given grid of values
+
+    experimental
+
+    Parameters
+    ----------
+    x : array_like
+        Values at which to evaluate the Bernstein polynomial.
+    fvals : ndarray
+        Grid values of coefficients for Bernstein polynomial basis in the
+        weighted sum.
+
+    Returns
+    -------
+    ndarray
+        Bernstein polynomial at evaluation points, weighted sum of Bernstein
+        polynomial basis.
+    """
+    k_terms = fvals.shape
+    k_dim = fvals.ndim
+    if k_dim != 2:
+        raise ValueError("`fval` needs to be 2-dimensional")
+    xx = np.atleast_2d(x)
+    if xx.shape[1] != 2:
+        raise ValueError("x needs to be bivariate and have 2 columns")
+
+    x1, x2 = xx.T
+    n1, n2 = k_terms[0] - 1, k_terms[1] - 1
+    k1 = np.arange(k_terms[0]).astype(float)
+    k2 = np.arange(k_terms[1]).astype(float)
+
+    # we are building a nobs x n1 x n2 array
+    poly_base = stats.binom.pmf(
+        k1[None, :, None], n1, x1[:, None, None]
+    ) * stats.binom.pmf(k2[None, None, :], n2, x2[:, None, None])
+    bp_values = (fvals * poly_base).sum(-1).sum(-1)
+
+    return bp_values
+
+
+def _eval_bernstein_dd(x, fvals):
+    """Evaluate d-dimensional bernstein polynomial given grid of values
+
+    experimental
+
+    Parameters
+    ----------
+    x : array_like
+        Values at which to evaluate the Bernstein polynomial.
+    fvals : ndarray
+        Grid values of coefficients for Bernstein polynomial basis in the
+        weighted sum.
+
+    Returns
+    -------
+    ndarray
+        Bernstein polynomial at evaluation points, weighted sum of Bernstein
+        polynomial basis.
+    """
+    k_terms = fvals.shape
+    k_dim = fvals.ndim
+    xx = np.atleast_2d(x)
+
+    # The following loop is a tricky
+    # we add terms for each x and expand dimension of poly base in each
+    # iteration using broadcasting
+
+    poly_base = np.zeros(x.shape[0])
+    for i in range(k_dim):
+        ki = np.arange(k_terms[i]).astype(float)
+        for _ in range(i + 1):
+            ki = ki[..., None]
+        ni = k_terms[i] - 1
+        xi = xx[:, i]
+        poly_base = poly_base[None, ...] + stats.binom._logpmf(ki, ni, xi)
+
+    poly_base = np.exp(poly_base)
+    bp_values = fvals.T[..., None] * poly_base
+
+    for _ in range(k_dim):
+        bp_values = bp_values.sum(0)
+
+    return bp_values
+
+
+def _ecdf_mv(data, method="seq", use_ranks=True):
+    """
+    Multivariate empirical distribution function, empirical copula
+
+    Parameters
+    ----------
+    data : array_like
+        Multivariate data with observations in rows and variables in
+        columns.
+    method : {"seq", "brute"}, optional
+        Method used to compute the empirical distribution function.
+    use_ranks : bool, optional
+        If True, then data is converted to ranks, without tie handling,
+        before computing the empirical distribution function.
+
+    Returns
+    -------
+    count : ndarray
+        Empirical distribution function counts for each observation.
+    x : ndarray
+        Data converted to ranks if `use_ranks` is True, otherwise the
+        original data as an array.
+
+    Notes
+    -----
+    Method "seq" is faster than method "brute", but supports mainly bivariate
+    case. Speed advantage of "seq" is increasing in number of observations
+    and decreasing in number of variables.
+    (see Segers ...)
+
+    Warning: This does not handle ties. The ecdf is based on univariate ranks
+    without ties. The assignment of ranks to ties depends on the sorting
+    algorithm and the initial ordering of the data.
+
+    When the original data is used instead of ranks, then method "brute"
+    computes the correct ecdf counts even in the case of ties.
+
+    """
+    x = np.asarray(data)
+    n = x.shape[0]
+    if use_ranks:
+        x = _rankdata_no_ties(x) / n
+    if method == "brute":
+        count = [((x <= x[i]).all(1)).sum() for i in range(n)]
+        count = np.asarray(count)
+    elif method.startswith("seq"):
+        sort_idx0 = np.argsort(x[:, 0])
+        x_s0 = x[sort_idx0]
+        x1 = x_s0[:, 1:]
+        count_smaller = [(x1[:i] <= x1[i]).all(1).sum() + 1 for i in range(n)]
+        count = np.empty(x.shape[0])
+        count[sort_idx0] = count_smaller
+    else:
+        raise ValueError("method not available")
+
+    return count, x
diff --git a/shared/package-files/3d/3d0ac11d0556f76d54996190ecde38f783579b161bbba926cd4e65d429fbec15/flatten_ops.h b/shared/package-files/3d/3d0ac11d0556f76d54996190ecde38f783579b161bbba926cd4e65d429fbec15/flatten_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..a982886ce6cc22541980e2755cd531a4e278e2c6
--- /dev/null
+++ b/shared/package-files/3d/3d0ac11d0556f76d54996190ecde38f783579b161bbba926cd4e65d429fbec15/flatten_ops.h
@@ -0,0 +1,61 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API flatten_using_ints {
+  using schema = at::Tensor (const at::Tensor &, int64_t, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::flatten")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "using_ints")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "flatten.using_ints(Tensor(a) self, int start_dim=0, int end_dim=-1) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self, int64_t start_dim, int64_t end_dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t start_dim, int64_t end_dim);
+};
+
+struct TORCH_API flatten_named_out_dim {
+  using schema = at::Tensor (const at::Tensor &, int64_t, int64_t, at::Dimname);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::flatten")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "named_out_dim")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "flatten.named_out_dim(Tensor(a) self, int start_dim, int end_dim, Dimname out_dim) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self, int64_t start_dim, int64_t end_dim, at::Dimname out_dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, int64_t start_dim, int64_t end_dim, at::Dimname out_dim);
+};
+
+struct TORCH_API flatten_using_names {
+  using schema = at::Tensor (const at::Tensor &, at::Dimname, at::Dimname, at::Dimname);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::flatten")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "using_names")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "flatten.using_names(Tensor(a) self, Dimname start_dim, Dimname end_dim, Dimname out_dim) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self, at::Dimname start_dim, at::Dimname end_dim, at::Dimname out_dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Dimname start_dim, at::Dimname end_dim, at::Dimname out_dim);
+};
+
+struct TORCH_API flatten_DimnameList {
+  using schema = at::Tensor (const at::Tensor &, at::DimnameList, at::Dimname);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::flatten")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "DimnameList")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "flatten.DimnameList(Tensor(a) self, Dimname[] dims, Dimname out_dim) -> Tensor(a)")
+  static at::Tensor call(const at::Tensor & self, at::DimnameList dims, at::Dimname out_dim);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::DimnameList dims, at::Dimname out_dim);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/3d/3d0e5b08e2bbeb8413969b1b6913389f4a708f2c26d9081ccc641d760f0380b8/vdot_native.h b/shared/package-files/3d/3d0e5b08e2bbeb8413969b1b6913389f4a708f2c26d9081ccc641d760f0380b8/vdot_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..7351d65e60247d5ad979d40ca9496839c5d34e68
--- /dev/null
+++ b/shared/package-files/3d/3d0e5b08e2bbeb8413969b1b6913389f4a708f2c26d9081ccc641d760f0380b8/vdot_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & vdot_out(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+TORCH_API at::Tensor vdot(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor vdot_cuda(const at::Tensor & self, const at::Tensor & other);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/3d/3d0e83180cd20a2715e54716ace4247473638357be646541cf0d8c8235f21814/guru-33-rv b/shared/package-files/3d/3d0e83180cd20a2715e54716ace4247473638357be646541cf0d8c8235f21814/guru-33-rv
new file mode 100644
index 0000000000000000000000000000000000000000..4092bc73c2bf11855223fdcbff4590a9eff7376f
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diff --git a/shared/package-files/3d/3d0e83180cd20a2715e54716ace4247473638357be646541cf0d8c8235f21814/guru-rv b/shared/package-files/3d/3d0e83180cd20a2715e54716ace4247473638357be646541cf0d8c8235f21814/guru-rv
new file mode 100644
index 0000000000000000000000000000000000000000..4092bc73c2bf11855223fdcbff4590a9eff7376f
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diff --git a/shared/package-files/3d/3d14f6862c51fa6571af65082ef1daf7d0ebe78105da1dec9c95c4fafdeb59c7/datasource.pyi b/shared/package-files/3d/3d14f6862c51fa6571af65082ef1daf7d0ebe78105da1dec9c95c4fafdeb59c7/datasource.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..345277d45370fe1442f6cf010528a3eefce07f29
--- /dev/null
+++ b/shared/package-files/3d/3d14f6862c51fa6571af65082ef1daf7d0ebe78105da1dec9c95c4fafdeb59c7/datasource.pyi
@@ -0,0 +1,15 @@
+from pathlib import Path
+import numpy as np
+
+path: Path
+d1: np.DataSource
+
+d1.abspath(path)  # E: incompatible type
+d1.abspath(b"...")  # E: incompatible type
+
+d1.exists(path)  # E: incompatible type
+d1.exists(b"...")  # E: incompatible type
+
+d1.open(path, "r")  # E: incompatible type
+d1.open(b"...", encoding="utf8")  # E: incompatible type
+d1.open(None, newline="/n")  # E: incompatible type
diff --git a/shared/package-files/3d/3d180008e36922a4e8daec11c34c7af264fed5962d07924aea928c38e8663c94/LICENSE b/shared/package-files/3d/3d180008e36922a4e8daec11c34c7af264fed5962d07924aea928c38e8663c94/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..33b7cdd2dbaeddce1e35aa1a13f63d71154dc42f
--- /dev/null
+++ b/shared/package-files/3d/3d180008e36922a4e8daec11c34c7af264fed5962d07924aea928c38e8663c94/LICENSE
@@ -0,0 +1,19 @@
+Copyright (c) 2009, 2010, 2013-2016 by the Brotli Authors.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
diff --git a/shared/package-files/3d/3d1b7a95a9168e26d9370fd1682f36e8ade61a08742ff681045bc2c6638458b6/__init__.py b/shared/package-files/3d/3d1b7a95a9168e26d9370fd1682f36e8ade61a08742ff681045bc2c6638458b6/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..83a394f4df276171e7e5b2a1eb0cee843f9d4e99
--- /dev/null
+++ b/shared/package-files/3d/3d1b7a95a9168e26d9370fd1682f36e8ade61a08742ff681045bc2c6638458b6/__init__.py
@@ -0,0 +1,5 @@
+from .linear import Linear
+
+__all__ = [
+    "Linear",
+]
diff --git a/shared/package-files/3d/3d1c04d976ff4d2841025a785aaab0cc4ee06c9c9b4e09d1e2456949fa273856/fix_urllib.py b/shared/package-files/3d/3d1c04d976ff4d2841025a785aaab0cc4ee06c9c9b4e09d1e2456949fa273856/fix_urllib.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab892bc52494c25bf5b46f92ca862965b4d99e5c
--- /dev/null
+++ b/shared/package-files/3d/3d1c04d976ff4d2841025a785aaab0cc4ee06c9c9b4e09d1e2456949fa273856/fix_urllib.py
@@ -0,0 +1,196 @@
+"""Fix changes imports of urllib which are now incompatible.
+   This is rather similar to fix_imports, but because of the more
+   complex nature of the fixing for urllib, it has its own fixer.
+"""
+# Author: Nick Edds
+
+# Local imports
+from lib2to3.fixes.fix_imports import alternates, FixImports
+from lib2to3.fixer_util import (Name, Comma, FromImport, Newline,
+                                find_indentation, Node, syms)
+
+MAPPING = {"urllib":  [
+                ("urllib.request",
+                    ["URLopener", "FancyURLopener", "urlretrieve",
+                     "_urlopener", "urlopen", "urlcleanup",
+                     "pathname2url", "url2pathname", "getproxies"]),
+                ("urllib.parse",
+                    ["quote", "quote_plus", "unquote", "unquote_plus",
+                     "urlencode", "splitattr", "splithost", "splitnport",
+                     "splitpasswd", "splitport", "splitquery", "splittag",
+                     "splittype", "splituser", "splitvalue", ]),
+                ("urllib.error",
+                    ["ContentTooShortError"])],
+           "urllib2" : [
+                ("urllib.request",
+                    ["urlopen", "install_opener", "build_opener",
+                     "Request", "OpenerDirector", "BaseHandler",
+                     "HTTPDefaultErrorHandler", "HTTPRedirectHandler",
+                     "HTTPCookieProcessor", "ProxyHandler",
+                     "HTTPPasswordMgr",
+                     "HTTPPasswordMgrWithDefaultRealm",
+                     "AbstractBasicAuthHandler",
+                     "HTTPBasicAuthHandler", "ProxyBasicAuthHandler",
+                     "AbstractDigestAuthHandler",
+                     "HTTPDigestAuthHandler", "ProxyDigestAuthHandler",
+                     "HTTPHandler", "HTTPSHandler", "FileHandler",
+                     "FTPHandler", "CacheFTPHandler",
+                     "UnknownHandler"]),
+                ("urllib.error",
+                    ["URLError", "HTTPError"]),
+           ]
+}
+
+# Duplicate the url parsing functions for urllib2.
+MAPPING["urllib2"].append(MAPPING["urllib"][1])
+
+
+def build_pattern():
+    bare = set()
+    for old_module, changes in MAPPING.items():
+        for change in changes:
+            new_module, members = change
+            members = alternates(members)
+            yield """import_name< 'import' (module=%r
+                                  | dotted_as_names< any* module=%r any* >) >
+                  """ % (old_module, old_module)
+            yield """import_from< 'from' mod_member=%r 'import'
+                       ( member=%s | import_as_name< member=%s 'as' any > |
+                         import_as_names< members=any*  >) >
+                  """ % (old_module, members, members)
+            yield """import_from< 'from' module_star=%r 'import' star='*' >
+                  """ % old_module
+            yield """import_name< 'import'
+                                  dotted_as_name< module_as=%r 'as' any > >
+                  """ % old_module
+            # bare_with_attr has a special significance for FixImports.match().
+            yield """power< bare_with_attr=%r trailer< '.' member=%s > any* >
+                  """ % (old_module, members)
+
+
+class FixUrllib(FixImports):
+
+    def build_pattern(self):
+        return "|".join(build_pattern())
+
+    def transform_import(self, node, results):
+        """Transform for the basic import case. Replaces the old
+           import name with a comma separated list of its
+           replacements.
+        """
+        import_mod = results.get("module")
+        pref = import_mod.prefix
+
+        names = []
+
+        # create a Node list of the replacement modules
+        for name in MAPPING[import_mod.value][:-1]:
+            names.extend([Name(name[0], prefix=pref), Comma()])
+        names.append(Name(MAPPING[import_mod.value][-1][0], prefix=pref))
+        import_mod.replace(names)
+
+    def transform_member(self, node, results):
+        """Transform for imports of specific module elements. Replaces
+           the module to be imported from with the appropriate new
+           module.
+        """
+        mod_member = results.get("mod_member")
+        pref = mod_member.prefix
+        member = results.get("member")
+
+        # Simple case with only a single member being imported
+        if member:
+            # this may be a list of length one, or just a node
+            if isinstance(member, list):
+                member = member[0]
+            new_name = None
+            for change in MAPPING[mod_member.value]:
+                if member.value in change[1]:
+                    new_name = change[0]
+                    break
+            if new_name:
+                mod_member.replace(Name(new_name, prefix=pref))
+            else:
+                self.cannot_convert(node, "This is an invalid module element")
+
+        # Multiple members being imported
+        else:
+            # a dictionary for replacements, order matters
+            modules = []
+            mod_dict = {}
+            members = results["members"]
+            for member in members:
+                # we only care about the actual members
+                if member.type == syms.import_as_name:
+                    as_name = member.children[2].value
+                    member_name = member.children[0].value
+                else:
+                    member_name = member.value
+                    as_name = None
+                if member_name != ",":
+                    for change in MAPPING[mod_member.value]:
+                        if member_name in change[1]:
+                            if change[0] not in mod_dict:
+                                modules.append(change[0])
+                            mod_dict.setdefault(change[0], []).append(member)
+
+            new_nodes = []
+            indentation = find_indentation(node)
+            first = True
+            def handle_name(name, prefix):
+                if name.type == syms.import_as_name:
+                    kids = [Name(name.children[0].value, prefix=prefix),
+                            name.children[1].clone(),
+                            name.children[2].clone()]
+                    return [Node(syms.import_as_name, kids)]
+                return [Name(name.value, prefix=prefix)]
+            for module in modules:
+                elts = mod_dict[module]
+                names = []
+                for elt in elts[:-1]:
+                    names.extend(handle_name(elt, pref))
+                    names.append(Comma())
+                names.extend(handle_name(elts[-1], pref))
+                new = FromImport(module, names)
+                if not first or node.parent.prefix.endswith(indentation):
+                    new.prefix = indentation
+                new_nodes.append(new)
+                first = False
+            if new_nodes:
+                nodes = []
+                for new_node in new_nodes[:-1]:
+                    nodes.extend([new_node, Newline()])
+                nodes.append(new_nodes[-1])
+                node.replace(nodes)
+            else:
+                self.cannot_convert(node, "All module elements are invalid")
+
+    def transform_dot(self, node, results):
+        """Transform for calls to module members in code."""
+        module_dot = results.get("bare_with_attr")
+        member = results.get("member")
+        new_name = None
+        if isinstance(member, list):
+            member = member[0]
+        for change in MAPPING[module_dot.value]:
+            if member.value in change[1]:
+                new_name = change[0]
+                break
+        if new_name:
+            module_dot.replace(Name(new_name,
+                                    prefix=module_dot.prefix))
+        else:
+            self.cannot_convert(node, "This is an invalid module element")
+
+    def transform(self, node, results):
+        if results.get("module"):
+            self.transform_import(node, results)
+        elif results.get("mod_member"):
+            self.transform_member(node, results)
+        elif results.get("bare_with_attr"):
+            self.transform_dot(node, results)
+        # Renaming and star imports are not supported for these modules.
+        elif results.get("module_star"):
+            self.cannot_convert(node, "Cannot handle star imports.")
+        elif results.get("module_as"):
+            self.cannot_convert(node, "This module is now multiple modules")
diff --git a/shared/package-files/3d/3d1cb16a5e1bcfd8d5666b8ba4a898c3e55a479f4b7a47f026058aa3b479b916/libs.py b/shared/package-files/3d/3d1cb16a5e1bcfd8d5666b8ba4a898c3e55a479f4b7a47f026058aa3b479b916/libs.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce3ed9c96e4057b9742f1a12021657fc40ea2580
--- /dev/null
+++ b/shared/package-files/3d/3d1cb16a5e1bcfd8d5666b8ba4a898c3e55a479f4b7a47f026058aa3b479b916/libs.py
@@ -0,0 +1,176 @@
+"""CUDA Toolkit libraries lookup utilities.
+
+CUDA Toolkit libraries can be available via either:
+
+- the `cuda-nvcc` and `cuda-nvrtc` conda packages for CUDA 12,
+- the `cudatoolkit` conda package for CUDA 11,
+- a user supplied location from CUDA_HOME,
+- a system wide location,
+- package-specific locations (e.g. the Debian NVIDIA packages),
+- or can be discovered by the system loader.
+"""
+
+import os
+import sys
+import ctypes
+
+from numba.misc.findlib import find_lib
+from numba.cuda.cuda_paths import get_cuda_paths
+from numba.cuda.cudadrv.driver import locate_driver_and_loader, load_driver
+from numba.cuda.cudadrv.error import CudaSupportError
+
+
+if sys.platform == 'win32':
+    _dllnamepattern = '%s.dll'
+    _staticnamepattern = '%s.lib'
+elif sys.platform == 'darwin':
+    _dllnamepattern = 'lib%s.dylib'
+    _staticnamepattern = 'lib%s.a'
+else:
+    _dllnamepattern = 'lib%s.so'
+    _staticnamepattern = 'lib%s.a'
+
+
+def get_libdevice():
+    d = get_cuda_paths()
+    paths = d['libdevice'].info
+    return paths
+
+
+def open_libdevice():
+    with open(get_libdevice(), 'rb') as bcfile:
+        return bcfile.read()
+
+
+def get_cudalib(lib, static=False):
+    """
+    Find the path of a CUDA library based on a search of known locations. If
+    the search fails, return a generic filename for the library (e.g.
+    'libnvvm.so' for 'nvvm') so that we may attempt to load it using the system
+    loader's search mechanism.
+    """
+    if lib == 'nvvm':
+        return get_cuda_paths()['nvvm'].info or _dllnamepattern % 'nvvm'
+    else:
+        dir_type = 'static_cudalib_dir' if static else 'cudalib_dir'
+        libdir = get_cuda_paths()[dir_type].info
+
+    candidates = find_lib(lib, libdir, static=static)
+    namepattern = _staticnamepattern if static else _dllnamepattern
+    return max(candidates) if candidates else namepattern % lib
+
+
+def open_cudalib(lib):
+    path = get_cudalib(lib)
+    return ctypes.CDLL(path)
+
+
+def check_static_lib(path):
+    if not os.path.isfile(path):
+        raise FileNotFoundError(f'{path} not found')
+
+
+def _get_source_variable(lib, static=False):
+    if lib == 'nvvm':
+        return get_cuda_paths()['nvvm'].by
+    elif lib == 'libdevice':
+        return get_cuda_paths()['libdevice'].by
+    else:
+        dir_type = 'static_cudalib_dir' if static else 'cudalib_dir'
+        return get_cuda_paths()[dir_type].by
+
+
+def test():
+    """Test library lookup.  Path info is printed to stdout.
+    """
+    failed = False
+
+    # Check for the driver
+    try:
+        dlloader, candidates = locate_driver_and_loader()
+        print('Finding driver from candidates:')
+        for location in candidates:
+            print(f'\t{location}')
+        print(f'Using loader {dlloader}')
+        print('\tTrying to load driver', end='...')
+        dll, path = load_driver(dlloader, candidates)
+        print('\tok')
+        print(f'\t\tLoaded from {path}')
+    except CudaSupportError as e:
+        print(f'\tERROR: failed to open driver: {e}')
+        failed = True
+
+    # Find the absolute location of the driver on Linux. Various driver-related
+    # issues have been reported by WSL2 users, and it is almost always due to a
+    # Linux (i.e. not- WSL2) driver being installed in a WSL2 system.
+    # Providing the absolute location of the driver indicates its version
+    # number in the soname (e.g. "libcuda.so.530.30.02"), which can be used to
+    # look up whether the driver was intended for "native" Linux.
+    if sys.platform == 'linux' and not failed:
+        pid = os.getpid()
+        mapsfile = os.path.join(os.path.sep, 'proc', f'{pid}', 'maps')
+        try:
+            with open(mapsfile) as f:
+                maps = f.read()
+        # It's difficult to predict all that might go wrong reading the maps
+        # file - in case various error conditions ensue (the file is not found,
+        # not readable, etc.) we use OSError to hopefully catch any of them.
+        except OSError:
+            # It's helpful to report that this went wrong to the user, but we
+            # don't set failed to True because this doesn't have any connection
+            # to actual CUDA functionality.
+            print(f'\tERROR: Could not open {mapsfile} to determine absolute '
+                  'path to libcuda.so')
+        else:
+            # In this case we could read the maps, so we can report the
+            # relevant ones to the user
+            locations = set(s for s in maps.split() if 'libcuda.so' in s)
+            print('\tMapped libcuda.so paths:')
+            for location in locations:
+                print(f'\t\t{location}')
+
+    # Checks for dynamic libraries
+    libs = 'nvvm nvrtc cudart'.split()
+    for lib in libs:
+        path = get_cudalib(lib)
+        print('Finding {} from {}'.format(lib, _get_source_variable(lib)))
+        print('\tLocated at', path)
+
+        try:
+            print('\tTrying to open library', end='...')
+            open_cudalib(lib)
+            print('\tok')
+        except OSError as e:
+            print('\tERROR: failed to open %s:\n%s' % (lib, e))
+            failed = True
+
+    # Check for cudadevrt (the only static library)
+    lib = 'cudadevrt'
+    path = get_cudalib(lib, static=True)
+    print('Finding {} from {}'.format(lib, _get_source_variable(lib,
+                                                                static=True)))
+    print('\tLocated at', path)
+
+    try:
+        print('\tChecking library', end='...')
+        check_static_lib(path)
+        print('\tok')
+    except FileNotFoundError as e:
+        print('\tERROR: failed to find %s:\n%s' % (lib, e))
+        failed = True
+
+    # Check for libdevice
+    where = _get_source_variable('libdevice')
+    print(f'Finding libdevice from {where}')
+    path = get_libdevice()
+    print('\tLocated at', path)
+
+    try:
+        print('\tChecking library', end='...')
+        check_static_lib(path)
+        print('\tok')
+    except FileNotFoundError as e:
+        print('\tERROR: failed to find %s:\n%s' % (lib, e))
+        failed = True
+
+    return not failed
diff --git a/shared/package-files/3d/3d1cb91908eef2975411f5ca418a36a85467c976b91bd977f5101cd3870d5d82/cpp_hook.h b/shared/package-files/3d/3d1cb91908eef2975411f5ca418a36a85467c976b91bd977f5101cd3870d5d82/cpp_hook.h
new file mode 100644
index 0000000000000000000000000000000000000000..e48c587296b2d2874231bb7676f88dbdec0aad9f
--- /dev/null
+++ b/shared/package-files/3d/3d1cb91908eef2975411f5ca418a36a85467c976b91bd977f5101cd3870d5d82/cpp_hook.h
@@ -0,0 +1,29 @@
+#pragma once
+#include 
+#include 
+#include 
+
+namespace torch::autograd {
+
+using hooks_list =
+    std::vector>;
+
+struct CppFunctionTensorPreHook : public FunctionPreHook {
+  CppFunctionTensorPreHook(std::shared_ptr hooks, size_t value_idx);
+  variable_list operator()(const variable_list& values) override;
+
+  std::shared_ptr hooks_;
+  size_t value_idx_;
+};
+
+struct CppFunctionSingleTensorPreHook : public FunctionPreHook {
+  CppFunctionSingleTensorPreHook(
+      std::function hook,
+      size_t value_idx);
+  variable_list operator()(const variable_list& values) override;
+
+  std::function hook_;
+  size_t value_idx_;
+};
+
+} // namespace torch::autograd
diff --git a/shared/package-files/3d/3d1d3adc3565bbcffeb62ec6b0b45bd1eee8e28033db1a4c8a5d2731e7b5f33d/objects.py b/shared/package-files/3d/3d1d3adc3565bbcffeb62ec6b0b45bd1eee8e28033db1a4c8a5d2731e7b5f33d/objects.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2db4886a559017422ed41bb8bd2246d6a3f0fb0
--- /dev/null
+++ b/shared/package-files/3d/3d1d3adc3565bbcffeb62ec6b0b45bd1eee8e28033db1a4c8a5d2731e7b5f33d/objects.py
@@ -0,0 +1,453 @@
+"""Indexer objects for computing start/end window bounds for rolling operations"""
+from __future__ import annotations
+
+from datetime import timedelta
+
+import numpy as np
+
+from pandas._libs.tslibs import BaseOffset
+from pandas._libs.window.indexers import calculate_variable_window_bounds
+from pandas.util._decorators import Appender
+
+from pandas.core.dtypes.common import ensure_platform_int
+
+from pandas.core.indexes.datetimes import DatetimeIndex
+
+from pandas.tseries.offsets import Nano
+
+get_window_bounds_doc = """
+Computes the bounds of a window.
+
+Parameters
+----------
+num_values : int, default 0
+    number of values that will be aggregated over
+window_size : int, default 0
+    the number of rows in a window
+min_periods : int, default None
+    min_periods passed from the top level rolling API
+center : bool, default None
+    center passed from the top level rolling API
+closed : str, default None
+    closed passed from the top level rolling API
+step : int, default None
+    step passed from the top level rolling API
+    .. versionadded:: 1.5
+win_type : str, default None
+    win_type passed from the top level rolling API
+
+Returns
+-------
+A tuple of ndarray[int64]s, indicating the boundaries of each
+window
+"""
+
+
+class BaseIndexer:
+    """
+    Base class for window bounds calculations.
+
+    Examples
+    --------
+    >>> from pandas.api.indexers import BaseIndexer
+    >>> class CustomIndexer(BaseIndexer):
+    ...     def get_window_bounds(self, num_values, min_periods, center, closed, step):
+    ...         start = np.empty(num_values, dtype=np.int64)
+    ...         end = np.empty(num_values, dtype=np.int64)
+    ...         for i in range(num_values):
+    ...             start[i] = i
+    ...             end[i] = i + self.window_size
+    ...         return start, end
+    >>> df = pd.DataFrame({"values": range(5)})
+    >>> indexer = CustomIndexer(window_size=2)
+    >>> df.rolling(indexer).sum()
+        values
+    0	1.0
+    1	3.0
+    2	5.0
+    3	7.0
+    4	4.0
+    """
+
+    def __init__(
+        self, index_array: np.ndarray | None = None, window_size: int = 0, **kwargs
+    ) -> None:
+        self.index_array = index_array
+        self.window_size = window_size
+        # Set user defined kwargs as attributes that can be used in get_window_bounds
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        raise NotImplementedError
+
+
+class FixedWindowIndexer(BaseIndexer):
+    """Creates window boundaries that are of fixed length."""
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        if center or self.window_size == 0:
+            offset = (self.window_size - 1) // 2
+        else:
+            offset = 0
+
+        end = np.arange(1 + offset, num_values + 1 + offset, step, dtype="int64")
+        start = end - self.window_size
+        if closed in ["left", "both"]:
+            start -= 1
+        if closed in ["left", "neither"]:
+            end -= 1
+
+        end = np.clip(end, 0, num_values)
+        start = np.clip(start, 0, num_values)
+
+        return start, end
+
+
+class VariableWindowIndexer(BaseIndexer):
+    """Creates window boundaries that are of variable length, namely for time series."""
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        # error: Argument 4 to "calculate_variable_window_bounds" has incompatible
+        # type "Optional[bool]"; expected "bool"
+        # error: Argument 6 to "calculate_variable_window_bounds" has incompatible
+        # type "Optional[ndarray]"; expected "ndarray"
+        return calculate_variable_window_bounds(
+            num_values,
+            self.window_size,
+            min_periods,
+            center,  # type: ignore[arg-type]
+            closed,
+            self.index_array,  # type: ignore[arg-type]
+        )
+
+
+class VariableOffsetWindowIndexer(BaseIndexer):
+    """
+    Calculate window boundaries based on a non-fixed offset such as a BusinessDay.
+
+    Examples
+    --------
+    >>> from pandas.api.indexers import VariableOffsetWindowIndexer
+    >>> df = pd.DataFrame(range(10), index=pd.date_range("2020", periods=10))
+    >>> offset = pd.offsets.BDay(1)
+    >>> indexer = VariableOffsetWindowIndexer(index=df.index, offset=offset)
+    >>> df
+                0
+    2020-01-01  0
+    2020-01-02  1
+    2020-01-03  2
+    2020-01-04  3
+    2020-01-05  4
+    2020-01-06  5
+    2020-01-07  6
+    2020-01-08  7
+    2020-01-09  8
+    2020-01-10  9
+    >>> df.rolling(indexer).sum()
+                   0
+    2020-01-01   0.0
+    2020-01-02   1.0
+    2020-01-03   2.0
+    2020-01-04   3.0
+    2020-01-05   7.0
+    2020-01-06  12.0
+    2020-01-07   6.0
+    2020-01-08   7.0
+    2020-01-09   8.0
+    2020-01-10   9.0
+    """
+
+    def __init__(
+        self,
+        index_array: np.ndarray | None = None,
+        window_size: int = 0,
+        index: DatetimeIndex | None = None,
+        offset: BaseOffset | None = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(index_array, window_size, **kwargs)
+        if not isinstance(index, DatetimeIndex):
+            raise ValueError("index must be a DatetimeIndex.")
+        self.index = index
+        if not isinstance(offset, BaseOffset):
+            raise ValueError("offset must be a DateOffset-like object.")
+        self.offset = offset
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        if step is not None:
+            raise NotImplementedError("step not implemented for variable offset window")
+        if num_values <= 0:
+            return np.empty(0, dtype="int64"), np.empty(0, dtype="int64")
+
+        # if windows is variable, default is 'right', otherwise default is 'both'
+        if closed is None:
+            closed = "right" if self.index is not None else "both"
+
+        right_closed = closed in ["right", "both"]
+        left_closed = closed in ["left", "both"]
+
+        if self.index[num_values - 1] < self.index[0]:
+            index_growth_sign = -1
+        else:
+            index_growth_sign = 1
+        offset_diff = index_growth_sign * self.offset
+
+        start = np.empty(num_values, dtype="int64")
+        start.fill(-1)
+        end = np.empty(num_values, dtype="int64")
+        end.fill(-1)
+
+        start[0] = 0
+
+        # right endpoint is closed
+        if right_closed:
+            end[0] = 1
+        # right endpoint is open
+        else:
+            end[0] = 0
+
+        zero = timedelta(0)
+        # start is start of slice interval (including)
+        # end is end of slice interval (not including)
+        for i in range(1, num_values):
+            end_bound = self.index[i]
+            start_bound = end_bound - offset_diff
+
+            # left endpoint is closed
+            if left_closed:
+                start_bound -= Nano(1)
+
+            # advance the start bound until we are
+            # within the constraint
+            start[i] = i
+            for j in range(start[i - 1], i):
+                start_diff = (self.index[j] - start_bound) * index_growth_sign
+                if start_diff > zero:
+                    start[i] = j
+                    break
+
+            # end bound is previous end
+            # or current index
+            end_diff = (self.index[end[i - 1]] - end_bound) * index_growth_sign
+            if end_diff == zero and not right_closed:
+                end[i] = end[i - 1] + 1
+            elif end_diff <= zero:
+                end[i] = i + 1
+            else:
+                end[i] = end[i - 1]
+
+            # right endpoint is open
+            if not right_closed:
+                end[i] -= 1
+
+        return start, end
+
+
+class ExpandingIndexer(BaseIndexer):
+    """Calculate expanding window bounds, mimicking df.expanding()"""
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        return (
+            np.zeros(num_values, dtype=np.int64),
+            np.arange(1, num_values + 1, dtype=np.int64),
+        )
+
+
+class FixedForwardWindowIndexer(BaseIndexer):
+    """
+    Creates window boundaries for fixed-length windows that include the current row.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
+    >>> df
+         B
+    0  0.0
+    1  1.0
+    2  2.0
+    3  NaN
+    4  4.0
+
+    >>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
+    >>> df.rolling(window=indexer, min_periods=1).sum()
+         B
+    0  1.0
+    1  3.0
+    2  2.0
+    3  4.0
+    4  4.0
+    """
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        if center:
+            raise ValueError("Forward-looking windows can't have center=True")
+        if closed is not None:
+            raise ValueError(
+                "Forward-looking windows don't support setting the closed argument"
+            )
+        if step is None:
+            step = 1
+
+        start = np.arange(0, num_values, step, dtype="int64")
+        end = start + self.window_size
+        if self.window_size:
+            end = np.clip(end, 0, num_values)
+
+        return start, end
+
+
+class GroupbyIndexer(BaseIndexer):
+    """Calculate bounds to compute groupby rolling, mimicking df.groupby().rolling()"""
+
+    def __init__(
+        self,
+        index_array: np.ndarray | None = None,
+        window_size: int | BaseIndexer = 0,
+        groupby_indices: dict | None = None,
+        window_indexer: type[BaseIndexer] = BaseIndexer,
+        indexer_kwargs: dict | None = None,
+        **kwargs,
+    ) -> None:
+        """
+        Parameters
+        ----------
+        index_array : np.ndarray or None
+            np.ndarray of the index of the original object that we are performing
+            a chained groupby operation over. This index has been pre-sorted relative to
+            the groups
+        window_size : int or BaseIndexer
+            window size during the windowing operation
+        groupby_indices : dict or None
+            dict of {group label: [positional index of rows belonging to the group]}
+        window_indexer : BaseIndexer
+            BaseIndexer class determining the start and end bounds of each group
+        indexer_kwargs : dict or None
+            Custom kwargs to be passed to window_indexer
+        **kwargs :
+            keyword arguments that will be available when get_window_bounds is called
+        """
+        self.groupby_indices = groupby_indices or {}
+        self.window_indexer = window_indexer
+        self.indexer_kwargs = indexer_kwargs.copy() if indexer_kwargs else {}
+        super().__init__(
+            index_array=index_array,
+            window_size=self.indexer_kwargs.pop("window_size", window_size),
+            **kwargs,
+        )
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        # 1) For each group, get the indices that belong to the group
+        # 2) Use the indices to calculate the start & end bounds of the window
+        # 3) Append the window bounds in group order
+        start_arrays = []
+        end_arrays = []
+        window_indices_start = 0
+        for key, indices in self.groupby_indices.items():
+            index_array: np.ndarray | None
+
+            if self.index_array is not None:
+                index_array = self.index_array.take(ensure_platform_int(indices))
+            else:
+                index_array = self.index_array
+            indexer = self.window_indexer(
+                index_array=index_array,
+                window_size=self.window_size,
+                **self.indexer_kwargs,
+            )
+            start, end = indexer.get_window_bounds(
+                len(indices), min_periods, center, closed, step
+            )
+            start = start.astype(np.int64)
+            end = end.astype(np.int64)
+            assert len(start) == len(
+                end
+            ), "these should be equal in length from get_window_bounds"
+            # Cannot use groupby_indices as they might not be monotonic with the object
+            # we're rolling over
+            window_indices = np.arange(
+                window_indices_start, window_indices_start + len(indices)
+            )
+            window_indices_start += len(indices)
+            # Extend as we'll be slicing window like [start, end)
+            window_indices = np.append(window_indices, [window_indices[-1] + 1]).astype(
+                np.int64, copy=False
+            )
+            start_arrays.append(window_indices.take(ensure_platform_int(start)))
+            end_arrays.append(window_indices.take(ensure_platform_int(end)))
+        if len(start_arrays) == 0:
+            return np.array([], dtype=np.int64), np.array([], dtype=np.int64)
+        start = np.concatenate(start_arrays)
+        end = np.concatenate(end_arrays)
+        return start, end
+
+
+class ExponentialMovingWindowIndexer(BaseIndexer):
+    """Calculate ewm window bounds (the entire window)"""
+
+    @Appender(get_window_bounds_doc)
+    def get_window_bounds(
+        self,
+        num_values: int = 0,
+        min_periods: int | None = None,
+        center: bool | None = None,
+        closed: str | None = None,
+        step: int | None = None,
+    ) -> tuple[np.ndarray, np.ndarray]:
+        return np.array([0], dtype=np.int64), np.array([num_values], dtype=np.int64)
diff --git a/shared/package-files/3d/3d1d7d82853ad115cbdda537854d82277572ae519e3fac7fe77d6723d9aabe6f/test_dagger.py b/shared/package-files/3d/3d1d7d82853ad115cbdda537854d82277572ae519e3fac7fe77d6723d9aabe6f/test_dagger.py
new file mode 100644
index 0000000000000000000000000000000000000000..8c99575262d9ca538d7bd0d94e1d5138d3f1fe0f
--- /dev/null
+++ b/shared/package-files/3d/3d1d7d82853ad115cbdda537854d82277572ae519e3fac7fe77d6723d9aabe6f/test_dagger.py
@@ -0,0 +1,83 @@
+from sympy.core.expr import Expr
+from sympy.core.mul import Mul
+from sympy.core.numbers import (I, Integer)
+from sympy.core.symbol import symbols
+from sympy.functions.elementary.complexes import conjugate
+from sympy.matrices.dense import Matrix
+
+from sympy.physics.quantum.dagger import adjoint, Dagger
+from sympy.external import import_module
+from sympy.testing.pytest import skip
+from sympy.physics.quantum.operator import Operator, IdentityOperator
+
+
+def test_scalars():
+    x = symbols('x', complex=True)
+    assert Dagger(x) == conjugate(x)
+    assert Dagger(I*x) == -I*conjugate(x)
+
+    i = symbols('i', real=True)
+    assert Dagger(i) == i
+
+    p = symbols('p')
+    assert isinstance(Dagger(p), adjoint)
+
+    i = Integer(3)
+    assert Dagger(i) == i
+
+    A = symbols('A', commutative=False)
+    assert Dagger(A).is_commutative is False
+
+
+def test_matrix():
+    x = symbols('x')
+    m = Matrix([[I, x*I], [2, 4]])
+    assert Dagger(m) == m.H
+
+
+def test_dagger_mul():
+    O = Operator('O')
+    I = IdentityOperator()
+    assert Dagger(O)*O == Dagger(O)*O
+    assert Dagger(O)*O*I == Mul(Dagger(O), O)*I
+    assert Dagger(O)*Dagger(O) == Dagger(O)**2
+    assert Dagger(O)*Dagger(I) == Dagger(O)
+
+
+class Foo(Expr):
+
+    def _eval_adjoint(self):
+        return I
+
+
+def test_eval_adjoint():
+    f = Foo()
+    d = Dagger(f)
+    assert d == I
+
+np = import_module('numpy')
+
+
+def test_numpy_dagger():
+    if not np:
+        skip("numpy not installed.")
+
+    a = np.array([[1.0, 2.0j], [-1.0j, 2.0]])
+    adag = a.copy().transpose().conjugate()
+    assert (Dagger(a) == adag).all()
+
+
+scipy = import_module('scipy', import_kwargs={'fromlist': ['sparse']})
+
+
+def test_scipy_sparse_dagger():
+    if not np:
+        skip("numpy not installed.")
+    if not scipy:
+        skip("scipy not installed.")
+    else:
+        sparse = scipy.sparse
+
+    a = sparse.csr_matrix([[1.0 + 0.0j, 2.0j], [-1.0j, 2.0 + 0.0j]])
+    adag = a.copy().transpose().conjugate()
+    assert np.linalg.norm((Dagger(a) - adag).todense()) == 0.0
diff --git a/shared/package-files/3d/3d24e0992c4f96d625edf2255cc11d8fa43b90c56c34bcffa9a82e581e899825/_zipfs.py b/shared/package-files/3d/3d24e0992c4f96d625edf2255cc11d8fa43b90c56c34bcffa9a82e581e899825/_zipfs.py
new file mode 100644
index 0000000000000000000000000000000000000000..668fedfa43001cf34b45fcda6477d29c1df5a5e2
--- /dev/null
+++ b/shared/package-files/3d/3d24e0992c4f96d625edf2255cc11d8fa43b90c56c34bcffa9a82e581e899825/_zipfs.py
@@ -0,0 +1,225 @@
+from __future__ import annotations
+
+import io
+import os
+import shutil
+import stat
+import typing
+import zipfile
+from datetime import datetime
+
+from ._base import FS
+from ._errors import (
+    FileExpected,
+    IllegalBackReference,
+    ResourceNotFound,
+    ResourceReadOnly,
+)
+from ._info import Info
+from ._path import dirname, escapes_root, forcedir, normpath, relpath
+from ._tempfs import TempFS
+
+if typing.TYPE_CHECKING:
+    from collections.abc import Collection
+    from typing import IO, Any
+
+    from ._subfs import SubFS
+
+
+class ZipFS(FS):
+    """Read and write zip files."""
+
+    def __new__(
+        cls, file: str | os.PathLike, write: bool = False, encoding: str = "utf-8"
+    ):
+        if write:
+            return WriteZipFS(file, encoding)
+        else:
+            return ReadZipFS(file, encoding)
+
+    if typing.TYPE_CHECKING:
+
+        def __init__(
+            self, file: str | os.PathLike, write: bool = False, encoding: str = "utf-8"
+        ):
+            pass
+
+
+class ReadZipFS(FS):
+    """A readable zip file."""
+
+    def __init__(self, file: str | os.PathLike, encoding: str = "utf-8"):
+        super().__init__()
+        self._file = os.fspath(file)
+        self.encoding = encoding  # unused
+        self._zip = zipfile.ZipFile(file, "r")
+        self._directory_fs = None
+
+    def __repr__(self) -> str:
+        return f"ReadZipFS({self._file!r})"
+
+    def __str__(self) -> str:
+        return f""
+
+    def _path_to_zip_name(self, path: str) -> str:
+        """Convert a path to a zip file name."""
+        path = relpath(normpath(path))
+        if self._directory.isdir(path):
+            path = forcedir(path)
+        return path
+
+    @property
+    def _directory(self) -> TempFS:
+        if self._directory_fs is None:
+            # Reject escaping member names up front, so the error names the zip
+            # entry rather than a path the caller never passed.
+            for zip_name in self._zip.namelist():
+                if escapes_root(zip_name):
+                    raise IllegalBackReference(zip_name)
+            # Populate a local mirror and only publish it once it is complete:
+            # an archive can fail partway through for reasons the pre-scan can't
+            # see (a member named both as a file and as a directory, an escaping
+            # name that only pathlib recognises on this platform), and a cached
+            # half-built mirror would answer later calls from a silently
+            # truncated view of the zip instead of failing again.
+            _fs = TempFS()
+            try:
+                for zip_name in self._zip.namelist():
+                    resource_name = zip_name
+                    if resource_name.endswith("/"):
+                        _fs.makedirs(resource_name, recreate=True)
+                    else:
+                        _fs.makedirs(dirname(resource_name), recreate=True)
+                        _fs.create(resource_name)
+            except Exception:
+                _fs.close()
+                raise
+            self._directory_fs = _fs
+        return self._directory_fs
+
+    def close(self):
+        super(ReadZipFS, self).close()
+        self._zip.close()
+        if self._directory_fs is not None:
+            self._directory_fs.close()
+
+    def getinfo(self, path: str, namespaces: Collection[str] | None = None) -> Info:
+        namespaces = namespaces or ()
+        raw_info = {}
+
+        if path == "/":
+            raw_info["basic"] = {"name": "", "is_dir": True}
+            if "details" in namespaces:
+                raw_info["details"] = {"type": stat.S_IFDIR}
+        else:
+            basic_info = self._directory.getinfo(path)
+            raw_info["basic"] = {"name": basic_info.name, "is_dir": basic_info.is_dir}
+
+            if "details" in namespaces:
+                zip_name = self._path_to_zip_name(path)
+                try:
+                    zip_info = self._zip.getinfo(zip_name)
+                except KeyError:
+                    pass
+                else:
+                    if "details" in namespaces:
+                        raw_info["details"] = {
+                            "size": zip_info.file_size,
+                            "type": int(
+                                stat.S_IFDIR if basic_info.is_dir else stat.S_IFREG
+                            ),
+                            "modified": datetime(*zip_info.date_time).timestamp(),
+                        }
+
+        return Info(raw_info)
+
+    def exists(self, path: str) -> bool:
+        self.check()
+        return self._directory.exists(path)
+
+    def isdir(self, path: str) -> bool:
+        self.check()
+        return self._directory.isdir(path)
+
+    def isfile(self, path: str) -> bool:
+        self.check()
+        return self._directory.isfile(path)
+
+    def listdir(self, path: str) -> str:
+        self.check()
+        return self._directory.listdir(path)
+
+    def makedir(self, path: str, recreate: bool = False) -> SubFS:
+        self.check()
+        raise ResourceReadOnly(path)
+
+    def makedirs(self, path: str, recreate: bool = False) -> SubFS:
+        self.check()
+        raise ResourceReadOnly(path)
+
+    def remove(self, path: str):
+        self.check()
+        raise ResourceReadOnly(path)
+
+    def removedir(self, path: str):
+        self.check()
+        raise ResourceReadOnly(path)
+
+    def removetree(self, path: str):
+        self.check()
+        raise ResourceReadOnly(path)
+
+    def movedir(self, src: str, dst: str, create: bool = False):
+        self.check()
+        raise ResourceReadOnly(src)
+
+    def readbytes(self, path: str) -> bytes:
+        self.check()
+        if not self._directory.isfile(path):
+            raise ResourceNotFound(path)
+        zip_name = self._path_to_zip_name(path)
+        zip_bytes = self._zip.read(zip_name)
+        return zip_bytes
+
+    def open(self, path: str, mode: str = "rb", **kwargs) -> IO[Any]:
+        self.check()
+        if self._directory.isdir(path):
+            raise FileExpected(f"{path!r} is a directory")
+
+        zip_mode = mode[0]
+        if zip_mode == "r" and not self._directory.exists(path):
+            raise ResourceNotFound(f"No such file or directory: {path!r}")
+
+        if any(m in mode for m in "wax+"):
+            raise ResourceReadOnly(path)
+
+        zip_name = self._path_to_zip_name(path)
+        stream = self._zip.open(zip_name, zip_mode)
+        if "b" in mode:
+            if kwargs:
+                raise ValueError("encoding args invalid for binary operation")
+            return stream
+        # Text mode
+        return io.TextIOWrapper(stream, **kwargs)
+
+
+class WriteZipFS(TempFS):
+    """A writable zip file."""
+
+    def __init__(self, file: str | os.PathLike, encoding: str = "utf-8"):
+        super().__init__()
+        self._file = os.fspath(file)
+        self.encoding = encoding  # unused
+
+    def __repr__(self) -> str:
+        return f"WriteZipFS({self._file!r})"
+
+    def __str__(self) -> str:
+        return f""
+
+    def close(self):
+        base_name = os.path.splitext(self._file)[0]
+        shutil.make_archive(base_name, format="zip", root_dir=self._temp_dir)
+        if self._file != base_name + ".zip":
+            shutil.move(base_name + ".zip", self._file)
+        super().close()
diff --git a/shared/package-files/3d/3d2594c948cdfb7c5eba0e73f0d8f0e67b19c946b6774790500c5165755de4d8/util.py b/shared/package-files/3d/3d2594c948cdfb7c5eba0e73f0d8f0e67b19c946b6774790500c5165755de4d8/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ff51bdee206467ce88c89e95cf10dec818b2c00
--- /dev/null
+++ b/shared/package-files/3d/3d2594c948cdfb7c5eba0e73f0d8f0e67b19c946b6774790500c5165755de4d8/util.py
@@ -0,0 +1,256 @@
+# coding: utf-8
+
+"""
+some helper functions that might be generally useful
+"""
+
+import datetime
+from functools import partial
+import re
+
+
+if False:  # MYPY
+    from typing import Any, Dict, Optional, List, Text  # NOQA
+    from .compat import StreamTextType  # NOQA
+
+
+class LazyEval:
+    """
+    Lightweight wrapper around lazily evaluated func(*args, **kwargs).
+
+    func is only evaluated when any attribute of its return value is accessed.
+    Every attribute access is passed through to the wrapped value.
+    (This only excludes special cases like method-wrappers, e.g., __hash__.)
+    The sole additional attribute is the lazy_self function which holds the
+    return value (or, prior to evaluation, func and arguments), in its closure.
+    """
+
+    def __init__(self, func, *args, **kwargs):
+        # type: (Any, Any, Any) -> None
+        def lazy_self():
+            # type: () -> Any
+            return_value = func(*args, **kwargs)
+            object.__setattr__(self, 'lazy_self', lambda: return_value)
+            return return_value
+
+        object.__setattr__(self, 'lazy_self', lazy_self)
+
+    def __getattribute__(self, name):
+        # type: (Any) -> Any
+        lazy_self = object.__getattribute__(self, 'lazy_self')
+        if name == 'lazy_self':
+            return lazy_self
+        return getattr(lazy_self(), name)
+
+    def __setattr__(self, name, value):
+        # type: (Any, Any) -> None
+        setattr(self.lazy_self(), name, value)
+
+
+RegExp = partial(LazyEval, re.compile)
+
+timestamp_regexp = RegExp(
+    """^(?P[0-9][0-9][0-9][0-9])
+       -(?P[0-9][0-9]?)
+       -(?P[0-9][0-9]?)
+       (?:((?P[Tt])|[ \\t]+)   # explictly not retaining extra spaces
+       (?P[0-9][0-9]?)
+       :(?P[0-9][0-9])
+       :(?P[0-9][0-9])
+       (?:\\.(?P[0-9]*))?
+        (?:[ \\t]*(?PZ|(?P[-+])(?P[0-9][0-9]?)
+       (?::(?P[0-9][0-9]))?))?)?$""",
+    re.X,
+)
+
+
+def create_timestamp(
+    year, month, day, t, hour, minute, second, fraction, tz, tz_sign, tz_hour, tz_minute
+):
+    # type: (Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any, Any) -> Any
+    # create a timestamp from match against timestamp_regexp
+    MAX_FRAC = 999999
+    year = int(year)
+    month = int(month)
+    day = int(day)
+    if not hour:
+        return datetime.date(year, month, day)
+    hour = int(hour)
+    minute = int(minute)
+    second = int(second)
+    frac = 0
+    if fraction:
+        frac_s = fraction[:6]
+        while len(frac_s) < 6:
+            frac_s += '0'
+        frac = int(frac_s)
+        if len(fraction) > 6 and int(fraction[6]) > 4:
+            frac += 1
+        if frac > MAX_FRAC:
+            fraction = 0
+        else:
+            fraction = frac
+    else:
+        fraction = 0
+    delta = None
+    if tz_sign:
+        tz_hour = int(tz_hour)
+        tz_minute = int(tz_minute) if tz_minute else 0
+        delta = datetime.timedelta(
+            hours=tz_hour, minutes=tz_minute, seconds=1 if frac > MAX_FRAC else 0
+        )
+        if tz_sign == '-':
+            delta = -delta
+    elif frac > MAX_FRAC:
+        delta = -datetime.timedelta(seconds=1)
+    # should do something else instead (or hook this up to the preceding if statement
+    # in reverse
+    #  if delta is None:
+    #      return datetime.datetime(year, month, day, hour, minute, second, fraction)
+    #  return datetime.datetime(year, month, day, hour, minute, second, fraction,
+    #                           datetime.timezone.utc)
+    # the above is not good enough though, should provide tzinfo. In Python3 that is easily
+    # doable drop that kind of support for Python2 as it has not native tzinfo
+    data = datetime.datetime(year, month, day, hour, minute, second, fraction)
+    if delta:
+        data -= delta
+    return data
+
+
+# originally as comment
+# https://github.com/pre-commit/pre-commit/pull/211#issuecomment-186466605
+# if you use this in your code, I suggest adding a test in your test suite
+# that check this routines output against a known piece of your YAML
+# before upgrades to this code break your round-tripped YAML
+def load_yaml_guess_indent(stream, **kw):
+    # type: (StreamTextType, Any) -> Any
+    """guess the indent and block sequence indent of yaml stream/string
+
+    returns round_trip_loaded stream, indent level, block sequence indent
+    - block sequence indent is the number of spaces before a dash relative to previous indent
+    - if there are no block sequences, indent is taken from nested mappings, block sequence
+      indent is unset (None) in that case
+    """
+    from .main import YAML
+
+    # load a YAML document, guess the indentation, if you use TABs you are on your own
+    def leading_spaces(line):
+        # type: (Any) -> int
+        idx = 0
+        while idx < len(line) and line[idx] == ' ':
+            idx += 1
+        return idx
+
+    if isinstance(stream, str):
+        yaml_str = stream  # type: Any
+    elif isinstance(stream, bytes):
+        # most likely, but the Reader checks BOM for this
+        yaml_str = stream.decode('utf-8')
+    else:
+        yaml_str = stream.read()
+    map_indent = None
+    indent = None  # default if not found for some reason
+    block_seq_indent = None
+    prev_line_key_only = None
+    key_indent = 0
+    for line in yaml_str.splitlines():
+        rline = line.rstrip()
+        lline = rline.lstrip()
+        if lline.startswith('- '):
+            l_s = leading_spaces(line)
+            block_seq_indent = l_s - key_indent
+            idx = l_s + 1
+            while line[idx] == ' ':  # this will end as we rstripped
+                idx += 1
+            if line[idx] == '#':  # comment after -
+                continue
+            indent = idx - key_indent
+            break
+        if map_indent is None and prev_line_key_only is not None and rline:
+            idx = 0
+            while line[idx] in ' -':
+                idx += 1
+            if idx > prev_line_key_only:
+                map_indent = idx - prev_line_key_only
+        if rline.endswith(':'):
+            key_indent = leading_spaces(line)
+            idx = 0
+            while line[idx] == ' ':  # this will end on ':'
+                idx += 1
+            prev_line_key_only = idx
+            continue
+        prev_line_key_only = None
+    if indent is None and map_indent is not None:
+        indent = map_indent
+    yaml = YAML()
+    return yaml.load(yaml_str, **kw), indent, block_seq_indent  # type: ignore
+
+
+def configobj_walker(cfg):
+    # type: (Any) -> Any
+    """
+    walks over a ConfigObj (INI file with comments) generating
+    corresponding YAML output (including comments
+    """
+    from configobj import ConfigObj  # type: ignore
+
+    assert isinstance(cfg, ConfigObj)
+    for c in cfg.initial_comment:
+        if c.strip():
+            yield c
+    for s in _walk_section(cfg):
+        if s.strip():
+            yield s
+    for c in cfg.final_comment:
+        if c.strip():
+            yield c
+
+
+def _walk_section(s, level=0):
+    # type: (Any, int) -> Any
+    from configobj import Section
+
+    assert isinstance(s, Section)
+    indent = '  ' * level
+    for name in s.scalars:
+        for c in s.comments[name]:
+            yield indent + c.strip()
+        x = s[name]
+        if '\n' in x:
+            i = indent + '  '
+            x = '|\n' + i + x.strip().replace('\n', '\n' + i)
+        elif ':' in x:
+            x = "'" + x.replace("'", "''") + "'"
+        line = '{0}{1}: {2}'.format(indent, name, x)
+        c = s.inline_comments[name]
+        if c:
+            line += ' ' + c
+        yield line
+    for name in s.sections:
+        for c in s.comments[name]:
+            yield indent + c.strip()
+        line = '{0}{1}:'.format(indent, name)
+        c = s.inline_comments[name]
+        if c:
+            line += ' ' + c
+        yield line
+        for val in _walk_section(s[name], level=level + 1):
+            yield val
+
+
+# def config_obj_2_rt_yaml(cfg):
+#     from .comments import CommentedMap, CommentedSeq
+#     from configobj import ConfigObj
+#     assert isinstance(cfg, ConfigObj)
+#     #for c in cfg.initial_comment:
+#     #    if c.strip():
+#     #        pass
+#     cm = CommentedMap()
+#     for name in s.sections:
+#         cm[name] = d = CommentedMap()
+#
+#
+#     #for c in cfg.final_comment:
+#     #    if c.strip():
+#     #        yield c
+#     return cm
diff --git a/shared/package-files/3d/3d276ab16e9d9edd73e73f7747a3f637a586833f6c0d90d12863f9dfd1bbeff4/__init__.py b/shared/package-files/3d/3d276ab16e9d9edd73e73f7747a3f637a586833f6c0d90d12863f9dfd1bbeff4/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..032cdc47f2f1a943d34beeb6104c6bb9a8d3d726
--- /dev/null
+++ b/shared/package-files/3d/3d276ab16e9d9edd73e73f7747a3f637a586833f6c0d90d12863f9dfd1bbeff4/__init__.py
@@ -0,0 +1,21 @@
+# Import seaborn objects
+from .rcmod import *  # noqa: F401,F403
+from .utils import *  # noqa: F401,F403
+from .palettes import *  # noqa: F401,F403
+from .relational import *  # noqa: F401,F403
+from .regression import *  # noqa: F401,F403
+from .categorical import *  # noqa: F401,F403
+from .distributions import *  # noqa: F401,F403
+from .matrix import *  # noqa: F401,F403
+from .miscplot import *  # noqa: F401,F403
+from .axisgrid import *  # noqa: F401,F403
+from .widgets import *  # noqa: F401,F403
+from .colors import xkcd_rgb, crayons  # noqa: F401
+from . import cm  # noqa: F401
+
+# Capture the original matplotlib rcParams
+import matplotlib as mpl
+_orig_rc_params = mpl.rcParams.copy()
+
+# Define the seaborn version
+__version__ = "0.13.2"
diff --git a/shared/package-files/3d/3d2b2c062c3ebfe3c37a7feaef973a3a434ee4fc3220f8466dff4f3098fb3f63/union_util.h b/shared/package-files/3d/3d2b2c062c3ebfe3c37a7feaef973a3a434ee4fc3220f8466dff4f3098fb3f63/union_util.h
new file mode 100644
index 0000000000000000000000000000000000000000..0f30d5a32781924a3c64904a203a03d9d3d48d79
--- /dev/null
+++ b/shared/package-files/3d/3d2b2c062c3ebfe3c37a7feaef973a3a434ee4fc3220f8466dff4f3098fb3f63/union_util.h
@@ -0,0 +1,31 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#include 
+#include "arrow/array/data.h"
+
+namespace arrow {
+namespace union_util {
+
+/// \brief Compute the number of of logical nulls in a sparse union array
+int64_t LogicalSparseUnionNullCount(const ArraySpan& span);
+
+/// \brief Compute the number of of logical nulls in a dense union array
+int64_t LogicalDenseUnionNullCount(const ArraySpan& span);
+
+}  // namespace union_util
+}  // namespace arrow
diff --git a/shared/package-files/3d/3d2d567d8d079b78f3f3b566ed52ad2f38af61bf832b7dc28858b0039a032d6b/gb2312.py b/shared/package-files/3d/3d2d567d8d079b78f3f3b566ed52ad2f38af61bf832b7dc28858b0039a032d6b/gb2312.py
new file mode 100644
index 0000000000000000000000000000000000000000..3c3b837d618ecd262007eec7d3bd786209e91c06
--- /dev/null
+++ b/shared/package-files/3d/3d2d567d8d079b78f3f3b566ed52ad2f38af61bf832b7dc28858b0039a032d6b/gb2312.py
@@ -0,0 +1,39 @@
+#
+# gb2312.py: Python Unicode Codec for GB2312
+#
+# Written by Hye-Shik Chang 
+#
+
+import _codecs_cn, codecs
+import _multibytecodec as mbc
+
+codec = _codecs_cn.getcodec('gb2312')
+
+class Codec(codecs.Codec):
+    encode = codec.encode
+    decode = codec.decode
+
+class IncrementalEncoder(mbc.MultibyteIncrementalEncoder,
+                         codecs.IncrementalEncoder):
+    codec = codec
+
+class IncrementalDecoder(mbc.MultibyteIncrementalDecoder,
+                         codecs.IncrementalDecoder):
+    codec = codec
+
+class StreamReader(Codec, mbc.MultibyteStreamReader, codecs.StreamReader):
+    codec = codec
+
+class StreamWriter(Codec, mbc.MultibyteStreamWriter, codecs.StreamWriter):
+    codec = codec
+
+def getregentry():
+    return codecs.CodecInfo(
+        name='gb2312',
+        encode=Codec().encode,
+        decode=Codec().decode,
+        incrementalencoder=IncrementalEncoder,
+        incrementaldecoder=IncrementalDecoder,
+        streamreader=StreamReader,
+        streamwriter=StreamWriter,
+    )
diff --git a/shared/package-files/3d/3d2f3d92970c3fa52f2552f22afe3d6e554e983423f8510eb427722967024deb/direct_url.json b/shared/package-files/3d/3d2f3d92970c3fa52f2552f22afe3d6e554e983423f8510eb427722967024deb/direct_url.json
new file mode 100644
index 0000000000000000000000000000000000000000..b23ea169d98acae2ce086929d9b2d9742c1b3d80
--- /dev/null
+++ b/shared/package-files/3d/3d2f3d92970c3fa52f2552f22afe3d6e554e983423f8510eb427722967024deb/direct_url.json
@@ -0,0 +1 @@
+{"dir_info": {}, "url": "file:///croot/conda_1689269889729/work"}
\ No newline at end of file
diff --git a/shared/package-files/3d/3d2fd34c866210b46b834f1abb64c5e4f9f93a327fefc90449a8554fbe788f68/MemoryFormat.h b/shared/package-files/3d/3d2fd34c866210b46b834f1abb64c5e4f9f93a327fefc90449a8554fbe788f68/MemoryFormat.h
new file mode 100644
index 0000000000000000000000000000000000000000..7f60a0ba0282c39cb8c72876a4288560ec280b93
--- /dev/null
+++ b/shared/package-files/3d/3d2fd34c866210b46b834f1abb64c5e4f9f93a327fefc90449a8554fbe788f68/MemoryFormat.h
@@ -0,0 +1,27 @@
+#pragma once
+
+#include 
+
+#include 
+
+#include 
+
+const int MEMORY_FORMAT_NAME_LEN = 64;
+
+struct THPMemoryFormat {
+  PyObject_HEAD at::MemoryFormat memory_format;
+  // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
+  char name[MEMORY_FORMAT_NAME_LEN + 1];
+};
+
+extern PyTypeObject THPMemoryFormatType;
+
+inline bool THPMemoryFormat_Check(PyObject* obj) {
+  return Py_TYPE(obj) == &THPMemoryFormatType;
+}
+
+PyObject* THPMemoryFormat_New(
+    at::MemoryFormat memory_format,
+    const std::string& name);
+
+void THPMemoryFormat_init(PyObject* module);
diff --git a/shared/package-files/3d/3d303d698834c3069728daa8095baadb545b8a01b383ef61d647b11d56a83852/connectivity.py b/shared/package-files/3d/3d303d698834c3069728daa8095baadb545b8a01b383ef61d647b11d56a83852/connectivity.py
new file mode 100644
index 0000000000000000000000000000000000000000..a9190620a6cc6d312e67054f88424585deed440e
--- /dev/null
+++ b/shared/package-files/3d/3d303d698834c3069728daa8095baadb545b8a01b383ef61d647b11d56a83852/connectivity.py
@@ -0,0 +1,415 @@
+""" Fast approximation for node connectivity
+"""
+import itertools
+from operator import itemgetter
+
+import networkx as nx
+
+__all__ = [
+    "local_node_connectivity",
+    "node_connectivity",
+    "all_pairs_node_connectivity",
+]
+
+
+def local_node_connectivity(G, source, target, cutoff=None):
+    """Compute node connectivity between source and target.
+
+    Pairwise or local node connectivity between two distinct and nonadjacent
+    nodes is the minimum number of nodes that must be removed (minimum
+    separating cutset) to disconnect them. By Menger's theorem, this is equal
+    to the number of node independent paths (paths that share no nodes other
+    than source and target). Which is what we compute in this function.
+
+    This algorithm is a fast approximation that gives an strict lower
+    bound on the actual number of node independent paths between two nodes [1]_.
+    It works for both directed and undirected graphs.
+
+    Parameters
+    ----------
+
+    G : NetworkX graph
+
+    source : node
+        Starting node for node connectivity
+
+    target : node
+        Ending node for node connectivity
+
+    cutoff : integer
+        Maximum node connectivity to consider. If None, the minimum degree
+        of source or target is used as a cutoff. Default value None.
+
+    Returns
+    -------
+    k: integer
+       pairwise node connectivity
+
+    Examples
+    --------
+    >>> # Platonic octahedral graph has node connectivity 4
+    >>> # for each non adjacent node pair
+    >>> from networkx.algorithms import approximation as approx
+    >>> G = nx.octahedral_graph()
+    >>> approx.local_node_connectivity(G, 0, 5)
+    4
+
+    Notes
+    -----
+    This algorithm [1]_ finds node independents paths between two nodes by
+    computing their shortest path using BFS, marking the nodes of the path
+    found as 'used' and then searching other shortest paths excluding the
+    nodes marked as used until no more paths exist. It is not exact because
+    a shortest path could use nodes that, if the path were longer, may belong
+    to two different node independent paths. Thus it only guarantees an
+    strict lower bound on node connectivity.
+
+    Note that the authors propose a further refinement, losing accuracy and
+    gaining speed, which is not implemented yet.
+
+    See also
+    --------
+    all_pairs_node_connectivity
+    node_connectivity
+
+    References
+    ----------
+    .. [1] White, Douglas R., and Mark Newman. 2001 A Fast Algorithm for
+        Node-Independent Paths. Santa Fe Institute Working Paper #01-07-035
+        http://eclectic.ss.uci.edu/~drwhite/working.pdf
+
+    """
+    if target == source:
+        raise nx.NetworkXError("source and target have to be different nodes.")
+
+    # Maximum possible node independent paths
+    if G.is_directed():
+        possible = min(G.out_degree(source), G.in_degree(target))
+    else:
+        possible = min(G.degree(source), G.degree(target))
+
+    K = 0
+    if not possible:
+        return K
+
+    if cutoff is None:
+        cutoff = float("inf")
+
+    exclude = set()
+    for i in range(min(possible, cutoff)):
+        try:
+            path = _bidirectional_shortest_path(G, source, target, exclude)
+            exclude.update(set(path))
+            K += 1
+        except nx.NetworkXNoPath:
+            break
+
+    return K
+
+
+def node_connectivity(G, s=None, t=None):
+    r"""Returns an approximation for node connectivity for a graph or digraph G.
+
+    Node connectivity is equal to the minimum number of nodes that
+    must be removed to disconnect G or render it trivial. By Menger's theorem,
+    this is equal to the number of node independent paths (paths that
+    share no nodes other than source and target).
+
+    If source and target nodes are provided, this function returns the
+    local node connectivity: the minimum number of nodes that must be
+    removed to break all paths from source to target in G.
+
+    This algorithm is based on a fast approximation that gives an strict lower
+    bound on the actual number of node independent paths between two nodes [1]_.
+    It works for both directed and undirected graphs.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Undirected graph
+
+    s : node
+        Source node. Optional. Default value: None.
+
+    t : node
+        Target node. Optional. Default value: None.
+
+    Returns
+    -------
+    K : integer
+        Node connectivity of G, or local node connectivity if source
+        and target are provided.
+
+    Examples
+    --------
+    >>> # Platonic octahedral graph is 4-node-connected
+    >>> from networkx.algorithms import approximation as approx
+    >>> G = nx.octahedral_graph()
+    >>> approx.node_connectivity(G)
+    4
+
+    Notes
+    -----
+    This algorithm [1]_ finds node independents paths between two nodes by
+    computing their shortest path using BFS, marking the nodes of the path
+    found as 'used' and then searching other shortest paths excluding the
+    nodes marked as used until no more paths exist. It is not exact because
+    a shortest path could use nodes that, if the path were longer, may belong
+    to two different node independent paths. Thus it only guarantees an
+    strict lower bound on node connectivity.
+
+    See also
+    --------
+    all_pairs_node_connectivity
+    local_node_connectivity
+
+    References
+    ----------
+    .. [1] White, Douglas R., and Mark Newman. 2001 A Fast Algorithm for
+        Node-Independent Paths. Santa Fe Institute Working Paper #01-07-035
+        http://eclectic.ss.uci.edu/~drwhite/working.pdf
+
+    """
+    if (s is not None and t is None) or (s is None and t is not None):
+        raise nx.NetworkXError("Both source and target must be specified.")
+
+    # Local node connectivity
+    if s is not None and t is not None:
+        if s not in G:
+            raise nx.NetworkXError(f"node {s} not in graph")
+        if t not in G:
+            raise nx.NetworkXError(f"node {t} not in graph")
+        return local_node_connectivity(G, s, t)
+
+    # Global node connectivity
+    if G.is_directed():
+        connected_func = nx.is_weakly_connected
+        iter_func = itertools.permutations
+
+        def neighbors(v):
+            return itertools.chain(G.predecessors(v), G.successors(v))
+
+    else:
+        connected_func = nx.is_connected
+        iter_func = itertools.combinations
+        neighbors = G.neighbors
+
+    if not connected_func(G):
+        return 0
+
+    # Choose a node with minimum degree
+    v, minimum_degree = min(G.degree(), key=itemgetter(1))
+    # Node connectivity is bounded by minimum degree
+    K = minimum_degree
+    # compute local node connectivity with all non-neighbors nodes
+    # and store the minimum
+    for w in set(G) - set(neighbors(v)) - {v}:
+        K = min(K, local_node_connectivity(G, v, w, cutoff=K))
+    # Same for non adjacent pairs of neighbors of v
+    for x, y in iter_func(neighbors(v), 2):
+        if y not in G[x] and x != y:
+            K = min(K, local_node_connectivity(G, x, y, cutoff=K))
+    return K
+
+
+def all_pairs_node_connectivity(G, nbunch=None, cutoff=None):
+    """Compute node connectivity between all pairs of nodes.
+
+    Pairwise or local node connectivity between two distinct and nonadjacent
+    nodes is the minimum number of nodes that must be removed (minimum
+    separating cutset) to disconnect them. By Menger's theorem, this is equal
+    to the number of node independent paths (paths that share no nodes other
+    than source and target). Which is what we compute in this function.
+
+    This algorithm is a fast approximation that gives an strict lower
+    bound on the actual number of node independent paths between two nodes [1]_.
+    It works for both directed and undirected graphs.
+
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    nbunch: container
+        Container of nodes. If provided node connectivity will be computed
+        only over pairs of nodes in nbunch.
+
+    cutoff : integer
+        Maximum node connectivity to consider. If None, the minimum degree
+        of source or target is used as a cutoff in each pair of nodes.
+        Default value None.
+
+    Returns
+    -------
+    K : dictionary
+        Dictionary, keyed by source and target, of pairwise node connectivity
+
+    Examples
+    --------
+    A 3 node cycle with one extra node attached has connectivity 2 between all
+    nodes in the cycle and connectivity 1 between the extra node and the rest:
+
+    >>> G = nx.cycle_graph(3)
+    >>> G.add_edge(2, 3)
+    >>> import pprint  # for nice dictionary formatting
+    >>> pprint.pprint(nx.all_pairs_node_connectivity(G))
+    {0: {1: 2, 2: 2, 3: 1},
+     1: {0: 2, 2: 2, 3: 1},
+     2: {0: 2, 1: 2, 3: 1},
+     3: {0: 1, 1: 1, 2: 1}}
+
+    See Also
+    --------
+    local_node_connectivity
+    node_connectivity
+
+    References
+    ----------
+    .. [1] White, Douglas R., and Mark Newman. 2001 A Fast Algorithm for
+        Node-Independent Paths. Santa Fe Institute Working Paper #01-07-035
+        http://eclectic.ss.uci.edu/~drwhite/working.pdf
+    """
+    if nbunch is None:
+        nbunch = G
+    else:
+        nbunch = set(nbunch)
+
+    directed = G.is_directed()
+    if directed:
+        iter_func = itertools.permutations
+    else:
+        iter_func = itertools.combinations
+
+    all_pairs = {n: {} for n in nbunch}
+
+    for u, v in iter_func(nbunch, 2):
+        k = local_node_connectivity(G, u, v, cutoff=cutoff)
+        all_pairs[u][v] = k
+        if not directed:
+            all_pairs[v][u] = k
+
+    return all_pairs
+
+
+def _bidirectional_shortest_path(G, source, target, exclude):
+    """Returns shortest path between source and target ignoring nodes in the
+    container 'exclude'.
+
+    Parameters
+    ----------
+
+    G : NetworkX graph
+
+    source : node
+        Starting node for path
+
+    target : node
+        Ending node for path
+
+    exclude: container
+        Container for nodes to exclude from the search for shortest paths
+
+    Returns
+    -------
+    path: list
+        Shortest path between source and target ignoring nodes in 'exclude'
+
+    Raises
+    ------
+    NetworkXNoPath
+        If there is no path or if nodes are adjacent and have only one path
+        between them
+
+    Notes
+    -----
+    This function and its helper are originally from
+    networkx.algorithms.shortest_paths.unweighted and are modified to
+    accept the extra parameter 'exclude', which is a container for nodes
+    already used in other paths that should be ignored.
+
+    References
+    ----------
+    .. [1] White, Douglas R., and Mark Newman. 2001 A Fast Algorithm for
+        Node-Independent Paths. Santa Fe Institute Working Paper #01-07-035
+        http://eclectic.ss.uci.edu/~drwhite/working.pdf
+
+    """
+    # call helper to do the real work
+    results = _bidirectional_pred_succ(G, source, target, exclude)
+    pred, succ, w = results
+
+    # build path from pred+w+succ
+    path = []
+    # from source to w
+    while w is not None:
+        path.append(w)
+        w = pred[w]
+    path.reverse()
+    # from w to target
+    w = succ[path[-1]]
+    while w is not None:
+        path.append(w)
+        w = succ[w]
+
+    return path
+
+
+def _bidirectional_pred_succ(G, source, target, exclude):
+    # does BFS from both source and target and meets in the middle
+    # excludes nodes in the container "exclude" from the search
+    if source is None or target is None:
+        raise nx.NetworkXException(
+            "Bidirectional shortest path called without source or target"
+        )
+    if target == source:
+        return ({target: None}, {source: None}, source)
+
+    # handle either directed or undirected
+    if G.is_directed():
+        Gpred = G.predecessors
+        Gsucc = G.successors
+    else:
+        Gpred = G.neighbors
+        Gsucc = G.neighbors
+
+    # predecesssor and successors in search
+    pred = {source: None}
+    succ = {target: None}
+
+    # initialize fringes, start with forward
+    forward_fringe = [source]
+    reverse_fringe = [target]
+
+    level = 0
+
+    while forward_fringe and reverse_fringe:
+        # Make sure that we iterate one step forward and one step backwards
+        # thus source and target will only trigger "found path" when they are
+        # adjacent and then they can be safely included in the container 'exclude'
+        level += 1
+        if level % 2 != 0:
+            this_level = forward_fringe
+            forward_fringe = []
+            for v in this_level:
+                for w in Gsucc(v):
+                    if w in exclude:
+                        continue
+                    if w not in pred:
+                        forward_fringe.append(w)
+                        pred[w] = v
+                    if w in succ:
+                        return pred, succ, w  # found path
+        else:
+            this_level = reverse_fringe
+            reverse_fringe = []
+            for v in this_level:
+                for w in Gpred(v):
+                    if w in exclude:
+                        continue
+                    if w not in succ:
+                        succ[w] = v
+                        reverse_fringe.append(w)
+                    if w in pred:
+                        return pred, succ, w  # found path
+
+    raise nx.NetworkXNoPath(f"No path between {source} and {target}.")
diff --git a/shared/package-files/3d/3d34d7fb66ae98e20b8acd1b29a53b3781031b018698efecee1ff0e6fd4d863b/direct_url.json b/shared/package-files/3d/3d34d7fb66ae98e20b8acd1b29a53b3781031b018698efecee1ff0e6fd4d863b/direct_url.json
new file mode 100644
index 0000000000000000000000000000000000000000..08af151ee7309af0d466e8f10f1117b691bc610a
--- /dev/null
+++ b/shared/package-files/3d/3d34d7fb66ae98e20b8acd1b29a53b3781031b018698efecee1ff0e6fd4d863b/direct_url.json
@@ -0,0 +1 @@
+{"dir_info": {}, "url": "file:///croot/rpds-py_1698945930462/work"}
\ No newline at end of file
diff --git a/shared/package-files/3d/3d3f7bb058cfef04d3ec03831675ea46531bdf23238b6ac1c094ba5fb3906772/multinomial.h b/shared/package-files/3d/3d3f7bb058cfef04d3ec03831675ea46531bdf23238b6ac1c094ba5fb3906772/multinomial.h
new file mode 100644
index 0000000000000000000000000000000000000000..1be8a5c74523f22ce226cd85c0b4c5ed33eddbbe
--- /dev/null
+++ b/shared/package-files/3d/3d3f7bb058cfef04d3ec03831675ea46531bdf23238b6ac1c094ba5fb3906772/multinomial.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::multinomial.out(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & multinomial_out(at::Tensor & out, const at::Tensor & self, int64_t num_samples, bool replacement=false, c10::optional generator=c10::nullopt) {
+    return at::_ops::multinomial_out::call(self, num_samples, replacement, generator, out);
+}
+// aten::multinomial.out(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & multinomial_outf(const at::Tensor & self, int64_t num_samples, bool replacement, c10::optional generator, at::Tensor & out) {
+    return at::_ops::multinomial_out::call(self, num_samples, replacement, generator, out);
+}
+
+// aten::multinomial(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None) -> Tensor
+inline at::Tensor multinomial(const at::Tensor & self, int64_t num_samples, bool replacement=false, c10::optional generator=c10::nullopt) {
+    return at::_ops::multinomial::call(self, num_samples, replacement, generator);
+}
+
+}
diff --git a/shared/package-files/3d/3d443e98d5a37ba6c500b07e98b54544f92f6cce09a4b5ee9c1eac8c16d3cb98/hook-numpy.py b/shared/package-files/3d/3d443e98d5a37ba6c500b07e98b54544f92f6cce09a4b5ee9c1eac8c16d3cb98/hook-numpy.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f24318adf5d9eea1897c26b42fa444efa5c8022
--- /dev/null
+++ b/shared/package-files/3d/3d443e98d5a37ba6c500b07e98b54544f92f6cce09a4b5ee9c1eac8c16d3cb98/hook-numpy.py
@@ -0,0 +1,37 @@
+"""This hook should collect all binary files and any hidden modules that numpy
+needs.
+
+Our (some-what inadequate) docs for writing PyInstaller hooks are kept here:
+https://pyinstaller.readthedocs.io/en/stable/hooks.html
+
+"""
+from PyInstaller.compat import is_conda, is_pure_conda
+from PyInstaller.utils.hooks import collect_dynamic_libs, is_module_satisfies
+
+# Collect all DLLs inside numpy's installation folder, dump them into built
+# app's root.
+binaries = collect_dynamic_libs("numpy", ".")
+
+# If using Conda without any non-conda virtual environment manager:
+if is_pure_conda:
+    # Assume running the NumPy from Conda-forge and collect it's DLLs from the
+    # communal Conda bin directory. DLLs from NumPy's dependencies must also be
+    # collected to capture MKL, OpenBlas, OpenMP, etc.
+    from PyInstaller.utils.hooks import conda_support
+    datas = conda_support.collect_dynamic_libs("numpy", dependencies=True)
+
+# Submodules PyInstaller cannot detect.  `_dtype_ctypes` is only imported
+# from C and `_multiarray_tests` is used in tests (which are not packed).
+hiddenimports = ['numpy.core._dtype_ctypes', 'numpy.core._multiarray_tests']
+
+# Remove testing and building code and packages that are referenced throughout
+# NumPy but are not really dependencies.
+excludedimports = [
+    "scipy",
+    "pytest",
+    "f2py",
+    "setuptools",
+    "numpy.f2py",
+    "distutils",
+    "numpy.distutils",
+]
diff --git a/shared/package-files/3d/3d465400792fbb08a5bb7e6e073642aeca373ab257a4598c16b02b7cbc010576/one_hot_categorical.py b/shared/package-files/3d/3d465400792fbb08a5bb7e6e073642aeca373ab257a4598c16b02b7cbc010576/one_hot_categorical.py
new file mode 100644
index 0000000000000000000000000000000000000000..37e62e874f5ebef13667b3a2d2bd7f66fc88f398
--- /dev/null
+++ b/shared/package-files/3d/3d465400792fbb08a5bb7e6e073642aeca373ab257a4598c16b02b7cbc010576/one_hot_categorical.py
@@ -0,0 +1,129 @@
+import torch
+from torch.distributions import constraints
+from torch.distributions.categorical import Categorical
+from torch.distributions.distribution import Distribution
+
+__all__ = ["OneHotCategorical", "OneHotCategoricalStraightThrough"]
+
+
+class OneHotCategorical(Distribution):
+    r"""
+    Creates a one-hot categorical distribution parameterized by :attr:`probs` or
+    :attr:`logits`.
+
+    Samples are one-hot coded vectors of size ``probs.size(-1)``.
+
+    .. note:: The `probs` argument must be non-negative, finite and have a non-zero sum,
+              and it will be normalized to sum to 1 along the last dimension. :attr:`probs`
+              will return this normalized value.
+              The `logits` argument will be interpreted as unnormalized log probabilities
+              and can therefore be any real number. It will likewise be normalized so that
+              the resulting probabilities sum to 1 along the last dimension. :attr:`logits`
+              will return this normalized value.
+
+    See also: :func:`torch.distributions.Categorical` for specifications of
+    :attr:`probs` and :attr:`logits`.
+
+    Example::
+
+        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
+        >>> m = OneHotCategorical(torch.tensor([ 0.25, 0.25, 0.25, 0.25 ]))
+        >>> m.sample()  # equal probability of 0, 1, 2, 3
+        tensor([ 0.,  0.,  0.,  1.])
+
+    Args:
+        probs (Tensor): event probabilities
+        logits (Tensor): event log probabilities (unnormalized)
+    """
+    arg_constraints = {"probs": constraints.simplex, "logits": constraints.real_vector}
+    support = constraints.one_hot
+    has_enumerate_support = True
+
+    def __init__(self, probs=None, logits=None, validate_args=None):
+        self._categorical = Categorical(probs, logits)
+        batch_shape = self._categorical.batch_shape
+        event_shape = self._categorical.param_shape[-1:]
+        super().__init__(batch_shape, event_shape, validate_args=validate_args)
+
+    def expand(self, batch_shape, _instance=None):
+        new = self._get_checked_instance(OneHotCategorical, _instance)
+        batch_shape = torch.Size(batch_shape)
+        new._categorical = self._categorical.expand(batch_shape)
+        super(OneHotCategorical, new).__init__(
+            batch_shape, self.event_shape, validate_args=False
+        )
+        new._validate_args = self._validate_args
+        return new
+
+    def _new(self, *args, **kwargs):
+        return self._categorical._new(*args, **kwargs)
+
+    @property
+    def _param(self):
+        return self._categorical._param
+
+    @property
+    def probs(self):
+        return self._categorical.probs
+
+    @property
+    def logits(self):
+        return self._categorical.logits
+
+    @property
+    def mean(self):
+        return self._categorical.probs
+
+    @property
+    def mode(self):
+        probs = self._categorical.probs
+        mode = probs.argmax(axis=-1)
+        return torch.nn.functional.one_hot(mode, num_classes=probs.shape[-1]).to(probs)
+
+    @property
+    def variance(self):
+        return self._categorical.probs * (1 - self._categorical.probs)
+
+    @property
+    def param_shape(self):
+        return self._categorical.param_shape
+
+    def sample(self, sample_shape=torch.Size()):
+        sample_shape = torch.Size(sample_shape)
+        probs = self._categorical.probs
+        num_events = self._categorical._num_events
+        indices = self._categorical.sample(sample_shape)
+        return torch.nn.functional.one_hot(indices, num_events).to(probs)
+
+    def log_prob(self, value):
+        if self._validate_args:
+            self._validate_sample(value)
+        indices = value.max(-1)[1]
+        return self._categorical.log_prob(indices)
+
+    def entropy(self):
+        return self._categorical.entropy()
+
+    def enumerate_support(self, expand=True):
+        n = self.event_shape[0]
+        values = torch.eye(n, dtype=self._param.dtype, device=self._param.device)
+        values = values.view((n,) + (1,) * len(self.batch_shape) + (n,))
+        if expand:
+            values = values.expand((n,) + self.batch_shape + (n,))
+        return values
+
+
+class OneHotCategoricalStraightThrough(OneHotCategorical):
+    r"""
+    Creates a reparameterizable :class:`OneHotCategorical` distribution based on the straight-
+    through gradient estimator from [1].
+
+    [1] Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
+    (Bengio et al, 2013)
+    """
+    has_rsample = True
+
+    def rsample(self, sample_shape=torch.Size()):
+        samples = self.sample(sample_shape)
+        probs = self._categorical.probs  # cached via @lazy_property
+        return samples + (probs - probs.detach())
diff --git a/shared/package-files/3d/3d4a866ec161e6a456ae22cd49f9461cd10a14ab1b2b10b2153cf6770db0eca0/linalg_diagonal_compositeimplicitautograd_dispatch.h b/shared/package-files/3d/3d4a866ec161e6a456ae22cd49f9461cd10a14ab1b2b10b2153cf6770db0eca0/linalg_diagonal_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..76359b305432ecaa7e027b8dad0ad15b0aa61194
--- /dev/null
+++ b/shared/package-files/3d/3d4a866ec161e6a456ae22cd49f9461cd10a14ab1b2b10b2153cf6770db0eca0/linalg_diagonal_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor linalg_diagonal(const at::Tensor & A, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/3d/3d4b64fc7b5b299adf96b6a8485a54f6677d2524736c2f5cf1fdbd9853a58463/RECORD b/shared/package-files/3d/3d4b64fc7b5b299adf96b6a8485a54f6677d2524736c2f5cf1fdbd9853a58463/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..f50e90c52637aff21664a8a965fa20b1c775e7c4
--- /dev/null
+++ b/shared/package-files/3d/3d4b64fc7b5b299adf96b6a8485a54f6677d2524736c2f5cf1fdbd9853a58463/RECORD
@@ -0,0 +1,26 @@
+interface_meta-2.0.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
+interface_meta-2.0.1.dist-info/METADATA,sha256=6fCmhF4T5d6LTulBl2DKFRfuPN0sQbGyBUmRIhF_AiI,6365
+interface_meta-2.0.1.dist-info/RECORD,,
+interface_meta-2.0.1.dist-info/WHEEL,sha256=QccIxa26bgl1E6uMy58deGWi-0aeIkkangHcxk2kWfw,87
+interface_meta-2.0.1.dist-info/licenses/LICENSE,sha256=hIl_ogmhhZEoJ8LPx7Zs-dFUhe8EHe_yIIIKpeR134k,1072
+interface_meta/__init__.py,sha256=-g7i_ylkKTVfWCv5GMn38U7lX2OBhI1uGJ5_oIVhhMs,295
+interface_meta/__pycache__/__init__.cpython-310.pyc,,
+interface_meta/__pycache__/_version.cpython-310.pyc,,
+interface_meta/__pycache__/decorators.cpython-310.pyc,,
+interface_meta/__pycache__/interface.cpython-310.pyc,,
+interface_meta/_version.py,sha256=AJvqZqEOrEKVHQbALacgWiM-1U44ZzqQ-ZsR-NbTbjg,520
+interface_meta/decorators.py,sha256=p-GKzRX9Jq4GFPLrbnQh6EaAk3HcEcVz43UZAkUgKOg,3383
+interface_meta/interface.py,sha256=mbtabgJZDC45K2-LIzEqp0kN4RqO7psW-3buXpF1sxM,8525
+interface_meta/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+interface_meta/utils/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
+interface_meta/utils/__pycache__/__init__.cpython-310.pyc,,
+interface_meta/utils/__pycache__/conformance.cpython-310.pyc,,
+interface_meta/utils/__pycache__/docs.cpython-310.pyc,,
+interface_meta/utils/__pycache__/errors.cpython-310.pyc,,
+interface_meta/utils/__pycache__/inspection.cpython-310.pyc,,
+interface_meta/utils/__pycache__/reporting.cpython-310.pyc,,
+interface_meta/utils/conformance.py,sha256=19KojSTTleFIC6EWclvBeAgRKTL-AYWxFqzLOLsr_es,6013
+interface_meta/utils/docs.py,sha256=WV0qSUPoWdANzyzNfyws6db38eHw5MDLHgD4oAy-rf0,5560
+interface_meta/utils/errors.py,sha256=Kvd4swY8BFXacQgZyEbB8uVFuIB-lyCGVBbRbABXn74,56
+interface_meta/utils/inspection.py,sha256=jt-2O742uPzqQwGPf7az1d5BTF5csllpFk0ubVP65sY,6192
+interface_meta/utils/reporting.py,sha256=xIYx-Dy-iaDh5r_1kADNfEmVgC4KakOO1I-qiHq-OFo,522
diff --git a/shared/package-files/3d/3d4ec629b325d62be04ca54ae788c1d9f808b9faff8430948338d8c8d5edfb81/version.pyi b/shared/package-files/3d/3d4ec629b325d62be04ca54ae788c1d9f808b9faff8430948338d8c8d5edfb81/version.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f55d01d1a172c34dbf57d66152c8af8ecc75c3be
--- /dev/null
+++ b/shared/package-files/3d/3d4ec629b325d62be04ca54ae788c1d9f808b9faff8430948338d8c8d5edfb81/version.pyi
@@ -0,0 +1,33 @@
+from abc import abstractmethod
+from typing import Optional, Pattern, Text, Tuple, TypeVar, Union
+
+_T = TypeVar("_T", bound=Version)
+
+class Version:
+    def __repr__(self) -> str: ...
+    @abstractmethod
+    def __init__(self, vstring: Optional[Text] = ...) -> None: ...
+    @abstractmethod
+    def parse(self: _T, vstring: Text) -> _T: ...
+    @abstractmethod
+    def __str__(self) -> str: ...
+    @abstractmethod
+    def __cmp__(self: _T, other: Union[_T, str]) -> bool: ...
+
+class StrictVersion(Version):
+    version_re: Pattern[str]
+    version: Tuple[int, int, int]
+    prerelease: Optional[Tuple[Text, int]]
+    def __init__(self, vstring: Optional[Text] = ...) -> None: ...
+    def parse(self: _T, vstring: Text) -> _T: ...
+    def __str__(self) -> str: ...
+    def __cmp__(self: _T, other: Union[_T, str]) -> bool: ...
+
+class LooseVersion(Version):
+    component_re: Pattern[str]
+    vstring: Text
+    version: Tuple[Union[Text, int], ...]
+    def __init__(self, vstring: Optional[Text] = ...) -> None: ...
+    def parse(self: _T, vstring: Text) -> _T: ...
+    def __str__(self) -> str: ...
+    def __cmp__(self: _T, other: Union[_T, str]) -> bool: ...
diff --git a/shared/package-files/3d/3d4f1a99ebfef1756f97807f25438e38cd687d231ba2da7edcc3c63c7a4fa13c/Riga b/shared/package-files/3d/3d4f1a99ebfef1756f97807f25438e38cd687d231ba2da7edcc3c63c7a4fa13c/Riga
new file mode 100644
index 0000000000000000000000000000000000000000..d99170b6420d4b91ec0c0e4652136f765974e0d8
Binary files /dev/null and b/shared/package-files/3d/3d4f1a99ebfef1756f97807f25438e38cd687d231ba2da7edcc3c63c7a4fa13c/Riga differ
diff --git a/shared/package-files/3d/3d4fa62890ff1db4f02d764fdfb74fada533897afb57b6fa8f9b903bc4380dbc/special_laguerre_polynomial_l_meta_dispatch.h b/shared/package-files/3d/3d4fa62890ff1db4f02d764fdfb74fada533897afb57b6fa8f9b903bc4380dbc/special_laguerre_polynomial_l_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..bde62bdd9257a845bc1c975a8c462bd2acc82a71
--- /dev/null
+++ b/shared/package-files/3d/3d4fa62890ff1db4f02d764fdfb74fada533897afb57b6fa8f9b903bc4380dbc/special_laguerre_polynomial_l_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_laguerre_polynomial_l(const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_out(at::Tensor & out, const at::Tensor & x, const at::Tensor & n);
+TORCH_API at::Tensor & special_laguerre_polynomial_l_outf(const at::Tensor & x, const at::Tensor & n, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/3d/3d5392088261606c559b603f385329c9f1ab45b5d667eb990687453b055d405e/metaschema.json b/shared/package-files/3d/3d5392088261606c559b603f385329c9f1ab45b5d667eb990687453b055d405e/metaschema.json
new file mode 100644
index 0000000000000000000000000000000000000000..746cde9690134529bdcde889f72c7ad5259f24ad
--- /dev/null
+++ b/shared/package-files/3d/3d5392088261606c559b603f385329c9f1ab45b5d667eb990687453b055d405e/metaschema.json
@@ -0,0 +1,166 @@
+{
+    "$schema": "http://json-schema.org/draft-07/schema#",
+    "$id": "http://json-schema.org/draft-07/schema#",
+    "title": "Core schema meta-schema",
+    "definitions": {
+        "schemaArray": {
+            "type": "array",
+            "minItems": 1,
+            "items": { "$ref": "#" }
+        },
+        "nonNegativeInteger": {
+            "type": "integer",
+            "minimum": 0
+        },
+        "nonNegativeIntegerDefault0": {
+            "allOf": [
+                { "$ref": "#/definitions/nonNegativeInteger" },
+                { "default": 0 }
+            ]
+        },
+        "simpleTypes": {
+            "enum": [
+                "array",
+                "boolean",
+                "integer",
+                "null",
+                "number",
+                "object",
+                "string"
+            ]
+        },
+        "stringArray": {
+            "type": "array",
+            "items": { "type": "string" },
+            "uniqueItems": true,
+            "default": []
+        }
+    },
+    "type": ["object", "boolean"],
+    "properties": {
+        "$id": {
+            "type": "string",
+            "format": "uri-reference"
+        },
+        "$schema": {
+            "type": "string",
+            "format": "uri"
+        },
+        "$ref": {
+            "type": "string",
+            "format": "uri-reference"
+        },
+        "$comment": {
+            "type": "string"
+        },
+        "title": {
+            "type": "string"
+        },
+        "description": {
+            "type": "string"
+        },
+        "default": true,
+        "readOnly": {
+            "type": "boolean",
+            "default": false
+        },
+        "examples": {
+            "type": "array",
+            "items": true
+        },
+        "multipleOf": {
+            "type": "number",
+            "exclusiveMinimum": 0
+        },
+        "maximum": {
+            "type": "number"
+        },
+        "exclusiveMaximum": {
+            "type": "number"
+        },
+        "minimum": {
+            "type": "number"
+        },
+        "exclusiveMinimum": {
+            "type": "number"
+        },
+        "maxLength": { "$ref": "#/definitions/nonNegativeInteger" },
+        "minLength": { "$ref": "#/definitions/nonNegativeIntegerDefault0" },
+        "pattern": {
+            "type": "string",
+            "format": "regex"
+        },
+        "additionalItems": { "$ref": "#" },
+        "items": {
+            "anyOf": [
+                { "$ref": "#" },
+                { "$ref": "#/definitions/schemaArray" }
+            ],
+            "default": true
+        },
+        "maxItems": { "$ref": "#/definitions/nonNegativeInteger" },
+        "minItems": { "$ref": "#/definitions/nonNegativeIntegerDefault0" },
+        "uniqueItems": {
+            "type": "boolean",
+            "default": false
+        },
+        "contains": { "$ref": "#" },
+        "maxProperties": { "$ref": "#/definitions/nonNegativeInteger" },
+        "minProperties": { "$ref": "#/definitions/nonNegativeIntegerDefault0" },
+        "required": { "$ref": "#/definitions/stringArray" },
+        "additionalProperties": { "$ref": "#" },
+        "definitions": {
+            "type": "object",
+            "additionalProperties": { "$ref": "#" },
+            "default": {}
+        },
+        "properties": {
+            "type": "object",
+            "additionalProperties": { "$ref": "#" },
+            "default": {}
+        },
+        "patternProperties": {
+            "type": "object",
+            "additionalProperties": { "$ref": "#" },
+            "propertyNames": { "format": "regex" },
+            "default": {}
+        },
+        "dependencies": {
+            "type": "object",
+            "additionalProperties": {
+                "anyOf": [
+                    { "$ref": "#" },
+                    { "$ref": "#/definitions/stringArray" }
+                ]
+            }
+        },
+        "propertyNames": { "$ref": "#" },
+        "const": true,
+        "enum": {
+            "type": "array",
+            "items": true
+        },
+        "type": {
+            "anyOf": [
+                { "$ref": "#/definitions/simpleTypes" },
+                {
+                    "type": "array",
+                    "items": { "$ref": "#/definitions/simpleTypes" },
+                    "minItems": 1,
+                    "uniqueItems": true
+                }
+            ]
+        },
+        "format": { "type": "string" },
+        "contentMediaType": { "type": "string" },
+        "contentEncoding": { "type": "string" },
+        "if": {"$ref": "#"},
+        "then": {"$ref": "#"},
+        "else": {"$ref": "#"},
+        "allOf": { "$ref": "#/definitions/schemaArray" },
+        "anyOf": { "$ref": "#/definitions/schemaArray" },
+        "oneOf": { "$ref": "#/definitions/schemaArray" },
+        "not": { "$ref": "#" }
+    },
+    "default": true
+}
diff --git a/shared/package-files/3d/3d53e8a01d233f986464450b482c02d3be39df65056d1d8fb60bb4239cf0982b/certs.py b/shared/package-files/3d/3d53e8a01d233f986464450b482c02d3be39df65056d1d8fb60bb4239cf0982b/certs.py
new file mode 100644
index 0000000000000000000000000000000000000000..38696a1fb3419dd810004d5aec9654e5224042ed
--- /dev/null
+++ b/shared/package-files/3d/3d53e8a01d233f986464450b482c02d3be39df65056d1d8fb60bb4239cf0982b/certs.py
@@ -0,0 +1,24 @@
+#!/usr/bin/env python
+
+"""
+requests.certs
+~~~~~~~~~~~~~~
+
+This module returns the preferred default CA certificate bundle. There is
+only one — the one from the certifi package.
+
+If you are packaging Requests, e.g., for a Linux distribution or a managed
+environment, you can change the definition of where() to return a separately
+packaged CA bundle.
+"""
+
+import os
+
+if "_PIP_STANDALONE_CERT" not in os.environ:
+    from pip._vendor.certifi import where
+else:
+    def where():
+        return os.environ["_PIP_STANDALONE_CERT"]
+
+if __name__ == "__main__":
+    print(where())
diff --git a/shared/package-files/3d/3d5410aa97577c88566586aedc0258389289aca9aedc034a11b8e06c49edb6fc/test_sidebar.py b/shared/package-files/3d/3d5410aa97577c88566586aedc0258389289aca9aedc034a11b8e06c49edb6fc/test_sidebar.py
new file mode 100644
index 0000000000000000000000000000000000000000..049531e66a414e09ad1beab39ed275b37010acff
--- /dev/null
+++ b/shared/package-files/3d/3d5410aa97577c88566586aedc0258389289aca9aedc034a11b8e06c49edb6fc/test_sidebar.py
@@ -0,0 +1,768 @@
+"""Test sidebar, coverage 85%"""
+from textwrap import dedent
+import sys
+
+from itertools import chain
+import unittest
+import unittest.mock
+from test.support import requires, swap_attr
+from test import support
+import tkinter as tk
+from idlelib.idle_test.tkinter_testing_utils import run_in_tk_mainloop
+
+from idlelib.delegator import Delegator
+from idlelib.editor import fixwordbreaks
+from idlelib.percolator import Percolator
+import idlelib.pyshell
+from idlelib.pyshell import fix_x11_paste, PyShell, PyShellFileList
+from idlelib.run import fix_scaling
+import idlelib.sidebar
+from idlelib.sidebar import get_end_linenumber, get_lineno
+
+
+class Dummy_editwin:
+    def __init__(self, text):
+        self.text = text
+        self.text_frame = self.text.master
+        self.per = Percolator(text)
+        self.undo = Delegator()
+        self.per.insertfilter(self.undo)
+
+    def setvar(self, name, value):
+        pass
+
+    def getlineno(self, index):
+        return int(float(self.text.index(index)))
+
+
+class LineNumbersTest(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        requires('gui')
+        cls.root = tk.Tk()
+        cls.root.withdraw()
+
+        cls.text_frame = tk.Frame(cls.root)
+        cls.text_frame.pack(side=tk.LEFT, fill=tk.BOTH, expand=True)
+        cls.text_frame.rowconfigure(1, weight=1)
+        cls.text_frame.columnconfigure(1, weight=1)
+
+        cls.text = tk.Text(cls.text_frame, width=80, height=24, wrap=tk.NONE)
+        cls.text.grid(row=1, column=1, sticky=tk.NSEW)
+
+        cls.editwin = Dummy_editwin(cls.text)
+        cls.editwin.vbar = tk.Scrollbar(cls.text_frame)
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.editwin.per.close()
+        cls.root.update()
+        cls.root.destroy()
+        del cls.text, cls.text_frame, cls.editwin, cls.root
+
+    def setUp(self):
+        self.linenumber = idlelib.sidebar.LineNumbers(self.editwin)
+
+        self.highlight_cfg = {"background": '#abcdef',
+                              "foreground": '#123456'}
+        orig_idleConf_GetHighlight = idlelib.sidebar.idleConf.GetHighlight
+        def mock_idleconf_GetHighlight(theme, element):
+            if element == 'linenumber':
+                return self.highlight_cfg
+            return orig_idleConf_GetHighlight(theme, element)
+        GetHighlight_patcher = unittest.mock.patch.object(
+            idlelib.sidebar.idleConf, 'GetHighlight', mock_idleconf_GetHighlight)
+        GetHighlight_patcher.start()
+        self.addCleanup(GetHighlight_patcher.stop)
+
+        self.font_override = 'TkFixedFont'
+        def mock_idleconf_GetFont(root, configType, section):
+            return self.font_override
+        GetFont_patcher = unittest.mock.patch.object(
+            idlelib.sidebar.idleConf, 'GetFont', mock_idleconf_GetFont)
+        GetFont_patcher.start()
+        self.addCleanup(GetFont_patcher.stop)
+
+    def tearDown(self):
+        self.text.delete('1.0', 'end')
+
+    def get_selection(self):
+        return tuple(map(str, self.text.tag_ranges('sel')))
+
+    def get_line_screen_position(self, line):
+        bbox = self.linenumber.sidebar_text.bbox(f'{line}.end -1c')
+        x = bbox[0] + 2
+        y = bbox[1] + 2
+        return x, y
+
+    def assert_state_disabled(self):
+        state = self.linenumber.sidebar_text.config()['state']
+        self.assertEqual(state[-1], tk.DISABLED)
+
+    def get_sidebar_text_contents(self):
+        return self.linenumber.sidebar_text.get('1.0', tk.END)
+
+    def assert_sidebar_n_lines(self, n_lines):
+        expected = '\n'.join(chain(map(str, range(1, n_lines + 1)), ['']))
+        self.assertEqual(self.get_sidebar_text_contents(), expected)
+
+    def assert_text_equals(self, expected):
+        return self.assertEqual(self.text.get('1.0', 'end'), expected)
+
+    def test_init_empty(self):
+        self.assert_sidebar_n_lines(1)
+
+    def test_init_not_empty(self):
+        self.text.insert('insert', 'foo bar\n'*3)
+        self.assert_text_equals('foo bar\n'*3 + '\n')
+        self.assert_sidebar_n_lines(4)
+
+    def test_toggle_linenumbering(self):
+        self.assertEqual(self.linenumber.is_shown, False)
+        self.linenumber.show_sidebar()
+        self.assertEqual(self.linenumber.is_shown, True)
+        self.linenumber.hide_sidebar()
+        self.assertEqual(self.linenumber.is_shown, False)
+        self.linenumber.hide_sidebar()
+        self.assertEqual(self.linenumber.is_shown, False)
+        self.linenumber.show_sidebar()
+        self.assertEqual(self.linenumber.is_shown, True)
+        self.linenumber.show_sidebar()
+        self.assertEqual(self.linenumber.is_shown, True)
+
+    def test_insert(self):
+        self.text.insert('insert', 'foobar')
+        self.assert_text_equals('foobar\n')
+        self.assert_sidebar_n_lines(1)
+        self.assert_state_disabled()
+
+        self.text.insert('insert', '\nfoo')
+        self.assert_text_equals('foobar\nfoo\n')
+        self.assert_sidebar_n_lines(2)
+        self.assert_state_disabled()
+
+        self.text.insert('insert', 'hello\n'*2)
+        self.assert_text_equals('foobar\nfoohello\nhello\n\n')
+        self.assert_sidebar_n_lines(4)
+        self.assert_state_disabled()
+
+        self.text.insert('insert', '\nworld')
+        self.assert_text_equals('foobar\nfoohello\nhello\n\nworld\n')
+        self.assert_sidebar_n_lines(5)
+        self.assert_state_disabled()
+
+    def test_delete(self):
+        self.text.insert('insert', 'foobar')
+        self.assert_text_equals('foobar\n')
+        self.text.delete('1.1', '1.3')
+        self.assert_text_equals('fbar\n')
+        self.assert_sidebar_n_lines(1)
+        self.assert_state_disabled()
+
+        self.text.insert('insert', 'foo\n'*2)
+        self.assert_text_equals('fbarfoo\nfoo\n\n')
+        self.assert_sidebar_n_lines(3)
+        self.assert_state_disabled()
+
+        # Deleting up to "2.end" doesn't delete the final newline.
+        self.text.delete('2.0', '2.end')
+        self.assert_text_equals('fbarfoo\n\n\n')
+        self.assert_sidebar_n_lines(3)
+        self.assert_state_disabled()
+
+        self.text.delete('1.3', 'end')
+        self.assert_text_equals('fba\n')
+        self.assert_sidebar_n_lines(1)
+        self.assert_state_disabled()
+
+        # Text widgets always keep a single '\n' character at the end.
+        self.text.delete('1.0', 'end')
+        self.assert_text_equals('\n')
+        self.assert_sidebar_n_lines(1)
+        self.assert_state_disabled()
+
+    def test_sidebar_text_width(self):
+        """
+        Test that linenumber text widget is always at the minimum
+        width
+        """
+        def get_width():
+            return self.linenumber.sidebar_text.config()['width'][-1]
+
+        self.assert_sidebar_n_lines(1)
+        self.assertEqual(get_width(), 1)
+
+        self.text.insert('insert', 'foo')
+        self.assert_sidebar_n_lines(1)
+        self.assertEqual(get_width(), 1)
+
+        self.text.insert('insert', 'foo\n'*8)
+        self.assert_sidebar_n_lines(9)
+        self.assertEqual(get_width(), 1)
+
+        self.text.insert('insert', 'foo\n')
+        self.assert_sidebar_n_lines(10)
+        self.assertEqual(get_width(), 2)
+
+        self.text.insert('insert', 'foo\n')
+        self.assert_sidebar_n_lines(11)
+        self.assertEqual(get_width(), 2)
+
+        self.text.delete('insert -1l linestart', 'insert linestart')
+        self.assert_sidebar_n_lines(10)
+        self.assertEqual(get_width(), 2)
+
+        self.text.delete('insert -1l linestart', 'insert linestart')
+        self.assert_sidebar_n_lines(9)
+        self.assertEqual(get_width(), 1)
+
+        self.text.insert('insert', 'foo\n'*90)
+        self.assert_sidebar_n_lines(99)
+        self.assertEqual(get_width(), 2)
+
+        self.text.insert('insert', 'foo\n')
+        self.assert_sidebar_n_lines(100)
+        self.assertEqual(get_width(), 3)
+
+        self.text.insert('insert', 'foo\n')
+        self.assert_sidebar_n_lines(101)
+        self.assertEqual(get_width(), 3)
+
+        self.text.delete('insert -1l linestart', 'insert linestart')
+        self.assert_sidebar_n_lines(100)
+        self.assertEqual(get_width(), 3)
+
+        self.text.delete('insert -1l linestart', 'insert linestart')
+        self.assert_sidebar_n_lines(99)
+        self.assertEqual(get_width(), 2)
+
+        self.text.delete('50.0 -1c', 'end -1c')
+        self.assert_sidebar_n_lines(49)
+        self.assertEqual(get_width(), 2)
+
+        self.text.delete('5.0 -1c', 'end -1c')
+        self.assert_sidebar_n_lines(4)
+        self.assertEqual(get_width(), 1)
+
+        # Text widgets always keep a single '\n' character at the end.
+        self.text.delete('1.0', 'end -1c')
+        self.assert_sidebar_n_lines(1)
+        self.assertEqual(get_width(), 1)
+
+    # The following tests are temporarily disabled due to relying on
+    # simulated user input and inspecting which text is selected, which
+    # are fragile and can fail when several GUI tests are run in parallel
+    # or when the windows created by the test lose focus.
+    #
+    # TODO: Re-work these tests or remove them from the test suite.
+
+    @unittest.skip('test disabled')
+    def test_click_selection(self):
+        self.linenumber.show_sidebar()
+        self.text.insert('1.0', 'one\ntwo\nthree\nfour\n')
+        self.root.update()
+
+        # Click on the second line.
+        x, y = self.get_line_screen_position(2)
+        self.linenumber.sidebar_text.event_generate('', x=x, y=y)
+        self.linenumber.sidebar_text.update()
+        self.root.update()
+
+        self.assertEqual(self.get_selection(), ('2.0', '3.0'))
+
+    def simulate_drag(self, start_line, end_line):
+        start_x, start_y = self.get_line_screen_position(start_line)
+        end_x, end_y = self.get_line_screen_position(end_line)
+
+        self.linenumber.sidebar_text.event_generate('',
+                                                    x=start_x, y=start_y)
+        self.root.update()
+
+        def lerp(a, b, steps):
+            """linearly interpolate from a to b (inclusive) in equal steps"""
+            last_step = steps - 1
+            for i in range(steps):
+                yield ((last_step - i) / last_step) * a + (i / last_step) * b
+
+        for x, y in zip(
+                map(int, lerp(start_x, end_x, steps=11)),
+                map(int, lerp(start_y, end_y, steps=11)),
+        ):
+            self.linenumber.sidebar_text.event_generate('', x=x, y=y)
+            self.root.update()
+
+        self.linenumber.sidebar_text.event_generate('',
+                                                    x=end_x, y=end_y)
+        self.root.update()
+
+    @unittest.skip('test disabled')
+    def test_drag_selection_down(self):
+        self.linenumber.show_sidebar()
+        self.text.insert('1.0', 'one\ntwo\nthree\nfour\nfive\n')
+        self.root.update()
+
+        # Drag from the second line to the fourth line.
+        self.simulate_drag(2, 4)
+        self.assertEqual(self.get_selection(), ('2.0', '5.0'))
+
+    @unittest.skip('test disabled')
+    def test_drag_selection_up(self):
+        self.linenumber.show_sidebar()
+        self.text.insert('1.0', 'one\ntwo\nthree\nfour\nfive\n')
+        self.root.update()
+
+        # Drag from the fourth line to the second line.
+        self.simulate_drag(4, 2)
+        self.assertEqual(self.get_selection(), ('2.0', '5.0'))
+
+    def test_scroll(self):
+        self.linenumber.show_sidebar()
+        self.text.insert('1.0', 'line\n' * 100)
+        self.root.update()
+
+        # Scroll down 10 lines.
+        self.text.yview_scroll(10, 'unit')
+        self.root.update()
+        self.assertEqual(self.text.index('@0,0'), '11.0')
+        self.assertEqual(self.linenumber.sidebar_text.index('@0,0'), '11.0')
+
+        # Generate a mouse-wheel event and make sure it scrolled up or down.
+        # The meaning of the "delta" is OS-dependant, so this just checks for
+        # any change.
+        self.linenumber.sidebar_text.event_generate('',
+                                                    x=0, y=0,
+                                                    delta=10)
+        self.root.update()
+        self.assertNotEqual(self.text.index('@0,0'), '11.0')
+        self.assertNotEqual(self.linenumber.sidebar_text.index('@0,0'), '11.0')
+
+    def test_font(self):
+        ln = self.linenumber
+
+        orig_font = ln.sidebar_text['font']
+        test_font = 'TkTextFont'
+        self.assertNotEqual(orig_font, test_font)
+
+        # Ensure line numbers aren't shown.
+        ln.hide_sidebar()
+
+        self.font_override = test_font
+        # Nothing breaks when line numbers aren't shown.
+        ln.update_font()
+
+        # Activate line numbers, previous font change is immediately effective.
+        ln.show_sidebar()
+        self.assertEqual(ln.sidebar_text['font'], test_font)
+
+        # Call the font update with line numbers shown, change is picked up.
+        self.font_override = orig_font
+        ln.update_font()
+        self.assertEqual(ln.sidebar_text['font'], orig_font)
+
+    def test_highlight_colors(self):
+        ln = self.linenumber
+
+        orig_colors = dict(self.highlight_cfg)
+        test_colors = {'background': '#222222', 'foreground': '#ffff00'}
+
+        def assert_colors_are_equal(colors):
+            self.assertEqual(ln.sidebar_text['background'], colors['background'])
+            self.assertEqual(ln.sidebar_text['foreground'], colors['foreground'])
+
+        # Ensure line numbers aren't shown.
+        ln.hide_sidebar()
+
+        self.highlight_cfg = test_colors
+        # Nothing breaks with inactive line numbers.
+        ln.update_colors()
+
+        # Show line numbers, previous colors change is immediately effective.
+        ln.show_sidebar()
+        assert_colors_are_equal(test_colors)
+
+        # Call colors update with no change to the configured colors.
+        ln.update_colors()
+        assert_colors_are_equal(test_colors)
+
+        # Call the colors update with line numbers shown, change is picked up.
+        self.highlight_cfg = orig_colors
+        ln.update_colors()
+        assert_colors_are_equal(orig_colors)
+
+
+class ShellSidebarTest(unittest.TestCase):
+    root: tk.Tk = None
+    shell: PyShell = None
+
+    @classmethod
+    def setUpClass(cls):
+        requires('gui')
+
+        cls.root = root = tk.Tk()
+        root.withdraw()
+
+        fix_scaling(root)
+        fixwordbreaks(root)
+        fix_x11_paste(root)
+
+        cls.flist = flist = PyShellFileList(root)
+        # See #43981 about macosx.setupApp(root, flist) causing failure.
+        root.update_idletasks()
+
+        cls.init_shell()
+
+    @classmethod
+    def tearDownClass(cls):
+        if cls.shell is not None:
+            cls.shell.executing = False
+            cls.shell.close()
+            cls.shell = None
+        cls.flist = None
+        cls.root.update_idletasks()
+        cls.root.destroy()
+        cls.root = None
+
+    @classmethod
+    def init_shell(cls):
+        cls.shell = cls.flist.open_shell()
+        cls.shell.pollinterval = 10
+        cls.root.update()
+        cls.n_preface_lines = get_lineno(cls.shell.text, 'end-1c') - 1
+
+    @classmethod
+    def reset_shell(cls):
+        cls.shell.per.bottom.delete(f'{cls.n_preface_lines+1}.0', 'end-1c')
+        cls.shell.shell_sidebar.update_sidebar()
+        cls.root.update()
+
+    def setUp(self):
+        # In some test environments, e.g. Azure Pipelines (as of
+        # Apr. 2021), sys.stdout is changed between tests. However,
+        # PyShell relies on overriding sys.stdout when run without a
+        # sub-process (as done here; see setUpClass).
+        self._saved_stdout = None
+        if sys.stdout != self.shell.stdout:
+            self._saved_stdout = sys.stdout
+            sys.stdout = self.shell.stdout
+
+        self.reset_shell()
+
+    def tearDown(self):
+        if self._saved_stdout is not None:
+            sys.stdout = self._saved_stdout
+
+    def get_sidebar_lines(self):
+        canvas = self.shell.shell_sidebar.canvas
+        texts = list(canvas.find(tk.ALL))
+        texts_by_y_coords = {
+            canvas.bbox(text)[1]: canvas.itemcget(text, 'text')
+            for text in texts
+        }
+        line_y_coords = self.get_shell_line_y_coords()
+        return [texts_by_y_coords.get(y, None) for y in line_y_coords]
+
+    def assert_sidebar_lines_end_with(self, expected_lines):
+        self.shell.shell_sidebar.update_sidebar()
+        self.assertEqual(
+            self.get_sidebar_lines()[-len(expected_lines):],
+            expected_lines,
+        )
+
+    def get_shell_line_y_coords(self):
+        text = self.shell.text
+        y_coords = []
+        index = text.index("@0,0")
+        if index.split('.', 1)[1] != '0':
+            index = text.index(f"{index} +1line linestart")
+        while (lineinfo := text.dlineinfo(index)) is not None:
+            y_coords.append(lineinfo[1])
+            index = text.index(f"{index} +1line")
+        return y_coords
+
+    def get_sidebar_line_y_coords(self):
+        canvas = self.shell.shell_sidebar.canvas
+        texts = list(canvas.find(tk.ALL))
+        texts.sort(key=lambda text: canvas.bbox(text)[1])
+        return [canvas.bbox(text)[1] for text in texts]
+
+    def assert_sidebar_lines_synced(self):
+        self.assertLessEqual(
+            set(self.get_sidebar_line_y_coords()),
+            set(self.get_shell_line_y_coords()),
+        )
+
+    def do_input(self, input):
+        shell = self.shell
+        text = shell.text
+        for line_index, line in enumerate(input.split('\n')):
+            if line_index > 0:
+                text.event_generate('<>')
+            text.insert('insert', line, 'stdin')
+
+    def test_initial_state(self):
+        sidebar_lines = self.get_sidebar_lines()
+        self.assertEqual(
+            sidebar_lines,
+            [None] * (len(sidebar_lines) - 1) + ['>>>'],
+        )
+        self.assert_sidebar_lines_synced()
+
+    @run_in_tk_mainloop()
+    def test_single_empty_input(self):
+        self.do_input('\n')
+        yield
+        self.assert_sidebar_lines_end_with(['>>>', '>>>'])
+
+    @run_in_tk_mainloop()
+    def test_single_line_statement(self):
+        self.do_input('1\n')
+        yield
+        self.assert_sidebar_lines_end_with(['>>>', None, '>>>'])
+
+    @run_in_tk_mainloop()
+    def test_multi_line_statement(self):
+        # Block statements are not indented because IDLE auto-indents.
+        self.do_input(dedent('''\
+            if True:
+            print(1)
+
+            '''))
+        yield
+        self.assert_sidebar_lines_end_with([
+            '>>>',
+            '...',
+            '...',
+            '...',
+            None,
+            '>>>',
+        ])
+
+    @run_in_tk_mainloop()
+    def test_single_long_line_wraps(self):
+        self.do_input('1' * 200 + '\n')
+        yield
+        self.assert_sidebar_lines_end_with(['>>>', None, '>>>'])
+        self.assert_sidebar_lines_synced()
+
+    @run_in_tk_mainloop()
+    def test_squeeze_multi_line_output(self):
+        shell = self.shell
+        text = shell.text
+
+        self.do_input('print("a\\nb\\nc")\n')
+        yield
+        self.assert_sidebar_lines_end_with(['>>>', None, None, None, '>>>'])
+
+        text.mark_set('insert', f'insert -1line linestart')
+        text.event_generate('<>')
+        yield
+        self.assert_sidebar_lines_end_with(['>>>', None, '>>>'])
+        self.assert_sidebar_lines_synced()
+
+        shell.squeezer.expandingbuttons[0].expand()
+        yield
+        self.assert_sidebar_lines_end_with(['>>>', None, None, None, '>>>'])
+        self.assert_sidebar_lines_synced()
+
+    @run_in_tk_mainloop()
+    def test_interrupt_recall_undo_redo(self):
+        text = self.shell.text
+        # Block statements are not indented because IDLE auto-indents.
+        initial_sidebar_lines = self.get_sidebar_lines()
+
+        self.do_input(dedent('''\
+            if True:
+            print(1)
+            '''))
+        yield
+        self.assert_sidebar_lines_end_with(['>>>', '...', '...'])
+        with_block_sidebar_lines = self.get_sidebar_lines()
+        self.assertNotEqual(with_block_sidebar_lines, initial_sidebar_lines)
+
+        # Control-C
+        text.event_generate('<>')
+        yield
+        self.assert_sidebar_lines_end_with(['>>>', '...', '...', None, '>>>'])
+
+        # Recall previous via history
+        text.event_generate('<>')
+        text.event_generate('<>')
+        yield
+        self.assert_sidebar_lines_end_with(['>>>', '...', None, '>>>'])
+
+        # Recall previous via recall
+        text.mark_set('insert', text.index('insert -2l'))
+        text.event_generate('<>')
+        yield
+
+        text.event_generate('<>')
+        yield
+        self.assert_sidebar_lines_end_with(['>>>'])
+
+        text.event_generate('<>')
+        yield
+        self.assert_sidebar_lines_end_with(['>>>', '...'])
+
+        text.event_generate('<>')
+        text.event_generate('<>')
+        yield
+        self.assert_sidebar_lines_end_with(
+            ['>>>', '...', '...', '...', None, '>>>']
+        )
+
+    @run_in_tk_mainloop()
+    def test_very_long_wrapped_line(self):
+        with support.adjust_int_max_str_digits(11_111), \
+                swap_attr(self.shell, 'squeezer', None):
+            self.do_input('x = ' + '1'*10_000 + '\n')
+            yield
+            self.assertEqual(self.get_sidebar_lines(), ['>>>'])
+
+    def test_font(self):
+        sidebar = self.shell.shell_sidebar
+
+        test_font = 'TkTextFont'
+
+        def mock_idleconf_GetFont(root, configType, section):
+            return test_font
+        GetFont_patcher = unittest.mock.patch.object(
+            idlelib.sidebar.idleConf, 'GetFont', mock_idleconf_GetFont)
+        GetFont_patcher.start()
+        def cleanup():
+            GetFont_patcher.stop()
+            sidebar.update_font()
+        self.addCleanup(cleanup)
+
+        def get_sidebar_font():
+            canvas = sidebar.canvas
+            texts = list(canvas.find(tk.ALL))
+            fonts = {canvas.itemcget(text, 'font') for text in texts}
+            self.assertEqual(len(fonts), 1)
+            return next(iter(fonts))
+
+        self.assertNotEqual(get_sidebar_font(), test_font)
+        sidebar.update_font()
+        self.assertEqual(get_sidebar_font(), test_font)
+
+    def test_highlight_colors(self):
+        sidebar = self.shell.shell_sidebar
+
+        test_colors = {"background": '#abcdef', "foreground": '#123456'}
+
+        orig_idleConf_GetHighlight = idlelib.sidebar.idleConf.GetHighlight
+        def mock_idleconf_GetHighlight(theme, element):
+            if element in ['linenumber', 'console']:
+                return test_colors
+            return orig_idleConf_GetHighlight(theme, element)
+        GetHighlight_patcher = unittest.mock.patch.object(
+            idlelib.sidebar.idleConf, 'GetHighlight',
+            mock_idleconf_GetHighlight)
+        GetHighlight_patcher.start()
+        def cleanup():
+            GetHighlight_patcher.stop()
+            sidebar.update_colors()
+        self.addCleanup(cleanup)
+
+        def get_sidebar_colors():
+            canvas = sidebar.canvas
+            texts = list(canvas.find(tk.ALL))
+            fgs = {canvas.itemcget(text, 'fill') for text in texts}
+            self.assertEqual(len(fgs), 1)
+            fg = next(iter(fgs))
+            bg = canvas.cget('background')
+            return {"background": bg, "foreground": fg}
+
+        self.assertNotEqual(get_sidebar_colors(), test_colors)
+        sidebar.update_colors()
+        self.assertEqual(get_sidebar_colors(), test_colors)
+
+    @run_in_tk_mainloop()
+    def test_mousewheel(self):
+        sidebar = self.shell.shell_sidebar
+        text = self.shell.text
+
+        # Enter a 100-line string to scroll the shell screen down.
+        self.do_input('x = """' + '\n'*100 + '"""\n')
+        yield
+        self.assertGreater(get_lineno(text, '@0,0'), 1)
+
+        last_lineno = get_end_linenumber(text)
+        self.assertIsNotNone(text.dlineinfo(text.index(f'{last_lineno}.0')))
+
+        # Scroll up using the  event.
+        # The meaning delta is platform-dependant.
+        delta = -1 if sys.platform == 'darwin' else 120
+        sidebar.canvas.event_generate('', x=0, y=0, delta=delta)
+        yield
+        self.assertIsNone(text.dlineinfo(text.index(f'{last_lineno}.0')))
+
+        # Scroll back down using the  event.
+        sidebar.canvas.event_generate('', x=0, y=0)
+        yield
+        self.assertIsNotNone(text.dlineinfo(text.index(f'{last_lineno}.0')))
+
+    @run_in_tk_mainloop()
+    def test_copy(self):
+        sidebar = self.shell.shell_sidebar
+        text = self.shell.text
+
+        first_line = get_end_linenumber(text)
+
+        self.do_input(dedent('''\
+            if True:
+            print(1)
+
+            '''))
+        yield
+
+        text.tag_add('sel', f'{first_line}.0', 'end-1c')
+        selected_text = text.get('sel.first', 'sel.last')
+        self.assertTrue(selected_text.startswith('if True:\n'))
+        self.assertIn('\n1\n', selected_text)
+
+        text.event_generate('<>')
+        self.addCleanup(text.clipboard_clear)
+
+        copied_text = text.clipboard_get()
+        self.assertEqual(copied_text, selected_text)
+
+    @run_in_tk_mainloop()
+    def test_copy_with_prompts(self):
+        sidebar = self.shell.shell_sidebar
+        text = self.shell.text
+
+        first_line = get_end_linenumber(text)
+        self.do_input(dedent('''\
+            if True:
+                print(1)
+
+            '''))
+        yield
+
+        text.tag_add('sel', f'{first_line}.3', 'end-1c')
+        selected_text = text.get('sel.first', 'sel.last')
+        self.assertTrue(selected_text.startswith('True:\n'))
+
+        selected_lines_text = text.get('sel.first linestart', 'sel.last')
+        selected_lines = selected_lines_text.split('\n')
+        selected_lines.pop()  # Final '' is a split artifact, not a line.
+        # Expect a block of input and a single output line.
+        expected_prompts = \
+            ['>>>'] + ['...'] * (len(selected_lines) - 2) + [None]
+        selected_text_with_prompts = '\n'.join(
+            line if prompt is None else prompt + ' ' + line
+            for prompt, line in zip(expected_prompts,
+                                    selected_lines,
+                                    strict=True)
+        ) + '\n'
+
+        text.event_generate('<>')
+        self.addCleanup(text.clipboard_clear)
+
+        copied_text = text.clipboard_get()
+        self.assertEqual(copied_text, selected_text_with_prompts)
+
+
+if __name__ == '__main__':
+    unittest.main(verbosity=2)
diff --git a/shared/package-files/3d/3d54e47334e91e55db4dcec840a942154f42ab85461befc98559d3d3f9feb750/cpu_avx512_knl.c b/shared/package-files/3d/3d54e47334e91e55db4dcec840a942154f42ab85461befc98559d3d3f9feb750/cpu_avx512_knl.c
new file mode 100644
index 0000000000000000000000000000000000000000..cb55e57aa220ebc8e1b638f7bfb470cff6725ea2
--- /dev/null
+++ b/shared/package-files/3d/3d54e47334e91e55db4dcec840a942154f42ab85461befc98559d3d3f9feb750/cpu_avx512_knl.c
@@ -0,0 +1,25 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #if !defined(__AVX512ER__) || !defined(__AVX512PF__)
+        #error "HOST/ARCH doesn't support Knights Landing AVX512 features"
+    #endif
+#endif
+
+#include 
+
+int main(int argc, char **argv)
+{
+    int base[128]={};
+    __m512d ad = _mm512_loadu_pd((const __m512d*)argv[argc-1]);
+    /* ER */
+    __m512i a = _mm512_castpd_si512(_mm512_exp2a23_pd(ad));
+    /* PF */
+    _mm512_mask_prefetch_i64scatter_pd(base, _mm512_cmpeq_epi64_mask(a, a), a, 1, _MM_HINT_T1);
+    return base[0];
+}
diff --git a/shared/package-files/3d/3d55a648feb63e7f2142477af3697ab9814b429d67c5996d30e13e16184ef4e5/mobilenetv3.py b/shared/package-files/3d/3d55a648feb63e7f2142477af3697ab9814b429d67c5996d30e13e16184ef4e5/mobilenetv3.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1fdcfec9570d35683efb10344e667d3f4487fce
--- /dev/null
+++ b/shared/package-files/3d/3d55a648feb63e7f2142477af3697ab9814b429d67c5996d30e13e16184ef4e5/mobilenetv3.py
@@ -0,0 +1,237 @@
+from functools import partial
+from typing import Any, List, Optional, Union
+
+import torch
+from torch import nn, Tensor
+from torch.ao.quantization import DeQuantStub, QuantStub
+
+from ...ops.misc import Conv2dNormActivation, SqueezeExcitation
+from ...transforms._presets import ImageClassification
+from .._api import register_model, Weights, WeightsEnum
+from .._meta import _IMAGENET_CATEGORIES
+from .._utils import _ovewrite_named_param, handle_legacy_interface
+from ..mobilenetv3 import (
+    _mobilenet_v3_conf,
+    InvertedResidual,
+    InvertedResidualConfig,
+    MobileNet_V3_Large_Weights,
+    MobileNetV3,
+)
+from .utils import _fuse_modules, _replace_relu
+
+
+__all__ = [
+    "QuantizableMobileNetV3",
+    "MobileNet_V3_Large_QuantizedWeights",
+    "mobilenet_v3_large",
+]
+
+
+class QuantizableSqueezeExcitation(SqueezeExcitation):
+    _version = 2
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        kwargs["scale_activation"] = nn.Hardsigmoid
+        super().__init__(*args, **kwargs)
+        self.skip_mul = nn.quantized.FloatFunctional()
+
+    def forward(self, input: Tensor) -> Tensor:
+        return self.skip_mul.mul(self._scale(input), input)
+
+    def fuse_model(self, is_qat: Optional[bool] = None) -> None:
+        _fuse_modules(self, ["fc1", "activation"], is_qat, inplace=True)
+
+    def _load_from_state_dict(
+        self,
+        state_dict,
+        prefix,
+        local_metadata,
+        strict,
+        missing_keys,
+        unexpected_keys,
+        error_msgs,
+    ):
+        version = local_metadata.get("version", None)
+
+        if hasattr(self, "qconfig") and (version is None or version < 2):
+            default_state_dict = {
+                "scale_activation.activation_post_process.scale": torch.tensor([1.0]),
+                "scale_activation.activation_post_process.activation_post_process.scale": torch.tensor([1.0]),
+                "scale_activation.activation_post_process.zero_point": torch.tensor([0], dtype=torch.int32),
+                "scale_activation.activation_post_process.activation_post_process.zero_point": torch.tensor(
+                    [0], dtype=torch.int32
+                ),
+                "scale_activation.activation_post_process.fake_quant_enabled": torch.tensor([1]),
+                "scale_activation.activation_post_process.observer_enabled": torch.tensor([1]),
+            }
+            for k, v in default_state_dict.items():
+                full_key = prefix + k
+                if full_key not in state_dict:
+                    state_dict[full_key] = v
+
+        super()._load_from_state_dict(
+            state_dict,
+            prefix,
+            local_metadata,
+            strict,
+            missing_keys,
+            unexpected_keys,
+            error_msgs,
+        )
+
+
+class QuantizableInvertedResidual(InvertedResidual):
+    # TODO https://github.com/pytorch/vision/pull/4232#pullrequestreview-730461659
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        super().__init__(*args, se_layer=QuantizableSqueezeExcitation, **kwargs)  # type: ignore[misc]
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x: Tensor) -> Tensor:
+        if self.use_res_connect:
+            return self.skip_add.add(x, self.block(x))
+        else:
+            return self.block(x)
+
+
+class QuantizableMobileNetV3(MobileNetV3):
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        """
+        MobileNet V3 main class
+
+        Args:
+           Inherits args from floating point MobileNetV3
+        """
+        super().__init__(*args, **kwargs)
+        self.quant = QuantStub()
+        self.dequant = DeQuantStub()
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.quant(x)
+        x = self._forward_impl(x)
+        x = self.dequant(x)
+        return x
+
+    def fuse_model(self, is_qat: Optional[bool] = None) -> None:
+        for m in self.modules():
+            if type(m) is Conv2dNormActivation:
+                modules_to_fuse = ["0", "1"]
+                if len(m) == 3 and type(m[2]) is nn.ReLU:
+                    modules_to_fuse.append("2")
+                _fuse_modules(m, modules_to_fuse, is_qat, inplace=True)
+            elif type(m) is QuantizableSqueezeExcitation:
+                m.fuse_model(is_qat)
+
+
+def _mobilenet_v3_model(
+    inverted_residual_setting: List[InvertedResidualConfig],
+    last_channel: int,
+    weights: Optional[WeightsEnum],
+    progress: bool,
+    quantize: bool,
+    **kwargs: Any,
+) -> QuantizableMobileNetV3:
+    if weights is not None:
+        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
+        if "backend" in weights.meta:
+            _ovewrite_named_param(kwargs, "backend", weights.meta["backend"])
+    backend = kwargs.pop("backend", "qnnpack")
+
+    model = QuantizableMobileNetV3(inverted_residual_setting, last_channel, block=QuantizableInvertedResidual, **kwargs)
+    _replace_relu(model)
+
+    if quantize:
+        # Instead of quantizing the model and then loading the quantized weights we take a different approach.
+        # We prepare the QAT model, load the QAT weights from training and then convert it.
+        # This is done to avoid extremely low accuracies observed on the specific model. This is rather a workaround
+        # for an unresolved bug on the eager quantization API detailed at: https://github.com/pytorch/vision/issues/5890
+        model.fuse_model(is_qat=True)
+        model.qconfig = torch.ao.quantization.get_default_qat_qconfig(backend)
+        torch.ao.quantization.prepare_qat(model, inplace=True)
+
+    if weights is not None:
+        model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
+
+    if quantize:
+        torch.ao.quantization.convert(model, inplace=True)
+        model.eval()
+
+    return model
+
+
+class MobileNet_V3_Large_QuantizedWeights(WeightsEnum):
+    IMAGENET1K_QNNPACK_V1 = Weights(
+        url="https://download.pytorch.org/models/quantized/mobilenet_v3_large_qnnpack-5bcacf28.pth",
+        transforms=partial(ImageClassification, crop_size=224),
+        meta={
+            "num_params": 5483032,
+            "min_size": (1, 1),
+            "categories": _IMAGENET_CATEGORIES,
+            "backend": "qnnpack",
+            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv3",
+            "unquantized": MobileNet_V3_Large_Weights.IMAGENET1K_V1,
+            "_metrics": {
+                "ImageNet-1K": {
+                    "acc@1": 73.004,
+                    "acc@5": 90.858,
+                }
+            },
+            "_ops": 0.217,
+            "_file_size": 21.554,
+            "_docs": """
+                These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized
+                weights listed below.
+            """,
+        },
+    )
+    DEFAULT = IMAGENET1K_QNNPACK_V1
+
+
+@register_model(name="quantized_mobilenet_v3_large")
+@handle_legacy_interface(
+    weights=(
+        "pretrained",
+        lambda kwargs: MobileNet_V3_Large_QuantizedWeights.IMAGENET1K_QNNPACK_V1
+        if kwargs.get("quantize", False)
+        else MobileNet_V3_Large_Weights.IMAGENET1K_V1,
+    )
+)
+def mobilenet_v3_large(
+    *,
+    weights: Optional[Union[MobileNet_V3_Large_QuantizedWeights, MobileNet_V3_Large_Weights]] = None,
+    progress: bool = True,
+    quantize: bool = False,
+    **kwargs: Any,
+) -> QuantizableMobileNetV3:
+    """
+    MobileNetV3 (Large) model from
+    `Searching for MobileNetV3 `_.
+
+    .. note::
+        Note that ``quantize = True`` returns a quantized model with 8 bit
+        weights. Quantized models only support inference and run on CPUs.
+        GPU inference is not yet supported.
+
+    Args:
+        weights (:class:`~torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V3_Large_Weights`, optional): The
+            pretrained weights for the model. See
+            :class:`~torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights` below for
+            more details, and possible values. By default, no pre-trained
+            weights are used.
+        progress (bool): If True, displays a progress bar of the
+            download to stderr. Default is True.
+        quantize (bool): If True, return a quantized version of the model. Default is False.
+        **kwargs: parameters passed to the ``torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights``
+            base class. Please refer to the `source code
+            `_
+            for more details about this class.
+
+    .. autoclass:: torchvision.models.quantization.MobileNet_V3_Large_QuantizedWeights
+        :members:
+    .. autoclass:: torchvision.models.MobileNet_V3_Large_Weights
+        :members:
+        :noindex:
+    """
+    weights = (MobileNet_V3_Large_QuantizedWeights if quantize else MobileNet_V3_Large_Weights).verify(weights)
+
+    inverted_residual_setting, last_channel = _mobilenet_v3_conf("mobilenet_v3_large", **kwargs)
+    return _mobilenet_v3_model(inverted_residual_setting, last_channel, weights, progress, quantize, **kwargs)
diff --git a/shared/package-files/3d/3d56b03c20d15e2ccf00b42aabfb24691f27bff588ef066f82e5bc29a53e527e/linalg_lstsq_cpu_dispatch.h b/shared/package-files/3d/3d56b03c20d15e2ccf00b42aabfb24691f27bff588ef066f82e5bc29a53e527e/linalg_lstsq_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4f803b9a9fd41aeee20374f382d5f0de95ab70e0
--- /dev/null
+++ b/shared/package-files/3d/3d56b03c20d15e2ccf00b42aabfb24691f27bff588ef066f82e5bc29a53e527e/linalg_lstsq_cpu_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple linalg_lstsq_out(at::Tensor & solution, at::Tensor & residuals, at::Tensor & rank, at::Tensor & singular_values, const at::Tensor & self, const at::Tensor & b, c10::optional rcond=c10::nullopt, c10::optional driver=c10::nullopt);
+TORCH_API ::std::tuple linalg_lstsq_outf(const at::Tensor & self, const at::Tensor & b, c10::optional rcond, c10::optional driver, at::Tensor & solution, at::Tensor & residuals, at::Tensor & rank, at::Tensor & singular_values);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/3d/3d58119237cfcc6cf62333f517e726f2791252deb92e358056a14956ce8e82dc/macos.py b/shared/package-files/3d/3d58119237cfcc6cf62333f517e726f2791252deb92e358056a14956ce8e82dc/macos.py
new file mode 100644
index 0000000000000000000000000000000000000000..7800fe101320efc6220071cc5fdea6ed7a30d4b7
--- /dev/null
+++ b/shared/package-files/3d/3d58119237cfcc6cf62333f517e726f2791252deb92e358056a14956ce8e82dc/macos.py
@@ -0,0 +1,101 @@
+"""macOS."""
+from __future__ import annotations
+
+import os.path
+
+from .api import PlatformDirsABC
+
+
+class MacOS(PlatformDirsABC):
+    """
+    Platform directories for the macOS operating system. Follows the guidance from `Apple documentation
+    `_.
+    Makes use of the `appname `,
+    `version `,
+    `ensure_exists `.
+    """
+
+    @property
+    def user_data_dir(self) -> str:
+        """:return: data directory tied to the user, e.g. ``~/Library/Application Support/$appname/$version``"""
+        return self._append_app_name_and_version(os.path.expanduser("~/Library/Application Support"))  # noqa: PTH111
+
+    @property
+    def site_data_dir(self) -> str:
+        """:return: data directory shared by users, e.g. ``/Library/Application Support/$appname/$version``"""
+        return self._append_app_name_and_version("/Library/Application Support")
+
+    @property
+    def user_config_dir(self) -> str:
+        """:return: config directory tied to the user, same as `user_data_dir`"""
+        return self.user_data_dir
+
+    @property
+    def site_config_dir(self) -> str:
+        """:return: config directory shared by the users, same as `site_data_dir`"""
+        return self.site_data_dir
+
+    @property
+    def user_cache_dir(self) -> str:
+        """:return: cache directory tied to the user, e.g. ``~/Library/Caches/$appname/$version``"""
+        return self._append_app_name_and_version(os.path.expanduser("~/Library/Caches"))  # noqa: PTH111
+
+    @property
+    def site_cache_dir(self) -> str:
+        """:return: cache directory shared by users, e.g. ``/Library/Caches/$appname/$version``"""
+        return self._append_app_name_and_version("/Library/Caches")
+
+    @property
+    def user_state_dir(self) -> str:
+        """:return: state directory tied to the user, same as `user_data_dir`"""
+        return self.user_data_dir
+
+    @property
+    def user_log_dir(self) -> str:
+        """:return: log directory tied to the user, e.g. ``~/Library/Logs/$appname/$version``"""
+        return self._append_app_name_and_version(os.path.expanduser("~/Library/Logs"))  # noqa: PTH111
+
+    @property
+    def user_documents_dir(self) -> str:
+        """:return: documents directory tied to the user, e.g. ``~/Documents``"""
+        return os.path.expanduser("~/Documents")  # noqa: PTH111
+
+    @property
+    def user_downloads_dir(self) -> str:
+        """:return: downloads directory tied to the user, e.g. ``~/Downloads``"""
+        return os.path.expanduser("~/Downloads")  # noqa: PTH111
+
+    @property
+    def user_pictures_dir(self) -> str:
+        """:return: pictures directory tied to the user, e.g. ``~/Pictures``"""
+        return os.path.expanduser("~/Pictures")  # noqa: PTH111
+
+    @property
+    def user_videos_dir(self) -> str:
+        """:return: videos directory tied to the user, e.g. ``~/Movies``"""
+        return os.path.expanduser("~/Movies")  # noqa: PTH111
+
+    @property
+    def user_music_dir(self) -> str:
+        """:return: music directory tied to the user, e.g. ``~/Music``"""
+        return os.path.expanduser("~/Music")  # noqa: PTH111
+
+    @property
+    def user_desktop_dir(self) -> str:
+        """:return: desktop directory tied to the user, e.g. ``~/Desktop``"""
+        return os.path.expanduser("~/Desktop")  # noqa: PTH111
+
+    @property
+    def user_runtime_dir(self) -> str:
+        """:return: runtime directory tied to the user, e.g. ``~/Library/Caches/TemporaryItems/$appname/$version``"""
+        return self._append_app_name_and_version(os.path.expanduser("~/Library/Caches/TemporaryItems"))  # noqa: PTH111
+
+    @property
+    def site_runtime_dir(self) -> str:
+        """:return: runtime directory shared by users, same as `user_runtime_dir`"""
+        return self.user_runtime_dir
+
+
+__all__ = [
+    "MacOS",
+]
diff --git a/shared/package-files/3d/3d5ca428163995da81117607ef4b4df0a130f99004bddfba1e48e8cdcaf9be41/_log_softmax_backward_data_native.h b/shared/package-files/3d/3d5ca428163995da81117607ef4b4df0a130f99004bddfba1e48e8cdcaf9be41/_log_softmax_backward_data_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..b36bba1c41a9978d510b739a62b65d9e089fcf64
--- /dev/null
+++ b/shared/package-files/3d/3d5ca428163995da81117607ef4b4df0a130f99004bddfba1e48e8cdcaf9be41/_log_softmax_backward_data_native.h
@@ -0,0 +1,26 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+namespace at {
+namespace native {
+struct TORCH_API structured_log_softmax_backward_cpu_out : public at::meta::structured__log_softmax_backward_data {
+void impl(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, const at::Tensor & out);
+};
+struct TORCH_API structured_log_softmax_backward_cuda_out : public at::meta::structured__log_softmax_backward_data {
+void impl(const at::Tensor & grad_output, const at::Tensor & output, int64_t dim, at::ScalarType input_dtype, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/3d/3d5d3ea9e1e3c88fb4772d2cb08b7b57fe6643b15d98a4a080d0347bbbbaa511/planarity.py b/shared/package-files/3d/3d5d3ea9e1e3c88fb4772d2cb08b7b57fe6643b15d98a4a080d0347bbbbaa511/planarity.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a5e4af89e9ebde6a2e2eb0de7bef885f1101b9a
--- /dev/null
+++ b/shared/package-files/3d/3d5d3ea9e1e3c88fb4772d2cb08b7b57fe6643b15d98a4a080d0347bbbbaa511/planarity.py
@@ -0,0 +1,1174 @@
+from collections import defaultdict
+
+import networkx as nx
+
+__all__ = ["check_planarity", "is_planar", "PlanarEmbedding"]
+
+
+def is_planar(G):
+    """Returns True if and only if `G` is planar.
+
+    A graph is *planar* iff it can be drawn in a plane without
+    any edge intersections.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    bool
+       Whether the graph is planar.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2)])
+    >>> nx.is_planar(G)
+    True
+    >>> nx.is_planar(nx.complete_graph(5))
+    False
+
+    See Also
+    --------
+    check_planarity :
+        Check if graph is planar *and* return a `PlanarEmbedding` instance if True.
+    """
+
+    return check_planarity(G, counterexample=False)[0]
+
+
+def check_planarity(G, counterexample=False):
+    """Check if a graph is planar and return a counterexample or an embedding.
+
+    A graph is planar iff it can be drawn in a plane without
+    any edge intersections.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+    counterexample : bool
+        A Kuratowski subgraph (to proof non planarity) is only returned if set
+        to true.
+
+    Returns
+    -------
+    (is_planar, certificate) : (bool, NetworkX graph) tuple
+        is_planar is true if the graph is planar.
+        If the graph is planar `certificate` is a PlanarEmbedding
+        otherwise it is a Kuratowski subgraph.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2)])
+    >>> is_planar, P = nx.check_planarity(G)
+    >>> print(is_planar)
+    True
+
+    When `G` is planar, a `PlanarEmbedding` instance is returned:
+
+    >>> P.get_data()
+    {0: [1, 2], 1: [0], 2: [0]}
+
+    Notes
+    -----
+    A (combinatorial) embedding consists of cyclic orderings of the incident
+    edges at each vertex. Given such an embedding there are multiple approaches
+    discussed in literature to drawing the graph (subject to various
+    constraints, e.g. integer coordinates), see e.g. [2].
+
+    The planarity check algorithm and extraction of the combinatorial embedding
+    is based on the Left-Right Planarity Test [1].
+
+    A counterexample is only generated if the corresponding parameter is set,
+    because the complexity of the counterexample generation is higher.
+
+    See also
+    --------
+    is_planar :
+        Check for planarity without creating a `PlanarEmbedding` or counterexample.
+
+    References
+    ----------
+    .. [1] Ulrik Brandes:
+        The Left-Right Planarity Test
+        2009
+        http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.9208
+    .. [2] Takao Nishizeki, Md Saidur Rahman:
+        Planar graph drawing
+        Lecture Notes Series on Computing: Volume 12
+        2004
+    """
+
+    planarity_state = LRPlanarity(G)
+    embedding = planarity_state.lr_planarity()
+    if embedding is None:
+        # graph is not planar
+        if counterexample:
+            return False, get_counterexample(G)
+        else:
+            return False, None
+    else:
+        # graph is planar
+        return True, embedding
+
+
+def check_planarity_recursive(G, counterexample=False):
+    """Recursive version of :meth:`check_planarity`."""
+    planarity_state = LRPlanarity(G)
+    embedding = planarity_state.lr_planarity_recursive()
+    if embedding is None:
+        # graph is not planar
+        if counterexample:
+            return False, get_counterexample_recursive(G)
+        else:
+            return False, None
+    else:
+        # graph is planar
+        return True, embedding
+
+
+def get_counterexample(G):
+    """Obtains a Kuratowski subgraph.
+
+    Raises nx.NetworkXException if G is planar.
+
+    The function removes edges such that the graph is still not planar.
+    At some point the removal of any edge would make the graph planar.
+    This subgraph must be a Kuratowski subgraph.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    Returns
+    -------
+    subgraph : NetworkX graph
+        A Kuratowski subgraph that proves that G is not planar.
+
+    """
+    # copy graph
+    G = nx.Graph(G)
+
+    if check_planarity(G)[0]:
+        raise nx.NetworkXException("G is planar - no counter example.")
+
+    # find Kuratowski subgraph
+    subgraph = nx.Graph()
+    for u in G:
+        nbrs = list(G[u])
+        for v in nbrs:
+            G.remove_edge(u, v)
+            if check_planarity(G)[0]:
+                G.add_edge(u, v)
+                subgraph.add_edge(u, v)
+
+    return subgraph
+
+
+def get_counterexample_recursive(G):
+    """Recursive version of :meth:`get_counterexample`."""
+
+    # copy graph
+    G = nx.Graph(G)
+
+    if check_planarity_recursive(G)[0]:
+        raise nx.NetworkXException("G is planar - no counter example.")
+
+    # find Kuratowski subgraph
+    subgraph = nx.Graph()
+    for u in G:
+        nbrs = list(G[u])
+        for v in nbrs:
+            G.remove_edge(u, v)
+            if check_planarity_recursive(G)[0]:
+                G.add_edge(u, v)
+                subgraph.add_edge(u, v)
+
+    return subgraph
+
+
+class Interval:
+    """Represents a set of return edges.
+
+    All return edges in an interval induce a same constraint on the contained
+    edges, which means that all edges must either have a left orientation or
+    all edges must have a right orientation.
+    """
+
+    def __init__(self, low=None, high=None):
+        self.low = low
+        self.high = high
+
+    def empty(self):
+        """Check if the interval is empty"""
+        return self.low is None and self.high is None
+
+    def copy(self):
+        """Returns a copy of this interval"""
+        return Interval(self.low, self.high)
+
+    def conflicting(self, b, planarity_state):
+        """Returns True if interval I conflicts with edge b"""
+        return (
+            not self.empty()
+            and planarity_state.lowpt[self.high] > planarity_state.lowpt[b]
+        )
+
+
+class ConflictPair:
+    """Represents a different constraint between two intervals.
+
+    The edges in the left interval must have a different orientation than
+    the one in the right interval.
+    """
+
+    def __init__(self, left=Interval(), right=Interval()):
+        self.left = left
+        self.right = right
+
+    def swap(self):
+        """Swap left and right intervals"""
+        temp = self.left
+        self.left = self.right
+        self.right = temp
+
+    def lowest(self, planarity_state):
+        """Returns the lowest lowpoint of a conflict pair"""
+        if self.left.empty():
+            return planarity_state.lowpt[self.right.low]
+        if self.right.empty():
+            return planarity_state.lowpt[self.left.low]
+        return min(
+            planarity_state.lowpt[self.left.low], planarity_state.lowpt[self.right.low]
+        )
+
+
+def top_of_stack(l):
+    """Returns the element on top of the stack."""
+    if not l:
+        return None
+    return l[-1]
+
+
+class LRPlanarity:
+    """A class to maintain the state during planarity check."""
+
+    __slots__ = [
+        "G",
+        "roots",
+        "height",
+        "lowpt",
+        "lowpt2",
+        "nesting_depth",
+        "parent_edge",
+        "DG",
+        "adjs",
+        "ordered_adjs",
+        "ref",
+        "side",
+        "S",
+        "stack_bottom",
+        "lowpt_edge",
+        "left_ref",
+        "right_ref",
+        "embedding",
+    ]
+
+    def __init__(self, G):
+        # copy G without adding self-loops
+        self.G = nx.Graph()
+        self.G.add_nodes_from(G.nodes)
+        for e in G.edges:
+            if e[0] != e[1]:
+                self.G.add_edge(e[0], e[1])
+
+        self.roots = []
+
+        # distance from tree root
+        self.height = defaultdict(lambda: None)
+
+        self.lowpt = {}  # height of lowest return point of an edge
+        self.lowpt2 = {}  # height of second lowest return point
+        self.nesting_depth = {}  # for nesting order
+
+        # None -> missing edge
+        self.parent_edge = defaultdict(lambda: None)
+
+        # oriented DFS graph
+        self.DG = nx.DiGraph()
+        self.DG.add_nodes_from(G.nodes)
+
+        self.adjs = {}
+        self.ordered_adjs = {}
+
+        self.ref = defaultdict(lambda: None)
+        self.side = defaultdict(lambda: 1)
+
+        # stack of conflict pairs
+        self.S = []
+        self.stack_bottom = {}
+        self.lowpt_edge = {}
+
+        self.left_ref = {}
+        self.right_ref = {}
+
+        self.embedding = PlanarEmbedding()
+
+    def lr_planarity(self):
+        """Execute the LR planarity test.
+
+        Returns
+        -------
+        embedding : dict
+            If the graph is planar an embedding is returned. Otherwise None.
+        """
+        if self.G.order() > 2 and self.G.size() > 3 * self.G.order() - 6:
+            # graph is not planar
+            return None
+
+        # make adjacency lists for dfs
+        for v in self.G:
+            self.adjs[v] = list(self.G[v])
+
+        # orientation of the graph by depth first search traversal
+        for v in self.G:
+            if self.height[v] is None:
+                self.height[v] = 0
+                self.roots.append(v)
+                self.dfs_orientation(v)
+
+        # Free no longer used variables
+        self.G = None
+        self.lowpt2 = None
+        self.adjs = None
+
+        # testing
+        for v in self.DG:  # sort the adjacency lists by nesting depth
+            # note: this sorting leads to non linear time
+            self.ordered_adjs[v] = sorted(
+                self.DG[v], key=lambda x: self.nesting_depth[(v, x)]
+            )
+        for v in self.roots:
+            if not self.dfs_testing(v):
+                return None
+
+        # Free no longer used variables
+        self.height = None
+        self.lowpt = None
+        self.S = None
+        self.stack_bottom = None
+        self.lowpt_edge = None
+
+        for e in self.DG.edges:
+            self.nesting_depth[e] = self.sign(e) * self.nesting_depth[e]
+
+        self.embedding.add_nodes_from(self.DG.nodes)
+        for v in self.DG:
+            # sort the adjacency lists again
+            self.ordered_adjs[v] = sorted(
+                self.DG[v], key=lambda x: self.nesting_depth[(v, x)]
+            )
+            # initialize the embedding
+            previous_node = None
+            for w in self.ordered_adjs[v]:
+                self.embedding.add_half_edge_cw(v, w, previous_node)
+                previous_node = w
+
+        # Free no longer used variables
+        self.DG = None
+        self.nesting_depth = None
+        self.ref = None
+
+        # compute the complete embedding
+        for v in self.roots:
+            self.dfs_embedding(v)
+
+        # Free no longer used variables
+        self.roots = None
+        self.parent_edge = None
+        self.ordered_adjs = None
+        self.left_ref = None
+        self.right_ref = None
+        self.side = None
+
+        return self.embedding
+
+    def lr_planarity_recursive(self):
+        """Recursive version of :meth:`lr_planarity`."""
+        if self.G.order() > 2 and self.G.size() > 3 * self.G.order() - 6:
+            # graph is not planar
+            return None
+
+        # orientation of the graph by depth first search traversal
+        for v in self.G:
+            if self.height[v] is None:
+                self.height[v] = 0
+                self.roots.append(v)
+                self.dfs_orientation_recursive(v)
+
+        # Free no longer used variable
+        self.G = None
+
+        # testing
+        for v in self.DG:  # sort the adjacency lists by nesting depth
+            # note: this sorting leads to non linear time
+            self.ordered_adjs[v] = sorted(
+                self.DG[v], key=lambda x: self.nesting_depth[(v, x)]
+            )
+        for v in self.roots:
+            if not self.dfs_testing_recursive(v):
+                return None
+
+        for e in self.DG.edges:
+            self.nesting_depth[e] = self.sign_recursive(e) * self.nesting_depth[e]
+
+        self.embedding.add_nodes_from(self.DG.nodes)
+        for v in self.DG:
+            # sort the adjacency lists again
+            self.ordered_adjs[v] = sorted(
+                self.DG[v], key=lambda x: self.nesting_depth[(v, x)]
+            )
+            # initialize the embedding
+            previous_node = None
+            for w in self.ordered_adjs[v]:
+                self.embedding.add_half_edge_cw(v, w, previous_node)
+                previous_node = w
+
+        # compute the complete embedding
+        for v in self.roots:
+            self.dfs_embedding_recursive(v)
+
+        return self.embedding
+
+    def dfs_orientation(self, v):
+        """Orient the graph by DFS, compute lowpoints and nesting order."""
+        # the recursion stack
+        dfs_stack = [v]
+        # index of next edge to handle in adjacency list of each node
+        ind = defaultdict(lambda: 0)
+        # boolean to indicate whether to skip the initial work for an edge
+        skip_init = defaultdict(lambda: False)
+
+        while dfs_stack:
+            v = dfs_stack.pop()
+            e = self.parent_edge[v]
+
+            for w in self.adjs[v][ind[v] :]:
+                vw = (v, w)
+
+                if not skip_init[vw]:
+                    if (v, w) in self.DG.edges or (w, v) in self.DG.edges:
+                        ind[v] += 1
+                        continue  # the edge was already oriented
+
+                    self.DG.add_edge(v, w)  # orient the edge
+
+                    self.lowpt[vw] = self.height[v]
+                    self.lowpt2[vw] = self.height[v]
+                    if self.height[w] is None:  # (v, w) is a tree edge
+                        self.parent_edge[w] = vw
+                        self.height[w] = self.height[v] + 1
+
+                        dfs_stack.append(v)  # revisit v after finishing w
+                        dfs_stack.append(w)  # visit w next
+                        skip_init[vw] = True  # don't redo this block
+                        break  # handle next node in dfs_stack (i.e. w)
+                    else:  # (v, w) is a back edge
+                        self.lowpt[vw] = self.height[w]
+
+                # determine nesting graph
+                self.nesting_depth[vw] = 2 * self.lowpt[vw]
+                if self.lowpt2[vw] < self.height[v]:  # chordal
+                    self.nesting_depth[vw] += 1
+
+                # update lowpoints of parent edge e
+                if e is not None:
+                    if self.lowpt[vw] < self.lowpt[e]:
+                        self.lowpt2[e] = min(self.lowpt[e], self.lowpt2[vw])
+                        self.lowpt[e] = self.lowpt[vw]
+                    elif self.lowpt[vw] > self.lowpt[e]:
+                        self.lowpt2[e] = min(self.lowpt2[e], self.lowpt[vw])
+                    else:
+                        self.lowpt2[e] = min(self.lowpt2[e], self.lowpt2[vw])
+
+                ind[v] += 1
+
+    def dfs_orientation_recursive(self, v):
+        """Recursive version of :meth:`dfs_orientation`."""
+        e = self.parent_edge[v]
+        for w in self.G[v]:
+            if (v, w) in self.DG.edges or (w, v) in self.DG.edges:
+                continue  # the edge was already oriented
+            vw = (v, w)
+            self.DG.add_edge(v, w)  # orient the edge
+
+            self.lowpt[vw] = self.height[v]
+            self.lowpt2[vw] = self.height[v]
+            if self.height[w] is None:  # (v, w) is a tree edge
+                self.parent_edge[w] = vw
+                self.height[w] = self.height[v] + 1
+                self.dfs_orientation_recursive(w)
+            else:  # (v, w) is a back edge
+                self.lowpt[vw] = self.height[w]
+
+            # determine nesting graph
+            self.nesting_depth[vw] = 2 * self.lowpt[vw]
+            if self.lowpt2[vw] < self.height[v]:  # chordal
+                self.nesting_depth[vw] += 1
+
+            # update lowpoints of parent edge e
+            if e is not None:
+                if self.lowpt[vw] < self.lowpt[e]:
+                    self.lowpt2[e] = min(self.lowpt[e], self.lowpt2[vw])
+                    self.lowpt[e] = self.lowpt[vw]
+                elif self.lowpt[vw] > self.lowpt[e]:
+                    self.lowpt2[e] = min(self.lowpt2[e], self.lowpt[vw])
+                else:
+                    self.lowpt2[e] = min(self.lowpt2[e], self.lowpt2[vw])
+
+    def dfs_testing(self, v):
+        """Test for LR partition."""
+        # the recursion stack
+        dfs_stack = [v]
+        # index of next edge to handle in adjacency list of each node
+        ind = defaultdict(lambda: 0)
+        # boolean to indicate whether to skip the initial work for an edge
+        skip_init = defaultdict(lambda: False)
+
+        while dfs_stack:
+            v = dfs_stack.pop()
+            e = self.parent_edge[v]
+            # to indicate whether to skip the final block after the for loop
+            skip_final = False
+
+            for w in self.ordered_adjs[v][ind[v] :]:
+                ei = (v, w)
+
+                if not skip_init[ei]:
+                    self.stack_bottom[ei] = top_of_stack(self.S)
+
+                    if ei == self.parent_edge[w]:  # tree edge
+                        dfs_stack.append(v)  # revisit v after finishing w
+                        dfs_stack.append(w)  # visit w next
+                        skip_init[ei] = True  # don't redo this block
+                        skip_final = True  # skip final work after breaking
+                        break  # handle next node in dfs_stack (i.e. w)
+                    else:  # back edge
+                        self.lowpt_edge[ei] = ei
+                        self.S.append(ConflictPair(right=Interval(ei, ei)))
+
+                # integrate new return edges
+                if self.lowpt[ei] < self.height[v]:
+                    if w == self.ordered_adjs[v][0]:  # e_i has return edge
+                        self.lowpt_edge[e] = self.lowpt_edge[ei]
+                    else:  # add constraints of e_i
+                        if not self.add_constraints(ei, e):
+                            # graph is not planar
+                            return False
+
+                ind[v] += 1
+
+            if not skip_final:
+                # remove back edges returning to parent
+                if e is not None:  # v isn't root
+                    self.remove_back_edges(e)
+
+        return True
+
+    def dfs_testing_recursive(self, v):
+        """Recursive version of :meth:`dfs_testing`."""
+        e = self.parent_edge[v]
+        for w in self.ordered_adjs[v]:
+            ei = (v, w)
+            self.stack_bottom[ei] = top_of_stack(self.S)
+            if ei == self.parent_edge[w]:  # tree edge
+                if not self.dfs_testing_recursive(w):
+                    return False
+            else:  # back edge
+                self.lowpt_edge[ei] = ei
+                self.S.append(ConflictPair(right=Interval(ei, ei)))
+
+            # integrate new return edges
+            if self.lowpt[ei] < self.height[v]:
+                if w == self.ordered_adjs[v][0]:  # e_i has return edge
+                    self.lowpt_edge[e] = self.lowpt_edge[ei]
+                else:  # add constraints of e_i
+                    if not self.add_constraints(ei, e):
+                        # graph is not planar
+                        return False
+
+        # remove back edges returning to parent
+        if e is not None:  # v isn't root
+            self.remove_back_edges(e)
+        return True
+
+    def add_constraints(self, ei, e):
+        P = ConflictPair()
+        # merge return edges of e_i into P.right
+        while True:
+            Q = self.S.pop()
+            if not Q.left.empty():
+                Q.swap()
+            if not Q.left.empty():  # not planar
+                return False
+            if self.lowpt[Q.right.low] > self.lowpt[e]:
+                # merge intervals
+                if P.right.empty():  # topmost interval
+                    P.right = Q.right.copy()
+                else:
+                    self.ref[P.right.low] = Q.right.high
+                P.right.low = Q.right.low
+            else:  # align
+                self.ref[Q.right.low] = self.lowpt_edge[e]
+            if top_of_stack(self.S) == self.stack_bottom[ei]:
+                break
+        # merge conflicting return edges of e_1,...,e_i-1 into P.L
+        while top_of_stack(self.S).left.conflicting(ei, self) or top_of_stack(
+            self.S
+        ).right.conflicting(ei, self):
+            Q = self.S.pop()
+            if Q.right.conflicting(ei, self):
+                Q.swap()
+            if Q.right.conflicting(ei, self):  # not planar
+                return False
+            # merge interval below lowpt(e_i) into P.R
+            self.ref[P.right.low] = Q.right.high
+            if Q.right.low is not None:
+                P.right.low = Q.right.low
+
+            if P.left.empty():  # topmost interval
+                P.left = Q.left.copy()
+            else:
+                self.ref[P.left.low] = Q.left.high
+            P.left.low = Q.left.low
+
+        if not (P.left.empty() and P.right.empty()):
+            self.S.append(P)
+        return True
+
+    def remove_back_edges(self, e):
+        u = e[0]
+        # trim back edges ending at parent u
+        # drop entire conflict pairs
+        while self.S and top_of_stack(self.S).lowest(self) == self.height[u]:
+            P = self.S.pop()
+            if P.left.low is not None:
+                self.side[P.left.low] = -1
+
+        if self.S:  # one more conflict pair to consider
+            P = self.S.pop()
+            # trim left interval
+            while P.left.high is not None and P.left.high[1] == u:
+                P.left.high = self.ref[P.left.high]
+            if P.left.high is None and P.left.low is not None:
+                # just emptied
+                self.ref[P.left.low] = P.right.low
+                self.side[P.left.low] = -1
+                P.left.low = None
+            # trim right interval
+            while P.right.high is not None and P.right.high[1] == u:
+                P.right.high = self.ref[P.right.high]
+            if P.right.high is None and P.right.low is not None:
+                # just emptied
+                self.ref[P.right.low] = P.left.low
+                self.side[P.right.low] = -1
+                P.right.low = None
+            self.S.append(P)
+
+        # side of e is side of a highest return edge
+        if self.lowpt[e] < self.height[u]:  # e has return edge
+            hl = top_of_stack(self.S).left.high
+            hr = top_of_stack(self.S).right.high
+
+            if hl is not None and (hr is None or self.lowpt[hl] > self.lowpt[hr]):
+                self.ref[e] = hl
+            else:
+                self.ref[e] = hr
+
+    def dfs_embedding(self, v):
+        """Completes the embedding."""
+        # the recursion stack
+        dfs_stack = [v]
+        # index of next edge to handle in adjacency list of each node
+        ind = defaultdict(lambda: 0)
+
+        while dfs_stack:
+            v = dfs_stack.pop()
+
+            for w in self.ordered_adjs[v][ind[v] :]:
+                ind[v] += 1
+                ei = (v, w)
+
+                if ei == self.parent_edge[w]:  # tree edge
+                    self.embedding.add_half_edge_first(w, v)
+                    self.left_ref[v] = w
+                    self.right_ref[v] = w
+
+                    dfs_stack.append(v)  # revisit v after finishing w
+                    dfs_stack.append(w)  # visit w next
+                    break  # handle next node in dfs_stack (i.e. w)
+                else:  # back edge
+                    if self.side[ei] == 1:
+                        self.embedding.add_half_edge_cw(w, v, self.right_ref[w])
+                    else:
+                        self.embedding.add_half_edge_ccw(w, v, self.left_ref[w])
+                        self.left_ref[w] = v
+
+    def dfs_embedding_recursive(self, v):
+        """Recursive version of :meth:`dfs_embedding`."""
+        for w in self.ordered_adjs[v]:
+            ei = (v, w)
+            if ei == self.parent_edge[w]:  # tree edge
+                self.embedding.add_half_edge_first(w, v)
+                self.left_ref[v] = w
+                self.right_ref[v] = w
+                self.dfs_embedding_recursive(w)
+            else:  # back edge
+                if self.side[ei] == 1:
+                    # place v directly after right_ref[w] in embed. list of w
+                    self.embedding.add_half_edge_cw(w, v, self.right_ref[w])
+                else:
+                    # place v directly before left_ref[w] in embed. list of w
+                    self.embedding.add_half_edge_ccw(w, v, self.left_ref[w])
+                    self.left_ref[w] = v
+
+    def sign(self, e):
+        """Resolve the relative side of an edge to the absolute side."""
+        # the recursion stack
+        dfs_stack = [e]
+        # dict to remember reference edges
+        old_ref = defaultdict(lambda: None)
+
+        while dfs_stack:
+            e = dfs_stack.pop()
+
+            if self.ref[e] is not None:
+                dfs_stack.append(e)  # revisit e after finishing self.ref[e]
+                dfs_stack.append(self.ref[e])  # visit self.ref[e] next
+                old_ref[e] = self.ref[e]  # remember value of self.ref[e]
+                self.ref[e] = None
+            else:
+                self.side[e] *= self.side[old_ref[e]]
+
+        return self.side[e]
+
+    def sign_recursive(self, e):
+        """Recursive version of :meth:`sign`."""
+        if self.ref[e] is not None:
+            self.side[e] = self.side[e] * self.sign_recursive(self.ref[e])
+            self.ref[e] = None
+        return self.side[e]
+
+
+class PlanarEmbedding(nx.DiGraph):
+    """Represents a planar graph with its planar embedding.
+
+    The planar embedding is given by a `combinatorial embedding
+    `_.
+
+    .. note:: `check_planarity` is the preferred way to check if a graph is planar.
+
+    **Neighbor ordering:**
+
+    In comparison to a usual graph structure, the embedding also stores the
+    order of all neighbors for every vertex.
+    The order of the neighbors can be given in clockwise (cw) direction or
+    counterclockwise (ccw) direction. This order is stored as edge attributes
+    in the underlying directed graph. For the edge (u, v) the edge attribute
+    'cw' is set to the neighbor of u that follows immediately after v in
+    clockwise direction.
+
+    In order for a PlanarEmbedding to be valid it must fulfill multiple
+    conditions. It is possible to check if these conditions are fulfilled with
+    the method :meth:`check_structure`.
+    The conditions are:
+
+    * Edges must go in both directions (because the edge attributes differ)
+    * Every edge must have a 'cw' and 'ccw' attribute which corresponds to a
+      correct planar embedding.
+    * A node with non zero degree must have a node attribute 'first_nbr'.
+
+    As long as a PlanarEmbedding is invalid only the following methods should
+    be called:
+
+    * :meth:`add_half_edge_ccw`
+    * :meth:`add_half_edge_cw`
+    * :meth:`connect_components`
+    * :meth:`add_half_edge_first`
+
+    Even though the graph is a subclass of nx.DiGraph, it can still be used
+    for algorithms that require undirected graphs, because the method
+    :meth:`is_directed` is overridden. This is possible, because a valid
+    PlanarGraph must have edges in both directions.
+
+    **Half edges:**
+
+    In methods like `add_half_edge_ccw` the term "half-edge" is used, which is
+    a term that is used in `doubly connected edge lists
+    `_. It is used
+    to emphasize that the edge is only in one direction and there exists
+    another half-edge in the opposite direction.
+    While conventional edges always have two faces (including outer face) next
+    to them, it is possible to assign each half-edge *exactly one* face.
+    For a half-edge (u, v) that is orientated such that u is below v then the
+    face that belongs to (u, v) is to the right of this half-edge.
+
+    See Also
+    --------
+    is_planar :
+        Preferred way to check if an existing graph is planar.
+
+    check_planarity :
+        A convenient way to create a `PlanarEmbedding`. If not planar,
+        it returns a subgraph that shows this.
+
+    Examples
+    --------
+
+    Create an embedding of a star graph (compare `nx.star_graph(3)`):
+
+    >>> G = nx.PlanarEmbedding()
+    >>> G.add_half_edge_cw(0, 1, None)
+    >>> G.add_half_edge_cw(0, 2, 1)
+    >>> G.add_half_edge_cw(0, 3, 2)
+    >>> G.add_half_edge_cw(1, 0, None)
+    >>> G.add_half_edge_cw(2, 0, None)
+    >>> G.add_half_edge_cw(3, 0, None)
+
+    Alternatively the same embedding can also be defined in counterclockwise
+    orientation. The following results in exactly the same PlanarEmbedding:
+
+    >>> G = nx.PlanarEmbedding()
+    >>> G.add_half_edge_ccw(0, 1, None)
+    >>> G.add_half_edge_ccw(0, 3, 1)
+    >>> G.add_half_edge_ccw(0, 2, 3)
+    >>> G.add_half_edge_ccw(1, 0, None)
+    >>> G.add_half_edge_ccw(2, 0, None)
+    >>> G.add_half_edge_ccw(3, 0, None)
+
+    After creating a graph, it is possible to validate that the PlanarEmbedding
+    object is correct:
+
+    >>> G.check_structure()
+
+    """
+
+    def get_data(self):
+        """Converts the adjacency structure into a better readable structure.
+
+        Returns
+        -------
+        embedding : dict
+            A dict mapping all nodes to a list of neighbors sorted in
+            clockwise order.
+
+        See Also
+        --------
+        set_data
+
+        """
+        embedding = {}
+        for v in self:
+            embedding[v] = list(self.neighbors_cw_order(v))
+        return embedding
+
+    def set_data(self, data):
+        """Inserts edges according to given sorted neighbor list.
+
+        The input format is the same as the output format of get_data().
+
+        Parameters
+        ----------
+        data : dict
+            A dict mapping all nodes to a list of neighbors sorted in
+            clockwise order.
+
+        See Also
+        --------
+        get_data
+
+        """
+        for v in data:
+            for w in reversed(data[v]):
+                self.add_half_edge_first(v, w)
+
+    def neighbors_cw_order(self, v):
+        """Generator for the neighbors of v in clockwise order.
+
+        Parameters
+        ----------
+        v : node
+
+        Yields
+        ------
+        node
+
+        """
+        if len(self[v]) == 0:
+            # v has no neighbors
+            return
+        start_node = self.nodes[v]["first_nbr"]
+        yield start_node
+        current_node = self[v][start_node]["cw"]
+        while start_node != current_node:
+            yield current_node
+            current_node = self[v][current_node]["cw"]
+
+    def check_structure(self):
+        """Runs without exceptions if this object is valid.
+
+        Checks that the following properties are fulfilled:
+
+        * Edges go in both directions (because the edge attributes differ).
+        * Every edge has a 'cw' and 'ccw' attribute which corresponds to a
+          correct planar embedding.
+        * A node with a degree larger than 0 has a node attribute 'first_nbr'.
+
+        Running this method verifies that the underlying Graph must be planar.
+
+        Raises
+        ------
+        NetworkXException
+            This exception is raised with a short explanation if the
+            PlanarEmbedding is invalid.
+        """
+        # Check fundamental structure
+        for v in self:
+            try:
+                sorted_nbrs = set(self.neighbors_cw_order(v))
+            except KeyError as err:
+                msg = f"Bad embedding. Missing orientation for a neighbor of {v}"
+                raise nx.NetworkXException(msg) from err
+
+            unsorted_nbrs = set(self[v])
+            if sorted_nbrs != unsorted_nbrs:
+                msg = "Bad embedding. Edge orientations not set correctly."
+                raise nx.NetworkXException(msg)
+            for w in self[v]:
+                # Check if opposite half-edge exists
+                if not self.has_edge(w, v):
+                    msg = "Bad embedding. Opposite half-edge is missing."
+                    raise nx.NetworkXException(msg)
+
+        # Check planarity
+        counted_half_edges = set()
+        for component in nx.connected_components(self):
+            if len(component) == 1:
+                # Don't need to check single node component
+                continue
+            num_nodes = len(component)
+            num_half_edges = 0
+            num_faces = 0
+            for v in component:
+                for w in self.neighbors_cw_order(v):
+                    num_half_edges += 1
+                    if (v, w) not in counted_half_edges:
+                        # We encountered a new face
+                        num_faces += 1
+                        # Mark all half-edges belonging to this face
+                        self.traverse_face(v, w, counted_half_edges)
+            num_edges = num_half_edges // 2  # num_half_edges is even
+            if num_nodes - num_edges + num_faces != 2:
+                # The result does not match Euler's formula
+                msg = "Bad embedding. The graph does not match Euler's formula"
+                raise nx.NetworkXException(msg)
+
+    def add_half_edge_ccw(self, start_node, end_node, reference_neighbor):
+        """Adds a half-edge from start_node to end_node.
+
+        The half-edge is added counter clockwise next to the existing half-edge
+        (start_node, reference_neighbor).
+
+        Parameters
+        ----------
+        start_node : node
+            Start node of inserted edge.
+        end_node : node
+            End node of inserted edge.
+        reference_neighbor: node
+            End node of reference edge.
+
+        Raises
+        ------
+        NetworkXException
+            If the reference_neighbor does not exist.
+
+        See Also
+        --------
+        add_half_edge_cw
+        connect_components
+        add_half_edge_first
+
+        """
+        if reference_neighbor is None:
+            # The start node has no neighbors
+            self.add_edge(start_node, end_node)  # Add edge to graph
+            self[start_node][end_node]["cw"] = end_node
+            self[start_node][end_node]["ccw"] = end_node
+            self.nodes[start_node]["first_nbr"] = end_node
+        else:
+            ccw_reference = self[start_node][reference_neighbor]["ccw"]
+            self.add_half_edge_cw(start_node, end_node, ccw_reference)
+
+            if reference_neighbor == self.nodes[start_node].get("first_nbr", None):
+                # Update first neighbor
+                self.nodes[start_node]["first_nbr"] = end_node
+
+    def add_half_edge_cw(self, start_node, end_node, reference_neighbor):
+        """Adds a half-edge from start_node to end_node.
+
+        The half-edge is added clockwise next to the existing half-edge
+        (start_node, reference_neighbor).
+
+        Parameters
+        ----------
+        start_node : node
+            Start node of inserted edge.
+        end_node : node
+            End node of inserted edge.
+        reference_neighbor: node
+            End node of reference edge.
+
+        Raises
+        ------
+        NetworkXException
+            If the reference_neighbor does not exist.
+
+        See Also
+        --------
+        add_half_edge_ccw
+        connect_components
+        add_half_edge_first
+        """
+        self.add_edge(start_node, end_node)  # Add edge to graph
+
+        if reference_neighbor is None:
+            # The start node has no neighbors
+            self[start_node][end_node]["cw"] = end_node
+            self[start_node][end_node]["ccw"] = end_node
+            self.nodes[start_node]["first_nbr"] = end_node
+            return
+
+        if reference_neighbor not in self[start_node]:
+            raise nx.NetworkXException(
+                "Cannot add edge. Reference neighbor does not exist"
+            )
+
+        # Get half-edge at the other side
+        cw_reference = self[start_node][reference_neighbor]["cw"]
+        # Alter half-edge data structures
+        self[start_node][reference_neighbor]["cw"] = end_node
+        self[start_node][end_node]["cw"] = cw_reference
+        self[start_node][cw_reference]["ccw"] = end_node
+        self[start_node][end_node]["ccw"] = reference_neighbor
+
+    def connect_components(self, v, w):
+        """Adds half-edges for (v, w) and (w, v) at some position.
+
+        This method should only be called if v and w are in different
+        components, or it might break the embedding.
+        This especially means that if `connect_components(v, w)`
+        is called it is not allowed to call `connect_components(w, v)`
+        afterwards. The neighbor orientations in both directions are
+        all set correctly after the first call.
+
+        Parameters
+        ----------
+        v : node
+        w : node
+
+        See Also
+        --------
+        add_half_edge_ccw
+        add_half_edge_cw
+        add_half_edge_first
+        """
+        self.add_half_edge_first(v, w)
+        self.add_half_edge_first(w, v)
+
+    def add_half_edge_first(self, start_node, end_node):
+        """The added half-edge is inserted at the first position in the order.
+
+        Parameters
+        ----------
+        start_node : node
+        end_node : node
+
+        See Also
+        --------
+        add_half_edge_ccw
+        add_half_edge_cw
+        connect_components
+        """
+        if start_node in self and "first_nbr" in self.nodes[start_node]:
+            reference = self.nodes[start_node]["first_nbr"]
+        else:
+            reference = None
+        self.add_half_edge_ccw(start_node, end_node, reference)
+
+    def next_face_half_edge(self, v, w):
+        """Returns the following half-edge left of a face.
+
+        Parameters
+        ----------
+        v : node
+        w : node
+
+        Returns
+        -------
+        half-edge : tuple
+        """
+        new_node = self[w][v]["ccw"]
+        return w, new_node
+
+    def traverse_face(self, v, w, mark_half_edges=None):
+        """Returns nodes on the face that belong to the half-edge (v, w).
+
+        The face that is traversed lies to the right of the half-edge (in an
+        orientation where v is below w).
+
+        Optionally it is possible to pass a set to which all encountered half
+        edges are added. Before calling this method, this set must not include
+        any half-edges that belong to the face.
+
+        Parameters
+        ----------
+        v : node
+            Start node of half-edge.
+        w : node
+            End node of half-edge.
+        mark_half_edges: set, optional
+            Set to which all encountered half-edges are added.
+
+        Returns
+        -------
+        face : list
+            A list of nodes that lie on this face.
+        """
+        if mark_half_edges is None:
+            mark_half_edges = set()
+
+        face_nodes = [v]
+        mark_half_edges.add((v, w))
+        prev_node = v
+        cur_node = w
+        # Last half-edge is (incoming_node, v)
+        incoming_node = self[v][w]["cw"]
+
+        while cur_node != v or prev_node != incoming_node:
+            face_nodes.append(cur_node)
+            prev_node, cur_node = self.next_face_half_edge(prev_node, cur_node)
+            if (prev_node, cur_node) in mark_half_edges:
+                raise nx.NetworkXException("Bad planar embedding. Impossible face.")
+            mark_half_edges.add((prev_node, cur_node))
+
+        return face_nodes
+
+    def is_directed(self):
+        """A valid PlanarEmbedding is undirected.
+
+        All reverse edges are contained, i.e. for every existing
+        half-edge (v, w) the half-edge in the opposite direction (w, v) is also
+        contained.
+        """
+        return False
diff --git a/shared/package-files/3d/3d5e4375ce2f12c417ad78422b20c870aa6d70d8578087be2a2635e33b1b544d/test_business_hour.py b/shared/package-files/3d/3d5e4375ce2f12c417ad78422b20c870aa6d70d8578087be2a2635e33b1b544d/test_business_hour.py
new file mode 100644
index 0000000000000000000000000000000000000000..2779100f5355cf7475b10d3de6bf7ceebc92af96
--- /dev/null
+++ b/shared/package-files/3d/3d5e4375ce2f12c417ad78422b20c870aa6d70d8578087be2a2635e33b1b544d/test_business_hour.py
@@ -0,0 +1,1445 @@
+"""
+Tests for offsets.BusinessHour
+"""
+from __future__ import annotations
+
+from datetime import (
+    datetime,
+    time as dt_time,
+)
+
+import pytest
+
+from pandas._libs.tslibs import (
+    Timedelta,
+    Timestamp,
+)
+from pandas._libs.tslibs.offsets import (
+    BDay,
+    BusinessHour,
+    Nano,
+)
+
+from pandas import (
+    DatetimeIndex,
+    _testing as tm,
+    date_range,
+)
+from pandas.tests.tseries.offsets.common import assert_offset_equal
+
+
+@pytest.fixture
+def dt():
+    return datetime(2014, 7, 1, 10, 00)
+
+
+@pytest.fixture
+def _offset():
+    return BusinessHour
+
+
+@pytest.fixture
+def offset1():
+    return BusinessHour()
+
+
+@pytest.fixture
+def offset2():
+    return BusinessHour(n=3)
+
+
+@pytest.fixture
+def offset3():
+    return BusinessHour(n=-1)
+
+
+@pytest.fixture
+def offset4():
+    return BusinessHour(n=-4)
+
+
+@pytest.fixture
+def offset5():
+    return BusinessHour(start=dt_time(11, 0), end=dt_time(14, 30))
+
+
+@pytest.fixture
+def offset6():
+    return BusinessHour(start="20:00", end="05:00")
+
+
+@pytest.fixture
+def offset7():
+    return BusinessHour(n=-2, start=dt_time(21, 30), end=dt_time(6, 30))
+
+
+@pytest.fixture
+def offset8():
+    return BusinessHour(start=["09:00", "13:00"], end=["12:00", "17:00"])
+
+
+@pytest.fixture
+def offset9():
+    return BusinessHour(n=3, start=["09:00", "22:00"], end=["13:00", "03:00"])
+
+
+@pytest.fixture
+def offset10():
+    return BusinessHour(n=-1, start=["23:00", "13:00"], end=["02:00", "17:00"])
+
+
+class TestBusinessHour:
+    @pytest.mark.parametrize(
+        "start,end,match",
+        [
+            (
+                dt_time(11, 0, 5),
+                "17:00",
+                "time data must be specified only with hour and minute",
+            ),
+            ("AAA", "17:00", "time data must match '%H:%M' format"),
+            ("14:00:05", "17:00", "time data must match '%H:%M' format"),
+            ([], "17:00", "Must include at least 1 start time"),
+            ("09:00", [], "Must include at least 1 end time"),
+            (
+                ["09:00", "11:00"],
+                "17:00",
+                "number of starting time and ending time must be the same",
+            ),
+            (
+                ["09:00", "11:00"],
+                ["10:00"],
+                "number of starting time and ending time must be the same",
+            ),
+            (
+                ["09:00", "11:00"],
+                ["12:00", "20:00"],
+                r"invalid starting and ending time\(s\): opening hours should not "
+                "touch or overlap with one another",
+            ),
+            (
+                ["12:00", "20:00"],
+                ["09:00", "11:00"],
+                r"invalid starting and ending time\(s\): opening hours should not "
+                "touch or overlap with one another",
+            ),
+        ],
+    )
+    def test_constructor_errors(self, start, end, match):
+        with pytest.raises(ValueError, match=match):
+            BusinessHour(start=start, end=end)
+
+    def test_different_normalize_equals(self, _offset):
+        # GH#21404 changed __eq__ to return False when `normalize` does not match
+        offset = _offset()
+        offset2 = _offset(normalize=True)
+        assert offset != offset2
+
+    def test_repr(
+        self,
+        offset1,
+        offset2,
+        offset3,
+        offset4,
+        offset5,
+        offset6,
+        offset7,
+        offset8,
+        offset9,
+        offset10,
+    ):
+        assert repr(offset1) == ""
+        assert repr(offset2) == "<3 * BusinessHours: bh=09:00-17:00>"
+        assert repr(offset3) == "<-1 * BusinessHour: bh=09:00-17:00>"
+        assert repr(offset4) == "<-4 * BusinessHours: bh=09:00-17:00>"
+
+        assert repr(offset5) == ""
+        assert repr(offset6) == ""
+        assert repr(offset7) == "<-2 * BusinessHours: bh=21:30-06:30>"
+        assert repr(offset8) == ""
+        assert repr(offset9) == "<3 * BusinessHours: bh=09:00-13:00,22:00-03:00>"
+        assert repr(offset10) == "<-1 * BusinessHour: bh=13:00-17:00,23:00-02:00>"
+
+    def test_with_offset(self, dt):
+        expected = Timestamp("2014-07-01 13:00")
+
+        assert dt + BusinessHour() * 3 == expected
+        assert dt + BusinessHour(n=3) == expected
+
+    @pytest.mark.parametrize(
+        "offset_name",
+        ["offset1", "offset2", "offset3", "offset4", "offset8", "offset9", "offset10"],
+    )
+    def test_eq_attribute(self, offset_name, request):
+        offset = request.getfixturevalue(offset_name)
+        assert offset == offset
+
+    @pytest.mark.parametrize(
+        "offset1,offset2",
+        [
+            (BusinessHour(start="09:00"), BusinessHour()),
+            (
+                BusinessHour(start=["23:00", "13:00"], end=["12:00", "17:00"]),
+                BusinessHour(start=["13:00", "23:00"], end=["17:00", "12:00"]),
+            ),
+        ],
+    )
+    def test_eq(self, offset1, offset2):
+        assert offset1 == offset2
+
+    @pytest.mark.parametrize(
+        "offset1,offset2",
+        [
+            (BusinessHour(), BusinessHour(-1)),
+            (BusinessHour(start="09:00"), BusinessHour(start="09:01")),
+            (
+                BusinessHour(start="09:00", end="17:00"),
+                BusinessHour(start="17:00", end="09:01"),
+            ),
+            (
+                BusinessHour(start=["13:00", "23:00"], end=["18:00", "07:00"]),
+                BusinessHour(start=["13:00", "23:00"], end=["17:00", "12:00"]),
+            ),
+        ],
+    )
+    def test_neq(self, offset1, offset2):
+        assert offset1 != offset2
+
+    @pytest.mark.parametrize(
+        "offset_name",
+        ["offset1", "offset2", "offset3", "offset4", "offset8", "offset9", "offset10"],
+    )
+    def test_hash(self, offset_name, request):
+        offset = request.getfixturevalue(offset_name)
+        assert offset == offset
+
+    def test_add_datetime(
+        self,
+        dt,
+        offset1,
+        offset2,
+        offset3,
+        offset4,
+        offset8,
+        offset9,
+        offset10,
+    ):
+        assert offset1 + dt == datetime(2014, 7, 1, 11)
+        assert offset2 + dt == datetime(2014, 7, 1, 13)
+        assert offset3 + dt == datetime(2014, 6, 30, 17)
+        assert offset4 + dt == datetime(2014, 6, 30, 14)
+        assert offset8 + dt == datetime(2014, 7, 1, 11)
+        assert offset9 + dt == datetime(2014, 7, 1, 22)
+        assert offset10 + dt == datetime(2014, 7, 1, 1)
+
+    def test_sub(self, dt, offset2, _offset):
+        off = offset2
+        msg = "Cannot subtract datetime from offset"
+        with pytest.raises(TypeError, match=msg):
+            off - dt
+        assert 2 * off - off == off
+
+        assert dt - offset2 == dt + _offset(-3)
+
+    def test_multiply_by_zero(self, dt, offset1, offset2):
+        assert dt - 0 * offset1 == dt
+        assert dt + 0 * offset1 == dt
+        assert dt - 0 * offset2 == dt
+        assert dt + 0 * offset2 == dt
+
+    def testRollback1(
+        self,
+        dt,
+        _offset,
+        offset1,
+        offset2,
+        offset3,
+        offset4,
+        offset5,
+        offset6,
+        offset7,
+        offset8,
+        offset9,
+        offset10,
+    ):
+        assert offset1.rollback(dt) == dt
+        assert offset2.rollback(dt) == dt
+        assert offset3.rollback(dt) == dt
+        assert offset4.rollback(dt) == dt
+        assert offset5.rollback(dt) == datetime(2014, 6, 30, 14, 30)
+        assert offset6.rollback(dt) == datetime(2014, 7, 1, 5, 0)
+        assert offset7.rollback(dt) == datetime(2014, 7, 1, 6, 30)
+        assert offset8.rollback(dt) == dt
+        assert offset9.rollback(dt) == dt
+        assert offset10.rollback(dt) == datetime(2014, 7, 1, 2)
+
+        datet = datetime(2014, 7, 1, 0)
+        assert offset1.rollback(datet) == datetime(2014, 6, 30, 17)
+        assert offset2.rollback(datet) == datetime(2014, 6, 30, 17)
+        assert offset3.rollback(datet) == datetime(2014, 6, 30, 17)
+        assert offset4.rollback(datet) == datetime(2014, 6, 30, 17)
+        assert offset5.rollback(datet) == datetime(2014, 6, 30, 14, 30)
+        assert offset6.rollback(datet) == datet
+        assert offset7.rollback(datet) == datet
+        assert offset8.rollback(datet) == datetime(2014, 6, 30, 17)
+        assert offset9.rollback(datet) == datet
+        assert offset10.rollback(datet) == datet
+
+        assert _offset(5).rollback(dt) == dt
+
+    def testRollback2(self, _offset):
+        assert _offset(-3).rollback(datetime(2014, 7, 5, 15, 0)) == datetime(
+            2014, 7, 4, 17, 0
+        )
+
+    def testRollforward1(
+        self,
+        dt,
+        _offset,
+        offset1,
+        offset2,
+        offset3,
+        offset4,
+        offset5,
+        offset6,
+        offset7,
+        offset8,
+        offset9,
+        offset10,
+    ):
+        assert offset1.rollforward(dt) == dt
+        assert offset2.rollforward(dt) == dt
+        assert offset3.rollforward(dt) == dt
+        assert offset4.rollforward(dt) == dt
+        assert offset5.rollforward(dt) == datetime(2014, 7, 1, 11, 0)
+        assert offset6.rollforward(dt) == datetime(2014, 7, 1, 20, 0)
+        assert offset7.rollforward(dt) == datetime(2014, 7, 1, 21, 30)
+        assert offset8.rollforward(dt) == dt
+        assert offset9.rollforward(dt) == dt
+        assert offset10.rollforward(dt) == datetime(2014, 7, 1, 13)
+
+        datet = datetime(2014, 7, 1, 0)
+        assert offset1.rollforward(datet) == datetime(2014, 7, 1, 9)
+        assert offset2.rollforward(datet) == datetime(2014, 7, 1, 9)
+        assert offset3.rollforward(datet) == datetime(2014, 7, 1, 9)
+        assert offset4.rollforward(datet) == datetime(2014, 7, 1, 9)
+        assert offset5.rollforward(datet) == datetime(2014, 7, 1, 11)
+        assert offset6.rollforward(datet) == datet
+        assert offset7.rollforward(datet) == datet
+        assert offset8.rollforward(datet) == datetime(2014, 7, 1, 9)
+        assert offset9.rollforward(datet) == datet
+        assert offset10.rollforward(datet) == datet
+
+        assert _offset(5).rollforward(dt) == dt
+
+    def testRollforward2(self, _offset):
+        assert _offset(-3).rollforward(datetime(2014, 7, 5, 16, 0)) == datetime(
+            2014, 7, 7, 9
+        )
+
+    def test_roll_date_object(self):
+        offset = BusinessHour()
+
+        dt = datetime(2014, 7, 6, 15, 0)
+
+        result = offset.rollback(dt)
+        assert result == datetime(2014, 7, 4, 17)
+
+        result = offset.rollforward(dt)
+        assert result == datetime(2014, 7, 7, 9)
+
+    normalize_cases = []
+    normalize_cases.append(
+        (
+            BusinessHour(normalize=True),
+            {
+                datetime(2014, 7, 1, 8): datetime(2014, 7, 1),
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 2),
+                datetime(2014, 7, 1, 16): datetime(2014, 7, 2),
+                datetime(2014, 7, 1, 23): datetime(2014, 7, 2),
+                datetime(2014, 7, 1, 0): datetime(2014, 7, 1),
+                datetime(2014, 7, 4, 15): datetime(2014, 7, 4),
+                datetime(2014, 7, 4, 15, 59): datetime(2014, 7, 4),
+                datetime(2014, 7, 4, 16, 30): datetime(2014, 7, 7),
+                datetime(2014, 7, 5, 23): datetime(2014, 7, 7),
+                datetime(2014, 7, 6, 10): datetime(2014, 7, 7),
+            },
+        )
+    )
+
+    normalize_cases.append(
+        (
+            BusinessHour(-1, normalize=True),
+            {
+                datetime(2014, 7, 1, 8): datetime(2014, 6, 30),
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 1),
+                datetime(2014, 7, 1, 16): datetime(2014, 7, 1),
+                datetime(2014, 7, 1, 10): datetime(2014, 6, 30),
+                datetime(2014, 7, 1, 0): datetime(2014, 6, 30),
+                datetime(2014, 7, 7, 10): datetime(2014, 7, 4),
+                datetime(2014, 7, 7, 10, 1): datetime(2014, 7, 7),
+                datetime(2014, 7, 5, 23): datetime(2014, 7, 4),
+                datetime(2014, 7, 6, 10): datetime(2014, 7, 4),
+            },
+        )
+    )
+
+    normalize_cases.append(
+        (
+            BusinessHour(1, normalize=True, start="17:00", end="04:00"),
+            {
+                datetime(2014, 7, 1, 8): datetime(2014, 7, 1),
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 1),
+                datetime(2014, 7, 1, 23): datetime(2014, 7, 2),
+                datetime(2014, 7, 2, 2): datetime(2014, 7, 2),
+                datetime(2014, 7, 2, 3): datetime(2014, 7, 2),
+                datetime(2014, 7, 4, 23): datetime(2014, 7, 5),
+                datetime(2014, 7, 5, 2): datetime(2014, 7, 5),
+                datetime(2014, 7, 7, 2): datetime(2014, 7, 7),
+                datetime(2014, 7, 7, 17): datetime(2014, 7, 7),
+            },
+        )
+    )
+
+    @pytest.mark.parametrize("case", normalize_cases)
+    def test_normalize(self, case):
+        offset, cases = case
+        for dt, expected in cases.items():
+            assert offset._apply(dt) == expected
+
+    on_offset_cases = []
+    on_offset_cases.append(
+        (
+            BusinessHour(),
+            {
+                datetime(2014, 7, 1, 9): True,
+                datetime(2014, 7, 1, 8, 59): False,
+                datetime(2014, 7, 1, 8): False,
+                datetime(2014, 7, 1, 17): True,
+                datetime(2014, 7, 1, 17, 1): False,
+                datetime(2014, 7, 1, 18): False,
+                datetime(2014, 7, 5, 9): False,
+                datetime(2014, 7, 6, 12): False,
+            },
+        )
+    )
+
+    on_offset_cases.append(
+        (
+            BusinessHour(start="10:00", end="15:00"),
+            {
+                datetime(2014, 7, 1, 9): False,
+                datetime(2014, 7, 1, 10): True,
+                datetime(2014, 7, 1, 15): True,
+                datetime(2014, 7, 1, 15, 1): False,
+                datetime(2014, 7, 5, 12): False,
+                datetime(2014, 7, 6, 12): False,
+            },
+        )
+    )
+
+    on_offset_cases.append(
+        (
+            BusinessHour(start="19:00", end="05:00"),
+            {
+                datetime(2014, 7, 1, 9, 0): False,
+                datetime(2014, 7, 1, 10, 0): False,
+                datetime(2014, 7, 1, 15): False,
+                datetime(2014, 7, 1, 15, 1): False,
+                datetime(2014, 7, 5, 12, 0): False,
+                datetime(2014, 7, 6, 12, 0): False,
+                datetime(2014, 7, 1, 19, 0): True,
+                datetime(2014, 7, 2, 0, 0): True,
+                datetime(2014, 7, 4, 23): True,
+                datetime(2014, 7, 5, 1): True,
+                datetime(2014, 7, 5, 5, 0): True,
+                datetime(2014, 7, 6, 23, 0): False,
+                datetime(2014, 7, 7, 3, 0): False,
+            },
+        )
+    )
+
+    on_offset_cases.append(
+        (
+            BusinessHour(start=["09:00", "13:00"], end=["12:00", "17:00"]),
+            {
+                datetime(2014, 7, 1, 9): True,
+                datetime(2014, 7, 1, 8, 59): False,
+                datetime(2014, 7, 1, 8): False,
+                datetime(2014, 7, 1, 17): True,
+                datetime(2014, 7, 1, 17, 1): False,
+                datetime(2014, 7, 1, 18): False,
+                datetime(2014, 7, 5, 9): False,
+                datetime(2014, 7, 6, 12): False,
+                datetime(2014, 7, 1, 12, 30): False,
+            },
+        )
+    )
+
+    on_offset_cases.append(
+        (
+            BusinessHour(start=["19:00", "23:00"], end=["21:00", "05:00"]),
+            {
+                datetime(2014, 7, 1, 9, 0): False,
+                datetime(2014, 7, 1, 10, 0): False,
+                datetime(2014, 7, 1, 15): False,
+                datetime(2014, 7, 1, 15, 1): False,
+                datetime(2014, 7, 5, 12, 0): False,
+                datetime(2014, 7, 6, 12, 0): False,
+                datetime(2014, 7, 1, 19, 0): True,
+                datetime(2014, 7, 2, 0, 0): True,
+                datetime(2014, 7, 4, 23): True,
+                datetime(2014, 7, 5, 1): True,
+                datetime(2014, 7, 5, 5, 0): True,
+                datetime(2014, 7, 6, 23, 0): False,
+                datetime(2014, 7, 7, 3, 0): False,
+                datetime(2014, 7, 4, 22): False,
+            },
+        )
+    )
+
+    @pytest.mark.parametrize("case", on_offset_cases)
+    def test_is_on_offset(self, case):
+        offset, cases = case
+        for dt, expected in cases.items():
+            assert offset.is_on_offset(dt) == expected
+
+    apply_cases = [
+        (
+            BusinessHour(),
+            {
+                datetime(2014, 7, 1, 11): datetime(2014, 7, 1, 12),
+                datetime(2014, 7, 1, 13): datetime(2014, 7, 1, 14),
+                datetime(2014, 7, 1, 15): datetime(2014, 7, 1, 16),
+                datetime(2014, 7, 1, 19): datetime(2014, 7, 2, 10),
+                datetime(2014, 7, 1, 16): datetime(2014, 7, 2, 9),
+                datetime(2014, 7, 1, 16, 30, 15): datetime(2014, 7, 2, 9, 30, 15),
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 2, 10),
+                datetime(2014, 7, 2, 11): datetime(2014, 7, 2, 12),
+                # out of business hours
+                datetime(2014, 7, 2, 8): datetime(2014, 7, 2, 10),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 3, 10),
+                datetime(2014, 7, 2, 23): datetime(2014, 7, 3, 10),
+                datetime(2014, 7, 3, 0): datetime(2014, 7, 3, 10),
+                # saturday
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 7, 10),
+                datetime(2014, 7, 4, 17): datetime(2014, 7, 7, 10),
+                datetime(2014, 7, 4, 16, 30): datetime(2014, 7, 7, 9, 30),
+                datetime(2014, 7, 4, 16, 30, 30): datetime(2014, 7, 7, 9, 30, 30),
+            },
+        ),
+        (
+            BusinessHour(4),
+            {
+                datetime(2014, 7, 1, 11): datetime(2014, 7, 1, 15),
+                datetime(2014, 7, 1, 13): datetime(2014, 7, 2, 9),
+                datetime(2014, 7, 1, 15): datetime(2014, 7, 2, 11),
+                datetime(2014, 7, 1, 16): datetime(2014, 7, 2, 12),
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 2, 13),
+                datetime(2014, 7, 2, 11): datetime(2014, 7, 2, 15),
+                datetime(2014, 7, 2, 8): datetime(2014, 7, 2, 13),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 3, 13),
+                datetime(2014, 7, 2, 23): datetime(2014, 7, 3, 13),
+                datetime(2014, 7, 3, 0): datetime(2014, 7, 3, 13),
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 7, 13),
+                datetime(2014, 7, 4, 17): datetime(2014, 7, 7, 13),
+                datetime(2014, 7, 4, 16, 30): datetime(2014, 7, 7, 12, 30),
+                datetime(2014, 7, 4, 16, 30, 30): datetime(2014, 7, 7, 12, 30, 30),
+            },
+        ),
+        (
+            BusinessHour(-1),
+            {
+                datetime(2014, 7, 1, 11): datetime(2014, 7, 1, 10),
+                datetime(2014, 7, 1, 13): datetime(2014, 7, 1, 12),
+                datetime(2014, 7, 1, 15): datetime(2014, 7, 1, 14),
+                datetime(2014, 7, 1, 16): datetime(2014, 7, 1, 15),
+                datetime(2014, 7, 1, 10): datetime(2014, 6, 30, 17),
+                datetime(2014, 7, 1, 16, 30, 15): datetime(2014, 7, 1, 15, 30, 15),
+                datetime(2014, 7, 1, 9, 30, 15): datetime(2014, 6, 30, 16, 30, 15),
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 1, 16),
+                datetime(2014, 7, 1, 5): datetime(2014, 6, 30, 16),
+                datetime(2014, 7, 2, 11): datetime(2014, 7, 2, 10),
+                # out of business hours
+                datetime(2014, 7, 2, 8): datetime(2014, 7, 1, 16),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 2, 16),
+                datetime(2014, 7, 2, 23): datetime(2014, 7, 2, 16),
+                datetime(2014, 7, 3, 0): datetime(2014, 7, 2, 16),
+                # saturday
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 4, 16),
+                datetime(2014, 7, 7, 9): datetime(2014, 7, 4, 16),
+                datetime(2014, 7, 7, 9, 30): datetime(2014, 7, 4, 16, 30),
+                datetime(2014, 7, 7, 9, 30, 30): datetime(2014, 7, 4, 16, 30, 30),
+            },
+        ),
+        (
+            BusinessHour(-4),
+            {
+                datetime(2014, 7, 1, 11): datetime(2014, 6, 30, 15),
+                datetime(2014, 7, 1, 13): datetime(2014, 6, 30, 17),
+                datetime(2014, 7, 1, 15): datetime(2014, 7, 1, 11),
+                datetime(2014, 7, 1, 16): datetime(2014, 7, 1, 12),
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 1, 13),
+                datetime(2014, 7, 2, 11): datetime(2014, 7, 1, 15),
+                datetime(2014, 7, 2, 8): datetime(2014, 7, 1, 13),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 2, 13),
+                datetime(2014, 7, 2, 23): datetime(2014, 7, 2, 13),
+                datetime(2014, 7, 3, 0): datetime(2014, 7, 2, 13),
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 4, 13),
+                datetime(2014, 7, 4, 18): datetime(2014, 7, 4, 13),
+                datetime(2014, 7, 7, 9, 30): datetime(2014, 7, 4, 13, 30),
+                datetime(2014, 7, 7, 9, 30, 30): datetime(2014, 7, 4, 13, 30, 30),
+            },
+        ),
+        (
+            BusinessHour(start="13:00", end="16:00"),
+            {
+                datetime(2014, 7, 1, 11): datetime(2014, 7, 1, 14),
+                datetime(2014, 7, 1, 13): datetime(2014, 7, 1, 14),
+                datetime(2014, 7, 1, 15): datetime(2014, 7, 2, 13),
+                datetime(2014, 7, 1, 19): datetime(2014, 7, 2, 14),
+                datetime(2014, 7, 1, 16): datetime(2014, 7, 2, 14),
+                datetime(2014, 7, 1, 15, 30, 15): datetime(2014, 7, 2, 13, 30, 15),
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 7, 14),
+                datetime(2014, 7, 4, 17): datetime(2014, 7, 7, 14),
+            },
+        ),
+        (
+            BusinessHour(n=2, start="13:00", end="16:00"),
+            {
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 2, 15),
+                datetime(2014, 7, 2, 14): datetime(2014, 7, 3, 13),
+                datetime(2014, 7, 2, 8): datetime(2014, 7, 2, 15),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 3, 15),
+                datetime(2014, 7, 2, 14, 30): datetime(2014, 7, 3, 13, 30),
+                datetime(2014, 7, 3, 0): datetime(2014, 7, 3, 15),
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 7, 15),
+                datetime(2014, 7, 4, 17): datetime(2014, 7, 7, 15),
+                datetime(2014, 7, 4, 14, 30): datetime(2014, 7, 7, 13, 30),
+                datetime(2014, 7, 4, 14, 30, 30): datetime(2014, 7, 7, 13, 30, 30),
+            },
+        ),
+        (
+            BusinessHour(n=-1, start="13:00", end="16:00"),
+            {
+                datetime(2014, 7, 2, 11): datetime(2014, 7, 1, 15),
+                datetime(2014, 7, 2, 13): datetime(2014, 7, 1, 15),
+                datetime(2014, 7, 2, 14): datetime(2014, 7, 1, 16),
+                datetime(2014, 7, 2, 15): datetime(2014, 7, 2, 14),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 2, 15),
+                datetime(2014, 7, 2, 16): datetime(2014, 7, 2, 15),
+                datetime(2014, 7, 2, 13, 30, 15): datetime(2014, 7, 1, 15, 30, 15),
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 4, 15),
+                datetime(2014, 7, 7, 11): datetime(2014, 7, 4, 15),
+            },
+        ),
+        (
+            BusinessHour(n=-3, start="10:00", end="16:00"),
+            {
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 1, 13),
+                datetime(2014, 7, 2, 14): datetime(2014, 7, 2, 11),
+                datetime(2014, 7, 2, 8): datetime(2014, 7, 1, 13),
+                datetime(2014, 7, 2, 13): datetime(2014, 7, 1, 16),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 2, 13),
+                datetime(2014, 7, 2, 11, 30): datetime(2014, 7, 1, 14, 30),
+                datetime(2014, 7, 3, 0): datetime(2014, 7, 2, 13),
+                datetime(2014, 7, 4, 10): datetime(2014, 7, 3, 13),
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 4, 13),
+                datetime(2014, 7, 4, 16): datetime(2014, 7, 4, 13),
+                datetime(2014, 7, 4, 12, 30): datetime(2014, 7, 3, 15, 30),
+                datetime(2014, 7, 4, 12, 30, 30): datetime(2014, 7, 3, 15, 30, 30),
+            },
+        ),
+        (
+            BusinessHour(start="19:00", end="05:00"),
+            {
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 1, 20),
+                datetime(2014, 7, 2, 14): datetime(2014, 7, 2, 20),
+                datetime(2014, 7, 2, 8): datetime(2014, 7, 2, 20),
+                datetime(2014, 7, 2, 13): datetime(2014, 7, 2, 20),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 2, 20),
+                datetime(2014, 7, 2, 4, 30): datetime(2014, 7, 2, 19, 30),
+                datetime(2014, 7, 3, 0): datetime(2014, 7, 3, 1),
+                datetime(2014, 7, 4, 10): datetime(2014, 7, 4, 20),
+                datetime(2014, 7, 4, 23): datetime(2014, 7, 5, 0),
+                datetime(2014, 7, 5, 0): datetime(2014, 7, 5, 1),
+                datetime(2014, 7, 5, 4): datetime(2014, 7, 7, 19),
+                datetime(2014, 7, 5, 4, 30): datetime(2014, 7, 7, 19, 30),
+                datetime(2014, 7, 5, 4, 30, 30): datetime(2014, 7, 7, 19, 30, 30),
+            },
+        ),
+        (
+            BusinessHour(n=-1, start="19:00", end="05:00"),
+            {
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 1, 4),
+                datetime(2014, 7, 2, 14): datetime(2014, 7, 2, 4),
+                datetime(2014, 7, 2, 8): datetime(2014, 7, 2, 4),
+                datetime(2014, 7, 2, 13): datetime(2014, 7, 2, 4),
+                datetime(2014, 7, 2, 20): datetime(2014, 7, 2, 5),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 2, 4),
+                datetime(2014, 7, 2, 19, 30): datetime(2014, 7, 2, 4, 30),
+                datetime(2014, 7, 3, 0): datetime(2014, 7, 2, 23),
+                datetime(2014, 7, 3, 6): datetime(2014, 7, 3, 4),
+                datetime(2014, 7, 4, 23): datetime(2014, 7, 4, 22),
+                datetime(2014, 7, 5, 0): datetime(2014, 7, 4, 23),
+                datetime(2014, 7, 5, 4): datetime(2014, 7, 5, 3),
+                datetime(2014, 7, 7, 19, 30): datetime(2014, 7, 5, 4, 30),
+                datetime(2014, 7, 7, 19, 30, 30): datetime(2014, 7, 5, 4, 30, 30),
+            },
+        ),
+        (
+            BusinessHour(n=4, start="00:00", end="23:00"),
+            {
+                datetime(2014, 7, 3, 22): datetime(2014, 7, 4, 3),
+                datetime(2014, 7, 4, 22): datetime(2014, 7, 7, 3),
+                datetime(2014, 7, 3, 22, 30): datetime(2014, 7, 4, 3, 30),
+                datetime(2014, 7, 3, 22, 20): datetime(2014, 7, 4, 3, 20),
+                datetime(2014, 7, 4, 22, 30, 30): datetime(2014, 7, 7, 3, 30, 30),
+                datetime(2014, 7, 4, 22, 30, 20): datetime(2014, 7, 7, 3, 30, 20),
+            },
+        ),
+        (
+            BusinessHour(n=-4, start="00:00", end="23:00"),
+            {
+                datetime(2014, 7, 4, 3): datetime(2014, 7, 3, 22),
+                datetime(2014, 7, 7, 3): datetime(2014, 7, 4, 22),
+                datetime(2014, 7, 4, 3, 30): datetime(2014, 7, 3, 22, 30),
+                datetime(2014, 7, 4, 3, 20): datetime(2014, 7, 3, 22, 20),
+                datetime(2014, 7, 7, 3, 30, 30): datetime(2014, 7, 4, 22, 30, 30),
+                datetime(2014, 7, 7, 3, 30, 20): datetime(2014, 7, 4, 22, 30, 20),
+            },
+        ),
+        (
+            BusinessHour(start=["09:00", "14:00"], end=["12:00", "18:00"]),
+            {
+                datetime(2014, 7, 1, 11): datetime(2014, 7, 1, 14),
+                datetime(2014, 7, 1, 15): datetime(2014, 7, 1, 16),
+                datetime(2014, 7, 1, 19): datetime(2014, 7, 2, 10),
+                datetime(2014, 7, 1, 16): datetime(2014, 7, 1, 17),
+                datetime(2014, 7, 1, 16, 30, 15): datetime(2014, 7, 1, 17, 30, 15),
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 2, 9),
+                datetime(2014, 7, 2, 11): datetime(2014, 7, 2, 14),
+                # out of business hours
+                datetime(2014, 7, 1, 13): datetime(2014, 7, 1, 15),
+                datetime(2014, 7, 2, 8): datetime(2014, 7, 2, 10),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 3, 10),
+                datetime(2014, 7, 2, 23): datetime(2014, 7, 3, 10),
+                datetime(2014, 7, 3, 0): datetime(2014, 7, 3, 10),
+                # saturday
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 7, 10),
+                datetime(2014, 7, 4, 17): datetime(2014, 7, 7, 9),
+                datetime(2014, 7, 4, 17, 30): datetime(2014, 7, 7, 9, 30),
+                datetime(2014, 7, 4, 17, 30, 30): datetime(2014, 7, 7, 9, 30, 30),
+            },
+        ),
+        (
+            BusinessHour(n=4, start=["09:00", "14:00"], end=["12:00", "18:00"]),
+            {
+                datetime(2014, 7, 1, 11): datetime(2014, 7, 1, 17),
+                datetime(2014, 7, 1, 13): datetime(2014, 7, 2, 9),
+                datetime(2014, 7, 1, 15): datetime(2014, 7, 2, 10),
+                datetime(2014, 7, 1, 16): datetime(2014, 7, 2, 11),
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 2, 14),
+                datetime(2014, 7, 2, 11): datetime(2014, 7, 2, 17),
+                datetime(2014, 7, 2, 8): datetime(2014, 7, 2, 15),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 3, 15),
+                datetime(2014, 7, 2, 23): datetime(2014, 7, 3, 15),
+                datetime(2014, 7, 3, 0): datetime(2014, 7, 3, 15),
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 7, 15),
+                datetime(2014, 7, 4, 17): datetime(2014, 7, 7, 14),
+                datetime(2014, 7, 4, 16, 30): datetime(2014, 7, 7, 11, 30),
+                datetime(2014, 7, 4, 16, 30, 30): datetime(2014, 7, 7, 11, 30, 30),
+            },
+        ),
+        (
+            BusinessHour(n=-4, start=["09:00", "14:00"], end=["12:00", "18:00"]),
+            {
+                datetime(2014, 7, 1, 11): datetime(2014, 6, 30, 16),
+                datetime(2014, 7, 1, 13): datetime(2014, 6, 30, 17),
+                datetime(2014, 7, 1, 15): datetime(2014, 6, 30, 18),
+                datetime(2014, 7, 1, 16): datetime(2014, 7, 1, 10),
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 1, 11),
+                datetime(2014, 7, 2, 11): datetime(2014, 7, 1, 16),
+                datetime(2014, 7, 2, 8): datetime(2014, 7, 1, 12),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 2, 12),
+                datetime(2014, 7, 2, 23): datetime(2014, 7, 2, 12),
+                datetime(2014, 7, 3, 0): datetime(2014, 7, 2, 12),
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 4, 12),
+                datetime(2014, 7, 4, 18): datetime(2014, 7, 4, 12),
+                datetime(2014, 7, 7, 9, 30): datetime(2014, 7, 4, 14, 30),
+                datetime(2014, 7, 7, 9, 30, 30): datetime(2014, 7, 4, 14, 30, 30),
+            },
+        ),
+        (
+            BusinessHour(n=-1, start=["19:00", "03:00"], end=["01:00", "05:00"]),
+            {
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 1, 4),
+                datetime(2014, 7, 2, 14): datetime(2014, 7, 2, 4),
+                datetime(2014, 7, 2, 8): datetime(2014, 7, 2, 4),
+                datetime(2014, 7, 2, 13): datetime(2014, 7, 2, 4),
+                datetime(2014, 7, 2, 20): datetime(2014, 7, 2, 5),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 2, 4),
+                datetime(2014, 7, 2, 4): datetime(2014, 7, 2, 1),
+                datetime(2014, 7, 2, 19, 30): datetime(2014, 7, 2, 4, 30),
+                datetime(2014, 7, 3, 0): datetime(2014, 7, 2, 23),
+                datetime(2014, 7, 3, 6): datetime(2014, 7, 3, 4),
+                datetime(2014, 7, 4, 23): datetime(2014, 7, 4, 22),
+                datetime(2014, 7, 5, 0): datetime(2014, 7, 4, 23),
+                datetime(2014, 7, 5, 4): datetime(2014, 7, 5, 0),
+                datetime(2014, 7, 7, 3, 30): datetime(2014, 7, 5, 0, 30),
+                datetime(2014, 7, 7, 19, 30): datetime(2014, 7, 7, 4, 30),
+                datetime(2014, 7, 7, 19, 30, 30): datetime(2014, 7, 7, 4, 30, 30),
+            },
+        ),
+    ]
+
+    # long business hours (see gh-26381)
+
+    # multiple business hours
+
+    @pytest.mark.parametrize("case", apply_cases)
+    def test_apply(self, case):
+        offset, cases = case
+        for base, expected in cases.items():
+            assert_offset_equal(offset, base, expected)
+
+    apply_large_n_cases = [
+        (
+            # A week later
+            BusinessHour(40),
+            {
+                datetime(2014, 7, 1, 11): datetime(2014, 7, 8, 11),
+                datetime(2014, 7, 1, 13): datetime(2014, 7, 8, 13),
+                datetime(2014, 7, 1, 15): datetime(2014, 7, 8, 15),
+                datetime(2014, 7, 1, 16): datetime(2014, 7, 8, 16),
+                datetime(2014, 7, 1, 17): datetime(2014, 7, 9, 9),
+                datetime(2014, 7, 2, 11): datetime(2014, 7, 9, 11),
+                datetime(2014, 7, 2, 8): datetime(2014, 7, 9, 9),
+                datetime(2014, 7, 2, 19): datetime(2014, 7, 10, 9),
+                datetime(2014, 7, 2, 23): datetime(2014, 7, 10, 9),
+                datetime(2014, 7, 3, 0): datetime(2014, 7, 10, 9),
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 14, 9),
+                datetime(2014, 7, 4, 18): datetime(2014, 7, 14, 9),
+                datetime(2014, 7, 7, 9, 30): datetime(2014, 7, 14, 9, 30),
+                datetime(2014, 7, 7, 9, 30, 30): datetime(2014, 7, 14, 9, 30, 30),
+            },
+        ),
+        (
+            # 3 days and 1 hour before
+            BusinessHour(-25),
+            {
+                datetime(2014, 7, 1, 11): datetime(2014, 6, 26, 10),
+                datetime(2014, 7, 1, 13): datetime(2014, 6, 26, 12),
+                datetime(2014, 7, 1, 9): datetime(2014, 6, 25, 16),
+                datetime(2014, 7, 1, 10): datetime(2014, 6, 25, 17),
+                datetime(2014, 7, 3, 11): datetime(2014, 6, 30, 10),
+                datetime(2014, 7, 3, 8): datetime(2014, 6, 27, 16),
+                datetime(2014, 7, 3, 19): datetime(2014, 6, 30, 16),
+                datetime(2014, 7, 3, 23): datetime(2014, 6, 30, 16),
+                datetime(2014, 7, 4, 9): datetime(2014, 6, 30, 16),
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 1, 16),
+                datetime(2014, 7, 6, 18): datetime(2014, 7, 1, 16),
+                datetime(2014, 7, 7, 9, 30): datetime(2014, 7, 1, 16, 30),
+                datetime(2014, 7, 7, 10, 30, 30): datetime(2014, 7, 2, 9, 30, 30),
+            },
+        ),
+        (
+            # 5 days and 3 hours later
+            BusinessHour(28, start="21:00", end="02:00"),
+            {
+                datetime(2014, 7, 1, 11): datetime(2014, 7, 9, 0),
+                datetime(2014, 7, 1, 22): datetime(2014, 7, 9, 1),
+                datetime(2014, 7, 1, 23): datetime(2014, 7, 9, 21),
+                datetime(2014, 7, 2, 2): datetime(2014, 7, 10, 0),
+                datetime(2014, 7, 3, 21): datetime(2014, 7, 11, 0),
+                datetime(2014, 7, 4, 1): datetime(2014, 7, 11, 23),
+                datetime(2014, 7, 4, 2): datetime(2014, 7, 12, 0),
+                datetime(2014, 7, 4, 3): datetime(2014, 7, 12, 0),
+                datetime(2014, 7, 5, 1): datetime(2014, 7, 14, 23),
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 15, 0),
+                datetime(2014, 7, 6, 18): datetime(2014, 7, 15, 0),
+                datetime(2014, 7, 7, 1): datetime(2014, 7, 15, 0),
+                datetime(2014, 7, 7, 23, 30): datetime(2014, 7, 15, 21, 30),
+            },
+        ),
+        (
+            # large n for multiple opening hours (3 days and 1 hour before)
+            BusinessHour(n=-25, start=["09:00", "14:00"], end=["12:00", "19:00"]),
+            {
+                datetime(2014, 7, 1, 11): datetime(2014, 6, 26, 10),
+                datetime(2014, 7, 1, 13): datetime(2014, 6, 26, 11),
+                datetime(2014, 7, 1, 9): datetime(2014, 6, 25, 18),
+                datetime(2014, 7, 1, 10): datetime(2014, 6, 25, 19),
+                datetime(2014, 7, 3, 11): datetime(2014, 6, 30, 10),
+                datetime(2014, 7, 3, 8): datetime(2014, 6, 27, 18),
+                datetime(2014, 7, 3, 19): datetime(2014, 6, 30, 18),
+                datetime(2014, 7, 3, 23): datetime(2014, 6, 30, 18),
+                datetime(2014, 7, 4, 9): datetime(2014, 6, 30, 18),
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 1, 18),
+                datetime(2014, 7, 6, 18): datetime(2014, 7, 1, 18),
+                datetime(2014, 7, 7, 9, 30): datetime(2014, 7, 1, 18, 30),
+                datetime(2014, 7, 7, 10, 30, 30): datetime(2014, 7, 2, 9, 30, 30),
+            },
+        ),
+        (
+            # 5 days and 3 hours later
+            BusinessHour(28, start=["21:00", "03:00"], end=["01:00", "04:00"]),
+            {
+                datetime(2014, 7, 1, 11): datetime(2014, 7, 9, 0),
+                datetime(2014, 7, 1, 22): datetime(2014, 7, 9, 3),
+                datetime(2014, 7, 1, 23): datetime(2014, 7, 9, 21),
+                datetime(2014, 7, 2, 2): datetime(2014, 7, 9, 23),
+                datetime(2014, 7, 3, 21): datetime(2014, 7, 11, 0),
+                datetime(2014, 7, 4, 1): datetime(2014, 7, 11, 23),
+                datetime(2014, 7, 4, 2): datetime(2014, 7, 11, 23),
+                datetime(2014, 7, 4, 3): datetime(2014, 7, 11, 23),
+                datetime(2014, 7, 4, 21): datetime(2014, 7, 12, 0),
+                datetime(2014, 7, 5, 0): datetime(2014, 7, 14, 22),
+                datetime(2014, 7, 5, 1): datetime(2014, 7, 14, 23),
+                datetime(2014, 7, 5, 15): datetime(2014, 7, 14, 23),
+                datetime(2014, 7, 6, 18): datetime(2014, 7, 14, 23),
+                datetime(2014, 7, 7, 1): datetime(2014, 7, 14, 23),
+                datetime(2014, 7, 7, 23, 30): datetime(2014, 7, 15, 21, 30),
+            },
+        ),
+    ]
+
+    @pytest.mark.parametrize("case", apply_large_n_cases)
+    def test_apply_large_n(self, case):
+        offset, cases = case
+        for base, expected in cases.items():
+            assert_offset_equal(offset, base, expected)
+
+    def test_apply_nanoseconds(self):
+        tests = [
+            (
+                BusinessHour(),
+                {
+                    Timestamp("2014-07-04 15:00")
+                    + Nano(5): Timestamp("2014-07-04 16:00")
+                    + Nano(5),
+                    Timestamp("2014-07-04 16:00")
+                    + Nano(5): Timestamp("2014-07-07 09:00")
+                    + Nano(5),
+                    Timestamp("2014-07-04 16:00")
+                    - Nano(5): Timestamp("2014-07-04 17:00")
+                    - Nano(5),
+                },
+            ),
+            (
+                BusinessHour(-1),
+                {
+                    Timestamp("2014-07-04 15:00")
+                    + Nano(5): Timestamp("2014-07-04 14:00")
+                    + Nano(5),
+                    Timestamp("2014-07-04 10:00")
+                    + Nano(5): Timestamp("2014-07-04 09:00")
+                    + Nano(5),
+                    Timestamp("2014-07-04 10:00")
+                    - Nano(5): Timestamp("2014-07-03 17:00")
+                    - Nano(5),
+                },
+            ),
+        ]
+
+        for offset, cases in tests:
+            for base, expected in cases.items():
+                assert_offset_equal(offset, base, expected)
+
+    @pytest.mark.parametrize("td_unit", ["s", "ms", "us", "ns"])
+    @pytest.mark.parametrize("unit", ["s", "ms", "us", "ns"])
+    def test_bday_ignores_timedeltas(self, unit, td_unit):
+        # GH#55608
+        idx = date_range("2010/02/01", "2010/02/10", freq="12h", unit=unit)
+        td = Timedelta(3, unit="h").as_unit(td_unit)
+        off = BDay(offset=td)
+        t1 = idx + off
+
+        exp_unit = tm.get_finest_unit(td.unit, idx.unit)
+
+        expected = DatetimeIndex(
+            [
+                "2010-02-02 03:00:00",
+                "2010-02-02 15:00:00",
+                "2010-02-03 03:00:00",
+                "2010-02-03 15:00:00",
+                "2010-02-04 03:00:00",
+                "2010-02-04 15:00:00",
+                "2010-02-05 03:00:00",
+                "2010-02-05 15:00:00",
+                "2010-02-08 03:00:00",
+                "2010-02-08 15:00:00",
+                "2010-02-08 03:00:00",
+                "2010-02-08 15:00:00",
+                "2010-02-08 03:00:00",
+                "2010-02-08 15:00:00",
+                "2010-02-09 03:00:00",
+                "2010-02-09 15:00:00",
+                "2010-02-10 03:00:00",
+                "2010-02-10 15:00:00",
+                "2010-02-11 03:00:00",
+            ],
+            freq=None,
+        ).as_unit(exp_unit)
+        tm.assert_index_equal(t1, expected)
+
+        # TODO(GH#55564): as_unit will be unnecessary
+        pointwise = DatetimeIndex([x + off for x in idx]).as_unit(exp_unit)
+        tm.assert_index_equal(pointwise, expected)
+
+    def test_add_bday_offset_nanos(self):
+        # GH#55608
+        idx = date_range("2010/02/01", "2010/02/10", freq="12h", unit="ns")
+        off = BDay(offset=Timedelta(3, unit="ns"))
+
+        result = idx + off
+        expected = DatetimeIndex([x + off for x in idx])
+        tm.assert_index_equal(result, expected)
+
+
+class TestOpeningTimes:
+    # opening time should be affected by sign of n, not by n's value and end
+    opening_time_cases = [
+        (
+            [
+                BusinessHour(),
+                BusinessHour(n=2),
+                BusinessHour(n=4),
+                BusinessHour(end="10:00"),
+                BusinessHour(n=2, end="4:00"),
+                BusinessHour(n=4, end="15:00"),
+            ],
+            {
+                datetime(2014, 7, 1, 11): (
+                    datetime(2014, 7, 2, 9),
+                    datetime(2014, 7, 1, 9),
+                ),
+                datetime(2014, 7, 1, 18): (
+                    datetime(2014, 7, 2, 9),
+                    datetime(2014, 7, 1, 9),
+                ),
+                datetime(2014, 7, 1, 23): (
+                    datetime(2014, 7, 2, 9),
+                    datetime(2014, 7, 1, 9),
+                ),
+                datetime(2014, 7, 2, 8): (
+                    datetime(2014, 7, 2, 9),
+                    datetime(2014, 7, 1, 9),
+                ),
+                # if timestamp is on opening time, next opening time is
+                # as it is
+                datetime(2014, 7, 2, 9): (
+                    datetime(2014, 7, 2, 9),
+                    datetime(2014, 7, 2, 9),
+                ),
+                datetime(2014, 7, 2, 10): (
+                    datetime(2014, 7, 3, 9),
+                    datetime(2014, 7, 2, 9),
+                ),
+                # 2014-07-05 is saturday
+                datetime(2014, 7, 5, 10): (
+                    datetime(2014, 7, 7, 9),
+                    datetime(2014, 7, 4, 9),
+                ),
+                datetime(2014, 7, 4, 10): (
+                    datetime(2014, 7, 7, 9),
+                    datetime(2014, 7, 4, 9),
+                ),
+                datetime(2014, 7, 4, 23): (
+                    datetime(2014, 7, 7, 9),
+                    datetime(2014, 7, 4, 9),
+                ),
+                datetime(2014, 7, 6, 10): (
+                    datetime(2014, 7, 7, 9),
+                    datetime(2014, 7, 4, 9),
+                ),
+                datetime(2014, 7, 7, 5): (
+                    datetime(2014, 7, 7, 9),
+                    datetime(2014, 7, 4, 9),
+                ),
+                datetime(2014, 7, 7, 9, 1): (
+                    datetime(2014, 7, 8, 9),
+                    datetime(2014, 7, 7, 9),
+                ),
+            },
+        ),
+        (
+            [
+                BusinessHour(start="11:15"),
+                BusinessHour(n=2, start="11:15"),
+                BusinessHour(n=3, start="11:15"),
+                BusinessHour(start="11:15", end="10:00"),
+                BusinessHour(n=2, start="11:15", end="4:00"),
+                BusinessHour(n=3, start="11:15", end="15:00"),
+            ],
+            {
+                datetime(2014, 7, 1, 11): (
+                    datetime(2014, 7, 1, 11, 15),
+                    datetime(2014, 6, 30, 11, 15),
+                ),
+                datetime(2014, 7, 1, 18): (
+                    datetime(2014, 7, 2, 11, 15),
+                    datetime(2014, 7, 1, 11, 15),
+                ),
+                datetime(2014, 7, 1, 23): (
+                    datetime(2014, 7, 2, 11, 15),
+                    datetime(2014, 7, 1, 11, 15),
+                ),
+                datetime(2014, 7, 2, 8): (
+                    datetime(2014, 7, 2, 11, 15),
+                    datetime(2014, 7, 1, 11, 15),
+                ),
+                datetime(2014, 7, 2, 9): (
+                    datetime(2014, 7, 2, 11, 15),
+                    datetime(2014, 7, 1, 11, 15),
+                ),
+                datetime(2014, 7, 2, 10): (
+                    datetime(2014, 7, 2, 11, 15),
+                    datetime(2014, 7, 1, 11, 15),
+                ),
+                datetime(2014, 7, 2, 11, 15): (
+                    datetime(2014, 7, 2, 11, 15),
+                    datetime(2014, 7, 2, 11, 15),
+                ),
+                datetime(2014, 7, 2, 11, 15, 1): (
+                    datetime(2014, 7, 3, 11, 15),
+                    datetime(2014, 7, 2, 11, 15),
+                ),
+                datetime(2014, 7, 5, 10): (
+                    datetime(2014, 7, 7, 11, 15),
+                    datetime(2014, 7, 4, 11, 15),
+                ),
+                datetime(2014, 7, 4, 10): (
+                    datetime(2014, 7, 4, 11, 15),
+                    datetime(2014, 7, 3, 11, 15),
+                ),
+                datetime(2014, 7, 4, 23): (
+                    datetime(2014, 7, 7, 11, 15),
+                    datetime(2014, 7, 4, 11, 15),
+                ),
+                datetime(2014, 7, 6, 10): (
+                    datetime(2014, 7, 7, 11, 15),
+                    datetime(2014, 7, 4, 11, 15),
+                ),
+                datetime(2014, 7, 7, 5): (
+                    datetime(2014, 7, 7, 11, 15),
+                    datetime(2014, 7, 4, 11, 15),
+                ),
+                datetime(2014, 7, 7, 9, 1): (
+                    datetime(2014, 7, 7, 11, 15),
+                    datetime(2014, 7, 4, 11, 15),
+                ),
+            },
+        ),
+        (
+            [
+                BusinessHour(-1),
+                BusinessHour(n=-2),
+                BusinessHour(n=-4),
+                BusinessHour(n=-1, end="10:00"),
+                BusinessHour(n=-2, end="4:00"),
+                BusinessHour(n=-4, end="15:00"),
+            ],
+            {
+                datetime(2014, 7, 1, 11): (
+                    datetime(2014, 7, 1, 9),
+                    datetime(2014, 7, 2, 9),
+                ),
+                datetime(2014, 7, 1, 18): (
+                    datetime(2014, 7, 1, 9),
+                    datetime(2014, 7, 2, 9),
+                ),
+                datetime(2014, 7, 1, 23): (
+                    datetime(2014, 7, 1, 9),
+                    datetime(2014, 7, 2, 9),
+                ),
+                datetime(2014, 7, 2, 8): (
+                    datetime(2014, 7, 1, 9),
+                    datetime(2014, 7, 2, 9),
+                ),
+                datetime(2014, 7, 2, 9): (
+                    datetime(2014, 7, 2, 9),
+                    datetime(2014, 7, 2, 9),
+                ),
+                datetime(2014, 7, 2, 10): (
+                    datetime(2014, 7, 2, 9),
+                    datetime(2014, 7, 3, 9),
+                ),
+                datetime(2014, 7, 5, 10): (
+                    datetime(2014, 7, 4, 9),
+                    datetime(2014, 7, 7, 9),
+                ),
+                datetime(2014, 7, 4, 10): (
+                    datetime(2014, 7, 4, 9),
+                    datetime(2014, 7, 7, 9),
+                ),
+                datetime(2014, 7, 4, 23): (
+                    datetime(2014, 7, 4, 9),
+                    datetime(2014, 7, 7, 9),
+                ),
+                datetime(2014, 7, 6, 10): (
+                    datetime(2014, 7, 4, 9),
+                    datetime(2014, 7, 7, 9),
+                ),
+                datetime(2014, 7, 7, 5): (
+                    datetime(2014, 7, 4, 9),
+                    datetime(2014, 7, 7, 9),
+                ),
+                datetime(2014, 7, 7, 9): (
+                    datetime(2014, 7, 7, 9),
+                    datetime(2014, 7, 7, 9),
+                ),
+                datetime(2014, 7, 7, 9, 1): (
+                    datetime(2014, 7, 7, 9),
+                    datetime(2014, 7, 8, 9),
+                ),
+            },
+        ),
+        (
+            [
+                BusinessHour(start="17:00", end="05:00"),
+                BusinessHour(n=3, start="17:00", end="03:00"),
+            ],
+            {
+                datetime(2014, 7, 1, 11): (
+                    datetime(2014, 7, 1, 17),
+                    datetime(2014, 6, 30, 17),
+                ),
+                datetime(2014, 7, 1, 18): (
+                    datetime(2014, 7, 2, 17),
+                    datetime(2014, 7, 1, 17),
+                ),
+                datetime(2014, 7, 1, 23): (
+                    datetime(2014, 7, 2, 17),
+                    datetime(2014, 7, 1, 17),
+                ),
+                datetime(2014, 7, 2, 8): (
+                    datetime(2014, 7, 2, 17),
+                    datetime(2014, 7, 1, 17),
+                ),
+                datetime(2014, 7, 2, 9): (
+                    datetime(2014, 7, 2, 17),
+                    datetime(2014, 7, 1, 17),
+                ),
+                datetime(2014, 7, 4, 17): (
+                    datetime(2014, 7, 4, 17),
+                    datetime(2014, 7, 4, 17),
+                ),
+                datetime(2014, 7, 5, 10): (
+                    datetime(2014, 7, 7, 17),
+                    datetime(2014, 7, 4, 17),
+                ),
+                datetime(2014, 7, 4, 10): (
+                    datetime(2014, 7, 4, 17),
+                    datetime(2014, 7, 3, 17),
+                ),
+                datetime(2014, 7, 4, 23): (
+                    datetime(2014, 7, 7, 17),
+                    datetime(2014, 7, 4, 17),
+                ),
+                datetime(2014, 7, 6, 10): (
+                    datetime(2014, 7, 7, 17),
+                    datetime(2014, 7, 4, 17),
+                ),
+                datetime(2014, 7, 7, 5): (
+                    datetime(2014, 7, 7, 17),
+                    datetime(2014, 7, 4, 17),
+                ),
+                datetime(2014, 7, 7, 17, 1): (
+                    datetime(2014, 7, 8, 17),
+                    datetime(2014, 7, 7, 17),
+                ),
+            },
+        ),
+        (
+            [
+                BusinessHour(-1, start="17:00", end="05:00"),
+                BusinessHour(n=-2, start="17:00", end="03:00"),
+            ],
+            {
+                datetime(2014, 7, 1, 11): (
+                    datetime(2014, 6, 30, 17),
+                    datetime(2014, 7, 1, 17),
+                ),
+                datetime(2014, 7, 1, 18): (
+                    datetime(2014, 7, 1, 17),
+                    datetime(2014, 7, 2, 17),
+                ),
+                datetime(2014, 7, 1, 23): (
+                    datetime(2014, 7, 1, 17),
+                    datetime(2014, 7, 2, 17),
+                ),
+                datetime(2014, 7, 2, 8): (
+                    datetime(2014, 7, 1, 17),
+                    datetime(2014, 7, 2, 17),
+                ),
+                datetime(2014, 7, 2, 9): (
+                    datetime(2014, 7, 1, 17),
+                    datetime(2014, 7, 2, 17),
+                ),
+                datetime(2014, 7, 2, 16, 59): (
+                    datetime(2014, 7, 1, 17),
+                    datetime(2014, 7, 2, 17),
+                ),
+                datetime(2014, 7, 5, 10): (
+                    datetime(2014, 7, 4, 17),
+                    datetime(2014, 7, 7, 17),
+                ),
+                datetime(2014, 7, 4, 10): (
+                    datetime(2014, 7, 3, 17),
+                    datetime(2014, 7, 4, 17),
+                ),
+                datetime(2014, 7, 4, 23): (
+                    datetime(2014, 7, 4, 17),
+                    datetime(2014, 7, 7, 17),
+                ),
+                datetime(2014, 7, 6, 10): (
+                    datetime(2014, 7, 4, 17),
+                    datetime(2014, 7, 7, 17),
+                ),
+                datetime(2014, 7, 7, 5): (
+                    datetime(2014, 7, 4, 17),
+                    datetime(2014, 7, 7, 17),
+                ),
+                datetime(2014, 7, 7, 18): (
+                    datetime(2014, 7, 7, 17),
+                    datetime(2014, 7, 8, 17),
+                ),
+            },
+        ),
+        (
+            [
+                BusinessHour(start=["11:15", "15:00"], end=["13:00", "20:00"]),
+                BusinessHour(n=3, start=["11:15", "15:00"], end=["12:00", "20:00"]),
+                BusinessHour(start=["11:15", "15:00"], end=["13:00", "17:00"]),
+                BusinessHour(n=2, start=["11:15", "15:00"], end=["12:00", "03:00"]),
+                BusinessHour(n=3, start=["11:15", "15:00"], end=["13:00", "16:00"]),
+            ],
+            {
+                datetime(2014, 7, 1, 11): (
+                    datetime(2014, 7, 1, 11, 15),
+                    datetime(2014, 6, 30, 15),
+                ),
+                datetime(2014, 7, 1, 18): (
+                    datetime(2014, 7, 2, 11, 15),
+                    datetime(2014, 7, 1, 15),
+                ),
+                datetime(2014, 7, 1, 23): (
+                    datetime(2014, 7, 2, 11, 15),
+                    datetime(2014, 7, 1, 15),
+                ),
+                datetime(2014, 7, 2, 8): (
+                    datetime(2014, 7, 2, 11, 15),
+                    datetime(2014, 7, 1, 15),
+                ),
+                datetime(2014, 7, 2, 9): (
+                    datetime(2014, 7, 2, 11, 15),
+                    datetime(2014, 7, 1, 15),
+                ),
+                datetime(2014, 7, 2, 10): (
+                    datetime(2014, 7, 2, 11, 15),
+                    datetime(2014, 7, 1, 15),
+                ),
+                datetime(2014, 7, 2, 11, 15): (
+                    datetime(2014, 7, 2, 11, 15),
+                    datetime(2014, 7, 2, 11, 15),
+                ),
+                datetime(2014, 7, 2, 11, 15, 1): (
+                    datetime(2014, 7, 2, 15),
+                    datetime(2014, 7, 2, 11, 15),
+                ),
+                datetime(2014, 7, 5, 10): (
+                    datetime(2014, 7, 7, 11, 15),
+                    datetime(2014, 7, 4, 15),
+                ),
+                datetime(2014, 7, 4, 10): (
+                    datetime(2014, 7, 4, 11, 15),
+                    datetime(2014, 7, 3, 15),
+                ),
+                datetime(2014, 7, 4, 23): (
+                    datetime(2014, 7, 7, 11, 15),
+                    datetime(2014, 7, 4, 15),
+                ),
+                datetime(2014, 7, 6, 10): (
+                    datetime(2014, 7, 7, 11, 15),
+                    datetime(2014, 7, 4, 15),
+                ),
+                datetime(2014, 7, 7, 5): (
+                    datetime(2014, 7, 7, 11, 15),
+                    datetime(2014, 7, 4, 15),
+                ),
+                datetime(2014, 7, 7, 9, 1): (
+                    datetime(2014, 7, 7, 11, 15),
+                    datetime(2014, 7, 4, 15),
+                ),
+                datetime(2014, 7, 7, 12): (
+                    datetime(2014, 7, 7, 15),
+                    datetime(2014, 7, 7, 11, 15),
+                ),
+            },
+        ),
+        (
+            [
+                BusinessHour(n=-1, start=["17:00", "08:00"], end=["05:00", "10:00"]),
+                BusinessHour(n=-2, start=["08:00", "17:00"], end=["10:00", "03:00"]),
+            ],
+            {
+                datetime(2014, 7, 1, 11): (
+                    datetime(2014, 7, 1, 8),
+                    datetime(2014, 7, 1, 17),
+                ),
+                datetime(2014, 7, 1, 18): (
+                    datetime(2014, 7, 1, 17),
+                    datetime(2014, 7, 2, 8),
+                ),
+                datetime(2014, 7, 1, 23): (
+                    datetime(2014, 7, 1, 17),
+                    datetime(2014, 7, 2, 8),
+                ),
+                datetime(2014, 7, 2, 8): (
+                    datetime(2014, 7, 2, 8),
+                    datetime(2014, 7, 2, 8),
+                ),
+                datetime(2014, 7, 2, 9): (
+                    datetime(2014, 7, 2, 8),
+                    datetime(2014, 7, 2, 17),
+                ),
+                datetime(2014, 7, 2, 16, 59): (
+                    datetime(2014, 7, 2, 8),
+                    datetime(2014, 7, 2, 17),
+                ),
+                datetime(2014, 7, 5, 10): (
+                    datetime(2014, 7, 4, 17),
+                    datetime(2014, 7, 7, 8),
+                ),
+                datetime(2014, 7, 4, 10): (
+                    datetime(2014, 7, 4, 8),
+                    datetime(2014, 7, 4, 17),
+                ),
+                datetime(2014, 7, 4, 23): (
+                    datetime(2014, 7, 4, 17),
+                    datetime(2014, 7, 7, 8),
+                ),
+                datetime(2014, 7, 6, 10): (
+                    datetime(2014, 7, 4, 17),
+                    datetime(2014, 7, 7, 8),
+                ),
+                datetime(2014, 7, 7, 5): (
+                    datetime(2014, 7, 4, 17),
+                    datetime(2014, 7, 7, 8),
+                ),
+                datetime(2014, 7, 7, 18): (
+                    datetime(2014, 7, 7, 17),
+                    datetime(2014, 7, 8, 8),
+                ),
+            },
+        ),
+    ]
+
+    @pytest.mark.parametrize("case", opening_time_cases)
+    def test_opening_time(self, case):
+        _offsets, cases = case
+        for offset in _offsets:
+            for dt, (exp_next, exp_prev) in cases.items():
+                assert offset._next_opening_time(dt) == exp_next
+                assert offset._prev_opening_time(dt) == exp_prev
diff --git a/shared/package-files/3d/3d62cffa6db491550926b39e34b1543a751dd6976612d23017dd8915ccebae46/macrodata_jmulti_c_lagorder.txt b/shared/package-files/3d/3d62cffa6db491550926b39e34b1543a751dd6976612d23017dd8915ccebae46/macrodata_jmulti_c_lagorder.txt
new file mode 100644
index 0000000000000000000000000000000000000000..efb5ea94e22e3a61ce88708c2cbc765833b5d71e
--- /dev/null
+++ b/shared/package-files/3d/3d62cffa6db491550926b39e34b1543a751dd6976612d23017dd8915ccebae46/macrodata_jmulti_c_lagorder.txt
@@ -0,0 +1,13 @@
+*** Sat, 3 Sep 2016 22:23:07 ***
+OPTIMAL ENDOGENOUS LAGS FROM INFORMATION CRITERIA
+
+endogenous variables:     realcons realgdp realinv 
+deterministic variables:  CONST 
+sample range:             [1961 Q3, 2009 Q3], T = 193
+
+optimal number of lags (searched up to 10 lags of levels):
+Akaike Info Criterion:    4            
+Final Prediction Error:   4            
+Hannan-Quinn Criterion:   2            
+Schwarz Criterion:        2            
+
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+# Experiments
+
+All learned weights are initialized here from scratch. Validation windows are used only for selection and diagnostics. Screen evaluations span all 97 configurations, sampling at most 192 instances per configuration with seed 2026 and recomputing Seasonal Naive on exactly that subset. Full validation uses the supplied reference. Neither scale is the held-out reward scale.
+
+## E0: contract bootstrap
+- Hypothesis: maintain a runnable package while training the first candidate.
+- Changed factor: none; provided Seasonal Naive formula, with no learned parameters.
+- Seed: deterministic. Measured compute: no training.
+- Checkpoint hash: not applicable.
+- Outcome: provisional package published with atomic directory exchange.
+
+## E1: direct long-horizon quantile forecasting
+- Hypothesis: a direct horizon objective and explicit seasonal context features reduce long-horizon median drift and the underestimation of uncertainty from short-patch autoregression.
+- Changed factors relative to the supplied baseline: 6,758,720-parameter encoder/decoder (4 encoder and 3 decoder layers, width 256), 32-point patches, variable 512–4096-point context, direct 32/128/512/1024-step objectives, monotone nine-quantile head. Context features contain recent levels, fixed damped slope and four-cycle primary/secondary seasonal averages; all weights are globally fixed at inference.
+- Corpus: all 95 mounted allow-listed directories, deterministic source-capped sample totaling 958,745,103 points / 3.835 GB and 357,789 series. Shortest retained series: 16 points. At most eight shuffled shards per source, up to approximately 24M points/source. Series exceeding 131,072 points are cropped at a random position. Packing took under two minutes on six workers.
+- Mixture: directory weight proportional to capped points^0.25, climate multiplier 0.35, low-frequency competition-source multiplier 3. Synthetic proportion 20%. Exact logic is in provenance/data.py.
+- Synthetic generator: newly generated damped trends, Fourier seasonality, AR noise, intermittent demand and changepoints. No downloaded synthetic assets.
+- Training seed: 17. AdamW 3e-4, batch 128, 400-step warmup, cosine decay, bf16, one H100. Run planned for 12,000 steps with snapshots every 1,000 steps.
+- Control: randomly initialized model with zero output projection (fixed seasonal mixture and fixed initial interval widths) saved at step 0 to distinguish learning from the forecasting prior.
+- Completed 12,000 steps in 349.9 seconds on one H100. Measured group skills, checkpoint hashes and outcomes are recorded below.
+
+## Known limits and next decisions
+- This first run jointly changes the architecture and objective; it does not isolate their individual causal contributions.
+- The initial corpus pack samples shuffled shards but takes early Arrow record batches within each selected shard. Later runs should test a wider source sample if validation plateaus.
+- 10-second daily seasonality exceeds the 4,096-point encoder window. Longer fixed context summaries are a planned ablation after initial diagnostics.
+
+## E1 control, step 0, screening
+- Checkpoint SHA-256: `81be0f24c44a558e29dffcabb4ac15ae3c944e1afe2860851683d155f920ec2f`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 7.037512302398682 seconds.
+- Overall skill 0.000921; relative MASE 1.194471; relative WQL 0.835649.
+- term skill: long=0.0466, medium=0.0284, short=-0.0280.
+- domain skill: Econ/Fin=-1.0735, Energy=-0.1019, Healthcare=-0.0966, Nature=0.2991, Sales=0.3167, Transport=0.2140, Web/CloudOps=-0.1294.
+- frequency skill: A=-0.6469, D=0.1541, H=0.1205, M=-0.3393, Q=-0.5490, S=-2.2691, T=0.1079, W=-0.0412.
+- variates skill: multivariate=0.0528, univariate=-0.0424.
+- Outcome: Untrained seasonal-mixture prior barely exceeds Seasonal Naive overall and fails badly on 10-second and low-frequency configurations.
+
+## E1 step 1000, seed 17, screening
+- Checkpoint SHA-256: `ebdd9eaa223c2bb6e38b8faec6f465a227fcd735b654a26f3b617a757addb0d3`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 6.581991195678711 seconds.
+- Overall skill 0.210443; relative MASE 0.932765; relative WQL 0.668336.
+- term skill: long=0.2339, medium=0.2517, short=0.1848.
+- domain skill: Econ/Fin=-0.1600, Energy=0.0730, Healthcare=-0.0312, Nature=0.3679, Sales=0.2919, Transport=0.3892, Web/CloudOps=0.2547.
+- frequency skill: A=0.3078, D=0.2651, H=0.3157, M=-0.3962, Q=0.0682, S=-0.6697, T=0.2839, W=0.0983.
+- variates skill: multivariate=0.2140, univariate=0.2076.
+- Outcome: Improves the control by 0.2095 screening skill after about 34 seconds on one H100. Selected for first neural publication and full validation.
+
+## E1 step 1000, full validation
+- Checkpoint SHA-256: `see model_manifest.json`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.206132; relative MASE 0.931220; relative WQL 0.676774.
+- term skill: long=0.2265, medium=0.2444, short=0.1829.
+- domain skill: Econ/Fin=-0.1543, Energy=0.0590, Healthcare=-0.0112, Nature=0.3688, Sales=0.3043, Transport=0.3878, Web/CloudOps=0.2461.
+- frequency skill: A=0.2746, D=0.2676, H=0.3116, M=-0.3493, Q=0.0397, S=-0.6697, T=0.2712, W=0.1025.
+- variates skill: multivariate=0.2097, univariate=0.2033.
+- Outcome: Full 151,170-instance validation skill 0.2061, with no failed configurations. Package inference took 29.5 seconds while training shared the sole GPU. First neural publication verified.
+
+## E1 step 2000, screening
+- Checkpoint SHA-256: `a513ac4f03aceb93a860d5e5eca70010c0101fdea794df4a9a8ae472647fb9c9`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 5.98806357383728 seconds.
+- Overall skill 0.249671; relative MASE 0.890745; relative WQL 0.632048.
+- term skill: long=0.2727, medium=0.2872, short=0.2257.
+- domain skill: Econ/Fin=-0.0533, Energy=0.1151, Healthcare=0.1665, Nature=0.4084, Sales=0.2984, Transport=0.3867, Web/CloudOps=0.2855.
+- frequency skill: A=0.3280, D=0.2894, H=0.3291, M=-0.1483, Q=0.1703, S=-0.4627, T=0.3239, W=0.1185.
+- variates skill: multivariate=0.2588, univariate=0.2423.
+- Outcome: Improves screening skill to 0.2497 after 66.8 seconds of training; candidate for replacement.
+
+## E1 step 3000, screening
+- Checkpoint SHA-256: `74da538d2365426496a12c965c0c0cdc1d3962a798bd2e2c5fed950c1f663f30`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 6.051600933074951 seconds.
+- Overall skill 0.246085; relative MASE 0.894210; relative WQL 0.635631.
+- term skill: long=0.2835, medium=0.2774, short=0.2187.
+- domain skill: Econ/Fin=-0.1111, Energy=0.1198, Healthcare=-0.0532, Nature=0.3956, Sales=0.3188, Transport=0.4123, Web/CloudOps=0.3004.
+- frequency skill: A=0.3478, D=0.2585, H=0.3298, M=-0.3174, Q=0.1517, S=-0.6889, T=0.3620, W=0.1126.
+- variates skill: multivariate=0.2552, univariate=0.2388.
+- Outcome: Slight regression to 0.2461 relative to step 2000. Negative checkpoint-selection result; not selected.
+
+## E2 fixed longer-history feature ablation, step 2000 seed 17
+- Checkpoint SHA-256: `25ac85db67c041c92870e2ea2e8b198bb768f5c5ed64f42b78d6ec207ffe3a18`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 6.066685438156128 seconds.
+- Overall skill 0.249089; relative MASE 0.890661; relative WQL 0.633088.
+- term skill: long=0.2741, medium=0.2855, short=0.2248.
+- domain skill: Econ/Fin=-0.0532, Energy=0.1146, Healthcare=0.1651, Nature=0.4048, Sales=0.2986, Transport=0.3880, Web/CloudOps=0.2859.
+- frequency skill: A=0.3273, D=0.2892, H=0.3291, M=-0.1495, Q=0.1705, S=-0.4627, T=0.3228, W=0.1166.
+- variates skill: multivariate=0.2572, univariate=0.2426.
+- Outcome: No training. Changed only history available to seasonal summaries from 4,096 to 16,384 points, with the encoder unchanged. Skill decreased 0.24967 to 0.24909. Rejected. Validation 10-second histories are shorter than one daily cycle, so additional history cannot supply that feature.
+
+## E1 step 4000, screening
+- Checkpoint SHA-256: `f1b4e5f798688a56a693daf799fa21e69ec21c9c1710cae8dafbdb833c9ee069`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 5.961170196533203 seconds.
+- Overall skill 0.266471; relative MASE 0.874789; relative WQL 0.615080.
+- term skill: long=0.2537, medium=0.2846, short=0.2643.
+- domain skill: Econ/Fin=-0.0360, Energy=0.1306, Healthcare=0.1810, Nature=0.3894, Sales=0.3301, Transport=0.4253, Web/CloudOps=0.3121.
+- frequency skill: A=0.3647, D=0.2978, H=0.3375, M=-0.0981, Q=0.1458, S=-0.2890, T=0.3375, W=0.1196.
+- variates skill: multivariate=0.2648, univariate=0.2678.
+- Outcome: Best ungated checkpoint so far; improved short-horizon performance, with some long-horizon regression.
+
+## E1 step 6000, screening
+- Checkpoint SHA-256: `f38cdb1b49938007c20bdfdfa7c4fc7d7aba6516192f388f37088891411eab7f`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 6.802650451660156 seconds.
+- Overall skill 0.258313; relative MASE 0.878300; relative WQL 0.626324.
+- term skill: long=0.2951, medium=0.3035, short=0.2254.
+- domain skill: Econ/Fin=-0.0599, Energy=0.1417, Healthcare=0.2750, Nature=0.3683, Sales=0.3181, Transport=0.4116, Web/CloudOps=0.2761.
+- frequency skill: A=0.3490, D=0.2962, H=0.3475, M=-0.0679, Q=0.1442, S=-0.6390, T=0.3416, W=0.0785.
+- variates skill: multivariate=0.2482, univariate=0.2662.
+- Outcome: Overall regression relative to step 4000 despite improvement on medium and long horizons. Not selected by overall skill.
+
+## E3 seasonal confidence gate, step 4000 seed 17
+- Checkpoint SHA-256: `f1b4e5f798688a56a693daf799fa21e69ec21c9c1710cae8dafbdb833c9ee069`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 9.475942373275757 seconds.
+- Overall skill 0.301779; relative MASE 0.827721; relative WQL 0.588981.
+- term skill: long=0.3338, medium=0.3656, short=0.2627.
+- domain skill: Econ/Fin=-0.0087, Energy=0.1326, Healthcare=0.1954, Nature=0.3891, Sales=0.3285, Transport=0.4242, Web/CloudOps=0.4509.
+- frequency skill: A=0.3647, D=0.2945, H=0.3455, M=-0.0632, Q=0.1395, S=0.3700, T=0.3389, W=0.1159.
+- variates skill: multivariate=0.3379, univariate=0.2716.
+- Outcome: No additional training. Fixed gate: weight=clip(1 - mean squared seasonal difference / (0.25 * context variance), 0, 0.98). Blend neural quantiles with a last-four-cycle seasonal mean and Gaussian intervals based on seasonal differences. Each instance uses only its own context; no optimized or adapted parameters. Skill increases 0.26647 to 0.30178; selected for full validation.
+
+## E3 step 4000 gate, full validation
+- Checkpoint SHA-256: `see model_manifest.json`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.296168; relative MASE 0.830141; relative WQL 0.596742.
+- term skill: long=0.3262, medium=0.3621, short=0.2569.
+- domain skill: Econ/Fin=-0.0444, Energy=0.1210, Healthcare=0.2106, Nature=0.3890, Sales=0.3389, Transport=0.4244, Web/CloudOps=0.4426.
+- frequency skill: A=0.2806, D=0.2946, H=0.3404, M=-0.0364, Q=0.1048, S=0.3700, T=0.3291, W=0.1093.
+- variates skill: multivariate=0.3332, univariate=0.2652.
+- Outcome: Selected and published. Full validation skill 0.296168 versus 0.206132 for the initial neural package; no failures. Full inference took 39.8 seconds while sharing one GPU with training. This is validation skill, not a held-out reward claim.
+
+## E1 step 8000, seed 17, ungated screening
+- Checkpoint SHA-256: `ebbae91cb50acf1b1436fcbcdce1637195dd6e5006580d3d8e94a4e102aed0ad`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 5.500704050064087 seconds.
+- Overall skill 0.276275; relative MASE 0.864160; relative WQL 0.606113.
+- term skill: long=0.2954, medium=0.3094, short=0.2557.
+- domain skill: Econ/Fin=-0.0085, Energy=0.1522, Healthcare=0.1826, Nature=0.3866, Sales=0.3192, Transport=0.4212, Web/CloudOps=0.3315.
+- frequency skill: A=0.3679, D=0.3262, H=0.3533, M=-0.2016, Q=0.1316, S=-0.4192, T=0.3605, W=0.1103.
+- variates skill: multivariate=0.2735, univariate=0.2785.
+- Outcome: Included in final checkpoint selection; compare to weight-averaged result below.
+
+## E1 step 8000, seed 17, gated screening
+- Checkpoint SHA-256: `ebbae91cb50acf1b1436fcbcdce1637195dd6e5006580d3d8e94a4e102aed0ad`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 6.228765964508057 seconds.
+- Overall skill 0.312772; relative MASE 0.813977; relative WQL 0.580216.
+- term skill: long=0.3671, medium=0.3752, short=0.2645.
+- domain skill: Econ/Fin=-0.0003, Energy=0.1512, Healthcare=0.1999, Nature=0.3863, Sales=0.3176, Transport=0.4184, Web/CloudOps=0.4791.
+- frequency skill: A=0.3679, D=0.3227, H=0.3559, M=-0.1576, Q=0.1357, S=0.3651, T=0.3604, W=0.1062.
+- variates skill: multivariate=0.3530, univariate=0.2790.
+- Outcome: Included in final checkpoint selection; compare to weight-averaged result below.
+
+## E1 step 10000, seed 17, ungated screening
+- Checkpoint SHA-256: `3845afb6035396030b9a5182037287db092814d68caa4c96a7c415eb1d3b3a0a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 6.094600439071655 seconds.
+- Overall skill 0.285838; relative MASE 0.855761; relative WQL 0.595993.
+- term skill: long=0.2974, medium=0.3249, short=0.2658.
+- domain skill: Econ/Fin=0.0083, Energy=0.1537, Healthcare=0.1904, Nature=0.3858, Sales=0.3164, Transport=0.4217, Web/CloudOps=0.3674.
+- frequency skill: A=0.3494, D=0.3058, H=0.3594, M=-0.1439, Q=0.1128, S=-0.2516, T=0.3685, W=0.1234.
+- variates skill: multivariate=0.2917, univariate=0.2812.
+- Outcome: Included in final checkpoint selection; compare to weight-averaged result below.
+
+## E1 step 10000, seed 17, gated screening
+- Checkpoint SHA-256: `3845afb6035396030b9a5182037287db092814d68caa4c96a7c415eb1d3b3a0a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 7.704133749008179 seconds.
+- Overall skill 0.317743; relative MASE 0.805746; relative WQL 0.577695.
+- term skill: long=0.3656, medium=0.3815, short=0.2718.
+- domain skill: Econ/Fin=0.0087, Energy=0.1540, Healthcare=0.2110, Nature=0.3855, Sales=0.3148, Transport=0.4194, Web/CloudOps=0.4916.
+- frequency skill: A=0.3494, D=0.3033, H=0.3612, M=-0.1027, Q=0.1231, S=0.3756, T=0.3699, W=0.1199.
+- variates skill: multivariate=0.3600, univariate=0.2821.
+- Outcome: Included in final checkpoint selection; compare to weight-averaged result below.
+
+## E1 step 12000, seed 17, ungated screening
+- Checkpoint SHA-256: `629130a69f89561219b286c1e805d89b8b4662fce0e4b75e731e8764d9066e98`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 5.807793855667114 seconds.
+- Overall skill 0.284471; relative MASE 0.856118; relative WQL 0.598027.
+- term skill: long=0.2877, medium=0.3155, short=0.2710.
+- domain skill: Econ/Fin=0.0294, Energy=0.1544, Healthcare=0.2252, Nature=0.3903, Sales=0.3151, Transport=0.4273, Web/CloudOps=0.3413.
+- frequency skill: A=0.3430, D=0.3172, H=0.3585, M=-0.1807, Q=0.1231, S=-0.2708, T=0.3565, W=0.1685.
+- variates skill: multivariate=0.2854, univariate=0.2837.
+- Outcome: Included in final checkpoint selection; compare to weight-averaged result below.
+
+## E1 step 12000, seed 17, gated screening
+- Checkpoint SHA-256: `629130a69f89561219b286c1e805d89b8b4662fce0e4b75e731e8764d9066e98`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 7.750269889831543 seconds.
+- Overall skill 0.315453; relative MASE 0.812477; relative WQL 0.576760.
+- term skill: long=0.3585, medium=0.3794, short=0.2715.
+- domain skill: Econ/Fin=0.0081, Energy=0.1534, Healthcare=0.2414, Nature=0.3899, Sales=0.3136, Transport=0.4251, Web/CloudOps=0.4722.
+- frequency skill: A=0.3430, D=0.3141, H=0.3577, M=-0.1400, Q=0.1249, S=0.3722, T=0.3572, W=0.1641.
+- variates skill: multivariate=0.3552, univariate=0.2820.
+- Outcome: Included in final checkpoint selection; compare to weight-averaged result below.
+
+## E4 stochastic weight average, seed 17, gated screening
+- Checkpoint SHA-256: `a346a09ba0b8d196b58acc22a76f7944109b2bba04ea2b7cf7566de24aa2d8db`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 7.737564325332642 seconds.
+- Overall skill 0.320612; relative MASE 0.803931; relative WQL 0.574139.
+- term skill: long=0.3654, medium=0.3823, short=0.2769.
+- domain skill: Econ/Fin=0.0210, Energy=0.1663, Healthcare=0.2341, Nature=0.3839, Sales=0.3192, Transport=0.4246, Web/CloudOps=0.4812.
+- frequency skill: A=0.3645, D=0.3114, H=0.3584, M=-0.0937, Q=0.1411, S=0.3729, T=0.3725, W=0.1398.
+- variates skill: multivariate=0.3557, univariate=0.2913.
+- Outcome: Arithmetic weight average of steps 8000, 9000, 10000, 11000 and 12000. Total underlying training 349.9 seconds on one H100 for 12,000 steps / 1,536,000 windows. Averaging uses no validation labels or training updates. Screening skill 0.320612 exceeds the best individual checkpoint 0.317743; selected for full validation.
+
+## E3 confidence and quantile-width sweep
+- Fixed global settings, selected only on validation; no model updates. Seed and checkpoint: E1 step 8000, seed 17.
+- Measured CPU time: 10.7 seconds. The original threshold 0.25 and width multiplier 1.0 won; no tuned change was adopted.
+- (variance threshold, width multiplier) (0.25, 1.0): skill 0.312772; term skills {'short': 0.2645319168347662, 'medium': 0.37524061562171485, 'long': 0.36711552655262625}.
+- (variance threshold, width multiplier) (0.5, 1.0): skill 0.311338; term skills {'short': 0.26128183571880415, 'medium': 0.3766834137570718, 'long': 0.36686872500364487}.
+- (variance threshold, width multiplier) (0.25, 0.75): skill 0.310114; term skills {'short': 0.26357949467631236, 'medium': 0.3710044838302994, 'long': 0.36223509497654094}.
+- (variance threshold, width multiplier) (0.1, 1.0): skill 0.309380; term skills {'short': 0.2645827018541862, 'medium': 0.3687788042390393, 'long': 0.3590704635989247}.
+- (variance threshold, width multiplier) (0.25, 1.25): skill 0.308716; term skills {'short': 0.25806263726485956, 'medium': 0.3732424245969126, 'long': 0.3664492202082874}.
+- (variance threshold, width multiplier) (0.5, 0.75): skill 0.308480; term skills {'short': 0.2599791649373039, 'medium': 0.3724274186615034, 'long': 0.36196930941343286}.
+- (variance threshold, width multiplier) (0.5, 1.25): skill 0.307830; term skills {'short': 0.25575126666422066, 'medium': 0.3747816871597687, 'long': 0.3663051398261168}.
+- (variance threshold, width multiplier) (0.1, 0.75): skill 0.306923; term skills {'short': 0.26386190560263856, 'medium': 0.3650190620286501, 'long': 0.35398875275507513}.
+- (variance threshold, width multiplier) (0.1, 1.25): skill 0.305043; term skills {'short': 0.2579337472130613, 'medium': 0.36611831669487493, 'long': 0.3583403545682865}.
+- (variance threshold, width multiplier) (1.0, 1.0): skill 0.300332; term skills {'short': 0.24300377373135085, 'medium': 0.37515035650362993, 'long': 0.3625716315021771}.
+- (variance threshold, width multiplier) (1.0, 0.75): skill 0.297470; term skills {'short': 0.24163138399697548, 'medium': 0.37101264403024414, 'long': 0.3577626408389879}.
+- (variance threshold, width multiplier) (1.0, 1.25): skill 0.296900; term skills {'short': 0.23759298244372884, 'medium': 0.3732464838258517, 'long': 0.3619922948184182}.
+- (variance threshold, width multiplier) (0.0, 1.0): skill 0.276275; term skills {'short': 0.25565045294773825, 'medium': 0.30935443355536374, 'long': 0.29541709470756516}.
+- (variance threshold, width multiplier) (0.0, 0.75): skill 0.274035; term skills {'short': 0.2552205537396286, 'medium': 0.30589300644316675, 'long': 0.289930620063797}.
+- (variance threshold, width multiplier) (0.0, 1.25): skill 0.271354; term skills {'short': 0.2486798921582145, 'medium': 0.30569315727356094, 'long': 0.29429049771287774}.
+
+## E5 capacity and exponential averaging
+- Hypothesis: increased capacity reduces residual errors after direct-horizon training; exponential weight averaging reduces checkpoint noise.
+- Changed factors: width 512, six encoder and four decoder layers, 37,002,816 parameters; 24,000-step schedule at 2e-4; seed 23. Same source sample, mixture, synthetic generator, batch 128 and horizon distribution as E1. Training-only EMA decay min(0.999, 1-1/(step+1)).
+- Completed on the sole H100; raw and EMA snapshots were evaluated across all 97 configurations. No causal claim isolates size from seed/schedule changes.
+
+## E4 averaged small model, full validation
+- Checkpoint SHA-256: `see model_manifest.json`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.315325; relative MASE 0.806359; relative WQL 0.581354.
+- term skill: long=0.3584, medium=0.3790, short=0.2715.
+- domain skill: Econ/Fin=-0.0034, Energy=0.1557, Healthcare=0.2457, Nature=0.3833, Sales=0.3308, Transport=0.4241, Web/CloudOps=0.4729.
+- frequency skill: A=0.2978, D=0.3142, H=0.3526, M=-0.0698, Q=0.1192, S=0.3729, T=0.3632, W=0.1318.
+- variates skill: multivariate=0.3509, univariate=0.2856.
+- Outcome: Full validation skill 0.315325, relative MASE 0.806359 and relative WQL 0.581354, all 97 configurations valid. Inference took 37.2 seconds while sharing one H100 with training. Selected over the step-4000 gated package.
+
+## E6 sign symmetry, averaged small model, seed 17
+- Checkpoint SHA-256: `a346a09ba0b8d196b58acc22a76f7944109b2bba04ea2b7cf7566de24aa2d8db`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 7.13059401512146 seconds.
+- Overall skill 0.328463; relative MASE 0.794174; relative WQL 0.567837.
+- term skill: long=0.3740, medium=0.3863, short=0.2861.
+- domain skill: Econ/Fin=0.0049, Energy=0.1627, Healthcare=0.2543, Nature=0.3978, Sales=0.3314, Transport=0.4439, Web/CloudOps=0.4889.
+- frequency skill: A=0.2986, D=0.3191, H=0.3677, M=-0.0077, Q=0.1661, S=0.3711, T=0.3792, W=0.1244.
+- variates skill: multivariate=0.3628, univariate=0.2998.
+- Outcome: No additional training. Average the forecasts from x and negated x with quantile order reversed, then apply positivity and the same fixed seasonal gate. Screening skill rises 0.320612 to 0.328463. Hypothesis: sign symmetry reduces finite-sample neural bias. Selected for full validation.
+
+## E6 sign symmetry, averaged small model, full validation
+- Checkpoint SHA-256: `see model_manifest.json`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.323978; relative MASE 0.795773; relative WQL 0.574292.
+- term skill: long=0.3666, medium=0.3832, short=0.2823.
+- domain skill: Econ/Fin=-0.0072, Energy=0.1517, Healthcare=0.2646, Nature=0.3974, Sales=0.3433, Transport=0.4439, Web/CloudOps=0.4816.
+- frequency skill: A=0.2459, D=0.3222, H=0.3623, M=0.0177, Q=0.1587, S=0.3711, T=0.3705, W=0.1158.
+- variates skill: multivariate=0.3586, univariate=0.2951.
+- Outcome: Full validation skill 0.323978 versus 0.315325 without sign symmetry. Relative MASE 0.795773, relative WQL 0.574292, zero failed configurations, 42.3 seconds of inference. Selected for atomic publication.
+
+## E7 coarse temporal resolution at inference, small averaged model
+- Checkpoint SHA-256: `a346a09ba0b8d196b58acc22a76f7944109b2bba04ea2b7cf7566de24aa2d8db`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 8.896855354309082 seconds.
+- Overall skill 0.320862; relative MASE 0.803289; relative WQL 0.574174.
+- term skill: long=0.3616, medium=0.3660, short=0.2861.
+- domain skill: Econ/Fin=0.0049, Energy=0.1601, Healthcare=0.2543, Nature=0.4043, Sales=0.3314, Transport=0.4281, Web/CloudOps=0.4698.
+- frequency skill: A=0.2986, D=0.3191, H=0.3444, M=-0.0077, Q=0.1661, S=0.3686, T=0.3804, W=0.1244.
+- variates skill: multivariate=0.3567, univariate=0.2909.
+- Outcome: Negative result: for horizons >=400, average the sign-symmetric prediction with a four-step-aggregated prediction from the same weights, then apply the unchanged seasonal gate. Screening skill falls from 0.328463 to 0.320862, so this is rejected. No training or fitted context parameters.
+
+## E8 median and predictable-series loss weighting
+- Hypothesis: optimizing median error explicitly and weighting by context seasonal error aligns pretraining with MASE and quantile loss.
+- Initial weights: E4 averaged small model, trained here from random initialization; no external pretrained assets.
+- Changed factor: loss = mean over valid steps of 0.5 times mean pinball loss plus 0.25 times absolute median error, weighted per instance by clip(1/(0.25 + normalized context seasonal absolute difference), 0.25, 4). Weights are normalized across the batch. Training-only calculation; inference model unchanged.
+- Seed 31, batch 256, 6,000 steps, AdamW 1e-4 with 400-step warmup and cosine decay, same source sample and synthetic generator. EMA as E5.
+- Compute: 272.9 seconds elapsed on the sole H100, shared concurrently with E5. Underlying initial pretraining used 349.9 seconds.
+
+## E8 step 1000, seed 31, symmetric screening
+- Checkpoint SHA-256: `e7cb5c4b4f04ba1ca873bc725f5e4345874520a96cbd3f079e4f631fd3c46d50`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 7.978966474533081 seconds.
+- Overall skill 0.331642; relative MASE 0.791898; relative WQL 0.564091.
+- term skill: long=0.3735, medium=0.3891, short=0.2910.
+- domain skill: Econ/Fin=-0.0197, Energy=0.1668, Healthcare=0.2894, Nature=0.4040, Sales=0.3301, Transport=0.4439, Web/CloudOps=0.4906.
+- frequency skill: A=0.2646, D=0.3158, H=0.3714, M=0.0669, Q=0.1387, S=0.3726, T=0.3764, W=0.1424.
+- variates skill: multivariate=0.3652, univariate=0.3036.
+- Outcome: EMA at 6000 is best overall and selected for full validation; raw checkpoints show greater variance.
+
+## E8 ema 1000, seed 31, symmetric screening
+- Checkpoint SHA-256: `dab309aff5a3a28936391b84b3ebd9d5dfc208e53c1e1602e4f5c599e3004c33`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 8.16230034828186 seconds.
+- Overall skill 0.332263; relative MASE 0.790121; relative WQL 0.564310.
+- term skill: long=0.3745, medium=0.3873, short=0.2925.
+- domain skill: Econ/Fin=0.0051, Energy=0.1638, Healthcare=0.2952, Nature=0.4011, Sales=0.3311, Transport=0.4450, Web/CloudOps=0.4919.
+- frequency skill: A=0.3239, D=0.3220, H=0.3698, M=0.0488, Q=0.1636, S=0.3751, T=0.3783, W=0.1317.
+- variates skill: multivariate=0.3653, univariate=0.3047.
+- Outcome: EMA at 6000 is best overall and selected for full validation; raw checkpoints show greater variance.
+
+## E8 step 2000, seed 31, symmetric screening
+- Checkpoint SHA-256: `111cf52c0662c60aebb44355135a65675a09d898995deeb4dd2f8f2b66339a0f`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 9.85259199142456 seconds.
+- Overall skill 0.326318; relative MASE 0.796087; relative WQL 0.570097.
+- term skill: long=0.3658, medium=0.3826, short=0.2873.
+- domain skill: Econ/Fin=-0.0159, Energy=0.1477, Healthcare=0.3155, Nature=0.4038, Sales=0.3247, Transport=0.4419, Web/CloudOps=0.4862.
+- frequency skill: A=0.2949, D=0.3163, H=0.3678, M=0.0654, Q=0.1519, S=0.3722, T=0.3655, W=0.1286.
+- variates skill: multivariate=0.3573, univariate=0.3006.
+- Outcome: EMA at 6000 is best overall and selected for full validation; raw checkpoints show greater variance.
+
+## E8 ema 2000, seed 31, symmetric screening
+- Checkpoint SHA-256: `5fa525d923b4af5949f2415cf0ad0b325dae7623f07aae06b3131bdbda08c209`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 14.634822130203247 seconds.
+- Overall skill 0.332843; relative MASE 0.789360; relative WQL 0.563874.
+- term skill: long=0.3745, medium=0.3875, short=0.2936.
+- domain skill: Econ/Fin=0.0007, Energy=0.1627, Healthcare=0.3135, Nature=0.4026, Sales=0.3309, Transport=0.4452, Web/CloudOps=0.4913.
+- frequency skill: A=0.3152, D=0.3249, H=0.3696, M=0.0640, Q=0.1700, S=0.3757, T=0.3784, W=0.1261.
+- variates skill: multivariate=0.3645, univariate=0.3065.
+- Outcome: EMA at 6000 is best overall and selected for full validation; raw checkpoints show greater variance.
+
+## E8 step 4000, seed 31, symmetric screening
+- Checkpoint SHA-256: `8ce9867a6005c28005b32d163254c088279f2a46febeadc433b403aeffc29066`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 20.403828620910645 seconds.
+- Overall skill 0.333292; relative MASE 0.789778; relative WQL 0.562816.
+- term skill: long=0.3723, medium=0.3857, short=0.2961.
+- domain skill: Econ/Fin=-0.0157, Energy=0.1603, Healthcare=0.3279, Nature=0.4080, Sales=0.3286, Transport=0.4450, Web/CloudOps=0.4921.
+- frequency skill: A=0.2821, D=0.3254, H=0.3697, M=0.0826, Q=0.1490, S=0.3718, T=0.3810, W=0.1182.
+- variates skill: multivariate=0.3645, univariate=0.3073.
+- Outcome: EMA at 6000 is best overall and selected for full validation; raw checkpoints show greater variance.
+
+## E8 ema 4000, seed 31, symmetric screening
+- Checkpoint SHA-256: `e61535b34efac923328f1dd464f5ab8ab05913438f86125d8b933f1b166281d9`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 10.288180112838745 seconds.
+- Overall skill 0.334221; relative MASE 0.787768; relative WQL 0.562681.
+- term skill: long=0.3735, medium=0.3879, short=0.2964.
+- domain skill: Econ/Fin=-0.0016, Energy=0.1638, Healthcare=0.3235, Nature=0.4037, Sales=0.3295, Transport=0.4477, Web/CloudOps=0.4916.
+- frequency skill: A=0.3041, D=0.3326, H=0.3704, M=0.0629, Q=0.1674, S=0.3746, T=0.3813, W=0.1128.
+- variates skill: multivariate=0.3653, univariate=0.3084.
+- Outcome: EMA at 6000 is best overall and selected for full validation; raw checkpoints show greater variance.
+
+## E8 step 6000, seed 31, symmetric screening
+- Checkpoint SHA-256: `15833e7668d9cd11ed55a4820b4700d71c6f734415d11490c7e8beb654abfa12`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 21.2668559551239 seconds.
+- Overall skill 0.334430; relative MASE 0.788174; relative WQL 0.562038.
+- term skill: long=0.3762, medium=0.3886, short=0.2953.
+- domain skill: Econ/Fin=-0.0108, Energy=0.1611, Healthcare=0.3518, Nature=0.4037, Sales=0.3298, Transport=0.4457, Web/CloudOps=0.4924.
+- frequency skill: A=0.2760, D=0.3360, H=0.3690, M=0.0736, Q=0.1605, S=0.3759, T=0.3813, W=0.1132.
+- variates skill: multivariate=0.3660, univariate=0.3081.
+- Outcome: EMA at 6000 is best overall and selected for full validation; raw checkpoints show greater variance.
+
+## E8 ema 6000, seed 31, symmetric screening
+- Checkpoint SHA-256: `70a3768d667df75f0d6565e831cdca8cf7c179c8546252e5902f7b7ec68ab7aa`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 11.340764284133911 seconds.
+- Overall skill 0.335038; relative MASE 0.787307; relative WQL 0.561630.
+- term skill: long=0.3762, medium=0.3878, short=0.2968.
+- domain skill: Econ/Fin=-0.0053, Energy=0.1637, Healthcare=0.3401, Nature=0.4031, Sales=0.3292, Transport=0.4464, Web/CloudOps=0.4934.
+- frequency skill: A=0.2894, D=0.3329, H=0.3693, M=0.0767, Q=0.1622, S=0.3763, T=0.3828, W=0.1162.
+- variates skill: multivariate=0.3659, univariate=0.3094.
+- Outcome: EMA at 6000 is best overall and selected for full validation; raw checkpoints show greater variance.
+
+## E9 neural context 512 for horizons <=128, seed 17
+- Checkpoint SHA-256: `a346a09ba0b8d196b58acc22a76f7944109b2bba04ea2b7cf7566de24aa2d8db`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 10.416622161865234 seconds.
+- Overall skill 0.320871; relative MASE 0.803968; relative WQL 0.573675.
+- term skill: long=0.3740, medium=0.3863, short=0.2718.
+- domain skill: Econ/Fin=0.0457, Energy=0.1466, Healthcare=0.2113, Nature=0.3887, Sales=0.3285, Transport=0.4330, Web/CloudOps=0.4911.
+- frequency skill: A=0.2986, D=0.2904, H=0.3587, M=-0.0099, Q=0.1658, S=0.3756, T=0.3687, W=0.1716.
+- variates skill: multivariate=0.3665, univariate=0.2822.
+- Outcome: Only the neural input is shortened; the seasonal confidence gate retains 4096 points. 512 loses skill; 1024/2048 improve by less than 0.001 over the control. Not adopted pending a larger effect.
+
+## E9 neural context 1024 for horizons <=128, seed 17
+- Checkpoint SHA-256: `a346a09ba0b8d196b58acc22a76f7944109b2bba04ea2b7cf7566de24aa2d8db`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 19.072535276412964 seconds.
+- Overall skill 0.329251; relative MASE 0.796444; relative WQL 0.564891.
+- term skill: long=0.3740, medium=0.3863, short=0.2876.
+- domain skill: Econ/Fin=0.0323, Energy=0.1629, Healthcare=0.1832, Nature=0.3978, Sales=0.3361, Transport=0.4415, Web/CloudOps=0.4998.
+- frequency skill: A=0.2986, D=0.3031, H=0.3649, M=-0.0077, Q=0.1661, S=0.3761, T=0.3907, W=0.1240.
+- variates skill: multivariate=0.3738, univariate=0.2915.
+- Outcome: Only the neural input is shortened; the seasonal confidence gate retains 4096 points. 512 loses skill; 1024/2048 improve by less than 0.001 over the control. Not adopted pending a larger effect.
+
+## E9 neural context 2048 for horizons <=128, seed 17
+- Checkpoint SHA-256: `a346a09ba0b8d196b58acc22a76f7944109b2bba04ea2b7cf7566de24aa2d8db`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 9.426023244857788 seconds.
+- Overall skill 0.329063; relative MASE 0.794285; relative WQL 0.566744.
+- term skill: long=0.3740, medium=0.3863, short=0.2872.
+- domain skill: Econ/Fin=0.0227, Energy=0.1628, Healthcare=0.2235, Nature=0.3975, Sales=0.3314, Transport=0.4448, Web/CloudOps=0.4931.
+- frequency skill: A=0.2986, D=0.3154, H=0.3656, M=-0.0077, Q=0.1661, S=0.3738, T=0.3847, W=0.1224.
+- variates skill: multivariate=0.3651, univariate=0.2989.
+- Outcome: Only the neural input is shortened; the seasonal confidence gate retains 4096 points. 512 loses skill; 1024/2048 improve by less than 0.001 over the control. Not adopted pending a larger effect.
+
+## E10 fixed 50/50 ensemble diagnostic
+- Checkpoint SHA-256: `see model_manifest.json`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 4.082070589065552 seconds.
+- Overall skill 0.332712; relative MASE 0.791350; relative WQL 0.562676.
+- term skill: long=0.3746, medium=0.3901, short=0.2921.
+- domain skill: Econ/Fin=-0.0029, Energy=0.1641, Healthcare=0.3036, Nature=0.4055, Sales=0.3288, Transport=0.4500, Web/CloudOps=0.4870.
+- frequency skill: A=0.2971, D=0.3243, H=0.3712, M=0.0353, Q=0.1843, S=0.3742, T=0.3778, W=0.1385.
+- variates skill: multivariate=0.3658, univariate=0.3052.
+- Outcome: Average of E8 EMA4000 and E5 EMA12000, both with sign symmetry and the same fixed seasonal gate. Skill 0.332712 is below E8 alone (0.334221). Rejected for now; both members would forecast every instance.
+
+## E8 EMA6000, full validation
+- Checkpoint SHA-256: `see model_manifest.json`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.330109; relative MASE 0.789610; relative WQL 0.568323.
+- term skill: long=0.3687, medium=0.3851, short=0.2920.
+- domain skill: Econ/Fin=-0.0223, Energy=0.1525, Healthcare=0.3475, Nature=0.4024, Sales=0.3415, Transport=0.4461, Web/CloudOps=0.4860.
+- frequency skill: A=0.2370, D=0.3358, H=0.3639, M=0.0960, Q=0.1447, S=0.3763, T=0.3738, W=0.1061.
+- variates skill: multivariate=0.3616, univariate=0.3039.
+- Outcome: Full validation skill 0.330109, relative MASE 0.789610, relative WQL 0.568323, zero failed configurations. Inference took 48.1 seconds while sharing the one H100. Improves the previous published 0.323978 and is selected for atomic publication.
+
+## E11 matched corpus-coverage experiment
+- Hypothesis: broader source coverage improves generalization after the initial model has plateaued.
+- Expanded sample: 95 directories, 1,032,415 series, 5,653,290,832 points (22.613 GB), versus 357,789 series and 958,745,103 points (3.835 GB). Per-source cap rises from 24M to 192M points, Arrow record batches are shuffled within each selected shard, and the per-series crop cap rises from 131,072 to 1,048,576. The same deterministic directory-hash seeds and at most eight sampled shards per directory are used.
+- Two matched continuations start from E8 EMA6000: original sample control and expanded sample. Both use seed 41, batch 256, the E8 loss, 6,000 steps, AdamW 1e-4 and the same schedule/EMA. Training uses only corpus/synthetic data.
+- Runs share the single H100 with E5; CPU threads are limited to three per new trainer. Completed matched runs: control 363.4 s, expanded 364.8 s; the expanded corpus did not improve either matched checkpoint. Detailed results are below.
+
+## E12 fixed continuity correction
+- Hypothesis: a direct-horizon head can miss the immediate level of a smooth history, and a small decaying correction should reduce short-horizon error. No training or context fitting. E8 EMA6000 weights, seed 31.
+- Fixed formula: ratio=mean(square(first differences))/variance over the last 512 points; w=clip(1-ratio/0.5,0,0.95); bias=w*(last value + median of the last eight differences - first forecast median). Add bias*exp(-step/tau) to each quantile, then retain the existing positivity rule. Skip sparse missing-value differences.
+- Initial diagnostic needed a missing-value guard; invalid intermediate scores were discarded. A positivity-lookback mismatch in the probe was also corrected so tau=0 exactly reproduces the control.
+- CPU sweep took 6.0 seconds; final verified control and all candidate scores follow. Tau 24 selected for package/full validation.
+- tau=0: skill 0.335038, relative MASE 0.787307, relative WQL 0.561630, group skills {'term': {'short': 0.2967927346774093, 'medium': 0.38776799350809343, 'long': 0.37616349665047677}, 'domain': {'Econ/Fin': -0.005273788909573085, 'Energy': 0.16374587322817125, 'Healthcare': 0.34007707190705616, 'Nature': 0.4030815329292825, 'Sales': 0.32924485226314826, 'Transport': 0.4464324047455728, 'Web/CloudOps': 0.4934311796492926}, 'freq': {'A-DEC': 0.2893784388532201, 'Q-DEC': 0.16218093798601307, 'M': 0.07665328507242941, 'W-SUN': -0.31450314757766273, 'D': 0.332876404786148, 'H': 0.3693479345200832, '15T': 0.14228566862247327, 'W-FRI': 0.032405497411887585, '10T': 0.4887742379514316, 'W-TUE': 0.309664207244038, 'W-THU': 0.14794979612329628, 'W-WED': 0.29198895648742507, '5T': 0.5120305409377247, '10S': 0.3762720731817548}}.
+- tau=2: skill 0.337508, relative MASE 0.783332, relative WQL 0.560293, group skills {'term': {'short': 0.30112159439110886, 'medium': 0.3882128168958763, 'long': 0.3763449044348186}, 'domain': {'Econ/Fin': 0.012010642573481944, 'Energy': 0.1687336419481582, 'Healthcare': 0.33847751009406857, 'Nature': 0.4038059435050727, 'Sales': 0.32920024652725177, 'Transport': 0.4463400575252827, 'Web/CloudOps': 0.4950030531002031}, 'freq': {'A-DEC': 0.2973561434987757, 'Q-DEC': 0.17698071591831765, 'M': 0.07233297868059718, 'W-SUN': -0.2582996069171355, 'D': 0.3346112879714367, 'H': 0.36855118351366944, '15T': 0.14569114456054433, 'W-FRI': 0.021432593572035197, '10T': 0.5064176027092252, 'W-TUE': 0.3132256284193611, 'W-THU': 0.1516542652623164, 'W-WED': 0.29198895648742507, '5T': 0.5121886153783087, '10S': 0.38181456512243606}}.
+- tau=8: skill 0.339730, relative MASE 0.781190, relative WQL 0.558067, group skills {'term': {'short': 0.30468677533784194, 'medium': 0.3890128603479147, 'long': 0.3768542253336602}, 'domain': {'Econ/Fin': 0.04357586882889164, 'Energy': 0.17060177779549268, 'Healthcare': 0.3292576172809155, 'Nature': 0.4040895491577191, 'Sales': 0.32915095007230155, 'Transport': 0.4455718893158739, 'Web/CloudOps': 0.498582535356759}, 'freq': {'A-DEC': 0.30427943534005153, 'Q-DEC': 0.18739632573970566, 'M': 0.05811644843967234, 'W-SUN': -0.14993519310493708, 'D': 0.33691177813304396, 'H': 0.36800946318922256, '15T': 0.15562275067857723, 'W-FRI': 0.015293948848938421, '10T': 0.5022238811405833, 'W-TUE': 0.308852394109824, 'W-THU': 0.16531962754891327, 'W-WED': 0.29198895648742507, '5T': 0.5124270255950129, '10S': 0.3959332124123227}}.
+- tau=24: skill 0.341604, relative MASE 0.780976, relative WQL 0.555056, group skills {'term': {'short': 0.30735425505077896, 'medium': 0.38984232067302926, 'long': 0.37791322023985785}, 'domain': {'Econ/Fin': 0.07789629542475163, 'Energy': 0.16814859423491146, 'Healthcare': 0.32219860777640197, 'Nature': 0.4027938268275436, 'Sales': 0.32911299366848257, 'Transport': 0.4440214473788372, 'Web/CloudOps': 0.505502554697025}, 'freq': {'A-DEC': 0.30701454754159097, 'Q-DEC': 0.18894276751813088, 'M': 0.04173508991923269, 'W-SUN': -0.06710914169849702, 'D': 0.33891978173454174, 'H': 0.36806701600948255, '15T': 0.16691265843751302, 'W-FRI': 0.011149977535114819, '10T': 0.48062823437029445, 'W-TUE': 0.3054618645258299, 'W-THU': 0.17135723270801106, 'W-WED': 0.29198895648742507, '5T': 0.5128629288387704, '10S': 0.42606586812368186}}.
+- tau=64: skill 0.340824, relative MASE 0.783313, relative WQL 0.554713, group skills {'term': {'short': 0.3064164746371565, 'medium': 0.38779908004530583, 'long': 0.3787937309532532}, 'domain': {'Econ/Fin': 0.09969949435020342, 'Energy': 0.15974598777640392, 'Healthcare': 0.3189527251466179, 'Nature': 0.4009254556920715, 'Sales': 0.32909545704191134, 'Transport': 0.44166143132149305, 'Web/CloudOps': 0.5103628293774518}, 'freq': {'A-DEC': 0.3079352359976604, 'Q-DEC': 0.18879190434187287, 'M': 0.03049481695615497, 'W-SUN': -0.03231570437562303, 'D': 0.33992332397958547, 'H': 0.36593420410508737, '15T': 0.1654947222236236, 'W-FRI': 0.009213828856730832, '10T': 0.46016772262416383, 'W-TUE': 0.3040006089982843, 'W-THU': 0.17343046632698478, 'W-WED': 0.29198895648742507, '5T': 0.5128850832828526, '10S': 0.4486615108502494}}.
+- tau=128: skill 0.338292, relative MASE 0.786639, relative WQL 0.556618, group skills {'term': {'short': 0.3043668884529205, 'medium': 0.38176748757743373, 'long': 0.37868472074438897}, 'domain': {'Econ/Fin': 0.10637792737302931, 'Energy': 0.1531270206619928, 'Healthcare': 0.31816668045459984, 'Nature': 0.39985518410953147, 'Sales': 0.3290895550941987, 'Transport': 0.4399018505526925, 'Web/CloudOps': 0.5082609219137205}, 'freq': {'A-DEC': 0.3082114553822066, 'Q-DEC': 0.1886335736447592, 'M': 0.026327746733658897, 'W-SUN': -0.021307580325764297, 'D': 0.34025742210101806, 'H': 0.3625439077766571, '15T': 0.1628300810039216, 'W-FRI': 0.00856370190545519, '10T': 0.4513327819863723, 'W-TUE': 0.30351149262807786, 'W-THU': 0.1740816133131421, 'W-WED': 0.29198895648742507, '5T': 0.512369368619311, '10S': 0.44352723463256316}}.
+
+## E12 continuity prior, full validation
+- Checkpoint SHA-256: `see model_manifest.json`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.336914; relative MASE 0.783869; relative WQL 0.560914.
+- term skill: long=0.3705, medium=0.3876, short=0.3028.
+- domain skill: Econ/Fin=0.0580, Energy=0.1590, Healthcare=0.3286, Nature=0.4018, Sales=0.3415, Transport=0.4438, Web/CloudOps=0.4980.
+- frequency skill: A=0.2499, D=0.3408, H=0.3619, M=0.0606, Q=0.1682, S=0.4261, T=0.3809, W=0.1368.
+- variates skill: multivariate=0.3731, univariate=0.3066.
+- Outcome: Full validation skill 0.336914 versus 0.330109 without the correction, relative MASE 0.783869 and relative WQL 0.560914; zero failures. Package inference took 69.8 seconds while three trainers shared the sole H100. Selected for atomic publication.
+
+## E11 corpus_control_ema1000
+- Checkpoint SHA-256: `f81bf26e2a17265332fdb3b29204d140703d7c3e9d9abdd7e3297715a7b1153a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 10.393452167510986 seconds.
+- Overall skill 0.335216; relative MASE 0.787169; relative WQL 0.561427.
+- term skill: long=0.3757, medium=0.3878, short=0.2973.
+- domain skill: Econ/Fin=-0.0001, Energy=0.1627, Healthcare=0.3427, Nature=0.4053, Sales=0.3297, Transport=0.4460, Web/CloudOps=0.4926.
+- frequency skill: A=0.3085, D=0.3314, H=0.3702, M=0.0765, Q=0.1659, S=0.3764, T=0.3820, W=0.1191.
+- variates skill: multivariate=0.3656, univariate=0.3100.
+- Outcome: Matched comparison: original sample control stays near the parent model; expanded corpus is consistently slightly worse. Neither continuation is selected. Compute: control 363.4 s, expanded 364.8 s elapsed while sharing one H100.
+
+## E11 corpus_control_ema3000
+- Checkpoint SHA-256: `c72daec0504f6082965d689dd9a9f1a7005ac0d9694fb8d4de3d2622db9d615b`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 10.011096715927124 seconds.
+- Overall skill 0.334832; relative MASE 0.787218; relative WQL 0.562040.
+- term skill: long=0.3765, medium=0.3877, short=0.2963.
+- domain skill: Econ/Fin=0.0014, Energy=0.1632, Healthcare=0.3213, Nature=0.4049, Sales=0.3317, Transport=0.4468, Web/CloudOps=0.4939.
+- frequency skill: A=0.2922, D=0.3214, H=0.3695, M=0.0723, Q=0.1640, S=0.3781, T=0.3850, W=0.1283.
+- variates skill: multivariate=0.3670, univariate=0.3081.
+- Outcome: Matched comparison: original sample control stays near the parent model; expanded corpus is consistently slightly worse. Neither continuation is selected. Compute: control 363.4 s, expanded 364.8 s elapsed while sharing one H100.
+
+## E11 corpus_control_ema6000
+- Checkpoint SHA-256: `fa23c895ff4926b3d0df6d15267718a1dda41ad9386abbfef1306becb262da1c`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 7.355180501937866 seconds.
+- Overall skill 0.334809; relative MASE 0.788444; relative WQL 0.561205.
+- term skill: long=0.3770, medium=0.3879, short=0.2960.
+- domain skill: Econ/Fin=-0.0139, Energy=0.1615, Healthcare=0.3361, Nature=0.4053, Sales=0.3305, Transport=0.4466, Web/CloudOps=0.4951.
+- frequency skill: A=0.2945, D=0.3215, H=0.3707, M=0.0639, Q=0.1501, S=0.3778, T=0.3855, W=0.1254.
+- variates skill: multivariate=0.3680, univariate=0.3071.
+- Outcome: Matched comparison: original sample control stays near the parent model; expanded corpus is consistently slightly worse. Neither continuation is selected. Compute: control 363.4 s, expanded 364.8 s elapsed while sharing one H100.
+
+## E11 corpus_expanded_ema1000
+- Checkpoint SHA-256: `8c0f66d7a2377fa37b4d3358c89ae9b5786a6f5f0223b1480fb7a0b20b17ebc8`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 10.571230411529541 seconds.
+- Overall skill 0.333782; relative MASE 0.789135; relative WQL 0.562448.
+- term skill: long=0.3749, medium=0.3867, short=0.2955.
+- domain skill: Econ/Fin=-0.0143, Energy=0.1614, Healthcare=0.3302, Nature=0.4029, Sales=0.3292, Transport=0.4472, Web/CloudOps=0.4938.
+- frequency skill: A=0.2932, D=0.3308, H=0.3695, M=0.0696, Q=0.1628, S=0.3754, T=0.3818, W=0.1103.
+- variates skill: multivariate=0.3653, univariate=0.3075.
+- Outcome: Matched comparison: original sample control stays near the parent model; expanded corpus is consistently slightly worse. Neither continuation is selected. Compute: control 363.4 s, expanded 364.8 s elapsed while sharing one H100.
+
+## E11 corpus_expanded_ema3000
+- Checkpoint SHA-256: `51586b5ce1a426dc2f3adf42aba9599b731b62ab316159dafe681e77317015a2`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 10.598361253738403 seconds.
+- Overall skill 0.333836; relative MASE 0.789352; relative WQL 0.562201.
+- term skill: long=0.3750, medium=0.3869, short=0.2954.
+- domain skill: Econ/Fin=-0.0087, Energy=0.1631, Healthcare=0.3250, Nature=0.4036, Sales=0.3303, Transport=0.4468, Web/CloudOps=0.4922.
+- frequency skill: A=0.3132, D=0.3259, H=0.3690, M=0.0696, Q=0.1693, S=0.3752, T=0.3829, W=0.1162.
+- variates skill: multivariate=0.3659, univariate=0.3071.
+- Outcome: Matched comparison: original sample control stays near the parent model; expanded corpus is consistently slightly worse. Neither continuation is selected. Compute: control 363.4 s, expanded 364.8 s elapsed while sharing one H100.
+
+## E11 corpus_expanded_ema6000
+- Checkpoint SHA-256: `b82b9da6afa6b7ec864a5c4f9829a2a68b29f0923d0c556a998b9d79623d9ba9`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 7.799206972122192 seconds.
+- Overall skill 0.333432; relative MASE 0.790380; relative WQL 0.562151.
+- term skill: long=0.3759, medium=0.3881, short=0.2938.
+- domain skill: Econ/Fin=-0.0272, Energy=0.1621, Healthcare=0.3299, Nature=0.4047, Sales=0.3298, Transport=0.4459, Web/CloudOps=0.4936.
+- frequency skill: A=0.3024, D=0.3227, H=0.3691, M=0.0639, Q=0.1574, S=0.3794, T=0.3834, W=0.1166.
+- variates skill: multivariate=0.3662, univariate=0.3061.
+- Outcome: Matched comparison: original sample control stays near the parent model; expanded corpus is consistently slightly worse. Neither continuation is selected. Compute: control 363.4 s, expanded 364.8 s elapsed while sharing one H100.
+
+## E5 direct_large_ema12000_gate
+- Checkpoint SHA-256: `202e23ba9d7560bf2bd67a609d024449facabdfd338c9268bc013ff6b613168f`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 11.02167820930481 seconds.
+- Overall skill 0.314336; relative MASE 0.813757; relative WQL 0.577734.
+- term skill: long=0.3670, medium=0.3868, short=0.2623.
+- domain skill: Econ/Fin=-0.0160, Energy=0.1404, Healthcare=0.2477, Nature=0.3897, Sales=0.3170, Transport=0.4355, Web/CloudOps=0.4760.
+- frequency skill: A=0.3627, D=0.3141, H=0.3649, M=-0.1520, Q=0.1593, S=0.3730, T=0.3518, W=0.1342.
+- variates skill: multivariate=0.3564, univariate=0.2788.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E5 direct_large_ema16000_gate
+- Checkpoint SHA-256: `8ba1220059af5a85180b3ad740402a48b2d38172598510da796339378e529a73`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 9.176010131835938 seconds.
+- Overall skill 0.322612; relative MASE 0.806599; relative WQL 0.568877.
+- term skill: long=0.3668, medium=0.3875, short=0.2777.
+- domain skill: Econ/Fin=0.0042, Energy=0.1444, Healthcare=0.3056, Nature=0.3937, Sales=0.3266, Transport=0.4361, Web/CloudOps=0.4847.
+- frequency skill: A=0.3573, D=0.3049, H=0.3646, M=-0.0462, Q=0.1603, S=0.3803, T=0.3607, W=0.1797.
+- variates skill: multivariate=0.3644, univariate=0.2874.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E5 direct_large_ema20000_gate
+- Checkpoint SHA-256: `1ec9d54fa1270442b0023da3f7fc23f5fa5291e58210a55f63878983afccc606`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 11.57221269607544 seconds.
+- Overall skill 0.324943; relative MASE 0.805088; relative WQL 0.566028.
+- term skill: long=0.3666, medium=0.3878, short=0.2821.
+- domain skill: Econ/Fin=0.0218, Energy=0.1439, Healthcare=0.2912, Nature=0.4039, Sales=0.3250, Transport=0.4352, Web/CloudOps=0.4880.
+- frequency skill: A=0.3533, D=0.3058, H=0.3660, M=-0.0485, Q=0.1544, S=0.3756, T=0.3665, W=0.1840.
+- variates skill: multivariate=0.3678, univariate=0.2887.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E5 direct_large_ema2000_gate
+- Checkpoint SHA-256: `bea7fbebdc754a6cef068a4b835e644d896305e928b4a63974c731874dc49028`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 7.918560028076172 seconds.
+- Overall skill 0.279056; relative MASE 0.848285; relative WQL 0.612718.
+- term skill: long=0.3485, medium=0.3540, short=0.2185.
+- domain skill: Econ/Fin=-0.0871, Energy=0.1232, Healthcare=0.1986, Nature=0.3855, Sales=0.3094, Transport=0.3731, Web/CloudOps=0.4269.
+- frequency skill: A=0.3054, D=0.2729, H=0.3360, M=-0.1424, Q=0.1034, S=0.3706, T=0.2968, W=0.1303.
+- variates skill: multivariate=0.3283, univariate=0.2373.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E5 direct_large_ema24000_gate
+- Checkpoint SHA-256: `e5bb7f70c0adbf61b4292cfc40a419e6757854a255ef0c47d3d47c7f2d68b7a2`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 8.641167879104614 seconds.
+- Overall skill 0.323234; relative MASE 0.806197; relative WQL 0.568114.
+- term skill: long=0.3665, medium=0.3864, short=0.2795.
+- domain skill: Econ/Fin=0.0385, Energy=0.1492, Healthcare=0.2588, Nature=0.4037, Sales=0.3217, Transport=0.4347, Web/CloudOps=0.4807.
+- frequency skill: A=0.3645, D=0.3062, H=0.3653, M=-0.0563, Q=0.1548, S=0.3766, T=0.3656, W=0.1664.
+- variates skill: multivariate=0.3653, univariate=0.2877.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E5 direct_large_ema4000_gate
+- Checkpoint SHA-256: `2f45a9ce8bed8e0e3ebe37ba98ac36ab8b9a630a615856760f46c0763fa78b5c`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 8.853939056396484 seconds.
+- Overall skill 0.296679; relative MASE 0.830007; relative WQL 0.595971.
+- term skill: long=0.3569, medium=0.3692, short=0.2413.
+- domain skill: Econ/Fin=-0.0411, Energy=0.1331, Healthcare=0.1511, Nature=0.3889, Sales=0.3253, Transport=0.4031, Web/CloudOps=0.4590.
+- frequency skill: A=0.3534, D=0.2760, H=0.3509, M=-0.1255, Q=0.1495, S=0.3734, T=0.3314, W=0.1185.
+- variates skill: multivariate=0.3480, univariate=0.2530.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E5 direct_large_ema8000_gate
+- Checkpoint SHA-256: `1b15662821b8c7a20d973f548dacabd237f470ffd1c75457c0b92011d3f0cc88`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 9.06697702407837 seconds.
+- Overall skill 0.310188; relative MASE 0.816631; relative WQL 0.582688.
+- term skill: long=0.3619, medium=0.3788, short=0.2604.
+- domain skill: Econ/Fin=-0.0143, Energy=0.1404, Healthcare=0.2015, Nature=0.3934, Sales=0.3255, Transport=0.4210, Web/CloudOps=0.4744.
+- frequency skill: A=0.3680, D=0.2929, H=0.3581, M=-0.0609, Q=0.1607, S=0.3679, T=0.3513, W=0.1178.
+- variates skill: multivariate=0.3533, univariate=0.2739.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E5 direct_large_step12000_gate
+- Checkpoint SHA-256: `8788d76e331dcd9cca1d6dab37db34b4c8b1257fc83da49b532c11643cd0ebfa`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 14.492464065551758 seconds.
+- Overall skill 0.304901; relative MASE 0.821804; relative WQL 0.587930.
+- term skill: long=0.3555, medium=0.3694, short=0.2575.
+- domain skill: Econ/Fin=-0.0110, Energy=0.1063, Healthcare=0.2456, Nature=0.3830, Sales=0.2991, Transport=0.4416, Web/CloudOps=0.4763.
+- frequency skill: A=0.3131, D=0.3025, H=0.3517, M=-0.0499, Q=0.1271, S=0.3821, T=0.3324, W=0.1350.
+- variates skill: multivariate=0.3451, univariate=0.2712.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E5 direct_large_step16000_gate
+- Checkpoint SHA-256: `beb43e784be8db8ee5ec14e83fa9c68f7ce897790441453eb6d72cceedb80650`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 9.434912919998169 seconds.
+- Overall skill 0.311197; relative MASE 0.820659; relative WQL 0.578132.
+- term skill: long=0.3577, medium=0.3731, short=0.2667.
+- domain skill: Econ/Fin=-0.0270, Energy=0.1289, Healthcare=0.2653, Nature=0.3904, Sales=0.3264, Transport=0.4341, Web/CloudOps=0.4732.
+- frequency skill: A=0.3445, D=0.2986, H=0.3593, M=-0.0768, Q=0.1596, S=0.3715, T=0.3463, W=0.1472.
+- variates skill: multivariate=0.3513, univariate=0.2775.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E5 direct_large_step20000_gate
+- Checkpoint SHA-256: `9332e5088244bd4a2a1ad5b6470a108ebaa6bc387a525202f6bf954d9796b1a6`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 11.937657117843628 seconds.
+- Overall skill 0.319529; relative MASE 0.811224; relative WQL 0.570793.
+- term skill: long=0.3661, medium=0.3842, short=0.2737.
+- domain skill: Econ/Fin=0.0113, Energy=0.1395, Healthcare=0.3332, Nature=0.3983, Sales=0.3058, Transport=0.4236, Web/CloudOps=0.4804.
+- frequency skill: A=0.3483, D=0.3114, H=0.3599, M=-0.0666, Q=0.1435, S=0.3671, T=0.3551, W=0.1967.
+- variates skill: multivariate=0.3633, univariate=0.2825.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E5 direct_large_step2000_gate
+- Checkpoint SHA-256: `f16c3270e2d4adafc8dbc1416687932a81b040bed29ad21887f7f8953437ba86`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 8.376563549041748 seconds.
+- Overall skill 0.284757; relative MASE 0.840864; relative WQL 0.608389.
+- term skill: long=0.3356, medium=0.3611, short=0.2319.
+- domain skill: Econ/Fin=-0.0840, Energy=0.1152, Healthcare=0.2242, Nature=0.3840, Sales=0.3264, Transport=0.3898, Web/CloudOps=0.4386.
+- frequency skill: A=0.2579, D=0.2746, H=0.3418, M=-0.0497, Q=0.1253, S=0.3787, T=0.2935, W=0.1458.
+- variates skill: multivariate=0.3309, univariate=0.2458.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E5 direct_large_step24000_gate
+- Checkpoint SHA-256: `b7e4f38aebcd2cf38ce484edaad8d19851317fb3b8df485e2faa0d0423cbcb48`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 7.831688165664673 seconds.
+- Overall skill 0.326279; relative MASE 0.802486; relative WQL 0.565617.
+- term skill: long=0.3671, medium=0.3874, short=0.2845.
+- domain skill: Econ/Fin=0.0453, Energy=0.1487, Healthcare=0.3262, Nature=0.4087, Sales=0.3234, Transport=0.4251, Web/CloudOps=0.4821.
+- frequency skill: A=0.3544, D=0.3106, H=0.3612, M=-0.0014, Q=0.1548, S=0.3765, T=0.3668, W=0.1910.
+- variates skill: multivariate=0.3662, univariate=0.2927.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E5 direct_large_step4000_gate
+- Checkpoint SHA-256: `a085c48b8bac06dba51118942d58b632dfc8fd934455232b2f1f7c7329f1ae76`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 8.682147979736328 seconds.
+- Overall skill 0.291006; relative MASE 0.830831; relative WQL 0.605023.
+- term skill: long=0.3451, medium=0.3504, short=0.2444.
+- domain skill: Econ/Fin=-0.0403, Energy=0.0877, Healthcare=0.2367, Nature=0.3707, Sales=0.3185, Transport=0.4275, Web/CloudOps=0.4638.
+- frequency skill: A=0.2862, D=0.2949, H=0.3327, M=-0.1134, Q=0.1567, S=0.3705, T=0.3243, W=0.1248.
+- variates skill: multivariate=0.3352, univariate=0.2538.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E5 direct_large_step8000_gate
+- Checkpoint SHA-256: `7c0ca8d6f3023ecefc12818560b9bac64e044102cb27d478978c48ce82276c38`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 8.785595417022705 seconds.
+- Overall skill 0.307945; relative MASE 0.812528; relative WQL 0.589444.
+- term skill: long=0.3650, medium=0.3688, short=0.2592.
+- domain skill: Econ/Fin=-0.0843, Energy=0.1539, Healthcare=0.1762, Nature=0.3845, Sales=0.3220, Transport=0.4223, Web/CloudOps=0.4730.
+- frequency skill: A=0.3235, D=0.2684, H=0.3578, M=-0.0688, Q=0.1156, S=0.3591, T=0.3615, W=0.1148.
+- variates skill: multivariate=0.3471, univariate=0.2751.
+- Outcome: 37M capacity run from random initialization; seed 23. Final run: 24,000 steps / 3,072,000 windows in 1073.9 s elapsed on the sole H100, shared with other experiments. Most checkpoints lag the small model; EMA reduces checkpoint variance. Final raw checkpoint is stronger than final EMA in gated inference, so it will receive a full inference-rule comparison.
+
+## E13/E14 matched large-model continuations
+- Parent: raw E5 step 24000 (random-init pretraining here, seed 23), selected after final gated and sign-symmetric/continuity comparisons.
+- Three runs share seed 43, batch 256, 6,000 steps, AdamW 8e-5 with 400-step warmup/cosine decay, the original packed sample, and the E8 median/seasonal-error-weighted objective. All use EMA. GPU: the one H100; three CPU threads per trainer.
+- large_loss: unchanged synthetic generator, control for applying E8 loss to the larger model.
+- large_synth: replace half of the 20% synthetic windows with newly generated integrated colored innovations, stochastic local-linear trends with level shifts, or sharp periodic pulses with slowly drifting amplitude. Generator is in large_synth/data.py; no external synthetic assets.
+- large_sign: independently negate each sampled context and target with probability 0.5. Context seasonal scales are sign-invariant. Data RNG is shared with augmentation, so later draws diverge from the control despite the matched seed; this is a single-seed comparison.
+- All inference rules remain fixed (sign symmetry, seasonal confidence gate and 24-step continuity). Results pending.
+
+## E15 final-checkpoint ensemble
+- Hypothesis: the differently trained small and large models have complementary median and distribution errors. Fixed equal weights, both members forecast every instance. E8 EMA6000 (seed31 continuation of seed17) plus E5 raw24000 (seed23). Same fixed inference rules applied separately before averaging.
+- Cached prediction diagnostic skill 0.344733 exceeds the small model 0.341604 and large model 0.343219. Actual bundled inference and full validation are required before publication.
+
+## E15 actual bundled ensemble screening
+- Checkpoint SHA-256: `91cb9b3fc2422fc376ecab7813213a7156b6baee963da620f06e82f1aaeaea39`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 21.611496925354004 seconds.
+- Overall skill 0.344715; relative MASE 0.780210; relative WQL 0.550363.
+- term skill: long=0.3780, medium=0.3928, short=0.3118.
+- domain skill: Econ/Fin=0.0849, Energy=0.1725, Healthcare=0.3305, Nature=0.4119, Sales=0.3307, Transport=0.4448, Web/CloudOps=0.5036.
+- frequency skill: A=0.2873, D=0.3348, H=0.3699, M=0.0563, Q=0.1938, S=0.4258, T=0.3912, W=0.1713.
+- variates skill: multivariate=0.3825, univariate=0.3130.
+- Outcome: Actual package inference skill 0.344715, within 0.000019 of cached averaging; zero failures. The difference comes from numerical batching. Full package inference remains required.
+
+## E15 bundled ensemble, full validation
+- Checkpoint SHA-256: `91cb9b3fc2422fc376ecab7813213a7156b6baee963da620f06e82f1aaeaea39`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.340239; relative MASE 0.782773; relative WQL 0.556081.
+- term skill: long=0.3706, medium=0.3906, short=0.3076.
+- domain skill: Econ/Fin=0.0668, Energy=0.1633, Healthcare=0.3369, Nature=0.4112, Sales=0.3436, Transport=0.4446, Web/CloudOps=0.4963.
+- frequency skill: A=0.2315, D=0.3371, H=0.3638, M=0.0773, Q=0.1704, S=0.4258, T=0.3837, W=0.1660.
+- variates skill: multivariate=0.3783, univariate=0.3083.
+- Outcome: Full skill 0.340239, relative MASE 0.782773, relative WQL 0.556081, zero failed configurations. Improves the previous published 0.336914. Relocated package inference took 119.3 seconds while three 37M trainers shared the sole H100. Repeated example calls were bitwise identical, finite and monotone. Selected for atomic publication.
+
+## E16 earlier-window diagnostic
+- Hypothesis: a second cutoff reveals whether gains on single-series configurations are stable over time.
+- Constructed the immediately preceding nonoverlapping window from each selected validation context (never used for training). All 97 configurations remain; short histories with fewer than H+8 points are omitted. Seasonal-error scales use the available capped prefix and Seasonal Naive is recomputed on that same input, so these are diagnostic scores on a separate scale.
+- The small E8 EMA6000 model improves from 0.308552 without continuity to 0.314127 with it. The large parent scores 0.312321; the first sign-augmented checkpoint scores 0.312240. Thus continuity is supported at both cutoffs, while the first sign-augmentation gain does not yet repeat on the earlier cutoff. No weights or rules are adapted on the diagnostic contexts.
+
+## Full-validation checkpoint identity audit
+- valid-report-1000.json: `ebdd9eaa223c2bb6e38b8faec6f465a227fcd735b654a26f3b617a757addb0d3`, skill 0.206132.
+- valid-report-4000-gate.json: `f1b4e5f798688a56a693daf799fa21e69ec21c9c1710cae8dafbdb833c9ee069`, skill 0.296168.
+- valid-report-swa-gate.json: `a346a09ba0b8d196b58acc22a76f7944109b2bba04ea2b7cf7566de24aa2d8db`, skill 0.315325.
+- valid-report-symmetric.json: `a346a09ba0b8d196b58acc22a76f7944109b2bba04ea2b7cf7566de24aa2d8db`, skill 0.323978.
+- valid-report-objective.json: `70a3768d667df75f0d6565e831cdca8cf7c179c8546252e5902f7b7ec68ab7aa`, skill 0.330109.
+- valid-report-continuity.json: `70a3768d667df75f0d6565e831cdca8cf7c179c8546252e5902f7b7ec68ab7aa`, skill 0.336914.
+- valid-report-ensemble.json: `91cb9b3fc2422fc376ecab7813213a7156b6baee963da620f06e82f1aaeaea39`, skill 0.340239.
+
+## E13/E14 large_loss_ema1000_continuity
+- Checkpoint SHA-256: `872c92ee27b0d63f310b7be7434ffe3d5342b19a42d48fefd0a468b87501b28e`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 14.880280256271362 seconds.
+- Overall skill 0.342546; relative MASE 0.782779; relative WQL 0.552194.
+- term skill: long=0.3792, medium=0.3907, short=0.3082.
+- domain skill: Econ/Fin=0.0879, Energy=0.1741, Healthcare=0.2781, Nature=0.4155, Sales=0.3277, Transport=0.4454, Web/CloudOps=0.5007.
+- frequency skill: A=0.2788, D=0.3141, H=0.3715, M=0.0511, Q=0.1869, S=0.4248, T=0.3925, W=0.1774.
+- variates skill: multivariate=0.3827, univariate=0.3087.
+- Outcome: Matched large-model continuation. Final EMA6000: sign augmentation wins (0.349298) over revised-loss control (0.346864) and richer synthetic generator (0.343184). Compute: control 618.3 s, synthetic 613.1 s, sign 617.9 s elapsed while sharing one H100.
+
+## E13/E14 large_loss_ema3000_continuity
+- Checkpoint SHA-256: `18c2788192e4028dd45255512b1c4bcddf12a50eb813e34fa0987b8949d75f15`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 14.631194829940796 seconds.
+- Overall skill 0.345993; relative MASE 0.778768; relative WQL 0.549233.
+- term skill: long=0.3793, medium=0.3902, short=0.3147.
+- domain skill: Econ/Fin=0.0850, Energy=0.1714, Healthcare=0.3423, Nature=0.4181, Sales=0.3281, Transport=0.4479, Web/CloudOps=0.5015.
+- frequency skill: A=0.2956, D=0.3232, H=0.3727, M=0.0705, Q=0.1917, S=0.4274, T=0.3904, W=0.1972.
+- variates skill: multivariate=0.3829, univariate=0.3150.
+- Outcome: Matched large-model continuation. Final EMA6000: sign augmentation wins (0.349298) over revised-loss control (0.346864) and richer synthetic generator (0.343184). Compute: control 618.3 s, synthetic 613.1 s, sign 617.9 s elapsed while sharing one H100.
+
+## E13/E14 large_loss_ema6000_continuity
+- Checkpoint SHA-256: `f5c7327b365832e8bd2d1b7b01e61c334f5bf620f5cae5f4d45feef2041090e8`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 9.797317266464233 seconds.
+- Overall skill 0.346864; relative MASE 0.778052; relative WQL 0.548275.
+- term skill: long=0.3803, medium=0.3907, short=0.3157.
+- domain skill: Econ/Fin=0.0848, Energy=0.1765, Healthcare=0.3460, Nature=0.4171, Sales=0.3275, Transport=0.4491, Web/CloudOps=0.4990.
+- frequency skill: A=0.2988, D=0.3201, H=0.3724, M=0.0715, Q=0.1909, S=0.4236, T=0.3942, W=0.2029.
+- variates skill: multivariate=0.3831, univariate=0.3165.
+- Outcome: Matched large-model continuation. Final EMA6000: sign augmentation wins (0.349298) over revised-loss control (0.346864) and richer synthetic generator (0.343184). Compute: control 618.3 s, synthetic 613.1 s, sign 617.9 s elapsed while sharing one H100.
+
+## E13/E14 large_sign_ema1000_continuity
+- Checkpoint SHA-256: `393950048f9d0487869b7aa5e6bd01e7ea1715dba16b1367ee8be353eac7f408`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 14.772316932678223 seconds.
+- Overall skill 0.347448; relative MASE 0.776694; relative WQL 0.548252.
+- term skill: long=0.3829, medium=0.3908, short=0.3157.
+- domain skill: Econ/Fin=0.0975, Energy=0.1743, Healthcare=0.3647, Nature=0.4116, Sales=0.3348, Transport=0.4489, Web/CloudOps=0.5001.
+- frequency skill: A=0.3764, D=0.3250, H=0.3728, M=0.0605, Q=0.1918, S=0.4294, T=0.3920, W=0.1973.
+- variates skill: multivariate=0.3811, univariate=0.3193.
+- Outcome: Matched large-model continuation. Final EMA6000: sign augmentation wins (0.349298) over revised-loss control (0.346864) and richer synthetic generator (0.343184). Compute: control 618.3 s, synthetic 613.1 s, sign 617.9 s elapsed while sharing one H100.
+
+## E13/E14 large_sign_ema3000_continuity
+- Checkpoint SHA-256: `24a38240b540c1311317103b880d4be47e17b7de883f535ef95977a3acc596dd`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 14.933598279953003 seconds.
+- Overall skill 0.348060; relative MASE 0.775887; relative WQL 0.547794.
+- term skill: long=0.3822, medium=0.3909, short=0.3171.
+- domain skill: Econ/Fin=0.1095, Energy=0.1761, Healthcare=0.3575, Nature=0.4106, Sales=0.3331, Transport=0.4503, Web/CloudOps=0.5000.
+- frequency skill: A=0.3842, D=0.3252, H=0.3741, M=0.0510, Q=0.1966, S=0.4324, T=0.3901, W=0.2089.
+- variates skill: multivariate=0.3815, univariate=0.3201.
+- Outcome: Matched large-model continuation. Final EMA6000: sign augmentation wins (0.349298) over revised-loss control (0.346864) and richer synthetic generator (0.343184). Compute: control 618.3 s, synthetic 613.1 s, sign 617.9 s elapsed while sharing one H100.
+
+## E13/E14 large_sign_ema6000_continuity
+- Checkpoint SHA-256: `96c9d350412c076594f4cc026aaf7396eacc45478f3d402c5d60dcf85dce8547`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 9.297121286392212 seconds.
+- Overall skill 0.349298; relative MASE 0.774499; relative WQL 0.546694.
+- term skill: long=0.3853, medium=0.3928, short=0.3172.
+- domain skill: Econ/Fin=0.1160, Energy=0.1799, Healthcare=0.3608, Nature=0.4057, Sales=0.3338, Transport=0.4511, Web/CloudOps=0.5017.
+- frequency skill: A=0.3836, D=0.3247, H=0.3752, M=0.0334, Q=0.1968, S=0.4300, T=0.3937, W=0.2168.
+- variates skill: multivariate=0.3827, univariate=0.3214.
+- Outcome: Matched large-model continuation. Final EMA6000: sign augmentation wins (0.349298) over revised-loss control (0.346864) and richer synthetic generator (0.343184). Compute: control 618.3 s, synthetic 613.1 s, sign 617.9 s elapsed while sharing one H100.
+
+## E13/E14 large_synth_ema1000_continuity
+- Checkpoint SHA-256: `c04fc8cad141754a2cb540b636cc3ac7744554cc85e201b4b291e777b2aee196`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 15.066582441329956 seconds.
+- Overall skill 0.343871; relative MASE 0.781301; relative WQL 0.551010.
+- term skill: long=0.3732, medium=0.3938, short=0.3118.
+- domain skill: Econ/Fin=0.1001, Energy=0.1746, Healthcare=0.2638, Nature=0.4169, Sales=0.3281, Transport=0.4461, Web/CloudOps=0.5042.
+- frequency skill: A=0.3050, D=0.3243, H=0.3716, M=-0.0066, Q=0.1903, S=0.4298, T=0.3943, W=0.1851.
+- variates skill: multivariate=0.3847, univariate=0.3094.
+- Outcome: Matched large-model continuation. Final EMA6000: sign augmentation wins (0.349298) over revised-loss control (0.346864) and richer synthetic generator (0.343184). Compute: control 618.3 s, synthetic 613.1 s, sign 617.9 s elapsed while sharing one H100.
+
+## E13/E14 large_synth_ema3000_continuity
+- Checkpoint SHA-256: `aa73bdf3e3b2d44e69c716f2dc788789949b96d69f4cd92c2b0169bfd4dc89ce`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 14.854033946990967 seconds.
+- Overall skill 0.343138; relative MASE 0.782085; relative WQL 0.551689.
+- term skill: long=0.3690, medium=0.3946, short=0.3119.
+- domain skill: Econ/Fin=0.1103, Energy=0.1771, Healthcare=0.2214, Nature=0.4199, Sales=0.3279, Transport=0.4472, Web/CloudOps=0.5016.
+- frequency skill: A=0.2856, D=0.3228, H=0.3725, M=0.0162, Q=0.1940, S=0.4277, T=0.3929, W=0.1727.
+- variates skill: multivariate=0.3849, univariate=0.3079.
+- Outcome: Matched large-model continuation. Final EMA6000: sign augmentation wins (0.349298) over revised-loss control (0.346864) and richer synthetic generator (0.343184). Compute: control 618.3 s, synthetic 613.1 s, sign 617.9 s elapsed while sharing one H100.
+
+## E13/E14 large_synth_ema6000_continuity
+- Checkpoint SHA-256: `f23469b8d187d0304e0accdc424ddd2233ed87dd77b97500176eec73644e0591`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 9.708917140960693 seconds.
+- Overall skill 0.343184; relative MASE 0.781828; relative WQL 0.551793.
+- term skill: long=0.3706, medium=0.3947, short=0.3112.
+- domain skill: Econ/Fin=0.1127, Energy=0.1747, Healthcare=0.2160, Nature=0.4195, Sales=0.3267, Transport=0.4485, Web/CloudOps=0.5041.
+- frequency skill: A=0.2764, D=0.3186, H=0.3736, M=0.0181, Q=0.1970, S=0.4302, T=0.3934, W=0.1723.
+- variates skill: multivariate=0.3851, univariate=0.3077.
+- Outcome: Matched large-model continuation. Final EMA6000: sign augmentation wins (0.349298) over revised-loss control (0.346864) and richer synthetic generator (0.343184). Compute: control 618.3 s, synthetic 613.1 s, sign 617.9 s elapsed while sharing one H100.
+
+## E16 earlier-window diagnostic large_loss6000
+- Checkpoint SHA-256: `f5c7327b365832e8bd2d1b7b01e61c334f5bf620f5cae5f4d45feef2041090e8`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 9.924073696136475 seconds.
+- Overall skill 0.311219; relative MASE 0.824887; relative WQL 0.575132.
+- term skill: long=0.3825, medium=0.3465, short=0.2673.
+- domain skill: Econ/Fin=0.1171, Energy=0.2168, Healthcare=0.3670, Nature=0.3256, Sales=0.3803, Transport=0.3468, Web/CloudOps=0.4260.
+- frequency skill: A=0.2543, D=0.3358, H=0.3410, M=0.1505, Q=0.2187, S=0.3402, T=0.3096, W=0.2343.
+- variates skill: multivariate=0.3301, univariate=0.2958.
+- Outcome: Model selection only. Seasonal Naive is recomputed on the earlier cutoff and capped-history scale. Final sign augmentation improves both main and earlier cutoffs; the richer synthetic generator does not beat its control. No diagnostic labels enter training.
+
+## E16 earlier-window diagnostic large_parent
+- Checkpoint SHA-256: `b7e4f38aebcd2cf38ce484edaad8d19851317fb3b8df485e2faa0d0423cbcb48`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 15.111237525939941 seconds.
+- Overall skill 0.312321; relative MASE 0.824141; relative WQL 0.573812.
+- term skill: long=0.3880, medium=0.3443, short=0.2678.
+- domain skill: Econ/Fin=0.1255, Energy=0.2150, Healthcare=0.3460, Nature=0.3326, Sales=0.3821, Transport=0.3403, Web/CloudOps=0.4349.
+- frequency skill: A=0.2124, D=0.3388, H=0.3399, M=0.1428, Q=0.2091, S=0.3387, T=0.3160, W=0.2326.
+- variates skill: multivariate=0.3359, univariate=0.2930.
+- Outcome: Model selection only. Seasonal Naive is recomputed on the earlier cutoff and capped-history scale. Final sign augmentation improves both main and earlier cutoffs; the richer synthetic generator does not beat its control. No diagnostic labels enter training.
+
+## E16 earlier-window diagnostic large_sign1000
+- Checkpoint SHA-256: `393950048f9d0487869b7aa5e6bd01e7ea1715dba16b1367ee8be353eac7f408`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 14.428916931152344 seconds.
+- Overall skill 0.312240; relative MASE 0.821151; relative WQL 0.576037.
+- term skill: long=0.3855, medium=0.3437, short=0.2691.
+- domain skill: Econ/Fin=0.1296, Energy=0.2106, Healthcare=0.3560, Nature=0.3340, Sales=0.3905, Transport=0.3466, Web/CloudOps=0.4302.
+- frequency skill: A=0.3161, D=0.3390, H=0.3442, M=0.1574, Q=0.2111, S=0.3426, T=0.3054, W=0.2304.
+- variates skill: multivariate=0.3287, univariate=0.2988.
+- Outcome: Model selection only. Seasonal Naive is recomputed on the earlier cutoff and capped-history scale. Final sign augmentation improves both main and earlier cutoffs; the richer synthetic generator does not beat its control. No diagnostic labels enter training.
+
+## E16 earlier-window diagnostic large_sign6000
+- Checkpoint SHA-256: `96c9d350412c076594f4cc026aaf7396eacc45478f3d402c5d60dcf85dce8547`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 9.61752462387085 seconds.
+- Overall skill 0.318367; relative MASE 0.813893; relative WQL 0.570866.
+- term skill: long=0.3826, medium=0.3453, short=0.2811.
+- domain skill: Econ/Fin=0.1456, Energy=0.2164, Healthcare=0.3902, Nature=0.3427, Sales=0.3912, Transport=0.3501, Web/CloudOps=0.4291.
+- frequency skill: A=0.3181, D=0.3498, H=0.3477, M=0.1673, Q=0.2198, S=0.3427, T=0.3048, W=0.2686.
+- variates skill: multivariate=0.3320, univariate=0.3073.
+- Outcome: Model selection only. Seasonal Naive is recomputed on the earlier cutoff and capped-history scale. Final sign augmentation improves both main and earlier cutoffs; the richer synthetic generator does not beat its control. No diagnostic labels enter training.
+
+## E16 earlier-window diagnostic large_synth6000
+- Checkpoint SHA-256: `f23469b8d187d0304e0accdc424ddd2233ed87dd77b97500176eec73644e0591`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 9.829549789428711 seconds.
+- Overall skill 0.311804; relative MASE 0.824530; relative WQL 0.574404.
+- term skill: long=0.3704, medium=0.3520, short=0.2715.
+- domain skill: Econ/Fin=0.1602, Energy=0.2091, Healthcare=0.3496, Nature=0.3283, Sales=0.3808, Transport=0.3477, Web/CloudOps=0.4301.
+- frequency skill: A=0.2270, D=0.3331, H=0.3416, M=0.1436, Q=0.2168, S=0.3434, T=0.3110, W=0.2438.
+- variates skill: multivariate=0.3289, univariate=0.2979.
+- Outcome: Model selection only. Seasonal Naive is recomputed on the earlier cutoff and capped-history scale. Final sign augmentation improves both main and earlier cutoffs; the richer synthetic generator does not beat its control. No diagnostic labels enter training.
+
+## E16 earlier-window diagnostic small_continuity
+- Checkpoint SHA-256: `70a3768d667df75f0d6565e831cdca8cf7c179c8546252e5902f7b7ec68ab7aa`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 12.578126668930054 seconds.
+- Overall skill 0.314127; relative MASE 0.818301; relative WQL 0.574876.
+- term skill: long=0.3797, medium=0.3430, short=0.2755.
+- domain skill: Econ/Fin=0.1152, Energy=0.2137, Healthcare=0.3801, Nature=0.3283, Sales=0.3845, Transport=0.3460, Web/CloudOps=0.4366.
+- frequency skill: A=0.2537, D=0.3362, H=0.3448, M=0.1858, Q=0.2208, S=0.3366, T=0.3083, W=0.2433.
+- variates skill: multivariate=0.3331, univariate=0.2986.
+- Outcome: Model selection only. Seasonal Naive is recomputed on the earlier cutoff and capped-history scale. Final sign augmentation improves both main and earlier cutoffs; the richer synthetic generator does not beat its control. No diagnostic labels enter training.
+
+## E16 earlier-window diagnostic small_no_continuity
+- Checkpoint SHA-256: `70a3768d667df75f0d6565e831cdca8cf7c179c8546252e5902f7b7ec68ab7aa`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 11.378507137298584 seconds.
+- Overall skill 0.308552; relative MASE 0.826361; relative WQL 0.578561.
+- term skill: long=0.3803, medium=0.3416, short=0.2654.
+- domain skill: Econ/Fin=0.0571, Energy=0.2115, Healthcare=0.3152, Nature=0.3257, Sales=0.3845, Transport=0.3471, Web/CloudOps=0.4427.
+- frequency skill: A=0.2363, D=0.3288, H=0.3546, M=0.1261, Q=0.1813, S=0.3403, T=0.3019, W=0.2031.
+- variates skill: multivariate=0.3334, univariate=0.2881.
+- Outcome: Model selection only. Seasonal Naive is recomputed on the earlier cutoff and capped-history scale. Final sign augmentation improves both main and earlier cutoffs; the richer synthetic generator does not beat its control. No diagnostic labels enter training.
+
+## E14 final sign-augmented single model, full validation
+- Checkpoint SHA-256: `96c9d350412c076594f4cc026aaf7396eacc45478f3d402c5d60dcf85dce8547`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.344508; relative MASE 0.777383; relative WQL 0.552713.
+- term skill: long=0.3776, medium=0.3912, short=0.3123.
+- domain skill: Econ/Fin=0.0926, Energy=0.1707, Healthcare=0.3648, Nature=0.4053, Sales=0.3446, Transport=0.4514, Web/CloudOps=0.4943.
+- frequency skill: A=0.2937, D=0.3275, H=0.3694, M=0.0521, Q=0.1776, S=0.4300, T=0.3859, W=0.2124.
+- variates skill: multivariate=0.3784, univariate=0.3162.
+- Outcome: Full skill 0.344508, relative MASE 0.777383, relative WQL 0.552713, zero failures. Outperforms the published two-model ensemble (0.340239) and also improves the earlier-cutoff diagnostic. Full inference 57.2 s. Relocated repeated example calls are bitwise deterministic, finite and monotone. Selected for atomic publication.
+
+## E18 remove symmetric inference after sign-augmented training
+- Checkpoint SHA-256: `96c9d350412c076594f4cc026aaf7396eacc45478f3d402c5d60dcf85dce8547`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 13.020774841308594 seconds.
+- Overall skill 0.345482; relative MASE 0.778014; relative WQL 0.550625.
+- term skill: long=0.3817, medium=0.3909, short=0.3125.
+- domain skill: Econ/Fin=0.1197, Energy=0.1784, Healthcare=0.3304, Nature=0.4074, Sales=0.3304, Transport=0.4474, Web/CloudOps=0.4960.
+- frequency skill: A=0.3782, D=0.3248, H=0.3710, M=0.0198, Q=0.1916, S=0.4321, T=0.3889, W=0.2113.
+- variates skill: multivariate=0.3767, univariate=0.3195.
+- Outcome: Negative result: removing the x/-x average lowers screening skill from 0.349298 to 0.345482 despite sign-augmented training. We retain symmetric inference; the speed saving is not needed for the contract budget.
+
+## E17 16-point projection conversion before continuation
+- Checkpoint SHA-256: `d69531bfde5bf801e7f36c515bf15fd0cae0bd84f7d1479d35f4a7b67f26b508`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 17.147535800933838 seconds.
+- Overall skill 0.341987; relative MASE 0.782323; relative WQL 0.553456.
+- term skill: long=0.3765, medium=0.3855, short=0.3105.
+- domain skill: Econ/Fin=0.0851, Energy=0.1664, Healthcare=0.3542, Nature=0.4067, Sales=0.3482, Transport=0.4386, Web/CloudOps=0.4996.
+- frequency skill: A=0.2894, D=0.3469, H=0.3634, M=-0.0442, Q=0.1930, S=0.4414, T=0.3853, W=0.1987.
+- variates skill: multivariate=0.3808, univariate=0.3093.
+- Outcome: Conversion of our own E14 EMA6000 checkpoint loses screening skill from 0.349298 to 0.341987 before updates. Input/query projection halves are folded by summation, constant-feature activation preserved, first 16 output rows retained, and physical sine positions remain in 32-point units. No external weights. Continuation is needed.
+
+## E17 matched patch-resolution continuation
+- Hypothesis: finer patches improve short-horizon detail and align common seasonal periods more closely without shortening context.
+- Control: 32-point patches, 37,002,816 parameters; treatment: 16-point patches, 36,764,960 parameters, initialized by conversion of the same owned parent E14 EMA6000. Only patch projections and positional spacing change. Physical position units remain 32 points; metadata horizon is floored at 32 to preserve short-horizon conditioning.
+- Both use the same window stream: seed47, batch256, 6000steps, AdamW8e-5, same sign-augmented sampler and median/seasonal-error-weighted loss/EMA. Context remains4096, maximum output1024. One H100 shared between runs.
+- Conversion code: patch16/convert.py. The converted checkpoint retains its parent hash and conversion description. Training and runtime require the model in patch16/model.py, copied to patch16_inference/model.py. Results pending final matched checkpoints.
+
+## E17 patch16_trial_ema1000
+- Checkpoint SHA-256: `396a7e827e8ddd068535f96f590e710f1931818c4e0b4cc589648bf68c830ba1`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 14.109450101852417 seconds.
+- Overall skill 0.348957; relative MASE 0.771301; relative WQL 0.549536.
+- term skill: long=0.3832, medium=0.3926, short=0.3175.
+- domain skill: Econ/Fin=0.0969, Energy=0.1735, Healthcare=0.3953, Nature=0.4077, Sales=0.3495, Transport=0.4526, Web/CloudOps=0.4982.
+- frequency skill: A=0.3699, D=0.3389, H=0.3688, M=0.0874, Q=0.1860, S=0.4381, T=0.3905, W=0.1934.
+- variates skill: multivariate=0.3799, univariate=0.3232.
+- Outcome: Matched seed47/window-stream comparison. Final patch16 skill 0.351398 exceeds patch32 0.350171, mainly through median error. Compute: control447.6s, patch16 563.2s, sharing one H100. Earlier-cutoff and full-package validation are pending before selection.
+
+## E17 patch16_trial_ema3000
+- Checkpoint SHA-256: `aad4bb2f079c957b1c2a064fc245e660bd9992bf0a01b57c9a48b0d33a44436e`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 13.450159549713135 seconds.
+- Overall skill 0.348611; relative MASE 0.772343; relative WQL 0.549378.
+- term skill: long=0.3843, medium=0.3974, short=0.3144.
+- domain skill: Econ/Fin=0.1014, Energy=0.1753, Healthcare=0.3863, Nature=0.4037, Sales=0.3479, Transport=0.4516, Web/CloudOps=0.4997.
+- frequency skill: A=0.3647, D=0.3298, H=0.3718, M=0.0756, Q=0.1866, S=0.4393, T=0.3930, W=0.1879.
+- variates skill: multivariate=0.3823, univariate=0.3204.
+- Outcome: Matched seed47/window-stream comparison. Final patch16 skill 0.351398 exceeds patch32 0.350171, mainly through median error. Compute: control447.6s, patch16 563.2s, sharing one H100. Earlier-cutoff and full-package validation are pending before selection.
+
+## E17 patch16_trial_ema6000
+- Checkpoint SHA-256: `0ff54447074e0271d2e331c7d3e6e9e0fdd73353e4d8cef0175ff627385f71c3`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 10.25124716758728 seconds.
+- Overall skill 0.351398; relative MASE 0.770359; relative WQL 0.546090.
+- term skill: long=0.3847, medium=0.3993, short=0.3185.
+- domain skill: Econ/Fin=0.1048, Energy=0.1768, Healthcare=0.3969, Nature=0.4061, Sales=0.3499, Transport=0.4528, Web/CloudOps=0.5033.
+- frequency skill: A=0.3673, D=0.3318, H=0.3738, M=0.0802, Q=0.1913, S=0.4391, T=0.3969, W=0.1924.
+- variates skill: multivariate=0.3843, univariate=0.3240.
+- Outcome: Matched seed47/window-stream comparison. Final patch16 skill 0.351398 exceeds patch32 0.350171, mainly through median error. Compute: control447.6s, patch16 563.2s, sharing one H100. Earlier-cutoff and full-package validation are pending before selection.
+
+## E17 patch32_control_ema1000
+- Checkpoint SHA-256: `56edd93dd7e80399be57a359574b2fb89eba843fa1ddf01f1d3f3a2d9c2fd73b`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 12.499926567077637 seconds.
+- Overall skill 0.347764; relative MASE 0.775926; relative WQL 0.548263.
+- term skill: long=0.3840, medium=0.3930, short=0.3148.
+- domain skill: Econ/Fin=0.1090, Energy=0.1783, Healthcare=0.3531, Nature=0.4057, Sales=0.3333, Transport=0.4508, Web/CloudOps=0.5005.
+- frequency skill: A=0.3826, D=0.3190, H=0.3738, M=0.0404, Q=0.1937, S=0.4305, T=0.3927, W=0.2146.
+- variates skill: multivariate=0.3815, univariate=0.3196.
+- Outcome: Matched seed47/window-stream comparison. Final patch16 skill 0.351398 exceeds patch32 0.350171, mainly through median error. Compute: control447.6s, patch16 563.2s, sharing one H100. Earlier-cutoff and full-package validation are pending before selection.
+
+## E17 patch32_control_ema3000
+- Checkpoint SHA-256: `fe46ac478011854b20eeba3bbde42023f299e2d8544d223f046be0e234ae7c6e`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 15.177376747131348 seconds.
+- Overall skill 0.349507; relative MASE 0.774280; relative WQL 0.546496.
+- term skill: long=0.3853, medium=0.3964, short=0.3160.
+- domain skill: Econ/Fin=0.1062, Energy=0.1803, Healthcare=0.3631, Nature=0.4048, Sales=0.3322, Transport=0.4510, Web/CloudOps=0.5041.
+- frequency skill: A=0.3765, D=0.3187, H=0.3755, M=0.0515, Q=0.1928, S=0.4362, T=0.3953, W=0.2095.
+- variates skill: multivariate=0.3848, univariate=0.3199.
+- Outcome: Matched seed47/window-stream comparison. Final patch16 skill 0.351398 exceeds patch32 0.350171, mainly through median error. Compute: control447.6s, patch16 563.2s, sharing one H100. Earlier-cutoff and full-package validation are pending before selection.
+
+## E17 patch32_control_ema6000
+- Checkpoint SHA-256: `3ee55007ebabdc41a5bbb6553dcd0c4b86e90b717dd1cff758d2318f538c0929`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 9.685351848602295 seconds.
+- Overall skill 0.350171; relative MASE 0.774332; relative WQL 0.545345.
+- term skill: long=0.3851, medium=0.3971, short=0.3171.
+- domain skill: Econ/Fin=0.1122, Energy=0.1816, Healthcare=0.3633, Nature=0.4023, Sales=0.3323, Transport=0.4516, Web/CloudOps=0.5054.
+- frequency skill: A=0.3791, D=0.3172, H=0.3768, M=0.0518, Q=0.1910, S=0.4349, T=0.3964, W=0.2119.
+- variates skill: multivariate=0.3854, univariate=0.3207.
+- Outcome: Matched seed47/window-stream comparison. Final patch16 skill 0.351398 exceeds patch32 0.350171, mainly through median error. Compute: control447.6s, patch16 563.2s, sharing one H100. Earlier-cutoff and full-package validation are pending before selection.
+
+## E19 prefix-consistent direct decoder
+- Hypothesis: requested total horizon should not change predictions of the same first steps. The context-only probe compared H48 versus H720 after all inference rules; mean absolute median-prefix differences were 0.0059–0.0582 context standard deviations across six sampled configurations (prefix_probe.json).
+- Change: future-query self-attention becomes causal and the total-horizon metadata feature is replaced by its constant training-mixture mean. Forecasts remain direct and parallel; no generated output is fed back. Encoder/cross attention and all data/loss settings stay unchanged.
+- Seed47, batch256, 6000steps, same original E14 EMA6000 parent and sampled-window stream as E17 patch32_control. Reuse that completed control instead of repeating it. Only the new decoder variant trains. Runtime/model: prefix_consistent/model.py, copied into prefix_inference.
+- Results pending.
+
+## E17 patch16, full validation
+- Checkpoint SHA-256: `0ff54447074e0271d2e331c7d3e6e9e0fdd73353e4d8cef0175ff627385f71c3`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.346729; relative MASE 0.773486; relative WQL 0.551739.
+- term skill: long=0.3768, medium=0.3979, short=0.3138.
+- domain skill: Econ/Fin=0.0839, Energy=0.1675, Healthcare=0.4022, Nature=0.4059, Sales=0.3593, Transport=0.4530, Web/CloudOps=0.4957.
+- frequency skill: A=0.2818, D=0.3356, H=0.3684, M=0.0967, Q=0.1741, S=0.4391, T=0.3887, W=0.1870.
+- variates skill: multivariate=0.3799, univariate=0.3190.
+- Outcome: Full skill 0.346729, relative MASE 0.773486 and relative WQL 0.551739, zero failures; improves the previous published 0.344508. Inference 57.5 seconds. Earlier-window skill 0.322486 versus control 0.319021. Relocated repeated example outputs are bitwise identical, finite and monotone. Initial assembly failed on a symlinked lineage path, was never published, and was rebuilt after canonicalizing paths; the missing-output score from that failed pipeline is discarded.
+
+## E19 prefix_consistent_ema1000
+- Checkpoint SHA-256: `aa797f3356c30e1859821bd807202298774b8d44b59b8325cc73956796a83b21`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 10.920389413833618 seconds.
+- Overall skill 0.347132; relative MASE 0.775797; relative WQL 0.549418.
+- term skill: long=0.3820, medium=0.3928, short=0.3146.
+- domain skill: Econ/Fin=0.1091, Energy=0.1769, Healthcare=0.3709, Nature=0.4082, Sales=0.3333, Transport=0.4492, Web/CloudOps=0.4954.
+- frequency skill: A=0.3824, D=0.3200, H=0.3727, M=0.0361, Q=0.1918, S=0.4265, T=0.3902, W=0.2278.
+- variates skill: multivariate=0.3798, univariate=0.3199.
+- Outcome: Final skill 0.349313 is below the reused matched patch32 control (0.350171). This training recipe does not improve the score; not selected. Training elapsed 288.9 seconds on one H100.
+
+## E19 prefix_consistent_ema3000
+- Checkpoint SHA-256: `11094f18080cd31f38a7f33d1df852adc957d9f1705653360b21e790e55b6648`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 10.28545594215393 seconds.
+- Overall skill 0.348148; relative MASE 0.775101; relative WQL 0.548201.
+- term skill: long=0.3814, medium=0.3951, short=0.3158.
+- domain skill: Econ/Fin=0.1060, Energy=0.1786, Healthcare=0.3758, Nature=0.4101, Sales=0.3323, Transport=0.4489, Web/CloudOps=0.4963.
+- frequency skill: A=0.3799, D=0.3215, H=0.3735, M=0.0487, Q=0.1876, S=0.4294, T=0.3915, W=0.2202.
+- variates skill: multivariate=0.3812, univariate=0.3206.
+- Outcome: Final skill 0.349313 is below the reused matched patch32 control (0.350171). This training recipe does not improve the score; not selected. Training elapsed 288.9 seconds on one H100.
+
+## E19 prefix_consistent_ema6000
+- Checkpoint SHA-256: `2572d0401c85966c3936b3feed906dee4206ca403a778c2356406aca57988ef7`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 9.322679996490479 seconds.
+- Overall skill 0.349313; relative MASE 0.774658; relative WQL 0.546555.
+- term skill: long=0.3800, medium=0.3960, short=0.3181.
+- domain skill: Econ/Fin=0.1130, Energy=0.1774, Healthcare=0.3765, Nature=0.4114, Sales=0.3328, Transport=0.4502, Web/CloudOps=0.4987.
+- frequency skill: A=0.3795, D=0.3224, H=0.3737, M=0.0589, Q=0.1895, S=0.4294, T=0.3930, W=0.2214.
+- variates skill: multivariate=0.3825, univariate=0.3216.
+- Outcome: Final skill 0.349313 is below the reused matched patch32 control (0.350171). This training recipe does not improve the score; not selected. Training elapsed 288.9 seconds on one H100.
+
+## E20: learned cross-variable adapter on the frozen E17 backbone
+Hypothesis: a jointly trained attention layer can exploit synchronized channels, especially on multivariate energy and weather series. Only a new 1.05M-parameter axial attention adapter is optimized; every backbone weight is frozen. Adapter output starts at zero, and univariate contexts bypass it exactly. Attention is confined to the channels of one instance. Context rows are grouped by whole instance in inference.
+Training: seed 53, 6,000 steps, 32 groups per batch, 2/3/4/8 variates, learning rate 2e-4 with 200-step warmup, AdamW, same median/quantile seasonal-scaled objective as E17, EMA. Joint pack has 334,868,547 aligned observations from 26 allow-listed directories. Thirty percent locally generated multivariate series use mixtures of latent seasonal/AR drivers with random channel lags, optional integrated noise, and observation noise. Channel signs/scales and missing observations are augmented. No validation observations are used by training. The new adapter is zero-initialized so step 0 supplies an inference grouping control.
+
+## E21: longer context at inference, without retraining
+Hypothesis: the encoder can benefit from histories longer than its 4,096-point training maximum. Hold E17 weights and all rules fixed and compare 8,192/16,384-point context caps with the current 4,096-point cap on all 97 screen configurations. This is a selection diagnostic, not adaptation; checkpoint tensor values are unchanged.
+
+## E22: function-preserving depth expansion
+Hypothesis: more learned capacity can improve the persistent energy and economic errors. Expand E17 from 6 encoder/4 decoder layers to 12 encoder/8 decoder layers, 72,499,488 parameters. Keep original blocks at even positions and insert random blocks with zero attention and feedforward output projections at odd positions. The initial enlarged model produces bitwise identical quantiles in a synthetic bf16 forward check. Conversion seed 59; both the expanded model and a continued 36.8M control will use seed 59, identical sampled windows, sign augmentation, revised seasonal-scaled median/quantile loss, and 24,000 AdamW steps, batch 256, learning rate 8e-5. Data remains packed_sample from 95 allowed sources plus the original local generator. GPU jobs share the single H100 if overlapping; elapsed training-process seconds will be reported.
+
+## E21 untrained 8192 context cap
+- Checkpoint SHA-256: `4cdcd096105c711926d1be912debeecf2999cc4e70389385ea60e516a211d035`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 11.263309001922607 seconds.
+- Overall skill 0.350147; relative MASE 0.769937; relative WQL 0.548498.
+- term skill: long=0.3906, medium=0.3940, short=0.3160.
+- domain skill: Econ/Fin=0.1042, Energy=0.1740, Healthcare=0.4046, Nature=0.3925, Sales=0.3499, Transport=0.4581, Web/CloudOps=0.5047.
+- frequency skill: A=0.3673, D=0.3338, H=0.3751, M=0.0802, Q=0.1913, S=0.4410, T=0.3905, W=0.1924.
+- variates skill: multivariate=0.3814, univariate=0.3241.
+- Outcome: Screen skill 0.350147 is below the 4096-point parent 0.351398. Not selected.
+
+## E21 untrained 16384 context cap
+- Checkpoint SHA-256: `a7504466f8d21bf752f3d99c32927a57cd63d0af5d5f15618ef4e8e1bdbeeb47`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 14.697236776351929 seconds.
+- Overall skill 0.347052; relative MASE 0.771167; relative WQL 0.552852.
+- term skill: long=0.3936, medium=0.3900, short=0.3107.
+- domain skill: Econ/Fin=0.1042, Energy=0.1694, Healthcare=0.4046, Nature=0.3838, Sales=0.3499, Transport=0.4577, Web/CloudOps=0.5032.
+- frequency skill: A=0.3673, D=0.3345, H=0.3752, M=0.0802, Q=0.1913, S=0.4410, T=0.3807, W=0.1924.
+- variates skill: multivariate=0.3779, univariate=0.3214.
+- Outcome: Below the 4096-point parent; longer context needs training and is not selected.
+
+## E23: fixed local-level diffusion prior (negative result)
+Hypothesis: smooth series might benefit from a decaying blend with their last value and random-walk intervals. Keep E17 forecasts fixed, estimate RMS consecutive differences from the last 512 context points, and blend Gaussian last-value forecasts with weight capped at 0.8, active only where squared-difference/context-variance ratio is below 0.05. Sweep decay 24/96/384 steps; no training, no fitting, no new random seed. All variants substantially regress on the 97-configuration screen, so none is adopted. Probe elapsed 5.3 CPU seconds. Parent checkpoint SHA-256: 0ff54447074e0271d2e331c7d3e6e9e0fdd73353e4d8cef0175ff627385f71c3.
+- Decay 0: skill 0.3513976241007074; term skills {"short": 0.3185422008330211, "medium": 0.3993234467710519, "long": 0.38466658538687104}; domain skills {"Econ/Fin": 0.1047749678551001, "Energy": 0.17678999618615132, "Healthcare": 0.3968888878939394, "Nature": 0.4060764075001204, "Sales": 0.349917064856452, "Transport": 0.4527521728604965, "Web/CloudOps": 0.50332745421581}.
+- Decay 24: skill 0.34165742688714285; term skills {"short": 0.30882663252618237, "medium": 0.38422047231265555, "long": 0.3803807240673608}; domain skills {"Econ/Fin": 0.09820862504146799, "Energy": 0.14638236482304767, "Healthcare": 0.3994101685341417, "Nature": 0.4097541459355638, "Sales": 0.34991701707454004, "Transport": 0.45281938688271317, "Web/CloudOps": 0.49437598362265067}.
+- Decay 96: skill 0.3235906270632306; term skills {"short": 0.30736381494165377, "medium": 0.31870169010171356, "long": 0.3688721255257502}; domain skills {"Econ/Fin": 0.09505897302445243, "Energy": 0.1285464282539771, "Healthcare": 0.3977611664977332, "Nature": 0.41574057336372194, "Sales": 0.34991700862256025, "Transport": 0.45284743770041946, "Web/CloudOps": 0.43886394048274135}.
+- Decay 384: skill 0.28712372958270105; term skills {"short": 0.30561653681994894, "medium": 0.2119589494208397, "long": 0.3091633781775974}; domain skills {"Econ/Fin": 0.09383616546291695, "Energy": 0.10801096739511007, "Healthcare": 0.3957240300486188, "Nature": 0.4179314810204363, "Sales": 0.3499170061816316, "Transport": 0.4526991548827556, "Web/CloudOps": 0.3016411139705769}.
+
+## E20 joint adapter full validation
+- Checkpoint SHA-256: `88beaafe52bd88300ebd46afc3a0d950246c280c738ac52367203d6c776a3053`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.348181; relative MASE 0.773070; relative WQL 0.549586.
+- term skill: long=0.3789, medium=0.3973, short=0.3159.
+- domain skill: Econ/Fin=0.0839, Energy=0.1670, Healthcare=0.4022, Nature=0.4151, Sales=0.3593, Transport=0.4530, Web/CloudOps=0.4958.
+- frequency skill: A=0.2818, D=0.3370, H=0.3714, M=0.0967, Q=0.1741, S=0.4354, T=0.3901, W=0.1878.
+- variates skill: multivariate=0.3830, univariate=0.3190.
+- Outcome: Full validation improves from 0.346729 to 0.348181 with no failures. Main screen improves from 0.351398 to 0.352838, but the earlier-window diagnostic regresses from 0.322486 to 0.321225. Selected on the task full validation metric, with this temporal limitation recorded. Training 157.8 process seconds on the single shared H100; full inference 63.7 seconds excluding startup. Frozen backbone equality, within-instance isolation, and repeated relocated example determinism verified.
+
+## E20 joint adapter screen
+- Checkpoint SHA-256: `88beaafe52bd88300ebd46afc3a0d950246c280c738ac52367203d6c776a3053`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 15.721086978912354 seconds.
+- Overall skill 0.352838; relative MASE 0.769945; relative WQL 0.543959.
+- term skill: long=0.3868, medium=0.3987, short=0.3205.
+- domain skill: Econ/Fin=0.1048, Energy=0.1763, Healthcare=0.3969, Nature=0.4152, Sales=0.3499, Transport=0.4528, Web/CloudOps=0.5034.
+- frequency skill: A=0.3673, D=0.3332, H=0.3768, M=0.0802, Q=0.1913, S=0.4354, T=0.3982, W=0.1932.
+- variates skill: multivariate=0.3874, univariate=0.3240.
+- Outcome: The added adapter improves weather forecasts most; some ETT and cloud configurations regress. Original univariate forecasts are unchanged.
+
+## E20 joint adapter earlier-window diagnostic
+- Checkpoint SHA-256: `88beaafe52bd88300ebd46afc3a0d950246c280c738ac52367203d6c776a3053`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 16.06979727745056 seconds.
+- Overall skill 0.321225; relative MASE 0.812345; relative WQL 0.567168.
+- term skill: long=0.3839, medium=0.3520, short=0.2830.
+- domain skill: Econ/Fin=0.1423, Energy=0.2143, Healthcare=0.3841, Nature=0.3485, Sales=0.3968, Transport=0.3596, Web/CloudOps=0.4341.
+- frequency skill: A=0.3026, D=0.3536, H=0.3503, M=0.1681, Q=0.2189, S=0.3405, T=0.3157, W=0.2466.
+- variates skill: multivariate=0.3338, univariate=0.3110.
+- Outcome: Temporal diagnostic regression; this limits confidence in the small primary-validation gain.
+
+## E24: seasonal-template recency
+Hypothesis: averaging the last four cycles induces bias when level or amplitude changes. Compare fixed 4/2/1-cycle means, four-cycle mean shifted to the latest cycle mean, and exponentially weighted four-cycle mean. The rest of the E17 forecaster and all confidence thresholds are fixed. Validation only; no parameter fitting. Centering improves the main screen 0.351398 to 0.353163 but decreases the earlier-window diagnostic 0.322486 to 0.321979. Other variants show larger temporal disagreement. Detailed per-configuration and group results remain in research/seasonal_recency/{screen,backcast}.json. Probe runtime: 15.3 and 15.0 CPU seconds. No variant published yet.
+
+## E25: learned recency features
+Hypothesis: adding level and amplitude change features to future queries lets the model learn when seasonal recency matters, instead of imposing a uniform template rule. Add a zero-initialized 32,768-parameter query projection receiving, for each of the two periods, latest-cycle minus four-cycle mean and the phase template times the latest/four-cycle standard deviation ratio minus one. Ratios are clipped to [0.5,2]; features require three observed cycles. Existing query and median heads stay structurally unchanged. Starting function is preserved; all weights are then trained. Same seed 59, parent E17, 24,000-step learning-rate schedule, batch 256 and identical sampler stream as E22's 36.8M control. Checkpoints every 3,000 steps; an unpromising trial can be stopped early. No validation data is used by training.
+
+## E26: second earlier-cutoff diagnostic
+Create a second nonoverlapping diagnostic window by removing another forecast horizon from E16 inputs. All observations remain within supplied validation histories and are never passed to trainers. 94 configurations and 8,934 instances have usable normalizers; Seasonal Naive and seasonal-error normalizers are recomputed for these prefixes. This diagnostic has its own scale and cannot be compared directly to primary validation or held-out reward. Purpose: assess temporal stability of the adapter and recency changes after the first earlier diagnostic disagreed with primary validation.
+
+E26 eligibility correction: SZ_TAXI long has exactly one seasonal period (96 points) left, so its prefix seasonal error is undefined. It is excluded from this diagnostic, alongside two configurations with insufficient context. An initial reference attempt detected the undefined metric; no candidate was selected on that invalid draft.
+
+E26 paired comparison on the 94 common configurations: adapter minus parent mean log geometric error by cutoff = [-0.0023015101916589752, 0.0019472848873323858, 0.0034041402255075566]. Positive means worse; the three-cutoff mean is 0.0010166383070603225. The adapter improves primary full validation but regresses on both earlier cutoffs, so its apparent gain is not temporally robust.
+
+E24 complete recency-rule ablation; parent checkpoint SHA-256 0ff54447074e0271d2e331c7d3e6e9e0fdd73353e4d8cef0175ff627385f71c3. No training, no random seed; model weights unchanged.
+
+screen diagnostic, 15.3 CPU seconds total:
+- (4, 'mean'): skill 0.3513976241007074; group skills {"term": {"short": 0.3185422008330211, "medium": 0.3993234467710519, "long": 0.38466658538687104}, "domain": {"Econ/Fin": 0.1047749678551001, "Energy": 0.17678999618615132, "Healthcare": 0.3968888878939394, "Nature": 0.4060764075001204, "Sales": 0.349917064856452, "Transport": 0.4527521728604965, "Web/CloudOps": 0.50332745421581}, "freq": {"A-DEC": 0.3672744916070976, "Q-DEC": 0.19127390925064935, "M": 0.08024694183722458, "W-SUN": 0.035758619864346475, "D": 0.3318015817783623, "H": 0.37383534533108864, "15T": 0.1940857197364374, "W-FRI": -0.03678057554518466, "10T": 0.4946120106982924, "W-TUE": 0.552193186183172, "W-THU": 0.16944597286444163, "W-WED": 0.3192454708946336, "5T": 0.5069054537063827, "10S": 0.43906058031696027}}
+- (2, 'mean'): skill 0.3515869592249251; group skills {"term": {"short": 0.31946055343361235, "medium": 0.395564454113736, "long": 0.3871567716641673}, "domain": {"Econ/Fin": 0.12253197026836216, "Energy": 0.18298948886090993, "Healthcare": 0.39741811166060514, "Nature": 0.40643913101885387, "Sales": 0.34900834734187824, "Transport": 0.45646747263938636, "Web/CloudOps": 0.4921796292797348}, "freq": {"A-DEC": 0.3672744916070976, "Q-DEC": 0.19172301173815798, "M": 0.07891640026491331, "W-SUN": 0.04042355013060539, "D": 0.3321769544067552, "H": 0.38073675441614074, "15T": 0.20117188206554237, "W-FRI": -0.03695866316509533, "10T": 0.49452343668884957, "W-TUE": 0.552193186183172, "W-THU": 0.16944597286444163, "W-WED": 0.3192454708946336, "5T": 0.5070472862431538, "10S": 0.3973951356554515}}
+- (1, 'mean'): skill 0.3435908999493934; group skills {"term": {"short": 0.3116909988543801, "medium": 0.38160533975903543, "long": 0.384670295110323}, "domain": {"Econ/Fin": 0.13426770743669836, "Energy": 0.18216526583305703, "Healthcare": 0.40167072012279914, "Nature": 0.4064404130881688, "Sales": 0.3492754711813856, "Transport": 0.4589127441324581, "Web/CloudOps": 0.45693278131755455}, "freq": {"A-DEC": 0.3672744916070976, "Q-DEC": 0.19143020771042418, "M": 0.08285832779020297, "W-SUN": 0.04327272071755062, "D": 0.33288417189993846, "H": 0.38267829909706397, "15T": 0.19937252608994493, "W-FRI": -0.037004579119138015, "10T": 0.4945270610078465, "W-TUE": 0.552193186183172, "W-THU": 0.16944597286444163, "W-WED": 0.3192454708946336, "5T": 0.5067187989779461, "10S": 0.25271387987938276}}
+- (4, 'centered'): skill 0.3531633226160329; group skills {"term": {"short": 0.3207293873053654, "medium": 0.3969450585806461, "long": 0.38964518179731034}, "domain": {"Econ/Fin": 0.12636396756466373, "Energy": 0.1800705780909143, "Healthcare": 0.3992839589819721, "Nature": 0.40602447440489264, "Sales": 0.34961552641405835, "Transport": 0.45585497734478375, "Web/CloudOps": 0.5005422814848317}, "freq": {"A-DEC": 0.3672744916070976, "Q-DEC": 0.19175192579993316, "M": 0.07909511983098594, "W-SUN": 0.042408325150654624, "D": 0.33302767036606196, "H": 0.37896395394765425, "15T": 0.19862358212653608, "W-FRI": -0.03667482636982777, "10T": 0.49458872561779677, "W-TUE": 0.552193186183172, "W-THU": 0.16944597286444163, "W-WED": 0.3192454708946336, "5T": 0.5067600572208003, "10S": 0.43120160792893125}}
+- (4, 'exponential'): skill 0.3506285650976836; group skills {"term": {"short": 0.31758444696147214, "medium": 0.39583120771209657, "long": 0.38712405721550214}, "domain": {"Econ/Fin": 0.11941316839573946, "Energy": 0.1820733009344513, "Healthcare": 0.39881728965443064, "Nature": 0.4063573767251085, "Sales": 0.3492900874450249, "Transport": 0.45600364485060096, "Web/CloudOps": 0.4900268042948257}, "freq": {"A-DEC": 0.3672744916070976, "Q-DEC": 0.19155754727480623, "M": 0.08126365276376935, "W-SUN": 0.0395554234837493, "D": 0.3321922474298862, "H": 0.3798686284876259, "15T": 0.19972610305133176, "W-FRI": -0.036894372612062964, "10T": 0.49455551047271673, "W-TUE": 0.552193186183172, "W-THU": 0.16944597286444163, "W-WED": 0.3192454708946336, "5T": 0.507021365412977, "10S": 0.38828731350299905}}
+
+backcast diagnostic, 15.0 CPU seconds total:
+- (4, 'mean'): skill 0.3224855500998881; group skills {"term": {"short": 0.2849500928667269, "medium": 0.35106641415497664, "long": 0.3858074289185036}, "domain": {"Econ/Fin": 0.14231294402320693, "Energy": 0.22033739564654398, "Healthcare": 0.38413791496286065, "Nature": 0.34419561689971845, "Sales": 0.3968014140531252, "Transport": 0.35961999575754544, "Web/CloudOps": 0.43501924471773445}, "freq": {"A-DEC": 0.30258726538887626, "Q-DEC": 0.21891811984740894, "M": 0.16812726492063224, "W-SUN": -0.010729530876487425, "D": 0.35222355718445886, "H": 0.3519096502404674, "15T": 0.2228803711964017, "W-FRI": 0.24958026063282224, "10T": 0.28160966000223076, "W-TUE": 0.5071080326855618, "W-THU": 0.17681316314370377, "W-WED": 0.3400228795320346, "5T": 0.41752089397176684, "10S": 0.3420520567149261}}
+- (2, 'mean'): skill 0.31641087380554034; group skills {"term": {"short": 0.2768742380592605, "medium": 0.34943272384716806, "long": 0.38006754913357355}, "domain": {"Econ/Fin": 0.1588024863579226, "Energy": 0.21997369353201524, "Healthcare": 0.371350308865825, "Nature": 0.34524192786763674, "Sales": 0.3968014140531252, "Transport": 0.3617399277122316, "Web/CloudOps": 0.4078808317401613}, "freq": {"A-DEC": 0.30258726538887626, "Q-DEC": 0.21832950345882607, "M": 0.1660037727174999, "W-SUN": -0.006617407115359519, "D": 0.3510532765644364, "H": 0.35322947177285846, "15T": 0.2251426669096147, "W-FRI": 0.2495544318494044, "10T": 0.28154255982923526, "W-TUE": 0.5071080326855618, "W-THU": 0.17681316314370377, "W-WED": 0.3400228795320346, "5T": 0.417584870977882, "10S": 0.23197125321775225}}
+- (1, 'mean'): skill 0.31723500000228033; group skills {"term": {"short": 0.28111182113787236, "medium": 0.34369676593720844, "long": 0.3794207668157681}, "domain": {"Econ/Fin": 0.16484876593565279, "Energy": 0.2163334841815111, "Healthcare": 0.34937996041769215, "Nature": 0.3461425603980003, "Sales": 0.3968014140531252, "Transport": 0.36432827783243815, "Web/CloudOps": 0.4170717985814322}, "freq": {"A-DEC": 0.30258726538887626, "Q-DEC": 0.21811526606513132, "M": 0.17155750755784438, "W-SUN": -0.004083126467642861, "D": 0.34319459279654896, "H": 0.35193709208671076, "15T": 0.22385514627514524, "W-FRI": 0.249535867076202, "10T": 0.28142605167103807, "W-TUE": 0.5071080326855618, "W-THU": 0.17681316314370377, "W-WED": 0.3400228795320346, "5T": 0.41764726501313276, "10S": 0.27470089184112967}}
+- (4, 'centered'): skill 0.3219793532222529; group skills {"term": {"short": 0.28513766744425706, "medium": 0.34881946541915476, "long": 0.3853894132385529}, "domain": {"Econ/Fin": 0.15718749322076342, "Energy": 0.220305778786681, "Healthcare": 0.3623301744180454, "Nature": 0.34528108993797224, "Sales": 0.3968014140531252, "Transport": 0.3641900819387449, "Web/CloudOps": 0.4312095523303556}, "freq": {"A-DEC": 0.30258726538887626, "Q-DEC": 0.21945119732749618, "M": 0.17114892410654747, "W-SUN": -0.005425657907346837, "D": 0.3478712397506638, "H": 0.3538545808265424, "15T": 0.22504062323947938, "W-FRI": 0.24955455634324242, "10T": 0.28170410454547445, "W-TUE": 0.5071080326855618, "W-THU": 0.17681316314370377, "W-WED": 0.3400228795320346, "5T": 0.4176757195719526, "10S": 0.32801909847059685}}
+- (4, 'exponential'): skill 0.3212088988374776; group skills {"term": {"short": 0.282531844032045, "medium": 0.3501930275891618, "long": 0.386723067036399}, "domain": {"Econ/Fin": 0.15471014663681715, "Energy": 0.22088089450488246, "Healthcare": 0.3714910427574707, "Nature": 0.3450102784118355, "Sales": 0.3968014140531252, "Transport": 0.3619509561390626, "Web/CloudOps": 0.4275124121699033}, "freq": {"A-DEC": 0.30258726538887626, "Q-DEC": 0.21855293965122158, "M": 0.16975398671452624, "W-SUN": -0.007489648663621784, "D": 0.3494275697398366, "H": 0.3534238823299253, "15T": 0.22624199923728783, "W-FRI": 0.24956090397954467, "10T": 0.28154140034619046, "W-TUE": 0.5071080326855618, "W-THU": 0.17681316314370377, "W-WED": 0.3400228795320346, "5T": 0.4176567607348064, "10S": 0.31322950674661665}}
+
+backcast2 diagnostic, 11.3 CPU seconds total:
+- (4, 'mean'): skill 0.3455098962011296; group skills {"term": {"short": 0.3290580626564209, "medium": 0.3700339540817553, "long": 0.36380280474970406}, "domain": {"Econ/Fin": 0.08514981056677962, "Energy": 0.22004402556428637, "Healthcare": 0.34077993948093876, "Nature": 0.45181894051147786, "Sales": 0.5552026876860963, "Transport": 0.4089471064366391, "Web/CloudOps": 0.4235634025112891}, "freq": {"A-DEC": 0.33512027886093443, "Q-DEC": 0.14846418792970395, "M": 0.1666731962215917, "W-SUN": 0.04822698344798482, "D": 0.4221052975732391, "H": 0.37954447462865715, "15T": 0.09066258444282005, "W-FRI": 0.10650868616967224, "10T": 0.4524242293365175, "W-TUE": 0.0831470254212826, "W-THU": 0.26812593887733527, "W-WED": 0.5394413576371667, "5T": 0.45460956828782484, "10S": 0.2830610169711695}}
+- (2, 'mean'): skill 0.34268288377692957; group skills {"term": {"short": 0.3260103214844039, "medium": 0.3650713522346687, "long": 0.36394907927583375}, "domain": {"Econ/Fin": 0.10626345560515471, "Energy": 0.21855508043330296, "Healthcare": 0.33494058443346464, "Nature": 0.45181894051147786, "Sales": 0.5552026876860963, "Transport": 0.41262361590216645, "Web/CloudOps": 0.40733619806789245}, "freq": {"A-DEC": 0.33512027886093443, "Q-DEC": 0.14853117890045908, "M": 0.15637649692786237, "W-SUN": 0.05109649829750129, "D": 0.4226979938774772, "H": 0.3810140667041473, "15T": 0.10084524164002151, "W-FRI": 0.10650868616967224, "10T": 0.4522402959584214, "W-TUE": 0.0831470254212826, "W-THU": 0.26812593887733527, "W-WED": 0.5394413576371667, "5T": 0.45675628129488266, "10S": 0.20569731261667445}}
+- (1, 'mean'): skill 0.3445039461555758; group skills {"term": {"short": 0.332077466515454, "medium": 0.3668808106296455, "long": 0.3542531601492346}, "domain": {"Econ/Fin": 0.10936035558443968, "Energy": 0.21764038800605967, "Healthcare": 0.3307656098951277, "Nature": 0.45181894051147786, "Sales": 0.5552026876860963, "Transport": 0.41137296882664487, "Web/CloudOps": 0.4178001938815905}, "freq": {"A-DEC": 0.33512027886093443, "Q-DEC": 0.14899214852623166, "M": 0.14771341656467907, "W-SUN": 0.05292218751297184, "D": 0.42303155789223856, "H": 0.3788342098211268, "15T": 0.10485120143271387, "W-FRI": 0.10650868616967224, "10T": 0.45177551404888616, "W-TUE": 0.0831470254212826, "W-THU": 0.26812593887733527, "W-WED": 0.5394413576371667, "5T": 0.4576208051300459, "10S": 0.24855961647506852}}
+- (4, 'centered'): skill 0.34598093527842033; group skills {"term": {"short": 0.3300325159298324, "medium": 0.3728154676891604, "long": 0.3603198134443867}, "domain": {"Econ/Fin": 0.10766883532595528, "Energy": 0.21948028584715717, "Healthcare": 0.3411805258187349, "Nature": 0.45181894051147786, "Sales": 0.5552026876860963, "Transport": 0.41219265214646406, "Web/CloudOps": 0.4193348965687418}, "freq": {"A-DEC": 0.33512027886093443, "Q-DEC": 0.14770244416717793, "M": 0.16665926477245963, "W-SUN": 0.05081321100071179, "D": 0.4226895384052014, "H": 0.3801908053318377, "15T": 0.10067740700220107, "W-FRI": 0.10650868616967224, "10T": 0.45281662963561176, "W-TUE": 0.0831470254212826, "W-THU": 0.26812593887733527, "W-WED": 0.5394413576371667, "5T": 0.4517952090885672, "10S": 0.27767832330559383}}
+- (4, 'exponential'): skill 0.3469706591030196; group skills {"term": {"short": 0.330791312305022, "medium": 0.3720978115862891, "long": 0.36385111952680094}, "domain": {"Econ/Fin": 0.1001234277395946, "Energy": 0.2202025077634595, "Healthcare": 0.336380045647639, "Nature": 0.45181894051147786, "Sales": 0.5552026876860963, "Transport": 0.41155494383831315, "Web/CloudOps": 0.4258605737282435}, "freq": {"A-DEC": 0.33512027886093443, "Q-DEC": 0.1486686416475177, "M": 0.15834250660686766, "W-SUN": 0.05058427650785369, "D": 0.42264469285933903, "H": 0.38114337578655855, "15T": 0.10061532381315941, "W-FRI": 0.10650868616967224, "10T": 0.4522176935148189, "W-TUE": 0.0831470254212826, "W-THU": 0.26812593887733527, "W-WED": 0.5394413576371667, "5T": 0.45594734272112136, "10S": 0.2839470264038111}}
+
+Centered four-cycle templates improve primary full validation and the second earlier diagnostic, while slightly reducing the first earlier diagnostic. They replace the joint adapter package: full validation 0.348797 versus adapter 0.348181, with better temporal stability. The joint adapter remains an archived experiment after worsening on both earlier cutoffs.
+
+## E24 centered seasonal template full validation
+- Checkpoint SHA-256: `0ff54447074e0271d2e331c7d3e6e9e0fdd73353e4d8cef0175ff627385f71c3`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.348797; relative MASE 0.771273; relative WQL 0.549826.
+- term skill: long=0.3827, medium=0.3957, short=0.3161.
+- domain skill: Econ/Fin=0.1051, Energy=0.1719, Healthcare=0.4042, Nature=0.4059, Sales=0.3592, Transport=0.4561, Web/CloudOps=0.4931.
+- frequency skill: A=0.2818, D=0.3368, H=0.3739, M=0.0954, Q=0.1765, S=0.4312, T=0.3905, W=0.1880.
+- variates skill: multivariate=0.3783, univariate=0.3243.
+- Outcome: Published. Same 36,764,960 learned parameters as E17; fixed seasonal template now preserves four-cycle shape while aligning its mean to the latest cycle. Full validation 0.348797 with zero failures; screen 0.353163; earlier diagnostics 0.321979 and 0.345981. Full inference 79.6 seconds while three training processes shared the one H100. Repeated relocated examples bitwise identical, finite, monotone.
+
+## E27: quantile width calibration diagnostic
+Hypothesis: the changed training objective and sign-averaged forecasts may change interval calibration relative to the early E3 width sweep. Hold current E24 medians fixed and compare global width multipliers 0.75, 1.0 and 1.25, preserving positivity and monotone deciles. No training or new random seed. Evaluate primary full validation and both earlier diagnostics; retain the same multiplier for every instance and horizon if adopted. Record per-configuration decile coverage to distinguish interval calibration from point-forecast errors.
+
+## E28: stronger seasonal-error scaling in the training loss
+Hypothesis: the existing floor 0.25 and maximum weight 4 underweight highly predictable series, whose small seasonal-error denominators make small biases costly in MASE. Change only this loss factor to floor 0.05 and maximum weight 20. The weight is derived from training contexts only; no validation observations enter gradients. Same E17 parent, seed 59, batch 256, sampled windows, 8e-5 learning rate and 24,000-step schedule as E22 control. Assess at 3,000 and 6,000 steps before deciding whether to continue. No architecture or inference changes. All training processes use the same single H100.
+
+E27 completed: global interval-width calibration is unchanged. The 1.0 multiplier wins on all three cutoffs; 0.75 and 1.25 are rejected. Parent checkpoint SHA-256: 0ff54447074e0271d2e331c7d3e6e9e0fdd73353e4d8cef0175ff627385f71c3.
+
+full, 21.961196422576904 CPU seconds:
+- Width 0.75: skill 0.3452164549545038; group skills {"term": {"short": 0.313465550024141, "medium": 0.3915340298847654, "long": 0.37755696642578507}, "domain": {"Econ/Fin": 0.10580171688867446, "Energy": 0.16637482564763484, "Healthcare": 0.40914268073244664, "Nature": 0.4019524649941506, "Sales": 0.3530144630644825, "Transport": 0.4556672339606386, "Web/CloudOps": 0.4876214368641769}, "freq": {"A-DEC": 0.28725493449470174, "Q-DEC": 0.1801084712445944, "M": 0.11175276948966328, "W-SUN": 0.0323370845377442, "D": 0.3312199981338694, "H": 0.3695069715804825, "15T": 0.18168253390246492, "W-FRI": -0.056487631535321325, "10T": 0.49352637886384465, "W-TUE": 0.5378585205934543, "W-THU": 0.18433827097062172, "W-WED": 0.31349093490925006, "5T": 0.49222999321979144, "10S": 0.42390324244202104}}
+- Width 1.0: skill 0.3487966789303172; group skills {"term": {"short": 0.3161336626027256, "medium": 0.3956796502512826, "long": 0.38269594016340247}, "domain": {"Econ/Fin": 0.10510484967413847, "Energy": 0.171919600185363, "Healthcare": 0.4042072222593659, "Nature": 0.40589812140398485, "Sales": 0.3591878025853771, "Transport": 0.4560779320618533, "Web/CloudOps": 0.4931146707071308}, "freq": {"A-DEC": 0.2818339423423022, "Q-DEC": 0.17653014662394806, "M": 0.09542703722254098, "W-SUN": 0.028486795465021086, "D": 0.336759380183841, "H": 0.3739148859162613, "15T": 0.18778774397400844, "W-FRI": -0.055524607369072276, "10T": 0.49458872561912703, "W-TUE": 0.552193186183172, "W-THU": 0.16944597281618878, "W-WED": 0.3192454710115562, "5T": 0.4977061545271937, "10S": 0.431201607928979}}
+- Width 1.25: skill 0.34610650213535965; group skills {"term": {"short": 0.31181715347067873, "medium": 0.39425414652966917, "long": 0.3825681229603841}, "domain": {"Econ/Fin": 0.09501414185590251, "Energy": 0.17012785606235192, "Healthcare": 0.3929707629727902, "Nature": 0.4039288459201795, "Sales": 0.3578978997303499, "Transport": 0.4512528387663022, "Web/CloudOps": 0.49362461046923467}, "freq": {"A-DEC": 0.2690929347470029, "Q-DEC": 0.16432266959209074, "M": 0.07072014222773326, "W-SUN": 0.01173769854460438, "D": 0.3347121204957273, "H": 0.37299271990393135, "15T": 0.18526873151735845, "W-FRI": -0.07163417179692866, "10T": 0.49168735086319126, "W-TUE": 0.562370097705067, "W-THU": 0.14458649403408286, "W-WED": 0.32018515385552937, "5T": 0.4992200637780666, "10S": 0.43174911263031324}}
+
+backcast, 8.311058759689331 CPU seconds:
+- Width 0.75: skill 0.31957721298205577; group skills {"term": {"short": 0.2849135227503923, "medium": 0.3430617259135954, "long": 0.3812505435660858}, "domain": {"Econ/Fin": 0.16020201234615272, "Energy": 0.21754177729190793, "Healthcare": 0.3694068614617043, "Nature": 0.3429441402339167, "Sales": 0.39134717268523767, "Transport": 0.36216325202164235, "Web/CloudOps": 0.42635659392097536}, "freq": {"A-DEC": 0.3052930254291859, "Q-DEC": 0.22988988761857532, "M": 0.17459013453754524, "W-SUN": 0.007398260950902569, "D": 0.3450062706602839, "H": 0.3480315684129901, "15T": 0.22233620685464417, "W-FRI": 0.30096387619142784, "10T": 0.27185481492835484, "W-TUE": 0.5329255178801061, "W-THU": 0.18370602390723068, "W-WED": 0.33814381573585706, "5T": 0.40916267988319677, "10S": 0.3313031636056427}}
+- Width 1.0: skill 0.3219793532184254; group skills {"term": {"short": 0.28513766744059266, "medium": 0.34881946541549136, "long": 0.3853894132342357}, "domain": {"Econ/Fin": 0.15718749322715975, "Energy": 0.22030577879293345, "Healthcare": 0.3623301744205766, "Nature": 0.34528108990726747, "Sales": 0.39680141405427904, "Transport": 0.36419008193684854, "Web/CloudOps": 0.431209552326686}, "freq": {"A-DEC": 0.30258726544387415, "Q-DEC": 0.2194511972962907, "M": 0.1711489239900802, "W-SUN": -0.005425657920283156, "D": 0.3478712397620801, "H": 0.35385458082566135, "15T": 0.22504062321275653, "W-FRI": 0.24955455634173984, "10T": 0.2817041045341433, "W-TUE": 0.5071080326855618, "W-THU": 0.17681316324492524, "W-WED": 0.3400228795175667, "5T": 0.41767571957575267, "10S": 0.32801909847013033}}
+- Width 1.25: skill 0.31790201775956073; group skills {"term": {"short": 0.2784084543186459, "medium": 0.34919365386981416, "long": 0.38309258280714664}, "domain": {"Econ/Fin": 0.14485063305112178, "Energy": 0.2160406174537144, "Healthcare": 0.34843021568184673, "Nature": 0.3414042631923321, "Sales": 0.3949701602051685, "Transport": 0.36064559654919026, "Web/CloudOps": 0.43041506837139865}, "freq": {"A-DEC": 0.289217486212048, "Q-DEC": 0.20086669955100855, "M": 0.15692994437261798, "W-SUN": -0.02840754711998872, "D": 0.34384490006115054, "H": 0.35403896763656184, "15T": 0.21854779190662021, "W-FRI": 0.19508006514698906, "10T": 0.2864956555409153, "W-TUE": 0.4740288982750931, "W-THU": 0.15896541765809558, "W-WED": 0.3390816757566152, "5T": 0.42177642249121394, "10S": 0.3165112734495428}}
+
+backcast2, 8.009549379348755 CPU seconds:
+- Width 0.75: skill 0.34409820450993134; group skills {"term": {"short": 0.3290162458924437, "medium": 0.3688383955368778, "long": 0.3584446274583232}, "domain": {"Econ/Fin": 0.1059670159637679, "Energy": 0.2168683811981844, "Healthcare": 0.3413880194250112, "Nature": 0.45062953387079285, "Sales": 0.5547232267111362, "Transport": 0.4100430663915112, "Web/CloudOps": 0.41729590695067253}, "freq": {"A-DEC": 0.34005157942217423, "Q-DEC": 0.1412869592685284, "M": 0.16881189749680703, "W-SUN": 0.047625092062016594, "D": 0.4231522928214516, "H": 0.37698957063440086, "15T": 0.0904235813541534, "W-FRI": 0.11812283700385673, "10T": 0.44881967454327965, "W-TUE": 0.07442882063540968, "W-THU": 0.2788710322691663, "W-WED": 0.5437864115591705, "5T": 0.444848784038935, "10S": 0.2913075712082248}}
+- Width 1.0: skill 0.34598093529199403; group skills {"term": {"short": 0.33003251595401095, "medium": 0.3728154676872124, "long": 0.3603198134466531}, "domain": {"Econ/Fin": 0.10766883533558247, "Energy": 0.2194802858514432, "Healthcare": 0.34118052581938385, "Nature": 0.4518189405691444, "Sales": 0.5552026877071643, "Transport": 0.4121926521487488, "Web/CloudOps": 0.41933489656663536}, "freq": {"A-DEC": 0.33512027884784024, "Q-DEC": 0.14770244414049571, "M": 0.16665926486843274, "W-SUN": 0.05081321100210512, "D": 0.42268953844805357, "H": 0.3801908053296258, "15T": 0.10067740700216166, "W-FRI": 0.10650868616810949, "10T": 0.45281662963431724, "W-TUE": 0.0831470254212826, "W-THU": 0.2681259389949434, "W-WED": 0.5394413576971099, "5T": 0.45179520908580906, "10S": 0.2776783233055614}}
+- Width 1.25: skill 0.3413902485820186; group skills {"term": {"short": 0.32419069585401805, "medium": 0.37023913422459476, "long": 0.35685627734091574}, "domain": {"Econ/Fin": 0.09962820431101826, "Energy": 0.21472017584206793, "Healthcare": 0.3337184668053308, "Nature": 0.44788650255350915, "Sales": 0.5519703061689909, "Transport": 0.4091113202517557, "Web/CloudOps": 0.41471225075769447}, "freq": {"A-DEC": 0.3220090566072281, "Q-DEC": 0.14689640454759956, "M": 0.15748943073554222, "W-SUN": 0.043428818988100804, "D": 0.4159786673838306, "H": 0.3773278172245832, "15T": 0.1020767647441726, "W-FRI": 0.08603022609757915, "10T": 0.45227993224331775, "W-TUE": 0.07815927778447074, "W-THU": 0.25035502046342106, "W-WED": 0.53163473233605, "5T": 0.4537783408350755, "10S": 0.2545513618912605}}
+
+Decile coverage uses <=, so ties at zero contribute excess lower-tail coverage on intermittent nonnegative series. Coverage alone is not evidence for changing intervals; the proper quantile loss favors the existing widths.
+
+## E22 control 3000-step checkpoint
+- Checkpoint SHA-256: `8fcb7dd923c415a0a93261be09f4b29a73674bc6188f33afb50130d615643715`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 16.52501678466797 seconds.
+- Overall skill 0.350784; relative MASE 0.771331; relative WQL 0.546434.
+- term skill: long=0.3855, medium=0.3992, short=0.3171.
+- domain skill: Econ/Fin=0.1025, Energy=0.1770, Healthcare=0.3901, Nature=0.4076, Sales=0.3484, Transport=0.4531, Web/CloudOps=0.5016.
+- frequency skill: A=0.3699, D=0.3270, H=0.3752, M=0.0777, Q=0.1894, S=0.4382, T=0.3953, W=0.1970.
+- variates skill: multivariate=0.3839, univariate=0.3232.
+- Outcome: Matched continuation control; primary screen 0.350784. Continuing training.
+
+## E22 control 6000-step checkpoint
+- Checkpoint SHA-256: `ae199bb31299e77f3035e227d61bf9b41fa84ae4eb9825f4d5d2bc7aef813934`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 15.943427324295044 seconds.
+- Overall skill 0.352399; relative MASE 0.769997; relative WQL 0.544661.
+- term skill: long=0.3861, medium=0.4001, short=0.3195.
+- domain skill: Econ/Fin=0.1064, Energy=0.1818, Healthcare=0.3888, Nature=0.4095, Sales=0.3482, Transport=0.4533, Web/CloudOps=0.5013.
+- frequency skill: A=0.3691, D=0.3297, H=0.3767, M=0.0899, Q=0.1951, S=0.4372, T=0.3967, W=0.1945.
+- variates skill: multivariate=0.3861, univariate=0.3242.
+- Outcome: Screen 0.352399; 869.5 elapsed training-process seconds through this checkpoint on shared single H100. Continuing training.
+
+## E22 expanded depth 3000-step checkpoint
+- Checkpoint SHA-256: `606808eaa8f41a4d8ffd7a336639c197cdb656b462d7b74cda0425a9c7b7ab88`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.74061632156372 seconds.
+- Overall skill 0.349903; relative MASE 0.771470; relative WQL 0.547820.
+- term skill: long=0.3846, medium=0.3995, short=0.3157.
+- domain skill: Econ/Fin=0.1036, Energy=0.1753, Healthcare=0.3821, Nature=0.4103, Sales=0.3478, Transport=0.4524, Web/CloudOps=0.5003.
+- frequency skill: A=0.3697, D=0.3271, H=0.3737, M=0.0851, Q=0.1912, S=0.4378, T=0.3939, W=0.1942.
+- variates skill: multivariate=0.3823, univariate=0.3229.
+- Outcome: Screen 0.349903 versus matched control 0.350784. 768.0 elapsed training-process seconds. Continuing to assess a longer run.
+
+## E25 learned recency 3000-step checkpoint
+- Checkpoint SHA-256: `ff6465dac03192cfc5f8837d8c6ab9524b0fb81c1a73867ff6e08aaf83a53398`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 18.54782772064209 seconds.
+- Overall skill 0.351702; relative MASE 0.770454; relative WQL 0.545509.
+- term skill: long=0.3852, medium=0.3988, short=0.3191.
+- domain skill: Econ/Fin=0.1014, Energy=0.1767, Healthcare=0.4078, Nature=0.4093, Sales=0.3476, Transport=0.4541, Web/CloudOps=0.5002.
+- frequency skill: A=0.3707, D=0.3341, H=0.3745, M=0.0812, Q=0.1898, S=0.4360, T=0.3954, W=0.1980.
+- variates skill: multivariate=0.3834, univariate=0.3253.
+- Outcome: Screen 0.351702 versus matched control 0.350784. 510.3 elapsed training-process seconds. This is a modest early improvement; further checkpoints and temporal diagnostics remain.
+
+## E22 control 6000 with centered seasonal template, full validation
+- Checkpoint SHA-256: `ae199bb31299e77f3035e227d61bf9b41fa84ae4eb9825f4d5d2bc7aef813934`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.349953; relative MASE 0.770650; relative WQL 0.548317.
+- term skill: long=0.3842, medium=0.3966, short=0.3172.
+- domain skill: Econ/Fin=0.1065, Energy=0.1770, Healthcare=0.3987, Nature=0.4093, Sales=0.3570, Transport=0.4567, Web/CloudOps=0.4912.
+- frequency skill: A=0.2855, D=0.3349, H=0.3766, M=0.1068, Q=0.1785, S=0.4296, T=0.3905, W=0.1906.
+- variates skill: multivariate=0.3802, univariate=0.3248.
+- Outcome: Published: full validation 0.349953 versus previous 0.348797, with zero failures. Earlier diagnostics improve to 0.322147 and 0.348634 from 0.321979 and 0.345981. Full inference took 98.8 seconds while four training processes shared the one H100. Repeated relocated example checks passed. Training continues beyond this milestone.
+
+## E22 control 6000 centered, earlier diagnostic
+- Checkpoint SHA-256: `ae199bb31299e77f3035e227d61bf9b41fa84ae4eb9825f4d5d2bc7aef813934`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 21.958669662475586 seconds.
+- Overall skill 0.322147; relative MASE 0.812166; relative WQL 0.565752.
+- term skill: long=0.3872, medium=0.3503, short=0.2840.
+- domain skill: Econ/Fin=0.1531, Energy=0.2205, Healthcare=0.3579, Nature=0.3475, Sales=0.3966, Transport=0.3647, Web/CloudOps=0.4317.
+- frequency skill: A=0.3153, D=0.3426, H=0.3545, M=0.1663, Q=0.2129, S=0.3286, T=0.3204, W=0.2542.
+- variates skill: multivariate=0.3358, univariate=0.3111.
+- Outcome: Small improvement over the preceding package.
+
+## E22 control 6000 centered, second earlier diagnostic
+- Checkpoint SHA-256: `ae199bb31299e77f3035e227d61bf9b41fa84ae4eb9825f4d5d2bc7aef813934`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 21.61665678024292 seconds.
+- Overall skill 0.348634; relative MASE 0.779607; relative WQL 0.544220.
+- term skill: long=0.3637, medium=0.3753, short=0.3325.
+- domain skill: Econ/Fin=0.1024, Energy=0.2199, Healthcare=0.3508, Nature=0.4590, Sales=0.5576, Transport=0.4153, Web/CloudOps=0.4205.
+- frequency skill: A=0.3380, D=0.4233, H=0.3833, M=0.1690, Q=0.1387, S=0.2800, T=0.3398, W=0.2426.
+- variates skill: multivariate=0.3512, univariate=0.3465.
+- Outcome: Improvement over the preceding package.
+
+## E29: coarse memory of older context (prepared)
+Hypothesis: a separately trained memory can extract useful information from points 4,097–16,384 without changing the frozen backbone’s treatment of its recent 4,096 points. Older observations are normalized using the recent window and averaged in groups of four, then encoded by two extra blocks. A zero-output cross-attention projection adds this memory to recent encoder states. Initial function is preserved; recent seasonal and continuity rules remain unchanged. The added embedding and two encoder blocks start from copies of our own trained weights, and the cross-attention output starts at zero. No external weights. Total 44,401,952 parameters; 7,636,992 trainable. Eight-step batch-16 smoke run on allowed packed_sample with seed 67 produced finite weights and updated only the history module. Full training has not started.
+
+## E30: seasonal amplitude alignment
+Hypothesis: four-cycle shape averaging also lags changes in seasonal amplitude. Starting from E24 centered templates, scale their deviations around the mean to the latest cycle standard deviation, correcting both variance estimates for observation noise inferred from seasonal differences. Scale ratio is clipped to [0.5,2], and amplitude alignment requires at least 75% finite observations in the latest cycle. All thresholds, intervals, learned weights and confidence gates remain fixed. Compare centered versus aligned templates on three diagnostic cutoffs using cached E17 raw forecasts; no training or new random seed.
+
+E30 initial amplitude-alignment results, parent checkpoint SHA-256 0ff54447074e0271d2e331c7d3e6e9e0fdd73353e4d8cef0175ff627385f71c3:
+
+screen, 7.7 CPU seconds total:
+- (4, 'centered'): skill 0.3531633226160329; group skills {"term": {"short": 0.3207293873053654, "medium": 0.3969450585806461, "long": 0.38964518179731034}, "domain": {"Econ/Fin": 0.12636396756466373, "Energy": 0.1800705780909143, "Healthcare": 0.3992839589819721, "Nature": 0.40602447440489264, "Sales": 0.34961552641405835, "Transport": 0.45585497734478375, "Web/CloudOps": 0.5005422814848317}, "freq": {"A-DEC": 0.3672744916070976, "Q-DEC": 0.19175192579993316, "M": 0.07909511983098594, "W-SUN": 0.042408325150654624, "D": 0.33302767036606196, "H": 0.37896395394765425, "15T": 0.19862358212653608, "W-FRI": -0.03667482636982777, "10T": 0.49458872561779677, "W-TUE": 0.552193186183172, "W-THU": 0.16944597286444163, "W-WED": 0.3192454708946336, "5T": 0.5067600572208003, "10S": 0.43120160792893125}}
+- (4, 'aligned'): skill 0.35398462796791796; group skills {"term": {"short": 0.3217411363550048, "medium": 0.39832598201137914, "long": 0.3894451118078128}, "domain": {"Econ/Fin": 0.128986533479437, "Energy": 0.1846259909517678, "Healthcare": 0.3989632452392111, "Nature": 0.40602447440489264, "Sales": 0.34961552641405835, "Transport": 0.45834957050423264, "Web/CloudOps": 0.4970505147956361}, "freq": {"A-DEC": 0.3672744916070976, "Q-DEC": 0.1913923357266032, "M": 0.07925704481777684, "W-SUN": 0.04248196090218248, "D": 0.332866037279251, "H": 0.38330094783509405, "15T": 0.20143473054459382, "W-FRI": -0.036466297970424444, "10T": 0.49454418004359824, "W-TUE": 0.552193186183172, "W-THU": 0.16944597286444163, "W-WED": 0.3192454708946336, "5T": 0.506404214769247, "10S": 0.4192979394083295}}
+
+backcast, 8.3 CPU seconds total:
+- (4, 'centered'): skill 0.3219793532222529; group skills {"term": {"short": 0.28513766744425706, "medium": 0.34881946541915476, "long": 0.3853894132385529}, "domain": {"Econ/Fin": 0.15718749322076342, "Energy": 0.220305778786681, "Healthcare": 0.3623301744180454, "Nature": 0.34528108993797224, "Sales": 0.3968014140531252, "Transport": 0.3641900819387449, "Web/CloudOps": 0.4312095523303556}, "freq": {"A-DEC": 0.30258726538887626, "Q-DEC": 0.21945119732749618, "M": 0.17114892410654747, "W-SUN": -0.005425657907346837, "D": 0.3478712397506638, "H": 0.3538545808265424, "15T": 0.22504062323947938, "W-FRI": 0.24955455634324242, "10T": 0.28170410454547445, "W-TUE": 0.5071080326855618, "W-THU": 0.17681316314370377, "W-WED": 0.3400228795320346, "5T": 0.4176757195719526, "10S": 0.32801909847059685}}
+- (4, 'aligned'): skill 0.32199923078598136; group skills {"term": {"short": 0.2839405542344856, "medium": 0.34882959423473925, "long": 0.38815021409803896}, "domain": {"Econ/Fin": 0.15740802100053652, "Energy": 0.21807242823144724, "Healthcare": 0.36213507616373974, "Nature": 0.3461350653810715, "Sales": 0.3968014140531252, "Transport": 0.3649195243648111, "Web/CloudOps": 0.4328434979914423}, "freq": {"A-DEC": 0.30258726538887626, "Q-DEC": 0.2183391142623865, "M": 0.1754893176389558, "W-SUN": -0.004377726385369485, "D": 0.3466775300728464, "H": 0.3524837610290207, "15T": 0.2254592175464284, "W-FRI": 0.24954835437149747, "10T": 0.2817431568124176, "W-TUE": 0.5071080326855618, "W-THU": 0.17681316314370377, "W-WED": 0.3400228795320346, "5T": 0.41754999889925704, "10S": 0.3353593007357636}}
+
+backcast2, 9.4 CPU seconds total:
+- (4, 'centered'): skill 0.34598093527842033; group skills {"term": {"short": 0.3300325159298324, "medium": 0.3728154676891604, "long": 0.3603198134443867}, "domain": {"Econ/Fin": 0.10766883532595528, "Energy": 0.21948028584715717, "Healthcare": 0.3411805258187349, "Nature": 0.45181894051147786, "Sales": 0.5552026876860963, "Transport": 0.41219265214646406, "Web/CloudOps": 0.4193348965687418}, "freq": {"A-DEC": 0.33512027886093443, "Q-DEC": 0.14770244416717793, "M": 0.16665926477245963, "W-SUN": 0.05081321100071179, "D": 0.4226895384052014, "H": 0.3801908053318377, "15T": 0.10067740700220107, "W-FRI": 0.10650868616967224, "10T": 0.45281662963561176, "W-TUE": 0.0831470254212826, "W-THU": 0.26812593887733527, "W-WED": 0.5394413576371667, "5T": 0.4517952090885672, "10S": 0.27767832330559383}}
+- (4, 'aligned'): skill 0.3467340001955017; group skills {"term": {"short": 0.3300823793890595, "medium": 0.3753689052243039, "long": 0.36094597324048516}, "domain": {"Econ/Fin": 0.10766542601267182, "Energy": 0.22063331078456605, "Healthcare": 0.33410091914860696, "Nature": 0.45181894051147786, "Sales": 0.5552026876860963, "Transport": 0.4141243473009397, "Web/CloudOps": 0.42142071217676347}, "freq": {"A-DEC": 0.33512027886093443, "Q-DEC": 0.14888883016351762, "M": 0.15492673811864788, "W-SUN": 0.05041601533475526, "D": 0.4227650502464607, "H": 0.38160118363487416, "15T": 0.10559542335780892, "W-FRI": 0.10650868616967224, "10T": 0.4528840561650268, "W-TUE": 0.0831470254212826, "W-THU": 0.26812593887733527, "W-WED": 0.5394413576371667, "5T": 0.45189525302575095, "10S": 0.2812440461970862}}
+
+The new rule improves all three aggregate diagnostics on the E17 weights. A complete package evaluation with the newer E22 control weights is in progress before adoption.
+
+E29 zero-initialization verification: all 97 screen configurations exactly reproduce the E17 centered runtime skill 0.3531633226160329. Evaluation 21.35 seconds on the shared H100. The eight-step smoke changed the history output projection by 1.4182e-5 maximum while all frozen backbone weights remained bitwise equal and all weights finite.
+
+## E25 learned recency 6000-step checkpoint
+- Checkpoint SHA-256: `dbbc4ff247028db1f339846b58c16c74d41a4a7b87e6a834988d95ee04c16734`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 19.591322422027588 seconds.
+- Overall skill 0.352851; relative MASE 0.769426; relative WQL 0.544305.
+- term skill: long=0.3876, medium=0.3997, short=0.3198.
+- domain skill: Econ/Fin=0.1086, Energy=0.1828, Healthcare=0.3943, Nature=0.4120, Sales=0.3475, Transport=0.4545, Web/CloudOps=0.4982.
+- frequency skill: A=0.3715, D=0.3323, H=0.3775, M=0.0905, Q=0.1942, S=0.4343, T=0.3965, W=0.1950.
+- variates skill: multivariate=0.3855, univariate=0.3256.
+- Outcome: Screen 0.352851 versus matched control 0.352399; modest improvement persists. Temporal diagnostics and later checkpoints remain.
+
+## E30 amplitude aligned template, full validation
+- Checkpoint SHA-256: `ae199bb31299e77f3035e227d61bf9b41fa84ae4eb9825f4d5d2bc7aef813934`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.350837; relative MASE 0.770162; relative WQL 0.547174.
+- term skill: long=0.3846, medium=0.3975, short=0.3183.
+- domain skill: Econ/Fin=0.1090, Energy=0.1818, Healthcare=0.3984, Nature=0.4093, Sales=0.3570, Transport=0.4592, Web/CloudOps=0.4877.
+- frequency skill: A=0.2855, D=0.3348, H=0.3812, M=0.1070, Q=0.1786, S=0.4173, T=0.3911, W=0.1906.
+- variates skill: multivariate=0.3791, univariate=0.3274.
+- Outcome: Published. With the E22 control 6000-step weights, full validation improves 0.349953 to 0.350837, first earlier diagnostic 0.322147 to 0.322219, and second earlier diagnostic 0.348634 to 0.349310. No failures; deterministic relocated example checks passed. Full inference 96.8 seconds while sharing the one H100 with four training processes. All template changes are fixed formulas; learned parameters remain unchanged.
+
+## E30 aligned template first earlier diagnostic
+- Checkpoint SHA-256: `ae199bb31299e77f3035e227d61bf9b41fa84ae4eb9825f4d5d2bc7aef813934`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 20.561070680618286 seconds.
+- Overall skill 0.322219; relative MASE 0.811128; relative WQL 0.566355.
+- term skill: long=0.3898, medium=0.3504, short=0.2830.
+- domain skill: Econ/Fin=0.1533, Energy=0.2182, Healthcare=0.3588, Nature=0.3483, Sales=0.3966, Transport=0.3655, Web/CloudOps=0.4333.
+- frequency skill: A=0.3153, D=0.3418, H=0.3532, M=0.1706, Q=0.2117, S=0.3358, T=0.3205, W=0.2543.
+- variates skill: multivariate=0.3369, univariate=0.3103.
+- Outcome: Small improvement over centered templates.
+
+## E30 aligned template second earlier diagnostic
+- Checkpoint SHA-256: `ae199bb31299e77f3035e227d61bf9b41fa84ae4eb9825f4d5d2bc7aef813934`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 20.08868408203125 seconds.
+- Overall skill 0.349310; relative MASE 0.778190; relative WQL 0.544080.
+- term skill: long=0.3643, medium=0.3777, short=0.3325.
+- domain skill: Econ/Fin=0.1024, Energy=0.2210, Healthcare=0.3434, Nature=0.4590, Sales=0.5576, Transport=0.4172, Web/CloudOps=0.4224.
+- frequency skill: A=0.3380, D=0.4234, H=0.3847, M=0.1568, Q=0.1399, S=0.2828, T=0.3413, W=0.2426.
+- variates skill: multivariate=0.3522, univariate=0.3470.
+- Outcome: Improvement over centered templates.
+
+## E28 stronger seasonal loss 3000-step checkpoint
+- Checkpoint SHA-256: `a514371332282d8ff1cf42e0c2127683e6bb95bfb98c1a6df6332c604d72c865`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 20.41924524307251 seconds.
+- Overall skill 0.350569; relative MASE 0.771378; relative WQL 0.546763.
+- term skill: long=0.3829, medium=0.3988, short=0.3180.
+- domain skill: Econ/Fin=0.1028, Energy=0.1748, Healthcare=0.4041, Nature=0.4086, Sales=0.3488, Transport=0.4526, Web/CloudOps=0.4996.
+- frequency skill: A=0.3725, D=0.3288, H=0.3734, M=0.0932, Q=0.1901, S=0.4387, T=0.3933, W=0.1988.
+- variates skill: multivariate=0.3818, univariate=0.3246.
+- Outcome: Screen 0.350569 versus matched control 0.350784. No gain at this checkpoint; continue to 6000 steps before deciding.
+
+E29 full trial started from the E22 control 6000-step backbone, seed 67, batch 64, 6,000 steps, learning rate 2e-4 with 200-step warmup and cosine decay. Only the 7.64M history-memory parameters train. Batch size is reduced to fit concurrent experiments on the one H100. Context lengths 8,192/16,384 with probabilities 0.4/0.6; horizon mixture remains 32/128/512/1024. Inference uses the current amplitude-aligned seasonal rule on the recent 4,096 points, so the additional history affects only the learned memory. The parent differs from the newly published E25 branch; zero-step evaluation supplies the exact matched control.
+
+## E25 learned recency 6000 with aligned templates, full validation
+- Checkpoint SHA-256: `dbbc4ff247028db1f339846b58c16c74d41a4a7b87e6a834988d95ee04c16734`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.351407; relative MASE 0.769733; relative WQL 0.546517.
+- term skill: long=0.3860, medium=0.3973, short=0.3189.
+- domain skill: Econ/Fin=0.1113, Energy=0.1828, Healthcare=0.4050, Nature=0.4118, Sales=0.3571, Transport=0.4604, Web/CloudOps=0.4845.
+- frequency skill: A=0.2870, D=0.3384, H=0.3819, M=0.1083, Q=0.1800, S=0.4144, T=0.3909, W=0.1904.
+- variates skill: multivariate=0.3786, univariate=0.3289.
+- Outcome: Published. Full validation 0.351407 versus preceding 0.350837; earlier diagnostics 0.323269 and 0.350071 versus 0.322219 and 0.349310. No failures; repeated relocated examples are bitwise identical, finite and monotone. Total 36,797,728 parameters. Full inference 96.7 seconds while the one H100 was shared with training. Training continues.
+
+## E25 recency 6000 aligned first earlier diagnostic
+- Checkpoint SHA-256: `dbbc4ff247028db1f339846b58c16c74d41a4a7b87e6a834988d95ee04c16734`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 20.321524143218994 seconds.
+- Overall skill 0.323269; relative MASE 0.809492; relative WQL 0.565744.
+- term skill: long=0.3896, medium=0.3509, short=0.2848.
+- domain skill: Econ/Fin=0.1588, Energy=0.2200, Healthcare=0.3660, Nature=0.3488, Sales=0.3987, Transport=0.3659, Web/CloudOps=0.4318.
+- frequency skill: A=0.3196, D=0.3433, H=0.3536, M=0.1780, Q=0.2165, S=0.3305, T=0.3218, W=0.2570.
+- variates skill: multivariate=0.3369, univariate=0.3122.
+- Outcome: Improves the matched continued model with the same aligned-template runtime.
+
+## E25 recency 6000 aligned second earlier diagnostic
+- Checkpoint SHA-256: `dbbc4ff247028db1f339846b58c16c74d41a4a7b87e6a834988d95ee04c16734`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 20.002116203308105 seconds.
+- Overall skill 0.350071; relative MASE 0.777410; relative WQL 0.543353.
+- term skill: long=0.3631, medium=0.3788, short=0.3338.
+- domain skill: Econ/Fin=0.1067, Energy=0.2234, Healthcare=0.3529, Nature=0.4590, Sales=0.5596, Transport=0.4165, Web/CloudOps=0.4198.
+- frequency skill: A=0.3409, D=0.4269, H=0.3856, M=0.1584, Q=0.1427, S=0.2730, T=0.3428, W=0.2398.
+- variates skill: multivariate=0.3525, univariate=0.3481.
+- Outcome: Improves the matched continued model with the same aligned-template runtime.
+
+E22 depth trial pruned after two matched comparisons: 3000-step skill 0.349903 versus control 0.350784, and 6000-step skill 0.351873 versus control 0.352399. The 6000-step checkpoint cost 1961.8 elapsed training-process seconds, versus 869.5 for the control under different shared-GPU contention. The planned 24,000 steps were not completed. This establishes no benefit at the tested budgets, not that greater depth can never help. Saved checkpoints remain available. Stop receipt: [{"pid": 4519, "kind": "training", "elapsed_seconds": 2108}, {"pid": 4914, "kind": "watcher", "elapsed_seconds": 1723}].
+
+## E22 expanded depth 6000-step checkpoint
+- Checkpoint SHA-256: `a1efd06cff13f7f47559d83d37ea63f6bc65692df49fd2bfe039502d33978632`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 29.713934898376465 seconds.
+- Overall skill 0.351873; relative MASE 0.770476; relative WQL 0.545206.
+- term skill: long=0.3864, medium=0.4005, short=0.3182.
+- domain skill: Econ/Fin=0.1070, Energy=0.1815, Healthcare=0.3779, Nature=0.4133, Sales=0.3487, Transport=0.4524, Web/CloudOps=0.4999.
+- frequency skill: A=0.3690, D=0.3292, H=0.3764, M=0.0873, Q=0.1951, S=0.4370, T=0.3963, W=0.1928.
+- variates skill: multivariate=0.3859, univariate=0.3234.
+- Outcome: Below the matched 36.8M control for the second comparison. Stop this trial and retain its checkpoints; prioritize the smaller learned-feature and history-memory experiments on one H100.
+
+## E31: stochastic seasonal amplitude in synthetic pretraining (prepared)
+Hypothesis: the original generator has constant Fourier amplitudes, whereas the added recency features describe changing amplitudes. Modulate the seasonal component in half of synthetic draws by a positive AR log-amplitude with time constant 100–10,000, stationary log standard deviation 0.05–0.4 and log-value clipping [-2,2]. The modulation is applied before trends, observation noise, missing values and intermittence. A separate RNG seed = main seed + 100003 preserves all source selection, corpus windows, horizon/context choices and original generator random draws relative to E25. Planned seed 59 and E17 initialization allow comparison with existing E25 checkpoints without retraining a control. No training started yet.
+
+## E29 history memory 1000-step checkpoint
+- Checkpoint SHA-256: `a3bdc3a7525ed869d48fc1ce2b54785bb82aa64573e3b3d8a0de98df10680af3`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.969539165496826 seconds.
+- Overall skill 0.355846; relative MASE 0.766420; relative WQL 0.541393.
+- term skill: long=0.3920, medium=0.3994, short=0.3237.
+- domain skill: Econ/Fin=0.1304, Energy=0.1908, Healthcare=0.3939, Nature=0.4095, Sales=0.3479, Transport=0.4592, Web/CloudOps=0.4963.
+- frequency skill: A=0.3691, D=0.3317, H=0.3867, M=0.0892, Q=0.1952, S=0.4178, T=0.4001, W=0.1953.
+- variates skill: multivariate=0.3846, univariate=0.3320.
+- Outcome: Improves the matched zero-step screen score 0.354979. Elapsed training-process time 104.3 seconds on the shared single H100. Temporal diagnostics determine whether the gain transfers across cutoffs.
+
+## E29 history memory 3000-step checkpoint
+- Checkpoint SHA-256: `81b1e8dfae60e87a20a2f0ff6d3464a608c8b86624cfc3383ca29441e9f1fd92`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 20.785941123962402 seconds.
+- Overall skill 0.356376; relative MASE 0.765914; relative WQL 0.540860.
+- term skill: long=0.3932, medium=0.3995, short=0.3240.
+- domain skill: Econ/Fin=0.1306, Energy=0.1923, Healthcare=0.3934, Nature=0.4102, Sales=0.3479, Transport=0.4591, Web/CloudOps=0.4965.
+- frequency skill: A=0.3691, D=0.3318, H=0.3878, M=0.0892, Q=0.1952, S=0.4179, T=0.4005, W=0.1953.
+- variates skill: multivariate=0.3854, univariate=0.3323.
+- Outcome: Improves the matched zero-step screen score 0.354979. Elapsed training-process time 283.3 seconds on the shared single H100. Temporal diagnostics determine whether the gain transfers across cutoffs.
+
+E31 training started: seed 59, batch 256, 24,000-step schedule, learning rate 8e-5, E17 initialization, same query-feature architecture as E25. Tests verified that all corpus-only rows are identical to E25 and that the main RNG states remain identical after both corpus-only and synthetic-only batches; 11 of 32 synthetic test rows changed. The independent modulation stream is seed 100062. Evaluate at 3000/6000 steps before extending this trial.
+
+## E22 control 12000 with aligned templates, full validation
+- Checkpoint SHA-256: `69e01d3fbc013287a1e63346e7cefbbe82acfb98f7d446459d52c931515bd8a8`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.355096; relative MASE 0.766848; relative WQL 0.542351.
+- term skill: long=0.3891, medium=0.3999, short=0.3233.
+- domain skill: Econ/Fin=0.1147, Energy=0.1853, Healthcare=0.3958, Nature=0.4163, Sales=0.3582, Transport=0.4612, Web/CloudOps=0.4938.
+- frequency skill: A=0.2887, D=0.3392, H=0.3848, M=0.1131, Q=0.1870, S=0.4202, T=0.3967, W=0.1906.
+- variates skill: multivariate=0.3845, univariate=0.3307.
+- Outcome: Published. Full validation 0.355096 versus preceding E25-6000 package 0.351407, first earlier diagnostic 0.325963 versus 0.323269, and second earlier diagnostic 0.352462 versus 0.350071. No failures; repeated relocated examples are bitwise identical, finite and monotone. Full inference 98.8 seconds under shared H100 contention. The matched E25-12000 checkpoint is still training.
+
+## E22 control12000 aligned first earlier diagnostic
+- Checkpoint SHA-256: `69e01d3fbc013287a1e63346e7cefbbe82acfb98f7d446459d52c931515bd8a8`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 22.49058723449707 seconds.
+- Overall skill 0.325963; relative MASE 0.809373; relative WQL 0.561330.
+- term skill: long=0.3895, medium=0.3521, short=0.2893.
+- domain skill: Econ/Fin=0.1619, Energy=0.2209, Healthcare=0.3713, Nature=0.3541, Sales=0.3962, Transport=0.3666, Web/CloudOps=0.4365.
+- frequency skill: A=0.3123, D=0.3490, H=0.3551, M=0.1691, Q=0.2293, S=0.3380, T=0.3258, W=0.2557.
+- variates skill: multivariate=0.3406, univariate=0.3141.
+- Outcome: Improves the preceding package.
+
+## E22 control12000 aligned second earlier diagnostic
+- Checkpoint SHA-256: `69e01d3fbc013287a1e63346e7cefbbe82acfb98f7d446459d52c931515bd8a8`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 21.788399934768677 seconds.
+- Overall skill 0.352462; relative MASE 0.775936; relative WQL 0.540386.
+- term skill: long=0.3649, medium=0.3793, short=0.3372.
+- domain skill: Econ/Fin=0.1068, Energy=0.2227, Healthcare=0.3661, Nature=0.4623, Sales=0.5608, Transport=0.4167, Web/CloudOps=0.4248.
+- frequency skill: A=0.3383, D=0.4276, H=0.3860, M=0.1616, Q=0.1520, S=0.2793, T=0.3458, W=0.2493.
+- variates skill: multivariate=0.3545, univariate=0.3508.
+- Outcome: Improves the preceding package.
+
+## E32: transfer the learned history memory to the improved backbone
+Hypothesis: the frozen history memory learns useful features that remain compatible with a later continuation of the same backbone architecture. Combine E29 history-memory tensors at 6000 steps with every backbone tensor from E22 control at 12000 steps. This is a deterministic combination of our own trained assets, with no new training and no external weights. Both parent checkpoint hashes and full training lineages are retained. The publication utility now traverses both parent branches and declares all their sources and generator seeds. Compare to the E22-12000 aligned-template runtime across three cutoffs before adoption.
+
+## E29 history memory 6000-step screen
+- Checkpoint SHA-256: `5c3b98e648e31f0d2aa61a150700be8147ba2638c975510fc15bdb311531cf43`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 43.3264684677124 seconds.
+- Overall skill 0.357127; relative MASE 0.765099; relative WQL 0.540174.
+- term skill: long=0.3941, medium=0.3993, short=0.3251.
+- domain skill: Econ/Fin=0.1303, Energy=0.1925, Healthcare=0.3999, Nature=0.4104, Sales=0.3479, Transport=0.4597, Web/CloudOps=0.4973.
+- frequency skill: A=0.3691, D=0.3342, H=0.3893, M=0.0892, Q=0.1952, S=0.4179, T=0.4002, W=0.1953.
+- variates skill: multivariate=0.3861, univariate=0.3331.
+- Outcome: Screen 0.357127 versus zero-step 0.354979. Training completed in 531.4 process seconds on the shared single H100. First earlier diagnostic regresses while the second improves; do not publish standalone without broader evidence.
+
+## E29 history memory first earlier diagnostic
+- Checkpoint SHA-256: `5c3b98e648e31f0d2aa61a150700be8147ba2638c975510fc15bdb311531cf43`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 22.55209970474243 seconds.
+- Overall skill 0.321189; relative MASE 0.812469; relative WQL 0.567141.
+- term skill: long=0.3883, medium=0.3470, short=0.2831.
+- domain skill: Econ/Fin=0.1541, Energy=0.2165, Healthcare=0.3555, Nature=0.3461, Sales=0.3966, Transport=0.3649, Web/CloudOps=0.4336.
+- frequency skill: A=0.3153, D=0.3408, H=0.3550, M=0.1706, Q=0.2117, S=0.3336, T=0.3162, W=0.2543.
+- variates skill: multivariate=0.3353, univariate=0.3098.
+- Outcome: Regresses from the matched parent 0.322219 to 0.321189.
+
+## E29 history memory second earlier diagnostic
+- Checkpoint SHA-256: `5c3b98e648e31f0d2aa61a150700be8147ba2638c975510fc15bdb311531cf43`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 22.82407259941101 seconds.
+- Overall skill 0.350315; relative MASE 0.776760; relative WQL 0.543398.
+- term skill: long=0.3664, medium=0.3780, short=0.3334.
+- domain skill: Econ/Fin=0.1037, Energy=0.2222, Healthcare=0.3446, Nature=0.4601, Sales=0.5576, Transport=0.4171, Web/CloudOps=0.4239.
+- frequency skill: A=0.3380, D=0.4241, H=0.3857, M=0.1568, Q=0.1399, S=0.2820, T=0.3431, W=0.2426.
+- variates skill: multivariate=0.3544, univariate=0.3470.
+- Outcome: Improves from the matched parent 0.349310 to 0.350315.
+
+## E28 stronger seasonal loss 6000-step checkpoint
+- Checkpoint SHA-256: `6208c74c3a6902d0410708741fd3ee609649e28732e5d5ee2696169685768e9b`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 19.053489208221436 seconds.
+- Overall skill 0.352533; relative MASE 0.769384; relative WQL 0.544869.
+- term skill: long=0.3857, medium=0.3986, short=0.3205.
+- domain skill: Econ/Fin=0.1093, Energy=0.1807, Healthcare=0.4083, Nature=0.4098, Sales=0.3497, Transport=0.4525, Web/CloudOps=0.4985.
+- frequency skill: A=0.3742, D=0.3386, H=0.3743, M=0.0874, Q=0.1946, S=0.4388, T=0.3951, W=0.1953.
+- variates skill: multivariate=0.3844, univariate=0.3260.
+- Outcome: Screen 0.352533 versus matched control 0.352399; first earlier diagnostic 0.322419 versus control 0.322598. Differences are small and inconsistent; continue to the 12000-step comparison. Training time through 6000: 1375.1 process seconds.
+
+## E22 pruned depth model earlier diagnostic
+- Checkpoint SHA-256: `a1efd06cff13f7f47559d83d37ea63f6bc65692df49fd2bfe039502d33978632`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 29.58867907524109 seconds.
+- Overall skill 0.322537; relative MASE 0.811436; relative WQL 0.565609.
+- term skill: long=0.3876, medium=0.3519, short=0.2839.
+- domain skill: Econ/Fin=0.1430, Energy=0.2205, Healthcare=0.3835, Nature=0.3473, Sales=0.3985, Transport=0.3592, Web/CloudOps=0.4331.
+- frequency skill: A=0.3156, D=0.3515, H=0.3521, M=0.1655, Q=0.2142, S=0.3417, T=0.3184, W=0.2490.
+- variates skill: multivariate=0.3364, univariate=0.3113.
+- Outcome: Score 0.322537 versus matched control 0.322598, confirming no useful advantage in the tested budgets.
+
+## E33: global quantile ensemble of the newer backbone and transferred memory
+Hypothesis: averaging the unmodified E22-12000 forecasts with E32 reduces the history memory’s temporal instability. Compare fixed backbone weights 0.25/0.5/0.75, with the complementary weight on the memory model, across all three diagnostics. Both members must forecast every instance in any deployed package. This is a cached-output diagnostic first, with no training or per-dataset choice. Actual package inference and full validation remain required before publication.
+
+## E32 transferred history memory screen
+- Checkpoint SHA-256: `c5a6d1fa7f637f2fa36f9176d66f39a71f8f3c8c84fef8098ec03a88a59f68e7`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.279379844665527 seconds.
+- Overall skill 0.361285; relative MASE 0.762270; relative WQL 0.535186.
+- term skill: long=0.3979, medium=0.4009, short=0.3306.
+- domain skill: Econ/Fin=0.1354, Energy=0.1960, Healthcare=0.4004, Nature=0.4175, Sales=0.3496, Transport=0.4614, Web/CloudOps=0.5026.
+- frequency skill: A=0.3746, D=0.3390, H=0.3931, M=0.0951, Q=0.1983, S=0.4210, T=0.4049, W=0.1967.
+- variates skill: multivariate=0.3911, univariate=0.3365.
+- Outcome: Main-screen gain persists on the newer backbone, but the first earlier diagnostic remains below its matched control. Not published standalone.
+
+## E32 transferred history memory first earlier
+- Checkpoint SHA-256: `c5a6d1fa7f637f2fa36f9176d66f39a71f8f3c8c84fef8098ec03a88a59f68e7`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.01271629333496 seconds.
+- Overall skill 0.324581; relative MASE 0.811091; relative WQL 0.562442.
+- term skill: long=0.3866, medium=0.3491, short=0.2893.
+- domain skill: Econ/Fin=0.1617, Energy=0.2185, Healthcare=0.3678, Nature=0.3513, Sales=0.3962, Transport=0.3659, Web/CloudOps=0.4368.
+- frequency skill: A=0.3123, D=0.3475, H=0.3561, M=0.1691, Q=0.2293, S=0.3357, T=0.3214, W=0.2557.
+- variates skill: multivariate=0.3385, univariate=0.3133.
+- Outcome: Main-screen gain persists on the newer backbone, but the first earlier diagnostic remains below its matched control. Not published standalone.
+
+## E32 transferred history memory second earlier
+- Checkpoint SHA-256: `c5a6d1fa7f637f2fa36f9176d66f39a71f8f3c8c84fef8098ec03a88a59f68e7`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 23.5328631401062 seconds.
+- Overall skill 0.352606; relative MASE 0.775583; relative WQL 0.540391.
+- term skill: long=0.3677, medium=0.3779, short=0.3370.
+- domain skill: Econ/Fin=0.1079, Energy=0.2224, Healthcare=0.3668, Nature=0.4615, Sales=0.5608, Transport=0.4165, Web/CloudOps=0.4263.
+- frequency skill: A=0.3383, D=0.4283, H=0.3855, M=0.1616, Q=0.1520, S=0.2804, T=0.3462, W=0.2493.
+- variates skill: multivariate=0.3553, univariate=0.3504.
+- Outcome: Main-screen gain persists on the newer backbone, but the first earlier diagnostic remains below its matched control. Not published standalone.
+
+E33 cached ensemble results. Neither member required new training. Backbone weights 0.25/0.5/0.75 apply globally to every instance; complementary weight is on E32. All tested mixtures retain a small first-earlier-cutoff regression. No ensemble is published yet; the simpler backbone continuations are still improving.
+- screen, base weight 0.25: skill 0.3609597107729168; elapsed 31.589441299438477 CPU seconds; groups {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7485062013261629, "rel_wql_gmean": 0.48497053192140965}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7187220659985801, "rel_wql_gmean": 0.49872959388387217}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7854775479522941, "rel_wql_gmean": 0.5715754588019885}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8766794111084217, "rel_wql_gmean": 0.8521904674160014}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9458381742339477, "rel_wql_gmean": 0.6841214470602749}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6357518097122528, "rel_wql_gmean": 0.5708050606184818}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8296139742081163, "rel_wql_gmean": 0.40898901267743815}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8283216000359614, "rel_wql_gmean": 0.510813353500822}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6491485866452709, "rel_wql_gmean": 0.4469444592152844}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.564775788716386, "rel_wql_gmean": 0.4386209893190716}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6092189862308618, "rel_wql_gmean": 0.6444119562488515}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7923969772018941, "rel_wql_gmean": 0.5529928138595462}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7344815207835862, "rel_wql_gmean": 0.502366984331498}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9818087731317099, "rel_wql_gmean": 0.8332936324629011}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7734447483921444, "rel_wql_gmean": 0.8291183442517216}, "S": {"n_configs": 6, "rel_mase_gmean": 0.662205210700223, "rel_wql_gmean": 0.5065947056042907}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7321702800096546, "rel_wql_gmean": 0.4836230433794967}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9308497588724332, "rel_wql_gmean": 0.6931956959471695}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7176036435313308, "rel_wql_gmean": 0.5172069567399432}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.800284652420953, "rel_wql_gmean": 0.5506355529979893}}}
+- screen, base weight 0.5: skill 0.36051700538130327; elapsed 1.6923091411590576 CPU seconds; groups {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7491443694259866, "rel_wql_gmean": 0.48552116951145263}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.717943946501363, "rel_wql_gmean": 0.49885742655549165}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7862261945539069, "rel_wql_gmean": 0.572174296426093}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8767314570553127, "rel_wql_gmean": 0.8518426772129285}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9464868100785462, "rel_wql_gmean": 0.6847020626617584}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6391927294627128, "rel_wql_gmean": 0.5728774819722747}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8288486290328975, "rel_wql_gmean": 0.4095777628918776}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.828359895013336, "rel_wql_gmean": 0.5108331535102516}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6492097960109925, "rel_wql_gmean": 0.44703510569395916}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5650725913141991, "rel_wql_gmean": 0.43904253902223594}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6107048585981317, "rel_wql_gmean": 0.6453081988530746}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7937577174837149, "rel_wql_gmean": 0.5536527716998109}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7349129886012886, "rel_wql_gmean": 0.5033244078192418}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9816729369571708, "rel_wql_gmean": 0.83284954955612}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7729642350253297, "rel_wql_gmean": 0.8282079386900584}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6624714487276662, "rel_wql_gmean": 0.5067064419332115}, "T": {"n_configs": 30, "rel_mase_gmean": 0.732172046143732, "rel_wql_gmean": 0.4837994768895142}, "W": {"n_configs": 8, "rel_mase_gmean": 0.930849298960232, "rel_wql_gmean": 0.6931955559830537}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.717793296521423, "rel_wql_gmean": 0.5179713349770367}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.8008210552601016, "rel_wql_gmean": 0.5508732181941426}}}
+- screen, base weight 0.75: skill 0.3599500839851063; elapsed 1.4231245517730713 CPU seconds; groups {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.750042489425022, "rel_wql_gmean": 0.48623104748726004}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7173897864210821, "rel_wql_gmean": 0.4991516788000004}, "short": {"n_configs": 55, "rel_mase_gmean": 0.78703598003334, "rel_wql_gmean": 0.5728330044823371}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8768876674480677, "rel_wql_gmean": 0.8515997053654789}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9473562016160542, "rel_wql_gmean": 0.6855548779843715}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.642561102977203, "rel_wql_gmean": 0.5749859696863521}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8285232450849535, "rel_wql_gmean": 0.4102456883415478}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8284086809446388, "rel_wql_gmean": 0.5108547074028001}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6493066620546145, "rel_wql_gmean": 0.44715612803391697}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5654188677782281, "rel_wql_gmean": 0.4395078746093791}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6122299433463864, "rel_wql_gmean": 0.6462324836335608}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7950974689907988, "rel_wql_gmean": 0.5543319910897211}, "H": {"n_configs": 31, "rel_mase_gmean": 0.735615332675693, "rel_wql_gmean": 0.5044620646385107}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9815783490405571, "rel_wql_gmean": 0.832453381029195}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7726561980040161, "rel_wql_gmean": 0.8274692503440843}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6627478709382408, "rel_wql_gmean": 0.5068543440740843}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7323316854508894, "rel_wql_gmean": 0.4840907541003884}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9308491431521891, "rel_wql_gmean": 0.6931954208513348}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7182293746317306, "rel_wql_gmean": 0.5189159515589695}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.8014062938450294, "rel_wql_gmean": 0.5511589238591919}}}
+- backcast, base weight 0.25: skill 0.32516390659459127; elapsed 25.728330373764038 CPU seconds; groups {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7877573688004119, "rel_wql_gmean": 0.4757505244331779}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7777646324659491, "rel_wql_gmean": 0.5427449141803178}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8320626409877416, "rel_wql_gmean": 0.6069233027764227}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8731645869233648, "rel_wql_gmean": 0.8046317212772931}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9000885899955989, "rel_wql_gmean": 0.6764463826118567}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6763673902063786, "rel_wql_gmean": 0.5893498915999001}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9498042213778831, "rel_wql_gmean": 0.44171639005646535}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7353166304138659, "rel_wql_gmean": 0.495785420303292}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.7449699119735571, "rel_wql_gmean": 0.5393127533664154}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6755793289890412, "rel_wql_gmean": 0.4696226318380198}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.651838123897038, "rel_wql_gmean": 0.7256129506047462}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8480816283629858, "rel_wql_gmean": 0.5013822816935581}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7872906478235149, "rel_wql_gmean": 0.5262643957494131}, "M": {"n_configs": 5, "rel_mase_gmean": 0.867272967035772, "rel_wql_gmean": 0.7959646950903766}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7634980658181472, "rel_wql_gmean": 0.7778879435047819}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8611665698543136, "rel_wql_gmean": 0.5114505986034271}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7774300159704559, "rel_wql_gmean": 0.5898942963102115}, "W": {"n_configs": 8, "rel_mase_gmean": 0.921455165311994, "rel_wql_gmean": 0.6011456983078217}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.8054278932888852, "rel_wql_gmean": 0.5417010935787644}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.8142697614393413, "rel_wql_gmean": 0.5786920991268066}}}
+- backcast, base weight 0.5: skill 0.3255906425540577; elapsed 7.025062561035156 CPU seconds; groups {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7868531100085023, "rel_wql_gmean": 0.47494194797983313}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7767176724724971, "rel_wql_gmean": 0.5419949281040547}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8318845792654018, "rel_wql_gmean": 0.606991975463184}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8731690390439605, "rel_wql_gmean": 0.8045484945388448}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8993476870381677, "rel_wql_gmean": 0.6755669234996251}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6753662260274707, "rel_wql_gmean": 0.5885861458424224}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9483032085189914, "rel_wql_gmean": 0.441350380267718}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7353166304138659, "rel_wql_gmean": 0.495785420303292}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.7446948865024733, "rel_wql_gmean": 0.5392154113246899}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6755391890283506, "rel_wql_gmean": 0.469748030722515}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.651838123897038, "rel_wql_gmean": 0.7256129506047462}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8475410369158077, "rel_wql_gmean": 0.5011421156521402}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7875436876281503, "rel_wql_gmean": 0.5262880067026078}, "M": {"n_configs": 5, "rel_mase_gmean": 0.867272967035772, "rel_wql_gmean": 0.7959646950903766}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7634980658181472, "rel_wql_gmean": 0.7778879435047819}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8607773505813321, "rel_wql_gmean": 0.5107840537486803}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7758285204666452, "rel_wql_gmean": 0.5890117516972101}, "W": {"n_configs": 8, "rel_mase_gmean": 0.921455165311994, "rel_wql_gmean": 0.6011456983078217}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.8047280897548712, "rel_wql_gmean": 0.5410867356076619}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.8138657265706295, "rel_wql_gmean": 0.5785877053648649}}}
+- backcast, base weight 0.75: skill 0.32585558132181525; elapsed 1.4801208972930908 CPU seconds; groups {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7864118030472989, "rel_wql_gmean": 0.47433966987370835}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7760856247602924, "rel_wql_gmean": 0.541419709057465}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8317549187410698, "rel_wql_gmean": 0.6071042148592315}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8731809073153128, "rel_wql_gmean": 0.8044818649657144}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8991776752660365, "rel_wql_gmean": 0.6750015559883032}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6743591342425898, "rel_wql_gmean": 0.5878184243392145}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9471433052106504, "rel_wql_gmean": 0.4410648488568812}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7353166304138659, "rel_wql_gmean": 0.495785420303292}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.744443044974495, "rel_wql_gmean": 0.5391442286013897}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6755193180086204, "rel_wql_gmean": 0.46990600477296696}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.651838123897038, "rel_wql_gmean": 0.7256129506047462}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8470002930114933, "rel_wql_gmean": 0.5009052086170435}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7882647172004155, "rel_wql_gmean": 0.5264910423859007}, "M": {"n_configs": 5, "rel_mase_gmean": 0.867272967035772, "rel_wql_gmean": 0.7959646950903766}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7634980658181472, "rel_wql_gmean": 0.7778879435047819}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8604064218762162, "rel_wql_gmean": 0.5101551146076984}, "T": {"n_configs": 30, "rel_mase_gmean": 0.774442079728573, "rel_wql_gmean": 0.5883004763096262}, "W": {"n_configs": 8, "rel_mase_gmean": 0.921455165311994, "rel_wql_gmean": 0.6011456983078217}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.8044577867215783, "rel_wql_gmean": 0.5406785466562373}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.8135190933822734, "rel_wql_gmean": 0.5785199260327762}}}
+- backcast2, base weight 0.25: skill 0.35287154434047796; elapsed 32.718568563461304 CPU seconds; groups {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7953937401265897, "rel_wql_gmean": 0.5030106493089533}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7678643593783896, "rel_wql_gmean": 0.5028055430553253}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7711270557942318, "rel_wql_gmean": 0.5695813024863108}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8805482364783594, "rel_wql_gmean": 0.9042209924611244}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9117289903338895, "rel_wql_gmean": 0.6620020860753241}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6839866145665877, "rel_wql_gmean": 0.5864976160474622}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7748252117778318, "rel_wql_gmean": 0.3733068662798137}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5489740065972382, "rel_wql_gmean": 0.3513233844778427}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6725766506204502, "rel_wql_gmean": 0.5061420572217049}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6870083960785978, "rel_wql_gmean": 0.4794994231705838}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5819327138083403, "rel_wql_gmean": 0.7523849571908706}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7299533930809837, "rel_wql_gmean": 0.4479740157977664}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7604275957811419, "rel_wql_gmean": 0.4953118153144008}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8158365215321529, "rel_wql_gmean": 0.8615342594759124}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8252565936089924, "rel_wql_gmean": 0.8713357271735944}, "S": {"n_configs": 6, "rel_mase_gmean": 0.9122753788008492, "rel_wql_gmean": 0.5679295721865012}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7657639848982667, "rel_wql_gmean": 0.5579672183603664}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8655383325021186, "rel_wql_gmean": 0.6510826830966135}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7902445203946336, "rel_wql_gmean": 0.5253027568920817}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7633236727962595, "rel_wql_gmean": 0.5525218103974238}}}
+- backcast2, base weight 0.5: skill 0.35293362223391267; elapsed 1.2891087532043457 CPU seconds; groups {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.795490811324907, "rel_wql_gmean": 0.5038189345476245}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7674326315874551, "rel_wql_gmean": 0.5023119643797436}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7711829671563578, "rel_wql_gmean": 0.5693457082014003}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8808378054392396, "rel_wql_gmean": 0.9044589957290904}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9113735276107076, "rel_wql_gmean": 0.6617472008949459}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6842243004747667, "rel_wql_gmean": 0.5865985548061023}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7748130011917327, "rel_wql_gmean": 0.37282859138217267}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5489740065972382, "rel_wql_gmean": 0.3513233844778427}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6724189210776539, "rel_wql_gmean": 0.5061490326925873}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6872511298933153, "rel_wql_gmean": 0.47991505019019964}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5819327138083403, "rel_wql_gmean": 0.7523849571908706}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7301681468376378, "rel_wql_gmean": 0.4481185117970856}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7599867700948899, "rel_wql_gmean": 0.49504239452481336}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8158365215321529, "rel_wql_gmean": 0.8615342594759124}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8252565936089924, "rel_wql_gmean": 0.8713357271735944}, "S": {"n_configs": 6, "rel_mase_gmean": 0.9125454167444352, "rel_wql_gmean": 0.5681445429462755}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7659127026855528, "rel_wql_gmean": 0.5579048015053354}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8655383325021186, "rel_wql_gmean": 0.6510826830966135}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7903908108689007, "rel_wql_gmean": 0.5252368408110905}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7631277150357363, "rel_wql_gmean": 0.5524454383766455}}}
+- backcast2, base weight 0.75: skill 0.35279424558075034; elapsed 1.3640234470367432 CPU seconds; groups {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7960013691972241, "rel_wql_gmean": 0.5048538794748333}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7673586435302413, "rel_wql_gmean": 0.5020153819167671}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7714553413789935, "rel_wql_gmean": 0.5691840862467166}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.881145657719554, "rel_wql_gmean": 0.9047199305925011}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9115950570456508, "rel_wql_gmean": 0.6618010993808187}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6844817312954508, "rel_wql_gmean": 0.5867080808867043}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7754005359113609, "rel_wql_gmean": 0.3724980616976047}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5489740065972382, "rel_wql_gmean": 0.3513233844778427}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.672286131185056, "rel_wql_gmean": 0.506183419259456}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6875722190026725, "rel_wql_gmean": 0.48037847185063465}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5819327138083403, "rel_wql_gmean": 0.7523849571908706}, "D": {"n_configs": 15, "rel_mase_gmean": 0.730395963063022, "rel_wql_gmean": 0.4482700955304752}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7602248518219639, "rel_wql_gmean": 0.4950255629831832}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8158365215321529, "rel_wql_gmean": 0.8615342594759124}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8252565936089924, "rel_wql_gmean": 0.8713357271735944}, "S": {"n_configs": 6, "rel_mase_gmean": 0.9128726300303336, "rel_wql_gmean": 0.5684332751933325}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7662563237934747, "rel_wql_gmean": 0.5579847239947101}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8655383325021186, "rel_wql_gmean": 0.6510826830966135}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7911061856256687, "rel_wql_gmean": 0.5254140703891469}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7629991415479856, "rel_wql_gmean": 0.5524144817860636}}}
+
+## E34: multivariate attention with end-to-end pretraining and univariate rehearsal
+Hypothesis: E20’s frozen backbone could not fully learn how to interpret cross-channel signals. Start from E22 control at 12000, add the same zero-output cross-variable attention layer, and train all 37,816,096 parameters. Three quarters of batches use the original 95-source univariate sampler, batch 128; one quarter use synchronized multivariate records, 32 groups with 2/3/4/8 channels. Six thousand steps, learning rate 4e-5 with 200-step warmup and cosine decay; main seed 73, independent joint-sampler seed 10073. Use only packed_sample and packed_joint and their local generators. This changes the training mixture and trainable parameter set together; it is a candidate method comparison, not a claim that either factor alone causes any gain. Zero-step output is the matched inference control. No validation observations enter gradients.
+
+## E25 learned recency 12000 with aligned templates, full validation
+- Checkpoint SHA-256: `8ccb6870a025e1097331a3a288b1183741a413f3460fbf3352d0c589c6a4394c`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.355628; relative MASE 0.765858; relative WQL 0.542156.
+- term skill: long=0.3899, medium=0.4001, short=0.3239.
+- domain skill: Econ/Fin=0.1162, Energy=0.1854, Healthcare=0.4030, Nature=0.4169, Sales=0.3571, Transport=0.4622, Web/CloudOps=0.4930.
+- frequency skill: A=0.2912, D=0.3393, H=0.3859, M=0.1107, Q=0.1840, S=0.4221, T=0.3964, W=0.1942.
+- variates skill: multivariate=0.3851, univariate=0.3311.
+- Outcome: Published. Full validation 0.355628 versus matched E22-12000 package 0.355096; earlier diagnostics 0.327844 and 0.354561 versus 0.325963 and 0.352462. No failures. Repeated relocated examples are bitwise identical, finite and monotone. This extends the recency-feature benefit to a third matched training checkpoint.
+
+## E25 recency12000 first earlier diagnostic
+- Checkpoint SHA-256: `8ccb6870a025e1097331a3a288b1183741a413f3460fbf3352d0c589c6a4394c`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 21.109707355499268 seconds.
+- Overall skill 0.327844; relative MASE 0.806911; relative WQL 0.559904.
+- term skill: long=0.3932, medium=0.3529, short=0.2909.
+- domain skill: Econ/Fin=0.1636, Energy=0.2224, Healthcare=0.3816, Nature=0.3533, Sales=0.3955, Transport=0.3666, Web/CloudOps=0.4404.
+- frequency skill: A=0.3134, D=0.3502, H=0.3567, M=0.1636, Q=0.2250, S=0.3402, T=0.3276, W=0.2656.
+- variates skill: multivariate=0.3437, univariate=0.3150.
+- Outcome: Improves the matched control.
+
+## E25 recency12000 second earlier diagnostic
+- Checkpoint SHA-256: `8ccb6870a025e1097331a3a288b1183741a413f3460fbf3352d0c589c6a4394c`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 20.91800332069397 seconds.
+- Overall skill 0.354561; relative MASE 0.773267; relative WQL 0.538742.
+- term skill: long=0.3642, medium=0.3828, short=0.3397.
+- domain skill: Econ/Fin=0.1095, Energy=0.2259, Healthcare=0.3738, Nature=0.4621, Sales=0.5594, Transport=0.4168, Web/CloudOps=0.4281.
+- frequency skill: A=0.3385, D=0.4319, H=0.3875, M=0.1660, Q=0.1478, S=0.2841, T=0.3480, W=0.2461.
+- variates skill: multivariate=0.3588, univariate=0.3511.
+- Outcome: Improves the matched control.
+
+Measured continuation compute (elapsed training-process seconds, shared single H100; these are not additive dedicated GPU-seconds):
+- depth36_control, step 1000: 124.8 seconds.
+- depth36_control, step 3000: 365.5 seconds.
+- depth36_control, step 6000: 869.5 seconds.
+- depth36_control, step 12000: 2211.3 seconds.
+- recency_features, step 1000: 168.9 seconds.
+- recency_features, step 3000: 510.3 seconds.
+- recency_features, step 6000: 1158.7 seconds.
+- recency_features, step 12000: 2481.6 seconds.
+- strong_loss, step 1000: 224.1 seconds.
+- strong_loss, step 3000: 672.8 seconds.
+- strong_loss, step 6000: 1375.1 seconds.
+- amplitude_synth, step 1000: 227.1 seconds.
+- amplitude_synth, step 3000: 678.7 seconds.
+- joint_rehearsal, step 1000: 172.6 seconds.
+
+## E35: explicit local trend increments in future queries (prepared)
+Hypothesis: the original damped trend feature clips normalized slopes at 0.03 per step, suppressing trends in short annual and quarterly contexts. Add two zero-initialized query features from differences of adjacent 4-point and 16-point means, extrapolated to each future step and softly capped at 3 context standard deviations. Each mean needs at least half its observations and at least two finite values. This is a fixed context calculation, not a fitted per-instance model. A separate 16,384-parameter query projection preserves the existing recency projection and all pretrained-in-this-workspace tensors. Planned E17 initialization, seed 59, batch 256 and 24,000-step schedule match E25 exactly; no new synthetic generator. Training not yet started.
+
+E28 stopped after the 12000-step comparison. Screen 0.355930 is below matched control 0.356664; earlier checkpoints gave small inconsistent differences. Training through 12000 took 2731.8 process seconds on the shared H100. Checkpoints are retained, but the stronger seasonal-error weighting is not adopted.
+
+## E28 stronger seasonal loss 12000-step checkpoint
+- Checkpoint SHA-256: `9f67f75960a568c9bc42fdb2ec99c8852bb5136ae869b7f63ac3fd6aeb359aab`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 21.34673237800598 seconds.
+- Overall skill 0.355930; relative MASE 0.766619; relative WQL 0.541111.
+- term skill: long=0.3886, medium=0.4019, short=0.3241.
+- domain skill: Econ/Fin=0.1123, Energy=0.1843, Healthcare=0.4078, Nature=0.4143, Sales=0.3492, Transport=0.4532, Web/CloudOps=0.5040.
+- frequency skill: A=0.3801, D=0.3350, H=0.3784, M=0.1213, Q=0.1929, S=0.4399, T=0.3989, W=0.1948.
+- variates skill: multivariate=0.3880, univariate=0.3292.
+- Outcome: Below matched control; stop this branch after three comparisons without a consistent advantage.
+
+E35 training started with the planned matched seed 59, batch 256, learning rate 8e-5 and 24000-step schedule, replacing E28 GPU work. A synthetic forward check verified bitwise equality at zero projection, and backward propagation produced finite nonzero gradients for the new trend projection. No additional training source or generator is introduced.
+
+## E31 synthetic amplitude 3000-step screen
+- Checkpoint SHA-256: `6f3e68f1b70e9cde6872473dc4d834c16195e72656ba974eb39d48413a10af63`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 22.430498361587524 seconds.
+- Overall skill 0.352625; relative MASE 0.768870; relative WQL 0.545079.
+- term skill: long=0.3854, medium=0.3995, short=0.3204.
+- domain skill: Econ/Fin=0.1023, Energy=0.1772, Healthcare=0.4136, Nature=0.4097, Sales=0.3467, Transport=0.4530, Web/CloudOps=0.5025.
+- frequency skill: A=0.3691, D=0.3356, H=0.3748, M=0.0875, Q=0.1887, S=0.4366, T=0.3965, W=0.1978.
+- variates skill: multivariate=0.3840, univariate=0.3265.
+- Outcome: Improves matched E25-3000 screen from 0.351702 to 0.352625. First earlier diagnostic improves 0.321665 to 0.322014. Keep training to assess persistence.
+
+## E34 joint rehearsal 1000-step screen
+- Checkpoint SHA-256: `394f2003a6c00cdcb0f2b944d1e9cc8ae4f7aca9e513bf46dbb1f8a2489c8670`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.026655912399292 seconds.
+- Overall skill 0.360583; relative MASE 0.762757; relative WQL 0.536022.
+- term skill: long=0.3975, medium=0.4014, short=0.3292.
+- domain skill: Econ/Fin=0.1394, Energy=0.1926, Healthcare=0.3956, Nature=0.4177, Sales=0.3466, Transport=0.4620, Web/CloudOps=0.5034.
+- frequency skill: A=0.3785, D=0.3382, H=0.3903, M=0.0963, Q=0.1994, S=0.4210, T=0.4059, W=0.1960.
+- variates skill: multivariate=0.3895, univariate=0.3366.
+- Outcome: Improves matched zero-step control on main screen and both earlier diagnostics. Later checkpoints are needed before selecting a package.
+
+## E34 joint rehearsal 3000-step screen
+- Checkpoint SHA-256: `6dd1560fb4d63eae0fa8d414252b55329371e397cee44dcff4c656ff3e2f3416`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 22.9136745929718 seconds.
+- Overall skill 0.358556; relative MASE 0.764675; relative WQL 0.538072.
+- term skill: long=0.3976, medium=0.4016, short=0.3253.
+- domain skill: Econ/Fin=0.1364, Energy=0.1912, Healthcare=0.3737, Nature=0.4189, Sales=0.3465, Transport=0.4616, Web/CloudOps=0.5016.
+- frequency skill: A=0.3793, D=0.3315, H=0.3904, M=0.0787, Q=0.2012, S=0.4202, T=0.4046, W=0.1966.
+- variates skill: multivariate=0.3887, univariate=0.3335.
+- Outcome: Screen falls below the zero-step control after the earlier gain; do not publish this checkpoint. Continue to the planned 6000-step endpoint to assess the lower learning rate.
+
+## E36: equal-weight control and recency ensemble
+Hypothesis: averaging two strong continuations reduces differences across validation cutoffs. Compare a fixed 50/50 quantile average of E25 recency12000 and E22 control18000, each forecasting every instance with the aligned seasonal rule. No training or new data; no dataset-specific selection. Screen and both earlier diagnostics are computed from cached forecasts before constructing a runtime package.
+
+## E37: fixed zero-mass projection
+Hypothesis: small positive forecasts on histories with an observed mass at exactly zero cause disproportionate median error. Diagnostic candidates snap nonnegative quantiles below 0, 0.001, 0.005 or 0.02 context standard deviations to zero, only when the recent4096 history is nonnegative and contains at least4 zeros comprising at least10% of observed points. All thresholds are fixed globally; no per-instance fitting. Test the same four candidates at all three validation cutoffs using E25 recency12000 aligned outputs. No training and no extra data.
+
+E37 initial sweep: threshold0.02 improved screen 0.359868→0.360509, first earlier0.327844→0.328180 and second earlier0.354561→0.354943. Smaller nonzero thresholds gave smaller gains. Build and fully evaluate the0.02 runtime. A separate follow-up will compare0.05 and0.1 to assess whether the original upper endpoint truncated the useful range; no additional fitted parameters.
+
+## E38: broader packed corpus for the 36.8M recency model (prepared)
+Hypothesis: the earlier negative corpus-expansion result at6.7M parameters may not hold for the larger, more-trained model. Compare packed_sample (0.959B points) with packed_expanded (5.653B points), initializing both from E25 recency12000. Keep source mixture weights calculated from packed_sample in both arms, isolating broader series and crop coverage from source-reweighting. Seed79, batch256, learning rate5e-5, 12000-step schedule, saves every3000, same architecture/loss/window mixture/synthetic generator. Each arm uses two Torch CPU threads. Training will start after current jobs release GPU capacity. No new corpus directories or generators.
+
+E37 wider sweep: thresholds0.02/0.05/0.1 gave screen0.360509/0.362121/0.364736, earlier1 0.328180/0.328643/0.329276, earlier2 0.354943/0.355475/0.356263. All three improve with the larger threshold. A final bracket at0.1/0.2/0.4/0.8 will locate an interior useful range before full runtime selection.
+
+## E39: bounded quantiles during pretraining (prepared)
+Hypothesis: the current continuous output head incurs small positive errors on genuinely zero-valued futures. Extend the existing nonnegativity heuristic symmetrically inside the training forward pass: histories entirely nonnegative clamp normalized output quantiles at standardized zero; entirely nonpositive histories clamp above at zero. This lets pinball loss learn exact boundary probability mass through negative latent quantiles, rather than requiring exact equality of a continuous prediction. No additional model parameters. Match E38 packed_sample control: own E25 recency12000 initialization, seed79, batch256, LR5e-5, 12000 steps, original source mixture and generator. Training not yet started; this is separate from the fixed E37 inference projection.
+
+E37 final bracket selected threshold0.4: screen/earlier1/earlier2 =0.370559/0.330696/0.357884, versus parent0.359868/0.327844/0.354561. Threshold0.8 regresses to0.364344/0.327637/0.350745, providing an interior optimum common to all three cutoffs. No finer sweep is planned. Assemble and fully evaluate threshold0.4 before publication.
+
+E36 equal-weight blend gives screen0.361191, earlier1 0.328179, earlier2 0.354067. The main screen remains below control18000 alone (0.361735), and second earlier remains below recency12000 (0.354561). Not selected; no ensemble runtime is published.
+
+E38 packed_sample control started with the planned seed79 and schedule after E34 completed. A sampler check confirmed bitwise equality with the original sampler and identical source probabilities for both packed corpora. The expanded arm remains prepared, awaiting GPU capacity.
+
+## E22 control18000 full validation with aligned seasonal rule
+- Checkpoint SHA-256: `b7bbb7473a317145235c133492a0d743a90a01ff252577a4c8a4651db008a138`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.357537; relative MASE 0.764724; relative WQL 0.539749.
+- term skill: long=0.3898, medium=0.4009, short=0.3270.
+- domain skill: Econ/Fin=0.1117, Energy=0.1855, Healthcare=0.4146, Nature=0.4204, Sales=0.3566, Transport=0.4642, Web/CloudOps=0.4949.
+- frequency skill: A=0.2833, D=0.3419, H=0.3872, M=0.1236, Q=0.1877, S=0.4206, T=0.3976, W=0.1997.
+- variates skill: multivariate=0.3862, univariate=0.3338.
+- Outcome: Improves full validation to0.357537; first and second earlier0.327746/0.352885 remain slightly below published recency12000. Candidate retained while matched recency18000 is evaluated.
+
+## E31 amplitude synthetic 6000-step screen
+- Checkpoint SHA-256: `7fe2746b237fb2699c2d8c812595666df0888cde656498043e01847bb552c783`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 21.376917362213135 seconds.
+- Overall skill 0.355543; relative MASE 0.766576; relative WQL 0.541792.
+- term skill: long=0.3869, medium=0.3997, short=0.3251.
+- domain skill: Econ/Fin=0.1054, Energy=0.1826, Healthcare=0.4173, Nature=0.4166, Sales=0.3471, Transport=0.4535, Web/CloudOps=0.5020.
+- frequency skill: A=0.3700, D=0.3444, H=0.3777, M=0.0986, Q=0.1941, S=0.4367, T=0.3980, W=0.1929.
+- variates skill: multivariate=0.3869, univariate=0.3294.
+- Outcome: Screen0.355543 improves matched E25-6000 0.352851. First earlier0.324002 is effectively tied with matched0.324008; second earlier0.349572 improves matched0.349098. Continue to12000.
+
+## E34 joint rehearsal 6000-step screen
+- Checkpoint SHA-256: `44aa5f702a58845a427ad76818631e808362b7dc236a3648232db47d621db219`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 21.278470993041992 seconds.
+- Overall skill 0.360025; relative MASE 0.763498; relative WQL 0.536437.
+- term skill: long=0.3986, medium=0.4027, short=0.3272.
+- domain skill: Econ/Fin=0.1375, Energy=0.1934, Healthcare=0.3748, Nature=0.4209, Sales=0.3465, Transport=0.4619, Web/CloudOps=0.5031.
+- frequency skill: A=0.3720, D=0.3320, H=0.3916, M=0.0873, Q=0.1998, S=0.4184, T=0.4071, W=0.1973.
+- variates skill: multivariate=0.3917, univariate=0.3337.
+- Outcome: Final screen0.360025 improves parent0.359242 but is below the1000-step peak0.360583. Evaluate both earlier cutoffs; do not publish solely from the small screen gain.
+
+E34 completed6000 steps in1049.5 training-process seconds. Earlier1/earlier2 =0.326344/0.350746: first improves parent0.325963, second regresses from0.352462. The1000-step checkpoint remains the better joint candidate, but neither is selected over the stronger recency model. All weights are archived.
+
+## E37 threshold0.02 full package validation
+- Checkpoint SHA-256: `8ccb6870a025e1097331a3a288b1183741a413f3460fbf3352d0c589c6a4394c`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.356254; relative MASE 0.764978; relative WQL 0.541726.
+- term skill: long=0.3904, medium=0.4005, short=0.3246.
+- domain skill: Econ/Fin=0.1162, Energy=0.1871, Healthcare=0.4030, Nature=0.4175, Sales=0.3571, Transport=0.4622, Web/CloudOps=0.4933.
+- frequency skill: A=0.2912, D=0.3393, H=0.3863, M=0.1108, Q=0.1840, S=0.4221, T=0.3979, W=0.1942.
+- variates skill: multivariate=0.3855, univariate=0.3320.
+- Outcome: Full skill0.356254 improves parent0.355628 with zero failed configurations. Repeated relocated examples are bitwise identical, finite and monotone. Temporarily published while the larger, consistently better threshold0.4 completes full validation.
+
+## E40: zero-mass projection with empirical phase probability
+E37 helps solar and several zero-heavy channels but slightly hurts some sales and cloud series. Hypothesis: a quantile should be snapped only when the observed zero probability supports its level. Keep threshold0.4 fixed; compare unrestricted E37, global observed zero fraction, and phase-specific zero frequencies from the last4 or8 primary seasonal cycles. For phase estimates with at least3 observations use (zero_count+0.5)/(count+1), otherwise the global fraction. Only levels at or below that zero probability are eligible. All rules are fixed context formulas, applied independently per variate. Compare cached E25 recency12000 forecasts on all three cutoffs; no training.
+
+E37 final selection: use the18,000-step recency checkpoint with threshold0.4. Full validation0.370171 exceeds12,000-step+0.4 at0.366390; earlier1 improves0.330696→0.332400, while earlier2 decreases0.357884→0.356697. The three-cutoff mean improves, and all three exceed the previously published12,000-step+0.02 package. Prefer the newer checkpoint for its full-validation and first-earlier gains; retain the second-earlier regression as a limitation. Both candidate packages passed repeated relocated example inference, with exact equality, finite outputs and monotone quantiles.
+- Measured depth36_control step18000: 3529.7 elapsed training-process seconds on the shared single H100.
+- Measured recency_features step18000: 4008.8 elapsed training-process seconds on the shared single H100.
+- Measured amplitude_synth step6000: 1498.1 elapsed training-process seconds on the shared single H100.
+- Measured joint_rehearsal step6000: 1049.5 elapsed training-process seconds on the shared single H100.
+
+## E37 threshold0.4 on recency12000 full validation
+- Checkpoint SHA-256: `8ccb6870a025e1097331a3a288b1183741a413f3460fbf3352d0c589c6a4394c`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.366390; relative MASE 0.754640; relative WQL 0.531990.
+- term skill: long=0.3917, medium=0.4037, short=0.3414.
+- domain skill: Econ/Fin=0.1162, Energy=0.2258, Healthcare=0.4030, Nature=0.4203, Sales=0.3559, Transport=0.4622, Web/CloudOps=0.4911.
+- frequency skill: A=0.2912, D=0.3374, H=0.3884, M=0.1159, Q=0.1840, S=0.4221, T=0.4262, W=0.1943.
+- variates skill: multivariate=0.3853, univariate=0.3509.
+- Outcome: Full0.366390, earlier1 0.330696, earlier2 0.357884. Clear improvement over the same checkpoint without zero projection. Superseded by the18000-step candidate on full validation and average across cutoffs.
+
+## E25 plus E37 recency18000 threshold0.4 full validation
+- Checkpoint SHA-256: `f6d2c41a690159a5b9c2034be45718c408a30b0d34d9607c9a768b9926781bbd`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.370171; relative MASE 0.750532; relative WQL 0.528537.
+- term skill: long=0.3932, medium=0.4057, short=0.3469.
+- domain skill: Econ/Fin=0.1099, Energy=0.2275, Healthcare=0.4340, Nature=0.4253, Sales=0.3541, Transport=0.4653, Web/CloudOps=0.4932.
+- frequency skill: A=0.2819, D=0.3468, H=0.3906, M=0.1220, Q=0.1866, S=0.4218, T=0.4285, W=0.2054.
+- variates skill: multivariate=0.3878, univariate=0.3558.
+- Outcome: Published atomically. Full skill0.370171; earlier1 0.332400 and earlier2 0.356697. All97 configurations and151170 instances scored, zero failures. Actual package repeated examples are bitwise deterministic, finite and monotone; full inference118.1 seconds under concurrent training.
+
+## Published recency18000 zero0.4 first earlier diagnostic
+- Checkpoint SHA-256: `f6d2c41a690159a5b9c2034be45718c408a30b0d34d9607c9a768b9926781bbd`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.86830711364746 seconds.
+- Overall skill 0.332400; relative MASE 0.801955; relative WQL 0.555755.
+- term skill: long=0.3980, medium=0.3574, short=0.2953.
+- domain skill: Econ/Fin=0.1526, Energy=0.2268, Healthcare=0.3995, Nature=0.3561, Sales=0.3986, Transport=0.3699, Web/CloudOps=0.4472.
+- frequency skill: A=0.2996, D=0.3516, H=0.3643, M=0.1721, Q=0.2330, S=0.3397, T=0.3325, W=0.2660.
+- variates skill: multivariate=0.3492, univariate=0.3187.
+- Outcome: Improves the previous published package and the12000-step zero0.4 candidate.
+
+## Published recency18000 zero0.4 second earlier diagnostic
+- Checkpoint SHA-256: `f6d2c41a690159a5b9c2034be45718c408a30b0d34d9607c9a768b9926781bbd`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 24.40335988998413 seconds.
+- Overall skill 0.356697; relative MASE 0.773731; relative WQL 0.534862.
+- term skill: long=0.3657, medium=0.3874, short=0.3411.
+- domain skill: Econ/Fin=0.1045, Energy=0.2331, Healthcare=0.3610, Nature=0.4656, Sales=0.5516, Transport=0.4179, Web/CloudOps=0.4303.
+- frequency skill: A=0.3474, D=0.4275, H=0.3891, M=0.1599, Q=0.1548, S=0.2798, T=0.3574, W=0.2434.
+- variates skill: multivariate=0.3611, univariate=0.3531.
+- Outcome: Improves the previous published package; slightly below the12000-step zero0.4 candidate.
+
+## E37 complete diagnostic sweep receipts
+All use the own recency12000 checkpoint SHA-256 `8ccb6870a025e1097331a3a288b1183741a413f3460fbf3352d0c589c6a4394c`; no training, fixed inference seed0. Compute below is CPU diagnostic elapsed time, not GPU training time.
+
+zero_mass, screen: 4.03 seconds.
+- Setting 0.0: skill 0.359868; term short=0.3284, medium=0.4016, long=0.3960; domain Econ/Fin=0.1374, Energy=0.1931, Healthcare=0.3970, Nature=0.4170, Sales=0.3479, Transport=0.4621, Web/CloudOps=0.5005.
+- Setting 0.001: skill 0.359874; term short=0.3284, medium=0.4016, long=0.3960; domain Econ/Fin=0.1374, Energy=0.1931, Healthcare=0.3970, Nature=0.4170, Sales=0.3479, Transport=0.4621, Web/CloudOps=0.5005.
+- Setting 0.005: skill 0.359953; term short=0.3285, medium=0.4017, long=0.3961; domain Econ/Fin=0.1374, Energy=0.1933, Healthcare=0.3970, Nature=0.4171, Sales=0.3479, Transport=0.4621, Web/CloudOps=0.5006.
+- Setting 0.02: skill 0.360509; term short=0.3292, medium=0.4020, long=0.3966; domain Econ/Fin=0.1374, Energy=0.1948, Healthcare=0.3970, Nature=0.4176, Sales=0.3480, Transport=0.4621, Web/CloudOps=0.5008.
+
+zero_mass, backcast: 3.65 seconds.
+- Setting 0.0: skill 0.327844; term short=0.2909, medium=0.3529, long=0.3932; domain Econ/Fin=0.1636, Energy=0.2224, Healthcare=0.3816, Nature=0.3533, Sales=0.3955, Transport=0.3666, Web/CloudOps=0.4404.
+- Setting 0.001: skill 0.327849; term short=0.2909, medium=0.3529, long=0.3932; domain Econ/Fin=0.1636, Energy=0.2224, Healthcare=0.3816, Nature=0.3533, Sales=0.3955, Transport=0.3666, Web/CloudOps=0.4404.
+- Setting 0.005: skill 0.327890; term short=0.2909, medium=0.3529, long=0.3933; domain Econ/Fin=0.1636, Energy=0.2225, Healthcare=0.3816, Nature=0.3534, Sales=0.3955, Transport=0.3666, Web/CloudOps=0.4404.
+- Setting 0.02: skill 0.328180; term short=0.2912, medium=0.3532, long=0.3937; domain Econ/Fin=0.1636, Energy=0.2229, Healthcare=0.3816, Nature=0.3537, Sales=0.3955, Transport=0.3666, Web/CloudOps=0.4409.
+
+zero_mass, backcast2: 3.62 seconds.
+- Setting 0.0: skill 0.354561; term short=0.3397, medium=0.3828, long=0.3642; domain Econ/Fin=0.1095, Energy=0.2259, Healthcare=0.3738, Nature=0.4621, Sales=0.5594, Transport=0.4168, Web/CloudOps=0.4281.
+- Setting 0.001: skill 0.354567; term short=0.3397, medium=0.3828, long=0.3642; domain Econ/Fin=0.1095, Energy=0.2259, Healthcare=0.3738, Nature=0.4621, Sales=0.5594, Transport=0.4168, Web/CloudOps=0.4282.
+- Setting 0.005: skill 0.354631; term short=0.3398, medium=0.3829, long=0.3643; domain Econ/Fin=0.1095, Energy=0.2260, Healthcare=0.3738, Nature=0.4622, Sales=0.5594, Transport=0.4168, Web/CloudOps=0.4282.
+- Setting 0.02: skill 0.354943; term short=0.3400, medium=0.3834, long=0.3646; domain Econ/Fin=0.1095, Energy=0.2266, Healthcare=0.3738, Nature=0.4627, Sales=0.5594, Transport=0.4168, Web/CloudOps=0.4283.
+
+zero_mass_wide, screen: 3.91 seconds.
+- Setting 0.0: skill 0.359868; term short=0.3284, medium=0.4016, long=0.3960; domain Econ/Fin=0.1374, Energy=0.1931, Healthcare=0.3970, Nature=0.4170, Sales=0.3479, Transport=0.4621, Web/CloudOps=0.5005.
+- Setting 0.02: skill 0.360509; term short=0.3292, medium=0.4020, long=0.3966; domain Econ/Fin=0.1374, Energy=0.1948, Healthcare=0.3970, Nature=0.4176, Sales=0.3480, Transport=0.4621, Web/CloudOps=0.5008.
+- Setting 0.05: skill 0.362121; term short=0.3316, medium=0.4026, long=0.3972; domain Econ/Fin=0.1374, Energy=0.1998, Healthcare=0.3970, Nature=0.4186, Sales=0.3483, Transport=0.4621, Web/CloudOps=0.5012.
+- Setting 0.1: skill 0.364736; term short=0.3357, medium=0.4036, long=0.3981; domain Econ/Fin=0.1374, Energy=0.2086, Healthcare=0.3970, Nature=0.4195, Sales=0.3487, Transport=0.4621, Web/CloudOps=0.5017.
+
+zero_mass_wide, backcast: 3.55 seconds.
+- Setting 0.0: skill 0.327844; term short=0.2909, medium=0.3529, long=0.3932; domain Econ/Fin=0.1636, Energy=0.2224, Healthcare=0.3816, Nature=0.3533, Sales=0.3955, Transport=0.3666, Web/CloudOps=0.4404.
+- Setting 0.02: skill 0.328180; term short=0.2912, medium=0.3532, long=0.3937; domain Econ/Fin=0.1636, Energy=0.2229, Healthcare=0.3816, Nature=0.3537, Sales=0.3955, Transport=0.3666, Web/CloudOps=0.4409.
+- Setting 0.05: skill 0.328643; term short=0.2914, medium=0.3540, long=0.3942; domain Econ/Fin=0.1636, Energy=0.2235, Healthcare=0.3816, Nature=0.3546, Sales=0.3957, Transport=0.3666, Web/CloudOps=0.4415.
+- Setting 0.1: skill 0.329276; term short=0.2917, medium=0.3550, long=0.3953; domain Econ/Fin=0.1636, Energy=0.2243, Healthcare=0.3816, Nature=0.3553, Sales=0.3959, Transport=0.3666, Web/CloudOps=0.4426.
+
+zero_mass_wide, backcast2: 3.53 seconds.
+- Setting 0.0: skill 0.354561; term short=0.3397, medium=0.3828, long=0.3642; domain Econ/Fin=0.1095, Energy=0.2259, Healthcare=0.3738, Nature=0.4621, Sales=0.5594, Transport=0.4168, Web/CloudOps=0.4281.
+- Setting 0.02: skill 0.354943; term short=0.3400, medium=0.3834, long=0.3646; domain Econ/Fin=0.1095, Energy=0.2266, Healthcare=0.3738, Nature=0.4627, Sales=0.5594, Transport=0.4168, Web/CloudOps=0.4283.
+- Setting 0.05: skill 0.355475; term short=0.3406, medium=0.3840, long=0.3651; domain Econ/Fin=0.1095, Energy=0.2278, Healthcare=0.3738, Nature=0.4637, Sales=0.5595, Transport=0.4168, Web/CloudOps=0.4284.
+- Setting 0.1: skill 0.356263; term short=0.3413, medium=0.3849, long=0.3660; domain Econ/Fin=0.1095, Energy=0.2295, Healthcare=0.3738, Nature=0.4650, Sales=0.5595, Transport=0.4168, Web/CloudOps=0.4287.
+
+zero_mass_range, screen: 3.63 seconds.
+- Setting 0.1: skill 0.364736; term short=0.3357, medium=0.4036, long=0.3981; domain Econ/Fin=0.1374, Energy=0.2086, Healthcare=0.3970, Nature=0.4195, Sales=0.3487, Transport=0.4621, Web/CloudOps=0.5017.
+- Setting 0.2: skill 0.368575; term short=0.3420, medium=0.4046, long=0.3988; domain Econ/Fin=0.1374, Energy=0.2225, Healthcare=0.3970, Nature=0.4210, Sales=0.3475, Transport=0.4621, Web/CloudOps=0.5015.
+- Setting 0.4: skill 0.370559; term short=0.3458, medium=0.4052, long=0.3979; domain Econ/Fin=0.1374, Energy=0.2332, Healthcare=0.3970, Nature=0.4205, Sales=0.3463, Transport=0.4621, Web/CloudOps=0.4986.
+- Setting 0.8: skill 0.364344; term short=0.3385, medium=0.4008, long=0.3925; domain Econ/Fin=0.1374, Energy=0.2220, Healthcare=0.3970, Nature=0.4198, Sales=0.3444, Transport=0.4621, Web/CloudOps=0.4869.
+
+zero_mass_range, backcast: 3.50 seconds.
+- Setting 0.1: skill 0.329276; term short=0.2917, medium=0.3550, long=0.3953; domain Econ/Fin=0.1636, Energy=0.2243, Healthcare=0.3816, Nature=0.3553, Sales=0.3959, Transport=0.3666, Web/CloudOps=0.4426.
+- Setting 0.2: skill 0.330173; term short=0.2923, medium=0.3563, long=0.3966; domain Econ/Fin=0.1636, Energy=0.2253, Healthcare=0.3816, Nature=0.3560, Sales=0.3970, Transport=0.3666, Web/CloudOps=0.4444.
+- Setting 0.4: skill 0.330696; term short=0.2928, medium=0.3550, long=0.3988; domain Econ/Fin=0.1636, Energy=0.2247, Healthcare=0.3816, Nature=0.3559, Sales=0.3972, Transport=0.3666, Web/CloudOps=0.4472.
+- Setting 0.8: skill 0.327637; term short=0.2916, medium=0.3471, long=0.3961; domain Econ/Fin=0.1636, Energy=0.2180, Healthcare=0.3815, Nature=0.3525, Sales=0.3939, Transport=0.3666, Web/CloudOps=0.4454.
+
+zero_mass_range, backcast2: 3.39 seconds.
+- Setting 0.1: skill 0.356263; term short=0.3413, medium=0.3849, long=0.3660; domain Econ/Fin=0.1095, Energy=0.2295, Healthcare=0.3738, Nature=0.4650, Sales=0.5595, Transport=0.4168, Web/CloudOps=0.4287.
+- Setting 0.2: skill 0.357389; term short=0.3423, medium=0.3862, long=0.3671; domain Econ/Fin=0.1095, Energy=0.2319, Healthcare=0.3738, Nature=0.4665, Sales=0.5592, Transport=0.4168, Web/CloudOps=0.4293.
+- Setting 0.4: skill 0.357884; term short=0.3425, medium=0.3883, long=0.3665; domain Econ/Fin=0.1095, Energy=0.2327, Healthcare=0.3738, Nature=0.4661, Sales=0.5573, Transport=0.4168, Web/CloudOps=0.4312.
+- Setting 0.8: skill 0.350745; term short=0.3358, medium=0.3808, long=0.3588; domain Econ/Fin=0.1095, Energy=0.2213, Healthcare=0.3737, Nature=0.4523, Sales=0.5499, Transport=0.4168, Web/CloudOps=0.4272.
+
+zero_phase, screen: 4.24 seconds.
+- Setting None: skill 0.370559; term short=0.3458, medium=0.4052, long=0.3979; domain Econ/Fin=0.1374, Energy=0.2332, Healthcare=0.3970, Nature=0.4205, Sales=0.3463, Transport=0.4621, Web/CloudOps=0.4986.
+- Setting 0: skill 0.367375; term short=0.3412, medium=0.4029, long=0.3972; domain Econ/Fin=0.1374, Energy=0.2196, Healthcare=0.3970, Nature=0.4183, Sales=0.3529, Transport=0.4621, Web/CloudOps=0.5008.
+- Setting 4: skill 0.369637; term short=0.3443, medium=0.4040, long=0.3986; domain Econ/Fin=0.1374, Energy=0.2277, Healthcare=0.3970, Nature=0.4202, Sales=0.3515, Transport=0.4621, Web/CloudOps=0.5001.
+- Setting 8: skill 0.369822; term short=0.3448, medium=0.4037, long=0.3986; domain Econ/Fin=0.1374, Energy=0.2278, Healthcare=0.3970, Nature=0.4202, Sales=0.3518, Transport=0.4621, Web/CloudOps=0.5007.
+
+zero_phase, backcast: 4.22 seconds.
+- Setting None: skill 0.330696; term short=0.2928, medium=0.3550, long=0.3988; domain Econ/Fin=0.1636, Energy=0.2247, Healthcare=0.3816, Nature=0.3559, Sales=0.3972, Transport=0.3666, Web/CloudOps=0.4472.
+- Setting 0: skill 0.328679; term short=0.2917, medium=0.3538, long=0.3940; domain Econ/Fin=0.1636, Energy=0.2231, Healthcare=0.3816, Nature=0.3547, Sales=0.4001, Transport=0.3666, Web/CloudOps=0.4412.
+- Setting 4: skill 0.329503; term short=0.2920, medium=0.3549, long=0.3957; domain Econ/Fin=0.1636, Energy=0.2254, Healthcare=0.3816, Nature=0.3555, Sales=0.3995, Transport=0.3666, Web/CloudOps=0.4415.
+- Setting 8: skill 0.329633; term short=0.2921, medium=0.3549, long=0.3960; domain Econ/Fin=0.1636, Energy=0.2252, Healthcare=0.3816, Nature=0.3555, Sales=0.4003, Transport=0.3666, Web/CloudOps=0.4420.
+
+zero_phase, backcast2: 3.98 seconds.
+- Setting None: skill 0.357884; term short=0.3425, medium=0.3883, long=0.3665; domain Econ/Fin=0.1095, Energy=0.2327, Healthcare=0.3738, Nature=0.4661, Sales=0.5573, Transport=0.4168, Web/CloudOps=0.4312.
+- Setting 0: skill 0.355941; term short=0.3416, medium=0.3833, long=0.3653; domain Econ/Fin=0.1095, Energy=0.2288, Healthcare=0.3738, Nature=0.4641, Sales=0.5605, Transport=0.4168, Web/CloudOps=0.4286.
+- Setting 4: skill 0.357313; term short=0.3424, medium=0.3856, long=0.3670; domain Econ/Fin=0.1095, Energy=0.2321, Healthcare=0.3738, Nature=0.4661, Sales=0.5605, Transport=0.4168, Web/CloudOps=0.4289.
+- Setting 8: skill 0.357230; term short=0.3422, medium=0.3858, long=0.3672; domain Econ/Fin=0.1095, Energy=0.2314, Healthcare=0.3738, Nature=0.4664, Sales=0.5608, Transport=0.4168, Web/CloudOps=0.4291.
+
+E40 outcome: unrestricted E37 threshold0.4 beats all global/phase-probability guards on every cutoff. Phase4 gives0.369637/0.329503/0.357313; phase8 gives0.369822/0.329633/0.357230; unrestricted gives0.370559/0.330696/0.357884. Do not add the probability guard.
+
+E36 component checkpoint hashes: recency12000 `8ccb6870a025e1097331a3a288b1183741a413f3460fbf3352d0c589c6a4394c`; control18000 `b7bbb7473a317145235c133492a0d743a90a01ff252577a4c8a4651db008a138`. These were cached diagnostic blends only, without additional training.
+
+## E41: learned zero-probability classifier with a frozen backbone
+Hypothesis: a classifier trained on allowed windows can make the zero projection conditional on future zero probability more accurately than the empirical guards rejected in E40. Freeze E25 recency18000 and train only a small MLP on decoder representations, global zero fraction, two four-cycle phase zero frequencies and absolute normalized median forecast. The classifier receives BCE loss for exact-zero futures only on sign-homogeneous contexts containing at least4 zeros and10% zeros. At inference, average probabilities from both sign orientations and allow the existing threshold0.4 projection only for quantile levels at or below that probability. Every instance passes through the same backbone and classifier. Bias5 initialization makes probability0.993 and preserves E37 forecasts. Own packed_sample and unchanged synthetic generator; seed83, batch128, LR2e-4, 6000-step schedule, one Torch CPU thread. This adds no external data and trains no backbone parameters.
+
+## E35 explicit trend inputs 3000-step screen
+- Checkpoint SHA-256: `17d346dde232341672545210839ea10fc6e147d6e1d65d2457497d790fc652f6`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.317448616027832 seconds.
+- Overall skill 0.351994; relative MASE 0.770114; relative WQL 0.545259.
+- term skill: long=0.3848, medium=0.3991, short=0.3197.
+- domain skill: Econ/Fin=0.1029, Energy=0.1770, Healthcare=0.4091, Nature=0.4082, Sales=0.3492, Transport=0.4538, Web/CloudOps=0.5011.
+- frequency skill: A=0.3685, D=0.3335, H=0.3745, M=0.0901, Q=0.1894, S=0.4379, T=0.3955, W=0.1971.
+- variates skill: multivariate=0.3833, univariate=0.3260.
+- Outcome: Small improvement over matched E25: screen0.351994 versus0.351702, first earlier0.321829 versus0.321665. Continue to6000 to determine whether this persists.
+
+E35 training through3000 took850.6 elapsed process seconds on the shared H100.
+
+## E42: match training decoder horizons to available labels (prepared)
+Hypothesis: short pretraining series supply fewer finite future labels than the requested32/128/512/1024 horizon, yet the current decoder sees every requested future token and receives that larger horizon in its metadata. During training only, infer each row's available horizon as its last finite target position, rounded up to the patch boundary; use that length for horizon metadata and mask unused future tokens as self-attention keys in every decoder layer. The label mask and loss remain unchanged. At inference the full requested horizon is used exactly as before, with no new inputs or fitting. Match E38 packed_sample control: own recency12000 initialization, seed79, batch256, LR5e-5, 12000 steps, unchanged sampler. No new model parameters or data. Planned to begin after the small frozen classifier releases memory; GPU evaluations are serialized to bound peak memory.
+
+E38 expanded corpus arm training started at 2026-10-22T04:02:57Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E41 initialization check: all original backbone tensors match the parent bitwise. With identical screen batches, every quantile in all97 configurations is bitwise equal to the parent; maximum difference0. The parent direct-screen score is0.374499664. The cached subset of full-validation predictions scores0.374469795 because batch composition and padding differ slightly; matched direct evaluations are used for E41 comparisons. The classifier has75,920 trainable parameters; total36,873,648. At1000/3000 steps, screen0.373402/0.374243 remains below the unchanged parent. Continue to the planned6000-step endpoint.
+
+E42 data diagnostic (only packed pretraining data, seed79, 640 windows per requested horizon): H32: 6.4% partial futures, mean available31.0; H128: 14.5% partial futures, mean available117.9; H512: 16.7% partial futures, mean available456.8; H1024: 19.2% partial futures, mean available886.4. CPU elapsed 0.93 seconds. A forward check verified bitwise unchanged ordinary inference and agreement within5.4e-7 between a padded masked short target and an actual short-horizon call.
+
+E35 first three cutoff comparisons at3000 steps: screen0.351994 versus matched0.351702; earlier1 0.321829 versus0.321665; earlier2 0.346465 versus0.346396. All gains are small; keep the6000-step comparison before drawing an architectural conclusion.
+
+## E22 depth36 control final24000-step screen
+- Checkpoint SHA-256: `d60cdc55e693df0caf601ded61031be859ee027b0da46585586a3c6a3b5276ba`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 22.044859170913696 seconds.
+- Overall skill 0.359867; relative MASE 0.762956; relative WQL 0.537082.
+- term skill: long=0.3922, medium=0.4034, short=0.3293.
+- domain skill: Econ/Fin=0.1126, Energy=0.1866, Healthcare=0.4038, Nature=0.4208, Sales=0.3464, Transport=0.4583, Web/CloudOps=0.5101.
+- frequency skill: A=0.3718, D=0.3396, H=0.3822, M=0.0911, Q=0.1974, S=0.4422, T=0.4054, W=0.2072.
+- variates skill: multivariate=0.3956, univariate=0.3299.
+- Outcome: Final mean-template screen0.359867 improves its18000-step0.359224 but remains below E25 recency18000 at0.361265. Finished5206.4 elapsed training-process seconds; no new package selected. GPU capacity transferred to E38 expanded corpus arm.
+
+E42 partial target horizons training started at 2026-10-22T04:06:37Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E43: recheck fixed prior strengths on the stronger backbone
+Hypothesis: the seasonal and continuity corrections selected on earlier models may be too strong after continued pretraining. Cache uncorrected symmetric neural forecasts from E25 recency18000, then compare all nine combinations of seasonal blend strength0/0.5/1 and continuity blend strength0/0.5/1. The candidate seasonal forecast is the existing aligned rule; partial strength interpolates it with the neural forecast. Continuity strength similarly interpolates the existing tau24 correction with its input. Keep the selected zero-mass threshold0.4 fixed and apply it last. Use the same global settings on all three cutoffs. No training or new data; inference seed0.
+
+## E41 frozen zero classifier3000-step screen
+- Checkpoint SHA-256: `08b5cc376746c87331cee4628ec56126adf48076aa08ffd2f2bd3f74427d4d96`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.868639707565308 seconds.
+- Overall skill 0.374243; relative MASE 0.746287; relative WQL 0.524694.
+- term skill: long=0.3998, medium=0.4060, short=0.3515.
+- domain skill: Econ/Fin=0.1332, Energy=0.2307, Healthcare=0.4319, Nature=0.4244, Sales=0.3502, Transport=0.4650, Web/CloudOps=0.5035.
+- frequency skill: A=0.3692, D=0.3463, H=0.3950, M=0.1023, Q=0.2006, S=0.4218, T=0.4344, W=0.2127.
+- variates skill: multivariate=0.3931, univariate=0.3588.
+- Outcome: Screen0.374243 is below unchanged parent0.374500. Earlier1 0.331330 versus0.332400; earlier2 0.356106 versus0.356697. The classifier repairs some sales and cloud errors but gives back more solar improvement; no package change.
+
+## E44: training-consistent trend summary on short contexts
+Code review found that the original trend basis chooses its averaging width from padded tensor width: min(128,max(4,c//4)). Training always pads to at least512, making this width128, whereas short inference batches often select smaller widths. This also makes the fixed trend summary sensitive to batch padding. Test a globally fixed128-point width at inference, preserving the exact formula used throughout training. It uses only each row's observed values; other features and all selected corrections remain unchanged. No new parameters, training or data. Compare the recency18000 checkpoint on the screen and both earlier windows before selecting a package.
+
+E44 first diagnostic launch failed before producing a forecast: a source-edit comment accidentally hid the adjacent recent-mean assignment. Corrected the assignment and restarted all three diagnostics; no failed output was scored or published.
+
+## E41 frozen zero classifier final6000-step screen
+- Checkpoint SHA-256: `9dcf4fa821ca636b648447e0516258a3da7056b5e98ec944d0251c73e5ae3006`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.14689803123474 seconds.
+- Overall skill 0.374576; relative MASE 0.746312; relative WQL 0.524117.
+- term skill: long=0.3999, medium=0.4061, short=0.3520.
+- domain skill: Econ/Fin=0.1332, Energy=0.2315, Healthcare=0.4319, Nature=0.4249, Sales=0.3502, Transport=0.4650, Web/CloudOps=0.5037.
+- frequency skill: A=0.3692, D=0.3465, H=0.3951, M=0.1017, Q=0.2006, S=0.4218, T=0.4353, W=0.2127.
+- variates skill: multivariate=0.3934, univariate=0.3592.
+- Outcome: Final screen0.374576 versus unchanged parent0.374500 is nearly tied. Both earlier cutoffs regress:0.331200 versus0.332400 and0.356162 versus0.356697. Do not select this classifier; retain fixed zero projection. Training took559.8 process seconds; backbone stayed frozen.
+
+## E43 complete prior-strength comparison
+Checkpoint SHA-256 `f6d2c41a690159a5b9c2034be45718c408a30b0d34d9607c9a768b9926781bbd`; fixed inference seed0; no training.
+
+screen: 10.68 CPU diagnostic seconds.
+- Seasonal0.0, continuity0.0: skill0.352854; term short=0.3372, medium=0.3820, long=0.3635; domain Econ/Fin=0.0649, Energy=0.2249, Healthcare=0.3918, Nature=0.4305, Sales=0.3458, Transport=0.4676, Web/CloudOps=0.4394.
+- Seasonal0.0, continuity0.5: skill0.358015; term short=0.3463, medium=0.3820, long=0.3639; domain Econ/Fin=0.1059, Energy=0.2325, Healthcare=0.4145, Nature=0.4284, Sales=0.3457, Transport=0.4662, Web/CloudOps=0.4421.
+- Seasonal0.0, continuity1.0: skill0.358419; term short=0.3472, medium=0.3814, long=0.3642; domain Econ/Fin=0.1280, Energy=0.2289, Healthcare=0.4280, Nature=0.4258, Sales=0.3457, Transport=0.4637, Web/CloudOps=0.4444.
+- Seasonal0.5, continuity0.0: skill0.361594; term short=0.3390, medium=0.3982, long=0.3816; domain Econ/Fin=0.0723, Energy=0.2304, Healthcare=0.3951, Nature=0.4304, Sales=0.3450, Transport=0.4683, Web/CloudOps=0.4668.
+- Seasonal0.5, continuity0.5: skill0.367185; term short=0.3491, medium=0.3978, long=0.3823; domain Econ/Fin=0.1132, Energy=0.2381, Healthcare=0.4171, Nature=0.4282, Sales=0.3449, Transport=0.4670, Web/CloudOps=0.4713.
+- Seasonal0.5, continuity1.0: skill0.367831; term short=0.3505, medium=0.3970, long=0.3826; domain Econ/Fin=0.1347, Energy=0.2340, Healthcare=0.4301, Nature=0.4256, Sales=0.3449, Transport=0.4647, Web/CloudOps=0.4751.
+- Seasonal1.0, continuity0.0: skill0.366912; term short=0.3385, medium=0.4065, long=0.3980; domain Econ/Fin=0.0640, Energy=0.2315, Healthcare=0.3974, Nature=0.4301, Sales=0.3440, Transport=0.4682, Web/CloudOps=0.4881.
+- Seasonal1.0, continuity0.5: skill0.373401; term short=0.3497, medium=0.4072, long=0.3989; domain Econ/Fin=0.1094, Energy=0.2393, Healthcare=0.4192, Nature=0.4280, Sales=0.3439, Transport=0.4672, Web/CloudOps=0.4952.
+- Seasonal1.0, continuity1.0: skill0.374500; term short=0.3517, medium=0.4069, long=0.3994; domain Econ/Fin=0.1332, Energy=0.2347, Healthcare=0.4319, Nature=0.4254, Sales=0.3438, Transport=0.4650, Web/CloudOps=0.5007.
+
+backcast: 11.26 CPU diagnostic seconds.
+- Seasonal0.0, continuity0.0: skill0.307142; term short=0.2826, medium=0.3351, long=0.3410; domain Econ/Fin=0.1076, Energy=0.2255, Healthcare=0.3430, Nature=0.3552, Sales=0.3986, Transport=0.3768, Web/CloudOps=0.3599.
+- Seasonal0.0, continuity0.5: skill0.312170; term short=0.2916, medium=0.3354, long=0.3411; domain Econ/Fin=0.1400, Energy=0.2277, Healthcare=0.3844, Nature=0.3556, Sales=0.3986, Transport=0.3764, Web/CloudOps=0.3620.
+- Seasonal0.0, continuity1.0: skill0.314938; term short=0.2967, medium=0.3353, long=0.3409; domain Econ/Fin=0.1539, Energy=0.2269, Healthcare=0.4209, Nature=0.3555, Sales=0.3986, Transport=0.3753, Web/CloudOps=0.3636.
+- Seasonal0.5, continuity0.0: skill0.318715; term short=0.2868, medium=0.3483, long=0.3683; domain Econ/Fin=0.1210, Energy=0.2277, Healthcare=0.3359, Nature=0.3556, Sales=0.3986, Transport=0.3747, Web/CloudOps=0.4076.
+- Seasonal0.5, continuity0.5: skill0.323130; term short=0.2948, medium=0.3486, long=0.3682; domain Econ/Fin=0.1519, Energy=0.2300, Healthcare=0.3781, Nature=0.3560, Sales=0.3986, Transport=0.3744, Web/CloudOps=0.4072.
+- Seasonal0.5, continuity1.0: skill0.325194; term short=0.2988, medium=0.3485, long=0.3679; domain Econ/Fin=0.1645, Energy=0.2291, Healthcare=0.4163, Nature=0.3559, Sales=0.3986, Transport=0.3733, Web/CloudOps=0.4059.
+- Seasonal1.0, continuity0.0: skill0.326613; term short=0.2844, medium=0.3570, long=0.3986; domain Econ/Fin=0.1011, Energy=0.2252, Healthcare=0.3156, Nature=0.3558, Sales=0.3986, Transport=0.3713, Web/CloudOps=0.4530.
+- Seasonal1.0, continuity0.5: skill0.330837; term short=0.2922, medium=0.3575, long=0.3984; domain Econ/Fin=0.1366, Energy=0.2277, Healthcare=0.3596, Nature=0.3563, Sales=0.3986, Transport=0.3710, Web/CloudOps=0.4510.
+- Seasonal1.0, continuity1.0: skill0.332400; term short=0.2953, medium=0.3574, long=0.3980; domain Econ/Fin=0.1526, Energy=0.2268, Healthcare=0.3995, Nature=0.3561, Sales=0.3986, Transport=0.3699, Web/CloudOps=0.4472.
+
+backcast2: 10.77 CPU diagnostic seconds.
+- Seasonal0.0, continuity0.0: skill0.342833; term short=0.3444, medium=0.3372, long=0.3447; domain Econ/Fin=0.0850, Energy=0.2421, Healthcare=0.3909, Nature=0.4651, Sales=0.5517, Transport=0.4200, Web/CloudOps=0.3492.
+- Seasonal0.0, continuity0.5: skill0.343238; term short=0.3448, medium=0.3374, long=0.3451; domain Econ/Fin=0.1046, Energy=0.2395, Healthcare=0.3778, Nature=0.4655, Sales=0.5516, Transport=0.4195, Web/CloudOps=0.3541.
+- Seasonal0.0, continuity1.0: skill0.341670; term short=0.3421, medium=0.3372, long=0.3453; domain Econ/Fin=0.1096, Energy=0.2345, Healthcare=0.3641, Nature=0.4656, Sales=0.5516, Transport=0.4183, Web/CloudOps=0.3571.
+- Seasonal0.5, continuity0.0: skill0.353176; term short=0.3476, medium=0.3630, long=0.3580; domain Econ/Fin=0.0889, Energy=0.2454, Healthcare=0.3896, Nature=0.4651, Sales=0.5517, Transport=0.4202, Web/CloudOps=0.3933.
+- Seasonal0.5, continuity0.5: skill0.353075; term short=0.3473, medium=0.3630, long=0.3584; domain Econ/Fin=0.1076, Energy=0.2421, Healthcare=0.3764, Nature=0.4655, Sales=0.5516, Transport=0.4198, Web/CloudOps=0.3966.
+- Seasonal0.5, continuity1.0: skill0.350860; term short=0.3435, medium=0.3626, long=0.3584; domain Econ/Fin=0.1107, Energy=0.2362, Healthcare=0.3627, Nature=0.4656, Sales=0.5516, Transport=0.4185, Web/CloudOps=0.3980.
+- Seasonal1.0, continuity0.0: skill0.359824; term short=0.3464, medium=0.3881, long=0.3655; domain Econ/Fin=0.0796, Energy=0.2431, Healthcare=0.3878, Nature=0.4651, Sales=0.5517, Transport=0.4196, Web/CloudOps=0.4291.
+- Seasonal1.0, continuity0.5: skill0.359396; term short=0.3456, medium=0.3880, long=0.3658; domain Econ/Fin=0.1008, Energy=0.2396, Healthcare=0.3747, Nature=0.4655, Sales=0.5516, Transport=0.4192, Web/CloudOps=0.4307.
+- Seasonal1.0, continuity1.0: skill0.356697; term short=0.3411, medium=0.3874, long=0.3657; domain Econ/Fin=0.1045, Energy=0.2331, Healthcare=0.3610, Nature=0.4656, Sales=0.5516, Transport=0.4179, Web/CloudOps=0.4303.
+
+E43 outcome: retain both corrections at full strength. Seasonal full strength wins at every cutoff. Full continuity wins the main screen and first earlier cutoff; removing or weakening it helps the second earlier cutoff. Half continuity has essentially the same three-cutoff average but lowers the main screen, so the current rule is retained. This confirms that the fixed corrections still contribute after additional pretraining.
+
+E39 pre-launch correction: require at least one strictly positive or negative observed value before imposing the corresponding training bound. All-zero contexts otherwise satisfy both signs and would force every quantile to zero. Such contexts retain the original unconstrained training forward pass; the existing final nonnegative inference projection remains unchanged. This correction was made before E39 training started.
+
+E44 selected: full validation0.371267 versus previous0.370171; matched direct screen0.375386 versus0.374500; earlier1 0.333401 versus0.332400; earlier2 0.357015 versus0.356697. All improve. Full package inference took116.2 seconds under concurrent training, with97 configurations and151170 instances, zero failures and no quantile crossings. Repeated relocated examples are bitwise identical. With the actual trained model, adding left padding to histories of lengths8/16/40 changes raw forecasts by at most3.82e-6 in float32, consistent with numerical reduction differences. The averaging width is now independent of batch padding and matches all pretraining context lengths. No training or parameter changes were needed.
+
+## E44 fixed128 trend summary full package validation
+- Checkpoint SHA-256: `f6d2c41a690159a5b9c2034be45718c408a30b0d34d9607c9a768b9926781bbd`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.371267; relative MASE 0.749824; relative WQL 0.527198.
+- term skill: long=0.3932, medium=0.4057, short=0.3489.
+- domain skill: Econ/Fin=0.1097, Energy=0.2283, Healthcare=0.4445, Nature=0.4270, Sales=0.3536, Transport=0.4653, Web/CloudOps=0.4932.
+- frequency skill: A=0.2900, D=0.3545, H=0.3906, M=0.1144, Q=0.1842, S=0.4218, T=0.4285, W=0.2081.
+- variates skill: multivariate=0.3889, univariate=0.3569.
+- Outcome: Published atomically. Full0.371267, screen0.375386, first earlier0.333401, second earlier0.357015. All improve over the previous package; repeated relocated inference is bitwise deterministic and all151170 instances score successfully.
+
+## E44 first earlier diagnostic
+- Checkpoint SHA-256: `f6d2c41a690159a5b9c2034be45718c408a30b0d34d9607c9a768b9926781bbd`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.190837860107422 seconds.
+- Overall skill 0.333401; relative MASE 0.800857; relative WQL 0.554849.
+- term skill: long=0.3980, medium=0.3574, short=0.2971.
+- domain skill: Econ/Fin=0.1546, Energy=0.2269, Healthcare=0.4152, Nature=0.3561, Sales=0.3982, Transport=0.3697, Web/CloudOps=0.4472.
+- frequency skill: A=0.3057, D=0.3535, H=0.3642, M=0.1858, Q=0.2322, S=0.3397, T=0.3325, W=0.2671.
+- variates skill: multivariate=0.3493, univariate=0.3205.
+- Outcome: Improves the preceding published package.
+
+## E44 second earlier diagnostic
+- Checkpoint SHA-256: `f6d2c41a690159a5b9c2034be45718c408a30b0d34d9607c9a768b9926781bbd`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 24.125259160995483 seconds.
+- Overall skill 0.357015; relative MASE 0.773709; relative WQL 0.534347.
+- term skill: long=0.3657, medium=0.3874, short=0.3417.
+- domain skill: Econ/Fin=0.1037, Energy=0.2341, Healthcare=0.3586, Nature=0.4669, Sales=0.5519, Transport=0.4175, Web/CloudOps=0.4304.
+- frequency skill: A=0.3474, D=0.4297, H=0.3890, M=0.1482, Q=0.1491, S=0.2798, T=0.3574, W=0.2484.
+- variates skill: multivariate=0.3619, univariate=0.3530.
+- Outcome: Improves the preceding published package.
+
+E35 follow-up: compare both6000-step models again with the now-selected E44 fixed128 basis and E37 zero0.4 rule. The old adaptive-width basis may interact with the new learned short-trend features, so this paired diagnostic will check whether their small gain changes once training/inference formulas agree. No retraining or new thresholds.
+
+## E31 amplitude synthetic12000-step screen
+- Checkpoint SHA-256: `88d6ebecc5342e428b803fcb60af38cafa8f94383976621f3f200abee9b2eb0e`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.205806970596313 seconds.
+- Overall skill 0.358165; relative MASE 0.764973; relative WQL 0.538519.
+- term skill: long=0.3902, medium=0.4022, short=0.3275.
+- domain skill: Econ/Fin=0.1122, Energy=0.1848, Healthcare=0.3959, Nature=0.4185, Sales=0.3479, Transport=0.4568, Web/CloudOps=0.5094.
+- frequency skill: A=0.3775, D=0.3345, H=0.3814, M=0.0974, Q=0.1993, S=0.4416, T=0.4040, W=0.1998.
+- variates skill: multivariate=0.3933, univariate=0.3287.
+- Outcome: Mean-template screen0.358165 remains above matched E25-12000 0.357241, after gains at3000 and6000. The advantage is smaller than at6000; continue to18000 and24000 before selecting a new package.
+
+## E35 explicit trend6000-step screen
+- Checkpoint SHA-256: `2ad3f9148954d7b3a16365ec529767cc3a0803b4488c7a475838725497c13565`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.423240423202515 seconds.
+- Overall skill 0.353152; relative MASE 0.769279; relative WQL 0.543902.
+- term skill: long=0.3873, medium=0.3990, short=0.3208.
+- domain skill: Econ/Fin=0.1103, Energy=0.1816, Healthcare=0.3998, Nature=0.4115, Sales=0.3496, Transport=0.4548, Web/CloudOps=0.4989.
+- frequency skill: A=0.3705, D=0.3396, H=0.3769, M=0.0764, Q=0.1986, S=0.4369, T=0.3960, W=0.1933.
+- variates skill: multivariate=0.3851, univariate=0.3265.
+- Outcome: Mean-template screen0.353152 versus matched E25 0.352851; earlier1 0.323894 versus0.324008; earlier2 0.349429 versus0.349098. With all selected fixed rules, screen0.366300 versus matched0.366032. The effect remains small; retain checkpoints and compare at12000.
+
+amplitude_synth through12000: 3311.5 elapsed training-process seconds on the shared H100.
+
+trend_features through6000: 1795.6 elapsed training-process seconds on the shared H100.
+
+E39 bounded quantiles training started at 2026-10-22T04:19:57Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E25 recency final24000-step matched mean-template screen
+- Checkpoint SHA-256: `1530fbb577a87ca4eb39da83d6fee9b8ee5bc924202e021e3a7b56493112f564`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.549001216888428 seconds.
+- Overall skill 0.361808; relative MASE 0.760201; relative WQL 0.535765.
+- term skill: long=0.3930, medium=0.4043, short=0.3321.
+- domain skill: Econ/Fin=0.1144, Energy=0.1875, Healthcare=0.4304, Nature=0.4242, Sales=0.3432, Transport=0.4595, Web/CloudOps=0.5080.
+- frequency skill: A=0.3731, D=0.3487, H=0.3832, M=0.0957, Q=0.1965, S=0.4423, T=0.4054, W=0.2079.
+- variates skill: multivariate=0.3953, univariate=0.3338.
+- Outcome: Final screen0.361808 versus matched E22-24000 0.359867. The learned recency features improve the matched comparison at every evaluated checkpoint. Completed5879.4 elapsed training-process seconds; full validation with selected fixed rules is pending.
+
+## E38 expanded corpus3000-step screen
+- Checkpoint SHA-256: `13cb3d2a222cd09f622eff3fe1657c4aebe2b1f2ca22a226462ecd0bb2c94026`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.664713144302368 seconds.
+- Overall skill 0.358667; relative MASE 0.763557; relative WQL 0.538674.
+- term skill: long=0.3925, medium=0.4038, short=0.3268.
+- domain skill: Econ/Fin=0.1145, Energy=0.1852, Healthcare=0.4021, Nature=0.4196, Sales=0.3470, Transport=0.4576, Web/CloudOps=0.5080.
+- frequency skill: A=0.3749, D=0.3373, H=0.3835, M=0.0979, Q=0.1983, S=0.4405, T=0.4029, W=0.1974.
+- variates skill: multivariate=0.3933, univariate=0.3297.
+- Outcome: Screen0.358667 versus matched packed_sample control0.358802: nearly tied. Broader corpus coverage has not yet provided a clear advantage. Continue to6000 and12000; source probabilities remain fixed to the original mixture.
+
+## E38 packed_sample control6000-step screen
+- Checkpoint SHA-256: `3b6a1f25cef4ecafcb7bde8ee9faf1cbe4c68a0c2fbb9594ce26eb3c222f3720`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.290119647979736 seconds.
+- Overall skill 0.358628; relative MASE 0.763471; relative WQL 0.538799.
+- term skill: long=0.3894, medium=0.4025, short=0.3286.
+- domain skill: Econ/Fin=0.1093, Energy=0.1839, Healthcare=0.4157, Nature=0.4245, Sales=0.3451, Transport=0.4579, Web/CloudOps=0.5041.
+- frequency skill: A=0.3762, D=0.3427, H=0.3805, M=0.1014, Q=0.1966, S=0.4404, T=0.4018, W=0.2035.
+- variates skill: multivariate=0.3910, univariate=0.3316.
+- Outcome: Screen0.358628 versus this restart at3000 0.358802. This restart has not surpassed uninterrupted E25 continuation. Retain as the matched control for corpus coverage, bounded quantiles and partial target horizons; assess after learning-rate decay.
+
+## E45: average late recency checkpoints
+Hypothesis: uniform parameter averaging of the same training trajectory can reduce checkpoint variation without adding inference cost. Average own E25 EMA checkpoints at18000/21000/24000 in FP32, retaining their shared configuration. This is one model with fixed averaged weights; every parent was trained here from the same random-initialization lineage, seed59. No additional data or gradient updates. Use the selected aligned seasonal rule, continuity, zero0.4 and fixed128 trend basis. Evaluate all three cutoffs before building a full candidate. The averaging script and every parent hash are preserved.
+
+E25 final24000-step package selected: full0.371479 versus18000-step0.371267; direct screen0.375502 versus0.375386; first earlier0.334180 versus0.333401; second earlier0.358275 versus0.357015. All improve. Full inference126.1 seconds under concurrent training;97 configurations,151170 instances, zero failures and no quantile crossings. Repeated relocated examples are bitwise identical, finite and monotone. Fixed rules are unchanged from E44.
+
+E45 outcome: the uniform18000/21000/24000 EMA average scores0.375408/0.333912/0.358081 on the three cutoffs, below the final24000 checkpoint at0.375502/0.334180/0.358275 on every cutoff. Not selected; no full package evaluation. No training compute; parameter averaging was not separately timed, and individual diagnostic evaluation times are recorded below.
+
+## E25 final24000-step package with selected fixed rules
+- Checkpoint SHA-256: `1530fbb577a87ca4eb39da83d6fee9b8ee5bc924202e021e3a7b56493112f564`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.371479; relative MASE 0.748783; relative WQL 0.527575.
+- term skill: long=0.3935, medium=0.4057, short=0.3491.
+- domain skill: Econ/Fin=0.1165, Energy=0.2279, Healthcare=0.4442, Nature=0.4287, Sales=0.3521, Transport=0.4652, Web/CloudOps=0.4925.
+- frequency skill: A=0.2919, D=0.3570, H=0.3902, M=0.1164, Q=0.1836, S=0.4232, T=0.4289, W=0.2028.
+- variates skill: multivariate=0.3893, univariate=0.3569.
+- Outcome: Published atomically. Full0.371479, screen0.375502, earlier1 0.334180, earlier2 0.358275. All improve over the preceding18000-step package. All151170 instances score; repeated relocated examples are bitwise deterministic, finite and monotone.
+
+## Published recency24000 first earlier diagnostic
+- Checkpoint SHA-256: `1530fbb577a87ca4eb39da83d6fee9b8ee5bc924202e021e3a7b56493112f564`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.669825792312622 seconds.
+- Overall skill 0.334180; relative MASE 0.799611; relative WQL 0.554415.
+- term skill: long=0.3968, medium=0.3571, short=0.2993.
+- domain skill: Econ/Fin=0.1593, Energy=0.2277, Healthcare=0.4063, Nature=0.3608, Sales=0.3980, Transport=0.3712, Web/CloudOps=0.4466.
+- frequency skill: A=0.3097, D=0.3512, H=0.3634, M=0.1820, Q=0.2352, S=0.3426, T=0.3343, W=0.2771.
+- variates skill: multivariate=0.3511, univariate=0.3204.
+- Outcome: Improves the preceding package.
+
+## Published recency24000 second earlier diagnostic
+- Checkpoint SHA-256: `1530fbb577a87ca4eb39da83d6fee9b8ee5bc924202e021e3a7b56493112f564`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.378834009170532 seconds.
+- Overall skill 0.358275; relative MASE 0.771140; relative WQL 0.534029.
+- term skill: long=0.3653, medium=0.3854, short=0.3449.
+- domain skill: Econ/Fin=0.1098, Energy=0.2362, Healthcare=0.3503, Nature=0.4701, Sales=0.5516, Transport=0.4194, Web/CloudOps=0.4299.
+- frequency skill: A=0.3501, D=0.4315, H=0.3891, M=0.1569, Q=0.1519, S=0.2833, T=0.3571, W=0.2551.
+- variates skill: multivariate=0.3625, univariate=0.3548.
+- Outcome: Improves the preceding package.
+
+## E45 averaged late EMA screen
+- Checkpoint SHA-256: `9b3541cfc195324574d22e27a8e3ac38edf7408712372933c645c892f39fa746`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.790673971176147 seconds.
+- Overall skill 0.375408; relative MASE 0.746595; relative WQL 0.522527.
+- term skill: long=0.3999, medium=0.4064, short=0.3534.
+- domain skill: Econ/Fin=0.1379, Energy=0.2352, Healthcare=0.4383, Nature=0.4274, Sales=0.3404, Transport=0.4653, Web/CloudOps=0.5004.
+- frequency skill: A=0.3761, D=0.3516, H=0.3953, M=0.0934, Q=0.2004, S=0.4222, T=0.4355, W=0.2141.
+- variates skill: multivariate=0.3931, univariate=0.3610.
+- Outcome: Below the final24000 checkpoint on all three cutoffs; not selected.
+
+## E42 partial target horizons3000-step screen
+- Checkpoint SHA-256: `fce955a709ee54429c943e3962df49055444d5a15453865a01e4883614425917`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.083299160003662 seconds.
+- Overall skill 0.358812; relative MASE 0.763592; relative WQL 0.538405.
+- term skill: long=0.3907, medium=0.4016, short=0.3287.
+- domain skill: Econ/Fin=0.1123, Energy=0.1842, Healthcare=0.4163, Nature=0.4220, Sales=0.3469, Transport=0.4589, Web/CloudOps=0.5047.
+- frequency skill: A=0.3754, D=0.3440, H=0.3817, M=0.1026, Q=0.1980, S=0.4409, T=0.3995, W=0.2076.
+- variates skill: multivariate=0.3908, univariate=0.3321.
+- Outcome: Screen0.358812 versus matched control0.358802 is effectively tied. Gains in annual, quarterly, monthly and weekly groups are offset by minute-frequency losses. Continue to6000 and12000 to assess persistence.
+
+## E46: use forecast slopes for the fixed continuity correction
+A per-channel diagnostic found that several ETT2 short-horizon forecasts underreact to a rebound after the latest trough: truth rises about1.1–1.9 context standard deviations in multiple channels, while the model rises only0.6–0.9. The current continuity anchor extrapolates the previous eight differences and may continue the old direction across a turn. Keep the smoothness gate, tau24, seasonal rule and zero0.4 fixed; compare past8 (current), zero slope, first forecast difference, and median of the first eight forecast differences. These are fixed transformations, with no fitting. Reuse the E25-18000 raw forecast caches for a paired diagnostic on all three cutoffs; their old short-context trend basis is unchanged within this comparison. Any selected variant must transfer to the latest fixed128 package before publication.
+
+## E47: frozen-backbone confidence-query projection (prepared)
+Hypothesis: fixed lag-difference statistics provide information about local noise and seasonal repeatability that the decoder may otherwise infer inefficiently from raw patches. Add a zero-initialized linear projection of six log-scaled normalized statistics: MAE and RMS difference at lag1 over512 points, and at each of the two frequency-derived periods over at most8 cycles. Fewer than8 valid pairs use neutral defaults1 and sqrt(2). The3072 new parameters affect decoder queries; freeze the complete E25 recency24000 backbone. Train with the original weighted quantile/median loss after averaging both sign orientations, matching inference. Own packed_sample and unchanged local generator, seed89, planned6000 steps atLR2e-4. Choose batch64 or32 after measuring maximum-shape memory, keeping the six existing trainers and serialized evaluations within the one H100. No new training data.
+
+E47 memory check at batch64 (paired batch128), context4096 and horizon1024 used1134.4 MiB peak allocated and1160.0 MiB peak reserved Torch memory. Gradients were finite; no optimizer update or checkpoint was made from the random fixture. Start the planned6000-step run at batch64 with only3072 trainable parameters.
+
+## E46 complete slope comparison
+No training; checkpoint SHA-256 `f6d2c41a690159a5b9c2034be45718c408a30b0d34d9607c9a768b9926781bbd`; fixed inference seed0.
+
+screen: 9.50 CPU diagnostic seconds.
+- past8: skill0.374500; term short=0.3517, medium=0.4069, long=0.3994; domain Econ/Fin=0.1332, Energy=0.2347, Healthcare=0.4319, Nature=0.4254, Sales=0.3438, Transport=0.4650, Web/CloudOps=0.5007; freq A-DEC=0.3692, Q-DEC=0.2006, M=0.1023, W-SUN=0.0521, D=0.3442, H=0.3953, 15T=0.2125, W-FRI=-0.0051, 10T=0.6138, W-TUE=0.5773, W-THU=0.1903, W-WED=0.3133, 5T=0.5112, 10S=0.4218.
+- none: skill0.370718; term short=0.3457, medium=0.4054, long=0.3986; domain Econ/Fin=0.1111, Energy=0.2246, Healthcare=0.4321, Nature=0.4266, Sales=0.3438, Transport=0.4648, Web/CloudOps=0.4996; freq A-DEC=0.3202, Q-DEC=0.1821, M=0.1025, W-SUN=0.0486, D=0.3438, H=0.3946, 15T=0.2087, W-FRI=0.0306, 10T=0.5876, W-TUE=0.5831, W-THU=0.1779, W-WED=0.3133, 5T=0.5117, 10S=0.4176.
+- future1: skill0.374205; term short=0.3511, medium=0.4072, long=0.3991; domain Econ/Fin=0.1137, Energy=0.2347, Healthcare=0.4268, Nature=0.4274, Sales=0.3439, Transport=0.4665, Web/CloudOps=0.5017; freq A-DEC=0.3598, Q-DEC=0.1662, M=0.1001, W-SUN=0.0181, D=0.3420, H=0.3984, 15T=0.2072, W-FRI=0.0122, 10T=0.6164, W-TUE=0.5675, W-THU=0.1755, W-WED=0.3133, 5T=0.5109, 10S=0.4256.
+- future8: skill0.373161; term short=0.3495, medium=0.4067, long=0.3990; domain Econ/Fin=0.1251, Energy=0.2298, Healthcare=0.4305, Nature=0.4272, Sales=0.3439, Transport=0.4643, Web/CloudOps=0.5016; freq A-DEC=0.3602, Q-DEC=0.1852, M=0.1008, W-SUN=0.0383, D=0.3439, H=0.3960, 15T=0.2096, W-FRI=-0.0017, 10T=0.6059, W-TUE=0.5789, W-THU=0.1738, W-WED=0.3133, 5T=0.5112, 10S=0.4252.
+
+backcast: 9.40 CPU diagnostic seconds.
+- past8: skill0.332400; term short=0.2953, medium=0.3574, long=0.3980; domain Econ/Fin=0.1526, Energy=0.2268, Healthcare=0.3995, Nature=0.3561, Sales=0.3986, Transport=0.3699, Web/CloudOps=0.4472; freq A-DEC=0.2996, Q-DEC=0.2330, M=0.1721, W-SUN=-0.0043, D=0.3516, H=0.3643, 15T=0.2254, W-FRI=0.2597, 10T=0.3130, W-TUE=0.5335, W-THU=0.2049, W-WED=0.3474, 5T=0.4329, 10S=0.3397.
+- none: skill0.329297; term short=0.2895, medium=0.3574, long=0.3980; domain Econ/Fin=0.1286, Energy=0.2253, Healthcare=0.3653, Nature=0.3586, Sales=0.3986, Transport=0.3702, Web/CloudOps=0.4470; freq A-DEC=0.2562, Q-DEC=0.2285, M=0.1591, W-SUN=-0.0090, D=0.3530, H=0.3630, 15T=0.2276, W-FRI=0.2653, 10T=0.3102, W-TUE=0.4340, W-THU=0.1790, W-WED=0.3474, 5T=0.4328, 10S=0.3392.
+- future1: skill0.331443; term short=0.2936, medium=0.3572, long=0.3981; domain Econ/Fin=0.1560, Energy=0.2259, Healthcare=0.3803, Nature=0.3573, Sales=0.3986, Transport=0.3708, Web/CloudOps=0.4467; freq A-DEC=0.2946, Q-DEC=0.2075, M=0.1779, W-SUN=-0.0420, D=0.3531, H=0.3699, 15T=0.2218, W-FRI=0.2626, 10T=0.3062, W-TUE=0.4321, W-THU=0.1844, W-WED=0.3474, 5T=0.4320, 10S=0.3387.
+- future8: skill0.330724; term short=0.2923, medium=0.3572, long=0.3980; domain Econ/Fin=0.1495, Energy=0.2260, Healthcare=0.3767, Nature=0.3568, Sales=0.3986, Transport=0.3706, Web/CloudOps=0.4462; freq A-DEC=0.2971, Q-DEC=0.2312, M=0.1706, W-SUN=-0.0239, D=0.3528, H=0.3662, 15T=0.2272, W-FRI=0.2646, 10T=0.3066, W-TUE=0.4419, W-THU=0.1812, W-WED=0.3474, 5T=0.4320, 10S=0.3363.
+
+backcast2: 9.16 CPU diagnostic seconds.
+- past8: skill0.356697; term short=0.3411, medium=0.3874, long=0.3657; domain Econ/Fin=0.1045, Energy=0.2331, Healthcare=0.3610, Nature=0.4656, Sales=0.5516, Transport=0.4179, Web/CloudOps=0.4303; freq A-DEC=0.3474, Q-DEC=0.1548, M=0.1599, W-SUN=0.0316, D=0.4275, H=0.3891, 15T=0.1072, W-FRI=0.1243, 10T=0.4930, W-TUE=0.0782, W-THU=0.3037, W-WED=0.5352, 5T=0.4648, 10S=0.2798.
+- none: skill0.356132; term short=0.3406, medium=0.3873, long=0.3643; domain Econ/Fin=0.0852, Energy=0.2311, Healthcare=0.3675, Nature=0.4661, Sales=0.5516, Transport=0.4189, Web/CloudOps=0.4316; freq A-DEC=0.3237, Q-DEC=0.1547, M=0.1618, W-SUN=0.0515, D=0.4258, H=0.3870, 15T=0.1053, W-FRI=0.1317, 10T=0.4935, W-TUE=0.1348, W-THU=0.2945, W-WED=0.5352, 5T=0.4648, 10S=0.2854.
+- future1: skill0.355261; term short=0.3386, medium=0.3872, long=0.3657; domain Econ/Fin=0.1174, Energy=0.2356, Healthcare=0.3303, Nature=0.4652, Sales=0.5516, Transport=0.4184, Web/CloudOps=0.4253; freq A-DEC=0.3497, Q-DEC=0.1386, M=0.1530, W-SUN=0.0379, D=0.4238, H=0.3954, 15T=0.1038, W-FRI=0.1264, 10T=0.4929, W-TUE=-0.1191, W-THU=0.2956, W-WED=0.5352, 5T=0.4644, 10S=0.2668.
+- future8: skill0.355137; term short=0.3386, medium=0.3873, long=0.3650; domain Econ/Fin=0.1032, Energy=0.2310, Healthcare=0.3342, Nature=0.4659, Sales=0.5516, Transport=0.4189, Web/CloudOps=0.4316; freq A-DEC=0.3492, Q-DEC=0.1479, M=0.1571, W-SUN=0.0462, D=0.4251, H=0.3890, 15T=0.1053, W-FRI=0.1264, 10T=0.4931, W-TUE=-0.1021, W-THU=0.2982, W-WED=0.5352, 5T=0.4643, 10S=0.2886.
+
+E46 outcome: retain the existing past-eight-difference slope. It beats zero slope and both forecast-derived slopes on every cutoff. The local turning-point explanation does not justify changing the global correction.
+
+## E48: phase diffusion in synthetic seasonal series (queued)
+Hypothesis: varying seasonal phase teaches the model when distant cycles should lose confidence, complementing E31's amplitude variation. Keep E31 unchanged except that half of synthetic calls add a Brownian phase to each Fourier period, shared across its harmonics. Draw one-cycle fundamental correlation log-uniformly from 0.90 to 0.999, with increment standard deviation sqrt(-2 log(correlation) / period). Phase RNG seed is 200062, independent of sampler seed 59 and amplitude seed 100062. Real corpus windows, frequency choices, all original generator draws, and amplitude draws remain matched to E31. A data-only check of 256 rows at each synthetic fraction 0, 0.2 and 1 verified equal original and amplitude RNG states; all corpus-only arrays were bitwise equal, and every finite output stayed finite (8.30 CPU seconds). No validation labels are used in this generator or training.
+
+Initialize from our E17 patch16 EMA6000; same E25 architecture, batch 256, LR 8e-5, 24000-step cosine schedule, seed 59, save every 3000. Compare to cached E31 at matched checkpoints and three cutoffs before publication. Queue after E38 control completes 12000, retaining at most six heavy trainers on the one H100. Directory phase_synth contains code and sampler_check.json; generator must be declared separately in sources_used if selected.
+
+## E49: uncertainty shape in the fixed seasonal correction
+Hypothesis: rare seasonal jumps inflate Gaussian RMS intervals in otherwise regular series. Keep the selected seasonal median, confidence gate, continuity correction and zero projection fixed; compare Gaussian RMS intervals with centered empirical seasonal-difference quantiles and Gaussian MAD intervals. All widths use the same sqrt((1+1/cycles)/2) averaging correction. Empirical residual median is subtracted so the median forecast is unchanged. These are fixed context formulas, with no optimization. Reuse E25-18000 raw caches for an inexpensive paired test across three cutoffs; transfer any consistent improvement to the current E25-24000 fixed128 runtime before publication. No training and no new sources.
+
+## E38 expanded-corpus 6000-step screen
+- Checkpoint SHA-256: `6f0068b79fbc501fb1ec9a76c88d91ceb9ffca46096900c18f5157a19784678f`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.27088236808777 seconds.
+- Overall skill 0.358532; relative MASE 0.764393; relative WQL 0.538311.
+- term skill: long=0.3918, medium=0.4028, short=0.3272.
+- domain skill: Econ/Fin=0.1142, Energy=0.1855, Healthcare=0.3972, Nature=0.4197, Sales=0.3475, Transport=0.4581, Web/CloudOps=0.5080.
+- frequency skill: A=0.3713, D=0.3310, H=0.3829, M=0.1077, Q=0.2043, S=0.4384, T=0.4038, W=0.2044.
+- variates skill: multivariate=0.3933, univariate=0.3295.
+- Outcome: Skill 0.358532 versus matched control 0.358628: near tie, with no demonstrated benefit from the larger packed corpus. Continue to 12000.
+
+## E39 bounded quantiles 3000-step screen
+- Checkpoint SHA-256: `3932f19e396a2ec597a047116ad2e4b680a0e7c9638472434388843bd358b4c7`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.178418159484863 seconds.
+- Overall skill 0.358466; relative MASE 0.764176; relative WQL 0.538575.
+- term skill: long=0.3917, medium=0.4021, short=0.3274.
+- domain skill: Econ/Fin=0.1098, Energy=0.1842, Healthcare=0.4143, Nature=0.4191, Sales=0.3505, Transport=0.4583, Web/CloudOps=0.5059.
+- frequency skill: A=0.3753, D=0.3428, H=0.3828, M=0.0891, Q=0.1957, S=0.4411, T=0.4011, W=0.1992.
+- variates skill: multivariate=0.3924, univariate=0.3301.
+- Outcome: Skill 0.358466 versus control 0.358802: a small regression. Continue to 6000 before selection.
+
+## E35 trend-query features 12000-step screen
+- Checkpoint SHA-256: `269be0627257c53343aac6d0609231865bd112be7e01944d966624160150d4a8`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.890631914138794 seconds.
+- Overall skill 0.357547; relative MASE 0.765787; relative WQL 0.538983.
+- term skill: long=0.3905, medium=0.4007, short=0.3268.
+- domain skill: Econ/Fin=0.1107, Energy=0.1852, Healthcare=0.4068, Nature=0.4159, Sales=0.3502, Transport=0.4572, Web/CloudOps=0.5058.
+- frequency skill: A=0.3786, D=0.3384, H=0.3803, M=0.0973, Q=0.2012, S=0.4413, T=0.4010, W=0.2023.
+- variates skill: multivariate=0.3916, univariate=0.3291.
+- Outcome: Skill 0.357547 versus recency control 0.357241: another small gain. Matched earlier-cutoff checks are running.
+
+## E31 amplitude variation 18000-step screen
+- Checkpoint SHA-256: `f0d8484d5f1bee554939ffbe769b95e42ba94e0739db7c680fac1c9a132f8f43`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.11006808280945 seconds.
+- Overall skill 0.361640; relative MASE 0.761418; relative WQL 0.535190.
+- term skill: long=0.3939, medium=0.4045, short=0.3314.
+- domain skill: Econ/Fin=0.1093, Energy=0.1877, Healthcare=0.4199, Nature=0.4206, Sales=0.3451, Transport=0.4594, Web/CloudOps=0.5124.
+- frequency skill: A=0.3693, D=0.3400, H=0.3834, M=0.0980, Q=0.2014, S=0.4419, T=0.4075, W=0.2126.
+- variates skill: multivariate=0.3966, univariate=0.3324.
+- Outcome: Skill 0.361640 versus recency control 0.361265: a small gain persists. Transfer to the current fixed inference rules on three cutoffs before selection.
+
+## E42 paired comparison: large_corpus_control 6000, screen
+- Checkpoint SHA-256: `3b6a1f25cef4ecafcb7bde8ee9faf1cbe4c68a0c2fbb9594ce26eb3c222f3720`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.290119647979736 seconds.
+- Overall skill 0.358628; relative MASE 0.763471; relative WQL 0.538799.
+- term skill: long=0.3894, medium=0.4025, short=0.3286.
+- domain skill: Econ/Fin=0.1093, Energy=0.1839, Healthcare=0.4157, Nature=0.4245, Sales=0.3451, Transport=0.4579, Web/CloudOps=0.5041.
+- frequency skill: A=0.3762, D=0.3427, H=0.3805, M=0.1014, Q=0.1966, S=0.4404, T=0.4018, W=0.2035.
+- variates skill: multivariate=0.3910, univariate=0.3316.
+- Outcome: Matched seed 79, same parent and data stream. This isolates training with per-row available target horizons; assess the three cutoffs jointly.
+
+## E42 paired comparison: partial_targets 6000, screen
+- Checkpoint SHA-256: `67562486b099a350da53c3caa5ff0932fe7fecdfe72c1879b064b2a9bb173dfb`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.865007400512695 seconds.
+- Overall skill 0.359222; relative MASE 0.762879; relative WQL 0.538220.
+- term skill: long=0.3896, medium=0.4026, short=0.3295.
+- domain skill: Econ/Fin=0.1138, Energy=0.1865, Healthcare=0.4140, Nature=0.4258, Sales=0.3420, Transport=0.4581, Web/CloudOps=0.5030.
+- frequency skill: A=0.3767, D=0.3422, H=0.3805, M=0.1063, Q=0.1975, S=0.4407, T=0.4017, W=0.2108.
+- variates skill: multivariate=0.3906, univariate=0.3331.
+- Outcome: Matched seed 79, same parent and data stream. This isolates training with per-row available target horizons; assess the three cutoffs jointly.
+
+## E42 paired comparison: large_corpus_control 6000, backcast
+- Checkpoint SHA-256: `3b6a1f25cef4ecafcb7bde8ee9faf1cbe4c68a0c2fbb9594ce26eb3c222f3720`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 27.584256172180176 seconds.
+- Overall skill 0.329632; relative MASE 0.806661; relative WQL 0.557102.
+- term skill: long=0.3889, medium=0.3561, short=0.2948.
+- domain skill: Econ/Fin=0.1442, Energy=0.2289, Healthcare=0.4117, Nature=0.3487, Sales=0.3954, Transport=0.3632, Web/CloudOps=0.4421.
+- frequency skill: A=0.3117, D=0.3564, H=0.3572, M=0.1570, Q=0.2265, S=0.3417, T=0.3299, W=0.2668.
+- variates skill: multivariate=0.3466, univariate=0.3158.
+- Outcome: Matched seed 79, same parent and data stream. This isolates training with per-row available target horizons; assess the three cutoffs jointly.
+
+## E42 paired comparison: partial_targets 6000, backcast
+- Checkpoint SHA-256: `67562486b099a350da53c3caa5ff0932fe7fecdfe72c1879b064b2a9bb173dfb`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 27.306365728378296 seconds.
+- Overall skill 0.330300; relative MASE 0.806062; relative WQL 0.556406.
+- term skill: long=0.3903, medium=0.3564, short=0.2953.
+- domain skill: Econ/Fin=0.1490, Energy=0.2297, Healthcare=0.4089, Nature=0.3480, Sales=0.3970, Transport=0.3635, Web/CloudOps=0.4436.
+- frequency skill: A=0.3192, D=0.3533, H=0.3582, M=0.1614, Q=0.2265, S=0.3433, T=0.3300, W=0.2726.
+- variates skill: multivariate=0.3477, univariate=0.3161.
+- Outcome: Matched seed 79, same parent and data stream. This isolates training with per-row available target horizons; assess the three cutoffs jointly.
+
+## E42 paired comparison: large_corpus_control 6000, backcast2
+- Checkpoint SHA-256: `3b6a1f25cef4ecafcb7bde8ee9faf1cbe4c68a0c2fbb9594ce26eb3c222f3720`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.882598161697388 seconds.
+- Overall skill 0.352570; relative MASE 0.776245; relative WQL 0.539992.
+- term skill: long=0.3679, medium=0.3749, short=0.3381.
+- domain skill: Econ/Fin=0.0867, Energy=0.2280, Healthcare=0.3674, Nature=0.4604, Sales=0.5586, Transport=0.4130, Web/CloudOps=0.4272.
+- frequency skill: A=0.3401, D=0.4293, H=0.3864, M=0.1768, Q=0.1515, S=0.2810, T=0.3448, W=0.2406.
+- variates skill: multivariate=0.3588, univariate=0.3475.
+- Outcome: Matched seed 79, same parent and data stream. This isolates training with per-row available target horizons; assess the three cutoffs jointly.
+
+## E42 paired comparison: partial_targets 6000, backcast2
+- Checkpoint SHA-256: `67562486b099a350da53c3caa5ff0932fe7fecdfe72c1879b064b2a9bb173dfb`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 27.302276849746704 seconds.
+- Overall skill 0.352901; relative MASE 0.775671; relative WQL 0.539838.
+- term skill: long=0.3689, medium=0.3760, short=0.3379.
+- domain skill: Econ/Fin=0.0894, Energy=0.2294, Healthcare=0.3710, Nature=0.4595, Sales=0.5574, Transport=0.4132, Web/CloudOps=0.4264.
+- frequency skill: A=0.3430, D=0.4260, H=0.3868, M=0.1778, Q=0.1500, S=0.2820, T=0.3450, W=0.2490.
+- variates skill: multivariate=0.3587, univariate=0.3482.
+- Outcome: Matched seed 79, same parent and data stream. This isolates training with per-row available target horizons; assess the three cutoffs jointly.
+
+## E49 complete seasonal interval comparison
+No training; inference seed 0. Own E25-18000 checkpoint SHA-256 f6d2c41a690159a5b9c2034be45718c408a30b0d34d9607c9a768b9926781bbd.
+
+screen: 12.53 CPU diagnostic seconds.
+- gaussian: skill 0.374500; term: short=0.3517, medium=0.4069, long=0.3994; domain: Econ/Fin=0.1332, Energy=0.2347, Healthcare=0.4319, Nature=0.4254, Sales=0.3438, Transport=0.4650, Web/CloudOps=0.5007; freq: A-DEC=0.3692, Q-DEC=0.2006, M=0.1023, W-SUN=0.0521, D=0.3442, H=0.3953, 15T=0.2125, W-FRI=-0.0051, 10T=0.6138, W-TUE=0.5773, W-THU=0.1903, W-WED=0.3133, 5T=0.5112, 10S=0.4218.
+- empirical: skill 0.374034; term: short=0.3515, medium=0.4062, long=0.3984; domain: Econ/Fin=0.1322, Energy=0.2332, Healthcare=0.4326, Nature=0.4254, Sales=0.3438, Transport=0.4650, Web/CloudOps=0.5005; freq: A-DEC=0.3692, Q-DEC=0.2007, M=0.1029, W-SUN=0.0515, D=0.3443, H=0.3941, 15T=0.2120, W-FRI=-0.0051, 10T=0.6138, W-TUE=0.5773, W-THU=0.1903, W-WED=0.3133, 5T=0.5112, 10S=0.4208.
+- mad: skill 0.373768; term: short=0.3513, medium=0.4061, long=0.3979; domain: Econ/Fin=0.1328, Energy=0.2325, Healthcare=0.4325, Nature=0.4254, Sales=0.3438, Transport=0.4649, Web/CloudOps=0.5001; freq: A-DEC=0.3692, Q-DEC=0.2007, M=0.1027, W-SUN=0.0515, D=0.3444, H=0.3938, 15T=0.2115, W-FRI=-0.0051, 10T=0.6138, W-TUE=0.5773, W-THU=0.1903, W-WED=0.3133, 5T=0.5112, 10S=0.4194.
+
+backcast: 12.04 CPU diagnostic seconds.
+- gaussian: skill 0.332400; term: short=0.2953, medium=0.3574, long=0.3980; domain: Econ/Fin=0.1526, Energy=0.2268, Healthcare=0.3995, Nature=0.3561, Sales=0.3986, Transport=0.3699, Web/CloudOps=0.4472; freq: A-DEC=0.2996, Q-DEC=0.2330, M=0.1721, W-SUN=-0.0043, D=0.3516, H=0.3643, 15T=0.2254, W-FRI=0.2597, 10T=0.3130, W-TUE=0.5335, W-THU=0.2049, W-WED=0.3474, 5T=0.4329, 10S=0.3397.
+- empirical: skill 0.332227; term: short=0.2954, medium=0.3563, long=0.3981; domain: Econ/Fin=0.1521, Energy=0.2257, Healthcare=0.3995, Nature=0.3558, Sales=0.3986, Transport=0.3697, Web/CloudOps=0.4482; freq: A-DEC=0.2996, Q-DEC=0.2331, M=0.1717, W-SUN=-0.0053, D=0.3516, H=0.3630, 15T=0.2257, W-FRI=0.2597, 10T=0.3130, W-TUE=0.5335, W-THU=0.2049, W-WED=0.3474, 5T=0.4330, 10S=0.3436.
+- mad: skill 0.331760; term: short=0.2949, medium=0.3559, long=0.3977; domain: Econ/Fin=0.1517, Energy=0.2248, Healthcare=0.3984, Nature=0.3554, Sales=0.3986, Transport=0.3696, Web/CloudOps=0.4480; freq: A-DEC=0.2996, Q-DEC=0.2330, M=0.1720, W-SUN=-0.0052, D=0.3511, H=0.3620, 15T=0.2253, W-FRI=0.2597, 10T=0.3129, W-TUE=0.5335, W-THU=0.2049, W-WED=0.3474, 5T=0.4330, 10S=0.3430.
+
+backcast2: 10.81 CPU diagnostic seconds.
+- gaussian: skill 0.356697; term: short=0.3411, medium=0.3874, long=0.3657; domain: Econ/Fin=0.1045, Energy=0.2331, Healthcare=0.3610, Nature=0.4656, Sales=0.5516, Transport=0.4179, Web/CloudOps=0.4303; freq: A-DEC=0.3474, Q-DEC=0.1548, M=0.1599, W-SUN=0.0316, D=0.4275, H=0.3891, 15T=0.1072, W-FRI=0.1243, 10T=0.4930, W-TUE=0.0782, W-THU=0.3037, W-WED=0.5352, 5T=0.4648, 10S=0.2798.
+- empirical: skill 0.356537; term: short=0.3408, medium=0.3876, long=0.3655; domain: Econ/Fin=0.1035, Energy=0.2316, Healthcare=0.3619, Nature=0.4656, Sales=0.5516, Transport=0.4178, Web/CloudOps=0.4315; freq: A-DEC=0.3474, Q-DEC=0.1549, M=0.1602, W-SUN=0.0311, D=0.4277, H=0.3876, 15T=0.1075, W-FRI=0.1243, 10T=0.4929, W-TUE=0.0782, W-THU=0.3037, W-WED=0.5352, 5T=0.4648, 10S=0.2846.
+- mad: skill 0.356717; term: short=0.3409, medium=0.3871, long=0.3665; domain: Econ/Fin=0.1039, Energy=0.2307, Healthcare=0.3619, Nature=0.4656, Sales=0.5516, Transport=0.4176, Web/CloudOps=0.4335; freq: A-DEC=0.3474, Q-DEC=0.1549, M=0.1602, W-SUN=0.0312, D=0.4278, H=0.3869, 15T=0.1072, W-FRI=0.1243, 10T=0.4929, W-TUE=0.0782, W-THU=0.3037, W-WED=0.5352, 5T=0.4648, 10S=0.2923.
+
+Outcome: retain Gaussian RMS intervals. Centered empirical intervals lose at all three cutoffs. MAD loses on the main and first earlier cutoff, with only a negligible second-earlier gain. Median MASE is exactly unchanged for every setting as intended. No transfer or package change.
+
+## E35 matched 12000 comparison: recency_features, screen
+- Checkpoint SHA-256: `8ccb6870a025e1097331a3a288b1183741a413f3460fbf3352d0c589c6a4394c`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 20.589985609054565 seconds.
+- Overall skill 0.357241; relative MASE 0.765658; relative WQL 0.539588.
+- term skill: long=0.3912, medium=0.4026, short=0.3252.
+- domain skill: Econ/Fin=0.1133, Energy=0.1855, Healthcare=0.3939, Nature=0.4171, Sales=0.3483, Transport=0.4566, Web/CloudOps=0.5065.
+- frequency skill: A=0.3781, D=0.3343, H=0.3817, M=0.0906, Q=0.1961, S=0.4411, T=0.4020, W=0.1998.
+- variates skill: multivariate=0.3920, univariate=0.3281.
+- Outcome: Trend features gain slightly on the main screen but lose on both earlier cutoffs. Stop this branch; its extra features are not selected.
+
+## E35 matched 12000 comparison: trend_features, screen
+- Checkpoint SHA-256: `269be0627257c53343aac6d0609231865bd112be7e01944d966624160150d4a8`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.890631914138794 seconds.
+- Overall skill 0.357547; relative MASE 0.765787; relative WQL 0.538983.
+- term skill: long=0.3905, medium=0.4007, short=0.3268.
+- domain skill: Econ/Fin=0.1107, Energy=0.1852, Healthcare=0.4068, Nature=0.4159, Sales=0.3502, Transport=0.4572, Web/CloudOps=0.5058.
+- frequency skill: A=0.3786, D=0.3384, H=0.3803, M=0.0973, Q=0.2012, S=0.4413, T=0.4010, W=0.2023.
+- variates skill: multivariate=0.3916, univariate=0.3291.
+- Outcome: Trend features gain slightly on the main screen but lose on both earlier cutoffs. Stop this branch; its extra features are not selected.
+
+## E35 matched 12000 comparison: recency_features, backcast
+- Checkpoint SHA-256: `8ccb6870a025e1097331a3a288b1183741a413f3460fbf3352d0c589c6a4394c`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 27.742298364639282 seconds.
+- Overall skill 0.328112; relative MASE 0.807765; relative WQL 0.558868.
+- term skill: long=0.3899, medium=0.3548, short=0.2920.
+- domain skill: Econ/Fin=0.1488, Energy=0.2248, Healthcare=0.4036, Nature=0.3514, Sales=0.3955, Transport=0.3613, Web/CloudOps=0.4413.
+- frequency skill: A=0.3134, D=0.3558, H=0.3562, M=0.1567, Q=0.2256, S=0.3420, T=0.3267, W=0.2651.
+- variates skill: multivariate=0.3435, univariate=0.3156.
+- Outcome: Trend features gain slightly on the main screen but lose on both earlier cutoffs. Stop this branch; its extra features are not selected.
+
+## E35 matched 12000 comparison: trend_features, backcast
+- Checkpoint SHA-256: `269be0627257c53343aac6d0609231865bd112be7e01944d966624160150d4a8`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 27.06494641304016 seconds.
+- Overall skill 0.327724; relative MASE 0.808759; relative WQL 0.558826.
+- term skill: long=0.3886, medium=0.3555, short=0.2916.
+- domain skill: Econ/Fin=0.1436, Energy=0.2247, Healthcare=0.3984, Nature=0.3522, Sales=0.3952, Transport=0.3621, Web/CloudOps=0.4411.
+- frequency skill: A=0.3149, D=0.3546, H=0.3559, M=0.1602, Q=0.2281, S=0.3419, T=0.3284, W=0.2545.
+- variates skill: multivariate=0.3441, univariate=0.3144.
+- Outcome: Trend features gain slightly on the main screen but lose on both earlier cutoffs. Stop this branch; its extra features are not selected.
+
+## E35 matched 12000 comparison: recency_features, backcast2
+- Checkpoint SHA-256: `8ccb6870a025e1097331a3a288b1183741a413f3460fbf3352d0c589c6a4394c`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 27.009191036224365 seconds.
+- Overall skill 0.353202; relative MASE 0.774939; relative WQL 0.539846.
+- term skill: long=0.3671, medium=0.3766, short=0.3388.
+- domain skill: Econ/Fin=0.0874, Energy=0.2254, Healthcare=0.3808, Nature=0.4621, Sales=0.5594, Transport=0.4116, Web/CloudOps=0.4293.
+- frequency skill: A=0.3385, D=0.4312, H=0.3856, M=0.1786, Q=0.1474, S=0.2819, T=0.3451, W=0.2459.
+- variates skill: multivariate=0.3594, univariate=0.3481.
+- Outcome: Trend features gain slightly on the main screen but lose on both earlier cutoffs. Stop this branch; its extra features are not selected.
+
+## E35 matched 12000 comparison: trend_features, backcast2
+- Checkpoint SHA-256: `269be0627257c53343aac6d0609231865bd112be7e01944d966624160150d4a8`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.192188262939453 seconds.
+- Overall skill 0.353075; relative MASE 0.775525; relative WQL 0.539650.
+- term skill: long=0.3664, medium=0.3761, short=0.3391.
+- domain skill: Econ/Fin=0.0837, Energy=0.2262, Healthcare=0.3801, Nature=0.4640, Sales=0.5598, Transport=0.4112, Web/CloudOps=0.4272.
+- frequency skill: A=0.3402, D=0.4277, H=0.3854, M=0.1847, Q=0.1490, S=0.2774, T=0.3477, W=0.2433.
+- variates skill: multivariate=0.3593, univariate=0.3480.
+- Outcome: Trend features gain slightly on the main screen but lose on both earlier cutoffs. Stop this branch; its extra features are not selected.
+
+## E50: paired-sign loss for a frozen-backbone output head
+Hypothesis: training against the averaged positive/negative forecasts used at inference can improve calibration over separately scoring the two orientations. Both arms initialize from our selected E25-24000 checkpoint with fixed128 trend features and freeze every parameter except the existing 147744-parameter output head. Both forward the same context and its sign reflection, consuming identical seed97 data windows. Control scores each orientation independently; candidate averages the two normalized forecasts before the original weighted quantile/median loss. This isolates the loss rather than data augmentation or forward compute. Same packed_sample, original local generator and independent sign augmentation, batch64, LR5e-5, 6000-step cosine schedule, save1000. No new data, no validation training, no parameter additions.
+
+A maximum-shape random fixture (paired batch128, context4096, horizon1024) verified finite gradients and measured peak Torch allocation697 MiB for the individual-loss control and729 MiB for the paired-loss candidate, with822 MiB reserved in each. No optimizer update or checkpoint was made from the fixture. Start the control after the rejected E35 trainer exits; start the paired arm after E47 finishes, keeping to one H100. The same fixed runtime evaluates both.
+
+E50 head_control training started at 2026-10-22T04:52:57Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E50 head_paired training started at 2026-10-22T04:53:18Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E35 stopped cleanly at step 13309, with 4327.0 elapsed process seconds at the stop request. Final checkpoint SHA-256 `be7cce34037750ef56be8a183b68af46dc2deec184ad228cd4c60ab91b51eb48`. The last validation was the paired 12000-step comparison above; the stop checkpoint was saved for provenance, not selected.
+
+## E31 amplitude 18000 with selected fixed rules, screen
+- Checkpoint SHA-256: `f0d8484d5f1bee554939ffbe769b95e42ba94e0739db7c680fac1c9a132f8f43`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.18380904197693 seconds.
+- Overall skill 0.375271; relative MASE 0.747344; relative WQL 0.522231.
+- term skill: long=0.4000, medium=0.4069, short=0.3529.
+- domain skill: Econ/Fin=0.1337, Energy=0.2361, Healthcare=0.4224, Nature=0.4258, Sales=0.3419, Transport=0.4649, Web/CloudOps=0.5043.
+- frequency skill: A=0.3721, D=0.3448, H=0.3951, M=0.0876, Q=0.2019, S=0.4223, T=0.4386, W=0.2158.
+- variates skill: multivariate=0.3950, univariate=0.3591.
+- Outcome: The earlier mean-template screening gain does not transfer to the selected aligned/zero0.4/fixed128 runtime: below recency18000 at every cutoff. Do not publish; finish the planned 24000 for a matched late comparison.
+
+## E31 amplitude 18000 with selected fixed rules, backcast
+- Checkpoint SHA-256: `f0d8484d5f1bee554939ffbe769b95e42ba94e0739db7c680fac1c9a132f8f43`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 27.348726511001587 seconds.
+- Overall skill 0.333200; relative MASE 0.800924; relative WQL 0.555137.
+- term skill: long=0.3941, medium=0.3576, short=0.2985.
+- domain skill: Econ/Fin=0.1568, Energy=0.2260, Healthcare=0.4100, Nature=0.3583, Sales=0.3987, Transport=0.3697, Web/CloudOps=0.4467.
+- frequency skill: A=0.3095, D=0.3534, H=0.3637, M=0.1993, Q=0.2273, S=0.3378, T=0.3326, W=0.2601.
+- variates skill: multivariate=0.3488, univariate=0.3205.
+- Outcome: The earlier mean-template screening gain does not transfer to the selected aligned/zero0.4/fixed128 runtime: below recency18000 at every cutoff. Do not publish; finish the planned 24000 for a matched late comparison.
+
+## E31 amplitude 18000 with selected fixed rules, backcast2
+- Checkpoint SHA-256: `f0d8484d5f1bee554939ffbe769b95e42ba94e0739db7c680fac1c9a132f8f43`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 27.135295629501343 seconds.
+- Overall skill 0.356896; relative MASE 0.774845; relative WQL 0.533762.
+- term skill: long=0.3678, medium=0.3811, short=0.3433.
+- domain skill: Econ/Fin=0.1063, Energy=0.2350, Healthcare=0.3543, Nature=0.4630, Sales=0.5538, Transport=0.4181, Web/CloudOps=0.4317.
+- frequency skill: A=0.3487, D=0.4308, H=0.3887, M=0.1298, Q=0.1454, S=0.2825, T=0.3581, W=0.2487.
+- variates skill: multivariate=0.3617, univariate=0.3530.
+- Outcome: The earlier mean-template screening gain does not transfer to the selected aligned/zero0.4/fixed128 runtime: below recency18000 at every cutoff. Do not publish; finish the planned 24000 for a matched late comparison.
+
+## E51: last optimizer weights versus EMA weights
+The late E25 EMA checkpoints continue to improve, and averaging older EMA checkpoints regressed in E45. Check whether the final optimizer iterate is ahead of its lagging EMA or instead suffers from update noise. No training or inference-rule change: evaluate step024000 against the selected ema024000 with the identical fixed128/aligned/zero0.4 runtime. Only transfer to earlier cutoffs and full validation if the main screen improves. Both candidates have the same seed59 training lineage.
+
+## E47 confidence projection 1000-step screen
+- Checkpoint SHA-256: `b2df8695413e53c7d62ec8f265affc31a43ca26e7b99b50e0a75f47782b95bfe`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.31796908378601 seconds.
+- Overall skill 0.375491; relative MASE 0.746025; relative WQL 0.522786.
+- term skill: long=0.3994, medium=0.4067, short=0.3536.
+- domain skill: Econ/Fin=0.1396, Energy=0.2349, Healthcare=0.4370, Nature=0.4288, Sales=0.3397, Transport=0.4653, Web/CloudOps=0.5002.
+- frequency skill: A=0.3765, D=0.3531, H=0.3948, M=0.0934, Q=0.1985, S=0.4235, T=0.4361, W=0.2100.
+- variates skill: multivariate=0.3936, univariate=0.3607.
+- Outcome: Backbone unchanged. Earlier checkpoints essentially tied; the 6000-step checkpoint gains 0.000291 over the parent. Earlier-cutoff checks are pending before any selection.
+
+## E47 confidence projection 3000-step screen
+- Checkpoint SHA-256: `653081261875b023a5d8dea02c999762fa8a2a4601ce7b6ad3b55559172290ab`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.364450693130493 seconds.
+- Overall skill 0.375482; relative MASE 0.745987; relative WQL 0.522828.
+- term skill: long=0.3995, medium=0.4066, short=0.3536.
+- domain skill: Econ/Fin=0.1394, Energy=0.2348, Healthcare=0.4379, Nature=0.4283, Sales=0.3403, Transport=0.4653, Web/CloudOps=0.5004.
+- frequency skill: A=0.3762, D=0.3534, H=0.3949, M=0.0912, Q=0.1983, S=0.4238, T=0.4362, W=0.2092.
+- variates skill: multivariate=0.3937, univariate=0.3606.
+- Outcome: Backbone unchanged. Earlier checkpoints essentially tied; the 6000-step checkpoint gains 0.000291 over the parent. Earlier-cutoff checks are pending before any selection.
+
+## E47 confidence projection 6000-step screen
+- Checkpoint SHA-256: `0bad97e8994aac49ea173443db0a4fa8d72d33f9eb9e2660f06de75f7f1403eb`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.593000650405884 seconds.
+- Overall skill 0.375793; relative MASE 0.745649; relative WQL 0.522544.
+- term skill: long=0.3998, medium=0.4067, short=0.3540.
+- domain skill: Econ/Fin=0.1396, Energy=0.2348, Healthcare=0.4406, Nature=0.4284, Sales=0.3399, Transport=0.4658, Web/CloudOps=0.5006.
+- frequency skill: A=0.3762, D=0.3542, H=0.3951, M=0.0954, Q=0.1982, S=0.4239, T=0.4362, W=0.2090.
+- variates skill: multivariate=0.3937, univariate=0.3611.
+- Outcome: Backbone unchanged. Earlier checkpoints essentially tied; the 6000-step checkpoint gains 0.000291 over the parent. Earlier-cutoff checks are pending before any selection.
+
+E47 completed 6000 steps in 818.8 elapsed training-loop seconds on the shared H100. Every original model tensor is bitwise equal to the frozen E25-24000 parent; only the 3072 confidence-projection parameters changed.
+
+E48 phase_synth training started at 2026-10-22T04:54:48Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E51 final optimizer iterate, screen
+- Checkpoint SHA-256: `32b70d0e88532fb98746ea032c10be587383d80ed7b0951b41f778a39c9dc68b`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.315549850463867 seconds.
+- Overall skill 0.374536; relative MASE 0.746570; relative WQL 0.524003.
+- term skill: long=0.3980, medium=0.4073, short=0.3522.
+- domain skill: Econ/Fin=0.1389, Energy=0.2328, Healthcare=0.4348, Nature=0.4279, Sales=0.3409, Transport=0.4652, Web/CloudOps=0.4998.
+- frequency skill: A=0.3701, D=0.3497, H=0.3936, M=0.0914, Q=0.1977, S=0.4222, T=0.4369, W=0.2087.
+- variates skill: multivariate=0.3931, univariate=0.3594.
+- Outcome: Skill 0.374536 is below the selected EMA24000 skill 0.375502. Retain EMA weights; no earlier-window or full evaluation is needed.
+
+## E39 bounded quantiles 6000-step screen
+- Checkpoint SHA-256: `4af38aa561df4d38c1f6df090f8a5dc364c38786eb2ab38933646d827cbe7669`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.320833683013916 seconds.
+- Overall skill 0.357854; relative MASE 0.764623; relative WQL 0.539287.
+- term skill: long=0.3907, medium=0.4019, short=0.3268.
+- domain skill: Econ/Fin=0.1103, Energy=0.1849, Healthcare=0.4072, Nature=0.4203, Sales=0.3470, Transport=0.4571, Web/CloudOps=0.5050.
+- frequency skill: A=0.3756, D=0.3402, H=0.3810, M=0.0920, Q=0.1957, S=0.4423, T=0.4023, W=0.1961.
+- variates skill: multivariate=0.3917, univariate=0.3295.
+- Outcome: Skill 0.357854 is below matched control 0.358628, following a smaller 3000-step regression. Stop this branch; retain the current inference-only positivity projection.
+
+## E52: varied requested horizons during partial-target training
+E42's correct per-row horizon conditioning improves all three cutoffs at 6000 steps. Hypothesis: exposing the decoder to intermediate requested horizons will also reduce mismatch between four fixed training horizon lengths and arbitrary inference lengths. Match E42's parent E25-12000, seed79, packed_sample, source probabilities, batch256, LR5e-5 and 12000-step schedule. Keep half the sampled target windows unchanged; for the other half, independently choose a prefix length uniformly from one-quarter to the full batch horizon, rounded upward to a 16-step patch boundary. Mask only later training labels. The existing E42 loss masks unused future tokens and sets each row's horizon metadata. Corpus contexts and periods, original generator and sampler random draws remain unchanged. Independent horizon RNG seed300082.
+
+A data-only check at all four horizon sizes verified identical context/period arrays and exact preservation of every retained target value (0.66 CPU seconds). No validation labels enter training. No new model parameters or inference behavior; this is a training-window augmentation. Start after rejected E39 exits and compare against cached E42 at 3000/6000/12000.
+
+E52 horizon_jitter training started at 2026-10-22T04:56:42Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E47 confidence projection 6000, backcast
+- Checkpoint SHA-256: `0bad97e8994aac49ea173443db0a4fa8d72d33f9eb9e2660f06de75f7f1403eb`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 25.725136041641235 seconds.
+- Overall skill 0.334126; relative MASE 0.799732; relative WQL 0.554420.
+- term skill: long=0.3968, medium=0.3571, short=0.2992.
+- domain skill: Econ/Fin=0.1592, Energy=0.2275, Healthcare=0.4065, Nature=0.3605, Sales=0.3971, Transport=0.3716, Web/CloudOps=0.4467.
+- frequency skill: A=0.3079, D=0.3510, H=0.3635, M=0.1810, Q=0.2346, S=0.3430, T=0.3344, W=0.2764.
+- variates skill: multivariate=0.3510, univariate=0.3204.
+- Outcome: Main screen gains 0.000291; first earlier cutoff loses only 0.000054; second earlier cutoff gains 0.000224. Proceed to full validation before deciding whether to replace the package.
+
+## E47 confidence projection 6000, backcast2
+- Checkpoint SHA-256: `0bad97e8994aac49ea173443db0a4fa8d72d33f9eb9e2660f06de75f7f1403eb`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 24.6521155834198 seconds.
+- Overall skill 0.358499; relative MASE 0.770987; relative WQL 0.533762.
+- term skill: long=0.3652, medium=0.3856, short=0.3452.
+- domain skill: Econ/Fin=0.1095, Energy=0.2361, Healthcare=0.3507, Nature=0.4706, Sales=0.5514, Transport=0.4202, Web/CloudOps=0.4300.
+- frequency skill: A=0.3495, D=0.4316, H=0.3891, M=0.1571, Q=0.1507, S=0.2825, T=0.3576, W=0.2564.
+- variates skill: multivariate=0.3624, univariate=0.3553.
+- Outcome: Main screen gains 0.000291; first earlier cutoff loses only 0.000054; second earlier cutoff gains 0.000224. Proceed to full validation before deciding whether to replace the package.
+
+E39 stopped cleanly at step 6298, with 2199.0 elapsed process seconds at the stop request. Final checkpoint SHA-256 `d1018a12abdb14c369ba55a561c3091bbc7dc4b25337345a0af68aa15e9afec2`. This stop checkpoint was retained for provenance and was not selected.
+
+## E53: within-cycle variance for seasonal confidence
+Hypothesis: total variance over up to eight cycles can be dominated by a long trend, artificially lowering the seasonal-difference/variance ratio and assigning high seasonal confidence even to a nonseasonal ramp. Change only the gate denominator to pooled within-cycle variance, subtracting each cycle's finite-observation mean before pooling squared deviations. Compare the existing global variance and this within-cycle variance, with the selected aligned template, Gaussian RMS intervals, continuity and zero0.4 unchanged. Use cached E25-18000 forecasts across three cutoffs; no training, optimization or new source data. A selected formula must transfer to the current checkpoint and fixed128 runtime before publication.
+
+## E47 confidence projection 6000, exact full package validation
+- Checkpoint SHA-256: `0bad97e8994aac49ea173443db0a4fa8d72d33f9eb9e2660f06de75f7f1403eb`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.371766; relative MASE 0.748576; relative WQL 0.527239.
+- term skill: long=0.3937, medium=0.4056, short=0.3496.
+- domain skill: Econ/Fin=0.1167, Energy=0.2278, Healthcare=0.4464, Nature=0.4283, Sales=0.3521, Transport=0.4660, Web/CloudOps=0.4929.
+- frequency skill: A=0.2915, D=0.3581, H=0.3903, M=0.1171, Q=0.1834, S=0.4239, T=0.4292, W=0.2017.
+- variates skill: multivariate=0.3895, univariate=0.3573.
+- Outcome: Selected for publication: skill 0.371766 versus 0.371479 for the previous package; relative MASE 0.748576 and WQL 0.527239. All 97 configurations and 151170 instances passed, with no quantile crossings. Actual package inference took 140.57 seconds on the one H100 while five heavy training runs and two head-only runs shared it. Relocation/determinism checks precede the atomic replacement.
+
+## E53 complete seasonal variance comparison
+No training; inference seed 0. E25-18000 checkpoint SHA-256 f6d2c41a690159a5b9c2034be45718c408a30b0d34d9607c9a768b9926781bbd.
+
+screen: 8.50 CPU diagnostic seconds.
+- global: skill 0.374500; term: short=0.3517, medium=0.4069, long=0.3994; domain: Econ/Fin=0.1332, Energy=0.2347, Healthcare=0.4319, Nature=0.4254, Sales=0.3438, Transport=0.4650, Web/CloudOps=0.5007; freq: A-DEC=0.3692, Q-DEC=0.2006, M=0.1023, W-SUN=0.0521, D=0.3442, H=0.3953, 15T=0.2125, W-FRI=-0.0051, 10T=0.6138, W-TUE=0.5773, W-THU=0.1903, W-WED=0.3133, 5T=0.5112, 10S=0.4218.
+- within_cycle: skill 0.374386; term: short=0.3515, medium=0.4068, long=0.3994; domain: Econ/Fin=0.1320, Energy=0.2346, Healthcare=0.4305, Nature=0.4254, Sales=0.3450, Transport=0.4650, Web/CloudOps=0.5007; freq: A-DEC=0.3692, Q-DEC=0.2024, M=0.1005, W-SUN=0.0487, D=0.3444, H=0.3951, 15T=0.2124, W-FRI=-0.0050, 10T=0.6139, W-TUE=0.5773, W-THU=0.1903, W-WED=0.3133, 5T=0.5112, 10S=0.4218.
+
+backcast: 8.12 CPU diagnostic seconds.
+- global: skill 0.332400; term: short=0.2953, medium=0.3574, long=0.3980; domain: Econ/Fin=0.1526, Energy=0.2268, Healthcare=0.3995, Nature=0.3561, Sales=0.3986, Transport=0.3699, Web/CloudOps=0.4472; freq: A-DEC=0.2996, Q-DEC=0.2330, M=0.1721, W-SUN=-0.0043, D=0.3516, H=0.3643, 15T=0.2254, W-FRI=0.2597, 10T=0.3130, W-TUE=0.5335, W-THU=0.2049, W-WED=0.3474, 5T=0.4329, 10S=0.3397.
+- within_cycle: skill 0.332143; term: short=0.2948, medium=0.3575, long=0.3979; domain: Econ/Fin=0.1529, Energy=0.2266, Healthcare=0.3952, Nature=0.3561, Sales=0.3986, Transport=0.3704, Web/CloudOps=0.4471; freq: A-DEC=0.2996, Q-DEC=0.2353, M=0.1665, W-SUN=-0.0082, D=0.3519, H=0.3640, 15T=0.2255, W-FRI=0.2597, 10T=0.3130, W-TUE=0.5335, W-THU=0.2049, W-WED=0.3474, 5T=0.4329, 10S=0.3396.
+
+backcast2: 7.99 CPU diagnostic seconds.
+- global: skill 0.356697; term: short=0.3411, medium=0.3874, long=0.3657; domain: Econ/Fin=0.1045, Energy=0.2331, Healthcare=0.3610, Nature=0.4656, Sales=0.5516, Transport=0.4179, Web/CloudOps=0.4303; freq: A-DEC=0.3474, Q-DEC=0.1548, M=0.1599, W-SUN=0.0316, D=0.4275, H=0.3891, 15T=0.1072, W-FRI=0.1243, 10T=0.4930, W-TUE=0.0782, W-THU=0.3037, W-WED=0.5352, 5T=0.4648, 10S=0.2798.
+- within_cycle: skill 0.356794; term: short=0.3414, medium=0.3873, long=0.3654; domain: Econ/Fin=0.1040, Energy=0.2329, Healthcare=0.3653, Nature=0.4656, Sales=0.5516, Transport=0.4179, Web/CloudOps=0.4301; freq: A-DEC=0.3474, Q-DEC=0.1544, M=0.1677, W-SUN=0.0298, D=0.4273, H=0.3891, 15T=0.1064, W-FRI=0.1243, 10T=0.4930, W-TUE=0.0782, W-THU=0.3037, W-WED=0.5352, 5T=0.4647, 10S=0.2791.
+
+Outcome: retain total history variance. Within-cycle variance loses on the main and first earlier cutoff, with only a small second-earlier gain. The trend-related failure mode does not justify changing the global formula.
+
+E47 relocation and determinism passed: the candidate and a separately copied package in a fresh /tmp directory produced bitwise identical, finite, monotone quantiles for univariate daily, univariate hourly and multivariate medium-horizon examples. Both ran with /tmp as the working directory. Publish the complete candidate atomically after these checks.
+
+## E54: train the full model with longer raw context
+E21's untrained extension and E29's frozen, coarsened history memory did not provide consistent gains. Hypothesis: adapting all existing parameters to raw 8192-point histories can use older cycles without the mismatch of an untrained extension or lossy extra memory. Two matched arms initialize from our E25-24000 EMA, with identical model size, original loss, seed101, packed_sample, source probabilities, batch128, LR3e-5 and 12000-step schedule. The sampler draws context sizes512/1024/2048/8192 with probabilities0.1/0.15/0.35/0.4 in both arms. The control then keeps only the last4096 points; the candidate keeps8192. Future labels and sampler RNG are matched exactly, including synthetic draws and aggregation. Both train every model parameter. This comparison changes only the retained raw history.
+
+At inference, positivity and the fixed seasonal rule continue to inspect only the recent4096 observations; continuity and zero projection retain their existing lookbacks. The candidate's neural encoder and normalization use up to8192. No external weights or new source data are introduced; the parameter count is unchanged. New generator/sign-augmentation seed101 must be declared if selected. Maximum-shape batch128/context8192/horizon1024 memory check, including allocated placeholders for Adam moments and EMA, measured9960 MiB peak allocated and10530 MiB reserved, with finite gradients in4.37 seconds. No optimizer update or checkpoint came from this random fixture. Queue the control after E50 head_control completes; queue the longer-context arm after E38 expanded corpus completes, preserving the one-H100 allocation.
+
+E54 data check passed in3.26 CPU seconds: all four sampled context sizes produced identical future labels, period metadata, overlapping recent history and sampler RNG states between the two arms.
+
+E54 long_context_control training started at 2026-10-22T05:04:35Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E55: transfer the matched partial-target training difference
+E42's 6000-step checkpoint improves all three cutoffs over its exact seed/data/initialization-matched E38 control, but both restarted trajectories began from an older E25 checkpoint. Hypothesis: the parameter difference between these matched runs isolates a useful training correction that can transfer to the selected later model. Form one fixed model with weights = current E47 confidence6000 + E42 partial6000 - E38 control6000 for their common backbone tensors, leaving the confidence projection unchanged. Use coefficient1 with no grid search. This is a deterministic composition of our own trained weights, with all three parent hashes recorded. No new training or data, and one model forecasts every instance. Evaluate the main screen first; if promising, require earlier-cutoff and exact full-package checks.
+
+## E50 head_control 1000-step screen
+- Checkpoint SHA-256: `a88f59494b256bf95addea9f00fa7b631cd67dc762228f523d95eec8047a05d8`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.136118412017822 seconds.
+- Overall skill 0.375630; relative MASE 0.745780; relative WQL 0.522726.
+- term skill: long=0.3996, medium=0.4068, short=0.3537.
+- domain skill: Econ/Fin=0.1403, Energy=0.2347, Healthcare=0.4405, Nature=0.4291, Sales=0.3396, Transport=0.4656, Web/CloudOps=0.4997.
+- frequency skill: A=0.3779, D=0.3539, H=0.3951, M=0.0943, Q=0.1980, S=0.4231, T=0.4359, W=0.2102.
+- variates skill: multivariate=0.3935, univariate=0.3610.
+- Outcome: Small gains at1000 are lost at3000 and6000. Paired loss trails the matched individual-loss control at the later checkpoints; neither arm surpasses the selected confidence model. No package replacement.
+
+## E50 head_control 3000-step screen
+- Checkpoint SHA-256: `75b9f4860e4c4345f2cedb6eb2f9ffe5c9e65aede03a314c72b20b1e8a1f7aaf`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.069400310516357 seconds.
+- Overall skill 0.375411; relative MASE 0.746024; relative WQL 0.522921.
+- term skill: long=0.3996, medium=0.4066, short=0.3534.
+- domain skill: Econ/Fin=0.1387, Energy=0.2345, Healthcare=0.4401, Nature=0.4293, Sales=0.3399, Transport=0.4650, Web/CloudOps=0.4996.
+- frequency skill: A=0.3759, D=0.3533, H=0.3949, M=0.0963, Q=0.1980, S=0.4231, T=0.4355, W=0.2102.
+- variates skill: multivariate=0.3933, univariate=0.3608.
+- Outcome: Small gains at1000 are lost at3000 and6000. Paired loss trails the matched individual-loss control at the later checkpoints; neither arm surpasses the selected confidence model. No package replacement.
+
+## E50 head_control 6000-step screen
+- Checkpoint SHA-256: `dd8fc459f097a0bc0c7cb133473130aa32e1dc520fd7f03ff50dc26366d1122e`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.82920217514038 seconds.
+- Overall skill 0.375304; relative MASE 0.746102; relative WQL 0.523045.
+- term skill: long=0.3996, medium=0.4065, short=0.3532.
+- domain skill: Econ/Fin=0.1404, Energy=0.2343, Healthcare=0.4375, Nature=0.4291, Sales=0.3402, Transport=0.4652, Web/CloudOps=0.4997.
+- frequency skill: A=0.3770, D=0.3531, H=0.3950, M=0.0965, Q=0.1987, S=0.4230, T=0.4355, W=0.2086.
+- variates skill: multivariate=0.3931, univariate=0.3607.
+- Outcome: Small gains at1000 are lost at3000 and6000. Paired loss trails the matched individual-loss control at the later checkpoints; neither arm surpasses the selected confidence model. No package replacement.
+
+E50 head_control completed6000 steps in 685.6 elapsed training-loop seconds. Every non-head tensor remains bitwise equal to the E25-24000 parent.
+
+## E50 head_paired 1000-step screen
+- Checkpoint SHA-256: `337e19aac481543a8ba78bd837f00c8a714315313e665b1b5a038d009a905a2a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.40800166130066 seconds.
+- Overall skill 0.375721; relative MASE 0.745631; relative WQL 0.522677.
+- term skill: long=0.3997, medium=0.4067, short=0.3539.
+- domain skill: Econ/Fin=0.1403, Energy=0.2344, Healthcare=0.4425, Nature=0.4292, Sales=0.3396, Transport=0.4658, Web/CloudOps=0.4998.
+- frequency skill: A=0.3786, D=0.3544, H=0.3951, M=0.0952, Q=0.1978, S=0.4234, T=0.4358, W=0.2099.
+- variates skill: multivariate=0.3935, univariate=0.3612.
+- Outcome: Small gains at1000 are lost at3000 and6000. Paired loss trails the matched individual-loss control at the later checkpoints; neither arm surpasses the selected confidence model. No package replacement.
+
+## E50 head_paired 3000-step screen
+- Checkpoint SHA-256: `1043bbd666890cb730f2931875860ed661f54bae5236a7c93bbbe03764487222`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.516828536987305 seconds.
+- Overall skill 0.375342; relative MASE 0.746117; relative WQL 0.522970.
+- term skill: long=0.3997, medium=0.4065, short=0.3533.
+- domain skill: Econ/Fin=0.1387, Energy=0.2340, Healthcare=0.4415, Nature=0.4295, Sales=0.3399, Transport=0.4651, Web/CloudOps=0.4994.
+- frequency skill: A=0.3750, D=0.3534, H=0.3947, M=0.0981, Q=0.1978, S=0.4235, T=0.4353, W=0.2097.
+- variates skill: multivariate=0.3930, univariate=0.3609.
+- Outcome: Small gains at1000 are lost at3000 and6000. Paired loss trails the matched individual-loss control at the later checkpoints; neither arm surpasses the selected confidence model. No package replacement.
+
+## E50 head_paired 6000-step screen
+- Checkpoint SHA-256: `8aacc5fadfa1a9d214aeba0cd1f39bde9aaf038b9ee2a487dafa57b9e904479e`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.942206621170044 seconds.
+- Overall skill 0.375234; relative MASE 0.746235; relative WQL 0.523068.
+- term skill: long=0.3997, medium=0.4064, short=0.3531.
+- domain skill: Econ/Fin=0.1405, Energy=0.2337, Healthcare=0.4385, Nature=0.4294, Sales=0.3403, Transport=0.4654, Web/CloudOps=0.4995.
+- frequency skill: A=0.3760, D=0.3533, H=0.3947, M=0.0981, Q=0.1988, S=0.4234, T=0.4354, W=0.2077.
+- variates skill: multivariate=0.3928, univariate=0.3609.
+- Outcome: Small gains at1000 are lost at3000 and6000. Paired loss trails the matched individual-loss control at the later checkpoints; neither arm surpasses the selected confidence model. No package replacement.
+
+E50 head_paired completed6000 steps in 687.3 elapsed training-loop seconds. Every non-head tensor remains bitwise equal to the E25-24000 parent.
+
+## E38 original corpus control12000, matched screening
+- Checkpoint SHA-256: `7f9ba5e01ac59c51a3b63d3067b442e23e15d0abc51ddbcccdd04d6083ae4add`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.037413597106934 seconds.
+- Overall skill 0.360533; relative MASE 0.761352; relative WQL 0.537094.
+- term skill: long=0.3910, medium=0.4047, short=0.3305.
+- domain skill: Econ/Fin=0.1118, Energy=0.1871, Healthcare=0.4084, Nature=0.4251, Sales=0.3445, Transport=0.4588, Web/CloudOps=0.5084.
+- frequency skill: A=0.3744, D=0.3432, H=0.3820, M=0.0997, Q=0.1999, S=0.4419, T=0.4062, W=0.2010.
+- variates skill: multivariate=0.3952, univariate=0.3315.
+- Outcome: The restarted control improves over its12000-step parent but remains below uninterrupted E25-24000. Retain as the paired reference for corpus expansion and partial-target training.
+
+## E38 original corpus control12000, selected fixed inference rules
+- Checkpoint SHA-256: `7f9ba5e01ac59c51a3b63d3067b442e23e15d0abc51ddbcccdd04d6083ae4add`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.83461022377014 seconds.
+- Overall skill 0.373819; relative MASE 0.747477; relative WQL 0.524569.
+- term skill: long=0.3971, medium=0.4072, short=0.3512.
+- domain skill: Econ/Fin=0.1366, Energy=0.2343, Healthcare=0.4105, Nature=0.4297, Sales=0.3412, Transport=0.4642, Web/CloudOps=0.5006.
+- frequency skill: A=0.3777, D=0.3471, H=0.3939, M=0.0915, Q=0.2002, S=0.4231, T=0.4362, W=0.2036.
+- variates skill: multivariate=0.3936, univariate=0.3576.
+- Outcome: The restarted control improves over its12000-step parent but remains below uninterrupted E25-24000. Retain as the paired reference for corpus expansion and partial-target training.
+
+E38 original-corpus control completed12000 steps in 3967.3 elapsed training-loop seconds. E48 phase_synth replaced it on the same H100 after completion.
+
+## E55 matched partial-target weight difference, screen
+- Checkpoint SHA-256: `031411940fdebc895e3cf681512924f680e7feaccdfbda46f85890397fe20bd0`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.02963423728943 seconds.
+- Overall skill 0.375848; relative MASE 0.745876; relative WQL 0.522292.
+- term skill: long=0.3998, medium=0.4069, short=0.3540.
+- domain skill: Econ/Fin=0.1449, Energy=0.2367, Healthcare=0.4308, Nature=0.4298, Sales=0.3356, Transport=0.4655, Web/CloudOps=0.5000.
+- frequency skill: A=0.3786, D=0.3503, H=0.3960, M=0.1012, Q=0.2008, S=0.4236, T=0.4351, W=0.2163.
+- variates skill: multivariate=0.3939, univariate=0.3611.
+- Outcome: Skill 0.375848 is only 0.000056 above the selected E47 model. Check both earlier cutoffs before a full-package evaluation; no replacement yet.
+
+## E56: bounded influence of difficult training windows
+Hypothesis: a linear average of normalized window losses gives unusually difficult or abruptly changing futures too much influence, while the benchmark aggregates relative configuration errors geometrically. Keep the original per-window weighted quantile/median loss and context-only seasonal weights, but replace each window loss L by 0.25*log1p(L/0.25) before averaging. The derivative is bounded by1 and declines for difficult windows; this is a robust surrogate, not a claim to exactly optimize benchmark skill. The scale0.25 is fixed in advance, with no sweep.
+
+Match E38 original-corpus control exactly: parent E25-12000, seed79, packed_sample, source weights, batch256, LR5e-5, 12000-step schedule and save3000. No new source data, architecture or inference rule. Queue after E42 completes12000, then compare against cached control checkpoints and earlier cutoffs. The loss scale is saved in training arguments.
+
+## E55 transferred partial-target update, backcast
+- Checkpoint SHA-256: `031411940fdebc895e3cf681512924f680e7feaccdfbda46f85890397fe20bd0`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.927225589752197 seconds.
+- Overall skill 0.335000; relative MASE 0.798832; relative WQL 0.553589.
+- term skill: long=0.3978, medium=0.3567, short=0.3005.
+- domain skill: Econ/Fin=0.1675, Energy=0.2279, Healthcare=0.4021, Nature=0.3600, Sales=0.3983, Transport=0.3718, Web/CloudOps=0.4491.
+- frequency skill: A=0.3286, D=0.3484, H=0.3640, M=0.1855, Q=0.2333, S=0.3447, T=0.3347, W=0.2831.
+- variates skill: multivariate=0.3522, univariate=0.3210.
+- Outcome: Mixed transfer: main +0.000056, first earlier +0.000874, second earlier -0.000595 versus E47. Keep the published package. Revisit the same coefficient with the final12000-step matched checkpoints; no full-package test of this6000-step composition.
+
+## E55 transferred partial-target update, backcast2
+- Checkpoint SHA-256: `031411940fdebc895e3cf681512924f680e7feaccdfbda46f85890397fe20bd0`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 23.91679048538208 seconds.
+- Overall skill 0.357904; relative MASE 0.771656; relative WQL 0.534289.
+- term skill: long=0.3649, medium=0.3860, short=0.3441.
+- domain skill: Econ/Fin=0.1130, Energy=0.2354, Healthcare=0.3520, Nature=0.4680, Sales=0.5482, Transport=0.4200, Web/CloudOps=0.4303.
+- frequency skill: A=0.3535, D=0.4265, H=0.3897, M=0.1535, Q=0.1485, S=0.2805, T=0.3574, W=0.2617.
+- variates skill: multivariate=0.3618, univariate=0.3547.
+- Outcome: Mixed transfer: main +0.000056, first earlier +0.000874, second earlier -0.000595 versus E47. Keep the published package. Revisit the same coefficient with the final12000-step matched checkpoints; no full-package test of this6000-step composition.
+
+E54 long_context_8192 training started at 2026-10-22T05:11:31Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E38 expanded corpus final12000, matched screen
+- Checkpoint SHA-256: `8aa07643add22cc1f8e45c069d19ff54bda8c0853c8e7076eb5923467942327b`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.707714796066284 seconds.
+- Overall skill 0.359331; relative MASE 0.763140; relative WQL 0.537852.
+- term skill: long=0.3931, medium=0.4039, short=0.3277.
+- domain skill: Econ/Fin=0.1164, Energy=0.1854, Healthcare=0.3936, Nature=0.4242, Sales=0.3431, Transport=0.4578, Web/CloudOps=0.5093.
+- frequency skill: A=0.3748, D=0.3312, H=0.3834, M=0.1029, Q=0.2013, S=0.4405, T=0.4056, W=0.2056.
+- variates skill: multivariate=0.3951, univariate=0.3293.
+- Outcome: Skill 0.359331 versus matched original-corpus control0.360533. The early near tie becomes a final regression. Do not select the expanded corpus for the current model; retain all packed data and receipts for future experiments.
+
+E38 expanded-corpus arm completed12000 steps in 4098.4 elapsed training-loop seconds. E54 long_context_8192 started after its process exited on the same H100.
+
+## E48 synthetic phase diffusion3000, matched screen
+- Checkpoint SHA-256: `06c74c2540e7fd208186a407ae6c38fcdeafe5e152887e1d7642d65c18084e64`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.34250235557556 seconds.
+- Overall skill 0.353459; relative MASE 0.767933; relative WQL 0.544338.
+- term skill: long=0.3849, medium=0.3994, short=0.3222.
+- domain skill: Econ/Fin=0.1025, Energy=0.1767, Healthcare=0.4158, Nature=0.4111, Sales=0.3476, Transport=0.4532, Web/CloudOps=0.5044.
+- frequency skill: A=0.3736, D=0.3385, H=0.3748, M=0.0857, Q=0.1907, S=0.4365, T=0.3973, W=0.1999.
+- variates skill: multivariate=0.3856, univariate=0.3267.
+- Outcome: Skill0.353459 versus amplitude-only E31 score0.352625 at the same step/seed/data stream. Earlier-cutoff checks are pending; continue the trial.
+
+E56 log_window_loss training started at 2026-10-22T05:14:05Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E57: period-relative phase projections with a frozen backbone
+Hypothesis: encoder and future-query attention can use seasonal history more efficiently when supplied explicit relative phase for each frequency-derived period, rather than having to reconstruct it from generic position embeddings and scalar period metadata. Add zero-initialized linear projections of per-point sine/cosine phase for the two periods to context patches and future-query patches. Positions are relative to the forecast origin; periods below2 yield zero features, and missing context positions are masked. There are65536 new parameters, with no calendar dates or dataset identifiers.
+
+Initialize from the selected E47 confidence6000 package; freeze every existing parameter, including the confidence projection. Train only the two phase projections on packed_sample and the unchanged local synthetic/sign generator, seed107, paired-sign weighted quantile/median loss, batch64, LR2e-4, 6000-step cosine schedule, save1000. Confirm zero-initialization equivalence and maximum-shape memory before starting alongside the six existing trainers on the one H100. No new source data.
+
+E57 checks passed: the zero-initialized model is bitwise equal to the parent on mixed-frequency, partially missing CPU contexts. Maximum-shape paired batch128/context4096/horizon1024 used3647 MiB peak allocated and3920 MiB reserved, including EMA and optimizer-state placeholders, with finite gradients in1.20 seconds. No optimizer update or checkpoint came from these fixtures. Start batch64 as planned; train only65536 projection parameters.
+
+## E52 varied requested horizons3000, matched screen
+- Checkpoint SHA-256: `ef30f8bd58927ec0a18dc5c68a7b18c0d8066c0b3ff6dbe31b6fec82af26eb0c`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.744144678115845 seconds.
+- Overall skill 0.359510; relative MASE 0.763513; relative WQL 0.537290.
+- term skill: long=0.3912, medium=0.4026, short=0.3294.
+- domain skill: Econ/Fin=0.1112, Energy=0.1841, Healthcare=0.4177, Nature=0.4232, Sales=0.3464, Transport=0.4589, Web/CloudOps=0.5065.
+- frequency skill: A=0.3765, D=0.3429, H=0.3831, M=0.1035, Q=0.1995, S=0.4418, T=0.4003, W=0.2081.
+- variates skill: multivariate=0.3922, univariate=0.3323.
+- Outcome: Skill0.359510 versus E42 partial-target control0.358812. The gain is promising; matched earlier-cutoff evaluations are queued.
+
+## E48 matched3000 comparison: amplitude_synth, backcast
+- Checkpoint SHA-256: `6f3e68f1b70e9cde6872473dc4d834c16195e72656ba974eb39d48413a10af63`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 23.832332372665405 seconds.
+- Overall skill 0.322014; relative MASE 0.811414; relative WQL 0.566499.
+- term skill: long=0.3860, medium=0.3513, short=0.2839.
+- domain skill: Econ/Fin=0.1393, Energy=0.2195, Healthcare=0.3717, Nature=0.3475, Sales=0.3988, Transport=0.3598, Web/CloudOps=0.4348.
+- frequency skill: A=0.3171, D=0.3485, H=0.3526, M=0.1707, Q=0.2099, S=0.3414, T=0.3170, W=0.2493.
+- variates skill: multivariate=0.3360, univariate=0.3107.
+- Outcome: Phase diffusion gains at all three cutoffs at3000: main+0.000834, earlier1+0.000612, earlier2+0.000501. Continue to6000 and later matched checkpoints.
+
+## E48 matched3000 comparison: phase_synth, backcast
+- Checkpoint SHA-256: `06c74c2540e7fd208186a407ae6c38fcdeafe5e152887e1d7642d65c18084e64`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 25.44191288948059 seconds.
+- Overall skill 0.322626; relative MASE 0.811284; relative WQL 0.565567.
+- term skill: long=0.3871, medium=0.3526, short=0.2840.
+- domain skill: Econ/Fin=0.1401, Energy=0.2216, Healthcare=0.3676, Nature=0.3459, Sales=0.3983, Transport=0.3597, Web/CloudOps=0.4368.
+- frequency skill: A=0.3175, D=0.3449, H=0.3527, M=0.1680, Q=0.2099, S=0.3428, T=0.3207, W=0.2496.
+- variates skill: multivariate=0.3385, univariate=0.3097.
+- Outcome: Phase diffusion gains at all three cutoffs at3000: main+0.000834, earlier1+0.000612, earlier2+0.000501. Continue to6000 and later matched checkpoints.
+
+## E48 matched3000 comparison: amplitude_synth, backcast2
+- Checkpoint SHA-256: `6f3e68f1b70e9cde6872473dc4d834c16195e72656ba974eb39d48413a10af63`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 27.063983917236328 seconds.
+- Overall skill 0.346071; relative MASE 0.781652; relative WQL 0.547076.
+- term skill: long=0.3649, medium=0.3705, short=0.3294.
+- domain skill: Econ/Fin=0.0773, Energy=0.2205, Healthcare=0.3375, Nature=0.4551, Sales=0.5561, Transport=0.4102, Web/CloudOps=0.4240.
+- frequency skill: A=0.3378, D=0.4197, H=0.3812, M=0.1601, Q=0.1370, S=0.2856, T=0.3361, W=0.2413.
+- variates skill: multivariate=0.3528, univariate=0.3406.
+- Outcome: Phase diffusion gains at all three cutoffs at3000: main+0.000834, earlier1+0.000612, earlier2+0.000501. Continue to6000 and later matched checkpoints.
+
+## E48 matched3000 comparison: phase_synth, backcast2
+- Checkpoint SHA-256: `06c74c2540e7fd208186a407ae6c38fcdeafe5e152887e1d7642d65c18084e64`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.462491750717163 seconds.
+- Overall skill 0.346572; relative MASE 0.781891; relative WQL 0.546071.
+- term skill: long=0.3645, medium=0.3710, short=0.3303.
+- domain skill: Econ/Fin=0.0768, Energy=0.2203, Healthcare=0.3341, Nature=0.4562, Sales=0.5562, Transport=0.4104, Web/CloudOps=0.4261.
+- frequency skill: A=0.3370, D=0.4191, H=0.3813, M=0.1474, Q=0.1380, S=0.2827, T=0.3397, W=0.2426.
+- variates skill: multivariate=0.3544, univariate=0.3402.
+- Outcome: Phase diffusion gains at all three cutoffs at3000: main+0.000834, earlier1+0.000612, earlier2+0.000501. Continue to6000 and later matched checkpoints.
+
+## E42 final12000, matched mean-template screen
+- Checkpoint SHA-256: `a0ef29e85d0cf01677a6a58e4a9f91b077073c4af95f85234eec2ddc223f4837`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.56895399093628 seconds.
+- Overall skill 0.361108; relative MASE 0.761463; relative WQL 0.536050.
+- term skill: long=0.3906, medium=0.4043, short=0.3319.
+- domain skill: Econ/Fin=0.1096, Energy=0.1895, Healthcare=0.4193, Nature=0.4256, Sales=0.3404, Transport=0.4593, Web/CloudOps=0.5063.
+- frequency skill: A=0.3721, D=0.3420, H=0.3825, M=0.1108, Q=0.1995, S=0.4420, T=0.4045, W=0.2122.
+- variates skill: multivariate=0.3937, univariate=0.3339.
+- Outcome: The partial-target change still improves over its matched original-corpus control. The direct model remains below the published later E25/E47 lineage. Earlier-cutoff checks and the prespecified final-checkpoint weight-difference transfer are pending.
+
+## E42 final12000, selected fixed inference rules
+- Checkpoint SHA-256: `a0ef29e85d0cf01677a6a58e4a9f91b077073c4af95f85234eec2ddc223f4837`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.01974844932556 seconds.
+- Overall skill 0.374349; relative MASE 0.747467; relative WQL 0.523688.
+- term skill: long=0.3969, medium=0.4067, short=0.3525.
+- domain skill: Econ/Fin=0.1345, Energy=0.2359, Healthcare=0.4230, Nature=0.4302, Sales=0.3371, Transport=0.4647, Web/CloudOps=0.4986.
+- frequency skill: A=0.3753, D=0.3458, H=0.3943, M=0.1064, Q=0.1999, S=0.4233, T=0.4344, W=0.2143.
+- variates skill: multivariate=0.3921, univariate=0.3599.
+- Outcome: The partial-target change still improves over its matched original-corpus control. The direct model remains below the published later E25/E47 lineage. Earlier-cutoff checks and the prespecified final-checkpoint weight-difference transfer are pending.
+
+E42 completed12000 steps in 4034.2 elapsed training-loop seconds. E56 replaced it on the same H100.
+
+## E52 matched3000 comparison: partial_targets, backcast
+- Checkpoint SHA-256: `fce955a709ee54429c943e3962df49055444d5a15453865a01e4883614425917`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 25.31913948059082 seconds.
+- Overall skill 0.329377; relative MASE 0.806571; relative WQL 0.557590.
+- term skill: long=0.3896, medium=0.3560, short=0.2940.
+- domain skill: Econ/Fin=0.1487, Energy=0.2246, Healthcare=0.4210, Nature=0.3485, Sales=0.3987, Transport=0.3621, Web/CloudOps=0.4433.
+- frequency skill: A=0.3183, D=0.3586, H=0.3563, M=0.1681, Q=0.2258, S=0.3434, T=0.3271, W=0.2657.
+- variates skill: multivariate=0.3451, univariate=0.3166.
+- Outcome: Horizon jitter gains main+0.000697 and earlier2+0.000423, but loses earlier1 by0.000300. Continue to6000 to assess whether the effect persists; no package change.
+
+## E52 matched3000 comparison: horizon_jitter, backcast
+- Checkpoint SHA-256: `ef30f8bd58927ec0a18dc5c68a7b18c0d8066c0b3ff6dbe31b6fec82af26eb0c`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 27.87836766242981 seconds.
+- Overall skill 0.329077; relative MASE 0.807099; relative WQL 0.557723.
+- term skill: long=0.3896, medium=0.3557, short=0.2936.
+- domain skill: Econ/Fin=0.1451, Energy=0.2244, Healthcare=0.4181, Nature=0.3490, Sales=0.3989, Transport=0.3628, Web/CloudOps=0.4429.
+- frequency skill: A=0.3192, D=0.3567, H=0.3569, M=0.1660, Q=0.2272, S=0.3431, T=0.3263, W=0.2674.
+- variates skill: multivariate=0.3446, univariate=0.3165.
+- Outcome: Horizon jitter gains main+0.000697 and earlier2+0.000423, but loses earlier1 by0.000300. Continue to6000 to assess whether the effect persists; no package change.
+
+## E52 matched3000 comparison: partial_targets, backcast2
+- Checkpoint SHA-256: `fce955a709ee54429c943e3962df49055444d5a15453865a01e4883614425917`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 27.110735416412354 seconds.
+- Overall skill 0.352022; relative MASE 0.776423; relative WQL 0.540782.
+- term skill: long=0.3678, medium=0.3760, short=0.3367.
+- domain skill: Econ/Fin=0.0867, Energy=0.2248, Healthcare=0.3701, Nature=0.4601, Sales=0.5593, Transport=0.4130, Web/CloudOps=0.4281.
+- frequency skill: A=0.3418, D=0.4285, H=0.3860, M=0.1767, Q=0.1499, S=0.2821, T=0.3437, W=0.2407.
+- variates skill: multivariate=0.3578, univariate=0.3473.
+- Outcome: Horizon jitter gains main+0.000697 and earlier2+0.000423, but loses earlier1 by0.000300. Continue to6000 to assess whether the effect persists; no package change.
+
+## E52 matched3000 comparison: horizon_jitter, backcast2
+- Checkpoint SHA-256: `ef30f8bd58927ec0a18dc5c68a7b18c0d8066c0b3ff6dbe31b6fec82af26eb0c`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.243326425552368 seconds.
+- Overall skill 0.352445; relative MASE 0.776496; relative WQL 0.540026.
+- term skill: long=0.3678, medium=0.3759, short=0.3375.
+- domain skill: Econ/Fin=0.0824, Energy=0.2247, Healthcare=0.3724, Nature=0.4606, Sales=0.5600, Transport=0.4138, Web/CloudOps=0.4293.
+- frequency skill: A=0.3405, D=0.4280, H=0.3865, M=0.1811, Q=0.1490, S=0.2826, T=0.3439, W=0.2424.
+- variates skill: multivariate=0.3585, univariate=0.3475.
+- Outcome: Horizon jitter gains main+0.000697 and earlier2+0.000423, but loses earlier1 by0.000300. Continue to6000 to assess whether the effect persists; no package change.
+
+## E57 period-relative phase projection, initial screen
+- Checkpoint SHA-256: `275e6d68dda364b71999bf311a8ce7acf09ae4fb4642b8e07c404dd1bd9849ca`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 29.636561393737793 seconds.
+- Overall skill 0.375793; relative MASE 0.745649; relative WQL 0.522544.
+- term skill: long=0.3998, medium=0.4067, short=0.3540.
+- domain skill: Econ/Fin=0.1396, Energy=0.2348, Healthcare=0.4406, Nature=0.4284, Sales=0.3399, Transport=0.4658, Web/CloudOps=0.5006.
+- frequency skill: A=0.3762, D=0.3542, H=0.3951, M=0.0954, Q=0.1982, S=0.4239, T=0.4362, W=0.2090.
+- variates skill: multivariate=0.3937, univariate=0.3611.
+- Outcome: Exactly equal to the E47 parent score0.3757929301726527, validating the zero-initialized runtime before training.
+
+## E55 final12000 matched training difference, screen
+- Checkpoint SHA-256: `721d1029284ea6f36d4c6f169eb001382a97a2848953fad84e3e62fb5a222802`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.509355783462524 seconds.
+- Overall skill 0.375956; relative MASE 0.746201; relative WQL 0.521885.
+- term skill: long=0.3991, medium=0.4059, short=0.3549.
+- domain skill: Econ/Fin=0.1387, Energy=0.2371, Healthcare=0.4442, Nature=0.4287, Sales=0.3348, Transport=0.4657, Web/CloudOps=0.4989.
+- frequency skill: A=0.3739, D=0.3506, H=0.3961, M=0.1054, Q=0.1984, S=0.4235, T=0.4345, W=0.2191.
+- variates skill: multivariate=0.3928, univariate=0.3622.
+- Outcome: Skill0.375956 versus selected E47 score0.375793. This is a larger screen gain than the6000-step difference; evaluate both earlier cutoffs before selecting.
+
+## E57 period-relative phase projections1000, screen
+- Checkpoint SHA-256: `2deeff17c98c6993f9da1713c973fe0f803358a717c021e81438cdf203ae5f51`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 29.055758476257324 seconds.
+- Overall skill 0.375590; relative MASE 0.745958; relative WQL 0.522667.
+- term skill: long=0.3989, medium=0.4062, short=0.3542.
+- domain skill: Econ/Fin=0.1398, Energy=0.2354, Healthcare=0.4399, Nature=0.4278, Sales=0.3393, Transport=0.4661, Web/CloudOps=0.4996.
+- frequency skill: A=0.3762, D=0.3549, H=0.3950, M=0.0942, Q=0.1970, S=0.4219, T=0.4359, W=0.2092.
+- variates skill: multivariate=0.3933, univariate=0.3612.
+- Outcome: Skill0.375590 is slightly below the parent0.375793. Continue to3000/6000 to assess whether training recovers, as the previous confidence projection did late in its schedule.
+
+## E42 final12000 matched comparison: large_corpus_control, backcast
+- Checkpoint SHA-256: `7f9ba5e01ac59c51a3b63d3067b442e23e15d0abc51ddbcccdd04d6083ae4add`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 27.9762020111084 seconds.
+- Overall skill 0.330213; relative MASE 0.806120; relative WQL 0.556510.
+- term skill: long=0.3888, medium=0.3557, short=0.2960.
+- domain skill: Econ/Fin=0.1456, Energy=0.2279, Healthcare=0.4182, Nature=0.3493, Sales=0.3975, Transport=0.3647, Web/CloudOps=0.4422.
+- frequency skill: A=0.2987, D=0.3583, H=0.3572, M=0.1590, Q=0.2332, S=0.3409, T=0.3303, W=0.2690.
+- variates skill: multivariate=0.3460, univariate=0.3174.
+- Outcome: Final partial-target model gains main+0.000575 and earlier1+0.000861, but loses earlier2 by0.000358. Direct model remains below the published later lineage.
+
+## E42 final12000 matched comparison: partial_targets, backcast
+- Checkpoint SHA-256: `a0ef29e85d0cf01677a6a58e4a9f91b077073c4af95f85234eec2ddc223f4837`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 27.47197651863098 seconds.
+- Overall skill 0.331074; relative MASE 0.805060; relative WQL 0.555811.
+- term skill: long=0.3901, medium=0.3561, short=0.2969.
+- domain skill: Econ/Fin=0.1467, Energy=0.2289, Healthcare=0.4192, Nature=0.3505, Sales=0.3984, Transport=0.3646, Web/CloudOps=0.4431.
+- frequency skill: A=0.3115, D=0.3560, H=0.3581, M=0.1677, Q=0.2337, S=0.3423, T=0.3297, W=0.2763.
+- variates skill: multivariate=0.3468, univariate=0.3183.
+- Outcome: Final partial-target model gains main+0.000575 and earlier1+0.000861, but loses earlier2 by0.000358. Direct model remains below the published later lineage.
+
+## E55 final12000 transferred difference, backcast
+- Checkpoint SHA-256: `721d1029284ea6f36d4c6f169eb001382a97a2848953fad84e3e62fb5a222802`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.976391792297363 seconds.
+- Overall skill 0.335609; relative MASE 0.797954; relative WQL 0.553184.
+- term skill: long=0.3979, medium=0.3572, short=0.3014.
+- domain skill: Econ/Fin=0.1631, Energy=0.2288, Healthcare=0.4091, Nature=0.3616, Sales=0.3971, Transport=0.3715, Web/CloudOps=0.4491.
+- frequency skill: A=0.3254, D=0.3493, H=0.3647, M=0.1918, Q=0.2328, S=0.3430, T=0.3345, W=0.2857.
+- variates skill: multivariate=0.3525, univariate=0.3218.
+- Outcome: Transfer gains main+0.000163 and earlier1+0.001483, but loses earlier2 by0.001028. Average gain across the three cutoffs is positive; run exact full validation before deciding whether to publish this tradeoff.
+
+## E42 final12000 matched comparison: large_corpus_control, backcast2
+- Checkpoint SHA-256: `7f9ba5e01ac59c51a3b63d3067b442e23e15d0abc51ddbcccdd04d6083ae4add`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.495740175247192 seconds.
+- Overall skill 0.353454; relative MASE 0.776299; relative WQL 0.538480.
+- term skill: long=0.3687, medium=0.3751, short=0.3393.
+- domain skill: Econ/Fin=0.0914, Energy=0.2288, Healthcare=0.3664, Nature=0.4591, Sales=0.5548, Transport=0.4142, Web/CloudOps=0.4303.
+- frequency skill: A=0.3473, D=0.4293, H=0.3865, M=0.1743, Q=0.1540, S=0.2821, T=0.3466, W=0.2433.
+- variates skill: multivariate=0.3605, univariate=0.3477.
+- Outcome: Final partial-target model gains main+0.000575 and earlier1+0.000861, but loses earlier2 by0.000358. Direct model remains below the published later lineage.
+
+## E42 final12000 matched comparison: partial_targets, backcast2
+- Checkpoint SHA-256: `a0ef29e85d0cf01677a6a58e4a9f91b077073c4af95f85234eec2ddc223f4837`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 27.049270629882812 seconds.
+- Overall skill 0.353096; relative MASE 0.776739; relative WQL 0.538771.
+- term skill: long=0.3692, medium=0.3760, short=0.3381.
+- domain skill: Econ/Fin=0.0920, Energy=0.2294, Healthcare=0.3671, Nature=0.4574, Sales=0.5540, Transport=0.4141, Web/CloudOps=0.4294.
+- frequency skill: A=0.3519, D=0.4253, H=0.3874, M=0.1772, Q=0.1555, S=0.2805, T=0.3464, W=0.2443.
+- variates skill: multivariate=0.3599, univariate=0.3476.
+- Outcome: Final partial-target model gains main+0.000575 and earlier1+0.000861, but loses earlier2 by0.000358. Direct model remains below the published later lineage.
+
+## E55 final12000 transferred difference, backcast2
+- Checkpoint SHA-256: `721d1029284ea6f36d4c6f169eb001382a97a2848953fad84e3e62fb5a222802`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.92556405067444 seconds.
+- Overall skill 0.357472; relative MASE 0.772231; relative WQL 0.534610.
+- term skill: long=0.3642, medium=0.3858, short=0.3437.
+- domain skill: Econ/Fin=0.1127, Energy=0.2350, Healthcare=0.3474, Nature=0.4678, Sales=0.5489, Transport=0.4199, Web/CloudOps=0.4300.
+- frequency skill: A=0.3556, D=0.4262, H=0.3901, M=0.1532, Q=0.1527, S=0.2796, T=0.3567, W=0.2577.
+- variates skill: multivariate=0.3616, univariate=0.3541.
+- Outcome: Transfer gains main+0.000163 and earlier1+0.001483, but loses earlier2 by0.001028. Average gain across the three cutoffs is positive; run exact full validation before deciding whether to publish this tradeoff.
+
+E31 amplitude-varying synthetic run completed24000 steps in7471.1 elapsed training-loop seconds on the shared H100. Final checkpoint is being checked with the selected fixed inference rules at all three cutoffs.
+
+## E58: increase width from our own trained model (preparing)
+Repeated continuations of the36.8M model show diminishing gains. Hypothesis: more capacity can improve learning from the allowed corpus after a function-preserving initialization, given a sufficiently long matched schedule. Widen the selected E47 model from d512/heads8 to d1024/heads16, retaining enc6/dec4 and patch16. Duplicate hidden representations; duplicate input columns with weights divided by2; duplicate output rows. Preserve attention K/V grouping, repeat layernorm vectors, and repeat the original512-dimensional positional vector. Verify the unperturbed conversion on fixed synthetic fixtures. Then add independently seeded1%-of-tensor-standard-deviation noise to matrix weights to break duplicated-neuron symmetry, measuring the resulting prediction change. This is an initialization derived only from our own trained weights.
+
+A matched original-width control will use the same parent, data windows, seed113 and original sign-augmented loss. Proposed batch128, LR3e-5 and24000-step schedule are subject to a maximum-shape memory check after E31 releases its training slot. For widened matrices with duplicated input columns, use half the base learning rate and twice the weight decay to retain comparable initial functional update/decay scales; other parameters use the original rate. These settings and any necessary memory-driven batch adjustment will be recorded before training. No external weights or data.
+
+## E55 final12000 transferred update, exact full package validation
+- Checkpoint SHA-256: `721d1029284ea6f36d4c6f169eb001382a97a2848953fad84e3e62fb5a222802`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.372143; relative MASE 0.748865; relative WQL 0.526402.
+- term skill: long=0.3930, medium=0.4049, short=0.3509.
+- domain skill: Econ/Fin=0.1163, Energy=0.2302, Healthcare=0.4510, Nature=0.4286, Sales=0.3487, Transport=0.4659, Web/CloudOps=0.4912.
+- frequency skill: A=0.2931, D=0.3551, H=0.3913, M=0.1284, Q=0.1851, S=0.4235, T=0.4275, W=0.2120.
+- variates skill: multivariate=0.3885, univariate=0.3588.
+- Outcome: Select for publication after relocation and determinism checks. Full skill0.372143 improves on E47 score0.371766. All97 configurations and151170 instances passed, with no quantile crossings; actual inference123.68 seconds. Earlier temporal diagnostics retain a tradeoff: first earlier cutoff+0.001483, second earlier cutoff-0.001028. The mean gain across the main and two earlier screens is positive; this is validation selection, not a held-out reward claim.
+
+E58 conversion produced 146477344 parameters. Maximum normalized-quantile difference before noise was 0.000001669; after the fixed1% matrix noise it was 0.018048. Conversion took 2.38 CPU seconds. Parent hash and noise seed113 are recorded in wide_capacity_initial.pt.json.
+
+Maximum-shape memory checks, including optimizer moments and EMA placeholders, used13326 MiB reserved at batch128 and10708 MiB at batch96; both had finite gradients. Choose batch96 for both arms to preserve evaluation headroom. Increase the planned schedule from24000 to32000 steps so the planned number of windows remains3072000 per arm. Save/evaluate4000,8000,16000,24000,32000. Base LR3e-5, seed113, unchanged context/horizon mixture and packed_sample. Wider-matrix LR multiplier0.5 and decay multiplier2 remain as declared. Start the wide arm now that E31 has exited; queue the original-width control after E57 completes. Research and package inference use batch64 for both widths to keep memory comparable.
+
+E55 relocation/determinism checks passed: the candidate and a separate fresh /tmp copy, both launched from /tmp, produced bitwise identical finite monotone quantiles for daily, hourly and multivariate medium-horizon examples. Publish the complete model/report/manifest/provenance/log bundle atomically.
+
+## E59: equal-weight ensemble of complementary training variants
+The final E31 amplitude-varying model improves both earlier cutoffs over its recency24000 control, while its main fixed-rule screen is lower. The selected E55 transfer has the opposite second-earlier tradeoff. Hypothesis: an equal-weight average can reduce these differences. Reuse cached quantiles for E55 partial_delta12k and E31 amplitude24000, with both members forecasting every instance. Test only the fixed50/50 average, with and without applying the selected zero0.4 projection once more after averaging; averaging two zero-projected forecasts can otherwise introduce small positive values. No weight sweep or training. If promising, build and validate the actual two-member runtime before publication.
+
+## E31 final24000 with selected fixed rules, screen
+- Checkpoint SHA-256: `ecab9102fc11909555044c67dffda85280e06a375f1733351c7e9a39bf2ce9bb`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.934752464294434 seconds.
+- Overall skill 0.375166; relative MASE 0.746944; relative WQL 0.522686.
+- term skill: long=0.3998, medium=0.4070, short=0.3527.
+- domain skill: Econ/Fin=0.1414, Energy=0.2355, Healthcare=0.4087, Nature=0.4266, Sales=0.3412, Transport=0.4655, Web/CloudOps=0.5053.
+- frequency skill: A=0.3762, D=0.3445, H=0.3950, M=0.0900, Q=0.2023, S=0.4234, T=0.4389, W=0.2104.
+- variates skill: multivariate=0.3958, univariate=0.3582.
+- Outcome: Relative to E25 recency24000: main-0.000336, earlier1+0.000565, earlier2+0.000635. Do not replace the main package with this model alone. Test the prespecified equal-weight blend with the selected E55 model.
+
+## E31 final24000 with selected fixed rules, backcast
+- Checkpoint SHA-256: `ecab9102fc11909555044c67dffda85280e06a375f1733351c7e9a39bf2ce9bb`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 23.675416946411133 seconds.
+- Overall skill 0.334745; relative MASE 0.799254; relative WQL 0.553722.
+- term skill: long=0.3965, medium=0.3574, short=0.3003.
+- domain skill: Econ/Fin=0.1603, Energy=0.2277, Healthcare=0.4000, Nature=0.3619, Sales=0.3979, Transport=0.3709, Web/CloudOps=0.4497.
+- frequency skill: A=0.3115, D=0.3485, H=0.3643, M=0.1990, Q=0.2289, S=0.3403, T=0.3365, W=0.2702.
+- variates skill: multivariate=0.3522, univariate=0.3205.
+- Outcome: Relative to E25 recency24000: main-0.000336, earlier1+0.000565, earlier2+0.000635. Do not replace the main package with this model alone. Test the prespecified equal-weight blend with the selected E55 model.
+
+## E31 final24000 with selected fixed rules, backcast2
+- Checkpoint SHA-256: `ecab9102fc11909555044c67dffda85280e06a375f1733351c7e9a39bf2ce9bb`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 24.473368406295776 seconds.
+- Overall skill 0.358910; relative MASE 0.771616; relative WQL 0.532644.
+- term skill: long=0.3672, medium=0.3833, short=0.3462.
+- domain skill: Econ/Fin=0.1126, Energy=0.2349, Healthcare=0.3493, Nature=0.4705, Sales=0.5536, Transport=0.4202, Web/CloudOps=0.4327.
+- frequency skill: A=0.3556, D=0.4300, H=0.3901, M=0.1370, Q=0.1497, S=0.2870, T=0.3573, W=0.2669.
+- variates skill: multivariate=0.3623, univariate=0.3562.
+- Outcome: Relative to E25 recency24000: main-0.000336, earlier1+0.000565, earlier2+0.000635. Do not replace the main package with this model alone. Test the prespecified equal-weight blend with the selected E55 model.
+
+## E59 cached equal-weight comparison
+No training; fixed inference seed0 and ensemble weight0.5 for each member. Member weights: selected E55 partial_delta12k SHA-256 721d1029284ea6f36d4c6f169eb001382a97a2848953fad84e3e62fb5a222802; amplitude24000 hash is recorded in its matched reports.
+
+screen: 2.65 CPU diagnostic seconds.
+- average: skill 0.376116; term: short=0.3545, medium=0.4069, long=0.3997; domain: Econ/Fin=0.1408, Energy=0.2367, Healthcare=0.4304, Nature=0.4279, Sales=0.3385, Transport=0.4658, Web/CloudOps=0.5025; freq: A-DEC=0.3755, Q-DEC=0.2008, M=0.0994, W-SUN=0.0865, D=0.3491, H=0.3958, 15T=0.2143, W-FRI=-0.0164, 10T=0.6125, W-TUE=0.5565, W-THU=0.2049, W-WED=0.3155, 5T=0.5141, 10S=0.4235.
+- average_then_zero: skill 0.376000; term: short=0.3545, medium=0.4068, long=0.3994; domain: Econ/Fin=0.1408, Energy=0.2367, Healthcare=0.4304, Nature=0.4277, Sales=0.3386, Transport=0.4658, Web/CloudOps=0.5021; freq: A-DEC=0.3755, Q-DEC=0.2008, M=0.0999, W-SUN=0.0865, D=0.3489, H=0.3957, 15T=0.2143, W-FRI=-0.0164, 10T=0.6124, W-TUE=0.5565, W-THU=0.2049, W-WED=0.3154, 5T=0.5138, 10S=0.4235.
+
+backcast: 2.59 CPU diagnostic seconds.
+- average: skill 0.335702; term: short=0.3015, medium=0.3577, long=0.3976; domain: Econ/Fin=0.1625, Energy=0.2286, Healthcare=0.4066, Nature=0.3622, Sales=0.3978, Transport=0.3714, Web/CloudOps=0.4500; freq: A-DEC=0.3193, Q-DEC=0.2313, M=0.1979, W-SUN=0.0360, D=0.3493, H=0.3648, 15T=0.2260, W-FRI=0.2606, 10T=0.3193, W-TUE=0.5296, W-THU=0.2330, W-WED=0.3440, 5T=0.4376, 10S=0.3419.
+- average_then_zero: skill 0.335612; term: short=0.3014, medium=0.3575, long=0.3976; domain: Econ/Fin=0.1625, Energy=0.2286, Healthcare=0.4066, Nature=0.3619, Sales=0.3980, Transport=0.3714, Web/CloudOps=0.4498; freq: A-DEC=0.3193, Q-DEC=0.2313, M=0.1983, W-SUN=0.0360, D=0.3491, H=0.3648, 15T=0.2260, W-FRI=0.2606, 10T=0.3188, W-TUE=0.5296, W-THU=0.2330, W-WED=0.3440, 5T=0.4372, 10S=0.3419.
+
+backcast2: 2.43 CPU diagnostic seconds.
+- average: skill 0.358667; term: short=0.3455, medium=0.3850, long=0.3661; domain: Econ/Fin=0.1134, Energy=0.2353, Healthcare=0.3493, Nature=0.4696, Sales=0.5519, Transport=0.4203, Web/CloudOps=0.4320; freq: A-DEC=0.3562, Q-DEC=0.1515, M=0.1452, W-SUN=0.0558, D=0.4288, H=0.3904, 15T=0.1044, W-FRI=0.1452, 10T=0.4937, W-TUE=0.1018, W-THU=0.3276, W-WED=0.5366, 5T=0.4664, 10S=0.2839.
+- average_then_zero: skill 0.358497; term: short=0.3453, medium=0.3849, long=0.3659; domain: Econ/Fin=0.1134, Energy=0.2352, Healthcare=0.3493, Nature=0.4693, Sales=0.5513, Transport=0.4203, Web/CloudOps=0.4318; freq: A-DEC=0.3562, Q-DEC=0.1515, M=0.1452, W-SUN=0.0558, D=0.4286, H=0.3904, 15T=0.1044, W-FRI=0.1452, 10T=0.4927, W-TUE=0.1018, W-THU=0.3276, W-WED=0.5364, 5T=0.4661, 10S=0.2839.
+
+Outcome: plain averaging wins all three cutoffs over the selected single model. Reapplying the zero projection after averaging is worse at every cutoff. Build the actual two-member package and verify cached/runtime agreement, full validation and relocation before publication.
+
+E58 wide_capacity_control training started at 2026-10-22T05:35:38Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E59 actual two-member runtime produced bitwise identical quantiles to the cached plain50/50 average for every point in all97 main-screen configurations. Its screening skill is0.37611643951533646, matching the probe exactly. Proceed to actual full-package inference and scoring.
+
+## E57 period-relative phase projections3000, screen
+- Checkpoint SHA-256: `740fed81313fbb2dc2060173ae11ed120d6cca9600620cd1c9d9331ccac699e5`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.340542793273926 seconds.
+- Overall skill 0.375400; relative MASE 0.745694; relative WQL 0.523171.
+- term skill: long=0.3995, medium=0.4066, short=0.3534.
+- domain skill: Econ/Fin=0.1392, Energy=0.2355, Healthcare=0.4394, Nature=0.4265, Sales=0.3394, Transport=0.4660, Web/CloudOps=0.4999.
+- frequency skill: A=0.3762, D=0.3544, H=0.3946, M=0.0943, Q=0.1976, S=0.4227, T=0.4359, W=0.2085.
+- variates skill: multivariate=0.3928, univariate=0.3611.
+- Outcome: The6000-step score0.375840 is only0.000047 above its E47 parent, after earlier regressions. Existing model tensors remain bitwise unchanged. Check earlier cutoffs before selecting any use of these projections.
+
+## E57 period-relative phase projections6000, screen
+- Checkpoint SHA-256: `956fb0c541a5ae81aaccd7b059143720c5b55d0c030df0162d2eae0736701832`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 29.05087399482727 seconds.
+- Overall skill 0.375840; relative MASE 0.745321; relative WQL 0.522695.
+- term skill: long=0.3997, medium=0.4065, short=0.3542.
+- domain skill: Econ/Fin=0.1396, Energy=0.2356, Healthcare=0.4472, Nature=0.4272, Sales=0.3377, Transport=0.4655, Web/CloudOps=0.4998.
+- frequency skill: A=0.3762, D=0.3562, H=0.3947, M=0.0979, Q=0.1989, S=0.4232, T=0.4357, W=0.2089.
+- variates skill: multivariate=0.3935, univariate=0.3614.
+- Outcome: The6000-step score0.375840 is only0.000047 above its E47 parent, after earlier regressions. Existing model tensors remain bitwise unchanged. Check earlier cutoffs before selecting any use of these projections.
+
+E57 completed6000 steps in1147.4 elapsed training-loop seconds. Every pre-existing tensor is bitwise equal to the E47 parent; only the65536 phase-projection parameters changed. E58 original-width control started after this process exited.
+
+## E58 widened initialization, main screen at batch64
+- Checkpoint SHA-256: `495eed9e1a09379b3cbcdadbee447d2c519a2cff8239c0756cf94f49f8a1b55e`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 43.54188299179077 seconds.
+- Overall skill 0.375710; relative MASE 0.745704; relative WQL 0.522645.
+- term skill: long=0.3998, medium=0.4067, short=0.3538.
+- domain skill: Econ/Fin=0.1394, Energy=0.2348, Healthcare=0.4397, Nature=0.4283, Sales=0.3400, Transport=0.4658, Web/CloudOps=0.5006.
+- frequency skill: A=0.3762, D=0.3540, H=0.3951, M=0.0946, Q=0.1984, S=0.4240, T=0.4362, W=0.2087.
+- variates skill: multivariate=0.3937, univariate=0.3610.
+- Outcome: Initial score0.375710 remains close to the original model before any larger-model training. The matched original-width initialization at the same inference batch size is being checked.
+
+## E56 log window loss3000, matched screen
+- Checkpoint SHA-256: `14dd06fb6dff3c336f5aa26b8525d7ccb921a80b3e7be4d22b285584c14ff3bd`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.476420640945435 seconds.
+- Overall skill 0.357135; relative MASE 0.765561; relative WQL 0.539835.
+- term skill: long=0.3893, medium=0.3998, short=0.3270.
+- domain skill: Econ/Fin=0.1142, Energy=0.1808, Healthcare=0.3930, Nature=0.4235, Sales=0.3538, Transport=0.4615, Web/CloudOps=0.5024.
+- frequency skill: A=0.3804, D=0.3293, H=0.3825, M=0.1125, Q=0.2032, S=0.4413, T=0.3984, W=0.2094.
+- variates skill: multivariate=0.3893, univariate=0.3303.
+- Outcome: Score0.357135 regresses versus original-loss control0.358802. Continue to6000 before deciding whether to stop this objective.
+
+## E48 phase diffusion6000, matched screen
+- Checkpoint SHA-256: `8d6371333e936bbfc2aa746819bfd0cedd392f873ce79c1d6f729b205c3f0b87`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.55935049057007 seconds.
+- Overall skill 0.356225; relative MASE 0.765189; relative WQL 0.541626.
+- term skill: long=0.3862, medium=0.3999, short=0.3266.
+- domain skill: Econ/Fin=0.1072, Energy=0.1811, Healthcare=0.4144, Nature=0.4178, Sales=0.3484, Transport=0.4543, Web/CloudOps=0.5048.
+- frequency skill: A=0.3702, D=0.3423, H=0.3775, M=0.0903, Q=0.1971, S=0.4361, T=0.4007, W=0.2000.
+- variates skill: multivariate=0.3887, univariate=0.3292.
+- Outcome: Score0.356225 versus amplitude-only0.355543: the main gain persists at6000. Earlier cutoffs are being evaluated.
+
+## E52 varied requested horizons6000, matched screen
+- Checkpoint SHA-256: `1276edf6f471ab96e61e61ead3cb813a58a4cadfac530d2c190072d9b211c182`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.603736400604248 seconds.
+- Overall skill 0.359683; relative MASE 0.762743; relative WQL 0.537542.
+- term skill: long=0.3900, medium=0.4024, short=0.3303.
+- domain skill: Econ/Fin=0.1129, Energy=0.1876, Healthcare=0.4160, Nature=0.4253, Sales=0.3425, Transport=0.4576, Web/CloudOps=0.5040.
+- frequency skill: A=0.3774, D=0.3429, H=0.3812, M=0.1107, Q=0.1984, S=0.4413, T=0.4021, W=0.2071.
+- variates skill: multivariate=0.3915, univariate=0.3332.
+- Outcome: Score0.359683 versus E42 partial-target control0.359222: a smaller main gain persists. Check both earlier cutoffs.
+
+## E54 original-context control6000, screen
+- Checkpoint SHA-256: `14d964cb2b828f8e81c8b656b33da583b2f5fb48a3f003d043ba7c0b88cd3137`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.69702458381653 seconds.
+- Overall skill 0.374515; relative MASE 0.746663; relative WQL 0.523973.
+- term skill: long=0.3995, medium=0.4071, short=0.3516.
+- domain skill: Econ/Fin=0.1381, Energy=0.2362, Healthcare=0.4110, Nature=0.4291, Sales=0.3380, Transport=0.4658, Web/CloudOps=0.5007.
+- frequency skill: A=0.3757, D=0.3496, H=0.3960, M=0.0674, Q=0.2005, S=0.4225, T=0.4371, W=0.2070.
+- variates skill: multivariate=0.3945, univariate=0.3581.
+- Outcome: Fresh continuation is below the E25 parent. Keep as the matched source-window/seed reference for the8192-context arm.
+
+## E48 matched6000 comparison: amplitude_synth, backcast
+- Checkpoint SHA-256: `7fe2746b237fb2699c2d8c812595666df0888cde656498043e01847bb552c783`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 21.44511580467224 seconds.
+- Overall skill 0.324002; relative MASE 0.810229; relative WQL 0.564005.
+- term skill: long=0.3880, medium=0.3521, short=0.2864.
+- domain skill: Econ/Fin=0.1415, Energy=0.2210, Healthcare=0.3855, Nature=0.3485, Sales=0.3980, Transport=0.3607, Web/CloudOps=0.4366.
+- frequency skill: A=0.3171, D=0.3509, H=0.3541, M=0.1703, Q=0.2160, S=0.3413, T=0.3204, W=0.2494.
+- variates skill: multivariate=0.3381, univariate=0.3126.
+- Outcome: Phase diffusion retains a main gain but loses about0.0011 at both earlier cutoffs. The earlier3000-step gains did not persist. Stop this branch; it is not selected.
+
+## E48 matched6000 comparison: phase_synth, backcast
+- Checkpoint SHA-256: `8d6371333e936bbfc2aa746819bfd0cedd392f873ce79c1d6f729b205c3f0b87`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.959611415863037 seconds.
+- Overall skill 0.322903; relative MASE 0.811500; relative WQL 0.564954.
+- term skill: long=0.3870, medium=0.3532, short=0.2843.
+- domain skill: Econ/Fin=0.1406, Energy=0.2220, Healthcare=0.3697, Nature=0.3497, Sales=0.3989, Transport=0.3583, Web/CloudOps=0.4353.
+- frequency skill: A=0.3141, D=0.3444, H=0.3533, M=0.1676, Q=0.2139, S=0.3417, T=0.3204, W=0.2546.
+- variates skill: multivariate=0.3382, univariate=0.3105.
+- Outcome: Phase diffusion retains a main gain but loses about0.0011 at both earlier cutoffs. The earlier3000-step gains did not persist. Stop this branch; it is not selected.
+
+## E48 matched6000 comparison: amplitude_synth, backcast2
+- Checkpoint SHA-256: `7fe2746b237fb2699c2d8c812595666df0888cde656498043e01847bb552c783`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 20.365211486816406 seconds.
+- Overall skill 0.349572; relative MASE 0.778615; relative WQL 0.543345.
+- term skill: long=0.3660, medium=0.3724, short=0.3345.
+- domain skill: Econ/Fin=0.0821, Energy=0.2234, Healthcare=0.3611, Nature=0.4581, Sales=0.5590, Transport=0.4121, Web/CloudOps=0.4247.
+- frequency skill: A=0.3403, D=0.4277, H=0.3841, M=0.1545, Q=0.1409, S=0.2820, T=0.3394, W=0.2470.
+- variates skill: multivariate=0.3548, univariate=0.3453.
+- Outcome: Phase diffusion retains a main gain but loses about0.0011 at both earlier cutoffs. The earlier3000-step gains did not persist. Stop this branch; it is not selected.
+
+## E48 matched6000 comparison: phase_synth, backcast2
+- Checkpoint SHA-256: `8d6371333e936bbfc2aa746819bfd0cedd392f873ce79c1d6f729b205c3f0b87`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.906906366348267 seconds.
+- Overall skill 0.348439; relative MASE 0.780846; relative WQL 0.543682.
+- term skill: long=0.3636, medium=0.3707, short=0.3341.
+- domain skill: Econ/Fin=0.0817, Energy=0.2219, Healthcare=0.3552, Nature=0.4580, Sales=0.5591, Transport=0.4110, Web/CloudOps=0.4239.
+- frequency skill: A=0.3402, D=0.4247, H=0.3827, M=0.1524, Q=0.1420, S=0.2780, T=0.3407, W=0.2441.
+- variates skill: multivariate=0.3541, univariate=0.3439.
+- Outcome: Phase diffusion retains a main gain but loses about0.0011 at both earlier cutoffs. The earlier3000-step gains did not persist. Stop this branch; it is not selected.
+
+## E59 equal-weight two-member ensemble, exact full package validation
+- Checkpoint SHA-256: `a5f575ec44defbc0ca0c4aeb29618bbcc0e09f726985851eee4027ea698cfadd`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.372254; relative MASE 0.748754; relative WQL 0.526294.
+- term skill: long=0.3937, medium=0.4059, short=0.3504.
+- domain skill: Econ/Fin=0.1179, Energy=0.2297, Healthcare=0.4376, Nature=0.4278, Sales=0.3512, Transport=0.4661, Web/CloudOps=0.4950.
+- frequency skill: A=0.2917, D=0.3537, H=0.3910, M=0.1216, Q=0.1860, S=0.4235, T=0.4302, W=0.2080.
+- variates skill: multivariate=0.3905, univariate=0.3573.
+- Outcome: Select after relocation/determinism checks: full skill0.372254 versus single-model0.372143; relative MASE0.748754 and WQL0.526294. All97 configurations and151170 instances passed with no quantile crossings. Actual package inference234.68 seconds while training shared the one H100. Main screening and both earlier cutoffs improve; every member forecasts every instance with fixed equal weights.
+
+E48 stopped cleanly at step7631, after 2736.0 elapsed process seconds at the stop request. Final checkpoint SHA-256 `702c419a404c2aad31011fb824ace504576f5e74244fc4228229fdfb2d970a9a`. The stop checkpoint is retained for provenance and is not selected.
+
+E59 relocation/determinism passed: the two-member candidate and a separate /tmp copy, both launched from /tmp, produced bitwise identical finite monotone forecasts on daily, hourly and multivariate medium-horizon examples. The complete two-member package is ready for atomic publication.
+
+## E57 phase projections final6000, backcast
+- Checkpoint SHA-256: `956fb0c541a5ae81aaccd7b059143720c5b55d0c030df0162d2eae0736701832`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 25.856826066970825 seconds.
+- Overall skill 0.333418; relative MASE 0.800235; relative WQL 0.555251.
+- term skill: long=0.3968, medium=0.3558, short=0.2984.
+- domain skill: Econ/Fin=0.1603, Energy=0.2271, Healthcare=0.4058, Nature=0.3594, Sales=0.3943, Transport=0.3716, Web/CloudOps=0.4455.
+- frequency skill: A=0.3079, D=0.3513, H=0.3638, M=0.1776, Q=0.2354, S=0.3427, T=0.3327, W=0.2745.
+- variates skill: multivariate=0.3498, univariate=0.3201.
+- Outcome: Reject this feature addition: main gain0.000047 is offset by earlier1 loss0.000708 and earlier2 loss0.000030 versus the frozen E47 parent. No package change from this branch.
+
+## E52 varied requested horizons6000, backcast
+- Checkpoint SHA-256: `1276edf6f471ab96e61e61ead3cb813a58a4cadfac530d2c190072d9b211c182`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.876362562179565 seconds.
+- Overall skill 0.330107; relative MASE 0.806559; relative WQL 0.556384.
+- term skill: long=0.3900, medium=0.3554, short=0.2954.
+- domain skill: Econ/Fin=0.1459, Energy=0.2294, Healthcare=0.4132, Nature=0.3489, Sales=0.3970, Transport=0.3634, Web/CloudOps=0.4423.
+- frequency skill: A=0.3187, D=0.3539, H=0.3583, M=0.1613, Q=0.2281, S=0.3442, T=0.3292, W=0.2708.
+- variates skill: multivariate=0.3469, univariate=0.3164.
+- Outcome: Relative to E42 partial-target control6000: main+0.000461, earlier1-0.000193, earlier2+0.000278. Continue to the final12000 checkpoint; a small positive average effect persists.
+
+## E57 phase projections final6000, backcast2
+- Checkpoint SHA-256: `956fb0c541a5ae81aaccd7b059143720c5b55d0c030df0162d2eae0736701832`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.712015867233276 seconds.
+- Overall skill 0.358470; relative MASE 0.770691; relative WQL 0.534015.
+- term skill: long=0.3646, medium=0.3858, short=0.3453.
+- domain skill: Econ/Fin=0.1102, Energy=0.2363, Healthcare=0.3561, Nature=0.4700, Sales=0.5508, Transport=0.4205, Web/CloudOps=0.4287.
+- frequency skill: A=0.3495, D=0.4331, H=0.3891, M=0.1593, Q=0.1505, S=0.2824, T=0.3568, W=0.2548.
+- variates skill: multivariate=0.3620, univariate=0.3556.
+- Outcome: Reject this feature addition: main gain0.000047 is offset by earlier1 loss0.000708 and earlier2 loss0.000030 versus the frozen E47 parent. No package change from this branch.
+
+## E52 varied requested horizons6000, backcast2
+- Checkpoint SHA-256: `1276edf6f471ab96e61e61ead3cb813a58a4cadfac530d2c190072d9b211c182`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 24.18545150756836 seconds.
+- Overall skill 0.353179; relative MASE 0.775830; relative WQL 0.539265.
+- term skill: long=0.3689, medium=0.3764, short=0.3382.
+- domain skill: Econ/Fin=0.0847, Energy=0.2293, Healthcare=0.3759, Nature=0.4588, Sales=0.5595, Transport=0.4134, Web/CloudOps=0.4274.
+- frequency skill: A=0.3432, D=0.4263, H=0.3869, M=0.1771, Q=0.1489, S=0.2829, T=0.3454, W=0.2495.
+- variates skill: multivariate=0.3591, univariate=0.3484.
+- Outcome: Relative to E42 partial-target control6000: main+0.000461, earlier1-0.000193, earlier2+0.000278. Continue to the final12000 checkpoint; a small positive average effect persists.
+
+## E60 filtered shared drivers for a frozen cross-channel adapter
+Hypothesis: earlier joint adapters saw lagged shared latent drivers but no channel-specific response filtering. A synthetic fast driver and a smoothed response may better represent thermal and energy relationships. Compare original joint generation against causal exponential filtering of half the channels in half the synthetic groups. Filtering time constant is log-uniform from1 to316 steps, applied before observation noise with256 warmup points and stationary initial level. Real windows, latent drivers, coefficients, noise, signs and scales use matched main RNG draws.
+- Parent: own E55 partial_delta12k checkpoint, SHA-256721d1029284ea6f36d4c6f169eb001382a97a2848953fad84e3e62fb5a222802. All original weights frozen. Add the previously tested zero-output cross-channel attention adapter (1051136 parameters). Every instance is forecast; one-channel inputs have the identity adapter.
+- Data: the26 allow-listed sources in packed_joint plus the original locally generated multivariate latent-driver generator. Synthetic fraction0.30. Candidate adds the declared filtered-response generator. No validation input or labels enter training.
+- Matched seed137; independent filter seed400140. Groups16, channels2/3/4/8 with probabilities0.4/0.2/0.2/0.2; contexts512/1024/2048/4096 and horizons32/128/512/1024 follow the original joint sampler. Paired-sign loss in both arms, AdamW LR0.0002,6000 steps, save1000/3000/6000 for diagnostics. One shared H100; training elapsed time will be recorded separately for each arm.
+- Selection: compare all97 configurations and both earlier cutoffs against the frozen parent and matched control. First verify sampler matching, identity initialization and maximum-shape memory without optimizer updates. No outcome yet.
+
+## E56 logarithmic window loss3000
+- Checkpoint SHA-256: `14dd06fb6dff3c336f5aa26b8525d7ccb921a80b3e7be4d22b285584c14ff3bd`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.476420640945435 seconds.
+- Overall skill 0.357135; relative MASE 0.765561; relative WQL 0.539835.
+- term skill: long=0.3893, medium=0.3998, short=0.3270.
+- domain skill: Econ/Fin=0.1142, Energy=0.1808, Healthcare=0.3930, Nature=0.4235, Sales=0.3538, Transport=0.4615, Web/CloudOps=0.5024.
+- frequency skill: A=0.3804, D=0.3293, H=0.3825, M=0.1125, Q=0.2032, S=0.4413, T=0.3984, W=0.2094.
+- variates skill: multivariate=0.3893, univariate=0.3303.
+- Outcome: Below matched control; the loss transformation is not selected.
+
+## E56 logarithmic window loss6000
+- Checkpoint SHA-256: `3346c86c6ce73e739fdd2a286b7cd4c1dab7430f6ee1ea8056b8db4943c48d66`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 21.688469886779785 seconds.
+- Overall skill 0.357689; relative MASE 0.764496; relative WQL 0.539655.
+- term skill: long=0.3904, medium=0.4011, short=0.3270.
+- domain skill: Econ/Fin=0.1167, Energy=0.1822, Healthcare=0.3871, Nature=0.4260, Sales=0.3525, Transport=0.4624, Web/CloudOps=0.5018.
+- frequency skill: A=0.3766, D=0.3301, H=0.3834, M=0.1073, Q=0.2061, S=0.4406, T=0.3995, W=0.2098.
+- variates skill: multivariate=0.3901, univariate=0.3306.
+- Outcome: Skill0.357689 versus matched control0.358628; second regression. Stop and retain existing linear window loss.
+
+## E54 recent4096 matched control final12000
+- Checkpoint SHA-256: `372edd6a03da9f577992a9cf6363b26efe7b11b35fc3aed2d50868566efd0c71`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.02079463005066 seconds.
+- Overall skill 0.375764; relative MASE 0.744880; relative WQL 0.523131.
+- term skill: long=0.4000, medium=0.4083, short=0.3532.
+- domain skill: Econ/Fin=0.1410, Energy=0.2368, Healthcare=0.4240, Nature=0.4300, Sales=0.3368, Transport=0.4653, Web/CloudOps=0.5016.
+- frequency skill: A=0.3753, D=0.3535, H=0.3960, M=0.0712, Q=0.2025, S=0.4230, T=0.4374, W=0.2130.
+- variates skill: multivariate=0.3961, univariate=0.3591.
+- Outcome: Finished12000 steps in2491.2 training-loop seconds. Small gain over frozen parent; await the8192 arm before interpretation.
+
+## E58 widened146M first4000
+- Checkpoint SHA-256: `a8e3dbf8f591804fb3ac6c72ac632dd9e18038e4da7f195208e56f85f41ecd29`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 34.12893605232239 seconds.
+- Overall skill 0.374121; relative MASE 0.747837; relative WQL 0.523810.
+- term skill: long=0.3989, medium=0.4070, short=0.3512.
+- domain skill: Econ/Fin=0.1400, Energy=0.2366, Healthcare=0.4144, Nature=0.4278, Sales=0.3414, Transport=0.4641, Web/CloudOps=0.4992.
+- frequency skill: A=0.3730, D=0.3465, H=0.3947, M=0.0955, Q=0.1987, S=0.4198, T=0.4367, W=0.2046.
+- variates skill: multivariate=0.3940, univariate=0.3578.
+- Outcome: Ahead of matched37M control by0.000510, but below initial checkpoint. Continue the prespecified longer capacity comparison.
+
+## E58 matched37M first4000
+- Checkpoint SHA-256: `65969ac2e314e6103c326ab0fe1c550d53a285833d9f983e1c1cdff0d7fb66b3`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.00419569015503 seconds.
+- Overall skill 0.373612; relative MASE 0.748644; relative WQL 0.524097.
+- term skill: long=0.3990, medium=0.4070, short=0.3502.
+- domain skill: Econ/Fin=0.1392, Energy=0.2363, Healthcare=0.4121, Nature=0.4272, Sales=0.3410, Transport=0.4638, Web/CloudOps=0.4989.
+- frequency skill: A=0.3725, D=0.3451, H=0.3948, M=0.0947, Q=0.1985, S=0.4190, T=0.4363, W=0.2034.
+- variates skill: multivariate=0.3937, univariate=0.3572.
+- Outcome: Below starting checkpoint; matched capacity comparison continues.
+
+E56 stop receipt: {"stopped_utc": "2026-10-22T05:53:09.078048+00:00", "reason": "Main screen below matched E38 control at both3000 and6000; prune negative branch.", "final_checkpoint": "/workspace/research/runs/log_window_loss/ema006767.pt", "final_checkpoint_sha256": "ec25440f0d11ac776874679e366255ce9cf54138f2e6802efba22bc88157d06c", "final_step": 6767, "elapsed_training_process_seconds": null}
+
+E56 measured training-process elapsed time was2298.259 seconds (the previous receipt field was null because the reader used the wrong key; corrected in log_window_loss_stop_receipt.json).
+
+E60 checks passed: {
+  "cpu_check_seconds": 1.9617000540019944,
+  "changed_synthetic_context_values": 21054,
+  "identity_matches_parent_bitwise": true,
+  "main_and_filter_rng_matched": true,
+  "real_windows_unchanged": true,
+  "optimizer_updates": 0,
+  "gpu_fixture_seconds": 1.0786702629411593,
+  "allocated_mib": 2625.63525390625,
+  "reserved_mib": 3872.0,
+  "parameters": 37851936,
+  "trainable_parameters": 1051136,
+  "loss": 0.3574138879776001
+}
+Both arms now start on the shared H100. No optimizer updates occurred during fixtures.
+
+## E61 log-uniform autoregressive memory in synthetic windows
+Hypothesis: the original AR coefficient uniform0.2..0.995 overwhelmingly supplies rapidly decorrelating noise. Energy and short-horizon electricity remain weak across cutoffs; slow stochastic level variation may be missing. Change only the AR coefficient draw to phi=exp(-1/tau), log10(tau) uniform0..4. Keep the existing zero initial state and sqrt(1-phi^2) innovation normalization. All Fourier seasons, trends, changepoints, intermittency, observation-noise draws, synthetic fraction and real-window draws remain matched.
+- Matched reference is the already completed E42 partial_targets run; do not retrain it. Same own E25 recency12000 initialization, seed79, packed_sample95-source corpus, B256, AdamW LR0.00005,12000 steps, saves3000/6000/12000. The E42 handling of partially observed targets is unchanged.
+- Source declaration if selected: original local generator plus the modified log-uniform AR-time-constant generator, seed79. No new external data or pretrained assets.
+- Scheduled after E52 releases its H100 allocation. Validate main screening and both earlier cutoffs against E42 at matched steps, then consider a matched parameter-difference transfer only if the learned effect is useful. No outcome yet.
+
+E61 sampler checks passed: {"cpu_seconds": 4.961035819957033, "main_rng_matched": true, "real_windows_unchanged": true, "periods_and_missing_masks_unchanged": true, "changed_synthetic_context_values": 31554, "optimizer_updates": 0}
+
+## E60 joint_filtered_control_ema0
+- Checkpoint SHA-256: `3a7e46a74247ccf366c4e2b950fe1e1cc3625c52f7db1f6fdebab261d1d66ae8`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.121415376663208 seconds.
+- Overall skill 0.375956; relative MASE 0.746201; relative WQL 0.521885.
+- term skill: long=0.3991, medium=0.4059, short=0.3549.
+- domain skill: Econ/Fin=0.1387, Energy=0.2371, Healthcare=0.4442, Nature=0.4287, Sales=0.3348, Transport=0.4657, Web/CloudOps=0.4989.
+- frequency skill: A=0.3739, D=0.3506, H=0.3961, M=0.1054, Q=0.1984, S=0.4235, T=0.4345, W=0.2191.
+- variates skill: multivariate=0.3928, univariate=0.3622.
+- Outcome: Initial identity is exact. At1000 both adapters show a small main-screen improvement; filtered versus control difference0.000043 is inconclusive. Continue to matched3000/6000 and earlier-cutoff diagnostics.
+
+## E60 joint_filtered_control_ema1000
+- Checkpoint SHA-256: `8f3493cddbf6579a69ed813b056f8a16110f6309d7589016703b68022bef205c`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.04537320137024 seconds.
+- Overall skill 0.376238; relative MASE 0.745929; relative WQL 0.521603.
+- term skill: long=0.4000, medium=0.4050, short=0.3554.
+- domain skill: Econ/Fin=0.1387, Energy=0.2375, Healthcare=0.4442, Nature=0.4301, Sales=0.3348, Transport=0.4657, Web/CloudOps=0.4986.
+- frequency skill: A=0.3739, D=0.3521, H=0.3962, M=0.1054, Q=0.1984, S=0.4213, T=0.4350, W=0.2191.
+- variates skill: multivariate=0.3934, univariate=0.3622.
+- Outcome: Initial identity is exact. At1000 both adapters show a small main-screen improvement; filtered versus control difference0.000043 is inconclusive. Continue to matched3000/6000 and earlier-cutoff diagnostics.
+
+## E60 joint_filtered_candidate_ema1000
+- Checkpoint SHA-256: `444ffa52005a89ef409a284a2a952d82da7f760e17f1ea865f118b1bb878fec5`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.930442571640015 seconds.
+- Overall skill 0.376281; relative MASE 0.745845; relative WQL 0.521590.
+- term skill: long=0.4002, medium=0.4050, short=0.3554.
+- domain skill: Econ/Fin=0.1387, Energy=0.2376, Healthcare=0.4442, Nature=0.4299, Sales=0.3348, Transport=0.4657, Web/CloudOps=0.4988.
+- frequency skill: A=0.3739, D=0.3524, H=0.3961, M=0.1054, Q=0.1984, S=0.4208, T=0.4351, W=0.2191.
+- variates skill: multivariate=0.3935, univariate=0.3622.
+- Outcome: Initial identity is exact. At1000 both adapters show a small main-screen improvement; filtered versus control difference0.000043 is inconclusive. Continue to matched3000/6000 and earlier-cutoff diagnostics.
+
+## E58 matched37M at8000
+- Checkpoint SHA-256: `2e8a53df1226d34548c14d2f6aa29531e9562825e077658040ad03cf1584e407`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.826340913772583 seconds.
+- Overall skill 0.374665; relative MASE 0.746940; relative WQL 0.523529.
+- term skill: long=0.3997, medium=0.4070, short=0.3518.
+- domain skill: Econ/Fin=0.1410, Energy=0.2366, Healthcare=0.4123, Nature=0.4301, Sales=0.3395, Transport=0.4651, Web/CloudOps=0.4997.
+- frequency skill: A=0.3794, D=0.3492, H=0.3961, M=0.0913, Q=0.2022, S=0.4225, T=0.4363, W=0.2001.
+- variates skill: multivariate=0.3935, univariate=0.3592.
+- Outcome: Recovered from the4000-step dip but remains below its parent. Await the matched146M checkpoint.
+
+## E31 amplitude synthesis final24000 with original mean-template runtime
+- Checkpoint SHA-256: `ecab9102fc11909555044c67dffda85280e06a375f1733351c7e9a39bf2ce9bb`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.28603458404541 seconds.
+- Overall skill 0.361862; relative MASE 0.761019; relative WQL 0.535099.
+- term skill: long=0.3937, medium=0.4047, short=0.3318.
+- domain skill: Econ/Fin=0.1167, Energy=0.1881, Healthcare=0.4079, Nature=0.4215, Sales=0.3445, Transport=0.4600, Web/CloudOps=0.5131.
+- frequency skill: A=0.3736, D=0.3413, H=0.3832, M=0.0977, Q=0.2001, S=0.4426, T=0.4085, W=0.2080.
+- variates skill: multivariate=0.3973, univariate=0.3321.
+- Outcome: Finished24000 steps in7471.1 training-loop seconds. Original-rule screening score slightly exceeds matched E25; current-rule and ensemble comparisons are recorded separately. This checkpoint is member1 of E59.
+
+## E60 joint_filtered_control_ema3000
+- Checkpoint SHA-256: `97b73dfac1a108dd79b419bef760ae281f25de78496cf982297bcd6c46f2bf3b`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.558463096618652 seconds.
+- Overall skill 0.376364; relative MASE 0.746340; relative WQL 0.521106.
+- term skill: long=0.4004, medium=0.4033, short=0.3562.
+- domain skill: Econ/Fin=0.1387, Energy=0.2353, Healthcare=0.4442, Nature=0.4338, Sales=0.3348, Transport=0.4657, Web/CloudOps=0.4989.
+- frequency skill: A=0.3739, D=0.3535, H=0.3966, M=0.1054, Q=0.1984, S=0.4217, T=0.4342, W=0.2193.
+- variates skill: multivariate=0.3937, univariate=0.3622.
+- Outcome: Both remain ahead of the frozen parent on the main screen. Unfiltered control now leads by 0.000084; earlier-cutoff evaluations are running. Continue both to 6000 as specified.
+
+## E60 joint_filtered_candidate_ema3000
+- Checkpoint SHA-256: `c37a8eddae4fc7419ea3ae0a28efc99dd6346e84f2d1807b5b6cb4013eeb2699`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.436699151992798 seconds.
+- Overall skill 0.376280; relative MASE 0.746320; relative WQL 0.521261.
+- term skill: long=0.4005, medium=0.4035, short=0.3559.
+- domain skill: Econ/Fin=0.1387, Energy=0.2352, Healthcare=0.4442, Nature=0.4324, Sales=0.3348, Transport=0.4657, Web/CloudOps=0.4997.
+- frequency skill: A=0.3739, D=0.3528, H=0.3961, M=0.1054, Q=0.1984, S=0.4201, T=0.4351, W=0.2193.
+- variates skill: multivariate=0.3935, univariate=0.3622.
+- Outcome: Both remain ahead of the frozen parent on the main screen. Unfiltered control now leads by 0.000084; earlier-cutoff evaluations are running. Continue both to 6000 as specified.
+
+## E62 matched horizon-training correction transferred to the selected backbone
+Hypothesis: E52's varied requested horizons may add to E42's partial-target correction when transferred to the later E47 backbone. Repeat the successful E55 matched-difference method with the E52 final checkpoint: E47 confidence6000 + E52 horizon_jitter12000 - E38 control12000. Coefficient is fixed at 1.0, with no sweep. This is equivalently the E55 backbone plus the E52-minus-E42 horizon-training difference, apart from floating-point order. Confidence projection remains unchanged. All three parents originate from our own random initialization and permitted sources.
+- No new training or data. CPU merge records parent hashes and elapsed time; the candidate will use the current fixed inference rules.
+- Compare the main screen and both earlier cutoffs against E55 and the current E59 package before any full validation or publication. A single main-screen gain is insufficient. No outcome yet.
+
+E61 long_memory_synth training started at 2026-10-22T06:01:39Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E60 joint_filtered_control at3000, backcast_evals
+- Checkpoint SHA-256: `97b73dfac1a108dd79b419bef760ae281f25de78496cf982297bcd6c46f2bf3b`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 25.748270750045776 seconds.
+- Overall skill 0.334618; relative MASE 0.799261; relative WQL 0.553928.
+- term skill: long=0.3961, medium=0.3571, short=0.3004.
+- domain skill: Econ/Fin=0.1631, Energy=0.2278, Healthcare=0.4091, Nature=0.3620, Sales=0.3971, Transport=0.3715, Web/CloudOps=0.4461.
+- frequency skill: A=0.3254, D=0.3492, H=0.3639, M=0.1918, Q=0.2328, S=0.3399, T=0.3325, W=0.2872.
+- variates skill: multivariate=0.3503, univariate=0.3218.
+- Outcome: Reject the adapter branch: both earlier cutoffs regress against E55, by more than the main-screen gain. Filtering does not fix this failure. Prune both runs before their planned6000 endpoint; retain stop checkpoints for provenance.
+
+## E60 joint_filtered_control at3000, backcast2_evals
+- Checkpoint SHA-256: `97b73dfac1a108dd79b419bef760ae281f25de78496cf982297bcd6c46f2bf3b`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 23.68830132484436 seconds.
+- Overall skill 0.355826; relative MASE 0.774983; relative WQL 0.535445.
+- term skill: long=0.3630, medium=0.3845, short=0.3417.
+- domain skill: Econ/Fin=0.1127, Energy=0.2316, Healthcare=0.3474, Nature=0.4679, Sales=0.5489, Transport=0.4199, Web/CloudOps=0.4270.
+- frequency skill: A=0.3556, D=0.4260, H=0.3879, M=0.1532, Q=0.1527, S=0.2768, T=0.3543, W=0.2578.
+- variates skill: multivariate=0.3579, univariate=0.3541.
+- Outcome: Reject the adapter branch: both earlier cutoffs regress against E55, by more than the main-screen gain. Filtering does not fix this failure. Prune both runs before their planned6000 endpoint; retain stop checkpoints for provenance.
+
+## E60 joint_filtered_candidate at3000, backcast_evals
+- Checkpoint SHA-256: `c37a8eddae4fc7419ea3ae0a28efc99dd6346e84f2d1807b5b6cb4013eeb2699`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.67229199409485 seconds.
+- Overall skill 0.334545; relative MASE 0.799017; relative WQL 0.554218.
+- term skill: long=0.3960, medium=0.3575, short=0.3001.
+- domain skill: Econ/Fin=0.1631, Energy=0.2272, Healthcare=0.4091, Nature=0.3627, Sales=0.3971, Transport=0.3715, Web/CloudOps=0.4460.
+- frequency skill: A=0.3254, D=0.3486, H=0.3640, M=0.1918, Q=0.2328, S=0.3383, T=0.3328, W=0.2870.
+- variates skill: multivariate=0.3502, univariate=0.3218.
+- Outcome: Reject the adapter branch: both earlier cutoffs regress against E55, by more than the main-screen gain. Filtering does not fix this failure. Prune both runs before their planned6000 endpoint; retain stop checkpoints for provenance.
+
+## E60 joint_filtered_candidate at3000, backcast2_evals
+- Checkpoint SHA-256: `c37a8eddae4fc7419ea3ae0a28efc99dd6346e84f2d1807b5b6cb4013eeb2699`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 24.532593488693237 seconds.
+- Overall skill 0.356065; relative MASE 0.774672; relative WQL 0.535261.
+- term skill: long=0.3639, medium=0.3845, short=0.3418.
+- domain skill: Econ/Fin=0.1127, Energy=0.2321, Healthcare=0.3474, Nature=0.4679, Sales=0.5489, Transport=0.4199, Web/CloudOps=0.4274.
+- frequency skill: A=0.3556, D=0.4256, H=0.3883, M=0.1532, Q=0.1527, S=0.2764, T=0.3550, W=0.2576.
+- variates skill: multivariate=0.3584, univariate=0.3541.
+- Outcome: Reject the adapter branch: both earlier cutoffs regress against E55, by more than the main-screen gain. Filtering does not fix this failure. Prune both runs before their planned6000 endpoint; retain stop checkpoints for provenance.
+
+## E63 corpus expansion at 146M capacity
+Hypothesis: E38's larger packed corpus did not improve the 37M model over 12,000 continuation steps, but a larger model with a longer schedule may benefit from the additional diversity. Add one matched data arm to E58: the same 146M initialization and optimization, using packed_expanded instead of packed_sample. The already-running E58 146M arm is the control; it is not retrained.
+- Both packs contain the same 95 allow-listed directories. Source probabilities are read from packed_sample in both arms, so this changes within-source diversity rather than source balance. The expanded pack contains 5,653,290,832 points versus 958,745,103 in the control. No new generator or external source.
+- Same own wide_capacity_initial checkpoint, seed 113, B96, 32,000 steps, base LR 0.00003, matched width-dependent AdamW groups, independently sign-augmented loss and EMA. Same runtime and batch64 for validation. All parameters train.
+- First verify that the new loader reproduces the control's sampler exactly when given the control pack, and that expanded source probabilities match. The model and maximum tensor shapes are unchanged from the measured E58 memory fixture.
+- Start after E60 adapter jobs stop. Compare checkpoints at 4000/8000/16000/24000/32000; evaluate earlier cutoffs before selection. This explicitly tests the interaction of corpus diversity and capacity, rather than repeating the small-model data comparison. No outcome yet.
+
+E63 checks passed: {"cpu_seconds": 6.780377898016013, "control_sampler_bitwise_equal": true, "source_probabilities_identical": true, "optimizer_updates": 0, "initial_checkpoint_sha256": "c3d41b52987eb033dcc60b4cd59e13e8adb75e319fe5f5bc043296faaac04849"}
+
+E60 joint_filtered_control stopped: {"final_step": 4691, "final_checkpoint": "/workspace/research/runs/joint_filtered_control/ema004691.pt", "checkpoint_sha256": "8d48a377b49cfd980a973aa9505565a537ed69c27c621ccf39b19d5787fb720b", "last_logged_training_seconds": 592.3, "last_logged_step": 4650, "all_original_tensors_bitwise_unchanged": true, "reason": "Both earlier cutoffs regress at3000 by more than the main gain; negative branch pruned."}
+
+E60 joint_filtered_candidate stopped: {"final_step": 4648, "final_checkpoint": "/workspace/research/runs/joint_filtered_candidate/ema004648.pt", "checkpoint_sha256": "1574e71b8877b5620a5929a55df22347c9475184194dec37ccb38c0eb71ed9c8", "last_logged_training_seconds": 590.2, "last_logged_step": 4600, "all_original_tensors_bitwise_unchanged": true, "reason": "Both earlier cutoffs regress at3000 by more than the main gain; negative branch pruned."}
+
+## E52 varied requested horizons final12000
+- Checkpoint SHA-256: `830179d763916a1065fc615d2c5e22f4a3b5b19390dde707ab0f057e13cadff6`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.63369631767273 seconds.
+- Overall skill 0.362379; relative MASE 0.760035; relative WQL 0.534923.
+- term skill: long=0.3913, medium=0.4055, short=0.3334.
+- domain skill: Econ/Fin=0.1099, Energy=0.1916, Healthcare=0.4150, Nature=0.4272, Sales=0.3410, Transport=0.4599, Web/CloudOps=0.5083.
+- frequency skill: A=0.3715, D=0.3433, H=0.3828, M=0.1086, Q=0.1994, S=0.4416, T=0.4075, W=0.2138.
+- variates skill: multivariate=0.3960, univariate=0.3343.
+- Outcome: Completed in3887.5 training-loop seconds (3897.218 process seconds). Main improves versus matched E42; inspect both earlier cutoffs and E62 transfer.
+
+## E52 final12000 first earlier cutoff
+- Checkpoint SHA-256: `830179d763916a1065fc615d2c5e22f4a3b5b19390dde707ab0f057e13cadff6`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 19.096883296966553 seconds.
+- Overall skill 0.331566; relative MASE 0.804478; relative WQL 0.555396.
+- term skill: long=0.3906, medium=0.3563, short=0.2975.
+- domain skill: Econ/Fin=0.1463, Energy=0.2302, Healthcare=0.4120, Nature=0.3520, Sales=0.3984, Transport=0.3650, Web/CloudOps=0.4442.
+- frequency skill: A=0.3138, D=0.3562, H=0.3588, M=0.1622, Q=0.2341, S=0.3415, T=0.3316, W=0.2747.
+- variates skill: multivariate=0.3480, univariate=0.3182.
+- Outcome: Improves over E42 by0.000492.
+
+## E52 final12000 second earlier cutoff
+- Checkpoint SHA-256: `830179d763916a1065fc615d2c5e22f4a3b5b19390dde707ab0f057e13cadff6`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 18.59932589530945 seconds.
+- Overall skill 0.353232; relative MASE 0.776549; relative WQL 0.538676.
+- term skill: long=0.3692, medium=0.3768, short=0.3380.
+- domain skill: Econ/Fin=0.0898, Energy=0.2297, Healthcare=0.3632, Nature=0.4567, Sales=0.5538, Transport=0.4144, Web/CloudOps=0.4314.
+- frequency skill: A=0.3506, D=0.4238, H=0.3876, M=0.1727, Q=0.1527, S=0.2824, T=0.3479, W=0.2436.
+- variates skill: multivariate=0.3614, univariate=0.3466.
+- Outcome: Improves over E42 by0.000136. The final horizon variation treatment improves all three matched screening windows.
+
+## E62 transferred horizon correction main screen
+- Checkpoint SHA-256: `22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.18038821220398 seconds.
+- Overall skill 0.377343; relative MASE 0.744367; relative WQL 0.520847.
+- term skill: long=0.3993, medium=0.4068, short=0.3570.
+- domain skill: Econ/Fin=0.1398, Energy=0.2376, Healthcare=0.4513, Nature=0.4321, Sales=0.3339, Transport=0.4661, Web/CloudOps=0.4995.
+- frequency skill: A=0.3721, D=0.3559, H=0.3958, M=0.1049, Q=0.1994, S=0.4226, T=0.4360, W=0.2230.
+- variates skill: multivariate=0.3942, univariate=0.3636.
+- Outcome: Main gain0.001387 over E55. CPU composition1.266 seconds; await both earlier-cutoff checks before full validation.
+
+## E62 transferred horizon correction first earlier cutoff
+- Checkpoint SHA-256: `22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 22.59067988395691 seconds.
+- Overall skill 0.336227; relative MASE 0.797258; relative WQL 0.552638.
+- term skill: long=0.3980, medium=0.3585, short=0.3020.
+- domain skill: Econ/Fin=0.1632, Energy=0.2299, Healthcare=0.4035, Nature=0.3641, Sales=0.3973, Transport=0.3720, Web/CloudOps=0.4497.
+- frequency skill: A=0.3264, D=0.3494, H=0.3653, M=0.1894, Q=0.2332, S=0.3419, T=0.3364, W=0.2851.
+- variates skill: multivariate=0.3539, univariate=0.3218.
+- Outcome: Improves over E55 by0.000618 and current E59 ensemble by0.000525; second earlier cutoff pending.
+
+## E64 replace the horizon-corrected member in the fixed equal-weight ensemble
+Hypothesis: E62 improves all three windows against E55, but the current E59 ensemble remains stronger at the second earlier cutoff. Replace only member0 of E59 with E62 and retain the amplitude-trained member1, applying the same equal quantile average after each member's fixed rules. No new weights, training, blend coefficient or postprocessing is introduced. Every member still forecasts every instance.
+- Use cached forecasts first on the main screen and both earlier cutoffs. Compare the candidate ensemble with E62 alone and E59. Validate the actual package if the composition offers a useful improvement.
+
+## E62 transferred horizon correction second earlier cutoff
+- Checkpoint SHA-256: `22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 21.85834312438965 seconds.
+- Overall skill 0.357693; relative MASE 0.772232; relative WQL 0.534241.
+- term skill: long=0.3647, medium=0.3858, short=0.3439.
+- domain skill: Econ/Fin=0.1095, Energy=0.2360, Healthcare=0.3457, Nature=0.4673, Sales=0.5494, Transport=0.4200, Web/CloudOps=0.4312.
+- frequency skill: A=0.3533, D=0.4246, H=0.3905, M=0.1533, Q=0.1508, S=0.2809, T=0.3574, W=0.2594.
+- variates skill: multivariate=0.3632, univariate=0.3532.
+- Outcome: Improves over E55 by0.000222. All three screening windows improve against the single-model parent; E59 still leads at this cutoff. Proceed to full validation and the fixed E64 ensemble comparison.
+
+E64 member checkpoint hashes: 22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c, ecab9102fc11909555044c67dffda85280e06a375f1733351c7e9a39bf2ce9bb.
+
+E64 cached screen: skill 0.376788946; relative MASE 0.745126449; relative WQL 0.521243098; CPU elapsed 4.12 seconds. Group skill: {"term": {"short": 0.3554964007817075, "medium": 0.4073693459600539, "long": 0.3998307241854733}, "domain": {"Econ/Fin": 0.14130300023652653, "Energy": 0.2369496172656066, "Healthcare": 0.4331271903068751, "Nature": 0.4295973732895463, "Sales": 0.3381125790990753, "Transport": 0.4660477929672022, "Web/CloudOps": 0.5028903628931379}, "freq": {"A-DEC": 0.37465521703273497, "Q-DEC": 0.20133386157730448, "M": 0.09799796329514798, "W-SUN": 0.09310304137899561, "D": 0.35177652554445227, "H": 0.39569724381500315, "15T": 0.21510285937582785, "W-FRI": -0.01717810068114023, "10T": 0.6134978939570246, "W-TUE": 0.5590219029222432, "W-THU": 0.20713023925638596, "W-WED": 0.3147893218102069, "5T": 0.5146554293758923, "10S": 0.42304338912946016}}.
+
+E64 cached backcast: skill 0.336038010; relative MASE 0.797789283; relative WQL 0.552583914; CPU elapsed 3.90 seconds. Group skill: {"term": {"short": 0.3017705847743448, "medium": 0.3583628431586988, "long": 0.3977762964359516}, "domain": {"Econ/Fin": 0.16247452271830498, "Energy": 0.22924210402608347, "Healthcare": 0.4037429866174578, "Nature": 0.3634291235876055, "Sales": 0.3979195783227222, "Transport": 0.3717322673047254, "Web/CloudOps": 0.45027750430374125}, "freq": {"A-DEC": 0.31962125220900417, "Q-DEC": 0.23151630865404227, "M": 0.19676862244049742, "W-SUN": 0.04104954385433246, "D": 0.3494141749986718, "H": 0.36512969667883677, "15T": 0.22647340624655954, "W-FRI": 0.26044659511767176, "10T": 0.3206375756185784, "W-TUE": 0.5251272756681418, "W-THU": 0.23336875275806457, "W-WED": 0.34380336710053805, "5T": 0.4388723441407405, "10S": 0.34124569112919356}}.
+
+E64 cached backcast2: skill 0.358773194; relative MASE 0.771487726; relative WQL 0.532959635; CPU elapsed 3.84 seconds. Group skill: {"term": {"short": 0.3455453575305818, "medium": 0.3850562697420571, "long": 0.3662794409758179}, "domain": {"Econ/Fin": 0.11178929070509958, "Energy": 0.2358139051710867, "Healthcare": 0.3485087569088834, "Nature": 0.46931745466541086, "Sales": 0.5520970513032182, "Transport": 0.4203697870790297, "Web/CloudOps": 0.4324624263189104}, "freq": {"A-DEC": 0.3552674881858966, "Q-DEC": 0.15052699940899794, "M": 0.14543941174604302, "W-SUN": 0.05780563284435891, "D": 0.4280091613762501, "H": 0.39053406736994467, "15T": 0.10558086695082369, "W-FRI": 0.14677601366514947, "10T": 0.4931102944101511, "W-TUE": 0.10259191794259981, "W-THU": 0.32777977287160787, "W-WED": 0.5368109233756173, "5T": 0.46691593361082795, "10S": 0.2842010268357835}}.
+
+E64 improves over current E59 on all three cutoffs. Versus E62 alone, it trails on the main screen and first earlier cutoff but gains at the second. Both complete packages will receive full validation; the current root remains E59 until that comparison and determinism checks finish.
+
+E64 actual package matches the cached equal-weight composition bitwise for every quantile in all 97 screening configurations. Proceed to exact full-package validation.
+
+## E65 recent normalization with an 8192-point encoder context
+Hypothesis: adding older history changes the normalization reference as well as supplying extra observations. E54's final8192 model scores0.375118 versus its matched4096 control0.375764. Test whether retaining the recent4096 mean and standard deviation makes older context easier to use. The encoder still receives all8192 points; every point and future target uses the recent reference frame. If the recent segment has no finite observations, fall back to the full available context.
+- Change only normalization. Same E54 initialization from own recency24000, seed101, matched packed_sample windows, B128, LR0.00003,12000 steps, maximum context8192 and sample context8192. Contexts of at most4096 retain the original formula. Fixed inference corrections still see only4096 observations. The existing E54 long-context run is the control. No new sources or generators.
+- Training-data diagnostic: 256 real allowed-corpus windows, seed151;101 had sufficient older and recent history. Full/recent standard-deviation ratio p10/p50/p90 was0.932/1.003/1.255; absolute mean shift in recent standard deviations was0.0068/0.0512/0.4089. This motivates testing the frame, without establishing it as the cause of the regression. Diagnostic CPU time1.90 seconds; no validation data used.
+- Verify exact equivalence for contexts at most4096 and finite fallback behavior before launch. Check main screens at3000/6000/12000 and earlier cutoffs before selection. No outcome yet.
+
+E65 checks passed: {"cpu_seconds": 0.7186618210980669, "short_context_normalization_bitwise_equal": true, "short_context_forecast_bitwise_equal": true, "recent_reference_and_empty_recent_fallback_verified": true, "all_missing_finite": true, "optimizer_updates": 0}
+
+## E62 exact full-package validation
+- Checkpoint SHA-256: `22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.373420; relative MASE 0.747096; relative WQL 0.525505.
+- term skill: long=0.3931, medium=0.4057, short=0.3528.
+- domain skill: Econ/Fin=0.1173, Energy=0.2305, Healthcare=0.4574, Nature=0.4321, Sales=0.3479, Transport=0.4663, Web/CloudOps=0.4919.
+- frequency skill: A=0.2918, D=0.3600, H=0.3910, M=0.1281, Q=0.1853, S=0.4226, T=0.4289, W=0.2156.
+- variates skill: multivariate=0.3899, univariate=0.3600.
+- Outcome: Full skill0.373420 versus E59 published0.372254. All97 configurations and151170 instances pass; no quantile crossings. Actual inference116.659 seconds. Await E64 full comparison and relocation/determinism before choosing the next publication.
+
+## E54 context8192 final12000
+- Checkpoint SHA-256: `90a1a9da78714594078b3cafd32b1812d633f5d6bf85ba8bfa60d09fbc3ebd38`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.50181746482849 seconds.
+- Overall skill 0.375118; relative MASE 0.744591; relative WQL 0.524419.
+- term skill: long=0.4042, medium=0.4057, short=0.3513.
+- domain skill: Econ/Fin=0.1426, Energy=0.2337, Healthcare=0.4381, Nature=0.4222, Sales=0.3378, Transport=0.4707, Web/CloudOps=0.5001.
+- frequency skill: A=0.3749, D=0.3573, H=0.3955, M=0.0779, Q=0.2010, S=0.4259, T=0.4332, W=0.2127.
+- variates skill: multivariate=0.3910, univariate=0.3622.
+- Outcome: Finished in3285.3 training-loop seconds (3296.255 process seconds). Main skill0.375118 remains below matched4096 control0.375764. Do not select this checkpoint; E65 isolates the normalization-frame hypothesis.
+
+## E58 widened146M at8000
+- Checkpoint SHA-256: `822fdf9f16fb741ab5e23728ad13cac2838a70ad4cfa23a191c9373294a6b4d5`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 33.286028146743774 seconds.
+- Overall skill 0.374330; relative MASE 0.747410; relative WQL 0.523759.
+- term skill: long=0.3993, medium=0.4074, short=0.3512.
+- domain skill: Econ/Fin=0.1416, Energy=0.2364, Healthcare=0.4073, Nature=0.4301, Sales=0.3397, Transport=0.4652, Web/CloudOps=0.4995.
+- frequency skill: A=0.3799, D=0.3459, H=0.3958, M=0.0911, Q=0.2002, S=0.4225, T=0.4364, W=0.2039.
+- variates skill: multivariate=0.3933, univariate=0.3588.
+- Outcome: Main skill0.374330 versus matched37M0.374665, reversing the small4000-step advantage. Both remain below initialization. Continue the prespecified32000-step capacity comparison; no capacity conclusion or package change yet.
+
+## E64 exact full-package validation after operator recovery
+- Checkpoint SHA-256: `35306ec57b81c42e8fbdfab14adc96293400eee0dbd808fadbb3c973f9ff427e`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.372871; relative MASE 0.747913; relative WQL 0.525850.
+- term skill: long=0.3938, medium=0.4063, short=0.3513.
+- domain skill: Econ/Fin=0.1184, Energy=0.2299, Healthcare=0.4400, Nature=0.4295, Sales=0.3508, Transport=0.4663, Web/CloudOps=0.4954.
+- frequency skill: A=0.2911, D=0.3562, H=0.3909, M=0.1203, Q=0.1862, S=0.4230, T=0.4309, W=0.2097.
+- variates skill: multivariate=0.3912, univariate=0.3579.
+- Outcome: Full skill0.372871 beats E59 but trails E62 single0.373420. Both pass relocation/determinism. Select E62 single, retaining E64 as a replayable alternative. E64 full inference79.236 seconds on an otherwise idle H100; do not compare that timing directly with concurrent-training measurements.
+
+## Operator interruption and recovery, 2026-10-22
+All previous container processes were confirmed ended; the H100 was idle. No credentials were inspected. Completed reports and atomic checkpoints were retained. E64 full validation was rerun because no completed report survived. Both E62 and E64 passed asset hashes and independent relocation: [
+  {
+    "package": "/run-state/workspace/research/candidate-horizon-delta12k",
+    "checkpoint_sha256": "22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c",
+    "configurations": 3,
+    "bitwise_equal_after_relocation": true,
+    "finite_and_monotone": true,
+    "seconds": 6.016964558046311
+  },
+  {
+    "package": "/run-state/workspace/research/candidate-horizon-ensemble",
+    "checkpoint_sha256": "35306ec57b81c42e8fbdfab14adc96293400eee0dbd808fadbb3c973f9ff427e",
+    "configurations": 3,
+    "bitwise_equal_after_relocation": true,
+    "finite_and_monotone": true,
+    "seconds": 6.803807514021173
+  }
+]
+
+
+Publication decision: E62 single has the highest exact full-validation skill among replayed candidates (0.373419858), improves all three screening cutoffs versus E55, and wins the main screen and first earlier cutoff against E64. E64 retains a second-earlier-cutoff advantage but is lower on full validation. Publish E62 with complete runtime, all own-checkpoint lineage, generator code, pinned requirements, this experiment log and the full report. Scores remain on the validation scale, not the held-out reward scale.
+
+## E66 matched recovery of interrupted research runs
+The operator interruption ended all trainers. E58 146M had step/EMA8000, while its37M control also had12000; neither had Adam or sampler state. Both arms restart from their own step8000 and EMA8000, preserving a common comparison point. Fresh AdamW and a shared new sampler seed179 begin at global step8001, with400-step rewarmup and the original cosine schedule through32000. This is a documented matched continuation, not an exact resumption of the old optimizer trajectory. Existing control12000 is retained but is not the recovered comparison control.
+- E63 expanded146M had only initialization saved, so it restarts from its own initial checkpoint with original seed113. It will receive the identical optimizer/stream reset at global8000 to match E58 recovery. E61 slow-memory and E65 recent normalization had no completed checkpoints, so they restart from their declared parent, original seeds and settings. No baseline or completed scientific comparison is retrained.
+- New driver saves weights, EMA, optimizer, CPU/CUDA RNG, sampler and batch RNG states, and the prefetched next batch atomically every1000 steps. Independent interrupted/continuous fixture: {"seed": 191, "steps": 4, "batch": 2, "fixture_parameters": 164384, "purpose": "recovery fixture only; never selected", "maximum_model_difference": 1.2101430968414206e-14, "maximum_ema_difference": 2.4147350785597155e-15, "maximum_optimizer_difference": 7.095019016745141e-16, "rng_and_prefetched_batch_bitwise_equal": true, "note": "Complete state restored; GPU training replay differs by floating-point roundoff, so training replay is not claimed bitwise identical. Package inference checks are separately bitwise."}. Four optimizer updates per fixture path used only permitted data; fixture weights are never candidates.
+- Work lost after the last usable checkpoints: E58 large at least800 steps, control at least1550 beyond its latest12000 (the matched restart also intentionally returns to8000); E63 at least1700; E61 at least2150; E65 at least300. Last logged elapsed times before interruption:2432.6,2101.5,388.0,542.2,71.8 seconds respectively. These are concurrent process times, not summed exclusive GPU time.
+
+## E67 end-to-end paired-sign training on the selected horizon-corrected model
+Hypothesis: E50 updated only the output head and did not sustain a paired-loss gain. The full encoder/decoder may be able to learn complementary sign-orientation forecasts when optimized through the exact neural average used in inference. Compare two full-model continuations from own E62: both forward x and -x, but the control scores each orientation separately while the candidate scores their ordered-quantile average. This changes the loss, not the sampled windows, number of forward passes, architecture or inference.
+- Both arms retain E52 random requested-horizon prefixes and E42 partial-target future-key masking, now integrated with the selected confidence features. At inference the requested horizon is fully available and the forward pass must remain bitwise identical at initialization to E62.
+- Own parent horizon_delta12k.pt (SHA-25622c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c); seed197, independent horizon seed300200, packed_sample95 allowed sources and the same local generator, B64 distinct windows /128 signed rows, LR0.00002,12000 steps, all parameters train. Same warmup400 and cosine schedule. Every runtime member forecasts every instance; no validation data are used for gradients.
+- Check initialization identity, paired-loss sign symmetry and the independence of unused future tokens. Measure maximum-shape memory before launch. Compare main and both earlier cutoffs; a main-only gain is insufficient. Complete recovery state includes both sampler RNGs. No outcome yet.
+
+E67 checks passed: {
+  "cpu_seconds": 3.4358641319558956,
+  "initial_forecast_bitwise_parent": true,
+  "unused_future_tokens_do_not_change_requested_prefix": true,
+  "paired_loss_sign_symmetric": true,
+  "matched_sampler_main_and_horizon_rng": true,
+  "gpu_fixture_seconds": 3.137407455011271,
+  "allocated_mib": 5783.81689453125,
+  "reserved_mib": 6266.0,
+  "optimizer_updates": 0
+}. Current allocated/reserved process footprint fits both additional paired arms within the single80GB H100. Each new arm uses about6.3GiB reserved at maximum shape.
+
+## E68 fixed upward quantile-level shift
+Hypothesis: E62 coverage diagnostics show upper-tail underprediction in several groups, while lower-tail excess inclusive coverage is largely due to ties at exact zero. Test one globally fixed quantile-function shift from level p to p+0.025, linearly interpolating between adjacent predicted deciles and extrapolating the uppermost interval. This is equivalent to75% of q(p)+25% of q(p+0.1), with upper-end linear extrapolation. It shifts the median as well as intervals. No dataset-specific parameters, context optimization, new training or weight fitting.
+- Apply after the existing fixed rules. Monotonicity and nonnegative quantiles for nonnegative histories are preserved by upward interpolation/extrapolation. Compare only unchanged and the predeclared+0.025 setting on the main screen and both earlier cutoffs. No coefficient sweep.
+- Descriptive coverage files horizon_delta_calibration_bounds.json and calibration_diagnostic.py retain strict/inclusive tie bounds. Parent weights SHA-25622c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c. No outcome yet.
+
+E68 screen, CPU elapsed2.34 seconds:
+- unchanged: skill0.377343216, relative MASE0.744366865, relative WQL0.520847298; group skill {"term": {"short": 0.35695456496017197, "medium": 0.4068421174843613, "long": 0.3992704749750494}, "domain": {"Econ/Fin": 0.1398359119561744, "Energy": 0.23759505750828103, "Healthcare": 0.4512562626750727, "Nature": 0.4321373180939323, "Sales": 0.33393397659338675, "Transport": 0.46608088355848554, "Web/CloudOps": 0.49950488336015364}, "freq": {"A-DEC": 0.37212708500173697, "Q-DEC": 0.19935115627243538, "M": 0.10494660003314427, "W-SUN": 0.09001322152715163, "D": 0.355915177294738, "H": 0.39579077444245214, "15T": 0.214120657226064, "W-FRI": -0.015604695485733178, "10T": 0.6146535484250835, "W-TUE": 0.5792731324380282, "W-THU": 0.21095087378926547, "W-WED": 0.3154347093297968, "5T": 0.5103163560669302, "10S": 0.42256110774455713}}.
+- shift_up_0025: skill0.376908525, relative MASE0.742757759, relative WQL0.522704720; group skill {"term": {"short": 0.3566597008609087, "medium": 0.404403536500499, "long": 0.4005186267241073}, "domain": {"Econ/Fin": 0.14287974364263867, "Energy": 0.23414603173384185, "Healthcare": 0.43949354133666974, "Nature": 0.4298630701436009, "Sales": 0.32658918319903174, "Transport": 0.470151191104459, "Web/CloudOps": 0.5032582354135502}, "freq": {"A-DEC": 0.380333567897149, "Q-DEC": 0.20181706747540706, "M": 0.08925268144424381, "W-SUN": 0.07477217948655135, "D": 0.35474663372376547, "H": 0.3940149463382252, "15T": 0.21958831724211048, "W-FRI": -0.0580596428780249, "10T": 0.6129399194069542, "W-TUE": 0.5699015681127257, "W-THU": 0.21136203731846115, "W-WED": 0.3102694893262884, "5T": 0.5147323912192434, "10S": 0.4303777414462714}}.
+
+E68 backcast, CPU elapsed2.34 seconds:
+- unchanged: skill0.336227035, relative MASE0.797257690, relative WQL0.552637565; group skill {"term": {"short": 0.3019534316321959, "medium": 0.35847999416902554, "long": 0.3980452217736976}, "domain": {"Econ/Fin": 0.16321936365108436, "Energy": 0.22989895953120598, "Healthcare": 0.4035339334978575, "Nature": 0.3640809439324144, "Sales": 0.39727796653561065, "Transport": 0.37202575172526886, "Web/CloudOps": 0.4496899590670599}, "freq": {"A-DEC": 0.32642568966436736, "Q-DEC": 0.23316387018660967, "M": 0.18942546327646903, "W-SUN": 0.03722616253430944, "D": 0.3493567465552936, "H": 0.3653486861566616, "15T": 0.22544143675041628, "W-FRI": 0.2612228517292393, "10T": 0.32002639781339237, "W-TUE": 0.5369423215086733, "W-THU": 0.24695593621438172, "W-WED": 0.34366857690318564, "5T": 0.43841920169431536, "10S": 0.34189056734107803}}.
+- shift_up_0025: skill0.333181127, relative MASE0.800191338, relative WQL0.555676359; group skill {"term": {"short": 0.30012284957976143, "medium": 0.3563718754867885, "long": 0.3913827408796766}, "domain": {"Econ/Fin": 0.16216091726465265, "Energy": 0.22830088335034404, "Healthcare": 0.4226177811841181, "Nature": 0.3541417493195709, "Sales": 0.38842247511482564, "Transport": 0.3660239875770892, "Web/CloudOps": 0.4469561601104274}, "freq": {"A-DEC": 0.3265608246880194, "Q-DEC": 0.22474554051269913, "M": 0.18267190809841738, "W-SUN": 0.03302751086137623, "D": 0.35103471245845674, "H": 0.36176195029219793, "15T": 0.21821690985575604, "W-FRI": 0.25908813449545354, "10T": 0.3119130318041733, "W-TUE": 0.5410511096762922, "W-THU": 0.24171213296902283, "W-WED": 0.34305917574568634, "5T": 0.43719448871859334, "10S": 0.33922568556395327}}.
+
+E68 backcast2, CPU elapsed2.31 seconds:
+- unchanged: skill0.357693384, relative MASE0.772231694, relative WQL0.534240944; group skill {"term": {"short": 0.34387576099815587, "medium": 0.38579809375160923, "long": 0.36474587566145333}, "domain": {"Econ/Fin": 0.10948663145426041, "Energy": 0.23595983020029976, "Healthcare": 0.3456833197210143, "Nature": 0.4672524394882832, "Sales": 0.5493882044206345, "Transport": 0.4200285125423293, "Web/CloudOps": 0.4311910731987415}, "freq": {"A-DEC": 0.3532937085689807, "Q-DEC": 0.15079546315571057, "M": 0.15334987951579737, "W-SUN": 0.053222430353438654, "D": 0.42461678912892176, "H": 0.39045164404809307, "15T": 0.1063009642103484, "W-FRI": 0.14702837959358672, "10T": 0.4925745835795794, "W-TUE": 0.10051793538767262, "W-THU": 0.31968494040001116, "W-WED": 0.5361596335033245, "5T": 0.4655530740312157, "10S": 0.280869499353007}}.
+- shift_up_0025: skill0.352929872, relative MASE0.775985000, relative WQL0.539571964; group skill {"term": {"short": 0.3397486152051552, "medium": 0.38072074318949967, "long": 0.3585956199908913}, "domain": {"Econ/Fin": 0.10581887733310036, "Energy": 0.23338234809030112, "Healthcare": 0.3418028155977272, "Nature": 0.4611553878945821, "Sales": 0.543948848409417, "Transport": 0.4138948091247504, "Web/CloudOps": 0.42581951888726777}, "freq": {"A-DEC": 0.3525707634011239, "Q-DEC": 0.14006676676474916, "M": 0.14537682622646142, "W-SUN": 0.05718515322639861, "D": 0.42470757896302946, "H": 0.38580382383546696, "15T": 0.0967294266428953, "W-FRI": 0.16016050379952163, "10T": 0.4943536981461871, "W-TUE": 0.05512451342569891, "W-THU": 0.29480125149705005, "W-WED": 0.5302395339818361, "5T": 0.4614399225720033, "10S": 0.2693556274034984}}.
+
+E68 rejected: the fixed upward quantile shift loses on all three windows, including -0.00305 and -0.00476 at the earlier cutoffs. Main median MASE improves slightly but WQL worsens. Descriptive coverage imbalance does not justify a global quantile shift. Retain E62 quantiles unchanged; no further coefficient sweep.
+
+## E69 persistent stochastic volatility in synthetic training windows — prepared, not launched
+Hypothesis: the local generator changes seasonal amplitude in E31 but its observation-noise scale stays constant throughout a window. Teach conditional uncertainty under persistent volatility by multiplying the Gaussian/AR noise component by an independent positive log-OU curve in half of synthetic windows. Leave signal, AR coefficient, observation-noise draws, real windows and the main RNG unchanged.
+- Proposed matched reference: completed E42 partial-target training. Same own recency12000 parent, seed79, packed_sample, B256, LR0.00005,12000 steps and original schedule. Only the noise envelope differs; no architecture or inference change.
+- Independent generator seed500082; log time constant uniform1.5..3.5 (about32..3162 steps), log standard deviation uniform0.1..0.8, stationary Gaussian initial log state, exp(logcurve-0.5*sd^2), log envelope clipped[-2.5,2.5]. Synthetic fraction remains0.2, so roughly10% of all windows receive the new envelope.
+- Verify real-window and main-RNG equality before launch. Queue only when a GPU allocation is released; no training has started. Final selection requires main and both earlier-cutoff evidence.
+
+E69 preparation checks: {"cpu_seconds": 4.379837638000026, "main_rng_matched": true, "real_windows_unchanged": true, "periods_and_missing_masks_unchanged": true, "changed_synthetic_context_values": 52057, "optimizer_updates": 0}
+
+## E67 full_paired_control at1000
+- Checkpoint SHA-256: `0aa7ece151109e13af73e7ca2d6b75baa95e67898a6273a081ed38e92cfc63cd`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.88213801383972 seconds.
+- Overall skill 0.376434; relative MASE 0.745243; relative WQL 0.521755.
+- term skill: long=0.3984, medium=0.4065, short=0.3558.
+- domain skill: Econ/Fin=0.1332, Energy=0.2361, Healthcare=0.4573, Nature=0.4305, Sales=0.3351, Transport=0.4660, Web/CloudOps=0.4983.
+- frequency skill: A=0.3675, D=0.3539, H=0.3948, M=0.1051, Q=0.1982, S=0.4219, T=0.4350, W=0.2252.
+- variates skill: multivariate=0.3925, univariate=0.3634.
+- Outcome: Paired objective leads the matched individual-orientation control by0.000426, but both trail the frozen E62 parent0.377343. Continue to3000 before interpreting the early optimization drop.
+
+## E67 full_paired_candidate at1000
+- Checkpoint SHA-256: `256329643ba1d7074386d227f4dafd6bdbf0faca5bf2280a60be51ddf2ad90ee`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.336730480194092 seconds.
+- Overall skill 0.376860; relative MASE 0.744723; relative WQL 0.521407.
+- term skill: long=0.3991, medium=0.4063, short=0.3564.
+- domain skill: Econ/Fin=0.1316, Energy=0.2364, Healthcare=0.4620, Nature=0.4301, Sales=0.3350, Transport=0.4669, Web/CloudOps=0.4985.
+- frequency skill: A=0.3655, D=0.3560, H=0.3951, M=0.1058, Q=0.1980, S=0.4230, T=0.4348, W=0.2256.
+- variates skill: multivariate=0.3926, univariate=0.3640.
+- Outcome: Paired objective leads the matched individual-orientation control by0.000426, but both trail the frozen E62 parent0.377343. Continue to3000 before interpreting the early optimization drop.
+
+Additional historical diagnostic: valid_backcast3 contains93 configurations and8812 instances, derived only from existing validation histories. This cutoff was not used in earlier selection. Compare E62 and its E55 predecessor, plus the fixed amplitude ensemble option; all outputs are selection/diagnostics only. Scales use capped prefixes and its own Seasonal Naive baseline.
+
+## Previously unused third earlier cutoff: horizon_delta12k
+- Checkpoint SHA-256: `22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 25.37537980079651 seconds.
+- Overall skill 0.387789; relative MASE 0.738165; relative WQL 0.507749.
+- term skill: long=0.3944, medium=0.3602, short=0.3954.
+- domain skill: Econ/Fin=0.1725, Energy=0.3191, Healthcare=0.2442, Nature=0.5053, Sales=0.3990, Transport=0.5060, Web/CloudOps=0.3880.
+- frequency skill: A=0.3565, D=0.4418, H=0.4032, M=0.1118, Q=0.2628, S=0.2833, T=0.4297, W=0.2606.
+- variates skill: multivariate=0.3722, univariate=0.3998.
+- Outcome: 93 configurations and8812 instances have enough remaining history. E62 retains a small0.000124 gain over E55 here; amplitude-only is lower. Scores are diagnostic and have their own Seasonal Naive reference. Retain the published E62 while checking the fixed ensemble combinations.
+
+## Previously unused third earlier cutoff: partial_delta12k
+- Checkpoint SHA-256: `721d1029284ea6f36d4c6f169eb001382a97a2848953fad84e3e62fb5a222802`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 25.088595867156982 seconds.
+- Overall skill 0.387664; relative MASE 0.738315; relative WQL 0.507852.
+- term skill: long=0.3943, medium=0.3601, short=0.3953.
+- domain skill: Econ/Fin=0.1728, Energy=0.3188, Healthcare=0.2482, Nature=0.5065, Sales=0.3984, Transport=0.5041, Web/CloudOps=0.3876.
+- frequency skill: A=0.3519, D=0.4408, H=0.4034, M=0.1157, Q=0.2629, S=0.2853, T=0.4287, W=0.2628.
+- variates skill: multivariate=0.3721, univariate=0.3997.
+- Outcome: 93 configurations and8812 instances have enough remaining history. E62 retains a small0.000124 gain over E55 here; amplitude-only is lower. Scores are diagnostic and have their own Seasonal Naive reference. Retain the published E62 while checking the fixed ensemble combinations.
+
+## Previously unused third earlier cutoff: amplitude_fixed_ema24000
+- Checkpoint SHA-256: `ecab9102fc11909555044c67dffda85280e06a375f1733351c7e9a39bf2ce9bb`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 24.98914361000061 seconds.
+- Overall skill 0.386555; relative MASE 0.740761; relative WQL 0.508011.
+- term skill: long=0.3906, medium=0.3617, short=0.3941.
+- domain skill: Econ/Fin=0.1763, Energy=0.3186, Healthcare=0.2144, Nature=0.5055, Sales=0.3950, Transport=0.5054, Web/CloudOps=0.3892.
+- frequency skill: A=0.3670, D=0.4397, H=0.4032, M=0.1018, Q=0.2609, S=0.2818, T=0.4298, W=0.2517.
+- variates skill: multivariate=0.3715, univariate=0.3982.
+- Outcome: 93 configurations and8812 instances have enough remaining history. E62 retains a small0.000124 gain over E55 here; amplitude-only is lower. Scores are diagnostic and have their own Seasonal Naive reference. Retain the published E62 while checking the fixed ensemble combinations.
+
+Third earlier cutoff, fixed horizon+amplitude ensemble: skill0.387712467, relative MASE0.738747107, relative WQL0.507475454; CPU1.61 seconds; group skill {"term": {"short": 0.3954701763614715, "medium": 0.36122355126648487, "long": 0.39279890497296066}, "domain": {"Econ/Fin": 0.1751736307959596, "Energy": 0.31921698125326037, "Healthcare": 0.23298033105692906, "Nature": 0.5060089627880903, "Sales": 0.3975145877969797, "Transport": 0.5058849307923443, "Web/CloudOps": 0.3889682376441502}, "freq": {"A-DEC": 0.3624040337196054, "Q-DEC": 0.2624518143609271, "M": 0.10707417310085743, "W-SUN": 0.1583361093273692, "D": 0.4418985852875217, "H": 0.403440739007801, "15T": 0.2601635557329961, "W-FRI": 0.014090974305712378, "10T": 0.6023775368816131, "W-TUE": 0.24266767576389037, "W-THU": 0.3814500446358562, "W-WED": 0.36993536616236844, "5T": 0.4629495965892555, "10S": 0.28289459770032044}}.
+
+Third earlier cutoff, fixed partial+amplitude ensemble: skill0.387583102, relative MASE0.738907219, relative WQL0.507579906; CPU1.63 seconds; group skill {"term": {"short": 0.39530402146176624, "medium": 0.36114921433277336, "long": 0.39271964935249504}, "domain": {"Econ/Fin": 0.17527256907786126, "Energy": 0.31900374729462777, "Healthcare": 0.23531552352603602, "Nature": 0.5063494545833882, "Sales": 0.3972024195554572, "Transport": 0.5049447049689553, "Web/CloudOps": 0.3887045854899489}, "freq": {"A-DEC": 0.36012619804016577, "Q-DEC": 0.2622822707122595, "M": 0.10906468169471106, "W-SUN": 0.15682778156902044, "D": 0.44126848409451547, "H": 0.4035052878161369, "15T": 0.26032721770481526, "W-FRI": 0.011894162492874005, "10T": 0.6016201297891219, "W-TUE": 0.2491326450741952, "W-THU": 0.38350286292612645, "W-WED": 0.3695417432322957, "5T": 0.4619676858363392, "10S": 0.2838781358710065}}.
+
+The new historical check does not overturn the publication: E62 beats E55 individually at this fourth available window, while the fixed ensemble comparison remains a documented alternative. Full validation and main screening still favor the published single model.
+
+## E70 training-only compiler throughput diagnostic
+Hypothesis: operator/kernel overhead limits the many small transformer operations on one H100. Benchmark torch.compile on the existing own E62 model and unpaired training loss, using64 permitted pretraining windows of context2048/horizon512, seed223. No optimizer updates; no validation examples; no compiler change to the published runtime. Compare eager and compiled loss, gradient direction and absolute differences, then alternate20-step forward/backward timing blocks three times under the existing shared-GPU load. Compilation uses two CPU workers. Any numerical or performance difference is recorded before considering a separate training experiment. No outcome yet.
+
+## E58/E66 recovered37M control at global12000
+- Checkpoint SHA-256: `5341c5600f5fd3f82a33a9a756ce30ee980973e90c0163455ea2ce545601b051`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 29.337642669677734 seconds.
+- Overall skill 0.374083; relative MASE 0.747749; relative WQL 0.523935.
+- term skill: long=0.3989, medium=0.4070, short=0.3511.
+- domain skill: Econ/Fin=0.1395, Energy=0.2360, Healthcare=0.4006, Nature=0.4331, Sales=0.3401, Transport=0.4656, Web/CloudOps=0.4984.
+- frequency skill: A=0.3744, D=0.3455, H=0.3952, M=0.0891, Q=0.1999, S=0.4216, T=0.4369, W=0.2041.
+- variates skill: multivariate=0.3932, univariate=0.3585.
+- Outcome: Skill0.374083, below its own8000 EMA0.374665 and initial parent0.375793. First4000 updates after the documented optimizer reset; await the matched146M result. Training losses over shared steps9550..10500 are nearly equal: large0.345200, control0.345264.
+
+## E67 full_paired_control at3000
+- Checkpoint SHA-256: `c98f8d82047e6e0ecd00f144307623afdd581ee6624d880063f810d090e1a473`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.129313945770264 seconds.
+- Overall skill 0.378018; relative MASE 0.743740; relative WQL 0.520156.
+- term skill: long=0.4008, medium=0.4068, short=0.3576.
+- domain skill: Econ/Fin=0.1354, Energy=0.2381, Healthcare=0.4531, Nature=0.4314, Sales=0.3329, Transport=0.4660, Web/CloudOps=0.5026.
+- frequency skill: A=0.3720, D=0.3538, H=0.3970, M=0.1035, Q=0.1971, S=0.4224, T=0.4379, W=0.2235.
+- variates skill: multivariate=0.3959, univariate=0.3634.
+- Outcome: Both exceed the E62 main screen0.377343. Paired0.378318 leads control0.378018; check both earlier cutoffs before selection. Training continues toward6000.
+
+## E67 full_paired_candidate at3000
+- Checkpoint SHA-256: `2a19ebe0d41c1a18feaba318041795be4d268ce890162a5035e6fc6bf1844238`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.954200506210327 seconds.
+- Overall skill 0.378318; relative MASE 0.743356; relative WQL 0.519924.
+- term skill: long=0.4020, medium=0.4066, short=0.3577.
+- domain skill: Econ/Fin=0.1339, Energy=0.2391, Healthcare=0.4511, Nature=0.4303, Sales=0.3325, Transport=0.4661, Web/CloudOps=0.5042.
+- frequency skill: A=0.3674, D=0.3531, H=0.3978, M=0.1030, Q=0.1972, S=0.4232, T=0.4383, W=0.2236.
+- variates skill: multivariate=0.3969, univariate=0.3631.
+- Outcome: Both exceed the E62 main screen0.377343. Paired0.378318 leads control0.378018; check both earlier cutoffs before selection. Training continues toward6000.
+
+## E67 full_paired_control_ema3000 evals
+- Checkpoint SHA-256: `c98f8d82047e6e0ecd00f144307623afdd581ee6624d880063f810d090e1a473`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.129313945770264 seconds.
+- Overall skill 0.378018; relative MASE 0.743740; relative WQL 0.520156.
+- term skill: long=0.4008, medium=0.4068, short=0.3576.
+- domain skill: Econ/Fin=0.1354, Energy=0.2381, Healthcare=0.4531, Nature=0.4314, Sales=0.3329, Transport=0.4660, Web/CloudOps=0.5026.
+- frequency skill: A=0.3720, D=0.3538, H=0.3970, M=0.1035, Q=0.1971, S=0.4224, T=0.4379, W=0.2235.
+- variates skill: multivariate=0.3959, univariate=0.3634.
+- Outcome: Matched whole-model sign-loss comparison; all parameters train, seed 197. Main screen improves; earlier cutoffs are mixed. Complete package validation follows.
+
+## E67 full_paired_control_ema3000 backcast_evals
+- Checkpoint SHA-256: `c98f8d82047e6e0ecd00f144307623afdd581ee6624d880063f810d090e1a473`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.22196912765503 seconds.
+- Overall skill 0.336172; relative MASE 0.797227; relative WQL 0.552750.
+- term skill: long=0.3984, medium=0.3579, short=0.3019.
+- domain skill: Econ/Fin=0.1591, Energy=0.2292, Healthcare=0.4105, Nature=0.3647, Sales=0.3944, Transport=0.3718, Web/CloudOps=0.4497.
+- frequency skill: A=0.3173, D=0.3530, H=0.3656, M=0.1894, Q=0.2304, S=0.3411, T=0.3359, W=0.2800.
+- variates skill: multivariate=0.3535, univariate=0.3220.
+- Outcome: Matched whole-model sign-loss comparison; all parameters train, seed 197. Main screen improves; earlier cutoffs are mixed. Complete package validation follows.
+
+## E67 full_paired_control_ema3000 backcast2_evals
+- Checkpoint SHA-256: `c98f8d82047e6e0ecd00f144307623afdd581ee6624d880063f810d090e1a473`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.114278316497803 seconds.
+- Overall skill 0.357963; relative MASE 0.772252; relative WQL 0.533779.
+- term skill: long=0.3643, medium=0.3867, short=0.3442.
+- domain skill: Econ/Fin=0.1073, Energy=0.2363, Healthcare=0.3529, Nature=0.4659, Sales=0.5494, Transport=0.4196, Web/CloudOps=0.4322.
+- frequency skill: A=0.3515, D=0.4255, H=0.3911, M=0.1494, Q=0.1504, S=0.2817, T=0.3589, W=0.2528.
+- variates skill: multivariate=0.3643, univariate=0.3528.
+- Outcome: Matched whole-model sign-loss comparison; all parameters train, seed 197. Main screen improves; earlier cutoffs are mixed. Complete package validation follows.
+
+## E67 full_paired_candidate_ema3000 evals
+- Checkpoint SHA-256: `2a19ebe0d41c1a18feaba318041795be4d268ce890162a5035e6fc6bf1844238`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.954200506210327 seconds.
+- Overall skill 0.378318; relative MASE 0.743356; relative WQL 0.519924.
+- term skill: long=0.4020, medium=0.4066, short=0.3577.
+- domain skill: Econ/Fin=0.1339, Energy=0.2391, Healthcare=0.4511, Nature=0.4303, Sales=0.3325, Transport=0.4661, Web/CloudOps=0.5042.
+- frequency skill: A=0.3674, D=0.3531, H=0.3978, M=0.1030, Q=0.1972, S=0.4232, T=0.4383, W=0.2236.
+- variates skill: multivariate=0.3969, univariate=0.3631.
+- Outcome: Matched whole-model sign-loss comparison; all parameters train, seed 197. Main screen improves; earlier cutoffs are mixed. Complete package validation follows.
+
+## E67 full_paired_candidate_ema3000 backcast_evals
+- Checkpoint SHA-256: `2a19ebe0d41c1a18feaba318041795be4d268ce890162a5035e6fc6bf1844238`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.824409246444702 seconds.
+- Overall skill 0.335739; relative MASE 0.797824; relative WQL 0.553058.
+- term skill: long=0.3982, medium=0.3579, short=0.3012.
+- domain skill: Econ/Fin=0.1554, Energy=0.2283, Healthcare=0.4138, Nature=0.3640, Sales=0.3938, Transport=0.3730, Web/CloudOps=0.4488.
+- frequency skill: A=0.3126, D=0.3515, H=0.3664, M=0.1838, Q=0.2305, S=0.3411, T=0.3345, W=0.2832.
+- variates skill: multivariate=0.3522, univariate=0.3223.
+- Outcome: Matched whole-model sign-loss comparison; all parameters train, seed 197. Main screen improves; earlier cutoffs are mixed. Complete package validation follows.
+
+## E67 full_paired_candidate_ema3000 backcast2_evals
+- Checkpoint SHA-256: `2a19ebe0d41c1a18feaba318041795be4d268ce890162a5035e6fc6bf1844238`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.085822582244873 seconds.
+- Overall skill 0.358235; relative MASE 0.771602; relative WQL 0.533775.
+- term skill: long=0.3637, medium=0.3876, short=0.3445.
+- domain skill: Econ/Fin=0.1066, Energy=0.2361, Healthcare=0.3468, Nature=0.4694, Sales=0.5483, Transport=0.4212, Web/CloudOps=0.4313.
+- frequency skill: A=0.3537, D=0.4237, H=0.3911, M=0.1509, Q=0.1498, S=0.2809, T=0.3593, W=0.2593.
+- variates skill: multivariate=0.3635, univariate=0.3540.
+- Outcome: Matched whole-model sign-loss comparison; all parameters train, seed 197. Main screen improves; earlier cutoffs are mixed. Complete package validation follows.
+
+## long_memory_synth_recovery_ema3000
+- Checkpoint SHA-256: `c8d675cce3857f6c065a231ccbd4f2dfb76c6c49236da051d13b450287006a6f`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.661465406417847 seconds.
+- Overall skill 0.360588; relative MASE 0.761649; relative WQL 0.536793.
+- term skill: long=0.3910, medium=0.4017, short=0.3318.
+- domain skill: Econ/Fin=0.1155, Energy=0.1829, Healthcare=0.4341, Nature=0.4242, Sales=0.3463, Transport=0.4592, Web/CloudOps=0.5066.
+- frequency skill: A=0.3750, D=0.3500, H=0.3816, M=0.1111, Q=0.2000, S=0.4415, T=0.4013, W=0.2067.
+- variates skill: multivariate=0.3919, univariate=0.3345.
+- Outcome: E61 slower synthetic AR dynamics; seed79; improves main screen over matched E42 at3000 steps. Continue and check earlier cutoffs.
+
+## recent_normalization_recovery_ema3000
+- Checkpoint SHA-256: `a55a5681f52782a142f44595f69d5aef59579fa7f64124c72b9de918189d8ff9`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 33.14950752258301 seconds.
+- Overall skill 0.373434; relative MASE 0.747021; relative WQL 0.525533.
+- term skill: long=0.3987, medium=0.4024, short=0.3519.
+- domain skill: Econ/Fin=0.1428, Energy=0.2320, Healthcare=0.4255, Nature=0.4235, Sales=0.3398, Transport=0.4701, Web/CloudOps=0.4974.
+- frequency skill: A=0.3847, D=0.3515, H=0.3961, M=0.0795, Q=0.2007, S=0.4234, T=0.4320, W=0.2036.
+- variates skill: multivariate=0.3889, univariate=0.3609.
+- Outcome: E65 recent4096 normalization with8192 context; seed101; improves matched E54 3000-step long-context result .372681. Continue.
+
+### E70 compiler cache infrastructure diagnostic
+Both the default and /tmp Triton caches reject child-directory-to-parent os.replace with EXDEV. Same-parent replacement succeeds. A local SiblingFileCacheManager preserves Triton keys and group formats while writing unique same-parent temporary files. Byte, text, overwrite and group fixtures passed (initial fixture used an invalid nonhex key; corrected before accepting the fixture). No installed library files, model parameters, optimizer state or inference runtime were changed. Retrying the identical training-only compile benchmark; no optimizer updates.
+
+### E70 fixed-shape compiler benchmark outcome
+Seed223, B64, C2048, H512, own E62 checkpoint 22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c; no optimizer updates and only allowed training windows. Compilation 51.065s; total benchmark 80.009s. Alternating three blocks: eager mean0.119890s versus compiled mean0.064745s, speedup1.852. Loss0.228576794 vs0.228607744; gradient cosine0.998925465, maximum absolute gradient difference0.000610351562. bf16 arithmetic differs slightly; this is not bitwise training equivalence. Other experiments shared the H100. Variable-shape compatibility is being checked before use in training; published inference is unchanged.
+
+## E71 finer eight-point patches — prepared, not launched
+Hypothesis: E17 benefited from32→16-point patches, and the current model still has short-horizon energy errors. Test16→8-point patches while preserving physical position units and all attention/MLP/confidence parameters. Fold the two halves of context, query and recency input projections by summation and keep the first8 output rows, as in the earlier resolution experiment. This is an own-weight initialization, not an exact function-preserving conversion. Initialize from E62, use seed197/B64/LR2e-5/12000 steps with paired sign loss, and compare to the already-running E67 paired16-point arm. Keep the identical sampler and its16-point random-prefix rounding; only model patch resolution changes. CPU fixtures and initial evaluation precede training. No additional data or synthetic generator. Waiting for training memory to become available.
+
+E70 variable-shape follow-up: torch.compile(dynamic=True, fullgraph=True) failed in the installed PyTorch tensorification pass: TensorReferenceAnalysis lacks python_mod. No optimizer updates occurred. Test static shape specialization with a64-graph cache limit instead, preserving the existing16 context/horizon combinations. Dynamic failure log: compiler_variable_shapes.log.
+
+## E72 convex mixtures of training windows — prepared, not launched
+Hypothesis: interpolating real dynamics can expand effective corpus diversity and regularize the forecast mapping beyond drawing additional series. On25% of sampled rows, choose a different row with the same two periods, normalize the donor using context-only mean/std into the primary context units, then combine context and future separately with a Beta(2,2) coefficient. Missing positions use mask intersection; require8 common observed context points and1 common future label. Both real and existing locally synthetic rows are eligible. Independent seed600200 preserves the main corpus/generator stream and requested-horizon stream. Planned own E62 initialization, seed197, paired loss, B64, LR2e-5,12000 steps, identical E67 paired control settings. No inference changes. Fixtures: {"cpu_seconds": 2.7141120220185257, "seed": 197, "mixture_seed": 600200, "rows": 192, "changed_context_rows": 43, "main_and_horizon_rng_matched": true, "changing_labels_does_not_change_contexts": true, "finite_masks_respected": true, "optimizer_updates": 0}. Await training allocation.
+
+### E58 symmetry diagnostic
+Seed239, four allowed training windows, no optimizer updates, CPU5.262225491984282s. At global12000, duplicate-stream encoder5 activations differ by only0.001181 relative RMS, versus0.001124 at initialization. Sampled duplicate-row matrix cosines remain0.99987–0.99997. The nearly redundant streams weaken the interpretation that this experiment tested useful extra capacity. Full measurements: wide_capacity_symmetry_diagnostic.json. No current weights changed.
+
+## E73 independently initialized extra feature stream — prepared, not launched
+Hypothesis: widening by near-identical copies keeps the extra neurons redundant. Build a146,477,344-parameter model with the original512 features and a separate randomly initialized512-feature stream. Inherited linear maps occupy their original blocks; new internal maps are independently random; cross-stream maps and new output-head columns start at zero. Layer normalization operates on each512-feature group separately so the parent function survives initialization. All maps, including cross-stream connections, will train. This changes initialization and normalization grouping relative to E58. Conversion seed241; own E47 parent. Same training seed113, B96, LR3e-5,32000-step schedule and optimizer/sampler reset at8000 with seed179 as the completed/recovered small control. Use unit LR multipliers because original columns are no longer duplicated or divided. No new data or generator. Conversion fixture: {"method": "independent random feature stream with block-diagonal inherited projections, zero cross-stream projections and zero new output columns; separate512-feature layer normalization", "parent_sha256": "0bad97e8994aac49ea173443db0a4fa8d72d33f9eb9e2660f06de75f7f1403eb", "random_initialization_seed": 241, "fixture_max_abs_difference": 1.6689300537109375e-06, "new_stream_activation": {"relative_rms_difference": 1.4169212579727173}, "learning_rate_multipliers": "one for every parameter", "checkpoint_sha256": "d164d8b12223032b23948d0dcb17389d10f8e739a30a1ade5f677d2a948e965b", "parameters": 146477344, "cpu_seconds": 2.802320774004329, "optimizer_updates": 0}. Initial screen and gradient checks precede training.
+
+### E70 static shape specialization outcome
+All four tested context/horizon shapes compiled and produced finite gradients; returning to2048/512 reused graph1, with four unique graphs total. Loss differences remained below0.00007. First calls took47.33,38.52,51.27,46.47 seconds; steady forward/backward speedups ranged1.60–1.94 under shared load. Complete result compiler_static_shapes_result.json, elapsed208.31172983598663s. Static specialization is usable, but16 training shapes incur compilation cost and bf16 arithmetic changes; existing active training runs remain eager.
+
+## E58/E66 recovered146M at global12000
+- Checkpoint SHA-256: `3d39cb54e80093bf97c2d95eda6bd30fefa65ec47b33ba70838dc881321e31ea`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 41.74531269073486 seconds.
+- Overall skill 0.373963; relative MASE 0.747878; relative WQL 0.524045.
+- term skill: long=0.3982, medium=0.4082, short=0.3507.
+- domain skill: Econ/Fin=0.1409, Energy=0.2357, Healthcare=0.3980, Nature=0.4325, Sales=0.3406, Transport=0.4658, Web/CloudOps=0.4987.
+- frequency skill: A=0.3756, D=0.3419, H=0.3949, M=0.0902, Q=0.1997, S=0.4220, T=0.4377, W=0.2068.
+- variates skill: multivariate=0.3930, univariate=0.3584.
+- Outcome: Diagnostic checkpoint; training continues. See experiment hypothesis and matched controls above.
+
+## E63 expanded corpus at146M first4000
+- Checkpoint SHA-256: `c89939a906b619e3d119a26d2edf1b567ff902930ca78b3e58cdce5903ed3077`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 41.43943977355957 seconds.
+- Overall skill 0.373186; relative MASE 0.749492; relative WQL 0.524216.
+- term skill: long=0.3994, medium=0.4061, short=0.3496.
+- domain skill: Econ/Fin=0.1406, Energy=0.2371, Healthcare=0.4020, Nature=0.4224, Sales=0.3395, Transport=0.4650, Web/CloudOps=0.5007.
+- frequency skill: A=0.3699, D=0.3383, H=0.3939, M=0.1020, Q=0.2066, S=0.4209, T=0.4379, W=0.2018.
+- variates skill: multivariate=0.3928, univariate=0.3571.
+- Outcome: Diagnostic checkpoint; training continues. See experiment hypothesis and matched controls above.
+
+## E61 slow AR3000 first earlier cutoff
+- Checkpoint SHA-256: `c8d675cce3857f6c065a231ccbd4f2dfb76c6c49236da051d13b450287006a6f`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.004189491271973 seconds.
+- Overall skill 0.329626; relative MASE 0.805342; relative WQL 0.558025.
+- term skill: long=0.3886, medium=0.3565, short=0.2947.
+- domain skill: Econ/Fin=0.1530, Energy=0.2229, Healthcare=0.4151, Nature=0.3515, Sales=0.3980, Transport=0.3624, Web/CloudOps=0.4448.
+- frequency skill: A=0.3216, D=0.3548, H=0.3555, M=0.1730, Q=0.2272, S=0.3450, T=0.3293, W=0.2673.
+- variates skill: multivariate=0.3459, univariate=0.3164.
+- Outcome: Diagnostic checkpoint; training continues. See experiment hypothesis and matched controls above.
+
+## E61 slow AR3000 second earlier cutoff
+- Checkpoint SHA-256: `c8d675cce3857f6c065a231ccbd4f2dfb76c6c49236da051d13b450287006a6f`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.961536407470703 seconds.
+- Overall skill 0.353271; relative MASE 0.774762; relative WQL 0.539854.
+- term skill: long=0.3691, medium=0.3752, short=0.3388.
+- domain skill: Econ/Fin=0.0921, Energy=0.2250, Healthcare=0.3726, Nature=0.4619, Sales=0.5589, Transport=0.4127, Web/CloudOps=0.4305.
+- frequency skill: A=0.3401, D=0.4317, H=0.3862, M=0.1746, Q=0.1512, S=0.2822, T=0.3452, W=0.2437.
+- variates skill: multivariate=0.3595, univariate=0.3482.
+- Outcome: Diagnostic checkpoint; training continues. See experiment hypothesis and matched controls above.
+
+## E67 paired3000 third earlier cutoff
+- Checkpoint SHA-256: `2a19ebe0d41c1a18feaba318041795be4d268ce890162a5035e6fc6bf1844238`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 26.884973287582397 seconds.
+- Overall skill 0.389069; relative MASE 0.736752; relative WQL 0.506597.
+- term skill: long=0.3965, medium=0.3605, short=0.3968.
+- domain skill: Econ/Fin=0.1707, Energy=0.3208, Healthcare=0.2592, Nature=0.5054, Sales=0.3991, Transport=0.5053, Web/CloudOps=0.3895.
+- frequency skill: A=0.3624, D=0.4436, H=0.4039, M=0.1198, Q=0.2578, S=0.2862, T=0.4305, W=0.2611.
+- variates skill: multivariate=0.3741, univariate=0.4006.
+- Outcome: Diagnostic checkpoint; training continues. See experiment hypothesis and matched controls above.
+
+## E67 candidate3000 complete package validation
+- Checkpoint SHA-256: `2a19ebe0d41c1a18feaba318041795be4d268ce890162a5035e6fc6bf1844238`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.374401; relative MASE 0.746075; relative WQL 0.524578.
+- term skill: long=0.3961, medium=0.4055, short=0.3534.
+- domain skill: Econ/Fin=0.1115, Energy=0.2320, Healthcare=0.4573, Nature=0.4303, Sales=0.3470, Transport=0.4663, Web/CloudOps=0.4964.
+- frequency skill: A=0.2888, D=0.3569, H=0.3930, M=0.1278, Q=0.1823, S=0.4232, T=0.4312, W=0.2160.
+- variates skill: multivariate=0.3926, univariate=0.3596.
+- Outcome: Full public-package validation. Training seed197, B64 distinct windows with both signs, LR2e-5;3000-step checkpoint at687.5s of training-loop elapsed time on the shared single H100. Exact public inference 129.863s. Paired candidate wins full validation and main screen; it improves earlier cutoffs2/3 versus E62, but loses0.000488 on cutoff1. Control full skill0.374180 versus paired0.374401. Select paired3000 with this temporal tradeoff recorded; training continues.
+
+## E67 control3000 complete package validation
+- Checkpoint SHA-256: `c98f8d82047e6e0ecd00f144307623afdd581ee6624d880063f810d090e1a473`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.374180; relative MASE 0.746358; relative WQL 0.524749.
+- term skill: long=0.3948, medium=0.4057, short=0.3535.
+- domain skill: Econ/Fin=0.1134, Energy=0.2311, Healthcare=0.4600, Nature=0.4313, Sales=0.3473, Transport=0.4663, Web/CloudOps=0.4949.
+- frequency skill: A=0.2923, D=0.3579, H=0.3922, M=0.1281, Q=0.1825, S=0.4224, T=0.4308, W=0.2163.
+- variates skill: multivariate=0.3915, univariate=0.3600.
+- Outcome: Full public-package validation. Training seed197, B64 distinct windows with both signs, LR2e-5;3000-step checkpoint at687.5s of training-loop elapsed time on the shared single H100. Exact public inference 131.546s. Paired candidate wins full validation and main screen; it improves earlier cutoffs2/3 versus E62, but loses0.000488 on cutoff1. Control full skill0.374180 versus paired0.374401. Select paired3000 with this temporal tradeoff recorded; training continues.
+
+E67 publication decision: paired3000 checkpoint2a19ebe0d41c1a18feaba318041795be4d268ce890162a5035e6fc6bf1844238, full0.3744010291; main0.3783180328; earlier cutoffs0.3357387876/0.3582353306/0.3890690875. Relative to published E62, full gain0.000981171, main gain0.000974817, earlier changes-0.000488248/+0.000541947/+0.001280498. All97 full configurations and151170 instances pass with no quantile crossings. Three example configurations are bitwise equal after independent relocation; runtime/weight hashes verified. The published inference remains eager.
+
+## E74 equal ensemble of E62 and its paired3000 continuation
+Hypothesis: fixed forecast averaging can retain the parent's stronger first earlier cutoff while preserving gains from paired-sign training. Test exactly one equal-weight quantile mean after each model's existing fixed rules, without a weight sweep or additional zero projection. Both models would forecast every instance. Reuse cached predictions for screening; no training, new seed or source. Evaluate main and all three earlier cutoffs, then full validation only if promising; an actual packaged replay remains required before publication. Members: E62 hash22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c, E67 hash2a19ebe0d41c1a18feaba318041795be4d268ce890162a5035e6fc6bf1844238.
+
+## full_paired_control_ema6000
+- Checkpoint SHA-256: `4517cc18d0d7b022c728a208b53bb0088fcefe249246640d26e688ba800cf84c`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.108938932418823 seconds.
+- Overall skill 0.377544; relative MASE 0.744699; relative WQL 0.520279.
+- term skill: long=0.3999, medium=0.4077, short=0.3567.
+- domain skill: Econ/Fin=0.1294, Energy=0.2391, Healthcare=0.4588, Nature=0.4300, Sales=0.3396, Transport=0.4652, Web/CloudOps=0.4999.
+- frequency skill: A=0.3728, D=0.3542, H=0.3955, M=0.1113, Q=0.1942, S=0.4228, T=0.4360, W=0.2279.
+- variates skill: multivariate=0.3946, univariate=0.3636.
+- Outcome: Paired6000 main improves to0.378455; matched control0.377544. Graft initialization reproduces parent0.375793 within numerical precision. Earlier paired diagnostics and future training determine selection.
+
+## full_paired_candidate_ema6000
+- Checkpoint SHA-256: `b797a18346b75d6cc1f541b56f880cd43055890f86b872bb37425b1c92609e71`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.94004225730896 seconds.
+- Overall skill 0.378455; relative MASE 0.743557; relative WQL 0.519554.
+- term skill: long=0.4019, medium=0.4071, short=0.3578.
+- domain skill: Econ/Fin=0.1262, Energy=0.2397, Healthcare=0.4622, Nature=0.4291, Sales=0.3379, Transport=0.4657, Web/CloudOps=0.5030.
+- frequency skill: A=0.3687, D=0.3544, H=0.3962, M=0.1031, Q=0.1932, S=0.4249, T=0.4385, W=0.2281.
+- variates skill: multivariate=0.3972, univariate=0.3631.
+- Outcome: Paired6000 main improves to0.378455; matched control0.377544. Graft initialization reproduces parent0.375793 within numerical precision. Earlier paired diagnostics and future training determine selection.
+
+## wide_graft_initial
+- Checkpoint SHA-256: `d164d8b12223032b23948d0dcb17389d10f8e739a30a1ade5f677d2a948e965b`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 44.365342140197754 seconds.
+- Overall skill 0.375793; relative MASE 0.745650; relative WQL 0.522544.
+- term skill: long=0.3998, medium=0.4067, short=0.3540.
+- domain skill: Econ/Fin=0.1396, Energy=0.2348, Healthcare=0.4405, Nature=0.4284, Sales=0.3399, Transport=0.4658, Web/CloudOps=0.5006.
+- frequency skill: A=0.3762, D=0.3542, H=0.3951, M=0.0953, Q=0.1981, S=0.4239, T=0.4362, W=0.2089.
+- variates skill: multivariate=0.3938, univariate=0.3611.
+- Outcome: Paired6000 main improves to0.378455; matched control0.377544. Graft initialization reproduces parent0.375793 within numerical precision. Earlier paired diagnostics and future training determine selection.
+
+E74 valid_screen: skill0.37807862274263027, relativeMASE0.7436448167242383, relativeWQL0.5201222287724669, CPU0.8619319740100764s; group skills {"term": {"short": 0.35759494914169054, "medium": 0.40699311016073325, "long": 0.40082709768107416}, "domain": {"Econ/Fin": 0.1373283443599086, "Energy": 0.23867787085456083, "Healthcare": 0.4514094815443358, "Nature": 0.4314394439880541, "Sales": 0.3334613777872122, "Transport": 0.46623492569656166, "Web/CloudOps": 0.5020547204722161}, "freq": {"A-DEC": 0.3700186215632586, "Q-DEC": 0.1986150873665018, "M": 0.10412662722790622, "W-SUN": 0.07901894069086524, "D": 0.35481222927845457, "H": 0.39699199743484126, "15T": 0.21413878856050306, "W-FRI": -0.0023901912492456656, "10T": 0.615926427622083, "W-TUE": 0.5812846025030445, "W-THU": 0.20706119312963223, "W-WED": 0.31744777723871154, "5T": 0.5126119145163621, "10S": 0.4229238092063159}}.
+
+E74 valid_backcast: skill0.3362925418425802, relativeMASE0.7972981372266922, relativeWQL0.552500463058947, CPU0.8971402349998243s; group skills {"term": {"short": 0.30190782919949666, "medium": 0.35857372233263296, "long": 0.3983345998796374}, "domain": {"Econ/Fin": 0.15970397473963605, "Energy": 0.22932627079926182, "Healthcare": 0.40921062898616656, "Nature": 0.3642338798926953, "Sales": 0.395796512384459, "Transport": 0.3726773780449273, "Web/CloudOps": 0.4497268240013762}, "freq": {"A-DEC": 0.31984516176318767, "Q-DEC": 0.23213469399614717, "M": 0.1867528733976035, "W-SUN": 0.027286458584816375, "D": 0.35084294376677716, "H": 0.36615049422003554, "15T": 0.22473053922570885, "W-FRI": 0.2587886969661052, "10T": 0.3198607928100037, "W-TUE": 0.5430562436146349, "W-THU": 0.24609593870252322, "W-WED": 0.3423884364810911, "5T": 0.4378462658914031, "10S": 0.34158786712809996}}.
+
+E74 valid_backcast2: skill0.3582180334801286, relativeMASE0.771690389912856, relativeWQL0.5337426744378997, CPU0.6840074959909543s; group skills {"term": {"short": 0.3444537095616398, "medium": 0.3869421164002306, "long": 0.36441392284164364}, "domain": {"Econ/Fin": 0.10841961881158191, "Energy": 0.2363021419988357, "Healthcare": 0.34644304914503876, "Nature": 0.4685938042819019, "Sales": 0.5492137665291525, "Transport": 0.4207857629370483, "Web/CloudOps": 0.4314575495135424}, "freq": {"A-DEC": 0.3538974701075839, "Q-DEC": 0.1504553008739551, "M": 0.15220470977886436, "W-SUN": 0.04613015348540839, "D": 0.42446352745465865, "H": 0.39096253798739566, "15T": 0.10670219795421398, "W-FRI": 0.14873972396574198, "10T": 0.4961638002828159, "W-TUE": 0.08195477095370607, "W-THU": 0.32491052813791066, "W-WED": 0.5372312006064026, "5T": 0.46600691242017644, "10S": 0.28101678592852175}}.
+
+E74 valid_backcast3: skill0.38866584889169165, relativeMASE0.7372042201081582, relativeWQL0.506955107034635, CPU0.6765452020335943s; group skills {"term": {"short": 0.39634584808261863, "medium": 0.36050403977747736, "long": 0.39564160116149116}, "domain": {"Econ/Fin": 0.17218827750208232, "Energy": 0.32018031275863146, "Healthcare": 0.251739940360198, "Nature": 0.505730039991936, "Sales": 0.3992528233665873, "Transport": 0.5057485203797758, "Web/CloudOps": 0.38900692766706535}, "freq": {"A-DEC": 0.35992280976221613, "Q-DEC": 0.2607290890980446, "M": 0.1158596568834448, "W-SUN": 0.14646809588021303, "D": 0.4430503070010823, "H": 0.40373923909167875, "15T": 0.26167249262649306, "W-FRI": 0.026229112650416053, "10T": 0.6033044583572338, "W-TUE": 0.2614261172432897, "W-THU": 0.3805020665242401, "W-WED": 0.37420834670061953, "5T": 0.4617999415682724, "10S": 0.28494980458180186}}.
+
+E74 fixed ensemble recovers cutoff1 but is below the paired member on main and cutoffs2/3. Check cached full validation before deciding; no mixture coefficient tuning.
+
+E73 gradient fixture: {"cpu_seconds": 3.0038612950011156, "seed": 251, "allowed_training_windows": 2, "loss": 0.6392828226089478, "initial_gradients": {"head_new_columns": 1.3039275407791138, "encoder0_new_to_old_attention": 0.16236796975135803, "decoder3_new_to_old_ff": 0.3286978602409363}, "all_gradients_finite": true, "optimizer_updates": 0}. All gradients finite, with nonzero gradient to new output columns and new-to-old attention/feedforward maps at initialization.
+
+## E67 paired6000 backcast_evals
+- Checkpoint SHA-256: `b797a18346b75d6cc1f541b56f880cd43055890f86b872bb37425b1c92609e71`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 27.112698793411255 seconds.
+- Overall skill 0.335119; relative MASE 0.798971; relative WQL 0.553296.
+- term skill: long=0.3996, medium=0.3567, short=0.2999.
+- domain skill: Econ/Fin=0.1484, Energy=0.2276, Healthcare=0.4122, Nature=0.3651, Sales=0.3962, Transport=0.3724, Web/CloudOps=0.4480.
+- frequency skill: A=0.3127, D=0.3525, H=0.3668, M=0.1712, Q=0.2359, S=0.3414, T=0.3328, W=0.2844.
+- variates skill: multivariate=0.3517, univariate=0.3216.
+- Outcome: Compared with paired3000, cutoff1 loses0.000620 and cutoff2 gains0.001211. Main gains0.000137. Complete package validation and third earlier cutoff follow; keep paired3000 published meanwhile.
+
+## E67 paired6000 backcast2_evals
+- Checkpoint SHA-256: `b797a18346b75d6cc1f541b56f880cd43055890f86b872bb37425b1c92609e71`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.65610408782959 seconds.
+- Overall skill 0.359446; relative MASE 0.769629; relative WQL 0.533126.
+- term skill: long=0.3637, medium=0.3914, short=0.3451.
+- domain skill: Econ/Fin=0.0947, Energy=0.2369, Healthcare=0.3640, Nature=0.4722, Sales=0.5483, Transport=0.4203, Web/CloudOps=0.4322.
+- frequency skill: A=0.3460, D=0.4239, H=0.3913, M=0.1623, Q=0.1478, S=0.2801, T=0.3609, W=0.2646.
+- variates skill: multivariate=0.3635, univariate=0.3561.
+- Outcome: Compared with paired3000, cutoff1 loses0.000620 and cutoff2 gains0.001211. Main gains0.000137. Complete package validation and third earlier cutoff follow; keep paired3000 published meanwhile.
+
+E74 cached full validation: skill0.37416157579966614, relativeMASE0.7463637996830615, relativeWQL0.5247758979895311, CPU1.7625104649923742s; group skills {"term": {"short": 0.35339006443372445, "medium": 0.4058776358425863, "long": 0.39478447597143573}, "domain": {"Econ/Fin": 0.1148260686606345, "Energy": 0.2315840307823489, "Healthcare": 0.45764579718017184, "Nature": 0.4314211475708052, "Sales": 0.3476654519885174, "Transport": 0.46645466438647154, "Web/CloudOps": 0.49435858431787905}, "freq": {"A-DEC": 0.2905258409636121, "Q-DEC": 0.18415390225031736, "M": 0.12808227393650784, "W-SUN": 0.042170378733840996, "D": 0.3587361531705563, "H": 0.39218267210684865, "15T": 0.20355479619501637, "W-FRI": -0.021429381368383904, "10T": 0.615926427622083, "W-TUE": 0.5812846025030445, "W-THU": 0.20706119312963223, "W-WED": 0.31744777723871154, "5T": 0.5037733014006005, "10S": 0.4229238092063159}}. Below paired3000 skill0.374401 by0.000239. Reject: the first earlier-cutoff benefit does not outweigh lower full/main/other-earlier scores. No ensemble package is published and no weight sweep follows.
+
+E71 initial8-point model main screen0.3622169411 versus E62 parent0.3773432163; conversion intentionally changes function. CPU fixture0.9008s verifies constant-input projection folding, unchanged inherited tensors, finite/ordered forecasts at6/18/720/900 steps. Training recovery must justify this initialization cost.
+
+E58/E63 resource decision fixed before the next results: at global16000, stop the near-duplicate width trial if it remains below both its E47 parent and the matched small control; preserve a complete recovery checkpoint. At8000, assess expanded-corpus146M against its same-width original-corpus8000. Prioritize the independent-stream E73 initialization when a large training slot is released. Existing runs retain eager arithmetic.
+
+## E67 paired6000 exact full package
+- Checkpoint SHA-256: `b797a18346b75d6cc1f541b56f880cd43055890f86b872bb37425b1c92609e71`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.374279; relative MASE 0.746472; relative WQL 0.524502.
+- term skill: long=0.3958, medium=0.4059, short=0.3532.
+- domain skill: Econ/Fin=0.1029, Energy=0.2327, Healthcare=0.4651, Nature=0.4291, Sales=0.3514, Transport=0.4660, Web/CloudOps=0.4952.
+- frequency skill: A=0.2886, D=0.3570, H=0.3913, M=0.1264, Q=0.1824, S=0.4249, T=0.4314, W=0.2203.
+- variates skill: multivariate=0.3928, univariate=0.3591.
+- Outcome: Paired6000 full0.374279 is below published3000 full0.374401 despite main/earlier2/earlier3 gains; retain3000 while training continues. Recent normalization6000 improves matched long-context baseline0.373979 and slightly exceeds4096 control0.374515; continue to12000.
+
+## E67 paired6000 third earlier cutoff
+- Checkpoint SHA-256: `b797a18346b75d6cc1f541b56f880cd43055890f86b872bb37425b1c92609e71`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 25.89822793006897 seconds.
+- Overall skill 0.389533; relative MASE 0.736193; relative WQL 0.506212.
+- term skill: long=0.3966, medium=0.3607, short=0.3974.
+- domain skill: Econ/Fin=0.1634, Energy=0.3178, Healthcare=0.2808, Nature=0.5081, Sales=0.3977, Transport=0.5055, Web/CloudOps=0.3908.
+- frequency skill: A=0.3558, D=0.4451, H=0.4045, M=0.1415, Q=0.2554, S=0.2895, T=0.4276, W=0.2643.
+- variates skill: multivariate=0.3722, univariate=0.4028.
+- Outcome: Paired6000 full0.374279 is below published3000 full0.374401 despite main/earlier2/earlier3 gains; retain3000 while training continues. Recent normalization6000 improves matched long-context baseline0.373979 and slightly exceeds4096 control0.374515; continue to12000.
+
+## E65 recent normalization6000
+- Checkpoint SHA-256: `1c5bc9b814879a43941f6e59b6d2024152ec9db43014e2b4431195e52366f1ea`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 34.534422874450684 seconds.
+- Overall skill 0.374709; relative MASE 0.745557; relative WQL 0.524426.
+- term skill: long=0.4014, medium=0.4044, short=0.3523.
+- domain skill: Econ/Fin=0.1384, Energy=0.2354, Healthcare=0.4238, Nature=0.4264, Sales=0.3389, Transport=0.4711, Web/CloudOps=0.4974.
+- frequency skill: A=0.3756, D=0.3531, H=0.3970, M=0.0716, Q=0.2013, S=0.4239, T=0.4343, W=0.2078.
+- variates skill: multivariate=0.3915, univariate=0.3610.
+- Outcome: Paired6000 full0.374279 is below published3000 full0.374401 despite main/earlier2/earlier3 gains; retain3000 while training continues. Recent normalization6000 improves matched long-context baseline0.373979 and slightly exceeds4096 control0.374515; continue to12000.
+
+E67 paired6000 full public inference131.433437s on the shared single H100; all97 configurations and151170 instances passed. The original3k publication remains selected by full validation, with the temporal tradeoff documented.
+
+## E75 training loss after fixed inference transformations — prepared, not launched
+Hypothesis: paired training aligns sign averaging but still scores raw neural forecasts, whereas inference changes them with seasonal and continuity priors and exact-zero projection. Compute the same fixed context statistics without model gradients, then apply the transforms differentiably to forecasts before the existing normalized median/quantile loss. Gradients include the continuity correction's dependence on the first forecast; zero/positivity thresholds remain fixed and piecewise differentiable. The target transform and seasonal loss weight stay unchanged. Planned E62 initialization, seed197, paired loss, B64, LR2e-5,12000 steps, identical data stream to E67 paired. No new data/generator and no inference changes. CPU implementation comparison: {"cpu_seconds": 0.11522011604392901, "seed": 263, "fixtures": [{"context": 8, "horizon": 6, "maximum_absolute_difference": 0.0, "finite_gradients": true}, {"context": 64, "horizon": 18, "maximum_absolute_difference": 2.384185791015625e-07, "finite_gradients": true}, {"context": 512, "horizon": 128, "maximum_absolute_difference": 1.1920928955078125e-06, "finite_gradients": true}, {"context": 4096, "horizon": 900, "maximum_absolute_difference": 7.152557373046875e-07, "finite_gradients": true}], "optimizer_updates": 0}. Fixture agreement is numerical, not bitwise, because training computes context statistics in float64. Await a training slot and one allowed-window end-to-end gradient check.
+
+## E61 slow AR6000 evals
+- Checkpoint SHA-256: `fea5b5290a3a9be5618eaeaf67e918e4f677d5be908d562040ce4bdfeb549bd9`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.034730434417725 seconds.
+- Overall skill 0.361281; relative MASE 0.760333; relative WQL 0.536557.
+- term skill: long=0.3915, medium=0.4029, short=0.3324.
+- domain skill: Econ/Fin=0.1215, Energy=0.1845, Healthcare=0.4313, Nature=0.4272, Sales=0.3429, Transport=0.4577, Web/CloudOps=0.5068.
+- frequency skill: A=0.3788, D=0.3481, H=0.3812, M=0.1116, Q=0.2006, S=0.4421, T=0.4034, W=0.2113.
+- variates skill: multivariate=0.3930, univariate=0.3349.
+- Outcome: Main skill0.361281 exceeds matched partial-target0.359222. Compare both earlier windows with E42; continue to12000.
+
+## E61 slow AR6000 backcast_evals
+- Checkpoint SHA-256: `fea5b5290a3a9be5618eaeaf67e918e4f677d5be908d562040ce4bdfeb549bd9`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 25.68802785873413 seconds.
+- Overall skill 0.331040; relative MASE 0.803953; relative WQL 0.556635.
+- term skill: long=0.3886, medium=0.3569, short=0.2972.
+- domain skill: Econ/Fin=0.1588, Energy=0.2277, Healthcare=0.4061, Nature=0.3526, Sales=0.3967, Transport=0.3629, Web/CloudOps=0.4452.
+- frequency skill: A=0.3244, D=0.3518, H=0.3577, M=0.1682, Q=0.2306, S=0.3442, T=0.3314, W=0.2762.
+- variates skill: multivariate=0.3484, univariate=0.3169.
+- Outcome: Main skill0.361281 exceeds matched partial-target0.359222. Compare both earlier windows with E42; continue to12000.
+
+## E61 slow AR6000 backcast2_evals
+- Checkpoint SHA-256: `fea5b5290a3a9be5618eaeaf67e918e4f677d5be908d562040ce4bdfeb549bd9`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.468894004821777 seconds.
+- Overall skill 0.354097; relative MASE 0.774005; relative WQL 0.539002.
+- term skill: long=0.3704, medium=0.3752, short=0.3398.
+- domain skill: Econ/Fin=0.0990, Energy=0.2275, Healthcare=0.3686, Nature=0.4617, Sales=0.5571, Transport=0.4131, Web/CloudOps=0.4308.
+- frequency skill: A=0.3441, D=0.4303, H=0.3862, M=0.1683, Q=0.1512, S=0.2846, T=0.3470, W=0.2514.
+- variates skill: multivariate=0.3602, univariate=0.3491.
+- Outcome: Main skill0.361281 exceeds matched partial-target0.359222. Compare both earlier windows with E42; continue to12000.
+
+E73 compiler fixture: {"seed": 271, "batch": 16, "context": 512, "horizon": 128, "eager_loss": 0.2625328004360199, "compiled_loss": 0.26252102851867676, "gradient_cosine": 0.9994836559992685, "maximum_gradient_difference": 0.003173828125, "compile_and_gradient_comparison_seconds": 65.4888255270198, "total_seconds": 66.77216628700262, "optimizer_updates": 0, "source": "allowed packed_sample and own generator"}. Static compilation is numerically close, not bitwise; use it for the32k-step independent-stream trial to amortize the16 shape compilations. This implementation difference from the existing eager small control is explicit. Loss formula, sampler, optimizer and schedule stay fixed.
+
+E75 allowed-window end-to-end fixture: {"cpu_seconds": 2.641128608956933, "seed": 269, "allowed_training_and_own_synthetic_windows": 8, "loss": 0.2808418869972229, "gradient_norm": 0.18120726943016052, "all_gradients_finite": true, "optimizer_updates": 0}. No optimizer updates.
+
+E69 is now queued after the slow-AR trainer PID980 exits with ema012000.pt, using its declared eager settings and the same allocation. Queue receipt volatility_synth_queue.json and watcher watch_volatility_synth.log identify the run. No E69 training has started yet.
+
+E71 maximum-shape GPU fixture: {"seed": 277, "batch": 64, "signed_rows": 128, "context": 4096, "horizon": 1024, "optimizer_updates": 0, "loss": 0.2055814117193222, "seconds": 10.965134899015538, "peak_allocated_mib": 10373.298828125, "peak_reserved_mib": 10992.0}. Passed without optimizer updates. Queue after both E67 trainers finish12000, so its larger working set reuses their combined allocation.
+
+E75 queued after E65 recent-normalization finishes12000. Both new queues require the predecessor final EMA before starting, and use the declared eager settings. E72 mixup remains prepared only; E73 awaits the prespecified width checkpoint decision.
+
+## E76 transfer the matched slow-AR6000 correction
+E61 improves the main and both earlier cutoffs at6000 versus E42. Hypothesis: this learned synthetic-dynamics correction also helps the selected paired3000 model. Form exactly base(E67 paired3000)+candidate(E61 slowAR6000)-control(E42 partial6000), coefficient1.0 on matching tensors; keep the base confidence projection unchanged. All three checkpoints were trained here on declared sources. No coefficient sweep, new training, data or inference changes. Compare the main and earlier screens before full replay.
+
+## E76 slow-AR correction evals
+- Checkpoint SHA-256: `8562cb05319ed04fdd8544a1af80bf1688ba55284e59b4689aa646e34e38a105`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.135655403137207 seconds.
+- Overall skill 0.379721; relative MASE 0.741564; relative WQL 0.518831.
+- term skill: long=0.4036, medium=0.4064, short=0.3597.
+- domain skill: Econ/Fin=0.1442, Energy=0.2362, Healthcare=0.4597, Nature=0.4330, Sales=0.3350, Transport=0.4655, Web/CloudOps=0.5071.
+- frequency skill: A=0.3715, D=0.3604, H=0.3974, M=0.0968, Q=0.1987, S=0.4260, T=0.4397, W=0.2230.
+- variates skill: multivariate=0.3981, univariate=0.3647.
+- Outcome: Fixed coefficient1.0 matched own-model correction improves all three screens over paired3000. Full public-package validation and the third earlier cutoff follow before publication. No additional optimization.
+
+## E76 slow-AR correction backcast_evals
+- Checkpoint SHA-256: `8562cb05319ed04fdd8544a1af80bf1688ba55284e59b4689aa646e34e38a105`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 25.913641452789307 seconds.
+- Overall skill 0.336781; relative MASE 0.795517; relative WQL 0.552923.
+- term skill: long=0.3965, medium=0.3587, short=0.3036.
+- domain skill: Econ/Fin=0.1684, Energy=0.2257, Healthcare=0.4092, Nature=0.3689, Sales=0.3927, Transport=0.3721, Web/CloudOps=0.4520.
+- frequency skill: A=0.3199, D=0.3498, H=0.3654, M=0.1876, Q=0.2323, S=0.3437, T=0.3374, W=0.2878.
+- variates skill: multivariate=0.3539, univariate=0.3228.
+- Outcome: Fixed coefficient1.0 matched own-model correction improves all three screens over paired3000. Full public-package validation and the third earlier cutoff follow before publication. No additional optimization.
+
+## E76 slow-AR correction backcast2_evals
+- Checkpoint SHA-256: `8562cb05319ed04fdd8544a1af80bf1688ba55284e59b4689aa646e34e38a105`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.258840799331665 seconds.
+- Overall skill 0.360577; relative MASE 0.768980; relative WQL 0.531693.
+- term skill: long=0.3677, medium=0.3876, short=0.3472.
+- domain skill: Econ/Fin=0.1192, Energy=0.2354, Healthcare=0.3433, Nature=0.4736, Sales=0.5465, Transport=0.4203, Web/CloudOps=0.4380.
+- frequency skill: A=0.3515, D=0.4289, H=0.3897, M=0.1383, Q=0.1499, S=0.2861, T=0.3643, W=0.2654.
+- variates skill: multivariate=0.3674, univariate=0.3550.
+- Outcome: Fixed coefficient1.0 matched own-model correction improves all three screens over paired3000. Full public-package validation and the third earlier cutoff follow before publication. No additional optimization.
+
+E76 CPU composition receipt: {"method": "matched training delta", "formula": "base + candidate - control", "coefficient": 1.0, "parent_hashes": {"/workspace/research/runs/full_paired_candidate/ema003000.pt": "2a19ebe0d41c1a18feaba318041795be4d268ce890162a5035e6fc6bf1844238", "/workspace/research/runs/long_memory_synth_recovery/ema006000.pt": "fea5b5290a3a9be5618eaeaf67e918e4f677d5be908d562040ce4bdfeb549bd9", "/workspace/research/runs/partial_targets/ema006000.pt": "67562486b099a350da53c3caa5ff0932fe7fecdfe72c1879b064b2a9bb173dfb"}, "seconds": 1.1477709169848822}.
+
+## E77 slow-AR synthesis during full paired-sign training — prepared, not launched
+E61's matched correction transfers favorably to the selected model on three screens. Hypothesis: training the confidence/horizon-aware model directly with the same slow-AR prior can improve on composing an older-backbone correction. Reuse E67's exact E62 initialization, seed197, B64, LR2e-5,12000-step schedule, paired-sign loss and independent horizon RNG300200. Change only synthetic AR phi fromU(.2,.995) toexp(-10**(-U(0,4))), preserving the main RNG calls and zero initial state. This yields time constants log-uniform1..10000 and keeps innovation variance normalized as before. Compare to the already-computed E67 paired control. No inference change, new corpus or validation training. Prioritize after a training allocation is released; E72 mixup remains an unlaunched alternative.
+
+## E76 slow-AR correction third earlier cutoff
+- Checkpoint SHA-256: `8562cb05319ed04fdd8544a1af80bf1688ba55284e59b4689aa646e34e38a105`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 25.890827178955078 seconds.
+- Overall skill 0.389281; relative MASE 0.737064; relative WQL 0.506031.
+- term skill: long=0.3949, medium=0.3619, short=0.3971.
+- domain skill: Econ/Fin=0.1812, Energy=0.3198, Healthcare=0.2311, Nature=0.5053, Sales=0.3996, Transport=0.5073, Web/CloudOps=0.3940.
+- frequency skill: A=0.3622, D=0.4457, H=0.4032, M=0.1047, Q=0.2602, S=0.2878, T=0.4313, W=0.2630.
+- variates skill: multivariate=0.3743, univariate=0.4008.
+- Outcome: Publish E76: full0.375612 improves paired3000 full0.374401; main0.379721 and earlier0.336781/0.360577/0.389281 all improve. All97 configurations and151170 instances pass; zero crossings. Fixed coefficient1.0 composition of own trained checkpoints; no additional training.
+
+## E76 slow-AR correction full package validation
+- Checkpoint SHA-256: `8562cb05319ed04fdd8544a1af80bf1688ba55284e59b4689aa646e34e38a105`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.375612; relative MASE 0.744415; relative WQL 0.523713.
+- term skill: long=0.3976, medium=0.4054, short=0.3551.
+- domain skill: Econ/Fin=0.1204, Energy=0.2291, Healthcare=0.4651, Nature=0.4330, Sales=0.3487, Transport=0.4658, Web/CloudOps=0.4992.
+- frequency skill: A=0.2896, D=0.3638, H=0.3924, M=0.1218, Q=0.1820, S=0.4260, T=0.4325, W=0.2151.
+- variates skill: multivariate=0.3936, univariate=0.3609.
+- Outcome: Publish E76: full0.375612 improves paired3000 full0.374401; main0.379721 and earlier0.336781/0.360577/0.389281 all improve. All97 configurations and151170 instances pass; zero crossings. Fixed coefficient1.0 composition of own trained checkpoints; no additional training.
+
+E76 published checkpoint SHA2568562cb05319ed04fdd8544a1af80bf1688ba55284e59b4689aa646e34e38a105. Public full inference132.224730s on the shared H100. Relocation fixture15.725434s: three example configurations, bitwise identical, finite and monotone; all runtime/weight hashes verified. The checker initially ran before candidate assembly completed and was restarted after the package existed; the infrastructure error log is retained separately. No inference runtime change.
+
+E77 sampler fixture: {"seed": 197, "main_and_horizon_rng_matched": true, "real_windows_bitwise_equal": true, "changed_synthetic_context_values": 37211, "cpu_seconds": 4.491440053971019, "optimizer_updates": 0}. Prepared only; no optimizer updates or launch yet.
+
+## wide_capacity_146m_recovery_ema16000
+- Checkpoint SHA-256: `ad4960e948e64ecf25ec3be4cdc50bae6c7076d15cd7470b028f6ac703bb1081`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 40.720494747161865 seconds.
+- Overall skill 0.375817; relative MASE 0.745785; relative WQL 0.522408.
+- term skill: long=0.3987, medium=0.4075, short=0.3541.
+- domain skill: Econ/Fin=0.1398, Energy=0.2372, Healthcare=0.4246, Nature=0.4298, Sales=0.3438, Transport=0.4655, Web/CloudOps=0.5003.
+- frequency skill: A=0.3787, D=0.3494, H=0.3971, M=0.0940, Q=0.1979, S=0.4232, T=0.4362, W=0.2114.
+- variates skill: multivariate=0.3941, univariate=0.3609.
+- Outcome: Original-corpus146M at16000 reaches0.375817, above its E47 parent0.375793 and matched small control0.375510: retain the planned continuation. Expanded corpus at8000 scores0.374266 versus original-corpus8000 .374330, an essentially tied result after a worse4000 checkpoint. Stop the expanded branch, preserve full recovery state, and prioritize an initialization that actually diversifies features. This is inconclusive for data scaling, not proof that expanded data is harmful.
+
+## wide_corpus_expanded_recovery_ema8000
+- Checkpoint SHA-256: `fbab74444b04d805cc04ec612964fcad5e66766d55c3f566a8dc870328ca4493`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 40.68104600906372 seconds.
+- Overall skill 0.374266; relative MASE 0.747150; relative WQL 0.524049.
+- term skill: long=0.4008, medium=0.4075, short=0.3504.
+- domain skill: Econ/Fin=0.1428, Energy=0.2366, Healthcare=0.4040, Nature=0.4268, Sales=0.3397, Transport=0.4655, Web/CloudOps=0.5014.
+- frequency skill: A=0.3790, D=0.3421, H=0.3958, M=0.1065, Q=0.2072, S=0.4225, T=0.4375, W=0.1966.
+- variates skill: multivariate=0.3942, univariate=0.3579.
+- Outcome: Original-corpus146M at16000 reaches0.375817, above its E47 parent0.375793 and matched small control0.375510: retain the planned continuation. Expanded corpus at8000 scores0.374266 versus original-corpus8000 .374330, an essentially tied result after a worse4000 checkpoint. Stop the expanded branch, preserve full recovery state, and prioritize an initialization that actually diversifies features. This is inconclusive for data scaling, not proof that expanded data is harmful.
+
+E63 stop requested after8000 evaluation. No validation gain justifies continuing the nearly duplicated-width expanded-data arm while E73 is ready; the complete recovery state will allow later continuation. E73 takes that allocation with seed113/B96/LR3e-5/32000, reset at8000 toseed179, and static compiled loss. Its separate feature-group normalization and independent stream are the research changes; compiler numerical differences were measured explicitly.
+
+E75 maximum-shape bf16 GPU fixture: {"seed": 281, "batch": 64, "context": 4096, "horizon": 1024, "optimizer_updates": 0, "loss": 0.22305740416049957, "seconds": 10.91669597796863, "peak_allocated_mib": 5362.6640625, "peak_reserved_mib": 5694.0}. All model gradients finite; no optimizer updates.
+
+E73 independent feature stream with static compilation, seed113 training started at 2026-10-22T07:07:01Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E71 eight-point patches, seed197 training started at 2026-10-22T07:08:06Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E63 stop receipt: {"experiment": "E63 expanded146M stopped", "step": 8501, "training_loop_seconds": 2892.9847088330425, "checkpoint_sha256": "1d578f0a2278a3a4ad7011e24957797dde52c70b7e2c6f8b369aaf8227ac7133", "ema": "/workspace/research/runs/wide_corpus_expanded_recovery/ema008501.pt", "complete_recovery": "/workspace/research/runs/wide_corpus_expanded_recovery/recovery.pt", "selection_checkpoint_step": 8000, "selection_skill": 0.3742660744379728, "outcome": "Essentially tied with original-corpus same-width8000 .3743298586; prioritize independent stream. Data scaling remains inconclusive."}.
+
+## E67 full_paired_control final12000
+- Checkpoint SHA-256: `b79cfdeefa1b2b20af89c8484a447dc96bfdd31234f2b08346468c8d9ece62bd`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 20.30943751335144 seconds.
+- Overall skill 0.377243; relative MASE 0.745277; relative WQL 0.520379.
+- term skill: long=0.4013, medium=0.4067, short=0.3560.
+- domain skill: Econ/Fin=0.1346, Energy=0.2365, Healthcare=0.4485, Nature=0.4306, Sales=0.3336, Transport=0.4666, Web/CloudOps=0.5025.
+- frequency skill: A=0.3681, D=0.3525, H=0.3948, M=0.1073, Q=0.2012, S=0.4245, T=0.4384, W=0.2186.
+- variates skill: multivariate=0.3950, univariate=0.3628.
+- Outcome: Seed197/B64/LR2e-5; final12000 is worse than the selected paired3000 and its published slow-AR correction. No publication. Matched control and candidate complete; no baseline rerun.
+
+## E67 full_paired_candidate final12000
+- Checkpoint SHA-256: `26d529aeaaee4484b0b4375fdc379779b08b38ac5077f0147587421f4f5fc8b0`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 21.91175127029419 seconds.
+- Overall skill 0.377427; relative MASE 0.744720; relative WQL 0.520460.
+- term skill: long=0.4032, medium=0.4055, short=0.3560.
+- domain skill: Econ/Fin=0.1326, Energy=0.2359, Healthcare=0.4464, Nature=0.4299, Sales=0.3326, Transport=0.4667, Web/CloudOps=0.5051.
+- frequency skill: A=0.3614, D=0.3505, H=0.3948, M=0.1036, Q=0.1986, S=0.4251, T=0.4404, W=0.2181.
+- variates skill: multivariate=0.3963, univariate=0.3620.
+- Outcome: Seed197/B64/LR2e-5; final12000 is worse than the selected paired3000 and its published slow-AR correction. No publication. Matched control and candidate complete; no baseline rerun.
+
+E67 complete measured training loop times on the shared H100: individual control2794.8358459s; paired candidate2794.8673859s. These are overlapping wall times, not additive exclusive GPU seconds.
+
+## Operator recovery2 — 2026-10-22
+The operator reports152594 research seconds remaining; all old processes ended. Published E76 verified SHA2568562cb05319ed04fdd8544a1af80bf1688ba55284e59b4689aa646e34e38a105 and full validation0.3756123475. Resume complete optimizer/EMA/RNG/prefetched-batch checkpoints: E58/E66 wide17000 and control23000, E61 slowAR8000, E65 recent normalization9000. Their prior last logged steps17200/23750/8450/9600 represent at least200/750/450/600 unsaved updates discarded. Original phase logs remain intact, latest elapsed3043.3/3041.0/3037.0/3034.3s respectively. E73 graft had no checkpoint or logged50-step interval; E71 patch8 reached200/63.0s with no checkpoint. Restart both from saved own initialization with unchanged seeds. Compiler caches were absent after recovery, so E73 recompiles static shapes. E63 remains stopped; E67 remains complete. Phase commands/PIDs/timing are recorded in recovery2_launch_receipt.json and *_recovery2.log; queued E69/E75/E77 retain predeclared settings.
+
+## E78 multiple persistent scales in synthetic residuals — prepared, not launched
+Hypothesis: the slow-AR improvement reflects missing persistent residual dynamics, but a single decay rate per synthetic sequence cannot represent fast variation superimposed on slow level changes. Compare four independent OU components to E77's one component. Keep the original component and main RNG draws; add three components with log-uniform time constants1..10000 from an independent RNG seed700200, then divide their sum by2 to preserve asymptotic stationary variance. All components retain zero initial state, isolating multiple scales from initialization. Other synthetic structure,20% synthetic fraction, real windows, horizon augmentation, architecture and loss remain unchanged. ParentE62, seed197/B64/LR2e-5/12000, paired-sign loss, main/horizon RNG identical to E77. This generator uses no external data. Prepare and check on allowed training windows; launch only after a suitable allocation is released.
+
+E78 sampler fixture passed: {   "seed": 197,   "extra_seed": 700200,   "real_windows_bitwise_equal": 211,   "synthetic_windows": 45,   "changed_context_values": 26747,   "main_and_horizon_rng_matched": true,   "finite_masks_and_periods_matched": true,   "cpu_seconds": 4.07623768504709,   "optimizer_updates": 0 }; prepared only, no optimizer updates.
+
+## E58/E66 recovered37M control24000
+- Checkpoint SHA-256: `0a77157c454bf7a913b41493f052666ed1cfce3d65206b6c969fb8d95e6314a7`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.700880765914917 seconds.
+- Overall skill 0.375681; relative MASE 0.745320; relative WQL 0.522962.
+- term skill: long=0.4008, medium=0.4066, short=0.3534.
+- domain skill: Econ/Fin=0.1417, Energy=0.2348, Healthcare=0.4210, Nature=0.4300, Sales=0.3397, Transport=0.4677, Web/CloudOps=0.5018.
+- frequency skill: A=0.3784, D=0.3533, H=0.3961, M=0.0763, Q=0.2029, S=0.4222, T=0.4370, W=0.2113.
+- variates skill: multivariate=0.3953, univariate=0.3596.
+- Outcome: Seed113, matched reset at8000 toseed179. Skill0.375681 remains slightly below E47parent0.375793; continue the predeclared schedule to32000. E58large24000 is pending.
+
+## E79 fresh pretraining at37M and146M — prepared, not launched
+Hypothesis: duplicating a mature smaller representation and continuing at a small learning rate limits the useful benefit of capacity. Test distinct random initializations under one matched modern recipe. Both arms use the same direct encoder/decoder architecture (patch16,6encoder/4decoder blocks, recency/confidence inputs and partial-target masks); widths512/1024 with8/16heads are the only architectural difference. Every tensor is initialized here with seed307; no parent weights. Both use packed_sample,20% own synthetic windows, the proven slow-AR time-constant prior1..10000, independent horizon-prefix augmentation seed300310, paired-sign objective, seed307 sampling, batch48 distinct/96signed rows, AdamW LR2e-4,400-step warmup and96000-step cosine schedule, EMA. Both use the same static compiled loss after a numerical/memory fixture. Save every1000; screen4000/8000/16000/24000/48000/72000/96000, with earlier cutoffs for promising candidates. This is a matched width comparison within the new recipe; comparisons to the old published lineage are model-selection comparisons, not clean causal claims about width. Reuse no released or baseline weights. Launch only after existing allocations release. The extended schedule supplies4.608M distinct sampled windows per arm; stop a clearly unpromising branch when its checkpoints establish that result.
+
+## E71 patch8 first1000
+- Checkpoint SHA-256: `67648eaef1f0a4fe5a7c5526f25c2c8724735720463d99250e814632841f9ada`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 32.271108627319336 seconds.
+- Overall skill 0.374708; relative MASE 0.748109; relative WQL 0.522638.
+- term skill: long=0.3990, medium=0.3996, short=0.3553.
+- domain skill: Econ/Fin=0.1322, Energy=0.2323, Healthcare=0.4676, Nature=0.4328, Sales=0.3481, Transport=0.4583, Web/CloudOps=0.4952.
+- frequency skill: A=0.3712, D=0.3577, H=0.3912, M=0.0847, Q=0.1944, S=0.4235, T=0.4328, W=0.2289.
+- variates skill: multivariate=0.3876, univariate=0.3643.
+- Outcome: Seed197/B64/LR2e-5; recovers most of the conversion loss, from initial0.362217 to0.374708. Still below matched patch16 paired1000 at0.376860 and parentE62 at0.377343; continue to3000/6000 before a decision.
+
+E79 initial checkpoint receipts: [   {     "path": "/workspace/research/random_capacity_512_initial.pt",     "sha256": "19da01694123fcb13df815689ca66460d0da596bd31dfabfabb08dda6f7801e4",     "parameters": 36800800,     "seed": 307,     "cpu_seconds": 0.5544250560924411,     "optimizer_updates": 0   },   {     "path": "/workspace/research/random_capacity_1024_initial.pt",     "sha256": "c9693619ca6277c26fef3b2bde37ff6796bec97b3af9f94588bbc5ddd329ec90",     "parameters": 146477344,     "seed": 307,     "cpu_seconds": 2.0617611361667514,     "optimizer_updates": 0   } ]
+
+E79 width512 fixture: {   "width": 512,   "initialization_seed": 307,   "fixture_seed": 313,   "optimizer_updates": 0,   "maximum_shape_memory": {     "batch": 48,     "signed_rows": 96,     "context": 4096,     "horizon": 1024,     "loss": 0.6031821966171265,     "peak_allocated_mib": 4520.62451171875,     "peak_reserved_mib": 4784.0   },   "eager_loss": 0.3412420153617859,   "compiled_loss": 0.3412419855594635,   "gradient_cosine": 0.9999952735476323,   "max_gradient_difference": 4.57763671875e-05,   "compile_comparison_seconds": 53.98739787377417,   "total_seconds": 63.50496790325269,   "source": "allowed packed_sample and own synthetic generator only" }
+
+E79 width1024 fixture: {   "width": 1024,   "initialization_seed": 307,   "fixture_seed": 313,   "optimizer_updates": 0,   "maximum_shape_memory": {     "batch": 48,     "signed_rows": 96,     "context": 4096,     "horizon": 1024,     "loss": 0.6031821966171265,     "peak_allocated_mib": 10162.86376953125,     "peak_reserved_mib": 10694.0   },   "eager_loss": 0.3412420153617859,   "compiled_loss": 0.3412419855594635,   "gradient_cosine": 0.9999932382274589,   "max_gradient_difference": 6.103515625e-05,   "compile_comparison_seconds": 62.02263342216611,   "total_seconds": 72.94260076899081,   "source": "allowed packed_sample and own synthetic generator only" }
+
+E79 both arms are queued after E58wide and E71patch8 release their allocations; their complete recovery files must exist. Queue receipts random_capacity_512_queue.json and random_capacity_1024_queue.json preserve exact commands. Both use B48/96000/seed307/static compilation. No training has started. Compiler caches are backed up under research/compiler_cache_backup and can be restored to the same /tmp paths after another recovery.
+
+postloss started 2026-10-22T07:27:21.677278+00:00 after recent_normalization_recovery completed. Original predeclared settings and seed retained.
+
+## Operator recovery3 — 2026-10-23
+Operator reports151596 research seconds remaining; all prior processes ended. Submission E76 hash verified unchanged. Resume complete state at wide20000/control28000/slowAR10000/patch8 2000. Last logged steps20100/28400/10850/2700 imply at least100/400/850/700 discarded unsaved updates; phase2 elapsed768.6/769.5/767.8/770.0s. Graft reached100 updates/722.7s without checkpoint; restart own seed113 initialization, restored compiler caches (13.8722s). Recent normalization completed12000 with final phase training loop708.0175s; do not retrain. Volatility/paired-slowAR and fresh-width queues restored with new dependencies. Postloss reached200/43.0s with nonfinite losses, no checkpoint; numerical diagnosis precedes restarting. Prior phase logs retained; no credential access.
+
+E75 numerical failure diagnosed on allowed-corpus step53, seed197, without optimizer updates: one row has context magnitude2.2842e18 and float32 variance accumulation overflow, yielding infinite normalization scale. The normalized neural output is finite, but conversion to raw units produces nonfinite values. Context postprocessing statistics themselves remain finite. Add a float64 fallback only on rows with nonfinite mean/scale; ordinary-row arithmetic remains bitwise unchanged. Restart this experiment from E62 after regression checks; no affected checkpoint will be selected. This correction is part of this comparison and will be retained in its inference runtime if selected.
+
+## E65 recent normalization final12000
+- Checkpoint SHA-256: `1fea846895698d0e33384a63e228b17de602d03154e41ad0c81d5cc060ed182f`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 31.676968097686768 seconds.
+- Overall skill 0.375806; relative MASE 0.744151; relative WQL 0.523574.
+- term skill: long=0.4022, medium=0.4055, short=0.3535.
+- domain skill: Econ/Fin=0.1427, Energy=0.2348, Healthcare=0.4380, Nature=0.4255, Sales=0.3383, Transport=0.4706, Web/CloudOps=0.4994.
+- frequency skill: A=0.3754, D=0.3572, H=0.3959, M=0.0785, Q=0.2042, S=0.4248, T=0.4352, W=0.2109.
+- variates skill: multivariate=0.3924, univariate=0.3622.
+- Outcome: Seed101/B128/LR3e-5. Main0.375806 narrowly exceeds4096-control0.375764 and8192-full-normalization0.375118; evaluate earlier windows against both matched controls. Final resumed phase708.0175 training-loop seconds; prior interrupted phases are retained separately.
+
+E75 numerical regression passed: ordinary rows bitwise original; observed overflowing row now finite loss2.6841469 and all gradients finite,7.0381s,0optimizer updates. Fixed-parent replay of250original training batches found no nonfinite postprocessed forecasts in21.9482s. Restarted seed197/B64/LR2e-5/12000 from E62. Future validation uses postloss_inference with the same overflow fallback. The first earlier-control evaluation launch used the old trend summary runtime by mistake and was cancelled; corrected comparisons are named long_context_control_fixed128_ema12000. No selection uses the cancelled diagnostic.
+
+E76 descriptive paired full-validation comparison against E67paired3000: improvement0.0012113184,51/97configurations improve. Resampling dataset-directory clusters (28keys, seed317,5000draws) gives skill-delta percentiles[-0.0006681,0.0012221,0.0031907]. The range crosses zero and adaptive validation reuse prevents a held-out significance claim. Retain E76 based on measured full/earlier scores, while recognizing this is a small improvement. Largest gains include us_births daily and bizitobs_l2c short; ETT2 minute short worsens. Full per-configuration log-error differences retained in slow_ar_delta6k_comparison.json.
+
+## E80 matched control for the overflow correction — prepared
+E75 needs a numerical fallback for rare overflowing training rows. To isolate its postprocessing-aware objective, prepare a control with exactly the same fallback but the original E67 paired-sign loss. ParentE62,seed197,B64,LR2e-5,12000steps, matching data/horizon RNG. No other inference or training change. This is a newly required numerical control, not a baseline rerun.
+
+## E81 equal ensemble of E76 and recent-normalization12000
+The longer-history candidate has a stronger second earlier cutoff but lower main/first-earlier performance than E76. Test whether averaging their postprocessed quantiles reduces complementary errors. Fixed equal weights; every member must forecast every instance if packaged. Compare cached main and both earlier predictions first. No coefficient sweep, new fitting, data or training. Reject unless the diagnostic tradeoff justifies full public-package replay.
+
+E80 overflow-only paired control queued alongside fresh-capacity arms after the old wide/patch8 allocations release. E78 multiscale-AR queued after postloss releases its allocation. Both use declared seed197/B64/LR2e-5/12000 settings; every command is retained in its queue receipt. Neither has started.
+
+## E65 matched comparison recent_normalization_recovery backcast_evals
+- Checkpoint SHA-256: `1fea846895698d0e33384a63e228b17de602d03154e41ad0c81d5cc060ed182f`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 23.444558143615723 seconds.
+- Overall skill 0.335105; relative MASE 0.796743; relative WQL 0.554866.
+- term skill: long=0.4010, medium=0.3564, short=0.2994.
+- domain skill: Econ/Fin=0.1646, Energy=0.2213, Healthcare=0.4212, Nature=0.3540, Sales=0.3970, Transport=0.3770, Web/CloudOps=0.4538.
+- frequency skill: A=0.3179, D=0.3541, H=0.3610, M=0.1841, Q=0.2288, S=0.3371, T=0.3395, W=0.2758.
+- variates skill: multivariate=0.3510, univariate=0.3221.
+- Outcome: Recent normalization main0.375806/earlier0.335105/0.361614; full-normalization8192 main0.375118/earlier0.335778/0.361619;4096control main0.375764/earlier0.335548/0.359190. Temporal tradeoff, no single-member publication. Test complementary equal ensemble with E76 as declared E81.
+
+## E65 matched comparison recent_normalization_recovery backcast2_evals
+- Checkpoint SHA-256: `1fea846895698d0e33384a63e228b17de602d03154e41ad0c81d5cc060ed182f`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 23.86247205734253 seconds.
+- Overall skill 0.361614; relative MASE 0.766245; relative WQL 0.531861.
+- term skill: long=0.3676, medium=0.3942, short=0.3463.
+- domain skill: Econ/Fin=0.1074, Energy=0.2351, Healthcare=0.3566, Nature=0.4647, Sales=0.5547, Transport=0.4203, Web/CloudOps=0.4480.
+- frequency skill: A=0.3496, D=0.4310, H=0.3890, M=0.1517, Q=0.1448, S=0.2876, T=0.3677, W=0.2567.
+- variates skill: multivariate=0.3709, univariate=0.3540.
+- Outcome: Recent normalization main0.375806/earlier0.335105/0.361614; full-normalization8192 main0.375118/earlier0.335778/0.361619;4096control main0.375764/earlier0.335548/0.359190. Temporal tradeoff, no single-member publication. Test complementary equal ensemble with E76 as declared E81.
+
+## E65 matched comparison long_context_control_fixed128 backcast_evals
+- Checkpoint SHA-256: `372edd6a03da9f577992a9cf6363b26efe7b11b35fc3aed2d50868566efd0c71`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 21.399088382720947 seconds.
+- Overall skill 0.335548; relative MASE 0.797825; relative WQL 0.553375.
+- term skill: long=0.3998, medium=0.3578, short=0.3002.
+- domain skill: Econ/Fin=0.1618, Energy=0.2279, Healthcare=0.4256, Nature=0.3575, Sales=0.3966, Transport=0.3721, Web/CloudOps=0.4487.
+- frequency skill: A=0.3187, D=0.3539, H=0.3656, M=0.1845, Q=0.2285, S=0.3427, T=0.3351, W=0.2751.
+- variates skill: multivariate=0.3528, univariate=0.3215.
+- Outcome: Recent normalization main0.375806/earlier0.335105/0.361614; full-normalization8192 main0.375118/earlier0.335778/0.361619;4096control main0.375764/earlier0.335548/0.359190. Temporal tradeoff, no single-member publication. Test complementary equal ensemble with E76 as declared E81.
+
+## E65 matched comparison long_context_control_fixed128 backcast2_evals
+- Checkpoint SHA-256: `372edd6a03da9f577992a9cf6363b26efe7b11b35fc3aed2d50868566efd0c71`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 21.050649642944336 seconds.
+- Overall skill 0.359190; relative MASE 0.769269; relative WQL 0.533801.
+- term skill: long=0.3663, medium=0.3893, short=0.3445.
+- domain skill: Econ/Fin=0.1046, Energy=0.2371, Healthcare=0.3554, Nature=0.4689, Sales=0.5535, Transport=0.4194, Web/CloudOps=0.4333.
+- frequency skill: A=0.3493, D=0.4307, H=0.3894, M=0.1531, Q=0.1429, S=0.2835, T=0.3601, W=0.2577.
+- variates skill: multivariate=0.3643, univariate=0.3551.
+- Outcome: Recent normalization main0.375806/earlier0.335105/0.361614; full-normalization8192 main0.375118/earlier0.335778/0.361619;4096control main0.375764/earlier0.335548/0.359190. Temporal tradeoff, no single-member publication. Test complementary equal ensemble with E76 as declared E81.
+
+## E65 matched comparison long_context_8192 backcast_evals
+- Checkpoint SHA-256: `90a1a9da78714594078b3cafd32b1812d633f5d6bf85ba8bfa60d09fbc3ebd38`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.02571153640747 seconds.
+- Overall skill 0.335778; relative MASE 0.796740; relative WQL 0.553746.
+- term skill: long=0.4000, medium=0.3562, short=0.3012.
+- domain skill: Econ/Fin=0.1625, Energy=0.2211, Healthcare=0.4240, Nature=0.3584, Sales=0.3963, Transport=0.3783, Web/CloudOps=0.4528.
+- frequency skill: A=0.3183, D=0.3550, H=0.3608, M=0.1825, Q=0.2283, S=0.3383, T=0.3410, W=0.2775.
+- variates skill: multivariate=0.3516, univariate=0.3229.
+- Outcome: Recent normalization main0.375806/earlier0.335105/0.361614; full-normalization8192 main0.375118/earlier0.335778/0.361619;4096control main0.375764/earlier0.335548/0.359190. Temporal tradeoff, no single-member publication. Test complementary equal ensemble with E76 as declared E81.
+
+## E65 matched comparison long_context_8192 backcast2_evals
+- Checkpoint SHA-256: `90a1a9da78714594078b3cafd32b1812d633f5d6bf85ba8bfa60d09fbc3ebd38`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 24.033729791641235 seconds.
+- Overall skill 0.361619; relative MASE 0.766647; relative WQL 0.531576.
+- term skill: long=0.3678, medium=0.3937, short=0.3464.
+- domain skill: Econ/Fin=0.1060, Energy=0.2331, Healthcare=0.3615, Nature=0.4677, Sales=0.5540, Transport=0.4209, Web/CloudOps=0.4469.
+- frequency skill: A=0.3484, D=0.4320, H=0.3875, M=0.1537, Q=0.1444, S=0.2887, T=0.3679, W=0.2592.
+- variates skill: multivariate=0.3695, univariate=0.3552.
+- Outcome: Recent normalization main0.375806/earlier0.335105/0.361614; full-normalization8192 main0.375118/earlier0.335778/0.361619;4096control main0.375764/earlier0.335548/0.359190. Temporal tradeoff, no single-member publication. Test complementary equal ensemble with E76 as declared E81.
+
+E81 main: {"composition": "equal quantile mean after each member applies its own fixed rules", "skill_score": 0.3792404374337234, "rel_mase_gmean": 0.7413362113099933, "rel_wql_gmean": 0.5197944315124549, "groups": {"term": {"short": 0.3580501678473965, "medium": 0.4075051608812147, "long": 0.40436165270683755}, "domain": {"Econ/Fin": 0.14514965737880325, "Energy": 0.23693703384364162, "Healthcare": 0.4508892358362413, "Nature": 0.43068317186356664, "Sales": 0.3372290847442204, "Transport": 0.46921029381701185, "Web/CloudOps": 0.5049832577126345}, "freq": {"A-DEC": 0.3741084316720569, "Q-DEC": 0.2032048606604645, "M": 0.09063626527124269, "W-SUN": 0.11024985736894433, "D": 0.35966521985300126, "H": 0.39764382964309264, "15T": 0.21216406035245483, "W-FRI": -0.006517286299776881, "10T": 0.6144670200764979, "W-TUE": 0.5505379760713061, "W-THU": 0.20290227669295913, "W-WED": 0.3148429295780334, "5T": 0.5200294927472697, "10S": 0.42580083885860065}}, "cpu_seconds": 1.3077636559028178, "members": ["/workspace/research/evals/slow_ar_delta6k", "/workspace/research/evals/recent_normalization_recovery_ema12000"], "selection_only": true}
+
+E81 backcast: {"composition": "equal quantile mean after each member applies its own fixed rules", "skill_score": 0.3372609628923202, "rel_mase_gmean": 0.7949387478174683, "rel_wql_gmean": 0.5525243706037937, "groups": {"term": {"short": 0.30267748548708406, "medium": 0.3587870572492281, "long": 0.4004504376989413}, "domain": {"Econ/Fin": 0.16791580188275335, "Energy": 0.2249147271500851, "Healthcare": 0.41565091499514417, "Nature": 0.36337531871749584, "Sales": 0.3953576986774914, "Transport": 0.37558039099633533, "Web/CloudOps": 0.45422426929296467}, "freq": {"A-DEC": 0.3198896505754395, "Q-DEC": 0.2324966250821987, "M": 0.1868807358115966, "W-SUN": 0.05675657061089712, "D": 0.35253814976953, "H": 0.3643205260222693, "15T": 0.2214260132321374, "W-FRI": 0.25870400902354673, "10T": 0.32373236597235966, "W-TUE": 0.534873854215015, "W-THU": 0.23833675077582528, "W-WED": 0.34309889609685684, "5T": 0.44852116184278545, "10S": 0.34093317446682636}}, "cpu_seconds": 0.9129793490283191, "members": ["/workspace/research/backcast_evals/slow_ar_delta6k", "/workspace/research/backcast_evals/recent_normalization_recovery_ema12000"], "selection_only": true}
+
+E81 backcast2: {"composition": "equal quantile mean after each member applies its own fixed rules", "skill_score": 0.36253066634723885, "rel_mase_gmean": 0.7663699578325764, "rel_wql_gmean": 0.5302493230514544, "groups": {"term": {"short": 0.3479807306166687, "medium": 0.39255912185019026, "long": 0.3693846383480154}, "domain": {"Econ/Fin": 0.1149608358698847, "Energy": 0.23668310113177404, "Healthcare": 0.35088547046370666, "Nature": 0.47069187269468127, "Sales": 0.5515284168852219, "Transport": 0.42149224872299274, "Web/CloudOps": 0.44469672192663035}, "freq": {"A-DEC": 0.3516200787127538, "Q-DEC": 0.14815814613753098, "M": 0.1464874832493296, "W-SUN": 0.06348262953040695, "D": 0.4307487103712574, "H": 0.3906466140437964, "15T": 0.10333300498128906, "W-FRI": 0.155407323458577, "10T": 0.5012397694349602, "W-TUE": 0.09030520980227696, "W-THU": 0.32093855640615543, "W-WED": 0.5365268550523004, "5T": 0.4842228628098807, "10S": 0.2879093531003366}}, "cpu_seconds": 0.9467690440360457, "members": ["/workspace/research/backcast2_evals/slow_ar_delta6k", "/workspace/research/backcast2_evals/recent_normalization_recovery_ema12000"], "selection_only": true}
+
+E81 actual candidate hash500fdde392c269ef1a6830477580bc7890b711824097ac711885bf0220e657cb,73,598,528parameters, contains original4096 E76 and recent-normalized8192 E65. Full package replay and relocation verification started. Candidate-only, submission unchanged.
+
+## E71 patch8 at3000
+- Checkpoint SHA-256: `5be1ccd5e5539619e4f5ff221a4088437e7267ceb34928015a0348187e4a575e`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.875902891159058 seconds.
+- Overall skill 0.378312; relative MASE 0.744947; relative WQL 0.518823.
+- term skill: long=0.4027, medium=0.4023, short=0.3592.
+- domain skill: Econ/Fin=0.1391, Energy=0.2401, Healthcare=0.4515, Nature=0.4387, Sales=0.3471, Transport=0.4584, Web/CloudOps=0.4997.
+- frequency skill: A=0.3807, D=0.3546, H=0.3955, M=0.0952, Q=0.1935, S=0.4235, T=0.4386, W=0.2325.
+- variates skill: multivariate=0.3942, univariate=0.3653.
+- Outcome: Seed197, interrupted then full-state resumed at2000. Main0.378312 essentially matches E67paired16-point3000 at0.378318. Continue to6000; no publication on this near tie.
+
+Recovery-frequency change verified on the tiny permitted-data fixture: {   "maximum_model_difference": 1.5543122344752192e-15,   "maximum_ema_difference": 3.0531133177191805e-16,   "batch_rng_equal": true,   "sampler_rng_equal": true,   "seconds": 29.735046536196023,   "selection_checkpoint_every": 4,   "recovery_checkpoint_every": 2,   "fixture_only": true }; increasing recovery-state saves preserves the sampler stream and model/EMA up to measured GPU roundoff. Newly launched jobs default to complete-state saves every250steps, while selection weights remain every1000. Already-running processes retain their original code/schedule.
+
+## E82 final slow-AR correction transfer — predeclared comparison
+When E61 completes12000, replace the6000-step correction with its12000-step counterpart: E67paired3000 + E61slowAR12000 - E42partial12000, coefficient1.0, confidence-only tensor retained from base. This is the already-planned final matched comparison, not addition on top of E76. No coefficient sweep or further training. Check all four screening cutoffs, then full public package only if justified. slow_ar_final_comparison.py executes the fixed composition and screens after the atomic EMA exists.
+
+## E81 equal long-history ensemble full package
+- Checkpoint SHA-256: `500fdde392c269ef1a6830477580bc7890b711824097ac711885bf0220e657cb`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.375233; relative MASE 0.744322; relative WQL 0.524415.
+- term skill: long=0.3984, medium=0.4064, short=0.3536.
+- domain skill: Econ/Fin=0.1215, Energy=0.2298, Healthcare=0.4558, Nature=0.4307, Sales=0.3504, Transport=0.4695, Web/CloudOps=0.4976.
+- frequency skill: A=0.2913, D=0.3631, H=0.3929, M=0.1149, Q=0.1839, S=0.4258, T=0.4330, W=0.2096.
+- variates skill: multivariate=0.3929, univariate=0.3608.
+- Outcome: Reject for publication: full0.375233 is below selected E76 0.375612, despite first/second-earlier gains. All97configs/151170instances valid, zero crossings. Public inference213.940586s on shared H100. No coefficient sweep; retain singleton E76.
+
+volatility_synth started 2026-10-23T02:55:46.196186+00:00 after long_memory_synth_recovery completed. Original predeclared settings and seed retained.
+
+## E75 corrected postloss first1000
+- Checkpoint SHA-256: `d33a29e773434d2f0faf2bc85b73151354dcb98bedc83f28c44e776835310afa`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 22.844736576080322 seconds.
+- Overall skill 0.376671; relative MASE 0.745163; relative WQL 0.521416.
+- term skill: long=0.3992, medium=0.4062, short=0.3560.
+- domain skill: Econ/Fin=0.1257, Energy=0.2385, Healthcare=0.4569, Nature=0.4304, Sales=0.3363, Transport=0.4666, Web/CloudOps=0.4975.
+- frequency skill: A=0.3694, D=0.3556, H=0.3962, M=0.0824, Q=0.1976, S=0.4232, T=0.4357, W=0.2250.
+- variates skill: multivariate=0.3926, univariate=0.3637.
+- Outcome: Finite training after the verified normalization fallback. Main0.376671 is slightly below E67paired1000 at0.376860; continue to3000/6000, with newly queued overflow-only E80 control needed to isolate the objective.
+
+paired_long_memory started 2026-10-23T02:56:31.210459+00:00 after wide_capacity_control_recovery completed. Original predeclared settings and seed retained.
+
+## E61 slowAR final12000
+- Checkpoint SHA-256: `c27613689514c4d0cd008f3f5e6bf35a14d2ff170c3786820981e52c373de10e`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.156561374664307 seconds.
+- Overall skill 0.362914; relative MASE 0.759071; relative WQL 0.534704.
+- term skill: long=0.3926, medium=0.4046, short=0.3342.
+- domain skill: Econ/Fin=0.1187, Energy=0.1874, Healthcare=0.4331, Nature=0.4271, Sales=0.3429, Transport=0.4595, Web/CloudOps=0.5091.
+- frequency skill: A=0.3722, D=0.3472, H=0.3824, M=0.1132, Q=0.2021, S=0.4423, T=0.4070, W=0.2137.
+- variates skill: multivariate=0.3958, univariate=0.3355.
+- Outcome: Seed79; main0.362914 improves matched E42partial12000 at0.361108. Completed final resumed phase569.4896 training-loop seconds; prior interrupted phase logs retained. E82 transfers the final matched correction to selected paired3000, compared with the already published6000 correction.
+
+E75 overflow source replay identifies bitcoin_with_missing (seed197,step53,row37), with daily periods7/365 and observed values from6.0e11 to2.28e18. These scales can be legitimate crypto quantities; no data-cleaning or filtering rule is inferred. Source diagnostic0.9295CPU seconds, no optimizer updates. The numerical fallback addresses the representation range.
+
+E81 relocated package checks passed: three example configurations bitwise equal, finite and monotone,21.2942s. Third-earlier cached ensemble0.389488 slightly exceeds E76 0.389281, but main/full remain lower, so the publication decision remains rejection. The long-history member alone scores0.386764 at this third cutoff.
+
+## E58/E66 recovered37M control final32000
+- Checkpoint SHA-256: `570983ddd4f6d8e136906f1669798d938ecd93738da55f624afc5b25606249bb`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.769165754318237 seconds.
+- Overall skill 0.375305; relative MASE 0.746308; relative WQL 0.522900.
+- term skill: long=0.3997, medium=0.4059, short=0.3535.
+- domain skill: Econ/Fin=0.1400, Energy=0.2324, Healthcare=0.4362, Nature=0.4284, Sales=0.3384, Transport=0.4676, Web/CloudOps=0.5011.
+- frequency skill: A=0.3825, D=0.3508, H=0.3940, M=0.0846, Q=0.2029, S=0.4232, T=0.4370, W=0.2159.
+- variates skill: multivariate=0.3936, univariate=0.3604.
+- Outcome: Final0.375305 below E47parent0.375793 and own24000 at0.375681. Final resumed phase616.7562 training-loop seconds; prior phase logs retained. No publication. Its allocation now trains E77pairedslowAR.
+
+E61 final12k EMA hashc27613689514c4d0cd008f3f5e6bf35a14d2ff170c3786820981e52c373de10e. Last resumed phase569.4895656s; phase2 reached10850 in767.8s, phase1 reached8450 in3037.0s, original pre-recovery attempt reached2150 in542.2s. These are overlapping process elapsed times, with discarded unsaved updates counted as spent compute; not exclusive GPU seconds. E65 final resumed phase708.0175s, prior phases3034.3s and71.8s.
+
+## E82 slowAR final correction evals
+- Checkpoint SHA-256: `f74bea6307738c331bd53618fde99f32238a72a0a85beb941f51446054e95c4c`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.064879655838013 seconds.
+- Overall skill 0.379181; relative MASE 0.741695; relative WQL 0.519643.
+- term skill: long=0.4028, medium=0.4057, short=0.3593.
+- domain skill: Econ/Fin=0.1465, Energy=0.2342, Healthcare=0.4607, Nature=0.4319, Sales=0.3354, Transport=0.4660, Web/CloudOps=0.5068.
+- frequency skill: A=0.3720, D=0.3593, H=0.3969, M=0.1007, Q=0.1994, S=0.4227, T=0.4390, W=0.2245.
+- variates skill: multivariate=0.3977, univariate=0.3640.
+- Outcome: Reject replacement of E76: final correction main0.379181/earlier0.336511/0.360111 loses three screens to6000 correction0.379721/0.336781/0.360577. Third-earlier improves0.389784 versus0.389281. Fixed coefficient1; no further sweep or full replay justified by this tradeoff. Retain E76.
+
+## E82 slowAR final correction backcast_evals
+- Checkpoint SHA-256: `f74bea6307738c331bd53618fde99f32238a72a0a85beb941f51446054e95c4c`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.045677423477173 seconds.
+- Overall skill 0.336511; relative MASE 0.796069; relative WQL 0.552989.
+- term skill: long=0.3975, medium=0.3593, short=0.3024.
+- domain skill: Econ/Fin=0.1707, Energy=0.2251, Healthcare=0.4072, Nature=0.3664, Sales=0.3935, Transport=0.3727, Web/CloudOps=0.4527.
+- frequency skill: A=0.3195, D=0.3490, H=0.3653, M=0.1930, Q=0.2358, S=0.3419, T=0.3374, W=0.2844.
+- variates skill: multivariate=0.3537, univariate=0.3225.
+- Outcome: Reject replacement of E76: final correction main0.379181/earlier0.336511/0.360111 loses three screens to6000 correction0.379721/0.336781/0.360577. Third-earlier improves0.389784 versus0.389281. Fixed coefficient1; no further sweep or full replay justified by this tradeoff. Retain E76.
+
+## E82 slowAR final correction backcast2_evals
+- Checkpoint SHA-256: `f74bea6307738c331bd53618fde99f32238a72a0a85beb941f51446054e95c4c`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 23.12555480003357 seconds.
+- Overall skill 0.360111; relative MASE 0.768417; relative WQL 0.532858.
+- term skill: long=0.3671, medium=0.3887, short=0.3461.
+- domain skill: Econ/Fin=0.1203, Energy=0.2346, Healthcare=0.3518, Nature=0.4695, Sales=0.5471, Transport=0.4199, Web/CloudOps=0.4385.
+- frequency skill: A=0.3524, D=0.4264, H=0.3900, M=0.1434, Q=0.1516, S=0.2859, T=0.3636, W=0.2642.
+- variates skill: multivariate=0.3674, univariate=0.3541.
+- Outcome: Reject replacement of E76: final correction main0.379181/earlier0.336511/0.360111 loses three screens to6000 correction0.379721/0.336781/0.360577. Third-earlier improves0.389784 versus0.389281. Fixed coefficient1; no further sweep or full replay justified by this tradeoff. Retain E76.
+
+## E82 slowAR final correction backcast3_evals
+- Checkpoint SHA-256: `f74bea6307738c331bd53618fde99f32238a72a0a85beb941f51446054e95c4c`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 23.64928960800171 seconds.
+- Overall skill 0.389784; relative MASE 0.736377; relative WQL 0.505671.
+- term skill: long=0.3953, medium=0.3617, short=0.3980.
+- domain skill: Econ/Fin=0.1860, Energy=0.3200, Healthcare=0.2362, Nature=0.5068, Sales=0.3992, Transport=0.5052, Web/CloudOps=0.3945.
+- frequency skill: A=0.3688, D=0.4453, H=0.4034, M=0.1130, Q=0.2642, S=0.2924, T=0.4309, W=0.2637.
+- variates skill: multivariate=0.3749, univariate=0.4013.
+- Outcome: Reject replacement of E76: final correction main0.379181/earlier0.336511/0.360111 loses three screens to6000 correction0.379721/0.336781/0.360577. Third-earlier improves0.389784 versus0.389281. Fixed coefficient1; no further sweep or full replay justified by this tradeoff. Retain E76.
+
+E82 composition receipt: {"method": "matched training delta", "formula": "base + candidate - control", "coefficient": 1.0, "parent_hashes": {"/workspace/research/runs/full_paired_candidate/ema003000.pt": "2a19ebe0d41c1a18feaba318041795be4d268ce890162a5035e6fc6bf1844238", "/workspace/research/runs/long_memory_synth_recovery/ema012000.pt": "c27613689514c4d0cd008f3f5e6bf35a14d2ff170c3786820981e52c373de10e", "/workspace/research/runs/partial_targets/ema012000.pt": "a0ef29e85d0cf01677a6a58e4a9f91b077073c4af95f85234eec2ddc223f4837"}, "seconds": 1.2579920410644263}
+
+## E61 slowAR final12k backcast_evals
+- Checkpoint SHA-256: `c27613689514c4d0cd008f3f5e6bf35a14d2ff170c3786820981e52c373de10e`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 23.161132097244263 seconds.
+- Overall skill 0.332462; relative MASE 0.802575; relative WQL 0.555221.
+- term skill: long=0.3889, medium=0.3582, short=0.2991.
+- domain skill: Econ/Fin=0.1592, Energy=0.2274, Healthcare=0.4159, Nature=0.3553, Sales=0.3990, Transport=0.3647, Web/CloudOps=0.4455.
+- frequency skill: A=0.3164, D=0.3542, H=0.3581, M=0.1781, Q=0.2386, S=0.3429, T=0.3325, W=0.2793.
+- variates skill: multivariate=0.3483, univariate=0.3196.
+- Outcome: Final direct slow-AR improves E42partial12k on both earlier cutoffs:0.332462 versus0.331074 and0.354079 versus0.353096. Matched synthetic-prior benefit survives the full schedule, while transferring its final delta is weaker than transferring6000 at the selected backbone.
+
+## E61 slowAR final12k backcast2_evals
+- Checkpoint SHA-256: `c27613689514c4d0cd008f3f5e6bf35a14d2ff170c3786820981e52c373de10e`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 22.80446219444275 seconds.
+- Overall skill 0.354079; relative MASE 0.774604; relative WQL 0.538615.
+- term skill: long=0.3715, medium=0.3752, short=0.3393.
+- domain skill: Econ/Fin=0.1039, Energy=0.2274, Healthcare=0.3615, Nature=0.4600, Sales=0.5536, Transport=0.4138, Web/CloudOps=0.4332.
+- frequency skill: A=0.3506, D=0.4274, H=0.3872, M=0.1661, Q=0.1581, S=0.2852, T=0.3480, W=0.2482.
+- variates skill: multivariate=0.3615, univariate=0.3480.
+- Outcome: Final direct slow-AR improves E42partial12k on both earlier cutoffs:0.332462 versus0.331074 and0.354079 versus0.353096. Matched synthetic-prior benefit survives the full schedule, while transferring its final delta is weaker than transferring6000 at the selected backbone.
+
+E73 usage diagnostic on8allowed-corpus windows,seed337,C512,H128,5.8388CPU seconds,0optimizer updates: initial added-head contribution is zero; atEMA2000 its output RMS is0.00924times the inherited head contribution. Cross-stream query-weight norm rises from0 to0.01672times within-stream norm; head-input streams remain distinct (relative RMS difference1.4274). Distinct features are being used, but this alone does not establish validation improvement. Full receipt wide_graft_usage2k.json includes hashes.
+
+## E75 corrected postloss3000
+- Checkpoint SHA-256: `82c785d40998e155a20290c9965c346c26cd397fa0f3cf36278377ad2aed5c7d`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.580218076705933 seconds.
+- Overall skill 0.378814; relative MASE 0.742602; relative WQL 0.519622.
+- term skill: long=0.4027, medium=0.4070, short=0.3582.
+- domain skill: Econ/Fin=0.1222, Energy=0.2441, Healthcare=0.4509, Nature=0.4321, Sales=0.3355, Transport=0.4650, Web/CloudOps=0.5021.
+- frequency skill: A=0.3710, D=0.3521, H=0.4006, M=0.0768, Q=0.1947, S=0.4220, T=0.4401, W=0.2249.
+- variates skill: multivariate=0.3963, univariate=0.3645.
+- Outcome: Main0.378814 exceeds original E67paired3000 at0.378318, but remains below selected E76 at0.379721. Check earlier cutoffs; numerical-only control E80 remains queued, so causal attribution to postprocessing loss is provisional.
+
+## E83 combine postprocessing-aware3000 with the selected slow-AR6000 correction
+E75 improves the original paired3000 main screen, while E76 demonstrates that the matched slow-AR6000 correction transfers across cutoffs. Test a fixed composition: E75postloss3000 + E61slowAR6000 - E42partial6000. Keep E75 confidence parameters, its verified overflow fallback and all inference rules. Coefficient1.0, no sweep; evaluate all four screening cutoffs before full replay/publication. This is a predictive composition test; E80 is still needed to separate the numerical correction from the postprocessing-aware loss. No additional training or validation gradients.
+
+## E77 pairedslowAR first1000
+- Checkpoint SHA-256: `278af3b837eee9032d80e20e70dc9155bd1f272983232826f16ff9df95547846`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.467870473861694 seconds.
+- Overall skill 0.377046; relative MASE 0.744404; relative WQL 0.521319.
+- term skill: long=0.3994, medium=0.4062, short=0.3566.
+- domain skill: Econ/Fin=0.1382, Energy=0.2363, Healthcare=0.4530, Nature=0.4321, Sales=0.3345, Transport=0.4673, Web/CloudOps=0.4987.
+- frequency skill: A=0.3685, D=0.3562, H=0.3951, M=0.1012, Q=0.1989, S=0.4234, T=0.4360, W=0.2232.
+- variates skill: multivariate=0.3931, univariate=0.3639.
+- Outcome: Seed197/B64/LR2e-5; main0.377046 slightly exceeds original paired1000 at0.376860. Early checkpoint only; continue matched3000/6000/12000 comparison.
+
+## E75 postloss3000 backcast_evals
+- Checkpoint SHA-256: `82c785d40998e155a20290c9965c346c26cd397fa0f3cf36278377ad2aed5c7d`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.485270261764526 seconds.
+- Overall skill 0.333947; relative MASE 0.800204; relative WQL 0.554393.
+- term skill: long=0.3971, medium=0.3587, short=0.2981.
+- domain skill: Econ/Fin=0.1484, Energy=0.2303, Healthcare=0.3976, Nature=0.3612, Sales=0.3967, Transport=0.3722, Web/CloudOps=0.4461.
+- frequency skill: A=0.3136, D=0.3467, H=0.3696, M=0.1659, Q=0.2280, S=0.3365, T=0.3319, W=0.2801.
+- variates skill: multivariate=0.3510, univariate=0.3200.
+- Outcome: Both earlier cutoffs regress against original E67paired3000:0.333947 versus0.335739 and0.357605 versus0.358235, exceeding the main gain. Prune corrected postloss continuation, preserve complete recovery and checkpoints, and release its allocation to E78multiscale. E83 composition screening continues; E80 numerical-only control remains needed for attribution.
+
+## E75 postloss3000 backcast2_evals
+- Checkpoint SHA-256: `82c785d40998e155a20290c9965c346c26cd397fa0f3cf36278377ad2aed5c7d`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 23.475824117660522 seconds.
+- Overall skill 0.357605; relative MASE 0.772196; relative WQL 0.534413.
+- term skill: long=0.3631, medium=0.3873, short=0.3437.
+- domain skill: Econ/Fin=0.1041, Energy=0.2395, Healthcare=0.3227, Nature=0.4712, Sales=0.5519, Transport=0.4212, Web/CloudOps=0.4278.
+- frequency skill: A=0.3532, D=0.4162, H=0.3932, M=0.1558, Q=0.1493, S=0.2753, T=0.3604, W=0.2566.
+- variates skill: multivariate=0.3621, univariate=0.3540.
+- Outcome: Both earlier cutoffs regress against original E67paired3000:0.333947 versus0.335739 and0.357605 versus0.358235, exceeding the main gain. Prune corrected postloss continuation, preserve complete recovery and checkpoints, and release its allocation to E78multiscale. E83 composition screening continues; E80 numerical-only control remains needed for attribution.
+
+multiscale_ar seed197 training started at 2026-10-23T03:01:47Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E75 stop receipt: {"experiment": "E75 corrected postloss stopped", "step": 3653, "checkpoint_sha256": "760314a6d65350417c4a58e32eb21cebaac953dc83820d300a4b6b24b0977cb1", "weights": "/workspace/research/runs/postloss/ema003653.pt", "recovery": "/workspace/research/runs/postloss/recovery.pt", "training_loop_final_record": {"completed_step": 3653, "training_process_loop_seconds": 799.9150489440653}, "process_receipt": {"command": ["python3", "/workspace/research/recovery/train.py", "--code-dir", "/workspace/research/postloss", "--packed", "/workspace/research/packed_sample", "--source-weights-from", "/workspace/research/packed_sample", "--out", "/workspace/research/runs/postloss", "--initialize-from", "/workspace/research/horizon_delta12k.pt", "--steps", "12000", "--batch", "64", "--lr", "2e-05", "--seed", "197", "--stop-file", "/workspace/research/postloss.stop", "--loss-mode", "paired"], "status": "complete", "pid": 1280, "log": "/workspace/research/postloss_recovery3.log", "started_utc": "2026-10-23T02:48:16Z", "note": "Restart own E62 initialization after verified float32 variance-overflow correction; original seed and recipe unchanged.", "returncode": 0, "process_seconds": 810.3138020890765}, "outcome": "Both earlier3000 cutoffs regress; E83 saved-checkpoint composition remains in screening, E80 numerical control queued."}
+
+## E83 postloss+slowAR evals
+- Checkpoint SHA-256: `0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.666229724884033 seconds.
+- Overall skill 0.380251; relative MASE 0.741085; relative WQL 0.518280.
+- term skill: long=0.4038, medium=0.4070, short=0.3603.
+- domain skill: Econ/Fin=0.1348, Energy=0.2414, Healthcare=0.4552, Nature=0.4344, Sales=0.3382, Transport=0.4643, Web/CloudOps=0.5059.
+- frequency skill: A=0.3743, D=0.3567, H=0.4003, M=0.0763, Q=0.1987, S=0.4254, T=0.4419, W=0.2242.
+- variates skill: multivariate=0.3980, univariate=0.3658.
+- Outcome: Main0.380251 improves selectedE76 0.379721, while all earlier scores0.335421/0.360099/0.389069 are below E76 0.336781/0.360577/0.389281. Complete public full validation to check whether the main gain extends beyond the capped screen; report the temporal regressions explicitly. No publication yet.
+
+## E83 postloss+slowAR backcast_evals
+- Checkpoint SHA-256: `0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 23.061537742614746 seconds.
+- Overall skill 0.335421; relative MASE 0.797508; relative WQL 0.553807.
+- term skill: long=0.3950, medium=0.3597, short=0.3013.
+- domain skill: Econ/Fin=0.1640, Energy=0.2281, Healthcare=0.3974, Nature=0.3657, Sales=0.3962, Transport=0.3713, Web/CloudOps=0.4494.
+- frequency skill: A=0.3211, D=0.3463, H=0.3688, M=0.1723, Q=0.2313, S=0.3404, T=0.3344, W=0.2853.
+- variates skill: multivariate=0.3526, univariate=0.3214.
+- Outcome: Main0.380251 improves selectedE76 0.379721, while all earlier scores0.335421/0.360099/0.389069 are below E76 0.336781/0.360577/0.389281. Complete public full validation to check whether the main gain extends beyond the capped screen; report the temporal regressions explicitly. No publication yet.
+
+## E83 postloss+slowAR backcast2_evals
+- Checkpoint SHA-256: `0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 23.91576099395752 seconds.
+- Overall skill 0.360099; relative MASE 0.769700; relative WQL 0.531990.
+- term skill: long=0.3671, medium=0.3869, short=0.3468.
+- domain skill: Econ/Fin=0.1196, Energy=0.2390, Healthcare=0.3200, Nature=0.4758, Sales=0.5509, Transport=0.4203, Web/CloudOps=0.4338.
+- frequency skill: A=0.3513, D=0.4228, H=0.3920, M=0.1454, Q=0.1495, S=0.2810, T=0.3646, W=0.2628.
+- variates skill: multivariate=0.3658, univariate=0.3555.
+- Outcome: Main0.380251 improves selectedE76 0.379721, while all earlier scores0.335421/0.360099/0.389069 are below E76 0.336781/0.360577/0.389281. Complete public full validation to check whether the main gain extends beyond the capped screen; report the temporal regressions explicitly. No publication yet.
+
+## E83 postloss+slowAR backcast3_evals
+- Checkpoint SHA-256: `0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 23.31220841407776 seconds.
+- Overall skill 0.389069; relative MASE 0.739022; relative WQL 0.505041.
+- term skill: long=0.3936, medium=0.3628, short=0.3969.
+- domain skill: Econ/Fin=0.1785, Energy=0.3245, Healthcare=0.2109, Nature=0.5039, Sales=0.4010, Transport=0.5082, Web/CloudOps=0.3908.
+- frequency skill: A=0.3606, D=0.4409, H=0.4062, M=0.1151, Q=0.2596, S=0.2759, T=0.4323, W=0.2589.
+- variates skill: multivariate=0.3734, univariate=0.4011.
+- Outcome: Main0.380251 improves selectedE76 0.379721, while all earlier scores0.335421/0.360099/0.389069 are below E76 0.336781/0.360577/0.389281. Complete public full validation to check whether the main gain extends beyond the capped screen; report the temporal regressions explicitly. No publication yet.
+
+E83 CPU composition receipt: {"method": "matched training delta", "formula": "base + candidate - control", "coefficient": 1.0, "parent_hashes": {"/workspace/research/runs/postloss/ema003000.pt": "82c785d40998e155a20290c9965c346c26cd397fa0f3cf36278377ad2aed5c7d", "/workspace/research/runs/long_memory_synth_recovery/ema006000.pt": "fea5b5290a3a9be5618eaeaf67e918e4f677d5be908d562040ce4bdfeb549bd9", "/workspace/research/runs/partial_targets/ema006000.pt": "67562486b099a350da53c3caa5ff0932fe7fecdfe72c1879b064b2a9bb173dfb"}, "seconds": 1.0092943429481238}
+
+## E83 postprocessing-aware3000 plus slowAR6000 full public-package replay
+- Checkpoint SHA-256: `0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.376131; relative MASE 0.743936; relative WQL 0.523180.
+- term skill: long=0.3978, medium=0.4059, short=0.3557.
+- domain skill: Econ/Fin=0.1127, Energy=0.2338, Healthcare=0.4604, Nature=0.4345, Sales=0.3517, Transport=0.4644, Web/CloudOps=0.4981.
+- frequency skill: A=0.2909, D=0.3608, H=0.3952, M=0.1011, Q=0.1844, S=0.4254, T=0.4346, W=0.2165.
+- variates skill: multivariate=0.3936, univariate=0.3619.
+- Outcome: Publish as best full primary validation: skill0.376130733 versus E76 0.375612348; relMASE0.743936224 and relWQL0.523180415, 97 configurations/151170 instances, zero failures. Public inference120.396952s; independent relocation14.861885s gave bitwise equal finite ordered forecasts on three examples. All three earlier cutoffs regress versus E76, so this is a narrow primary-validation selection, not evidence of broad temporal improvement. Keep E76 as the more stable rollback. No training in this composition; coefficient1.0 fixed, source training seeds197/79; hashes and parent compute above. E80 numerical-only control remains queued.
+
+E83 descriptive cluster comparison: {"candidate": "/workspace/research/valid-report-postloss-slow-ar.json", "parent": "/workspace/research/valid-report-slow-ar-delta6k.json", "candidate_hash": "0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "parent_hash": "8562cb05319ed04fdd8544a1af80bf1688ba55284e59b4689aa646e34e38a105", "skill_difference": 0.0005183850211532004, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 51, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": -0.0027720070621055277, "p50": 0.0002985771391603165, "p975": 0.004193731829011101}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}
+
+## E58/E66 recovered146M at global24000
+- Checkpoint SHA-256: `8f9a786a85c8fcd3d4aaba9e5651a68b6f2df74d5cdc0e30907a4293dd297492`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 37.02190327644348 seconds.
+- Overall skill 0.375978; relative MASE 0.744657; relative WQL 0.522931.
+- term skill: long=0.4006, medium=0.4073, short=0.3537.
+- domain skill: Econ/Fin=0.1411, Energy=0.2353, Healthcare=0.4235, Nature=0.4300, Sales=0.3398, Transport=0.4674, Web/CloudOps=0.5021.
+- frequency skill: A=0.3775, D=0.3521, H=0.3962, M=0.0785, Q=0.2030, S=0.4233, T=0.4372, W=0.2148.
+- variates skill: multivariate=0.3957, univariate=0.3598.
+- Outcome: Matched 37M control24000 is0.375680951 and original E47parent0.375792930. Widened model0.375977511 is modestly higher; continue planned32000, no publication against stronger E83. Seed113 then recovery reset179, batch96, baseLR3e-5; complete recovery retained.
+
+## E73 independent feature stream first4000
+- Checkpoint SHA-256: `c16f146c285d9e5c8c9ae60e54af3d9d1dae3f48e1cf2104e37676062df8ad5e`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 40.91307091712952 seconds.
+- Overall skill 0.372085; relative MASE 0.750057; relative WQL 0.525664.
+- term skill: long=0.3976, medium=0.4060, short=0.3484.
+- domain skill: Econ/Fin=0.1401, Energy=0.2348, Healthcare=0.4070, Nature=0.4248, Sales=0.3416, Transport=0.4634, Web/CloudOps=0.4971.
+- frequency skill: A=0.3733, D=0.3439, H=0.3937, M=0.0971, Q=0.1987, S=0.4185, T=0.4336, W=0.2014.
+- variates skill: multivariate=0.3914, univariate=0.3563.
+- Outcome: Main0.372084568 regresses versus own function-preserving initialization0.375792503 and original-width parent. New-stream usage was nonzero at2000; use planned8000 to distinguish transient feature learning from persistent degradation. Seed113/B96/LR3e-5, compiled static loss; no publication.
+
+## E78 multiscale AR first1000
+- Checkpoint SHA-256: `a3c57dba6318728692a87990bf652a9a8469fa4fb24bf925e79a6f72510936d3`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.617778062820435 seconds.
+- Overall skill 0.377072; relative MASE 0.744319; relative WQL 0.521335.
+- term skill: long=0.3993, medium=0.4061, short=0.3568.
+- domain skill: Econ/Fin=0.1341, Energy=0.2362, Healthcare=0.4612, Nature=0.4310, Sales=0.3345, Transport=0.4671, Web/CloudOps=0.4985.
+- frequency skill: A=0.3662, D=0.3563, H=0.3950, M=0.1046, Q=0.1987, S=0.4232, T=0.4355, W=0.2248.
+- variates skill: multivariate=0.3928, univariate=0.3643.
+- Outcome: Main0.377071682 is effectively tied with E77 single slowAR1000 at0.377046018 and above E67 original AR1000 at0.376859515. Early checkpoint, continue matched3000/6000/12000. Seed197, extraRNG700200, B64/LR2e-5, unchanged corpus and main/horizon RNG.
+
+## E84 daily and weekly aggregation of permitted real series
+Hypothesis: the original two/four-fold aggregation rarely exposes coarse energy and other dynamics. On20% of real rows, independently consider a daily or weekly block average of the same allowed series at the original cutoff. Only exact integral frequency ratios are used; require8 context blocks and1 future block. Select equally among eligible daily/weekly choices. Finite-value averages use float64 sums and disjoint past/future blocks. Context and target can be shorter than requested and remain masked; periods become[7,365] or[52,1]. All original sampling/random draws and original local synthetic windows remain unchanged; extra RNGseed900200. Planned own E62 initialization, seed197, paired B64, LR2e-5/12000steps match E67. This tests coarser real-series augmentation, not additional external data. Queue after E77 to stay within one H100.
+Training-only fixture: {"seed": 197, "aggregation_seed": 900200, "diagnostic_shapes_seed": 331, "cpu_seconds": 3.8464299628976732, "rows": 4096, "aggregated_rows": 459, "main_and_horizon_rng_matched": true, "nonaggregated_rows_bitwise_equal": true, "changing_future_does_not_change_context": true, "before_period_counts": {"(24, 168)": 932, "(1440, 10080)": 160, "(288, 2016)": 805, "(12, 1)": 214, "(48, 336)": 249, "(4, 1)": 235, "(7, 365)": 513, "(96, 672)": 244, "(52, 1)": 148, "(144, 1008)": 149, "(2, 91)": 5, "(1, 1)": 153, "(900, 21600)": 23, "(360, 8640)": 73, "(4, 28)": 54, "(12, 84)": 30, "(720, 5040)": 14, "(6, 42)": 24, "(360, 2520)": 7, "(450, 10800)": 3, "(72, 504)": 51, "(4, 182)": 6, "(36, 252)": 3, "(1, 7)": 1}, "after_period_counts": {"(52, 1)": 352, "(1440, 10080)": 131, "(288, 2016)": 692, "(12, 1)": 214, "(48, 336)": 231, "(4, 1)": 235, "(7, 365)": 704, "(96, 672)": 223, "(24, 168)": 778, "(1, 1)": 153, "(900, 21600)": 23, "(360, 8640)": 73, "(144, 1008)": 133, "(4, 28)": 38, "(12, 84)": 26, "(720, 5040)": 10, "(360, 2520)": 6, "(450, 10800)": 3, "(72, 504)": 41, "(2, 91)": 3, "(4, 182)": 6, "(36, 252)": 3, "(6, 42)": 17, "(1, 7)": 1}, "factor_counts": {"168": 73, "288": 97, "2016": 37, "7": 66, "1440": 26, "24": 90, "48": 14, "96": 20, "28": 12, "144": 4, "336": 6, "4": 4, "10080": 8, "1008": 1, "672": 1}, "aggregated_context_percentiles": [8.0, 48.0, 4096.0], "aggregated_target_percentiles": [1.0, 60.0, 1024.0], "optimizer_updates": 0}
+
+## E71 patch8 at6000
+- Checkpoint SHA-256: `308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 32.29093050956726 seconds.
+- Overall skill 0.381179; relative MASE 0.741215; relative WQL 0.516638.
+- term skill: long=0.4036, medium=0.4049, short=0.3630.
+- domain skill: Econ/Fin=0.1328, Energy=0.2446, Healthcare=0.4760, Nature=0.4387, Sales=0.3481, Transport=0.4598, Web/CloudOps=0.5005.
+- frequency skill: A=0.3764, D=0.3587, H=0.3961, M=0.1191, Q=0.1895, S=0.4235, T=0.4409, W=0.2396.
+- variates skill: multivariate=0.3970, univariate=0.3683.
+- Outcome: Main0.381178739 exceeds matched E67paired6000 0.378455389 and selected E83main0.380250568. Test all three earlier cutoffs and full public replay before publication. Architecture change only; seed197/B64/LR2e-5; checkpoints resumed with full optimizer and RNG state.
+
+## E77 paired slowAR at3000
+- Checkpoint SHA-256: `0691cb2a1f55203c949dcdd159bc9da17d56a3cccbba57fa2c61a9f74b769376`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.542114734649658 seconds.
+- Overall skill 0.378238; relative MASE 0.743200; relative WQL 0.520166.
+- term skill: long=0.4019, medium=0.4056, short=0.3580.
+- domain skill: Econ/Fin=0.1408, Energy=0.2382, Healthcare=0.4444, Nature=0.4319, Sales=0.3325, Transport=0.4662, Web/CloudOps=0.5040.
+- frequency skill: A=0.3686, D=0.3549, H=0.3971, M=0.0926, Q=0.1967, S=0.4234, T=0.4392, W=0.2222.
+- variates skill: multivariate=0.3972, univariate=0.3627.
+- Outcome: Main0.378238485 is slightly below original E67paired3000 0.378318033; continue planned6000 before choosing. Seed197/B64/LR2e-5, main and requested-horizon sampling matched.
+
+## E85 transfer the slow-AR correction to the finer-patch model
+E71patch8 at6000 improves the main screen and first earlier cutoff. E76 previously showed a matched slow-AR6000 correction improves all four cutoffs on patch16. Test one fixed composition: patch8_6000 + fold16to8(slowAR6000 - partial6000), where the linear fold is exactly E71 initialization: sum projection halves, retain first8 output positions, other tensors unchanged. Confidence parameters remain those of patch8. This is a transfer hypothesis, not exact function preservation. Coefficient1 with no sweep, original inference rules, all four cutoffs before full replay/publication. No new optimizer updates or random draws; original training seeds197 and79. Script patch8_delta/merge.py and all parent hashes retained.
+
+## E86 learn the added feature stream while freezing inherited parameters — prepared
+E73 at4000 underperforms its function-preserving starting model despite using the new stream. Hypothesis: a low-rate update of every parameter disturbs the mature function while learning the random additions too slowly. Reuse exactly E73 independent-stream initialization, but protect all36,800,800 inherited entries and train the109,676,544 new/cross-stream entries atLR2e-4 for8000steps. Original seed113/B96/source/context/horizon/loss unchanged. This changes trainable scope and learning rate together and is a method comparison, not a one-factor causal attribution. Gradient entries for protected blocks are zeroed before clipping; inherited values are restored after AdamW to cancel its weight decay. References reconstruct from the saved checkpoint on resume. All outputs still come from one146M model; inference has no adaptation or freezing machinery. Await E73next8000 before allocation.
+Fixture on6 allowed-corpus windows, discarded copy only: {"cpu_seconds": 8.51362281711772, "seed": 347, "optimizer_updates_on_discarded_fixture_copy": 3, "allowed_corpus_windows": 6, "protected_parameters": 36800800, "trainable_parameters": 109676544, "all_inherited_parameters_bitwise_equal_to_parent": true, "recovery_reference_reconstruction_equal": true, "new_head_max_change": 0.0006009560311213136, "losses": [0.3154084384441376, 0.5418640375137329, 3.4678804874420166], "selection_checkpoint_written": false}
+
+## E71 patch8 at6000 first earlier
+- Checkpoint SHA-256: `308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 31.848629474639893 seconds.
+- Overall skill 0.337263; relative MASE 0.796725; relative WQL 0.551283.
+- term skill: long=0.4004, medium=0.3547, short=0.3044.
+- domain skill: Econ/Fin=0.1652, Energy=0.2281, Healthcare=0.4272, Nature=0.3699, Sales=0.4068, Transport=0.3692, Web/CloudOps=0.4462.
+- frequency skill: A=0.3305, D=0.3545, H=0.3635, M=0.2050, Q=0.2352, S=0.3470, T=0.3351, W=0.2877.
+- variates skill: multivariate=0.3553, univariate=0.3226.
+- Outcome: Earlier skills0.337262777/0.360619944/0.384512704. First two exceed E83 and E76; third regresses materially against both, including E83 0.389069333. Record this temporal tradeoff. Full primary validation0.377160271 exceeds E83 0.376130733.
+
+## E71 patch8 at6000 second earlier
+- Checkpoint SHA-256: `308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 30.961294412612915 seconds.
+- Overall skill 0.360620; relative MASE 0.769294; relative WQL 0.531405.
+- term skill: long=0.3643, medium=0.3909, short=0.3471.
+- domain skill: Econ/Fin=0.1073, Energy=0.2373, Healthcare=0.3614, Nature=0.4729, Sales=0.5535, Transport=0.4233, Web/CloudOps=0.4312.
+- frequency skill: A=0.3265, D=0.4302, H=0.3900, M=0.1513, Q=0.1529, S=0.2830, T=0.3613, W=0.2737.
+- variates skill: multivariate=0.3599, univariate=0.3612.
+- Outcome: Earlier skills0.337262777/0.360619944/0.384512704. First two exceed E83 and E76; third regresses materially against both, including E83 0.389069333. Record this temporal tradeoff. Full primary validation0.377160271 exceeds E83 0.376130733.
+
+## E71 patch8 at6000 third earlier
+- Checkpoint SHA-256: `308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 30.769143104553223 seconds.
+- Overall skill 0.384513; relative MASE 0.743664; relative WQL 0.509403.
+- term skill: long=0.3930, medium=0.3668, short=0.3880.
+- domain skill: Econ/Fin=0.1775, Energy=0.3064, Healthcare=0.2392, Nature=0.5093, Sales=0.3974, Transport=0.5038, Web/CloudOps=0.3894.
+- frequency skill: A=0.3445, D=0.4405, H=0.4005, M=0.1373, Q=0.2471, S=0.2972, T=0.4240, W=0.2382.
+- variates skill: multivariate=0.3641, univariate=0.4002.
+- Outcome: Earlier skills0.337262777/0.360619944/0.384512704. First two exceed E83 and E76; third regresses materially against both, including E83 0.389069333. Record this temporal tradeoff. Full primary validation0.377160271 exceeds E83 0.376130733.
+
+## E71 patch8 at6000 full public-package replay
+- Checkpoint SHA-256: `308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.377160; relative MASE 0.744341; relative WQL 0.521172.
+- term skill: long=0.3977, medium=0.4038, short=0.3585.
+- domain skill: Econ/Fin=0.1125, Energy=0.2381, Healthcare=0.4791, Nature=0.4387, Sales=0.3613, Transport=0.4600, Web/CloudOps=0.4920.
+- frequency skill: A=0.2981, D=0.3626, H=0.3904, M=0.1423, Q=0.1804, S=0.4235, T=0.4341, W=0.2328.
+- variates skill: multivariate=0.3923, univariate=0.3649.
+- Outcome: Publish strongest full primary candidate: skill0.377160271, relMASE0.744340532 and relWQL0.521171845; 97 configurations/151170 instances and zero failures. Inference149.631765s under shared H100; relocation14.860671s, three configurations bitwise equal, finite and ordered. Earlier cutoffs1/2 improve but cutoff3 falls to0.384512704 from E83 0.389069333. This selection is based on primary full validation, with a material earlier-window tradeoff. Seed197/B64/LR2e-5; model36,665,488 parameters. Training continues to12000; E85slowAR transfer currently lowers main skill.
+
+E71 descriptive full comparison: {"candidate": "/workspace/research/valid-report-patch8-6k.json", "parent": "/workspace/research/valid-report-postloss-slow-ar.json", "candidate_hash": "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "parent_hash": "0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "skill_difference": 0.001029538022933929, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 51, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": -0.0037342410536253257, "p50": 0.0011012262742491896, "p975": 0.005930261648940039}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}
+
+## E87 equal ensemble of postloss+slowAR and patch8_6000
+E71 improves full and first two earlier scores but regresses on the third. Its per-configuration differences against E83 run in both directions. Test a single predeclared equal average of their final quantiles, every member on every instance, to reduce these disagreements. No coefficient sweep or per-dataset rules; no additional training. Screen all four cutoffs with cached forecasts, then require a complete public-package replay if promising. Member hashes0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49 and308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988.
+
+E87 cached valid_screen: {"composition": "equal quantile mean after each member applies its own fixed rules", "skill_score": 0.38368555666405846, "rel_mase_gmean": 0.7380746260326486, "rel_wql_gmean": 0.5146410398989778, "groups": {"term": {"short": 0.36538266249250273, "medium": 0.4082837973759923, "long": 0.4054283252594051}, "domain": {"Econ/Fin": 0.13784772212566998, "Energy": 0.24686600590076013, "Healthcare": 0.4744445530216731, "Nature": 0.43858582844670857, "Sales": 0.34499044135263635, "Transport": 0.4641713711179224, "Web/CloudOps": 0.504852655777996}, "freq": {"A-DEC": 0.37872174128763647, "Q-DEC": 0.19576783224250183, "M": 0.10352329460914667, "W-SUN": 0.0815487165237122, "D": 0.3619759620437517, "H": 0.40136898241411867, "15T": 0.21618729924976976, "W-FRI": 0.0005727425187482815, "10T": 0.6270627319464949, "W-TUE": 0.6195479435809044, "W-THU": 0.20656160899192244, "W-WED": 0.3284622519828829, "5T": 0.5178170596317864, "10S": 0.4250896621077831}}, "cpu_seconds": 0.8467225290369242, "members": ["/workspace/research/evals/postloss_slow_ar_delta6k", "/workspace/research/evals/patch8_ema6000"], "selection_only": true}
+
+E87 cached valid_backcast: {"composition": "equal quantile mean after each member applies its own fixed rules", "skill_score": 0.33897078175106665, "rel_mase_gmean": 0.7947425391953629, "rel_wql_gmean": 0.549812808335635, "groups": {"term": {"short": 0.30566345086890123, "medium": 0.3596853813715456, "long": 0.4000253823922667}, "domain": {"Econ/Fin": 0.16860733481230095, "Energy": 0.23127409233361262, "Healthcare": 0.41508771848830917, "Nature": 0.3697364579642294, "Sales": 0.40314036211800997, "Transport": 0.3731226096810256, "Web/CloudOps": 0.44992972632456585}, "freq": {"A-DEC": 0.32848231511958337, "Q-DEC": 0.23562309290129158, "M": 0.19041732638938724, "W-SUN": 0.025351943698138202, "D": 0.3534437856481293, "H": 0.3688302027123801, "15T": 0.22312808413257845, "W-FRI": 0.2396493514491056, "10T": 0.3237622247956604, "W-TUE": 0.5543034150109358, "W-THU": 0.2604484095221261, "W-WED": 0.35736004737028026, "5T": 0.4409641017446271, "10S": 0.3450492950879055}}, "cpu_seconds": 0.7932982260826975, "members": ["/workspace/research/backcast_evals/postloss_slow_ar_delta6k", "/workspace/research/backcast_evals/patch8_ema6000"], "selection_only": true}
+
+E87 cached valid_backcast2: {"composition": "equal quantile mean after each member applies its own fixed rules", "skill_score": 0.36315845377936284, "rel_mase_gmean": 0.7669826024462578, "rel_wql_gmean": 0.5287827307935709, "groups": {"term": {"short": 0.3500710560941004, "medium": 0.39152062222827344, "long": 0.36789234736191556}, "domain": {"Econ/Fin": 0.11764473029610945, "Energy": 0.2411172995160572, "Healthcare": 0.3456990033484809, "Nature": 0.4766227701429041, "Sales": 0.5542499449658524, "Transport": 0.4240681895536792, "Web/CloudOps": 0.43520532523252287}, "freq": {"A-DEC": 0.3426922609629939, "Q-DEC": 0.15231664461723582, "M": 0.15186824306557356, "W-SUN": 0.05088825731569291, "D": 0.4296455306851713, "H": 0.39379373998466227, "15T": 0.10856607437428756, "W-FRI": 0.14720063917754334, "10T": 0.5085351252790775, "W-TUE": 0.07220161720253104, "W-THU": 0.3450882445488662, "W-WED": 0.5601701661454508, "5T": 0.47180636145224786, "10S": 0.2848018841721206}}, "cpu_seconds": 0.8004084089770913, "members": ["/workspace/research/backcast2_evals/postloss_slow_ar_delta6k", "/workspace/research/backcast2_evals/patch8_ema6000"], "selection_only": true}
+
+E87 cached valid_backcast3: {"composition": "equal quantile mean after each member applies its own fixed rules", "skill_score": 0.3893067316884232, "rel_mase_gmean": 0.7388677045007551, "rel_wql_gmean": 0.5047537816165226, "groups": {"term": {"short": 0.3954965650939142, "medium": 0.3665319698411411, "long": 0.3951340525388045}, "domain": {"Econ/Fin": 0.18260879016430553, "Energy": 0.31821834617690903, "Healthcare": 0.23007490883688486, "Nature": 0.5085775372702099, "Sales": 0.40095301819850215, "Transport": 0.5083874509785007, "Web/CloudOps": 0.39193111101214995}, "freq": {"A-DEC": 0.3560016768547195, "Q-DEC": 0.2553722438340734, "M": 0.12861913252670398, "W-SUN": 0.15123578049216402, "D": 0.44398634475249976, "H": 0.40571804459976457, "15T": 0.25382261362712966, "W-FRI": 0.03470000993227129, "10T": 0.6063950584074473, "W-TUE": 0.24994144625076442, "W-THU": 0.35649129369337096, "W-WED": 0.3788996637883131, "5T": 0.4648714119960353, "10S": 0.2886025572123534}}, "cpu_seconds": 0.7890921849757433, "members": ["/workspace/research/backcast3_evals/postloss_slow_ar_delta6k", "/workspace/research/backcast3_evals/patch8_ema6000"], "selection_only": true}
+
+E87 cached equal ensemble improves all four cutoffs over both members:0.383686/0.338971/0.363158/0.389307. Assemble exact per-member model code and start full public-package validation. No coefficient search or new optimization.
+
+## E88 matched late slow-AR continuation of patch8 — prepared
+E71patch8 improves primary validation, while mapping the E61slowAR weight correction onto it lowers the main screen. Test direct training instead: preserve E71 complete model/EMA/AdamW/RNG/prefetched-batch state exactly at8000, then continue to12000 with only the synthetic AR coefficient law changed fromU(.2,.995) toexp(-10**(-U(0,4))). Reuse the already verified E77 data implementation and E71 model. All corpus windows, signs, horizon prefixes, batch64, seed197, original LR2e-5 cosine schedule and optimizer state stay matched. The saved prefetched batch is consumed once unchanged, so the changed generator starts with the following batch. No new random initialization, external data or validation training. Queue after E69 volatility releases its memory. Compare the unchanged E71 final12000 and the candidate; screen9000/12000 and earlier cutoffs for promising results.
+
+## E85 patch8 slowAR correction evals
+- Checkpoint SHA-256: `7047bf350ed55e595b47c5f8e0b1712429d049d00967b5b126d1cd039b1c7ba4`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 32.3618426322937 seconds.
+- Overall skill 0.379635; relative MASE 0.743623; relative WQL 0.517537.
+- term skill: long=0.4040, medium=0.4057, short=0.3597.
+- domain skill: Econ/Fin=0.1422, Energy=0.2420, Healthcare=0.4441, Nature=0.4393, Sales=0.3494, Transport=0.4592, Web/CloudOps=0.5028.
+- frequency skill: A=0.3775, D=0.3606, H=0.3953, M=0.0567, Q=0.1914, S=0.4261, T=0.4427, W=0.2368.
+- variates skill: multivariate=0.3979, univariate=0.3647.
+- Outcome: Reject for primary selection: patch8 slowAR transfer main0.379634840 is below patch8 parent0.381178739; earlier1/2 improve to0.339492623/0.363603669, but earlier3 slightly regresses to0.384319115 versus0.384512704. This transfer trades main performance for earlier1/2 and does not dominate. No full replay; E88 tests direct training on slowAR instead. Fixed coefficient1, CPU composition1.039392s, no optimization.
+
+## E85 patch8 slowAR correction backcast_evals
+- Checkpoint SHA-256: `7047bf350ed55e595b47c5f8e0b1712429d049d00967b5b126d1cd039b1c7ba4`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 32.34789538383484 seconds.
+- Overall skill 0.339493; relative MASE 0.792721; relative WQL 0.550345.
+- term skill: long=0.4006, medium=0.3577, short=0.3072.
+- domain skill: Econ/Fin=0.1745, Energy=0.2254, Healthcare=0.4254, Nature=0.3780, Sales=0.4071, Transport=0.3673, Web/CloudOps=0.4527.
+- frequency skill: A=0.3339, D=0.3537, H=0.3634, M=0.2145, Q=0.2368, S=0.3480, T=0.3408, W=0.2893.
+- variates skill: multivariate=0.3590, univariate=0.3235.
+- Outcome: Reject for primary selection: patch8 slowAR transfer main0.379634840 is below patch8 parent0.381178739; earlier1/2 improve to0.339492623/0.363603669, but earlier3 slightly regresses to0.384319115 versus0.384512704. This transfer trades main performance for earlier1/2 and does not dominate. No full replay; E88 tests direct training on slowAR instead. Fixed coefficient1, CPU composition1.039392s, no optimization.
+
+## E85 patch8 slowAR correction backcast2_evals
+- Checkpoint SHA-256: `7047bf350ed55e595b47c5f8e0b1712429d049d00967b5b126d1cd039b1c7ba4`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 31.240530729293823 seconds.
+- Overall skill 0.363604; relative MASE 0.765287; relative WQL 0.529214.
+- term skill: long=0.3689, medium=0.3919, short=0.3503.
+- domain skill: Econ/Fin=0.1175, Energy=0.2366, Healthcare=0.3619, Nature=0.4770, Sales=0.5513, Transport=0.4209, Web/CloudOps=0.4416.
+- frequency skill: A=0.3278, D=0.4365, H=0.3885, M=0.1369, Q=0.1530, S=0.2924, T=0.3673, W=0.2792.
+- variates skill: multivariate=0.3657, univariate=0.3619.
+- Outcome: Reject for primary selection: patch8 slowAR transfer main0.379634840 is below patch8 parent0.381178739; earlier1/2 improve to0.339492623/0.363603669, but earlier3 slightly regresses to0.384319115 versus0.384512704. This transfer trades main performance for earlier1/2 and does not dominate. No full replay; E88 tests direct training on slowAR instead. Fixed coefficient1, CPU composition1.039392s, no optimization.
+
+## E85 patch8 slowAR correction backcast3_evals
+- Checkpoint SHA-256: `7047bf350ed55e595b47c5f8e0b1712429d049d00967b5b126d1cd039b1c7ba4`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 30.950783491134644 seconds.
+- Overall skill 0.384319; relative MASE 0.743701; relative WQL 0.509698.
+- term skill: long=0.3926, medium=0.3666, short=0.3878.
+- domain skill: Econ/Fin=0.1844, Energy=0.3057, Healthcare=0.2112, Nature=0.5108, Sales=0.3977, Transport=0.5024, Web/CloudOps=0.3936.
+- frequency skill: A=0.3446, D=0.4387, H=0.3996, M=0.1104, Q=0.2522, S=0.2976, T=0.4263, W=0.2439.
+- variates skill: multivariate=0.3656, univariate=0.3987.
+- Outcome: Reject for primary selection: patch8 slowAR transfer main0.379634840 is below patch8 parent0.381178739; earlier1/2 improve to0.339492623/0.363603669, but earlier3 slightly regresses to0.384319115 versus0.384512704. This transfer trades main performance for earlier1/2 and does not dominate. No full replay; E88 tests direct training on slowAR instead. Fixed coefficient1, CPU composition1.039392s, no optimization.
+
+## E69 stochastic volatility first3000
+- Checkpoint SHA-256: `f68cd2d694ecf03ff038c6a2f49657eb2f211f213d68c7dec3ed5cc0c5592c5c`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.498825311660767 seconds.
+- Overall skill 0.359482; relative MASE 0.763227; relative WQL 0.537539.
+- term skill: long=0.3909, medium=0.4022, short=0.3296.
+- domain skill: Econ/Fin=0.1126, Energy=0.1841, Healthcare=0.4243, Nature=0.4229, Sales=0.3462, Transport=0.4587, Web/CloudOps=0.5052.
+- frequency skill: A=0.3751, D=0.3459, H=0.3825, M=0.1061, Q=0.1992, S=0.4403, T=0.3990, W=0.2102.
+- variates skill: multivariate=0.3912, univariate=0.3331.
+- Outcome: Main0.359481571 exceeds matched E42partial3000 0.358812323, while E61slowAR3000 was0.360587999. Continue6000 to assess persistence before transfer or earlier-window selection. Seed79/B256/LR5e-5, independent generatorRNG500082, same real windows.
+
+## E78 multiscale AR at3000
+- Checkpoint SHA-256: `185d1a3d5d32a3cad539fb3a83bf98ae06a56606a35bafaf8deecd75c8b496d1`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.39926815032959 seconds.
+- Overall skill 0.378048; relative MASE 0.743609; relative WQL 0.520198.
+- term skill: long=0.4028, medium=0.4059, short=0.3572.
+- domain skill: Econ/Fin=0.1377, Energy=0.2383, Healthcare=0.4460, Nature=0.4316, Sales=0.3318, Transport=0.4664, Web/CloudOps=0.5035.
+- frequency skill: A=0.3675, D=0.3526, H=0.3972, M=0.1004, Q=0.1981, S=0.4242, T=0.4386, W=0.2228.
+- variates skill: multivariate=0.3966, univariate=0.3629.
+- Outcome: Main0.378048480 is slightly below E77single slowAR3000 0.378238485 and E67original AR3000 0.378318033. Continue planned6000; no publication or evidence of a gain yet. Seed197/B64/LR2e-5, extraRNG700200, main/horizon draws matched.
+
+E85 exact composition receipt: {
+  "method": "matched training delta with fixed linear patch conversion",
+  "formula": "patch8_base + fold16to8(slowAR6000 - partial6000)",
+  "coefficient": 1.0,
+  "mapping": "sum input/query/recency halves; take first8 output positions; other tensors unchanged",
+  "exact_function_preservation": false,
+  "parent_hashes": {
+    "/workspace/research/runs/patch8/ema006000.pt": "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988",
+    "/workspace/research/runs/long_memory_synth_recovery/ema006000.pt": "fea5b5290a3a9be5618eaeaf67e918e4f677d5be908d562040ce4bdfeb549bd9",
+    "/workspace/research/runs/partial_targets/ema006000.pt": "67562486b099a350da53c3caa5ff0932fe7fecdfe72c1879b064b2a9bb173dfb"
+  },
+  "seconds": 1.0393919900525361,
+  "checkpoint_sha256": "7047bf350ed55e595b47c5f8e0b1712429d049d00967b5b126d1cd039b1c7ba4",
+  "optimizer_updates": 0
+}
+
+Measured checkpoint compute through current selection milestones:
+```json
+{
+  "E71 patch8 selected6000": {
+    "phase_log": "patch8_recovery3.log",
+    "checkpoint_loop_record": {
+      "step": 6000,
+      "loss": 0.27341755777597426,
+      "seconds": 1302.8,
+      "lr": 1.2000000000000002e-05,
+      "grad_norm": 0.12276209145784378,
+      "context": 2048,
+      "horizon": 512
+    },
+    "prior_interrupted_logged_seconds": {
+      "initial_unsaved_attempt": 63.0,
+      "recovery2_last_logged2700_resumed_from2000": 770.0
+    },
+    "total_logged_process_loop_seconds_including_repeated_work": 2135.8
+  },
+  "E58 recoveredwide24000": {
+    "phase_log": "wide_capacity_146m_recovery_recovery3.log",
+    "checkpoint_loop_record": {
+      "step": 24000,
+      "loss": 0.3451157429814339,
+      "seconds": 1117.3,
+      "lr": 9.51471862576143e-06,
+      "grad_norm": 0.31578314304351807,
+      "context": 2048,
+      "horizon": 1024
+    }
+  },
+  "E73 graft4000": {
+    "phase_log": "wide_graft_recovery3.log",
+    "checkpoint_loop_record": {
+      "step": 4000,
+      "loss": 0.34949749410152436,
+      "seconds": 1146.4,
+      "lr": 2.908655439013544e-05,
+      "grad_norm": 0.47331908345222473,
+      "context": 1024,
+      "horizon": 1024
+    }
+  },
+  "E69 volatility3000": {
+    "phase_log": "volatility_synth_recovery3.log",
+    "checkpoint_loop_record": {
+      "step": 3000,
+      "loss": 0.33109228551387787,
+      "seconds": 977.8,
+      "lr": 4.4142135623730954e-05,
+      "grad_norm": 0.13540834188461304,
+      "context": 2048,
+      "horizon": 1024
+    }
+  },
+  "E77 pairedslowAR3000": {
+    "phase_log": "paired_long_memory_recovery3.log",
+    "checkpoint_loop_record": {
+      "step": 3000,
+      "loss": 0.31684702143073085,
+      "seconds": 624.8,
+      "lr": 1.765685424949238e-05,
+      "grad_norm": 1.3133997917175293,
+      "context": 2048,
+      "horizon": 1024
+    }
+  },
+  "E78 multiscale3000": {
+    "phase_log": "multiscale_ar_train.log",
+    "checkpoint_loop_record": {
+      "step": 3000,
+      "loss": 0.3144953328371048,
+      "seconds": 637.3,
+      "lr": 1.765685424949238e-05,
+      "grad_norm": 1.2501322031021118,
+      "context": 2048,
+      "horizon": 1024
+    }
+  },
+  "note": "These are concurrent training-process loop measurements on the one H100, not exclusive GPU seconds. Prefix records exclude startup and may exclude unlogged tails before operator interruption. Earlier parent training is recorded separately in the lineage experiments."
+}
+```
+
+E88 launcher correction: naming the launcher queue.py shadowed Python standard-library queue during Torch import, causing a circular-import failure before snapshotting or training. Renamed it launch.py and restarted while E71recovery8000 was still present. Also renamed the E84launcher to avoid the same import hazard in future sampler checks. Existing E84 queued process is unchanged. No optimizer updates or validation results were affected.
+
+E88 preserved complete exact8000 state: {
+  "snapshot": "/workspace/research/patch8_recovery008000.pt",
+  "step": 8000,
+  "sha256": "2fa4b6f18df4a5f2eed7e1bc153f8f5e3755584305569db42e047f3b1312f5d5",
+  "snapshot_process_seconds_including_wait": 2.3576558139175177,
+  "prefetched_batch_preserved": true
+}
+
+## E87 equal patch16/patch8 ensemble complete public-package replay
+- Checkpoint SHA-256: `65e5698c0ee4fe1e9ae0607db6ef56c573aa4b82ec009b63ea1a861e4aec741d`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.379630; relative MASE 0.741025; relative WQL 0.519360.
+- term skill: long=0.3995, medium=0.4072, short=0.3609.
+- domain skill: Econ/Fin=0.1168, Energy=0.2399, Healthcare=0.4788, Nature=0.4386, Sales=0.3583, Transport=0.4643, Web/CloudOps=0.4967.
+- frequency skill: A=0.2977, D=0.3660, H=0.3960, M=0.1275, Q=0.1843, S=0.4251, T=0.4366, W=0.2280.
+- variates skill: multivariate=0.3954, univariate=0.3668.
+- Outcome: Publish strongest replayable candidate: full skill0.379629893, relMASE0.741025239, relWQL0.519360272; 97 configurations/151170 instances, zero failures. Both members forecast every instance with fixed .5/.5 weights, total73,466,288 parameters. Full inference229.446591s on the shared H100, relocation22.524727s with bitwise-equal finite ordered forecasts on3 examples. Actual package quantiles are bitwise equal to the independent member average on electricity hourly short, solar minute short and ETT2hourly long. Cached diagnostic skill0.383685557/0.338970782/0.363158454/0.389306732 exceeds each member at all four cutoffs. No additional training or coefficient sweep; member training seeds/compute recorded above.
+
+E87 composition verification: {
+  "cpu_seconds": 0.016339811962097883,
+  "comparisons": [
+    {
+      "config_id": "electricity__H__short",
+      "shape": [
+        370,
+        1,
+        9,
+        48
+      ],
+      "bitwise_equal_to_fixed_member_average": true,
+      "max_abs_difference": 0.0,
+      "max_scaled_difference": 0.0
+    },
+    {
+      "config_id": "solar__T__short",
+      "shape": [
+        137,
+        1,
+        9,
+        48
+      ],
+      "bitwise_equal_to_fixed_member_average": true,
+      "max_abs_difference": 0.0,
+      "max_scaled_difference": 0.0
+    },
+    {
+      "config_id": "ett2__H__long",
+      "shape": [
+        1,
+        7,
+        9,
+        720
+      ],
+      "bitwise_equal_to_fixed_member_average": true,
+      "max_abs_difference": 0.0,
+      "max_scaled_difference": 0.0
+    }
+  ],
+  "no_new_inference_or_training": true
+}
+
+E87 descriptive cluster comparison: {"candidate": "/workspace/research/valid-report-mixed-patch-ensemble.json", "parent": "/workspace/research/valid-report-patch8-6k.json", "candidate_hash": "65e5698c0ee4fe1e9ae0607db6ef56c573aa4b82ec009b63ea1a861e4aec741d", "parent_hash": "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "skill_difference": 0.002469622689308837, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 53, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": 0.0003627411169234291, "p50": 0.002411418344102745, "p975": 0.004671754347801495}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}
+
+## E77 paired slowAR at6000
+- Checkpoint SHA-256: `d471571ef423a63db1ab1b70922d691c31df6099238b1d2e53cb0b48b343a353`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.37148094177246 seconds.
+- Overall skill 0.378677; relative MASE 0.743065; relative WQL 0.519526.
+- term skill: long=0.4020, medium=0.4060, short=0.3586.
+- domain skill: Econ/Fin=0.1354, Energy=0.2384, Healthcare=0.4567, Nature=0.4305, Sales=0.3378, Transport=0.4664, Web/CloudOps=0.5036.
+- frequency skill: A=0.3690, D=0.3556, H=0.3958, M=0.1000, Q=0.1944, S=0.4250, T=0.4395, W=0.2269.
+- variates skill: multivariate=0.3981, univariate=0.3628.
+- Outcome: Main0.378677489 modestly exceeds matched E67paired6000 0.378455389, after3000 was slightly behind. Continue planned12000 and check three earlier cutoffs; this remains below selected individual E71 and ensemble E87. Seed197/B64/LR2e-5, same real-window and requested-horizon RNG.
+
+## E73 independent stream at8000, prune continuation
+- Checkpoint SHA-256: `c75d9f9ceae94e22370c2cc8ce4aac6ca83f5474d32e9ca3c571d5aa381d8d4a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 41.07528495788574 seconds.
+- Overall skill 0.373597; relative MASE 0.747769; relative WQL 0.524735.
+- term skill: long=0.3982, medium=0.4061, short=0.3509.
+- domain skill: Econ/Fin=0.1426, Energy=0.2349, Healthcare=0.4024, Nature=0.4298, Sales=0.3401, Transport=0.4643, Web/CloudOps=0.4998.
+- frequency skill: A=0.3809, D=0.3443, H=0.3950, M=0.0990, Q=0.2037, S=0.4221, T=0.4345, W=0.2060.
+- variates skill: multivariate=0.3921, univariate=0.3585.
+- Outcome: Main0.373596872 recovers from4000 0.372084568 but remains below its function-preserving start0.375792503 and matched original-width parent. Prune after this second negative checkpoint to prioritize E86 frozen-inherited higher-rate training; this does not establish that further E73 training could never recover. Checkpoint8000 training loop2058.7s this recovery plus722.7s logged in the prior interrupted attempt; complete stop receipt follows. Seed113/B96/LR3e-5, compiled static loss; resetseed179 occurred at8001 while waiting for evaluation.
+
+E86 frozen inherited stream seed113 training started at 2026-10-23T03:23:23Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E73 stop receipt: {"experiment": "E73 stopped after negative8000", "final_loop": {"completed_step": 8683, "training_process_loop_seconds": 2219.4671748820692}, "checkpoint": "/workspace/research/runs/wide_graft/ema008683.pt", "sha256": "36c21e4662c5c6e07cd3c2a34ecba515bc986d29deaef5e1856e6950c30e7db7", "complete_recovery": "/workspace/research/runs/wide_graft/recovery.pt"}
+
+## E77 paired slowAR6000 backcast_evals
+- Checkpoint SHA-256: `d471571ef423a63db1ab1b70922d691c31df6099238b1d2e53cb0b48b343a353`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.434251308441162 seconds.
+- Overall skill 0.337098; relative MASE 0.795494; relative WQL 0.552410.
+- term skill: long=0.3992, medium=0.3581, short=0.3032.
+- domain skill: Econ/Fin=0.1599, Energy=0.2277, Healthcare=0.4115, Nature=0.3693, Sales=0.3963, Transport=0.3724, Web/CloudOps=0.4511.
+- frequency skill: A=0.3147, D=0.3511, H=0.3673, M=0.1858, Q=0.2368, S=0.3433, T=0.3357, W=0.2891.
+- variates skill: multivariate=0.3547, univariate=0.3227.
+- Outcome: Earlier0.337098369/0.360407088/0.389754840 each exceeds matched E67paired6000 0.335118632/0.359446144/0.389533029, with main0.378677489 also above0.378455389. Supports slower synthetic AR dynamics in the actual paired objective. Continue12000 and queued E88 patch8 continuation; selected E87ensemble remains stronger on primary validation.
+
+## E77 paired slowAR6000 backcast2_evals
+- Checkpoint SHA-256: `d471571ef423a63db1ab1b70922d691c31df6099238b1d2e53cb0b48b343a353`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 23.03375768661499 seconds.
+- Overall skill 0.360407; relative MASE 0.768163; relative WQL 0.532542.
+- term skill: long=0.3654, medium=0.3912, short=0.3462.
+- domain skill: Econ/Fin=0.1057, Energy=0.2366, Healthcare=0.3597, Nature=0.4721, Sales=0.5486, Transport=0.4198, Web/CloudOps=0.4360.
+- frequency skill: A=0.3457, D=0.4251, H=0.3918, M=0.1564, Q=0.1512, S=0.2811, T=0.3637, W=0.2618.
+- variates skill: multivariate=0.3657, univariate=0.3561.
+- Outcome: Earlier0.337098369/0.360407088/0.389754840 each exceeds matched E67paired6000 0.335118632/0.359446144/0.389533029, with main0.378677489 also above0.378455389. Supports slower synthetic AR dynamics in the actual paired objective. Continue12000 and queued E88 patch8 continuation; selected E87ensemble remains stronger on primary validation.
+
+## E77 paired slowAR6000 backcast3_evals
+- Checkpoint SHA-256: `d471571ef423a63db1ab1b70922d691c31df6099238b1d2e53cb0b48b343a353`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 21.342447519302368 seconds.
+- Overall skill 0.389755; relative MASE 0.736028; relative WQL 0.505958.
+- term skill: long=0.3958, medium=0.3615, short=0.3978.
+- domain skill: Econ/Fin=0.1734, Energy=0.3172, Healthcare=0.2610, Nature=0.5086, Sales=0.3982, Transport=0.5051, Web/CloudOps=0.3947.
+- frequency skill: A=0.3572, D=0.4437, H=0.4041, M=0.1316, Q=0.2594, S=0.2895, T=0.4297, W=0.2664.
+- variates skill: multivariate=0.3731, univariate=0.4026.
+- Outcome: Earlier0.337098369/0.360407088/0.389754840 each exceeds matched E67paired6000 0.335118632/0.359446144/0.389533029, with main0.378677489 also above0.378455389. Supports slower synthetic AR dynamics in the actual paired objective. Continue12000 and queued E88 patch8 continuation; selected E87ensemble remains stronger on primary validation.
+
+## E89 matched patch4 versus patch8 continuation — prepared
+E71 finer patches improved primary full validation. Test another halving of patch length. Both arms initialize from own E71EMA6000, with candidate applying the same fixed linear fold8to4 and first4 output rows. Positions preserve physical time units. Candidate starts functionally different, so record its zero-step screen. Width512/heads8/6encoder/4decoder/context4096/horizon1024 unchanged. New matched sampling seed401, B32 distinct/64signed, paired loss, LR2e-5/12000steps and fresh AdamW/EMA. Both use original synthetic generator and the same source and horizon-prefix streams; horizon truncation remains rounded16 in both to isolate model patch length. Candidate versus control changes patch length, while comparisons against E71 are method selections with a new optimizer/batch/seed. Measure maximum-shape memory before queuing both arms after E78 and E86 release their allocations. Screen1000/3000/6000/12000; earlier windows and full package replay for promising candidates. No external data or weights.
+
+## E89 patch4 zero-step conversion
+- Checkpoint SHA-256: `60a834e264452f3acddfdf191115c6af7ce6e972d959909bf701bac5517420ed`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 52.6757493019104 seconds.
+- Overall skill 0.370553; relative MASE 0.752738; relative WQL 0.526349.
+- term skill: long=0.3989, medium=0.3945, short=0.3498.
+- domain skill: Econ/Fin=0.1324, Energy=0.2289, Healthcare=0.4183, Nature=0.4394, Sales=0.3492, Transport=0.4525, Web/CloudOps=0.4931.
+- frequency skill: A=0.3342, D=0.3388, H=0.3842, M=0.1611, Q=0.1857, S=0.4158, T=0.4284, W=0.2377.
+- variates skill: multivariate=0.3847, univariate=0.3590.
+- Outcome: Initial screen0.370553128 is below source E71patch8_6000 0.381178739, as expected from a non-function-preserving patch fold. This is the baseline before new training, not an improvement. Candidate/control maximum-shape checks are finite and fit: reserved11376/6224MiB including EMA and AdamW moment storage. Both B32/seed401 arms are queued after E78 and E86.
+
+E89 patch4_initial.json: {
+  "method": "sum input/query/recency halves and keep first4 output rows; same physical position coordinates",
+  "exact_function_preservation": false,
+  "parent_sha256": "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988",
+  "checkpoint_sha256": "60a834e264452f3acddfdf191115c6af7ce6e972d959909bf701bac5517420ed",
+  "parameters": 36597832,
+  "cpu_seconds": 0.6177915919106454,
+  "optimizer_updates": 0
+}
+
+E89 patch4_memory_check.json: {
+  "seed": 401,
+  "batch": 32,
+  "signed_rows": 64,
+  "patch": 4,
+  "context": 4096,
+  "horizon": 1024,
+  "EMA_and_AdamW_moment_storage_present": true,
+  "optimizer_updates": 0,
+  "loss": 0.2863434851169586,
+  "seconds": 10.160149822942913,
+  "peak_allocated_mib": 10789.2275390625,
+  "peak_reserved_mib": 11376.0
+}
+
+E89 patch4_control_memory_check.json: {
+  "seed": 401,
+  "batch": 32,
+  "signed_rows": 64,
+  "patch": 8,
+  "context": 4096,
+  "horizon": 1024,
+  "EMA_and_AdamW_moment_storage_present": true,
+  "optimizer_updates": 0,
+  "loss": 0.30326029658317566,
+  "seconds": 10.05252459505573,
+  "peak_allocated_mib": 5778.71923828125,
+  "peak_reserved_mib": 6224.0
+}
+
+E89 candidate and control queued after PIDs2497(E78) and3946(E86), with their complete recovery files required; inferred total memory replaces those two allocations while preserving evaluation headroom.
+
+## E86 protected inherited stream first1000
+- Checkpoint SHA-256: `048d9f14388ad2409cd2a3f0b352d5fc1b8525f2e1e5e196707a36fe84a193bc`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 41.35012197494507 seconds.
+- Overall skill 0.372364; relative MASE 0.749500; relative WQL 0.525586.
+- term skill: long=0.3978, medium=0.4046, short=0.3494.
+- domain skill: Econ/Fin=0.1443, Energy=0.2339, Healthcare=0.4085, Nature=0.4257, Sales=0.3367, Transport=0.4639, Web/CloudOps=0.4980.
+- frequency skill: A=0.3876, D=0.3511, H=0.3944, M=0.0661, Q=0.1958, S=0.4208, T=0.4323, W=0.2051.
+- variates skill: multivariate=0.3906, univariate=0.3575.
+- Outcome: Main0.372363971 is below own initialization0.375792503. Higher-rate added/cross-stream learning still changes predictions despite protecting inherited tensors. Continue predeclared3000 comparison before deciding. Seed113/B96/LR2e-4, compiled loss, 36,800,800 inherited parameters protected and109,676,544 new/cross-stream parameters train.
+
+## E78 multiscale AR at6000
+- Checkpoint SHA-256: `d517ab35a8bdaeb1b0d14d4fb1879a984cedec0663bc09e3a602497facbcf473`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 21.888899564743042 seconds.
+- Overall skill 0.378668; relative MASE 0.743166; relative WQL 0.519472.
+- term skill: long=0.4027, medium=0.4063, short=0.3582.
+- domain skill: Econ/Fin=0.1300, Energy=0.2384, Healthcare=0.4669, Nature=0.4301, Sales=0.3370, Transport=0.4663, Web/CloudOps=0.5026.
+- frequency skill: A=0.3680, D=0.3559, H=0.3959, M=0.1037, Q=0.1941, S=0.4255, T=0.4387, W=0.2279.
+- variates skill: multivariate=0.3972, univariate=0.3635.
+- Outcome: Main0.378667589 is effectively tied with E77single slowAR6000 0.378677489; both slightly exceed original E67paired6000 0.378455389. No demonstrated gain from adding AR components. Continue planned12000 endpoint; no publication.
+
+## E69 stochastic volatility at6000
+- Checkpoint SHA-256: `1161e76a6d17ace780f0bf63d6961a2ea28a4036ee5a29ee78b9e9897e7661ee`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.00778031349182 seconds.
+- Overall skill 0.359792; relative MASE 0.762404; relative WQL 0.537598.
+- term skill: long=0.3895, medium=0.4029, short=0.3305.
+- domain skill: Econ/Fin=0.1133, Energy=0.1860, Healthcare=0.4240, Nature=0.4263, Sales=0.3419, Transport=0.4578, Web/CloudOps=0.5034.
+- frequency skill: A=0.3769, D=0.3446, H=0.3811, M=0.1104, Q=0.1982, S=0.4402, T=0.4011, W=0.2124.
+- variates skill: multivariate=0.3908, univariate=0.3340.
+- Outcome: Main0.359791528 exceeds matched E42partial6000 0.359221833; E61slowAR6000 remains stronger at0.361281186. Check the first two earlier cutoffs to decide whether the volatility gain generalizes. Seed79/B256/LR5e-5, extraRNG500082; full continuation is planned12000.
+
+E86 actual GPU-trained EMA1000 preservation audit: {
+  "checkpoint": "/workspace/research/runs/wide_graft_frozen/ema001000.pt",
+  "step": 1000,
+  "cpu_seconds": 1.7478242949582636,
+  "inherited_parameters": 36800800,
+  "inherited_bitwise_equal_to_E47": true,
+  "changed_added_or_cross_stream_parameters": 109676526,
+  "optimizer_updates": 0
+}
+
+## E69 volatility6000 backcast_evals
+- Checkpoint SHA-256: `1161e76a6d17ace780f0bf63d6961a2ea28a4036ee5a29ee78b9e9897e7661ee`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 22.790717124938965 seconds.
+- Overall skill 0.330896; relative MASE 0.805425; relative WQL 0.555856.
+- term skill: long=0.3909, medium=0.3566, short=0.2960.
+- domain skill: Econ/Fin=0.1469, Energy=0.2301, Healthcare=0.4125, Nature=0.3497, Sales=0.3988, Transport=0.3637, Web/CloudOps=0.4436.
+- frequency skill: A=0.3198, D=0.3550, H=0.3590, M=0.1648, Q=0.2278, S=0.3421, T=0.3294, W=0.2744.
+- variates skill: multivariate=0.3479, univariate=0.3170.
+- Outcome: Main0.359791528 and earlier1 0.330895782 improve matched E42partial6000 0.359221833/0.330299984; earlier2 0.352505400 is below control0.352900879. Mixed temporal evidence, so no transfer or publication. Finish predeclared12000 before deciding; E88 remains queued after this allocation.
+
+## E69 volatility6000 backcast2_evals
+- Checkpoint SHA-256: `1161e76a6d17ace780f0bf63d6961a2ea28a4036ee5a29ee78b9e9897e7661ee`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 22.497740983963013 seconds.
+- Overall skill 0.352505; relative MASE 0.776021; relative WQL 0.540255.
+- term skill: long=0.3678, medium=0.3769, short=0.3372.
+- domain skill: Econ/Fin=0.0890, Energy=0.2282, Healthcare=0.3738, Nature=0.4582, Sales=0.5585, Transport=0.4133, Web/CloudOps=0.4263.
+- frequency skill: A=0.3446, D=0.4275, H=0.3868, M=0.1756, Q=0.1499, S=0.2802, T=0.3442, W=0.2458.
+- variates skill: multivariate=0.3580, univariate=0.3481.
+- Outcome: Main0.359791528 and earlier1 0.330895782 improve matched E42partial6000 0.359221833/0.330299984; earlier2 0.352505400 is below control0.352900879. Mixed temporal evidence, so no transfer or publication. Finish predeclared12000 before deciding; E88 remains queued after this allocation.
+
+## E86 protected inherited stream at3000
+- Checkpoint SHA-256: `45ac18a80765f03bca329ccef8d4c81f39fae8cb3f555e4e261b9a38c5fd253f`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 41.0149462223053 seconds.
+- Overall skill 0.373573; relative MASE 0.748714; relative WQL 0.524113.
+- term skill: long=0.3985, medium=0.4063, short=0.3506.
+- domain skill: Econ/Fin=0.1401, Energy=0.2337, Healthcare=0.4215, Nature=0.4251, Sales=0.3422, Transport=0.4636, Web/CloudOps=0.5006.
+- frequency skill: A=0.3797, D=0.3460, H=0.3949, M=0.0960, Q=0.1979, S=0.4211, T=0.4348, W=0.2041.
+- variates skill: multivariate=0.3931, univariate=0.3576.
+- Outcome: Main0.373573070 improves from1000 0.372363971 and reaches roughly E73unrestricted8000 0.373596872 in fewer updates, but still trails the starting model0.375792503. Continue planned6000/8000 to test recovery. This compares different trainable scopes and learning rates together. Actual EMA1000 audit showed all36,800,800 inherited entries bitwise unchanged; added connections can still change the inherited stream activations.
+
+E84 coarse aggregation seed197 training started at 2026-10-23T03:37:37Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E77 paired slowAR final12000
+- Checkpoint SHA-256: `61cdf64f284aaa5bd97be010a29178d8b6dc796af35898d9b0ea2a76dce7dd0e`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.205326557159424 seconds.
+- Overall skill 0.377696; relative MASE 0.743749; relative WQL 0.520690.
+- term skill: long=0.4033, medium=0.4049, short=0.3568.
+- domain skill: Econ/Fin=0.1435, Energy=0.2349, Healthcare=0.4451, Nature=0.4300, Sales=0.3324, Transport=0.4672, Web/CloudOps=0.5053.
+- frequency skill: A=0.3634, D=0.3546, H=0.3953, M=0.0987, Q=0.2009, S=0.4254, T=0.4404, W=0.2126.
+- variates skill: multivariate=0.3967, univariate=0.3621.
+- Outcome: Final main0.377696007 exceeds matched E67paired12000 0.377427077 but is below its own6000 checkpoint0.378677489; retain6000 for the method comparison. Training completed12000 in2453.330776 process-loop seconds on the shared H100, seed197/B64/LR2e-5. E84coarse aggregation started after this trainer exited. No publication against stronger E87ensemble.
+
+overflow_control seed197 training started at 2026-10-23T03:43:42Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E90 training loss scale for daily and weekly windows — prepared
+The task metadata confirms daily and weekly MASE uses lag1, while the current training weight uses the first modeled seasonal period,7/52. Hypothesis: matching this scale improves coarse-frequency forecasting. Change only the lag used by the context seasonal-MAE loss weight for exact period pairs[7,365] and[52,1] to1; all other periods, weighting formula, forecasts and inference rules remain unchanged. Match E80overflow-only control: own E62 initialization, seed197/B64/paired loss/LR2e-5/12000steps, same original sampler and horizon RNG, verified float64 overflow fallback in both. Compare against E80, not the older normalization implementation. No generator or training source change. Queue after E84 releases its allocation.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:43:44Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:43:44Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:43:56Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:43:56Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:43:57Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:43:57Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:10Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:10Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:11Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:11Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:12Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:12Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:12Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:12Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:24Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:24Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:25Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:25Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:26Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:26Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:26Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:26Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:27Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:27Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:27Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:27Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:28Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:28Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:28Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:28Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:39Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:39Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:41Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:41Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:41Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:41Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:42Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:42Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:45Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:45Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:46Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:46Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:50Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:50Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:50Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:50Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:50Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:50Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:44:51Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:44:51Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E79 startup failure and clean matched restart
+{
+  "started_utc": "2026-10-23T03:43:44Z",
+  "cleanup_utc": "2026-10-23T03:47:40.492921+00:00",
+  "cause": "legacy random_capacity/queue.py shadowed stdlib queue inside compiler workers and launched recursive duplicate E79 trainers; OOM then closed pool errors",
+  "saved_checkpoints": 0,
+  "discarded_partial_optimizer_updates": "not recoverable from interleaved/overwritten logs; no model selected",
+  "established_trainers_affected": false,
+  "terminated_related_processes": 50,
+  "remedy": "rename and guard launchers; exclusive output-directory and receipt locks; clean fresh restart",
+  "numerical_recipe_correction": "both fresh arms now include previously verified float64 statistics fallback only for nonfinite float32 mean/scale; initial weights/seeds/source streams unchanged"
+}
+The incident interval is wall time on the sole shared H100; duplicate per-process compute cannot be reconstructed exactly because legacy launchers reused log paths. Original failed logs and receipts are retained. No valid E79 checkpoint exists from this attempt. The updated numerical normalization is shared by both fresh widths; width remains the only difference between their recipes.
+
+E79 corrected-normalization fixture: {"normalization_overflow_fallback": true, "width": 512, "initialization_seed": 307, "fixture_seed": 313, "optimizer_updates": 0, "maximum_shape_memory": {"batch": 48, "signed_rows": 96, "context": 4096, "horizon": 1024, "loss": 0.6031821966171265, "peak_allocated_mib": 4521.12451171875, "peak_reserved_mib": 4784.0}, "eager_loss": 0.3412420153617859, "compiled_loss": 0.3412419855594635, "gradient_cosine": 0.9999952735476323, "max_gradient_difference": 4.57763671875e-05, "compile_comparison_seconds": 43.313792017987, "total_seconds": 50.912394708022475, "source": "allowed packed_sample and own synthetic generator only"}
+
+E90 fixture and queue: {
+  "seed": 419,
+  "cpu_seconds": 1.048940374981612,
+  "forward_bitwise_unchanged": true,
+  "hourly_monthly_loss_bitwise_unchanged": true,
+  "mixed_loss_before": 0.4770006537437439,
+  "mixed_loss_after": 0.4904267191886902,
+  "all_gradients_finite": true,
+  "optimizer_updates": 0,
+  "diagnostic_input": "four locally generated random/smooth contexts; never used in selected weights"
+}
+Queued after E84(PID4254), using E80 as the matched control.
+
+E79 duplicate-output guard fixture: {
+  "duplicate_rejected_before_model_loading": true,
+  "optimizer_updates": 0,
+  "cpu_seconds": 1.6497254162095487
+}
+With the training code directory on the import path, queue now resolves to the Python standard library. No queue.py/torch.py/multiprocessing.py/concurrent.py shadow files remain under research.
+
+E79 corrected-normalization fixture: {"normalization_overflow_fallback": true, "width": 1024, "initialization_seed": 307, "fixture_seed": 313, "optimizer_updates": 0, "maximum_shape_memory": {"batch": 48, "signed_rows": 96, "context": 4096, "horizon": 1024, "loss": 0.6031821966171265, "peak_allocated_mib": 10162.86376953125, "peak_reserved_mib": 10696.0}, "eager_loss": 0.3412420153617859, "compiled_loss": 0.3412419855594635, "gradient_cosine": 0.9999932382274589, "max_gradient_difference": 6.103515625e-05, "compile_comparison_seconds": 40.99431764590554, "total_seconds": 49.15364895504899, "source": "allowed packed_sample and own synthetic generator only"}
+
+E79 fresh1024 seed307 training started at 2026-10-23T03:50:34Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 fresh512 seed307 training started at 2026-10-23T03:50:34Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E89 patch4_candidate seed401 training started at 2026-10-23T03:50:45Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E89 patch4_control seed401 training started at 2026-10-23T03:50:45Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E58/E66 widened146M final32000
+- Checkpoint SHA-256: `ecbb1f28a9bced1c7ecc078593908435320a618cde23bf034719b92b14c12b96`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.13470983505249 seconds.
+- Overall skill 0.375306; relative MASE 0.746016; relative WQL 0.523103.
+- term skill: long=0.3994, medium=0.4059, short=0.3536.
+- domain skill: Econ/Fin=0.1403, Energy=0.2325, Healthcare=0.4376, Nature=0.4286, Sales=0.3380, Transport=0.4675, Web/CloudOps=0.5006.
+- frequency skill: A=0.3821, D=0.3507, H=0.3942, M=0.0875, Q=0.2028, S=0.4235, T=0.4362, W=0.2179.
+- variates skill: multivariate=0.3934, univariate=0.3605.
+- Outcome: Final0.375305635 is effectively identical to matched37Mcontrol0.375304864 and below own E47parent0.375792930. The small24000 advantage did not persist. This rejects a benefit from this duplicated-width continuation, not from fresh larger pretraining. Final recovery3 loop3443.875463s; previous phases recorded separately; seed113/reset179/B96/LR3e-5. E79 tests fresh capacity instead.
+
+## E86 protected inherited stream at6000
+- Checkpoint SHA-256: `73dfe63b77e9f981fec42ceff6b2c9ec8b69e887c6d1d42830629c729c989625`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 30.87101984024048 seconds.
+- Overall skill 0.373645; relative MASE 0.748615; relative WQL 0.524061.
+- term skill: long=0.3977, medium=0.4063, short=0.3511.
+- domain skill: Econ/Fin=0.1394, Energy=0.2337, Healthcare=0.4357, Nature=0.4267, Sales=0.3377, Transport=0.4634, Web/CloudOps=0.4976.
+- frequency skill: A=0.3780, D=0.3487, H=0.3934, M=0.1091, Q=0.2016, S=0.4221, T=0.4332, W=0.2062.
+- variates skill: multivariate=0.3912, univariate=0.3593.
+- Outcome: Main0.373645290 barely improves3000 0.373573070 and remains below starting model0.375792503. Finish planned8000, then release allocation to E89. Seed113/B96/LR2e-4; no publication.
+
+## E84 coarse-data first1000
+- Checkpoint SHA-256: `207329fa0c16814d1fa72b3683219761f5ca3b692fa7059730f76c3fe0e93712`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.292750597000122 seconds.
+- Overall skill 0.377433; relative MASE 0.744088; relative WQL 0.520892.
+- term skill: long=0.4001, medium=0.4067, short=0.3569.
+- domain skill: Econ/Fin=0.1311, Energy=0.2365, Healthcare=0.4620, Nature=0.4308, Sales=0.3347, Transport=0.4671, Web/CloudOps=0.5002.
+- frequency skill: A=0.3669, D=0.3545, H=0.3965, M=0.1063, Q=0.1974, S=0.4228, T=0.4351, W=0.2289.
+- variates skill: multivariate=0.3942, univariate=0.3638.
+- Outcome: Main0.377433346 exceeds matched E67paired1000 0.376859515. Early evidence only; same seed197/B64/LR2e-5, independent aggregationRNG900200; no inference changes.
+
+## E84 coarse-data at3000
+- Checkpoint SHA-256: `75b4b41869254617288521d3c0c3539c9d60c6cad6b259b6ff166d1c9fd78c29`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 18.35591197013855 seconds.
+- Overall skill 0.378023; relative MASE 0.743608; relative WQL 0.520241.
+- term skill: long=0.4019, medium=0.4064, short=0.3573.
+- domain skill: Econ/Fin=0.1364, Energy=0.2363, Healthcare=0.4484, Nature=0.4319, Sales=0.3319, Transport=0.4672, Web/CloudOps=0.5044.
+- frequency skill: A=0.3677, D=0.3514, H=0.3990, M=0.1053, Q=0.1979, S=0.4219, T=0.4378, W=0.2193.
+- variates skill: multivariate=0.3966, univariate=0.3628.
+- Outcome: Main0.378023141 is slightly below matched E67paired3000 0.378318033 after the first1000 gain. Continue6000/12000 and inspect frequency groups; no consistent gain yet.
+
+## E80 overflow-only control first1000
+- Checkpoint SHA-256: `fb64456ba0c2ebef979b8525274132618fe542f67e7193036e4a013de6629f5c`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 18.763219356536865 seconds.
+- Overall skill 0.376804; relative MASE 0.744768; relative WQL 0.521468.
+- term skill: long=0.3990, medium=0.4062, short=0.3563.
+- domain skill: Econ/Fin=0.1319, Energy=0.2364, Healthcare=0.4621, Nature=0.4300, Sales=0.3347, Transport=0.4668, Web/CloudOps=0.4982.
+- frequency skill: A=0.3657, D=0.3559, H=0.3950, M=0.1061, Q=0.1979, S=0.4228, T=0.4347, W=0.2257.
+- variates skill: multivariate=0.3925, univariate=0.3640.
+- Outcome: Main0.376804394 is effectively tied with original E67paired1000 0.376859515, and slightly above E75postloss1000 0.376670711. Numerical correction alone has not shown a predictive gain yet. Continue matched3000/6000/12000.
+
+## E78 multiscale AR final12000
+- Checkpoint SHA-256: `a9b6f03e211513684787db1d518696925351987e524cb969c3d3a184afde6f25`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 18.768662929534912 seconds.
+- Overall skill 0.377173; relative MASE 0.744921; relative WQL 0.520745.
+- term skill: long=0.4032, medium=0.4047, short=0.3559.
+- domain skill: Econ/Fin=0.1376, Energy=0.2351, Healthcare=0.4499, Nature=0.4288, Sales=0.3321, Transport=0.4667, Web/CloudOps=0.5043.
+- frequency skill: A=0.3628, D=0.3520, H=0.3946, M=0.1047, Q=0.2008, S=0.4249, T=0.4400, W=0.2136.
+- variates skill: multivariate=0.3959, univariate=0.3619.
+- Outcome: Final0.377172928 is below matched E77single slowAR12000 0.377696007 and original E67paired12000 0.377427077. At6000 it nearly tied single slowAR; no gain from extra components. Completed planned endpoint; retain negative result and release this allocation for queued E89 after E86 finishes.
+
+E88 patch8 late slowAR seed197 training started at 2026-10-23T03:55:05Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E86 protected inherited stream final8000
+- Checkpoint SHA-256: `8c868d08f583249ae38cd9173b7f19c530825dda785fe66011be60d3e1a6b2f4`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 30.288848638534546 seconds.
+- Overall skill 0.373894; relative MASE 0.748106; relative WQL 0.524001.
+- term skill: long=0.3986, medium=0.4065, short=0.3511.
+- domain skill: Econ/Fin=0.1434, Energy=0.2349, Healthcare=0.4214, Nature=0.4283, Sales=0.3364, Transport=0.4640, Web/CloudOps=0.4986.
+- frequency skill: A=0.3812, D=0.3459, H=0.3944, M=0.1025, Q=0.2000, S=0.4216, T=0.4346, W=0.2079.
+- variates skill: multivariate=0.3920, univariate=0.3591.
+- Outcome: Final0.373894042 remains below its own initialization0.375792503. Protecting inherited tensors while training additions at a higher rate improved recovery speed but did not improve the forecast model. Completed8000 in1625.057884loopseconds on the shared H100. Effective protected/trainable counts36,800,800/109,676,544; seed113/B96/LR2e-4. Released allocation to E89; retain as negative result.
+
+## E80 overflow-only control at3000
+- Checkpoint SHA-256: `2084f1888f8d9e5afe2c740769ea2b4167086cdd62dfb4014bc3a338c00d4c12`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 22.62967348098755 seconds.
+- Overall skill 0.378664; relative MASE 0.743020; relative WQL 0.519581.
+- term skill: long=0.4024, medium=0.4069, short=0.3581.
+- domain skill: Econ/Fin=0.1345, Energy=0.2391, Healthcare=0.4537, Nature=0.4314, Sales=0.3327, Transport=0.4661, Web/CloudOps=0.5041.
+- frequency skill: A=0.3682, D=0.3548, H=0.3976, M=0.1042, Q=0.1968, S=0.4234, T=0.4386, W=0.2234.
+- variates skill: multivariate=0.3971, univariate=0.3636.
+- Outcome: Main0.378663825 exceeds original E67paired3000 0.378318033 and nearly matches E75postloss3000 0.378813891. Most of the earlier raw main gain may come from the numerical correction rather than the postprocessing-aware loss. Check earlier cutoffs using the same overflow-safe runtime before attributing the remaining difference. Same seed197/B64/LR2e-5.
+
+## E84 coarse data at6000
+- Checkpoint SHA-256: `79cce8767ec35ed8ba6045957f2ef4147adb8fe312b03c50dc20ae635255926f`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 22.568254947662354 seconds.
+- Overall skill 0.378304; relative MASE 0.743340; relative WQL 0.519958.
+- term skill: long=0.4018, medium=0.4073, short=0.3574.
+- domain skill: Econ/Fin=0.1325, Energy=0.2353, Healthcare=0.4579, Nature=0.4332, Sales=0.3368, Transport=0.4657, Web/CloudOps=0.5045.
+- frequency skill: A=0.3681, D=0.3543, H=0.3963, M=0.1037, Q=0.1944, S=0.4243, T=0.4385, W=0.2257.
+- variates skill: multivariate=0.3970, univariate=0.3630.
+- Outcome: Main0.378304411 remains slightly below matched E67paired6000 0.378455389. Continue planned12000, then assess overall/frequency behavior; no consistent aggregate gain yet. Seed197/B64/LR2e-5, same base/source/horizon streams apart from independent coarse aggregation.
+
+## E89 patch8 matched control first1000
+- Checkpoint SHA-256: `ebf6cd1845311b149118beb13364892445388f124e9f706bffe7a317ab97b762`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 29.399544715881348 seconds.
+- Overall skill 0.380274; relative MASE 0.743092; relative WQL 0.516841.
+- term skill: long=0.4036, medium=0.4039, short=0.3617.
+- domain skill: Econ/Fin=0.1303, Energy=0.2425, Healthcare=0.4624, Nature=0.4416, Sales=0.3473, Transport=0.4610, Web/CloudOps=0.5001.
+- frequency skill: A=0.3689, D=0.3561, H=0.3952, M=0.1038, Q=0.1908, S=0.4222, T=0.4419, W=0.2419.
+- variates skill: multivariate=0.3965, univariate=0.3670.
+- Outcome: Control0.380274173 is below its shared starting E71patch8_6000 0.381178739. The patch4 candidate is still approaching1000. Fresh AdamW/EMA, seed401/B32/LR2e-5; wait for the matched comparison.
+
+Completed process loop receipts: E71 recovery3 final12000=3333.747876572s (earlier interrupted prefixes separately logged); E78 final12000=2448.526446433s; E86 final8000=1625.057883863s. All overlapped other training on the one H100 and are not exclusive GPU times.
+
+## E71 patch8 final12000
+- Checkpoint SHA-256: `313d035b782188253ecd27c0da37030b397b82e4fa596f3a944994be3f1d203f`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.39552927017212 seconds.
+- Overall skill 0.379468; relative MASE 0.743587; relative WQL 0.517842.
+- term skill: long=0.4043, medium=0.4042, short=0.3599.
+- domain skill: Econ/Fin=0.1408, Energy=0.2402, Healthcare=0.4302, Nature=0.4416, Sales=0.3430, Transport=0.4619, Web/CloudOps=0.5048.
+- frequency skill: A=0.3766, D=0.3605, H=0.3949, M=0.0689, Q=0.1914, S=0.4241, T=0.4445, W=0.2231.
+- variates skill: multivariate=0.3987, univariate=0.3637.
+- Outcome: Final main0.379467684 is below selected6000 0.381178739. Retain6000 as published ensemble member. Seed197/B64/LR2e-5; recovery3 loop3333.747877s, previous recovery phases recorded separately; no further continuation in this run.
+
+## E69 volatility final12000
+- Checkpoint SHA-256: `adddab6f07cb65843a1356ee2a3b36985fd56ecacc6fdb7703c465b0cfe21f5a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 18.7578706741333 seconds.
+- Overall skill 0.360846; relative MASE 0.761644; relative WQL 0.536363.
+- term skill: long=0.3901, medium=0.4048, short=0.3314.
+- domain skill: Econ/Fin=0.1094, Energy=0.1887, Healthcare=0.4132, Nature=0.4274, Sales=0.3422, Transport=0.4584, Web/CloudOps=0.5066.
+- frequency skill: A=0.3708, D=0.3402, H=0.3826, M=0.1173, Q=0.1994, S=0.4419, T=0.4041, W=0.2107.
+- variates skill: multivariate=0.3938, univariate=0.3333.
+- Outcome: Final main0.360845700 is below matched E42partial12000 0.361108160. Earlier6000 diagnostics were mixed. The early main gain did not persist; no transfer or publication. Seed79/B256/LR5e-5; final recovery3 loop3542.503298s. Released allocation to E88.
+
+## E89 patch4 candidate first1000
+- Checkpoint SHA-256: `a0b4f56f9b3bbf26a94ee72a8f3487c1fb42c009c79ccb8fcef8538ffdb96e7f`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 47.17473244667053 seconds.
+- Overall skill 0.378251; relative MASE 0.744567; relative WQL 0.519191.
+- term skill: long=0.4036, medium=0.4019, short=0.3589.
+- domain skill: Econ/Fin=0.1342, Energy=0.2339, Healthcare=0.4544, Nature=0.4415, Sales=0.3520, Transport=0.4631, Web/CloudOps=0.5002.
+- frequency skill: A=0.3613, D=0.3424, H=0.3929, M=0.1696, Q=0.1890, S=0.4219, T=0.4368, W=0.2442.
+- variates skill: multivariate=0.3925, univariate=0.3667.
+- Outcome: Main0.378250742 improves folded initialization0.370553128 but trails matched patch8control1000 0.380274173 and shared original patch8parent0.381178739. Continue planned comparison; seed401/B32/LR2e-5.
+
+## E80 overflow-only3000 earlier cutoff1
+- Checkpoint SHA-256: `2084f1888f8d9e5afe2c740769ea2b4167086cdd62dfb4014bc3a338c00d4c12`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 22.02164888381958 seconds.
+- Overall skill 0.336120; relative MASE 0.797369; relative WQL 0.552739.
+- term skill: long=0.3981, medium=0.3582, short=0.3019.
+- domain skill: Econ/Fin=0.1557, Energy=0.2287, Healthcare=0.4162, Nature=0.3643, Sales=0.3941, Transport=0.3729, Web/CloudOps=0.4489.
+- frequency skill: A=0.3135, D=0.3521, H=0.3666, M=0.1852, Q=0.2300, S=0.3411, T=0.3348, W=0.2843.
+- variates skill: multivariate=0.3527, univariate=0.3226.
+- Outcome: Earlier0.336119626 exceeds E75postloss0.333946620 and original E67paired0.335738788. Numerical fix alone is a better base here; seed197/B64/LR2e-5, no extra optimization.
+
+## E80 overflow-only3000 earlier cutoff2
+- Checkpoint SHA-256: `2084f1888f8d9e5afe2c740769ea2b4167086cdd62dfb4014bc3a338c00d4c12`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 21.807528972625732 seconds.
+- Overall skill 0.358235; relative MASE 0.771665; relative WQL 0.533732.
+- term skill: long=0.3635, medium=0.3875, short=0.3446.
+- domain skill: Econ/Fin=0.1067, Energy=0.2360, Healthcare=0.3485, Nature=0.4691, Sales=0.5486, Transport=0.4210, Web/CloudOps=0.4314.
+- frequency skill: A=0.3545, D=0.4236, H=0.3910, M=0.1500, Q=0.1497, S=0.2812, T=0.3593, W=0.2599.
+- variates skill: multivariate=0.3633, univariate=0.3541.
+- Outcome: Earlier0.358234785 exceeds E75postloss0.357604589 and essentially ties original E67paired0.358235331. Main and first two earlier cutoffs warrant one fixed slowAR delta composition; no validation gradients.
+
+## E80 overflow-only3000 earlier cutoff3
+- Checkpoint SHA-256: `2084f1888f8d9e5afe2c740769ea2b4167086cdd62dfb4014bc3a338c00d4c12`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 21.630401849746704 seconds.
+- Overall skill 0.388792; relative MASE 0.737171; relative WQL 0.506769.
+- term skill: long=0.3965, medium=0.3607, short=0.3962.
+- domain skill: Econ/Fin=0.1707, Energy=0.3203, Healthcare=0.2577, Nature=0.5049, Sales=0.3995, Transport=0.5052, Web/CloudOps=0.3898.
+- frequency skill: A=0.3632, D=0.4429, H=0.4039, M=0.1158, Q=0.2578, S=0.2865, T=0.4303, W=0.2614.
+- variates skill: multivariate=0.3742, univariate=0.4001.
+- Outcome: Earlier0.388791691 is slightly below original E67paired0.389069088. Numerical correction does not improve every cutoff. Keep all outcomes; assess one predeclared fixed composition across all four cutoffs.
+
+## E91 predeclared overflow-safe base plus slow-AR correction
+Hypothesis: the overflow-only E80 paired3000 base retains the useful numerical correction without E75 postprocessing-aware objective regressions, while the independently matched E61 slowAR6000 minus E42partial6000 correction retains its previous benefit. Apply exactly one coefficient1 composition: E80paired3000 + E61slowAR6000 - E42partial6000. No coefficient sweep, no additional optimizer updates, no validation targets used in weight construction. All three parents were trained on allowed corpus/synthetic data from our own scratch lineage. Base seed197/B64/LR2e-5; matched delta parents seed79/B256/LR5e-5. Use the exact postloss_inference overflow-safe model with unchanged fixed inference rules. Evaluate all four established cutoffs, and only then consider equal-weight ensemble replacement with E71patch8_6000 and a full public-contract replay.
+
+## E80 overflow-only control at6000
+- Checkpoint SHA-256: `b19875c52ed5024383fb69ccc48cb07a5629ad8011abf134c7dcfcdc50fae2af`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 22.57701802253723 seconds.
+- Overall skill 0.378289; relative MASE 0.743925; relative WQL 0.519575.
+- term skill: long=0.4014, medium=0.4069, short=0.3578.
+- domain skill: Econ/Fin=0.1256, Energy=0.2394, Healthcare=0.4638, Nature=0.4293, Sales=0.3386, Transport=0.4657, Web/CloudOps=0.5023.
+- frequency skill: A=0.3687, D=0.3551, H=0.3958, M=0.1068, Q=0.1918, S=0.4249, T=0.4380, W=0.2268.
+- variates skill: multivariate=0.3966, univariate=0.3633.
+- Outcome: Main0.378288765 is below its3000 0.378663825 and matched original E67paired6000 0.378455389. The numerical correction is required for rare finite overflows but its forecasting gain is not monotonic. Continue planned12000; seed197/B64/LR2e-5.
+
+## E89 patch8 matched control at3000
+- Checkpoint SHA-256: `9083c6be954ec65ec6248801e719d6f182cd3d12bf116f8fc5fc4796675141c4`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 33.57180857658386 seconds.
+- Overall skill 0.378946; relative MASE 0.744701; relative WQL 0.517938.
+- term skill: long=0.4043, medium=0.4024, short=0.3597.
+- domain skill: Econ/Fin=0.1384, Energy=0.2401, Healthcare=0.4325, Nature=0.4444, Sales=0.3407, Transport=0.4620, Web/CloudOps=0.5013.
+- frequency skill: A=0.3736, D=0.3524, H=0.3933, M=0.0834, Q=0.1970, S=0.4232, T=0.4439, W=0.2376.
+- variates skill: multivariate=0.3975, univariate=0.3638.
+- Outcome: Control0.378945686 is below first1000 and original selected6000 parent. Continue matched patch4 comparison; seed401/B32/LR2e-5.
+
+## E88 late slowAR first9000
+- Checkpoint SHA-256: `fbd4b0d4f29fb162929a3cf9cd4bb0ae264a2e15d860519e0fc08fa314b6820d`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 30.04508352279663 seconds.
+- Overall skill 0.380434; relative MASE 0.741856; relative WQL 0.517434.
+- term skill: long=0.4054, medium=0.4044, short=0.3611.
+- domain skill: Econ/Fin=0.1423, Energy=0.2409, Healthcare=0.4434, Nature=0.4404, Sales=0.3436, Transport=0.4625, Web/CloudOps=0.5050.
+- frequency skill: A=0.3803, D=0.3586, H=0.3955, M=0.0778, Q=0.1920, S=0.4256, T=0.4443, W=0.2331.
+- variates skill: multivariate=0.3993, univariate=0.3650.
+- Outcome: Main0.380434469 exceeds original E71final12000 but is below selected E71earlier6000. Evaluate the exact original9000 checkpoint before attributing a gain to the late AR change. Resume8000 preserved optimizer/EMA/allRNG/prefetched batch; seed197/B64/global12kLRschedule.
+
+## E91 overflow-safe plus slowAR valid_screen
+- Checkpoint SHA-256: `59b90e76823bd4b339c7b5d290c7afbdca9d1ba81848760aa5f80ee941cdd089`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.561925172805786 seconds.
+- Overall skill 0.379922; relative MASE 0.741449; relative WQL 0.518575.
+- term skill: long=0.4039, medium=0.4067, short=0.3598.
+- domain skill: Econ/Fin=0.1450, Energy=0.2359, Healthcare=0.4601, Nature=0.4339, Sales=0.3351, Transport=0.4657, Web/CloudOps=0.5073.
+- frequency skill: A=0.3727, D=0.3616, H=0.3973, M=0.0940, Q=0.1982, S=0.4264, T=0.4400, W=0.2228.
+- variates skill: multivariate=0.3984, univariate=0.3648.
+- Outcome: Fixed coefficient1 composition; zero optimizer updates, merge CPU1.173140s. Main0.379921968 is slightly below E83main0.380250568, but all three earlier cutoffs improve E83. Next compare one fixed equal ensemble with selected E71patch8_6000 across all four cutoffs; validation selection only.
+
+## E91 overflow-safe plus slowAR valid_backcast
+- Checkpoint SHA-256: `59b90e76823bd4b339c7b5d290c7afbdca9d1ba81848760aa5f80ee941cdd089`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.259706020355225 seconds.
+- Overall skill 0.337178; relative MASE 0.795032; relative WQL 0.552597.
+- term skill: long=0.3962, medium=0.3594, short=0.3042.
+- domain skill: Econ/Fin=0.1688, Energy=0.2262, Healthcare=0.4114, Nature=0.3695, Sales=0.3929, Transport=0.3721, Web/CloudOps=0.4520.
+- frequency skill: A=0.3205, D=0.3501, H=0.3656, M=0.1901, Q=0.2319, S=0.3439, T=0.3378, W=0.2886.
+- variates skill: multivariate=0.3544, univariate=0.3232.
+- Outcome: Fixed coefficient1 composition; zero optimizer updates, merge CPU1.173140s. Main0.379921968 is slightly below E83main0.380250568, but all three earlier cutoffs improve E83. Next compare one fixed equal ensemble with selected E71patch8_6000 across all four cutoffs; validation selection only.
+
+## E91 overflow-safe plus slowAR valid_backcast2
+- Checkpoint SHA-256: `59b90e76823bd4b339c7b5d290c7afbdca9d1ba81848760aa5f80ee941cdd089`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.369709968566895 seconds.
+- Overall skill 0.360453; relative MASE 0.769235; relative WQL 0.531723.
+- term skill: long=0.3677, medium=0.3870, short=0.3472.
+- domain skill: Econ/Fin=0.1196, Energy=0.2351, Healthcare=0.3437, Nature=0.4735, Sales=0.5473, Transport=0.4202, Web/CloudOps=0.4377.
+- frequency skill: A=0.3522, D=0.4289, H=0.3897, M=0.1376, Q=0.1497, S=0.2867, T=0.3638, W=0.2656.
+- variates skill: multivariate=0.3670, univariate=0.3551.
+- Outcome: Fixed coefficient1 composition; zero optimizer updates, merge CPU1.173140s. Main0.379921968 is slightly below E83main0.380250568, but all three earlier cutoffs improve E83. Next compare one fixed equal ensemble with selected E71patch8_6000 across all four cutoffs; validation selection only.
+
+## E91 overflow-safe plus slowAR valid_backcast3
+- Checkpoint SHA-256: `59b90e76823bd4b339c7b5d290c7afbdca9d1ba81848760aa5f80ee941cdd089`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 25.158844232559204 seconds.
+- Overall skill 0.389176; relative MASE 0.737055; relative WQL 0.506212.
+- term skill: long=0.3949, medium=0.3621, short=0.3969.
+- domain skill: Econ/Fin=0.1809, Energy=0.3196, Healthcare=0.2279, Nature=0.5059, Sales=0.4000, Transport=0.5071, Web/CloudOps=0.3940.
+- frequency skill: A=0.3624, D=0.4461, H=0.4034, M=0.1001, Q=0.2604, S=0.2871, T=0.4311, W=0.2634.
+- variates skill: multivariate=0.3742, univariate=0.4008.
+- Outcome: Fixed coefficient1 composition; zero optimizer updates, merge CPU1.173140s. Main0.379921968 is slightly below E83main0.380250568, but all three earlier cutoffs improve E83. Next compare one fixed equal ensemble with selected E71patch8_6000 across all four cutoffs; validation selection only.
+
+## E92 robust normalization — prepared, not launched
+Hypothesis: rare large history observations dominate the current mean/RMS scale and compress typical dynamics. Change only the context normalization to winsorized statistics: center a clipping interval on the finite-value median, radius6 times the mean absolute deviation from that median; estimate mean and standard deviation of the clipped history in float64, retain the existing relative scale floor, and normalize the original history with the existing ±30 bound. This uses a mean absolute deviation, rather than median deviation, to retain scale for intermittent series. All-missing and constant histories use the same safe count/floor conventions. No future observations enter normalization. Initialize own E62 checkpoint; seed197/B64/LR2e-5/12000 paired steps, same original corpus/synthetic/horizon streams as E80overflow-only control. No new parameters, sources, or synthetic generator. A separate runtime will use identical robust statistics. Check finite outputs/gradients, affine and sign behavior, and unchanged loss machinery on locally generated fixtures before launch. Queue only after E80 releases its allocation.
+
+### E91 fixed ensemble outcome — retain E87
+[
+  {
+    "cutoff": "valid_screen",
+    "candidate": 0.38295755880742377,
+    "published": 0.38368555666405846,
+    "cpu_seconds": 0.7978230791632086,
+    "groups": {
+      "term": {
+        "short": 0.3641412739810047,
+        "medium": 0.4081748699742944,
+        "long": 0.4053387686104565
+      },
+      "domain": {
+        "Econ/Fin": 0.14196055084083636,
+        "Energy": 0.2436181245717931,
+        "Healthcare": 0.47044789997945524,
+        "Nature": 0.43843771854824554,
+        "Sales": 0.3435316624897278,
+        "Transport": 0.4646935796341666,
+        "Web/CloudOps": 0.50560991569805
+      },
+      "freq": {
+        "A-DEC": 0.37806371948108464,
+        "Q-DEC": 0.1953743898001734,
+        "M": 0.10835758037283183,
+        "W-SUN": 0.09200217288388479,
+        "D": 0.36288028663668526,
+        "H": 0.39989339265058577,
+        "15T": 0.21574107475831517,
+        "W-FRI": 0.00590529614346913,
+        "10T": 0.6203515795555475,
+        "W-TUE": 0.5998659430846229,
+        "W-THU": 0.20928566575491192,
+        "W-WED": 0.32755565821641586,
+        "5T": 0.5196292524760446,
+        "10S": 0.4253796526525012
+      }
+    }
+  },
+  {
+    "cutoff": "valid_backcast",
+    "candidate": 0.33965252002132096,
+    "published": 0.33897078175106665,
+    "cpu_seconds": 0.8413949671667069,
+    "groups": {
+      "term": {
+        "short": 0.30682882244621934,
+        "medium": 0.3594584814302525,
+        "long": 0.4004578178388881
+      },
+      "domain": {
+        "Econ/Fin": 0.17015181674919277,
+        "Energy": 0.23013645693798135,
+        "Healthcare": 0.4218451623665679,
+        "Nature": 0.3716837571447873,
+        "Sales": 0.4016515190787069,
+        "Transport": 0.3732465934225949,
+        "Web/CloudOps": 0.450994086374909
+      },
+      "freq": {
+        "A-DEC": 0.3282816505284657,
+        "Q-DEC": 0.23566016127363243,
+        "M": 0.19914077895872262,
+        "W-SUN": 0.03865936211094867,
+        "D": 0.355074708701307,
+        "H": 0.36721923281513913,
+        "15T": 0.22292257615547317,
+        "W-FRI": 0.23702394178468034,
+        "10T": 0.32706954225689555,
+        "W-TUE": 0.5531057781818784,
+        "W-THU": 0.26381006300655674,
+        "W-WED": 0.35364720411869544,
+        "5T": 0.44270238618668056,
+        "10S": 0.34631632390506784
+      }
+    }
+  },
+  {
+    "cutoff": "valid_backcast2",
+    "candidate": 0.36318271604290864,
+    "published": 0.36315845377936284,
+    "cpu_seconds": 0.7982894480228424,
+    "groups": {
+      "term": {
+        "short": 0.3500873123601329,
+        "medium": 0.39140163998387134,
+        "long": 0.3681031208336688
+      },
+      "domain": {
+        "Econ/Fin": 0.11631521324076821,
+        "Energy": 0.23897422330833784,
+        "Healthcare": 0.3563145060249002,
+        "Nature": 0.4759919302878802,
+        "Sales": 0.5526652479550637,
+        "Transport": 0.42387192299481313,
+        "Web/CloudOps": 0.436824697056448
+      },
+      "freq": {
+        "A-DEC": 0.34293470421447503,
+        "Q-DEC": 0.15231508648525816,
+        "M": 0.14735330101758526,
+        "W-SUN": 0.05191699540908579,
+        "D": 0.4322785270529358,
+        "H": 0.39262772604483,
+        "15T": 0.10591962461225035,
+        "W-FRI": 0.14794480975291435,
+        "10T": 0.5077330623307716,
+        "W-TUE": 0.09268217269468071,
+        "W-THU": 0.34555097504792365,
+        "W-WED": 0.5579990641356007,
+        "5T": 0.4724634827891697,
+        "10S": 0.2869202644785588
+      }
+    }
+  },
+  {
+    "cutoff": "valid_backcast3",
+    "candidate": 0.3889402057921222,
+    "published": 0.3893067316884232,
+    "cpu_seconds": 1.0883019380271435,
+    "groups": {
+      "term": {
+        "short": 0.3948070762587602,
+        "medium": 0.36614080286851436,
+        "long": 0.3957100819716167
+      },
+      "domain": {
+        "Econ/Fin": 0.18258306326920215,
+        "Energy": 0.3151836976978215,
+        "Healthcare": 0.23576266671317847,
+        "Nature": 0.5094192054942891,
+        "Sales": 0.4006642315236326,
+        "Transport": 0.5077376839877803,
+        "Web/CloudOps": 0.39340413209496183
+      },
+      "freq": {
+        "A-DEC": 0.3560985343152149,
+        "Q-DEC": 0.25541091598546395,
+        "M": 0.11989423475215888,
+        "W-SUN": 0.15976888148282853,
+        "D": 0.4460752118885134,
+        "H": 0.40415283128676627,
+        "15T": 0.2542013440411708,
+        "W-FRI": 0.03652589348537405,
+        "10T": 0.6009630559934178,
+        "W-TUE": 0.25771359383581927,
+        "W-THU": 0.35674611651454924,
+        "W-WED": 0.37808927325032227,
+        "5T": 0.4654632139858025,
+        "10S": 0.29366540153879894
+      }
+    }
+  }
+]
+The E91/E71 equal ensemble loses main0.382957559 versus E870.383685557 and third earlier0.388940206 versus0.389306732. First earlier improves0.339652520 versus0.338970782; second is effectively tied0.363182716 versus0.363158454. No consistent replacement gain; retain E87 and do not spend a full replay. E91 checkpoint SHA59b90e76823bd4b339c7b5d290c7afbdca9d1ba81848760aa5f80ee941cdd089, coefficient1, no optimization. The postloss-related earlier regressions were real, but removing that objective does not improve the selected equal ensemble overall.
+
+E92 CPU fixture passed: finite outputs/gradients, ordered quantiles, midpoint-median sign symmetry, affine behavior on nonconstant rows, eight own-generated contexts, seed431, zero updates,1.487883s. GPU autocast fixture queued.
+
+## E88 exact original patch8 control at9000
+- Checkpoint SHA-256: `342436626bc9219f068c118aefbe6ffb106f91e9f88acb01a9ee704cdfb7aa6b`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 34.10145950317383 seconds.
+- Overall skill 0.380329; relative MASE 0.741988; relative WQL 0.517518.
+- term skill: long=0.4051, medium=0.4042, short=0.3611.
+- domain skill: Econ/Fin=0.1410, Energy=0.2410, Healthcare=0.4471, Nature=0.4399, Sales=0.3435, Transport=0.4623, Web/CloudOps=0.5043.
+- frequency skill: A=0.3807, D=0.3585, H=0.3953, M=0.0838, Q=0.1923, S=0.4254, T=0.4436, W=0.2332.
+- variates skill: multivariate=0.3986, univariate=0.3654.
+- Outcome: Original9000 main0.380329158 versus E88late slowAR9000 0.380434469, a small0.000105310 gain after1000 changed steps. Both below original selected6000 0.381178739; continue exact final12000 comparison. One failed evaluator invocation used a nonexistent path and did no inference; rerun with full_paired_inference succeeded.
+
+E92 GPU bf16 paired-gradient fixture passed, seed433,8.773601s, zero optimizer updates. Queue behind E80PID4371; unchanged declared12k schedule.
+
+Provenance hardening for E88: publisher now includes the immutable external recovery008000 snapshot as a lineage parent and records its hash/step, in addition to the original initialization. This preserves the original8000-step recipe before the late slowAR change. Mutable within-run recovery files remain excluded to avoid self-referencing lineage; no training state or weights changed.
+
+## Permitted-sampler coverage diagnostic, seed449
+Independent E79sampler audit generated48000 windows in4.262272CPU seconds, zero optimizer updates, no validation data read, active training streams untouched. Full source probabilities and finite-history/target summaries are sampler_coverage_audit.json. Monthly[12,1] accounts for5.1104% of rows: medianfinitecontext43, medianfinitetarget57,42.68%histories below32. Quarterly[4,1] accounts for5.2792%: mediancontext33,target46,47.95%histories below32. Weekly[52,1] accounts for3.8188%: mediancontext51,target82,30.44%histories below32. Current synthetic-period list contains no[52,1]weekly case. These are training-distribution diagnostics, not performance evidence; context and target marginal medians do not imply every row has that ratio. Original sampler20%synthetic/randomC-H recipe preserved.
+
+## E79 fresh37M first4000
+- Checkpoint SHA-256: `dc04a05abefdb903497a3311e7eac289ccdeae75db0ada66d02e05e431ba4f1a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 29.426799774169922 seconds.
+- Overall skill 0.341862; relative MASE 0.775670; relative WQL 0.558415.
+- term skill: long=0.3667, medium=0.3845, short=0.3148.
+- domain skill: Econ/Fin=0.1118, Energy=0.2099, Healthcare=0.3250, Nature=0.4095, Sales=0.3041, Transport=0.4280, Web/CloudOps=0.4683.
+- frequency skill: A=0.3520, D=0.3280, H=0.3608, M=-0.0493, Q=0.1682, S=0.4263, T=0.3918, W=0.2320.
+- variates skill: multivariate=0.3630, univariate=0.3246.
+- Outcome: Fresh random seed307/B48/LR2e-4/96000 static-compiled paired training, original random initialization hash19da01694123fcb13df815689ca66460d0da596bd31dfabfabb08dda6f7801e4. Main0.341861940 is below the published mature lineage but a promising early learning curve. Wait for the matched146M4000 result and planned8000/16000 checkpoints. Both arms share the corrected float64 overflow fallback and slowAR generator. Startup failures and discarded work were recorded separately; no baseline weights used.
+
+## E89 patch4 candidate at3000
+- Checkpoint SHA-256: `87fcd88001d63389726ff55885ef09933f912034662816cf2080d2f08a041542`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 58.48912262916565 seconds.
+- Overall skill 0.379843; relative MASE 0.742829; relative WQL 0.517743.
+- term skill: long=0.4040, medium=0.4031, short=0.3611.
+- domain skill: Econ/Fin=0.1466, Energy=0.2356, Healthcare=0.4515, Nature=0.4449, Sales=0.3476, Transport=0.4665, Web/CloudOps=0.4992.
+- frequency skill: A=0.3695, D=0.3423, H=0.3937, M=0.1494, Q=0.2020, S=0.4211, T=0.4409, W=0.2524.
+- variates skill: multivariate=0.3948, univariate=0.3676.
+- Outcome: Candidate main0.379842915 now exceeds matched patch8control3000 0.378945686 by0.000897230, though below selected starting parent0.381178739. Monthly skill0.1494 and weekly0.2524 are encouraging; daily0.3423 remains weak. Continue the declared6000/12000 comparisons before considering ensemble complementarity. Seed401/B32/LR2e-5; same original generator and matched fresh optimizer.
+
+E90 daily-weekly loss scale seed197 training started at 2026-10-23T04:12:55Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E93 patch8 encoder with patch4 forecast blocks — prepared
+Hypothesis: E89patch4 improves monthly/weekly behavior by refining forecast blocks; part of the gain may survive with the cheaper patch8 encoder. Decouple input and output patch sizes, keeping input8 and output4. Initialize exactly from own E71patch8 EMA6000: retain all encoder/input tensors, fold query/recency halves and keep the first4 head rows as in E89. No function-preservation claim for output4; input8/output8 must reproduce the old implementation exactly. All other training settings match E89: seed401/B32/LR2e-5/12000 paired steps, original normalization and synthetic generator, horizonRNG300404, fresh optimizer/EMA. Compare with existing E89patch8control and fullpatch4 at1000/3000/6000/12000. No new data/generator or external weights. Queue after E88 releases its allocation, after equivalence and maximum-shape CUDA checks.
+
+## E84 coarse aggregation final12000
+- Checkpoint SHA-256: `bb3e1d1ef6b57ad9517e69c5f3c4fd5caac6e4fd0f50fa9273df2cd2b13b8a70`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.56629729270935 seconds.
+- Overall skill 0.377144; relative MASE 0.744785; relative WQL 0.520888.
+- term skill: long=0.4036, medium=0.4057, short=0.3553.
+- domain skill: Econ/Fin=0.1335, Energy=0.2339, Healthcare=0.4474, Nature=0.4322, Sales=0.3330, Transport=0.4658, Web/CloudOps=0.5048.
+- frequency skill: A=0.3623, D=0.3529, H=0.3953, M=0.1038, Q=0.1971, S=0.4244, T=0.4404, W=0.2071.
+- variates skill: multivariate=0.3962, univariate=0.3616.
+- Outcome: Final0.377143853 is below matched E67paired12000 0.377427077. Daily skill improves0.352881088 versus0.350503003, but weekly drops0.207079293 versus0.218111206. Short term loses0.000773 while long/medium gain slightly. The targeted daily/weekly augmentation is mixed and not selected. Seed197/B64/LR2e-5, final training loop2103.526387s. E90loss-scale-only starts in the released allocation.
+
+## E89 patch8 matched control at6000
+- Checkpoint SHA-256: `9e8f798ceb2e1b49d8bd92ba334c42470e780b8d6b62a14195bb268ae8ba872a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 33.938090801239014 seconds.
+- Overall skill 0.377735; relative MASE 0.746288; relative WQL 0.518853.
+- term skill: long=0.4024, medium=0.4020, short=0.3584.
+- domain skill: Econ/Fin=0.1414, Energy=0.2382, Healthcare=0.4376, Nature=0.4420, Sales=0.3419, Transport=0.4600, Web/CloudOps=0.4997.
+- frequency skill: A=0.3730, D=0.3564, H=0.3927, M=0.0689, Q=0.1989, S=0.4229, T=0.4411, W=0.2355.
+- variates skill: multivariate=0.3951, univariate=0.3636.
+- Outcome: Main0.377734884 continues below original selected parent0.381178739 and control3000 0.378945686. Preserve this matched reference for fullpatch4 and E93splitpatch; seed401/B32/LR2e-5. Continue planned12000.
+
+E93 initialization SHA0719b4dce554764ef69181d8b19d8da11849ef32b35391ffee6700e639a73114;36,601,928parameters, CPUconversion0.663439s. CPUfixture1.433205s proves equal8/8 implementation bitwise unchanged and all converted tensors exactly match E89full4 except the retained input8 projection. CUDAmaximumshapeB32C4096H1024, EMA/moments present, finite loss/gradients,7082MiB reserved,10.871036s. Zero optimizer updates in fixtures. Queued behind E88PID7349 as declared.
+
+## E93 input8/output4 zero-step conversion
+- Checkpoint SHA-256: `0719b4dce554764ef69181d8b19d8da11849ef32b35391ffee6700e639a73114`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 36.244373083114624 seconds.
+- Overall skill 0.375817; relative MASE 0.747921; relative WQL 0.520916.
+- term skill: long=0.4024, medium=0.4024, short=0.3547.
+- domain skill: Econ/Fin=0.1375, Energy=0.2390, Healthcare=0.4236, Nature=0.4343, Sales=0.3477, Transport=0.4607, Web/CloudOps=0.4989.
+- frequency skill: A=0.3643, D=0.3468, H=0.3925, M=0.0737, Q=0.1911, S=0.4237, T=0.4382, W=0.2424.
+- variates skill: multivariate=0.3939, univariate=0.3610.
+- Outcome: Initial main0.375817443 is below shared E71patch8parent0.381178739 but above fullpatch4 conversion0.370553128. Keeping the input projection preserves more initial accuracy; this is initialization behavior, not evidence for trained superiority. Conversion0.663439CPU seconds; no optimizer updates; queued matched seed401/B32/LR2e-5/12000 continuation.
+
+## E87 validation-only context/error audit
+Read the established9473-instance screen, both selected member prediction caches, and the cached SeasonalNaive reference; no optimization or changed inference. Compute fixed context4096/recent512 level shifts, scale ratios, and RMS/mean-absolute-deviation ratios, then macro aggregate conditional median-error ratios over represented configurations. CPU2.732488s, zero optimizer updates; complete bins/counts/per-channel metrics in context_error_audit.json. Median-error ratios across recent-level-shift bins[0,.25),[.25,.75),[.75,1.5),[1.5,inf) are.73083,.71495,.77366,.88600. Last bin has only40rows/eightconfigurations. Outlier-dominance bins do not show a monotonic weakness; the highest ratio bin performs relatively well against SeasonalNaive. These are descriptive validation subsets, with different configurations represented; not benchmark skill, independent evidence, or causal estimates. No inference-rule change follows from this audit.
+
+## E79 fresh146M first4000
+- Checkpoint SHA-256: `ce1d3cf0e49de7acc168ca818424fef4c58feebc49e35ebccd79e6a7450c810a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 41.41028594970703 seconds.
+- Overall skill 0.328134; relative MASE 0.791714; relative WQL 0.570160.
+- term skill: long=0.3561, medium=0.3699, short=0.3002.
+- domain skill: Econ/Fin=0.1210, Energy=0.2038, Healthcare=0.2653, Nature=0.3940, Sales=0.2893, Transport=0.4160, Web/CloudOps=0.4518.
+- frequency skill: A=0.3724, D=0.3122, H=0.3525, M=-0.0778, Q=0.2007, S=0.4142, T=0.3812, W=0.1775.
+- variates skill: multivariate=0.3500, univariate=0.3102.
+- Outcome: Main0.328133919 trails matched37M4000 0.341861940. Both use own random seed307, batch48, LR2e-4/96000, identical sampled windows and modern paired/slowAR/horizon recipe. This is early optimization, not evidence that larger capacity cannot improve. Continue planned8000/16000 while inspecting matched training loss. Static-compiler startup/abandoned duplicate launches were recorded separately.
+
+E93 input8/output4 seed401 training started at 2026-10-23T04:18:59Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E79 matched training-loss diagnostic at first4000: mean50-step-block losses37M/146M over steps1..400=.434227/.430111,401..1000=.397594/.402231,1001..2000=.381371/.386349,2001..3000=.374289/.377926,3001..4000=.361665/.366583. The larger model starts slightly better during warmup but trails after reachingLR2e-4. This is consistent with an optimization mismatch, not proof of its cause. First4000 training-loop times including static compilation:37M1106.1s,146M1563.7s; both share the sole H100.
+
+## E94 fresh146M with half learning rate — prepared
+Hypothesis: the commonLR2e-4 is too aggressive at width1024, obscuring capacity benefits. Add one matched fresh146M arm withLR1e-4 only; same original random initialization SHA c9693619ca6277c26fef3b2bde37ff6796bec97b3af9f94588bbc5ddd329ec90, seed307, B48,96000-step cosine horizon,400 warmup, static compiled paired loss, allowed packed_sample/source weights, slowAR synthetic generator, requested-horizon augmentation, overflow fallback, optimizer/EMA, save/checkpoint cadence. All original E79 arms continue unchanged. Reuse the exact code and already verified width1024 maximum-shape/compiled-gradient fixtures; no architecture or memory change warrants rerunning them. Screen4000/8000/16000/24000, continuing longer only if supported. Queue after E89patch8control releases its allocation. No pretrained assets, new sources or generators.
+
+## E79 fresh37M at8000
+- Checkpoint SHA-256: `5e821b81ac42babdc67ed0f5368da07941be075784d1ded6ab6c65539458faf0`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 29.999473094940186 seconds.
+- Overall skill 0.353509; relative MASE 0.763149; relative WQL 0.547666.
+- term skill: long=0.3727, medium=0.3917, short=0.3307.
+- domain skill: Econ/Fin=0.1196, Energy=0.2092, Healthcare=0.3930, Nature=0.4203, Sales=0.3250, Transport=0.4394, Web/CloudOps=0.4807.
+- frequency skill: A=0.3577, D=0.3366, H=0.3677, M=0.0393, Q=0.1747, S=0.4203, T=0.4060, W=0.2420.
+- variates skill: multivariate=0.3701, univariate=0.3400.
+- Outcome: Main0.353508836 improves4000 0.341861940, still below the mature published model. Continue declared16k/24k/48k; own random seed307/B48/LR2e-4/96k, no pretrained assets. Fresh146M is still before its matched8k.
+
+## E90 daily-weekly loss scale first1000
+- Checkpoint SHA-256: `e0e1e3967da656eaa8184317b080a5f2064081d0d53e969c4f8a4894fa92d4f2`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.965925931930542 seconds.
+- Overall skill 0.376961; relative MASE 0.744602; relative WQL 0.521322.
+- term skill: long=0.3990, medium=0.4061, short=0.3567.
+- domain skill: Econ/Fin=0.1300, Energy=0.2364, Healthcare=0.4663, Nature=0.4300, Sales=0.3344, Transport=0.4667, Web/CloudOps=0.4984.
+- frequency skill: A=0.3661, D=0.3567, H=0.3951, M=0.1038, Q=0.1981, S=0.4227, T=0.4348, W=0.2267.
+- variates skill: multivariate=0.3927, univariate=0.3641.
+- Outcome: Main0.376961315 slightly exceeds matched overflow-only E801000 0.376804394. Too early for a method-selection claim; inspect3k/6k and frequency groups. Seed197/B64/LR2e-5, only exactdaily/weeklyseasonallosslag changes.
+
+## E88 late patch8 slowAR final12000
+- Checkpoint SHA-256: `81e020113b23e84d1df64458e7c1b4a4f6ad6ee24f7cdb601aeb3fbe5d20251f`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 33.93403601646423 seconds.
+- Overall skill 0.379111; relative MASE 0.744397; relative WQL 0.517873.
+- term skill: long=0.4044, medium=0.4045, short=0.3590.
+- domain skill: Econ/Fin=0.1445, Energy=0.2396, Healthcare=0.4185, Nature=0.4414, Sales=0.3428, Transport=0.4624, Web/CloudOps=0.5058.
+- frequency skill: A=0.3764, D=0.3621, H=0.3953, M=0.0393, Q=0.1919, S=0.4250, T=0.4450, W=0.2233.
+- variates skill: multivariate=0.3996, univariate=0.3623.
+- Outcome: Final0.379111337 is below exact original E71final12000 0.379467684. Its small9k gain did not persist, and neither checkpoint exceeds selected6k parent0.381178739. Reject the late slowAR continuation; no new package or earlier-cutoff selection. Resumed8000->12000 completed in1415.491672loopseconds, preserving original optimizer/EMA/RNG/prefetched batch; seed197/B64/same globalcosine. E93 starts in the released allocation.
+
+E92 robust normalization seed197 training started at 2026-10-23T04:21:37Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E95 broader source coverage at fixed packed volume — preparation
+Hypothesis: restricting each large source to eight shards and early record batches limits the useful diversity, especially for buildings (127shards) and each climate source (96shards). Prepare packed_stratified with the same95allowlisted directories,24Mpoint/source soft cap, min16, maxcrop131072 as packed_sample. Select a uniform shuffled order of flattened series across every shard and record batch; take the first finite crops until the cap. Keep exact per-row frequency, exclude all-missing crops, and use independent deterministic per-series crop RNG. Thus coverage, row-frequency handling and empty-crop filtering are declared changes; this is not a claim that each can be isolated if actual mixed-frequency cases occur. Source mixture probabilities will remain those from packed_sample in any training comparison. No external data or validation values enter packing.
+
+Packer fixtures cover scalar, variable-channel and fixed-channel Arrow targets, null parents/variates, all-missing series, differing row frequencies and long crops:44retained series replay exactly, two fresh outputs have identical hashes,0.240463CPU seconds, zero updates. Initial fixture caught Arrow boolean conversion requiring zero_copy_only=False; fixed before any real-data packing. Per-directory receipts record selected source coordinates, shard coverage, binary hashes and elapsed time; output-wide lock and final atomic meta.json prevent reading an incomplete pack.
+
+Planned first training comparison, contingent on the completed real-data audit: own E62 parent, seed197/B64/LR2e-5/12000 paired steps with the exact E80overflow-control model/data code, changing only the packed corpus while retaining source probabilities from packed_sample. Screen1k/3k/6k/12k. No fresh training is launched yet; prefer the E90allocation after it finishes. A later fresh-capacity/data comparison remains a separate decision.
+
+## E80 overflow-only final12000
+- Checkpoint SHA-256: `f11d056b95f1c5eeef174265ebc51742952600e0dae1fa52900860447d5be819`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.899142265319824 seconds.
+- Overall skill 0.377424; relative MASE 0.744802; relative WQL 0.520408.
+- term skill: long=0.4025, medium=0.4056, short=0.3563.
+- domain skill: Econ/Fin=0.1303, Energy=0.2363, Healthcare=0.4508, Nature=0.4285, Sales=0.3332, Transport=0.4665, Web/CloudOps=0.5051.
+- frequency skill: A=0.3608, D=0.3513, H=0.3946, M=0.1037, Q=0.1974, S=0.4251, T=0.4405, W=0.2175.
+- variates skill: multivariate=0.3963, univariate=0.3620.
+- Outcome: Final0.377423946 effectively ties original E67paired12000 0.377427077; strongest main checkpoint remained3000. Numerical correction is still required for rare overflow but does not cause a consistent predictive gain by itself. Completed2264.013627training-loop seconds on sharedH100, seed197/B64/LR2e-5. E92robust normalization starts in its allocation.
+
+## E90 daily-weekly loss scale at3000
+- Checkpoint SHA-256: `a985b1af082ccd6e23a9ec3fd6d6bdd8e8f9ae44900d2c0e1f8d3737cd9fd8d3`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.59094786643982 seconds.
+- Overall skill 0.378417; relative MASE 0.743144; relative WQL 0.519906.
+- term skill: long=0.4018, medium=0.4067, short=0.3579.
+- domain skill: Econ/Fin=0.1345, Energy=0.2395, Healthcare=0.4551, Nature=0.4311, Sales=0.3328, Transport=0.4655, Web/CloudOps=0.5031.
+- frequency skill: A=0.3691, D=0.3557, H=0.3972, M=0.1020, Q=0.1974, S=0.4233, T=0.4378, W=0.2245.
+- variates skill: multivariate=0.3966, univariate=0.3635.
+- Outcome: Main0.378417277 trails matched E803000 0.378663825 after a small first1000 gain. Continue6000/12000 and inspect targeted frequency groups; no publication. Seed197/B64/LR2e-5.
+
+## E92 robust normalization first1000
+- Checkpoint SHA-256: `0067828e667139743a9908ff3a6d68f704197a1609935c64ae6f7a15c84884fa`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.180058240890503 seconds.
+- Overall skill 0.377596; relative MASE 0.742948; relative WQL 0.521418.
+- term skill: long=0.3986, medium=0.4060, short=0.3580.
+- domain skill: Econ/Fin=0.1327, Energy=0.2358, Healthcare=0.4628, Nature=0.4326, Sales=0.3487, Transport=0.4670, Web/CloudOps=0.4977.
+- frequency skill: A=0.3673, D=0.3569, H=0.3949, M=0.1225, Q=0.1981, S=0.4216, T=0.4346, W=0.2284.
+- variates skill: multivariate=0.3923, univariate=0.3656.
+- Outcome: Main0.377596201 exceeds matched E801000 0.376804394 by0.000791807. Early evidence only; changes fixed context mean/RMS estimation to median-centered winsorized statistics, retaining original history normalization bounds. Continue3000/6000 and earlier cutoffs if the gain persists. Seed197/B64/LR2e-5.
+
+## E93 input8/output4 first1000
+- Checkpoint SHA-256: `0aecc5af0ac9b0caf9949bd6601f2fe4ad96c5fdc9e1287c1ff2237439367b7a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 35.963313579559326 seconds.
+- Overall skill 0.379099; relative MASE 0.744060; relative WQL 0.518128.
+- term skill: long=0.4046, medium=0.4041, short=0.3591.
+- domain skill: Econ/Fin=0.1359, Energy=0.2378, Healthcare=0.4437, Nature=0.4406, Sales=0.3497, Transport=0.4667, Web/CloudOps=0.5000.
+- frequency skill: A=0.3584, D=0.3472, H=0.3946, M=0.1244, Q=0.1932, S=0.4224, T=0.4398, W=0.2474.
+- variates skill: multivariate=0.3943, univariate=0.3667.
+- Outcome: Main0.379098968 exceeds fullpatch4 E891000 0.378250742 but trails matched patch8control1000 0.380274173. Keeping input8 retains initial accuracy; assess later recovery against both existing arms before an architectural conclusion. Seed401/B32/LR2e-5.
+
+E95 pack completed in109.068074s on two CPUworkers:95directories,342661series,952527771points (3.810GB), versus original357789series/958745103points. Identical softcaps, actualpointcount0.65%lower because overshoot/serieslengths differ. Building127/127shards, borg31/31, eachlargeclimate96/96 are represented. No selected candidate row differed from its shardfirst frequency;103all-missing crops excluded. Exact real-data audit replayed260series acrossall95dirs against independent Arrow row/var access, including frequencies and cropstarts; source probabilities bitwise equal to original. Seed461,7.897138CPU seconds, no validationdata/optimizerupdates. Training nowqueued afterE90PID8949 under the predeclared E80matched recipe.
+
+## E89 fullpatch4 at6000
+- Checkpoint SHA-256: `e50a2d43bb3b6b66a94a6a2d9d62f0cb3e404310352e0357cfe0f2a874a21727`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 58.3371467590332 seconds.
+- Overall skill 0.380541; relative MASE 0.742008; relative WQL 0.517151.
+- term skill: long=0.4046, medium=0.4023, short=0.3625.
+- domain skill: Econ/Fin=0.1470, Energy=0.2361, Healthcare=0.4746, Nature=0.4451, Sales=0.3448, Transport=0.4655, Web/CloudOps=0.4969.
+- frequency skill: A=0.3637, D=0.3535, H=0.3958, M=0.1449, Q=0.2068, S=0.4213, T=0.4378, W=0.2471.
+- variates skill: multivariate=0.3932, univariate=0.3703.
+- Outcome: Main0.380540506 improves3000 0.379842915 and beats matched patch8control6000 0.377734884, but remains below selected starting E71patch8parent0.381178739. Check all three earlier cutoffs to assess stability and possible ensemble complementarity. Seed401/B32/LR2e-5; same original generator and fresh optimizer.
+
+## E93 input8/output4 at3000
+- Checkpoint SHA-256: `1b82ca84aca9aaa2b8b6b1f944a865b6aad7560257f88b053ea5f62bf63a4eb8`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 35.112743854522705 seconds.
+- Overall skill 0.379808; relative MASE 0.743342; relative WQL 0.517444.
+- term skill: long=0.4052, medium=0.4025, short=0.3608.
+- domain skill: Econ/Fin=0.1447, Energy=0.2358, Healthcare=0.4455, Nature=0.4421, Sales=0.3442, Transport=0.4692, Web/CloudOps=0.5011.
+- frequency skill: A=0.3652, D=0.3489, H=0.3934, M=0.1161, Q=0.2001, S=0.4227, T=0.4417, W=0.2532.
+- variates skill: multivariate=0.3949, univariate=0.3675.
+- Outcome: Main0.379808189 nearly matches fullpatch4_3000 0.379842915 and exceeds matched patch8control3000 0.378945686. Much of the early finer-patch gain may be retained by output blocks alone, with7082MiB max-shape reserve versus11376MiB fullpatch4. Continue6000/12000; seed401/B32/LR2e-5.
+
+## E92 robust normalization at3000
+- Checkpoint SHA-256: `348934d9fbcc8f2a8c45f7ebdc6cd971678d190bcf6ec0c0bd1cf80a12334bbc`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.601238489151 seconds.
+- Overall skill 0.378938; relative MASE 0.741788; relative WQL 0.519985.
+- term skill: long=0.4019, medium=0.4060, short=0.3591.
+- domain skill: Econ/Fin=0.1339, Energy=0.2386, Healthcare=0.4552, Nature=0.4314, Sales=0.3481, Transport=0.4662, Web/CloudOps=0.5030.
+- frequency skill: A=0.3665, D=0.3532, H=0.3965, M=0.1254, Q=0.1981, S=0.4218, T=0.4381, W=0.2295.
+- variates skill: multivariate=0.3961, univariate=0.3649.
+- Outcome: Main0.378937598 exceeds matched overflow-only E803000 0.378663825 by0.000273773 and postlossE753000 0.378813891 by0.000123707. Small primary gain persists; check all three earlier cutoffs before any composition or selection. Same seed197/B64/LR2e-5. Float64 robust statistics replace the old normalization throughout both network and context loss-scale calculation.
+
+## E96 equal three-resolution ensemble — predeclared selection probe
+Hypothesis: fullpatch4 E89EMA6000 adds complementary monthly/weekly behavior to the published E87patch16/patch8 pair. Test exactly equal1/3 quantile averaging after each member applies its own unchanged rules, ordered E83postloss+slowAR, E71patch8_6000, E89patch4_6000. No coefficient sweep, no additional optimization, no dataset/frequency selection. Every member must forecast every instance if packaged. Compare all four established cached cutoffs; require a complete public-contract replay and relocation/composition checks before any publication. E89patch4_6000 hash e50a2d43bb3b6b66a94a6a2d9d62f0cb3e404310352e0357cfe0f2a874a21727; earlier cutoffs currently running.
+
+E94 fresh146M halfLR seed307 training started at 2026-10-23T04:36:27Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E96 main cached probe: skill0.384594982171353,relMASE0.737391433574142,relWQL0.513598773629635,CPU1.411123s; currentE87main0.383685556664058. Promising0.000909426 main improvement with fixed1/3 weights; assemble a separate candidate while the remaining earlier-cutoff predictions finish. No publication before full public-contract replay/checks.
+
+## E89 patch4_6000 backcast_evals
+- Checkpoint SHA-256: `e50a2d43bb3b6b66a94a6a2d9d62f0cb3e404310352e0357cfe0f2a874a21727`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 57.45131206512451 seconds.
+- Overall skill 0.336072; relative MASE 0.796655; relative WQL 0.553314.
+- term skill: long=0.4016, medium=0.3589, short=0.2999.
+- domain skill: Econ/Fin=0.1775, Energy=0.2230, Healthcare=0.4006, Nature=0.3706, Sales=0.4131, Transport=0.3680, Web/CloudOps=0.4502.
+- frequency skill: A=0.3160, D=0.3472, H=0.3715, M=0.2107, Q=0.2381, S=0.3496, T=0.3318, W=0.2614.
+- variates skill: multivariate=0.3526, univariate=0.3226.
+- Outcome: All three earlier cutoffs trail selected E71patch8_6000, so no standalone replacement. The predeclared equal three-model ensemble gains on main/earlier1/earlier2 and nearly ties earlier3; assess exact package replay.
+
+## E89 patch4_6000 backcast2_evals
+- Checkpoint SHA-256: `e50a2d43bb3b6b66a94a6a2d9d62f0cb3e404310352e0357cfe0f2a874a21727`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 50.43167591094971 seconds.
+- Overall skill 0.360368; relative MASE 0.769660; relative WQL 0.531571.
+- term skill: long=0.3665, medium=0.3905, short=0.3460.
+- domain skill: Econ/Fin=0.1116, Energy=0.2401, Healthcare=0.3295, Nature=0.4708, Sales=0.5563, Transport=0.4201, Web/CloudOps=0.4365.
+- frequency skill: A=0.2963, D=0.4200, H=0.3926, M=0.1575, Q=0.1533, S=0.2829, T=0.3608, W=0.2868.
+- variates skill: multivariate=0.3615, univariate=0.3594.
+- Outcome: All three earlier cutoffs trail selected E71patch8_6000, so no standalone replacement. The predeclared equal three-model ensemble gains on main/earlier1/earlier2 and nearly ties earlier3; assess exact package replay.
+
+## E89 patch4_6000 backcast3_evals
+- Checkpoint SHA-256: `e50a2d43bb3b6b66a94a6a2d9d62f0cb3e404310352e0357cfe0f2a874a21727`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 55.502692461013794 seconds.
+- Overall skill 0.383331; relative MASE 0.748104; relative WQL 0.508326.
+- term skill: long=0.3963, medium=0.3680, short=0.3844.
+- domain skill: Econ/Fin=0.1767, Energy=0.3066, Healthcare=0.2424, Nature=0.4983, Sales=0.3975, Transport=0.5047, Web/CloudOps=0.3929.
+- frequency skill: A=0.3000, D=0.4228, H=0.4042, M=0.1613, Q=0.2599, S=0.3033, T=0.4260, W=0.2276.
+- variates skill: multivariate=0.3668, univariate=0.3960.
+- Outcome: All three earlier cutoffs trail selected E71patch8_6000, so no standalone replacement. The predeclared equal three-model ensemble gains on main/earlier1/earlier2 and nearly ties earlier3; assess exact package replay.
+
+## E92 robust_3000 backcast_evals
+- Checkpoint SHA-256: `348934d9fbcc8f2a8c45f7ebdc6cd971678d190bcf6ec0c0bd1cf80a12334bbc`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.711654901504517 seconds.
+- Overall skill 0.335781; relative MASE 0.797334; relative WQL 0.553328.
+- term skill: long=0.3997, medium=0.3581, short=0.3006.
+- domain skill: Econ/Fin=0.1550, Energy=0.2286, Healthcare=0.4082, Nature=0.3646, Sales=0.3970, Transport=0.3731, Web/CloudOps=0.4489.
+- frequency skill: A=0.3150, D=0.3499, H=0.3669, M=0.1858, Q=0.2309, S=0.3411, T=0.3343, W=0.2844.
+- variates skill: multivariate=0.3520, univariate=0.3226.
+- Outcome: Earlier cutoff is below matched E80overflow-only3000. The main gain does not repeat temporally; continue the declared6k/12k training but do not select or transfer this3k checkpoint.
+
+## E92 robust_3000 backcast2_evals
+- Checkpoint SHA-256: `348934d9fbcc8f2a8c45f7ebdc6cd971678d190bcf6ec0c0bd1cf80a12334bbc`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.730652570724487 seconds.
+- Overall skill 0.358075; relative MASE 0.771535; relative WQL 0.534088.
+- term skill: long=0.3627, medium=0.3886, short=0.3441.
+- domain skill: Econ/Fin=0.1063, Energy=0.2373, Healthcare=0.3471, Nature=0.4675, Sales=0.5490, Transport=0.4200, Web/CloudOps=0.4314.
+- frequency skill: A=0.3543, D=0.4241, H=0.3906, M=0.1525, Q=0.1489, S=0.2804, T=0.3600, W=0.2550.
+- variates skill: multivariate=0.3640, univariate=0.3532.
+- Outcome: Earlier cutoff is below matched E80overflow-only3000. The main gain does not repeat temporally; continue the declared6k/12k training but do not select or transfer this3k checkpoint.
+
+## E92 robust_3000 backcast3_evals
+- Checkpoint SHA-256: `348934d9fbcc8f2a8c45f7ebdc6cd971678d190bcf6ec0c0bd1cf80a12334bbc`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 25.396876335144043 seconds.
+- Overall skill 0.387946; relative MASE 0.738909; relative WQL 0.506977.
+- term skill: long=0.3950, medium=0.3615, short=0.3950.
+- domain skill: Econ/Fin=0.1698, Energy=0.3214, Healthcare=0.2587, Nature=0.5024, Sales=0.3958, Transport=0.5033, Web/CloudOps=0.3888.
+- frequency skill: A=0.3650, D=0.4381, H=0.4044, M=0.1203, Q=0.2538, S=0.2862, T=0.4293, W=0.2622.
+- variates skill: multivariate=0.3739, univariate=0.3988.
+- Outcome: Earlier cutoff is below matched E80overflow-only3000. The main gain does not repeat temporally; continue the declared6k/12k training but do not select or transfer this3k checkpoint.
+
+### E96 four-cutoff equal ensemble selection
+{
+  "valid_screen": {
+    "candidate": 0.384594982171353,
+    "E87": 0.38368555666405846,
+    "cpu_seconds": 1.4111226599197835,
+    "groups": {
+      "term": {
+        "short": 0.3668858469093572,
+        "medium": 0.4077230710992221,
+        "long": 0.40635338876251526
+      },
+      "domain": {
+        "Econ/Fin": 0.14395536397825648,
+        "Energy": 0.24582427470581425,
+        "Healthcare": 0.4767476122577817,
+        "Nature": 0.44212246483547213,
+        "Sales": 0.34646504369353526,
+        "Transport": 0.46693421316839256,
+        "Web/CloudOps": 0.5034023313828211
+      },
+      "freq": {
+        "A-DEC": 0.37656040036707195,
+        "Q-DEC": 0.20077338922637145,
+        "M": 0.1185115893357509,
+        "W-SUN": 0.10525031522270123,
+        "D": 0.36189985298754856,
+        "H": 0.40144834519193306,
+        "15T": 0.21610876368843102,
+        "W-FRI": 0.015068318308339101,
+        "10T": 0.6264434570332911,
+        "W-TUE": 0.6245141515082602,
+        "W-THU": 0.20878118292775294,
+        "W-WED": 0.32985721333447826,
+        "5T": 0.5176096384720621,
+        "10S": 0.4242433666861485
+      }
+    }
+  },
+  "valid_backcast": {
+    "candidate": 0.33975008510701665,
+    "E87": 0.33897078175106665,
+    "cpu_seconds": 1.2138579529710114,
+    "groups": {
+      "term": {
+        "short": 0.30570414138317814,
+        "medium": 0.3609015682591047,
+        "long": 0.4020582836765434
+      },
+      "domain": {
+        "Econ/Fin": 0.1743856017696448,
+        "Energy": 0.23046498734519416,
+        "Healthcare": 0.4113970603320174,
+        "Nature": 0.3716618234366038,
+        "Sales": 0.408030987603086,
+        "Transport": 0.3731794302285525,
+        "Web/CloudOps": 0.451509629697311
+      },
+      "freq": {
+        "A-DEC": 0.3279717662601809,
+        "Q-DEC": 0.2380599535136222,
+        "M": 0.19905386528004188,
+        "W-SUN": 0.04880471728272662,
+        "D": 0.35315023295661196,
+        "H": 0.3711978117933119,
+        "15T": 0.22358807419379623,
+        "W-FRI": 0.22824822674434042,
+        "10T": 0.322600023714811,
+        "W-TUE": 0.547413555209586,
+        "W-THU": 0.24438059706818105,
+        "W-WED": 0.3628856336883284,
+        "5T": 0.4412301876136191,
+        "10S": 0.3475276102698931
+      }
+    }
+  },
+  "valid_backcast2": {
+    "candidate": 0.36472017597795625,
+    "E87": 0.36315845377936284,
+    "cpu_seconds": 1.3266870640218258,
+    "groups": {
+      "term": {
+        "short": 0.35192307261410094,
+        "medium": 0.39289736169887557,
+        "long": 0.36886086281920194
+      },
+      "domain": {
+        "Econ/Fin": 0.11936723839740782,
+        "Energy": 0.24270008941522314,
+        "Healthcare": 0.34837441457268725,
+        "Nature": 0.4771559710187644,
+        "Sales": 0.556902251507494,
+        "Transport": 0.4248139750422676,
+        "Web/CloudOps": 0.43759149119826346
+      },
+      "freq": {
+        "A-DEC": 0.3311026308933668,
+        "Q-DEC": 0.15378540101465998,
+        "M": 0.16020062601089513,
+        "W-SUN": 0.06801117727416384,
+        "D": 0.4305843857907744,
+        "H": 0.3953546731641614,
+        "15T": 0.10577250483830014,
+        "W-FRI": 0.15138877778445536,
+        "10T": 0.5095975609862264,
+        "W-TUE": 0.10847360375070303,
+        "W-THU": 0.3459885667095576,
+        "W-WED": 0.5660655173116367,
+        "5T": 0.4731902812063754,
+        "10S": 0.28592028110498247
+      }
+    }
+  },
+  "valid_backcast3": {
+    "candidate": 0.389238119545644,
+    "E87": 0.3893067316884232,
+    "cpu_seconds": 1.2415199128445238,
+    "groups": {
+      "term": {
+        "short": 0.39417395687284207,
+        "medium": 0.36821605147108216,
+        "long": 0.396861173789945
+      },
+      "domain": {
+        "Econ/Fin": 0.18370373648624572,
+        "Energy": 0.31629238352886546,
+        "Healthcare": 0.2377535675450031,
+        "Nature": 0.5072143926034133,
+        "Sales": 0.40117499834543047,
+        "Transport": 0.5090476109622039,
+        "Web/CloudOps": 0.39339169169310506
+      },
+      "freq": {
+        "A-DEC": 0.3399962169002898,
+        "Q-DEC": 0.25825750036173534,
+        "M": 0.14131510737146213,
+        "W-SUN": 0.1672885644844745,
+        "D": 0.4408980632536098,
+        "H": 0.4067494659465881,
+        "15T": 0.2551059021117046,
+        "W-FRI": 0.016583036267497464,
+        "10T": 0.6063638743127748,
+        "W-TUE": 0.2532467022883038,
+        "W-THU": 0.3464821396686204,
+        "W-WED": 0.3777825141129817,
+        "5T": 0.46382868467749705,
+        "10S": 0.29461185618122454
+      }
+    }
+  }
+}
+Three gains and effectively tied third cutoff justify full public-interface replay. Candidate descriptor1503d92ce031ec0c0e0533ece20d0516a0eceb134802d51746e24a44c4b5ee9b,110064120parameters. Every member retains its exact model code; shared postprocessing hashes checked during assembly. E87 remains published pending complete replay and checks.
+
+Fresh37M state preservation: wait for its complete recovery024000 state and archive model/EMA/Adam/allRNG/prefetchedbatch to random_capacity_512_recovery024000.pt. This is a zero-update checkpoint snapshot, not a new experiment or a changed active schedule. It preserves the option of a later controlled annealing comparison without repeating pretraining. No continuation is queued.
+
+Data preparation extension, no training scheduled: create packed_stratified_large with192Mpoint/source softcaps using the identical E95all-shard code/min16/maxcrop131072 and directory/per-series RNGs. Because global series order and crops are cap-independent, the24Mpack should be an exact per-source binary/record prefix of the192Mpack. Check that invariant before any future data-scaling experiment. This artifact is separate from E95training, which remains queued on the24Mpack. No new source directories/generators or validation data. Two CPUworkers; GPUtraining schedules unchanged.
+
+## E79 fresh146M at8000
+- Checkpoint SHA-256: `12ad3e4b1be6b38b31a3dee057805e2a532f48fe3723f6bcefb15ba63ca8ee97`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 39.91020727157593 seconds.
+- Overall skill 0.343979; relative MASE 0.773251; relative WQL 0.556563.
+- term skill: long=0.3755, medium=0.3854, short=0.3146.
+- domain skill: Econ/Fin=0.1160, Energy=0.1969, Healthcare=0.3682, Nature=0.4144, Sales=0.3129, Transport=0.4343, Web/CloudOps=0.4719.
+- frequency skill: A=0.3662, D=0.3440, H=0.3640, M=-0.0514, Q=0.1891, S=0.4174, T=0.3968, W=0.1930.
+- variates skill: multivariate=0.3619, univariate=0.3293.
+- Outcome: Main0.343979357 improves4000 0.328133919; still below matched37M8000 0.353508836, with a narrowing gap. Continue declared16k/24k; seed307/B48/LR2e-4/96k. E94halfLR is a separate matched optimization arm.
+
+## E79 fresh37M at16000
+- Checkpoint SHA-256: `a83c2bee0b284ab22ebb5107d79e2ff2ecd29a7de9a384d46d175dbffcf2c5b4`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 29.48415732383728 seconds.
+- Overall skill 0.355318; relative MASE 0.763624; relative WQL 0.544266.
+- term skill: long=0.3802, medium=0.3960, short=0.3290.
+- domain skill: Econ/Fin=0.1292, Energy=0.2122, Healthcare=0.3475, Nature=0.4103, Sales=0.3304, Transport=0.4455, Web/CloudOps=0.4937.
+- frequency skill: A=0.3719, D=0.3210, H=0.3755, M=0.0179, Q=0.1815, S=0.4231, T=0.4098, W=0.2504.
+- variates skill: multivariate=0.3787, univariate=0.3361.
+- Outcome: Main0.355318072 improves8000 0.353508836 only modestly. Continue original96k schedule unchanged and preserve complete24kstate for a possible later annealing comparison. Seed307/B48/LR2e-4, own scratch initialization.
+
+## E90 daily-weekly loss scale at6000
+- Checkpoint SHA-256: `d6b3d3e30290a9360789d236565ecb0acf410d4605bb50821f30b7886ed2d349`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.801913261413574 seconds.
+- Overall skill 0.378435; relative MASE 0.743624; relative WQL 0.519542.
+- term skill: long=0.4018, medium=0.4074, short=0.3577.
+- domain skill: Econ/Fin=0.1254, Energy=0.2397, Healthcare=0.4663, Nature=0.4297, Sales=0.3355, Transport=0.4644, Web/CloudOps=0.5032.
+- frequency skill: A=0.3677, D=0.3561, H=0.3960, M=0.0989, Q=0.1934, S=0.4243, T=0.4382, W=0.2295.
+- variates skill: multivariate=0.3971, univariate=0.3632.
+- Outcome: Main0.378434765 slightly exceeds matched E806000 0.378288765, reversing its small3k deficit. Effect is inconsistent and small; finish12k, then assess targeted frequency groups. Seed197/B64/LR2e-5.
+
+## E89 patch8 control final12000
+- Checkpoint SHA-256: `80519b37e670050b23c231718210bf49aaa64b5396e301a44975140d8075a85a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 35.017725229263306 seconds.
+- Overall skill 0.380847; relative MASE 0.742398; relative WQL 0.516367.
+- term skill: long=0.4032, medium=0.4046, short=0.3626.
+- domain skill: Econ/Fin=0.1449, Energy=0.2438, Healthcare=0.4363, Nature=0.4433, Sales=0.3387, Transport=0.4634, Web/CloudOps=0.5028.
+- frequency skill: A=0.3691, D=0.3616, H=0.3940, M=0.0837, Q=0.1978, S=0.4249, T=0.4461, W=0.2290.
+- variates skill: multivariate=0.3988, univariate=0.3662.
+- Outcome: Final0.380847096 rebounds from6000 0.377734884 as the learning rate falls; still below initial selected E71parent0.381178739. Preserve as the final matched reference for fullpatch4 and input8/output4, which are still training. Seed401/B32/LR2e-5; do not infer final architectural superiority from the6k comparison alone. Released allocation to E94fresh146M halfLR.
+
+## Larger all-shard data preparation, no optimizer updates
+Same fixed global-series order and crop rules as E95, raising only the per-directory cap from24M to192M points. Packed5,354,822,975points in1,059,868series across95allowed directories;125.173232s with2CPU workers. Independent260series Arrow replay plus binary/record-prefix checks for all95directories passed in19.984999s, seed461; source probabilities bitwise match the original source mixture. The24M pack is exactly a prefix within every source. Audit stratified_large_audit.json; no validation read and no training on this larger pack scheduled yet.
+
+E89patch8control final12000 training loop2729.052105s on shared singleH100; validation/hash/group results recorded above. No continuation of completed control.
+
+## E92 robust normalization at6000
+- Checkpoint SHA-256: `3c76ff01356bdf0de6219a0937fe43c25175caef19d4324b4e73c39310659fce`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.452584981918335 seconds.
+- Overall skill 0.377780; relative MASE 0.743560; relative WQL 0.520681.
+- term skill: long=0.4010, medium=0.4074, short=0.3568.
+- domain skill: Econ/Fin=0.1250, Energy=0.2380, Healthcare=0.4626, Nature=0.4291, Sales=0.3516, Transport=0.4654, Web/CloudOps=0.5006.
+- frequency skill: A=0.3659, D=0.3537, H=0.3957, M=0.1211, Q=0.1920, S=0.4237, T=0.4359, W=0.2278.
+- variates skill: multivariate=0.3949, univariate=0.3638.
+- Outcome: Main0.377780381 is below matched overflow-only E80at6000 0.378288765; earlier3k already regressed at all three cutoffs. Continue the predeclared final12k but no6k selection or composition. Seed197/B64/LR2e-5; normalization is the only changed factor.
+
+## E96 three-patch equal ensemble full public replay — selected
+- Checkpoint SHA-256: `1503d92ce031ec0c0e0533ece20d0516a0eceb134802d51746e24a44c4b5ee9b`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.380721; relative MASE 0.740204; relative WQL 0.518109.
+- term skill: long=0.4003, medium=0.4066, short=0.3628.
+- domain skill: Econ/Fin=0.1244, Energy=0.2393, Healthcare=0.4812, Nature=0.4421, Sales=0.3603, Transport=0.4671, Web/CloudOps=0.4951.
+- frequency skill: A=0.2988, D=0.3660, H=0.3960, M=0.1437, Q=0.1866, S=0.4242, T=0.4364, W=0.2370.
+- variates skill: multivariate=0.3947, univariate=0.3694.
+- Outcome: Full validation skill0.380721445 improves E87by0.001091552; relativeMASE0.740203875 andrelativeWQL0.518108512 both improve. All97configs/151170instances,0failures,441.765573 inference seconds on shared singleH100. Every member forecasts every instance, equal1/3 weights fixed before replay; no extra training. Relocation bitwise/finite/ordered checks passed in33.315708s; independent third-member public replay10.848689s and bitwise three-member composition0.027575CPU s passed. Three of four selection cutoffs improve; third earlier loss0.0000686. Full55/97configs improve; descriptive dataset-bootstrap interval spans zero, so the small adaptive-validation gain is not a held-out significance claim. Publish the strongest complete replayable candidate; continue research.
+
+## E97 explicit weekly synthetic coverage — preparation
+Hypothesis: the existing20% synthetic mixture omits[52,1]weekly seasonality, contributing to weak weekly transfer. Replace exactly a Bernoulli1/11 share of synthetic windows with the same own Fourier/trend/AR/intermittency/changepoint generator atperiods[52,1]. The total synthetic fraction remains20%; all ten original period families retain expected weight1/11 and the new weekly family gains1/11. Always consume the original generator first; independent RNGseed1000200 chooses/re-generates weekly windows, preserving every real row and non-replaced synthetic row and the original sampler/horizon streams bitwise. No other history/target-length change. Match E80overflow-only: ownE62 initialization,seed197/B64/LR2e-5/12000paired steps,original packed_sample/source probabilities/model/loss/runtime. Screen1/3/6/12k; require sampler stream/recovery checks before queueing after E92 completes. No validation used for generation or optimization; declare the new local generator if published.
+
+E97sampler check: {"seed": 467, "rows": 7680, "original_synthetic_rows": 1543, "replaced_weekly_rows": 134, "probability": 0.09090909090909091, "original_and_horizon_rng_bitwise_preserved": true, "unmodified_rows_bitwise_preserved": true, "finite_masks_unchanged": true, "all_numpy_rng_recovery_exact": true, "model_sha256": "44eeb23c037e91b8f67e432615ce46540a7ac4242919785f9c4218de23121499", "model_bitwise_unchanged": true, "seconds": 3.6882259079720825, "optimizer_updates": 0, "validation_data_read": false}
+Queue declared after E92PID9354, requiring its final recovery. Existing model code is byte-identical to E80, so no new GPU arithmetic fixture is needed for this data-only change. Full command is weekly_synth_queue.json; launcher weekly_synth/launch.py.
+
+## E93 input8/output4 at6000
+- Checkpoint SHA-256: `8afbf4d2008aa7d5dab4ecbdcd1685f1a59b8fcb739a39f65fa8ba4b8cb4be9d`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 36.958359479904175 seconds.
+- Overall skill 0.380224; relative MASE 0.742564; relative WQL 0.517292.
+- term skill: long=0.4044, medium=0.4022, short=0.3620.
+- domain skill: Econ/Fin=0.1461, Energy=0.2357, Healthcare=0.4662, Nature=0.4406, Sales=0.3432, Transport=0.4673, Web/CloudOps=0.5003.
+- frequency skill: A=0.3656, D=0.3580, H=0.3941, M=0.1089, Q=0.2009, S=0.4225, T=0.4400, W=0.2482.
+- variates skill: multivariate=0.3938, univariate=0.3692.
+- Outcome: Main0.380224037 is between matched fullpatch4at6000 0.380540506 and patch8control0.377734884, below selected starting E71parent0.381178739. The decoder-only refinement recovers most of the full-resolution gain at lower encoder cost; continue final12k before any ensemble replacement. Seed401/B32/LR2e-5, same original generator.
+
+## E98 completion of deliberately pruned E75 schedule — declared revisit
+E75 was intentionally stopped at3653 after earlier-cutoff regressions; that decision and its stopfile remain intact. E89patch8control subsequently recovered from0.377734884 at6k to0.380847096 at12k as learning rate decayed, showing that an intermediate trough can conceal a late recovery. Revisit the incomplete E75 schedule under a separate output, postloss_completion, by preserving and restoring its exact3653 model/EMA/Adam/allRNG/prefetched state. Continue original steps3654..12000 with the unchanged12k cosine schedule,seed197/B64/LR2e-5,original data/generator and postprocessing-aware objective. No new recipe factor and no reset; this is a completion of a previously pruned experiment, not an independent replication. Model/data/torch_post code byte-match the original archived provenance. Compare6k/12k against E80overflow-only, and examine earlier cutoffs before any composition. Queue only after E93 releases its allocation; retain original postloss.stop and all negative results.
+
+E98exact restart snapshot: {"source": "/workspace/research/runs/postloss/recovery.pt", "output": "/workspace/research/postloss_recovery003653.pt", "step": 3653, "sha256": "7d81547596356906fbac3a9aa3d47f84c27eabf845ba26e2c3c74ec369923c01", "seconds": 1.7649966378230602, "preserves": "model, EMA, optimizer, all RNG states and prefetched batch", "optimizer_updates": 0}
+Queued after E93PID9274; launcher postloss/launch_completion.py, completecommand postloss_completion_queue.json. No GPU fixture repeated because model/loss/data code is identical to already verified E75, and no new optimizer update occurred during preparation.
+
+## E79 fresh37M at24000
+- Checkpoint SHA-256: `fbdb51213b3cf165273c4ea83c2386ec0bd496b182e31d57f421eee29c0da28a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 29.551169872283936 seconds.
+- Overall skill 0.357902; relative MASE 0.763517; relative WQL 0.539988.
+- term skill: long=0.3831, medium=0.3912, short=0.3346.
+- domain skill: Econ/Fin=0.1259, Energy=0.2164, Healthcare=0.3681, Nature=0.4073, Sales=0.3362, Transport=0.4526, Web/CloudOps=0.4919.
+- frequency skill: A=0.3818, D=0.3384, H=0.3762, M=0.0189, Q=0.1970, S=0.4195, T=0.4130, W=0.2313.
+- variates skill: multivariate=0.3766, univariate=0.3426.
+- Outcome: Main0.357901602 improves16k0.355318072 but still trails the mature scratch lineage. Continue original96k schedule unchanged. Exact24k model/EMA/Adam/allRNG/prefetch snapshot preserved for a separately declared cooldown comparison; no continuation launched yet. Seed307/B48/LR2e-4/static compiled paired loss/slowAR original source mixture.
+
+E79small immutable24k snapshot: {"source": "/workspace/research/runs/random_capacity_512/recovery.pt", "output": "/workspace/research/random_capacity_512_recovery024000.pt", "step": 24000, "sha256": "32de72f3d09f8eee785c1d19a279541348de7ba129f79d62e5824e1223e19322", "seconds": 2.042371174087748, "preserves": "model, EMA, optimizer, all RNG states and prefetched batch", "optimizer_updates": 0}
+
+## E99 matched early cooldown of fresh37M model — declared
+Hypothesis: E79fresh37M is held back by the still-high96k-schedule learning rate, rather than only a lack of capacity. Branch from the exact immutable24000 training state (SHA32de72f3d09f8eee785c1d19a279541348de7ba129f79d62e5824e1223e19322), preserving model/EMA/Adam/allRNG/prefetched batch. Change only the next12000-step learning-rate schedule: smoothly cosine-decay from the original step24000 rate0.00017656854249492381 to0.00001 at36000, with no optimizer reset or rewarmup. Original E79continues its96k schedule unchanged. Same seed307/B48/static compiled paired objective/slowAR generator/original pack and source probabilities. Compare candidate and original at global30000/36000; earlier windows only for a promising candidate. Queue after E89fullpatch4 releases its allocation. Default schedule arithmetic was verified bitwise at140000steps across three existing recipes; cooldown joins exactly and decreases monotonically. No GPU arithmetic or model change; reuse existing compiled/eager-gradient fixtures.
+E99schedule checks: {"default_steps_bitwise_equal": 140000, "join_step": 24000, "join_lr": 0.00017656854249492381, "end_step": 36000, "end_lr": 1e-05, "schedule": "cosine from original step24000 rate to1e-5 by36000, no warmup/reset", "optimizer_updates": 0, "validation_data_read": false, "seconds": 0.14022667310200632}
+
+## E94 fresh146M halfLR first4000
+- Checkpoint SHA-256: `62c59ba50647d301fd5afd846b25c03271fe35b7d97fe63f5b2f7b931957090b`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 41.465235233306885 seconds.
+- Overall skill 0.336895; relative MASE 0.784082; relative WQL 0.560794.
+- term skill: long=0.3621, medium=0.3772, short=0.3107.
+- domain skill: Econ/Fin=0.1179, Energy=0.2132, Healthcare=0.2861, Nature=0.4034, Sales=0.2941, Transport=0.4230, Web/CloudOps=0.4607.
+- frequency skill: A=0.3563, D=0.3278, H=0.3600, M=-0.0301, Q=0.1751, S=0.4173, T=0.3872, W=0.1853.
+- variates skill: multivariate=0.3571, univariate=0.3204.
+- Outcome: Lower LR1e-4 improves matched146M4k main0.328133919 to0.336895196, still below37Mcontrol0.341861940. All three terms improve over high-LR146M; large capacity is not yet better than the smaller model. Afterwarmup its matched training loss beats high-LR146M consistently and slightly beats37M by steps2001..4000. Continue8k/16k before selecting a larger-corpus fresh recipe. Same initial weights/seed307/B48/data/objective; only learning rate changed.
+
+E79/E94matched first4k loss/compute: {"random_capacity_512": {"1-400": 0.4342267720028758, "401-1000": 0.3975941499322653, "1001-2000": 0.381371116399765, "2001-3000": 0.37428912545740606, "3001-4000": 0.3616653003469109, "through4000_loop_seconds": 1106.1}, "random_capacity_1024": {"1-400": 0.4301105997338891, "401-1000": 0.40223139939208824, "1001-2000": 0.3863489354848862, "2001-3000": 0.3779264837577939, "3001-4000": 0.36658306304365396, "through4000_loop_seconds": 1563.7}, "random_capacity_1024_halflr": {"1-400": 0.4339301012828946, "401-1000": 0.39748335411151253, "1001-2000": 0.3812341860383749, "2001-3000": 0.3728542738109827, "3001-4000": 0.36091782905161385, "through4000_loop_seconds": 1064.5}}. Shared-H100 process times include differing compiler-cache warm-up and are not exclusiveGPU efficiency measurements.
+
+E95 all-shard corpus seed197 training started at 2026-10-23T04:58:12Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E90 daily-weekly loss scale final12000
+- Checkpoint SHA-256: `4c5641b649529f9ac36c8679289c64bacf0b30e11dae8428620a0d59e56ffabc`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.18997550010681 seconds.
+- Overall skill 0.377373; relative MASE 0.744776; relative WQL 0.520511.
+- term skill: long=0.4024, medium=0.4060, short=0.3561.
+- domain skill: Econ/Fin=0.1328, Energy=0.2359, Healthcare=0.4455, Nature=0.4306, Sales=0.3355, Transport=0.4657, Web/CloudOps=0.5050.
+- frequency skill: A=0.3617, D=0.3526, H=0.3946, M=0.1059, Q=0.1975, S=0.4251, T=0.4399, W=0.2154.
+- variates skill: multivariate=0.3960, univariate=0.3621.
+- Outcome: Final main0.377373344 is effectively tied but slightly below matched E80overflow-only0.377423946. The isolated seasonal-loss-lag change has no sustained overall gain across1k/3k/6k/12k. Seed197/B64/LR2e-5; completed2702.166704 shared-H100 training-loop seconds. No candidate publication; E95broader-source-coverage starts in the released allocation.
+
+## E100 equal probability-distribution pool — predeclared cached comparison
+Hypothesis: E96quantile averaging omits dispersion due to disagreement among its three members. Keep exactly the E83/E71/E89fixed models and equalweights, but average their CDFs and invert for the nine deciles. Each member distribution is piecewise-linear through its predicted deciles, extended to probabilities0/1 by one adjacent endpoint spacing; repeated quantiles retain atoms. Evaluate all four cached cutoffs, with no coefficient/tail-setting sweep and zero optimizer updates. Every member still forecasts every instance. Initial local fixture found only5.55e-17roundoff around exactzero; enforcing the mathematical bound between member quantiles at each level corrected it before any validation read. Identical-member, uniform-mixture, sign/affine, finite/ordered and chunk-size checks passed: {"seed": 479, "identical_members_bitwise_identity": true, "uniform_mixture_exact_within_float32": true, "atoms_at_zero_preserved": true, "ordered_finite": true, "chunk_size_bitwise_independent": true, "sign_reflection_and_affine_equivariance": true, "optimizer_updates": 0, "seconds": 0.09868191904388368}
+
+## E95 all-shard source coverage first1000
+- Checkpoint SHA-256: `17b2a03d282c141dbd68ac7f68df4626802f1a0323fff313f1a28192c5068b5b`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.06695008277893 seconds.
+- Overall skill 0.377028; relative MASE 0.744820; relative WQL 0.521057.
+- term skill: long=0.3997, medium=0.4068, short=0.3562.
+- domain skill: Econ/Fin=0.1378, Energy=0.2376, Healthcare=0.4487, Nature=0.4311, Sales=0.3344, Transport=0.4669, Web/CloudOps=0.4993.
+- frequency skill: A=0.3724, D=0.3543, H=0.3951, M=0.1080, Q=0.1986, S=0.4218, T=0.4370, W=0.2193.
+- variates skill: multivariate=0.3940, univariate=0.3632.
+- Outcome: Main0.377028371 exceeds matched E80at1000 0.376804394 by0.000223977. Medium/long improve while short is nearly unchanged; still below E62initialization0.377343216, so no selection claim yet. Continue3k/6k/12k. Seed197/B64/LR2e-5; pack coverage changes at nearly identical volume, same original source probabilities/model/loss/generator.
+
+E97 weekly synthetic coverage seed197 training started at 2026-10-23T05:06:47Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+### E100 probability-pool outcome — rejected
+{"cutoff": "valid_screen", "skill_score": 0.38447708223972143, "rel_mase_gmean": 0.7377458188534619, "rel_wql_gmean": 0.5135487760227904, "groups": {"term": {"short": 0.36645074748329265, "medium": 0.40785519113120783, "long": 0.4067636672388967}, "domain": {"Econ/Fin": 0.14348007471775803, "Energy": 0.2450499585597603, "Healthcare": 0.4778661939355727, "Nature": 0.44167938695480613, "Sales": 0.34589247896287667, "Transport": 0.46735333815132574, "Web/CloudOps": 0.5036629603237621}, "freq": {"A-DEC": 0.37569899250005157, "Q-DEC": 0.20013922343000312, "M": 0.12148678908575261, "W-SUN": 0.10281895779812733, "D": 0.3613770705448509, "H": 0.40132102430667216, "15T": 0.21622161251725824, "W-FRI": 0.010849753325401168, "10T": 0.6253364743600922, "W-TUE": 0.6270406616896087, "W-THU": 0.2100424900094875, "W-WED": 0.3297769734384489, "5T": 0.5175830091172531, "10S": 0.424444633099896}}, "cpu_seconds": 59.980443592881784, "member_checkpoint_hashes": ["0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "e50a2d43bb3b6b66a94a6a2d9d62f0cb3e404310352e0357cfe0f2a874a21727"], "quantile_mean_skill": 0.384594982171353, "skill_difference": -0.00011789993163158208}
+{"cutoff": "valid_backcast", "skill_score": 0.3396884750452879, "rel_mase_gmean": 0.7937686109009007, "rel_wql_gmean": 0.5492927082278539, "groups": {"term": {"short": 0.305359424616949, "medium": 0.3615421761351466, "long": 0.4019782542502155}, "domain": {"Econ/Fin": 0.17397857577775933, "Energy": 0.23077587806776068, "Healthcare": 0.4105170158829923, "Nature": 0.37090555838048966, "Sales": 0.40754241032517846, "Transport": 0.3735854351257373, "Web/CloudOps": 0.4515115490492597}, "freq": {"A-DEC": 0.3285015352119629, "Q-DEC": 0.23809442338781928, "M": 0.19846718923537676, "W-SUN": 0.044551228029835244, "D": 0.3527581961201992, "H": 0.37195542467805676, "15T": 0.22380832764902536, "W-FRI": 0.22713834170412128, "10T": 0.3209843494303689, "W-TUE": 0.5471337430335201, "W-THU": 0.24671528715780322, "W-WED": 0.36065042452889085, "5T": 0.440176624512733, "10S": 0.34765351732372307}}, "cpu_seconds": 57.84474289207719, "member_checkpoint_hashes": ["0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "e50a2d43bb3b6b66a94a6a2d9d62f0cb3e404310352e0357cfe0f2a874a21727"], "quantile_mean_skill": 0.33975008510701665, "skill_difference": -6.161006172877848e-05}
+{"cutoff": "valid_backcast2", "skill_score": 0.3643569364713283, "rel_mase_gmean": 0.7656814703595871, "rel_wql_gmean": 0.5276895417390272, "groups": {"term": {"short": 0.35141014761082046, "medium": 0.3925477275360366, "long": 0.36889675295988844}, "domain": {"Econ/Fin": 0.11837724372992486, "Energy": 0.24257682706622608, "Healthcare": 0.3484957338430069, "Nature": 0.4765403493474547, "Sales": 0.5563991023520016, "Transport": 0.425156500116473, "Web/CloudOps": 0.43670286593915697}, "freq": {"A-DEC": 0.32966975290636025, "Q-DEC": 0.15381819951222442, "M": 0.15901923467685497, "W-SUN": 0.06330956349285344, "D": 0.43027727492156853, "H": 0.39548623856812215, "15T": 0.10618336189249866, "W-FRI": 0.14996401232455647, "10T": 0.5090292906528928, "W-TUE": 0.11021265661815505, "W-THU": 0.34484214889524656, "W-WED": 0.565895835912684, "5T": 0.47197167812560914, "10S": 0.2855930392816969}}, "cpu_seconds": 55.48255044897087, "member_checkpoint_hashes": ["0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "e50a2d43bb3b6b66a94a6a2d9d62f0cb3e404310352e0357cfe0f2a874a21727"], "quantile_mean_skill": 0.36472017597795625, "skill_difference": -0.000363239506627977}
+{"cutoff": "valid_backcast3", "skill_score": 0.3894908456851319, "rel_mase_gmean": 0.7395191359520792, "rel_wql_gmean": 0.5040051154624993, "groups": {"term": {"short": 0.3944986218711739, "medium": 0.3683905810196345, "long": 0.3969887061132117}, "domain": {"Econ/Fin": 0.18408496490995196, "Energy": 0.3168114104271367, "Healthcare": 0.23983131130227353, "Nature": 0.5064220935312094, "Sales": 0.40140535360155616, "Transport": 0.5096985006120188, "Web/CloudOps": 0.393462743970424}, "freq": {"A-DEC": 0.34218212975771833, "Q-DEC": 0.25834447555512075, "M": 0.14460929216407215, "W-SUN": 0.16537651572223833, "D": 0.4408650435087358, "H": 0.40698403946159434, "15T": 0.25514902945862916, "W-FRI": 0.019067740904430974, "10T": 0.6070205489033722, "W-TUE": 0.25089200168813297, "W-THU": 0.3450365084609506, "W-WED": 0.378097845878244, "5T": 0.46374932773306954, "10S": 0.2954018250276097}}, "cpu_seconds": 54.93674235115759, "member_checkpoint_hashes": ["0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "e50a2d43bb3b6b66a94a6a2d9d62f0cb3e404310352e0357cfe0f2a874a21727"], "quantile_mean_skill": 0.389238119545644, "skill_difference": 0.0002527261394879021}
+Probability pooling loses main and the first two earlier cutoffs, improving only the third. Retain E96 equal quantile averaging; no full replay or runtime publication. Zero optimizer updates; full CPU comparison time228.244479s, local fixture seed479.
+
+## E101 own-ensemble distillation — prepared, not queued
+
+
+Prepared design, not yet queued. Complete the listed checks and record the exact experiment before launch. No external models, weights or data.
+
+Hypothesis: the larger fresh model can learn useful conditional forecasts faster when its proper real-target objective is supplemented by dense quantile targets from our own mature scratch-trained ensemble. This differs from the failed direct width conversions and protected random feature grafts: all student parameters begin at the same random initialization as E94.
+
+Match E94 exactly: own random_capacity_1024_initial.pt, seed307, batch48 distinct windows/96 signed rows, learning rate1e-4,96000-step schedule,400warmup, compiled paired-sign loss, original packed_sample/source probabilities,20% own slow-AR synthetic generator, original requested-horizon stream. Only the additional teacher loss changes. E94 remains the real-target-only control.
+
+Teacher: frozen E96 three members, descriptor1503d92ce031ec0c0e0533ece20d0516a0eceb134802d51746e24a44c4b5ee9b. Use each member's sign-averaged normalized neural quantiles BEFORE fixed postprocessing, then equal averaging. All members share the same ordinary-row normalization at training shapes (contexts are multiples of512); verify that. Pass the same per-row available forecast lengths to each member's partial-target mask. Mask teacher loss on rows with any nonfinite member normalization/output; retain the original real-target objective on those rows. The student loss and runtime remain the original E94 neural objective and fixed inference rules, so there is no simultaneous postprocessing-aware-loss change.
+
+Add coefficient0.5 times a teacher L1 objective: per valid target step,0.5*mean(abs(student_quantiles-teacher_quantiles)) +0.25*abs(student_median-teacher_median); average over the same valid target positions and apply the same context seasonal-error weight, normalized over teacher-valid rows. No coefficient sweep. The real-target objective is unchanged and retains weight1. Teacher targets are constants, with no gradients or updates to teacher weights. All teacher windows come only from the allowed sampler; no validation inputs or labels enter training.
+
+Compute teacher targets outside the compiled student objective. Student model state/config must retain exactly E94 keys and parameter count; teacher modules must not be registered on the student or enter its optimizer/EMA/checkpoints. Record teacher descriptor and all three checkpoint hashes in training args/lineage, and automatically declare own-teacher pseudo-target generation in sources_used on publication. Archive teacher code and generation code in provenance; deployed inference loads only the student.
+
+Required checks: zero teacher coefficient reproduces original loss/gradients on identical own-generated fixtures; teacher sign averaging/normalization matches independent raw-member forward passes; teacher parameters remain unchanged and have no gradients; student gradients finite; complete sampler/RNG recovery unchanged; eager/compiled gradient comparison; maximum-shape memory including student EMA/Adam and teacher weights. Initial fixture has zero optimizer updates. Runtime is unchanged random_capacity_inference.
+
+Potential allocation: queue only after both E99cooldown and E97weekly training finish, using their queue processes/checkpoint dependencies rather than unknown future trainer PIDs. Their released combined memory is sufficient for146M student plus frozen teacher. Watch4/8/16/24k before deciding longer allocation. Do not launch until code, checks, exact recipe declaration and source-lineage handling are complete.
+
+CPUfixture: {"seed": 487, "seconds": 5.1773834731429815, "teacher_descriptor_sha256": "1503d92ce031ec0c0e0533ece20d0516a0eceb134802d51746e24a44c4b5ee9b", "teacher_parameters": 110064120, "teacher_weights_frozen_bitwise": true, "teacher_gradients_absent": true, "independent_raw_member_average_bitwise": true, "ordinary_normalization_bitwise_same": true, "overflow_teacher_row_excluded_only_from_auxiliary": true, "sign_reflection_equivariant": true, "zero_coefficient_loss_and_gradients_bitwise_control": true, "student_state_keys_unchanged": true, "sampler_code_bitwise_control": true, "all_student_gradients_finite": true, "real_loss": 7.174529552459717, "combined_loss": 7.284832954406738, "optimizer_updates": 0, "validation_data_read": false}
+Frozen teacher assets copied to distill_teacher_E96; descriptor unchanged. No student optimizer updates yet.
+
+## E92 robust normalization final12000 — rejected
+- Checkpoint SHA-256: `25be8396d4a8c6ea98c67bca79060c4979a2829661016f0ed51b4e98c897c688`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.238795042037964 seconds.
+- Overall skill 0.377099; relative MASE 0.744448; relative WQL 0.521199.
+- term skill: long=0.4035, medium=0.4066, short=0.3549.
+- domain skill: Econ/Fin=0.1295, Energy=0.2365, Healthcare=0.4448, Nature=0.4270, Sales=0.3471, Transport=0.4672, Web/CloudOps=0.5036.
+- frequency skill: A=0.3570, D=0.3466, H=0.3946, M=0.1215, Q=0.1971, S=0.4235, T=0.4392, W=0.2223.
+- variates skill: multivariate=0.3955, univariate=0.3620.
+- Outcome: Final main0.377099312 is below matched overflow-only E80final0.377423946. The3k main gain did not transfer to any earlier cutoff, and6k/final main also regress. Reject this normalization change; retain ordinary statistics with the overflow-only fallback. Seed197/B64/LR2e-5, no new sources/generator. E97weekly-synthetic run has started in the released allocation.
+
+E92finalcompute: {"completed_step": 12000, "training_process_loop_seconds": 2692.7444765449036}
+
+## E79 fresh37M at30000, E99 future matched control
+- Checkpoint SHA-256: `7965d1fe4b831143de6280ae873ec61a8024dbb6a14da5dfae1ef58098c33a59`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.895771265029907 seconds.
+- Overall skill 0.358564; relative MASE 0.761438; relative WQL 0.540346.
+- term skill: long=0.3844, medium=0.3900, short=0.3358.
+- domain skill: Econ/Fin=0.1162, Energy=0.2193, Healthcare=0.3709, Nature=0.4110, Sales=0.3395, Transport=0.4511, Web/CloudOps=0.4907.
+- frequency skill: A=0.3754, D=0.3312, H=0.3793, M=0.0636, Q=0.1845, S=0.4178, T=0.4155, W=0.2110.
+- variates skill: multivariate=0.3768, univariate=0.3436.
+- Outcome: Main0.358563990 improves24k0.357901602 by0.000662387 under the original96k schedule. This is the exact global30k control for the separately queued E99cooldown; no E99outcome yet. Seed307/B48/LR2e-4; continue unchanged original schedule.
+
+## E79 fresh146M at16000
+- Checkpoint SHA-256: `959ff6c885777e971d4ee25729d3406485404a69a0bbfe320959506b180e9e35`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 39.40332913398743 seconds.
+- Overall skill 0.349499; relative MASE 0.768924; relative WQL 0.550316.
+- term skill: long=0.3865, medium=0.3939, short=0.3166.
+- domain skill: Econ/Fin=0.1199, Energy=0.2111, Healthcare=0.2664, Nature=0.4047, Sales=0.3301, Transport=0.4402, Web/CloudOps=0.4965.
+- frequency skill: A=0.3766, D=0.3221, H=0.3700, M=-0.0589, Q=0.1818, S=0.4218, T=0.4156, W=0.1984.
+- variates skill: multivariate=0.3768, univariate=0.3269.
+- Outcome: Main0.349499286 improves8k0.343979357 but still trails matched37M16k0.355318072. Gap narrows to0.005818787; no capacity advantage established. Same seed307/B48/LR2e-4; continue24k while E94halfLR comparison matures.
+
+## E95 all-shard source coverage at3000
+- Checkpoint SHA-256: `ed98a18948ae0ad7cc18f1e2eb653f9af6bd6545ad81daa897b167b5ccf529ce`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.239446878433228 seconds.
+- Overall skill 0.376742; relative MASE 0.745627; relative WQL 0.520972.
+- term skill: long=0.4002, medium=0.4075, short=0.3552.
+- domain skill: Econ/Fin=0.1328, Energy=0.2374, Healthcare=0.4444, Nature=0.4331, Sales=0.3350, Transport=0.4645, Web/CloudOps=0.5005.
+- frequency skill: A=0.3721, D=0.3540, H=0.3968, M=0.0980, Q=0.1953, S=0.4214, T=0.4362, W=0.2177.
+- variates skill: multivariate=0.3948, univariate=0.3620.
+- Outcome: Main0.376741687 falls below matched E80at3000 0.378663825, reversing the small1k advantage. Broader uniform source coverage has not shown a sustained benefit in this mature-model continuation. Continue declared6k/12k before any data-mixture conclusion. Seed197/B64/LR2e-5, nearly matched packed volume and identical source probabilities.
+
+E98 complete original E75 schedule training started at 2026-10-23T05:11:48Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E101GPUfixture: {"initialization_seed": 307, "fixture_seed": 491, "optimizer_updates": 0, "maximum_shape_memory": {"batch": 48, "signed_rows": 96, "context": 4096, "horizon": 1024, "student_parameters": 146477344, "teacher_parameters": 110064120, "loss": 0.7845079898834229, "teacher_valid_fraction": 1.0, "peak_allocated_mib": 10583.0390625, "peak_reserved_mib": 11116.0}, "eager_loss": 0.3347933888435364, "compiled_loss": 0.3347933888435364, "gradient_cosine": 0.9999896108063672, "max_gradient_difference": 6.866455078125e-05, "teacher_gradients_absent": true, "compile_comparison_seconds": 54.64838738180697, "total_seconds": 79.89753107703291, "source": "allowed packed_sample and own slowAR generator only; frozen scratch-trained E96 neural teacher"}
+Checks passed, zero optimizer updates. E101nowqueued after E99queuePID10701 and E97queuePID10513 both end, requiring their saved recovery checkpoints. These are dependency-launcher PIDs, not unknown future trainer PIDs. Exact recipe/command in distill_ensemble_queue.json; immutable teacher package distill_teacher_E96. Compare E94real-target-only at matching checkpoints. Student inference uses unchanged random_capacity_inference and contains no teacher dependency.
+
+## E93 input8/output4 final12000
+- Checkpoint SHA-256: `1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 35.806113958358765 seconds.
+- Overall skill 0.381712; relative MASE 0.740742; relative WQL 0.516077.
+- term skill: long=0.4058, medium=0.4052, short=0.3629.
+- domain skill: Econ/Fin=0.1468, Energy=0.2396, Healthcare=0.4600, Nature=0.4409, Sales=0.3373, Transport=0.4706, Web/CloudOps=0.5017.
+- frequency skill: A=0.3633, D=0.3624, H=0.3942, M=0.1027, Q=0.1968, S=0.4248, T=0.4434, W=0.2445.
+- variates skill: multivariate=0.3961, univariate=0.3700.
+- Outcome: Final main0.381711986 exceeds matched E89patch8control12k0.380847096 and starting selected E71parent0.381178739. The smaller output blocks help at the final schedule; check all three earlier cutoffs and matched fullpatch4 final before any selection or architectural conclusion. Training loop3153.565149s, sharedH100; seed401/B32/LR2e-5, unchanged generator. E98exactpostloss completion started in the released allocation.
+
+## E97 weekly synthetic coverage first1000
+- Checkpoint SHA-256: `abd335075c429adc73f20e1e291146903a767a3e5c5a52669768863d5585031a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.452547311782837 seconds.
+- Overall skill 0.378319; relative MASE 0.743092; relative WQL 0.520107.
+- term skill: long=0.3993, medium=0.4061, short=0.3590.
+- domain skill: Econ/Fin=0.1270, Energy=0.2360, Healthcare=0.4896, Nature=0.4309, Sales=0.3352, Transport=0.4671, Web/CloudOps=0.4981.
+- frequency skill: A=0.3664, D=0.3562, H=0.3957, M=0.1213, Q=0.1976, S=0.4232, T=0.4346, W=0.2365.
+- variates skill: multivariate=0.3927, univariate=0.3666.
+- Outcome: Main0.378319040 exceeds matched E80at1000 0.376804394 by0.001514645. Monthly skill rises0.10610to0.12127 and weekly0.22573to0.23646; most overall gain is short-term. Original real and unchanged synthetic windows/RNG remain bitwise matched. Continue3k/6k/12k before selection. Seed197/B64/LR2e-5, independentweeklyseed1000200, total20%synthetic unchanged.
+
+## E94 fresh146M halfLR at8000
+- Checkpoint SHA-256: `650a1a4177cae63f3ceab8a2255fbd2f9d281a7e83e8d380a34bb956bd690884`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 38.053096294403076 seconds.
+- Overall skill 0.347153; relative MASE 0.771841; relative WQL 0.552198.
+- term skill: long=0.3712, medium=0.3767, short=0.3259.
+- domain skill: Econ/Fin=0.1161, Energy=0.2093, Healthcare=0.3516, Nature=0.4137, Sales=0.3243, Transport=0.4423, Web/CloudOps=0.4676.
+- frequency skill: A=0.3714, D=0.3436, H=0.3637, M=0.0157, Q=0.1571, S=0.4182, T=0.3966, W=0.2130.
+- variates skill: multivariate=0.3599, univariate=0.3368.
+- Outcome: Main0.347153427 beats matched high-LR146M8k0.343979357, but remains below37M8k0.353508836. The rate benefit persists but shrinks versus4k; continue16k/24k before choosing a larger-corpus fresh recipe. Same owninit/seed307/B48/96k, only LR differs from E79wide.
+
+## E102 fixed E96 third-member replacement with E93final — predeclared
+Replace only the fullpatch4 EMA6000 member with split input8/output4 EMA12000. Retain E83patch16 and E71patch8_6000, ordered equal1/3 quantile averaging after each unchanged member rule. Every member forecasts every instance. No new optimization, no coefficient sweep or configuration-specific selection. Compare all four established cached cutoffs, requiring full public replay and relocation/composition checks before publication. E93final individual main0.381711986 exceeds its control and parent; earlier cutoffs are currently running. The E101teacher remains the immutable E96 ensemble regardless of this selection result.
+
+E99 fresh37M early cooldown training started at 2026-10-23T05:15:29Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E89 fullpatch4 final12000
+- Checkpoint SHA-256: `a263e545cafb309e8b22dde151e6a8bd37166e1db346170ad7bd33f4a11cd6a3`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 51.19871735572815 seconds.
+- Overall skill 0.381640; relative MASE 0.741276; relative WQL 0.515825.
+- term skill: long=0.4055, medium=0.4047, short=0.3631.
+- domain skill: Econ/Fin=0.1486, Energy=0.2408, Healthcare=0.4740, Nature=0.4424, Sales=0.3396, Transport=0.4680, Web/CloudOps=0.4971.
+- frequency skill: A=0.3603, D=0.3552, H=0.3960, M=0.1392, Q=0.2025, S=0.4227, T=0.4401, W=0.2492.
+- variates skill: multivariate=0.3948, univariate=0.3709.
+- Outcome: Final main0.381640078 exceeds matched patch8control0.380847096, essentially ties the cheaper E93input8/output4 final0.381711986, and exceeds selected starting E71parent0.381178739. Training loop5066.016522s on sharedH100, seed401/B32/LR2e-5; actual memory advantage of E93 is supported by maximum-shape checks, while process timings are concurrent rather than exclusive. Evaluate all three earlier cutoffs before choosing a replacement for E96thirdmember. E99cooldown starts in the released allocation.
+
+## E103 fixed E96 third-member replacement with E89final — predeclared
+Test the completed fullpatch4 EMA12000 as the third member, retaining E83patch16/E71patch8 and equal1/3 quantile averaging with unchanged per-member rules. This is the counterpart to E102splitpatch-final replacement, motivated by the near-tie of the matched architecture continuations at12k. No coefficient or per-config choice; every member forecasts every instance. Compare all four cached cutoffs against E96/E102 before selecting the next full replay. No additional optimizer updates.
+
+Recovery-provenance maintenance: trainer now carries immutable external resume snapshots in parent_checkpoints across subsequent restarts from a run's own mutable recovery.pt. This preserves E98/E99prefix ancestry after another operator recovery and avoids linking mutable source-run recovery files. Metadata only, no training arithmetic changed. CURRENT snapshots now explicitly record the known compiler environment for compiled jobs, including those whose launchers passed it via subprocess environment rather than an env command prefix. No credentials were read.
+
+### E102 four-cutoff replacement results
+{"cutoff": "valid_screen", "skill_score": 0.38490788212550675, "rel_mase_gmean": 0.7369849187976256, "rel_wql_gmean": 0.5133596411831329, "groups": {"term": {"short": 0.3670856470642414, "medium": 0.4084718855262034, "long": 0.40650615955402314}, "domain": {"Econ/Fin": 0.1435289463184154, "Energy": 0.2468980568665663, "Healthcare": 0.4746000842796365, "Nature": 0.44061471938672714, "Sales": 0.3438839562187085, "Transport": 0.4681588575437192, "Web/CloudOps": 0.5046164356104859}, "freq": {"A-DEC": 0.37543835684208393, "Q-DEC": 0.19718024023430158, "M": 0.10850755704828396, "W-SUN": 0.10914931929038174, "D": 0.364713649661322, "H": 0.40088295006065944, "15T": 0.21696396975859744, "W-FRI": 0.016303274872958684, "10T": 0.6274098133659913, "W-TUE": 0.6163821514248893, "W-THU": 0.20854279501175432, "W-WED": 0.32935722968495507, "5T": 0.5196886329209323, "10S": 0.42527389349857736}}, "cpu_seconds": 1.1513165449723601, "member_checkpoint_hashes": ["0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "E96skill": 0.384594982171353, "difference": 0.0003128999541537336}
+{"cutoff": "valid_backcast", "skill_score": 0.3397943577904946, "rel_mase_gmean": 0.7942938342926791, "rel_wql_gmean": 0.5487534602272348, "groups": {"term": {"short": 0.3060609208737005, "medium": 0.36022438378162913, "long": 0.40207177797998805}, "domain": {"Econ/Fin": 0.17314936720715002, "Energy": 0.2319064310330724, "Healthcare": 0.40898749701599635, "Nature": 0.3714427052655884, "Sales": 0.4054545766888219, "Transport": 0.37346761267845907, "Web/CloudOps": 0.4512796397157435}, "freq": {"A-DEC": 0.32636255116793356, "Q-DEC": 0.2369053047170233, "M": 0.1881408754401731, "W-SUN": 0.05514520346862939, "D": 0.35254747200062386, "H": 0.37162220001802326, "15T": 0.2225810499701737, "W-FRI": 0.23076747912755158, "10T": 0.32531767521178967, "W-TUE": 0.5449536336522042, "W-THU": 0.2550583257373391, "W-WED": 0.36148774693113794, "5T": 0.441771392840713, "10S": 0.34781169339869}}, "cpu_seconds": 0.9792960570193827, "member_checkpoint_hashes": ["0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "E96skill": 0.33975008510701665, "difference": 4.427268347795277e-05}
+{"cutoff": "valid_backcast2", "skill_score": 0.3650870167535245, "rel_mase_gmean": 0.7649173761305604, "rel_wql_gmean": 0.527003973075039, "groups": {"term": {"short": 0.3525196178413804, "medium": 0.3930479998451024, "long": 0.3688394487554716}, "domain": {"Econ/Fin": 0.12187550267974279, "Energy": 0.24171273109616453, "Healthcare": 0.3558210371956918, "Nature": 0.4786187053429537, "Sales": 0.5559745534466993, "Transport": 0.42514365668969034, "Web/CloudOps": 0.4369290048270271}, "freq": {"A-DEC": 0.3369941826448156, "Q-DEC": 0.15634597829751584, "M": 0.16659776433783968, "W-SUN": 0.07034227201826782, "D": 0.4307838193118658, "H": 0.3947195332904091, "15T": 0.10843788342786231, "W-FRI": 0.15292520112219465, "10T": 0.5097026958567001, "W-TUE": 0.1163232645897253, "W-THU": 0.349832844039044, "W-WED": 0.5634413265331784, "5T": 0.4725475721543746, "10S": 0.2849619587969022}}, "cpu_seconds": 1.3131735338829458, "member_checkpoint_hashes": ["0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "E96skill": 0.36472017597795625, "difference": 0.00036684077556825567}
+{"cutoff": "valid_backcast3", "skill_score": 0.38962589808404846, "rel_mase_gmean": 0.7393031561148107, "rel_wql_gmean": 0.5039293302189662, "groups": {"term": {"short": 0.3950146881429296, "medium": 0.3683351816334438, "long": 0.3962356024661119}, "domain": {"Econ/Fin": 0.1866323514229733, "Energy": 0.31708138786412254, "Healthcare": 0.2340457735690984, "Nature": 0.5075633858787092, "Sales": 0.3999844088004907, "Transport": 0.5091845709202749, "Web/CloudOps": 0.3939387141464573}, "freq": {"A-DEC": 0.35740138324250814, "Q-DEC": 0.2545638826926907, "M": 0.14196420345262517, "W-SUN": 0.1692422167486961, "D": 0.44092551174968075, "H": 0.4060276831713958, "15T": 0.2544847257167575, "W-FRI": 0.03945021050804143, "10T": 0.6073743483329828, "W-TUE": 0.25124832856283164, "W-THU": 0.3470992258549631, "W-WED": 0.3775958266712779, "5T": 0.46513388285277635, "10S": 0.29273333259972834}}, "cpu_seconds": 0.9749888752121478, "member_checkpoint_hashes": ["0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "E96skill": 0.389238119545644, "difference": 0.00038777853840443743}
+
+### E103 four-cutoff replacement results
+{"cutoff": "valid_screen", "skill_score": 0.38500541763348517, "rel_mase_gmean": 0.7370491942191689, "rel_wql_gmean": 0.5131520925694101, "groups": {"term": {"short": 0.36722493789915567, "medium": 0.40845783827201154, "long": 0.4066128546537624}, "domain": {"Econ/Fin": 0.14488968734284446, "Energy": 0.2473756772173874, "Healthcare": 0.4777300437443104, "Nature": 0.4411115514129288, "Sales": 0.3448077959265311, "Transport": 0.46774095296357987, "Web/CloudOps": 0.5033387584756153}, "freq": {"A-DEC": 0.37541665630455645, "Q-DEC": 0.19911678292032942, "M": 0.11881300866622979, "W-SUN": 0.1096477052731838, "D": 0.362528588146091, "H": 0.4015705047657928, "15T": 0.21804762920144782, "W-FRI": 0.016363727924622262, "10T": 0.6271485862780906, "W-TUE": 0.6209733756670539, "W-THU": 0.20999017480896343, "W-WED": 0.3297749874361884, "5T": 0.5175349566789766, "10S": 0.42462313787098094}}, "cpu_seconds": 1.0732698428910226, "member_checkpoint_hashes": ["0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "a263e545cafb309e8b22dde151e6a8bd37166e1db346170ad7bd33f4a11cd6a3"], "E96skill": 0.384594982171353, "difference": 0.00041043546213215265}
+{"cutoff": "valid_backcast", "skill_score": 0.3398214546118604, "rel_mase_gmean": 0.7942044163673427, "rel_wql_gmean": 0.5487701941828703, "groups": {"term": {"short": 0.305847769661334, "medium": 0.3615040967825627, "long": 0.40146856729517433}, "domain": {"Econ/Fin": 0.17464373027255042, "Energy": 0.23108171914379005, "Healthcare": 0.41082426569988595, "Nature": 0.3718443434112704, "Sales": 0.40646820364012926, "Transport": 0.3728762819285243, "Web/CloudOps": 0.4515441161888809}, "freq": {"A-DEC": 0.32427594695442885, "Q-DEC": 0.23945276396724646, "M": 0.19719617479508833, "W-SUN": 0.052844558516275786, "D": 0.35260466772957, "H": 0.3721367040211806, "15T": 0.2223065777034271, "W-FRI": 0.22845992938029724, "10T": 0.3234396022330981, "W-TUE": 0.5459245154169383, "W-THU": 0.24711082469129164, "W-WED": 0.36312061525273154, "5T": 0.4404099962380955, "10S": 0.3485327044406994}}, "cpu_seconds": 0.9945498188026249, "member_checkpoint_hashes": ["0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "a263e545cafb309e8b22dde151e6a8bd37166e1db346170ad7bd33f4a11cd6a3"], "E96skill": 0.33975008510701665, "difference": 7.13695048437657e-05}
+{"cutoff": "valid_backcast2", "skill_score": 0.3647180336171173, "rel_mase_gmean": 0.7654029032858002, "rel_wql_gmean": 0.5272820041297973, "groups": {"term": {"short": 0.3518878749308295, "medium": 0.3927580540668256, "long": 0.3691077522689188}, "domain": {"Econ/Fin": 0.12218990276622621, "Energy": 0.24251138907504843, "Healthcare": 0.34933847363414383, "Nature": 0.4781212829897, "Sales": 0.55694372735719, "Transport": 0.4243899770061409, "Web/CloudOps": 0.43650428576145894}, "freq": {"A-DEC": 0.33629748314335295, "Q-DEC": 0.1560373253956937, "M": 0.16237816345656342, "W-SUN": 0.07189010798504825, "D": 0.43022756178029165, "H": 0.39543965655443114, "15T": 0.10619855263453626, "W-FRI": 0.15256288862137302, "10T": 0.5099262713842705, "W-TUE": 0.11275167806759523, "W-THU": 0.3476561865337633, "W-WED": 0.565409644514056, "5T": 0.4712731877671763, "10S": 0.2859728774327921}}, "cpu_seconds": 0.9891702111344784, "member_checkpoint_hashes": ["0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "a263e545cafb309e8b22dde151e6a8bd37166e1db346170ad7bd33f4a11cd6a3"], "E96skill": 0.36472017597795625, "difference": -2.142360838952584e-06}
+{"cutoff": "valid_backcast3", "skill_score": 0.38952531921448574, "rel_mase_gmean": 0.7397344287798532, "rel_wql_gmean": 0.5038015284686523, "groups": {"term": {"short": 0.39470492326109163, "medium": 0.3679378045902095, "long": 0.39702622151428124}, "domain": {"Econ/Fin": 0.18635232828843817, "Energy": 0.3167555032477224, "Healthcare": 0.2321529881554648, "Nature": 0.5080109601079104, "Sales": 0.40020731613048277, "Transport": 0.5096409724474877, "Web/CloudOps": 0.3934863582665823}, "freq": {"A-DEC": 0.34966366686932726, "Q-DEC": 0.25774901005566275, "M": 0.13921842550571784, "W-SUN": 0.1678888068666432, "D": 0.4412824281729495, "H": 0.40605210717475726, "15T": 0.2556303886920226, "W-FRI": 0.03145225561173515, "10T": 0.6068260917454812, "W-TUE": 0.2595682801406983, "W-THU": 0.3474528636749996, "W-WED": 0.3763281957599267, "5T": 0.4644139584813969, "10S": 0.29384233335279064}}, "cpu_seconds": 1.3381098771933466, "member_checkpoint_hashes": ["0ae7875b49b99a44c295dc2b8243327aeacadc25b2354988abb2c37250359a49", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "a263e545cafb309e8b22dde151e6a8bd37166e1db346170ad7bd33f4a11cd6a3"], "E96skill": 0.389238119545644, "difference": 0.00028719966884172}
+Next full replay: E102 improves all four cutoffs and has the cheaper input8 encoder. E103 is slightly stronger on main/first earlier but weaker thanE102 onsecond/third; secondearlier effectivelyties E96. E103is retained as a cached alternative, without a claimed full-suite result. No new optimizer updates in either ensemble composition.
+
+## E97 weekly synthetic coverage at3000
+- Checkpoint SHA-256: `a5619c3da9cb9049d92c9582e7fd74dea3e3d474bf5cf8499522e739cd202305`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.279794216156006 seconds.
+- Overall skill 0.380663; relative MASE 0.740647; relative WQL 0.517896.
+- term skill: long=0.4025, medium=0.4068, short=0.3617.
+- domain skill: Econ/Fin=0.1317, Energy=0.2379, Healthcare=0.4904, Nature=0.4332, Sales=0.3335, Transport=0.4661, Web/CloudOps=0.5036.
+- frequency skill: A=0.3690, D=0.3546, H=0.3977, M=0.1269, Q=0.1964, S=0.4224, T=0.4386, W=0.2419.
+- variates skill: multivariate=0.3970, univariate=0.3674.
+- Outcome: Main0.380663183 exceeds matched E80at3000 0.378663825 by0.001999358, sustaining the1k gain. Earlier cutoffs are now running; no independent-model publication yet. Seed197/B64/LR2e-5, exact original real/non-replaced synthetic streams preserved, weekly replacement1/11 of20%synthetic.
+
+## E104 transfer the matched weekly-synthesis correction — predeclared
+Hypothesis: E97weekly3000 minus E80overflow-control3000 isolates the new weekly synthetic coverage and can improve E83, while retaining E83postprocessing-aware and slowAR contributions. Apply exactly coefficient1: E83postloss_slow_ar_delta6k + E97weekly_synthEMA3000 - E80overflow_controlEMA3000. All model keys/configurations align; all weights were trained here on allowed data, with the delta pair sharing seed197/B64/LR2e-5 and original sampler/horizon streams. No coefficient sweep or optimizer updates. E97earlier results are not yet available at declaration. Evaluate the fixed composition at all four cutoffs and then its fixed equal1/3 ensemble with E71patch8_6000 and E93splitpatch_12000, replacing only E102firstmember. Use unchanged postloss_inference numerical fallback and fixed rules. This is a predictive composition test, with full public replay/checks required for any publication. E101teacher remains frozen E96.
+
+## E93 split patch final earlier cutoff1
+- Checkpoint SHA-256: `1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 35.36992645263672 seconds.
+- Overall skill 0.336872; relative MASE 0.797481; relative WQL 0.551410.
+- term skill: long=0.4020, medium=0.3579, short=0.3016.
+- domain skill: Econ/Fin=0.1748, Energy=0.2280, Healthcare=0.3931, Nature=0.3699, Sales=0.4068, Transport=0.3691, Web/CloudOps=0.4509.
+- frequency skill: A=0.3160, D=0.3464, H=0.3726, M=0.1800, Q=0.2360, S=0.3514, T=0.3341, W=0.2756.
+- variates skill: multivariate=0.3569, univariate=0.3205.
+- Outcome: Fixed final12000 checkpoint, seed401/B32/LR2e-5. Used for the predeclared equal-member replacement comparison E102/E103; validation only, no further optimizer updates.
+
+## E93 split patch final earlier cutoff2
+- Checkpoint SHA-256: `1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 31.873882293701172 seconds.
+- Overall skill 0.361104; relative MASE 0.770556; relative WQL 0.529731.
+- term skill: long=0.3665, medium=0.3913, short=0.3470.
+- domain skill: Econ/Fin=0.1207, Energy=0.2377, Healthcare=0.3430, Nature=0.4729, Sales=0.5545, Transport=0.4214, Web/CloudOps=0.4356.
+- frequency skill: A=0.3185, D=0.4195, H=0.3908, M=0.1553, Q=0.1616, S=0.2820, T=0.3625, W=0.2958.
+- variates skill: multivariate=0.3609, univariate=0.3613.
+- Outcome: Fixed final12000 checkpoint, seed401/B32/LR2e-5. Used for the predeclared equal-member replacement comparison E102/E103; validation only, no further optimizer updates.
+
+## E93 split patch final earlier cutoff3
+- Checkpoint SHA-256: `1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 33.14549136161804 seconds.
+- Overall skill 0.385555; relative MASE 0.745119; relative WQL 0.506687.
+- term skill: long=0.3946, medium=0.3685, short=0.3886.
+- domain skill: Econ/Fin=0.1861, Energy=0.3097, Healthcare=0.2354, Nature=0.5014, Sales=0.3950, Transport=0.5057, Web/CloudOps=0.3950.
+- frequency skill: A=0.3543, D=0.4270, H=0.4025, M=0.1610, Q=0.2496, S=0.2977, T=0.4286, W=0.2436.
+- variates skill: multivariate=0.3696, univariate=0.3979.
+- Outcome: Fixed final12000 checkpoint, seed401/B32/LR2e-5. Used for the predeclared equal-member replacement comparison E102/E103; validation only, no further optimizer updates.
+
+## E89 full patch4 final earlier cutoff1
+- Checkpoint SHA-256: `a263e545cafb309e8b22dde151e6a8bd37166e1db346170ad7bd33f4a11cd6a3`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 55.011173486709595 seconds.
+- Overall skill 0.336345; relative MASE 0.797972; relative WQL 0.551947.
+- term skill: long=0.3997, medium=0.3611, short=0.3004.
+- domain skill: Econ/Fin=0.1781, Energy=0.2248, Healthcare=0.3992, Nature=0.3709, Sales=0.4088, Transport=0.3668, Web/CloudOps=0.4509.
+- frequency skill: A=0.3067, D=0.3457, H=0.3740, M=0.2061, Q=0.2429, S=0.3533, T=0.3298, W=0.2654.
+- variates skill: multivariate=0.3532, univariate=0.3226.
+- Outcome: Fixed final12000 checkpoint, seed401/B32/LR2e-5. Used for the predeclared equal-member replacement comparison E102/E103; validation only, no further optimizer updates.
+
+## E89 full patch4 final earlier cutoff2
+- Checkpoint SHA-256: `a263e545cafb309e8b22dde151e6a8bd37166e1db346170ad7bd33f4a11cd6a3`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 54.35282373428345 seconds.
+- Overall skill 0.359845; relative MASE 0.771620; relative WQL 0.531089.
+- term skill: long=0.3668, medium=0.3902, short=0.3451.
+- domain skill: Econ/Fin=0.1192, Energy=0.2393, Healthcare=0.3219, Nature=0.4739, Sales=0.5564, Transport=0.4186, Web/CloudOps=0.4338.
+- frequency skill: A=0.3129, D=0.4161, H=0.3924, M=0.1622, Q=0.1597, S=0.2850, T=0.3585, W=0.2927.
+- variates skill: multivariate=0.3606, univariate=0.3592.
+- Outcome: Fixed final12000 checkpoint, seed401/B32/LR2e-5. Used for the predeclared equal-member replacement comparison E102/E103; validation only, no further optimizer updates.
+
+## E89 full patch4 final earlier cutoff3
+- Checkpoint SHA-256: `a263e545cafb309e8b22dde151e6a8bd37166e1db346170ad7bd33f4a11cd6a3`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 54.33600902557373 seconds.
+- Overall skill 0.384241; relative MASE 0.747472; relative WQL 0.507256.
+- term skill: long=0.3960, medium=0.3667, short=0.3865.
+- domain skill: Econ/Fin=0.1847, Energy=0.3079, Healthcare=0.2301, Nature=0.5006, Sales=0.3948, Transport=0.5062, Web/CloudOps=0.3930.
+- frequency skill: A=0.3322, D=0.4254, H=0.4016, M=0.1568, Q=0.2587, S=0.3002, T=0.4273, W=0.2401.
+- variates skill: multivariate=0.3670, univariate=0.3975.
+- Outcome: Fixed final12000 checkpoint, seed401/B32/LR2e-5. Used for the predeclared equal-member replacement comparison E102/E103; validation only, no further optimizer updates.
+
+## E95 all-shard coverage at6000
+- Checkpoint SHA-256: `78a3c47718545643662029245141e6d39e64cf0853d2502f20222123ce3cd5c7`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.874247789382935 seconds.
+- Overall skill 0.376043; relative MASE 0.746531; relative WQL 0.521508.
+- term skill: long=0.4012, medium=0.4068, short=0.3538.
+- domain skill: Econ/Fin=0.1318, Energy=0.2380, Healthcare=0.4414, Nature=0.4294, Sales=0.3318, Transport=0.4670, Web/CloudOps=0.4991.
+- frequency skill: A=0.3714, D=0.3507, H=0.3948, M=0.0936, Q=0.1963, S=0.4213, T=0.4386, W=0.2145.
+- variates skill: multivariate=0.3939, univariate=0.3614.
+- Outcome: Skill0.376043220 falls below matched E80control6000 0.378288765. Continue the declared12000 schedule; broader coverage at fixed volume has not improved the mature model.
+
+## E98 original postloss schedule at6000
+- Checkpoint SHA-256: `7f1a2200b0f6a2cd63a378b001010e4a4a9776eba76c800ee1d61c23fc8bf9cb`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.866560697555542 seconds.
+- Overall skill 0.377594; relative MASE 0.744160; relative WQL 0.520572.
+- term skill: long=0.4019, medium=0.4066, short=0.3564.
+- domain skill: Econ/Fin=0.1081, Energy=0.2440, Healthcare=0.4585, Nature=0.4289, Sales=0.3383, Transport=0.4638, Web/CloudOps=0.5007.
+- frequency skill: A=0.3681, D=0.3493, H=0.3983, M=0.0713, Q=0.1895, S=0.4227, T=0.4403, W=0.2264.
+- variates skill: multivariate=0.3955, univariate=0.3630.
+- Outcome: Exact optimizer/RNG continuation from originalE75step3653; no reset. Skill0.377594313 is below E75at3000 0.378813891 and E80at6000 0.378288765. Continue to predeclared12000; no checkpoint replacement.
+
+## E79 fresh37M original schedule at36000
+- Checkpoint SHA-256: `06778302f2e405ecd30fd00407523d03be8756c7a6029c38a26360cbeb98becf`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 29.79560947418213 seconds.
+- Overall skill 0.364859; relative MASE 0.756433; relative WQL 0.533298.
+- term skill: long=0.3873, medium=0.3910, short=0.3457.
+- domain skill: Econ/Fin=0.1266, Energy=0.2248, Healthcare=0.4265, Nature=0.4125, Sales=0.3402, Transport=0.4559, Web/CloudOps=0.4912.
+- frequency skill: A=0.3896, D=0.3497, H=0.3830, M=0.0639, Q=0.1985, S=0.4187, T=0.4195, W=0.2201.
+- variates skill: multivariate=0.3809, univariate=0.3518.
+- Outcome: Original96k schedule control, seed307/B48/LR2e-4. Skill0.364859034 improves on30k0.358563990; compare E99cooldown at the same step when available.
+
+## E105 nested corpus volume at fixed source probabilities — predeclared
+Hypothesis: the mature model may benefit from more distinct allowed series beyond the all-shard coverage change. Compare against E95 using the exactly nested packed_stratified_large instead of packed_stratified. Source probabilities stay bitwise those of packed_sample, all95directories, all model/optimizer/EMA/horizon settings unchanged: ownE62initial SHA22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c, seed197/B64/LR2e-5/12000steps, paired loss, overflow_control code, postloss_inference runtime. Per-directory softcap192M versus24M points; total5,354,822,975points/1,059,868series versus952,527,771points/342,661series. Same global series order and per-series crop rules, exact binary/record prefix checked for all95dirs. stratified_large_audit.json replays260actualseries and verifies source probabilities, seed461,19.984999CPU seconds,0optimizerupdates/no validationreads. More series changes the sampler trajectory; this is a single-seed volume comparison, not identical sampled rows. E95at6000 currently trails E80; E105 tests whether volume recovers that deficit. Queue after E95 in its allocation. Screens1000/3000/6000/12000; earlier cutoffs and full replay required before publication. No added training data sources or new generator.
+
+## E106 spectral smoothness prior in synthetic residuals — preparation
+Hypothesis: the original white/AR1 synthetic residuals underrepresent smooth stochastic changes. Replace only the noise residual in half of synthetic windows (total synthetic share unchanged20%) with a unit-marginal-variance stationary Gaussian draw generated here by FFT. Equal kernel-family prior: Matérn nu0.5/1.5/2.5 or squared-exponential; length scale10**U(0,4) steps. Generate a periodic FFT field of next-power-of-two length at least4times the window and keep its prefix; normalize by theoretical spectral variance, never by realized future values. Keep the original Fourier components, trend, changepoint, intermittent and positivity transforms, periods and observed masks. Always consume the original residual RNG before replacement; independent spectral seed1100200 preserves the full original sampler/horizon streams and all non-replaced rows. This differs from E14's multi-family whole-series replacement and E78's four-AR-component sum. Comparator E80: ownE62initial SHA22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c, seed197/B64/LR2e-5/12000steps, paired loss, oldpacked_sample/sourceweights, overflow-safe model unchanged. CPU distribution/RNG checks required before launch; no validation in generator or training. Queue after E98 when checks pass. Screen1000/3000/6000/12000 and earlier cutoffs for promising checkpoints; no coefficient sweep.
+E106 CPU audit passed: seed499,7680paired candidate/control windows,1535synthetic rows,743spectral replacements. All non-replaced rows, period labels, finite masks and original/horizon RNG states are bitwise unchanged; saving/restoring all NumPy states replays the next batch exactly. Model file byte-identical toE80 SHA44eeb23c037e91b8f67e432615ce46540a7ac4242919785f9c4218de23121499. Four kernel-family empirical variances/autocovariances over1024draws each match their declared discrete spectral covariances within0.005 (tolerance0.045); extreme length scales1/10000 and window lengths8/40/5120 remain finite. Sampler comparison4.179206CPU seconds,total4.517388seconds,0optimizerupdates/no validationread. Receipt spectral_synth_checks.json. Queued after E98queue10664; full training metadata and publisher generator provenance added.
+
+## E102 split-patch replacement full public replay
+- Checkpoint SHA-256: `2dedb7fd1052bf72a38fc005cc8c89f65d42a50260830de0f0efbb5f16a30b4f`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.381163; relative MASE 0.739673; relative WQL 0.517741.
+- term skill: long=0.4007, medium=0.4074, short=0.3631.
+- domain skill: Econ/Fin=0.1245, Energy=0.2404, Healthcare=0.4799, Nature=0.4407, Sales=0.3581, Transport=0.4683, Web/CloudOps=0.4964.
+- frequency skill: A=0.2968, D=0.3691, H=0.3956, M=0.1343, Q=0.1868, S=0.4253, T=0.4379, W=0.2354.
+- variates skill: multivariate=0.3952, univariate=0.3698.
+- Outcome: Fixed equal1/3 ensemble E83patch16/E71patch8_6000/E93input8output4_12000. Full skill0.38116304998662165 exceeds E96full0.3807214453622526 by0.00044160462436904; zero failures across97configurations/151170instances. Inference358.197612shared-H100 seconds. Relocation already bitwise; independent component/composition checks pending before atomic publication. Validation selection only; no held-out reward claim.
+
+E102 final checks all passed before publication: split_ensemble_validation_receipt.json completes allfive steps in510.069582wall seconds including lock waits. Public inference358.197612seconds. Relocated three-example inference is bitwise identical/finite/ordered,31.745146seconds. Independent public third-member replay plus orderedfloat32 component mean exactly match bundled output for three complete actual configurations; see split_patch_composition_replay.json and split_ensemble_composition_checks.json. Descriptor2dedb7fd1052bf72a38fc005cc8c89f65d42a50260830de0f0efbb5f16a30b4f;110,068,216parameters. Selected for atomic publication over E96. Allfour diagnostic cutoffs improve; full-term skills long0.400651,medium0.407396,short0.363109. Descriptive group/config comparison and cluster bootstrap saved in split_ensemble_comparison.json; no held-out significance claim. E101teacher remains immutable E96.
+
+## E107 learned use of sixteen-cycle centered seasonal summaries — preparation
+Hypothesis: within the existing4096-point context, averaging more observed cycles can reduce phase-pattern noise, while centering each cycle prevents old levels from biasing the forecast. Add fixed context features for each of the two existing frequency periods: the16cycle-minus4cycle mean of cycle-centered phase observations and the16cycle support fraction. Enable a phase only with at least8observed cycles, at least2recent cycles and period>=2. A zero-initialized linear query projection exposes these four features to the existing decoder; a separate zero-initialized two-gate head adds tanh-bounded multiples of the two template differences to the median. All quantiles shift together; original interval widths are unchanged initially. The initial function must be exactly preserved and all parameters train. No increased context, raw-memory encoder or fitted seasonal parameters; this differs from E29/E54 older-history experiments. Comparator E80uses the same ownE62initial, seed197/B64/LR2e-5/12000steps, paired loss, original corpus/generator/horizon streams and overflow fallback. New weights start exactlyzero, conversion seed503; no external weights. Audit independent feature arithmetic, initialization equivalence, gradient flow and max-shape GPU memory before launch. Screen1000/3000/6000/12000, earlier/fullreplay for selection. Queue after E105 when ready; no launch yet.
+
+## E97 weekly synthetic coverage at6000
+- Checkpoint SHA-256: `de053e5baf9641e9245519990d6e5193fc91392cb76e72ceffee1ce63f578fca`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.010923624038696 seconds.
+- Overall skill 0.380716; relative MASE 0.741210; relative WQL 0.517415.
+- term skill: long=0.4022, medium=0.4073, short=0.3617.
+- domain skill: Econ/Fin=0.1254, Energy=0.2389, Healthcare=0.4994, Nature=0.4301, Sales=0.3392, Transport=0.4658, Web/CloudOps=0.5031.
+- frequency skill: A=0.3704, D=0.3555, H=0.3964, M=0.1253, Q=0.1936, S=0.4249, T=0.4385, W=0.2461.
+- variates skill: multivariate=0.3971, univariate=0.3673.
+- Outcome: Main0.380715730 exceeds matched E80at6000 0.378288765 by0.002426965; nearly ties E97at3000. Continue declared12000 schedule.
+
+## E99 early cooldown at30000
+- Checkpoint SHA-256: `ffa1f38d24165a7167a319a609bc03fa14cbe471ea7785659f10d7c953889661`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.24539875984192 seconds.
+- Overall skill 0.363004; relative MASE 0.756412; relative WQL 0.536432.
+- term skill: long=0.3857, medium=0.3920, short=0.3425.
+- domain skill: Econ/Fin=0.1135, Energy=0.2193, Healthcare=0.4094, Nature=0.4163, Sales=0.3443, Transport=0.4551, Web/CloudOps=0.4933.
+- frequency skill: A=0.3737, D=0.3414, H=0.3797, M=0.0903, Q=0.1811, S=0.4171, T=0.4196, W=0.2193.
+- variates skill: multivariate=0.3812, univariate=0.3481.
+- Outcome: Skill0.363004205 exceeds original E79at30000 0.358563990 by0.004440216. Same immutable24000optimizer/EMA/RNG state, only the declared LR cooldown differs. Continue to36000 before schedule selection.
+
+## E97 weekly3000 earlier cutoff1
+- Checkpoint SHA-256: `a5619c3da9cb9049d92c9582e7fd74dea3e3d474bf5cf8499522e739cd202305`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.978241682052612 seconds.
+- Overall skill 0.336324; relative MASE 0.797606; relative WQL 0.552235.
+- term skill: long=0.3979, medium=0.3586, short=0.3021.
+- domain skill: Econ/Fin=0.1518, Energy=0.2284, Healthcare=0.4155, Nature=0.3672, Sales=0.3939, Transport=0.3733, Web/CloudOps=0.4489.
+- frequency skill: A=0.3129, D=0.3516, H=0.3666, M=0.1788, Q=0.2305, S=0.3410, T=0.3350, W=0.2907.
+- variates skill: multivariate=0.3530, univariate=0.3227.
+- Outcome: Fixed3000checkpoint, seed197/B64/LR2e-5. Matched E80earlier scores0.336119626/0.358234785/0.388791691. Candidate first/third improve slightly and second regresses; mixed standalone evidence. E104 matched-delta composition evaluated separately.
+
+## E97 weekly3000 earlier cutoff2
+- Checkpoint SHA-256: `a5619c3da9cb9049d92c9582e7fd74dea3e3d474bf5cf8499522e739cd202305`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.026545524597168 seconds.
+- Overall skill 0.357608; relative MASE 0.772348; relative WQL 0.534302.
+- term skill: long=0.3634, medium=0.3874, short=0.3435.
+- domain skill: Econ/Fin=0.1055, Energy=0.2359, Healthcare=0.3296, Nature=0.4704, Sales=0.5497, Transport=0.4211, Web/CloudOps=0.4319.
+- frequency skill: A=0.3536, D=0.4229, H=0.3913, M=0.1492, Q=0.1501, S=0.2808, T=0.3592, W=0.2527.
+- variates skill: multivariate=0.3632, univariate=0.3530.
+- Outcome: Fixed3000checkpoint, seed197/B64/LR2e-5. Matched E80earlier scores0.336119626/0.358234785/0.388791691. Candidate first/third improve slightly and second regresses; mixed standalone evidence. E104 matched-delta composition evaluated separately.
+
+## E97 weekly3000 earlier cutoff3
+- Checkpoint SHA-256: `a5619c3da9cb9049d92c9582e7fd74dea3e3d474bf5cf8499522e739cd202305`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 25.23224711418152 seconds.
+- Overall skill 0.389288; relative MASE 0.737704; relative WQL 0.505581.
+- term skill: long=0.3960, medium=0.3604, short=0.3973.
+- domain skill: Econ/Fin=0.1690, Energy=0.3192, Healthcare=0.2786, Nature=0.5046, Sales=0.3991, Transport=0.5056, Web/CloudOps=0.3897.
+- frequency skill: A=0.3644, D=0.4424, H=0.4032, M=0.1383, Q=0.2578, S=0.2856, T=0.4308, W=0.2610.
+- variates skill: multivariate=0.3738, univariate=0.4012.
+- Outcome: Fixed3000checkpoint, seed197/B64/LR2e-5. Matched E80earlier scores0.336119626/0.358234785/0.388791691. Candidate first/third improve slightly and second regresses; mixed standalone evidence. E104 matched-delta composition evaluated separately.
+
+## E104 weekly matched-delta standalone valid_screen
+- Checkpoint SHA-256: `094c7c694406075967f392a0d9ac1146bac984a90ab468c59d660a1b92fa7ba2`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.348880529403687 seconds.
+- Overall skill 0.381830; relative MASE 0.739086; relative WQL 0.517036.
+- term skill: long=0.4034, medium=0.4069, short=0.3634.
+- domain skill: Econ/Fin=0.1309, Energy=0.2397, Healthcare=0.4832, Nature=0.4388, Sales=0.3386, Transport=0.4639, Web/CloudOps=0.5053.
+- frequency skill: A=0.3743, D=0.3550, H=0.4003, M=0.1128, Q=0.1977, S=0.4242, T=0.4413, W=0.2371.
+- variates skill: multivariate=0.3976, univariate=0.3690.
+- Outcome: Fixed E83 + E97weekly3000 - E80overflow3000, coefficient1. Merge1.392576CPU seconds, no optimizer updates. Standalone improves main/first earlier versus E83 and declines at second/third; fixed equal ensemble assessed separately.
+
+## E104 weekly matched-delta standalone valid_backcast
+- Checkpoint SHA-256: `094c7c694406075967f392a0d9ac1146bac984a90ab468c59d660a1b92fa7ba2`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 31.152572631835938 seconds.
+- Overall skill 0.336131; relative MASE 0.797469; relative WQL 0.552651.
+- term skill: long=0.3953, medium=0.3602, short=0.3023.
+- domain skill: Econ/Fin=0.1601, Energy=0.2285, Healthcare=0.3995, Nature=0.3691, Sales=0.3960, Transport=0.3717, Web/CloudOps=0.4496.
+- frequency skill: A=0.3204, D=0.3464, H=0.3692, M=0.1755, Q=0.2317, S=0.3401, T=0.3348, W=0.2897.
+- variates skill: multivariate=0.3531, univariate=0.3223.
+- Outcome: Fixed E83 + E97weekly3000 - E80overflow3000, coefficient1. Merge1.392576CPU seconds, no optimizer updates. Standalone improves main/first earlier versus E83 and declines at second/third; fixed equal ensemble assessed separately.
+
+## E104 weekly matched-delta standalone valid_backcast2
+- Checkpoint SHA-256: `094c7c694406075967f392a0d9ac1146bac984a90ab468c59d660a1b92fa7ba2`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 24.71313166618347 seconds.
+- Overall skill 0.359266; relative MASE 0.771102; relative WQL 0.532407.
+- term skill: long=0.3665, medium=0.3870, short=0.3455.
+- domain skill: Econ/Fin=0.1182, Energy=0.2385, Healthcare=0.3103, Nature=0.4745, Sales=0.5516, Transport=0.4204, Web/CloudOps=0.4341.
+- frequency skill: A=0.3507, D=0.4222, H=0.3921, M=0.1412, Q=0.1508, S=0.2801, T=0.3641, W=0.2573.
+- variates skill: multivariate=0.3652, univariate=0.3544.
+- Outcome: Fixed E83 + E97weekly3000 - E80overflow3000, coefficient1. Merge1.392576CPU seconds, no optimizer updates. Standalone improves main/first earlier versus E83 and declines at second/third; fixed equal ensemble assessed separately.
+
+## E104 weekly matched-delta standalone valid_backcast3
+- Checkpoint SHA-256: `094c7c694406075967f392a0d9ac1146bac984a90ab468c59d660a1b92fa7ba2`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 27.16672372817993 seconds.
+- Overall skill 0.388450; relative MASE 0.741068; relative WQL 0.504669.
+- term skill: long=0.3933, medium=0.3623, short=0.3962.
+- domain skill: Econ/Fin=0.1766, Energy=0.3223, Healthcare=0.2292, Nature=0.5006, Sales=0.4004, Transport=0.5087, Web/CloudOps=0.3907.
+- frequency skill: A=0.3616, D=0.4377, H=0.4053, M=0.1372, Q=0.2595, S=0.2755, T=0.4323, W=0.2534.
+- variates skill: multivariate=0.3728, univariate=0.4005.
+- Outcome: Fixed E83 + E97weekly3000 - E80overflow3000, coefficient1. Merge1.392576CPU seconds, no optimizer updates. Standalone improves main/first earlier versus E83 and declines at second/third; fixed equal ensemble assessed separately.
+
+## E104 fixed equal ensemble four-cutoff result
+{"composition": "equal quantile mean after each member applies its own fixed rules", "skill_score": 0.3854686334562757, "rel_mase_gmean": 0.736317060829138, "rel_wql_gmean": 0.5128888362858817, "groups": {"term": {"short": 0.36816414362245353, "medium": 0.408440542958879, "long": 0.40638695737391495}, "domain": {"Econ/Fin": 0.1425263103369776, "Energy": 0.24632269833233222, "Healthcare": 0.4841127370370696, "Nature": 0.4419832586228435, "Sales": 0.34402063532792737, "Transport": 0.46813360862898734, "Web/CloudOps": 0.5044110005980553}, "freq": {"A-DEC": 0.3754462639697367, "Q-DEC": 0.19683288984300373, "M": 0.12041158797480767, "W-SUN": 0.10445402624428435, "D": 0.36437209338558496, "H": 0.40083310684490536, "15T": 0.21665767736788544, "W-FRI": 0.012382553366723226, "10T": 0.627237041718931, "W-TUE": 0.6419405160293805, "W-THU": 0.2063343746335743, "W-WED": 0.3296903939083894, "5T": 0.5194953263110145, "10S": 0.42496228760958743}}, "configs": [{"config_id": "m4_yearly__A__short", 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"n_mase_elements": 1800, "n_seasonal_error_zero": 0, "n_seasonal_error_nan": 0}, "relative": {"mase": 0.9567305254553605, "wql": 0.8227330180228468}}, {"config_id": "bizitobs_service__S__short", "leaderboard_key": "bizitobs_service/10S/short", "dataset": "bizitobs_service", "term": "short", "freq": "10S", "prediction_length": 60, "seasonality": 360, "n_variates": 2, "n_instances": 21, "domain": "Web/CloudOps", "n_cohorts": 1, "metrics": {"mase": 0.7324628601848132, "wql": 0.012468266168907526, "wsql_by_level": [0.008215030129070992, 0.012485755828051727, 0.014889145286160158, 0.016000871359545917, 0.016105768183720788, 0.015278630292856189, 0.013275651753476963, 0.010118624988524415, 0.005844917698760588], "n_valid_labels": 2520, "n_masked_labels": 0, "mae_median": 16.491911796350326, "n_mase_elements": 2520, "n_seasonal_error_zero": 0, "n_seasonal_error_nan": 0}, "relative": {"mase": 0.7696556880179742, "wql": 0.503318066478902}}, {"config_id": "bizitobs_service__S__medium", "leaderboard_key": "bizitobs_service/10S/medium", "dataset": "bizitobs_service", "term": "medium", "freq": "10S", "prediction_length": 600, "seasonality": 360, "n_variates": 2, "n_instances": 21, "domain": "Web/CloudOps", "n_cohorts": 1, "metrics": {"mase": 0.7984312321992689, "wql": 0.011001039924959608, "wsql_by_level": [0.007518777611379767, 0.010577598061715378, 0.012318945521906922, 0.013262129629493939, 0.013534719675842093, 0.013194749673509063, 0.01211883979469059, 0.01007703901860633, 0.006406560337492398], "n_valid_labels": 25200, "n_masked_labels": 0, "mae_median": 17.09932730270047, "n_mase_elements": 25200, "n_seasonal_error_zero": 0, "n_seasonal_error_nan": 0}, "relative": {"mase": 0.8673632301139702, "wql": 0.45992525896546305}}, {"config_id": "bizitobs_service__S__long", "leaderboard_key": "bizitobs_service/10S/long", "dataset": "bizitobs_service", "term": "long", "freq": "10S", "prediction_length": 900, "seasonality": 360, "n_variates": 2, "n_instances": 21, "domain": "Web/CloudOps", "n_cohorts": 1, "metrics": {"mase": 0.646613369495283, "wql": 0.010402683917260002, "wsql_by_level": [0.0062267995817844955, 0.008538692518941748, 0.010108725971076577, 0.011376764214893436, 0.012436127694055487, 0.013155873427271378, 0.012787249144287667, 0.011013426596545082, 0.007980496106484125], "n_valid_labels": 37800, "n_masked_labels": 0, "mae_median": 15.606604814255048, "n_mase_elements": 37800, "n_seasonal_error_zero": 0, "n_seasonal_error_nan": 0}, "relative": {"mase": 0.8002403081371326, "wql": 0.3728878713837755}}, {"config_id": "bizitobs_l2c__T__short", "leaderboard_key": "bizitobs_l2c/5T/short", "dataset": "bizitobs_l2c/5T", "term": "short", "freq": "5T", "prediction_length": 48, "seasonality": 288, "n_variates": 7, "n_instances": 1, "domain": "Web/CloudOps", "n_cohorts": 1, "metrics": {"mase": 0.28996107175004043, "wql": 0.08935709313411225, "wsql_by_level": [0.03476836707761851, 0.05700713165647231, 0.0733345462727266, 0.08690633538697647, 0.10064485729347158, 0.11181830219704957, 0.11767262198806329, 0.11786800047570861, 0.10419367585892322], "n_valid_labels": 336, "n_masked_labels": 0, "mae_median": 2.548459583804721, "n_mase_elements": 336, "n_seasonal_error_zero": 0, "n_seasonal_error_nan": 0}, "relative": {"mase": 0.3641853237606104, "wql": 0.4278913576057856}}, {"config_id": "bizitobs_l2c__T__medium", "leaderboard_key": "bizitobs_l2c/5T/medium", "dataset": "bizitobs_l2c/5T", "term": "medium", "freq": "5T", "prediction_length": 480, "seasonality": 288, "n_variates": 7, "n_instances": 1, "domain": "Web/CloudOps", "n_cohorts": 1, "metrics": {"mase": 0.4321503031836867, "wql": 0.18432258806548052, "wsql_by_level": [0.09018466538428084, 0.14253605337405711, 0.18038778677145245, 0.20804248017819088, 0.22944895995378836, 0.2356383014527587, 0.22753973179059417, 0.19975734445073456, 0.1453679692334674], "n_valid_labels": 3360, "n_masked_labels": 0, "mae_median": 4.786018498880523, "n_mase_elements": 3360, "n_seasonal_error_zero": 0, "n_seasonal_error_nan": 0}, "relative": {"mase": 0.8225710682193722, "wql": 0.7122892501188703}}, {"config_id": "bizitobs_l2c__T__long", "leaderboard_key": "bizitobs_l2c/5T/long", "dataset": "bizitobs_l2c/5T", "term": "long", "freq": "5T", "prediction_length": 720, "seasonality": 288, "n_variates": 7, "n_instances": 1, "domain": "Web/CloudOps", "n_cohorts": 1, "metrics": {"mase": 0.699228756292291, "wql": 0.34073021038707835, "wsql_by_level": [0.1277722036256575, 0.22579297019476952, 0.31174281595824704, 0.37821302407612, 0.4231092894326387, 0.45462519155466463, 0.4514393600779259, 0.4146039750481884, 0.27927306351549336], "n_valid_labels": 5040, "n_masked_labels": 0, "mae_median": 6.934133305663746, "n_mase_elements": 5040, "n_seasonal_error_zero": 0, "n_seasonal_error_nan": 0}, "relative": {"mase": 0.4479447069213554, "wql": 0.4461418940946688}}, {"config_id": "bizitobs_l2c__H__short", "leaderboard_key": "bizitobs_l2c/H/short", "dataset": "bizitobs_l2c/H", "term": "short", "freq": "H", "prediction_length": 48, "seasonality": 24, "n_variates": 7, "n_instances": 1, "domain": "Web/CloudOps", "n_cohorts": 1, "metrics": {"mase": 0.47224914020222897, "wql": 0.182787381279163, "wsql_by_level": [0.1240704270771492, 0.20018397060257917, 0.2164037952932672, 0.22330980998235528, 0.21512922606792495, 0.20148822613798775, 0.18792831045147373, 0.1614166682162592, 0.11515599768347046], "n_valid_labels": 336, "n_masked_labels": 0, "mae_median": 4.900850257703236, "n_mase_elements": 336, "n_seasonal_error_zero": 0, "n_seasonal_error_nan": 0}, "relative": {"mase": 0.3716596580112479, "wql": 0.47618212083808953}}], "cpu_seconds": 1.03408993803896, "members": ["/workspace/research/backcast3_evals/weekly_slow_ar_delta3k", "/workspace/research/backcast3_evals/patch8_ema6000", "/workspace/research/backcast3_evals/split_patch_ema12000"], "member_checkpoint_hashes": ["094c7c694406075967f392a0d9ac1146bac984a90ab468c59d660a1b92fa7ba2", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "weights": [0.3333333333333333, 0.3333333333333333, 0.3333333333333333], "selection_only": true, "cutoff": "valid_backcast3", "E102skill": 0.38962589808404846, "difference": -6.989045150118667e-05}
+Main+0.000560751 and firstearlier+0.000432658; secondearlier-0.000137495 and thirdearlier-0.000069890. Mixed cutoff evidence, positive average diagnostic gain; select this one fixed candidate for full public replay and relocation/composition checks. No coefficient sweep. Publish only if fullscore exceeds E102 and all replay checks pass; do not claim held-out significance.
+E107 audits passed before launch. CPU seed503:1.633014seconds; independent float64 NumPy feature reference, per-cycle-offset invariance, sampler byte identity, inherited parameters and CPU forward outputs bitwise preserved. Added49,184zero-initialized parameters,total36,849,984. Initialcheckpoint SHA470ef98d69d00b17defbf41c70a92b22574154cdb6e0317993bccf113e4af93b. GPU seed509: initial BF16 outputs bitwise identical; maxB64/C4096/H1024 paired objective0.314552695 and finite gradients; new query/head gradient norms0.00467448/0.01857908,headbias0.00118291. Peakreserved6290MiB/allocated5785.632MiB including EMA and Adam placeholders. Check wall45.549493seconds includes waiting on global evaluation lock;0optimizerupdates/no validationread. Model embeds its helper function directly, so generic standalone/ensemble publishers need no extra runtime module. Queued after E105queue12006; unchanged control stream and full-model optimizer.
+
+## E108 complete eligible-series corpus packing — resource preparation, no model training
+Hypothesis for later data scaling: repeated training on the first approximately1B or5.35B packed points may limit fresh larger models even with fixed source balancing. Prepare every allowed series of length>=16 whose deterministic crop has a finite observation, using the already audited stratified_corpus/pack.py with a nonbinding1e12per-directory pointcap. Preserve the exact shuffled global series order, min length16, maximum deterministic crop131072, row-specific frequency, and95allowlisted directories. This extends packed_stratified_large as an exact prefix; it does not imply retaining every observation of series longer than131072. Use2CPUworkers, no GPU/model optimization. Audit every directory's exact binary/record prefix against the5.35Bpoint pack, independent Arrow replays, and bitwise original source probabilities before any training. No data downloads, no validation reads. Any fresh-model training recipe will be declared separately after current E94/E101 evidence.
+
+## Training exposure diagnostic for fresh capacity
+Allowed-corpus/local-generator audit seed521,20480windows,3.303616CPU seconds,0optimizerupdates/no validationread. Meanfinite context1599.5786,target444.9583,total2044.537points per sampled row. Thus fresh96k/B48 presents approximately9.421Bfinite observations over4.608Mrows before sign duplication, including resampled/overlapping windows and synthetic values. Published E102ancestry contains22.848Msampled rows when taking each unique trained-run maximum; this includes subtracted-control branches and is compute invested, not an equivalent single-model trajectory. Complete per-run audit training_exposure_audit.json. Fresh146M currently has substantially less training exposure than the mature lineage; early capacity comparisons should not be interpreted as a scaling limit. Full eligible-series pack E108 is preparation for later longer training; no new training recipe selected by this audit alone.
+
+## E104 weekly matched-delta ensemble full public replay
+- Checkpoint SHA-256: `1669bc65e42eaf879ed99fab670b8c12a950c4245c341a6f0bd8354c8494a2b8`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.381736; relative MASE 0.738984; relative WQL 0.517264.
+- term skill: long=0.4005, medium=0.4074, short=0.3642.
+- domain skill: Econ/Fin=0.1238, Energy=0.2398, Healthcare=0.4894, Nature=0.4421, Sales=0.3581, Transport=0.4683, Web/CloudOps=0.4962.
+- frequency skill: A=0.2968, D=0.3688, H=0.3955, M=0.1458, Q=0.1868, S=0.4250, T=0.4377, W=0.2400.
+- variates skill: multivariate=0.3951, univariate=0.3709.
+- Outcome: Fixed equal1/3 ensemble E104weekly correction/E71patch8_6000/E93splitpatch_12000. Full skill0.381736117455058 exceeds E102full0.38116304998662165 by0.00057306746843635. RelativeMASE0.7389844981008465 and WQL0.517264204380349 both improve;97configurations/151170instances/0failures,inference358.357500shared-H100 seconds. Two of four diagnostic cutoffs improve and two slightly decline; fullscore supports selecting this candidate after independent component/composition checks. No held-out reward or significance claim.
+
+E105 nested corpus volume seed197 training started at 2026-10-23T05:42:47Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E99 early cooldown final36000
+- Checkpoint SHA-256: `b985ce73ed119243ef51e5f0579bc43633419ce375eb7ce2fad7d1a1103acb51`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.4186589717865 seconds.
+- Overall skill 0.363307; relative MASE 0.758030; relative WQL 0.534779.
+- term skill: long=0.3893, medium=0.3898, short=0.3425.
+- domain skill: Econ/Fin=0.1268, Energy=0.2221, Healthcare=0.4006, Nature=0.4132, Sales=0.3453, Transport=0.4557, Web/CloudOps=0.4925.
+- frequency skill: A=0.3909, D=0.3370, H=0.3814, M=0.1134, Q=0.1976, S=0.4152, T=0.4195, W=0.2100.
+- variates skill: multivariate=0.3805, univariate=0.3493.
+- Outcome: Final0.363306598 trails original E79at36000 0.364859034 by0.001552437. The30k gain did not persist. Declared cooldown branch complete; no schedule replacement and no standalone publication. Exact24000state restored, same seed307/B48; only learning-rate schedule differs.
+
+## E79 fresh146M original schedule at24000
+- Checkpoint SHA-256: `e03ce08a9c1c357bce9d0aca3871c8817627cc660d16706cd85fddba9b5fe5eb`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 34.343666791915894 seconds.
+- Overall skill 0.354494; relative MASE 0.763098; relative WQL 0.546035.
+- term skill: long=0.3782, medium=0.3903, short=0.3308.
+- domain skill: Econ/Fin=0.1204, Energy=0.2113, Healthcare=0.3859, Nature=0.4116, Sales=0.3203, Transport=0.4432, Web/CloudOps=0.4876.
+- frequency skill: A=0.3886, D=0.3283, H=0.3717, M=0.0151, Q=0.1916, S=0.4207, T=0.4085, W=0.2469.
+- variates skill: multivariate=0.3720, univariate=0.3402.
+- Outcome: Skill0.354493848 improves on16000 0.349499286 but trails matched37M24000 0.357901602. Continue declared schedule; await halfLR at matched checkpoints. All own random initialization, seed307/B48/LR2e-4.
+
+E104 final replay suite passed in438.071371wall seconds including lock waits. Relocated examples bitwise identical,finite/ordered,31.678684seconds. Public inference358.357500seconds. Independently replayed changed first member and orderedfloat32 component average match allthree complete actual configurations exactly; receipts weekly_slow_ar_composition_replay.json and weekly_slow_ensemble_composition_checks.json. Descriptor1669bc65e42eaf879ed99fab670b8c12a950c4245c341a6f0bd8354c8494a2b8,110,068,216parameters. Selected for atomic publication over E102. Descriptive comparison: {"improved_configs": 42, "dataset_cluster_delta_percentiles": {"p025": -0.00031799803137674555, "p50": 0.0005000199376024583, "p975": 0.002059979380489236}, "bootstrap_seed": 317, "bootstrap_samples": 5000}. No held-out significance claim; two earlier diagnostic cutoffs have small regressions. E101teacher stays frozen E96.
+
+E99cooldown24k-to36k branch completed in1603.635706shared-H100 training-loop seconds; final rejected as recorded. E95all-shard24Mcap run completed12000 in2657.504791loop seconds; final evaluation pending while E105nested-volume starts in its allocation.
+
+## E109 duration of the matched weekly-synthetic correction — predeclared
+E104's fixed3000-step correction improves the full replay but has two small earlier-cutoff regressions. At6000, E97weekly skill0.380715730 exceeds its matched E80control0.378288765 by0.002426965. Test the next already scheduled checkpoint duration with exactly the same coefficient1 formula E83 + E97weeklyEMA6000 - E80overflowEMA6000, then the same equal1/3 ensemble with E71patch8_6000 and E93splitpatch_12000. No coefficient search, optimizer updates or change to any inference rule. Evaluate all four diagnostic cutoffs before choosing a full replay; compare directly to currently published E104. This is validation checkpoint selection along the declared training run, not independent replication. All weights and synthetic generators were trained/generated here on allowed data.
+
+E106 spectral synthetic residuals seed197 training started at 2026-10-23T05:45:28Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E95 all-shard fixed-volume final12000
+- Checkpoint SHA-256: `453c1e5c5ff189f961d2b4001e9e299c72e26f86d3c4afe5b5a6a46fe5acaafe`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.615615367889404 seconds.
+- Overall skill 0.377983; relative MASE 0.744078; relative WQL 0.519979.
+- term skill: long=0.4039, medium=0.4059, short=0.3566.
+- domain skill: Econ/Fin=0.1366, Energy=0.2370, Healthcare=0.4437, Nature=0.4338, Sales=0.3305, Transport=0.4678, Web/CloudOps=0.5031.
+- frequency skill: A=0.3715, D=0.3556, H=0.3975, M=0.0998, Q=0.1933, S=0.4243, T=0.4390, W=0.2113.
+- variates skill: multivariate=0.3973, univariate=0.3622.
+- Outcome: Final0.377983250 recovers from weak3k/6k and exceeds matched E80final0.377423946 by0.000559304, while remaining below the old-corpus best3000checkpoint0.378663825. Training2657.504791loop seconds, seed197/B64/LR2e-5. Earlier matched-final cutoffs queued to test whether the late gain persists. E105nested-volume training now active.
+
+## E79 fresh37M original schedule at48000
+- Checkpoint SHA-256: `855f37f248e8104578ae2b356d2b7326a73236476f888fd1468e988be4238509`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.904500484466553 seconds.
+- Overall skill 0.366773; relative MASE 0.755679; relative WQL 0.530618.
+- term skill: long=0.3907, medium=0.3907, short=0.3479.
+- domain skill: Econ/Fin=0.1392, Energy=0.2294, Healthcare=0.4025, Nature=0.4162, Sales=0.3289, Transport=0.4575, Web/CloudOps=0.4950.
+- frequency skill: A=0.3873, D=0.3499, H=0.3856, M=0.0362, Q=0.2082, S=0.4181, T=0.4257, W=0.2180.
+- variates skill: multivariate=0.3836, univariate=0.3530.
+- Outcome: Skill0.366772722 improves over36000 0.364859034. Original96k schedule continues after E99cooldown failed its final comparison. Seed307/B48/LR2e-4, own random initialization and slow-AR generator; no publication yet.
+
+## E105 output-path collision incident and correction
+At05:46UTC the E105watcher reported the exact E95checkpoint hash, revealing a launcher path error: packed_stratified_large training and its watcher both pointed to completed runs/stratified_corpus instead of runs/stratified_volume. This was an implementation error, not a model result. All four mislabeled E105cached evaluations were quarantined under incidents/stratified_volume_output_collision and are invalid for selection. The new trainer was stopped; its complete model/EMA/Adam/allRNG/prefetched-batch state at1000 was saved immutably as stratified_volume_recovery001000.pt SHA f153b05103d51042525b0ba765dc0a826241791ea7611baba8cfd07a54b5bca8. Its actual1000EMA SHA dd2b7db79f33a230724fe26a33e07793547329639046e32e4a380cc8b1a23485 and raw checkpoint were moved into the corrected directory. Resume from1000 preserves the intended training trajectory apart from normal numerical restart effects; unsaved later work is discarded and counted as incident compute. Original E95EMA/raw1000 and final optimizer recovery were overwritten and are no longer replayable; original E95EMA/raw2000..12000 including all selected later checkpoints remain intact. FinalE95EMA SHA453c1e5c5ff189f961d2b4001e9e299c72e26f86d3c4afe5b5a6a46fe5acaafe was verified unchanged. E95is complete and must not be resumed. Published E104and its assets were untouched; no E105result was published. Corrected E105launcher uses runs/stratified_volume and explicit immutable resume, watcher rescoring actual1000. E107queue was stopped while waiting and will be requeued against the new E105launcherPID. A process-start guard now rejects any fresh trainer output containing EMA/raw/recovery checkpoints unless --resume is explicit; CPU fixture verifies refusal before GPU initialization and preserves the existing marker. Incident logs/receipt/checkpoints retained.
+
+E105 nested corpus volume seed197 training started at 2026-10-23T05:47:49Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E101 own-ensemble distillation fresh146M training started at 2026-10-23T05:50:23Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E105 incident first-phase last measured record: {"step": 1000, "loss": 0.2910519391298294, "seconds": 202.5, "lr": 1.9727406610312548e-05, "grad_norm": 0.13067036867141724, "context": 4096, "horizon": 512}. Corrected trainer13473 restored1000 exactly from immutable rescue and is progressing in runs/stratified_volume. E107requeued against corrected queue13470. New watch_evaluate.py verifies every cached/result checkpoint SHA and optionally the expected packed corpus; CPU behavioral fixture rejects wrong corpus/hash and accepts matching cached reports in3.652652seconds with no evaluator/GPU/optimizer execution (watch_evaluation_guard_checks.json). The completed sourceE95run now has no recovery.pt, preventing accidental use of the overwritten optimizer state.
+
+## E94 fresh146M half learning rate at16000
+- Checkpoint SHA-256: `19f9ead156ea9bc4a3edca1303bc5595057b2e1b89ff88e7bb7d9acf39618ba5`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 33.574928522109985 seconds.
+- Overall skill 0.354616; relative MASE 0.765691; relative WQL 0.543981.
+- term skill: long=0.3807, medium=0.3858, short=0.3318.
+- domain skill: Econ/Fin=0.1226, Energy=0.2154, Healthcare=0.3836, Nature=0.4162, Sales=0.3402, Transport=0.4491, Web/CloudOps=0.4736.
+- frequency skill: A=0.3784, D=0.3518, H=0.3719, M=0.0534, Q=0.1915, S=0.4189, T=0.4000, W=0.2223.
+- variates skill: multivariate=0.3672, univariate=0.3444.
+- Outcome: Skill0.354615664 exceeds highLR146Mat16000 0.349499286 by0.005116378, nearly matches highLRat24000 0.354493848, and trails matched37M16000 0.355318072 by0.000702409. Consistent rate advantage across4/8/16k supports LR1e-4 for a later fresh146Mdata comparison. Continue96k schedule.
+
+## E108 complete corpus pack audited
+Complete pack contains62,399,537,510points and7,652,636series from all95allowed directories. Packer550.026052wall seconds with2CPUworkers; independent audit80.076772seconds; preparation total631.880773seconds. Exact binary and record prefix against packed_stratified_large in all95directories,260independent Arrow series replays with row-specific frequencies/crops, source probabilities bitwise equal to packed_sample. Receipts stratified_full_prepare_receipt.json and stratified_full_audit.json. No optimizer updates or validation reads. Runtime publication never depends on this training pack.
+
+## E110 fresh146M full eligible-series data comparison — predeclared
+Hypothesis: greater allowed-series diversity improves a fresh larger model beyond the narrow original pack. E94halfLR beats E79wide at matched4k/8k/16k, so use E94's exact recipe and own random initial checkpoint SHA c9693619ca6277c26fef3b2bde37ff6796bec97b3af9f94588bbc5ddd329ec90. Same d1024/heads16/enc6/dec4/patch16/context4096/horizon1024,146,477,344parameters, seed307/B48/LR1e-4/96000steps/400warmup, paired loss,20%slow-AR synthetic, horizon RNG300310, static compilation, Adam/EMA settings. Change only packed_sample to audited packed_stratified_full; retain original source probabilities via source-weights-from packed_sample. This is a joint coverage+volume comparison against E94, not an isolation of either factor, and differing series lengths change subsequent sampling trajectories despite the same seed. No teacher or newly tested synthetic generator in this arm. Queue after E106spectral run in its released allocation; max-shape memory is covered by the unchanged E79/E94 architecture check at10,696MiB reserved. Screen4/8/16/24/48/72/96k; longer continuation decisions remain open for later evidence. Expected-output and corpus/hash guards apply. No external/pretrained assets.
+
+## E98 original postloss completion final12000
+- Checkpoint SHA-256: `b4b36749b469b0a5024747e6407351cc77fbfc0e77cd1776eabdc07318299f4c`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.10327959060669 seconds.
+- Overall skill 0.376034; relative MASE 0.745577; relative WQL 0.522191.
+- term skill: long=0.4024, medium=0.4041, short=0.3544.
+- domain skill: Econ/Fin=0.1158, Energy=0.2406, Healthcare=0.4413, Nature=0.4287, Sales=0.3336, Transport=0.4650, Web/CloudOps=0.5007.
+- frequency skill: A=0.3675, D=0.3453, H=0.3958, M=0.0708, Q=0.1957, S=0.4234, T=0.4412, W=0.2183.
+- variates skill: multivariate=0.3933, univariate=0.3620.
+- Outcome: Final0.376034254 is below originalE75at3000 0.378813891 and matchedE80final0.377423946. Completion3654..12000 cost1995.386513training-loop seconds, plus original799.915049seconds. Reject late completion; selected postloss contribution stays at3000. Full-state branch restored optimizer/EMA/allRNG without reset.
+
+## E105 corrected actual larger-volume first1000
+- Checkpoint SHA-256: `dd2b7db79f33a230724fe26a33e07793547329639046e32e4a380cc8b1a23485`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.012547969818115 seconds.
+- Overall skill 0.377047; relative MASE 0.745081; relative WQL 0.520844.
+- term skill: long=0.3978, medium=0.4060, short=0.3574.
+- domain skill: Econ/Fin=0.1346, Energy=0.2367, Healthcare=0.4623, Nature=0.4324, Sales=0.3353, Transport=0.4653, Web/CloudOps=0.4978.
+- frequency skill: A=0.3704, D=0.3548, H=0.3960, M=0.1135, Q=0.1955, S=0.4239, T=0.4334, W=0.2282.
+- variates skill: multivariate=0.3927, univariate=0.3643.
+- Outcome: Actual packed_stratified_large checkpoint SHA dd2b7db79f33a230724fe26a33e07793547329639046e32e4a380cc8b1a23485; skill0.377046610 nearly ties E95smallpack1000 0.377028371. Earlier mislabeled cached E105results were quarantined and are invalid. This correctly rescored checkpoint was moved from the incident directory and retains its original metadata/hash; continued training now uses a separate output.
+
+## E106 spectral synthetic residuals first1000
+- Checkpoint SHA-256: `6d81b602d598a452ab114d27433d713831611e69925cf81857839a083d14925c`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 22.007343530654907 seconds.
+- Overall skill 0.377778; relative MASE 0.743525; relative WQL 0.520710.
+- term skill: long=0.3998, medium=0.4063, short=0.3578.
+- domain skill: Econ/Fin=0.1359, Energy=0.2365, Healthcare=0.4618, Nature=0.4329, Sales=0.3341, Transport=0.4674, Web/CloudOps=0.4992.
+- frequency skill: A=0.3667, D=0.3581, H=0.3950, M=0.1087, Q=0.1987, S=0.4227, T=0.4366, W=0.2240.
+- variates skill: multivariate=0.3935, univariate=0.3650.
+- Outcome: Skill0.377777744 exceeds matched E80at1000 0.376804394 by0.000973350. Only half of locally generated residuals changed, original real/non-replaced streams preserved. Seed197/B64/LR2e-5, continue declared3000/6000/12000 before selection.
+
+## E109 fixed6000correction ensemble four-cutoff result
+{"skill_score": 0.38571041915894055, "rel_mase_gmean": 0.7360297117883622, "rel_wql_gmean": 0.5126854026218823, "groups": {"term": {"short": 0.3684054892093498, "medium": 0.40861792906707406, "long": 0.40669389489538343}, "domain": {"Econ/Fin": 0.14352283454252435, "Energy": 0.24673599474557084, "Healthcare": 0.4852425866482829, "Nature": 0.44163298463938605, "Sales": 0.34379367871415345, "Transport": 0.46820747636240867, "Web/CloudOps": 0.5046932217026704}, "freq": {"A-DEC": 0.3760321167863795, "Q-DEC": 0.19766380569346853, "M": 0.12098833477007476, "W-SUN": 0.10769661791141583, "D": 0.3645387551794308, "H": 0.40107642673673183, "15T": 0.21690311587788813, "W-FRI": 0.015770663763870485, "10T": 0.6272364379377529, "W-TUE": 0.6408660517579567, "W-THU": 0.20503433411830196, "W-WED": 0.32981773944316883, "5T": 0.5197675944998887, "10S": 0.4252552385586551}}, "cpu_seconds": 1.219195496989414, "member_checkpoint_hashes": ["c8adf9cadf97dac5e120d8d4d2c8c025bffa2e8b55d8664bf900cce55d00a70b", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cutoff": "valid_screen", "E104skill": 0.3854686334562757, "difference": 0.0002417857026648651}
+{"skill_score": 0.34038453448144423, "rel_mase_gmean": 0.7936376163611775, "rel_wql_gmean": 0.5482257309654212, "groups": {"term": {"short": 0.30705150791287494, "medium": 0.36037259523502396, "long": 0.40216620546988446}, "domain": {"Econ/Fin": 0.17253359368549226, "Energy": 0.23204288671949924, "Healthcare": 0.4136249395275713, "Nature": 0.3730211654221537, "Sales": 0.40518403019805194, "Transport": 0.3736095722850602, "Web/CloudOps": 0.45146748825459326}, "freq": {"A-DEC": 0.32644250202504, "Q-DEC": 0.23690729834334368, "M": 0.19096290262857263, "W-SUN": 0.05220186917057601, "D": 0.35265301620275713, "H": 0.3717913194271458, "15T": 0.22264807081310156, "W-FRI": 0.2309648955865844, "10T": 0.3251814465668018, "W-TUE": 0.5629848975400014, "W-THU": 0.2613953386383071, "W-WED": 0.3604296268765198, "5T": 0.44169178473335347, "10S": 0.3476495261147444}}, "cpu_seconds": 1.4167720968835056, "member_checkpoint_hashes": ["c8adf9cadf97dac5e120d8d4d2c8c025bffa2e8b55d8664bf900cce55d00a70b", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cutoff": "valid_backcast", "E104skill": 0.34022701617855455, "difference": 0.00015751830288968094}
+{"skill_score": 0.3650268512866791, "rel_mase_gmean": 0.7650984666186498, "rel_wql_gmean": 0.5269790976955032, "groups": {"term": {"short": 0.35239958764222634, "medium": 0.3931690597943601, "long": 0.3687369083593992}, "domain": {"Econ/Fin": 0.12213255276326718, "Energy": 0.24166462607625983, "Healthcare": 0.3517132635055068, "Nature": 0.47875836217412926, "Sales": 0.5562721649607756, "Transport": 0.42517847470928805, "Web/CloudOps": 0.43741099158446717}, "freq": {"A-DEC": 0.33656065874085495, "Q-DEC": 0.15684832945264293, "M": 0.16665053208003056, "W-SUN": 0.07150895572001315, "D": 0.43035508666570155, "H": 0.39490487280100917, "15T": 0.10882809267173599, "W-FRI": 0.15456262907954288, "10T": 0.5092255418082989, "W-TUE": 0.08691901966493587, "W-THU": 0.35389841775428443, "W-WED": 0.5635809755138526, "5T": 0.4725753025882409, "10S": 0.2851839750626278}}, "cpu_seconds": 1.239942419109866, "member_checkpoint_hashes": ["c8adf9cadf97dac5e120d8d4d2c8c025bffa2e8b55d8664bf900cce55d00a70b", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cutoff": "valid_backcast2", "E104skill": 0.36494952219954613, "difference": 7.732908713298237e-05}
+{"skill_score": 0.38931035191213115, "rel_mase_gmean": 0.7402180023142099, "rel_wql_gmean": 0.5038270416495189, "groups": {"term": {"short": 0.3946100958444625, "medium": 0.3680969397604268, "long": 0.39609503944824875}, "domain": {"Econ/Fin": 0.18617468677704974, "Energy": 0.31652315087317884, "Healthcare": 0.24343015434307236, "Nature": 0.5047900396770414, "Sales": 0.4000084000969678, "Transport": 0.5092713174572736, "Web/CloudOps": 0.3939880442254168}, "freq": {"A-DEC": 0.3575108227176348, "Q-DEC": 0.2547450248116794, "M": 0.148792543537967, "W-SUN": 0.17031337870235352, "D": 0.4392381685936584, "H": 0.40578214745866226, "15T": 0.2545743158438908, "W-FRI": 0.03969737538520679, "10T": 0.6065987129573612, "W-TUE": 0.2789739520044787, "W-THU": 0.3351510988039397, "W-WED": 0.3774145654877956, "5T": 0.46539939355428894, "10S": 0.2923169693971158}}, "cpu_seconds": 1.0821592810098082, "member_checkpoint_hashes": ["c8adf9cadf97dac5e120d8d4d2c8c025bffa2e8b55d8664bf900cce55d00a70b", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cutoff": "valid_backcast3", "E104skill": 0.3895560076325473, "difference": -0.00024565572041612693}
+Three diagnostic gains and one decline: main+0.000241786,first+0.000157518,second+0.000077329,third-0.000245656. Small positive average; select this one fixed checkpoint-duration candidate for full public replay. No coefficient sweep or new optimizer updates. Merge2.075168CPU seconds, standalone SHA c8adf9cadf97dac5e120d8d4d2c8c025bffa2e8b55d8664bf900cce55d00a70b.
+
+## E109 weekly6000 matched-delta standalone valid_screen
+- Checkpoint SHA-256: `c8adf9cadf97dac5e120d8d4d2c8c025bffa2e8b55d8664bf900cce55d00a70b`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.40149998664856 seconds.
+- Overall skill 0.382470; relative MASE 0.738351; relative WQL 0.516480.
+- term skill: long=0.4043, medium=0.4074, short=0.3640.
+- domain skill: Econ/Fin=0.1333, Energy=0.2409, Healthcare=0.4856, Nature=0.4375, Sales=0.3381, Transport=0.4644, Web/CloudOps=0.5062.
+- frequency skill: A=0.3761, D=0.3559, H=0.4008, M=0.1140, Q=0.2001, S=0.4253, T=0.4418, W=0.2372.
+- variates skill: multivariate=0.3982, univariate=0.3696.
+- Outcome: Fixed E83+E97weekly6000-E80overflow6000, coefficient1; no optimizer updates. Ensemble comparison is the selection criterion; third-earlier standalone performance declines.
+
+## E109 weekly6000 matched-delta standalone valid_backcast
+- Checkpoint SHA-256: `c8adf9cadf97dac5e120d8d4d2c8c025bffa2e8b55d8664bf900cce55d00a70b`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 21.963260173797607 seconds.
+- Overall skill 0.336472; relative MASE 0.796772; relative WQL 0.552567.
+- term skill: long=0.3951, medium=0.3601, short=0.3030.
+- domain skill: Econ/Fin=0.1606, Energy=0.2286, Healthcare=0.4001, Nature=0.3703, Sales=0.3955, Transport=0.3719, Web/CloudOps=0.4498.
+- frequency skill: A=0.3210, D=0.3470, H=0.3693, M=0.1803, Q=0.2309, S=0.3394, T=0.3343, W=0.2926.
+- variates skill: multivariate=0.3535, univariate=0.3226.
+- Outcome: Fixed E83+E97weekly6000-E80overflow6000, coefficient1; no optimizer updates. Ensemble comparison is the selection criterion; third-earlier standalone performance declines.
+
+## E109 weekly6000 matched-delta standalone valid_backcast2
+- Checkpoint SHA-256: `c8adf9cadf97dac5e120d8d4d2c8c025bffa2e8b55d8664bf900cce55d00a70b`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 22.833601474761963 seconds.
+- Overall skill 0.359364; relative MASE 0.770770; relative WQL 0.532473.
+- term skill: long=0.3665, medium=0.3870, short=0.3457.
+- domain skill: Econ/Fin=0.1191, Energy=0.2389, Healthcare=0.3074, Nature=0.4743, Sales=0.5518, Transport=0.4204, Web/CloudOps=0.4347.
+- frequency skill: A=0.3504, D=0.4213, H=0.3925, M=0.1454, Q=0.1508, S=0.2803, T=0.3644, W=0.2553.
+- variates skill: multivariate=0.3658, univariate=0.3541.
+- Outcome: Fixed E83+E97weekly6000-E80overflow6000, coefficient1; no optimizer updates. Ensemble comparison is the selection criterion; third-earlier standalone performance declines.
+
+## E109 weekly6000 matched-delta standalone valid_backcast3
+- Checkpoint SHA-256: `c8adf9cadf97dac5e120d8d4d2c8c025bffa2e8b55d8664bf900cce55d00a70b`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 21.22951889038086 seconds.
+- Overall skill 0.387648; relative MASE 0.742514; relative WQL 0.505006.
+- term skill: long=0.3931, medium=0.3619, short=0.3950.
+- domain skill: Econ/Fin=0.1745, Energy=0.3225, Healthcare=0.2373, Nature=0.4950, Sales=0.4012, Transport=0.5086, Web/CloudOps=0.3906.
+- frequency skill: A=0.3606, D=0.4357, H=0.4053, M=0.1367, Q=0.2600, S=0.2740, T=0.4318, W=0.2506.
+- variates skill: multivariate=0.3723, univariate=0.3995.
+- Outcome: Fixed E83+E97weekly6000-E80overflow6000, coefficient1; no optimizer updates. Ensemble comparison is the selection criterion; third-earlier standalone performance declines.
+
+## E95 all-shard final earlier cutoff1
+- Checkpoint SHA-256: `453c1e5c5ff189f961d2b4001e9e299c72e26f86d3c4afe5b5a6a46fe5acaafe`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 22.185170888900757 seconds.
+- Overall skill 0.337351; relative MASE 0.796681; relative WQL 0.551166.
+- term skill: long=0.3975, medium=0.3600, short=0.3037.
+- domain skill: Econ/Fin=0.1590, Energy=0.2333, Healthcare=0.4170, Nature=0.3635, Sales=0.3931, Transport=0.3751, Web/CloudOps=0.4471.
+- frequency skill: A=0.3146, D=0.3506, H=0.3671, M=0.1813, Q=0.2349, S=0.3420, T=0.3372, W=0.2919.
+- variates skill: multivariate=0.3535, univariate=0.3242.
+- Outcome: Matched seed197/B64/LR2e-5 final12000 comparison. E95gains main/first/third earlier but loses0.001849350at second earlier; average four-cutoff gain is essentially zero. Do not select a standalone or transferred coverage correction; E105volume and E110freshfull data comparisons remain separate experiments.
+
+## E80 matched original-corpus final earlier cutoff1
+- Checkpoint SHA-256: `f11d056b95f1c5eeef174265ebc51742952600e0dae1fa52900860447d5be819`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 22.276308298110962 seconds.
+- Overall skill 0.336528; relative MASE 0.797567; relative WQL 0.551922.
+- term skill: long=0.3974, medium=0.3587, short=0.3027.
+- domain skill: Econ/Fin=0.1549, Energy=0.2300, Healthcare=0.4194, Nature=0.3635, Sales=0.3947, Transport=0.3732, Web/CloudOps=0.4488.
+- frequency skill: A=0.3168, D=0.3524, H=0.3669, M=0.1806, Q=0.2286, S=0.3418, T=0.3360, W=0.2847.
+- variates skill: multivariate=0.3529, univariate=0.3232.
+- Outcome: Matched seed197/B64/LR2e-5 final12000 comparison. E95gains main/first/third earlier but loses0.001849350at second earlier; average four-cutoff gain is essentially zero. Do not select a standalone or transferred coverage correction; E105volume and E110freshfull data comparisons remain separate experiments.
+
+## E95 all-shard final earlier cutoff2
+- Checkpoint SHA-256: `453c1e5c5ff189f961d2b4001e9e299c72e26f86d3c4afe5b5a6a46fe5acaafe`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 22.514131546020508 seconds.
+- Overall skill 0.359124; relative MASE 0.770269; relative WQL 0.533219.
+- term skill: long=0.3649, medium=0.3876, short=0.3456.
+- domain skill: Econ/Fin=0.1057, Energy=0.2383, Healthcare=0.3457, Nature=0.4701, Sales=0.5518, Transport=0.4215, Web/CloudOps=0.4312.
+- frequency skill: A=0.3487, D=0.4241, H=0.3889, M=0.1590, Q=0.1439, S=0.2824, T=0.3606, W=0.2711.
+- variates skill: multivariate=0.3622, univariate=0.3566.
+- Outcome: Matched seed197/B64/LR2e-5 final12000 comparison. E95gains main/first/third earlier but loses0.001849350at second earlier; average four-cutoff gain is essentially zero. Do not select a standalone or transferred coverage correction; E105volume and E110freshfull data comparisons remain separate experiments.
+
+## E80 matched original-corpus final earlier cutoff2
+- Checkpoint SHA-256: `f11d056b95f1c5eeef174265ebc51742952600e0dae1fa52900860447d5be819`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 22.563663959503174 seconds.
+- Overall skill 0.360973; relative MASE 0.768847; relative WQL 0.531126.
+- term skill: long=0.3646, medium=0.3902, short=0.3479.
+- domain skill: Econ/Fin=0.1009, Energy=0.2385, Healthcare=0.3613, Nature=0.4749, Sales=0.5481, Transport=0.4204, Web/CloudOps=0.4340.
+- frequency skill: A=0.3509, D=0.4270, H=0.3905, M=0.1611, Q=0.1432, S=0.2801, T=0.3626, W=0.2745.
+- variates skill: multivariate=0.3648, univariate=0.3579.
+- Outcome: Matched seed197/B64/LR2e-5 final12000 comparison. E95gains main/first/third earlier but loses0.001849350at second earlier; average four-cutoff gain is essentially zero. Do not select a standalone or transferred coverage correction; E105volume and E110freshfull data comparisons remain separate experiments.
+
+## E95 all-shard final earlier cutoff3
+- Checkpoint SHA-256: `453c1e5c5ff189f961d2b4001e9e299c72e26f86d3c4afe5b5a6a46fe5acaafe`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 22.70902395248413 seconds.
+- Overall skill 0.387881; relative MASE 0.740491; relative WQL 0.506003.
+- term skill: long=0.3979, medium=0.3625, short=0.3935.
+- domain skill: Econ/Fin=0.1693, Energy=0.3185, Healthcare=0.2544, Nature=0.4987, Sales=0.3991, Transport=0.5048, Web/CloudOps=0.3958.
+- frequency skill: A=0.3523, D=0.4336, H=0.4042, M=0.1203, Q=0.2579, S=0.2981, T=0.4301, W=0.2615.
+- variates skill: multivariate=0.3768, univariate=0.3965.
+- Outcome: Matched seed197/B64/LR2e-5 final12000 comparison. E95gains main/first/third earlier but loses0.001849350at second earlier; average four-cutoff gain is essentially zero. Do not select a standalone or transferred coverage correction; E105volume and E110freshfull data comparisons remain separate experiments.
+
+## E80 matched original-corpus final earlier cutoff3
+- Checkpoint SHA-256: `f11d056b95f1c5eeef174265ebc51742952600e0dae1fa52900860447d5be819`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 22.906182527542114 seconds.
+- Overall skill 0.387481; relative MASE 0.740121; relative WQL 0.506916.
+- term skill: long=0.3953, medium=0.3592, short=0.3949.
+- domain skill: Econ/Fin=0.1649, Energy=0.3182, Healthcare=0.2719, Nature=0.5016, Sales=0.3989, Transport=0.5041, Web/CloudOps=0.3891.
+- frequency skill: A=0.3478, D=0.4406, H=0.4026, M=0.1367, Q=0.2596, S=0.2884, T=0.4271, W=0.2609.
+- variates skill: multivariate=0.3727, univariate=0.3989.
+- Outcome: Matched seed197/B64/LR2e-5 final12000 comparison. E95gains main/first/third earlier but loses0.001849350at second earlier; average four-cutoff gain is essentially zero. Do not select a standalone or transferred coverage correction; E105volume and E110freshfull data comparisons remain separate experiments.
+
+## E111 independent-seed replication of weekly synthetic coverage — predeclared
+Hypothesis: the weekly-coverage gain is a reproducible training-data effect, rather than a favorable seed197continuation. Train two new matched arms from the same ownE62initial checkpoint with seed523/B64/LR2e-5/12000steps, paired loss, packed_sample and original source weights. Control uses overflow_control; candidate uses the already checked weekly_synth generator (unchanged20%synthetic; independent Bernoulli1/11 weekly replacement, weekly seed1000526, horizon seed300526). Both consume identical original real/non-replaced streams for this new seed. The primary matched comparison is at6000;1000/3000 are learning-curve diagnostics and12000tests durability. This is a new causal replication of the generator effect, not a retraining of any supplied reference baseline. Queue control after E107 and candidate after control, using one released allocation sequentially. Every output name is distinct and guarded; expected packed corpus and checkpoint hashes are checked by watchers. Earlier cutoffs required before any transfer or averaging of seed-specific corrections. No new model-soup rule is selected yet.
+
+## E97 weekly synthetic coverage final12000
+- Checkpoint SHA-256: `5104b21697e423740d2fa0fb55c4010759a83ab7f376b8704b0b992f56850203`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 20.680010318756104 seconds.
+- Overall skill 0.380440; relative MASE 0.741395; relative WQL 0.517747.
+- term skill: long=0.4027, medium=0.4063, short=0.3614.
+- domain skill: Econ/Fin=0.1282, Energy=0.2358, Healthcare=0.4938, Nature=0.4316, Sales=0.3351, Transport=0.4668, Web/CloudOps=0.5051.
+- frequency skill: A=0.3616, D=0.3526, H=0.3946, M=0.1286, Q=0.1969, S=0.4251, T=0.4412, W=0.2425.
+- variates skill: multivariate=0.3967, univariate=0.3672.
+- Outcome: Final0.380439539 remains above matchedE80final0.377423946 by0.003015593, though slightly below its own6000 0.380715730. Complete12000 in2600.472750shared-H100 training-loop seconds. The fixed6000delta ensemble E109 is being fully replayed; E111seed523 matched replication is queued. No held-out claim.
+
+## E109 weekly6000 correction ensemble full public replay
+- Checkpoint SHA-256: `6bb9ddf58411d410255e32206c4ef484576a3818cbc27bb574159ebe56957573`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.381981; relative MASE 0.738680; relative WQL 0.517067.
+- term skill: long=0.4009, medium=0.4075, short=0.3645.
+- domain skill: Econ/Fin=0.1246, Energy=0.2403, Healthcare=0.4904, Nature=0.4418, Sales=0.3580, Transport=0.4684, Web/CloudOps=0.4965.
+- frequency skill: A=0.2972, D=0.3689, H=0.3957, M=0.1465, Q=0.1871, S=0.4253, T=0.4379, W=0.2406.
+- variates skill: multivariate=0.3953, univariate=0.3712.
+- Outcome: Full0.38198119952338705 exceeds publishedE1040.381736117455058 by0.00024508206832905, with both relativeMASE0.7386798626058647 and WQL0.5170673482219265 improved.97configs151170instances0fail,inference295.661474shared-H100 seconds. Three of four diagnostic cutoffs improve, third earlier declines0.000245656. Publish only after independent component/composition checks complete; adaptive validation result, not held-out significance.
+
+Recovery robustness: future trainer starts/resumes now save the already audited complete recovery state after their first completed update, in addition to every250steps. This protects progress during later static-shape compiler warmup; no objective, sampler or optimizer arithmetic changes. Already running trainers retain their loaded code. E101first50updates completed with finite loss0.744227227/gradientnorm0.361719310,teacher-valid1.0in logged batch,458.4loop seconds including compilation; remaining rare shapes may still compile. Compiler cache backup refreshed independently.
+
+E109 complete replay/check suite375.233383wall seconds includinglockwaits; inference295.661474seconds. Relocation bitwise/finite/ordered25.588799seconds. Independent changed-member replay and fixed orderedfloat32 composition match three complete actual configurations exactly (weekly_slow_ar6_composition_replay.json,weekly_slow6_ensemble_composition_checks.json). Descriptor6bb9ddf58411d410255e32206c4ef484576a3818cbc27bb574159ebe56957573,110068216parameters. Selected for atomic publication. Descriptive comparison {"improved_configs": 52, "dataset_cluster_delta_percentiles": {"p025": -4.58150388659706e-05, "p50": 0.00024309038144720851, "p975": 0.0005397384010364019}, "bootstrap_seed": 317, "bootstrap_samples": 5000}; no held-out significance claim. E101teacher staysE96.
+
+## E105 corrected larger-volume at3000
+- Checkpoint SHA-256: `df5bc1e16aa79d1074d7f2fb12f9b66ebdc8debf302fae7e7f388a89d2d2793a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 21.78273630142212 seconds.
+- Overall skill 0.376761; relative MASE 0.744501; relative WQL 0.521728.
+- term skill: long=0.3980, medium=0.4051, short=0.3571.
+- domain skill: Econ/Fin=0.1385, Energy=0.2347, Healthcare=0.4694, Nature=0.4324, Sales=0.3388, Transport=0.4642, Web/CloudOps=0.4967.
+- frequency skill: A=0.3747, D=0.3532, H=0.3950, M=0.1208, Q=0.1966, S=0.4235, T=0.4334, W=0.2280.
+- variates skill: multivariate=0.3912, univariate=0.3650.
+- Outcome: Skill0.376761202 nearly ties E95smallpack3000 0.376741687 and trails E80original-pack3000 0.378663825. Corrected separate output;1001onward restored full1000state. Continue declared6000/12000, no selection.
+
+## E106 spectral residuals at3000
+- Checkpoint SHA-256: `a2445412f640657c3396806783fbcbbed5243b9443fe08615a45760c4becd98e`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 21.065938234329224 seconds.
+- Overall skill 0.378295; relative MASE 0.743170; relative WQL 0.520091.
+- term skill: long=0.4031, medium=0.4065, short=0.3573.
+- domain skill: Econ/Fin=0.1451, Energy=0.2383, Healthcare=0.4329, Nature=0.4339, Sales=0.3314, Transport=0.4654, Web/CloudOps=0.5053.
+- frequency skill: A=0.3698, D=0.3530, H=0.3972, M=0.1006, Q=0.2014, S=0.4236, T=0.4398, W=0.2184.
+- variates skill: multivariate=0.3981, univariate=0.3621.
+- Outcome: Skill0.378295411 trails matched E80at3000 0.378663825 by0.000368414. The1000gain did not persist. Continue declared6000/12000 before any earlier-cutoff selection; no transfer or publication.
+
+## E112 final-duration matched weekly correction — predeclared
+E104at3000 and E109at6000 improved complete public replay with coefficient1 and unchanged equal-member composition. Finish the checkpoint-duration comparison at the already scheduled final12000: E83 + E97weeklyEMA12000 - E80overflowEMA12000, coefficient1. E97final0.380439539 minus E80final0.377423946 is a larger standalone matched gap than at6000, despite the candidate's own slight decline. Evaluate the fixed merged checkpoint at all four cutoffs, then the same equal1/3 ensemble with E71patch8_6000 and E93splitpatch_12000. No optimizer updates, coefficient sweep or change to any runtime rule. Compare to published E109; full public replay and checks required for publication. This completes the declared3k/6k/12k duration sequence, not a new training run or independent replication. E111seed523 pair separately tests reproducibility.
+
+## E113 frequency-scaled short training windows — preparation
+Hypothesis: the sampler coverage audit shows many low-frequency real windows with short histories and long future labels, while low-frequency synthetic contexts are usually hundreds/thousands of steps. Change only the training-window distribution for half of rows whose existing period pair is[first<=52,second=1]. First consume the original sampler and requested-horizon augmentation completely. Using independent RNG seed1200200, choose a new forecast endpoint uniformly among finite original target positions; choose an integer horizon log-uniform from1 to min(originalH,max(8,2*period),available_span-8); retain the most recent min(originalC,max(64,32*period),available_span-newH) context positions. Require at least4finite context values. Move only existing contiguous allowed-series/local-generator observations across the new training cutoff, preserving all internal missing values and period labels; no new values, validation reads or cross-series information. Unselected rows and original/horizon RNG streams must remain bitwise unchanged. This is one joint history/horizon sampling hypothesis, not a separate causal claim about either subfactor. ComparatorE80: ownE62initial,seed197/B64/LR2e-5/12000,paired loss,original20%synthetic and source probabilities, unchanged overflow-safe model/runtime. CPU index/mask/RNG checks required before queueing after the E111pair. Screens1000/3000/6000/12000; earlier/full replay for selection.
+E113 pretraining-only design revision before any optimizer step: initial CPU audit seed541/15360windows found median requested horizon2 for annual/quarterly changed rows. To avoid concentrating the new supervision on1–2step forecasts, set the log-uniform lower bound to4 whenever at least4future positions are available (otherwise the available maximum). All other rules are unchanged. No validation outcome was used for this revision. Initial audit retained as lowfreq_windows_checks_initial_horizon1.json,4.844810CPU seconds; rerun the index/RNG audit on the final sampler before queueing.
+
+E113 final sampler audit passes: seed541,15360paired control/candidate windows,2679eligible,1360changed,4.688498CPU seconds,0optimizerupdates/no validationread. Exact contiguous source indices verified for every changed context/target, all unselected rows and period labels unchanged, original/horizon RNG states bitwise identical, all three NumPy streams recover exactly. Model file byte-identical toE80 SHA44eeb23c037e91b8f67e432615ce46540a7ac4242919785f9c4218de23121499. Sampling diagnostics {"4": {"rows": 392, "median_context_before": 38.5, "median_context_after": 56.0, "median_target_before": 48.0, "median_target_after": 5.0}, "1": {"rows": 255, "median_context_before": 714.0, "median_context_after": 64.0, "median_target_before": 112.0, "median_target_after": 5.0}, "52": {"rows": 298, "median_context_before": 53.0, "median_context_after": 76.0, "median_target_before": 84.0, "median_target_after": 16.0}, "12": {"rows": 415, "median_context_before": 60.0, "median_context_after": 88.0, "median_target_before": 66.0, "median_target_after": 9.0}}. Queue after E111candidate14350. Final generator/rule metadata and publication provenance added; runtime remains postloss_inference.
+
+## E112 final-duration ensemble four-cutoff result
+{"skill_score": 0.3858712532527797, "rel_mase_gmean": 0.7358504086107674, "rel_wql_gmean": 0.512541833459536, "groups": {"term": {"short": 0.36862786434327777, "medium": 0.4088431767481189, "long": 0.4066382857473849}, "domain": {"Econ/Fin": 0.1427335412380505, "Energy": 0.24665585766807274, "Healthcare": 0.4865626055775951, "Nature": 0.4421324797730376, "Sales": 0.3441164280500456, "Transport": 0.46824759257656046, "Web/CloudOps": 0.5048162617852268}, "freq": {"A-DEC": 0.3751097673324215, "Q-DEC": 0.1969044501960543, "M": 0.12093463923233339, "W-SUN": 0.1050444592094002, "D": 0.36501445271701716, "H": 0.40096290360047526, "15T": 0.21688931337366202, "W-FRI": 0.01363906764599443, "10T": 0.6277697339150299, "W-TUE": 0.6448188288204655, "W-THU": 0.20576781810192735, "W-WED": 0.33017644200384, "5T": 0.5200746054287371, "10S": 0.42522083651418774}}, "cpu_seconds": 1.3133022310212255, "member_checkpoint_hashes": ["6f1f0f6025afbe565633fee0b937a14f1efedf83ffbe7de65641c3800f60d8c7", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cutoff": "valid_screen", "E109skill": 0.38571041915894055, "difference": 0.00016083409383915725}
+{"skill_score": 0.34026453805586865, "rel_mase_gmean": 0.7938743991145676, "rel_wql_gmean": 0.5482616396650213, "groups": {"term": {"short": 0.3069591344201037, "medium": 0.360270352157806, "long": 0.401968127644735}, "domain": {"Econ/Fin": 0.1719491915504786, "Energy": 0.23186920778828224, "Healthcare": 0.4136537686496582, "Nature": 0.3727904917107857, "Sales": 0.40577071577146684, "Transport": 0.3736370692721874, "Web/CloudOps": 0.4513164517386834}, "freq": {"A-DEC": 0.3259591929532375, "Q-DEC": 0.23688125417263828, "M": 0.19019710487662123, "W-SUN": 0.04859397725596748, "D": 0.35267813857571817, "H": 0.3715520522339072, "15T": 0.22235983728090003, "W-FRI": 0.23099317599917168, "10T": 0.3253705594505678, "W-TUE": 0.5636487610465628, "W-THU": 0.2596937123136588, "W-WED": 0.3622295587104454, "5T": 0.44209582636515465, "10S": 0.34758760809251354}}, "cpu_seconds": 1.2864791969768703, "member_checkpoint_hashes": ["6f1f0f6025afbe565633fee0b937a14f1efedf83ffbe7de65641c3800f60d8c7", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cutoff": "valid_backcast", "E109skill": 0.34038453448144423, "difference": -0.00011999642557558587}
+{"skill_score": 0.36500280105746885, "rel_mase_gmean": 0.7650616372712448, "rel_wql_gmean": 0.5270443883489382, "groups": {"term": {"short": 0.3522992147072812, "medium": 0.39323093590574965, "long": 0.36882551999825675}, "domain": {"Econ/Fin": 0.12172400330140187, "Energy": 0.24177568201657473, "Healthcare": 0.351634580322554, "Nature": 0.47856500731963436, "Sales": 0.556767594467451, "Transport": 0.42520135798126146, "Web/CloudOps": 0.43731641066930993}, "freq": {"A-DEC": 0.3368001909919832, "Q-DEC": 0.15714702439125794, "M": 0.166107338147097, "W-SUN": 0.06814222014322535, "D": 0.4306717956748586, "H": 0.394613806291076, "15T": 0.10893648874402395, "W-FRI": 0.1553435881509515, "10T": 0.5096714148955988, "W-TUE": 0.0852329830425479, "W-THU": 0.35258387477718955, "W-WED": 0.5645835048514585, "5T": 0.4726250053861992, "10S": 0.2856493230118694}}, "cpu_seconds": 1.1841424808371812, "member_checkpoint_hashes": ["6f1f0f6025afbe565633fee0b937a14f1efedf83ffbe7de65641c3800f60d8c7", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cutoff": "valid_backcast2", "E109skill": 0.3650268512866791, "difference": -2.4050229210259566e-05}
+{"skill_score": 0.38948675360484886, "rel_mase_gmean": 0.7399201304191406, "rel_wql_gmean": 0.5037387262498307, "groups": {"term": {"short": 0.3948199478576392, "medium": 0.3681915847463416, "long": 0.39625870682346687}, "domain": {"Econ/Fin": 0.1861914584696166, "Energy": 0.3162396550654769, "Healthcare": 0.2449973656886668, "Nature": 0.5053241835290441, "Sales": 0.400142202691233, "Transport": 0.5092939425383289, "Web/CloudOps": 0.39439674653047185}, "freq": {"A-DEC": 0.3579795125033336, "Q-DEC": 0.25418213861585404, "M": 0.14903096998383114, "W-SUN": 0.16834143185233952, "D": 0.44005142265195263, "H": 0.4058611111478776, "15T": 0.25413569489604193, "W-FRI": 0.03894964894687247, "10T": 0.6066278797992326, "W-TUE": 0.2775687319750957, "W-THU": 0.33593939814117346, "W-WED": 0.37754976142332153, "5T": 0.4657926027011683, "10S": 0.29225163220286654}}, "cpu_seconds": 1.1745618879795074, "member_checkpoint_hashes": ["6f1f0f6025afbe565633fee0b937a14f1efedf83ffbe7de65641c3800f60d8c7", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cutoff": "valid_backcast3", "E109skill": 0.38931035191213115, "difference": 0.00017640169271770922}
+Main+0.000160834,firstearlier-0.000119996,secondearlier-0.000024050,thirdearlier+0.000176402. Mixed evidence with small positive average, select the final planned duration for one full public replay; no coefficient search or further intermediate-duration scouting. Merge1.324673CPU seconds, standaloneSHA6f1f0f6025afbe565633fee0b937a14f1efedf83ffbe7de65641c3800f60d8c7.
+
+## E112 weekly12000 matched-delta standalone valid_screen
+- Checkpoint SHA-256: `6f1f0f6025afbe565633fee0b937a14f1efedf83ffbe7de65641c3800f60d8c7`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.014628887176514 seconds.
+- Overall skill 0.382923; relative MASE 0.737814; relative WQL 0.516097.
+- term skill: long=0.4041, medium=0.4080, short=0.3647.
+- domain skill: Econ/Fin=0.1314, Energy=0.2405, Healthcare=0.4896, Nature=0.4389, Sales=0.3390, Transport=0.4645, Web/CloudOps=0.5065.
+- frequency skill: A=0.3734, D=0.3574, H=0.4005, M=0.1143, Q=0.1979, S=0.4251, T=0.4425, W=0.2393.
+- variates skill: multivariate=0.3987, univariate=0.3701.
+- Outcome: Fixed E83+E97weekly12000-E80overflow12000, coefficient1; no optimizer updates. Final declared duration; equal-ensemble four-cutoff comparison recorded separately.
+
+## E112 weekly12000 matched-delta standalone valid_backcast
+- Checkpoint SHA-256: `6f1f0f6025afbe565633fee0b937a14f1efedf83ffbe7de65641c3800f60d8c7`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.712749242782593 seconds.
+- Overall skill 0.336057; relative MASE 0.797482; relative WQL 0.552765.
+- term skill: long=0.3943, medium=0.3598, short=0.3028.
+- domain skill: Econ/Fin=0.1596, Energy=0.2279, Healthcare=0.4002, Nature=0.3697, Sales=0.3973, Transport=0.3719, Web/CloudOps=0.4492.
+- frequency skill: A=0.3195, D=0.3471, H=0.3685, M=0.1781, Q=0.2311, S=0.3391, T=0.3345, W=0.2912.
+- variates skill: multivariate=0.3529, univariate=0.3224.
+- Outcome: Fixed E83+E97weekly12000-E80overflow12000, coefficient1; no optimizer updates. Final declared duration; equal-ensemble four-cutoff comparison recorded separately.
+
+## E112 weekly12000 matched-delta standalone valid_backcast2
+- Checkpoint SHA-256: `6f1f0f6025afbe565633fee0b937a14f1efedf83ffbe7de65641c3800f60d8c7`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 22.29803729057312 seconds.
+- Overall skill 0.359294; relative MASE 0.770653; relative WQL 0.532671.
+- term skill: long=0.3666, medium=0.3870, short=0.3456.
+- domain skill: Econ/Fin=0.1183, Energy=0.2393, Healthcare=0.3076, Nature=0.4737, Sales=0.5535, Transport=0.4205, Web/CloudOps=0.4340.
+- frequency skill: A=0.3509, D=0.4224, H=0.3915, M=0.1439, Q=0.1518, S=0.2810, T=0.3649, W=0.2544.
+- variates skill: multivariate=0.3654, univariate=0.3543.
+- Outcome: Fixed E83+E97weekly12000-E80overflow12000, coefficient1; no optimizer updates. Final declared duration; equal-ensemble four-cutoff comparison recorded separately.
+
+## E112 weekly12000 matched-delta standalone valid_backcast3
+- Checkpoint SHA-256: `6f1f0f6025afbe565633fee0b937a14f1efedf83ffbe7de65641c3800f60d8c7`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 23.48830485343933 seconds.
+- Overall skill 0.388305; relative MASE 0.741401; relative WQL 0.504680.
+- term skill: long=0.3936, medium=0.3621, short=0.3959.
+- domain skill: Econ/Fin=0.1754, Energy=0.3218, Healthcare=0.2432, Nature=0.4966, Sales=0.4018, Transport=0.5087, Web/CloudOps=0.3917.
+- frequency skill: A=0.3624, D=0.4385, H=0.4055, M=0.1372, Q=0.2587, S=0.2738, T=0.4319, W=0.2514.
+- variates skill: multivariate=0.3722, univariate=0.4007.
+- Outcome: Fixed E83+E97weekly12000-E80overflow12000, coefficient1; no optimizer updates. Final declared duration; equal-ensemble four-cutoff comparison recorded separately.
+
+## E94 fresh146M halfLR at24000
+- Checkpoint SHA-256: `ee69006629037179a8fd6d09233755cb8efceafb0a47494abceceea3cb7fae52`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 37.77550005912781 seconds.
+- Overall skill 0.355452; relative MASE 0.766772; relative WQL 0.541806.
+- term skill: long=0.3783, medium=0.3844, short=0.3349.
+- domain skill: Econ/Fin=0.1190, Energy=0.2184, Healthcare=0.3835, Nature=0.4109, Sales=0.3450, Transport=0.4523, Web/CloudOps=0.4748.
+- frequency skill: A=0.3831, D=0.3537, H=0.3714, M=0.0510, Q=0.1900, S=0.4180, T=0.4033, W=0.2182.
+- variates skill: multivariate=0.3639, univariate=0.3487.
+- Outcome: Skill0.355452449 exceeds highLR146M24000 0.354493848 by0.000958601; smaller than the16k advantage. Below37M24000 0.357901602. Continue existing schedule; E110full-corpus comparison retains this rate, unchanged recipe otherwise.
+
+## E105 corrected larger-volume at6000
+- Checkpoint SHA-256: `ec7daab696252802849e28ee1b8348e500ebe094e8ece39a956f01daff5a6943`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.547231912612915 seconds.
+- Overall skill 0.376970; relative MASE 0.744376; relative WQL 0.521465.
+- term skill: long=0.3995, medium=0.4061, short=0.3565.
+- domain skill: Econ/Fin=0.1376, Energy=0.2329, Healthcare=0.4645, Nature=0.4332, Sales=0.3377, Transport=0.4673, Web/CloudOps=0.4981.
+- frequency skill: A=0.3771, D=0.3531, H=0.3960, M=0.1187, Q=0.1962, S=0.4239, T=0.4342, W=0.2227.
+- variates skill: multivariate=0.3915, univariate=0.3652.
+- Outcome: Skill0.376970385 exceeds E95smallpack6000 0.376043220 by0.000927165 but remains below original-corpus E80at6000 0.378288765. Correct output and checkpoint hash; continue12000 before selection.
+
+## E106 spectral residuals at6000
+- Checkpoint SHA-256: `683f3824d3c1f605d17bb08d3eef127a54c67eca3867cfbcf1158710a55c4b24`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.36519145965576 seconds.
+- Overall skill 0.379248; relative MASE 0.742652; relative WQL 0.518861.
+- term skill: long=0.4037, medium=0.4079, short=0.3582.
+- domain skill: Econ/Fin=0.1453, Energy=0.2390, Healthcare=0.4421, Nature=0.4321, Sales=0.3360, Transport=0.4646, Web/CloudOps=0.5073.
+- frequency skill: A=0.3711, D=0.3532, H=0.3962, M=0.1095, Q=0.1992, S=0.4251, T=0.4421, W=0.2198.
+- variates skill: multivariate=0.3998, univariate=0.3624.
+- Outcome: Skill0.379247830 exceeds matched E80at6000 0.378288765 by0.000959065, recovering the3k deficit. Queue matched earlier-cutoff evaluations before any fixed weight transfer or publication. Original sampler/horizon streams preserved; only the declared stochastic residual law differs.
+
+## E106 spectral residuals at6000 valid_backcast
+- Checkpoint SHA-256: `683f3824d3c1f605d17bb08d3eef127a54c67eca3867cfbcf1158710a55c4b24`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.24050521850586 seconds.
+- Overall skill 0.337595; relative MASE 0.795167; relative WQL 0.551809.
+- term skill: long=0.3992, medium=0.3598, short=0.3034.
+- domain skill: Econ/Fin=0.1714, Energy=0.2258, Healthcare=0.4113, Nature=0.3681, Sales=0.3959, Transport=0.3715, Web/CloudOps=0.4544.
+- frequency skill: A=0.3144, D=0.3510, H=0.3671, M=0.1923, Q=0.2368, S=0.3431, T=0.3372, W=0.2876.
+- variates skill: multivariate=0.3548, univariate=0.3236.
+- Outcome: Matched seed197/B64/LR2e-5 comparison. E106 minus E80: main +0.000959065, first earlier +0.002438307, second +0.001622232, third -0.002173652. Three gains and one regression; use all four for transfer selection.
+
+## E106 spectral residuals at6000 valid_backcast2
+- Checkpoint SHA-256: `683f3824d3c1f605d17bb08d3eef127a54c67eca3867cfbcf1158710a55c4b24`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 23.82012963294983 seconds.
+- Overall skill 0.361328; relative MASE 0.767561; relative WQL 0.531426.
+- term skill: long=0.3654, medium=0.3919, short=0.3475.
+- domain skill: Econ/Fin=0.1166, Energy=0.2360, Healthcare=0.3636, Nature=0.4724, Sales=0.5443, Transport=0.4198, Web/CloudOps=0.4383.
+- frequency skill: A=0.3461, D=0.4280, H=0.3911, M=0.1469, Q=0.1522, S=0.2809, T=0.3651, W=0.2687.
+- variates skill: multivariate=0.3660, univariate=0.3575.
+- Outcome: Matched seed197/B64/LR2e-5 comparison. E106 minus E80: main +0.000959065, first earlier +0.002438307, second +0.001622232, third -0.002173652. Three gains and one regression; use all four for transfer selection.
+
+## E106 spectral residuals at6000 valid_backcast3
+- Checkpoint SHA-256: `683f3824d3c1f605d17bb08d3eef127a54c67eca3867cfbcf1158710a55c4b24`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 22.619502544403076 seconds.
+- Overall skill 0.387494; relative MASE 0.739218; relative WQL 0.507514.
+- term skill: long=0.3976, medium=0.3604, short=0.3937.
+- domain skill: Econ/Fin=0.1834, Energy=0.3158, Healthcare=0.2403, Nature=0.5028, Sales=0.3975, Transport=0.5052, Web/CloudOps=0.3936.
+- frequency skill: A=0.3594, D=0.4421, H=0.4034, M=0.1060, Q=0.2636, S=0.2895, T=0.4283, W=0.2584.
+- variates skill: multivariate=0.3719, univariate=0.3995.
+- Outcome: Matched seed197/B64/LR2e-5 comparison. E106 minus E80: main +0.000959065, first earlier +0.002438307, second +0.001622232, third -0.002173652. Three gains and one regression; use all four for transfer selection.
+
+## E80 matched overflow control at6000 valid_backcast
+- Checkpoint SHA-256: `b19875c52ed5024383fb69ccc48cb07a5629ad8011abf134c7dcfcdc50fae2af`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.008483409881592 seconds.
+- Overall skill 0.335156; relative MASE 0.799092; relative WQL 0.553149.
+- term skill: long=0.3994, medium=0.3566, short=0.3001.
+- domain skill: Econ/Fin=0.1475, Energy=0.2281, Healthcare=0.4131, Nature=0.3644, Sales=0.3963, Transport=0.3724, Web/CloudOps=0.4481.
+- frequency skill: A=0.3117, D=0.3511, H=0.3667, M=0.1735, Q=0.2355, S=0.3418, T=0.3331, W=0.2854.
+- variates skill: multivariate=0.3519, univariate=0.3215.
+- Outcome: Matched seed197/B64/LR2e-5 comparison. E106 minus E80: main +0.000959065, first earlier +0.002438307, second +0.001622232, third -0.002173652. Three gains and one regression; use all four for transfer selection.
+
+## E80 matched overflow control at6000 valid_backcast2
+- Checkpoint SHA-256: `b19875c52ed5024383fb69ccc48cb07a5629ad8011abf134c7dcfcdc50fae2af`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 22.792612314224243 seconds.
+- Overall skill 0.359706; relative MASE 0.769403; relative WQL 0.532850.
+- term skill: long=0.3636, medium=0.3907, short=0.3458.
+- domain skill: Econ/Fin=0.0946, Energy=0.2378, Healthcare=0.3660, Nature=0.4719, Sales=0.5485, Transport=0.4205, Web/CloudOps=0.4319.
+- frequency skill: A=0.3470, D=0.4246, H=0.3913, M=0.1616, Q=0.1478, S=0.2802, T=0.3612, W=0.2648.
+- variates skill: multivariate=0.3639, univariate=0.3563.
+- Outcome: Matched seed197/B64/LR2e-5 comparison. E106 minus E80: main +0.000959065, first earlier +0.002438307, second +0.001622232, third -0.002173652. Three gains and one regression; use all four for transfer selection.
+
+## E80 matched overflow control at6000 valid_backcast3
+- Checkpoint SHA-256: `b19875c52ed5024383fb69ccc48cb07a5629ad8011abf134c7dcfcdc50fae2af`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 23.62287187576294 seconds.
+- Overall skill 0.389668; relative MASE 0.736019; relative WQL 0.506109.
+- term skill: long=0.3966, medium=0.3611, short=0.3975.
+- domain skill: Econ/Fin=0.1636, Energy=0.3183, Healthcare=0.2809, Nature=0.5072, Sales=0.3984, Transport=0.5059, Web/CloudOps=0.3910.
+- frequency skill: A=0.3571, D=0.4456, H=0.4044, M=0.1427, Q=0.2560, S=0.2903, T=0.4280, W=0.2623.
+- variates skill: multivariate=0.3727, univariate=0.4027.
+- Outcome: Matched seed197/B64/LR2e-5 comparison. E106 minus E80: main +0.000959065, first earlier +0.002438307, second +0.001622232, third -0.002173652. Three gains and one regression; use all four for transfer selection.
+
+## E114 spectral-prior transfer — predeclared
+E1066000 improves three of four cutoffs over matched E80, with a regression on the oldest cutoff. Test two fixed coefficient-one corrections on the E109 first member. A (additive): weekly_slow_ar_delta6k + spectral_synth/ema006000 - overflow_control/ema006000. B (replacement): A + partial_targets/ema006000 - long_memory_synth_recovery/ema006000; this removes the earlier slow-AR correction, testing overlap between the two stochastic priors. Keep the weekly6000 correction fixed for both even if E112publishes. All ancestors are our own allowed-data training. No optimizer updates, new training seed, coefficient search, runtime change or per-instance routing. Evaluate both standalone at all four cutoffs and as the unchanged equal1/3 ensemble with E71patch8_6000 and E93splitpatch_12000. Select for full replay only if the complete diagnostic pattern supports it; compare to E109 and the current published model. CPU merges and all evaluation times/hashes recorded in spectral_transfer_receipt.json and reports.
+
+## E112 final weekly12000 ensemble full public replay — selected
+- Checkpoint SHA-256: `f01498eb73e4aa0539e81c67a8a80e56af47d1437f4d65ecc882d551468241a1`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.382143; relative MASE 0.738501; relative WQL 0.516922.
+- term skill: long=0.4008, medium=0.4078, short=0.3647.
+- domain skill: Econ/Fin=0.1239, Energy=0.2402, Healthcare=0.4917, Nature=0.4423, Sales=0.3583, Transport=0.4684, Web/CloudOps=0.4966.
+- frequency skill: A=0.2967, D=0.3694, H=0.3956, M=0.1464, Q=0.1869, S=0.4252, T=0.4382, W=0.2411.
+- variates skill: multivariate=0.3955, univariate=0.3713.
+- Outcome: Full skill0.382143195 exceeds E1090.381981200 by0.000161996. All97configs151170instances succeeded. Relocation deterministic and finite/ordered; independent three-configuration composition exact. Four-cutoff evidence is mixed with small positive average. Validation selection, not a held-out significance claim.
+
+E112 complete public suite {"package": "/workspace/research/candidate-weekly-slow12-ensemble", "status": "complete", "steps": [{"name": "relocation", "returncode": 0, "seconds": 61.15590557013638}, {"name": "full_public_replay", "returncode": 0, "seconds": 382.10087375016883}, {"name": "independent_weekly_slow_replay", "returncode": 0, "seconds": 31.673624618910253}, {"name": "composition", "returncode": 0, "seconds": 0.6345367848407477}, {"name": "descriptive_comparison", "returncode": 0, "seconds": 0.2616488521452993}], "current_step": "descriptive_comparison", "seconds": 475.838925806107, "skill": 0.3821431951028217}. Descriptive comparison {"candidate": "/workspace/research/valid-report-weekly-slow12-ensemble.json", "parent": "/workspace/research/valid-report-weekly-slow6-ensemble.json", "candidate_hash": "f01498eb73e4aa0539e81c67a8a80e56af47d1437f4d65ecc882d551468241a1", "parent_hash": "6bb9ddf58411d410255e32206c4ef484576a3818cbc27bb574159ebe56957573", "skill_difference": 0.00016199557943463727, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 55, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": -4.304766264538462e-05, "p50": 0.00016053828879819543, "p975": 0.0003767170761483723}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}. Atomic publication selected. Model tensors, runtime code and equal weights are unchanged from replay. E101teacher remains frozen E96.
+
+E107 learned sixteen-cycle summary seed197 training started at 2026-10-23T06:21:44Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E114 spectral_add_delta6k valid_screen
+- Checkpoint SHA-256: `c3dfc433d51355228fc13d04913db1a7edf83030c33283b150bdcc50f5ae6ea2`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.866043090820312 seconds.
+- Overall skill 0.382009; relative MASE 0.739202; relative WQL 0.516656.
+- term skill: long=0.4047, medium=0.4077, short=0.3629.
+- domain skill: Econ/Fin=0.1448, Energy=0.2399, Healthcare=0.4635, Nature=0.4395, Sales=0.3355, Transport=0.4625, Web/CloudOps=0.5089.
+- frequency skill: A=0.3780, D=0.3567, H=0.4013, M=0.0935, Q=0.2062, S=0.4246, T=0.4432, W=0.2297.
+- variates skill: multivariate=0.3997, univariate=0.3676.
+- Outcome: Fixed coefficient-one spectral transfer; additive and old-slow-AR replacement are the two predeclared ablations. No optimization. Ensemble selection recorded separately.
+
+## E114 spectral_add_delta6k valid_backcast
+- Checkpoint SHA-256: `c3dfc433d51355228fc13d04913db1a7edf83030c33283b150bdcc50f5ae6ea2`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 23.915529251098633 seconds.
+- Overall skill 0.338230; relative MASE 0.793941; relative WQL 0.551602.
+- term skill: long=0.3953, medium=0.3603, short=0.3061.
+- domain skill: Econ/Fin=0.1783, Energy=0.2263, Healthcare=0.4002, Nature=0.3726, Sales=0.3946, Transport=0.3707, Web/CloudOps=0.4554.
+- frequency skill: A=0.3234, D=0.3463, H=0.3697, M=0.2025, Q=0.2286, S=0.3392, T=0.3373, W=0.2912.
+- variates skill: multivariate=0.3554, univariate=0.3242.
+- Outcome: Fixed coefficient-one spectral transfer; additive and old-slow-AR replacement are the two predeclared ablations. No optimization. Ensemble selection recorded separately.
+
+## E114 spectral_add_delta6k valid_backcast2
+- Checkpoint SHA-256: `c3dfc433d51355228fc13d04913db1a7edf83030c33283b150bdcc50f5ae6ea2`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 22.574910163879395 seconds.
+- Overall skill 0.360737; relative MASE 0.769308; relative WQL 0.531202.
+- term skill: long=0.3675, medium=0.3883, short=0.3472.
+- domain skill: Econ/Fin=0.1344, Energy=0.2381, Healthcare=0.3027, Nature=0.4740, Sales=0.5467, Transport=0.4200, Web/CloudOps=0.4411.
+- frequency skill: A=0.3495, D=0.4226, H=0.3924, M=0.1272, Q=0.1523, S=0.2785, T=0.3691, W=0.2610.
+- variates skill: multivariate=0.3682, univariate=0.3546.
+- Outcome: Fixed coefficient-one spectral transfer; additive and old-slow-AR replacement are the two predeclared ablations. No optimization. Ensemble selection recorded separately.
+
+## E114 spectral_add_delta6k valid_backcast3
+- Checkpoint SHA-256: `c3dfc433d51355228fc13d04913db1a7edf83030c33283b150bdcc50f5ae6ea2`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 21.270376443862915 seconds.
+- Overall skill 0.385446; relative MASE 0.745841; relative WQL 0.506377.
+- term skill: long=0.3926, medium=0.3611, short=0.3917.
+- domain skill: Econ/Fin=0.1886, Energy=0.3194, Healthcare=0.2003, Nature=0.4920, Sales=0.4005, Transport=0.5087, Web/CloudOps=0.3919.
+- frequency skill: A=0.3636, D=0.4339, H=0.4034, M=0.1065, Q=0.2631, S=0.2741, T=0.4316, W=0.2453.
+- variates skill: multivariate=0.3711, univariate=0.3965.
+- Outcome: Fixed coefficient-one spectral transfer; additive and old-slow-AR replacement are the two predeclared ablations. No optimization. Ensemble selection recorded separately.
+
+E110 fresh146M full eligible-series corpus training started at 2026-10-23T06:22:29Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E105 corrected larger-volume final12000
+- Checkpoint SHA-256: `c14daa56b23ee830ac5e0e50bcab588d8beac92ae756f038bed83431031c4b4d`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.560805082321167 seconds.
+- Overall skill 0.377529; relative MASE 0.743201; relative WQL 0.521353.
+- term skill: long=0.4045, medium=0.4073, short=0.3550.
+- domain skill: Econ/Fin=0.1278, Energy=0.2347, Healthcare=0.4489, Nature=0.4326, Sales=0.3346, Transport=0.4664, Web/CloudOps=0.5052.
+- frequency skill: A=0.3643, D=0.3476, H=0.3969, M=0.0868, Q=0.1944, S=0.4248, T=0.4408, W=0.2231.
+- variates skill: multivariate=0.3969, univariate=0.3616.
+- Outcome: Final0.377529200 trails E95nested-small0.377983250 by0.000454050 and is nearly tied with E80oldpack0.377423946. No selection from the main score; all three earlier cutoffs are running to complete the volume comparison. Continuation preserves exact1000optimizer/EMA/RNG/prefetch state; the original output collision and lost E95weights remain documented.
+
+E105 completed12000: corrected continuation loop2013.982814125s, process2034.180478418s. Original interrupted phase process237.888408643s, last logged step1000 at202.5loopseconds; full1000state resumed exactly. Total observed process wall2272.068887061s includes setup and incident handling inside those phases. E106 completed12000,loop2206.272277840s,process2220.040782971s. These are shared-H100 process wall times, not isolated GPU hours. Both final checkpoints now get all three earlier-cutoff diagnostics; no new training or changes to their declared recipes. E107started06:21:44trainer15845; E110started06:22:29trainer15925.
+
+## E115 end-to-end period-phase conditioning — predeclared preparation
+E57 added sine/cosine period-relative inputs but froze the entire existing backbone; its +0.000047 main gain was offset by earlier losses and it was rejected. Test the different hypothesis that attention and projections must co-adapt. Add the same two zero-initialized per-point phase projections (context and future query, two frequency periods, sine/cosine, origin at forecast start, periods<2 zero, missing context positions zero) to the current overflow-safe E62 model. Train all parameters jointly, matching E80 seed197/B64/LR2e-5/12000/paired loss/original source probabilities, generator and requested-horizon stream exactly. No calendar dates, dataset identities or new sources. This compares phase conditioning against E80; it is not a matched causal comparison of training scope against the older E57 run, which used another parent/rate/seed. Verify initial function/inherited tensors and phase arithmetic, then max-shape finite gradients including backbone and new projections before queueing after E79fresh37M96000. Screens1000/3000/6000/12000; earlier/full replay before any selection.
+
+## E106 spectral residuals final12000 valid_screen
+- Checkpoint SHA-256: `1c5c735147bcd1679bc761d091aaf4369c37ce4c14efd65e19b91ffd7a524756`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 21.81789803504944 seconds.
+- Overall skill 0.377641; relative MASE 0.743803; relative WQL 0.520743.
+- term skill: long=0.4039, medium=0.4060, short=0.3559.
+- domain skill: Econ/Fin=0.1485, Energy=0.2343, Healthcare=0.4336, Nature=0.4316, Sales=0.3325, Transport=0.4654, Web/CloudOps=0.5076.
+- frequency skill: A=0.3624, D=0.3521, H=0.3945, M=0.1015, Q=0.2039, S=0.4248, T=0.4419, W=0.2125.
+- variates skill: multivariate=0.3976, univariate=0.3613.
+- Outcome: Final main0.377641399 versus E80.377423946; earlier0.337647555/0.361626447/0.385497870 versus control0.336528337/0.360973470/0.387481485. Three small gains offset by oldest-cutoff regression; average gap about0.0000015. The6000checkpoint remains the only transfer under consideration. No final-duration transfer selected.
+
+## E106 spectral residuals final12000 valid_backcast
+- Checkpoint SHA-256: `1c5c735147bcd1679bc761d091aaf4369c37ce4c14efd65e19b91ffd7a524756`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 20.256327152252197 seconds.
+- Overall skill 0.337648; relative MASE 0.795467; relative WQL 0.551513.
+- term skill: long=0.3962, medium=0.3603, short=0.3046.
+- domain skill: Econ/Fin=0.1743, Energy=0.2267, Healthcare=0.4112, Nature=0.3671, Sales=0.3950, Transport=0.3721, Web/CloudOps=0.4535.
+- frequency skill: A=0.3210, D=0.3506, H=0.3659, M=0.1951, Q=0.2332, S=0.3426, T=0.3392, W=0.2845.
+- variates skill: multivariate=0.3547, univariate=0.3237.
+- Outcome: Final main0.377641399 versus E80.377423946; earlier0.337647555/0.361626447/0.385497870 versus control0.336528337/0.360973470/0.387481485. Three small gains offset by oldest-cutoff regression; average gap about0.0000015. The6000checkpoint remains the only transfer under consideration. No final-duration transfer selected.
+
+## E106 spectral residuals final12000 valid_backcast2
+- Checkpoint SHA-256: `1c5c735147bcd1679bc761d091aaf4369c37ce4c14efd65e19b91ffd7a524756`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 24.78632402420044 seconds.
+- Overall skill 0.361626; relative MASE 0.767884; relative WQL 0.530706.
+- term skill: long=0.3650, medium=0.3904, short=0.3489.
+- domain skill: Econ/Fin=0.1188, Energy=0.2375, Healthcare=0.3551, Nature=0.4741, Sales=0.5473, Transport=0.4192, Web/CloudOps=0.4377.
+- frequency skill: A=0.3482, D=0.4309, H=0.3898, M=0.1385, Q=0.1489, S=0.2820, T=0.3651, W=0.2748.
+- variates skill: multivariate=0.3657, univariate=0.3583.
+- Outcome: Final main0.377641399 versus E80.377423946; earlier0.337647555/0.361626447/0.385497870 versus control0.336528337/0.360973470/0.387481485. Three small gains offset by oldest-cutoff regression; average gap about0.0000015. The6000checkpoint remains the only transfer under consideration. No final-duration transfer selected.
+
+## E106 spectral residuals final12000 valid_backcast3
+- Checkpoint SHA-256: `1c5c735147bcd1679bc761d091aaf4369c37ce4c14efd65e19b91ffd7a524756`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 22.51108193397522 seconds.
+- Overall skill 0.385498; relative MASE 0.742905; relative WQL 0.508292.
+- term skill: long=0.3945, medium=0.3594, short=0.3917.
+- domain skill: Econ/Fin=0.1811, Energy=0.3163, Healthcare=0.2183, Nature=0.5001, Sales=0.3991, Transport=0.5041, Web/CloudOps=0.3915.
+- frequency skill: A=0.3530, D=0.4381, H=0.4016, M=0.0934, Q=0.2652, S=0.2859, T=0.4287, W=0.2549.
+- variates skill: multivariate=0.3723, univariate=0.3957.
+- Outcome: Final main0.377641399 versus E80.377423946; earlier0.337647555/0.361626447/0.385497870 versus control0.336528337/0.360973470/0.387481485. Three small gains offset by oldest-cutoff regression; average gap about0.0000015. The6000checkpoint remains the only transfer under consideration. No final-duration transfer selected.
+
+## E105 corrected larger-volume final12000 valid_backcast
+- Checkpoint SHA-256: `c14daa56b23ee830ac5e0e50bcab588d8beac92ae756f038bed83431031c4b4d`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 22.00239086151123 seconds.
+- Overall skill 0.336714; relative MASE 0.797414; relative WQL 0.551719.
+- term skill: long=0.3996, medium=0.3592, short=0.3019.
+- domain skill: Econ/Fin=0.1536, Energy=0.2291, Healthcare=0.4203, Nature=0.3659, Sales=0.3940, Transport=0.3743, Web/CloudOps=0.4484.
+- frequency skill: A=0.3155, D=0.3546, H=0.3681, M=0.1764, Q=0.2336, S=0.3396, T=0.3370, W=0.2766.
+- variates skill: multivariate=0.3543, univariate=0.3224.
+- Outcome: Compared with E95nested-small: main-0.000454050, earlier1-0.000637726, earlier2+0.000411577, earlier3+0.002468634. Mixed results, strongest gain on the oldest cutoff; no clear uniform benefit or publication selected. Original output collision remains documented; these reports use the corrected actual E105weights.
+
+## E105 corrected larger-volume final12000 valid_backcast2
+- Checkpoint SHA-256: `c14daa56b23ee830ac5e0e50bcab588d8beac92ae756f038bed83431031c4b4d`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 23.669189453125 seconds.
+- Overall skill 0.359536; relative MASE 0.770749; relative WQL 0.532203.
+- term skill: long=0.3638, medium=0.3913, short=0.3452.
+- domain skill: Econ/Fin=0.1019, Energy=0.2364, Healthcare=0.3499, Nature=0.4709, Sales=0.5511, Transport=0.4222, Web/CloudOps=0.4343.
+- frequency skill: A=0.3525, D=0.4266, H=0.3904, M=0.1615, Q=0.1480, S=0.2795, T=0.3626, W=0.2558.
+- variates skill: multivariate=0.3641, univariate=0.3558.
+- Outcome: Compared with E95nested-small: main-0.000454050, earlier1-0.000637726, earlier2+0.000411577, earlier3+0.002468634. Mixed results, strongest gain on the oldest cutoff; no clear uniform benefit or publication selected. Original output collision remains documented; these reports use the corrected actual E105weights.
+
+## E105 corrected larger-volume final12000 valid_backcast3
+- Checkpoint SHA-256: `c14daa56b23ee830ac5e0e50bcab588d8beac92ae756f038bed83431031c4b4d`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 24.15644383430481 seconds.
+- Overall skill 0.390349; relative MASE 0.736786; relative WQL 0.504453.
+- term skill: long=0.3958, medium=0.3608, short=0.3990.
+- domain skill: Econ/Fin=0.1696, Energy=0.3184, Healthcare=0.2721, Nature=0.5101, Sales=0.3969, Transport=0.5048, Web/CloudOps=0.3932.
+- frequency skill: A=0.3711, D=0.4486, H=0.4049, M=0.1322, Q=0.2537, S=0.2882, T=0.4301, W=0.2569.
+- variates skill: multivariate=0.3757, univariate=0.4016.
+- Outcome: Compared with E95nested-small: main-0.000454050, earlier1-0.000637726, earlier2+0.000411577, earlier3+0.002468634. Mixed results, strongest gain on the oldest cutoff; no clear uniform benefit or publication selected. Original output collision remains documented; these reports use the corrected actual E105weights.
+
+## E114 spectral_replace_delta6k valid_screen
+- Checkpoint SHA-256: `b01ff54ba0bd947f4806bd08793288a8202a1c8de153db482666aee3458395ac`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 22.1757493019104 seconds.
+- Overall skill 0.382039; relative MASE 0.738892; relative WQL 0.516823.
+- term skill: long=0.4049, medium=0.4087, short=0.3624.
+- domain skill: Econ/Fin=0.1375, Energy=0.2428, Healthcare=0.4698, Nature=0.4360, Sales=0.3332, Transport=0.4634, Web/CloudOps=0.5079.
+- frequency skill: A=0.3761, D=0.3509, H=0.4018, M=0.1029, Q=0.2052, S=0.4222, T=0.4437, W=0.2355.
+- variates skill: multivariate=0.4001, univariate=0.3672.
+- Outcome: Fixed coefficient-one spectral transfer; additive and old-slow-AR replacement are the two predeclared ablations. No optimization. Ensemble selection recorded separately.
+
+## E114 spectral_replace_delta6k valid_backcast
+- Checkpoint SHA-256: `b01ff54ba0bd947f4806bd08793288a8202a1c8de153db482666aee3458395ac`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 22.24388575553894 seconds.
+- Overall skill 0.337298; relative MASE 0.795806; relative WQL 0.551861.
+- term skill: long=0.3970, medium=0.3617, short=0.3030.
+- domain skill: Econ/Fin=0.1666, Energy=0.2287, Healthcare=0.3986, Nature=0.3693, Sales=0.3952, Transport=0.3715, Web/CloudOps=0.4531.
+- frequency skill: A=0.3150, D=0.3467, H=0.3702, M=0.1914, Q=0.2292, S=0.3361, T=0.3366, W=0.2879.
+- variates skill: multivariate=0.3553, univariate=0.3226.
+- Outcome: Fixed coefficient-one spectral transfer; additive and old-slow-AR replacement are the two predeclared ablations. No optimization. Ensemble selection recorded separately.
+
+## E114 spectral_replace_delta6k valid_backcast2
+- Checkpoint SHA-256: `b01ff54ba0bd947f4806bd08793288a8202a1c8de153db482666aee3458395ac`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 23.31834578514099 seconds.
+- Overall skill 0.358167; relative MASE 0.771957; relative WQL 0.533643.
+- term skill: long=0.3638, medium=0.3887, short=0.3438.
+- domain skill: Econ/Fin=0.1247, Energy=0.2380, Healthcare=0.3032, Nature=0.4702, Sales=0.5491, Transport=0.4202, Web/CloudOps=0.4344.
+- frequency skill: A=0.3507, D=0.4174, H=0.3934, M=0.1411, Q=0.1551, S=0.2738, T=0.3644, W=0.2504.
+- variates skill: multivariate=0.3640, univariate=0.3534.
+- Outcome: Fixed coefficient-one spectral transfer; additive and old-slow-AR replacement are the two predeclared ablations. No optimization. Ensemble selection recorded separately.
+
+## E114 spectral_replace_delta6k valid_backcast3
+- Checkpoint SHA-256: `b01ff54ba0bd947f4806bd08793288a8202a1c8de153db482666aee3458395ac`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 24.276151657104492 seconds.
+- Overall skill 0.385845; relative MASE 0.745123; relative WQL 0.506207.
+- term skill: long=0.3948, medium=0.3605, short=0.3918.
+- domain skill: Econ/Fin=0.1804, Energy=0.3205, Healthcare=0.2235, Nature=0.4938, Sales=0.4003, Transport=0.5072, Web/CloudOps=0.3888.
+- frequency skill: A=0.3630, D=0.4320, H=0.4048, M=0.1164, Q=0.2614, S=0.2715, T=0.4319, W=0.2461.
+- variates skill: multivariate=0.3719, univariate=0.3966.
+- Outcome: Fixed coefficient-one spectral transfer; additive and old-slow-AR replacement are the two predeclared ablations. No optimization. Ensemble selection recorded separately.
+
+E115 checks complete: {"seed": 563, "independent_phase_arithmetic": true, "phase_left_padding_invariance": true, "periods_below_two_zero": true, "initial_function_bitwise_equal": true, "inherited_weights_bitwise_equal": true, "sampler_byte_identical": true, "all_parameters_trainable": true, "added_parameters": 65536, "total_parameters": 36866336, "checkpoint_sha256": "70d0fe416fc633d9bd5d8432735ae1725cdf16bcc56824fbd2b973276d90116b", "seconds": 1.7632095960434526, "optimizer_updates": 0, "validation_data_read": false}; GPU {"seed": 569, "gpu_initial_function_bitwise_equal": true, "max_shape": [64, 4096, 1024], "loss": 0.54439777135849, "selected_gradient_norms": {"phase_context.weight": 0.014588589780032635, "phase_query.weight": 0.01659703068435192, "encoder.0.attn.q.weight": 0.002245454117655754, "head.weight": 0.029972484335303307}, "all_parameters_trainable": true, "reserved_mib": 6272.0, "allocated_mib": 5789.81689453125, "includes_ema_and_adam_placeholders": true, "seconds": 125.7952905651182, "optimizer_updates": 0, "validation_data_read": false}. GPU seconds include serialized evaluation-lock wait; no optimizer update or validation read. Zero initialization preserves inherited tensors and forecasts bitwise; both added projections and inherited attention/head receive finite nonzero gradients. Runtime model is identical to the training model. Queue after E79small96000. Publisher documentation now derives frozen-versus-joint phase training from actual lineage; no runtime changes to published packages.
+
+## E114 fixed spectral transfer ensemble outcomes
+{"skill_score": 0.3859105542007717, "rel_mase_gmean": 0.7358684109009667, "rel_wql_gmean": 0.5124636984760504, "groups": {"term": {"short": 0.36841730807118356, "medium": 0.4090050160435663, "long": 0.40716915412299237}, "domain": {"Econ/Fin": 0.14811696868566626, "Energy": 0.24660436752069403, "Healthcare": 0.47910058788021426, "Nature": 0.44240542330694954, "Sales": 0.34307605991799384, "Transport": 0.46771149823781855, "Web/CloudOps": 0.5062219614466995}, "freq": {"A-DEC": 0.37669669863368394, "Q-DEC": 0.200340326472501, "M": 0.11908763465172645, "W-SUN": 0.12532801227165324, "D": 0.36422932549961984, "H": 0.40123365786788323, "15T": 0.21729188974855052, "W-FRI": 0.015453408823068893, "10T": 0.6278668799855711, "W-TUE": 0.6255256773761033, "W-THU": 0.2055099177488643, "W-WED": 0.3287751407658812, "5T": 0.5215363726041493, "10S": 0.42516355809197104}}, "cpu_seconds": 1.0599841580260545, "member_checkpoint_hashes": ["c3dfc433d51355228fc13d04913db1a7edf83030c33283b150bdcc50f5ae6ea2", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "candidate": "spectral_add_delta6k", "cutoff": "valid_screen", "E112skill": 0.3858712532527797, "difference": 3.930094799198258e-05}
+{"skill_score": 0.3412336108260463, "rel_mase_gmean": 0.7923075693116662, "rel_wql_gmean": 0.5477331939190134, "groups": {"term": {"short": 0.3082380704419988, "medium": 0.36087755992185644, "long": 0.40256738708364537}, "domain": {"Econ/Fin": 0.17928739133939553, "Energy": 0.23149418505070973, "Healthcare": 0.4142126388904961, "Nature": 0.3739148820510916, "Sales": 0.40499124016503796, "Transport": 0.37331945935003474, "Web/CloudOps": 0.45366968215211745}, "freq": {"A-DEC": 0.32728365516656444, "Q-DEC": 0.236748044898076, "M": 0.19835812917105478, "W-SUN": 0.07360873539461676, "D": 0.35259911130364885, "H": 0.3719231643339844, "15T": 0.2213306919945408, "W-FRI": 0.2288725313306662, "10T": 0.32822175650292673, "W-TUE": 0.559366215155875, "W-THU": 0.25990001317199096, "W-WED": 0.360055735229183, "5T": 0.44455182384807246, "10S": 0.347846510903316}}, "cpu_seconds": 1.0289423430804163, "member_checkpoint_hashes": ["c3dfc433d51355228fc13d04913db1a7edf83030c33283b150bdcc50f5ae6ea2", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "candidate": "spectral_add_delta6k", "cutoff": "valid_backcast", "E112skill": 0.34026453805586865, "difference": 0.0009690727701776591}
+{"skill_score": 0.36575134769765616, "rel_mase_gmean": 0.7643280805916521, "rel_wql_gmean": 0.5263071751020174, "groups": {"term": {"short": 0.35313765392056196, "medium": 0.3940145011079407, "long": 0.36928346944084967}, "domain": {"Econ/Fin": 0.1278772851976926, "Energy": 0.24145749844953146, "Healthcare": 0.35086323444911927, "Nature": 0.4791524685105649, "Sales": 0.5547328500628512, "Transport": 0.4250780374879125, "Web/CloudOps": 0.43991341613268353}, "freq": {"A-DEC": 0.33586764940966785, "Q-DEC": 0.15779445087152755, "M": 0.16375055154356122, "W-SUN": 0.08390542109684229, "D": 0.4307789359841945, "H": 0.3948583749831561, "15T": 0.10818558944800316, "W-FRI": 0.15292053290873164, "10T": 0.5107332028367493, "W-TUE": 0.09532113367661221, "W-THU": 0.35588558530569137, "W-WED": 0.5627227566205757, "5T": 0.4760121317899746, "10S": 0.2848510509521789}}, "cpu_seconds": 1.175089841010049, "member_checkpoint_hashes": ["c3dfc433d51355228fc13d04913db1a7edf83030c33283b150bdcc50f5ae6ea2", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "candidate": "spectral_add_delta6k", "cutoff": "valid_backcast2", "E112skill": 0.36500280105746885, "difference": 0.0007485466401873087}
+{"skill_score": 0.388763997234149, "rel_mase_gmean": 0.7411269672326161, "rel_wql_gmean": 0.5041099131397703, "groups": {"term": {"short": 0.3936943170093701, "medium": 0.36798445243024747, "long": 0.39615921182618374}, "domain": {"Econ/Fin": 0.1909220354405753, "Energy": 0.31559006097512954, "Healthcare": 0.23199415024755954, "Nature": 0.5039982625361157, "Sales": 0.39988753803404353, "Transport": 0.5093753126309916, "Web/CloudOps": 0.3947283481141741}, "freq": {"A-DEC": 0.3583022117406983, "Q-DEC": 0.2566121509078392, "M": 0.13878112747670346, "W-SUN": 0.17907630757955872, "D": 0.4388632040643935, "H": 0.405288967030186, "15T": 0.2540225780499259, "W-FRI": 0.03649862826525585, "10T": 0.6060275093557081, "W-TUE": 0.26615750127784543, "W-THU": 0.33504744208892767, "W-WED": 0.37704039372015596, "5T": 0.46645862961129225, "10S": 0.29251927680018197}}, "cpu_seconds": 1.1375589109957218, "member_checkpoint_hashes": ["c3dfc433d51355228fc13d04913db1a7edf83030c33283b150bdcc50f5ae6ea2", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "candidate": "spectral_add_delta6k", "cutoff": "valid_backcast3", "E112skill": 0.38948675360484886, "difference": -0.0007227563706998508}
+{"skill_score": 0.38538242748882945, "rel_mase_gmean": 0.736575164356215, "rel_wql_gmean": 0.5128529696893745, "groups": {"term": {"short": 0.3675950378502798, "medium": 0.4090321072718678, "long": 0.4068080010769052}, "domain": {"Econ/Fin": 0.1452107005254969, "Energy": 0.24720144190287108, "Healthcare": 0.4773960655138917, "Nature": 0.4411401520810895, "Sales": 0.3422136708191351, "Transport": 0.46776594940797556, "Web/CloudOps": 0.5053750267603281}, "freq": {"A-DEC": 0.3762239031537611, "Q-DEC": 0.19964405404447882, "M": 0.11772763314619139, "W-SUN": 0.11426532226198283, "D": 0.3618358770783556, "H": 0.4012391303794067, "15T": 0.21828910758964182, "W-FRI": 0.0158268795014036, "10T": 0.6269679416145453, "W-TUE": 0.6374273197245699, "W-THU": 0.20444633086852815, "W-WED": 0.3293023694518882, "5T": 0.520446433050282, "10S": 0.4243254373932551}}, "cpu_seconds": 1.0959253669716418, "member_checkpoint_hashes": ["b01ff54ba0bd947f4806bd08793288a8202a1c8de153db482666aee3458395ac", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "candidate": "spectral_replace_delta6k", "cutoff": "valid_screen", "E112skill": 0.3858712532527797, "difference": -0.0004888257639502624}
+{"skill_score": 0.3406802195876929, "rel_mase_gmean": 0.7932581807222594, "rel_wql_gmean": 0.5479963313421329, "groups": {"term": {"short": 0.3072113460670465, "medium": 0.3609289231624365, "long": 0.4025228284048611}, "domain": {"Econ/Fin": 0.17519659434666768, "Energy": 0.23205216431370745, "Healthcare": 0.41457631581368715, "Nature": 0.37248553469222323, "Sales": 0.4051051613684693, "Transport": 0.37330292125603537, "Web/CloudOps": 0.4524628983508361}, "freq": {"A-DEC": 0.3247221726362275, "Q-DEC": 0.23679225957378447, "M": 0.19494676959754076, "W-SUN": 0.06360311378805661, "D": 0.35276837361420454, "H": 0.372002261486453, "15T": 0.22209503268624742, "W-FRI": 0.22895441819580886, "10T": 0.3247372972264523, "W-TUE": 0.5627629693024542, "W-THU": 0.2585092983543258, "W-WED": 0.35938474562086387, "5T": 0.4432621496867426, "10S": 0.34700427098942754}}, "cpu_seconds": 1.5319079000037163, "member_checkpoint_hashes": ["b01ff54ba0bd947f4806bd08793288a8202a1c8de153db482666aee3458395ac", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "candidate": "spectral_replace_delta6k", "cutoff": "valid_backcast", "E112skill": 0.34026453805586865, "difference": 0.0004156815318242524}
+{"skill_score": 0.3645638328287083, "rel_mase_gmean": 0.7655529003321527, "rel_wql_gmean": 0.5274346454361977, "groups": {"term": {"short": 0.35186763351956485, "medium": 0.39321263800713, "long": 0.3678828400958726}, "domain": {"Econ/Fin": 0.12424108633012076, "Energy": 0.24135890916218927, "Healthcare": 0.35067839600874395, "Nature": 0.4774941308360753, "Sales": 0.5553672377118917, "Transport": 0.42513094655931627, "Web/CloudOps": 0.43686388883653227}, "freq": {"A-DEC": 0.336016644671164, "Q-DEC": 0.1585853880353536, "M": 0.16597963033077545, "W-SUN": 0.07588756093646032, "D": 0.42914434851192584, "H": 0.3951850713687237, "15T": 0.10848567130668929, "W-FRI": 0.15162235166311566, "10T": 0.508604696908483, "W-TUE": 0.08008393561824934, "W-THU": 0.35379846085908884, "W-WED": 0.5621221829283422, "5T": 0.4723996609971295, "10S": 0.28287996534579274}}, "cpu_seconds": 1.1693297398742288, "member_checkpoint_hashes": ["b01ff54ba0bd947f4806bd08793288a8202a1c8de153db482666aee3458395ac", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "candidate": "spectral_replace_delta6k", "cutoff": "valid_backcast2", "E112skill": 0.36500280105746885, "difference": -0.0004389682287605501}
+{"skill_score": 0.3887657720103035, "rel_mase_gmean": 0.7410046782980017, "rel_wql_gmean": 0.504190179101556, "groups": {"term": {"short": 0.3936432761250075, "medium": 0.3675850585692205, "long": 0.3967135529057608}, "domain": {"Econ/Fin": 0.18838704424082175, "Energy": 0.31576821178040126, "Healthcare": 0.23963002301962777, "Nature": 0.5044876407125312, "Sales": 0.39975495300854236, "Transport": 0.5088397838295604, "Web/CloudOps": 0.393445049293746}, "freq": {"A-DEC": 0.3588124703265817, "Q-DEC": 0.2560486792582567, "M": 0.1429024757751649, "W-SUN": 0.1749136920607539, "D": 0.4382471228895556, "H": 0.4056385181220228, "15T": 0.2544805853997113, "W-FRI": 0.0366545562900642, "10T": 0.606714665207392, "W-TUE": 0.27687389203140456, "W-THU": 0.33308740205358955, "W-WED": 0.37665824463477027, "5T": 0.465156868376136, "10S": 0.29156742693850557}}, "cpu_seconds": 1.2852319721132517, "member_checkpoint_hashes": ["b01ff54ba0bd947f4806bd08793288a8202a1c8de153db482666aee3458395ac", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "candidate": "spectral_replace_delta6k", "cutoff": "valid_backcast3", "E112skill": 0.38948675360484886, "difference": -0.0007209815945453668}
+Additive wins three cutoffs over replacement and ties the fourth within0.000002. Versus publishedE112, additive gains main0.000039301,firstearlier0.000969073,secondearlier0.000748547 and loses thirdearlier0.000722756. Select additive for one full public replay. Reject replacement; do not tune a coefficient. Total two-ablation suite623.425876410wallseconds includes evaluation-lock waits; zero optimizer updates. Merge CPU times1.420432522 and1.159398519seconds; candidate hashes in spectral_transfer_receipt.json.
+
+## E107 sixteen-cycle learned summaries at1000
+- Checkpoint SHA-256: `31a9cdfb13341bf068d62dcd67df55a48ecc8f539676fc9aa18b17a87967aaaf`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.538329362869263 seconds.
+- Overall skill 0.378727; relative MASE 0.742222; relative WQL 0.520032.
+- term skill: long=0.4007, medium=0.4068, short=0.3589.
+- domain skill: Econ/Fin=0.1331, Energy=0.2370, Healthcare=0.4629, Nature=0.4319, Sales=0.3365, Transport=0.4721, Web/CloudOps=0.4995.
+- frequency skill: A=0.3659, D=0.3628, H=0.3966, M=0.1077, Q=0.1987, S=0.4237, T=0.4357, W=0.2244.
+- variates skill: multivariate=0.3931, univariate=0.3671.
+- Outcome: First skill0.378727349 exceeds matched E80at1000 0.376804394 by0.001922954. Initial function was preserved; whole model and new query/head train jointly. Continue declared3000/6000/12000 and earlier-cutoff diagnostics before selection.
+
+## E79 fresh37M at72000
+- Checkpoint SHA-256: `8439114a6db9c3441824266cca82575413c81d4d06843f89c3ce365220bdd491`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.264113903045654 seconds.
+- Overall skill 0.368966; relative MASE 0.753206; relative WQL 0.528678.
+- term skill: long=0.3954, medium=0.3943, short=0.3484.
+- domain skill: Econ/Fin=0.1436, Energy=0.2321, Healthcare=0.4098, Nature=0.4180, Sales=0.3528, Transport=0.4585, Web/CloudOps=0.4929.
+- frequency skill: A=0.3884, D=0.3520, H=0.3888, M=0.0702, Q=0.2092, S=0.4184, T=0.4268, W=0.2065.
+- variates skill: multivariate=0.3853, univariate=0.3556.
+- Outcome: Skill0.368966239 improves over48000 0.366772722 by0.002193517. Still below the mature trained lineage. Keep the declared96000schedule; earlier cooldown lost its final matched comparison. No new learning-rate changes or model selection yet.
+
+Fresh capacity compute audit at 2026-10-23T06:30:58Z: capacity_learning_curves.json/csv join each measured checkpoint report to its actual training log, with no inferred missing times. Latest selected checkpoints: random_capacity_512 seed307 step72000 logged shared-H100 loop9277.7seconds; random_capacity_1024 seed307 step24000 logged shared-H100 loop6492.7seconds; random_capacity_1024_halflr seed307 step24000 logged shared-H100 loop5576.8seconds; All original96k schedules unchanged. No new optimization or inference in this audit.
+
+## E114 additive spectral ensemble full public replay — selected
+- Checkpoint SHA-256: `9a31b69cbb9c32bdc51541d93b569334a408445233579f43935adc92072526bc`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.382197; relative MASE 0.738486; relative WQL 0.516841.
+- term skill: long=0.4013, medium=0.4079, short=0.3645.
+- domain skill: Econ/Fin=0.1297, Energy=0.2401, Healthcare=0.4846, Nature=0.4425, Sales=0.3573, Transport=0.4679, Web/CloudOps=0.4980.
+- frequency skill: A=0.2970, D=0.3687, H=0.3959, M=0.1449, Q=0.1880, S=0.4252, T=0.4390, W=0.2388.
+- variates skill: multivariate=0.3963, univariate=0.3708.
+- Outcome: Full0.382197314 exceeds E1120.382143195 by0.000054118. All97configs151170instances passed. Relocation and independent fixed-average composition checks passed. Three diagnostic gains and one regression; tiny full improvement is validation selection, not held-out significance.
+
+E114 complete public suite {"package": "/workspace/research/candidate-spectral-add-ensemble", "status": "complete", "steps": [{"name": "relocation", "returncode": 0, "seconds": 31.251009764848277}, {"name": "full_public_replay", "returncode": 0, "seconds": 333.7630249520298}, {"name": "independent_spectral_replay", "returncode": 0, "seconds": 72.58775101904757}, {"name": "composition", "returncode": 0, "seconds": 0.2565292678773403}, {"name": "descriptive_comparison", "returncode": 0, "seconds": 0.17433986882679164}], "current_step": "descriptive_comparison", "seconds": 438.11250560800545, "skill": 0.3821973136018404}. Descriptive comparison {"candidate": "/workspace/research/valid-report-spectral-add-ensemble.json", "parent": "/workspace/research/valid-report-weekly-slow12-ensemble.json", "candidate_hash": "9a31b69cbb9c32bdc51541d93b569334a408445233579f43935adc92072526bc", "parent_hash": "f01498eb73e4aa0539e81c67a8a80e56af47d1437f4d65ecc882d551468241a1", "skill_difference": 5.4118499018707134e-05, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 53, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": -0.0012920618845766562, "p50": 6.553719107660338e-05, "p975": 0.001266637510104884}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}.
+Provenance repair before publication: the old hard-coded archive filename list omitted spectral.py and recovery/schedules.py. The publisher now archives every Python module in each explicitly declared trainer directory and records their hashes. Reassembled three source packages with complete archives:81/61/67modules. Every runtime-code hash and weight hash is identical to its replayed source package; no inference behavior changed and no extra forecast run was required. Candidate package in the full replay remains immutable.
+
+## E101 own-ensemble distillation first4000
+- Checkpoint SHA-256: `907bbfc1252d0f62e5be920598b4579ab2576009a357c20d9e0d0f3328fe41b3`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 35.88508892059326 seconds.
+- Overall skill 0.347881; relative MASE 0.772644; relative WQL 0.550395.
+- term skill: long=0.3742, medium=0.3899, short=0.3205.
+- domain skill: Econ/Fin=0.1324, Energy=0.2134, Healthcare=0.2766, Nature=0.4049, Sales=0.3337, Transport=0.4471, Web/CloudOps=0.4785.
+- frequency skill: A=0.3694, D=0.3330, H=0.3712, M=-0.0892, Q=0.1903, S=0.4170, T=0.4063, W=0.2105.
+- variates skill: multivariate=0.3681, univariate=0.3314.
+- Outcome: Skill0.347881090 exceeds matched E94at4000 0.336895196 by0.010985894 and slightly exceeds E94at8000 0.347153427. This is forecast validation, not the teacher-augmented training loss. Same own random146M initialization/seed307/B48/LR1e-4; only the declared frozen E96raw-neural teacher auxiliary differs. Keep the teacher/coefficient and96000schedule unchanged; next matched screen8000.
+
+## E107 sixteen-cycle learned summaries at3000
+- Checkpoint SHA-256: `92e575ca3cb053d9bdf75a010d83ded0025fe97c906fb9930c74dc1a28e1e892`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.18501567840576 seconds.
+- Overall skill 0.381751; relative MASE 0.739110; relative WQL 0.517152.
+- term skill: long=0.4051, medium=0.4080, short=0.3621.
+- domain skill: Econ/Fin=0.1362, Energy=0.2403, Healthcare=0.4508, Nature=0.4350, Sales=0.3352, Transport=0.4748, Web/CloudOps=0.5063.
+- frequency skill: A=0.3677, D=0.3668, H=0.4009, M=0.1024, Q=0.1977, S=0.4243, T=0.4398, W=0.2205.
+- variates skill: multivariate=0.3984, univariate=0.3682.
+- Outcome: Skill0.381750592 exceeds E80at3000 0.378663825 by0.003086767, following the1000gain. Run all three earlier cutoffs before any transfer or publication. New seasonal query/head learned jointly with the original model from the function-preserving E62 initialization; no runtime rule change.
+
+## E107 sixteen-cycle summaries3000 valid_backcast
+- Checkpoint SHA-256: `92e575ca3cb053d9bdf75a010d83ded0025fe97c906fb9930c74dc1a28e1e892`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.551977157592773 seconds.
+- Overall skill 0.339635; relative MASE 0.793581; relative WQL 0.549512.
+- term skill: long=0.4013, medium=0.3614, short=0.3056.
+- domain skill: Econ/Fin=0.1561, Energy=0.2309, Healthcare=0.3961, Nature=0.3764, Sales=0.4008, Transport=0.3807, Web/CloudOps=0.4508.
+- frequency skill: A=0.3131, D=0.3627, H=0.3730, M=0.1797, Q=0.2320, S=0.3414, T=0.3350, W=0.2818.
+- variates skill: multivariate=0.3548, univariate=0.3273.
+- Outcome: E1073000 improves all four matched cutoffs versus E80: main0.381750592 vs0.378663825; earlier0.339634914/0.361559427/0.392953200 vs0.336119626/0.358234785/0.388791691. Strongest observed consistent feature result in this round. Continue declared6000/12000; predeclare a coefficient-one transfer to the current first ensemble member.
+
+## E107 sixteen-cycle summaries3000 valid_backcast2
+- Checkpoint SHA-256: `92e575ca3cb053d9bdf75a010d83ded0025fe97c906fb9930c74dc1a28e1e892`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.749686002731323 seconds.
+- Overall skill 0.361559; relative MASE 0.768149; relative WQL 0.530634.
+- term skill: long=0.3661, medium=0.3924, short=0.3475.
+- domain skill: Econ/Fin=0.1055, Energy=0.2372, Healthcare=0.3468, Nature=0.4779, Sales=0.5514, Transport=0.4272, Web/CloudOps=0.4325.
+- frequency skill: A=0.3541, D=0.4282, H=0.3964, M=0.1616, Q=0.1507, S=0.2837, T=0.3600, W=0.2592.
+- variates skill: multivariate=0.3654, univariate=0.3584.
+- Outcome: E1073000 improves all four matched cutoffs versus E80: main0.381750592 vs0.378663825; earlier0.339634914/0.361559427/0.392953200 vs0.336119626/0.358234785/0.388791691. Strongest observed consistent feature result in this round. Continue declared6000/12000; predeclare a coefficient-one transfer to the current first ensemble member.
+
+## E107 sixteen-cycle summaries3000 valid_backcast3
+- Checkpoint SHA-256: `92e575ca3cb053d9bdf75a010d83ded0025fe97c906fb9930c74dc1a28e1e892`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 24.80188488960266 seconds.
+- Overall skill 0.392953; relative MASE 0.731991; relative WQL 0.503429.
+- term skill: long=0.3993, medium=0.3624, short=0.4017.
+- domain skill: Econ/Fin=0.1691, Energy=0.3244, Healthcare=0.2777, Nature=0.5077, Sales=0.4044, Transport=0.5103, Web/CloudOps=0.3919.
+- frequency skill: A=0.3641, D=0.4523, H=0.4083, M=0.1380, Q=0.2600, S=0.2876, T=0.4311, W=0.2621.
+- variates skill: multivariate=0.3774, univariate=0.4049.
+- Outcome: E1073000 improves all four matched cutoffs versus E80: main0.381750592 vs0.378663825; earlier0.339634914/0.361559427/0.392953200 vs0.336119626/0.358234785/0.388791691. Strongest observed consistent feature result in this round. Continue declared6000/12000; predeclare a coefficient-one transfer to the current first ensemble member.
+
+## E116 matched long-seasonal feature transfer — predeclared
+E1073000 improves all four matched cutoffs over E80 by about0.0031/0.0035/0.0033/0.0042. Test complementarity with the current E114firstmember using exactly extend_zero(spectral_add_delta6k) + E107EMA3000 - extend_zero(E80EMA3000), coefficient1. Extend the base and control with the same three zero-initialized long_query/long_head tensors; copy the candidate's new tensors and add candidate-control differences to every shared tensor. The zero-extended base must preserve forecasts bitwise before transfer. Check keys/config/shapes, finite merged weights, parent hashes and explicit arithmetic; CPU fixture seed571, no optimizer updates or validation data in conversion. Preserve all inference rules and the equal1/3 other membersE71patch8_6000/E93split_12000. Evaluate standalone and the fixed ensemble at all four cutoffs, then require complete public replay/relocation/independent composition before any publication. No coefficient or per-instance rule search.
+
+## E116 long-seasonal matched transfer3000 valid_screen
+- Checkpoint SHA-256: `23b775110747f72747a342fb2282366c2a7d7ae74bfab2137d4c6e38df99dc0a`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.276195764541626 seconds.
+- Overall skill 0.385452; relative MASE 0.734312; relative WQL 0.514317.
+- term skill: long=0.4073, medium=0.4087, short=0.3677.
+- domain skill: Econ/Fin=0.1474, Energy=0.2410, Healthcare=0.4699, Nature=0.4427, Sales=0.3383, Transport=0.4712, Web/CloudOps=0.5106.
+- frequency skill: A=0.3775, D=0.3705, H=0.4046, M=0.0994, Q=0.2071, S=0.4258, T=0.4438, W=0.2263.
+- variates skill: multivariate=0.4004, univariate=0.3733.
+- Outcome: Fixed coefficient-one zero-extended E1073000-E80control3000 transfer onto E114firstmember; no optimizer update. Ensemble selection recorded separately.
+
+## E116 long-seasonal matched transfer3000 valid_backcast
+- Checkpoint SHA-256: `23b775110747f72747a342fb2282366c2a7d7ae74bfab2137d4c6e38df99dc0a`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 27.110499620437622 seconds.
+- Overall skill 0.342024; relative MASE 0.789652; relative WQL 0.548257.
+- term skill: long=0.3991, medium=0.3634, short=0.3102.
+- domain skill: Econ/Fin=0.1785, Energy=0.2289, Healthcare=0.3769, Nature=0.3859, Sales=0.4015, Transport=0.3788, Web/CloudOps=0.4575.
+- frequency skill: A=0.3232, D=0.3572, H=0.3764, M=0.1959, Q=0.2304, S=0.3395, T=0.3380, W=0.2889.
+- variates skill: multivariate=0.3578, univariate=0.3292.
+- Outcome: Fixed coefficient-one zero-extended E1073000-E80control3000 transfer onto E114firstmember; no optimizer update. Ensemble selection recorded separately.
+
+## E110 fresh146M full corpus first4000
+- Checkpoint SHA-256: `687830f4718f327cbbc64dbf00233a78035447e3a3c46015bc45b9496d8bec0e`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 36.46506428718567 seconds.
+- Overall skill 0.337869; relative MASE 0.781543; relative WQL 0.560964.
+- term skill: long=0.3669, medium=0.3753, short=0.3113.
+- domain skill: Econ/Fin=0.1151, Energy=0.2150, Healthcare=0.2791, Nature=0.4107, Sales=0.2977, Transport=0.4192, Web/CloudOps=0.4616.
+- frequency skill: A=0.3312, D=0.3394, H=0.3632, M=-0.0643, Q=0.1689, S=0.4240, T=0.3860, W=0.1775.
+- variates skill: multivariate=0.3630, univariate=0.3171.
+- Outcome: Skill0.337869059 exceeds matched E94oldpack4000 0.336895196 by0.000973864. This changes corpus coverage and volume together with original source probabilities fixed. Small initial gain; continue scheduled8000/16000 before deciding on any interaction with distillation or more capacity.
+
+E116 conversion/check receipt: {"method": "matched training delta after zero extension of new seasonal query/head parameters", "formula": "extend_zero(base) + candidate - extend_zero(control)", "coefficient": 1.0, "new_parameters_from_candidate": ["long_head.bias", "long_head.weight", "long_query.weight"], "parent_hashes": {"/workspace/research/spectral_add_delta6k.pt": "c3dfc433d51355228fc13d04913db1a7edf83030c33283b150bdcc50f5ae6ea2", "/workspace/research/runs/long_seasonal/ema003000.pt": "92e575ca3cb053d9bdf75a010d83ded0025fe97c906fb9930c74dc1a28e1e892", "/workspace/research/runs/overflow_control/ema003000.pt": "2084f1888f8d9e5afe2c740769ea2b4167086cdd62dfb4014bc3a338c00d4c12"}, "base_extension_forward_bitwise_equal": true, "check_seed": 571, "seconds": 2.1952822851017118, "optimizer_updates": 0, "validation_data_read": false, "checkpoint_sha256": "23b775110747f72747a342fb2282366c2a7d7ae74bfab2137d4c6e38df99dc0a", "parameters": 36849984}. Shared forward implementations were also compared: overflow_control and E114postloss_inference differ only inside the unused training loss function; forecast class is identical. No runtime mismatch introduced by the base-extension audit.
+
+## E116 long-seasonal matched transfer3000 valid_backcast2
+- Checkpoint SHA-256: `23b775110747f72747a342fb2282366c2a7d7ae74bfab2137d4c6e38df99dc0a`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.600340604782104 seconds.
+- Overall skill 0.364198; relative MASE 0.765491; relative WQL 0.528085.
+- term skill: long=0.3700, medium=0.3935, short=0.3503.
+- domain skill: Econ/Fin=0.1339, Energy=0.2393, Healthcare=0.3008, Nature=0.4830, Sales=0.5489, Transport=0.4261, Web/CloudOps=0.4427.
+- frequency skill: A=0.3494, D=0.4268, H=0.3979, M=0.1404, Q=0.1534, S=0.2812, T=0.3700, W=0.2612.
+- variates skill: multivariate=0.3706, univariate=0.3590.
+- Outcome: Fixed coefficient-one zero-extended E1073000-E80control3000 transfer onto E114firstmember; no optimizer update. Ensemble selection recorded separately.
+
+## E116 long-seasonal matched transfer3000 valid_backcast3
+- Checkpoint SHA-256: `23b775110747f72747a342fb2282366c2a7d7ae74bfab2137d4c6e38df99dc0a`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 25.46401309967041 seconds.
+- Overall skill 0.389892; relative MASE 0.740054; relative WQL 0.502979.
+- term skill: long=0.3954, medium=0.3628, short=0.3977.
+- domain skill: Econ/Fin=0.1867, Energy=0.3231, Healthcare=0.2223, Nature=0.4962, Sales=0.4052, Transport=0.5141, Web/CloudOps=0.3942.
+- frequency skill: A=0.3640, D=0.4445, H=0.4080, M=0.1294, Q=0.2645, S=0.2754, T=0.4321, W=0.2475.
+- variates skill: multivariate=0.3746, univariate=0.4017.
+- Outcome: Fixed coefficient-one zero-extended E1073000-E80control3000 transfer onto E114firstmember; no optimizer update. Ensemble selection recorded separately.
+
+## E116 fixed three-member ensemble four-cutoff results
+{"skill_score": 0.38720541337659986, "rel_mase_gmean": 0.7342507813740501, "rel_wql_gmean": 0.5114290851583633, "groups": {"term": {"short": 0.3700839213601874, "medium": 0.40957250804893053, "long": 0.40827611548402953}, "domain": {"Econ/Fin": 0.14908608660199785, "Energy": 0.24714375369431862, "Healthcare": 0.4787534904014933, "Nature": 0.4440184955426868, "Sales": 0.3442242595915186, "Transport": 0.4710390859143697, "Web/CloudOps": 0.5070560078179032}, "freq": {"A-DEC": 0.3765552064197536, "Q-DEC": 0.2007920342778422, "M": 0.11880155273345816, "W-SUN": 0.1264948417953352, "D": 0.36884318033535124, "H": 0.40281260223169946, "15T": 0.21749181645510862, "W-FRI": 0.015285001640822693, "10T": 0.6279931658141638, "W-TUE": 0.6163675116775146, "W-THU": 0.2084538535547953, "W-WED": 0.32879841207319593, "5T": 0.5222434535959196, "10S": 0.42555374873861473}}, "cpu_seconds": 1.8546344628557563, "member_checkpoint_hashes": ["23b775110747f72747a342fb2282366c2a7d7ae74bfab2137d4c6e38df99dc0a", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cutoff": "valid_screen", "E114skill": 0.3859105542007717, "difference": 0.001294859175828167}
+{"skill_score": 0.3427206795875515, "rel_mase_gmean": 0.7906423475529908, "rel_wql_gmean": 0.5464115429421713, "groups": {"term": {"short": 0.3098546944929881, "medium": 0.3621091931043331, "long": 0.403988714940432}, "domain": {"Econ/Fin": 0.17965067851574157, "Energy": 0.2326316747235092, "Healthcare": 0.4068086635720405, "Nature": 0.37873253762946857, "Sales": 0.4075537380987566, "Transport": 0.37635767380141383, "Web/CloudOps": 0.45437737080841945}, "freq": {"A-DEC": 0.3272530340462253, "Q-DEC": 0.23755513606501621, "M": 0.19617972821818674, "W-SUN": 0.07381440729472832, "D": 0.35641808245133066, "H": 0.3746335453552856, "15T": 0.22156520469438523, "W-FRI": 0.2285171150198988, "10T": 0.32790408335401755, "W-TUE": 0.5487565883106209, "W-THU": 0.26369997407283086, "W-WED": 0.360495064206409, "5T": 0.44527223827225215, "10S": 0.34795150353597804}}, "cpu_seconds": 1.4879765149671584, "member_checkpoint_hashes": ["23b775110747f72747a342fb2282366c2a7d7ae74bfab2137d4c6e38df99dc0a", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cutoff": "valid_backcast", "E114skill": 0.3412336108260463, "difference": 0.0014870687615051725}
+{"skill_score": 0.3671371671308241, "rel_mase_gmean": 0.7627223884596792, "rel_wql_gmean": 0.5251128999058764, "groups": {"term": {"short": 0.35442618611968046, "medium": 0.39594551851033577, "long": 0.3703088425246239}, "domain": {"Econ/Fin": 0.12773114801545526, "Energy": 0.2420703081614609, "Healthcare": 0.3503190659637698, "Nature": 0.48266906273662424, "Sales": 0.5556522652473174, "Transport": 0.42748257591759653, "Web/CloudOps": 0.4404570123243067}, "freq": {"A-DEC": 0.33580577495994546, "Q-DEC": 0.1582829183514055, "M": 0.16797269143179927, "W-SUN": 0.08432615590486037, "D": 0.43228327170696623, "H": 0.39712101579766446, "15T": 0.1083039694235629, "W-FRI": 0.15224002631060463, "10T": 0.5116096591114121, "W-TUE": 0.10029684309090381, "W-THU": 0.35590715421034425, "W-WED": 0.5629822534882516, "5T": 0.47636409225147436, "10S": 0.2857396389239164}}, "cpu_seconds": 1.1572973690927029, "member_checkpoint_hashes": ["23b775110747f72747a342fb2282366c2a7d7ae74bfab2137d4c6e38df99dc0a", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cutoff": "valid_backcast2", "E114skill": 0.36575134769765616, "difference": 0.0013858194331679563}
+{"skill_score": 0.3902787157172325, "rel_mase_gmean": 0.7392609091902026, "rel_wql_gmean": 0.5028807013678822, "groups": {"term": {"short": 0.3956114073839023, "medium": 0.3687662234436596, "long": 0.3972699880915276}, "domain": {"Econ/Fin": 0.19055643421598678, "Energy": 0.3170395049778194, "Healthcare": 0.2375244592154213, "Nature": 0.5052031712338959, "Sales": 0.40168219976171715, "Transport": 0.511473215788659, "Web/CloudOps": 0.39553570955988127}, "freq": {"A-DEC": 0.35850397733087536, "Q-DEC": 0.25725670014643276, "M": 0.1465996371340087, "W-SUN": 0.1773439469690138, "D": 0.44211913796350977, "H": 0.4070414678036135, "15T": 0.2546177459094544, "W-FRI": 0.03688784934804279, "10T": 0.6055501765507136, "W-TUE": 0.27303119495126527, "W-THU": 0.33344051421477383, "W-WED": 0.37619790832892397, "5T": 0.46709627787109553, "10S": 0.2930890553634443}}, "cpu_seconds": 1.1736454339697957, "member_checkpoint_hashes": ["23b775110747f72747a342fb2282366c2a7d7ae74bfab2137d4c6e38df99dc0a", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cutoff": "valid_backcast3", "E114skill": 0.388763997234149, "difference": 0.0015147184830834881}
+All four improve: main+0.001294859,earlier1+0.001487069,earlier2+0.001385819,earlier3+0.001514718. Select exactly this coefficient-one three-member package for full public replay. Added49184parameters in firstmember; no change to fixed rules or ensemble weights, no further coefficient search.
+
+## E107 sixteen-cycle learned summaries at6000
+- Checkpoint SHA-256: `ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.15697693824768 seconds.
+- Overall skill 0.382275; relative MASE 0.739038; relative WQL 0.516326.
+- term skill: long=0.4050, medium=0.4082, short=0.3631.
+- domain skill: Econ/Fin=0.1279, Energy=0.2411, Healthcare=0.4596, Nature=0.4341, Sales=0.3406, Transport=0.4765, Web/CloudOps=0.5056.
+- frequency skill: A=0.3682, D=0.3698, H=0.4004, M=0.1080, Q=0.1932, S=0.4259, T=0.4395, W=0.2214.
+- variates skill: multivariate=0.3987, univariate=0.3689.
+- Outcome: Main0.382274667 exceeds matched E80at6000 0.378288765 by0.003985903 and E107at3000 by0.000524075. Continue the declared final12000. Earlier-cutoff checks precede any new duration transfer; E1163000transfer is in full validation.
+
+## Validation coverage diagnostic on E114, no candidate calibration rule
+Hypothesis: check whether interval errors show a broad common scaling direction before considering a fixed calibration change. No changed model factor, optimizer update, fitting or new inference; deterministic CPU analysis of the completed public replay,1.366043157seconds,checkpoint9a31b69cbb9c32bdc51541d93b569334a408445233579f43935adc92072526bc. Strict and inclusive coverage are reported separately to account for exact-zero/discrete atoms; equal configuration weights. Strict=[0.09649615502089019, 0.17808724598272557, 0.2561047718942836, 0.3396853273325143, 0.42947219602440895, 0.5244340215216663, 0.6224077059098905, 0.7293349801457444, 0.8349448227403538]; inclusive=[0.18646994735201108, 0.26716067726233894, 0.3442348151253366, 0.4263376847833035, 0.5138603926323156, 0.6012624616842667, 0.6942390398914879, 0.7962815528178204, 0.8967020109729391]. Mean per-config distance outside the nominal quantile bracket=0.07068066327514765; term deviations={"long": 0.06334532807343082, "medium": 0.05332049010856118, "short": 0.08010985737940889}. Overall coverage brackets contain most nominal levels; upper levels show only slight aggregate undercoverage. Per-config deviations mix finite-sample noise, bias and nonstationarity. No universal interval rescaling is supported or selected. Full descriptive output spectral_add_ensemble_coverage.json; analysis code recovery/quantile_coverage.py.
+
+## E116 long-seasonal3000 ensemble full public replay — selected
+- Checkpoint SHA-256: `d5320aa1bc1d23983eb3f438d4ab3171de0b5d8afe93846fb853acd99629d092`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.383511; relative MASE 0.736870; relative WQL 0.515774.
+- term skill: long=0.4024, medium=0.4084, short=0.3662.
+- domain skill: Econ/Fin=0.1307, Energy=0.2407, Healthcare=0.4843, Nature=0.4441, Sales=0.3584, Transport=0.4712, Web/CloudOps=0.4989.
+- frequency skill: A=0.2969, D=0.3733, H=0.3975, M=0.1446, Q=0.1892, S=0.4256, T=0.4394, W=0.2377.
+- variates skill: multivariate=0.3969, univariate=0.3726.
+- Outcome: Full0.383511269 exceeds E1140.382197314 by0.001313955. All97configs151170instances passed. All four diagnostic cutoffs improve. Relocation and independent fixed-average composition checks pass. Validation selection only; no held-out reward claim.
+
+E116 complete public suite {"package": "/workspace/research/candidate-long-seasonal3-ensemble", "status": "complete", "steps": [{"name": "relocation", "returncode": 0, "seconds": 30.231913516065106}, {"name": "full_public_replay", "returncode": 0, "seconds": 386.0414331450593}, {"name": "independent_long_seasonal_replay", "returncode": 0, "seconds": 36.198165599955246}, {"name": "composition", "returncode": 0, "seconds": 0.30810956098139286}, {"name": "descriptive_comparison", "returncode": 0, "seconds": 0.19051235518418252}], "current_step": "descriptive_comparison", "seconds": 452.98110670782626, "skill": 0.3835112690884035}. Descriptive comparison {"candidate": "/workspace/research/valid-report-long-seasonal3-ensemble.json", "parent": "/workspace/research/valid-report-spectral-add-ensemble.json", "candidate_hash": "d5320aa1bc1d23983eb3f438d4ab3171de0b5d8afe93846fb853acd99629d092", "parent_hash": "9a31b69cbb9c32bdc51541d93b569334a408445233579f43935adc92072526bc", "skill_difference": 0.001313955486563101, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 70, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": 0.0005376346973493762, "p50": 0.0013050301765117345, "p975": 0.002204697056569243}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}. Publish with identical runtime/weight hashes. Firstmember adds49184learned seasonal query/head parameters; all three members forecast every instance with fixed equal weights. E101teacher remains E96.
+
+## E107 sixteen-cycle summaries6000 valid_backcast
+- Checkpoint SHA-256: `ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.52558135986328 seconds.
+- Overall skill 0.339853; relative MASE 0.793528; relative WQL 0.549186.
+- term skill: long=0.4027, medium=0.3608, short=0.3056.
+- domain skill: Econ/Fin=0.1478, Energy=0.2308, Healthcare=0.3866, Nature=0.3818, Sales=0.4043, Transport=0.3807, Web/CloudOps=0.4514.
+- frequency skill: A=0.3114, D=0.3658, H=0.3746, M=0.1689, Q=0.2379, S=0.3419, T=0.3334, W=0.2824.
+- variates skill: multivariate=0.3553, univariate=0.3273.
+- Outcome: Allfour6000scores exceed both the matched E80control and E1073000. Main0.382274667,earlier0.339852539/0.363254728/0.394498411. Matched E80 gaps about0.003986/0.004696/0.003549/0.004831. E117 tests the fixed transfer duration, retaining E114as the unchanged zero-extended base.
+
+## E107 sixteen-cycle summaries6000 valid_backcast2
+- Checkpoint SHA-256: `ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.47639751434326 seconds.
+- Overall skill 0.363255; relative MASE 0.765724; relative WQL 0.529492.
+- term skill: long=0.3665, medium=0.3958, short=0.3490.
+- domain skill: Econ/Fin=0.0929, Energy=0.2388, Healthcare=0.3619, Nature=0.4815, Sales=0.5514, Transport=0.4275, Web/CloudOps=0.4336.
+- frequency skill: A=0.3460, D=0.4300, H=0.3971, M=0.1795, Q=0.1488, S=0.2836, T=0.3617, W=0.2611.
+- variates skill: multivariate=0.3659, univariate=0.3611.
+- Outcome: Allfour6000scores exceed both the matched E80control and E1073000. Main0.382274667,earlier0.339852539/0.363254728/0.394498411. Matched E80 gaps about0.003986/0.004696/0.003549/0.004831. E117 tests the fixed transfer duration, retaining E114as the unchanged zero-extended base.
+
+## E107 sixteen-cycle summaries6000 valid_backcast3
+- Checkpoint SHA-256: `ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 26.297444581985474 seconds.
+- Overall skill 0.394498; relative MASE 0.730199; relative WQL 0.502099.
+- term skill: long=0.3995, medium=0.3628, short=0.4041.
+- domain skill: Econ/Fin=0.1617, Energy=0.3232, Healthcare=0.3074, Nature=0.5097, Sales=0.4041, Transport=0.5118, Web/CloudOps=0.3930.
+- frequency skill: A=0.3574, D=0.4564, H=0.4093, M=0.1720, Q=0.2585, S=0.2910, T=0.4288, W=0.2635.
+- variates skill: multivariate=0.3764, univariate=0.4084.
+- Outcome: Allfour6000scores exceed both the matched E80control and E1073000. Main0.382274667,earlier0.339852539/0.363254728/0.394498411. Matched E80 gaps about0.003986/0.004696/0.003549/0.004831. E117 tests the fixed transfer duration, retaining E114as the unchanged zero-extended base.
+
+## E117 duration of the learned seasonal correction — predeclared
+E1163000transfer improves all four diagnostic cutoffs and full replay. E1076000 now improves all four standalone cutoffs over both its3000checkpoint and matched E80control. Complete the duration comparison at6000 and the already scheduled final12000, using exactly the same zero-extended base spectral_add_delta6k (E114firstmember), matching candidate/control checkpoints at each duration, coefficient1, and E71patch8_6000/E93split_12000 as unchanged equal-weight ensemble members. Do not add the new duration on top of E116's already-added3000correction. Each merged candidate gets all four diagnostics; full public replay and checks required for publication. No coefficient or intermediate-checkpoint sweep; no optimizer updates in transfer. The12000arm waits for E107's planned completion.
+
+## E117 long-seasonal matched transfer6000 valid_screen
+- Checkpoint SHA-256: `02ac8f37ed3a54fb12b209f6d8d9eb64e01a5cb862dc63f77ab8e3f8633a4c44`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 70.3311378955841 seconds.
+- Overall skill 0.386484; relative MASE 0.733317; relative WQL 0.513287.
+- term skill: long=0.4079, medium=0.4093, short=0.3690.
+- domain skill: Econ/Fin=0.1470, Energy=0.2417, Healthcare=0.4724, Nature=0.4438, Sales=0.3382, Transport=0.4729, Web/CloudOps=0.5114.
+- frequency skill: A=0.3771, D=0.3739, H=0.4062, M=0.0986, Q=0.2068, S=0.4262, T=0.4439, W=0.2255.
+- variates skill: multivariate=0.4014, univariate=0.3743.
+- Outcome: Fixed coefficient-one zero-extended E1076000-E80control6000 transfer onto E114firstmember; no optimizer update. Ensemble selection recorded separately.
+
+## E118 independent-seed replication of learned seasonal summaries — predeclared
+E107seed197 improves all four cutoffs at3000and6000; E116transfer improves full validation by0.001313955 across70of97configurations. Replicate the feature treatment at independent seed523, reusing the already-planned E111weekly_replication_control as the exactly matched control. Candidate initializes from the same function-preserving long_seasonal_initial (ownE62 plus49184zero parameters), with original packed_sample/source probabilities/generator, seed523/B64/LR2e-5/12000/paired loss and horizon seed300526. Train all parameters. The only factor versus E111control is the same16-cycle learned summaries; no weekly replacement or other synthetic change. Primary comparison6000;1000/3000diagnostic and12000durability. Require all earlier cutoffs before any transfer/averaging decision; no new model-soup rule declared. Reuse E107's exact initialization, arithmetic and maximum-shape GPU audits because model/sampler/batch are unchanged; verify sampler byte identity and initial checkpoint hash before queueing after E113lowfreq_windows14975. This adds one candidate run, no second control or supplied-baseline retraining.
+
+## E117 long-seasonal matched transfer6000 valid_backcast
+- Checkpoint SHA-256: `02ac8f37ed3a54fb12b209f6d8d9eb64e01a5cb862dc63f77ab8e3f8633a4c44`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 33.026755571365356 seconds.
+- Overall skill 0.342409; relative MASE 0.789019; relative WQL 0.548055.
+- term skill: long=0.3990, medium=0.3637, short=0.3108.
+- domain skill: Econ/Fin=0.1781, Energy=0.2290, Healthcare=0.3699, Nature=0.3893, Sales=0.4030, Transport=0.3792, Web/CloudOps=0.4578.
+- frequency skill: A=0.3230, D=0.3600, H=0.3772, M=0.1942, Q=0.2303, S=0.3398, T=0.3376, W=0.2870.
+- variates skill: multivariate=0.3584, univariate=0.3294.
+- Outcome: Fixed coefficient-one zero-extended E1076000-E80control6000 transfer onto E114firstmember; no optimizer update. Ensemble selection recorded separately.
+
+## E117 long-seasonal matched transfer6000 valid_backcast2
+- Checkpoint SHA-256: `02ac8f37ed3a54fb12b209f6d8d9eb64e01a5cb862dc63f77ab8e3f8633a4c44`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.222503900527954 seconds.
+- Overall skill 0.364638; relative MASE 0.764781; relative WQL 0.527844.
+- term skill: long=0.3704, medium=0.3939, short=0.3508.
+- domain skill: Econ/Fin=0.1343, Energy=0.2389, Healthcare=0.2996, Nature=0.4841, Sales=0.5492, Transport=0.4268, Web/CloudOps=0.4437.
+- frequency skill: A=0.3483, D=0.4277, H=0.3984, M=0.1458, Q=0.1537, S=0.2830, T=0.3697, W=0.2602.
+- variates skill: multivariate=0.3707, univariate=0.3597.
+- Outcome: Fixed coefficient-one zero-extended E1076000-E80control6000 transfer onto E114firstmember; no optimizer update. Ensemble selection recorded separately.
+
+## E117 long-seasonal matched transfer6000 valid_backcast3
+- Checkpoint SHA-256: `02ac8f37ed3a54fb12b209f6d8d9eb64e01a5cb862dc63f77ab8e3f8633a4c44`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 52.90152072906494 seconds.
+- Overall skill 0.391181; relative MASE 0.738393; relative WQL 0.501983.
+- term skill: long=0.3957, medium=0.3634, short=0.3996.
+- domain skill: Econ/Fin=0.1862, Energy=0.3239, Healthcare=0.2347, Nature=0.4974, Sales=0.4062, Transport=0.5153, Web/CloudOps=0.3945.
+- frequency skill: A=0.3626, D=0.4483, H=0.4086, M=0.1380, Q=0.2650, S=0.2751, T=0.4326, W=0.2479.
+- variates skill: multivariate=0.3753, univariate=0.4034.
+- Outcome: Fixed coefficient-one zero-extended E1076000-E80control6000 transfer onto E114firstmember; no optimizer update. Ensemble selection recorded separately.
+
+E117 duration6000 equal-ensemble diagnostics: [{"cutoff": "valid_screen", "skill_score": 0.38758775695568237, "rel_mase_gmean": 0.7338581642688206, "rel_wql_gmean": 0.5110643632400712, "groups": {"term": {"short": 0.3705854772097721, "medium": 0.4098116884577624, "long": 0.40850774015255753}, "domain": {"Econ/Fin": 0.1491110309849244, "Energy": 0.2473872111540495, "Healthcare": 0.47967500211204583, "Nature": 0.4443342058731611, "Sales": 0.34435330099487205, "Transport": 0.47178883199133137, "Web/CloudOps": 0.507316811565411}, "freq": {"A-DEC": 0.37657388175046513, "Q-DEC": 0.20077233812299533, "M": 0.11934165027075339, "W-SUN": 0.1260870289350683, "D": 0.37011118914329555, "H": 0.4033355658667086, "15T": 0.2177918137607776, "W-FRI": 0.01580119721245088, "10T": 0.6278398409268195, "W-TUE": 0.6156563794482202, "W-THU": 0.20846408250449933, "W-WED": 0.3285721069115759, "5T": 0.522168695958841, "10S": 0.4256937093047316}}, "member_checkpoint_hashes": ["02ac8f37ed3a54fb12b209f6d8d9eb64e01a5cb862dc63f77ab8e3f8633a4c44", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 1.5809676200151443, "delta_vs_E116": 0.0003823435790825114}, {"cutoff": "valid_backcast", "skill_score": 0.3429158754584547, "rel_mase_gmean": 0.7903227473802326, "rel_wql_gmean": 0.5463078826412736, "groups": {"term": {"short": 0.3101741450136747, "medium": 0.36225183532087923, "long": 0.4039504121923163}, "domain": {"Econ/Fin": 0.1796638005766118, "Energy": 0.23274562514662867, "Healthcare": 0.4050454901624251, "Nature": 0.37989567626268006, "Sales": 0.4082253173536764, "Transport": 0.3765529479337325, "Web/CloudOps": 0.45441716485204564}, "freq": {"A-DEC": 0.3272205609581892, "Q-DEC": 0.23759099706252007, "M": 0.19588675468651817, "W-SUN": 0.07375509478160447, "D": 0.35756967295528785, "H": 0.3748935561837421, "15T": 0.22174583342116727, "W-FRI": 0.2283220401322632, "10T": 0.32775359942485127, "W-TUE": 0.5476232952821192, "W-THU": 0.2634820217181426, "W-WED": 0.3605171603714096, "5T": 0.444928923907442, "10S": 0.3480376209492927}}, "member_checkpoint_hashes": ["02ac8f37ed3a54fb12b209f6d8d9eb64e01a5cb862dc63f77ab8e3f8633a4c44", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 1.486426532967016, "delta_vs_E116": 0.00019519587090321977}, {"cutoff": "valid_backcast2", "skill_score": 0.3673138699400975, "rel_mase_gmean": 0.7624413618664356, "rel_wql_gmean": 0.5250131474901527, "groups": {"term": {"short": 0.35464549923886646, "medium": 0.396044483486757, "long": 0.3704566659810776}, "domain": {"Econ/Fin": 0.1278986000738499, "Energy": 0.24201205303512197, "Healthcare": 0.35011514635967966, "Nature": 0.4829607673467684, "Sales": 0.5559108719880497, "Transport": 0.42779567927039897, "Web/CloudOps": 0.440788541797849}, "freq": {"A-DEC": 0.33548760475433637, "Q-DEC": 0.15846843703525426, "M": 0.16977470264226235, "W-SUN": 0.08500199629088767, "D": 0.4327290855838334, "H": 0.3972970456246422, "15T": 0.10794550576938078, "W-FRI": 0.15172550191595102, "10T": 0.5117883242263219, "W-TUE": 0.09682533580849628, "W-THU": 0.3563552961839991, "W-WED": 0.5628050187009171, "5T": 0.4762546590382627, "10S": 0.2862951112159169}}, "member_checkpoint_hashes": ["02ac8f37ed3a54fb12b209f6d8d9eb64e01a5cb862dc63f77ab8e3f8633a4c44", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 1.5663821888156235, "delta_vs_E116": 0.0001767028092733991}, {"cutoff": "valid_backcast3", "skill_score": 0.39076445217348055, "rel_mase_gmean": 0.7386290023225965, "rel_wql_gmean": 0.5025093132930779, "groups": {"term": {"short": 0.3962936650066763, "medium": 0.36903122355860374, "long": 0.3974199700061841}, "domain": {"Econ/Fin": 0.19052380239240974, "Energy": 0.31738542877172005, "Healthcare": 0.2410422058404026, "Nature": 0.5056637262017856, "Sales": 0.40214858215010085, "Transport": 0.5120028986153148, "Web/CloudOps": 0.39565427543069853}, "freq": {"A-DEC": 0.3580331842712011, "Q-DEC": 0.2574659133377385, "M": 0.14984257763476883, "W-SUN": 0.17713719254991567, "D": 0.44344232532232797, "H": 0.40729030186015236, "15T": 0.25503817655546823, "W-FRI": 0.03718352110646361, "10T": 0.6055530546744756, "W-TUE": 0.2759842620088724, "W-THU": 0.33285112284477014, "W-WED": 0.37606870679901383, "5T": 0.46723753191341555, "10S": 0.2931530897878887}}, "member_checkpoint_hashes": ["02ac8f37ed3a54fb12b209f6d8d9eb64e01a5cb862dc63f77ab8e3f8633a4c44", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 1.2575315169524401, "delta_vs_E116": 0.00048573645624805373}]. All four improve E116; advance to full public replay. Initial assembly helper incorrectly passed the ensemble-probe schema to the single-report recorder and failed before package creation; corrected to direct group recording. No model/runtime/publication mutation occurred.
+
+## E119 learned phase-specific seasonal dispersion — predeclared
+E107 mean summaries improve all four cutoffs, suggesting useful information remains in per-phase repeated cycles. Hypothesis: exposing phase-specific variability helps the neural median/quantile decoder distinguish reliable and noisy seasonal positions. Add four query features: log1p(unbiased sample standard deviation) of the last4 and last16 cycle-centered matching-phase observations, for each of two frequency-derived periods. Use exactly E107 support gate (period>=2,recent>=2,total>=8); inactive features zero. Add one zero-initialized patch*4 to d projection (32768parameters); retain E107 mean query/head and train all parameters. No explicit output rescaling, direct variance head, new generator or loss. Initialize by zero-extending E107's original function-preserving initial checkpoint, not a validation-selected trained endpoint. Match E107 seed197/B64/LR2e-5/12000/paired, original packed_sample/source probabilities/generator/horizon RNG. Primary comparison6000 versus E1076000;1000/3000diagnostic,12000durability. Earlier cutoffs before transfer/publication. Queue after E115phase_joint; no coefficient sweep or transfer rule yet. CPU arithmetic/invariance and maximum-shape GPU audits precede launch.
+
+E119 CPU audit {"seed": 577, "independent_feature_arithmetic": true, "centered_cycle_offset_invariance": true, "old_features_bitwise_equal": true, "initial_function_bitwise_equal": true, "inherited_weights_bitwise_equal": true, "all_parameters_trainable": true, "sampler_byte_identical": true, "added_parameters": 32768, "total_parameters": 36882752, "checkpoint_sha256": "5f07cac4fe7de3e878a01cb90384e79905fdb3156122b568e82c5923526aca3c", "seconds": 1.8492389279417694, "optimizer_updates": 0, "validation_data_read": false}. Distinct from rejected E53: E53 changed a fixed seasonal-confidence denominator pooled across all phases; E119 retains that formula and trains a zero-initialized query projection of phase-specific dispersion with all weights trainable. No validation targets or optimizer updates enter this audit.
+
+E1108000 matched diagnostic {"experiment": "E110full-corpus8000 versus E94matched8000", "overall_delta": -0.003203082653086664, "groups": {"term": {"long": 0.0035847827270744093, "medium": 0.0029933799435938324, "short": -0.008583522930157517}, "domain": {"Econ/Fin": 0.0073067524243448645, "Energy": 0.0010232563174896736, "Healthcare": -0.10046309213785809, "Nature": -0.005525316527005608, "Sales": 0.020186513009092466, "Transport": -0.004776228457146647, "Web/CloudOps": 0.007967761013063401}, "frequency": {"A": 0.0024475638858065807, "D": -0.00816465345146733, "H": 0.003737303868274089, "M": -0.0603011923470429, "Q": 0.023677353819767433, "S": 0.0010874878225529683, "T": 3.1634525451962325e-05, "W": -0.021925362852562746}}}. Small improvements on medium/long horizons are outweighed by short-horizon Healthcare and monthly/weekly regressions; not sufficient evidence for combining this data change with distillation. Preserve original96000schedule; no training-data adaptation to these validation values.
+
+Context-only seasonal-support diagnostic: {"kind": "optimistic potential support before missing-value masks; not measured forecast improvement", "input_metadata_only": true, "validation_targets_read": false, "optimizer_updates": 0, "changed_configurations": 21, "changed_domains": ["Energy", "Nature", "Transport", "Web/CloudOps"], "seconds": 1.966426236089319}. At16384instead of4096,21configurations gain potential>=8cycle support before missing masks. Example: electricity15min weekly672period gains6to16availablecycles; solar10min weekly1008 gains4to16. This is potential feature availability, not forecast benefit. No training or inference rule changed.
+
+E111 weekly seed523 control training started at 2026-10-23T07:04:07Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E119 maximum-shape GPU audit {"seed": 587, "gpu_initial_function_bitwise_equal": true, "max_shape": [64, 4096, 1024], "loss": 0.30101367831230164, "selected_gradient_norms": {"dispersion_query.weight": 0.0080416863784194, "long_query.weight": 0.02340359427034855, "long_head.weight": 0.013128682039678097, "head.weight": 0.02928958274424076}, "all_parameters_trainable": true, "reserved_mib": 6162.0, "allocated_mib": 5787.1318359375, "includes_ema_and_adam_placeholders": true, "seconds": 351.92002349486575, "optimizer_updates": 0, "validation_data_read": false}. Reported351.920023s is total process wall including waiting for the shared evaluation lock, not isolated GPU compute. BF16 initialization is bitwise equal; all trainable gradients finite and new-projection gradient nonzero. Queue after E115final12000; no optimizer update in audit.
+
+E107 completed {"completed_step": 12000, "training_process_loop_seconds": 2526.470098217018}, process wall 2542.7628201858606s, final checkpoint SHA256d512cdac520239f0c10e021721424d87cee30086fa70f6cbc41f21e2e04988c3. E111control seed523 started automatically after completion, trainer19130. E117final-duration coefficient-one transfer created SHA2568a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4, merge2.527997653CPU s; four-cutoff evaluation pending.
+
+E1018000 matched comparison {"comparison": "E101 versus E94 at8000updates", "candidate_hash": "dca6ae35fabe5a4608ef2207587fe6dfc4b66b7f07c826e640ef0d9887e1fef8", "control_hash": "650a1a4177cae63f3ceab8a2255fbd2f9d281a7e83e8d380a34bb956bd690884", "skill_delta": 0.007141103867715826, "groups": {"term": {"long": 0.00950527008347335, "medium": 0.015714162767370032, "short": 0.0025782486269768645}, "domain": {"Econ/Fin": 0.014946719158874244, "Energy": 0.011541932738351024, "Healthcare": -0.029374142922637292, "Nature": -0.0037363199637078237, "Sales": 0.020011161284304135, "Transport": 0.017766071964885777, "Web/CloudOps": 0.005429339989242221}, "frequency": {"A": 0.01843941003486016, "D": -0.004802637871195281, "H": 0.014959072868680146, "M": -0.03208366163078247, "Q": 0.04314910869141775, "S": -0.007253560764663325, "T": 0.013793862179354521, "W": -0.010157605065865938}}}. Main0.3542945304341839 versus E940.3471534265664681. Update-count advantage remains; three teacher forward passes add cost, so this is not evidence of wall-time efficiency. Preserve teacherE96/coefficient0.5/original96000schedule.
+
+## E120 inference ablation of learned seasonal summaries — predeclared
+E1076000 improves matched E80, but both inherited weights and newly introduced feature projections changed during training. Diagnostic hypothesis: the learned sixteen-cycle query/head features themselves contribute to forecasting beyond the inherited-weight changes. Take exactly E107EMA6000 and disable both long_query and long_head by setting long_seasonal_features=False and removing only their three tensors; preserve every common tensor bitwise. Compare this fixed ablation to the original E1076000 at all four diagnostic cutoffs. No optimizer update, retraining, new generator or coefficient search. This is a diagnostic of the trained representation, not a matched-training causal estimate and not a publication candidate. Keep the current E117 publication decision independent. The result will inform whether extending only seasonal-summary history is worth a later controlled training experiment.
+
+## E117 seasonal6000 ensemble full public replay — selected
+- Checkpoint SHA-256: `2e4fd324abe8fae2bb2b0d93c132b48bc047985da4d05171edbff36d83edb76c`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.383907; relative MASE 0.736461; relative WQL 0.515398.
+- term skill: long=0.4027, medium=0.4087, short=0.3667.
+- domain skill: Econ/Fin=0.1307, Energy=0.2409, Healthcare=0.4852, Nature=0.4445, Sales=0.3586, Transport=0.4720, Web/CloudOps=0.4992.
+- frequency skill: A=0.2968, D=0.3745, H=0.3981, M=0.1452, Q=0.1893, S=0.4257, T=0.4394, W=0.2376.
+- variates skill: multivariate=0.3972, univariate=0.3731.
+- Outcome: Full skill 0.383907276221 exceeds published 0.383511269088 by 0.000396007133. All97configs151170instances pass; relocation and independent fixed-average composition checks pass. Validation selection only, not held-out reward.
+
+E117 complete public suite {"package": "/workspace/research/candidate-long-seasonal6-ensemble", "status": "complete", "steps": [{"name": "relocation", "returncode": 0, "seconds": 27.76716866483912}, {"name": "full_public_replay", "returncode": 0, "seconds": 364.8008179310709}, {"name": "independent_long_seasonal_replay", "returncode": 0, "seconds": 185.7471042801626}, {"name": "composition", "returncode": 0, "seconds": 0.46263073617592454}, {"name": "descriptive_comparison", "returncode": 0, "seconds": 0.17200843803584576}], "current_step": "descriptive_comparison", "seconds": 578.9992693390232, "skill": 0.38390727622107634}. Descriptive comparison {"candidate": "/workspace/research/valid-report-long-seasonal6-ensemble.json", "parent": "/workspace/research/valid-report-long-seasonal3-ensemble.json", "candidate_hash": "2e4fd324abe8fae2bb2b0d93c132b48bc047985da4d05171edbff36d83edb76c", "parent_hash": "d5320aa1bc1d23983eb3f438d4ab3171de0b5d8afe93846fb853acd99629d092", "skill_difference": 0.0003960071326728487, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 64, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": 0.00022268027543086432, "p50": 0.00038841478230000615, "p975": 0.0006178172436181516}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}. Identical runtime/weight hashes verified before atomic publication. Every member forecasts every instance. E101teacher remains frozenE96.
+
+E117 publication bookkeeping: successful atomic swap at07:08:28UTC preceded a TypeError in the subsequent research-state update, because predeclared durations was a list. Corrected future writer to duration_results and reconciled CURRENT against the actual published manifest/report; no model, validation or runtime error, and no partial package was exposed.
+
+E79 matched48000 capacity comparison {"experiment": "E79 matched fresh146M versus37M at48000", "wide_skill": 0.3685434345005941, "small_skill": 0.36677272226065616, "delta": 0.001770712239937966, "candidate_hash": "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c", "control_hash": "855f37f248e8104578ae2b356d2b7326a73236476f888fd1468e988be4238509", "groups": {"term": {"long": -0.004614403570790682, "medium": 0.0001880975934308582, "short": 0.005003156709554091}, "domain": {"Econ/Fin": -0.016997682096246525, "Energy": -0.003545869646366051, "Healthcare": 0.04853217938401477, "Nature": 0.005359137774352152, "Sales": -0.0067723756343403, "Transport": -0.0027269222078922795, "Web/CloudOps": 0.0022497021003726747}, "frequency": {"A": 0.004470802964257836, "D": 0.005424165034339068, "H": -0.003437844355180242, "M": 0.07518726555589927, "Q": -0.013178225536983335, "S": -0.003760199536136266, "T": -0.0021534007709399505, "W": 0.007093310437459088}}}. The146M branch now exceeds37M at equal updates, reversing the earlier ranking. Retain scheduled72000/96000checkpoints; no capacity extension or329Mrun declared.
+
+E115 joint phase conditioning seed197 training started at 2026-10-23T07:10:28Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E107 seasonal12000 valid_backcast
+- Checkpoint SHA-256: `d512cdac520239f0c10e021721424d87cee30086fa70f6cbc41f21e2e04988c3`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 27.891539812088013 seconds.
+- Overall skill 0.341379; relative MASE 0.792273; relative WQL 0.547515.
+- term skill: long=0.4011, medium=0.3630, short=0.3083.
+- domain skill: Econ/Fin=0.1541, Energy=0.2330, Healthcare=0.3908, Nature=0.3814, Sales=0.4040, Transport=0.3820, Web/CloudOps=0.4524.
+- frequency skill: A=0.3164, D=0.3688, H=0.3749, M=0.1725, Q=0.2300, S=0.3427, T=0.3364, W=0.2805.
+- variates skill: multivariate=0.3562, univariate=0.3294.
+- Outcome: Final E107 versus matched E8012000 delta 0.004850916529; versus own6000 delta 0.001526715303. Final duration improves three cutoffs over6000 but loses the oldest; E117 predeclared transferred composition assessed separately.
+
+## E107 seasonal12000 valid_backcast2
+- Checkpoint SHA-256: `d512cdac520239f0c10e021721424d87cee30086fa70f6cbc41f21e2e04988c3`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.684894800186157 seconds.
+- Overall skill 0.365060; relative MASE 0.764718; relative WQL 0.527186.
+- term skill: long=0.3677, medium=0.3957, short=0.3518.
+- domain skill: Econ/Fin=0.0984, Energy=0.2396, Healthcare=0.3583, Nature=0.4847, Sales=0.5527, Transport=0.4280, Web/CloudOps=0.4370.
+- frequency skill: A=0.3501, D=0.4330, H=0.3965, M=0.1801, Q=0.1448, S=0.2846, T=0.3637, W=0.2713.
+- variates skill: multivariate=0.3674, univariate=0.3631.
+- Outcome: Final E107 versus matched E8012000 delta 0.004086370453; versus own6000 delta 0.001805111594. Final duration improves three cutoffs over6000 but loses the oldest; E117 predeclared transferred composition assessed separately.
+
+## E107 seasonal12000 valid_backcast3
+- Checkpoint SHA-256: `d512cdac520239f0c10e021721424d87cee30086fa70f6cbc41f21e2e04988c3`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 27.912177085876465 seconds.
+- Overall skill 0.392833; relative MASE 0.733445; relative WQL 0.502630.
+- term skill: long=0.3982, medium=0.3609, short=0.4024.
+- domain skill: Econ/Fin=0.1609, Energy=0.3232, Healthcare=0.3044, Nature=0.5056, Sales=0.4052, Transport=0.5100, Web/CloudOps=0.3912.
+- frequency skill: A=0.3472, D=0.4525, H=0.4075, M=0.1705, Q=0.2608, S=0.2906, T=0.4279, W=0.2644.
+- variates skill: multivariate=0.3764, univariate=0.4055.
+- Outcome: Final E107 versus matched E8012000 delta 0.005351688124; versus own6000 delta -0.001665237983. Final duration improves three cutoffs over6000 but loses the oldest; E117 predeclared transferred composition assessed separately.
+
+E107 complete final-duration comparison [{"cutoff": "valid_screen", "skill": 0.38231375879048757, "delta_matched": 0.004889812714840014, "delta_vs6000": 3.9091567565430196e-05}, {"cutoff": "valid_backcast", "skill": 0.34137925383664736, "delta_matched": 0.0048509165293714185, "delta_vs6000": 0.0015267153027013203}, {"cutoff": "valid_backcast2", "skill": 0.3650598400595585, "delta_matched": 0.004086370453054822, "delta_vs6000": 0.0018051115942698814}, {"cutoff": "valid_backcast3", "skill": 0.3928331731047856, "delta_matched": 0.005351688124433984, "delta_vs6000": -0.0016652379826666008}]. Final is better than matched E80 on all four cutoffs, and mixed versus own6000. No extra intermediate checkpoint search declared.
+
+## E117 long-seasonal matched transfer12000 valid_screen
+- Checkpoint SHA-256: `8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.930054187774658 seconds.
+- Overall skill 0.386692; relative MASE 0.733194; relative WQL 0.513026.
+- term skill: long=0.4082, medium=0.4096, short=0.3692.
+- domain skill: Econ/Fin=0.1468, Energy=0.2415, Healthcare=0.4724, Nature=0.4442, Sales=0.3390, Transport=0.4736, Web/CloudOps=0.5117.
+- frequency skill: A=0.3769, D=0.3743, H=0.4066, M=0.0984, Q=0.2059, S=0.4258, T=0.4442, W=0.2250.
+- variates skill: multivariate=0.4015, univariate=0.3746.
+- Outcome: Fixed coefficient-one zero-extended E10712000-E80control12000 transfer onto E114firstmember; no optimizer update. Ensemble selection recorded separately.
+
+## E117 long-seasonal matched transfer12000 valid_backcast
+- Checkpoint SHA-256: `8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.0443058013916 seconds.
+- Overall skill 0.342530; relative MASE 0.789143; relative WQL 0.547767.
+- term skill: long=0.3986, medium=0.3634, short=0.3113.
+- domain skill: Econ/Fin=0.1773, Energy=0.2292, Healthcare=0.3696, Nature=0.3902, Sales=0.4038, Transport=0.3796, Web/CloudOps=0.4572.
+- frequency skill: A=0.3234, D=0.3615, H=0.3769, M=0.1938, Q=0.2299, S=0.3391, T=0.3378, W=0.2870.
+- variates skill: multivariate=0.3582, univariate=0.3298.
+- Outcome: Fixed coefficient-one zero-extended E10712000-E80control12000 transfer onto E114firstmember; no optimizer update. Ensemble selection recorded separately.
+
+## E117 long-seasonal matched transfer12000 valid_backcast2
+- Checkpoint SHA-256: `8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 29.031078815460205 seconds.
+- Overall skill 0.365057; relative MASE 0.764511; relative WQL 0.527333.
+- term skill: long=0.3707, medium=0.3938, short=0.3515.
+- domain skill: Econ/Fin=0.1339, Energy=0.2390, Healthcare=0.2992, Nature=0.4848, Sales=0.5508, Transport=0.4274, Web/CloudOps=0.4443.
+- frequency skill: A=0.3487, D=0.4280, H=0.3986, M=0.1466, Q=0.1539, S=0.2839, T=0.3704, W=0.2603.
+- variates skill: multivariate=0.3711, univariate=0.3602.
+- Outcome: Fixed coefficient-one zero-extended E10712000-E80control12000 transfer onto E114firstmember; no optimizer update. Ensemble selection recorded separately.
+
+## E117 long-seasonal matched transfer12000 valid_backcast3
+- Checkpoint SHA-256: `8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 30.16946768760681 seconds.
+- Overall skill 0.391080; relative MASE 0.738672; relative WQL 0.501959.
+- term skill: long=0.3957, medium=0.3634, short=0.3994.
+- domain skill: Econ/Fin=0.1854, Energy=0.3237, Healthcare=0.2319, Nature=0.4975, Sales=0.4068, Transport=0.5154, Web/CloudOps=0.3948.
+- frequency skill: A=0.3628, D=0.4472, H=0.4084, M=0.1398, Q=0.2640, S=0.2757, T=0.4328, W=0.2481.
+- variates skill: multivariate=0.3752, univariate=0.4033.
+- Outcome: Fixed coefficient-one zero-extended E10712000-E80control12000 transfer onto E114firstmember; no optimizer update. Ensemble selection recorded separately.
+
+## E120 no-seasonal-feature ablation valid_screen
+- Checkpoint SHA-256: `5d016bdaaae5682039289ff9d0078b8e7361a85c7d073cb3d1d172a682696635`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.446512699127197 seconds.
+- Overall skill 0.379037; relative MASE 0.743615; relative WQL 0.518542.
+- term skill: long=0.4013, medium=0.4070, short=0.3591.
+- domain skill: Econ/Fin=0.1239, Energy=0.2394, Healthcare=0.4687, Nature=0.4304, Sales=0.3369, Transport=0.4663, Web/CloudOps=0.5035.
+- frequency skill: A=0.3682, D=0.3569, H=0.3975, M=0.1045, Q=0.1916, S=0.4253, T=0.4375, W=0.2293.
+- variates skill: multivariate=0.3973, univariate=0.3641.
+- Outcome: Ablated minus original E1076000 skill -0.003237981869. Only the three newly introduced query/head tensors removed; all common weights identical. Diagnostic only; no optimizer update or publication.
+
+## E120 no-seasonal-feature ablation valid_backcast
+- Checkpoint SHA-256: `5d016bdaaae5682039289ff9d0078b8e7361a85c7d073cb3d1d172a682696635`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 25.945672273635864 seconds.
+- Overall skill 0.335291; relative MASE 0.798422; relative WQL 0.553388.
+- term skill: long=0.3978, medium=0.3572, short=0.3008.
+- domain skill: Econ/Fin=0.1449, Energy=0.2271, Healthcare=0.4135, Nature=0.3656, Sales=0.3969, Transport=0.3715, Web/CloudOps=0.4497.
+- frequency skill: A=0.3114, D=0.3509, H=0.3670, M=0.1826, Q=0.2353, S=0.3420, T=0.3323, W=0.2844.
+- variates skill: multivariate=0.3521, univariate=0.3216.
+- Outcome: Ablated minus original E1076000 skill -0.004561070524. Only the three newly introduced query/head tensors removed; all common weights identical. Diagnostic only; no optimizer update or publication.
+
+## E120 no-seasonal-feature ablation valid_backcast2
+- Checkpoint SHA-256: `5d016bdaaae5682039289ff9d0078b8e7361a85c7d073cb3d1d172a682696635`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.085110902786255 seconds.
+- Overall skill 0.358680; relative MASE 0.770361; relative WQL 0.533894.
+- term skill: long=0.3640, medium=0.3891, short=0.3445.
+- domain skill: Econ/Fin=0.0925, Energy=0.2359, Healthcare=0.3637, Nature=0.4703, Sales=0.5469, Transport=0.4203, Web/CloudOps=0.4325.
+- frequency skill: A=0.3460, D=0.4234, H=0.3903, M=0.1635, Q=0.1471, S=0.2816, T=0.3596, W=0.2637.
+- variates skill: multivariate=0.3624, univariate=0.3556.
+- Outcome: Ablated minus original E1076000 skill -0.004574561662. Only the three newly introduced query/head tensors removed; all common weights identical. Diagnostic only; no optimizer update or publication.
+
+## E120 no-seasonal-feature ablation valid_backcast3
+- Checkpoint SHA-256: `5d016bdaaae5682039289ff9d0078b8e7361a85c7d073cb3d1d172a682696635`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 23.88756275177002 seconds.
+- Overall skill 0.390053; relative MASE 0.735691; relative WQL 0.505695.
+- term skill: long=0.3964, medium=0.3611, short=0.3982.
+- domain skill: Econ/Fin=0.1627, Energy=0.3183, Healthcare=0.2887, Nature=0.5063, Sales=0.3981, Transport=0.5070, Web/CloudOps=0.3912.
+- frequency skill: A=0.3574, D=0.4489, H=0.4042, M=0.1424, Q=0.2562, S=0.2901, T=0.4282, W=0.2599.
+- variates skill: multivariate=0.3722, univariate=0.4037.
+- Outcome: Ablated minus original E1076000 skill -0.004445083448. Only the three newly introduced query/head tensors removed; all common weights identical. Diagnostic only; no optimizer update or publication.
+
+E120 completed diagnostic {"experiment": "E120 fixed inference ablation", "pid": 19358, "status": "complete", "optimizer_updates": 0, "checkpoint_sha256": "5d016bdaaae5682039289ff9d0078b8e7361a85c7d073cb3d1d172a682696635", "seconds": 313.96637652395293, "comparisons": [{"cutoff": "valid_screen", "ablated_skill": 0.37903668535385004, "original_skill": 0.38227466722292214, "ablation_delta": -0.003237981869072093}, {"cutoff": "valid_backcast", "ablated_skill": 0.335291468009684, "original_skill": 0.33985253853394604, "ablation_delta": -0.004561070524262023}, {"cutoff": "valid_backcast2", "ablated_skill": 0.3586801668029278, "original_skill": 0.3632547284652886, "ablation_delta": -0.00457456166236081}, {"cutoff": "valid_backcast3", "ablated_skill": 0.3900533276391289, "original_skill": 0.3944984110874522, "ablation_delta": -0.004445083448323284}]}. Preparation {"method": "inference-only ablation: disable long seasonal query and median head", "removed_parameters": ["long_head.bias", "long_head.weight", "long_query.weight"], "parent_sha256": "ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253", "common_weights_bitwise_equal": true, "equivalent_to_zeroing_added_tensors": true, "fixture_max_normalized_quantile_change": 0.3484839200973511, "seed": 593, "optimizer_updates": 0, "validation_data_read": false, "seconds": 1.7483407920226455, "checkpoint_sha256": "5d016bdaaae5682039289ff9d0078b8e7361a85c7d073cb3d1d172a682696635", "parameters": 36800800}. No model selection or inference rule change made from this ablation.
+
+E117 final12000 ensemble selection decision: main0.3877185003381878(+0.000130743),earlier0.3429732095231861(+0.000057334),0.3674637814642573(+0.000149912),0.3907349048993719(-0.000029547) versus6000. Three small gains and a smaller oldest-cutoff regression justify full public replay of the predeclared endpoint. No new duration or coefficient searched. Keep published6000 unless complete full validation improves and all package checks pass.
+
+E117 final-duration diagnostic comparison [{"cutoff": "valid_screen", "skill_score": 0.3877185003381878, "rel_mase_gmean": 0.7337351974885521, "rel_wql_gmean": 0.5109317858967326, "groups": {"term": {"short": 0.3707558524930944, "medium": 0.4098771901633257, "long": 0.408606031650835}, "domain": {"Econ/Fin": 0.14892832484818552, "Energy": 0.2472971852687753, "Healthcare": 0.4803016863042562, "Nature": 0.44452367697019235, "Sales": 0.34469848608770726, "Transport": 0.4720713678216161, "Web/CloudOps": 0.5074289619200675}, "freq": {"A-DEC": 0.37644303940062507, "Q-DEC": 0.20040073790916524, "M": 0.11941330678885598, "W-SUN": 0.1251202483389996, "D": 0.37055191866195825, "H": 0.40349640640320394, "15T": 0.21781231621749508, "W-FRI": 0.015126576068462239, "10T": 0.6280390649679213, "W-TUE": 0.6163537412047071, "W-THU": 0.20809838353336907, "W-WED": 0.3286161379110919, "5T": 0.5222559385117231, "10S": 0.4255389538943257}}, "member_checkpoint_hashes": ["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 2.2789630780462176, "delta_vs_6000": 0.00013074338250540496}, {"cutoff": "valid_backcast", "skill_score": 0.3429732095231861, "rel_mase_gmean": 0.7903406487887271, "rel_wql_gmean": 0.546200178449458, "groups": {"term": {"short": 0.3103732976664231, "medium": 0.36211673724743143, "long": 0.40386614844229607}, "domain": {"Econ/Fin": 0.1793467759174172, "Energy": 0.23278752845843642, "Healthcare": 0.4053028690714259, "Nature": 0.38023084245127514, "Sales": 0.4085492421507243, "Transport": 0.3767345389715574, "Web/CloudOps": 0.45420439383545774}, "freq": {"A-DEC": 0.3273754770611558, "Q-DEC": 0.23747069130587617, "M": 0.1957831138190873, "W-SUN": 0.07263667297699061, "D": 0.35807825619545863, "H": 0.3747906004450807, "15T": 0.2215635320385274, "W-FRI": 0.22848872660315322, "10T": 0.32771035120558367, "W-TUE": 0.5486117454705075, "W-THU": 0.26369218944162776, "W-WED": 0.36062902681612985, "5T": 0.44514303798334387, "10S": 0.34786208551915265}}, "member_checkpoint_hashes": ["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 2.2636594069190323, "delta_vs_6000": 5.73340647314069e-05}, {"cutoff": "valid_backcast2", "skill_score": 0.3674637814642573, "rel_mase_gmean": 0.7623553176057026, "rel_wql_gmean": 0.5248236072073068, "groups": {"term": {"short": 0.35484310895705573, "medium": 0.3960867437395954, "long": 0.3705980799617601}, "domain": {"Econ/Fin": 0.1277070545929031, "Energy": 0.24205256210757975, "Healthcare": 0.3497431968929344, "Nature": 0.48319278386757325, "Sales": 0.5564586584486519, "Transport": 0.4280488353299794, "Web/CloudOps": 0.4410272745304028}, "freq": {"A-DEC": 0.3355904988679629, "Q-DEC": 0.1585150327453716, "M": 0.17016606054242045, "W-SUN": 0.08409061455177957, "D": 0.43282659921195, "H": 0.39736790791666765, "15T": 0.10774153963042221, "W-FRI": 0.15202567498019182, "10T": 0.5120408099663714, "W-TUE": 0.0966837112065353, "W-THU": 0.3563038710687719, "W-WED": 0.5630246967574166, "5T": 0.4766927315668852, "10S": 0.2867261076297586}}, "member_checkpoint_hashes": ["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 2.4177008508704603, "delta_vs_6000": 0.0001499115241597737}, {"cutoff": "valid_backcast3", "skill_score": 0.3907349048993719, "rel_mase_gmean": 0.7387169220065166, "rel_wql_gmean": 0.5024982439818847, "groups": {"term": {"short": 0.396276610094657, "medium": 0.3689977724231216, "long": 0.39735893151620383}, "domain": {"Econ/Fin": 0.19020523838220893, "Energy": 0.31728610571853166, "Healthcare": 0.2403015174198082, "Nature": 0.5057342681593209, "Sales": 0.40241599684588714, "Transport": 0.5120862680709422, "Web/CloudOps": 0.3957049292064879}, "freq": {"A-DEC": 0.35800855596973136, "Q-DEC": 0.257067773847507, "M": 0.15038777612792043, "W-SUN": 0.17671013337471464, "D": 0.4432142959019776, "H": 0.4072206064146562, "15T": 0.2549314225171735, "W-FRI": 0.037548757757445705, "10T": 0.6057445314692966, "W-TUE": 0.27568951945628684, "W-THU": 0.333108346801685, "W-WED": 0.37617633114340077, "5T": 0.467212550495147, "10S": 0.29328450918527416}}, "member_checkpoint_hashes": ["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 2.134492114884779, "delta_vs_6000": -2.954727410864333e-05}]. Advance the predeclared fixed final-duration composition to complete public replay; only an improved verified package can replace the submission.
+
+## E121 older history for learned seasonal summaries — predeclared
+E120 removing the seasonal query/head lowers skill at all four cutoffs by0.00324/0.00456/0.00457/0.00445; a context-metadata audit identifies21configurations with additional potential8cycle support at16384instead of4096. Test longer history only for E107's learned sixteen-cycle summaries. Encoder, metadata, normalization, training loss weighting, positivity and fixed postprocessing remain at4096. Extend only originally full4096 real training contexts with up to16384 preceding observations from the same sampled series/cutoff; preserve original targets, recent contexts, intentional short-context draws, aggregation alignment, random masking draws, source probabilities and all sampler/horizon RNG states exactly. Synthetic windows keep their original generated values with missing extra prefix; no new generator or random draw. For aggregated rows, extend by whole earlier aggregation blocks ending at the same forecast cutoff. Normalize the added prefix with the recent4096 statistics, preserving recent normalized values exactly. No new parameters; initialize from E107's original zero-feature own initialization with only context metadata changed. Match E107 seed197/B64/LR2e-5/12000/paired/originalpack; exact sampler audits permit using completed E107 as control without retraining it. Primary6000,1000/3000diagnostic,12000durability; all earlier cutoffs before selection. Queue after E119 only after CPU/GPU audits. No change to shared published ensemble runtime during E117 replay; any later ensemble packaging must explicitly honor each member's fixed rule-context cap.
+
+E121 CPU audit {"seed": 599, "sampler_seed": 601, "initial_function_bitwise_equal": true, "initial_loss_bitwise_equal": true, "all_parameter_tensors_bitwise_equal": true, "encoder_and_rule_context": 4096, "summary_context": 16384, "sampler_rows": 5120, "extended_real_rows": 610, "extended_aggregated_rows": 74, "synthetic_rows": 1032, "extra_finite_history_observations": 4575621, "original_recent_contexts_targets_periods_bitwise_equal": true, "original_sampler_and_horizon_rng_states_equal": true, "source_probabilities_bitwise_equal": true, "extra_history_independently_verified_against_same_source_before_cutoff": true, "synthetic_extra_prefix_missing": true, "total_parameters": 36849984, "checkpoint_sha256": "e15ab884442dacf5b0d2017e515732e2569ab3c520ecf465841e5e91bcd0dd88", "seconds": 9.032198722008616, "optimizer_updates": 0, "validation_data_read": false}.
+
+E121 CPU audit {"seed": 197, "separate_batch_rng_seed": 197, "requested_horizon_seed": 300200, "batches": 100, "rows": 6400, "extended_rows": 824, "shape_counts": {"4096/1024": 23, "2048/1024": 25, "4096/32": 3, "512/128": 3, "4096/128": 7, "1024/128": 2, "512/1024": 7, "4096/512": 9, "1024/1024": 5, "2048/128": 4, "512/32": 2, "2048/512": 6, "2048/32": 3, "512/512": 1}, "actual_training_rng_layout": true, "recent_context_targets_periods_bitwise_equal": true, "all_three_rng_states_bitwise_equal": true, "seconds": 4.592217224882916, "optimizer_updates": 0, "validation_data_read": false}.
+
+E121 runtime preparation: standalone predict.py uses16384only as the learned-summary input cap; positivity and all fixed transforms explicitly receive recent4096. Shared three_patch_ensemble/runtime remains unchanged during E117replay. If E121 is selected later, its ensemble runtime must honor per-model rule_context; do not publish it through the old hard-coded per-member input/postprocessing path without modifying and validating that path. No new ensemble transfer rule declared.
+
+## E117 seasonal12000 ensemble full public replay — selected
+- Checkpoint SHA-256: `90199d09666c1653819ef17813f69fc4014fca075f99cc3766d24f457b50ca79`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.384041; relative MASE 0.736336; relative WQL 0.515260.
+- term skill: long=0.4028, medium=0.4087, short=0.3669.
+- domain skill: Econ/Fin=0.1306, Energy=0.2408, Healthcare=0.4858, Nature=0.4446, Sales=0.3589, Transport=0.4723, Web/CloudOps=0.4993.
+- frequency skill: A=0.2968, D=0.3749, H=0.3983, M=0.1452, Q=0.1891, S=0.4255, T=0.4395, W=0.2374.
+- variates skill: multivariate=0.3973, univariate=0.3733.
+- Outcome: Full skill 0.384041465826 exceeds published 0.383907276221 by 0.000134189605. All97configs151170instances pass; relocation and independent fixed-average composition checks pass. Validation selection only, not held-out reward.
+
+E117 complete public suite {"package": "/workspace/research/candidate-long-seasonal12-ensemble", "status": "complete", "steps": [{"name": "relocation", "returncode": 0, "seconds": 32.01111192582175}, {"name": "full_public_replay", "returncode": 0, "seconds": 385.4564341898076}, {"name": "independent_long_seasonal_replay", "returncode": 0, "seconds": 46.03750956710428}, {"name": "composition", "returncode": 0, "seconds": 0.3832244158256799}, {"name": "descriptive_comparison", "returncode": 0, "seconds": 0.31505064899101853}], "current_step": "descriptive_comparison", "seconds": 464.2120908219367, "skill": 0.3840414658263811}. Descriptive comparison {"candidate": "/workspace/research/valid-report-long-seasonal12-ensemble.json", "parent": "/workspace/research/valid-report-long-seasonal6-ensemble.json", "candidate_hash": "90199d09666c1653819ef17813f69fc4014fca075f99cc3766d24f457b50ca79", "parent_hash": "2e4fd324abe8fae2bb2b0d93c132b48bc047985da4d05171edbff36d83edb76c", "skill_difference": 0.00013418960530475754, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 55, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": 2.5936228850581572e-06, "p50": 0.000133406498457056, "p975": 0.0002505207149228361}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}. Identical runtime/weight hashes verified before atomic publication. Every member forecasts every instance. E101teacher remains frozenE96.
+
+E121 GPU audit {"seed": 607, "gpu_initial_function_bitwise_equal": true, "gpu_initial_loss_bitwise_equal": true, "max_input_shape": [64, 16384, 1024], "encoder_context": 4096, "loss": 0.2252708375453949, "gradient_norms": {"long_query.weight": 0.005559232551604509, "long_head.weight": 0.013920275494456291, "head.weight": 0.015998471528291702}, "reserved_mib": 6302.0, "allocated_mib": 5788.6318359375, "includes_ema_and_adam_placeholders": true, "lock_wait_seconds": 247.51019481010735, "post_lock_seconds": 9.868129789829254, "seconds": 258.1824177131057, "optimizer_updates": 0, "validation_data_read": false}. Active post-lock wall9.868130s, lockwait247.510195s, no optimizer updates. Queue after E11919255; exactly matched completed E107 remains the control.
+
+E115early matched diagnostics:1000skill0.376851746607891 versus E800.37680439430493307;3000skill0.3785995231369078 versus E800.37866382508046315. Effects are near zero so far; retain predeclared6000primary and12000durability. E119/E121 remain queued as independent factors, not conditioned on a claimed phase benefit.
+
+E11016000 matched comparison {"experiment": "E110full-corpus16000 versus E94matched16000", "candidate_skill": 0.35154579999964164, "control_skill": 0.35461566373218645, "skill_delta": -0.0030698637325448086, "candidate_hash": "29daf0a92679ccac5de356e78cdbce9ad7d88fa971229948b131194277972687", "control_hash": "19f9ead156ea9bc4a3edca1303bc5595057b2e1b89ff88e7bb7d9acf39618ba5", "groups": {"term": {"long": 0.002939641461384479, "medium": 0.0044219426650220495, "short": -0.008705471161239875}, "domain": {"Econ/Fin": 0.0077186974426632915, "Energy": 0.00047905371405398967, "Healthcare": -0.08545128617031428, "Nature": -0.006472746365275395, "Sales": 0.0047153529000921734, "Transport": -0.005534397382311318, "Web/CloudOps": 0.01049812646266357}, "frequency": {"A": 0.008032668576188895, "D": -0.0168735229638608, "H": 0.004955057897488513, "M": -0.09069678098773792, "Q": -0.005391239762853384, "S": -0.000881347834741697, "T": 0.008664048847818129, "W": -0.02936746335393836}}}. Full-corpus coverage/volume change remains below the small-pack control at8000and16000; original96000schedule retained. No full-corpus+distillation factorial arm is selected from these results.
+
+Allowed-pack metadata/offset audit:0.520215079CPU seconds,0optimizer updates,no series values or validation labels read. Both packs retain min16/maxcrop131072, all95directories have no mixed-frequency rows, source probabilities are unchanged. Conditional real-data length-bin total-variation distance=0.008112016801623342. Largest weighted within-source length shifts: taxi_30min, borg_cluster_data_2011, favorita_sales. No packing-boundary or frequency-label explanation for E110regression was found; broader series/crop diversity and stochastic training remain unresolved. Artifact pack_composition_comparison.json.
+
+Research queue status at2026-10-23T07:29UTC: E119seasonal_dispersion queue19255 waits on E115phase_joint final12000; E121seasonal_history queue20306 waits on E119final12000. E121CPU/GPU audits complete. E120ablation diagnostic complete. E110full-corpus16000 remains below matched E94; no full-corpus+distillation combination launched. Current strongest replayable model remains E117final12000. Refresh packaged journal atomically with identical model/runtime assets.
+
+E115 predeclared6000primary: skill0.37858897993545326,matched E800.3782887645248565,delta+0.00030021541059677137,SHA25603cf2357994ad0c372408331c08129b1f915d049f924eab9fce43fbd7af532e7,eval27.917009s,logged trainingloop1196.1s. Earlier cutoffs running before any transfer decision; no combined phase/seasonal-summary architecture or transfer coefficient is declared yet.
+
+## E115 joint phase6000 valid_backcast
+- Checkpoint SHA-256: `03cf2357994ad0c372408331c08129b1f915d049f924eab9fce43fbd7af532e7`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 25.034706830978394 seconds.
+- Overall skill 0.334541; relative MASE 0.799530; relative WQL 0.553870.
+- term skill: long=0.3988, medium=0.3563, short=0.2994.
+- domain skill: Econ/Fin=0.1510, Energy=0.2274, Healthcare=0.4048, Nature=0.3645, Sales=0.3958, Transport=0.3718, Web/CloudOps=0.4480.
+- frequency skill: A=0.3123, D=0.3497, H=0.3674, M=0.1732, Q=0.2359, S=0.3416, T=0.3315, W=0.2841.
+- variates skill: multivariate=0.3514, univariate=0.3208.
+- Outcome: Matched E80 delta -0.000615748726. All three earlier cutoffs regress; reject6000transfer. Finish predeclared12000durability; no combined phase/long-summary runtime or coefficient sweep.
+
+## E115 joint phase6000 valid_backcast2
+- Checkpoint SHA-256: `03cf2357994ad0c372408331c08129b1f915d049f924eab9fce43fbd7af532e7`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.29227328300476 seconds.
+- Overall skill 0.359464; relative MASE 0.769678; relative WQL 0.533062.
+- term skill: long=0.3641, medium=0.3901, short=0.3455.
+- domain skill: Econ/Fin=0.0960, Energy=0.2364, Healthcare=0.3661, Nature=0.4716, Sales=0.5493, Transport=0.4208, Web/CloudOps=0.4321.
+- frequency skill: A=0.3464, D=0.4240, H=0.3920, M=0.1619, Q=0.1475, S=0.2820, T=0.3595, W=0.2656.
+- variates skill: multivariate=0.3630, univariate=0.3566.
+- Outcome: Matched E80 delta -0.000241820058. All three earlier cutoffs regress; reject6000transfer. Finish predeclared12000durability; no combined phase/long-summary runtime or coefficient sweep.
+
+## E115 joint phase6000 valid_backcast3
+- Checkpoint SHA-256: `03cf2357994ad0c372408331c08129b1f915d049f924eab9fce43fbd7af532e7`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 24.796042680740356 seconds.
+- Overall skill 0.389560; relative MASE 0.736442; relative WQL 0.505996.
+- term skill: long=0.3970, medium=0.3611, short=0.3972.
+- domain skill: Econ/Fin=0.1652, Energy=0.3182, Healthcare=0.2782, Nature=0.5064, Sales=0.3978, Transport=0.5062, Web/CloudOps=0.3915.
+- frequency skill: A=0.3587, D=0.4447, H=0.4046, M=0.1439, Q=0.2561, S=0.2914, T=0.4277, W=0.2621.
+- variates skill: multivariate=0.3724, univariate=0.4028.
+- Outcome: Matched E80 delta -0.000107694093. All three earlier cutoffs regress; reject6000transfer. Finish predeclared12000durability; no combined phase/long-summary runtime or coefficient sweep.
+
+E1156000 selection outcome [{"cutoff": "valid_screen", "skill": 0.37858897993545326, "matched_delta": 0.00030021541059677137}, {"cutoff": "valid_backcast", "skill": 0.3345407161426459, "matched_delta": -0.0006157487262733463}, {"cutoff": "valid_backcast2", "skill": 0.3594639786785041, "matched_delta": -0.00024182005829043174}, {"cutoff": "valid_backcast3", "skill": 0.3895599872359743, "matched_delta": -0.00010769409279154374}]. Reject transfer: main gain is not robust to all three earlier cutoffs. Existing12kdurability checkpoint remains scheduled.
+
+## E122 original AR prior during fresh146M pretraining — predeclared
+The log-uniform slow-AR prior helped a mature patch16 model, but it was not tested as the sole AR law throughout fresh large-model pretraining. E79wide now exceeds37M at48000, while its absolute skill remains below mature models; transferring a late-training prior to an entire random-init schedule is an untested assumption. Compare a fresh146M run that changes only the AR coefficient law in the original20% synthetic stream: U(0.2,0.995), replacing exp(-10**(-U(0,4))). Preserve asymptotic variance scaling, AR branch probability, all other generator components, real windows, source weights, all RNG streams, original own random initialization, seed307/B48/LR2e-4/96000/paired/static-compiled recipe. Control is the already-running E79random_capacity_1024, not a retrained supplied baseline. Reuse its exact model code, initial checkpoint and maximum-shape allocation; no architecture change. Primary48000,4k/8k/16k/24kdiagnostics,72k/96kdurability. Queue only after E79wide finishes96000, replacing an identical memory allocation. CPU audit must show that only synthetic AR rows change and all RNG states/real/non-ARrows match. No new capacity jump, full-corpus combination or teacher is included.
+
+E122 CPU audit {"seed": 307, "batch_rng_seed": 307, "requested_horizon_seed": 300310, "rows": 4800, "synthetic_ar_rows": 481, "changed_rows": 457, "unchanged_rows": 4343, "only_ar_formula_differs_in_sampler_code": true, "model_code_byte_identical": true, "real_and_non_ar_rows_bitwise_equal": true, "periods_and_rng_states_bitwise_equal": true, "source_probabilities_bitwise_equal": true, "initial_checkpoint_sha256": "c9693619ca6277c26fef3b2bde37ff6796bec97b3af9f94588bbc5ddd329ec90", "seconds": 6.526560466038063, "optimizer_updates": 0, "validation_data_read": false}. Only the AR coefficient expression differs in data.py; model code byte-identical to E79. Reuse the exact already-running E79B48/C4096/H1024 compiled allocation after it completes; no extra GPU-memory fixture or baseline retraining needed for a sampler-only change.
+
+E111 weekly seed523 candidate training started at 2026-10-23T07:42:47Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+
+### E94 matched48000 learning-rate comparison — 2026-10-23T07:46:50.490098+00:00
+
+{
+  "experiment": "E94 half-rate versus E79 original-rate fresh146M at48000",
+  "candidate_skill": 0.36570581958166615,
+  "control_skill": 0.3685434345005941,
+  "delta": -0.0028376149189279776,
+  "candidate_hash": "ea9055d683b93a4691e77bd64d46e301302765673055fd9b32ddb3a3e2f749df",
+  "control_hash": "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c",
+  "groups": {
+    "term": {
+      "long": -0.006308080487552492,
+      "medium": 0.0023686540592120497,
+      "short": -0.0035639256416847287
+    },
+    "domain": {
+      "Econ/Fin": -0.0038752234446699507,
+      "Energy": -0.00047244108396105045,
+      "Healthcare": 0.0023903022656310835,
+      "Nature": -0.011893232420320698,
+      "Sales": 0.024694181264939052,
+      "Transport": 0.004949441498781182,
+      "Web/CloudOps": -0.00991799147726491
+    },
+    "frequency": {
+      "A": -0.0009237625994724663,
+      "D": 0.014366973944888062,
+      "H": -0.0027944742503555053,
+      "M": -0.04634901411084125,
+      "Q": 0.013385915439624863,
+      "S": 0.0019980780959804045,
+      "T": -0.0036583096822225825,
+      "W": -0.022089407739222522
+    }
+  },
+  "decision": "Early lower-rate advantage reversed; preserve both predeclared96000 schedules. Validation selection only; no new inference or training in this comparison."
+}
+Logged training-phase wall: E94 10829.8s, E79 11564.9s, both on the shared H100; evaluation38.238023s and37.596497s. No isolated GPU-time claim.
+
+
+### E121 ensemble runtime preparation: first synthetic audit exposed an existing extreme-value composition difference
+
+At 2026-10-23T07:49:34.397201+00:00, the separate history-aware ensemble runtime matched legacy output on all fixtures and matched standalone composition for ordinary, missing, constant, long-history sign/variance-shift rows. Only deliberately scaled1e22 rows failed independent composition: E71/E93 standalone runtimes replaced nonfinite forecasts before output, while the legacy ensemble replaced nonfinites only after averaging, erasing the finite first member contribution. No validation labels were read, no optimizer steps or publication occurred. Preserved first audit code/log/outputs. The candidate runtime now applies each member's existing finite-value cleanup before averaging; rerun checks ordinary legacy equality and exact standalone composition including extremes. Current published runtime remains unchanged pending candidate full replay.
+
+E119 learned phase-specific dispersion seed197 training started at 2026-10-23T07:49:44Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+
+## E123 logarithmically spaced requested horizons — predeclared preparation
+
+2026-10-23T07:50:54.279494+00:00: The current ensemble has lower short-term validation skill than medium/long. E52 already established requested-horizon diversity, but its active prefixes are uniformly betweenH/4 andH, while half of batch horizons are1024. Hypothesis: exposing the same allowed histories/cutoffs to shorter requested horizons improves short-term forecasts without changing source mixture or model. Change only the active half of per-row horizon truncations: reuse each original integer endpoint draw as a quantile, map geometrically frommin(16,max(1,H//4)) toH, then apply the same patch16 rounding. The other half retainH. Consume identical sampler/horizon/batch RNG draws and keep all target values; only mask suffixes earlier. Match completed E80: ownE62initial22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c,seed197/B64/LR2e-5/12000/paired/originalgenerator/oldpack. This differs from queuedE113, which shifts cutoffs and contexts only for low-frequency rows. Primary6000,1000/3000diagnostics,12000durability; compare all terms and all four cutoffs before any transfer. No new transfer or horizon-specific inference rule declared. Planned afterE118, no extra concurrent allocation. CPU stream/mask audit before launch; model/runtime/shapes unchanged fromE80, so no redundant GPU allocation fixture.
+
+
+### E123 CPU stream audit and queue — 2026-10-23T07:51:41.253390+00:00
+
+{
+  "seed": 197,
+  "horizon_seed": 300200,
+  "batches": 100,
+  "rows": 6400,
+  "changed_masks": 2582,
+  "contexts_periods_and_retained_targets_bitwise_equal": true,
+  "sampler_and_horizon_rng_states_bitwise_equal": true,
+  "finite_targets_only_removed": true,
+  "model_code_byte_identical": true,
+  "source_probabilities_bitwise_equal": true,
+  "control_lengths": {
+    "mean": 510.11921875,
+    "median": 505.0,
+    "at_most32": 0.11453125,
+    "at_most128": 0.324375,
+    "above512": 0.4240625,
+    "empty": 28
+  },
+  "candidate_lengths": {
+    "mean": 396.1734375,
+    "median": 144.0,
+    "at_most32": 0.20390625,
+    "at_most128": 0.4959375,
+    "above512": 0.3,
+    "empty": 35
+  },
+  "initial_checkpoint_sha256": "22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c",
+  "seconds": 6.1131663920823485,
+  "optimizer_updates": 0,
+  "validation_data_read": false
+}
+Queued launcher21256/watch21257 afterE118queue18381. Initial/checkpoint/model/runtime unchanged fromE80; no extra concurrent GPU allocation. Seven additional empty-target rows in6400 follow from the shorter masks; the existing finite-target loss handles them without nonfinite loss. No labels used for training or audit.
+
+
+### E121 separate ensemble runtime audit passed — 2026-10-23T07:51:41.253390+00:00
+
+{
+  "seed": 613,
+  "generated_utc": "2026-10-23T07:50:46.528945+00:00",
+  "checks": [
+    {
+      "config": "synthetic_H",
+      "legacy_ordinary_rows_bitwise": true,
+      "composition_bitwise": true,
+      "extreme_legacy_behavior_changed": true,
+      "finite": true,
+      "ordered": true,
+      "n_instances": 12
+    },
+    {
+      "config": "synthetic_15T",
+      "legacy_ordinary_rows_bitwise": true,
+      "composition_bitwise": true,
+      "extreme_legacy_behavior_changed": true,
+      "finite": true,
+      "ordered": true,
+      "n_instances": 12
+    }
+  ],
+  "steps": [
+    {
+      "label": "old_current",
+      "seconds": 14.261059219017625
+    },
+    {
+      "label": "new_current",
+      "seconds": 15.096283270977437
+    },
+    {
+      "label": "mixed",
+      "seconds": 15.924891367089003
+    },
+    {
+      "label": "standalone0",
+      "seconds": 8.318170101149008
+    },
+    {
+      "label": "standalone1",
+      "seconds": 7.596355753019452
+    },
+    {
+      "label": "standalone2",
+      "seconds": 8.01823993306607
+    }
+  ],
+  "lock_wait_seconds": 0.000759027898311615,
+  "seconds": 70.38110470818356,
+  "optimizer_updates": 0,
+  "validation_read": false,
+  "runtime_sha256": "c1876617c78330ba89353f706cfacf7d3671e02a669620a48d9eb7c63e9aa7ef",
+  "note": "First audit exposed pre-existing final-only nonfinite cleanup: one nonfinite member erased finite member contributions. New runtime matches each standalone member cleanup before averaging. Synthetic fixture only; no forecast-score selection, no publication. Own E107 nonzero weights with E121 history limits verify the extended path."
+}
+The original failed extreme-value fixture and code are retained. This is infrastructure only, with no model scoring or publication; any later candidate requires full public replay and composition/relocation checks.
+
+
+### E110 matched24000 corpus comparison — 2026-10-23T07:53:20.957033+00:00
+
+{
+  "experiment": "E110 full corpus versus E94 old pack at24000",
+  "candidate_skill": 0.35536752960369333,
+  "control_skill": 0.3554524492447929,
+  "delta": -8.491964109957895e-05,
+  "candidate_hash": "076fc28ac9f8ee5d8b2c9bc8336f24163a224e2dd26c11dea6320174d716acad",
+  "control_hash": "ee69006629037179a8fd6d09233755cb8efceafb0a47494abceceea3cb7fae52",
+  "groups": {
+    "term": {
+      "long": 0.0066183483633208695,
+      "medium": 0.005359114456888858,
+      "short": -0.00511225167712015
+    },
+    "domain": {
+      "Econ/Fin": 0.01560774310449653,
+      "Energy": 0.005550732858429663,
+      "Healthcare": -0.04555123338041678,
+      "Nature": -0.001542728980177377,
+      "Sales": -0.0010462981174947927,
+      "Transport": -0.002019735208625084,
+      "Web/CloudOps": 0.00285868770816311
+    },
+    "frequency": {
+      "A": 0.0014241864241971713,
+      "D": -0.019689610482476638,
+      "H": 0.006733299077081645,
+      "M": -0.06480975110791165,
+      "Q": -0.0047313751091847545,
+      "S": -0.00307137574664218,
+      "T": 0.0058982745621553034,
+      "W": 0.01652420279074107
+    }
+  },
+  "decision": "Earlier deficit nearly disappears; preserve predeclared96000 schedule. No new full-corpus/distillation combination yet."
+}
+Analysis uses already-completed validation reports, no additional inference or training.
+
+
+### Training-window dynamics audit — 2026-10-23T07:53:20.957033+00:00
+
+{
+  "seed": 619,
+  "rows": 10240,
+  "batches": 160,
+  "groups": {
+    "other_frequency_real": {
+      "rows": 7020,
+      "median_finite_context": 1024.0,
+      "context_at_most128": 0.09373219373219373,
+      "context_above512": 0.6876068376068376,
+      "median_finite_target": 510.0,
+      "dynamic_rows": 6892,
+      "median_abs_recent_change": 0.3054065606608507,
+      "median_abs_future_change": 0.2920584181814329,
+      "recent_future_change_correlation": -0.040732554648212585
+    },
+    "other_frequency_synthetic": {
+      "rows": 1452,
+      "median_finite_context": 2048.0,
+      "context_at_most128": 0.01790633608815427,
+      "context_above512": 0.8271349862258953,
+      "median_finite_target": 512.0,
+      "dynamic_rows": 1449,
+      "median_abs_recent_change": 0.2098600382721559,
+      "median_abs_future_change": 0.20923267260726575,
+      "recent_future_change_correlation": 0.0034826008291495387
+    },
+    "low_frequency_synthetic": {
+      "rows": 612,
+      "median_finite_context": 2048.0,
+      "context_at_most128": 0.024509803921568627,
+      "context_above512": 0.8137254901960784,
+      "median_finite_target": 512.0,
+      "dynamic_rows": 610,
+      "median_abs_recent_change": 0.09875074862712509,
+      "median_abs_future_change": 0.10918120886961286,
+      "recent_future_change_correlation": 0.16770165507227477
+    },
+    "low_frequency_real": {
+      "rows": 1156,
+      "median_finite_context": 22.0,
+      "context_at_most128": 0.9160899653979239,
+      "context_above512": 0.025086505190311418,
+      "median_finite_target": 39.0,
+      "dynamic_rows": 757,
+      "median_abs_recent_change": 0.6538894101925867,
+      "median_abs_future_change": 0.6044534213626241,
+      "recent_future_change_correlation": 0.15421289193025312
+    }
+  },
+  "seconds": 4.563704397995025,
+  "generated_utc": "2026-10-23T07:52:57.305581+00:00",
+  "instrumentation_first640_rows_and_rng_bitwise": true,
+  "optimizer_updates": 0,
+  "validation_data_read": false,
+  "decision": "Descriptive audit only. E113 remains unchanged; no new generator selected."
+}
+The low-frequency context-length mismatch motivates the already queued E113. The normalized change statistic depends on context length and is descriptive, not evidence that a specific synthetic trend law is wrong. No generator modification is selected.
+
+## E115 joint phase final12000 backcast_evals
+- Checkpoint SHA-256: `3881901e309cfe590e5a38fbf36b6d953f1e447a8c1655d30e2d0dcd53b21947`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 25.93891978263855 seconds.
+- Overall skill 0.336448; relative MASE 0.797586; relative WQL 0.552043.
+- term skill: long=0.3976, medium=0.3580, short=0.3028.
+- domain skill: Econ/Fin=0.1584, Energy=0.2302, Healthcare=0.4175, Nature=0.3635, Sales=0.3939, Transport=0.3734, Web/CloudOps=0.4481.
+- frequency skill: A=0.3169, D=0.3539, H=0.3669, M=0.1794, Q=0.2287, S=0.3432, T=0.3347, W=0.2848.
+- variates skill: multivariate=0.3523, univariate=0.3236.
+- Outcome: Final main gain does not extend to any of three earlier cutoffs. Reject transfer; entire scheduled12000 run completed. E119 now uses its allocation.
+
+## E115 joint phase final12000 backcast2_evals
+- Checkpoint SHA-256: `3881901e309cfe590e5a38fbf36b6d953f1e447a8c1655d30e2d0dcd53b21947`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.23729419708252 seconds.
+- Overall skill 0.360471; relative MASE 0.769208; relative WQL 0.531712.
+- term skill: long=0.3639, medium=0.3900, short=0.3474.
+- domain skill: Econ/Fin=0.1009, Energy=0.2376, Healthcare=0.3660, Nature=0.4734, Sales=0.5482, Transport=0.4209, Web/CloudOps=0.4327.
+- frequency skill: A=0.3494, D=0.4273, H=0.3907, M=0.1606, Q=0.1427, S=0.2811, T=0.3608, W=0.2731.
+- variates skill: multivariate=0.3634, univariate=0.3581.
+- Outcome: Final main gain does not extend to any of three earlier cutoffs. Reject transfer; entire scheduled12000 run completed. E119 now uses its allocation.
+
+## E115 joint phase final12000 backcast3_evals
+- Checkpoint SHA-256: `3881901e309cfe590e5a38fbf36b6d953f1e447a8c1655d30e2d0dcd53b21947`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 24.674077033996582 seconds.
+- Overall skill 0.387451; relative MASE 0.740469; relative WQL 0.506727.
+- term skill: long=0.3956, medium=0.3594, short=0.3947.
+- domain skill: Econ/Fin=0.1657, Energy=0.3176, Healthcare=0.2691, Nature=0.5021, Sales=0.3988, Transport=0.5045, Web/CloudOps=0.3895.
+- frequency skill: A=0.3495, D=0.4401, H=0.4024, M=0.1393, Q=0.2584, S=0.2912, T=0.4267, W=0.2614.
+- variates skill: multivariate=0.3719, univariate=0.3994.
+- Outcome: Final main gain does not extend to any of three earlier cutoffs. Reject transfer; entire scheduled12000 run completed. E119 now uses its allocation.
+
+
+### E115 final durability decision — 2026-10-23T07:53:57.917713+00:00
+
+{
+  "experiment": "E115 final12000 durability",
+  "results": [
+    {
+      "cutoff": "evals",
+      "candidate_skill": 0.3778925567049696,
+      "control_skill": 0.37742394607564755,
+      "delta": 0.00046861062932201936,
+      "candidate_hash": "3881901e309cfe590e5a38fbf36b6d953f1e447a8c1655d30e2d0dcd53b21947",
+      "control_hash": "f11d056b95f1c5eeef174265ebc51742952600e0dae1fa52900860447d5be819",
+      "groups": {
+        "term": {
+          "long": 0.0007551829471643678,
+          "medium": 0.0005399265973557998,
+          "short": 0.0003205426244053955
+        },
+        "domain": {
+          "Econ/Fin": 0.0026749976350706195,
+          "Energy": 0.0010280848367260065,
+          "Healthcare": 0.002513727759610118,
+          "Nature": 0.0009755955854692244,
+          "Sales": -0.0010722281827152402,
+          "Transport": 7.34692219485078e-05,
+          "Web/CloudOps": -0.0008110839721524599
+        },
+        "frequency": {
+          "A": 0.0019621222069610145,
+          "D": 0.0017268222442292647,
+          "H": 0.0006945679359758872,
+          "M": 0.0018297098176588422,
+          "Q": -0.0003820175145841098,
+          "S": 0.0005249370631543737,
+          "T": -0.0001720234383183472,
+          "W": -0.0011357933291050726
+        }
+      }
+    },
+    {
+      "cutoff": "backcast_evals",
+      "candidate_skill": 0.3364479011265926,
+      "control_skill": 0.33652833730727594,
+      "delta": -8.04361806833187e-05,
+      "candidate_hash": "3881901e309cfe590e5a38fbf36b6d953f1e447a8c1655d30e2d0dcd53b21947",
+      "control_hash": "f11d056b95f1c5eeef174265ebc51742952600e0dae1fa52900860447d5be819",
+      "groups": {
+        "term": {
+          "long": 0.00016716033873442893,
+          "medium": -0.0007783077314573728,
+          "short": 9.999343723732235e-05
+        },
+        "domain": {
+          "Econ/Fin": 0.0034523972337110465,
+          "Energy": 0.00019134656742725298,
+          "Healthcare": -0.0018918585547146316,
+          "Nature": 6.700484163635334e-05,
+          "Sales": -0.0008093804403472848,
+          "Transport": 0.00011479441302009175,
+          "Web/CloudOps": -0.0007443198776549975
+        },
+        "frequency": {
+          "A": 2.8931234940632322e-05,
+          "D": 0.0015561664379444284,
+          "H": 6.460395876151104e-05,
+          "M": -0.001121458080245663,
+          "Q": 0.00010242765184809155,
+          "S": 0.0013966664704375686,
+          "T": -0.0012845052122840706,
+          "W": 7.914588676039003e-05
+        }
+      }
+    },
+    {
+      "cutoff": "backcast2_evals",
+      "candidate_skill": 0.36047103767996824,
+      "control_skill": 0.36097346960650367,
+      "delta": -0.0005024319265354293,
+      "candidate_hash": "3881901e309cfe590e5a38fbf36b6d953f1e447a8c1655d30e2d0dcd53b21947",
+      "control_hash": "f11d056b95f1c5eeef174265ebc51742952600e0dae1fa52900860447d5be819",
+      "groups": {
+        "term": {
+          "long": -0.0006005654328086196,
+          "medium": -0.0002021528350056867,
+          "short": -0.0005915736701820329
+        },
+        "domain": {
+          "Econ/Fin": 6.3013984400450695e-06,
+          "Energy": -0.0009007624833653471,
+          "Healthcare": 0.0047114825921613646,
+          "Nature": -0.0015395945891927276,
+          "Sales": 0.00014432948317333327,
+          "Transport": 0.0005150338454551484,
+          "Web/CloudOps": -0.0012703692235170871
+        },
+        "frequency": {
+          "A": -0.0015608772340366395,
+          "D": 0.00030188862261526506,
+          "H": 0.0001943772823068679,
+          "M": -0.0005187099890650027,
+          "Q": -0.0005106669012917564,
+          "S": 0.0009339296164969646,
+          "T": -0.0017296888300678548,
+          "W": -0.00136940093009974
+        }
+      }
+    },
+    {
+      "cutoff": "backcast3_evals",
+      "candidate_skill": 0.387451491496983,
+      "control_skill": 0.3874814849803516,
+      "delta": -2.9993483368628837e-05,
+      "candidate_hash": "3881901e309cfe590e5a38fbf36b6d953f1e447a8c1655d30e2d0dcd53b21947",
+      "control_hash": "f11d056b95f1c5eeef174265ebc51742952600e0dae1fa52900860447d5be819",
+      "groups": {
+        "term": {
+          "long": 0.00030593631400455124,
+          "medium": 0.0002832620919390738,
+          "short": -0.0002578885558356392
+        },
+        "domain": {
+          "Econ/Fin": 0.0007833141379801623,
+          "Energy": -0.0006056144184419798,
+          "Healthcare": -0.0027216797213800437,
+          "Nature": 0.00048301125176675175,
+          "Sales": -8.841270514148292e-05,
+          "Transport": 0.0003993064983340844,
+          "Web/CloudOps": 0.0003902518340567207
+        },
+        "frequency": {
+          "A": 0.0016810475853026308,
+          "D": -0.00056261654858758,
+          "H": -0.00019302241203689174,
+          "M": 0.002605153074515587,
+          "Q": -0.0012757918233360366,
+          "S": 0.0028281089083405098,
+          "T": -0.000444785271267234,
+          "W": 0.0005135461601460944
+        }
+      }
+    }
+  ],
+  "mean_diagnostic_skill_delta": -3.606274031633938e-05,
+  "decision": "Reject transfer; main-only gain, all three earlier diagnostics lower. E115 complete, no combined phase/seasonal model created."
+}
+Trainingloop2333.941144s; process2350.583369s on sharedH100. No transfer assembled or published.
+
+
+### E111 seed523 matched3000 diagnostic
+
+{
+  "experiment": "E111 seed523 weekly synthesis at3000",
+  "candidate_skill": 0.3797743795241497,
+  "control_skill": 0.37742318829464827,
+  "delta": 0.002351191229501448,
+  "candidate_hash": "ba3fcd7179fb75b8b62a3907ef4dff96c49145d230bf299c2fc36f362b85ebd3",
+  "control_hash": "feaf2ac11b13882c968b73d445e377276bb0278c9a59e1345806ac33f90e1901",
+  "groups": {
+    "term": {
+      "long": -0.0003734133982308263,
+      "medium": -0.00016266512388696253,
+      "short": 0.004502223173963982
+    },
+    "domain": {
+      "Econ/Fin": -0.004187422115931705,
+      "Energy": 0.0001287333424395687,
+      "Healthcare": 0.040625614337162164,
+      "Nature": 0.0017461734714656618,
+      "Sales": 0.0005922345668298856,
+      "Transport": 0.0003277958817268223,
+      "Web/CloudOps": -0.001337662723777444
+    },
+    "frequency": {
+      "A": -0.00028746342219676535,
+      "D": -3.56775488330463e-05,
+      "H": -6.780036292286518e-05,
+      "M": 0.02708306492322632,
+      "Q": 0.00040066853047870943,
+      "S": 3.753362014269346e-05,
+      "T": -0.0001576457412183485,
+      "W": 0.021646690122148926
+    }
+  },
+  "decision": "Positive early replication; wait primary6000 and earlier cutoffs. No new transfer/averaging rule."
+}
+Uses completed watcher reports only; no additional training or inference.
+
+
+## E124 equal-seed weekly correction — predeclared conditional selection
+
+Hypothesis: averaging the matched weekly-synthetic training differences across seeds197 and523 reduces seed-specific noise without adding inference cost. Freeze the current E117final12000 first member as base (SHA8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4). One candidate, coefficient0.5, duration6000 only: base +0.5*((weekly523_6000-control523_6000)-(weekly197_6000-control197_6000)). Apply this to shared parameters only; the base's three added long-seasonal tensors stay bitwise unchanged. Existing spectral/slow-AR/long-seasonal corrections remain present. E71patch8_6000/E93split_12000 stay unchanged, fixed equal three-member output average, all members forecast every instance. No coefficient/duration sweep.
+
+Proceed only if E111primary6000 beats its seed523control on at least3 of4diagnostic cutoffs with positive mean skill difference. Then evaluate the single fixed merged checkpoint and equal ensemble at all four cutoffs; advance to full replay only for at least3of4ensemble gains and positive mean versus immutableE117final12000. Publication additionally requires full97configuration improvement, no failures, relocation/determinism and independent composition checks. No optimizer updates or validation training. The comparison of3k is already positive; primary6k and earlier candidate metrics are still unavailable at declaration. Use the unchanged published ensemble runtime for this study; the separately prepared E121runtime is a different factor.
+Declared 2026-10-23T07:57:32.400427+00:00.
+
+
+### E119 matched3000 dispersion diagnostic
+
+{
+  "experiment": "E119 dispersion versus E107 at3000",
+  "candidate_skill": 0.3815441525201153,
+  "control_skill": 0.3817505923699258,
+  "delta": -0.00020643984981050245,
+  "candidate_hash": "a78e34a619ace1c6ba1867d98496b7537c8ce8e449444e42ce7eb9e09f8f1a54",
+  "control_hash": "92e575ca3cb053d9bdf75a010d83ded0025fe97c906fb9930c74dc1a28e1e892",
+  "groups": {
+    "term": {
+      "long": -1.2229681438435946e-05,
+      "medium": 7.023840094277922e-05,
+      "short": -0.0003995973540478204
+    },
+    "domain": {
+      "Econ/Fin": 0.00027946829089275926,
+      "Energy": -7.99416262584618e-05,
+      "Healthcare": -0.0020021058239552136,
+      "Nature": -0.00020712969475200627,
+      "Sales": -4.6786221165273645e-05,
+      "Transport": 0.00018673388204337726,
+      "Web/CloudOps": -0.0003041789689921526
+    },
+    "frequency": {
+      "A": 0.00036916672182307764,
+      "D": -0.0009145361219375925,
+      "H": -0.00031670885705470386,
+      "M": 0.00037779472561139116,
+      "Q": 0.00012017765676031189,
+      "S": -0.000309243994029762,
+      "T": 4.948408615323974e-05,
+      "W": 0.0003294878154047609
+    }
+  },
+  "decision": "No early gain; retain predeclared6000primary/12000durability. No transfer."
+}
+Comparison uses completed reports only; no additional inference or optimizer updates.
+
+
+### Frozen-teacher compiler audit — engineering preparation, no training change
+
+Hypothesis: compiling the three frozen E96 teacher forwards can reduce the large per-update cost of E101. Benchmark the existing eager teacher versus torch.compile(fullgraph=True,dynamic=False) on locally generated fixture contexts/targets at training shapes, with unchanged E96 weights, sign pairing, finite masks and forecast lengths. Compare quantiles/masks and shared-H100 timing; no validation data or optimizer updates. This does not authorize silently changing the active E101 implementation. If outputs differ, any later adoption must be explicitly recorded and evaluated as a continuation variant. No compiler target or training coefficient is changed by this audit.
+
+## E111 primary6000 candidate valid_backcast
+- Checkpoint SHA-256: `8c67d1285a467d32a4e758292bb1668dbaaa96c3fd6599a04653285db2e67792`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.258385181427002 seconds.
+- Overall skill 0.336681; relative MASE 0.798249; relative WQL 0.551197.
+- term skill: long=0.3978, medium=0.3581, short=0.3031.
+- domain skill: Econ/Fin=0.1578, Energy=0.2287, Healthcare=0.4099, Nature=0.3658, Sales=0.3992, Transport=0.3748, Web/CloudOps=0.4492.
+- frequency skill: A=0.3239, D=0.3524, H=0.3666, M=0.1758, Q=0.2291, S=0.3414, T=0.3354, W=0.2922.
+- variates skill: multivariate=0.3524, univariate=0.3239.
+- Outcome: Independent-seed matched replication; no validation training. Four-cutoff comparison determines the predeclared E124 gate.
+
+## E111 primary6000 control valid_backcast
+- Checkpoint SHA-256: `674137ac14bce26f6983cb68123e34d01ca598bb3ad72e81cbd197d138ed6104`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 25.904212474822998 seconds.
+- Overall skill 0.336863; relative MASE 0.797598; relative WQL 0.551344.
+- term skill: long=0.3975, medium=0.3578, short=0.3036.
+- domain skill: Econ/Fin=0.1614, Energy=0.2300, Healthcare=0.4123, Nature=0.3633, Sales=0.3981, Transport=0.3741, Web/CloudOps=0.4494.
+- frequency skill: A=0.3229, D=0.3533, H=0.3666, M=0.1824, Q=0.2284, S=0.3417, T=0.3358, W=0.2875.
+- variates skill: multivariate=0.3531, univariate=0.3237.
+- Outcome: Independent-seed matched replication; no validation training. Four-cutoff comparison determines the predeclared E124 gate.
+
+## E111 primary6000 candidate valid_backcast2
+- Checkpoint SHA-256: `8c67d1285a467d32a4e758292bb1668dbaaa96c3fd6599a04653285db2e67792`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 24.067421197891235 seconds.
+- Overall skill 0.358345; relative MASE 0.770722; relative WQL 0.534203.
+- term skill: long=0.3618, medium=0.3869, short=0.3456.
+- domain skill: Econ/Fin=0.1061, Energy=0.2376, Healthcare=0.3412, Nature=0.4706, Sales=0.5470, Transport=0.4219, Web/CloudOps=0.4299.
+- frequency skill: A=0.3474, D=0.4283, H=0.3890, M=0.1688, Q=0.1508, S=0.2742, T=0.3595, W=0.2563.
+- variates skill: multivariate=0.3623, univariate=0.3551.
+- Outcome: Independent-seed matched replication; no validation training. Four-cutoff comparison determines the predeclared E124 gate.
+
+## E111 primary6000 control valid_backcast2
+- Checkpoint SHA-256: `674137ac14bce26f6983cb68123e34d01ca598bb3ad72e81cbd197d138ed6104`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 23.221482515335083 seconds.
+- Overall skill 0.359281; relative MASE 0.770398; relative WQL 0.532868.
+- term skill: long=0.3623, medium=0.3874, short=0.3469.
+- domain skill: Econ/Fin=0.1069, Energy=0.2374, Healthcare=0.3682, Nature=0.4678, Sales=0.5477, Transport=0.4215, Web/CloudOps=0.4305.
+- frequency skill: A=0.3465, D=0.4288, H=0.3891, M=0.1732, Q=0.1502, S=0.2762, T=0.3596, W=0.2634.
+- variates skill: multivariate=0.3628, univariate=0.3565.
+- Outcome: Independent-seed matched replication; no validation training. Four-cutoff comparison determines the predeclared E124 gate.
+
+## E111 primary6000 candidate valid_backcast3
+- Checkpoint SHA-256: `8c67d1285a467d32a4e758292bb1668dbaaa96c3fd6599a04653285db2e67792`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 24.324751377105713 seconds.
+- Overall skill 0.389207; relative MASE 0.737424; relative WQL 0.505907.
+- term skill: long=0.3942, medium=0.3592, short=0.3983.
+- domain skill: Econ/Fin=0.1642, Energy=0.3173, Healthcare=0.2941, Nature=0.5034, Sales=0.3981, Transport=0.5056, Web/CloudOps=0.3912.
+- frequency skill: A=0.3470, D=0.4390, H=0.4042, M=0.1470, Q=0.2543, S=0.2835, T=0.4301, W=0.2684.
+- variates skill: multivariate=0.3734, univariate=0.4014.
+- Outcome: Independent-seed matched replication; no validation training. Four-cutoff comparison determines the predeclared E124 gate.
+
+## E111 primary6000 control valid_backcast3
+- Checkpoint SHA-256: `674137ac14bce26f6983cb68123e34d01ca598bb3ad72e81cbd197d138ed6104`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 23.96277379989624 seconds.
+- Overall skill 0.389075; relative MASE 0.736946; relative WQL 0.506454.
+- term skill: long=0.3937, medium=0.3598, short=0.3979.
+- domain skill: Econ/Fin=0.1644, Energy=0.3181, Healthcare=0.2751, Nature=0.5053, Sales=0.3977, Transport=0.5053, Web/CloudOps=0.3920.
+- frequency skill: A=0.3453, D=0.4399, H=0.4047, M=0.1292, Q=0.2542, S=0.2852, T=0.4297, W=0.2702.
+- variates skill: multivariate=0.3739, univariate=0.4008.
+- Outcome: Independent-seed matched replication; no validation training. Four-cutoff comparison determines the predeclared E124 gate.
+
+
+### E111 primary replication / E124 conditional gate outcome — 2026-10-23T08:04:25.847630+00:00
+
+{
+  "results": [
+    {
+      "cutoff": "valid_screen",
+      "candidate_skill": 0.37996963634005054,
+      "control_skill": 0.3764059869702334,
+      "delta": 0.0035636493698171234,
+      "candidate_hash": "8c67d1285a467d32a4e758292bb1668dbaaa96c3fd6599a04653285db2e67792",
+      "control_hash": "674137ac14bce26f6983cb68123e34d01ca598bb3ad72e81cbd197d138ed6104"
+    },
+    {
+      "cutoff": "valid_backcast",
+      "candidate_skill": 0.3366810094726821,
+      "control_skill": 0.33686271636038767,
+      "delta": -0.0001817068877055794,
+      "candidate_hash": "8c67d1285a467d32a4e758292bb1668dbaaa96c3fd6599a04653285db2e67792",
+      "control_hash": "674137ac14bce26f6983cb68123e34d01ca598bb3ad72e81cbd197d138ed6104"
+    },
+    {
+      "cutoff": "valid_backcast2",
+      "candidate_skill": 0.3583446416420619,
+      "control_skill": 0.3592811180029256,
+      "delta": -0.0009364763608636917,
+      "candidate_hash": "8c67d1285a467d32a4e758292bb1668dbaaa96c3fd6599a04653285db2e67792",
+      "control_hash": "674137ac14bce26f6983cb68123e34d01ca598bb3ad72e81cbd197d138ed6104"
+    },
+    {
+      "cutoff": "valid_backcast3",
+      "candidate_skill": 0.38920731977391243,
+      "control_skill": 0.38907521863271344,
+      "delta": 0.00013210114119899252,
+      "candidate_hash": "8c67d1285a467d32a4e758292bb1668dbaaa96c3fd6599a04653285db2e67792",
+      "control_hash": "674137ac14bce26f6983cb68123e34d01ca598bb3ad72e81cbd197d138ed6104"
+    }
+  ],
+  "mean_skill_delta": 0.0006443918156117112,
+  "decision": "E124 not run: primary E111replication has only2of4positive cutoffs, failing predeclared3of4gate despite positive mean. No merged checkpoint, no ensemble probe/full replay or publication. E11112000durability remains planned."
+}
+E124selection runner 326.826085990062seconds wall including waiting; only E111diagnostic inference was performed. E124model algebra, CPUforward and ensemble/full-suite code were prepared but not executed.
+
+
+## E125 seasonal-query versus direct-median contribution — predeclared diagnostic
+
+2026-10-23T08:05:46.953758+00:00: E120 established that removing both learned seasonal paths hurts all four cutoffs. Resolve which path drives that effect using two fixed inference ablations of the immutable E107EMA6000(ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253): (a)zero long_query.weight only, leaving learned direct median gates; (b)zero long_head.weight and bias only, leaving decoder query features. All other weights, model graph, normalization and fixed forecast rules stay unchanged. No optimizer updates, no new training data or coefficients. CPUfixture seed647 checks unchanged tensors, identical normalization, finite ordered forecasts and nonzero functional change; then evaluate all four cutoffs. This is a mechanism diagnostic, not an automatic publication candidate or transfer. Any follow-up architecture decision must be declared separately.
+
+## E125 seasonal_no_query6k evals
+- Checkpoint SHA-256: `3670e55131f07c6d4b31daaeb57e6678c3722d0eb11308f7cadc3b01730dc8e4`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.681535482406616 seconds.
+- Overall skill 0.381850; relative MASE 0.739570; relative WQL 0.516664.
+- term skill: long=0.4049, medium=0.4080, short=0.3624.
+- domain skill: Econ/Fin=0.1268, Energy=0.2410, Healthcare=0.4571, Nature=0.4342, Sales=0.3405, Transport=0.4758, Web/CloudOps=0.5052.
+- frequency skill: A=0.3682, D=0.3687, H=0.4005, M=0.1021, Q=0.1933, S=0.4260, T=0.4394, W=0.2208.
+- variates skill: multivariate=0.3987, univariate=0.3681.
+- Outcome: Fixed component ablation of E1076000; mechanism diagnostic only, no training or automatic publication.
+
+## E125 seasonal_no_query6k backcast_evals
+- Checkpoint SHA-256: `3670e55131f07c6d4b31daaeb57e6678c3722d0eb11308f7cadc3b01730dc8e4`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.842697381973267 seconds.
+- Overall skill 0.339286; relative MASE 0.794169; relative WQL 0.549686.
+- term skill: long=0.4023, medium=0.3606, short=0.3049.
+- domain skill: Econ/Fin=0.1476, Energy=0.2303, Healthcare=0.3882, Nature=0.3808, Sales=0.4041, Transport=0.3795, Web/CloudOps=0.4509.
+- frequency skill: A=0.3114, D=0.3658, H=0.3739, M=0.1675, Q=0.2373, S=0.3420, T=0.3328, W=0.2810.
+- variates skill: multivariate=0.3547, univariate=0.3267.
+- Outcome: Fixed component ablation of E1076000; mechanism diagnostic only, no training or automatic publication.
+
+## E125 seasonal_no_query6k backcast2_evals
+- Checkpoint SHA-256: `3670e55131f07c6d4b31daaeb57e6678c3722d0eb11308f7cadc3b01730dc8e4`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 27.00600552558899 seconds.
+- Overall skill 0.362807; relative MASE 0.766129; relative WQL 0.529956.
+- term skill: long=0.3663, medium=0.3955, short=0.3484.
+- domain skill: Econ/Fin=0.0932, Energy=0.2386, Healthcare=0.3622, Nature=0.4806, Sales=0.5520, Transport=0.4264, Web/CloudOps=0.4333.
+- frequency skill: A=0.3460, D=0.4301, H=0.3964, M=0.1803, Q=0.1484, S=0.2837, T=0.3610, W=0.2608.
+- variates skill: multivariate=0.3655, univariate=0.3606.
+- Outcome: Fixed component ablation of E1076000; mechanism diagnostic only, no training or automatic publication.
+
+## E125 seasonal_no_query6k backcast3_evals
+- Checkpoint SHA-256: `3670e55131f07c6d4b31daaeb57e6678c3722d0eb11308f7cadc3b01730dc8e4`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 27.12690496444702 seconds.
+- Overall skill 0.394311; relative MASE 0.730710; relative WQL 0.502059.
+- term skill: long=0.3994, medium=0.3622, short=0.4040.
+- domain skill: Econ/Fin=0.1623, Energy=0.3228, Healthcare=0.3091, Nature=0.5086, Sales=0.4035, Transport=0.5116, Web/CloudOps=0.3936.
+- frequency skill: A=0.3574, D=0.4569, H=0.4096, M=0.1695, Q=0.2587, S=0.2912, T=0.4283, W=0.2619.
+- variates skill: multivariate=0.3765, univariate=0.4080.
+- Outcome: Fixed component ablation of E1076000; mechanism diagnostic only, no training or automatic publication.
+
+
+## E126 bounded compiled-teacher continuation — predeclared preparation
+
+2026-10-23T08:12:21.857671+00:00: The synthetic compiler audit shows1.48–2.00x faster frozen E96teacher calls under sharedH100 load, with identical validity masks but non-bitwise quantiles (relative RMSdifference~0.00035–0.00047). E101distillation remains useful at16000:0.363473693 versus matchedE940.354615664. Test numerical training impact before changing the original implementation. Start from immutable complete E101step16000 recovery SHA81e71006dd9cd52612d203e96c8cc658b80118c061b0b5d8720b37da1c3e0935, preserve Adam/EMA/allRNG/prefetched batch and original96000LRschedule, seed307/B48/oldpack/slowAR/paired/studentcompiled/teacherE96coefficient0.5. Only change frozen teacher calls to torch.compile(fullgraph=True,dynamic=False). Stop at17000 (1000new updates), compare with the original E101saved17000 on all four diagnostic cutoffs. Keep the original E101running.
+
+Before launch, a maxB48C4096H1024 audit with the mature16000student must show finite gradients, loss agreement within0.3%, gradientcosine>.995, identical teacher validity, and reservedmemory<14GiB including Adam/EMA placeholders. A temporary seventh process is permitted only after measuring enough free memory on the same singleH100; no second GPU or additional CPU allocation. This trial makes no publication or automatic takeover. Later adoption requires explicit review of timings/numerical divergence and model forecasts; do not silently combine with teacher-coefficient annealing, teacher refresh, full corpus or another factor. The1000-update probe also warms teacher compiled shapes; report cold compile and training time honestly.
+
+Completed teacher compiler audit: {"status": "complete", "seed": 641, "teacher_descriptor_sha256": "1503d92ce031ec0c0e0533ece20d0516a0eceb134802d51746e24a44c4b5ee9b", "rows": [{"context": 512, "horizon": 32, "batch": 48, "compile_first_call_seconds": 78.96409600507468, "mask_bitwise_equal": true, "quantiles_bitwise_equal": false, "maximum_absolute_difference": 0.01088714599609375, "mean_absolute_difference": 0.0005115967942401767, "rms_difference": 0.0007116682245396078, "eager_quantile_rms": 1.515346646308899, "valid_rows": 47, "seconds_per_call": {"eager_first": 0.095995718089398, "compiled_first": 0.04834699417309215, "compiled_second": 0.04806781424364696, "eager_second": 0.09657300100661814}, "peak_reserved_mib": 764.0}, {"context": 2048, "horizon": 512, "batch": 48, "compile_first_call_seconds": 70.28295603999868, "mask_bitwise_equal": true, "quantiles_bitwise_equal": false, "maximum_absolute_difference": 0.03644561767578125, "mean_absolute_difference": 0.0005734049482271075, "rms_difference": 0.0012666580732911825, "eager_quantile_rms": 3.636091947555542, "valid_rows": 47, "seconds_per_call": {"eager_first": 0.24695850332500413, "compiled_first": 0.15215913150071478, "compiled_second": 0.1510773518239148, "eager_second": 0.23846306465566158}, "peak_reserved_mib": 1586.0}, {"context": 4096, "horizon": 1024, "batch": 48, "compile_first_call_seconds": 71.95634523383342, "mask_bitwise_equal": true, "quantiles_bitwise_equal": false, "maximum_absolute_difference": 0.05052947998046875, "mean_absolute_difference": 0.0005942085990682244, "rms_difference": 0.001294782035984099, "eager_quantile_rms": 3.725360870361328, "valid_rows": 47, "seconds_per_call": {"eager_first": 0.45826623084334034, "compiled_first": 0.30095767183229327, "compiled_second": 0.34795630965769914, "eager_second": 0.5011981244121367}, "peak_reserved_mib": 2738.0}], "lock_wait_seconds": 0.000400397926568985, "seconds": 271.9287391670514, "generated_utc": "2026-10-23T08:08:27.316747+00:00", "optimizer_updates": 0, "validation_data_read": false, "active_E101_modified": false, "note": "Times are wall-clock measurements under shared single-H100 load, not isolated kernel speed. No training implementation selected by this audit."}
+
+Immutable16000 recovery snapshot: {
+  "path": "/workspace/research/distill_ensemble_recovery016000.pt",
+  "sha256": "81e71006dd9cd52612d203e96c8cc658b80118c061b0b5d8720b37da1c3e0935",
+  "step": 16000,
+  "complete_optimizer_ema_rng_prefetched_batch": true,
+  "verification_and_hash_seconds": 3.710943895857781,
+  "utc": "2026-10-23T08:07:09.849318+00:00",
+  "reason": "Immutable branch point for possible later declared distillation schedule/compiler study; no variant selected or launched.",
+  "note": "Initial copy succeeded at16000; assertion incorrectly expected key ema rather than ema_state. Verified/reused that same immutable temporary copy, without rereading mutable live recovery. No training state modified."
+}
+
+
+### E101 matched16000 distillation diagnostic — 2026-10-23T08:13:48.893370+00:00
+
+{
+  "experiment": "E101 own-ensemble distillation versus E94 at16000",
+  "candidate_skill": 0.3634736933529894,
+  "control_skill": 0.35461566373218645,
+  "delta": 0.008858029620802954,
+  "candidate_hash": "7adc72c9070576fd9c3d4d0f24f0540426e65b83badbe59537ca0e58b4843d34",
+  "control_hash": "19f9ead156ea9bc4a3edca1303bc5595057b2e1b89ff88e7bb7d9acf39618ba5",
+  "groups": {
+    "term": {
+      "long": 0.005875203966622888,
+      "medium": 0.008206541750095275,
+      "short": 0.010341209368318616
+    },
+    "domain": {
+      "Econ/Fin": 0.011618545269489577,
+      "Energy": 0.012045294079029989,
+      "Healthcare": 0.03736270098484029,
+      "Nature": -0.007342708743686921,
+      "Sales": -8.327111726524894e-05,
+      "Transport": 0.007870063310169595,
+      "Web/CloudOps": 0.01106951784068222
+    },
+    "frequency": {
+      "A": 0.014232599133729873,
+      "D": -0.013505393467374338,
+      "H": 0.011650739340949401,
+      "M": -0.015525930901423846,
+      "Q": 0.016462852241035608,
+      "S": 0.0030705164143619568,
+      "T": 0.020171322356402066,
+      "W": 0.004779721931400305
+    }
+  },
+  "decision": "Strong update-count gain persists. OriginalE101 recipe stays fixed; separate E126 bounded compiler probe prepared, not coefficient annealing. Shared-wall efficiency requires measured comparison, not update counts alone."
+}
+E101loop8126.2s at16000; evaluation37.404650s under sharedH100.
+
+
+### E125 component preparation audit
+
+{
+  "seed": 647,
+  "rows": [
+    {
+      "name": "seasonal_no_query6k",
+      "checkpoint_sha256": "3670e55131f07c6d4b31daaeb57e6678c3722d0eb11308f7cadc3b01730dc8e4",
+      "method": "fixed single-component inference ablation",
+      "zeroed_tensors": [
+        "long_query.weight"
+      ],
+      "all_other_tensors_bitwise_unchanged": true,
+      "normalization_bitwise_unchanged": true,
+      "finite_ordered_fixture": true,
+      "fixture_maximum_quantile_change": 0.019054830074310303,
+      "check_seed": 647,
+      "parent_sha256": "ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253",
+      "optimizer_updates": 0,
+      "validation_data_read": false
+    },
+    {
+      "name": "seasonal_no_head6k",
+      "checkpoint_sha256": "97318093a014742f4968ef363909b072748176288da86032153f97601960afcc",
+      "method": "fixed single-component inference ablation",
+      "zeroed_tensors": [
+        "long_head.weight",
+        "long_head.bias"
+      ],
+      "all_other_tensors_bitwise_unchanged": true,
+      "normalization_bitwise_unchanged": true,
+      "finite_ordered_fixture": true,
+      "fixture_maximum_quantile_change": 0.2622612714767456,
+      "check_seed": 647,
+      "parent_sha256": "ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253",
+      "optimizer_updates": 0,
+      "validation_data_read": false
+    }
+  ],
+  "seconds": 2.5344709930941463,
+  "optimizer_updates": 0,
+  "validation_data_read": false
+}
+Controller22069 started08:06:33UTC, awaited compiler audit, now evaluates all four cutoffs for both ablations.
+
+## E125 seasonal_no_head6k evals
+- Checkpoint SHA-256: `97318093a014742f4968ef363909b072748176288da86032153f97601960afcc`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 31.10630750656128 seconds.
+- Overall skill 0.379404; relative MASE 0.743137; relative WQL 0.518263.
+- term skill: long=0.4015, medium=0.4071, short=0.3597.
+- domain skill: Econ/Fin=0.1249, Energy=0.2395, Healthcare=0.4706, Nature=0.4303, Sales=0.3367, Transport=0.4669, Web/CloudOps=0.5039.
+- frequency skill: A=0.3682, D=0.3577, H=0.3975, M=0.1101, Q=0.1913, S=0.4253, T=0.4375, W=0.2295.
+- variates skill: multivariate=0.3973, univariate=0.3648.
+- Outcome: Fixed component ablation of E1076000; mechanism diagnostic only, no training or automatic publication.
+
+## E125 seasonal_no_head6k backcast_evals
+- Checkpoint SHA-256: `97318093a014742f4968ef363909b072748176288da86032153f97601960afcc`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.683140993118286 seconds.
+- Overall skill 0.335854; relative MASE 0.797806; relative WQL 0.552878.
+- term skill: long=0.3983, medium=0.3574, short=0.3016.
+- domain skill: Econ/Fin=0.1452, Energy=0.2276, Healthcare=0.4119, Nature=0.3667, Sales=0.3969, Transport=0.3728, Web/CloudOps=0.4503.
+- frequency skill: A=0.3114, D=0.3508, H=0.3678, M=0.1843, Q=0.2358, S=0.3419, T=0.3329, W=0.2854.
+- variates skill: multivariate=0.3526, univariate=0.3222.
+- Outcome: Fixed component ablation of E1076000; mechanism diagnostic only, no training or automatic publication.
+
+## E125 seasonal_no_head6k backcast2_evals
+- Checkpoint SHA-256: `97318093a014742f4968ef363909b072748176288da86032153f97601960afcc`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.467807054519653 seconds.
+- Overall skill 0.359182; relative MASE 0.769824; relative WQL 0.533430.
+- term skill: long=0.3643, medium=0.3894, short=0.3452.
+- domain skill: Econ/Fin=0.0923, Energy=0.2361, Healthcare=0.3632, Nature=0.4715, Sales=0.5464, Transport=0.4215, Web/CloudOps=0.4328.
+- frequency skill: A=0.3460, D=0.4231, H=0.3914, M=0.1626, Q=0.1474, S=0.2815, T=0.3603, W=0.2637.
+- variates skill: multivariate=0.3629, univariate=0.3562.
+- Outcome: Fixed component ablation of E1076000; mechanism diagnostic only, no training or automatic publication.
+
+## E125 seasonal_no_head6k backcast3_evals
+- Checkpoint SHA-256: `97318093a014742f4968ef363909b072748176288da86032153f97601960afcc`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 27.682344436645508 seconds.
+- Overall skill 0.390150; relative MASE 0.735335; relative WQL 0.505780.
+- term skill: long=0.3966, medium=0.3616, short=0.3981.
+- domain skill: Econ/Fin=0.1622, Energy=0.3185, Healthcare=0.2876, Nature=0.5074, Sales=0.3983, Transport=0.5072, Web/CloudOps=0.3905.
+- frequency skill: A=0.3574, D=0.4483, H=0.4039, M=0.1441, Q=0.2559, S=0.2900, T=0.4285, W=0.2615.
+- variates skill: multivariate=0.3721, univariate=0.4040.
+- Outcome: Fixed component ablation of E1076000; mechanism diagnostic only, no training or automatic publication.
+
+E126 bounded compiled-teacher probe16000to17000 training started at 2026-10-23T08:15:29Z, as an authorized immediate bounded trial; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+
+## E127 preserve each member's finite forecast fallback before averaging — declared numerical fix
+
+The E121 synthetic runtime audit exposed an existing composition mismatch on1e22contexts: E71/E93 standalone outputs replace nonfinite forecasts before writing, while the ensemble replaced nonfinites only after averaging, erasing the finite first-member contribution. Stage the checked history_three_patch_ensemble runtime (predict SHA c1876617c78330ba89353f706cfacf7d3671e02a669620a48d9eb7c63e9aa7ef) with exactly the published E117final/E71/E93weights. The added per-member history-cap support is inactive for these4096-context models. No model parameter, training data, source mixture or ensemble weight changes. Fixed inference seed0; synthetic fixture seed613.
+
+Require a fresh full public replay on97configs/151170instances with0failures, bitwise equality of every mean/quantile array to the existing E117full predictions, relocation/determinism, existing independent three-config member composition, and an independent synthetic extreme-value composition test using the actual published first member. If any ordinary/full output differs, stop and diagnose rather than automatically publish. On success the validation skill is unchanged and the numerical contract is more faithful; publish atomically and record the new runtime hash. This is not a forecast-score gain or a new trained model.
+
+
+### E125 component diagnostic completed — 2026-10-23T08:19:13.642566+00:00
+
+[
+  {
+    "name": "seasonal_no_query6k",
+    "cutoff": "evals",
+    "skill": 0.3818501008693248,
+    "control_skill": 0.38227466722292214,
+    "delta": -0.0004245663535973332,
+    "checkpoint_sha256": "3670e55131f07c6d4b31daaeb57e6678c3722d0eb11308f7cadc3b01730dc8e4"
+  },
+  {
+    "name": "seasonal_no_query6k",
+    "cutoff": "backcast_evals",
+    "skill": 0.33928591943676045,
+    "control_skill": 0.33985253853394604,
+    "delta": -0.0005666190971855833,
+    "checkpoint_sha256": "3670e55131f07c6d4b31daaeb57e6678c3722d0eb11308f7cadc3b01730dc8e4"
+  },
+  {
+    "name": "seasonal_no_query6k",
+    "cutoff": "backcast2_evals",
+    "skill": 0.3628070915496776,
+    "control_skill": 0.3632547284652886,
+    "delta": -0.00044763691561100494,
+    "checkpoint_sha256": "3670e55131f07c6d4b31daaeb57e6678c3722d0eb11308f7cadc3b01730dc8e4"
+  },
+  {
+    "name": "seasonal_no_query6k",
+    "cutoff": "backcast3_evals",
+    "skill": 0.39431067840000456,
+    "control_skill": 0.3944984110874522,
+    "delta": -0.00018773268744765037,
+    "checkpoint_sha256": "3670e55131f07c6d4b31daaeb57e6678c3722d0eb11308f7cadc3b01730dc8e4"
+  },
+  {
+    "name": "seasonal_no_head6k",
+    "cutoff": "evals",
+    "skill": 0.37940363906366914,
+    "control_skill": 0.38227466722292214,
+    "delta": -0.0028710281592529974,
+    "checkpoint_sha256": "97318093a014742f4968ef363909b072748176288da86032153f97601960afcc"
+  },
+  {
+    "name": "seasonal_no_head6k",
+    "cutoff": "backcast_evals",
+    "skill": 0.33585434720380203,
+    "control_skill": 0.33985253853394604,
+    "delta": -0.0039981913301440075,
+    "checkpoint_sha256": "97318093a014742f4968ef363909b072748176288da86032153f97601960afcc"
+  },
+  {
+    "name": "seasonal_no_head6k",
+    "cutoff": "backcast2_evals",
+    "skill": 0.3591824992979731,
+    "control_skill": 0.3632547284652886,
+    "delta": -0.004072229167315533,
+    "checkpoint_sha256": "97318093a014742f4968ef363909b072748176288da86032153f97601960afcc"
+  },
+  {
+    "name": "seasonal_no_head6k",
+    "cutoff": "backcast3_evals",
+    "skill": 0.3901496414883441,
+    "control_skill": 0.3944984110874522,
+    "delta": -0.0043487695991081265,
+    "checkpoint_sha256": "97318093a014742f4968ef363909b072748176288da86032153f97601960afcc"
+  }
+]
+Both ablations lose on all four cutoffs. Removing the direct median head has the larger effect. This is conditional component sensitivity, not an additive causal decomposition; retain both paths. Future direct uncertainty-output features remain a hypothesis, not a selected architecture. Total controllerwall462.65167835797183seconds includes waiting for the compiler audit.
+
+
+### E126 combined mature-student GPU audit and bounded launch
+
+{
+  "seed": 653,
+  "student_step": 16000,
+  "teacher_descriptor_sha256": "1503d92ce031ec0c0e0533ece20d0516a0eceb134802d51746e24a44c4b5ee9b",
+  "batch": 48,
+  "context": 4096,
+  "horizon": 1024,
+  "includes_adam_ema_placeholders": true,
+  "eager_teacher_loss": 0.20429196953773499,
+  "compiled_teacher_loss": 0.20429277420043945,
+  "gradient_cosine": 0.9999876529317515,
+  "max_gradient_difference": 1.621246337890625e-05,
+  "teacher_quantile_rms_difference": 0.0016051846323534846,
+  "teacher_masks_bitwise_equal": true,
+  "teacher_gradients_absent": true,
+  "reserved_mib": 10186.0,
+  "allocated_mib": 10124.892578125,
+  "free_mib_before_audit": 22865.6875,
+  "lock_wait_seconds": 5.014136218931526,
+  "seconds": 55.19193267985247,
+  "optimizer_updates": 0,
+  "validation_data_read": false,
+  "generated_utc": "2026-10-23T08:13:47.847944+00:00"
+}
+First launcher attempt stopped in the queue argument parser before any trainer started because wait-pid was required. Added an explicit immediate-job mode (empty dependency list) to the shared queue utility; existing queued dependencies are unchanged. Corrected launch22506/watch22507/trainer22508 started08:15:29UTC, free23388MiB. Complete16000state restored; original96k schedule, halt17000. Corrected launch explicitly carries the saved compiler cache environment. Original E101 remains unchanged.
+
+E113 frequency-scaled short windows seed197 training started at 2026-10-23T08:21:23Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+E126 numerical diagnostic tolerance declared before either17000validation result: absolute skill difference at most0.0005 on each of the four cutoffs. This is a review criterion, not automatic takeover or a claim of statistical equivalence. Compare matching17000weights and record compile startup separately; shared-H100 training wall is not isolated GPU time. 2026-10-23T08:22:24.705079+00:00
+
+## E127 finite-member numerical correction full public replay
+- Checkpoint SHA-256: `90199d09666c1653819ef17813f69fc4014fca075f99cc3766d24f457b50ca79`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.384041; relative MASE 0.736336; relative WQL 0.515260.
+- term skill: long=0.4028, medium=0.4087, short=0.3669.
+- domain skill: Econ/Fin=0.1306, Energy=0.2408, Healthcare=0.4858, Nature=0.4446, Sales=0.3589, Transport=0.4723, Web/CloudOps=0.4993.
+- frequency skill: A=0.2968, D=0.3749, H=0.3983, M=0.1452, Q=0.1891, S=0.4255, T=0.4395, W=0.2374.
+- variates skill: multivariate=0.3973, univariate=0.3733.
+- Outcome: All97full mean/quantile arrays are bitwise identical to E117. Actual-member extreme synthetic composition now preserves finite first-member contribution; relocation/determinism and real composition pass. No new model training or forecast-score gain.
+
+E127 full validation suite: {"package": "/workspace/research/candidate-finite-member-ensemble", "status": "complete", "steps": [{"name": "relocation", "returncode": 0, "seconds": 31.570269790943712}, {"name": "full_public_replay", "returncode": 0, "seconds": 365.88558935699984}, {"name": "independent_real_composition", "returncode": 0, "seconds": 0.27913929894566536}, {"name": "full_bitwise_and_extreme_composition", "returncode": 0, "seconds": 99.88748204382136}], "current_step": "full_bitwise_and_extreme_composition", "seconds": 497.63583351206034, "skill": 0.3840414658263811, "full_forecasts_bitwise_equal": true}. RuntimepredictSHA c1876617c78330ba89353f706cfacf7d3671e02a669620a48d9eb7c63e9aa7ef. Publish same trained weights with numerical composition correction; retain immutable E96teacher.
+
+E119 primary6000 matched four-cutoff comparison: {"rows": [{"cutoff": "evals", "candidate_skill": 0.38225879938174245, "control_skill": 0.38227466722292214, "delta": -1.5867841179684206e-05, "candidate_hash": "6b640ad1ce200544da2d530578e59eb353f8b9dabc5cc98473403cfe7383c49f", "control_hash": "ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253", "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7400506225960969, "rel_wql_gmean": 0.47808876994740046}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7101268693563747, "rel_wql_gmean": 0.49314710783510285}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7500860933248864, "rel_wql_gmean": 0.5411083364362582}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8886871163750968, "rel_wql_gmean": 0.8551794904776345}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8972051207193078, "rel_wql_gmean": 0.6417640311927593}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.578364137997311, "rel_wql_gmean": 0.5117456137390363}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8023291284024608, "rel_wql_gmean": 0.3989034387003474}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8416251755891729, "rel_wql_gmean": 0.5159970993971862}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6323129527317001, "rel_wql_gmean": 0.4332903678255558}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5615569274522425, "rel_wql_gmean": 0.43470539309584183}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6177358739303225, "rel_wql_gmean": 0.6470843841362995}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7613833385246014, "rel_wql_gmean": 0.5227797118737059}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7261807487698988, "rel_wql_gmean": 0.4956422642796265}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9602049092528727, "rel_wql_gmean": 0.8288042520798957}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7839745097436424, "rel_wql_gmean": 0.8298460141507311}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6554424134583847, "rel_wql_gmean": 0.5033779169908231}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6921043444047297, "rel_wql_gmean": 0.45319095997947384}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9031138478320136, "rel_wql_gmean": 0.6694602750281545}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7086008455661859, "rel_wql_gmean": 0.5102395060491531}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7643044334835595, "rel_wql_gmean": 0.5212065466992852}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7402530882128023, "rel_wql_gmean": 0.47826753514082926}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.710265197438745, "rel_wql_gmean": 0.4931672821758772}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7498589240481417, "rel_wql_gmean": 0.5410407688872414}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8892029615768211, "rel_wql_gmean": 0.85541375158805}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8972340808013708, "rel_wql_gmean": 0.6419310799963354}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5749908408040473, "rel_wql_gmean": 0.507856444781463}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8028566989060131, "rel_wql_gmean": 0.39889278962700064}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8421494671087474, "rel_wql_gmean": 0.5162975438622042}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6323849322594254, "rel_wql_gmean": 0.43339253239569303}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5616650304722973, "rel_wql_gmean": 0.43523931575775593}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.61692642682572, "rel_wql_gmean": 0.6469875043190525}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7604157827225532, "rel_wql_gmean": 0.5223254859979091}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7256889701473604, "rel_wql_gmean": 0.49536285168895955}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9611837530785082, "rel_wql_gmean": 0.8278616039641923}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7839583182238047, "rel_wql_gmean": 0.830360300010519}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6548591866261223, "rel_wql_gmean": 0.5032338833413911}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6926762306120622, "rel_wql_gmean": 0.45361994505793396}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9042490502981876, "rel_wql_gmean": 0.6703742252767426}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7084778976708436, "rel_wql_gmean": 0.510408775576821}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7643134428569803, "rel_wql_gmean": 0.5210866729603522}}}, "eval_seconds": 28.624332427978516}, {"cutoff": "backcast_evals", "candidate_skill": 0.34048229409326713, "control_skill": 0.33985253853394604, "delta": 0.0006297555593210991, "candidate_hash": "6b640ad1ce200544da2d530578e59eb353f8b9dabc5cc98473403cfe7383c49f", "control_hash": "ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253", "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7699386641350829, "rel_wql_gmean": 0.4620092164574338}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7700252574476023, "rel_wql_gmean": 0.5312660160823937}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8114763680106447, "rel_wql_gmean": 0.5925124306653264}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8851214996579603, "rel_wql_gmean": 0.81706534284058}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8880075540028629, "rel_wql_gmean": 0.6669917238344288}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6607679512516224, "rel_wql_gmean": 0.5676047563389465}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9044039298942245, "rel_wql_gmean": 0.4227147422454011}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7233763880081109, "rel_wql_gmean": 0.49051933957001853}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.7306764832685102, "rel_wql_gmean": 0.5237199503158482}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6585663042744694, "rel_wql_gmean": 0.453530474116577}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6519157656498028, "rel_wql_gmean": 0.7264149528026873}, "D": {"n_configs": 15, "rel_mase_gmean": 0.820638386519995, "rel_wql_gmean": 0.48899927341307703}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7669137207994428, "rel_wql_gmean": 0.5089765668356441}, "M": {"n_configs": 5, "rel_mase_gmean": 0.8681664151706637, "rel_wql_gmean": 0.7970725004451346}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7591685936267477, "rel_wql_gmean": 0.7657209715884332}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8528003711266274, "rel_wql_gmean": 0.5088102011002493}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7701790682032242, "rel_wql_gmean": 0.57528943509888}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8741170955888644, "rel_wql_gmean": 0.5874303282064617}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7864590781265922, "rel_wql_gmean": 0.5275771051125318}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.798685143563512, "rel_wql_gmean": 0.5654498581714905}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7709573168881856, "rel_wql_gmean": 0.46268425970990196}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7695230479033899, "rel_wql_gmean": 0.5310215600398799}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8117841847086917, "rel_wql_gmean": 0.593906337357885}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8871103723322559, "rel_wql_gmean": 0.8186104803199865}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8873228160456544, "rel_wql_gmean": 0.666828211814892}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6602366692845489, "rel_wql_gmean": 0.5698594545952637}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9037307861253031, "rel_wql_gmean": 0.42282624120616724}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7231485364548405, "rel_wql_gmean": 0.49076711071441}, 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0.48427195356442543}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8071916126513461, "rel_wql_gmean": 0.8356014190262583}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8308749825896053, "rel_wql_gmean": 0.8716517553922709}, "S": {"n_configs": 6, "rel_mase_gmean": 0.904213435588803, "rel_wql_gmean": 0.5675780199167809}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7537381736791354, "rel_wql_gmean": 0.539091137142719}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8509266671504966, "rel_wql_gmean": 0.6398335731372661}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7792766449889658, "rel_wql_gmean": 0.5144707856195212}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7538788385219328, "rel_wql_gmean": 0.5404416000493212}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7969398609040024, "rel_wql_gmean": 0.5035562825504419}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7490067352384399, "rel_wql_gmean": 0.48743000572367984}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7615427910500057, "rel_wql_gmean": 0.5565614531092009}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.901205213255903, "rel_wql_gmean": 0.9129519979778129}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.895984498209844, "rel_wql_gmean": 0.6466953024058848}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6906395512407255, "rel_wql_gmean": 0.5895403412546308}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7520688777995285, "rel_wql_gmean": 0.35747495688522557}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5597836473152902, "rel_wql_gmean": 0.3594495182358377}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6614283420298616, "rel_wql_gmean": 0.49546817917989344}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6809574130261864, "rel_wql_gmean": 0.47110941162938547}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.581464401116573, "rel_wql_gmean": 0.7355000101927864}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7270580675006559, "rel_wql_gmean": 0.44686466314745266}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7494927915333071, "rel_wql_gmean": 0.48496142310648743}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8066081312673148, "rel_wql_gmean": 0.8347163141766332}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8311725894461938, "rel_wql_gmean": 0.8717568945686304}, "S": {"n_configs": 6, "rel_mase_gmean": 0.9046061716746755, "rel_wql_gmean": 0.5673078591497167}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7542500727562019, "rel_wql_gmean": 0.5401993598021697}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8508075542526838, "rel_wql_gmean": 0.6417280462795324}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7799074480129385, "rel_wql_gmean": 0.515543995911792}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7544561978137869, "rel_wql_gmean": 0.5410325795112644}}}, "eval_seconds": 26.783769130706787}, {"cutoff": "backcast3_evals", "candidate_skill": 0.3950124598405723, "control_skill": 0.3944984110874522, "delta": 0.000514048753120111, "candidate_hash": "6b640ad1ce200544da2d530578e59eb353f8b9dabc5cc98473403cfe7383c49f", "control_hash": "ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253", "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7599778232892921, "rel_wql_gmean": 0.47414979341796676}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7672348822762877, "rel_wql_gmean": 0.5287703093503114}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7061176910432736, "rel_wql_gmean": 0.501699365937218}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8450238929299057, "rel_wql_gmean": 0.8287968523167164}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8287237417525989, "rel_wql_gmean": 0.5534837186870216}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7575935562899834, "rel_wql_gmean": 0.6315003633340103}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7076556931072816, "rel_wql_gmean": 0.3366919094754201}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7497066029652214, "rel_wql_gmean": 0.47462593240681566}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5363319189417703, "rel_wql_gmean": 0.44423886834819754}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.71171989190133, "rel_wql_gmean": 0.5165221199292769}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5305049486199563, "rel_wql_gmean": 0.7748462903644002}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7121431317868887, "rel_wql_gmean": 0.4145144835788314}, "H": {"n_configs": 29, "rel_mase_gmean": 0.719562525136373, "rel_wql_gmean": 0.48383364301897663}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8317622022092529, "rel_wql_gmean": 0.8214020589097163}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7154303032341603, "rel_wql_gmean": 0.7695070959424496}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8351733673081517, "rel_wql_gmean": 0.5991170712501708}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7049645906490642, "rel_wql_gmean": 0.4635720688691023}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8041936712430771, "rel_wql_gmean": 0.6676297088347317}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7580934176036076, "rel_wql_gmean": 0.5121324030131259}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.708087351250063, "rel_wql_gmean": 0.49341443724470835}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7605445792601405, "rel_wql_gmean": 0.4740600920237114}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7673937404796206, "rel_wql_gmean": 0.529050128812873}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7066888727036074, "rel_wql_gmean": 0.5025271114107714}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8465750633042362, "rel_wql_gmean": 0.8301832550203931}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8280900345851895, "rel_wql_gmean": 0.5531225386420328}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7582600194472988, "rel_wql_gmean": 0.632623318642284}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7096275569559131, "rel_wql_gmean": 0.3386957610122997}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7487935010952289, "rel_wql_gmean": 0.47423739993986525}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5364607716260557, "rel_wql_gmean": 0.4442170483634485}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7128912578353812, "rel_wql_gmean": 0.5168699930573769}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5308138227989901, "rel_wql_gmean": 0.777900736251483}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7112195255614516, "rel_wql_gmean": 0.4154980506806888}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7198763379573593, "rel_wql_gmean": 0.48466211242450724}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8330944575177875, "rel_wql_gmean": 0.8230031202022723}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7156105446358054, "rel_wql_gmean": 0.7684011151446023}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8371984068688675, "rel_wql_gmean": 0.6003957122289793}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7049982843940813, "rel_wql_gmean": 0.46277982987038807}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8087944681865019, "rel_wql_gmean": 0.670755534033392}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7587335902791335, "rel_wql_gmean": 0.5125489110619184}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7084600116309258, "rel_wql_gmean": 0.494009506622114}}}, "eval_seconds": 26.883931159973145}], "positive": 3, "mean_delta": 0.00046659020753248015}. Three earlier cutoffs improve; latest cutoff near-ties. Proceed to one fixed transfer experiment.
+
+## E128 matched seasonal-dispersion transfer, declared 2026-10-23T08:30:05.084134+00:00
+Hypothesis: the E119 variance-query improvement complements the published learned seasonal mean correction. Fixed coefficient-one formula: extend_zero(E117final firstmember) + E119EMA6000 - extend_zero(E107EMA6000); only new dispersion_query.weight uses candidate directly. Keep E71EMA6000 and E93EMA12000, fixed ordered equal thirds for every instance. Primary duration6000; duration12000 is a predeclared durability comparison, only if its standalone matched four-cutoff delta is positive in at least3/4 and has positive mean. No coefficient sweep. Require ensemble improvements in at least3/4 cutoffs and positive mean before full public replay; publish only on improved full97 score with relocation, determinism and independent composition checks. Check seed659, no optimizer updates or new training data; parent training seed197. Retain E127 finite-member runtime. Main and earlier scores are validation diagnostics, not held-out reward.
+
+## E129 direct seasonal uncertainty head, declared 2026-10-23T08:31:03.466366+00:00
+Hypothesis: E125 showed the direct seasonal median path contributes more than query conditioning; a direct learned width path may similarly exploit phase-specific variance more effectively. Matched control E119. Start from its own zero-feature initialization, add one zero Linear(d,patch*2) head, train all parameters with identical seed197/B64/LR2e-5/12000/paired/oldpack/original synthetic generator and original horizon stream. For each active period (p>=2,n4>=2,n16>=8), delta=log1p(std16)-log2, otherwise0. Width multiplier=exp(clamp(sum_j(tanh(g_j)*delta_j),-log4,log4)), applied to all eight positive width increments; median path unchanged directly. This bounded positive factor preserves order; zero head preserves initialization bitwise. Added16416parameters atd512/patch16. Primary6000,1000/3000diagnostics,12000durability; require four-cutoff matched evidence before any transfer, no formula selected. CPUfixtureseed661/GPUfixtureseed673, no validation training. Queue after E121 on the same small-model allocation, not an additional concurrent trainer.
+
+E79 fresh capacity72000 matched width comparison: {"label": "E79 fresh capacity72000 matched width comparison", "utc": "2026-10-23T08:31:54.152494+00:00", "candidate": "random_capacity_1024_ema72000", "control": "random_capacity_512_ema72000", "rows": [{"cutoff": "evals", "candidate_skill": 0.36736566458978936, "control_skill": 0.3689662390668288, "delta": -0.0016005744770394292, "candidate_checkpoint": "baa71611dff2da76515319d144b23e31e19304772ad4401fc4a18ea03f07a499", "control_checkpoint": "8439114a6db9c3441824266cca82575413c81d4d06843f89c3ce365220bdd491", "candidate_eval_seconds": 38.030234575271606, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7596192288082486, "rel_wql_gmean": 0.4951141955204966}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7363375295734231, "rel_wql_gmean": 0.5056560133396528}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7606418888845478, "rel_wql_gmean": 0.5538790094120064}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.9033681839460348, "rel_wql_gmean": 0.8510306246351569}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9132964142466047, "rel_wql_gmean": 0.6613834360775507}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6008629515181685, "rel_wql_gmean": 0.5128134103327899}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8241897772379998, "rel_wql_gmean": 0.4190277961561824}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8456240759834974, "rel_wql_gmean": 0.5233104342109804}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6511786830134689, "rel_wql_gmean": 0.4507742495174751}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5716616669502825, "rel_wql_gmean": 0.4393165312128719}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5958283325931863, "rel_wql_gmean": 0.6144059264519328}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7777281919380308, "rel_wql_gmean": 0.5386241189298943}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7363614335969476, "rel_wql_gmean": 0.5119011863402705}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9955332689841764, "rel_wql_gmean": 0.8441275404151479}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.758645213417042, "rel_wql_gmean": 0.8163566812421911}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6688662611218593, "rel_wql_gmean": 0.5094982599379567}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7151571878390335, "rel_wql_gmean": 0.4685405119829564}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9157681504509959, "rel_wql_gmean": 0.6694775206808375}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7270035470605405, "rel_wql_gmean": 0.5268439726526699}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7782347336583278, "rel_wql_gmean": 0.5325909632877113}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7477127171883448, "rel_wql_gmean": 0.48882740108669653}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7306495550237877, "rel_wql_gmean": 0.5021287839095168}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7641343685367114, "rel_wql_gmean": 0.5555590515607122}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8717773670750196, "rel_wql_gmean": 0.8412173322578796}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9021940945935126, "rel_wql_gmean": 0.6536787310638513}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6334859224455053, "rel_wql_gmean": 0.549868320414668}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8188967077625586, "rel_wql_gmean": 0.41366299417792224}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8213689730266212, "rel_wql_gmean": 0.5099084850083979}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6500708909219332, "rel_wql_gmean": 0.45106083264865515}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5813623780087182, "rel_wql_gmean": 0.44228012583177345}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.598628298464793, "rel_wql_gmean": 0.6247848200123971}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7826762333854071, "rel_wql_gmean": 0.5364548400819831}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7353501027725676, "rel_wql_gmean": 0.5079346769936453}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9822856143120433, "rel_wql_gmean": 0.880135503372167}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7612651746529128, "rel_wql_gmean": 0.8214245943555902}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6646723400893781, "rel_wql_gmean": 0.5089169068334075}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7095018788257277, "rel_wql_gmean": 0.46315795989437103}, "W": {"n_configs": 8, "rel_mase_gmean": 0.920309627928256, "rel_wql_gmean": 0.6842471513104053}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7244533893383394, "rel_wql_gmean": 0.5215434143342278}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7769162726418792, "rel_wql_gmean": 0.5344288966305734}}}, "group_skill_delta": {"term": {"long": -0.008700510302014908, "medium": -0.004485021335244421, "short": 0.0024743264837086}, "domain": {"Econ/Fin": -0.020447941266737324, "Energy": -0.009250912004791112, "Healthcare": 0.03510302802888143, "Nature": -0.005652053507493315, "Sales": -0.018059336432825535, "Transport": -0.0002889950786101325, "Web/CloudOps": 0.005935847541222006}, "frequency": {"A": 0.006520905028241986, "D": 0.0007468270688206768, "H": -0.0028033954635036107, "M": 0.013098916095136914, "Q": 0.0038008982664397006, "S": -0.002165148240247605, "T": -0.005614643117016205, "W": 0.010550306030891465}, "variates": {"multivariate": -0.004202079812957171, "univariate": 0.0005633740723954839}}}], "mean_delta": -0.0016005744770394292, "positive_cutoffs": 0, "outcome": "Wide146M main0.367365665 trails small37M0.368966239 and its own48k; no publication. Retain original96k schedule and predeclared final endpoint."}
+
+E113 shorter low-frequency windows1000 diagnostic: {"label": "E113 shorter low-frequency windows1000 diagnostic", "utc": "2026-10-23T08:31:54.193313+00:00", "candidate": "lowfreq_windows_ema1000", "control": "overflow_control_ema1000", "rows": [{"cutoff": "evals", "candidate_skill": 0.3758305357946752, "control_skill": 0.37680439430493307, "delta": -0.0009738585102578856, "candidate_checkpoint": "c51d67222deadc8cfecf976a088f25ef6827dc3aa5c4300166b9c77691f4a790", "control_checkpoint": "fb64456ba0c2ebef979b8525274132618fe542f67e7193036e4a013de6629f5c", "candidate_eval_seconds": 25.86383891105652, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7476696916740688, "rel_wql_gmean": 0.48332445505372645}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7120845469848178, "rel_wql_gmean": 0.49524848737069915}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7584508635396726, "rel_wql_gmean": 0.5491110528440255}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8811467462843773, "rel_wql_gmean": 0.8498343555017993}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9019820514397988, "rel_wql_gmean": 0.6470806620758618}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5815059473667801, "rel_wql_gmean": 0.5236532312845102}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8132112237916583, "rel_wql_gmean": 0.4003876735372719}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8491948108207495, "rel_wql_gmean": 0.522553119214883}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6437619246945891, "rel_wql_gmean": 0.44182702692804765}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5680496436874133, "rel_wql_gmean": 0.44315944166758076}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6181702887464274, "rel_wql_gmean": 0.6428211885395657}, "D": {"n_configs": 15, "rel_mase_gmean": 0.775382385015581, "rel_wql_gmean": 0.5381442776598314}, "H": {"n_configs": 31, "rel_mase_gmean": 0.733049566384431, "rel_wql_gmean": 0.5000120743901492}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9788847533271087, "rel_wql_gmean": 0.8373503963086395}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7791907444316182, "rel_wql_gmean": 0.8262408383947131}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6583317867171721, "rel_wql_gmean": 0.505377904469957}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6966646523006679, "rel_wql_gmean": 0.45839507431764615}, "W": {"n_configs": 8, "rel_mase_gmean": 0.903496904137189, "rel_wql_gmean": 0.67048369687104}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.715270494684413, "rel_wql_gmean": 0.5161414769252024}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7711288204924687, "rel_wql_gmean": 0.5273322512747626}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7475949248841576, "rel_wql_gmean": 0.48313009060709816}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7121646285296794, "rel_wql_gmean": 0.49507829874922954}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7565109440013578, "rel_wql_gmean": 0.5476487193118825}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8836242320820453, "rel_wql_gmean": 0.852790735489267}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9015830729546991, "rel_wql_gmean": 0.6466569995775542}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5682109895409901, "rel_wql_gmean": 0.50913591597259}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8118689127220183, "rel_wql_gmean": 0.4001636631853673}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8482726060427207, "rel_wql_gmean": 0.5217876667686607}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.643592872708551, "rel_wql_gmean": 0.4417185877096313}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5682037417522958, "rel_wql_gmean": 0.443077587356112}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6211129140655237, "rel_wql_gmean": 0.6476852381416378}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7731968641003847, "rel_wql_gmean": 0.5364961216672471}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7326719219666206, "rel_wql_gmean": 0.4996436801378994}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9679265081606557, "rel_wql_gmean": 0.825526925103439}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7785677705735384, "rel_wql_gmean": 0.8263634046573236}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6589846090055789, "rel_wql_gmean": 0.5055237904571999}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6968498829283847, "rel_wql_gmean": 0.45851341856614997}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8985566548600665, "rel_wql_gmean": 0.6671788546802966}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7151909536351672, "rel_wql_gmean": 0.5159951052379306}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7691918342794635, "rel_wql_gmean": 0.5258681687194484}}}, "group_skill_delta": {"term": {"long": -0.00015093475640626242, "medium": -6.865930295374412e-05, "short": -0.001684626978899928}, "domain": {"Econ/Fin": 0.0027216616996312837, "Energy": -0.0004190682125532197, "Healthcare": -0.01395893874619547, "Nature": -0.0006306454184297206, "Sales": -0.0008496159775570611, "Transport": -0.00013547259975099646, "Web/CloudOps": 2.170436372661566e-05}, "frequency": {"A": 0.003884682295869024, "D": -0.0018995551640285102, "H": -0.00037897852469348514, "M": -0.011460399143613631, "Q": -0.00026133085398794, "S": 0.00036920690243569254, "T": 0.00014807337877076332, "W": -0.004046103336208362}, "variates": {"multivariate": -0.00011994069529930318, "univariate": -0.0016861314715301834}}}], "mean_delta": -0.0009738585102578856, "positive_cutoffs": 0, "outcome": "Early diagnostic only; retain declared6000 primary and12000 durability."}
+
+E129 initialization/CPU audit: {"seed": 661, "initial_function_bitwise_equal": true, "inherited_features_and_weights_bitwise_equal": true, "independent_width_arithmetic": true, "bounded_multiplier": [0.25, 4.0], "unchanged_median_for_fixed_hidden": true, "finite_ordered": true, "sampler_byte_identical": true, "all_parameters_trainable": true, "added_parameters": 16416, "total_parameters": 36899168, "checkpoint_sha256": "94ba155b90c6b510bccf92358cc225afe8f317285a19637ab5ca68072bbb83d1", "seconds": 1.7288333191536367, "optimizer_updates": 0, "validation_data_read": false}. Maximum-shape GPU audit: {"seed": 673, "gpu_initial_function_bitwise_equal": true, "gpu_initial_loss_bitwise_equal": true, "max_shape": [64, 4096, 1024], "loss": 0.3557884097099304, "selected_gradient_norms": {"width_head.weight": 0.0037436725106090307, "width_head.bias": 0.000249308388447389, "dispersion_query.weight": 0.001482732011936605, "long_head.weight": 0.01620558276772499, "head.weight": 0.024969521909952164}, "all_parameters_trainable": true, "reserved_mib": 6428.0, "allocated_mib": 5787.44677734375, "free_before_mib": 11792.125, "includes_ema_and_adam_placeholders": true, "lock_wait_seconds": 38.27195146121085, "seconds": 50.22554790391587, "optimizer_updates": 0, "validation_data_read": false}. Queue24583 waits for E12120306; no extra concurrent training allocation. Publisher now documents the learned bounded width head.
+
+E128 primary merge receipt: {
+  "method": "matched training delta after zero extension of new seasonal dispersion query parameters",
+  "formula": "extend_zero(base) + candidate - extend_zero(control)",
+  "coefficient": 1.0,
+  "new_parameters_from_candidate": [
+    "dispersion_query.weight"
+  ],
+  "parent_hashes": {
+    "/workspace/research/long_seasonal_transfer12k.pt": "8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4",
+    "/workspace/research/runs/seasonal_dispersion/ema006000.pt": "6b640ad1ce200544da2d530578e59eb353f8b9dabc5cc98473403cfe7383c49f",
+    "/workspace/research/runs/long_seasonal/ema006000.pt": "ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253"
+  },
+  "base_extension_forward_bitwise_equal": true,
+  "check_seed": 659,
+  "seconds": 2.03242999012582,
+  "optimizer_updates": 0,
+  "validation_data_read": false,
+  "checkpoint_sha256": "993f03042c049bd7e6de5ba5631caa855723eab1a5d8f7e487e7721bca5deb26",
+  "parameters": 36882752
+}. Four-cutoff evaluation running; no publication.
+
+E121 older history only for learned seasonal summaries seed197 training started at 2026-10-23T08:33:35Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E128 primary dispersion transfer6000 valid_screen
+- Checkpoint SHA-256: `993f03042c049bd7e6de5ba5631caa855723eab1a5d8f7e487e7721bca5deb26`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.288978338241577 seconds.
+- Overall skill 0.386450; relative MASE 0.733378; relative WQL 0.513300.
+- term skill: long=0.4083, medium=0.4093, short=0.3688.
+- domain skill: Econ/Fin=0.1468, Energy=0.2418, Healthcare=0.4703, Nature=0.4437, Sales=0.3392, Transport=0.4738, Web/CloudOps=0.5111.
+- frequency skill: A=0.3770, D=0.3737, H=0.4062, M=0.0981, Q=0.2061, S=0.4253, T=0.4442, W=0.2254.
+- variates skill: multivariate=0.4010, univariate=0.3746.
+- Outcome: Fixed zero-extended E117first + E1196000 - E1076000; no optimizer updates. Equal ensemble selected separately.
+
+## E128 primary dispersion transfer6000 valid_backcast
+- Checkpoint SHA-256: `993f03042c049bd7e6de5ba5631caa855723eab1a5d8f7e487e7721bca5deb26`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.401955604553223 seconds.
+- Overall skill 0.343151; relative MASE 0.788790; relative WQL 0.546978.
+- term skill: long=0.3990, medium=0.3631, short=0.3124.
+- domain skill: Econ/Fin=0.1780, Energy=0.2290, Healthcare=0.3703, Nature=0.3907, Sales=0.4041, Transport=0.3805, Web/CloudOps=0.4586.
+- frequency skill: A=0.3239, D=0.3622, H=0.3775, M=0.1942, Q=0.2296, S=0.3388, T=0.3385, W=0.2880.
+- variates skill: multivariate=0.3586, univariate=0.3306.
+- Outcome: Fixed zero-extended E117first + E1196000 - E1076000; no optimizer updates. Equal ensemble selected separately.
+
+## E128 primary dispersion transfer6000 valid_backcast2
+- Checkpoint SHA-256: `993f03042c049bd7e6de5ba5631caa855723eab1a5d8f7e487e7721bca5deb26`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.512702226638794 seconds.
+- Overall skill 0.365859; relative MASE 0.763853; relative WQL 0.526456.
+- term skill: long=0.3708, medium=0.3948, short=0.3525.
+- domain skill: Econ/Fin=0.1343, Energy=0.2387, Healthcare=0.2990, Nature=0.4858, Sales=0.5513, Transport=0.4280, Web/CloudOps=0.4468.
+- frequency skill: A=0.3497, D=0.4278, H=0.3995, M=0.1471, Q=0.1537, S=0.2838, T=0.3716, W=0.2622.
+- variates skill: multivariate=0.3721, univariate=0.3608.
+- Outcome: Fixed zero-extended E117first + E1196000 - E1076000; no optimizer updates. Equal ensemble selected separately.
+
+## E128 primary dispersion transfer6000 valid_backcast3
+- Checkpoint SHA-256: `993f03042c049bd7e6de5ba5631caa855723eab1a5d8f7e487e7721bca5deb26`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 28.568291425704956 seconds.
+- Overall skill 0.391471; relative MASE 0.738149; relative WQL 0.501669.
+- term skill: long=0.3959, medium=0.3634, short=0.4000.
+- domain skill: Econ/Fin=0.1857, Energy=0.3233, Healthcare=0.2336, Nature=0.4990, Sales=0.4064, Transport=0.5155, Web/CloudOps=0.3955.
+- frequency skill: A=0.3650, D=0.4475, H=0.4088, M=0.1384, Q=0.2638, S=0.2780, T=0.4325, W=0.2508.
+- variates skill: multivariate=0.3755, univariate=0.4037.
+- Outcome: Fixed zero-extended E117first + E1196000 - E1076000; no optimizer updates. Equal ensemble selected separately.
+
+E128 primary6000 fixed equal-ensemble diagnostics: {"pid": 24358, "status": "complete; review gate before full replay", "steps": [{"label": "standalone four cutoffs", "returncode": 0, "seconds": 221.39954217500053}, {"label": "record valid_screen", "returncode": 0, "seconds": 0.03905554208904505}, {"label": "ensemble valid_screen", "returncode": 0, "seconds": 1.7885562051087618}, {"label": "record valid_backcast", "returncode": 0, "seconds": 0.04613363998942077}, {"label": "ensemble valid_backcast", "returncode": 0, "seconds": 1.7479637148790061}, {"label": "record valid_backcast2", "returncode": 0, "seconds": 0.03942001913674176}, {"label": "ensemble valid_backcast2", "returncode": 0, "seconds": 1.7197186860721558}, {"label": "record valid_backcast3", "returncode": 0, "seconds": 0.03546834201551974}, {"label": "ensemble valid_backcast3", "returncode": 0, "seconds": 1.706985381897539}], "started_utc": "2026-10-23T08:30:28.293934+00:00", "seconds": 228.63339753285982, "results": [{"cutoff": "valid_screen", "skill": 0.3876423359084613, "control_skill": 0.3877185003381878, "delta": -7.616442972646631e-05, "groups": {"term": {"short": 0.3706423870097789, "medium": 0.40976716485729514, "long": 0.4086557681538596}, "domain": {"Econ/Fin": 0.14906237351000928, "Energy": 0.24738905555743995, "Healthcare": 0.4791388969316559, "Nature": 0.4443686917605315, "Sales": 0.3447262790585014, "Transport": 0.4721227859861077, "Web/CloudOps": 0.5073504404679972}, "freq": {"A-DEC": 0.37647878173928195, "Q-DEC": 0.20042557356367063, "M": 0.1189364431714729, "W-SUN": 0.12637957687199874, "D": 0.3702481661005801, "H": 0.403349929380743, "15T": 0.21784830817653156, "W-FRI": 0.01471989794806583, "10T": 0.6278900860567873, "W-TUE": 0.6155125042124696, "W-THU": 0.20881506767137004, "W-WED": 0.3285469750480611, "5T": 0.5225769045144781, "10S": 0.4253617269575647}}, "member_checkpoint_hashes": ["993f03042c049bd7e6de5ba5631caa855723eab1a5d8f7e487e7721bca5deb26", 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0.10761587986155563, "W-FRI": 0.15220755311382572, "10T": 0.5120125447976647, "W-TUE": 0.09639129778711786, "W-THU": 0.35710660219593915, "W-WED": 0.5632462034802572, "5T": 0.477596257783802, "10S": 0.286655372051118}}, "member_checkpoint_hashes": ["993f03042c049bd7e6de5ba5631caa855723eab1a5d8f7e487e7721bca5deb26", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 1.570545946015045}, {"cutoff": "valid_backcast3", "skill": 0.3908039139644831, "control_skill": 0.3907349048993719, "delta": 6.900906511120297e-05, "groups": {"term": {"short": 0.396384608836137, "medium": 0.36898160880763275, "long": 0.3974028861047404}, "domain": {"Econ/Fin": 0.19037938198384174, "Energy": 0.3171391114512614, "Healthcare": 0.24031991333738278, "Nature": 0.5062131856742668, "Sales": 0.4022669958061532, "Transport": 0.5121071712995844, "Web/CloudOps": 0.3957592747320986}, "freq": {"A-DEC": 0.35875772722129007, "Q-DEC": 0.25705448484714655, "M": 0.14981550609481042, "W-SUN": 0.17752544894384303, "D": 0.44329034770347153, "H": 0.4072363705194667, "15T": 0.25497834281865817, "W-FRI": 0.03726145753696919, "10T": 0.6056597097039726, "W-TUE": 0.2758258081083025, "W-THU": 0.33505679968046076, "W-WED": 0.3759736817716426, "5T": 0.4669889752425598, "10S": 0.2939738197189754}}, "member_checkpoint_hashes": ["993f03042c049bd7e6de5ba5631caa855723eab1a5d8f7e487e7721bca5deb26", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 1.457663832930848}], "positive_cutoffs": 3, "mean_delta": 0.00011613907637808563, "selection_gate_passed": true}. No publication yet.
+
+E113 shorter low-frequency windows3000 diagnostic: {"label": "E113 shorter low-frequency windows3000 diagnostic", "utc": "2026-10-23T08:35:11.851754+00:00", "candidate": "lowfreq_windows_ema3000", "control": "overflow_control_ema3000", "rows": [{"cutoff": "evals", "candidate_skill": 0.3776574984788966, "control_skill": 0.37866382508046315, "delta": -0.0010063266015665207, "candidate_checkpoint": "959a677cbd33d2d64bad236fb249895e9c3f6e34f48885fac4f545282fa9fd09", "control_checkpoint": "2084f1888f8d9e5afe2c740769ea2b4167086cdd62dfb4014bc3a338c00d4c12", "candidate_eval_seconds": 24.51565384864807, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7451218625482054, "rel_wql_gmean": 0.4809596828119359}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.711351800336926, "rel_wql_gmean": 0.495079186867095}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7570785534009797, "rel_wql_gmean": 0.5464658580413575}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8817171083012888, "rel_wql_gmean": 0.8508619719806925}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9007550043958699, "rel_wql_gmean": 0.6455026968039306}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5865091484539705, "rel_wql_gmean": 0.521860625658197}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8108198685722532, "rel_wql_gmean": 0.3989956949190282}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8569380284582788, "rel_wql_gmean": 0.52336231069808}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6445588627176548, "rel_wql_gmean": 0.44213570216420356}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5622075107700881, "rel_wql_gmean": 0.4376069902777912}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6146587844971091, "rel_wql_gmean": 0.6434926649836787}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7774028542287973, "rel_wql_gmean": 0.5367206013548418}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7301987942753132, "rel_wql_gmean": 0.49786948216978816}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9825361125273154, "rel_wql_gmean": 0.843890448437557}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7840439342906915, "rel_wql_gmean": 0.8283104400124692}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6584863106408512, "rel_wql_gmean": 0.5060624950687049}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6936356874055993, "rel_wql_gmean": 0.45476141585564783}, "W": {"n_configs": 8, "rel_mase_gmean": 0.903387224991496, "rel_wql_gmean": 0.6692509608863554}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7111451661730048, "rel_wql_gmean": 0.5122727594544467}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7719262320902942, "rel_wql_gmean": 0.5268222689333825}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7439855686659663, "rel_wql_gmean": 0.4800673380532462}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.710969314529407, "rel_wql_gmean": 0.4947258837580571}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7552601359377148, "rel_wql_gmean": 0.5456283669131529}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8797820000958861, "rel_wql_gmean": 0.851465626766978}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8986925381034403, "rel_wql_gmean": 0.6442585108586064}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5798649151393334, "rel_wql_gmean": 0.5145973168091904}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8106570659226684, "rel_wql_gmean": 0.39879082771082475}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8534880943118137, "rel_wql_gmean": 0.5217738153838526}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6447411683656122, "rel_wql_gmean": 0.442132284996167}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5617243007193845, "rel_wql_gmean": 0.4378082514816697}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6174355033007595, "rel_wql_gmean": 0.6464320790726874}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7765018646063141, "rel_wql_gmean": 0.5360963442907659}, "H": {"n_configs": 31, "rel_mase_gmean": 0.729523943107356, "rel_wql_gmean": 0.4974199163124135}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9688155615793533, "rel_wql_gmean": 0.8282121039558453}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7789548898684013, "rel_wql_gmean": 0.8281810669626073}, "S": {"n_configs": 6, "rel_mase_gmean": 0.657424827907701, "rel_wql_gmean": 0.5057742486433215}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6929995095863842, "rel_wql_gmean": 0.45475081881278595}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9011291244909845, "rel_wql_gmean": 0.6693011160359584}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.710039655336745, "rel_wql_gmean": 0.511942587644631}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7703738146766376, "rel_wql_gmean": 0.5257441499310483}}}, "group_skill_delta": {"term": {"long": -0.0010118106313061759, "medium": -0.00037129576378203133, "short": -0.0012653961738797737}, "domain": {"Econ/Fin": -0.0006441364096760882, "Energy": -0.0016078574902267384, "Healthcare": -0.006984185169627399, "Nature": -0.00020313198054944248, "Sales": -0.002364454118613857, "Transport": 7.342614009608894e-05, "Web/CloudOps": -9.920427523435471e-05}, "frequency": {"A": 0.0028569523115398177, "D": -0.0007499672727220297, "H": -0.0005508457283032175, "M": -0.014818971593210772, "Q": -0.0026823567864709963, "S": -0.000629756848426255, "T": -0.0002641576452013794, "W": -0.0009432955023421785}, "variates": {"multivariate": -0.0006637132182860661, "univariate": -0.0012937612212758909}}}], "mean_delta": -0.0010063266015665207, "positive_cutoffs": 0, "outcome": "Main diagnostic trails control; preserve declared6000 primary and12000 durability before transfer decisions."}
+
+E119 full12000 training completed: {"launcher_pid": 19255, "label": "E119 learned phase-specific dispersion seed197", "command": ["python3", "/workspace/research/recovery/train.py", "--code-dir", "/workspace/research/seasonal_dispersion", "--packed", "/workspace/research/packed_sample", "--source-weights-from", "/workspace/research/packed_sample", "--out", "/workspace/research/runs/seasonal_dispersion", "--initialize-from", "/workspace/research/seasonal_dispersion_initial.pt", "--steps", "12000", "--batch", "64", "--lr", ".00002", "--seed", "197", "--loss-mode", "paired", "--stop-file", "/workspace/research/seasonal_dispersion.stop"], "status": "complete", "queued_utc": "2026-10-23T07:05:49Z", "wait_pids": [16340], "required_checkpoints": ["/workspace/research/runs/phase_joint/ema012000.pt"], "started_utc": "2026-10-23T07:49:44Z", "training_pid": 21134, "returncode": 0, "training_process_seconds": 2628.503051322885, "completed_utc": "2026-10-23T08:33:32Z"}. Training loop2611.556469116s. E121 older-summary-history trainer24729 started08:33:35.
+
+E128 selection clarification before12000 ensemble results: if primary6000 earns publication, durability12000 must additionally pass the same3of4/mean-positive ensemble gate versus that newly published6000 candidate, then improve its full score. This strengthens the original current-package comparison; no coefficient/duration sweep or automatic promotion.
+
+## E130 fixed fourth fresh-capacity member, declared 2026-10-23T08:36:18.970909+00:00
+Hypothesis: a separately initialized146M model may contribute error diversity despite its weaker standalone score. Primary checkpoint E79EMA48000 (chosen because it is the strongest observed E79wide main checkpoint through72000, not a blind checkpoint choice), seed307, SHA227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c. Add it after E117finalfirst/E71EMA6000/E93EMA12000, ordered equal quarters for every instance. No new optimization, no additional data or fitted weights. One checkpoint and one weighting; no smaller-weight fallback if rejected. Evaluate all four cutoffs, require3of4positive and positive mean versus frozen E127triple before full replay. Any later publication must also improve the then-current full package and pass all runtime checks. If E128 earns publication first, require the same four-cutoff gate versus E128 as well. No four-member runtime or package is built until this diagnostic gate passes.
+
+E126 mandatory17000stop reached. Measured compute/timing: {"utc": "2026-10-23T08:41:07.993229+00:00", "checkpoint_sha256": "d8be56a82c73a0426710742a1468df3b3d8abdaad541da4716e58cc607ee6e18", "training_seed": 307, "optimizer_updates": 1000, "starting_step": 16000, "ending_step": 17000, "original_lr_schedule_steps": 96000, "compiled_training_process_seconds": 1500.9277165979147, "compiled_training_loop_seconds": 1456.545469535049, "original_16000to17000_logged_seconds": 494.5000000000009, "warm_intervals": [{"start": 16150, "end": 17000, "original_seconds": 419.90000000000146, "compiled_seconds": 398.70000000000005, "ratio_original_over_compiled": 1.0531728116378265}, {"start": 16500, "end": 17000, "original_seconds": 246.8000000000011, "compiled_seconds": 239.10000000000014, "ratio_original_over_compiled": 1.0322040987034753}, {"start": 16600, "end": 17000, "original_seconds": 192.90000000000146, "compiled_seconds": 187.0, "ratio_original_over_compiled": 1.0315508021390452}], "matched_logged_context_horizon_all": true, "notes": "Descriptive wall time on the same shared H100 at different times. Cold teacher compilation dominates the bounded probe. Warm intervals were inspected after training and are not an isolated randomized speed comparison. No adoption selected."}. Four-cutoff forecast comparison still pending; originalE101 unchanged.
+
+E111 final12000 durability: {"results": [{"cutoff": "evals", "delta": 0.003587592890627267, "candidate_skill": 0.3800888522094774, "control_skill": 0.37650125931885015, "candidate_hash": "c46f6b8de8c5b982dd84a7f3fd9a6b00069a30a6e32a01dcf92be9f88cbc09e1", "control_hash": "dc03098da92746d3974cdb5fd3df955a8b52e689260f1fdcd0dd7b63a91fe17e", "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7420863095161662, "rel_wql_gmean": 0.4796187815866674}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7116412034410219, "rel_wql_gmean": 0.49528650860865264}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7527363095622707, "rel_wql_gmean": 0.5433350136140453}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8790538298381169, "rel_wql_gmean": 0.8493909121009501}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8986566430347296, "rel_wql_gmean": 0.6478984415102629}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5436082622122895, "rel_wql_gmean": 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"T": {"n_configs": 29, "rel_mase_gmean": 0.7039614590005732, "rel_wql_gmean": 0.46207823333750453}, "W": {"n_configs": 8, "rel_mase_gmean": 0.810577145353702, "rel_wql_gmean": 0.6793951900954402}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7616930972618384, "rel_wql_gmean": 0.5118981392017699}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7193535077794512, "rel_wql_gmean": 0.5027824181292397}}}}], "positive": 2, "mean_delta": 0.0008029682732766941, "seconds": 841.7242407109588}. E124 primary6000 gate remains failed; no new averaging rule.
+
+E119 dispersion12000 durability: {"label": "E119 dispersion12000 durability", "utc": "2026-10-23T08:44:56.875956+00:00", "candidate": "seasonal_dispersion_ema12000", "control": "long_seasonal_ema12000", "rows": [{"cutoff": "evals", "candidate_skill": 0.3820478868373609, "control_skill": 0.38231375879048757, "delta": -0.0002658719531266618, "candidate_checkpoint": "b0204fa8dd3371d9160fc00545d6b065ccb23bb18e345b3b63cd39c024c9acf5", "control_checkpoint": "d512cdac520239f0c10e021721424d87cee30086fa70f6cbc41f21e2e04988c3", "candidate_eval_seconds": 29.899949550628662, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7389032030803768, "rel_wql_gmean": 0.4775255930846139}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7110090800376195, "rel_wql_gmean": 0.49416801903946805}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7504609946788687, "rel_wql_gmean": 0.5413698790489025}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8804929993505196, 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"T": {"n_configs": 29, "rel_mase_gmean": 0.7538170530124115, "rel_wql_gmean": 0.5356891156947962}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8384463511921755, "rel_wql_gmean": 0.6337414984658606}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7796481341539873, "rel_wql_gmean": 0.5122582767435926}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.751963109653387, "rel_wql_gmean": 0.5386190816130975}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.796127056742376, "rel_wql_gmean": 0.5022139410634582}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7505942901123125, "rel_wql_gmean": 0.4865575819153698}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7594504520679526, "rel_wql_gmean": 0.5532537195147663}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8929618274343588, "rel_wql_gmean": 0.9103813793769899}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8959209226146972, "rel_wql_gmean": 0.6453740580861129}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6961784435164479, "rel_wql_gmean": 0.5915734081229318}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7476406868387996, "rel_wql_gmean": 0.35522212901676864}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5597759096460994, "rel_wql_gmean": 0.35738676117830037}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6611597485381723, "rel_wql_gmean": 0.4949322755161753}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6805375314732472, "rel_wql_gmean": 0.4657391485089796}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.582687321216151, "rel_wql_gmean": 0.7249374834569997}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7228644523548966, "rel_wql_gmean": 0.4448013254664766}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7512801448992653, "rel_wql_gmean": 0.48470980713170986}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8061257673025353, "rel_wql_gmean": 0.8338421947073288}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8329742195879615, "rel_wql_gmean": 0.8780190666063756}, "S": {"n_configs": 6, "rel_mase_gmean": 0.903952356548986, "rel_wql_gmean": 0.5661338988037351}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7547233557063331, "rel_wql_gmean": 0.536425314767226}, "W": {"n_configs": 8, "rel_mase_gmean": 0.83773127724725, "rel_wql_gmean": 0.6339155481728359}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.780210137456359, "rel_wql_gmean": 0.5128749991570595}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7524307203251008, "rel_wql_gmean": 0.5390362822620333}}}, "group_skill_delta": {"term": {"long": 0.00015265790254148648, "medium": 0.0009280507553094397, "short": 0.0004779586831510363}, "domain": {"Econ/Fin": 0.0011479018275053487, "Energy": -0.0004483809788876636, "Healthcare": -0.001978698982208482, "Nature": 0.0007765227244096451, "Sales": 0.0007763039411929351, "Transport": 0.0008374732354174963, "Web/CloudOps": 0.0016263021837661373}, "frequency": {"A": 0.00024450622227922825, "D": 0.0006825072861625925, "H": 0.0003934483458349902, "M": 0.0006153275895631749, "Q": -0.00025640136438909167, "S": 0.00011450777265553658, "T": 0.0008186590134711125, "W": -0.00021085977279267087}, "variates": {"multivariate": 0.00060817659956236, "univariate": 0.00044435063951930864}}}, {"cutoff": "backcast3_evals", "candidate_skill": 0.39329641531340254, "control_skill": 0.3928331731047856, "delta": 0.0004632422086169319, "candidate_checkpoint": "b0204fa8dd3371d9160fc00545d6b065ccb23bb18e345b3b63cd39c024c9acf5", "control_checkpoint": "d512cdac520239f0c10e021721424d87cee30086fa70f6cbc41f21e2e04988c3", "candidate_eval_seconds": 26.51674461364746, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7620982278020191, "rel_wql_gmean": 0.47426314781212775}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7694130571916938, "rel_wql_gmean": 0.5293555168619524}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7102997952164497, "rel_wql_gmean": 0.5023655717094271}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8390596184536422, "rel_wql_gmean": 0.8364410318397068}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8301807382152379, "rel_wql_gmean": 0.552148460325587}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7582486561812802, "rel_wql_gmean": 0.6324647241609418}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7235102474851808, "rel_wql_gmean": 0.33685653081795286}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7486059713981105, "rel_wql_gmean": 0.47352466437330154}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5373740999001123, "rel_wql_gmean": 0.4458528549155647}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7139121494179991, "rel_wql_gmean": 0.5182975401922782}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5305412273194996, "rel_wql_gmean": 0.8003515074386365}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7189848782726174, "rel_wql_gmean": 0.4168506671427805}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7218929902246265, "rel_wql_gmean": 0.48494043868982045}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8339835183539945, "rel_wql_gmean": 0.8257482517849524}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7130270287878929, "rel_wql_gmean": 0.7661662375161}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8378690834528353, "rel_wql_gmean": 0.5986481829064408}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7071701445283383, "rel_wql_gmean": 0.4630581494762598}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8116701266923606, "rel_wql_gmean": 0.6622668197361823}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7597234443787527, "rel_wql_gmean": 0.5110609184057446}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7127385729927525, "rel_wql_gmean": 0.49516529836403683}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7631434004686845, "rel_wql_gmean": 0.4745780224475118}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7702543592823002, "rel_wql_gmean": 0.5302072122994279}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7102740757127205, "rel_wql_gmean": 0.5028426749263977}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8404794249042948, "rel_wql_gmean": 0.8377611830280824}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8299149339539488, "rel_wql_gmean": 0.5519405024214632}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7621826867136657, "rel_wql_gmean": 0.6349175346276966}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7231739407449111, "rel_wql_gmean": 0.3380432271049275}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7474335142271239, "rel_wql_gmean": 0.4733071421517686}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5378627602598968, "rel_wql_gmean": 0.4463114549472672}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7146709097664398, "rel_wql_gmean": 0.5186863440823273}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5306073447296601, "rel_wql_gmean": 0.8030982774337949}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7175124768607152, "rel_wql_gmean": 0.41780178227764675}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7223996277645064, "rel_wql_gmean": 0.4859456509788173}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8339193215840682, "rel_wql_gmean": 0.8250296214792107}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7133214644953572, "rel_wql_gmean": 0.7659591311067668}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8388577706002066, "rel_wql_gmean": 0.5998597273487538}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7077596915926687, "rel_wql_gmean": 0.46245587762920626}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8141976774865708, "rel_wql_gmean": 0.6645660569936594}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7600456915083991, "rel_wql_gmean": 0.511644654751976}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7131300545794724, "rel_wql_gmean": 0.49563420928448887}}}, "group_skill_delta": {"term": {"long": 0.0006117877094232727, "medium": 0.0008622961767622561, "short": 0.00027276941172516}, "domain": {"Econ/Fin": 0.0013699012635308705, "Energy": -0.00023588449424616353, "Healthcare": 0.003139154413493772, "Nature": 0.0007538624280253869, "Sales": -0.0006030840572849305, "Transport": 0.00047428946123651006, "Web/CloudOps": 0.0005514018196188797}, "frequency": {"A": 0.001157893796062348, "D": 6.270950468445502e-05, "H": 0.0008207092403196281, "M": -0.00039310727445363014, "Q": 5.266435663608959e-05, "S": 0.0011344504917797371, "T": -0.00013393555935359291, "W": 0.0024142457340984302}, "variates": {"multivariate": 0.00048796919081894874, "univariate": 0.00044442732081340086}}}], "mean_delta": 0.00030630640965662037, "positive_cutoffs": 3, "outcome": "Predeclared E12812000 transfer requires at least3of4 positive and positive mean; no coefficient selection."}
+
+## E128 primary6000 dispersion ensemble full public replay — not selected
+- Checkpoint SHA-256: `a388035c9461a7bdb079a931484d9c505428ea0086e2f5456313d995872a584e`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.383964; relative MASE 0.736451; relative WQL 0.515310.
+- term skill: long=0.4028, medium=0.4086, short=0.3668.
+- domain skill: Econ/Fin=0.1308, Energy=0.2409, Healthcare=0.4847, Nature=0.4445, Sales=0.3589, Transport=0.4723, Web/CloudOps=0.4992.
+- frequency skill: A=0.2968, D=0.3746, H=0.3981, M=0.1447, Q=0.1890, S=0.4254, T=0.4397, W=0.2375.
+- variates skill: multivariate=0.3972, univariate=0.3732.
+- Outcome: Full skill0.383963658607839 is below E1270.3840414658263811 by0.0000778072185421. All97configs151170instances succeed; inference377.680531184s. Three-of-four diagnostic gains did not transfer to improved full validation. Keep E127 published; no coefficient sweep. Independent composition suite is finishing.
+
+## E131 fresh146M with learned long-seasonal summaries, declared 2026-10-23T08:48:34.312475+00:00
+Hypothesis: the robust E107/E120/E125 evidence for direct seasonal summaries can address the fresh146M plateau without another capacity increase. Control E79wide originalLR. Start from its own untrained random initialization c9693619ca6277c26fef3b2bde37ff6796bec97b3af9f94588bbc5ddd329ec90 and add exactly E107 zero long_query/long_head parameters (98336 atd1024/patch16). Existing weights and initial forecasts must match bitwise. Same seed307/B48distinct96signed/LR2e-4/96000/400warmup/cosinefloor0.2/paired/staticcompiledloss/oldpack/sourceweights/20%slowAR and horizon300310. All parameters train. Primary48000,4000/8000/16000/24000diagnostics,72000/96000durability; primary and final require4cutoffs before transfer or ensemble decisions. No transfer formula selected. CPUfixture677/GPUfixture683; allowed training data only. Queue after E94halfLR final96000 frees its allocation; require completedE79wide96000 control. No additional concurrent trainer and no pretrained weights.
+
+E130 fixed fourth fresh-capacity member diagnostics: {"pid": 24896, "status": "complete; review gate", "steps": [{"label": "fresh146M48000 earlier cutoffs", "seconds": 801.8997166370973}, {"label": "fixed equal quarters valid_screen", "seconds": 2.2401710469275713}, {"label": "fixed equal quarters valid_backcast", "seconds": 2.280684475088492}, {"label": "fixed equal quarters valid_backcast2", "seconds": 2.503589668078348}, {"label": "fixed equal quarters valid_backcast3", "seconds": 1.973427789984271}], "started_utc": "2026-10-23T08:36:19.032374+00:00", "seconds": 811.2990581667982, "results": [{"cutoff": "valid_screen", "skill": 0.3878507268780589, "control_skill": 0.3877185003381878, "delta": 0.00013222653987110533, "groups": {"term": {"short": 0.3715987371883521, "medium": 0.4102540213663538, "long": 0.4067391588891215}, "domain": {"Econ/Fin": 0.1489434280268307, "Energy": 0.24776307784165708, "Healthcare": 0.4797658368125918, "Nature": 0.4432112385396936, "Sales": 0.34253568790603184, "Transport": 0.4728212016626797, "Web/CloudOps": 0.5082519165180346}, "freq": {"A-DEC": 0.38359463669798244, "Q-DEC": 0.20462915621155287, "M": 0.12236644601227109, "W-SUN": 0.09441595412894432, "D": 0.37245225750954924, "H": 0.40230869575473505, "15T": 0.21611411756632326, "W-FRI": 0.018963917247772133, "10T": 0.6269659382966359, "W-TUE": 0.6129270175490279, "W-THU": 0.226657792022391, "W-WED": 0.3223843548790608, "5T": 0.5238648554081364, "10S": 0.4236329368087466}}, "member_checkpoint_hashes": ["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 2.065567785874009}, {"cutoff": "valid_backcast", "skill": 0.34441110518308204, "control_skill": 0.3429732095231861, "delta": 0.001437895659895938, "groups": {"term": {"short": 0.31207922757792406, "medium": 0.36328071712981613, "long": 0.40494216606009625}, "domain": {"Econ/Fin": 0.18128449966132598, "Energy": 0.2353103687221182, "Healthcare": 0.4025407461850268, "Nature": 0.3790544216342604, "Sales": 0.40723401747223886, "Transport": 0.3760760439577333, "Web/CloudOps": 0.4588042844416619}, "freq": {"A-DEC": 0.3352639380629199, "Q-DEC": 0.23254852316486108, "M": 0.19688259960835408, "W-SUN": 0.055031869484658325, "D": 0.36179297351401374, "H": 0.3749856405146871, "15T": 0.22389670167242903, "W-FRI": 0.23918097287302442, "10T": 0.3333771556820778, "W-TUE": 0.5274661574021489, "W-THU": 0.2692923481092909, "W-WED": 0.3578389948455023, "5T": 0.4461528818794869, "10S": 0.34927794955924096}}, "member_checkpoint_hashes": ["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 2.097238216083497}, {"cutoff": "valid_backcast2", "skill": 0.3685168155758046, "control_skill": 0.3674637814642573, "delta": 0.0010530341115473352, "groups": {"term": {"short": 0.3556161382479862, "medium": 0.3973117710813864, "long": 0.37222700110278417}, "domain": {"Econ/Fin": 0.1278105572382463, "Energy": 0.23987340597263695, "Healthcare": 0.36790127898546965, "Nature": 0.48227924309884174, "Sales": 0.5594797440115518, "Transport": 0.42629751766299717, "Web/CloudOps": 0.44556758902381643}, "freq": {"A-DEC": 0.3385306293437419, "Q-DEC": 0.15198048444107892, "M": 0.1698592409978169, "W-SUN": 0.07808183431207394, "D": 0.43567816912377244, "H": 0.3995244931978722, "15T": 0.10576200082748655, "W-FRI": 0.15725296166613179, "10T": 0.5103532273533584, "W-TUE": 0.17179354019954174, "W-THU": 0.3330508401365113, "W-WED": 0.5583853279496267, "5T": 0.4773643023111328, "10S": 0.2905566372491195}}, "member_checkpoint_hashes": ["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 2.3007850870490074}, {"cutoff": "valid_backcast3", "skill": 0.3912834616582097, "control_skill": 0.3907349048993719, "delta": 0.0005485567588378037, "groups": {"term": {"short": 0.3962793017121322, "medium": 0.3721673520503136, "long": 0.3968136230237693}, "domain": {"Econ/Fin": 0.18895322636819234, "Energy": 0.3197131861773106, "Healthcare": 0.23539860466957807, "Nature": 0.5079580913352499, "Sales": 0.3989970371269539, "Transport": 0.5091360867168643, "Web/CloudOps": 0.3972175212799016}, "freq": {"A-DEC": 0.35355855949676074, "Q-DEC": 0.26171742025055167, "M": 0.1494414083112504, "W-SUN": 0.1701606617044591, "D": 0.4409486975966336, "H": 0.407339108595274, "15T": 0.2629345850074465, "W-FRI": 0.04507153357762772, "10T": 0.6019138427315083, "W-TUE": 0.2793354887817199, "W-THU": 0.3516119599992523, "W-WED": 0.3654380765447295, "5T": 0.46940708121690167, "10S": 0.2877855071020504}}, "member_checkpoint_hashes": ["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 1.7579908831976354}], "positive_cutoffs": 4, "mean_delta": 0.0007929282675380456, "selection_gate_passed": true}. No runtime/package constructed, no weight sweep.
+
+E1286000 complete replayability suite: {"package": "/workspace/research/candidate-dispersion6-ensemble", "status": "complete", "steps": [{"name": "relocation", "returncode": 0, "seconds": 84.77749159513041}, {"name": "full_public_replay", "returncode": 0, "seconds": 436.0959661989473}, {"name": "independent_dispersion_replay", "returncode": 0, "seconds": 184.11096227006055}, {"name": "composition", "returncode": 0, "seconds": 0.16595014580525458}, {"name": "descriptive_comparison", "returncode": 0, "seconds": 0.1356765329837799}], "current_step": "descriptive_comparison", "seconds": 705.2942320329603, "skill": 0.383963658607839}. All package checks passed, but full score remains below E127; do not publish. Descriptive comparison {"candidate": "/workspace/research/valid-report-dispersion6-ensemble.json", "parent": "/workspace/research/valid-report-finite-member-ensemble.json", "candidate_hash": "a388035c9461a7bdb079a931484d9c505428ea0086e2f5456313d995872a584e", "parent_hash": "90199d09666c1653819ef17813f69fc4014fca075f99cc3766d24f457b50ca79", "skill_difference": -7.780721854211414e-05, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 47, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": -0.00024062329890107149, "p50": -7.43316017622897e-05, "p975": 6.257681725268526e-05}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}.
+
+E131 CPU initialization checks: {"fixture_seed": 677, "training_seed": 307, "initial_forward_and_loss_bitwise_equal": true, "inherited_weights_bitwise_equal": true, "sampler_byte_identical": true, "model_code_byte_identical_to_E107": true, "all_parameters_trainable": true, "added_parameters": 98336, "parameters": 146575680, "checkpoint_sha256": "f7e35a6341ffc6c1ce53cfd8934256dde937171f5002f1fc34b1fd169f1a15d6", "seconds": 8.978978032013401, "optimizer_updates": 0, "validation_data_read": false}. GPU maximum/compiled audit pending. Pending E129 and E131 trainer metadata now explicitly records requested-horizon seed and normalization fallback; E131 also records slow-AR law. E129 has not started training, so no past checkpoint metadata or arithmetic changed.
+
+## E126 compiled candidate evals
+- Checkpoint SHA-256: `d8be56a82c73a0426710742a1468df3b3d8abdaad541da4716e58cc607ee6e18`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 36.09797239303589 seconds.
+- Overall skill 0.364306; relative MASE 0.760934; relative WQL 0.531067.
+- term skill: long=0.3859, medium=0.3952, short=0.3435.
+- domain skill: Econ/Fin=0.1306, Energy=0.2277, Healthcare=0.4224, Nature=0.4112, Sales=0.3396, Transport=0.4561, Web/CloudOps=0.4871.
+- frequency skill: A=0.3907, D=0.3389, H=0.3847, M=0.0270, Q=0.2057, S=0.4209, T=0.4222, W=0.2298.
+- variates skill: multivariate=0.3789, univariate=0.3524.
+- Outcome: Bounded1000-update teacher compiler numerical comparison from identical full16000state; original schedule and all other factors unchanged. No automatic takeover or publication.
+
+## E126 original control evals
+- Checkpoint SHA-256: `0699dba2a0b78d61ec78a7f4d8855eb887e0e5844009c85b73c2280bdeda201d`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 39.142066955566406 seconds.
+- Overall skill 0.364321; relative MASE 0.759462; relative WQL 0.532071.
+- term skill: long=0.3871, medium=0.3956, short=0.3429.
+- domain skill: Econ/Fin=0.1313, Energy=0.2286, Healthcare=0.4285, Nature=0.4088, Sales=0.3419, Transport=0.4555, Web/CloudOps=0.4863.
+- frequency skill: A=0.3912, D=0.3367, H=0.3857, M=0.0483, Q=0.2050, S=0.4208, T=0.4213, W=0.2241.
+- variates skill: multivariate=0.3798, univariate=0.3517.
+- Outcome: Bounded1000-update teacher compiler numerical comparison from identical full16000state; original schedule and all other factors unchanged. No automatic takeover or publication.
+
+## E126 compiled candidate backcast_evals
+- Checkpoint SHA-256: `d8be56a82c73a0426710742a1468df3b3d8abdaad541da4716e58cc607ee6e18`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 36.27298712730408 seconds.
+- Overall skill 0.331744; relative MASE 0.800194; relative WQL 0.558073.
+- term skill: long=0.4018, medium=0.3533, short=0.2941.
+- domain skill: Econ/Fin=0.1556, Energy=0.2263, Healthcare=0.3584, Nature=0.3628, Sales=0.4053, Transport=0.3596, Web/CloudOps=0.4547.
+- frequency skill: A=0.3343, D=0.3419, H=0.3664, M=0.1982, Q=0.2234, S=0.3430, T=0.3284, W=0.2638.
+- variates skill: multivariate=0.3545, univariate=0.3130.
+- Outcome: Bounded1000-update teacher compiler numerical comparison from identical full16000state; original schedule and all other factors unchanged. No automatic takeover or publication.
+
+## E126 original control backcast_evals
+- Checkpoint SHA-256: `0699dba2a0b78d61ec78a7f4d8855eb887e0e5844009c85b73c2280bdeda201d`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 36.57498288154602 seconds.
+- Overall skill 0.332181; relative MASE 0.799229; relative WQL 0.558016.
+- term skill: long=0.4034, medium=0.3538, short=0.2940.
+- domain skill: Econ/Fin=0.1540, Energy=0.2252, Healthcare=0.3557, Nature=0.3694, Sales=0.4058, Transport=0.3593, Web/CloudOps=0.4544.
+- frequency skill: A=0.3315, D=0.3453, H=0.3660, M=0.2015, Q=0.2195, S=0.3408, T=0.3291, W=0.2623.
+- variates skill: multivariate=0.3535, univariate=0.3147.
+- Outcome: Bounded1000-update teacher compiler numerical comparison from identical full16000state; original schedule and all other factors unchanged. No automatic takeover or publication.
+
+## E126 compiled candidate backcast2_evals
+- Checkpoint SHA-256: `d8be56a82c73a0426710742a1468df3b3d8abdaad541da4716e58cc607ee6e18`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 36.39142870903015 seconds.
+- Overall skill 0.357844; relative MASE 0.770908; relative WQL 0.534907.
+- term skill: long=0.3688, medium=0.3877, short=0.3419.
+- domain skill: Econ/Fin=0.0992, Energy=0.2353, Healthcare=0.3101, Nature=0.4711, Sales=0.5544, Transport=0.4164, Web/CloudOps=0.4409.
+- frequency skill: A=0.3243, D=0.4196, H=0.3963, M=0.1582, Q=0.1367, S=0.2899, T=0.3536, W=0.2590.
+- variates skill: multivariate=0.3696, univariate=0.3482.
+- Outcome: Bounded1000-update teacher compiler numerical comparison from identical full16000state; original schedule and all other factors unchanged. No automatic takeover or publication.
+
+## E126 original control backcast2_evals
+- Checkpoint SHA-256: `0699dba2a0b78d61ec78a7f4d8855eb887e0e5844009c85b73c2280bdeda201d`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 39.81012320518494 seconds.
+- Overall skill 0.359808; relative MASE 0.767154; relative WQL 0.534241.
+- term skill: long=0.3687, medium=0.3897, short=0.3446.
+- domain skill: Econ/Fin=0.1002, Energy=0.2345, Healthcare=0.3380, Nature=0.4734, Sales=0.5563, Transport=0.4167, Web/CloudOps=0.4416.
+- frequency skill: A=0.3233, D=0.4245, H=0.3964, M=0.1619, Q=0.1366, S=0.2886, T=0.3543, W=0.2699.
+- variates skill: multivariate=0.3701, univariate=0.3513.
+- Outcome: Bounded1000-update teacher compiler numerical comparison from identical full16000state; original schedule and all other factors unchanged. No automatic takeover or publication.
+
+## E126 compiled candidate backcast3_evals
+- Checkpoint SHA-256: `d8be56a82c73a0426710742a1468df3b3d8abdaad541da4716e58cc607ee6e18`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 36.27197313308716 seconds.
+- Overall skill 0.379547; relative MASE 0.749469; relative WQL 0.513646.
+- term skill: long=0.3893, medium=0.3660, short=0.3811.
+- domain skill: Econ/Fin=0.1606, Energy=0.3132, Healthcare=0.1758, Nature=0.5022, Sales=0.3883, Transport=0.4989, Web/CloudOps=0.3846.
+- frequency skill: A=0.3186, D=0.4271, H=0.3989, M=0.1041, Q=0.2597, S=0.2577, T=0.4316, W=0.2193.
+- variates skill: multivariate=0.3648, univariate=0.3909.
+- Outcome: Bounded1000-update teacher compiler numerical comparison from identical full16000state; original schedule and all other factors unchanged. No automatic takeover or publication.
+
+## E126 original control backcast3_evals
+- Checkpoint SHA-256: `0699dba2a0b78d61ec78a7f4d8855eb887e0e5844009c85b73c2280bdeda201d`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 35.707090616226196 seconds.
+- Overall skill 0.377589; relative MASE 0.751555; relative WQL 0.515459.
+- term skill: long=0.3887, medium=0.3647, short=0.3784.
+- domain skill: Econ/Fin=0.1619, Energy=0.3136, Healthcare=0.1792, Nature=0.4931, Sales=0.3888, Transport=0.4990, Web/CloudOps=0.3821.
+- frequency skill: A=0.3235, D=0.4228, H=0.3981, M=0.0995, Q=0.2578, S=0.2577, T=0.4299, W=0.2151.
+- variates skill: multivariate=0.3640, univariate=0.3881.
+- Outcome: Bounded1000-update teacher compiler numerical comparison from identical full16000state; original schedule and all other factors unchanged. No automatic takeover or publication.
+
+E131 maximum GPU/compiled audit: {"fixture_seed": 683, "training_seed": 307, "gpu_initial_forward_and_loss_bitwise_equal": true, "all_parameters_trainable": true, "maximum_shape_memory": {"batch": 48, "signed_rows": 96, "context": 4096, "horizon": 1024, "loss": 0.5064497590065002, "peak_allocated_mib": 10163.2412109375, "peak_reserved_mib": 10602.0, "free_before_mib": 22465.6875, "includes_adam_and_ema_placeholders": true, "selected_gradient_norms": {"long_query.weight": 0.0, "long_head.weight": 0.022453932091593742, "long_head.bias": 0.0007150184828788042, "head.weight": 0.21377567946910858}}, "eager_loss": 0.2832183539867401, "compiled_loss": 0.2832183241844177, "gradient_cosine": 0.999991608022376, "max_gradient_difference": 6.103515625e-05, "compile_comparison_seconds": 70.16511336597614, "lock_wait_seconds": 88.56701109395362, "seconds": 173.39559380686842, "optimizer_updates": 0, "validation_data_read": false}. New-query gradient is exactly0 at initial random zero output heads, as expected; direct long_head and original head gradients are nonzero. Queue25692 waits for E94halfLR and E79wide final96000; watcher/controller {"queue_pid": 25692, "watch_pid": 25693, "comparison_pid": 25694}. No additional concurrent allocation.
+
+E130 prepared public package descriptorfa97880dd132b1cdff459be4798f8f360d7df59b13e78026a9d74a95d63df0db, 256594744parameters; member weights["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"]. Separate four_member_ensemble runtime keeps per-member finite cleanup/rule caps and ordered equal quarters. New146M member packaged directly with only its actual seed307 own-model lineage and generator declarations, without inherited mature-model seed labels. Synthetic composition, relocation, full replay and independent real composition suite active.
+
+E113 6000 four-cutoff comparison: {"label": "E113 6000 four-cutoff comparison", "utc": "2026-10-23T08:53:56.106260+00:00", "candidate": "lowfreq_windows_ema6000", "control": "overflow_control_ema6000", "rows": [{"cutoff": "evals", "candidate_skill": 0.37689889834307033, "control_skill": 0.3782887645248565, "delta": -0.001389866181786159, "candidate_checkpoint": "6022f0a4bfa1062ad14abf079449bbc0aea2c8378c4e8c24a5a88d6c7eed568f", "control_checkpoint": "b19875c52ed5024383fb69ccc48cb07a5629ad8011abf134c7dcfcdc50fae2af", "candidate_eval_seconds": 24.637309312820435, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7461642946563414, "rel_wql_gmean": 0.48185140448625774}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7114818476673104, "rel_wql_gmean": 0.4937134846824009}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7587744836539531, "rel_wql_gmean": 0.5474521404602561}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8833786145123239, "rel_wql_gmean": 0.8531518487365242}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9008291449851727, "rel_wql_gmean": 0.6443880390924023}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5841826675598496, "rel_wql_gmean": 0.5185655742165746}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8182743754041456, "rel_wql_gmean": 0.40277193564568253}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8481685562000009, "rel_wql_gmean": 0.5201665790427301}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6442200391898962, "rel_wql_gmean": 0.44227361720756947}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5642905007500406, "rel_wql_gmean": 0.43825160894025916}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6091689454127692, "rel_wql_gmean": 0.638276521684612}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7785626516441129, "rel_wql_gmean": 0.5355665107799793}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7314284480466448, "rel_wql_gmean": 0.4981263653987478}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9986128574835264, "rel_wql_gmean": 0.8624410357490886}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7903739563981046, "rel_wql_gmean": 0.8299815110338646}, "S": {"n_configs": 6, "rel_mase_gmean": 0.656301620925384, "rel_wql_gmean": 0.5047620788741458}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6945298685094353, "rel_wql_gmean": 0.4553332014533837}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9014486439579953, "rel_wql_gmean": 0.6668717099327689}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7126021042582767, "rel_wql_gmean": 0.5125054838159262}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.772902623640725, "rel_wql_gmean": 0.5274131827426548}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7451158192819246, "rel_wql_gmean": 0.4809631678035488}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7120497644863014, "rel_wql_gmean": 0.4940812105058145}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7560063232433744, "rel_wql_gmean": 0.5455016162773587}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.891060652656678, "rel_wql_gmean": 0.8581226203181075}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8989380404519756, "rel_wql_gmean": 0.6435803440684917}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5710382040222995, "rel_wql_gmean": 0.5034944242351712}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8133075715765863, "rel_wql_gmean": 0.4004091491239218}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8446340518950459, "rel_wql_gmean": 0.5179589899563962}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6447526741433901, "rel_wql_gmean": 0.4427981539703199}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5645988459931921, "rel_wql_gmean": 0.4387078987240053}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6165351258073414, "rel_wql_gmean": 0.6464531739881929}, "D": {"n_configs": 15, "rel_mase_gmean": 0.777944381816132, "rel_wql_gmean": 0.5346411774818413}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7317515640788856, "rel_wql_gmean": 0.49890798111520296}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9655032792454371, "rel_wql_gmean": 0.8262938781338912}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7858131466267005, "rel_wql_gmean": 0.8312500357519329}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6555214893191091, "rel_wql_gmean": 0.504479335142133}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6939507700541674, "rel_wql_gmean": 0.45508640655176025}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8990798911693764, "rel_wql_gmean": 0.664987514515424}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7111159189621905, "rel_wql_gmean": 0.5120760043186128}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7711295953953445, "rel_wql_gmean": 0.5256252376142441}}}, "group_skill_delta": {"term": {"long": -0.0009739529555055082, "medium": 0.0004572613129861791, "short": -0.0023238162415889496}, "domain": {"Econ/Fin": 0.0063028758916267025, "Energy": -0.001277279116533303, "Healthcare": -0.01419331378718447, "Nature": -0.003426212345731483, "Sales": -0.002793454166251652, "Transport": 0.0005371855991479491, "Web/CloudOps": 0.00039473405139495155}, "frequency": {"A": 0.007764034867115299, "D": -0.0008143027489134758, "H": 0.0006067942733988252, "M": -0.03484210742464455, "Q": -0.0017233061297405872, "S": -0.0005033122269536561, "T": -0.000386852040670127, "W": -0.002114020721949994}, "variates": {"multivariate": -0.0008835170618891608, "univariate": -0.0018146214185645881}}}, {"cutoff": "backcast_evals", "candidate_skill": 0.3343961576413551, "control_skill": 0.33515646486891926, "delta": -0.0007603072275641498, "candidate_checkpoint": "6022f0a4bfa1062ad14abf079449bbc0aea2c8378c4e8c24a5a88d6c7eed568f", "control_checkpoint": "b19875c52ed5024383fb69ccc48cb07a5629ad8011abf134c7dcfcdc50fae2af", "candidate_eval_seconds": 23.87971806526184, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7775995665582034, "rel_wql_gmean": 0.4660524143600413}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7742979584754606, "rel_wql_gmean": 0.5343745587246692}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8201142133986199, "rel_wql_gmean": 0.598685800269491}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8832386329140982, "rel_wql_gmean": 0.8125278417445753}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8897858431693706, "rel_wql_gmean": 0.6676356286156242}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6492080161186249, "rel_wql_gmean": 0.5505110265373835}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9342301107590483, "rel_wql_gmean": 0.4342352628337986}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7396632725324492, "rel_wql_gmean": 0.5033836939140032}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.7390030781599123, "rel_wql_gmean": 0.5323292524398107}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6651103369438204, "rel_wql_gmean": 0.4599139822296968}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6614347761202842, "rel_wql_gmean": 0.7332772756783581}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8395380907736223, "rel_wql_gmean": 0.49858292016697153}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7782944268201338, "rel_wql_gmean": 0.5161951267114039}, "M": {"n_configs": 5, "rel_mase_gmean": 0.8850314011347771, "rel_wql_gmean": 0.8093835670278922}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7641972179731484, "rel_wql_gmean": 0.7595910327353056}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8520874716612998, "rel_wql_gmean": 0.5072036983501343}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7698429755624734, "rel_wql_gmean": 0.5785867598697058}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8751127568750565, "rel_wql_gmean": 0.5813954254548376}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.790903324120952, "rel_wql_gmean": 0.531769678844664}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.8085692176449468, "rel_wql_gmean": 0.571082070761698}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7750842285105053, "rel_wql_gmean": 0.46538535076726445}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7739741905332832, "rel_wql_gmean": 0.534827984372569}, "short": {"n_configs": 55, "rel_mase_gmean": 0.818371751293216, "rel_wql_gmean": 0.5985153782895438}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.887916619787545, "rel_wql_gmean": 0.8185770809200361}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8901417181128463, "rel_wql_gmean": 0.6693878286037928}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6334453098405051, "rel_wql_gmean": 0.5437528735999733}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9321426691826549, "rel_wql_gmean": 0.43340604882484346}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7321691335963859, "rel_wql_gmean": 0.4977340558431129}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.7394280206962279, "rel_wql_gmean": 0.5327250385233748}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6634585442750054, "rel_wql_gmean": 0.4591816165371809}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6523615189029875, "rel_wql_gmean": 0.7262242686636398}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8405439241384891, "rel_wql_gmean": 0.5009731755165275}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7770655988729349, "rel_wql_gmean": 0.5160636056826207}, "M": {"n_configs": 5, "rel_mase_gmean": 0.8620023507565068, "rel_wql_gmean": 0.7924280387937057}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7600409188395882, "rel_wql_gmean": 0.7689369191610511}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8521465319527592, "rel_wql_gmean": 0.5084037872669025}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7696065791080199, "rel_wql_gmean": 0.577900906515431}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8747966347792652, "rel_wql_gmean": 0.5837934687567188}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7902103277834961, "rel_wql_gmean": 0.5315871703126607}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.8062350844786952, "rel_wql_gmean": 0.5709428531788484}}}, "group_skill_delta": {"term": {"long": -0.0014047237485829145, "medium": 0.00013828872268262238, "short": -0.0008444102595704939}, "domain": {"Econ/Fin": 0.005396410804951124, "Energy": 0.001165065198261761, "Healthcare": -0.010938049139590511, "Nature": -0.0013197186448916653, "Sales": -0.006515463640201058, "Transport": 0.00041349253828093513, "Web/CloudOps": -0.0011271926475546756}, "frequency": {"A": -0.008127431914985217, "D": 0.0019373580889670006, "H": -0.0005812622851624205, "M": -0.019879317803983554, "Q": 0.002585334672893569, "S": 0.000800090015471544, "T": -0.0004980990138920838, "W": 0.001340411461665303}, "variates": {"multivariate": -0.00039543175315481793, "univariate": -0.0010642347373726047}}}, {"cutoff": "backcast2_evals", "candidate_skill": 0.3599979390330963, "control_skill": 0.35970579873679454, "delta": 0.0002921402963017572, "candidate_checkpoint": "6022f0a4bfa1062ad14abf079449bbc0aea2c8378c4e8c24a5a88d6c7eed568f", "control_checkpoint": "b19875c52ed5024383fb69ccc48cb07a5629ad8011abf134c7dcfcdc50fae2af", "candidate_eval_seconds": 23.407413244247437, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.8008593621710156, "rel_wql_gmean": 0.5057430926868616}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.757468445904617, "rel_wql_gmean": 0.49256746030833815}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7633722415209746, "rel_wql_gmean": 0.558605012241182}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8946216965321081, "rel_wql_gmean": 0.9088687371623195}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8965201542322633, "rel_wql_gmean": 0.6464378810161919}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6785194902800584, "rel_wql_gmean": 0.5849201303195053}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7655843123264524, "rel_wql_gmean": 0.36490172511656266}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5678061820242641, "rel_wql_gmean": 0.3644855914883543}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6685465658076019, "rel_wql_gmean": 0.5017399429608845}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6849965213617552, "rel_wql_gmean": 0.4724282525707762}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5825163531960122, "rel_wql_gmean": 0.7346377536602618}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7324508940634272, "rel_wql_gmean": 0.45212722264136573}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7556124624084898, "rel_wql_gmean": 0.4899134119816834}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8103905761748015, "rel_wql_gmean": 0.8492327228669642}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8353441047809755, "rel_wql_gmean": 0.8671883110574543}, "S": {"n_configs": 6, "rel_mase_gmean": 0.9088419438141119, "rel_wql_gmean": 0.5678755021819691}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7573691463179116, "rel_wql_gmean": 0.5419241988811554}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8434646356234143, "rel_wql_gmean": 0.6305198670040683}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7829043173773316, "rel_wql_gmean": 0.5178062400149124}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7587866385127666, "rel_wql_gmean": 0.5443359350107977}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.8007838015734439, "rel_wql_gmean": 0.5057608145580296}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7549215312107497, "rel_wql_gmean": 0.49176726739734045}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7642838919526312, "rel_wql_gmean": 0.5599238938428742}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8997976584221669, "rel_wql_gmean": 0.9109410837720314}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8969828992939779, "rel_wql_gmean": 0.6477417063864679}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6849313799294706, "rel_wql_gmean": 0.586932347321718}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7624727427382104, "rel_wql_gmean": 0.3657161858629389}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5639287429478652, "rel_wql_gmean": 0.36145769293128766}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6689876132941273, "rel_wql_gmean": 0.5020608717418806}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6837785683593893, "rel_wql_gmean": 0.4719606763184108}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5811330636284028, "rel_wql_gmean": 0.7336592636608978}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7329769702098001, "rel_wql_gmean": 0.45167764562697754}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7553820928392463, "rel_wql_gmean": 0.4904273479694985}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8254186064766647, "rel_wql_gmean": 0.8515799856418855}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8324909356393811, "rel_wql_gmean": 0.8724694350490023}, "S": {"n_configs": 6, "rel_mase_gmean": 0.9100541095744552, "rel_wql_gmean": 0.5693729069160721}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7550799772409099, "rel_wql_gmean": 0.540387432745741}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8437173750489751, "rel_wql_gmean": 0.6406981711171287}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7818465318697899, "rel_wql_gmean": 0.5175870710427966}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7594977958239505, "rel_wql_gmean": 0.545505930798867}}}, "group_skill_delta": {"term": {"long": -1.8873745696090793e-05, "medium": -0.0015232986214765987, "short": 0.0011607039431776878}, "domain": {"Econ/Fin": 0.0036351470036239952, "Energy": 0.0009639811195998327, "Healthcare": 0.004057414263133219, "Nature": -0.0004868526346849533, "Sales": -0.0034430301775181804, "Transport": 0.00037626934528212086, "Web/CloudOps": -0.0007872938922964812}, "frequency": {"A": -0.0012124673197710933, "D": -7.965945370269623e-05, "H": 0.0002262434472751762, "M": 0.008812908587369161, "Q": 0.0011285045089141077, "S": 0.0014261024011577028, "T": -0.0018765679611807418, "W": 0.005972689804801923}, "variates": {"multivariate": -0.0005649429922075511, "univariate": 0.0009917364796149242}}}, {"cutoff": "backcast3_evals", "candidate_skill": 0.38993403866336573, "control_skill": 0.38966768132876584, "delta": 0.0002663573345998982, "candidate_checkpoint": "6022f0a4bfa1062ad14abf079449bbc0aea2c8378c4e8c24a5a88d6c7eed568f", "control_checkpoint": "b19875c52ed5024383fb69ccc48cb07a5629ad8011abf134c7dcfcdc50fae2af", "candidate_eval_seconds": 23.662298679351807, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7653115593218499, "rel_wql_gmean": 0.47599461710723834}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7715691889827463, "rel_wql_gmean": 0.5313719143376657}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7126844177529106, "rel_wql_gmean": 0.5076569047682628}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8352132728850933, "rel_wql_gmean": 0.8170711397518996}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8348557462103663, "rel_wql_gmean": 0.5561697329589659}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7733153411204075, "rel_wql_gmean": 0.6546403085337337}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7140141482679682, "rel_wql_gmean": 0.34196831793742916}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7565061085453817, "rel_wql_gmean": 0.48058309322338083}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5437907447481647, "rel_wql_gmean": 0.449614701170043}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7165702979219388, "rel_wql_gmean": 0.5200051777411084}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5261551139322042, "rel_wql_gmean": 0.7330240240629913}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7252372471132796, "rel_wql_gmean": 0.4239024663817478}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7268124573646887, "rel_wql_gmean": 0.4893130921486449}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8545447134934323, "rel_wql_gmean": 0.8493178662981181}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7127776195257158, "rel_wql_gmean": 0.7718161257451915}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8395472897334526, "rel_wql_gmean": 0.5993236231307666}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7069092537460439, "rel_wql_gmean": 0.4641162361923377}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8015863686794376, "rel_wql_gmean": 0.6701047974351558}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7632290984309094, "rel_wql_gmean": 0.515189244156747}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7144971245676118, "rel_wql_gmean": 0.49880868819086793}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7650747158835133, "rel_wql_gmean": 0.4758888221311058}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7703157746376595, "rel_wql_gmean": 0.5298503011269557}, "short": {"n_configs": 54, "rel_mase_gmean": 0.713915671760059, "rel_wql_gmean": 0.5084844315057424}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8452403065316382, "rel_wql_gmean": 0.8277399249864468}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8346950467324626, "rel_wql_gmean": 0.5567599695672222}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7876375654704325, "rel_wql_gmean": 0.6565412569519505}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.710238739553539, "rel_wql_gmean": 0.34191077293082645}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7563686587019305, "rel_wql_gmean": 0.4784563222634317}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5433317817040721, "rel_wql_gmean": 0.4493168613990946}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7161526533494015, "rel_wql_gmean": 0.5178489398382691}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5311143622209655, "rel_wql_gmean": 0.7781586196577971}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7234708846594775, "rel_wql_gmean": 0.42486757337507386}, "H": {"n_configs": 29, "rel_mase_gmean": 0.725973846929309, "rel_wql_gmean": 0.48863382838793346}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8623268790180476, "rel_wql_gmean": 0.8523275939235515}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7182632014266673, "rel_wql_gmean": 0.7707135583961491}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8408993113146807, "rel_wql_gmean": 0.5989715147638536}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7064723895859075, "rel_wql_gmean": 0.463139705670842}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8096257056910313, "rel_wql_gmean": 0.6721758032092567}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7638007775200529, "rel_wql_gmean": 0.5152023145873739}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7148285566222029, "rel_wql_gmean": 0.49905178580637044}}}, "group_skill_delta": {"term": {"long": -0.00016046718080775513, "medium": -0.0014369850536211093, "short": 0.001009829034523957}, "domain": {"Econ/Fin": 0.010351943848399103, "Energy": 0.00029585934007825276, "Healthcare": 0.00760033702944507, "Nature": -0.0013495907178020516, "Sales": -0.0013903114325046761, "Transport": -0.0003724434991111414, "Web/CloudOps": -0.0014444489650127235}, "frequency": {"A": 0.02184240353004696, "D": -4.55761836851698e-05, "H": -0.0007579762655841593, "M": 0.005385376877326431, "Q": 0.0023166477508324013, "S": 0.00036236562580005405, "T": -0.0007797412974028806, "W": 0.004803408269482556}, "variates": {"multivariate": 0.00024275664332440439, "univariate": 0.0002839356919598357}}}], "mean_delta": -0.00039791894461216337, "positive_cutoffs": 2, "outcome": "Declared primary6000/durability12000 diagnostic. No transfer formula or automatic publication is selected by this controller."}
+
+## E128 durability dispersion transfer12000 valid_screen
+- Checkpoint SHA-256: `807ab7579fc823c783e57a0cd9758125c02b67b383e6d13364f0d34ee63ee4c9`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.033636569976807 seconds.
+- Overall skill 0.386522; relative MASE 0.733364; relative WQL 0.513191.
+- term skill: long=0.4078, medium=0.4094, short=0.3691.
+- domain skill: Econ/Fin=0.1471, Energy=0.2419, Healthcare=0.4732, Nature=0.4432, Sales=0.3390, Transport=0.4742, Web/CloudOps=0.5107.
+- frequency skill: A=0.3779, D=0.3743, H=0.4062, M=0.0993, Q=0.2064, S=0.4247, T=0.4441, W=0.2254.
+- variates skill: multivariate=0.4008, univariate=0.3749.
+- Outcome: Fixed zero-extended E117first + E11912000 - E10712000; no optimizer updates. Equal ensemble selected separately.
+
+## E128 durability dispersion transfer12000 valid_backcast
+- Checkpoint SHA-256: `807ab7579fc823c783e57a0cd9758125c02b67b383e6d13364f0d34ee63ee4c9`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 26.6574444770813 seconds.
+- Overall skill 0.343249; relative MASE 0.788490; relative WQL 0.547022.
+- term skill: long=0.3992, medium=0.3634, short=0.3124.
+- domain skill: Econ/Fin=0.1782, Energy=0.2295, Healthcare=0.3698, Nature=0.3898, Sales=0.4038, Transport=0.3810, Web/CloudOps=0.4588.
+- frequency skill: A=0.3236, D=0.3617, H=0.3779, M=0.1942, Q=0.2298, S=0.3387, T=0.3387, W=0.2875.
+- variates skill: multivariate=0.3588, univariate=0.3306.
+- Outcome: Fixed zero-extended E117first + E11912000 - E10712000; no optimizer updates. Equal ensemble selected separately.
+
+## E128 durability dispersion transfer12000 valid_backcast2
+- Checkpoint SHA-256: `807ab7579fc823c783e57a0cd9758125c02b67b383e6d13364f0d34ee63ee4c9`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 25.614221811294556 seconds.
+- Overall skill 0.365699; relative MASE 0.763918; relative WQL 0.526676.
+- term skill: long=0.3705, medium=0.3949, short=0.3522.
+- domain skill: Econ/Fin=0.1341, Energy=0.2390, Healthcare=0.2968, Nature=0.4855, Sales=0.5515, Transport=0.4283, Web/CloudOps=0.4462.
+- frequency skill: A=0.3489, D=0.4280, H=0.3996, M=0.1482, Q=0.1531, S=0.2824, T=0.3716, W=0.2600.
+- variates skill: multivariate=0.3719, univariate=0.3607.
+- Outcome: Fixed zero-extended E117first + E11912000 - E10712000; no optimizer updates. Equal ensemble selected separately.
+
+## E128 durability dispersion transfer12000 valid_backcast3
+- Checkpoint SHA-256: `807ab7579fc823c783e57a0cd9758125c02b67b383e6d13364f0d34ee63ee4c9`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 25.73096227645874 seconds.
+- Overall skill 0.391781; relative MASE 0.737942; relative WQL 0.501300.
+- term skill: long=0.3966, medium=0.3639, short=0.4001.
+- domain skill: Econ/Fin=0.1860, Energy=0.3235, Healthcare=0.2382, Nature=0.4989, Sales=0.4063, Transport=0.5159, Web/CloudOps=0.3954.
+- frequency skill: A=0.3641, D=0.4486, H=0.4092, M=0.1381, Q=0.2641, S=0.2774, T=0.4329, W=0.2497.
+- variates skill: multivariate=0.3758, univariate=0.4041.
+- Outcome: Fixed zero-extended E117first + E11912000 - E10712000; no optimizer updates. Equal ensemble selected separately.
+
+E128 durability12000 fixed equal-ensemble diagnostics: {"pid": 25137, "status": "complete; review gate before full replay", "steps": [{"label": "standalone four cutoffs", "returncode": 0, "seconds": 611.6175360109191}, {"label": "record valid_screen", "returncode": 0, "seconds": 0.03854229603894055}, {"label": "ensemble valid_screen", "returncode": 0, "seconds": 2.4362374609336257}, {"label": "record valid_backcast", "returncode": 0, "seconds": 0.03839866095222533}, {"label": "ensemble valid_backcast", "returncode": 0, "seconds": 2.195331864990294}, {"label": "record valid_backcast2", "returncode": 0, "seconds": 0.053376773139461875}, {"label": "ensemble valid_backcast2", "returncode": 0, "seconds": 2.065167659893632}, {"label": "record valid_backcast3", "returncode": 0, "seconds": 0.044758826959878206}, {"label": "ensemble valid_backcast3", "returncode": 0, "seconds": 1.7165518840774894}], "started_utc": "2026-10-23T08:45:01.960793+00:00", "seconds": 620.504908550065, "results": [{"cutoff": "valid_screen", "skill": 0.387631434012613, "control_skill": 0.3877185003381878, "delta": -8.706632557475835e-05, "groups": {"term": {"short": 0.37068194474462157, "medium": 0.4097796176395536, "long": 0.40849729366372367}, "domain": {"Econ/Fin": 0.14906153028819058, "Energy": 0.24740579222476355, "Healthcare": 0.4796934504715721, "Nature": 0.44410850413953806, "Sales": 0.3446703538346755, "Transport": 0.4722712030334615, "Web/CloudOps": 0.5072367984448052}, "freq": {"A-DEC": 0.37673348328274414, "Q-DEC": 0.20049341302210122, "M": 0.11935346179130535, "W-SUN": 0.12581403312344364, "D": 0.3701949353166585, "H": 0.40333433785598394, "15T": 0.2179069338160644, "W-FRI": 0.014432437224305206, "10T": 0.6277687306843087, "W-TUE": 0.6171748330046889, "W-THU": 0.2082801402344301, "W-WED": 0.328702914013944, "5T": 0.5225236050449166, "10S": 0.4252257258157742}}, "member_checkpoint_hashes": ["807ab7579fc823c783e57a0cd9758125c02b67b383e6d13364f0d34ee63ee4c9", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 2.199216665001586}, {"cutoff": "valid_backcast", "skill": 0.34321236138030986, "control_skill": 0.3429732095231861, "delta": 0.00023915185712375386, "groups": {"term": {"short": 0.31071983654199253, "medium": 0.3621282754770089, "long": 0.4040730316333201}, "domain": {"Econ/Fin": 0.17961750811991928, "Energy": 0.23286215379520836, "Healthcare": 0.40563890895352117, "Nature": 0.3800791233546058, "Sales": 0.40853347688296815, "Transport": 0.3771851486377281, "Web/CloudOps": 0.4547587636856828}, "freq": {"A-DEC": 0.32749370401535927, "Q-DEC": 0.23741514828197818, "M": 0.19585493095249495, "W-SUN": 0.07316053633867003, "D": 0.35815449501673347, "H": 0.3751489668493323, "15T": 0.22170922698884998, "W-FRI": 0.2287214171356433, "10T": 0.32681881547325076, "W-TUE": 0.5493191419071376, "W-THU": 0.2637606809467842, "W-WED": 0.36054426279868534, "5T": 0.4460139993900665, "10S": 0.3477728598170654}}, "member_checkpoint_hashes": ["807ab7579fc823c783e57a0cd9758125c02b67b383e6d13364f0d34ee63ee4c9", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 1.956491030054167}, {"cutoff": "valid_backcast2", "skill": 0.3676028026728825, "control_skill": 0.3674637814642573, "delta": 0.00013902120862518963, "groups": {"term": {"short": 0.3549456260609696, "medium": 0.39649710524299164, "long": 0.3705253993820533}, "domain": {"Econ/Fin": 0.12780519012554215, "Energy": 0.24201604645162877, "Healthcare": 0.3486275063249036, "Nature": 0.48331614416756874, "Sales": 0.5566475041330979, "Transport": 0.42832467643451233, "Web/CloudOps": 0.4415709197426039}, "freq": {"A-DEC": 0.3357231130775328, "Q-DEC": 0.15829172951663628, "M": 0.17048883573879448, "W-SUN": 0.08504117481065188, "D": 0.43269402341806407, "H": 0.39766899225608077, "15T": 0.10795846596782255, "W-FRI": 0.15201232164010592, "10T": 0.5119145223555399, "W-TUE": 0.09108782450582265, "W-THU": 0.3566400804239148, "W-WED": 0.5633665322888896, "5T": 0.4772805736081409, "10S": 0.2862378937571334}}, "member_checkpoint_hashes": ["807ab7579fc823c783e57a0cd9758125c02b67b383e6d13364f0d34ee63ee4c9", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 1.8519201409071684}, {"cutoff": "valid_backcast3", "skill": 0.3908557586831448, "control_skill": 0.3907349048993719, "delta": 0.00012085378377291178, "groups": {"term": {"short": 0.3963510949367022, "medium": 0.36911963139497816, "long": 0.3976102068749139}, "domain": {"Econ/Fin": 0.19051417812242089, "Energy": 0.31718667596118966, "Healthcare": 0.2415112051403444, "Nature": 0.506115793531148, "Sales": 0.4022035041012628, "Transport": 0.5122293135267391, "Web/CloudOps": 0.3956455185229273}, "freq": {"A-DEC": 0.3585566380206566, "Q-DEC": 0.25722061236867333, "M": 0.14972205914197367, "W-SUN": 0.17775151306254877, "D": 0.4434997099668646, "H": 0.40733288525473155, "15T": 0.2554130986399531, "W-FRI": 0.03689607565197617, "10T": 0.6055719949289415, "W-TUE": 0.27616117961258635, "W-THU": 0.3340485207038867, "W-WED": 0.3759002450046014, "5T": 0.46697145262020834, "10S": 0.29374694337261253}}, "member_checkpoint_hashes": ["807ab7579fc823c783e57a0cd9758125c02b67b383e6d13364f0d34ee63ee4c9", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "cpu_seconds": 1.4953445647843182}], "positive_cutoffs": 3, "mean_delta": 0.00010299013098677423, "selection_gate_passed": true}. No publication yet.
+
+E126 final decision 2026-10-23T08:55:33.634889+00:00: reject teacher compiler takeover. Four-cutoff skill differences [-1.5293212246692e-05, -0.0004372110796623252, -0.0019640869934627325, 0.001958147733838289], maximum absolute0.001964086993 exceeds predeclared0.0005 diagnostic tolerance; late shared-device timing gain only3to5percent, cold process1500.927716598s. Keep original E101 teacher execution/coefficient/package and96k schedule unchanged. Bounded probe stays stopped17000; no further training on this branch. The tolerance is an engineering diagnostic, not a significance or equivalence test.
+
+E113 primary6000 selection: two cutoffs improve, two regress, mean delta-0.000397918945 versus E80. Do not transfer primary; complete declared12000 durability. All group metrics are in lowfreq_windows6k_four_cutoff_comparison.json and the matched journal entry.
+
+E118 independent-seed long-seasonal replication seed523 training started at 2026-10-23T09:03:52Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E130 fixed fourth fresh146M48000 member — full replay selected
+- Checkpoint SHA-256: `fa97880dd132b1cdff459be4798f8f360d7df59b13e78026a9d74a95d63df0db`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.384150; relative MASE 0.735921; relative WQL 0.515370.
+- term skill: long=0.4008, medium=0.4088, short=0.3679.
+- domain skill: Econ/Fin=0.1298, Energy=0.2412, Healthcare=0.4860, Nature=0.4432, Sales=0.3560, Transport=0.4730, Web/CloudOps=0.5004.
+- frequency skill: A=0.3027, D=0.3766, H=0.3971, M=0.1475, Q=0.1890, S=0.4236, T=0.4396, W=0.2403.
+- variates skill: multivariate=0.3963, univariate=0.3743.
+- Outcome: Fixed equal quarters improve full skill from 0.384041465826 to 0.384149718182; all97configs151170instances pass. Inference 481.492s on shared H100. Relocation and actual-member real/extreme synthetic composition are bitwise exact. Validation selection only, not held-out reward.
+
+E130 complete suite {"package": "/workspace/research/candidate-fresh-capacity4-ensemble", "status": "complete", "steps": [{"name": "synthetic_four_member_composition", "returncode": 0, "seconds": 140.04258152702823}, {"name": "relocation", "returncode": 0, "seconds": 67.24213584186509}, {"name": "full_public_replay", "returncode": 0, "seconds": 484.4881066719536}, {"name": "independent_fresh146m_replay", "returncode": 0, "seconds": 99.68134234496392}, {"name": "composition", "returncode": 0, "seconds": 0.18438137485645711}, {"name": "descriptive_comparison", "returncode": 0, "seconds": 0.18302110210061073}], "current_step": "descriptive_comparison", "seconds": 791.8420735518448, "skill": 0.38414971818207}. Synthetic actual-four-member runtime checks {"checkpoint_sha256": "fa97880dd132b1cdff459be4798f8f360d7df59b13e78026a9d74a95d63df0db", "runtime_code_sha256": {"seasonal.py": "b946e63048196ab5ea8b249137d48c80d8510943caccd0f180e4fb86d9b24b37", "ts_io.py": "1064d1a95a59e7edd6ed33e9a6d58c83c17fa3886acb1fb54e3652d9326ee436", "model.py": "c27748fa4b1186b513f76eca26052753b8dcd1794994e5989addaf371ca70f2e", "predict.py": "15b86dbaa19128df1105b6dd4243f2aa1321aff3fc45685a0a9811d8862d7add", "infer.sh": "5fe66b4fe9a4787285d830cfd96975701e97286a75ba7c3687bd8891475edf68", "continuity.py": "dbb08d2229ad80ad00f638b0b9f2f6ca13c663e0fd6730058e5fee8ff54545d5", "zero_mass.py": "f079d509009bcda409b80eb64467c869b838cfbab11b2bd4c660c2875b6642fe", "model1.py": "c27748fa4b1186b513f76eca26052753b8dcd1794994e5989addaf371ca70f2e", "model3.py": "44eeb23c037e91b8f67e432615ce46540a7ac4242919785f9c4218de23121499", "model0.py": "3eaa4aed0a1c62b80d94be6c3084b10a594e9fce17444eac17ca3f1b0b5c549e", "model2.py": "89bb87b2501b117ac02daf001a68f389ec488931ce18905445d15874760685be"}, "member_hashes": ["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "checks": [{"config_id": "synthetic_H", "instances": 12, "bitwise_fixed_average": true, "finite": true, "ordered": true}, {"config_id": "synthetic_15T", "instances": 12, "bitwise_fixed_average": true, "finite": true, "ordered": true}], "steps": [{"name": "standalone0", "seconds": 7.54584995820187}, {"name": "standalone1", "seconds": 7.568851496092975}, {"name": "standalone2", "seconds": 7.905901189893484}, {"name": "standalone3", "seconds": 8.563424529973418}, {"name": "ensemble", "seconds": 20.146188400918618}], "lock_wait_seconds": 88.12057089991868, "seconds": 139.89567038416862, "optimizer_updates": 0, "validation_data_read": false}. Descriptive comparison {"candidate": "/workspace/research/valid-report-fresh-capacity4-ensemble.json", "parent": "/workspace/research/valid-report-finite-member-ensemble.json", "candidate_hash": "fa97880dd132b1cdff459be4798f8f360d7df59b13e78026a9d74a95d63df0db", "parent_hash": "90199d09666c1653819ef17813f69fc4014fca075f99cc3766d24f457b50ca79", "skill_difference": 0.00010825235568889813, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 41, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": -0.00215013782076347, "p50": 0.00017516257300170812, "p975": 0.0022812793702812035}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}. Every member forecasts every instance. Four cutoffs improve; latest-cutoff long-term group regresses while aggregate improves, as detailed in group metrics. E101teacher remains immutableE96.
+
+## E132 final-duration fresh fourth member, declared 2026-10-23T09:06:54.075039+00:00
+Hypothesis: since E130 showed error diversity from the separately initialized146M model, its scheduled96000 endpoint may improve the same ensemble beyond48000. Replace only E79wideEMA48000 with its scheduledEMA96000; freeze E117finalfirst/E71EMA6000/E93EMA12000 and ordered equal quarters. Control E130published. The96000main result is not yet available; no additional training or altered schedule. Training seed307, no new generator/data. Evaluate all4cutoffs regardless of standalone ranking, require3of4positive and positive mean versusE130 before one full public replay. Publication also requires better full score and checks versus the then-current package. No intermediate checkpoint or weight sweep.
+
+## E133 conditional equal-seed seasonal correction, declared 2026-10-23T09:08:19.714091+00:00
+Hypothesis: if the large E107 seasonal-summary effect replicates with seed523, averaging matched corrections across seeds may improve robustness. This is separate from rejected E124weekly replication. Require BOTH E1186000 and E11812000 to improve their matched E111control at at least3of4cutoffs with positive mean. Only then construct one duration12000 checkpoint: E117final firstmember +0.5*((E118EMA12000-zero_extend(E111controlEMA12000))-(E107EMA12000-zero_extend(E80EMA12000))). Control tensors for long_query/long_head are exactlyzero. Other parameters use the literal float32 formula in that order. Keep E71EMA6000, E93EMA12000 and E79wideEMA48000 fixed; equal ordered quarters, every member forecasts every instance. Require3of4positive ensemble comparisons and positive mean against E130 before full replay; publication must improve the then-current package and pass runtime checks. No6000merged alternative, coefficient sweep, new optimizer updates or data. Training seeds197/523; merge fixtureseed691. E1186000/12000results are not yet available.
+
+## E134 fixed visible-window curriculum from random146M, declared 2026-10-23T09:14:07.033062+00:00
+Hypothesis: the fresh large-model plateau partly reflects optimization on long contexts/futures from the first update; the mature smaller lineage began with shorter windows. Match E79wide own random initialization c969..., model/data/sampler/sourceweights/seed307/B48/LR2e-4/96k/paired/staticcompile/slowAR/horizon300310/400warmup/cosinefloor0.2. Always sample and prefetch the ORIGINAL full windows with identical RNG consumption. Only the arrays supplied to the loss are cropped: steps1..16000 take lastC/4 context and firstmax(16,H/4) target;16001..32000 take lastC/2 and firstmax(16,H/2);32001..96000 use original arrays unchanged. Context lengths are already divisible by16; every target view is at least16. Crop after consuming the prefetched full batch; recovery stores the unmodified next full batch and uses the resumed global step, with no extra counter/RNG. Source windows, values and late-stage stream are identical to control; visible observation exposure/compute differ and will be measured. No teacher, new data, new model parameters or inference changes. Primary96000;16/32/48/72kdiagnostics,48/96four-cutoff comparisons. Queue after E110full-corpus96000 frees its allocation; require E79wide96000. CPUfixture701/GPUfixture709; no validation training. No schedule alternatives or transfer formula selected.
+
+E121 6000 four-cutoff comparison: {"label": "E121 6000 four-cutoff comparison", "utc": "2026-10-23T09:15:40.991339+00:00", "candidate": "seasonal_history_ema6000", "control": "long_seasonal_ema6000", "rows": [{"cutoff": "evals", "candidate_skill": 0.3817685810005982, "control_skill": 0.38227466722292214, "delta": -0.0005060862223239537, "candidate_checkpoint": "3cb20017da82189fb26998bf3129d05dd234da77d0ab9368c6a4124ae732aa19", "control_checkpoint": "ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253", "candidate_eval_seconds": 28.41759157180786, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7427798851547502, "rel_wql_gmean": 0.480705859626928}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7084344807368257, "rel_wql_gmean": 0.49372688802898496}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7498345696608782, "rel_wql_gmean": 0.5411656310255009}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8896377050023038, "rel_wql_gmean": 0.8552925713089266}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.895357514468135, "rel_wql_gmean": 0.6408807905896997}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5733389714741494, "rel_wql_gmean": 0.5060436634788081}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8080292177977787, "rel_wql_gmean": 0.4045547054558028}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8418605092641899, "rel_wql_gmean": 0.5165249207431287}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6322814717091779, "rel_wql_gmean": 0.4335633189446315}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5617078170865537, "rel_wql_gmean": 0.43513795058882027}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6180475502592851, "rel_wql_gmean": 0.6469105480568086}, "D": {"n_configs": 15, "rel_mase_gmean": 0.761525834042119, "rel_wql_gmean": 0.5233823774456857}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7257276749680774, "rel_wql_gmean": 0.4955113716516702}, "M": {"n_configs": 5, "rel_mase_gmean": 0.96171751117691, "rel_wql_gmean": 0.825323398780684}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7837107393854835, "rel_wql_gmean": 0.8294027653998538}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6557024709255037, "rel_wql_gmean": 0.5035040985336133}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6924773974760391, "rel_wql_gmean": 0.4554542637411677}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9029678802897229, "rel_wql_gmean": 0.6700409831152518}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7076350430583279, "rel_wql_gmean": 0.5112125809875208}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7652589747638697, "rel_wql_gmean": 0.5218170330181122}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7402530882128023, "rel_wql_gmean": 0.47826753514082926}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.710265197438745, "rel_wql_gmean": 0.4931672821758772}, "short": {"n_configs": 55, "rel_mase_gmean": 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No transfer formula or automatic publication is selected by this controller."}
+
+E113 12000 four-cutoff comparison: {"label": "E113 12000 four-cutoff comparison", "utc": "2026-10-23T09:16:05.247965+00:00", "candidate": "lowfreq_windows_ema12000", "control": "overflow_control_ema12000", "rows": [{"cutoff": "evals", "candidate_skill": 0.37612597264381065, "control_skill": 0.37742394607564755, "delta": -0.0012979734318369074, "candidate_checkpoint": "f1deb47b05e0dddb2c882081f6371d4a6c27e5b644446ab2d3848ec6af789fd8", "control_checkpoint": "f11d056b95f1c5eeef174265ebc51742952600e0dae1fa52900860447d5be819", "candidate_eval_seconds": 26.301554441452026, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7428750481395252, "rel_wql_gmean": 0.48016983627031584}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7127112192835677, "rel_wql_gmean": 0.4949026837866265}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7603970824570427, "rel_wql_gmean": 0.5494707164558967}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.875268873201828, 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0.5682478578118126}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7564710377088042, "rel_wql_gmean": 0.5386795987319778}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8308220008623086, "rel_wql_gmean": 0.6230353765403394}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7827378337570088, "rel_wql_gmean": 0.5158567345122158}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7577139368382865, "rel_wql_gmean": 0.5428398324072662}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7998283729290717, "rel_wql_gmean": 0.5048537403572065}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7567348346553658, "rel_wql_gmean": 0.49139307103243485}, "short": {"n_configs": 54, "rel_mase_gmean": 0.762930601340605, "rel_wql_gmean": 0.5572913926751922}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8902018742574637, "rel_wql_gmean": 0.9080828126396094}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8967112467195689, "rel_wql_gmean": 0.6466666398782791}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6908821587788814, "rel_wql_gmean": 0.5905411593557701}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7593906111647103, "rel_wql_gmean": 0.3630649835252516}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5660049516474805, "rel_wql_gmean": 0.36083556795060334}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6692141236302525, "rel_wql_gmean": 0.5019337855870213}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6840578993103755, "rel_wql_gmean": 0.46836362238870816}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5819380993584167, "rel_wql_gmean": 0.723948814143525}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7300802327867975, "rel_wql_gmean": 0.44973000047782175}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7573152418388004, "rel_wql_gmean": 0.4905218098483386}, "M": {"n_configs": 4, "rel_mase_gmean": 0.825511662103245, "rel_wql_gmean": 0.8525433006612966}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8348196873036913, "rel_wql_gmean": 0.8792738256086735}, "S": {"n_configs": 6, "rel_mase_gmean": 0.9101844597610753, "rel_wql_gmean": 0.5693250695338232}, "T": {"n_configs": 29, "rel_mase_gmean": 0.75556464168385, "rel_wql_gmean": 0.5377684583538125}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8322603650379057, "rel_wql_gmean": 0.6324937767638193}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.782445957809565, "rel_wql_gmean": 0.5156982204614814}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7580358480370438, "rel_wql_gmean": 0.543923941651618}}}, "group_skill_delta": {"term": {"long": 0.00019595986944986876, "medium": -0.0011963062249945589, "short": 0.0010126495337633434}, "domain": {"Econ/Fin": 0.0027723913674168044, "Energy": 0.0015796820841680326, "Healthcare": -0.0036576861574247888, "Nature": -9.370861462276725e-05, "Sales": 0.0012622036778165424, "Transport": -2.795885354567318e-06, "Web/CloudOps": -0.00040073261232353996}, "frequency": {"A": -0.00021690098198479113, "D": 0.00012167144878916236, "H": -0.00010740488144889149, "M": 0.0030857545471283387, "Q": 0.004291405638661705, "S": 0.0007251447805141087, "T": -0.0009223202400732156, "W": 0.006067822151289648}, "variates": {"multivariate": -0.0002161043626571768, "univariate": 0.0007764498089196659}}}, {"cutoff": "backcast3_evals", "candidate_skill": 0.38719770172424695, "control_skill": 0.3874814849803516, "delta": -0.0002837832561046749, "candidate_checkpoint": "f1deb47b05e0dddb2c882081f6371d4a6c27e5b644446ab2d3848ec6af789fd8", "control_checkpoint": "f11d056b95f1c5eeef174265ebc51742952600e0dae1fa52900860447d5be819", "candidate_eval_seconds": 24.347827434539795, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7684886444337029, "rel_wql_gmean": 0.47700055161764726}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7719012970032346, "rel_wql_gmean": 0.5309111874339674}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7205395887196173, "rel_wql_gmean": 0.5088842083422412}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8312087228420175, "rel_wql_gmean": 0.8186588543234923}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8351368839049325, "rel_wql_gmean": 0.5545932357253937}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.8175567972220871, "rel_wql_gmean": 0.6760837299667757}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7299909306564671, "rel_wql_gmean": 0.3420605153189345}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7548733967003611, "rel_wql_gmean": 0.47934273977909336}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5451842768000186, "rel_wql_gmean": 0.4515764701679637}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7188771721303677, "rel_wql_gmean": 0.5204081485529428}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5241640857947558, "rel_wql_gmean": 0.7291166786507837}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7350980533969118, "rel_wql_gmean": 0.4276860378056021}, "H": {"n_configs": 29, "rel_mase_gmean": 0.728816078273106, "rel_wql_gmean": 0.49012599470467744}, "M": {"n_configs": 4, "rel_mase_gmean": 0.884158227262403, "rel_wql_gmean": 0.8735522520241081}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7090887722467131, "rel_wql_gmean": 0.7680357872508405}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8460699141449298, "rel_wql_gmean": 0.5998981264388689}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7086527442094294, "rel_wql_gmean": 0.4630233384268165}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8169291712293834, "rel_wql_gmean": 0.6644383983004994}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7633479802491316, "rel_wql_gmean": 0.5131305442375695}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7238025548687982, "rel_wql_gmean": 0.501861868079696}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7675699458528298, "rel_wql_gmean": 0.476452132158557}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.773141221801274, "rel_wql_gmean": 0.5311688105422268}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7189800356855841, "rel_wql_gmean": 0.5092012481102669}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8366494448197422, "rel_wql_gmean": 0.8335501433013701}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8362673307045482, "rel_wql_gmean": 0.5558565966414833}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7992789325648234, "rel_wql_gmean": 0.663313586611861}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7270958326307262, "rel_wql_gmean": 0.3416485868534862}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7558002187371488, "rel_wql_gmean": 0.47806361106286926}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5447632408986202, "rel_wql_gmean": 0.45146185813227047}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7184671561660497, "rel_wql_gmean": 0.5194115372422989}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5301621193542202, "rel_wql_gmean": 0.8023624756576537}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7320022964105988, "rel_wql_gmean": 0.42747341049868826}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7284419538124913, "rel_wql_gmean": 0.48986156448616525}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8692971155035826, "rel_wql_gmean": 0.8573561489309673}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7145578258684864, "rel_wql_gmean": 0.7671041544075885}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8434376506034783, "rel_wql_gmean": 0.6004240857745707}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7091796225957213, "rel_wql_gmean": 0.4627952794263764}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8186104789687557, "rel_wql_gmean": 0.6672650133055504}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7652944038188929, "rel_wql_gmean": 0.5141740641844628}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7208570784782143, "rel_wql_gmean": 0.5012656144320912}}}, "group_skill_delta": {"term": {"long": -0.0007099465499640356, "medium": 0.0006693751847272056, "short": -0.00046727777101984724}, "domain": {"Econ/Fin": 0.010188433449565881, "Energy": 0.001235688152959713, "Healthcare": -0.015333237527605181, "Nature": -0.0012922529180409148, "Sales": -0.00043446352071674355, "Transport": -0.0002545772391819856, "Web/CloudOps": -0.0007602336179508562}, "frequency": {"A": 0.03400882289466778, "D": -0.0013210139532306986, "H": -0.00031462845811736084, "M": -0.01553325343887657, "Q": 0.0023910101617380386, "S": -0.0007973503823683892, "T": 7.176516391471655e-05, "W": 0.002324819735960215}, "variates": {"multivariate": 0.0014342827367879174, "univariate": -0.0015849874659156216}}}], "mean_delta": -0.000496019701602235, "positive_cutoffs": 1, "outcome": "Declared primary6000/durability12000 diagnostic. No transfer formula or automatic publication is selected by this controller."}
+
+## E128 twelve-thousand-step dispersion transfer full replay, rejected
+- Checkpoint SHA-256: `e473ba2608f2cc15100e181259634d9f553dcc311dedee8b0f896b133703a17b`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.383953; relative MASE 0.736493; relative WQL 0.515300.
+- term skill: long=0.4027, medium=0.4086, short=0.3668.
+- domain skill: Econ/Fin=0.1306, Energy=0.2409, Healthcare=0.4852, Nature=0.4442, Sales=0.3589, Transport=0.4725, Web/CloudOps=0.4991.
+- frequency skill: A=0.2970, D=0.3746, H=0.3981, M=0.1452, Q=0.1890, S=0.4252, T=0.4396, W=0.2376.
+- variates skill: multivariate=0.3971, univariate=0.3733.
+- Outcome: Full skill0.38395261653633084 below E1270.3840414658263811 and current E1300.38414971818207. All97 configurations/151170instances finite; inference358.537563328s; complete relocation/independent-composition suite1004.778254749s passed. No publication or coefficient sweep.
+
+E101 own-ensemble distillation 24000 matched duration: {"label": "E101 own-ensemble distillation 24000 matched duration", "utc": "2026-10-23T09:19:15.570271+00:00", "candidate": "distill_ensemble_ema24000", "control": "random_capacity_1024_halflr_ema24000", "rows": [{"cutoff": "evals", "candidate_skill": 0.36786899672815143, "control_skill": 0.3554524492447929, "delta": 0.01241654748335852, "candidate_checkpoint": "ad5c0444cb3da069662fa016561f8cf246470d52b5149d5e5ecff4f4635ac6fd", "control_checkpoint": "ee69006629037179a8fd6d09233755cb8efceafb0a47494abceceea3cb7fae52", "candidate_eval_seconds": 36.978116512298584, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7613534532855317, "rel_wql_gmean": 0.4933355126988402}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7218139585351127, "rel_wql_gmean": 0.4992284050428652}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7672268173513697, "rel_wql_gmean": 0.5547476172046656}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8932646398208068, "rel_wql_gmean": 0.8458136656436929}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9078583500592542, "rel_wql_gmean": 0.6488988402003546}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6171945120239629, "rel_wql_gmean": 0.5508778445646585}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8172291112406892, "rel_wql_gmean": 0.40581862290030846}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8418608823902648, "rel_wql_gmean": 0.5159447884287781}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6528920710132115, "rel_wql_gmean": 0.45070297408715015}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5812822617907885, "rel_wql_gmean": 0.4520217678361691}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5927671163749784, "rel_wql_gmean": 0.6245031317118163}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7851739820802963, "rel_wql_gmean": 0.5406958904898744}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7405818217057821, "rel_wql_gmean": 0.5060231638513055}, "M": {"n_configs": 5, "rel_mase_gmean": 1.0030369609877634, "rel_wql_gmean": 0.8643308347760176}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7675602608974536, "rel_wql_gmean": 0.8301407913586738}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6599527631730835, "rel_wql_gmean": 0.5067737473086187}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7092137567823373, "rel_wql_gmean": 0.46706903964030133}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9238536075967602, "rel_wql_gmean": 0.670954136490052}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7261235492551099, "rel_wql_gmean": 0.5251452485440882}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.780482086012812, "rel_wql_gmean": 0.5314168783649992}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7672963823883818, "rel_wql_gmean": 0.5037463097000351}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7411598138375365, "rel_wql_gmean": 0.5112523421889252}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7765805771888272, "rel_wql_gmean": 0.5695687308167366}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8947865329147192, "rel_wql_gmean": 0.8675019552981618}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9189851213071903, "rel_wql_gmean": 0.6647129956643829}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.66522448244886, "rel_wql_gmean": 0.5713422747891131}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8229663797697023, "rel_wql_gmean": 0.42175005100395146}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.836731066077329, "rel_wql_gmean": 0.5127605811341385}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6568620754928051, "rel_wql_gmean": 0.45664954453268697}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5941838738109626, "rel_wql_gmean": 0.4642586168661542}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6090797274199198, "rel_wql_gmean": 0.6248561761913869}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7816538456892409, "rel_wql_gmean": 0.5343645406533337}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7518484970509849, "rel_wql_gmean": 0.5255707125287721}, "M": {"n_configs": 5, "rel_mase_gmean": 1.015039576827218, "rel_wql_gmean": 0.8873267820580395}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7756154804231246, "rel_wql_gmean": 0.8458050910642245}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6668506293769108, "rel_wql_gmean": 0.507924296219694}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7306344744783564, "rel_wql_gmean": 0.48726927054167557}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9160794156665825, "rel_wql_gmean": 0.6672608817274759}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.742970470874668, "rel_wql_gmean": 0.5446766637662609}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7862695054442648, "rel_wql_gmean": 0.539530442255707}}}, "group_skill_delta": {"term": {"long": 0.00884517749183844, "medium": 0.01527290829046124, "short": 0.0126749508916848}, "domain": {"Econ/Fin": 0.011823213626144025, "Energy": 0.014042335073513823, "Healthcare": 0.0334047345528341, "Nature": 0.01325231705496166, "Sales": -0.004041666279614886, "Transport": 0.005224438942843523, "Web/CloudOps": 0.012625350236096566}, "frequency": {"A": 0.008489284622378968, "D": -0.005279668651802849, "H": 0.016439670250736338, "M": 0.017932703667306926, "Q": 0.011712835768662666, "S": 0.0036739608508072896, "T": 0.02112575984504239, "W": -0.005480325776542672}, "variates": {"multivariate": 0.01863219594085297, "univariate": 0.007299234511382369}}}], "mean_delta": 0.01241654748335852, "positive_cutoffs": 1, "outcome": "Positive matched main diagnostic; preserve immutable E96 teacher, coefficient0.5 and original96000 schedule. Main validation only; no package selection yet."}
+
+E94 halfLR 72000 matched duration: {"label": "E94 halfLR 72000 matched duration", "utc": "2026-10-23T09:19:15.611756+00:00", "candidate": "random_capacity_1024_halflr_ema72000", "control": "random_capacity_1024_ema72000", "rows": [{"cutoff": "evals", "candidate_skill": 0.36653362293692227, "control_skill": 0.36736566458978936, "delta": -0.0008320416528670993, "candidate_checkpoint": "1f20fbc689daeb4c9761714dbe10f442ab41488d027207e7899a0cd3de1add3c", "control_checkpoint": "baa71611dff2da76515319d144b23e31e19304772ad4401fc4a18ea03f07a499", "candidate_eval_seconds": 37.33427858352661, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7680606899024435, "rel_wql_gmean": 0.4988503915569091}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7332786035110377, "rel_wql_gmean": 0.5041170760030442}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7585705226561933, "rel_wql_gmean": 0.5555576562163199}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8890425784326859, "rel_wql_gmean": 0.8521745095508803}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9159891215471997, "rel_wql_gmean": 0.6608606448062099}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5774882181873465, "rel_wql_gmean": 0.4922066250724507}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8219319248209576, "rel_wql_gmean": 0.4164103507345064}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8152705090073499, "rel_wql_gmean": 0.5099477192627261}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6500269528069342, "rel_wql_gmean": 0.4497591516823354}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5834637275048629, "rel_wql_gmean": 0.4552808833310693}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5922899838046344, "rel_wql_gmean": 0.6089813388580939}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7654652588047858, "rel_wql_gmean": 0.5272959028606339}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7427422246535798, "rel_wql_gmean": 0.5161376804225851}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9636735950001094, "rel_wql_gmean": 0.8438060966501667}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7597462095052908, "rel_wql_gmean": 0.8168824191722339}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6643227595707758, "rel_wql_gmean": 0.5113951504875184}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7203630323482785, "rel_wql_gmean": 0.47429870706475213}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9107516280265588, "rel_wql_gmean": 0.6648932917668241}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.731662111685473, "rel_wql_gmean": 0.531806127274585}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7742101724432567, "rel_wql_gmean": 0.5311843649137367}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7596192288082486, "rel_wql_gmean": 0.4951141955204966}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7363375295734231, "rel_wql_gmean": 0.5056560133396528}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7606418888845478, "rel_wql_gmean": 0.5538790094120064}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.9033681839460348, "rel_wql_gmean": 0.8510306246351569}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9132964142466047, "rel_wql_gmean": 0.6613834360775507}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6008629515181685, "rel_wql_gmean": 0.5128134103327899}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8241897772379998, "rel_wql_gmean": 0.4190277961561824}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8456240759834974, "rel_wql_gmean": 0.5233104342109804}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6511786830134689, "rel_wql_gmean": 0.4507742495174751}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5716616669502825, "rel_wql_gmean": 0.4393165312128719}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5958283325931863, "rel_wql_gmean": 0.6144059264519328}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7777281919380308, "rel_wql_gmean": 0.5386241189298943}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7363614335969476, "rel_wql_gmean": 0.5119011863402705}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9955332689841764, "rel_wql_gmean": 0.8441275404151479}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.758645213417042, "rel_wql_gmean": 0.8163566812421911}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6688662611218593, "rel_wql_gmean": 0.5094982599379567}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7151571878390335, "rel_wql_gmean": 0.4685405119829564}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9157681504509959, "rel_wql_gmean": 0.6694775206808375}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7270035470605405, "rel_wql_gmean": 0.5268439726526699}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7782347336583278, "rel_wql_gmean": 0.5325909632877113}}}, "group_skill_delta": {"term": {"long": -0.005720486590518714, "medium": 0.0021960790531426744, "short": -9.711935889122891e-05}, "domain": {"Econ/Fin": 0.00639561796267607, "Energy": -0.000837198123813554, "Healthcare": 0.021950168491802202, "Nature": 0.002641304295429303, "Sales": 0.02044153041057939, "Transport": 0.0010891685181155797, "Web/CloudOps": -0.01426351698227335}, "frequency": {"A": 0.0044681538800170895, "D": 0.011911071514982718, "H": -0.005200610093547331, "M": 0.014959576574749334, "Q": -0.0008243959861216066, "S": 0.0009041042351182194, "T": -0.005662050596863866, "W": 0.0048255724159554925}, "variates": {"multivariate": -0.004896700591577097, "univariate": 0.002515350672037786}}}], "mean_delta": -0.0008320416528670993, "positive_cutoffs": 0, "outcome": "Half learning rate remains below original at72000; continue declared96000 schedules. Main validation only."}
+
+E129 direct learned seasonal uncertainty head seed197 training started at 2026-10-23T09:19:29Z, after the declared dependency processes exited; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+2026-10-23T09:19:50.754277+00:00: E12812 full replay rejected at0.38395261653633084; all checks passed, suite1004.778254749s. E113 final12k1of4positive/mean-0.000496019702 after primary2of4/mean-0.000397918945: no transfer. E121primary6k1of4/mean-0.000485590081: no primary transfer, scheduled12k still finishing. E10124k0.367868996728, matched E94+0.012416547483; E94halfLR72k0.366533622937, matched E79-0.000832041653. Original schedules and immutable teacher preserved. E132controller26132 and E133controller26167 wait for declared endpoints.
+
+2026-10-23T09:20:48.920259+00:00: E134 visibility curriculum audit and queue. CPU seed701 plus actualstream307,6144rows,4.915062141837552s: full source windows, masks, periods and RNG bitwise matched; resumed boundary views exact; original prefetched batch unmodified. Early/middle/full finite target means135.0555/251.7554/415.6190; empty target rows47/44/37 of6144 are preserved rather than resampled. GPU seed709,B48signed96,C128,H16,gradient cosine0.9999875115719722,eager/compiled loss0.4359123110771179/0.43591222167015076,peak reserved3644MiB with Adam/EMA placeholders,compile51.421730180s,total73.688431652s,0optimizer updates/no validation. Queue{"queue_pid": 26704, "watch_pid": 26705, "comparison_pid": 26706} waits E110full13879 and E79wide5650 final96000. Reuses own scratch initialc9693619...,146477344params; sampler/model byte-identical to E79. Training-only deterministic views archived; publisher records schedule and sources. No extra concurrent allocation. Primary96k,16/32/48/72diagnostics,48/96four-cutoff comparisons.
+
+## E135: own-ensemble distilled fourth member, predeclared48k/96k
+2026-10-23T09:22:02.738349+00:00: E101distillation gains0.012416547 versus matchedE94 at24k. Hypothesis: at48k its improved learned forecasts can replace fresh E79wide48k in the fixed four-member package, despite potentially reduced member diversity. Training is unchanged; immutableE96teacher/coefficient0.5/seed307/B48/LR1e-4/96k/oldpack remain. Primary48k,final96kdurability, no earlier checkpoint alternatives or weight sweep. Every instance gets E117final/E71/E93/student with ordered equal quarters. Scientific standalone control is E94 at the same duration; package comparator is E130frozenquad48 and then-current publication. Require3of4 positive diagnostic cutoffs and positive mean before a full public replay; publication also requires better full aggregate and complete relocation/composition checks. No new training, no runtime modification or model constructed now.
+
+E121 12000 four-cutoff comparison: {"label": "E121 12000 four-cutoff comparison", "utc": "2026-10-23T09:22:44.476099+00:00", "candidate": "seasonal_history_ema12000", "control": "long_seasonal_ema12000", "rows": [{"cutoff": "evals", "candidate_skill": 0.3813059147504362, "control_skill": 0.38231375879048757, "delta": -0.0010078440400513866, "candidate_checkpoint": "50257b1c120f798238c78920e28863d3b0f6a7bdb45e93c6ec82ad3ee6c92127", "control_checkpoint": "d512cdac520239f0c10e021721424d87cee30086fa70f6cbc41f21e2e04988c3", "candidate_eval_seconds": 27.915850400924683, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7410925529327445, "rel_wql_gmean": 0.4801896489521486}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7101927496760675, "rel_wql_gmean": 0.4951777893325377}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7505336641958719, "rel_wql_gmean": 0.5416629459896614}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8808621122321908, "rel_wql_gmean": 0.8553613464740869}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8986528839717891, "rel_wql_gmean": 0.6427356452925028}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5866079781217807, "rel_wql_gmean": 0.515878502724205}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8088402776655573, "rel_wql_gmean": 0.4071886269488133}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.849054821935193, "rel_wql_gmean": 0.5194197842337527}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6312577037012914, "rel_wql_gmean": 0.43131584054457206}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5577419814097542, "rel_wql_gmean": 0.4320730752302784}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6267917500028767, "rel_wql_gmean": 0.6531109131553342}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7645604573802218, "rel_wql_gmean": 0.5268011431202}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7264806634838061, "rel_wql_gmean": 0.4958712859698789}, "M": {"n_configs": 5, "rel_mase_gmean": 0.968665847380227, "rel_wql_gmean": 0.8244191704080674}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7789616355653816, "rel_wql_gmean": 0.8261807474647432}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6540890253739396, "rel_wql_gmean": 0.5027568624383535}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6901059067030996, "rel_wql_gmean": 0.4533119317454891}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9072082556593281, "rel_wql_gmean": 0.6801331584162394}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7073920580377491, "rel_wql_gmean": 0.51149932132313}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7662561432987753, "rel_wql_gmean": 0.5224500380303261}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7385624256035537, "rel_wql_gmean": 0.4774480306015298}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7112970077840257, "rel_wql_gmean": 0.49399950873586423}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7495810559140079, "rel_wql_gmean": 0.5412990087697497}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8812498422781989, "rel_wql_gmean": 0.8555240839274089}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9003124134337842, "rel_wql_gmean": 0.6439831822912251}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5795579602545416, "rel_wql_gmean": 0.5129229324649281}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8014187192027531, "rel_wql_gmean": 0.39917690533601163}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8487678656214006, "rel_wql_gmean": 0.5191938831467846}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6315950761584922, "rel_wql_gmean": 0.4315386790157564}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5583373635107705, "rel_wql_gmean": 0.433162583197897}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6267725580318051, "rel_wql_gmean": 0.6538606856911642}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7599509242283058, "rel_wql_gmean": 0.524915385039173}, "H": {"n_configs": 31, "rel_mase_gmean": 0.726196004396462, "rel_wql_gmean": 0.4955559356320016}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9682906777404066, "rel_wql_gmean": 0.8249007339255652}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7794132143007206, "rel_wql_gmean": 0.826151122683708}, "S": {"n_configs": 6, "rel_mase_gmean": 0.654344138587591, "rel_wql_gmean": 0.5028751847848326}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6900127153197991, "rel_wql_gmean": 0.4513560570693673}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9068583895899763, "rel_wql_gmean": 0.679330225432368}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7082895004717786, "rel_wql_gmean": 0.5105014816296383}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7639387308045943, "rel_wql_gmean": 0.5212587544154408}}}, "group_skill_delta": {"term": {"long": -0.002721680002506388, "medium": -0.0002456613235772842, "short": -0.0006188626122706209}, "domain": {"Econ/Fin": 0.00027360420030342336, "Energy": 0.0014393074330932842, "Healthcare": -0.0048842555794103015, "Nature": -0.008286759016696288, "Sales": -0.0002566324573352041, "Transport": 0.00027422820848521656, "Web/CloudOps": 0.0008808120760050997}, "frequency": {"A": 0.00035734856289149075, "D": -0.003049503732827308, "H": -0.00030844311715227946, "M": 8.783697678704794e-05, "Q": 0.00021811127813176068, "S": 0.0001793098017012218, "T": -0.0012456082559563209, "W": -0.0006151947572433647}, "variates": {"multivariate": -0.0002059442254825905, "univariate": -0.001678172759489227}}}, {"cutoff": "backcast_evals", "candidate_skill": 0.3401379378099838, "control_skill": 0.34137925383664736, "delta": -0.0012413160266635526, "candidate_checkpoint": "50257b1c120f798238c78920e28863d3b0f6a7bdb45e93c6ec82ad3ee6c92127", "control_checkpoint": "d512cdac520239f0c10e021721424d87cee30086fa70f6cbc41f21e2e04988c3", "candidate_eval_seconds": 27.87614369392395, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7771656159812894, "rel_wql_gmean": 0.4641464670148968}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7698937625348257, "rel_wql_gmean": 0.5323994679990453}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8092395007488731, "rel_wql_gmean": 0.5916383173869478}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8758967019430145, "rel_wql_gmean": 0.8175201210563051}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8887780515899812, "rel_wql_gmean": 0.6669633025938924}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6516566774021227, "rel_wql_gmean": 0.5621621277653412}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9098315297713278, "rel_wql_gmean": 0.4282329830782216}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7242751469093448, "rel_wql_gmean": 0.4914362361956457}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.7294937757164066, "rel_wql_gmean": 0.5226388793304544}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6610373665420268, "rel_wql_gmean": 0.4520737202274516}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6450259219422076, "rel_wql_gmean": 0.7246543179459486}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8154620853085416, "rel_wql_gmean": 0.4875051030541513}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7695450570086102, "rel_wql_gmean": 0.5086618227538536}, "M": {"n_configs": 5, "rel_mase_gmean": 0.8612498214867264, "rel_wql_gmean": 0.7897578092562838}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7636484321563957, "rel_wql_gmean": 0.7759964686434834}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8532172145869841, "rel_wql_gmean": 0.5069283220084164}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7723414529338847, "rel_wql_gmean": 0.5772230378425738}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8727212488016272, "rel_wql_gmean": 0.5935061529318592}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7894050496926538, "rel_wql_gmean": 0.5288493492410338}, "univariate": {"n_configs": 54, "rel_mase_gmean": 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"rel_wql_gmean": 0.5230662990235599}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6616757881069665, "rel_wql_gmean": 0.45318260421936735}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6445112721993314, "rel_wql_gmean": 0.7250031265258748}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8169867958549157, "rel_wql_gmean": 0.48765941110091987}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7687868921949567, "rel_wql_gmean": 0.5082857531718749}, "M": {"n_configs": 5, "rel_mase_gmean": 0.8647112860240415, "rel_wql_gmean": 0.791872490233388}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7635201146060429, "rel_wql_gmean": 0.7764383165590925}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8527947748812648, "rel_wql_gmean": 0.5066866363631385}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7680903138861567, "rel_wql_gmean": 0.5733326508067024}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8719229424762637, "rel_wql_gmean": 0.5937682323059945}}, "variates": {"multivariate": {"n_configs": 43, 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"T": {"n_configs": 29, "rel_mase_gmean": 0.7528268453636292, "rel_wql_gmean": 0.5352915340790136}, "W": {"n_configs": 8, "rel_mase_gmean": 0.83682418441124, "rel_wql_gmean": 0.6327957387084753}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.779687197088358, "rel_wql_gmean": 0.5128211016965539}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7517044391242127, "rel_wql_gmean": 0.538809108170691}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.796127056742376, "rel_wql_gmean": 0.5022139410634582}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7505942901123125, "rel_wql_gmean": 0.4865575819153698}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7594504520679526, "rel_wql_gmean": 0.5532537195147663}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8929618274343588, "rel_wql_gmean": 0.9103813793769899}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8959209226146972, "rel_wql_gmean": 0.6453740580861129}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6961784435164479, "rel_wql_gmean": 0.5915734081229318}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7476406868387996, "rel_wql_gmean": 0.35522212901676864}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5597759096460994, "rel_wql_gmean": 0.35738676117830037}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6611597485381723, "rel_wql_gmean": 0.4949322755161753}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6805375314732472, "rel_wql_gmean": 0.4657391485089796}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.582687321216151, "rel_wql_gmean": 0.7249374834569997}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7228644523548966, "rel_wql_gmean": 0.4448013254664766}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7512801448992653, "rel_wql_gmean": 0.48470980713170986}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8061257673025353, "rel_wql_gmean": 0.8338421947073288}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8329742195879615, "rel_wql_gmean": 0.8780190666063756}, "S": {"n_configs": 6, "rel_mase_gmean": 0.903952356548986, "rel_wql_gmean": 0.5661338988037351}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7547233557063331, "rel_wql_gmean": 0.536425314767226}, "W": {"n_configs": 8, "rel_mase_gmean": 0.83773127724725, "rel_wql_gmean": 0.6339155481728359}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.780210137456359, "rel_wql_gmean": 0.5128749991570595}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7524307203251008, "rel_wql_gmean": 0.5390362822620333}}}, "group_skill_delta": {"term": {"long": -0.0007324544050971715, "medium": 0.0008555081358375682, "short": 0.0005351722743626164}, "domain": {"Econ/Fin": 0.0005327151568488286, "Energy": 0.00130703949975719, "Healthcare": -9.377891999895471e-05, "Nature": 0.00027799860786281716, "Sales": -0.0002771384946731459, "Transport": 0.00032181580708878776, "Web/CloudOps": -0.0005818248027860129}, "frequency": {"A": 0.0005868221601658119, "D": -0.00030131064454019807, "H": -0.0004838666325538421, "M": 0.001131549891915773, "Q": -0.0005303241709088136, "S": -7.056133095595385e-07, "T": 0.0014718692998841876, "W": 0.0010382287478243324}, "variates": {"multivariate": 0.00024525683429399425, "univariate": 0.00044158599169175083}}}, {"cutoff": "backcast3_evals", "candidate_skill": 0.391803142720465, "control_skill": 0.3928331731047856, "delta": -0.0010300303843205905, "candidate_checkpoint": "50257b1c120f798238c78920e28863d3b0f6a7bdb45e93c6ec82ad3ee6c92127", "control_checkpoint": "d512cdac520239f0c10e021721424d87cee30086fa70f6cbc41f21e2e04988c3", "candidate_eval_seconds": 27.31839394569397, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7624346703659255, "rel_wql_gmean": 0.4743580833851574}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7702307781273624, "rel_wql_gmean": 0.5292770399476574}, "short": {"n_configs": 54, "rel_mase_gmean": 0.713144124249704, "rel_wql_gmean": 0.504332011833272}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8405708171366003, "rel_wql_gmean": 0.838069378872038}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8318396604722257, "rel_wql_gmean": 0.553433996992084}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7566464181949424, "rel_wql_gmean": 0.6344870408459748}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7319190534029946, "rel_wql_gmean": 0.3400004103072392}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7475116719207561, "rel_wql_gmean": 0.4734936758666793}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.53750914128304, "rel_wql_gmean": 0.4457451914431652}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7142643694503665, "rel_wql_gmean": 0.517548965180728}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5302117589970107, "rel_wql_gmean": 0.8007710428899867}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7180517709674819, "rel_wql_gmean": 0.4178382031926381}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7228091606224635, "rel_wql_gmean": 0.4862435953137423}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8359939305521914, "rel_wql_gmean": 0.8275991910591551}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7127980309953568, "rel_wql_gmean": 0.7659555307794623}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8420996207058596, "rel_wql_gmean": 0.6005482331608448}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7105038030240836, "rel_wql_gmean": 0.4635245614583766}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8170490407004515, "rel_wql_gmean": 0.6656752726023953}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7628515792966292, "rel_wql_gmean": 0.5124410538658243}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7137943050574351, "rel_wql_gmean": 0.49613017247229274}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7631434004686845, "rel_wql_gmean": 0.4745780224475118}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7702543592823002, "rel_wql_gmean": 0.5302072122994279}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7102740757127205, "rel_wql_gmean": 0.5028426749263977}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8404794249042948, "rel_wql_gmean": 0.8377611830280824}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8299149339539488, "rel_wql_gmean": 0.5519405024214632}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7621826867136657, "rel_wql_gmean": 0.6349175346276966}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7231739407449111, "rel_wql_gmean": 0.3380432271049275}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7474335142271239, "rel_wql_gmean": 0.4733071421517686}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5378627602598968, "rel_wql_gmean": 0.4463114549472672}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7146709097664398, "rel_wql_gmean": 0.5186863440823273}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5306073447296601, "rel_wql_gmean": 0.8030982774337949}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7175124768607152, "rel_wql_gmean": 0.41780178227764675}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7223996277645064, "rel_wql_gmean": 0.4859456509788173}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8339193215840682, "rel_wql_gmean": 0.8250296214792107}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7133214644953572, "rel_wql_gmean": 0.7659591311067668}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8388577706002066, "rel_wql_gmean": 0.5998597273487538}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7077596915926687, "rel_wql_gmean": 0.46245587762920626}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8141976774865708, "rel_wql_gmean": 0.6645660569936594}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7600456915083991, "rel_wql_gmean": 0.511644654751976}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7131300545794724, "rel_wql_gmean": 0.49563420928448887}}}, "group_skill_delta": {"term": {"long": 0.0004189156912476344, "medium": 0.0005705870804755264, "short": -0.0020923781938803065}, "domain": {"Econ/Fin": -0.0001999628363876793, "Energy": -0.0017004855606553848, "Healthcare": 0.002766103506972928, "Nature": -0.0044183974353669275, "Sales": -0.00014829528630255773, "Transport": 0.0004719007179080359, "Web/CloudOps": 0.0008409088440183288}, "frequency": {"A": 0.0011895443852505405, "D": -0.00022959662975885475, "H": -0.0003495788044701742, "M": -0.0023234081767862236, "Q": 0.00027298758276950164, "S": -0.0017771470506958709, "T": -0.0017699468197419632, "W": -0.001901601654469287}, "variates": {"multivariate": -0.0016360525211348564, "univariate": -0.0005743401329708631}}}], "mean_delta": -0.0007314110640462834, "positive_cutoffs": 1, "outcome": "Declared primary6000/durability12000 diagnostic. No transfer formula or automatic publication is selected by this controller."}
+
+## E136: direct bounded local-trend head
+2026-10-23T09:22:46.982190+00:00: Hypothesis: direct learned forecast gates on fixed short-window trend increments can improve short/low-frequency extrapolation. E35query-only local-trend features failed earlier cutoffs; this is a distinct output-path addition motivated by E125seasonal direct-head sensitivity, not a claim that E35 worked or a direct head-versus-query comparison. Start exactly E107zero-extended E62 initial470ef98d..., retain its long-seasonal query/head, add only zero Linear512→32 with bias (16416parameters). For each k=4,16, take means of lastk and previousk normalized observations, require>=max(2,k/2) finite in each; slope=(recent-older)/k; delta(t)=3*tanh(slope*(t+k/2)/3), t starts0. Invalid support gives0. Add sum(tanh(learned_gate)*delta) to median/all nine quantiles, bounded by6context-standard-deviation units; no query addition, width change or frequency selector. All parameters train. Same seed197/B64/LR2e-5/12kpaired/oldpack/originalgenerator/horizon300200 as E107; code/sampler and inherited tensors preserved. Primary6k,1/3diagnostic,12durability; allfourcutoffs comparedtoE107 at6/12. CPUfixture719/GPU727. Queue after E129width final12k only after audits. No new concurrent trainer; no transfer coefficient or ensemble selected. No validation training.
+
+E110 full allowed corpus 48000 matched comparison: {"label": "E110 full allowed corpus 48000 matched comparison", "utc": "2026-10-23T09:23:20.926275+00:00", "candidate": "random_capacity_full_ema48000", "control": "random_capacity_1024_halflr_ema48000", "rows": [{"cutoff": "evals", "candidate_skill": 0.36449796840324333, "control_skill": 0.36570581958166615, "delta": -0.001207851178422814, "candidate_checkpoint": "926f61e5d5f8ffebc8a5d73285832ebedc28aca794b0b06b55fc3c48a07330d1", "control_checkpoint": "ea9055d683b93a4691e77bd64d46e301302765673055fd9b32ddb3a3e2f749df", "candidate_eval_seconds": 36.223931074142456, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7548553746218293, "rel_wql_gmean": 0.4912987773239935}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7352999993476327, "rel_wql_gmean": 0.504363562451463}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7710300090434509, "rel_wql_gmean": 0.559031484306989}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8770103389279252, "rel_wql_gmean": 0.8491199347133979}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9116653965880178, "rel_wql_gmean": 0.6580222595386767}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.658803814018058, "rel_wql_gmean": 0.5396972664532947}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8280404956337558, "rel_wql_gmean": 0.41423369227737183}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8220888925613495, "rel_wql_gmean": 0.5100769909819354}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6548033152870734, "rel_wql_gmean": 0.45244139184364884}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5808419471264448, "rel_wql_gmean": 0.4489494375356866}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5935959285501078, "rel_wql_gmean": 0.6250048870270299}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7751260416854967, "rel_wql_gmean": 0.5273604629524228}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7390325632700983, "rel_wql_gmean": 0.5118806853040565}, "M": {"n_configs": 5, "rel_mase_gmean": 1.1155244631113046, "rel_wql_gmean": 0.9182662011196825}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7722896053051759, "rel_wql_gmean": 0.8394501877623775}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6682567022663544, "rel_wql_gmean": 0.5122682378733995}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7187462560447286, "rel_wql_gmean": 0.4734288271006955}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8925511740053919, "rel_wql_gmean": 0.6530704845861032}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7303154554027623, "rel_wql_gmean": 0.5275234582063876}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7838594930079051, "rel_wql_gmean": 0.5349400339090976}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7662260094156811, "rel_wql_gmean": 0.5020347958679111}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7301951069360759, "rel_wql_gmean": 0.504117847493669}, "short": {"n_configs": 55, "rel_mase_gmean": 0.760169630057802, "rel_wql_gmean": 0.5569997956352839}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.9028304922158942, "rel_wql_gmean": 0.8610209443587438}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9109454293905939, "rel_wql_gmean": 0.6586383074299472}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5906912914017803, "rel_wql_gmean": 0.5056722543269967}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8235409860259677, "rel_wql_gmean": 0.4231966180830639}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.827511700634232, "rel_wql_gmean": 0.5156149712576104}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6493612164587901, "rel_wql_gmean": 0.44948344031797927}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.579824052847299, "rel_wql_gmean": 0.45322822077685615}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.602630505709834, "rel_wql_gmean": 0.6156602399781036}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7586530974855328, "rel_wql_gmean": 0.5235946298319726}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7435902027632983, "rel_wql_gmean": 0.5180310279395838}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9983606322195729, "rel_wql_gmean": 0.8755961609165552}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.763496329349679, "rel_wql_gmean": 0.8207236971111334}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6652066754808894, "rel_wql_gmean": 0.512126307398621}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7125876645731054, "rel_wql_gmean": 0.4722827561143623}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9331507389139616, "rel_wql_gmean": 0.6806266270092172}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7296537216262745, "rel_wql_gmean": 0.5340631203218278}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7755777292850502, "rel_wql_gmean": 0.5321111949818653}}}, "group_skill_delta": {"term": {"long": 0.011237044940272, "medium": -0.002265482424887666, "short": -0.0058258391281375}, "domain": {"Econ/Fin": 0.018725456927130568, "Energy": 5.6439664597340844e-05, "Healthcare": -0.04975294984969725, "Nature": 0.00469166361832507, "Sales": 0.00564960889904409, "Transport": -0.004041300246997448, "Web/CloudOps": 0.001977896791272138}, "frequency": {"A": 1.254752371049328e-05, "D": -0.00909265869243725, "H": 0.005588956120089361, "M": -0.07713535452581188, "Q": -0.013576920435537154, "S": -0.001417613835157483, "T": -0.003207973776989448, "W": 0.033470593806027016}, "variates": {"multivariate": 0.0035524775192712355, "univariate": -0.005135222886763047}}}], "mean_delta": -0.001207851178422814, "positive_cutoffs": 0, "outcome": "At48k the full-corpus arm remains below the matched original-pack model. Continue declared72k/96k schedule; no source-mixture or teacher change."}
+
+2026-10-23T09:26:09.408870+00:00: E136CPU/GPU audits complete; queued{"queue_pid": 26926, "watch_pid": 26927, "comparison_pid": 26928} after E12924583final12000. InitialSHA87158329c5c8bfc69025843fa82c14482931b67d7e6c6b728aa461d45ef56bd4,36866400params. CPU2.943994993s, all original forecasts/loss bitwise; independent trend float64 maxerror4.44e-16 and float32 maxerror2.01856e-6. Initial1e-6 absolute float32 audit tolerance was too tight due mean-rounding amplified by forecast lead; failures retained as direct_trend_head_checks_failure1/2.log, tolerance amendedto1e-5 plus independent float64<1e-12. Model arithmetic unchanged by audit amendment. GPU727maximumB64C4096H1024 finiteallgradients/nonzeronewheadgrads, original BF16forecast/lossbitwise,6294MiBreserved/5785.947allocated includingAdam/EMAplaceholders,12.204455994s,0optimizerupdates/no validation. E121final complete12k main0.381305914750, fourcutoffs1positive/mean-0.000731411064; no transfer. E11048k0.364497968403, matched E94-0.001207851178; original96kschedule kept.
+
+E130 validation-only error diagnostics: coverage1.232385601s and median-error structure1.231693947s, same published descriptorfa97880d..., no new inference/updates/fitting. Lower-bound absolute per-window mean-error fraction of medianMAE averages0.56189long/0.55437medium/0.68098short; annual0.92223 and quarterly0.86332, but these are descriptive Jensen bounds, not recoverable improvements. Last-quarter normalizedMAE ratios1.09692long/1.09527medium/1.19979short. Atom-aware strict/inclusive quantile coverage retained; mixed signed bias by domain argues against a global offset. Files fresh_capacity4_ensemble_coverage.json and fresh_capacity4_median_error_structure.json contain all configuration groups.
+
+## E137: fresh depth/width allocation at nearly fixed146M parameter count
+2026-10-23T09:26:40.530388+00:00: Hypothesis: the fresh E79wide model may use its parameter budget better with more sequential layers and narrower hidden states. E22mature identity-grafted depth failed and is not resumed; this is a new fresh initialization with different depth/width allocation. One fixed configuration d768/12heads/11encoder/7decoder versus E79d1024/16heads/6encoder/4decoder, same64head dimension and patch16. Meta-device count146328864 versus146477344, difference-0.101367percent. No actual weights created in the count probe. Initialize locally with seed307 and the same zero-output seasonal-mixture/interval biases; require identical initial forecasts/loss. Same sampler/sourceweights/packed_sample/slowAR20percent/horizon300310, seed307/B48/LR2e-4/96k/paired/staticcompile/400warmupcosfloor0.2. Compare equal updates and record walltime; compute is not exactly matched despite nearly fixed parameters. Primary48k,4/8/16/24diagnostics,72/96durability;48/96all4cutoffs versusE79. CPUfixture733/GPU739; queue after E122originalAR96000, also require E79wide96000, replacing one allocation. No long-seasonal features/teacher/curriculum/newdata. No additional configuration or LR sweep, no transfer/ensemble selected. Audit before queue.
+
+## E118 seasonal replication seed523 scheduled1000 diagnostic
+- Checkpoint SHA-256: `6a98b9da2fe0328e79dc5918ac7a3633ca68848f124de9211021c00cb223b971`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 26.95650577545166 seconds.
+- Overall skill 0.378318; relative MASE 0.742595; relative WQL 0.520457.
+- term skill: long=0.4020, medium=0.4076, short=0.3573.
+- domain skill: Econ/Fin=0.1385, Energy=0.2383, Healthcare=0.4379, Nature=0.4331, Sales=0.3369, Transport=0.4715, Web/CloudOps=0.5009.
+- frequency skill: A=0.3664, D=0.3564, H=0.3977, M=0.1105, Q=0.1974, S=0.4226, T=0.4371, W=0.2196.
+- variates skill: multivariate=0.3951, univariate=0.3646.
+- Outcome: Early diagnostic only; primary6000 and final12000 four-cutoff replication gates remain unchanged.
+
+## E118 seasonal replication seed523 scheduled3000 diagnostic
+- Checkpoint SHA-256: `5bf12017a324cbc63b04b472e9a4a16e6f31de1f91075e5dbdaa9b5877f0e778`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.29305911064148 seconds.
+- Overall skill 0.380865; relative MASE 0.739639; relative WQL 0.518264.
+- term skill: long=0.4047, medium=0.4074, short=0.3609.
+- domain skill: Econ/Fin=0.1410, Energy=0.2368, Healthcare=0.4548, Nature=0.4339, Sales=0.3391, Transport=0.4753, Web/CloudOps=0.5046.
+- frequency skill: A=0.3691, D=0.3683, H=0.3972, M=0.1066, Q=0.1997, S=0.4244, T=0.4398, W=0.2187.
+- variates skill: multivariate=0.3956, univariate=0.3689.
+- Outcome: Early diagnostic only; primary6000 and final12000 four-cutoff replication gates remain unchanged.
+
+## E129 direct uncertainty head scheduled1000 diagnostic
+- Checkpoint SHA-256: `f35c7a8244c69445476be414b9945a8182382c20eba1704e3439e64c2b7b2835`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.25899314880371 seconds.
+- Overall skill 0.378918; relative MASE 0.742329; relative WQL 0.519639.
+- term skill: long=0.4009, medium=0.4070, short=0.3591.
+- domain skill: Econ/Fin=0.1331, Energy=0.2373, Healthcare=0.4623, Nature=0.4321, Sales=0.3365, Transport=0.4726, Web/CloudOps=0.4996.
+- frequency skill: A=0.3655, D=0.3628, H=0.3970, M=0.1081, Q=0.1988, S=0.4236, T=0.4359, W=0.2240.
+- variates skill: multivariate=0.3932, univariate=0.3673.
+- Outcome: Early diagnostic only; compare6000/12000 against E119 across all four cutoffs before considering any transfer.
+
+E121 final compute:12000updates, training loop2735.054694893s/process2752.4546950480435s on sharedH100, completed2026-10-23T09:19:28Z. E129width trainer26493 started09:19:29. E136queued after its scheduled completion.
+
+E118 6000 four-cutoff comparison: {"label": "E118 6000 four-cutoff comparison", "utc": "2026-10-23T09:27:51.420228+00:00", "candidate": "long_seasonal_replication_ema6000", "control": "weekly_replication_control_ema6000", "rows": [{"cutoff": "evals", "candidate_skill": 0.38139534722395463, "control_skill": 0.3764059869702334, "delta": 0.004989360253721209, "candidate_checkpoint": "a35444a7dd833f0988143c8b6f304dee50256258d32528f097dfd83e78aebd29", "control_checkpoint": "674137ac14bce26f6983cb68123e34d01ca598bb3ad72e81cbd197d138ed6104", "candidate_eval_seconds": 27.10453462600708, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7384436160182325, "rel_wql_gmean": 0.47633347487090744}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7083770552963923, "rel_wql_gmean": 0.4942262244660524}, "short": {"n_configs": 55, "rel_mase_gmean": 0.751548854583905, "rel_wql_gmean": 0.543994941613593}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8746099669867802, "rel_wql_gmean": 0.8444329440894982}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8974004296196013, "rel_wql_gmean": 0.6447860628281569}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5916091617456333, "rel_wql_gmean": 0.5181079300960174}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8010054886688159, "rel_wql_gmean": 0.39794714882168664}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8452719229795517, "rel_wql_gmean": 0.5198290418551154}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6348508365518176, "rel_wql_gmean": 0.43540848427535833}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.559670606308481, "rel_wql_gmean": 0.4359745561616292}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6166679480695255, "rel_wql_gmean": 0.6474656804701975}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7642165704351264, "rel_wql_gmean": 0.526273641696534}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7252102222235688, "rel_wql_gmean": 0.49585443092368514}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9617184700594594, "rel_wql_gmean": 0.8119569708130686}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7728364193780128, "rel_wql_gmean": 0.8232647673811775}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6562575033932274, "rel_wql_gmean": 0.5039290527300556}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6916904149917935, "rel_wql_gmean": 0.45467693411097854}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9047060491506385, "rel_wql_gmean": 0.6824196495981721}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7081151587667208, "rel_wql_gmean": 0.511629619486321}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7648591098010137, "rel_wql_gmean": 0.5226011134261795}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7423520793155982, "rel_wql_gmean": 0.4787760930516074}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7108957302184202, "rel_wql_gmean": 0.49544398256669214}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7601781653409367, "rel_wql_gmean": 0.5498223036137193}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8774576028138481, "rel_wql_gmean": 0.8475316890174351}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8988661021414461, "rel_wql_gmean": 0.6463834516983328}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6006104281692445, "rel_wql_gmean": 0.524233793726234}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8134935230540142, "rel_wql_gmean": 0.3997489668388505}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8499103394938109, "rel_wql_gmean": 0.5219975994261501}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6467190684113404, "rel_wql_gmean": 0.44452081778083646}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5634416431639562, "rel_wql_gmean": 0.4401382781033821}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6161471234928296, "rel_wql_gmean": 0.6463468715167274}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7848816529444701, "rel_wql_gmean": 0.5402760226239798}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7309935655071853, "rel_wql_gmean": 0.4990723913425074}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9727178622288029, "rel_wql_gmean": 0.816056242029619}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7737575967731107, "rel_wql_gmean": 0.823275820013628}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6574784363502988, "rel_wql_gmean": 0.5055662052073743}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6939173128959706, "rel_wql_gmean": 0.45688555231972555}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9049382682975748, "rel_wql_gmean": 0.6792210269186987}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7113196954989369, "rel_wql_gmean": 0.5136253484474166}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7736792477740673, "rel_wql_gmean": 0.5282222076131846}}}, "group_skill_delta": {"term": {"long": 0.0030901879764350104, "medium": 0.001780762492295751, "short": 0.007095493847238954}, "domain": {"Econ/Fin": 0.002975834665022492, "Energy": 0.001563367974052876, "Healthcare": 0.007483990472556434, "Nature": 0.005670688999450224, "Sales": 0.0032012415147016915, "Transport": 0.010415631896715682, "Web/CloudOps": 0.004022455454145979}, "frequency": {"A": -0.000812835465225703, "D": 0.01701113810261179, "H": 0.004336748931600631, "M": 0.007279563828444857, "Q": 0.00048059417615298194, "S": 0.0014689462587585966, "T": 0.002264615663313485, "W": -0.001743018809113317}, "variates": {"multivariate": 0.002535860099677434, "univariate": 0.007045445011520823}}}, {"cutoff": "backcast_evals", "candidate_skill": 0.34148204276995875, "control_skill": 0.33686271636038767, "delta": 0.004619326409571078, "candidate_checkpoint": "a35444a7dd833f0988143c8b6f304dee50256258d32528f097dfd83e78aebd29", "control_checkpoint": "674137ac14bce26f6983cb68123e34d01ca598bb3ad72e81cbd197d138ed6104", "candidate_eval_seconds": 28.566162824630737, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7723731416377819, "rel_wql_gmean": 0.4632107172403023}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7671954794368977, "rel_wql_gmean": 0.5312350959721146}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8100582121563055, "rel_wql_gmean": 0.5899282692731751}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8687261450618122, "rel_wql_gmean": 0.8082961609981344}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8862173381254191, "rel_wql_gmean": 0.6651047976556796}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6594478212047317, "rel_wql_gmean": 0.5645231469319925}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9089209431299469, "rel_wql_gmean": 0.42467952077275695}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7202692807552361, "rel_wql_gmean": 0.48857346078291813}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.7288704777178566, "rel_wql_gmean": 0.5222328100597717}, "Web/CloudOps": {"n_configs": 20, 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"Q": {"n_configs": 1, "rel_mase_gmean": 0.8287813793543607, "rel_wql_gmean": 0.8713871032122515}, "S": {"n_configs": 6, "rel_mase_gmean": 0.9172508855779987, "rel_wql_gmean": 0.5711595531807128}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7574199269295314, "rel_wql_gmean": 0.5414015001184371}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8487218235695173, "rel_wql_gmean": 0.6393469997194787}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7842051278389427, "rel_wql_gmean": 0.5178220809914736}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7594234945005031, "rel_wql_gmean": 0.5453398108764536}}}, "group_skill_delta": {"term": {"long": 0.002457539093086325, "medium": 0.005998211480277904, "short": 0.0030417477421523786}, "domain": {"Econ/Fin": -0.002273957709553409, "Energy": 0.0009015022908251957, "Healthcare": -0.0022612411372170405, "Nature": 0.009016440918215407, "Sales": 0.0024790220967471632, "Transport": 0.007354386508216848, "Web/CloudOps": 0.0022573579863649496}, "frequency": {"A": -0.00019577464888176355, "D": 0.00414510007665303, "H": 0.006642513192105515, "M": 0.01506639415788269, "Q": 0.000628474278988711, "S": 0.0031934833206613833, "T": 0.0006857901330149518, "W": -0.0033932466881767542}, "variates": {"multivariate": 0.0027634126167848105, "univariate": 0.0043115265641551215}}}, {"cutoff": "backcast3_evals", "candidate_skill": 0.3942150741055669, "control_skill": 0.38907521863271344, "delta": 0.00513985547285345, "candidate_checkpoint": "a35444a7dd833f0988143c8b6f304dee50256258d32528f097dfd83e78aebd29", "control_checkpoint": "674137ac14bce26f6983cb68123e34d01ca598bb3ad72e81cbd197d138ed6104", "candidate_eval_seconds": 27.25320529937744, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7629635636061852, "rel_wql_gmean": 0.47738458152400776}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.766392308286767, "rel_wql_gmean": 0.5313314815939195}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7071218260479499, "rel_wql_gmean": 0.5006775766138106}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8381102666984516, "rel_wql_gmean": 0.8394034451197796}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8289543246911923, "rel_wql_gmean": 0.5533980426446239}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7646070931321529, "rel_wql_gmean": 0.6340273684119219}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7124818665930959, "rel_wql_gmean": 0.3362710472055882}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.749228232992685, "rel_wql_gmean": 0.4762895479100841}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5376228806838672, "rel_wql_gmean": 0.4444847778365197}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7113197969938141, "rel_wql_gmean": 0.5172381497265214}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5285683023786453, "rel_wql_gmean": 0.8081021206308033}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7180947935486091, "rel_wql_gmean": 0.4166198174883141}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7187802155486654, "rel_wql_gmean": 0.48447543037731317}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8446392308099292, "rel_wql_gmean": 0.8352147404850153}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7170557043368275, "rel_wql_gmean": 0.7716033464240198}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8418253577208561, "rel_wql_gmean": 0.6034466207614427}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7040554267349227, "rel_wql_gmean": 0.4621152504325736}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8007557024739949, "rel_wql_gmean": 0.6624691609661729}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7573616152833316, "rel_wql_gmean": 0.5115793885293797}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7103904231097383, "rel_wql_gmean": 0.49493523556054253}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7671142731116414, "rel_wql_gmean": 0.47916971922185897}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7691789126794641, "rel_wql_gmean": 0.5327740132487557}, "short": {"n_configs": 54, "rel_mase_gmean": 0.715186469179385, "rel_wql_gmean": 0.5068184655107872}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8354342596603832, "rel_wql_gmean": 0.8357377255274399}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8342617788501976, "rel_wql_gmean": 0.5573075943847307}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7972742291272131, "rel_wql_gmean": 0.6590388193539706}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7177945317735281, "rel_wql_gmean": 0.3409481388214971}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7564395059888536, "rel_wql_gmean": 0.4796335352468516}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5443616570651463, "rel_wql_gmean": 0.4495153491412182}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7144590925124263, "rel_wql_gmean": 0.5173883213055337}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5289225996632382, "rel_wql_gmean": 0.8103046826779774}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7349960465094477, "rel_wql_gmean": 0.4267705081645206}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7251125032253354, "rel_wql_gmean": 0.4886750306028493}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8759774015027841, "rel_wql_gmean": 0.8655991309312683}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.719352309068614, "rel_wql_gmean": 0.7733057960523231}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8471349906333493, "rel_wql_gmean": 0.6030978365582621}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7042550964089376, "rel_wql_gmean": 0.46188222870125517}, "W": {"n_configs": 8, "rel_mase_gmean": 0.799674894951322, "rel_wql_gmean": 0.6659800353030906}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7624402331967736, "rel_wql_gmean": 0.5141833494144036}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.717447678119841, "rel_wql_gmean": 0.5004410335678261}}}, "group_skill_delta": {"term": {"long": 0.0027698008657701667, "medium": 0.0020262913449556885, "short": 0.0070419325939486965}, "domain": {"Econ/Fin": -0.0031706278671020938, "Energy": 0.004560668439750781, "Healthcare": 0.028605969372706652, "Nature": 0.0052263731253747725, "Sales": 0.00497135350332778, "Transport": 0.005829861229354971, "Web/CloudOps": 0.0014252569820584204}, "frequency": {"A": 0.0011093614220064607, "D": 0.013099980427090374, "H": 0.0051570149204880344, "M": 0.030859037748264706, "Q": 0.002011671661662273, "S": 0.002037535338632379, "T": -6.297375685770934e-05, "W": 0.0014344297457079547}, "variates": {"multivariate": 0.003670950319457855, "univariate": 0.006243315369551361}}}], "mean_delta": 0.004591270374809914, "positive_cutoffs": 4, "outcome": "Declared primary6000/durability12000 diagnostic. No transfer formula or automatic publication is selected by this controller."}
+
+2026-10-23T09:29:59.791282+00:00: E118 seed523 long-seasonal primary6000 passes allfour matched cutoffs. CandidateSHAa35444a7dd833f0988143c8b6f304dee50256258d32528f097dfd83e78aebd29; main0.381395347224, deltas[0.004989360253721209, 0.004619326409571078, 0.003616539363093918, 0.00513985547285345], mean0.004591270374809914. E133conditional averaged correction still waits for mandatory final12000 replication gate; no averaged checkpoint created. This replicates the feature benefit across data/initial optimizer streamseed, not a held-out test claim.
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+2026-10-23T09:31:30.337349+00:00: E137fresh depth allocation audited/queued{"queue_pid": 27276, "watch_pid": 27277, "comparison_pid": 27278} after E122queue20741 and E79queue5650 final96000. InitialSHA3991760771cd77bea0eb79a13091eeff912853dce8afa77ea272cf14840dd234;146328864params. CPU733initialforecast/lossbitwise,9.490635959s. GPU739maximumB48signed96C4096H1024 withAdam/EMA placeholders peak13370MiBreserved/12748.821allocated; finitegrads; compiledcomparison85.653620400s,total119.975613646 incl17.560618830lockwait. Initialzerohead compilegradientcos0.999966065. Additional temporary randomheadstd0.001 fixture ensures nonzero trunkgradients: allcos0.999967261,enc0.999929597,dec0.999963870; losses0.617865443/0.617864728;114.944255652s incl58.508098071lockwait,compiledcomparison10.392613893s. No optimizerupdates, initialcheckpointhashunchanged, no validation. This second audit addresses zerohead suppressing trunkgradient checks; no new model weights saved. Only queueafteraudits.
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+2026-10-23T09:32:37.930781+00:00: E110additional diagnostic scope, selected after viewing48kmain: full-corpus model loses aggregate0.001207851 toE94 but gains long-term0.011237045 and loses monthly0.077135355. Evaluate matched48k and final96k across allfour cutoffs to distinguish stable corpus tradeoffs from cutoff variation. This diagnostic expansion is post-main, not a predeclared48k selection claim. No training change, source mixture adjustment, checkpoint sweep or package automatically selected. ReuseE94cached earlier outputs where available; immutable ownweights only.
+
+E110 post-main diagnostic expansion 48000 four-cutoff comparison: {"label": "E110 post-main diagnostic expansion 48000 four-cutoff comparison", "utc": "2026-10-23T09:36:31.329060+00:00", "candidate": "random_capacity_full_ema48000", "control": "random_capacity_1024_halflr_ema48000", "rows": [{"cutoff": "evals", "candidate_skill": 0.36449796840324333, "control_skill": 0.36570581958166615, "delta": -0.001207851178422814, "candidate_checkpoint": "926f61e5d5f8ffebc8a5d73285832ebedc28aca794b0b06b55fc3c48a07330d1", "control_checkpoint": "ea9055d683b93a4691e77bd64d46e301302765673055fd9b32ddb3a3e2f749df", "candidate_eval_seconds": 36.223931074142456, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7548553746218293, "rel_wql_gmean": 0.4912987773239935}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7352999993476327, "rel_wql_gmean": 0.504363562451463}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7710300090434509, "rel_wql_gmean": 0.559031484306989}}, "domain": 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"rel_wql_gmean": 0.5349992288028624}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8341454143816802, "rel_wql_gmean": 0.8338715773308747}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8515634140277826, "rel_wql_gmean": 0.569558846056249}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 1.0036883931983478, "rel_wql_gmean": 0.7939359543280723}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7284353100394492, "rel_wql_gmean": 0.3541163927462603}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7596670123428141, "rel_wql_gmean": 0.48310093816362193}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5606603701520327, "rel_wql_gmean": 0.4662600653320203}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7196411952930366, "rel_wql_gmean": 0.5292738980605055}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5409193903073758, "rel_wql_gmean": 0.7917996023000913}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7959225789371909, "rel_wql_gmean": 0.45488532912713076}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7327074432980089, "rel_wql_gmean": 0.4985937197066213}, "M": {"n_configs": 4, "rel_mase_gmean": 1.014151462247973, "rel_wql_gmean": 0.9863094244425376}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7189041697429438, "rel_wql_gmean": 0.7579482002697641}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8426818571883371, "rel_wql_gmean": 0.6178592102432287}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7104899807629945, "rel_wql_gmean": 0.47423851084596597}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8247509389073472, "rel_wql_gmean": 0.6824107191878224}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7843110069902741, "rel_wql_gmean": 0.532604424215474}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7372506716076969, "rel_wql_gmean": 0.5168855843039583}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7752169604856247, "rel_wql_gmean": 0.4888518915184839}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7650403788019358, "rel_wql_gmean": 0.5308175670777368}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7467627077568808, "rel_wql_gmean": 0.5327531090955401}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8620913672622541, "rel_wql_gmean": 0.8547318170618328}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8441748859081236, "rel_wql_gmean": 0.5646289945902436}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.9376598848526552, "rel_wql_gmean": 0.7818664221169428}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7456434741968527, "rel_wql_gmean": 0.3539286239241533}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7618130413241728, "rel_wql_gmean": 0.48417506611634803}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5607487049943833, "rel_wql_gmean": 0.46645306914905216}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.716046053229904, "rel_wql_gmean": 0.5316021041051098}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5347671576614936, "rel_wql_gmean": 0.8122502371876661}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8007894976355243, "rel_wql_gmean": 0.45286612793735087}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7340762162135239, "rel_wql_gmean": 0.500516438169534}, "M": {"n_configs": 4, "rel_mase_gmean": 0.9220553371250699, "rel_wql_gmean": 0.9123288561939542}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7146849143809129, "rel_wql_gmean": 0.7593485849767106}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8558536051666373, "rel_wql_gmean": 0.6309694826905562}, "T": {"n_configs": 29, "rel_mase_gmean": 0.711372174563038, "rel_wql_gmean": 0.4773308031413116}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8219606642085736, "rel_wql_gmean": 0.6657419281727769}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7816389778004437, "rel_wql_gmean": 0.5342976538479055}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7370987880405361, "rel_wql_gmean": 0.5143752681169628}}}, "group_skill_delta": {"term": {"long": 0.0031508306340294334, "medium": 0.0005175502310403868, "short": -0.0029336236856895015}, "domain": {"Econ/Fin": 0.024395220415046714, "Energy": -0.00603526299653101, "Healthcare": -0.036445501021621585, "Nature": 0.005827774867388058, "Sales": 0.0015291263619008344, "Transport": 0.0001460946109080874, "Web/CloudOps": -0.00019099449491710718}, "frequency": {"A": 0.00461739459561572, "D": 0.0004958286645164733, "H": 0.0017296683701544247, "M": -0.08295436023909741, "Q": -0.0014897437080618747, "S": 0.01329196518146214, "T": 0.0022508390441152715, "W": -0.010473632248902076}, "variates": {"multivariate": -7.708756783786352e-05, "univariate": -0.0015642852251873673}}}], "mean_delta": -0.0010022002317872647, "positive_cutoffs": 1, "outcome": "Declared comparison checkpoints 48000,96000. No transfer formula or automatic publication is selected by this controller."}
+
+## E138: learned bounded ensemble gate on frozen E130 members
+2026-10-23T09:37:40.524485+00:00: Hypothesis: context statistics and expert disagreement can predict which own-trained member is more reliable, improving on fixed equal quarters. Freeze exactly E130descriptorfa97880dd132b1cdff459be4798f8f360d7df59b13e78026a9d74a95d63df0db and all four expert tensors. Every expert forecasts every instance. Train only a fresh gate MLP29→64→64→4, twoGELUactivations, zero final layer,6340parameters; fixed positive per-lead weights0.125+0.5*softmax(logits), so each lies[0.125,0.625] and sums1. All nine quantiles share each lead's weights. No weight/coefficient sweep or expert updates. Gate features29: log finitecount/8; two log periods/8; log requestedH/8; leadfraction and log lead/8; finitecount/observed-span (exclude left padding); three log normalized mean-absolute differences at lag1/period1/period2 with E47support/default rules; three absolute adjacent-mean differences at widths4/16/128 requiring>=max(2,k/2)finite inbothmeans; perexpert absolute normalized median difference from equal-expert median (4), log1p normalized80percent width(4), absolute median difference from lastfinitecontextvalue(4), and from last32mean(4). Bounded feature transforms, recent4096canonical normalization; no dataset/domain identity feature, no other instance. Exact existing NumPy seasonal/continuity/zero-mass/finite transforms for expert outputs in both training and inference; training per-row available horizons use existing decoder masks. Gate FP32 and frozen expert BF16 with TF32disabled, as package runtime. Initial gate must reproduce equal quarters on real/synthetic fixtures; preserve ordered quantiles and deterministic relocation.
+Training planned after E123log_horizon final12000, replacing one allocation: original E80sampler/packed_sample/sourceweights, seed751/B64/LR1e-3/12000, existing400warmup/cosfloor0.2, fusedAdamW(.9,.95),wd0.05,clip1,EMAexistingformula. Supervised targets only allowed corpus/own generator; no distillation labels or validation training. Same weighted quantile-plus-median loss and context seasonal scale; no extra entropy penalty. Primary6000,1000/3000diagnostics,12000durability; all4cutoffs versus frozenE130 at6/12, and then-current publication before any selection. Save complete optimizer/EMA/RNG/prefetched batches; experts never registered in optimizer/EMA. CPUfixture757/GPU761. Implement and audit before queue. Runtime and publication need a separate package that includes the frozen experts, gate and provenance; current submission remains unchanged. No candidate/training launched yet.
+
+2026-10-23T09:40:45.598974+00:00: E138implementation detail fixed before audits/training: normalized median-distance and adjacent-mean features clip30; width feature log1p(clamp(normalizedwidth,0,60)); lag-difference statistics log1p(clamp(absdiff,0,10)), default1 whenfewer8pairs; allfeaturevalues nan_to_num andclip[0,30]. Canonical recent4096 normalization pads left to16, with original E79normalizer onordinaryvalues; extreme-invalidmu/scale usefinite0/1 fallback. Finite fraction denominator is observed span fromfirstfinite toforecastcutoff, excluding batch-left-padding. Combination uses ordered FP32positive-weight accumulation, thenfinitecleanup andcummaxalongquantiles forroundoff; same operation training/inference. Expert helper uses exact frozen package NumPytransforms, not a differentiable approximation. Core files prepared; no initialgateweights/audit/training yet.
+
+E129 6000 four-cutoff comparison: {"label": "E129 6000 four-cutoff comparison", "utc": "2026-10-23T09:43:02.720550+00:00", "candidate": "seasonal_width_ema6000", "control": "seasonal_dispersion_ema6000", "rows": [{"cutoff": "evals", "candidate_skill": 0.3831603776984651, "control_skill": 0.38225879938174245, "delta": 0.0009015783167226621, "candidate_checkpoint": "4565c9b6447aab0d457738b65543c5cbee2a90f79e4a87dd9afa7f567aaf685a", "control_checkpoint": "6b640ad1ce200544da2d530578e59eb353f8b9dabc5cc98473403cfe7383c49f", "candidate_eval_seconds": 28.452057600021362, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7401317804403728, "rel_wql_gmean": 0.4766136103886538}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7099436471829155, "rel_wql_gmean": 0.49190059521229146}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7499662116453987, "rel_wql_gmean": 0.5396013043488593}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8875791722920614, "rel_wql_gmean": 0.8559903021736716}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8975039761261759, "rel_wql_gmean": 0.6401064409980725}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5785767170315638, "rel_wql_gmean": 0.5100206072446433}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8021508818788523, "rel_wql_gmean": 0.3970075037303459}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8427232478989891, "rel_wql_gmean": 0.516173636798094}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6323989932515749, "rel_wql_gmean": 0.43099113410972134}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5609720769542301, "rel_wql_gmean": 0.4341063609833436}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6181463973582709, "rel_wql_gmean": 0.6507339136112732}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7607294744792854, "rel_wql_gmean": 0.5213621876812429}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7261758240903428, "rel_wql_gmean": 0.4928362425659036}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9610257004140478, "rel_wql_gmean": 0.8222510272919312}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.783320722154848, "rel_wql_gmean": 0.829193391980513}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6555292549472804, "rel_wql_gmean": 0.5033031593079214}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6919888167355419, "rel_wql_gmean": 0.4527208999963679}, "W": {"n_configs": 8, "rel_mase_gmean": 0.903260761731007, "rel_wql_gmean": 0.670479427208697}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7082974659593699, "rel_wql_gmean": 0.5088761663351993}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7643965343949494, "rel_wql_gmean": 0.5196992592514946}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7400506225960969, "rel_wql_gmean": 0.47808876994740046}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7101268693563747, "rel_wql_gmean": 0.49314710783510285}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7500860933248864, "rel_wql_gmean": 0.5411083364362582}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8886871163750968, "rel_wql_gmean": 0.8551794904776345}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8972051207193078, "rel_wql_gmean": 0.6417640311927593}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.578364137997311, "rel_wql_gmean": 0.5117456137390363}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8023291284024608, "rel_wql_gmean": 0.3989034387003474}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8416251755891729, "rel_wql_gmean": 0.5159970993971862}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6323129527317001, "rel_wql_gmean": 0.4332903678255558}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5615569274522425, "rel_wql_gmean": 0.43470539309584183}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6177358739303225, "rel_wql_gmean": 0.6470843841362995}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7613833385246014, "rel_wql_gmean": 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0.00013068122954484895, "Energy": 0.0008543822424174063, "Healthcare": 0.0008178971143827374, "Nature": 0.001408719730444874, "Sales": -0.0005425522983093511, "Transport": 0.0013550988926180896, "Web/CloudOps": 0.0005977160740209264}, "frequency": {"A": -0.0019910343743771985, "D": 0.0011265229391850262, "H": 0.0017026795855628052, "M": 0.0031541100335809036, "Q": 0.0006534857098969837, "S": 4.606356713177107e-06, "T": 0.0003372434812615177, "W": -0.000654923649645367}, "variates": {"multivariate": 0.0009324167005572148, "univariate": 0.000875317916272933}}}, {"cutoff": "backcast_evals", "candidate_skill": 0.3412465968938221, "control_skill": 0.34048229409326713, "delta": 0.0007643028005549501, "candidate_checkpoint": "4565c9b6447aab0d457738b65543c5cbee2a90f79e4a87dd9afa7f567aaf685a", "control_checkpoint": "6b640ad1ce200544da2d530578e59eb353f8b9dabc5cc98473403cfe7383c49f", "candidate_eval_seconds": 28.581611156463623, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7703378422663418, "rel_wql_gmean": 0.46078608846829733}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7698204801258983, "rel_wql_gmean": 0.5298063248192315}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8116946984363662, "rel_wql_gmean": 0.591096395267947}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8840896172674156, "rel_wql_gmean": 0.8166116323804048}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8877976324574343, "rel_wql_gmean": 0.6655471766191752}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6603789214388155, "rel_wql_gmean": 0.566295641779891}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9061232280904533, "rel_wql_gmean": 0.4211354757782516}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7234756545220536, "rel_wql_gmean": 0.4901133073734373}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.7310010585990251, "rel_wql_gmean": 0.5212183995094899}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6586290512783582, "rel_wql_gmean": 0.452861684924056}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.65209515758716, "rel_wql_gmean": 0.7285952892015877}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8208196574267836, "rel_wql_gmean": 0.48847822436902455}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7664815515194227, "rel_wql_gmean": 0.5059676547535986}, "M": {"n_configs": 5, "rel_mase_gmean": 0.8689281957906105, "rel_wql_gmean": 0.794920967030779}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7592445698251972, "rel_wql_gmean": 0.7638082257229809}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8532703653418005, "rel_wql_gmean": 0.5072000942524026}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7706738261523969, "rel_wql_gmean": 0.5752670274035184}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8748739987149731, "rel_wql_gmean": 0.5865464292807087}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7867476548525202, "rel_wql_gmean": 0.5267846995784454}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7987490424547307, "rel_wql_gmean": 0.5635638286829712}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7699386641350829, "rel_wql_gmean": 0.4620092164574338}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7700252574476023, "rel_wql_gmean": 0.5312660160823937}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8114763680106447, "rel_wql_gmean": 0.5925124306653264}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8851214996579603, "rel_wql_gmean": 0.81706534284058}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8880075540028629, "rel_wql_gmean": 0.6669917238344288}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6607679512516224, "rel_wql_gmean": 0.5676047563389465}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9044039298942245, "rel_wql_gmean": 0.4227147422454011}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7233763880081109, "rel_wql_gmean": 0.49051933957001853}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.7306764832685102, "rel_wql_gmean": 0.5237199503158482}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6585663042744694, "rel_wql_gmean": 0.453530474116577}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6519157656498028, "rel_wql_gmean": 0.7264149528026873}, "D": {"n_configs": 15, "rel_mase_gmean": 0.820638386519995, "rel_wql_gmean": 0.48899927341307703}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7669137207994428, "rel_wql_gmean": 0.5089765668356441}, "M": {"n_configs": 5, "rel_mase_gmean": 0.8681664151706637, "rel_wql_gmean": 0.7970725004451346}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7591685936267477, "rel_wql_gmean": 0.7657209715884332}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8528003711266274, "rel_wql_gmean": 0.5088102011002493}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7701790682032242, "rel_wql_gmean": 0.57528943509888}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8741170955888644, "rel_wql_gmean": 0.5874303282064617}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7864590781265922, "rel_wql_gmean": 0.5275771051125318}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.798685143563512, "rel_wql_gmean": 0.5654498581714905}}}, "group_skill_delta": {"term": {"long": 0.0006356256917187109, "medium": 0.0009642128129954131, "short": 0.0007359098911582374}, "domain": {"Econ/Fin": 0.00073186311553608, "Energy": 0.0009247175050326462, "Healthcare": 0.0008867426062801886, "Nature": 0.0005697499193413336, "Sales": 0.00020573643967503052, "Transport": 0.001342097068590009, "Web/CloudOps": 0.000377087225496342}, "frequency": {"A": -0.0011267980889990703, "D": 0.0002676642157174447, "H": 0.0020250082092524435, "M": 0.0007590877118247574, "Q": 0.0009147646145672983, "S": 0.0008618680731700135, "T": -0.00020079915287640748, "W": 0.0002293716185552963}, "variates": {"multivariate": 0.0003658441739100571, "univariate": 0.0010948501022474133}}}, {"cutoff": "backcast2_evals", "candidate_skill": 0.3644066589741426, "control_skill": 0.363993152824157, "delta": 0.00041350614998558743, "candidate_checkpoint": "4565c9b6447aab0d457738b65543c5cbee2a90f79e4a87dd9afa7f567aaf685a", "control_checkpoint": "6b640ad1ce200544da2d530578e59eb353f8b9dabc5cc98473403cfe7383c49f", "candidate_eval_seconds": 26.675612211227417, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7961637137411435, "rel_wql_gmean": 0.5007940348863654}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7484221381570295, "rel_wql_gmean": 0.485156960968829}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7617928321348254, "rel_wql_gmean": 0.5553202191044332}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8983375494012568, "rel_wql_gmean": 0.9111196456378068}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8970999925485309, "rel_wql_gmean": 0.6465759688739209}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6900039445090415, "rel_wql_gmean": 0.5873506576265198}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.753971127095037, "rel_wql_gmean": 0.35660829387334064}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.559306557818693, "rel_wql_gmean": 0.35840925915226857}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6609125705540779, "rel_wql_gmean": 0.4922875880959694}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6788931301438412, "rel_wql_gmean": 0.46737025685144046}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5819217025358413, "rel_wql_gmean": 0.7362151675065224}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7263619812328097, "rel_wql_gmean": 0.44648252900082386}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7489365313492822, "rel_wql_gmean": 0.4825391145611769}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8068542040559062, "rel_wql_gmean": 0.8311865589547313}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8304298982253768, "rel_wql_gmean": 0.8709755134471618}, "S": {"n_configs": 6, "rel_mase_gmean": 0.9042383200336376, "rel_wql_gmean": 0.5635594174773026}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7542243885634808, "rel_wql_gmean": 0.5386059549725625}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8531264230830305, "rel_wql_gmean": 0.6402967795704403}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7793372878914676, "rel_wql_gmean": 0.5133124388244873}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7546525685383496, "rel_wql_gmean": 0.5395671141334875}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7965511154806201, "rel_wql_gmean": 0.5034490772862287}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7475606718658245, "rel_wql_gmean": 0.48624034441677194}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7612052477427849, "rel_wql_gmean": 0.5556449965470085}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.89958164144685, "rel_wql_gmean": 0.9115144320697872}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.896485898505093, "rel_wql_gmean": 0.6474484136174568}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6888981722703484, "rel_wql_gmean": 0.5885334564659844}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7520768524740646, "rel_wql_gmean": 0.3570202752625398}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5587359331147982, "rel_wql_gmean": 0.35793010096876227}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.660805281297565, "rel_wql_gmean": 0.49469607344952504}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6791821336817456, "rel_wql_gmean": 0.46839071952339456}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5812757221445022, "rel_wql_gmean": 0.7331639098314356}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7257815402854406, "rel_wql_gmean": 0.44620200724347436}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7487818727109079, "rel_wql_gmean": 0.48427195356442543}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8071916126513461, "rel_wql_gmean": 0.8356014190262583}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8308749825896053, "rel_wql_gmean": 0.8716517553922709}, "S": {"n_configs": 6, "rel_mase_gmean": 0.904213435588803, "rel_wql_gmean": 0.5675780199167809}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7537381736791354, "rel_wql_gmean": 0.539091137142719}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8509266671504966, "rel_wql_gmean": 0.6398335731372661}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7792766449889658, "rel_wql_gmean": 0.5144707856195212}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7538788385219328, "rel_wql_gmean": 0.5404416000493212}}}, "group_skill_delta": {"term": {"long": 0.0018256362898133593, "medium": 0.00032513775489051433, "short": -6.0791157643347304e-05}, "domain": {"Econ/Fin": 0.0008223572185477313, "Energy": 0.0002527633729961787, "Healthcare": 0.0001298555167734694, "Nature": -0.00035272546643028146, "Sales": -0.0005276844252504187, "Transport": 0.001347213227943067, "Web/CloudOps": 0.0007346247241569737}, "frequency": {"A": -0.0017204222675391012, "D": -0.00040644018907909363, "H": 0.0010162523283209213, "M": 0.002343664706180748, "Q": 0.0005580611792563861, "S": 0.0025307903614832927, "T": 8.14415756567044e-05, "W": -0.0012205150129873665}, "variates": {"multivariate": 0.0006886035795363732, "univariate": 0.00018941944022621282}}}, {"cutoff": "backcast3_evals", "candidate_skill": 0.3961657067203871, "control_skill": 0.3950124598405723, "delta": 0.0011532468798147733, "candidate_checkpoint": "4565c9b6447aab0d457738b65543c5cbee2a90f79e4a87dd9afa7f567aaf685a", "control_checkpoint": "6b640ad1ce200544da2d530578e59eb353f8b9dabc5cc98473403cfe7383c49f", "candidate_eval_seconds": 26.97839665412903, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7600822781889456, "rel_wql_gmean": 0.47156145703998736}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7672561850318086, "rel_wql_gmean": 0.5262698059262596}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7060643596741657, "rel_wql_gmean": 0.5002597010370469}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8435836894499562, "rel_wql_gmean": 0.8286522484966601}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8286134000476382, "rel_wql_gmean": 0.552199267649125}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7590223214303585, "rel_wql_gmean": 0.6285189373705079}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7073540721342023, "rel_wql_gmean": 0.3344540092898926}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.749361533677848, "rel_wql_gmean": 0.4742470990102958}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5361846691866137, "rel_wql_gmean": 0.44176777190687744}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7123442250159027, "rel_wql_gmean": 0.5143913685997483}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5305604036884269, "rel_wql_gmean": 0.779803421151957}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7107983893316233, "rel_wql_gmean": 0.4137604149503069}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7200755579098317, "rel_wql_gmean": 0.4812770795680051}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8335078176436748, "rel_wql_gmean": 0.8183553058482521}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.715305611813047, "rel_wql_gmean": 0.768650862326446}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8362260390859402, "rel_wql_gmean": 0.5930371374430102}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7050883321055527, "rel_wql_gmean": 0.4629634772372099}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8027681335712222, "rel_wql_gmean": 0.6622988737579344}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7582430169224578, "rel_wql_gmean": 0.5101819672871283}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7079647802504616, "rel_wql_gmean": 0.4915435820229081}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7599778232892921, "rel_wql_gmean": 0.47414979341796676}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7672348822762877, "rel_wql_gmean": 0.5287703093503114}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7061176910432736, "rel_wql_gmean": 0.501699365937218}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8450238929299057, "rel_wql_gmean": 0.8287968523167164}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8287237417525989, "rel_wql_gmean": 0.5534837186870216}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7575935562899834, "rel_wql_gmean": 0.6315003633340103}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7076556931072816, "rel_wql_gmean": 0.3366919094754201}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7497066029652214, "rel_wql_gmean": 0.47462593240681566}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5363319189417703, "rel_wql_gmean": 0.44423886834819754}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.71171989190133, "rel_wql_gmean": 0.5165221199292769}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5305049486199563, "rel_wql_gmean": 0.7748462903644002}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7121431317868887, "rel_wql_gmean": 0.4145144835788314}, "H": {"n_configs": 29, "rel_mase_gmean": 0.719562525136373, "rel_wql_gmean": 0.48383364301897663}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8317622022092529, "rel_wql_gmean": 0.8214020589097163}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7154303032341603, "rel_wql_gmean": 0.7695070959424496}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8351733673081517, "rel_wql_gmean": 0.5991170712501708}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7049645906490642, "rel_wql_gmean": 0.4635720688691023}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8041936712430771, "rel_wql_gmean": 0.6676297088347317}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7580934176036076, "rel_wql_gmean": 0.5121324030131259}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.708087351250063, "rel_wql_gmean": 0.49341443724470835}}}, "group_skill_delta": {"term": {"long": 0.0015995540392709229, "medium": 0.0014989739965263205, "short": 0.0008770395154084065}, "domain": {"Econ/Fin": 0.0007864054919420882, "Energy": 0.0008313438803890794, "Healthcare": 0.0009843228945447535, "Nature": 0.0017285978088057985, "Sales": 0.0003753492055548202, "Transport": 0.0014263063002810106, "Web/CloudOps": 0.0009865481512651986}, "frequency": {"A": -0.00208121262018246, "D": 0.0010071651112283364, "H": 0.0013511956487944898, "M": 0.0006690864341446501, "Q": 0.0004775407083540406, "S": 0.0031550000420319524, "T": 0.0003252367850553606, "W": 0.0035783370798538883}, "variates": {"multivariate": 0.0011262840063563928, "univariate": 0.001172720569471708}}}], "mean_delta": 0.0008081585367694932, "positive_cutoffs": 4, "outcome": "Declared primary6000/durability12000 diagnostic. No transfer formula or automatic publication is selected by this controller."}
+
+## E139: transfer the matched direct-width-head learning difference
+2026-10-23T09:45:05.586904+00:00: E129primary6000 improves allfour matched E119cutoffs, deltas+0.000901578317/+0.000764302801/+0.000413506150/+0.001153246880,mean+0.000808158537. Fixed candidate first member=zero_extend(E117final first)+E129n-zero_extend(E119n), coefficient1, n6000primary and12000predeclared durability. E117 is extended with zero dispersion_query and width_head; E119 is extended only with zero width_head. Thus this transfers the matched width treatment, NOT the rejected E128dispersion correction; dispersion_query receives only its E129-minus-E119 difference. Othermembers frozen E71/E93/E79wide48000, equal quarters, everymemberforecastsallinstances. Match allfour diagnostics against E130; require3positive/meanpositive beforefull replay and improved full score plusallpackagechecks beforepublication. If12k E129matched replication comparison passes3of4/meanpositive, constructone12kdurability candidate; no coefficient/schedule sweep. Mergefixture769,0optimizerupdates; model/sourcecode seasonal_width. Declared before transfer diagnostics and before E12912k results. No publication yet.
+
+E123 logarithmic requested horizons seed197 training started at 2026-10-23T09:46:52Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+## E139 width-treatment transfer6000 valid_screen
+- Checkpoint SHA-256: `82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 29.684224367141724 seconds.
+- Overall skill 0.387281; relative MASE 0.733506; relative WQL 0.511821.
+- term skill: long=0.4089, medium=0.4104, short=0.3696.
+- domain skill: Econ/Fin=0.1462, Energy=0.2423, Healthcare=0.4723, Nature=0.4455, Sales=0.3387, Transport=0.4749, Web/CloudOps=0.5115.
+- frequency skill: A=0.3746, D=0.3745, H=0.4081, M=0.1014, Q=0.2067, S=0.4255, T=0.4442, W=0.2252.
+- variates skill: multivariate=0.4019, univariate=0.3754.
+- Outcome: Fixed zero-extended E117first + E129 - zero-extended E119, coefficient1. No E128dispersion correction; no optimizer update. Equal-quarter ensemble selected separately.
+
+## E139 width-treatment transfer6000 valid_backcast
+- Checkpoint SHA-256: `82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 24.04444932937622 seconds.
+- Overall skill 0.343524; relative MASE 0.788892; relative WQL 0.546286.
+- term skill: long=0.3997, medium=0.3643, short=0.3123.
+- domain skill: Econ/Fin=0.1778, Energy=0.2298, Healthcare=0.3716, Nature=0.3921, Sales=0.4041, Transport=0.3809, Web/CloudOps=0.4578.
+- frequency skill: A=0.3222, D=0.3623, H=0.3789, M=0.1952, Q=0.2314, S=0.3395, T=0.3376, W=0.2891.
+- variates skill: multivariate=0.3587, univariate=0.3312.
+- Outcome: Fixed zero-extended E117first + E129 - zero-extended E119, coefficient1. No E128dispersion correction; no optimizer update. Equal-quarter ensemble selected separately.
+
+## E139 width-treatment transfer6000 valid_backcast2
+- Checkpoint SHA-256: `82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 28.53399968147278 seconds.
+- Overall skill 0.365608; relative MASE 0.764906; relative WQL 0.526148.
+- term skill: long=0.3726, medium=0.3943, short=0.3516.
+- domain skill: Econ/Fin=0.1343, Energy=0.2391, Healthcare=0.3004, Nature=0.4850, Sales=0.5506, Transport=0.4291, Web/CloudOps=0.4448.
+- frequency skill: A=0.3474, D=0.4277, H=0.4001, M=0.1476, Q=0.1544, S=0.2867, T=0.3700, W=0.2611.
+- variates skill: multivariate=0.3717, univariate=0.3607.
+- Outcome: Fixed zero-extended E117first + E129 - zero-extended E119, coefficient1. No E128dispersion correction; no optimizer update. Equal-quarter ensemble selected separately.
+
+## E139 width-treatment transfer6000 valid_backcast3
+- Checkpoint SHA-256: `82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 27.095674514770508 seconds.
+- Overall skill 0.392582; relative MASE 0.738306; relative WQL 0.499735.
+- term skill: long=0.3971, medium=0.3650, short=0.4009.
+- domain skill: Econ/Fin=0.1858, Energy=0.3244, Healthcare=0.2332, Nature=0.5013, Sales=0.4069, Transport=0.5171, Web/CloudOps=0.3956.
+- frequency skill: A=0.3609, D=0.4501, H=0.4099, M=0.1413, Q=0.2642, S=0.2792, T=0.4330, W=0.2513.
+- variates skill: multivariate=0.3762, univariate=0.4052.
+- Outcome: Fixed zero-extended E117first + E129 - zero-extended E119, coefficient1. No E128dispersion correction; no optimizer update. Equal-quarter ensemble selected separately.
+
+E139 fixed width-treatment equal-quarter ensemble diagnostics: {"duration": 6, "rows": [{"cutoff": "valid_screen", "skill": 0.3880317541577587, "control_skill": 0.3878507268780589, "delta": 0.00018102727969981913, "groups": {"term": {"short": 0.37174692534094356, "medium": 0.4104634567232286, "long": 0.40697243288781937}, "domain": {"Econ/Fin": 0.14882625072549704, "Energy": 0.24798215465423723, "Healthcare": 0.4800533641456496, "Nature": 0.4434996766578704, "Sales": 0.34247960895884155, "Transport": 0.47319081725353807, "Web/CloudOps": 0.5082390957555601}, "freq": {"A-DEC": 0.3831377797724955, "Q-DEC": 0.20484619744979937, "M": 0.12322323211250241, "W-SUN": 0.09482411725596462, "D": 0.3725900248579562, "H": 0.4026924018136455, "15T": 0.21607087174951745, "W-FRI": 0.019077364661426954, "10T": 0.6269864195810371, "W-TUE": 0.6126385479039982, "W-THU": 0.2269308168035562, "W-WED": 0.3226025717551765, "5T": 0.5238937427115736, "10S": 0.4235713253665808}}, "member_checkpoint_hashes": 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0.23921873033237973, "10T": 0.33344297150578384, "W-TUE": 0.5282042216778233, "W-THU": 0.2698543998117874, "W-WED": 0.3579008773064647, "5T": 0.4461284480238702, "10S": 0.3494794048194889}}, "member_checkpoint_hashes": ["82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 2.001161159016192}, {"cutoff": "valid_backcast2", "skill": 0.36868215125404136, "control_skill": 0.3685168155758046, "delta": 0.00016533567823673323, "groups": {"term": {"short": 0.3556510051989389, "medium": 0.3974721406909896, "long": 0.37275885646173146}, "domain": {"Econ/Fin": 0.1278350538167331, "Energy": 0.23989795894300459, "Healthcare": 0.36838771510735646, "Nature": 0.48237820683547394, "Sales": 0.5594074671789011, "Transport": 0.4268006508532327, "Web/CloudOps": 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"domain": {"Econ/Fin": 0.18899820568391779, "Energy": 0.31993369866181876, "Healthcare": 0.23581997212058814, "Nature": 0.5089980057615181, "Sales": 0.3990322628469123, "Transport": 0.5095836076719016, "Web/CloudOps": 0.3974158416525724}, "freq": {"A-DEC": 0.35311095791291525, "Q-DEC": 0.2617586514931013, "M": 0.14978985778396314, "W-SUN": 0.17046968017254482, "D": 0.4417285783653847, "H": 0.4077405117239663, "15T": 0.26309995341217174, "W-FRI": 0.04575059601407794, "10T": 0.6023021299968281, "W-TUE": 0.28116664140750425, "W-THU": 0.3527749294198317, "W-WED": 0.3653636163804277, "5T": 0.4692890959676005, "10S": 0.28864130736535976}}, "member_checkpoint_hashes": ["82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 2.0130097379442304}], "positive_cutoffs": 4, "mean_delta": 0.0002636822078205536, "selection_gate_passed": true}. Full replay/runtime checks and comparison to current publication required before any promotion.
+
+E118 12000 four-cutoff comparison: {"label": "E118 12000 four-cutoff comparison", "utc": "2026-10-23T09:50:18.814803+00:00", "candidate": "long_seasonal_replication_ema12000", "control": "weekly_replication_control_ema12000", "rows": [{"cutoff": "evals", "candidate_skill": 0.38143987517227207, "control_skill": 0.37650125931885015, "delta": 0.004938615853421924, "candidate_checkpoint": "db021fe2a29e80abf906556b2e3627f61128cedc984c852d63ca9bc7d8af4c42", "control_checkpoint": "dc03098da92746d3974cdb5fd3df955a8b52e689260f1fdcd0dd7b63a91fe17e", "candidate_eval_seconds": 28.141856908798218, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7387209289896919, "rel_wql_gmean": 0.4774699824212779}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7091967584947031, "rel_wql_gmean": 0.4940210001993474}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7510955101931623, "rel_wql_gmean": 0.543458407121595}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 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"M": {"n_configs": 5, "rel_mase_gmean": 0.9868972926114846, "rel_wql_gmean": 0.8492660442835309}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7750580859699931, "rel_wql_gmean": 0.8210487526626195}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6555272835229642, "rel_wql_gmean": 0.5036801719831989}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6911027159279228, "rel_wql_gmean": 0.4542264629034677}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9130790737285582, "rel_wql_gmean": 0.6878939835146572}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7084528048450579, "rel_wql_gmean": 0.5121287241091885}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.764554441506531, "rel_wql_gmean": 0.5220703038894595}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7423258223657083, "rel_wql_gmean": 0.4798378587550141}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7111249588305558, "rel_wql_gmean": 0.49538817078279485}, "short": {"n_configs": 55, 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"rel_mase_gmean": 0.7948399949221965, "rel_wql_gmean": 0.5643740247582992}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7790696804591536, "rel_wql_gmean": 0.4675903653980333}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7721877709531506, "rel_wql_gmean": 0.5359033743134146}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8157056524541806, "rel_wql_gmean": 0.595945142576227}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8702083459344165, "rel_wql_gmean": 0.8095153005807372}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8915064371785775, "rel_wql_gmean": 0.6716980091534934}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6240249575542719, "rel_wql_gmean": 0.5402160092228716}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.935694134093894, "rel_wql_gmean": 0.4349550600914482}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7315038063745323, "rel_wql_gmean": 0.4978004030239261}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.735787693330345, "rel_wql_gmean": 0.5289930405755158}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6650226795797447, "rel_wql_gmean": 0.45792804555178057}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6325356225251678, "rel_wql_gmean": 0.7103481618244573}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8394331932394111, "rel_wql_gmean": 0.4979820769791658}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7778562933336808, "rel_wql_gmean": 0.5158436068239591}, "M": {"n_configs": 5, "rel_mase_gmean": 0.8454913331318379, "rel_wql_gmean": 0.7816333262802584}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7629077893451893, "rel_wql_gmean": 0.7702380946329767}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8544025093684732, "rel_wql_gmean": 0.5071247498858551}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7689059517346652, "rel_wql_gmean": 0.5775992603673705}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8751544074195073, "rel_wql_gmean": 0.5931769268978029}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7925885195053555, "rel_wql_gmean": 0.5316423918477458}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.8025184091177678, "rel_wql_gmean": 0.5698895064907624}}}, "group_skill_delta": {"term": {"long": 0.0035890893532852797, "medium": 0.003887027467982529, "short": 0.005547081250184327}, "domain": {"Econ/Fin": -0.0012610064603308935, "Energy": 0.0029406593819746307, "Healthcare": -0.02506731684725938, "Nature": 0.01709394079797677, "Sales": 0.009365737541987995, "Transport": 0.008740482680035688, "Web/CloudOps": 0.002992758665532058}, "frequency": {"A": -0.00019568717236961852, "D": 0.014835117409109921, "H": 0.007864892077899865, "M": -0.0037258810104657814, "Q": 0.0018620474427203382, "S": -3.284729439079204e-05, "T": -0.00018451554590293018, "W": -0.0012900170359010366}, "variates": {"multivariate": 0.0025567012317949667, "univariate": 0.006507807373265728}}}, {"cutoff": "backcast2_evals", "candidate_skill": 0.3634886085731699, "control_skill": 0.35985891378123624, "delta": 0.0036296947919336464, "candidate_checkpoint": "db021fe2a29e80abf906556b2e3627f61128cedc984c852d63ca9bc7d8af4c42", "control_checkpoint": "dc03098da92746d3974cdb5fd3df955a8b52e689260f1fdcd0dd7b63a91fe17e", "candidate_eval_seconds": 28.65201234817505, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7979339375294328, "rel_wql_gmean": 0.5031687023634543}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7517105222461405, "rel_wql_gmean": 0.4892541087028114}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7609992194334069, "rel_wql_gmean": 0.5545688770684942}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8873807551276736, "rel_wql_gmean": 0.9093265549869165}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8956398235921643, "rel_wql_gmean": 0.6483653832582555}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6952661999275426, "rel_wql_gmean": 0.5936962934257054}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.761731002314024, "rel_wql_gmean": 0.35442280401471654}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5539411001711044, "rel_wql_gmean": 0.35520389573354616}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.659555354887592, "rel_wql_gmean": 0.49395721637685036}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6813938014578171, "rel_wql_gmean": 0.47069748314484394}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5794197948834124, "rel_wql_gmean": 0.7245475951431574}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7206962810623505, "rel_wql_gmean": 0.44288001392257026}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7532809555218374, "rel_wql_gmean": 0.48608171516723486}, "M": {"n_configs": 4, "rel_mase_gmean": 0.7940742107722347, "rel_wql_gmean": 0.8263573179324762}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8266970748771869, "rel_wql_gmean": 0.8663850701039977}, "S": {"n_configs": 6, "rel_mase_gmean": 0.9067302485516102, "rel_wql_gmean": 0.5663452379244183}, "T": {"n_configs": 29, "rel_mase_gmean": 0.755670252212079, "rel_wql_gmean": 0.5405076823867628}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8555340530410969, "rel_wql_gmean": 0.6409546763971603}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7822914405953071, "rel_wql_gmean": 0.5150446640209236}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7534790730878452, "rel_wql_gmean": 0.5401056636501812}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.8013559838363913, "rel_wql_gmean": 0.5054128770623004}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.75788321794871, "rel_wql_gmean": 0.49421017421244823}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7639898471755044, "rel_wql_gmean": 0.5577406704739787}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.884582495524887, "rel_wql_gmean": 0.9052218281574911}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8962501330896433, "rel_wql_gmean": 0.6495487799423033}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6926973403721509, "rel_wql_gmean": 0.5937869785059188}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7713675171303247, "rel_wql_gmean": 0.3616973051331611}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5592522419628495, "rel_wql_gmean": 0.35866016979444243}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6679748662601066, "rel_wql_gmean": 0.501187884126286}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6844749650355838, "rel_wql_gmean": 0.4715640721636921}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5794331362019375, "rel_wql_gmean": 0.7249032130584426}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7274908943977823, "rel_wql_gmean": 0.447185381910289}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7592674956924574, "rel_wql_gmean": 0.4915230345874937}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8160225626258149, "rel_wql_gmean": 0.8474243321015386}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8281711532324068, "rel_wql_gmean": 0.8673446644194264}, "S": {"n_configs": 6, "rel_mase_gmean": 0.9115517503849181, "rel_wql_gmean": 0.567299277514214}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7565118858787296, "rel_wql_gmean": 0.5413823048088119}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8461374522214398, "rel_wql_gmean": 0.6382218477885009}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7845688942217784, "rel_wql_gmean": 0.5172999150208025}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7584619130014335, "rel_wql_gmean": 0.5444927422951403}}}, "group_skill_delta": {"term": {"long": 0.0027717516851422097, "medium": 0.005561254867336496, "short": 0.0031339914168337923}, "domain": {"Econ/Fin": -0.0034439799050458575, "Energy": 0.0009549465604122842, "Healthcare": -0.0011390306533090033, "Nature": 0.00861494586213929, "Sales": 0.0042845945669905405, "Transport": 0.007820517934064686, "Web/CloudOps": 0.0018012534719821227}, "frequency": {"A": 0.00016644892837114345, "D": 0.005409262153120986, "H": 0.0057905615066219385, "M": 0.021520279148344645, "Q": 0.001223154328716336, "S": 0.002507660417394919, "T": 0.0008729573481850439, "W": -0.00564950788779961}, "variates": {"multivariate": 0.0023135175521127316, "univariate": 0.0047000259684004675}}}, {"cutoff": "backcast3_evals", "candidate_skill": 0.3943023790374164, "control_skill": 0.388556531456951, "delta": 0.005745847580465369, "candidate_checkpoint": "db021fe2a29e80abf906556b2e3627f61128cedc984c852d63ca9bc7d8af4c42", "control_checkpoint": "dc03098da92746d3974cdb5fd3df955a8b52e689260f1fdcd0dd7b63a91fe17e", "candidate_eval_seconds": 27.054030179977417, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7614581674687556, "rel_wql_gmean": 0.47713168997385613}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.763155807467953, "rel_wql_gmean": 0.527881218926757}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7080535789039192, "rel_wql_gmean": 0.502206170658625}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8399782271529793, "rel_wql_gmean": 0.8468463431331842}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8280279149960056, "rel_wql_gmean": 0.5521607358800456}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7634445488727849, "rel_wql_gmean": 0.6498400190900193}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7147165005724686, "rel_wql_gmean": 0.3370924827377741}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7472792918336342, "rel_wql_gmean": 0.4748198841860076}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5368064984461026, "rel_wql_gmean": 0.4441624330587873}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7097838608816313, "rel_wql_gmean": 0.514446028980707}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5330343006916491, "rel_wql_gmean": 0.8373654758558289}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7156020798224476, "rel_wql_gmean": 0.4146576329269315}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7194889038392792, "rel_wql_gmean": 0.4825765897020932}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8488669982115364, "rel_wql_gmean": 0.8419122250539668}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.712681896042176, "rel_wql_gmean": 0.7723553807508527}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8377146489365882, "rel_wql_gmean": 0.5993714084744902}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7028536424030993, "rel_wql_gmean": 0.4624405725314098}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8036808005194592, "rel_wql_gmean": 0.6759113276790494}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7563804722547655, "rel_wql_gmean": 0.5094859554824934}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7104189888412888, "rel_wql_gmean": 0.49676960319334235}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7663754402347989, "rel_wql_gmean": 0.4788938791084861}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7658739230595875, "rel_wql_gmean": 0.529347553216718}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7178692269632083, "rel_wql_gmean": 0.5086998134122838}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8358354885094386, "rel_wql_gmean": 0.8418146966445984}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8338897723615496, "rel_wql_gmean": 0.5562121498093919}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.8060949747388733, "rel_wql_gmean": 0.6796354438827096}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7222059595204405, "rel_wql_gmean": 0.3416807049403571}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7557238523302612, "rel_wql_gmean": 0.47948838435897584}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5443370693639568, "rel_wql_gmean": 0.4497401377123374}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7130988319811894, "rel_wql_gmean": 0.5141627844030099}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5331048942504498, "rel_wql_gmean": 0.8394069703462528}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7335602695405524, "rel_wql_gmean": 0.42553600401076375}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7260346680667362, "rel_wql_gmean": 0.4869693265129284}, "M": {"n_configs": 4, "rel_mase_gmean": 0.881837749964207, "rel_wql_gmean": 0.8756527000622875}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7161603254136085, "rel_wql_gmean": 0.7742932145513032}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8420519451582145, "rel_wql_gmean": 0.5981276922561349}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7039614590005732, "rel_wql_gmean": 0.46207823333750453}, "W": {"n_configs": 8, "rel_mase_gmean": 0.810577145353702, "rel_wql_gmean": 0.6793951900954402}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7616930972618384, "rel_wql_gmean": 0.5118981392017699}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7193535077794512, "rel_wql_gmean": 0.5027824181292397}}}, "group_skill_delta": {"term": {"long": 0.0030587204091698794, "medium": 0.0020118051577781015, "short": 0.007988467440151692}, "domain": {"Econ/Fin": -0.004585528987425813, "Energy": 0.004874050603654867, "Healthcare": 0.035813776933601216, "Nature": 0.005911615406058501, "Sales": 0.006293859142192937, "Transport": 0.0064908035304042655, "Web/CloudOps": 0.0012426906558737905}, "frequency": {"A": 0.0008581975930802566, "D": 0.013980334142363549, "H": 0.005362258994278446, "M": 0.03335732356890664, "Q": 0.0027407788769321373, "S": 0.0010945510822769622, "T": 0.0002255482293131994, "W": 0.00506059561025618}, "variates": {"multivariate": 0.0036492394557235963, "univariate": 0.007330830293845181}}}], "mean_delta": 0.004758515134392016, "positive_cutoffs": 4, "outcome": "Declared primary6000/durability12000 diagnostic. No transfer formula or automatic publication is selected by this controller."}
+
+## E133 equal-seed seasonal correction12000 valid_screen
+- Checkpoint SHA-256: `7536ba6a485c65a3c5ff64791aedb2ae97ce1a3f011b010152ab8d396d67b5f1`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 27.239246129989624 seconds.
+- Overall skill 0.386677; relative MASE 0.733124; relative WQL 0.513099.
+- term skill: long=0.4077, medium=0.4094, short=0.3694.
+- domain skill: Econ/Fin=0.1473, Energy=0.2415, Healthcare=0.4717, Nature=0.4443, Sales=0.3394, Transport=0.4736, Web/CloudOps=0.5116.
+- frequency skill: A=0.3770, D=0.3747, H=0.4065, M=0.0987, Q=0.2065, S=0.4256, T=0.4440, W=0.2250.
+- variates skill: multivariate=0.4014, univariate=0.3747.
+- Outcome: Conditional fixed coefficient0.5 mean of197/523 matched seasonal corrections; no optimizer update. Fixed equal-quarter ensemble selected separately.
+
+## E133 equal-seed seasonal correction12000 valid_backcast
+- Checkpoint SHA-256: `7536ba6a485c65a3c5ff64791aedb2ae97ce1a3f011b010152ab8d396d67b5f1`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 28.115806102752686 seconds.
+- Overall skill 0.342371; relative MASE 0.789258; relative WQL 0.547952.
+- term skill: long=0.3985, medium=0.3632, short=0.3112.
+- domain skill: Econ/Fin=0.1773, Energy=0.2289, Healthcare=0.3694, Nature=0.3897, Sales=0.4041, Transport=0.3794, Web/CloudOps=0.4573.
+- frequency skill: A=0.3233, D=0.3608, H=0.3768, M=0.1928, Q=0.2304, S=0.3391, T=0.3376, W=0.2878.
+- variates skill: multivariate=0.3581, univariate=0.3296.
+- Outcome: Conditional fixed coefficient0.5 mean of197/523 matched seasonal corrections; no optimizer update. Fixed equal-quarter ensemble selected separately.
+
+## E133 equal-seed seasonal correction12000 valid_backcast2
+- Checkpoint SHA-256: `7536ba6a485c65a3c5ff64791aedb2ae97ce1a3f011b010152ab8d396d67b5f1`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.665321111679077 seconds.
+- Overall skill 0.364812; relative MASE 0.764623; relative WQL 0.527663.
+- term skill: long=0.3704, medium=0.3935, short=0.3513.
+- domain skill: Econ/Fin=0.1335, Energy=0.2389, Healthcare=0.2996, Nature=0.4845, Sales=0.5511, Transport=0.4273, Web/CloudOps=0.4436.
+- frequency skill: A=0.3490, D=0.4281, H=0.3981, M=0.1474, Q=0.1534, S=0.2827, T=0.3700, W=0.2610.
+- variates skill: multivariate=0.3706, univariate=0.3601.
+- Outcome: Conditional fixed coefficient0.5 mean of197/523 matched seasonal corrections; no optimizer update. Fixed equal-quarter ensemble selected separately.
+
+## E133 equal-seed seasonal correction12000 valid_backcast3
+- Checkpoint SHA-256: `7536ba6a485c65a3c5ff64791aedb2ae97ce1a3f011b010152ab8d396d67b5f1`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 28.653014183044434 seconds.
+- Overall skill 0.391369; relative MASE 0.738034; relative WQL 0.501918.
+- term skill: long=0.3956, medium=0.3633, short=0.4000.
+- domain skill: Econ/Fin=0.1856, Energy=0.3240, Healthcare=0.2323, Nature=0.4987, Sales=0.4068, Transport=0.5156, Web/CloudOps=0.3942.
+- frequency skill: A=0.3628, D=0.4482, H=0.4085, M=0.1414, Q=0.2652, S=0.2758, T=0.4325, W=0.2498.
+- variates skill: multivariate=0.3752, univariate=0.4038.
+- Outcome: Conditional fixed coefficient0.5 mean of197/523 matched seasonal corrections; no optimizer update. Fixed equal-quarter ensemble selected separately.
+
+E133 fixed equal-seed ensemble diagnostics: {"pid": 26167, "status": "complete; review before full replay", "gates": [{"step": 6000, "passed": true, "positive_cutoffs": 4, "mean_delta": 0.004591270374809914}, {"step": 12000, "passed": true, "positive_cutoffs": 4, "mean_delta": 0.004758515134392016}], "steps": [{"label": "literal equal-seed correction", "returncode": 0, "seconds": 5.973382799187675}, {"label": "averaged first member four cutoffs", "returncode": 0, "seconds": 222.38261847989634}, {"label": "record first member valid_screen", "returncode": 0, "seconds": 0.053908178117126226}, {"label": "fixed equal quarters valid_screen", "returncode": 0, "seconds": 2.1986292800866067}, {"label": "record first member valid_backcast", "returncode": 0, "seconds": 0.03650985099375248}, {"label": "fixed equal quarters valid_backcast", "returncode": 0, "seconds": 2.4613293861038983}, {"label": "record first member valid_backcast2", "returncode": 0, "seconds": 0.046597677981480956}, {"label": "fixed equal quarters valid_backcast2", "returncode": 0, "seconds": 2.7123846088070422}, {"label": "record first member valid_backcast3", "returncode": 0, "seconds": 0.035643117036670446}, {"label": "fixed equal quarters valid_backcast3", "returncode": 0, "seconds": 2.093629617942497}], "started_utc": "2026-10-23T09:09:36.735017+00:00", "seconds": 2682.319190497976, "results": [{"cutoff": "valid_screen", "skill": 0.38785477359333786, "control_skill": 0.3878507268780589, "delta": 4.046715278982482e-06, "groups": {"term": {"short": 0.3716688240670073, "medium": 0.4102061312283073, "long": 0.40663213236586226}, "domain": {"Econ/Fin": 0.14910671083514682, "Energy": 0.24779407759100036, "Healthcare": 0.4796362241251608, "Nature": 0.4432257200931151, "Sales": 0.34263328492967204, "Transport": 0.47281367736453117, "Web/CloudOps": 0.5082186463976825}, "freq": {"A-DEC": 0.38361040506867294, "Q-DEC": 0.2047727574845134, "M": 0.12235083900991339, "W-SUN": 0.09504638639083018, "D": 0.3726119388862481, "H": 0.40231866250150483, "15T": 0.2160294681894075, "W-FRI": 0.019025160984217138, "10T": 0.6268611393112872, "W-TUE": 0.6127482002482068, "W-THU": 0.22649492107079372, "W-WED": 0.322412323258104, "5T": 0.5238514547309681, "10S": 0.42360793679136877}}, "member_checkpoint_hashes": ["7536ba6a485c65a3c5ff64791aedb2ae97ce1a3f011b010152ab8d396d67b5f1", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 2.025519290007651}, {"cutoff": "valid_backcast", "skill": 0.3443769768533582, "control_skill": 0.34441110518308204, "delta": -3.4128329723825956e-05, "groups": {"term": {"short": 0.3120715038835057, "medium": 0.36323690871433056, "long": 0.40485751694448247}, "domain": {"Econ/Fin": 0.18132606483484726, "Energy": 0.2352649367029942, "Healthcare": 0.40246542897179693, "Nature": 0.37896904212325055, "Sales": 0.40730909035583496, "Transport": 0.3760850708501421, "Web/CloudOps": 0.45876412740906936}, "freq": {"A-DEC": 0.33522309274397843, "Q-DEC": 0.2326549879741392, "M": 0.19676557042031928, "W-SUN": 0.055587438311497706, "D": 0.36169717439461113, "H": 0.37498125694225815, "15T": 0.22378951311057127, "W-FRI": 0.23940264536567812, "10T": 0.33326681335631403, "W-TUE": 0.5281002284753433, "W-THU": 0.2692759146010608, "W-WED": 0.3578859908552052, "5T": 0.446077101202502, "10S": 0.3492609613018596}}, "member_checkpoint_hashes": ["7536ba6a485c65a3c5ff64791aedb2ae97ce1a3f011b010152ab8d396d67b5f1", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 2.254887686111033}, {"cutoff": "valid_backcast2", "skill": 0.36841439344351123, "control_skill": 0.3685168155758046, "delta": -0.00010242213229338937, "groups": {"term": {"short": 0.35552365985295686, "medium": 0.3971638855088674, "long": 0.37214956870206606}, "domain": {"Econ/Fin": 0.1277548758760082, "Energy": 0.2398498072114984, "Healthcare": 0.36787885831191636, "Nature": 0.4821151755691415, "Sales": 0.5595396999848431, "Transport": 0.4262953026044375, "Web/CloudOps": 0.44529635994193606}, "freq": {"A-DEC": 0.3386271977921971, "Q-DEC": 0.1518556119745832, "M": 0.1700427088684615, "W-SUN": 0.07891128103101364, "D": 0.43569590527514224, "H": 0.3994239112440673, "15T": 0.1057285187519148, "W-FRI": 0.15752145079538982, "10T": 0.5102086564402654, "W-TUE": 0.1723122815774616, "W-THU": 0.3323162174688119, "W-WED": 0.5583938634798413, "5T": 0.4771079073110027, "10S": 0.29027439342671546}}, "member_checkpoint_hashes": ["7536ba6a485c65a3c5ff64791aedb2ae97ce1a3f011b010152ab8d396d67b5f1", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 2.4400427860673517}, {"cutoff": "valid_backcast3", "skill": 0.3913542571468349, "control_skill": 0.3912834616582097, "delta": 7.079548862520824e-05, "groups": {"term": {"short": 0.3964180019964655, "medium": 0.3721100151164026, "long": 0.39682110562604944}, "domain": {"Econ/Fin": 0.18900810965416615, "Energy": 0.3197848021845332, "Healthcare": 0.23556871517140554, "Nature": 0.5082105126117362, "Sales": 0.39899103549503967, "Transport": 0.509194768973212, "Web/CloudOps": 0.3971032627533564}, "freq": {"A-DEC": 0.3535777547272728, "Q-DEC": 0.2620099084176857, "M": 0.14996383163337845, "W-SUN": 0.1706123710194779, "D": 0.44116674873533124, "H": 0.40736720014009664, "15T": 0.26291011118119134, "W-FRI": 0.045188100099548656, "10T": 0.6020458063592252, "W-TUE": 0.2794904572630179, "W-THU": 0.35227177770877793, "W-WED": 0.3653817058269325, "5T": 0.46918117859237574, "10S": 0.28781180529292705}}, "member_checkpoint_hashes": ["7536ba6a485c65a3c5ff64791aedb2ae97ce1a3f011b010152ab8d396d67b5f1", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 1.9175813610199839}], "positive_cutoffs": 2, "mean_delta": -1.542706452825615e-05, "selection_gate_passed": false}. No automatic publication.
+
+2026-10-23T09:56:25.284546+00:00: E133 conditional seasonal seed average rejected at diagnostics: source replication E118 passed both6k and12k (4of4each, mean+0.004591270375/+0.004758515134), but literal averaged correction7536ba6a485c65a3c5ff64791aedb2ae97ce1a3f011b010152ab8d396d67b5f1 gives ensemble deltas+0.000004046715/-0.000034128330/-0.000102422132/+0.000070795489 versus frozenE130, only2of4positive, mean-0.0000154270645283. No full replay/publication or coefficient alternatives. CPU merge3.657563774s, nooptimizerupdates; selection controller2682.319190498s includes dependency waiting, first-member4cutoffs222.382618480s. Replication source finalSHA db021fe2a29e80abf906556b2e3627f61128cedc984c852d63ca9bc7d8af4c42, main0.38143987517227207, trainingloop2564.504476471s. E123log_horizon started after its completion.
+
+2026-10-23T09:56:25.284546+00:00: E138 engineering implementation, no selection training queued yet. Initial6340gateSHA9d1401cd4d5b5f1c3ddd25a4a74e15e5db7ef708116cdf5c4fa7e90580b58da1. Independent scalar CPUaudit caught float32 subtraction cancellation in normalized expert disagreement (0.000256836414337 at constant-context fixture); fixed feature computation tofloat64 thenfloat32 MLP. Model arithmetic changed before anytraining; original failed logs retained, audit tolerance unchanged. Final scalar error6.556510925292969e-7, padding0, rowpermutation and initial equal-quarters bitwise, CPU0.107544080s. GPUaudit first failed missing frequency export (fixed), then mismatched auditbatch12 against cachedbatch4 (corrected fixtureto4; no modelchange/tolerance relaxation). SuccessfulGPU761: allfour expert outputs and zerohead ensemble bitwise match E130 on both syntheticconfigs including1e22contexts; B64C4096H1024, finite nonzero gradients through everygate layer with temporaryhead, no expert gradients,2048.220214844MiBallocated/2472MiBreserved includinggateAdam/EMAplaceholders,27.484272173s including4.875554147lockwait,0optimizerupdates. Optional --gate-package trainer branch implemented; gateonlyoptimizer/EMA, frozenexpert hashes preserved, complete recovery unchanged. Private portable initialpackage descriptor8c299fa9fe2a63d1e1d90154b662479487ac8ed69968c048a718f930749a1c6b includes immutableE130experts. Continuous/interrupted recoveryrehearsal andpublicruntime/relocationchecks nowrunning beforequeue.
+
+2026-10-23T09:56:25.284546+00:00: E139 primary width-treatment firstmemberSHA82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca,36899168params; CPUmergefixture769,2.319647101s, nooptimizerupdates. Equal-quarter diagnostic deltas+0.000181027280/+0.000289166747/+0.000165335678/+0.000419199126, all4positive, mean+0.000263682208. Fullsuite active at width6_ensemble_validation_receipt.json; synthetic standalonecomposition and relocation alreadypass. Fullreplay andindependentrealcomposition remain mandatory; no publicationyet.
+
+2026-10-23T09:58:45.421335+00:00: E118final process elapsed2575.852544286987s; completed09:46:48UTC, source loop2564.5044764708728s. Replication source source benefit remains replicated; E133mean correction separately rejected. E138 feature/rule review confirmed fixed seasonal, continuity and zero-mass transforms are prefix-local in forecast lead; training supplies each row's finite requested horizon to expert decoder and gate, preserving prior mask handling. No new experiments or training modifications from this review.
+
+E122 fresh146M original AR prior seed307 training started at 2026-10-23T09:59:37Z, after the preceding trainer exited with its final checkpoint. Settings are unchanged from the declared comparison.
+
+2026-10-23T10:00:33.031842+00:00: E79fresh146M originalLR completed its declared96000updates, checkpointSHAd7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33, trainingprocess22140.291222665925s, final log {"completed_step": 96000, "training_process_loop_seconds": 22110.386884229025}. E122originalAR started under its existing queue after completion; no new concurrent allocation. E132final fourthmember comparison awaits main thenearlier diagnostics; no endpoint selected from unseen results. Energy per-config full E130 diagnostics show largest negative skills ETT2/T/short(-0.3974313), electricity/H/short(-0.0809033), electricity/W/short(-0.0504676), ETT1/T/medium(-0.0323996), ETT2/D/short(-0.0167731). These heterogeneous failures do not establish a broad building-source deficiency; no source reweight selected. E136direct-trend remains the declared next controlled architecture study.
+
+2026-10-23T10:02:42.915649+00:00: E139primary6000 full public replay positive, pending remaining mandatory checks: skill0.3843356309048914 versusE130 0.38414971818207, delta0.00018591272282142413; relativeMASE0.7359642442141643, relativeWQL0.5150285742183113; all97configs151170instances,0failures, inference469.96330363093875s onsharedH100. Descriptordfc20235aeb4b9fc799001d69f5be390d9892448dbbf51fc2abb8ca0be453bf7. Allthree termgroups improve; medianMASE worsens slightly while WQL improves. Independent firstmember realcomposition still running/queued, so not published yet. No held-out reward claim.
+
+E136 direct bounded local-trend head seed197 training started at 2026-10-23T10:02:48Z, after the declared dependency processes exited; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+2026-10-23T10:04:10.104732+00:00: E138full recovery rehearsal passed exact equality for model, optimizer, EMA, complete NumPy/Torch/CUDA RNG states and prefetched batch after6updates versus interruptionafter3/restoredto6. Seed751,B4engineering fixture, original12000LRschedule;12total fixture updates across duplicate trajectories, no validation data and no selection weights. Six gate optimizer tensors only,6340params; experts excluded. Threeprocesses26.724943044/23.340486976/25.177071764s, total495.167764166s including419.907799349s evaluationlockwait. InitialSHA9d1401cd... unchanged. Publicruntime/relocation check stillrequired beforequeue. E129width completed12000 at2026-10-23T10:02:44Z, process2594.787767793052s, {"completed_step": 12000, "training_process_loop_seconds": 2581.1645341650583}, finalSHA8a8c75cafcc7b42266aaab7d8458cb383b65313ce6da8c396e76760f7ab6e33c. E136directtrend started in same allocation; E13912kdurability waits declared source gate before merge.
+
+## E140: bounded runtime loader study
+2026-10-23T10:05:33.226872+00:00: Hypothesis: portable inference startup can avoid initializing dense weights that are immediately overwritten. Changed factor only construction/loading: create model on meta device, load own saved tensors with assign=True, then move toCUDA; preserve all saved tensor values/strides and nonparameter attributes. No model/data/training/forecast-rule changes or optimizerupdates. CPUfixture787 compares each of E139primary four members against the original loader, including exact parameters/strides and absence of buffers; measure constructor/load elapsed. This engineering probe was described before running its CPU test; no runtime file has been modified. Only pursue a separate portable candidate if CPU savings justify actual GPU/cohort checks. Runtime adoption requires exact synthetic/real/full public forecast identity and relocation, plus measured startup improvement; descriptor/weights stay fixed and runtime hashes change. E138training/immutableexpert loader remains unchanged during its pending audits.
+
+## E139 direct-width treatment6000 equal-quarter full replay selected
+- Checkpoint SHA-256: `dfc20235aeb4b9fc799001d69f5be390d9892448dbbf51fc2abb8ca0be453bf7`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.384336; relative MASE 0.735964; relative WQL 0.515029.
+- term skill: long=0.4011, medium=0.4090, short=0.3680.
+- domain skill: Econ/Fin=0.1298, Energy=0.2414, Healthcare=0.4863, Nature=0.4435, Sales=0.3559, Transport=0.4733, Web/CloudOps=0.5003.
+- frequency skill: A=0.3025, D=0.3768, H=0.3975, M=0.1484, Q=0.1892, S=0.4236, T=0.4396, W=0.2404.
+- variates skill: multivariate=0.3965, univariate=0.3745.
+- Outcome: Matched width-treatment correction at 6000 steps improves full skill from 0.384149718182 to 0.384335630905. All97configs151170instances pass. Inference 469.963s on shared H100. Relocation and actual-member real/extreme synthetic composition are bitwise exact. Validation selection only, not held-out reward.
+
+E139 complete suite {"package": "/workspace/research/candidate-width6-ensemble", "status": "complete", "steps": [{"name": "synthetic_composition", "returncode": 0, "seconds": 32.99152904795483}, {"name": "relocation", "returncode": 0, "seconds": 68.7697308328934}, {"name": "full_public_replay", "returncode": 0, "seconds": 503.14482915005647}, {"name": "independent_first_member", "returncode": 0, "seconds": 196.6843510561157}, {"name": "composition", "returncode": 0, "seconds": 0.22877190494909883}, {"name": "descriptive_comparison", "returncode": 0, "seconds": 0.1512721918988973}], "current_step": "descriptive_comparison", "seconds": 801.9830046950374, "skill": 0.3843356309048914}. Synthetic checks {"duration": 6, "descriptor_sha256": "dfc20235aeb4b9fc799001d69f5be390d9892448dbbf51fc2abb8ca0be453bf7", "members": ["82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "checks": [{"config_id": "synthetic_H", "instances": 12, "bitwise_composition": true, "finite_ordered": true}, {"config_id": "synthetic_15T", "instances": 12, "bitwise_composition": true, "finite_ordered": true}], "unchanged_member_references_reused": "/workspace/research/fresh_capacity4_fixture", "input_files_bitwise_identical": true, "seconds": 32.86732584401034}. Descriptive comparison {"candidate": "/workspace/research/valid-report-width6-ensemble.json", "parent": "/workspace/research/valid-report-fresh-capacity4-ensemble.json", "candidate_hash": "dfc20235aeb4b9fc799001d69f5be390d9892448dbbf51fc2abb8ca0be453bf7", "parent_hash": "fa97880dd132b1cdff459be4798f8f360d7df59b13e78026a9d74a95d63df0db", "skill_difference": 0.00018591272282142413, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 59, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": 5.3236006803086824e-05, "p50": 0.00018019965585558406, "p975": 0.000338037622301765}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}. Current-publication diagnostic deltas [0.00018102727969981913, 0.0002891667470349413, 0.00016533567823673323, 0.00041919912631072087]. Every member forecasts every instance. E101teacher and E138experts remain their immutable declared snapshots.
+
+2026-10-23T10:07:06.353692+00:00: E140CPUfixture787 passed exact saved values/strides for all four members and no buffers; constructor+load sums1.7415185519494116s original versus0.0662954212166369s meta, total3.905531459953636s,0optimizerupdates. Proceed to one separate loader-only candidate, ABBAAB isolated GPUprocess timing (three repeats each), same synthetic fixtures/batch4. Before seeing GPU results, require median load reduction at least0.25s AND5percent plus exact forecasts before anyfull public replay; record shared-device timing uncertainty. No training loader or existing runtime modified.
+
+2026-10-23T10:07:38.902305+00:00: E138all engineering checks passed and queued{"queue_pid": 28517, "watch_pid": 28518, "comparison_pid": 28519} after E123log_horizon queue21256/final12000, replacing one allocation. Private initialpackage8c299fa9... actualsynthetic two configs/batch4 and three realconfigs/batch128 reproduce E130bitwise; real inputs only, no validation labels orupdates. Public synthetic21.942501016s/real24.590036898s; relocation43.007742864s, fullruntimeaudit640.823434885s includes evaluationlockwait. CPU/GPU/recovery checks and frozen initial/expert hashes are enforced by launcher. Plannedseed751/B64/LR1e-3/12koriginalsampler,6kprimary/12kdurability,1/3diag unchanged. Expert snapshot remains immutableE130 despite new E139publishedmodel; no test-time fitting. Sources and exact generator archived by separate gatepublisher.
+
+## E141: matched146M patch-resolution continuation
+2026-10-23T10:09:20.174946+00:00: Hypothesis: the patch8 improvement seen in mature37M E71 can transfer to fresh146M, whose final E79wide96000 main0.371180826606 exceeds own48000 and matched37Mfinal. This is a new continuation comparison, not fresh-architecture equivalence or a source-weight change. Both arms start from own E79wide EMA96000 SHA d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33; control keeps patch16, candidate applies the fixed existing E71conversion: sum corresponding halves of embedding/query/recency input weights, retain first8 of16 output rows, preserve all backbone tensors and physical position units. Conversion is NOT function-preserving; no coefficient choices. Same random_capacity model/data code (including overflow fallback and20percent slowAR), oldpacked_sample/sourceweights, seed809/B32/LR2e-5/12000/paired/staticcompiledloss/400warmup/cosfloor0.2; reset Adam/EMA schedules identically, requestedhorizonseed300812. Controlthen candidate sequentially replace E136directtrend after its final12000, no extra concurrent allocation. Primary6000,1000/3000diagnostics,12000durability; matched6/12allfourcutoffs, positive mean and>=3of4 before any later ensemble proposal/full replay. No ensemble formula or sweep selected now. CPUfixture811 independent conversion arithmetic/inherited tensor/config/finiteordered forward checks; GPUfixture821 maximumB32signed64,C4096,H1024 includingAdam/EMA placeholders and eager/compiledloss-gradient comparison. Queue only after audits and memory headroom confirmation. No validation training; no supplied baseline retraining.
+
+2026-10-23T10:10:57.554558+00:00: E140ABBAAB GPUprocess audit passed: allsix synthetic outputs bitwiseequal, finiteordered. Median load15.83080534520559s original versus8.647635334869847s meta, saving7.183170010335743s (0.4537463416231815relative), predeclared>=0.25s/5percent gatepassed. Total182.9963137828745s includeslockwait83.4526801398024s. Shared-device timing, no idle-verifier speed claim. Separate candidate-width6-meta-loader now enters full97publicreplay, exact forecastarray comparison andrelocation before any adoption; no trainingloader changed. E141CPUconversion fixture811 passed,146206864params,initialSHA3fef8f613e5fad3633e4fafe697453e2fe3691678fa581c8a2fea3e6da3de2c1,5.976632893s,0updates; maximumGPUaudit821queuedunder evaluationlock.
+
+E132 fixed fourth-member96000 durability diagnostics: {"pid": 26132, "status": "complete; review gate", "steps": [{"label": "fresh146M96000 earlier cutoffs", "seconds": 423.53272597002797}, {"label": "fixed equal quarters valid_screen", "seconds": 2.2404548309277743}, {"label": "fixed equal quarters valid_backcast", "seconds": 2.999124326976016}, {"label": "fixed equal quarters valid_backcast2", "seconds": 2.5167792509309947}, {"label": "fixed equal quarters valid_backcast3", "seconds": 1.6545759898144752}], "started_utc": "2026-10-23T09:06:54.167150+00:00", "seconds": 3932.743177816039, "results": [{"cutoff": "valid_screen", "skill": 0.3879917387071412, "control_skill": 0.3878507268780589, "delta": 0.000141011829082327, "groups": {"term": {"short": 0.3720792785727258, "medium": 0.41069529613285427, "long": 0.40573692158328545}, "domain": {"Econ/Fin": 0.14912424149140913, "Energy": 0.2472009579845904, "Healthcare": 0.49398090397936345, "Nature": 0.4410833912066744, "Sales": 0.34459481519961455, "Transport": 0.4723428197004681, "Web/CloudOps": 0.5073838557087357}, "freq": {"A-DEC": 0.3841737407457373, "Q-DEC": 0.2060401345601105, "M": 0.12526071807642203, "W-SUN": 0.089538132740977, "D": 0.3718749043263354, "H": 0.40319630992500677, "15T": 0.21786386051711848, "W-FRI": 0.020439090559231654, "10T": 0.6250252751000003, "W-TUE": 0.6420547528849962, "W-THU": 0.2178823087924573, "W-WED": 0.32696192850760064, "5T": 0.5207951537698052, "10S": 0.423966557020225}}, "member_checkpoint_hashes": ["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "cpu_seconds": 2.0663202931173146}, {"cutoff": "valid_backcast", "skill": 0.34527215391567845, "control_skill": 0.34441110518308204, "delta": 0.0008610487325964034, "groups": {"term": {"short": 0.3126884325864907, "medium": 0.3637688978553446, "long": 0.4067147191362456}, "domain": {"Econ/Fin": 0.18044693535148892, "Energy": 0.2346014580725676, "Healthcare": 0.40656080024519137, "Nature": 0.38070856949276444, "Sales": 0.4074086766757222, "Transport": 0.377223390101862, "Web/CloudOps": 0.46044469275016797}, "freq": {"A-DEC": 0.33204316302436765, "Q-DEC": 0.23677528035988427, "M": 0.18718300392526344, "W-SUN": 0.0459689931600773, "D": 0.36367425225660344, "H": 0.37572279841566447, "15T": 0.2232591251920133, "W-FRI": 0.24169192652833082, "10T": 0.33505387846121126, "W-TUE": 0.5507201309892467, "W-THU": 0.2670975311600482, "W-WED": 0.3624139496117442, "5T": 0.4485217500942056, "10S": 0.3496660874149985}}, "member_checkpoint_hashes": ["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "cpu_seconds": 2.82790120691061}, {"cutoff": "valid_backcast2", "skill": 0.3693722441131262, "control_skill": 0.3685168155758046, "delta": 0.0008554285373215675, "groups": {"term": {"short": 0.3561034685363469, "medium": 0.3986579369207216, "long": 0.37353436597025813}, "domain": {"Econ/Fin": 0.12736988675734873, "Energy": 0.24060967952411283, "Healthcare": 0.361205844869546, "Nature": 0.48217194835329746, "Sales": 0.5592933136533227, "Transport": 0.42733602595255205, "Web/CloudOps": 0.4493810855468827}, "freq": {"A-DEC": 0.33839273225074207, "Q-DEC": 0.1555223115262534, "M": 0.16902905410628355, "W-SUN": 0.07216335576924882, "D": 0.43526279365396536, "H": 0.40005187132697984, "15T": 0.10638589959756006, "W-FRI": 0.14483234056274708, "10T": 0.5091239135399137, "W-TUE": 0.15194949116265377, "W-THU": 0.3407213866845825, "W-WED": 0.5605028503844405, "5T": 0.483629147626099, "10S": 0.28991830224876325}}, "member_checkpoint_hashes": ["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "cpu_seconds": 2.3098160580266267}, {"cutoff": "valid_backcast3", "skill": 0.3917440412921396, "control_skill": 0.3912834616582097, "delta": 0.00046057963392986956, "groups": {"term": {"short": 0.39609380043847486, "medium": 0.3726609326428316, "long": 0.3990714746962174}, "domain": {"Econ/Fin": 0.19146841603554976, "Energy": 0.3169493285921843, "Healthcare": 0.23363305321771521, "Nature": 0.5079864794927875, "Sales": 0.402341900108644, "Transport": 0.5097972319661975, "Web/CloudOps": 0.4024376021492734}, "freq": {"A-DEC": 0.36339584495699273, "Q-DEC": 0.26360078602725334, "M": 0.14532839562199418, "W-SUN": 0.17018654203393757, "D": 0.4395955029815283, "H": 0.40764297620236134, "15T": 0.25800645318468773, "W-FRI": 0.043820152478523, "10T": 0.6034667895013048, "W-TUE": 0.28576803587190736, "W-THU": 0.34695837014955566, "W-WED": 0.3741862411400826, "5T": 0.4737863955972218, "10S": 0.2930816557144478}}, "member_checkpoint_hashes": ["8a2b252b733cc79c31778ea27ba00fbe61206fef50454514b667c54a928d80a4", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "cpu_seconds": 1.4660730550531298}], "positive_cutoffs": 4, "mean_delta": 0.0005795171832325419, "selection_gate_passed": true}. No runtime/package constructed, no weight sweep.
+
+## E142: fixed two-factor composition of width treatment and final fresh member
+2026-10-23T10:13:32.739522+00:00: E132final96000 fourthmember passes4of4 versus frozenE130, mean+0.000579517183; versus currentE139width6000 it passes3of4, mean+0.000315834975. E139width6000 alreadypassed4of4/fullreplay. Hypothesis: these changes to separate members remain useful together. One fixed equal-quarter combination uses E1396000first SHA82d50d0b..., E71patch8/E93split unchanged, and E79fresh146M96000 SHAd7cfcd72.... No training, weight/epoch/coefficient sweep, or substitution of E13912000. This completes a fixed2x2 comparison against E130(E117first/fresh48), E139(widthfirst/fresh48), and E132(E117first/fresh96). Evaluate allfour cached diagnostic cutoffs; require>=3positive andmeanpositive versus BOTH isolated E139andE132 before joint fullreplay. E132isolated fullreplay remains eligible and will also be measured, rather than inferring its fullscore from diagnostics. Everymember forecasts everyinstance; best complete fullvalidated candidate selected. Assemble candidates after E140loader decision to keep runtime lineage consistent; sourceindividual member replay/relocation/extreme composition remain required. No model constructed or joint diagnostic measured before this declaration.
+
+2026-10-23T10:13:39.741097+00:00: E142 fixed width6000/fresh96000 quartet diagnostics {"status": "complete; review before package/fullreplay", "results": [{"cutoff": "valid_screen", "skill": 0.3881718681051801, "width_control_skill": 0.3880317541577587, "fresh_control_skill": 0.3879917387071412, "delta_width": 0.00014011394742141636, "delta_fresh": 0.00018012939803890848, "groups": {"term": {"short": 0.3722239066082397, "medium": 0.41091591005070605, "long": 0.4059638616914294}, "domain": {"Econ/Fin": 0.14901755367760694, "Energy": 0.24741422430120008, "Healthcare": 0.4943318904343714, "Nature": 0.44134594243138947, "Sales": 0.3445241159318412, "Transport": 0.47269779445274607, "Web/CloudOps": 0.507385308125947}, "freq": {"A-DEC": 0.38370002946456816, "Q-DEC": 0.20626693520370054, "M": 0.12614029249200875, "W-SUN": 0.08996291768496012, "D": 0.37201944875933657, "H": 0.4035710103676592, "15T": 0.21784009071870558, "W-FRI": 0.02046942787274386, "10T": 0.6250497434629132, "W-TUE": 0.6417672377765984, "W-THU": 0.2180478165537424, "W-WED": 0.32714945388803274, "5T": 0.5208370369159045, "10S": 0.4239060992367861}}, "member_checkpoint_hashes": ["82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "cpu_seconds": 1.9977810359559953}, {"cutoff": "valid_backcast", "skill": 0.3455586213321383, "width_control_skill": 0.344700271930117, "fresh_control_skill": 0.34527215391567845, "delta_width": 0.0008583494020213411, "delta_fresh": 0.00028646741645987905, "groups": {"term": {"short": 0.31298317888014404, "medium": 0.36399688922917317, "long": 0.4070347944383135}, "domain": {"Econ/Fin": 0.18055394020674953, "Energy": 0.2348134086852961, "Healthcare": 0.4070841731588888, "Nature": 0.38116047615930115, "Sales": 0.4074762260349628, "Transport": 0.37765229926233534, "Web/CloudOps": 0.4606241441368746}, "freq": {"A-DEC": 0.3317879932584423, "Q-DEC": 0.2371616106420198, "M": 0.18757363725282472, "W-SUN": 0.046528360729501284, "D": 0.3640158957717563, "H": 0.37629546776645173, "15T": 0.22308590849540832, "W-FRI": 0.24172781290644818, "10T": 0.33510910697177476, "W-TUE": 0.5514604320698773, "W-THU": 0.2676131040680497, "W-WED": 0.3624466093693053, "5T": 0.44848635160409267, "10S": 0.3498688987777634}}, "member_checkpoint_hashes": ["82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "cpu_seconds": 1.2962471970822662}, {"cutoff": "valid_backcast2", "skill": 0.3695446326773957, "width_control_skill": 0.36868215125404136, "fresh_control_skill": 0.3693722441131262, "delta_width": 0.0008624814233543221, "delta_fresh": 0.0001723885642694878, "groups": {"term": {"short": 0.3561553236693098, "medium": 0.39881193758154254, "long": 0.37406076222597684}, "domain": {"Econ/Fin": 0.12739021872720024, "Energy": 0.24065421574153212, "Healthcare": 0.36170573519895977, "Nature": 0.4822797751725245, "Sales": 0.559215597626701, "Transport": 0.4278274284034268, "Web/CloudOps": 0.44953046241749317}, "freq": {"A-DEC": 0.33803240362938913, "Q-DEC": 0.1556366215688766, "M": 0.16938575239717812, "W-SUN": 0.07265591665507165, "D": 0.4351869375859423, "H": 0.4005074918768775, "15T": 0.106183720406681, "W-FRI": 0.14466976658042907, "10T": 0.5090032236270038, "W-TUE": 0.15262124206805772, "W-THU": 0.3413719074247239, "W-WED": 0.5604533363402475, "5T": 0.4835166085060454, "10S": 0.2906753670786497}}, "member_checkpoint_hashes": ["82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "cpu_seconds": 1.5180778538342565}, {"cutoff": "valid_backcast3", "skill": 0.3921644754508403, "width_control_skill": 0.39170266078452043, "fresh_control_skill": 0.3917440412921396, "delta_width": 0.000461814666319893, "delta_fresh": 0.0004204341587007443, "groups": {"term": {"short": 0.396523119774537, "medium": 0.3731115929789489, "long": 0.39943603289581653}, "domain": {"Econ/Fin": 0.1915138068470047, "Energy": 0.31716941229808127, "Healthcare": 0.23407160495463708, "Nature": 0.5090366665812751, "Sales": 0.40236628008580433, "Transport": 0.5102391473978511, "Web/CloudOps": 0.4026365427407549}, "freq": {"A-DEC": 0.36291095579230737, "Q-DEC": 0.2636137569190712, "M": 0.1456802398010142, "W-SUN": 0.17057037719788581, "D": 0.44035582757394454, "H": 0.4080361547461918, "15T": 0.2581899488833542, "W-FRI": 0.044476826738058306, "10T": 0.6038475830013132, "W-TUE": 0.28764820094432986, "W-THU": 0.34829791806939636, "W-WED": 0.37407395307293756, "5T": 0.4736677362009297, "10S": 0.293970489003357}}, "member_checkpoint_hashes": ["82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "cpu_seconds": 1.375803180038929}], "seconds": 6.957909902790561, "optimizer_updates": 0, "new_neural_inference": false, "positive_cutoffs_vs_width": 4, "mean_delta_vs_width": 0.0005806898597792431, "positive_cutoffs_vs_fresh": 4, "mean_delta_vs_fresh": 0.0002648548843672549, "selection_gate_passed": true}. No package/fullscore inferred.
+
+2026-10-23T10:17:26.773523+00:00: E142fixed width6000/fresh96000 joint quartet passed allfour versus BOTH isolated parents; mean+0.0005806898597792431versusE139 and+0.0002648548843672549versusE132, CPUdiagnostic total6.957909902790561s,0optimizerupdates/no newneuralinference. Individual fresh96000 portable package assembled with actual random_capacity/recovery provenance andseed307; first/fourth models unchanged. Separate E132andE142 fullsuite assemblers, extremeactual-member tests and guardedpromoters prepared, explicitly waiting E140loaderdecision. No fullscore inferred fromcached diagnostics.
+
+E129 12000 four-cutoff comparison: {"label": "E129 12000 four-cutoff comparison", "utc": "2026-10-23T10:21:49.863964+00:00", "candidate": "seasonal_width_ema12000", "control": "seasonal_dispersion_ema12000", "rows": [{"cutoff": "evals", "candidate_skill": 0.3829575592439247, "control_skill": 0.3820478868373609, "delta": 0.0009096724065638107, "candidate_checkpoint": "8a8c75cafcc7b42266aaab7d8458cb383b65313ce6da8c396e76760f7ab6e33c", "control_checkpoint": "b0204fa8dd3371d9160fc00545d6b065ccb23bb18e345b3b63cd39c024c9acf5", "candidate_eval_seconds": 28.068697452545166, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7385649404930517, "rel_wql_gmean": 0.4756621939681783}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7107455375873698, "rel_wql_gmean": 0.4927860639379416}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7504165521104995, "rel_wql_gmean": 0.5401485601257813}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8800466291473449, "rel_wql_gmean": 0.8559796352527203}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9009572046437253, "rel_wql_gmean": 0.6425888264141222}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5853876912825183, "rel_wql_gmean": 0.5139823506378491}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8016996186602896, "rel_wql_gmean": 0.3975658345447008}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.849913221695885, "rel_wql_gmean": 0.5190371903971364}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.631497689549554, "rel_wql_gmean": 0.42906996252022056}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5575551391566088, "rel_wql_gmean": 0.4322196856913945}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6270302637021805, "rel_wql_gmean": 0.6576369760217897}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7618012319908849, "rel_wql_gmean": 0.5242249531312764}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7269862252820123, "rel_wql_gmean": 0.4928489449999714}, "M": {"n_configs": 5, "rel_mase_gmean": 0.969136840910731, "rel_wql_gmean": 0.8195237194400399}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7793562279359187, "rel_wql_gmean": 0.8252555262342453}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6553789645227944, "rel_wql_gmean": 0.5029910279440831}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6890059169894194, "rel_wql_gmean": 0.4509720506518005}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9073663673513933, "rel_wql_gmean": 0.6788515847406241}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7084149227770673, "rel_wql_gmean": 0.5093822506937259}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7644685786621924, "rel_wql_gmean": 0.5197841025612521}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7389032030803768, "rel_wql_gmean": 0.4775255930846139}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7110090800376195, "rel_wql_gmean": 0.49416801903946805}, "short": {"n_configs": 55, "rel_mase_gmean": 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{"n_configs": 15, "rel_mase_gmean": 0.7189848782726174, "rel_wql_gmean": 0.4168506671427805}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7218929902246265, "rel_wql_gmean": 0.48494043868982045}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8339835183539945, "rel_wql_gmean": 0.8257482517849524}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7130270287878929, "rel_wql_gmean": 0.7661662375161}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8378690834528353, "rel_wql_gmean": 0.5986481829064408}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7071701445283383, "rel_wql_gmean": 0.4630581494762598}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8116701266923606, "rel_wql_gmean": 0.6622668197361823}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7597234443787527, "rel_wql_gmean": 0.5110609184057446}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7127385729927525, "rel_wql_gmean": 0.49516529836403683}}}, "group_skill_delta": {"term": {"long": 0.0007026268936359825, "medium": 0.0014848786465334607, "short": 0.0011429872371131466}, "domain": {"Econ/Fin": 0.00028237705186429096, "Energy": 0.0005617783994764647, "Healthcare": 0.0012375905460411118, "Nature": 0.0021296078883320524, "Sales": 0.0002384836357876008, "Transport": 0.001034410096170113, "Web/CloudOps": 0.0013177282123868483}, "frequency": {"A": -0.0007981187712625681, "D": 0.001338827871026238, "H": 0.001105189791952066, "M": 0.003771657692377972, "Q": -0.0002543051765319104, "S": 0.003126441306062322, "T": 0.0001692224676280718, "W": 0.002699192407964013}, "variates": {"multivariate": 0.0009954700011264306, "univariate": 0.0012174798532288422}}}], "mean_delta": 0.0010185270802024193, "positive_cutoffs": 4, "outcome": "Declared primary6000/durability12000 diagnostic. No transfer formula or automatic publication is selected by this controller."}
+
+2026-10-23T10:22:49.515280+00:00: E141maximumGPUaudit821 passed: B32signed64/C4096/H1024, peak13306MiBreserved/12671.094238281allocated includingAdam/EMAplaceholders, freebefore23499.6875MiB, allgradientsfinite. Eager/compiledloss0.349611848593/0.349606692791, gradientcos0.999655369179 (>predeclared0.999), maxgradientdifference0.000694274902344, compilecomparison52.566907400s, total621.825011299s including557.020459669lockwait,0updates/no validation, initial/source hashesunchanged. Queued[{"name": "capacity_patch8_control", "queue_pid": 29105, "watch_pid": 29106}, {"name": "capacity_patch8_candidate", "queue_pid": 29107, "watch_pid": 29108}, {"comparison_pid": 29109}] sequentially: control waitsE13626926final12000; patch8candidate waitscontrolfinal12000. Actualsharedcode random_capacity, seed809/B32/LR2e-5/12kpairedcompiled, oldpack/slowAR/horizon300812, no newconcurrentallocation. E129final12000 matched width effect passed4of4, mean0.0010185270802024193; E139predeclared12ktransfer controller proceeds. E140fullscore exactlymatchesE1390.3843356309048914,0fail, inference478.566845837s; relocation andall97arrayidentity finalcheckpending. Cachebackup10:22:14 copied826files34431587bytes in37.224307003s.
+
+## E140 loader-only full replay, same E139model — selected
+- Checkpoint SHA-256: `dfc20235aeb4b9fc799001d69f5be390d9892448dbbf51fc2abb8ca0be453bf7`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.384336; relative MASE 0.735964; relative WQL 0.515029.
+- term skill: long=0.4011, medium=0.4090, short=0.3680.
+- domain skill: Econ/Fin=0.1298, Energy=0.2414, Healthcare=0.4863, Nature=0.4435, Sales=0.3559, Transport=0.4733, Web/CloudOps=0.5003.
+- frequency skill: A=0.3025, D=0.3768, H=0.3975, M=0.1484, Q=0.1892, S=0.4236, T=0.4396, W=0.2404.
+- variates skill: multivariate=0.3965, univariate=0.3745.
+- Outcome: Runtime-only meta-device construction preserves all97configurations/151170instances bitwise and complete scored metrics. No parameter, forecast arithmetic or training change. Shared-H100 median model load15.830805345s to8.647635335s acrossABBAAB six processes; relocation bitwise. Gate E138training/experts remain unchanged.
+
+2026-10-23T10:24:27.185694+00:00: E140 runtime adoption. Runtime-only meta-device construction preserves all97configurations/151170instances bitwise and complete scored metrics. No parameter, forecast arithmetic or training change. Shared-H100 median model load15.830805345s to8.647635335s acrossABBAAB six processes; relocation bitwise. Gate E138training/experts remain unchanged. Fullsuite {"status": "complete", "steps": [{"name": "full_public_replay", "returncode": 0, "seconds": 560.3487957720645}, {"name": "relocation", "returncode": 0, "seconds": 204.34823257708922}], "current_step": "relocation", "seconds": 765.6101541731041, "skill": 0.3843356309048914, "all97_bitwise_equal": true, "descriptor_sha256": "dfc20235aeb4b9fc799001d69f5be390d9892448dbbf51fc2abb8ca0be453bf7"}. Timing {"fixture": "unchanged E130 synthetic fixture, extreme/short/missing/constant histories; batch4", "order": "ABBAAB", "measurements": [{"order": 0, "loader": "original", "process_seconds": 20.24588763108477, "import_seconds": 1.4443256838712841, "load_seconds": 15.852897667093202, "forecast_seconds": 2.1752711760345846, "python_body_seconds": 19.472512820968404, "synthetic_forecasts_bitwise_equal": true}, {"order": 1, "loader": "meta", "process_seconds": 13.65355687099509, "import_seconds": 1.548527258913964, "load_seconds": 9.021844693925232, "forecast_seconds": 2.333743426948786, "python_body_seconds": 12.904134886106476, "synthetic_forecasts_bitwise_equal": true}, {"order": 2, "loader": "meta", "process_seconds": 12.357821244047955, "import_seconds": 1.4167155490722507, "load_seconds": 8.278879324207082, "forecast_seconds": 2.01220401795581, "python_body_seconds": 11.707830937113613, "synthetic_forecasts_bitwise_equal": true}, {"order": 3, "loader": "original", "process_seconds": 19.96071141399443, "import_seconds": 1.514954593963921, "load_seconds": 15.629903526045382, "forecast_seconds": 2.1864277741406113, "python_body_seconds": 19.331303440965712, "synthetic_forecasts_bitwise_equal": true}, {"order": 4, "loader": "original", "process_seconds": 20.090617951005697, "import_seconds": 1.3624230169225484, "load_seconds": 15.83080534520559, "forecast_seconds": 2.1589162528980523, "python_body_seconds": 19.352180532878265, "synthetic_forecasts_bitwise_equal": true}, {"order": 5, "loader": "meta", "process_seconds": 13.16387436306104, "import_seconds": 1.5226807829458266, "load_seconds": 8.647635334869847, "forecast_seconds": 2.1828685391228646, "python_body_seconds": 12.353214598959312, "synthetic_forecasts_bitwise_equal": true}], "original_load_median_seconds": 15.83080534520559, "meta_load_median_seconds": 8.647635334869847, "load_seconds_saved": 7.183170010335743, "relative_load_reduction": 0.4537463416231815, "all_predictions_bitwise_equal": true, "full_replay_gate_passed": true, "lock_wait_seconds": 83.4526801398024, "seconds": 182.9963137828745, "optimizer_updates": 0, "validation_data_read": false, "runtime_published": false}. All model/source hashes retained; runtime predict hash 4de2b53c821ad17baf1b78341dc4fb0bc170faab2493a4a64e0ec63c1e459c5c.
+
+E123 6000 four-cutoff comparison: {"label": "E123 6000 four-cutoff comparison", "utc": "2026-10-23T10:24:35.220389+00:00", "candidate": "log_horizon_ema6000", "control": "overflow_control_ema6000", "rows": [{"cutoff": "evals", "candidate_skill": 0.37865532008536973, "control_skill": 0.3782887645248565, "delta": 0.00036655556051323757, "candidate_checkpoint": "93e04b5c87662d05dfac2501f3d338fe6635300763ba7d66ed99ec753b000b54", "control_checkpoint": "b19875c52ed5024383fb69ccc48cb07a5629ad8011abf134c7dcfcdc50fae2af", "candidate_eval_seconds": 25.514965057373047, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7462064122793782, "rel_wql_gmean": 0.4817524267954395}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7121443784430227, "rel_wql_gmean": 0.49387218509984704}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7553622741098462, "rel_wql_gmean": 0.5442475085950527}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8866145181132203, "rel_wql_gmean": 0.8542324051707179}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8982853999565202, "rel_wql_gmean": 0.6438231700329504}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5653862223410185, "rel_wql_gmean": 0.4962622080758084}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8157537033224999, "rel_wql_gmean": 0.39922883495294803}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8466047087571053, "rel_wql_gmean": 0.5197982234309718}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6438808411251333, "rel_wql_gmean": 0.4424786699975036}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5661691110447804, "rel_wql_gmean": 0.4393097303977116}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6158224993578367, "rel_wql_gmean": 0.6419430340937796}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7721125920488169, "rel_wql_gmean": 0.5325599077168136}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7322206942759663, "rel_wql_gmean": 0.49840524191518587}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9806447422858212, "rel_wql_gmean": 0.8291341847839103}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7872225408587343, "rel_wql_gmean": 0.8305665588898373}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6576880286758843, "rel_wql_gmean": 0.50582210837238}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6933712031833571, "rel_wql_gmean": 0.454206485450446}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8998061918261053, "rel_wql_gmean": 0.6667766940735769}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7117852314964168, "rel_wql_gmean": 0.5118139617090144}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7703613664450539, "rel_wql_gmean": 0.5248564981862198}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7451158192819246, "rel_wql_gmean": 0.4809631678035488}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7120497644863014, "rel_wql_gmean": 0.4940812105058145}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7560063232433744, "rel_wql_gmean": 0.5455016162773587}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.891060652656678, "rel_wql_gmean": 0.8581226203181075}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8989380404519756, "rel_wql_gmean": 0.6435803440684917}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5710382040222995, "rel_wql_gmean": 0.5034944242351712}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8133075715765863, "rel_wql_gmean": 0.4004091491239218}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8446340518950459, "rel_wql_gmean": 0.5179589899563962}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6447526741433901, "rel_wql_gmean": 0.4427981539703199}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5645988459931921, "rel_wql_gmean": 0.4387078987240053}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6165351258073414, "rel_wql_gmean": 0.6464531739881929}, "D": {"n_configs": 15, "rel_mase_gmean": 0.777944381816132, "rel_wql_gmean": 0.5346411774818413}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7317515640788856, "rel_wql_gmean": 0.49890798111520296}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9655032792454371, "rel_wql_gmean": 0.8262938781338912}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7858131466267005, "rel_wql_gmean": 0.8312500357519329}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6555214893191091, "rel_wql_gmean": 0.504479335142133}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6939507700541674, "rel_wql_gmean": 0.45508640655176025}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8990798911693764, "rel_wql_gmean": 0.664987514515424}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7111159189621905, "rel_wql_gmean": 0.5120760043186128}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7711295953953445, "rel_wql_gmean": 0.5256252376142441}}}, "group_skill_delta": {"term": {"long": -0.0009292866400005817, "medium": 8.608201648374614e-05, "short": 0.001011902873017334}, "domain": {"Econ/Fin": 0.004163701849280277, "Energy": 0.0001327324287423437, "Healthcare": 0.0065059814819156125, "Nature": -1.4551210729463193e-05, "Sales": -0.0019458206083642349, "Transport": 0.0005540362326413861, "Web/CloudOps": -0.0010333352414531038}, "frequency": {"A": 0.002569809970611181, "D": 0.003673625478569753, "H": 0.0001109500640047445, "M": -0.0085222577976527, "Q": -0.00039182591339348427, "S": -0.0017156017197644813, "T": 0.0007780433766471084, "W": -0.001352172614520919}, "variates": {"multivariate": -0.00012942702152285435, "univariate": 0.0007827047080517513}}}, {"cutoff": "backcast_evals", "candidate_skill": 0.3355749732404827, "control_skill": 0.33515646486891926, "delta": 0.0004185083715634308, "candidate_checkpoint": "93e04b5c87662d05dfac2501f3d338fe6635300763ba7d66ed99ec753b000b54", "control_checkpoint": "b19875c52ed5024383fb69ccc48cb07a5629ad8011abf134c7dcfcdc50fae2af", "candidate_eval_seconds": 25.02743911743164, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7745769572441988, "rel_wql_gmean": 0.46493400574379684}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7751495127352199, "rel_wql_gmean": 0.5332025260186516}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8173284976358717, "rel_wql_gmean": 0.5986697059120546}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8823174592655021, "rel_wql_gmean": 0.8140986667594712}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8888687691141154, "rel_wql_gmean": 0.6687728487218596}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6292078899714746, "rel_wql_gmean": 0.5404132417192778}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9361702976595294, "rel_wql_gmean": 0.43476436500743626}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7331386222696689, "rel_wql_gmean": 0.4985191261071979}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.7392860085399551, "rel_wql_gmean": 0.532495053175624}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6633982972820373, "rel_wql_gmean": 0.4586366826408607}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.65213406143564, "rel_wql_gmean": 0.7240462300238811}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8394329557085416, "rel_wql_gmean": 0.5006199813037504}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7767400572890936, "rel_wql_gmean": 0.5159920514316604}, "M": {"n_configs": 5, "rel_mase_gmean": 0.8694179661107201, "rel_wql_gmean": 0.8006468014326326}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7601338042952481, "rel_wql_gmean": 0.7677264103120457}, "S": {"n_configs": 6, "rel_mase_gmean": 0.85406082311329, "rel_wql_gmean": 0.5085109918154127}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7681372055210357, "rel_wql_gmean": 0.576462101174773}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8728363344447978, "rel_wql_gmean": 0.5817005287102098}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7895875858567248, "rel_wql_gmean": 0.5309359385983964}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.8059642824146065, "rel_wql_gmean": 0.5707588405936942}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7750842285105053, "rel_wql_gmean": 0.46538535076726445}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7739741905332832, "rel_wql_gmean": 0.534827984372569}, "short": {"n_configs": 55, "rel_mase_gmean": 0.818371751293216, "rel_wql_gmean": 0.5985153782895438}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.887916619787545, "rel_wql_gmean": 0.8185770809200361}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8901417181128463, "rel_wql_gmean": 0.6693878286037928}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6334453098405051, "rel_wql_gmean": 0.5437528735999733}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9321426691826549, "rel_wql_gmean": 0.43340604882484346}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7321691335963859, "rel_wql_gmean": 0.4977340558431129}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.7394280206962279, "rel_wql_gmean": 0.5327250385233748}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6634585442750054, "rel_wql_gmean": 0.4591816165371809}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6523615189029875, "rel_wql_gmean": 0.7262242686636398}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8405439241384891, "rel_wql_gmean": 0.5009731755165275}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7770655988729349, "rel_wql_gmean": 0.5160636056826207}, "M": {"n_configs": 5, "rel_mase_gmean": 0.8620023507565068, "rel_wql_gmean": 0.7924280387937057}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7600409188395882, "rel_wql_gmean": 0.7689369191610511}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8521465319527592, "rel_wql_gmean": 0.5084037872669025}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7696065791080199, "rel_wql_gmean": 0.577900906515431}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8747966347792652, "rel_wql_gmean": 0.5837934687567188}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7902103277834961, "rel_wql_gmean": 0.5315871703126607}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.8062350844786952, "rel_wql_gmean": 0.5709428531788484}}}, "group_skill_delta": {"term": {"long": 0.00048778063938359484, "medium": 0.0004908568774791844, "short": 0.0003560652924083918}, "domain": {"Econ/Fin": 0.005020238482787098, "Energy": 0.0009065493098369126, "Healthcare": 0.003765290994097392, "Nature": -0.002369074951698802, "Sales": -0.0008757551686336962, "Transport": 0.00019575153275108725, "Web/CloudOps": 0.0003526564281545186}, "frequency": {"A": 0.001152753642942761, "D": 0.0006576219064280409, "H": 0.00017655561039986445, "M": -0.007840692723027232, "Q": 0.0005553045660217082, "S": -0.0008083634787563465, "T": 0.0014668633412805443, "W": 0.002081864480944051}, "variates": {"multivariate": 0.000652398893651096, "univariate": 0.00022327616229211866}}}, {"cutoff": "backcast2_evals", "candidate_skill": 0.3602013522615043, "control_skill": 0.35970579873679454, "delta": 0.0004955535247097576, "candidate_checkpoint": "93e04b5c87662d05dfac2501f3d338fe6635300763ba7d66ed99ec753b000b54", "control_checkpoint": "b19875c52ed5024383fb69ccc48cb07a5629ad8011abf134c7dcfcdc50fae2af", "candidate_eval_seconds": 23.198112964630127, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.8007882701401953, "rel_wql_gmean": 0.5047283084069593}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7557560073891201, "rel_wql_gmean": 0.4916203116994603}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7630878462331969, "rel_wql_gmean": 0.5595177016373934}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8944037265353993, "rel_wql_gmean": 0.9103819438899337}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8966901108295002, "rel_wql_gmean": 0.6460978068690744}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.688975950767024, "rel_wql_gmean": 0.592555229178715}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7610450511402209, "rel_wql_gmean": 0.36488567517584497}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.562690295341662, "rel_wql_gmean": 0.3611688235519399}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.668656336123287, "rel_wql_gmean": 0.5017149578822506}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6836872181760263, "rel_wql_gmean": 0.47194042832761307}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5812476180954884, "rel_wql_gmean": 0.7344030570354311}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7298461049411038, "rel_wql_gmean": 0.4501889971326194}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7556004160164702, "rel_wql_gmean": 0.49005904403471334}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8207563558352381, "rel_wql_gmean": 0.8461681486884118}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8332490430298408, "rel_wql_gmean": 0.8745223235906323}, "S": {"n_configs": 6, "rel_mase_gmean": 0.9104095668288751, "rel_wql_gmean": 0.5690729222581083}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7550352242447472, "rel_wql_gmean": 0.5407368948130437}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8453218961356106, "rel_wql_gmean": 0.6402431587905859}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7819390810291392, "rel_wql_gmean": 0.5177722839678629}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7585309693695085, "rel_wql_gmean": 0.5444650192108228}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.8007838015734439, "rel_wql_gmean": 0.5057608145580296}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7549215312107497, "rel_wql_gmean": 0.49176726739734045}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7642838919526312, "rel_wql_gmean": 0.5599238938428742}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8997976584221669, "rel_wql_gmean": 0.9109410837720314}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8969828992939779, "rel_wql_gmean": 0.6477417063864679}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6849313799294706, "rel_wql_gmean": 0.586932347321718}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7624727427382104, "rel_wql_gmean": 0.3657161858629389}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5639287429478652, "rel_wql_gmean": 0.36145769293128766}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6689876132941273, "rel_wql_gmean": 0.5020608717418806}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6837785683593893, "rel_wql_gmean": 0.4719606763184108}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5811330636284028, "rel_wql_gmean": 0.7336592636608978}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7329769702098001, "rel_wql_gmean": 0.45167764562697754}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7553820928392463, "rel_wql_gmean": 0.4904273479694985}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8254186064766647, "rel_wql_gmean": 0.8515799856418855}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8324909356393811, "rel_wql_gmean": 0.8724694350490023}, "S": {"n_configs": 6, "rel_mase_gmean": 0.9100541095744552, "rel_wql_gmean": 0.5693729069160721}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7550799772409099, "rel_wql_gmean": 0.540387432745741}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8437173750489751, "rel_wql_gmean": 0.6406981711171287}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7818465318697899, "rel_wql_gmean": 0.5175870710427966}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7594977958239505, "rel_wql_gmean": 0.545505930798867}}}, "group_skill_delta": {"term": {"long": 0.0006481605837891369, "medium": -0.0002455649395277648, "short": 0.0007492039088975444}, "domain": {"Econ/Fin": 0.0029947568262879143, "Energy": 0.0010921150561006288, "Healthcare": -0.004908064064340523, "Nature": 0.0010939856920688662, "Sales": 0.0006762696777590227, "Transport": 0.0003431454765271402, "Web/CloudOps": 5.013321128011228e-05}, "frequency": {"A": -0.00039529044588337836, "D": 0.002177116752944852, "H": 0.00014066995196726495, "M": 0.005031861953157191, "Q": -0.0013904788500725962, "S": 4.9124966616798105e-05, "T": -0.00018757494776133754, "W": -0.00043740566160055394}, "variates": {"multivariate": -0.00015146401492227568, "univariate": 0.0010238338735453212}}}, {"cutoff": "backcast3_evals", "candidate_skill": 0.38907558897446926, "control_skill": 0.38966768132876584, "delta": -0.0005920923542965806, "candidate_checkpoint": "93e04b5c87662d05dfac2501f3d338fe6635300763ba7d66ed99ec753b000b54", "control_checkpoint": "b19875c52ed5024383fb69ccc48cb07a5629ad8011abf134c7dcfcdc50fae2af", "candidate_eval_seconds": 23.680344820022583, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7664246505375552, "rel_wql_gmean": 0.47534894929206173}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7709957271601952, "rel_wql_gmean": 0.5294283944280077}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7149741232259509, "rel_wql_gmean": 0.5093015850008509}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8424499300311005, "rel_wql_gmean": 0.8303231924998296}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8374647244682922, "rel_wql_gmean": 0.5575694446439766}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7857226277604137, "rel_wql_gmean": 0.6537514857046645}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7124684885559982, "rel_wql_gmean": 0.3430594466733129}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7566325282058416, "rel_wql_gmean": 0.4795753787008562}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5435574634738487, "rel_wql_gmean": 0.44919808683312046}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7162827342267492, "rel_wql_gmean": 0.5164612267993445}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.531608948400588, "rel_wql_gmean": 0.7861714391766957}, "D": {"n_configs": 15, "rel_mase_gmean": 0.725097373265854, "rel_wql_gmean": 0.42550690476480174}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7273265837892093, "rel_wql_gmean": 0.48888496583136964}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8647563430569731, "rel_wql_gmean": 0.8541402251007794}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7205367518569639, "rel_wql_gmean": 0.7742457404206831}, "S": {"n_configs": 6, "rel_mase_gmean": 0.84233390958665, "rel_wql_gmean": 0.6016125828203523}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7064579182180134, "rel_wql_gmean": 0.46230456148625915}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8114993968088045, "rel_wql_gmean": 0.6733854704706034}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7662618656430447, "rel_wql_gmean": 0.5154197000500024}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7148181916718245, "rel_wql_gmean": 0.49935817984419606}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7650747158835133, "rel_wql_gmean": 0.4758888221311058}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7703157746376595, "rel_wql_gmean": 0.5298503011269557}, "short": {"n_configs": 54, "rel_mase_gmean": 0.713915671760059, "rel_wql_gmean": 0.5084844315057424}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8452403065316382, "rel_wql_gmean": 0.8277399249864468}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8346950467324626, "rel_wql_gmean": 0.5567599695672222}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7876375654704325, "rel_wql_gmean": 0.6565412569519505}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.710238739553539, "rel_wql_gmean": 0.34191077293082645}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7563686587019305, "rel_wql_gmean": 0.4784563222634317}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5433317817040721, "rel_wql_gmean": 0.4493168613990946}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7161526533494015, "rel_wql_gmean": 0.5178489398382691}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5311143622209655, "rel_wql_gmean": 0.7781586196577971}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7234708846594775, "rel_wql_gmean": 0.42486757337507386}, "H": {"n_configs": 29, "rel_mase_gmean": 0.725973846929309, "rel_wql_gmean": 0.48863382838793346}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8623268790180476, "rel_wql_gmean": 0.8523275939235515}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7182632014266673, "rel_wql_gmean": 0.7707135583961491}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8408993113146807, "rel_wql_gmean": 0.5989715147638536}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7064723895859075, "rel_wql_gmean": 0.463139705670842}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8096257056910313, "rel_wql_gmean": 0.6721758032092567}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7638007775200529, "rel_wql_gmean": 0.5152023145873739}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7148285566222029, "rel_wql_gmean": 0.49905178580637044}}}, "group_skill_delta": {"term": {"long": -0.00018943603376742058, "medium": -2.7379716044273472e-05, "short": -0.0009307633618287348}, "domain": {"Econ/Fin": 7.976553499244865e-05, "Energy": -0.00162629021511429, "Healthcare": 0.0024022762439499523, "Nature": -0.0016013077695871125, "Sales": -0.0008081416482458303, "Transport": -3.728085080112464e-05, "Web/CloudOps": 0.0007612806484328383}, "frequency": {"A": -0.0036022247600432378, "D": -0.0010403139190640065, "H": -0.0007078203907763259, "M": -0.002119234073469034, "Q": -0.0028823008291861685, "S": -0.0021693930154538243, "T": 0.0005218167541891638, "W": -0.0015174000286153344}, "variates": {"multivariate": -0.0011423683985309907, "univariate": -0.00017898927207837811}}}], "mean_delta": 0.00017213127562246133, "positive_cutoffs": 3, "outcome": "Declared primary6000/durability12000 diagnostic. No transfer formula or automatic publication is selected by this controller."}
+
+## E143: low-frequency synthetic generation-origin window study
+2026-10-23T10:27:02.767973+00:00: E113short low-frequency windows failed primary/final; E123log horizons primary passes3of4 overall but monthlyregresses3of4. Hypothesis: E113selected synthetic shortviews are often late crops of long damped-trend/zero-initialized-AR paths, hiding early dynamics. Change only the origin of selected low-frequency SYNTHETIC views: retain exactly E113selected rows, context/horizon lengths, sign, period, masks and all three RNG streams; for a successfully shortened selected synthetic row, use the first nc+nh finite observations of that same already-generated raw path, split at nc. No regeneration, new random draws, changed source probabilities or real-row changes. Original synthetic paths start at generator time0; keep real and unselected rows bitwise equal toE113. This changes process age, including its noise/seasonal path slice, not window-length distributions. Architecture remains exactE80/E113model; initialize same ownE62 22c08571...,seed197/B64/LR2e-5/12kpaired/oldpack/originalAR/horizon300200/lowfreq1200200. Primary6k,1/3diagnostics,12durability; allfour matchedagainstE113, also report originalE80 reference before any later transfer proposal. No transfer/ensemble formula selected. Queue only after audits, following E138gate12000 in its allocation. CPUfixture829 compares candidate, unchangedE113 and rawOriginalSampler over matched batches: masks/counts/periods, all RNGs, exact prefix content, unchanged real/unselected rows, measured age distribution andchanged-row fraction. GPUfixture839 checks maximumB64C4096H1024 gradients and modelinitiality,0updates. No validation training; no extra concurrent trainer.
+
+E138 bounded learned gate with frozen E130 experts seed751 training started at 2026-10-23T10:27:05Z, after the declared dependency processes exited; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+## E139 width-treatment transfer12000 valid_screen
+- Checkpoint SHA-256: `0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.310531616210938 seconds.
+- Overall skill 0.387520; relative MASE 0.733331; relative WQL 0.511544.
+- term skill: long=0.4091, medium=0.4104, short=0.3700.
+- domain skill: Econ/Fin=0.1459, Energy=0.2422, Healthcare=0.4758, Nature=0.4455, Sales=0.3392, Transport=0.4746, Web/CloudOps=0.5119.
+- frequency skill: A=0.3742, D=0.3751, H=0.4081, M=0.1041, Q=0.2058, S=0.4261, T=0.4443, W=0.2245.
+- variates skill: multivariate=0.4021, univariate=0.3757.
+- Outcome: Fixed zero-extended E117first + E129 - zero-extended E119, coefficient1. No E128dispersion correction; no optimizer update. Equal-quarter ensemble selected separately.
+
+## E139 width-treatment transfer12000 valid_backcast
+- Checkpoint SHA-256: `0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 27.502727031707764 seconds.
+- Overall skill 0.343890; relative MASE 0.788701; relative WQL 0.545809.
+- term skill: long=0.4003, medium=0.3644, short=0.3127.
+- domain skill: Econ/Fin=0.1771, Energy=0.2297, Healthcare=0.3741, Nature=0.3925, Sales=0.4042, Transport=0.3810, Web/CloudOps=0.4586.
+- frequency skill: A=0.3220, D=0.3633, H=0.3791, M=0.1943, Q=0.2316, S=0.3397, T=0.3382, W=0.2887.
+- variates skill: multivariate=0.3594, univariate=0.3313.
+- Outcome: Fixed zero-extended E117first + E129 - zero-extended E119, coefficient1. No E128dispersion correction; no optimizer update. Equal-quarter ensemble selected separately.
+
+## E139 width-treatment transfer12000 valid_backcast2
+- Checkpoint SHA-256: `0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 24.989196062088013 seconds.
+- Overall skill 0.366048; relative MASE 0.764590; relative WQL 0.525635.
+- term skill: long=0.3729, medium=0.3948, short=0.3520.
+- domain skill: Econ/Fin=0.1344, Energy=0.2395, Healthcare=0.3026, Nature=0.4850, Sales=0.5507, Transport=0.4293, Web/CloudOps=0.4457.
+- frequency skill: A=0.3479, D=0.4284, H=0.4004, M=0.1462, Q=0.1543, S=0.2879, T=0.3704, W=0.2617.
+- variates skill: multivariate=0.3723, univariate=0.3610.
+- Outcome: Fixed zero-extended E117first + E129 - zero-extended E119, coefficient1. No E128dispersion correction; no optimizer update. Equal-quarter ensemble selected separately.
+
+## E139 width-treatment transfer12000 valid_backcast3
+- Checkpoint SHA-256: `0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 26.174396991729736 seconds.
+- Overall skill 0.392430; relative MASE 0.738716; relative WQL 0.499707.
+- term skill: long=0.3967, medium=0.3652, short=0.4007.
+- domain skill: Econ/Fin=0.1856, Energy=0.3243, Healthcare=0.2332, Nature=0.5002, Sales=0.4071, Transport=0.5169, Web/CloudOps=0.3961.
+- frequency skill: A=0.3623, D=0.4497, H=0.4099, M=0.1429, Q=0.2639, S=0.2800, T=0.4328, W=0.2495.
+- variates skill: multivariate=0.3764, univariate=0.4048.
+- Outcome: Fixed zero-extended E117first + E129 - zero-extended E119, coefficient1. No E128dispersion correction; no optimizer update. Equal-quarter ensemble selected separately.
+
+E139 fixed width-treatment equal-quarter ensemble diagnostics: {"duration": 12, "rows": [{"cutoff": "valid_screen", "skill": 0.3880940716647685, "control_skill": 0.3878507268780589, "delta": 0.00024334478670962056, "groups": {"term": {"short": 0.3718361126433063, "medium": 0.41047787687286896, "long": 0.4070163737461161}, "domain": {"Econ/Fin": 0.1488229748585247, "Energy": 0.247964055732541, "Healthcare": 0.4809027473693035, "Nature": 0.44352650768790447, "Sales": 0.34256789285366673, "Transport": 0.47310196832544604, "Web/CloudOps": 0.5083316851449597}, "freq": {"A-DEC": 0.3830952988223687, "Q-DEC": 0.20467537519703682, "M": 0.12390254877378837, "W-SUN": 0.09450096808121189, "D": 0.37275151579100685, "H": 0.4027073080058312, "15T": 0.21611553985351117, "W-FRI": 0.018967985259529585, "10T": 0.6269959285933837, "W-TUE": 0.6124123403873277, "W-THU": 0.2268102199847829, "W-WED": 0.3225344724675915, "5T": 0.5239433705854297, "10S": 0.4236958122200234}}, "member_checkpoint_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 1.6809547890443355}, {"cutoff": "valid_backcast", "skill": 0.3448104609077517, "control_skill": 0.34441110518308204, "delta": 0.00039935572466964775, "groups": {"term": {"short": 0.31245915335733965, "medium": 0.36362389340881796, "long": 0.4054350296920991}, "domain": {"Econ/Fin": 0.18126455901870764, "Energy": 0.23553281949198923, "Healthcare": 0.40340386557544006, "Nature": 0.3796461943741479, "Sales": 0.40735666854345987, "Transport": 0.3765206196971741, "Web/CloudOps": 0.45926117581551074}, "freq": {"A-DEC": 0.33497124765846853, "Q-DEC": 0.2330478195417287, "M": 0.1971724324218872, "W-SUN": 0.054978160901529294, "D": 0.3623003627918472, "H": 0.37563617715620035, "15T": 0.2237740903317258, "W-FRI": 0.23889383471934988, "10T": 0.33382559175453563, "W-TUE": 0.5282101735908391, "W-THU": 0.2699481170031097, "W-WED": 0.3579223791574039, "5T": 0.44639393817344386, "10S": 0.3494866279841966}}, "member_checkpoint_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 1.3834715059492737}, {"cutoff": "valid_backcast2", "skill": 0.36880549401636464, "control_skill": 0.3685168155758046, "delta": 0.00028867844056001957, "groups": {"term": {"short": 0.3557792125839013, "medium": 0.3976234159515164, "long": 0.37283637415331294}, "domain": {"Econ/Fin": 0.1279512813804744, "Energy": 0.24000021529474358, "Healthcare": 0.36887674104298807, "Nature": 0.4823897557215715, "Sales": 0.5594334224806643, "Transport": 0.42685460130823527, "Web/CloudOps": 0.4459311887907168}, "freq": {"A-DEC": 0.338334357927663, "Q-DEC": 0.15206458144058743, "M": 0.1698603515590016, "W-SUN": 0.07845860079783107, "D": 0.4357602074273158, "H": 0.40005281620046274, "15T": 0.10570069438527652, "W-FRI": 0.15750144786315068, "10T": 0.5103185468905502, "W-TUE": 0.17377501969975284, "W-THU": 0.33380296904542994, "W-WED": 0.5582895692455323, "5T": 0.4774074622724199, "10S": 0.2915269307225671}}, "member_checkpoint_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 1.9184009530581534}, {"cutoff": "valid_backcast3", "skill": 0.3916700540609932, "control_skill": 0.3912834616582097, "delta": 0.00038659240278349216, "groups": {"term": {"short": 0.39665594368150614, "medium": 0.3726936875567748, "long": 0.3970868799589885}, "domain": {"Econ/Fin": 0.18900504765867832, "Energy": 0.31991046115705724, "Healthcare": 0.23584447967070232, "Nature": 0.5087575358785668, "Sales": 0.3990721070656553, "Transport": 0.5095530506864195, "Web/CloudOps": 0.39754690098606627}, "freq": {"A-DEC": 0.35351115435794767, "Q-DEC": 0.2617314512834785, "M": 0.15027823393361794, "W-SUN": 0.1701517578741646, "D": 0.4415953734285615, "H": 0.40774874323266697, "15T": 0.2630174882655655, "W-FRI": 0.04539106764289902, "10T": 0.6020760681644033, "W-TUE": 0.28156959554735794, "W-THU": 0.35210625055136835, "W-WED": 0.36540966729932056, "5T": 0.4693489584952465, "10S": 0.2889018833518858}}, "member_checkpoint_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 1.126626624027267}], "positive_cutoffs": 4, "mean_delta": 0.000329492838680695, "selection_gate_passed": true}. Full replay/runtime checks and comparison to current publication required before any promotion.
+
+2026-10-23T10:31:23.864747+00:00: E140runtime published10:24:27.185694UTC with all97forecasts/151170instances bitwiseunchanged; same descriptor dfc20235..., predictSHA4de2b53c821ad17baf1b78341dc4fb0bc170faab2493a4a64e0ec63c1e459c5c. Fullsuite765.610154173s, fullinference478.566845837s, relocation24.125692660s; score0.3843356309048914. Shared four_member_ensemble/runtime/predict.py nowuses auditedmeta construction; E138training/experts loader unchanged. E132andE142fullpipeline active; E13912transfer0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960 passed4of4vsE130,3of4vscurrentwidth6; fullsuite queued, no publication. E142latest longterm diagnostic regresses0.001008571versusE139width6 despitepositiveaggregate; earlier longgroups improve. All tradeoffs remain inreports.
+
+2026-10-23T10:31:23.864747+00:00: E123log_horizon completed2026-10-23T10:27:03Z, process2411.322815848049s, {"completed_step": 12000, "training_process_loop_seconds": 2398.8343433369882}. Primary6k3of4positive/mean+0.000172131276; monthlynegative3of4 andlatestlongnegative0.000929287. No transferselected yet; awaitscheduledfinaldurability. E138gate started10:27 afterE123; only6340paramsoptimized withfixedE130experts. E143synthetic-origin CPUaudit8192rows acrossseeds197/829:251originresetrows/235actuallychanged, fraction0.0286865234; all7941otherrowsbitwiseequal; masks/counts/periods/allRNGstreams exact. Oldage mean2442.697211/median2269/max4984→0; meancontext/horizon197.4741/7.54183 unchanged. Originalsynthetic flags independentlychecked againstrecordedgeneratorcalls. CPU9.716736370s,0updates/no validation. Samplerrecovery audit andGPU839 maxshape pending; NOTQUEUED. Shared trainer/publisher metadata-only support added for newlowfreq_age codepath; all existing paths unchanged.
+
+E131 fresh146M learned seasonal summaries seed307 training started at 2026-10-23T10:36:09Z, after the declared dependency processes exited; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+## E132 final146M fourth replacement full replay — not selected
+- Checkpoint SHA-256: `2eb9f5a1c466d0b134bd6e1654084872257eb5250debdf70bb5ff91b4f992788`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.384297; relative MASE 0.736378; relative WQL 0.514803.
+- term skill: long=0.4000, medium=0.4094, short=0.3682.
+- domain skill: Econ/Fin=0.1297, Energy=0.2406, Healthcare=0.4991, Nature=0.4411, Sales=0.3580, Transport=0.4725, Web/CloudOps=0.4997.
+- frequency skill: A=0.3046, D=0.3758, H=0.3980, M=0.1500, Q=0.1918, S=0.4240, T=0.4382, W=0.2445.
+- variates skill: multivariate=0.3962, univariate=0.3747.
+- Outcome: All 97 configurations and 151170 instances passed, but full skill 0.384297081868 is below current E139 0.384335630905 by 0.000038549037. Diagnostic improvement did not carry to the full validation panel. Runtime relocation and independent composition passed. Keep current package; fixed E142 combination is evaluated separately.
+
+2026-10-23T10:37:49.051282+00:00: E132 final fresh146M replacement complete, rejected against current E139. {"candidate": "/workspace/research/valid-report-fresh-capacity96-ensemble.json", "parent": "/workspace/research/valid-report-width6-ensemble.json", "candidate_hash": "2eb9f5a1c466d0b134bd6e1654084872257eb5250debdf70bb5ff91b4f992788", "parent_hash": "dfc20235aeb4b9fc799001d69f5be390d9892448dbbf51fc2abb8ca0be453bf7", "skill_difference": -3.8549037296498057e-05, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 58, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": -0.0017987775569960063, "p50": -6.69091715608805e-05, "p975": 0.00186898954741802}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}. Fullsuite {"package": "/workspace/research/candidate-fresh-capacity96-ensemble", "status": "complete", "steps": [{"name": "synthetic_composition", "returncode": 0, "seconds": 36.56581411208026}, {"name": "relocation", "returncode": 0, "seconds": 79.25890716700815}, {"name": "full_public_replay", "returncode": 0, "seconds": 517.8466667379253}, {"name": "independent_fresh_member", "returncode": 0, "seconds": 116.02587331691757}, {"name": "composition", "returncode": 0, "seconds": 0.2465963999275118}, {"name": "descriptive_comparison", "returncode": 0, "seconds": 0.14853956899605691}], "current_step": "descriptive_comparison", "seconds": 750.1023201830685, "skill": 0.3842970818675949}. E142 fixed joint remains separately in full validation; no new coefficient search.
+
+2026-10-23T10:37:49.051282+00:00: E143 process-age study queued after E138 final12000. CPU 8192 rows and exact sampler recovery passed; GPU {"fixture_seed": 839, "training_seed": 197, "initial_checkpoint_sha256": "22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c", "parameters": 36800800, "batch": 64, "signed_rows": 128, "context": 4096, "horizon": 1024, "changed_origin_rows": 5, "loss": 0.27946507930755615, "all_gradients_finite": true, "initial_parameters_bitwise_unchanged": true, "model_source_byte_identical": true, "peak_allocated_mib": 5783.81689453125, "peak_reserved_mib": 6266.0, "includes_adam_and_ema_placeholders": true, "lock_wait_seconds": 398.92589370394126, "seconds": 408.2387276128866, "optimizer_updates": 0, "validation_data_read": false}. Recovery {"seed": 197, "changed_origin_rows": 1, "rng_names": ["rng", "horizon_rng", "lowfreq_rng"], "arrays_and_age_metadata_bitwise_equal": true, "rng_end_states_bitwise_equal": true, "additional_persistent_sampler_state_required": false, "seconds": 3.593586537055671, "optimizer_updates": 0, "validation_data_read": false}. Seed197/B64/LR2e-5/original12k schedule, same E113 initial22c085... and architecture. No validation used for training. Queue29836/watch29837/matched29838/reference29839; persistent session61462. E94 final96000 completed10:36:05, process21577.931560865s/loop21549.731918170s; E131 started10:36:09 on freed allocation, final E94 evaluation pending.
+
+E136 6000 four-cutoff comparison: {"label": "E136 6000 four-cutoff comparison", "utc": "2026-10-23T10:38:33.022534+00:00", "candidate": "direct_trend_head_ema6000", "control": "long_seasonal_ema6000", "rows": [{"cutoff": "evals", "candidate_skill": 0.38278101206010395, "control_skill": 0.38227466722292214, "delta": 0.0005063448371818158, "candidate_checkpoint": "fb0c9d03d2553d9f54c2e10a65c0d4be1a24378569a8a23260bae026fab464b7", "control_checkpoint": "ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253", "candidate_eval_seconds": 28.18398880958557, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7405286822510679, "rel_wql_gmean": 0.47815289625292473}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7098466409104937, "rel_wql_gmean": 0.49273280479882475}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7487902344550451, "rel_wql_gmean": 0.5405241839882021}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8885757607542428, "rel_wql_gmean": 0.8560105262486205}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.896865896857373, "rel_wql_gmean": 0.6417519337394582}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.57210565628962, "rel_wql_gmean": 0.5054745063696781}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8025539119446234, "rel_wql_gmean": 0.3983162431465755}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8419405550999579, "rel_wql_gmean": 0.5158994080757854}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6321298413405132, "rel_wql_gmean": 0.4333564919287113}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5608853879574, "rel_wql_gmean": 0.4347651717202102}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6176051344653356, "rel_wql_gmean": 0.6475870918735755}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7576437755821, "rel_wql_gmean": 0.5207339801102654}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7253222127375288, "rel_wql_gmean": 0.4952653450154947}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9618996553464796, "rel_wql_gmean": 0.8274106367297241}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.783576833064113, "rel_wql_gmean": 0.8303585401738374}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6546604495433772, "rel_wql_gmean": 0.5030010524751672}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6923259345252674, "rel_wql_gmean": 0.453162524716402}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9042614158747945, "rel_wql_gmean": 0.6712662529903971}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7081206446758993, "rel_wql_gmean": 0.510165348306591}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.763446087094821, "rel_wql_gmean": 0.5205507164597446}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7402530882128023, "rel_wql_gmean": 0.47826753514082926}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.710265197438745, "rel_wql_gmean": 0.4931672821758772}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7498589240481417, "rel_wql_gmean": 0.5410407688872414}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8892029615768211, "rel_wql_gmean": 0.85541375158805}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8972340808013708, "rel_wql_gmean": 0.6419310799963354}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5749908408040473, "rel_wql_gmean": 0.507856444781463}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8028566989060131, "rel_wql_gmean": 0.39889278962700064}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8421494671087474, "rel_wql_gmean": 0.5162975438622042}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6323849322594254, "rel_wql_gmean": 0.43339253239569303}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5616650304722973, "rel_wql_gmean": 0.43523931575775593}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.61692642682572, "rel_wql_gmean": 0.6469875043190525}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7604157827225532, "rel_wql_gmean": 0.5223254859979091}, 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-0.0004597905397619728, "medium": 0.00018422987265309665, "short": 0.00030738935638796594}, "domain": {"Econ/Fin": 0.0015237723652161694, "Energy": 0.0007489717871447255, "Healthcare": -0.00144674446587445, "Nature": 0.000548645028838668, "Sales": -0.0003258680649546042, "Transport": -0.0002121764956774519, "Web/CloudOps": -0.000873586992585218}, "frequency": {"A": 0.001494721860401227, "D": 0.00136457137212187, "H": -0.00027805241520506563, "M": -0.0008668391590758162, "Q": -0.0002569295404494243, "S": -0.00047665778926297975, "T": 4.368260905673882e-05, "W": -8.594630509117795e-05}, "variates": {"multivariate": 0.00018596595228337343, "univariate": 7.81871842889803e-05}}}], "mean_delta": 0.00024245069528067154, "positive_cutoffs": 4, "outcome": "Declared comparison checkpoints 6000,12000. No transfer formula or automatic publication is selected by this controller."}
+
+## E144 matched direct-trend correction on the published width first member — declared 2026-10-23T10:38:59.356642+00:00
+Hypothesis: the E136 direct local-trend head provides a complementary improvement to E139 seasonal-width treatment. E136 primary6000 now beats matched E107 at all four cutoffs (mean 0.00024245069528067154); this transfer declaration follows those source results, before constructing/evaluating any merged candidate. Freeze base E139seasonal_width_transfer6k SHA82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca. Fixed formula: zero_extend(base)+zero_extend(E136_duration)-zero_extend(E107_duration), coefficient1; new trend head receives E136 values, base dispersion/width paths remain bitwise unchanged. No further training, no coefficient sweep. Add direct-trend path to width model with zero-extension forward equivalence checked for both parents. Primary6000; conditional12000 durability constructed only if matched E136/E107 final passes >=3of4 and positive mean. Both ensemble probes use the same frozen E139 quartet: new first/E71patch8/E93split/E79wide48000, equal quarters, compare to E139width6 on all four panels. Require >=3of4 and positive mean before any full replay; full replay and comparison to current publication plus relocation/composition checks before selection. No E142/fresh96 or width12 substitution. Sources/seeds remain their recorded scratch parents: E136/E107 seed197/B64/LR2e-5/12k, no optimizer updates in this experiment. CPU fixture seed853. Validation only for selection.
+
+2026-10-23T10:39:48.392903+00:00: E144 primary merge CPU passed. {"method": "matched direct-trend treatment added to frozen E139width6", "formula": "zero_extend(E139width6)+zero_extend(E136)-zero_extend(E107)", "coefficient": 1.0, "parent_hashes": {"/workspace/research/seasonal_width_transfer6k.pt": "82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca", "/workspace/research/runs/direct_trend_head/ema006000.pt": "fb0c9d03d2553d9f54c2e10a65c0d4be1a24378569a8a23260bae026fab464b7", "/workspace/research/runs/long_seasonal/ema006000.pt": "ad45e0cfae574d3dc1be0b9720c9be0863db9ffe33891fdb22c86dfa83667253"}, "new_parameters": ["trend_head.bias", "trend_head.weight"], "unchanged_base_paths": ["dispersion_query.weight", "width_head.bias", "width_head.weight"], "base_only_parameters_bitwise_unchanged": true, "zero_extension_fixtures": [{"context": 16, "horizon": 6, "base_bitwise_equal": true, "candidate_bitwise_equal": true}, {"context": 31, "horizon": 29, "base_bitwise_equal": true, "candidate_bitwise_equal": true}, {"context": 128, "horizon": 128, "base_bitwise_equal": true, "candidate_bitwise_equal": true}, {"context": 512, "horizon": 300, "base_bitwise_equal": true, "candidate_bitwise_equal": true}], "finite_ordered_fixture": true, "check_seed": 853, "optimizer_updates": 0, "validation_data_read": false, "seconds": 3.217405413975939, "checkpoint_sha256": "9ba4af36e21a47aa761adac79fd6de599de475dde35083e7f8e6f16df77a07ff", "parameters": 36915584}. All4cutoff first-member and fixedquarter probes now active; final waits its separate source gate. No fullsuite or publication selected.
+
+## E145 full-corpus simulated-building allocation — declared 2026-10-23T10:41:45.560832+00:00
+Hypothesis: fixed per-directory weighting underuses the diversity of buildings_900k (about1.875percent of real-row draws despite15.7Bpoints), limiting transfer to some energy series. This is exploratory; heterogeneous Energy failures do not establish that buildings are their cause. Test exactly one mixture change against E110: multiply the unnormalized buildings_900k weight by4, renormalize all95 probabilities. Keep original20percent synthetic share, conditional weights of all other sources in the same ratios, full packed_stratified_full corpus, E110ownc969freshinitial/seed307/B48/LR1e-4/96000/static-compiled paired loss/slowAR/horizon300310. No source added, no new generator or validation-driven training samples. Different source draws also change later sampler trajectories; this is a matched recipe, not a claim of identical windows. Primary48000,4/8/16/24learning diagnostics,72/96durability; compare48/96 across allfour cutoffs againstE110. Require>=3positive andmeanpositive plusfull validation/current-best checks before selection. No mixture coefficient sweep, no distillation or architectural factor combined. Queue after E131final96000, replacing its allocation, only after CPU probability/stream/recovery audits; model/runtime/maximum shapes identical to E110 so reuse prior GPU allocation audit. This deliberately retains E110lowerLR to isolate mixture despite E94inferior final main. No new independent trainer slot.
+
+2026-10-23T10:42:23.193026+00:00: E145 full-corpus source allocation audit passed {"fixture_seed": 857, "training_seed": 307, "initial_checkpoint_sha256": "c9693619ca6277c26fef3b2bde37ff6796bec97b3af9f94588bbc5ddd329ec90", "model_source_byte_identical": true, "all95_full_corpus_directories_present": true, "source_probability_formula_verified": true, "unchanged_other_source_probability_ratios": true, "old_building_probability_conditional_real": 0.018752266675677916, "new_building_probability_conditional_real": 0.07101404383933141, "unnormalized_building_weight_multiplier": 4.0, "synthetic_fraction": 0.2, "restored_probability_rows_bitwise_equal": 1024, "synthetic_rows_bitwise_equal": 256, "corresponding_rng_streams_bitwise_equal": true, "sampler_recovery_bitwise_equal": true, "additional_persistent_sampler_state_required": false, "source_draws": 200000, "maximum_sampling_zscore": 2.4399169706579604, "seconds": 4.10820398805663, "optimizer_updates": 0, "validation_data_read": false}. Queued {"queue_pid": 30898, "watch_pid": 30899, "comparison_pid": 30900} afterE131final96000, replacing thatallocation. Newbuildingprobability7.101404percent conditionalreal versus1.875227percent; allothersratiosunchanged,20percentownsyntheticunchanged. Reusebyteidentical146Mmodel/compiledmaxshapes; no redundantGPUaudit. Sharedtrainer/publisher modifications are metadata-only for thisnew codepath.
+
+## E94 fresh146M halfLR final96000
+- Checkpoint SHA-256: `3ecb2892396bc62c203e0d72ce266d2c6df978c24e68cf87cd6dc0ec34db5d30`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 30.012848377227783 seconds.
+- Overall skill 0.368620; relative MASE 0.751743; relative WQL 0.530288.
+- term skill: long=0.3835, medium=0.3897, short=0.3545.
+- domain skill: Econ/Fin=0.1382, Energy=0.2181, Healthcare=0.4969, Nature=0.4180, Sales=0.3477, Transport=0.4597, Web/CloudOps=0.4867.
+- frequency skill: A=0.3961, D=0.3653, H=0.3830, M=0.1145, Q=0.2113, S=0.4148, T=0.4172, W=0.2262.
+- variates skill: multivariate=0.3769, univariate=0.3619.
+- Outcome: Declared96k schedule completed. Seed307/B48/LR1e-4; loop21549.731918170s/process21577.931560865s on shared H100. Main0.368620401816 trails matched E79originalLR final0.371180826606. No extension or publication; E131 takes released allocation.
+
+## E122 original AR fresh146M early8000
+- Checkpoint SHA-256: `3815be6275dbd603cc84c52fd4123d93049838c9ddebc474e7238d4fc73fe38d`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 31.772407054901123 seconds.
+- Overall skill 0.337168; relative MASE 0.783591; relative WQL 0.560683.
+- term skill: long=0.3644, medium=0.3800, short=0.3091.
+- domain skill: Econ/Fin=0.1166, Energy=0.2035, Healthcare=0.2278, Nature=0.4136, Sales=0.3185, Transport=0.4316, Web/CloudOps=0.4664.
+- frequency skill: A=0.3649, D=0.3230, H=0.3597, M=-0.0185, Q=0.1862, S=0.4188, T=0.3897, W=0.1790.
+- variates skill: multivariate=0.3568, univariate=0.3211.
+- Outcome: Early learning diagnostic only; primary48000/final96000 unchanged. Same ownc969init/seed307/B48/LR2e-4 asE79; only original AR coefficient law differs.
+
+## E138 bounded learned gate early1000
+- Checkpoint SHA-256: `633ae8ba586c610bc328df8d2641a1c47546856fa302f9d6374e517a2dd7d2f6`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 92.65112614980899 seconds.
+- Overall skill 0.388769; relative MASE 0.732345; relative WQL 0.510147.
+- term skill: long=0.4085, medium=0.4111, short=0.3722.
+- domain skill: Econ/Fin=0.1505, Energy=0.2480, Healthcare=0.4796, Nature=0.4449, Sales=0.3432, Transport=0.4743, Web/CloudOps=0.5091.
+- frequency skill: A=0.3803, D=0.3731, H=0.4043, M=0.1196, Q=0.2039, S=0.4248, T=0.4474, W=0.2434.
+- variates skill: multivariate=0.4022, univariate=0.3779.
+- Outcome: Diagnostic only; declared primary6000/final12000 unchanged. Own gate seed751/B64/LR1e-3; all four E130 experts frozen and every member forecasts every instance. Main gain versus fixed equal-quarter control is not yet a four-cutoff or full-validation selection.
+
+## E123 log requested horizon final12000 main
+- Checkpoint SHA-256: `5fda67cc9f9137162d71f0fba5507a849dd19a23f2ac77cf0ffc08fb1b53aa06`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 24.62970805168152 seconds.
+- Overall skill 0.377701; relative MASE 0.744479; relative WQL 0.520170.
+- term skill: long=0.4025, medium=0.4065, short=0.3564.
+- domain skill: Econ/Fin=0.1304, Energy=0.2371, Healthcare=0.4530, Nature=0.4303, Sales=0.3343, Transport=0.4662, Web/CloudOps=0.5037.
+- frequency skill: A=0.3651, D=0.3530, H=0.3955, M=0.0923, Q=0.1965, S=0.4247, T=0.4405, W=0.2192.
+- variates skill: multivariate=0.3966, univariate=0.3622.
+- Outcome: Completed original12k schedule, seed197/B64/LR2e-5; loop2398.834343337s/process2411.322815848s. Primary6k3of4 small meanpositive with monthly regressions; final earlier cutoffs pending. No transfer or publication selected.
+
+E141 matched146M capacity_patch8_control seed809 training started at 2026-10-23T10:45:42Z, after the declared dependency processes exited; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+2026-10-23T10:46:25.714004+00:00: E138 conditional fullsuite tooling prepared: ensemble_gate/validate.py --step6000|12000, check_learned_runtime.py, promote.py. No execution or new model-selection decision before declared source/current-cutoff gates. Learned runtime check reconstructs final forecasts via independent ordered NumPyFP32 weighting of four cached actual standalone experts on2extremesynthetic and3realconfigs; gateweights are computed on matching contexts/batches, bounded positive weights and sums checked. Relocation and fullpublicreplay remain mandatory. E136 completed10:45:38, process2570.288663981s; E141matched146Mcontrol started10:45:42 after it. No schedule changes.
+
+E123 12000 four-cutoff comparison: {"label": "E123 12000 four-cutoff comparison", "utc": "2026-10-23T10:48:46.660817+00:00", "candidate": "log_horizon_ema12000", "control": "overflow_control_ema12000", "rows": [{"cutoff": "evals", "candidate_skill": 0.37770146684887507, "control_skill": 0.37742394607564755, "delta": 0.0002775207732275131, "candidate_checkpoint": "5fda67cc9f9137162d71f0fba5507a849dd19a23f2ac77cf0ffc08fb1b53aa06", "control_checkpoint": "f11d056b95f1c5eeef174265ebc51742952600e0dae1fa52900860447d5be819", "candidate_eval_seconds": 24.62970805168152, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7430244452363175, "rel_wql_gmean": 0.4804276434758998}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.71174036218895, "rel_wql_gmean": 0.49498041435724505}, "short": {"n_configs": 55, "rel_mase_gmean": 0.757938731122603, "rel_wql_gmean": 0.5464559229342405}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8823340676061324, "rel_wql_gmean": 0.8570935896860983}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9010399263979435, "rel_wql_gmean": 0.6458671428713116}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5871495040245798, "rel_wql_gmean": 0.5095924394827762}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8116493904861689, "rel_wql_gmean": 0.39992570204571326}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8514443092466814, "rel_wql_gmean": 0.5205516445093903}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.644241067703261, "rel_wql_gmean": 0.4422575250312542}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5625657110738383, "rel_wql_gmean": 0.4378510875844917}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6219123128672049, "rel_wql_gmean": 0.6481432034462807}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7800627693155953, "rel_wql_gmean": 0.5365845741616493}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7321846779907237, "rel_wql_gmean": 0.49908772182893135}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9862393811466624, "rel_wql_gmean": 0.8354474711123536}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.780436277156429, "rel_wql_gmean": 0.8272006430543747}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6555124581113588, "rel_wql_gmean": 0.5049553035175368}, "T": {"n_configs": 30, "rel_mase_gmean": 0.690853971937905, "rel_wql_gmean": 0.4531219975212009}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9035088801916387, "rel_wql_gmean": 0.6747538235234236}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7106567590422463, "rel_wql_gmean": 0.5122945948988381}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7725593099787635, "rel_wql_gmean": 0.5265270775754183}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7430904415695668, "rel_wql_gmean": 0.4803877697002309}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7132918126148858, "rel_wql_gmean": 0.4952594111267713}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7578628359813909, "rel_wql_gmean": 0.5467970503421377}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8820089901245588, "rel_wql_gmean": 0.8576370610661226}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9027748652932497, "rel_wql_gmean": 0.6461208417787276}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5860981811462721, "rel_wql_gmean": 0.5145960110698827}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8131107048238599, "rel_wql_gmean": 0.4016810458988802}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8527673973406287, "rel_wql_gmean": 0.5213825017868637}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6445705043805339, "rel_wql_gmean": 0.44151518253827515}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5611845549171244, "rel_wql_gmean": 0.43637179177338026}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6256068773709946, "rel_wql_gmean": 0.6531531717628165}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7810498071209858, "rel_wql_gmean": 0.5387984968910804}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7331074392934215, "rel_wql_gmean": 0.4999416190250625}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9734095428884114, "rel_wql_gmean": 0.8253812557813238}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7799909436165716, "rel_wql_gmean": 0.8257926959351992}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6551233447531275, "rel_wql_gmean": 0.5045264853695348}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6915094659095177, "rel_wql_gmean": 0.4527256621835321}, "W": {"n_configs": 8, "rel_mase_gmean": 0.9056914149873438, "rel_wql_gmean": 0.6760727114446506}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7112247286748321, "rel_wql_gmean": 0.5124312426851864}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7726699203226439, "rel_wql_gmean": 0.5268484105597512}}}, "group_skill_delta": {"term": {"long": 1.737862102779708e-06, "medium": 0.0008139892845643315, "short": 0.00016861148852720387}, "domain": 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"rel_wql_gmean": 0.8023624756576537}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7320022964105988, "rel_wql_gmean": 0.42747341049868826}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7284419538124913, "rel_wql_gmean": 0.48986156448616525}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8692971155035826, "rel_wql_gmean": 0.8573561489309673}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7145578258684864, "rel_wql_gmean": 0.7671041544075885}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8434376506034783, "rel_wql_gmean": 0.6004240857745707}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7091796225957213, "rel_wql_gmean": 0.4627952794263764}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8186104789687557, "rel_wql_gmean": 0.6672650133055504}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7652944038188929, "rel_wql_gmean": 0.5141740641844628}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7208570784782143, "rel_wql_gmean": 0.5012656144320912}}}, "group_skill_delta": {"term": {"long": -0.0012904633289524403, "medium": 0.0010647283136636654, "short": 0.0009111954474430028}, "domain": {"Econ/Fin": -0.002346644763757122, "Energy": -0.0011830578548855941, "Healthcare": 0.009354439317742047, "Nature": -0.0002729779172935265, "Sales": -0.00020057697608455438, "Transport": 0.0009457687699090944, "Web/CloudOps": 0.002177994622508672}, "frequency": {"A": -0.0016684445871615328, "D": 0.001953686501656615, "H": -0.00010396030723058836, "M": 0.0084743150621307, "Q": -0.0016226046240747083, "S": 0.0012697116624826865, "T": 0.0007157424949919555, "W": -0.005096647356290673}, "variates": {"multivariate": -4.83182755077749e-05, "univariate": 0.0008928733582169723}}}], "mean_delta": 0.00010899903530839361, "positive_cutoffs": 3, "outcome": "Declared primary6000/durability12000 diagnostic. No transfer formula or automatic publication is selected by this controller."}
+
+## E146 independent-seed replication of the direct-trend head — declared 2026-10-23T10:50:18.939746+00:00
+E136 primary6000 improved all four matched panels with mean+0.000242450695, mainly short-term gains and mixed long-term changes. Test whether this architecture effect repeats with independent seed523, reusing the already-completed E118long-seasonal seed523 control (no control retraining). Candidate uses exactly E136direct_trend_head code and own zero-extended initial87158329c5c8bfc69025843fa82c14482931b67d7e6c6b728aa461d45ef56bd4; seed523/B64/LR2e-5/12000/paired/originalAR/oldpack, requested-horizon RNG300526. All other settings match E118. Primary6000,1000/3000learningdiagnostics,12000durability; allfourcutoffs at6/12. No new correction average or ensemble formula is selected. Queue after E143final12000 in the same allocation. Reuse E136exact architecture/initial-function/loss/gradient/memory audits; source sampler is byte-identical to E118. Only independent seed and output identity change from E136. No new training sources or generators; validation is never training.
+
+2026-10-23T10:50:39.592468+00:00: E146 queued32647/watch32648/matched32649 afterE14329836final12000, persistent session45629. Same audited E136code/initial, E118seed523matchedcontrol reused; no newGPUallocation. E123final allfour diagnostics3positive/mean+0.000108999035, monthlydeltas−0.011372781/−0.012153947/+0.003684070/+0.008474315; longdeltas+0.000001738/+0.000377348/−0.000306568/−0.001290463. No transferselected. E13912full.384393497061 abovecurrent.384335630905; independentcompositionpending. Allthree termgroups improve, EnergyandTransportslightlydecline. E131first50updates include678.1s coldcompilation,100updates777.3s; firststeprecovery saved10:37:24. No stall/restart.
+
+E13912 durability versus currently published E1396: {"candidate": "/workspace/research/valid-report-width12-ensemble.json", "parent": "/workspace/research/valid-report-width6-ensemble.json", "candidate_hash": "9048d5792bd22df21846386abd745e4aacd41d92eb35ba4183274a1799443e20", "parent_hash": "dfc20235aeb4b9fc799001d69f5be390d9892448dbbf51fc2abb8ca0be453bf7", "skill_difference": 5.786615561642616e-05, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 50, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": -1.2850248546755758e-05, "p50": 5.484836126623538e-05, "p975": 0.0001611356463262853}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}. Full term gains long+0.000050819687/medium+0.000013234524/short+0.000078879171; Energy−0.000021019095,Transport−0.000088978265. Both relative metrics improve. Adaptive validation comparison, not held-out significance.
+
+## E139 direct-width treatment12000 equal-quarter full replay selected
+- Checkpoint SHA-256: `9048d5792bd22df21846386abd745e4aacd41d92eb35ba4183274a1799443e20`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.384393; relative MASE 0.735909; relative WQL 0.514970.
+- term skill: long=0.4011, medium=0.4090, short=0.3681.
+- domain skill: Econ/Fin=0.1299, Energy=0.2413, Healthcare=0.4870, Nature=0.4435, Sales=0.3560, Transport=0.4732, Web/CloudOps=0.5004.
+- frequency skill: A=0.3025, D=0.3769, H=0.3975, M=0.1490, Q=0.1893, S=0.4237, T=0.4396, W=0.2403.
+- variates skill: multivariate=0.3965, univariate=0.3746.
+- Outcome: Matched width-treatment correction at 12000 steps improves full skill from 0.384335630905 to 0.384393497061. All97configs151170instances pass. Inference 390.180s on shared H100. Relocation and actual-member real/extreme synthetic composition are bitwise exact. Validation selection only, not held-out reward.
+
+E139 complete suite {"package": "/workspace/research/candidate-width12-ensemble", "status": "complete", "steps": [{"name": "synthetic_composition", "returncode": 0, "seconds": 414.75948875397444}, {"name": "relocation", "returncode": 0, "seconds": 233.04726853198372}, {"name": "full_public_replay", "returncode": 0, "seconds": 451.77311267494224}, {"name": "independent_first_member", "returncode": 0, "seconds": 124.7392677241005}, {"name": "composition", "returncode": 0, "seconds": 0.27263093483634293}, {"name": "descriptive_comparison", "returncode": 0, "seconds": 0.18503591907210648}], "current_step": "descriptive_comparison", "seconds": 1224.7863074848428, "skill": 0.38439349706050785}. Synthetic checks {"duration": 12, "descriptor_sha256": "9048d5792bd22df21846386abd745e4aacd41d92eb35ba4183274a1799443e20", "members": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "checks": [{"config_id": "synthetic_H", "instances": 12, "bitwise_composition": true, "finite_ordered": true}, {"config_id": "synthetic_15T", "instances": 12, "bitwise_composition": true, "finite_ordered": true}], "unchanged_member_references_reused": "/workspace/research/fresh_capacity4_fixture", "input_files_bitwise_identical": true, "seconds": 414.61180081614293}. Descriptive comparison {"candidate": "/workspace/research/valid-report-width12-ensemble.json", "parent": "/workspace/research/valid-report-fresh-capacity4-ensemble.json", "candidate_hash": "9048d5792bd22df21846386abd745e4aacd41d92eb35ba4183274a1799443e20", "parent_hash": "fa97880dd132b1cdff459be4798f8f360d7df59b13e78026a9d74a95d63df0db", "skill_difference": 0.0002437788784378503, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 55, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": 0.00010064760811145934, "p50": 0.00023652036685539723, "p975": 0.0004220867463148285}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}. Current-publication diagnostic deltas [6.231750700980143e-05, 0.00011018897763470648, 0.00012334276232328634, -3.260672352722871e-05]. Every member forecasts every instance. E101teacher and E138experts remain their immutable declared snapshots.
+
+## E147 complete the fresh146M corpus-by-learning-rate comparison — declared 2026-10-23T10:52:42.366573+00:00
+E110full-corpus72k main0.368203045574 now exceeds matched E94old-corpus halfLR72k0.366533622937, reversing its48k deficit. E79old-corpus originalLR final0.371180826606 exceeds E94halfLR final0.368620401816. Hypothesis: larger corpus coverage changes the useful learning-rate regime, and E110may also benefit from the original2e-4 peak. Add the missing full-corpus/highLR cell: identical E110random_capacity code/ownc969freshinitial/seed307/B48/96k/paired/staticcompile/slowAR/horizon300310/fullpacked_stratified_full/original packed_sample source probabilities, changing only LR1e-4to2e-4. This completes four cells with E79old-high,E94old-low,E110full-low. Source/shape/window/RNG laws are identical to E110; no data change or additional generator. Primary48k,4/8/16/24diagnostics,72/96durability; allfourcutoffs48/96 againstE110, with existingE79/E94 reports used for descriptive interaction analysis. No higherLR sweep or automatic extension. Queue after E141patch8 candidate final12000, replacing that allocation; reuse identical model/sampler/GPU audits. No additional concurrent trainer. Final full validation and current-publication checks remain required before selection.
+
+## E110 full-corpus146M learning diagnostic72000
+- Checkpoint SHA-256: `9905ad079d2a5d0590708039830e22f4d6fa74912810b8eb6d928e91dcda883d`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 28.039432287216187 seconds.
+- Overall skill 0.368203; relative MASE 0.756794; relative WQL 0.527445.
+- term skill: long=0.3878, medium=0.3938, short=0.3504.
+- domain skill: Econ/Fin=0.1411, Energy=0.2334, Healthcare=0.4159, Nature=0.4163, Sales=0.3433, Transport=0.4594, Web/CloudOps=0.4897.
+- frequency skill: A=0.4005, D=0.3570, H=0.3882, M=0.0195, Q=0.2086, S=0.4137, T=0.4226, W=0.2367.
+- variates skill: multivariate=0.3827, univariate=0.3564.
+- Outcome: Main0.368203045574 exceeds matched E94halfLR0.366533622937 by0.001669422638, reversing48k deficit. Original seed307/B48/LR1e-4/96k schedule unchanged; final96k four-cutoff comparison pending. Recorded72000 training step: [{"step": 72000, "loss": 0.33998720556497575, "seconds": 15811.1, "lr": 3.17157287525381e-05, "grad_norm": 0.10857579112052917, "context": 2048, "horizon": 1024}]
+
+2026-10-23T10:53:27.068533+00:00: E13912 published10:50:59, fullskill0.38439349706050785, descriptor9048d5792bd22df21846386abd745e4aacd41d92eb35ba4183274a1799443e20; meta loader retained. Suite1224.786307485s, actualfullinference390.179507027s. E147 queued33025/watch33026/matched33027 afterE141candidate29107final12000; persistent48765. Only LR value and output/stopfile identities differ fromE110command. E142stillfullreplay; E144primaryearliercutoffs pending.
+
+E142 full comparison to current E13912: {"candidate": "/workspace/research/valid-report-width6-fresh96-ensemble.json", "parent": "/workspace/research/valid-report-width12-ensemble.json", "candidate_hash": "49440d1daee26c41a9bbb1f1580bbb35317a35553a58dafbd778cecd027c05fe", "parent_hash": "9048d5792bd22df21846386abd745e4aacd41d92eb35ba4183274a1799443e20", "skill_difference": 8.871195194093406e-05, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 58, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": -0.001613355806511571, "p50": 6.775720109708505e-05, "p975": 0.0019363009444379672}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}. Group skill differences {"term": {"long": -0.0009279178231075269, "medium": 0.0005735524763520461, "short": 0.0002997885484163909}, "domain": {"Econ/Fin": -0.00015088664572748822, "Energy": -0.0005078681590492407, "Healthcare": 0.012435113021739164, "Nature": -0.0021745525299137203, "Sales": 0.0020021432451777788, "Transport": -0.000371935301136217, "Web/CloudOps": -0.0007407729720235734}, "frequency": {"A": 0.001840451213197536, "D": -0.000886131628639375, "H": 0.0008664188718713328, "M": 0.0018450121584902002, "Q": 0.0027381957217106345, "S": 0.00021028701676273354, "T": -0.001380162056435874, "W": 0.0043002785147818745}, "variates": {"multivariate": -0.00023108088179579767, "univariate": 0.0003524789140099127}}. MASE worsens from0.7359092460760267to0.7364206184269699 while WQL improves from0.5149702473261757to0.5144643448895588. Declared2x2 log-error interaction 1.2040144307112755e-06, additive-log-error predicted skill 0.38448295010430533, observed 0.3844822090124488. This is descriptive validation selection; no additional blend coefficient chosen.
+
+## E142 final fresh146M quartet full replay — selected
+- Checkpoint SHA-256: `49440d1daee26c41a9bbb1f1580bbb35317a35553a58dafbd778cecd027c05fe`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.384482; relative MASE 0.736421; relative WQL 0.514464.
+- term skill: long=0.4002, medium=0.4096, short=0.3684.
+- domain skill: Econ/Fin=0.1297, Energy=0.2408, Healthcare=0.4995, Nature=0.4414, Sales=0.3580, Transport=0.4729, Web/CloudOps=0.4997.
+- frequency skill: A=0.3043, D=0.3760, H=0.3984, M=0.1508, Q=0.1920, S=0.4239, T=0.4382, W=0.2446.
+- variates skill: multivariate=0.3963, univariate=0.3749.
+- Outcome: Fixed quartet improves full skill from 0.384393497061 to 0.384482209012. All97configs151170instances pass; inference 422.063s on shared H100. Relocation and real/extreme synthetic standalone composition are bitwise exact. Validation selection only, not held-out reward.
+
+E142 fullsuite {"package": "/workspace/research/candidate-width6-fresh96-ensemble", "status": "complete", "steps": [{"name": "synthetic_composition", "returncode": 0, "seconds": 199.80909308814444}, {"name": "relocation", "returncode": 0, "seconds": 461.85226410697214}, {"name": "full_public_replay", "returncode": 0, "seconds": 528.3381450260058}, {"name": "composition", "returncode": 0, "seconds": 0.30109393992461264}, {"name": "descriptive_comparison", "returncode": 0, "seconds": 0.2489329711534083}], "current_step": "descriptive_comparison", "seconds": 1190.5941559779458, "skill": 0.3844822090124488}. Synthetic {"kind": "joint", "descriptor_sha256": "49440d1daee26c41a9bbb1f1580bbb35317a35553a58dafbd778cecd027c05fe", "members": ["82d50d0b2f7abfb963562f5f7966644b05612a49725f066392c022ccd6ba18ca", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "checks": [{"config_id": "synthetic_H", "instances": 12, "bitwise_composition": true, "finite_ordered": true}, {"config_id": "synthetic_15T", "instances": 12, "bitwise_composition": true, "finite_ordered": true}], "references": ["/workspace/research/width6_fixture/standalone0", "/workspace/research/fresh_capacity4_fixture/standalone1", "/workspace/research/fresh_capacity4_fixture/standalone2", "/workspace/research/fresh_capacity96_fixture/standalone3"], "fresh_member_cached": true, "seconds": 199.67343028006144}. Descriptive comparison {"candidate": "/workspace/research/valid-report-width6-fresh96-ensemble.json", "parent": "/workspace/research/valid-report-width6-ensemble.json", "candidate_hash": "49440d1daee26c41a9bbb1f1580bbb35317a35553a58dafbd778cecd027c05fe", "parent_hash": "dfc20235aeb4b9fc799001d69f5be390d9892448dbbf51fc2abb8ca0be453bf7", "skill_difference": 0.00014657810755736023, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 60, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": -0.0015682766075555549, "p50": 0.00011755044460248776, "p975": 0.0020397091338542946}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}. Current-publication diagnostic deltas [7.779644041161493e-05, 0.0007481604243866347, 0.0007391386610310358, 0.0004944213898471217]. All members forecast every instance. E101teacher/E138experts remain their immutable snapshots.
+
+## E148 fixed durability combination of the selected width and fresh checkpoints — declared 2026-10-23T10:59:43.449019+00:00
+E139width12 beat width6 with fresh48 (3of4diagnostics and full+0.000057866156). E142width6+fresh96 is now published and beats both isolated alternatives on full validation. Test the remaining fixed combination: E139width12/E71patch8/E93split/E79fresh96, ordered equal quarters. No new training, coefficient search, per-frequency switch, trend correction or gate. This follows individual-parent results and precedes any combined predictions. Compare allfourcutoffs against BOTH E139width12/fresh48 and currentE142width6/fresh96; require>=3positive/positive mean againsteach before a fullsuite. Full replay, standalone composition, relocation and current-best guards still mandatory. Existing scratch checkpoints remain unchanged: first0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960, fourthd7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33. Same source manifests/seeds asparents;0optimizerupdates. No change to E144fixed width6/fresh48 contrast or immutableE101/E138assets.
+
+2026-10-23T10:59:53.191745+00:00: E148 fixed width12000/fresh96000 quartet diagnostics {"status": "complete; review before package/fullreplay", "results": [{"cutoff": "valid_screen", "skill": 0.3882279509777624, "width12_parent_skill": 0.3880940716647685, "joint6_parent_skill": 0.3881718681051801, "delta_width12": 0.00013387931299391198, "delta_joint6": 5.608287258229705e-05, "groups": {"term": {"short": 0.3723017702532888, "medium": 0.41093067600045863, "long": 0.4060075172607863}, "domain": {"Econ/Fin": 0.14901328476060638, "Energy": 0.24740035323875065, "Healthcare": 0.49506927931058364, "Nature": 0.4413654800615009, "Sales": 0.3446101717591584, "Transport": 0.47260963824857904, "Web/CloudOps": 0.507475767308865}, "freq": {"A-DEC": 0.38365057239921063, "Q-DEC": 0.20606735650303087, "M": 0.12682244603724124, "W-SUN": 0.08968565726055233, "D": 0.37213466457612654, "H": 0.4035878747807796, "15T": 0.21788743064766847, "W-FRI": 0.020360259620060495, "10T": 0.6250681642313944, "W-TUE": 0.6415415405419468, "W-THU": 0.21792947739631574, "W-WED": 0.32708506145318983, "5T": 0.5208812969571452, "10S": 0.42402778171174516}}, "member_checkpoint_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "cpu_seconds": 2.5732416899409145}, {"cutoff": "valid_backcast", "skill": 0.3456661156587746, "width12_parent_skill": 0.3448104609077517, "joint6_parent_skill": 0.3455586213321383, "delta_width12": 0.000855654751022894, "delta_joint6": 0.00010749432663625935, "groups": {"term": {"short": 0.3130693683031155, "medium": 0.36409584759645663, "long": 0.4071975759527283}, "domain": {"Econ/Fin": 0.18043527478544963, "Energy": 0.23481540394451372, "Healthcare": 0.4074352868061085, "Nature": 0.3813032447441278, "Sales": 0.4075242245178744, "Transport": 0.37766486704604607, "Web/CloudOps": 0.4608848578894076}, "freq": {"A-DEC": 0.331781124887934, "Q-DEC": 0.23724173166905804, "M": 0.1874780089767496, "W-SUN": 0.0459263068532203, "D": 0.36418901298748285, "H": 0.3763686663732764, "15T": 0.22314757314382871, "W-FRI": 0.24140491835961553, "10T": 0.33548373093161554, "W-TUE": 0.5514665885251284, "W-THU": 0.2677180644187378, "W-WED": 0.3624726313886212, "5T": 0.4487294283202319, "10S": 0.34987753064947524}}, "member_checkpoint_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "cpu_seconds": 2.0671064669732004}, {"cutoff": "valid_backcast2", "skill": 0.36966272417010304, "width12_parent_skill": 0.36880549401636464, "joint6_parent_skill": 0.3695446326773957, "delta_width12": 0.0008572301537383975, "delta_joint6": 0.00011809149270736174, "groups": {"term": {"short": 0.35627742731482626, "medium": 0.3989526857240734, "long": 0.3741414686574598}, "domain": {"Econ/Fin": 0.12750789956106479, "Energy": 0.2407648322615341, "Healthcare": 0.362169648295931, "Nature": 0.4822691288379122, "Sales": 0.5592426971862643, "Transport": 0.4278832683656373, "Web/CloudOps": 0.4497407632446214}, "freq": {"A-DEC": 0.3381861793580494, "Q-DEC": 0.15558369062139554, "M": 0.16912368457803728, "W-SUN": 0.07258683839700397, "D": 0.43534732833058165, "H": 0.4005937901940083, "15T": 0.10632763061011052, "W-FRI": 0.14511791304527222, "10T": 0.5090961154114221, "W-TUE": 0.15381305847280335, "W-THU": 0.3413042930188698, "W-WED": 0.5603979936450694, "5T": 0.48366119612793756, "10S": 0.29090095581623265}}, "member_checkpoint_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "cpu_seconds": 1.789065261138603}, {"cutoff": "valid_backcast3", "skill": 0.3921296782846404, "width12_parent_skill": 0.3916700540609932, "joint6_parent_skill": 0.3921644754508403, "delta_width12": 0.0004596242236472037, "delta_joint6": -3.479716619991802e-05, "groups": {"term": {"short": 0.3964714460684823, "medium": 0.373180111862915, "long": 0.39934479213703844}, "domain": {"Econ/Fin": 0.191514835075003, "Energy": 0.3171497763613005, "Healthcare": 0.23406912051970086, "Nature": 0.5087773123381991, "Sales": 0.40240992387298036, "Transport": 0.5102150645411062, "Web/CloudOps": 0.4027691414578687}, "freq": {"A-DEC": 0.3632879895614767, "Q-DEC": 0.2635672907077069, "M": 0.1461601048112755, "W-SUN": 0.17023500340733289, "D": 0.4402406107863014, "H": 0.40804718949837626, "15T": 0.2581134771316893, "W-FRI": 0.044141466152163744, "10T": 0.6036240151562728, "W-TUE": 0.28794685929485997, "W-THU": 0.3474718313178913, "W-WED": 0.37413351061197164, "5T": 0.47372805877658364, "10S": 0.29420374901523283}}, "member_checkpoint_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "cpu_seconds": 1.8740738260094076}], "seconds": 9.421103393193334, "optimizer_updates": 0, "new_neural_inference": false, "positive_cutoffs_vs_width12": 4, "mean_delta_vs_width12": 0.0005765971103506018, "positive_cutoffs_vs_joint6": 3, "mean_delta_vs_joint6": 6.171788143150003e-05, "selection_gate_passed": true}. No package/fullscore inferred.
+
+## E138 bounded learned gate early3000
+- Checkpoint SHA-256: `2d9192426b7d544ce10d9ba66e1f527c935ba467df0a7aa12b58898f2f9dfcd1`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 103.82110226619989 seconds.
+- Overall skill 0.389319; relative MASE 0.731431; relative WQL 0.509864.
+- term skill: long=0.4087, medium=0.4116, short=0.3729.
+- domain skill: Econ/Fin=0.1514, Energy=0.2477, Healthcare=0.4805, Nature=0.4451, Sales=0.3428, Transport=0.4754, Web/CloudOps=0.5103.
+- frequency skill: A=0.3812, D=0.3750, H=0.4052, M=0.1167, Q=0.2047, S=0.4249, T=0.4479, W=0.2417.
+- variates skill: multivariate=0.4026, univariate=0.3785.
+- Outcome: Learning diagnostic only; primary6000/final12000 unchanged. Main0.389319492423 is higher than1k0.388768978765; no earlier-cutoff/full-selection decision from3k. All E130experts remain immutable; only6340gateparams trained.
+
+2026-10-23T11:00:13.778380+00:00: E148 fixedwidth12+fresh96 passed all4versusE139width12/fresh48 (mean+0.000576597110) and3of4versusE142width6/fresh96 (mean+0.000061717881), oldestcutoffdelta−0.000034797166. CPUprobes9.421103393s,0trainingupdates. Completepublicsuite nowlaunched, no automaticpromotion. Gate3kmain0.389319492423 remains diagnostic only.
+
+## E144 direct-trend treatment transfer6000 valid_screen
+- Checkpoint SHA-256: `9ba4af36e21a47aa761adac79fd6de599de475dde35083e7f8e6f16df77a07ff`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 25.811277151107788 seconds.
+- Overall skill 0.387424; relative MASE 0.733394; relative WQL 0.511661.
+- term skill: long=0.4094, medium=0.4106, short=0.3696.
+- domain skill: Econ/Fin=0.1452, Energy=0.2423, Healthcare=0.4725, Nature=0.4456, Sales=0.3389, Transport=0.4748, Web/CloudOps=0.5122.
+- frequency skill: A=0.3744, D=0.3748, H=0.4080, M=0.1001, Q=0.2063, S=0.4257, T=0.4446, W=0.2254.
+- variates skill: multivariate=0.4021, univariate=0.3755.
+- Outcome: Fixed zero-extended E139width6 + zero-extended E136 - zero-extended E107, coefficient1. Base dispersion/width paths bitwise unchanged; no optimizer update. Equal-quarter ensemble selected separately.
+
+## E144 direct-trend treatment transfer6000 valid_backcast
+- Checkpoint SHA-256: `9ba4af36e21a47aa761adac79fd6de599de475dde35083e7f8e6f16df77a07ff`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 22.062713384628296 seconds.
+- Overall skill 0.343499; relative MASE 0.788973; relative WQL 0.546272.
+- term skill: long=0.3998, medium=0.3643, short=0.3122.
+- domain skill: Econ/Fin=0.1782, Energy=0.2293, Healthcare=0.3754, Nature=0.3914, Sales=0.4040, Transport=0.3806, Web/CloudOps=0.4580.
+- frequency skill: A=0.3226, D=0.3626, H=0.3785, M=0.1977, Q=0.2306, S=0.3402, T=0.3377, W=0.2876.
+- variates skill: multivariate=0.3585, univariate=0.3313.
+- Outcome: Fixed zero-extended E139width6 + zero-extended E136 - zero-extended E107, coefficient1. Base dispersion/width paths bitwise unchanged; no optimizer update. Equal-quarter ensemble selected separately.
+
+## E144 direct-trend treatment transfer6000 valid_backcast2
+- Checkpoint SHA-256: `9ba4af36e21a47aa761adac79fd6de599de475dde35083e7f8e6f16df77a07ff`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 26.243786096572876 seconds.
+- Overall skill 0.365818; relative MASE 0.764709; relative WQL 0.525935.
+- term skill: long=0.3723, medium=0.3945, short=0.3520.
+- domain skill: Econ/Fin=0.1349, Energy=0.2394, Healthcare=0.3020, Nature=0.4854, Sales=0.5512, Transport=0.4290, Web/CloudOps=0.4446.
+- frequency skill: A=0.3482, D=0.4281, H=0.3999, M=0.1492, Q=0.1537, S=0.2854, T=0.3707, W=0.2609.
+- variates skill: multivariate=0.3720, univariate=0.3608.
+- Outcome: Fixed zero-extended E139width6 + zero-extended E136 - zero-extended E107, coefficient1. Base dispersion/width paths bitwise unchanged; no optimizer update. Equal-quarter ensemble selected separately.
+
+## E144 direct-trend treatment transfer6000 valid_backcast3
+- Checkpoint SHA-256: `9ba4af36e21a47aa761adac79fd6de599de475dde35083e7f8e6f16df77a07ff`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 26.943432569503784 seconds.
+- Overall skill 0.392476; relative MASE 0.738388; relative WQL 0.499852.
+- term skill: long=0.3965, medium=0.3649, short=0.4010.
+- domain skill: Econ/Fin=0.1864, Energy=0.3247, Healthcare=0.2290, Nature=0.5022, Sales=0.4068, Transport=0.5167, Web/CloudOps=0.3947.
+- frequency skill: A=0.3628, D=0.4508, H=0.4093, M=0.1376, Q=0.2634, S=0.2789, T=0.4331, W=0.2517.
+- variates skill: multivariate=0.3762, univariate=0.4050.
+- Outcome: Fixed zero-extended E139width6 + zero-extended E136 - zero-extended E107, coefficient1. Base dispersion/width paths bitwise unchanged; no optimizer update. Equal-quarter ensemble selected separately.
+
+E144 fixed direct-trend equal-quarter ensemble diagnostics: {"duration": 6, "rows": [{"cutoff": "valid_screen", "skill": 0.38810101106304606, "control_skill": 0.3880317541577587, "delta": 6.925690528736261e-05, "groups": {"term": {"short": 0.37180827298086194, "medium": 0.4105115509520151, "long": 0.40708238805037744}, "domain": {"Econ/Fin": 0.1486111186027611, "Energy": 0.2479695849755097, "Healthcare": 0.4804056795233387, "Nature": 0.44349612366523783, "Sales": 0.34254579297464094, "Transport": 0.47319670526932023, "Web/CloudOps": 0.5084644098846547}, "freq": {"A-DEC": 0.3830620791547408, "Q-DEC": 0.20477654488786246, "M": 0.12309867469533453, "W-SUN": 0.09539518012564197, "D": 0.37279528788580674, "H": 0.40267616846564325, "15T": 0.2159286858582814, "W-FRI": 0.018629318922964266, "10T": 0.626998082431836, "W-TUE": 0.611936508038109, "W-THU": 0.22735602930060128, "W-WED": 0.32285500325028815, "5T": 0.5242567984118436, "10S": 0.42364215749954115}}, "member_checkpoint_hashes": 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0.23889301677977803, "10T": 0.3334497373457339, "W-TUE": 0.5283866471678982, "W-THU": 0.2693442052960473, "W-WED": 0.3576817387194232, "5T": 0.4462591487900791, "10S": 0.34964818068776293}}, "member_checkpoint_hashes": ["9ba4af36e21a47aa761adac79fd6de599de475dde35083e7f8e6f16df77a07ff", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 2.079201564891264}, {"cutoff": "valid_backcast2", "skill": 0.3687334161573663, "control_skill": 0.36868215125404136, "delta": 5.1264903324965694e-05, "groups": {"term": {"short": 0.3557059534653181, "medium": 0.3976216095200944, "long": 0.37268682260890473}, "domain": {"Econ/Fin": 0.12803076624568543, "Energy": 0.24000255570986928, "Healthcare": 0.36855583170472794, "Nature": 0.4823647970325259, "Sales": 0.559496370437498, "Transport": 0.42676585305163917, "Web/CloudOps": 0.44574525321486713}, "freq": {"A-DEC": 0.33838286634019077, "Q-DEC": 0.15194832993661667, "M": 0.17048569213442577, "W-SUN": 0.07880746890627799, "D": 0.4356860699349723, "H": 0.3999234407371479, "15T": 0.10623917997605214, "W-FRI": 0.15689116677662518, "10T": 0.5102047202290378, "W-TUE": 0.17135603071174732, "W-THU": 0.33338011043972227, "W-WED": 0.5580671789826079, "5T": 0.47741806564473255, "10S": 0.29101676393121934}}, "member_checkpoint_hashes": ["9ba4af36e21a47aa761adac79fd6de599de475dde35083e7f8e6f16df77a07ff", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 1.8924103130120784}, {"cutoff": "valid_backcast3", "skill": 0.39163165430208413, "control_skill": 0.39170266078452043, "delta": -7.10064824362977e-05, "groups": {"term": {"short": 0.3966795855907692, "medium": 0.37253068675023604, "long": 0.39699833653896155}, "domain": {"Econ/Fin": 0.18919906291191568, "Energy": 0.31995206851807945, "Healthcare": 0.2347697994876441, "Nature": 0.5091382470970252, "Sales": 0.39897801119857224, "Transport": 0.5094799840851572, "Web/CloudOps": 0.39716820937506025}, "freq": {"A-DEC": 0.3535253256647043, "Q-DEC": 0.2615670338345317, "M": 0.14866387047266738, "W-SUN": 0.17110165811244527, "D": 0.4418909540008449, "H": 0.4075936421084978, "15T": 0.2631269004445399, "W-FRI": 0.045083749104135484, "10T": 0.6025336205792375, "W-TUE": 0.27966201942927393, "W-THU": 0.35336214420145295, "W-WED": 0.36513539720826094, "5T": 0.46905294221375926, "10S": 0.2884859565550919}}, "member_checkpoint_hashes": ["9ba4af36e21a47aa761adac79fd6de599de475dde35083e7f8e6f16df77a07ff", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 1.8629437680356205}], "positive_cutoffs": 3, "mean_delta": 1.589353232439583e-05, "selection_gate_passed": true}. Full replay/runtime checks and comparison to current publication required before any promotion.
+
+E136 12000 four-cutoff comparison: {"label": "E136 12000 four-cutoff comparison", "utc": "2026-10-23T11:02:40.051142+00:00", "candidate": "direct_trend_head_ema12000", "control": "long_seasonal_ema12000", "rows": [{"cutoff": "evals", "candidate_skill": 0.3827065485549006, "control_skill": 0.38231375879048757, "delta": 0.00039278976441303737, "candidate_checkpoint": "a58331b22849ed42145fa59ede34ac1110d547ca2ca8d22fd7ae1e7758275c02", "control_checkpoint": "d512cdac520239f0c10e021721424d87cee30086fa70f6cbc41f21e2e04988c3", "candidate_eval_seconds": 23.143726587295532, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.738528048869986, "rel_wql_gmean": 0.4773385847991587}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7109918585197693, "rel_wql_gmean": 0.4936608059965356}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7488759735294819, "rel_wql_gmean": 0.540881489964601}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8803309837829927, "rel_wql_gmean": 0.8557476438004471}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9004161113893661, "rel_wql_gmean": 0.6438547292784662}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5770128564480608, "rel_wql_gmean": 0.510787104123025}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8024521154915958, "rel_wql_gmean": 0.39959211009188234}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8487728274242853, "rel_wql_gmean": 0.5190797723691057}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6311631791945154, "rel_wql_gmean": 0.43133647192395125}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5570474112338696, "rel_wql_gmean": 0.4322179728387257}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6266150997143095, "rel_wql_gmean": 0.6537532278074971}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7574597056782264, "rel_wql_gmean": 0.5230914635499829}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7263796018128642, "rel_wql_gmean": 0.49601851117568424}, "M": 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0.5306073447296601, "rel_wql_gmean": 0.8030982774337949}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7175124768607152, "rel_wql_gmean": 0.41780178227764675}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7223996277645064, "rel_wql_gmean": 0.4859456509788173}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8339193215840682, "rel_wql_gmean": 0.8250296214792107}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7133214644953572, "rel_wql_gmean": 0.7659591311067668}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8388577706002066, "rel_wql_gmean": 0.5998597273487538}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7077596915926687, "rel_wql_gmean": 0.46245587762920626}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8141976774865708, "rel_wql_gmean": 0.6645660569936594}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7600456915083991, "rel_wql_gmean": 0.511644654751976}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7131300545794724, "rel_wql_gmean": 0.49563420928448887}}}, "group_skill_delta": {"term": {"long": -6.440524793560609e-05, "medium": 1.2553848545149293e-06, "short": -0.0002814460074814784}, "domain": {"Econ/Fin": 0.0018779845248143578, "Energy": 0.0007408019089427675, "Healthcare": -0.0018966403921262254, "Nature": 0.00023079957426602737, "Sales": -0.00027143369330284717, "Transport": -0.00040530308944747295, "Web/CloudOps": -0.0019090683135004616}, "frequency": {"A": 0.001944684287676024, "D": 0.0014042712874474672, "H": -2.5497132614016316e-05, "M": -0.005617694492873415, "Q": 0.0006765617207961094, "S": -0.0019162076973660014, "T": -0.0001888943515314656, "W": -0.0014572062836881727}, "variates": {"multivariate": -0.00036533865836885226, "univariate": -3.8938555586565116e-05}}}], "mean_delta": 0.00011268297958977325, "positive_cutoffs": 2, "outcome": "Declared comparison checkpoints 6000,12000. No transfer formula or automatic publication is selected by this controller."}
+
+E144source gate failed for12k; no additional transfer constructed. {"pid": 30367, "status": "complete; matched source gate failed12", "steps": [{"label": "first member four cutoffs6", "returncode": 0, "seconds": 1269.0724992570467}, {"label": "record first 6 valid_screen", "returncode": 0, "seconds": 0.046739127952605486}, {"label": "fixed equal quarters 6 valid_screen", "returncode": 0, "seconds": 1.9155653920024633}, {"label": "record first 6 valid_backcast", "returncode": 0, "seconds": 0.038436006056144834}, {"label": "fixed equal quarters 6 valid_backcast", "returncode": 0, "seconds": 2.262516180984676}, {"label": "record first 6 valid_backcast2", "returncode": 0, "seconds": 0.042515057139098644}, {"label": "fixed equal quarters 6 valid_backcast2", "returncode": 0, "seconds": 2.07946516177617}, {"label": "record first 6 valid_backcast3", "returncode": 0, "seconds": 0.03795478004030883}, {"label": "fixed equal quarters 6 valid_backcast3", "returncode": 0, "seconds": 2.0126948000397533}], "results": [{"duration": 6, "rows": [{"cutoff": "valid_screen", "skill": 0.38810101106304606, "control_skill": 0.3880317541577587, "delta": 6.925690528736261e-05, "groups": {"term": {"short": 0.37180827298086194, "medium": 0.4105115509520151, "long": 0.40708238805037744}, "domain": {"Econ/Fin": 0.1486111186027611, "Energy": 0.2479695849755097, "Healthcare": 0.4804056795233387, "Nature": 0.44349612366523783, "Sales": 0.34254579297464094, "Transport": 0.47319670526932023, "Web/CloudOps": 0.5084644098846547}, "freq": {"A-DEC": 0.3830620791547408, "Q-DEC": 0.20477654488786246, "M": 0.12309867469533453, "W-SUN": 0.09539518012564197, "D": 0.37279528788580674, "H": 0.40267616846564325, "15T": 0.2159286858582814, "W-FRI": 0.018629318922964266, "10T": 0.626998082431836, "W-TUE": 0.611936508038109, "W-THU": 0.22735602930060128, "W-WED": 0.32285500325028815, "5T": 0.5242567984118436, "10S": 0.42364215749954115}}, "member_checkpoint_hashes": ["9ba4af36e21a47aa761adac79fd6de599de475dde35083e7f8e6f16df77a07ff", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 1.7184872501529753}, {"cutoff": "valid_backcast", "skill": 0.34471433073323854, "control_skill": 0.344700271930117, "delta": 1.4058803121552721e-05, "groups": {"term": {"short": 0.31238304922869986, "medium": 0.36347247982590625, "long": 0.405345914099144}, "domain": {"Econ/Fin": 0.18144711659913626, "Energy": 0.23536769711757133, "Healthcare": 0.40411138472347474, "Nature": 0.37935065740310636, "Sales": 0.40728275158059335, "Transport": 0.37651708169313824, "Web/CloudOps": 0.4590755997389684}, "freq": {"A-DEC": 0.3350593911160711, "Q-DEC": 0.23266418596417293, "M": 0.19783596100298428, "W-SUN": 0.05626178192469, "D": 0.3622504039590009, "H": 0.3754732207508753, "15T": 0.22359339214094542, "W-FRI": 0.23889301677977803, "10T": 0.3334497373457339, "W-TUE": 0.5283866471678982, "W-THU": 0.2693442052960473, "W-WED": 0.3576817387194232, "5T": 0.4462591487900791, "10S": 0.34964818068776293}}, "member_checkpoint_hashes": ["9ba4af36e21a47aa761adac79fd6de599de475dde35083e7f8e6f16df77a07ff", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 2.079201564891264}, {"cutoff": "valid_backcast2", "skill": 0.3687334161573663, "control_skill": 0.36868215125404136, "delta": 5.1264903324965694e-05, "groups": {"term": {"short": 0.3557059534653181, "medium": 0.3976216095200944, "long": 0.37268682260890473}, "domain": {"Econ/Fin": 0.12803076624568543, "Energy": 0.24000255570986928, "Healthcare": 0.36855583170472794, "Nature": 0.4823647970325259, "Sales": 0.559496370437498, "Transport": 0.42676585305163917, "Web/CloudOps": 0.44574525321486713}, "freq": {"A-DEC": 0.33838286634019077, "Q-DEC": 0.15194832993661667, "M": 0.17048569213442577, "W-SUN": 0.07880746890627799, "D": 0.4356860699349723, "H": 0.3999234407371479, "15T": 0.10623917997605214, "W-FRI": 0.15689116677662518, "10T": 0.5102047202290378, "W-TUE": 0.17135603071174732, "W-THU": 0.33338011043972227, "W-WED": 0.5580671789826079, "5T": 0.47741806564473255, "10S": 0.29101676393121934}}, "member_checkpoint_hashes": ["9ba4af36e21a47aa761adac79fd6de599de475dde35083e7f8e6f16df77a07ff", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 1.8924103130120784}, {"cutoff": "valid_backcast3", "skill": 0.39163165430208413, "control_skill": 0.39170266078452043, "delta": -7.10064824362977e-05, "groups": {"term": {"short": 0.3966795855907692, "medium": 0.37253068675023604, "long": 0.39699833653896155}, "domain": {"Econ/Fin": 0.18919906291191568, "Energy": 0.31995206851807945, "Healthcare": 0.2347697994876441, "Nature": 0.5091382470970252, "Sales": 0.39897801119857224, "Transport": 0.5094799840851572, "Web/CloudOps": 0.39716820937506025}, "freq": {"A-DEC": 0.3535253256647043, "Q-DEC": 0.2615670338345317, "M": 0.14866387047266738, "W-SUN": 0.17110165811244527, "D": 0.4418909540008449, "H": 0.4075936421084978, "15T": 0.2631269004445399, "W-FRI": 0.045083749104135484, "10T": 0.6025336205792375, "W-TUE": 0.27966201942927393, "W-THU": 0.35336214420145295, "W-WED": 0.36513539720826094, "5T": 0.46905294221375926, "10S": 0.2884859565550919}}, "member_checkpoint_hashes": ["9ba4af36e21a47aa761adac79fd6de599de475dde35083e7f8e6f16df77a07ff", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c"], "cpu_seconds": 1.8629437680356205}], "positive_cutoffs": 3, "mean_delta": 1.589353232439583e-05, "selection_gate_passed": true}], "started_utc": "2026-10-23T10:39:47.219310+00:00", "seconds": 1376.4727018228732}
+
+## E136 direct bounded trend final12000 main
+- Checkpoint SHA-256: `a58331b22849ed42145fa59ede34ac1110d547ca2ca8d22fd7ae1e7758275c02`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 23.143726587295532 seconds.
+- Overall skill 0.382707; relative MASE 0.738279; relative WQL 0.516134.
+- term skill: long=0.4063, medium=0.4076, short=0.3636.
+- domain skill: Econ/Fin=0.1320, Energy=0.2386, Healthcare=0.4571, Nature=0.4337, Sales=0.3362, Transport=0.4782, Web/CloudOps=0.5093.
+- frequency skill: A=0.3600, D=0.3705, H=0.3998, M=0.1044, Q=0.1976, S=0.4271, T=0.4425, W=0.2153.
+- variates skill: multivariate=0.3991, univariate=0.3693.
+- Outcome: Final main0.382706548555 exceeds matched E107, but complete4cutoff comparison improves only2of4 (mean+0.000112682980), failing declared durability gate. E144final transfer not constructed. E146seed523 replication remains a separately predeclared primary6000 test. Seed197/B64/LR2e-5; process2570.288663981s; original12k schedule complete.
+
+## E122 original AR fresh146M early16000
+- Checkpoint SHA-256: `0f48d177218bcd7531f3d54fea58f5ca08036886a457008259c70485858848f2`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 36.974172592163086 seconds.
+- Overall skill 0.352504; relative MASE 0.766166; relative WQL 0.547207.
+- term skill: long=0.3797, medium=0.3879, short=0.3275.
+- domain skill: Econ/Fin=0.1324, Energy=0.2149, Healthcare=0.3369, Nature=0.4109, Sales=0.3337, Transport=0.4397, Web/CloudOps=0.4847.
+- frequency skill: A=0.3850, D=0.3235, H=0.3719, M=0.0535, Q=0.1967, S=0.4166, T=0.4073, W=0.2191.
+- variates skill: multivariate=0.3741, univariate=0.3347.
+- Outcome: Early diagnostic0.352503682323 exceeds same-update E79slowAR0.349499285513 by0.003004396810. Same seed307/B48/LR2e-4/96k, ownc969init; only AR coefficient law changes. Primary48k/final96k unchanged; no selection from this early main panel.
+
+2026-10-23T11:07:51.052221+00:00: E144primary6k merged quartet only+0.000015893532mean versus frozenE1396 (3of4), but currentE142 deltas [-7.085704213405375e-05, -0.0008442905988997884, -0.0008112165200293564, -0.0005328211487561907] (0of4). No full replay or publication. E136final12k source gate fails2of4 despite+0.000112682980mean; E144final not constructed. E146seed523primary replication remains queued as declared.
+
+E148 full group differences against current E142 {"term": {"long": 5.078989562323777e-05, "medium": 1.3970767767879089e-05, "short": 7.037013199329145e-05}, "domain": {"Econ/Fin": 4.251436603996961e-05, "Energy": -1.6436239628325744e-05, "Healthcare": 0.0006581007257682181, "Nature": 1.51650260322711e-05, "Sales": 5.69484299851597e-05, "Transport": -8.820947061149997e-05, "Web/CloudOps": 9.93649148453013e-05}, "frequency": {"A": -4.305730237130767e-06, "D": 5.600023718677072e-05, "H": 1.1796898732430172e-05, "M": 0.0006410945366341902, "Q": 3.291114509240689e-05, "S": 0.0001216824749590284, "T": 5.1314657622425486e-05, "W": -0.00012932300104695482}, "variates": {"multivariate": 6.773523247716184e-05, "univariate": 4.1424287538505133e-05}}. Both aggregate relative metrics improve. Primary3of4/meanpositive current-cutoff gate passed; earliest cutoff declines0.000034797166. No training or ensemble-weight adjustment; complete runtime/relocation/composition checks passed.
+
+## E148 final fresh146M quartet full replay — selected
+- Checkpoint SHA-256: `a1b4ab426901be8e1ee299eeeb3f041bac7cea3d4e59c4608824fc3d05c83e03`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.384536; relative MASE 0.736368; relative WQL 0.514412.
+- term skill: long=0.4002, medium=0.4096, short=0.3685.
+- domain skill: Econ/Fin=0.1298, Energy=0.2408, Healthcare=0.5001, Nature=0.4414, Sales=0.3581, Transport=0.4728, Web/CloudOps=0.4998.
+- frequency skill: A=0.3043, D=0.3760, H=0.3984, M=0.1515, Q=0.1921, S=0.4240, T=0.4383, W=0.2444.
+- variates skill: multivariate=0.3964, univariate=0.3750.
+- Outcome: Fixed quartet improves full skill from 0.384482209012 to 0.384535531421. All97configs151170instances pass; inference 459.216s on shared H100. Relocation and real/extreme synthetic standalone composition are bitwise exact. Validation selection only, not held-out reward.
+
+E148 fullsuite {"package": "/workspace/research/candidate-width12-fresh96-ensemble", "status": "complete", "steps": [{"name": "synthetic_composition", "returncode": 0, "seconds": 115.8998016428668}, {"name": "relocation", "returncode": 0, "seconds": 51.69444114388898}, {"name": "full_public_replay", "returncode": 0, "seconds": 462.5455177319236}, {"name": "composition", "returncode": 0, "seconds": 0.33620940893888474}, {"name": "descriptive_comparison", "returncode": 0, "seconds": 0.16860568895936012}], "current_step": "descriptive_comparison", "seconds": 630.6624612889718, "skill": 0.384535531420913}. Synthetic {"kind": "joint12", "descriptor_sha256": "a1b4ab426901be8e1ee299eeeb3f041bac7cea3d4e59c4608824fc3d05c83e03", "members": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33"], "checks": [{"config_id": "synthetic_H", "instances": 12, "bitwise_composition": true, "finite_ordered": true}, {"config_id": "synthetic_15T", "instances": 12, "bitwise_composition": true, "finite_ordered": true}], "references": ["/workspace/research/width12_fixture/standalone0", "/workspace/research/fresh_capacity4_fixture/standalone1", "/workspace/research/fresh_capacity4_fixture/standalone2", "/workspace/research/fresh_capacity96_fixture/standalone3"], "fresh_member_cached": true, "seconds": 115.75572869600728}. Descriptive comparison {"candidate": "/workspace/research/valid-report-width12-fresh96-ensemble.json", "parent": "/workspace/research/valid-report-width6-fresh96-ensemble.json", "candidate_hash": "a1b4ab426901be8e1ee299eeeb3f041bac7cea3d4e59c4608824fc3d05c83e03", "parent_hash": "49440d1daee26c41a9bbb1f1580bbb35317a35553a58dafbd778cecd027c05fe", "skill_difference": 5.33224084642292e-05, "n_configs": 97, "n_dataset_clusters": 28, "improved_configs": 50, "bootstrap_seed": 317, "bootstrap_samples": 5000, "dataset_cluster_delta_percentiles": {"p025": -1.3563314704279072e-05, "p50": 5.152464910823662e-05, "p975": 0.00014571112637890002}, "note": "Descriptive resampling only. Adaptive validation selection and reused windows prevent treating this as a held-out significance test."}. Current-publication diagnostic deltas [5.608287258229705e-05, 0.00010749432663625935, 0.00011809149270736174, -3.479716619991802e-05]. All members forecast every instance. E101teacher/E138experts remain their immutable snapshots.
+
+## E131 fresh146M long-seasonal4000 learning diagnostic
+- Checkpoint SHA-256: `3a6bdd72a63e36e30295843c1865cbe1682370d01d12ac9c932a2db0c99ad241`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 37.18789601325989 seconds.
+- Overall skill 0.334468; relative MASE 0.779164; relative WQL 0.568471.
+- term skill: long=0.3617, medium=0.3834, short=0.3037.
+- domain skill: Econ/Fin=0.1144, Energy=0.2028, Healthcare=0.2995, Nature=0.3963, Sales=0.2941, Transport=0.4410, Web/CloudOps=0.4524.
+- frequency skill: A=0.3792, D=0.3362, H=0.3639, M=-0.0957, Q=0.1802, S=0.4239, T=0.3739, W=0.1963.
+- variates skill: multivariate=0.3490, univariate=0.3227.
+- Outcome: Seed307/B48/LR2e-4 original96k schedule, own random initialization plus zero seasonal features. Main0.334468421976 versus matched E79at4000 0.328133919419, delta+0.006334502556. Early main panel only; primary48000/final96000 and all four cutoffs remain required. No selection or schedule change.
+
+## E141 patch16 matched continuation3000 learning diagnostic
+- Checkpoint SHA-256: `351a29a13179757eed91b8115a4933cb638076d1daa332cacb8a899f0bc56687`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 36.242040157318115 seconds.
+- Overall skill 0.371632; relative MASE 0.749634; relative WQL 0.526718.
+- term skill: long=0.3840, medium=0.3952, short=0.3575.
+- domain skill: Econ/Fin=0.1345, Energy=0.2268, Healthcare=0.5035, Nature=0.4186, Sales=0.3293, Transport=0.4591, Web/CloudOps=0.4914.
+- frequency skill: A=0.4042, D=0.3634, H=0.3884, M=0.1131, Q=0.2064, S=0.4166, T=0.4190, W=0.2388.
+- variates skill: multivariate=0.3799, univariate=0.3650.
+- Outcome: Seed809/B32/LR2e-5/12000 from own E79wide96000. Control learning diagnostic only; matched foldedpatch8 candidate has not started. Primary6000/final12000 comparison unchanged.
+
+2026-10-23T11:16:03.558184+00:00: E148 cached four-cutoff failure diagnosis (0.01057158992625773s CPU;0updates/no newinference), receiptE148_failure_structure.json. Top recurrent weaknesses [{"leaderboard_key": "m4_daily/D/short", "geometric_mean_relative_error": 1.0802223136613158, "cutoffs_worse_than_seasonal_naive": 4, "cutoff_count": 4}, {"leaderboard_key": "electricity/H/short", "geometric_mean_relative_error": 1.0080920609749786, "cutoffs_worse_than_seasonal_naive": 2, "cutoff_count": 4}, {"leaderboard_key": "ett1/15T/medium", "geometric_mean_relative_error": 0.9428132186885196, "cutoffs_worse_than_seasonal_naive": 1, "cutoff_count": 4}, {"leaderboard_key": "ett2/15T/long", "geometric_mean_relative_error": 0.9332305533762528, "cutoffs_worse_than_seasonal_naive": 1, "cutoff_count": 4}, {"leaderboard_key": "ett1/W/short", "geometric_mean_relative_error": 0.9328546779617872, "cutoffs_worse_than_seasonal_naive": 1, "cutoff_count": 4}, {"leaderboard_key": "saugeen/M/short", "geometric_mean_relative_error": 0.9322337865102405, "cutoffs_worse_than_seasonal_naive": 2, "cutoff_count": 4}, {"leaderboard_key": "ett1/15T/long", "geometric_mean_relative_error": 0.9321693285748703, "cutoffs_worse_than_seasonal_naive": 1, "cutoff_count": 4}, {"leaderboard_key": "ett2/15T/short", "geometric_mean_relative_error": 0.9270483647160915, "cutoffs_worse_than_seasonal_naive": 2, "cutoff_count": 4}]. Diagnostic only; no new training rule or inference switch selected.
+
+2026-10-23T11:17:38.943532+00:00: E148 full-validation descriptive quantile/median audit, cached forecasts only,0updates/no new inference. Quantile audit1.235771079082042s;median audit1.2367559450212866s. Equal-config mean quantile coverage-band deviation0.07028782858028769. M4daily median coverage0.47945833899687373,coverage-band deviation0.021089136574388708,MASE3.289412851999819,signed scaled error-0.3332592876743655,absolute window-mean error/MAE0.8980850580730646. The last quantity is a Jensen bound, not a recoverable correction. Aggregate coverage does not establish conditional calibration or prove error causality. No forecast adjustment or new generator selected. Detailed group/config receiptsE148_quantile_coverage.json andE148_median_error_structure.json.
+
+## E149 integrated local-level noise in synthetic training — declared 2026-10-23T11:18:57.764896+00:00
+Hypothesis: the corpus and current synthetic mixture underrepresent persistent stochastic level motion. Current E148 M4daily trails Seasonal Naive on all four reused cutoffs despite fairly close aggregate quantile coverage; this motivates, but does not establish, the mechanism. Test one training-only generator change against completed E80: add an independent Gaussian random walk to25percent of synthetic paths, before existing intermittent/positivity transforms. At each selected path draw per-step innovation standard deviation10**Uniform(-3,-1); the level begins at0 and subsequent levels are cumulative independent N(0,sigma) innovations. The original Fourier, damped-trend, AR, changepoint and observation-noise draws remain unchanged, as do every real row, source probability, length, mask, sign and requested-horizon draw. A separate RNG(seed+1300003) controls selection, scale and innovations and is fully checkpointed. Synthetic fraction remains20percent, so about5percent of all rows receive the new component; no frequency or dataset conditions.
+Candidate integrated_level uses byte-identical E80model and own E62initial22c08571978be31ba57368eb983089a40a44d56d4ae5e17da41afd2a761a258c, seed197/B64/LR2e-5/12000/paired/uncompiled/oldpack/originalAR, horizon RNG300200. Primary6000, final12000 across allfourcutoffs versus E80;1000/3000main learning diagnostics. No coefficient sweep, transfer formula, new full-corpus combination or inference adjustment is selected. This differs from E61slow stationary AR and failed E69stochastic volatility: it adds a unit-root level component without rescaling existing noise. Queue after E146final12000 in the same allocation, after CPUstream/recovery and maximum-shape finite-gradient checks. No extra concurrent trainer. No validation training.
+
+2026-10-23T21:22:15.117446+00:00: Storage recovery4: six complete optimizer/EMA/RNG/prefetch states restored; original recipes and endpoint schedules unchanged. Checkpoint audit {"utc": "2026-10-23T21:20:45.083665+00:00", "rows": [{"run": "distill_ensemble", "step": 39750, "end_step": 96000, "sha256": "5c45a2060c0307118bf112b92938704c807bc587d6886ed2808a143526c8e61a", "bytes": 2344995351, "sampler_rngs": ["rng", "horizon_rng"], "seconds": 5.320158665068448, "zip_crc_valid": true, "full_recovery_fields": true, "model_and_ema_finite": true, "prefetch_shapes": [[48, 4096], [48, 32], [48, 2]]}, {"run": "random_capacity_full", "step": 81250, "end_step": 96000, "sha256": "17851f8b68a8fd6ced263aa86771e03f57e6f2bbf8e6c631a5fda77d720db4b2", "bytes": 2344759575, "sampler_rngs": ["rng", "horizon_rng"], "seconds": 5.329595515038818, "zip_crc_valid": true, "full_recovery_fields": true, "model_and_ema_finite": true, "prefetch_shapes": [[48, 2048], [48, 1024], [48, 2]]}, {"run": "random_capacity_original_ar", "step": 22250, "end_step": 96000, "sha256": "9f3740eb601f053d5689bbe905578adb92f84be060980372e5a410cf3ccf636e", "bytes": 2345041495, "sampler_rngs": ["rng", "horizon_rng"], "seconds": 5.150818087160587, "zip_crc_valid": true, "full_recovery_fields": true, "model_and_ema_finite": true, "prefetch_shapes": [[48, 4096], [48, 128], [48, 2]]}, {"run": "random_capacity_long_seasonal", "step": 8250, "end_step": 96000, "sha256": "cbe4ca6ee65878592e97c3aa0fe849b6d11f674720272a6d16b8def26957f252", "bytes": 2346840508, "sampler_rngs": ["rng", "horizon_rng"], "seconds": 5.352568831061944, "zip_crc_valid": true, "full_recovery_fields": true, "model_and_ema_finite": true, "prefetch_shapes": [[48, 4096], [48, 1024], [48, 2]]}, {"run": "ensemble_gate", "step": 6250, "end_step": 12000, "sha256": "1e4ca8d91132469baaba2a4a6c0c3e7a642cb59bdfd47bc6920d0b8b27cbfac2", "bytes": 1890843, "sampler_rngs": ["rng", "horizon_rng"], "seconds": 0.012273023836314678, "zip_crc_valid": true, "full_recovery_fields": true, "model_and_ema_finite": true, "prefetch_shapes": [[64, 4096], [64, 1024], [64, 2]]}, {"run": "capacity_patch8_control", "step": 7500, "end_step": 12000, "sha256": "cd7d2c28375f521563f721cd04eff9da81c2ef6e68e0dd59e0ba3cc5fe1ae286", "bytes": 2344128471, "sampler_rngs": ["rng", "horizon_rng"], "seconds": 4.977336599025875, "zip_crc_valid": true, "full_recovery_fields": true, "model_and_ema_finite": true, "prefetch_shapes": [[32, 512], [32, 512], [32, 2]]}], "seconds": 26.474200160009786}. Queued dependencies rewired to new live queue PIDs. Incomplete .tmp files quarantined; no model state inferred from them. All selected weights preserved. Original externally enforced research budget remains authoritative; operator downtime is not research compute.
+
+E110 fresh146M full eligible-series corpus storage recovery training started at 2026-10-23T21:22:18Z, as an authorized immediate bounded trial; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+E122 fresh146M original AR prior seed307 storage recovery training started at 2026-10-23T21:22:18Z, as an authorized immediate bounded trial; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+E131 fresh146M learned seasonal summaries seed307 storage recovery training started at 2026-10-23T21:22:18Z, as an authorized immediate bounded trial; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+E138 bounded learned gate with frozen E130 experts seed751 storage recovery training started at 2026-10-23T21:22:18Z, as an authorized immediate bounded trial; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+E141 matched146M capacity_patch8_control seed809 storage recovery training started at 2026-10-23T21:22:18Z, as an authorized immediate bounded trial; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+E101 own-ensemble distillation fresh146M storage recovery training started at 2026-10-23T21:22:19Z, as an authorized immediate bounded trial; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+2026-10-23T21:22:57.780616+00:00: Recovery4 resumes six jobs from verified full states {"distill_ensemble": 39750, "random_capacity_full": 81250, "random_capacity_original_ar": 22250, "random_capacity_long_seasonal": 8250, "ensemble_gate": 6250, "capacity_patch8_control": 7500}. All six original schedules and seven queued studies preserved; new dependencies recorded in recovery4_launch_receipt.json, persistent session23105. E148publication hashes and relocated outputs pass unchanged(11.567369470s). Compilerrestore83.745881014s; checkpointCRC/hash/finite/fullstateaudit26.474200160s. Original externally enforced budget retained; approximately10h operator downtime must not be inferred as research compute. E149is only declared, not implemented or queued. Storage inventory:raw step317.80GiB,EMA317.44GiB,fullrecovery45.44GiB; no valid checkpoints pruned. Disk currently13TBfree.
+
+## E138 learned gate6000 valid_screen
+- Checkpoint SHA-256: `2115a57946abe740b5b8e9a107d0ceca1697b3c71168fe9b565b7a404ee2ea80`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 101.05418701795861 seconds.
+- Overall skill 0.389154; relative MASE 0.731523; relative WQL 0.510077.
+- term skill: long=0.4082, medium=0.4121, short=0.3726.
+- domain skill: Econ/Fin=0.1515, Energy=0.2476, Healthcare=0.4815, Nature=0.4451, Sales=0.3430, Transport=0.4755, Web/CloudOps=0.5095.
+- frequency skill: A=0.3822, D=0.3763, H=0.4042, M=0.1131, Q=0.2031, S=0.4247, T=0.4482, W=0.2419.
+- variates skill: multivariate=0.4019, univariate=0.3789.
+- Outcome: All four own E130 experts frozen; only6340gateparameters trained on allowed corpus. Fixed bounded per-lead quantile weights. Compare immutableE130 then current publication before any full package selection.
+
+## E138 learned gate6000 valid_backcast
+- Checkpoint SHA-256: `2115a57946abe740b5b8e9a107d0ceca1697b3c71168fe9b565b7a404ee2ea80`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 45.48357278504409 seconds.
+- Overall skill 0.344511; relative MASE 0.788286; relative WQL 0.545063.
+- term skill: long=0.4034, medium=0.3630, short=0.3131.
+- domain skill: Econ/Fin=0.1828, Energy=0.2349, Healthcare=0.3965, Nature=0.3832, Sales=0.4075, Transport=0.3783, Web/CloudOps=0.4565.
+- frequency skill: A=0.3291, D=0.3623, H=0.3776, M=0.1964, Q=0.2350, S=0.3446, T=0.3405, W=0.2904.
+- variates skill: multivariate=0.3619, univariate=0.3303.
+- Outcome: All four own E130 experts frozen; only6340gateparameters trained on allowed corpus. Fixed bounded per-lead quantile weights. Compare immutableE130 then current publication before any full package selection.
+
+## E138 learned gate6000 valid_backcast2
+- Checkpoint SHA-256: `2115a57946abe740b5b8e9a107d0ceca1697b3c71168fe9b565b7a404ee2ea80`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 82.1311989359092 seconds.
+- Overall skill 0.369255; relative MASE 0.759487; relative WQL 0.523826.
+- term skill: long=0.3728, medium=0.3967, short=0.3570.
+- domain skill: Econ/Fin=0.1325, Energy=0.2423, Healthcare=0.3544, Nature=0.4854, Sales=0.5581, Transport=0.4279, Web/CloudOps=0.4445.
+- frequency skill: A=0.3395, D=0.4346, H=0.3996, M=0.1756, Q=0.1552, S=0.2883, T=0.3697, W=0.2846.
+- variates skill: multivariate=0.3711, univariate=0.3678.
+- Outcome: All four own E130 experts frozen; only6340gateparameters trained on allowed corpus. Fixed bounded per-lead quantile weights. Compare immutableE130 then current publication before any full package selection.
+
+## E138 learned gate6000 valid_backcast3
+- Checkpoint SHA-256: `2115a57946abe740b5b8e9a107d0ceca1697b3c71168fe9b565b7a404ee2ea80`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 97.45909608481452 seconds.
+- Overall skill 0.391481; relative MASE 0.737187; relative WQL 0.502310.
+- term skill: long=0.3970, medium=0.3699, short=0.3974.
+- domain skill: Econ/Fin=0.1925, Energy=0.3204, Healthcare=0.2353, Nature=0.5044, Sales=0.4028, Transport=0.5122, Web/CloudOps=0.3963.
+- frequency skill: A=0.3598, D=0.4441, H=0.4083, M=0.1462, Q=0.2620, S=0.2841, T=0.4336, W=0.2498.
+- variates skill: multivariate=0.3742, univariate=0.4047.
+- Outcome: All four own E130 experts frozen; only6340gateparameters trained on allowed corpus. Fixed bounded per-lead quantile weights. Compare immutableE130 then current publication before any full package selection.
+
+2026-10-23T21:26:11.191817+00:00: E138 bounded ensemble gate selection: {"step": 6000, "rows": [{"cutoff": "valid_screen", "skill": 0.3891538952530367, "control_skill": 0.3878507268780589, "delta": 0.0013031683749777923, "groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7364459507042054, "rel_wql_gmean": 0.4755697971765997}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.705725254641002, "rel_wql_gmean": 0.4896858518745376}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7397234471502719, "rel_wql_gmean": 0.5321278452570799}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8615237874881659, "rel_wql_gmean": 0.8356013887604252}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8913262218388278, "rel_wql_gmean": 0.6351339086754909}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5440759106430966, "rel_wql_gmean": 0.4941361567456849}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.794973161669226, "rel_wql_gmean": 0.3873664651416758}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8415413625242938, "rel_wql_gmean": 0.5129205524308298}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6348171905006708, "rel_wql_gmean": 0.43339778932077555}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5555030792807922, "rel_wql_gmean": 0.4331316646849878}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6081119055047446, "rel_wql_gmean": 0.6277298132615224}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7500973483663572, "rel_wql_gmean": 0.5186179442639502}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7249079419744635, "rel_wql_gmean": 0.4897508699794607}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9643106633501736, "rel_wql_gmean": 0.8157244325106289}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7681238260199779, "rel_wql_gmean": 0.8266990024699747}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6565228342396154, "rel_wql_gmean": 0.5040952618525022}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6808048286769331, "rel_wql_gmean": 0.4471886333896679}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8784946548259455, "rel_wql_gmean": 0.6542720159132867}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7052490192101172, "rel_wql_gmean": 0.5073126711779109}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7531434617083017, "rel_wql_gmean": 0.5122885690189363}}}, "checkpoint_sha256": "2115a57946abe740b5b8e9a107d0ceca1697b3c71168fe9b565b7a404ee2ea80", "eval_seconds": 101.05418701795861}, {"cutoff": "valid_backcast", "skill": 0.34451133233877995, "control_skill": 0.34441110518308204, "delta": 0.0001002271556979073, "groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7691953335821761, "rel_wql_gmean": 0.4627303531901571}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7674526053408633, "rel_wql_gmean": 0.5287604498603796}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8038778359075024, "rel_wql_gmean": 0.5869980231872798}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8511829323181022, "rel_wql_gmean": 0.7846465334380613}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8833613884303286, "rel_wql_gmean": 0.6626592834516265}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6514007845060794, "rel_wql_gmean": 0.5590515139682694}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9036967508528725, "rel_wql_gmean": 0.4209643778642046}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7204696211529373, "rel_wql_gmean": 0.4872923571092192}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.7323964069146874, "rel_wql_gmean": 0.5276686564187033}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6539206259797139, "rel_wql_gmean": 0.4517774173910834}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6353979663319429, "rel_wql_gmean": 0.7083966062237425}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8287186929492704, "rel_wql_gmean": 0.49075342638505814}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7660022323916221, "rel_wql_gmean": 0.5057883369260989}, "M": {"n_configs": 5, "rel_mase_gmean": 0.840421736009188, "rel_wql_gmean": 0.7683604511312051}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7562360316720279, "rel_wql_gmean": 0.7737834341734877}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8502063314002679, "rel_wql_gmean": 0.5052710666666878}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7602126510941328, "rel_wql_gmean": 0.5722159579505812}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8614098951222435, "rel_wql_gmean": 0.584569871704564}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7780281184441407, "rel_wql_gmean": 0.5233484481580956}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7965501176333248, "rel_wql_gmean": 0.5629970553979425}}}, "checkpoint_sha256": "2115a57946abe740b5b8e9a107d0ceca1697b3c71168fe9b565b7a404ee2ea80", "eval_seconds": 45.48357278504409}, {"cutoff": "valid_backcast2", "skill": 0.3692552712826589, "control_skill": 0.3685168155758046, "delta": 0.0007384557068542685, "groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7902938318860995, "rel_wql_gmean": 0.4977963298037035}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7463131012782198, "rel_wql_gmean": 0.48763633945412027}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7540488204132838, "rel_wql_gmean": 0.5483608127979848}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8594751460854615, "rel_wql_gmean": 0.8756215264384803}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8954190802700714, "rel_wql_gmean": 0.641128974585506}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6964824489811069, "rel_wql_gmean": 0.5984297586733128}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7410304083167673, "rel_wql_gmean": 0.35740800615553536}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5554524169073445, "rel_wql_gmean": 0.3516202979967578}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6616274111297696, "rel_wql_gmean": 0.49475736037429535}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6715129139772495, "rel_wql_gmean": 0.45959833905594105}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5780595196142971, "rel_wql_gmean": 0.7545958139506606}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7219608611737873, "rel_wql_gmean": 0.4428203116356558}, "H": {"n_configs": 30, "rel_mase_gmean": 0.748318765635403, "rel_wql_gmean": 0.4817412143295903}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8035534035507537, "rel_wql_gmean": 0.8457821464270225}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8251525142814832, "rel_wql_gmean": 0.8649272471317476}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8990795032628562, "rel_wql_gmean": 0.5633706894917235}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7465724484386546, "rel_wql_gmean": 0.5320522780653318}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8241740973365012, "rel_wql_gmean": 0.6209745017214765}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7752240721939954, "rel_wql_gmean": 0.5102244240010131}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7470092930299288, "rel_wql_gmean": 0.5350762099823907}}}, "checkpoint_sha256": "2115a57946abe740b5b8e9a107d0ceca1697b3c71168fe9b565b7a404ee2ea80", "eval_seconds": 82.1311989359092}, {"cutoff": "valid_backcast3", "skill": 0.3914805032078633, "control_skill": 0.3912834616582097, "delta": 0.00019704154965360932, "groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7646717541571438, "rel_wql_gmean": 0.47555963242062804}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7594180694866295, "rel_wql_gmean": 0.5227464782406109}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7197882441060385, "rel_wql_gmean": 0.5045674541132409}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8175686322071732, "rel_wql_gmean": 0.7975380490966467}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8345733264249693, "rel_wql_gmean": 0.5534624809981347}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.8550465666013121, "rel_wql_gmean": 0.6839536770499075}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7259385500618593, "rel_wql_gmean": 0.3383276541439344}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.750738046214297, "rel_wql_gmean": 0.4750531542135627}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5378722099557868, "rel_wql_gmean": 0.44230129042133576}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.707336756570856, "rel_wql_gmean": 0.5151988413209527}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5292421996814161, "rel_wql_gmean": 0.7743843306970275}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7340676984424483, "rel_wql_gmean": 0.4210138345405265}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7248830714988798, "rel_wql_gmean": 0.48302222853420806}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8635071248662185, "rel_wql_gmean": 0.8442363626325257}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.71477390446079, "rel_wql_gmean": 0.7619380112945435}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8429649893060646, "rel_wql_gmean": 0.6079035249422998}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7003204534968781, "rel_wql_gmean": 0.4580988621337273}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8316528981631645, "rel_wql_gmean": 0.6767266332674158}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7628262991295707, "rel_wql_gmean": 0.5133217775879014}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7175797463477325, "rel_wql_gmean": 0.4937939191412373}}}, "checkpoint_sha256": "2115a57946abe740b5b8e9a107d0ceca1697b3c71168fe9b565b7a404ee2ea80", "eval_seconds": 97.45909608481452}], "positive_cutoffs": 4, "mean_delta": 0.0005847231967958944, "selection_gate_passed": true}. No automatic full replay or promotion; current-publication comparison also required.
+
+2026-10-23T21:27:49.561099+00:00: E138primary6000 all4positive versus frozenE130,mean+0.0005847231967958944; versus currentE148 deltas[0.0009259442752742597, -0.0011547833199946345, -0.00040745288744414854, -0.0006491750767770865],mean-0.00032136675223540245(1of4positive). No full replay/publication. Original12000schedule continues; no bound or expert change. E149CPU8192rows405selected387changed, real/unselected/masks/periods/originalRNGs exact, newRNG recoveryexact; CPU12.01417627511546s. GPU101.85472289007157s including92.41605444904417s lockwait;maxreserved6266.0MiB, finitegradients,0updates. Queued1280afterE146queue[962],persistent96256. Original E80model/checkpoint/seed/schedule retained; generator and provenance declared.
+
+## E131 fresh146M long-seasonal8000 learning diagnostic
+- Checkpoint SHA-256: `7d839d749a8fbb5801df4b2e425de5b256cc1a2c7afc13d73d2e4a67f0555dfb`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 35.940014362335205 seconds.
+- Overall skill 0.352440; relative MASE 0.763553; relative WQL 0.549188.
+- term skill: long=0.3746, medium=0.3928, short=0.3275.
+- domain skill: Econ/Fin=0.1119, Energy=0.2084, Healthcare=0.3483, Nature=0.4136, Sales=0.3193, Transport=0.4552, Web/CloudOps=0.4823.
+- frequency skill: A=0.3639, D=0.3550, H=0.3741, M=-0.0611, Q=0.1716, S=0.4194, T=0.4022, W=0.2220.
+- variates skill: multivariate=0.3726, univariate=0.3359.
+- Outcome: Seed307/B48/LR2e-4 original96000schedule. Main0.352439815529 versus matched E79at8000 0.343979356935, delta+0.008460458594. Early main panel only; primary48000/final96000 and four-cutoff comparisons remain unchanged. No model selection or extension.
+
+2026-10-23T21:29:56.949237+00:00: Compute accounting after storage recovery: recovery/capacity_curves.py now records all declared fresh/gate/continuation arms and separate original/resumed training phases in capacity_learning_curves.json/CSV. Accumulated logged loop wall includes repeated updates after recovery and excludes operator downtime and startup. Interrupted/unlogged phase tails are not estimated, so values can be lower bounds; concurrent shared-H100 process times must not be summed as isolated GPU usage or the external budget. No learning, inference or model-selection changes.
+
+## E122 original-AR fresh146M24000 learning diagnostic
+- Checkpoint SHA-256: `da1d36f9655ef643187be503031b1db6f79713635f521710318fd93d708bb109`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 35.425286054611206 seconds.
+- Overall skill 0.350517; relative MASE 0.768574; relative WQL 0.548845.
+- term skill: long=0.3777, medium=0.3772, short=0.3292.
+- domain skill: Econ/Fin=0.1296, Energy=0.2129, Healthcare=0.3727, Nature=0.4089, Sales=0.3343, Transport=0.4401, Web/CloudOps=0.4733.
+- frequency skill: A=0.3877, D=0.3287, H=0.3694, M=0.0695, Q=0.2006, S=0.4133, T=0.3990, W=0.2254.
+- variates skill: multivariate=0.3647, univariate=0.3390.
+- Outcome: Seed307/B48/LR2e-4 original96000schedule. Main0.350516880404 versus E79slowAR matched24000 0.354493848082, delta-0.003976967678, reversing positive16000gap. Main learning diagnostic only; primary48000/final96000 four-cutoff comparisons unchanged. No selection or schedule change.
+
+2026-10-23T21:34:43.157549+00:00: Training-only pack exposure audit(seed943,24576rows perpack,54.10828941594809s CPU,0updates, no validation read): [{"pack": "packed_sample", "sampled_rows": 24576, "mean_finite_context": 1606.4626871744792, "mean_finite_target": 444.7574869791667, "mean_finite_total": 2051.220174153646, "batch_mean_monte_carlo_standard_error": 40.775650142597264, "fraction_target_le32": 0.13675944010416666, "fraction_target_ge480": 0.4447835286458333, "fraction_target_ge720": 0.2779947916666667, "fraction_target_ge900": 0.22127278645833334, "finite_target_length_quantiles": [0.0, 32.0, 96.0, 384.0, 800.0, 1024.0, 1024.0], "estimated_presentations_at96000_B48": 9452022562.5}, {"pack": "packed_stratified_full", "sampled_rows": 24576, "mean_finite_context": 1603.9788818359375, "mean_finite_target": 447.0040283203125, "mean_finite_total": 2050.98291015625, "batch_mean_monte_carlo_standard_error": 40.60792077573359, "fraction_target_le32": 0.13724772135416666, "fraction_target_ge480": 0.4491780598958333, "fraction_target_ge720": 0.2822265625, "fraction_target_ge900": 0.22310384114583334, "finite_target_length_quantiles": [0.0, 32.0, 96.0, 389.5, 804.25, 1024.0, 1024.0], "estimated_presentations_at96000_B48": 9450929250.0}]. Both packs present about9.45Bfinite observations by96000B48; fullcoverage changes available series, not sampled observation count under this recipe. These are overlapping/resampled presentations and include synthetics; no uniqueness claim. Meta-device architecture count(no weights/noGPU,.036116143s):d1536/24heads/6encoder/4decoder/patch16=329029920parameters, FP32parameters+gradients+twoAdammoments+EMA6.128659845GiB before activations/workspaces. No larger run, batch change or extension selected; await E110final review.
+
+E141 matched146M capacity_patch8_candidate seed809 storage recovery training started at 2026-10-23T21:35:28Z, after the declared dependency processes exited; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+## E141 patch16 continuation final12000 main
+- Checkpoint SHA-256: `78e34c37073a8114d02d99f3d191343386337430495420337e8bf81f518fba12`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 30.86342692375183 seconds.
+- Overall skill 0.369708; relative MASE 0.751895; relative WQL 0.528356.
+- term skill: long=0.3833, medium=0.3945, short=0.3546.
+- domain skill: Econ/Fin=0.1347, Energy=0.2247, Healthcare=0.4946, Nature=0.4128, Sales=0.3372, Transport=0.4584, Web/CloudOps=0.4913.
+- frequency skill: A=0.4002, D=0.3580, H=0.3865, M=0.1059, Q=0.2125, S=0.4155, T=0.4182, W=0.2402.
+- variates skill: multivariate=0.3781, univariate=0.3629.
+- Outcome: Original scheduled control complete after storage recovery; seed809/B32/LR2e-5/12000 from own E79wide96000. Main0.369708108064 versus starting parent0.371180826606, delta-0.001472718543. Candidate patch8 comparison pending. No inference selection. Recovery4 process788.670280825s plus interrupted original phase recorded1983.7s loop lower bound; summed shared-process times exclude operator downtime and are not isolatedGPUtime.
+
+2026-10-23T21:42:22.387354+00:00: Actual cross-container repeated-update log audit: {"utc": "2026-10-23T21:39:35.512633+00:00", "overlapping_logged_intervals": 18, "all_logged_values_exact": false, "maximum_loss_difference": 0.0011714527010917575, "maximum_gradient_norm_difference": 0.10824991762638092, "seconds": 0.017175609013065696, "new_training_or_inference": false, "note": "Observed repeated50-update intervals following restoration of250-step checkpoints. Same logged values are evidence of consistent recovery, not an exhaustive equality check of every tensor or proof of future determinism. E138has no repeated logged interval because its last log and saved checkpoint were both6250."}. Replayed shape/LR records match, but compiled training losses/gradient norms are not bitwise identical. Do not attribute the cause yet. Engineering diagnostic recovery/check_compiled_restart.py launched: two fresh uninterrupted6-update repeats and one3+resume3 trajectory from the same own mature E79wide96 checkpoint, seed953/B48, no validation data. Compare optimizer/model/EMA/allRNG/prefetch states; fixtureweights are not selection candidates. Active studies keep their original recipes and endpoints; published inference remains bitwise verified.
+
+2026-10-23T21:46:34.771264+00:00: Compiled recovery diagnostic complete: seed953/own E79wide96 parent,18fixture optimizerupdates total, 186.96378611098044s wall. {"uninterrupted_repeat": {"model": {"bitwise_equal": false, "different_tensors": 198, "maximum_abs_difference": 1.6439706087112427e-05}, "ema_state": {"bitwise_equal": false, "different_tensors": 198, "maximum_abs_difference": 6.292015314102173e-06}, "optimizer": {"bitwise_equal": false, "different_tensors": 396, "maximum_abs_difference": 6.755226058885455e-05}, "batch_rng_state": {"bitwise_equal": true, "different_tensors": 0, "maximum_abs_difference": 0.0}, "sampler_rng_states": {"bitwise_equal": true, "different_tensors": 0, "maximum_abs_difference": 0.0}, "torch_rng_state": {"bitwise_equal": true, "different_tensors": 0, "maximum_abs_difference": 0.0}, "cuda_rng_state": {"bitwise_equal": true, "different_tensors": 0, "maximum_abs_difference": 0.0}, "prefetched_batch": {"bitwise_equal": true, "different_tensors": 0, "maximum_abs_difference": 0.0}}, "uninterrupted_vs_restart": {"model": {"bitwise_equal": false, "different_tensors": 198, "maximum_abs_difference": 1.9418075680732727e-05}, "ema_state": {"bitwise_equal": false, "different_tensors": 198, "maximum_abs_difference": 8.985400199890137e-06}, "optimizer": {"bitwise_equal": false, "different_tensors": 396, "maximum_abs_difference": 5.0059868954122066e-05}, "batch_rng_state": {"bitwise_equal": true, "different_tensors": 0, "maximum_abs_difference": 0.0}, "sampler_rng_states": {"bitwise_equal": true, "different_tensors": 0, "maximum_abs_difference": 0.0}, "torch_rng_state": {"bitwise_equal": true, "different_tensors": 0, "maximum_abs_difference": 0.0}, "cuda_rng_state": {"bitwise_equal": true, "different_tensors": 0, "maximum_abs_difference": 0.0}, "prefetched_batch": {"bitwise_equal": true, "different_tensors": 0, "maximum_abs_difference": 0.0}}}. Both uninterrupted repeats and the3+resume3 trajectory differ numerically in learned tensors, while all random states, prefetched arrays and ancestry are exact. Thus ordinary same-container compiled training already varies; this provides no evidence of a unique sampler-state loss at restart and is not a claim of bitwise training replay. It does not identify a particular GPU kernel or prove all cross-container variation has one cause. Production recipes and checkpoints unchanged; fixtureweights excluded from selection. Published inference remains bitwise checked.
+
+## E150 engineering: weighted gradient accumulation for the one-H100 allocation — declared 2026-10-23T21:49:15.781616+00:00
+Prepare an optional training-only microbatch path while preserving effective batch size and the original loss. The objective is a weighted mean over rows, so each microbatch loss must be scaled by its context-derived weight sum divided by the whole-batch weight sum; averaging microbatch means would change the objective. Clip gradients, take one AdamW update and one EMA update only after all microbatches. Preserve the original sampler, prefetched full batch, all RNGs, requested-horizon masks and learning-rate step count. The default non-microbatch branch must retain its original arithmetic.
+This is readiness engineering, not a selected model, larger-capacity run, batch-size experiment or schedule extension. CPU checks use unequal row weights, missing targets and nonzero heads to compare full-batch loss/gradients with accumulation. GPU checks use own mature scratch weights, measure BF16 compiled gradient differences against ordinary repeat variability, and verify maximum-shape memory with effectiveB48/micro16. Original active/queued recipes receive no flag. Decide any later training study only after current full-corpus/fresh-model evidence. No validation data in engineering checks; no fixture weights selected.
+
+## E131 fresh146M long-seasonal16000 learning diagnostic
+- Checkpoint SHA-256: `212a7ca2db307aba7701c508178767ebab976f47993fe7771a627d0b7f9bf06c`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 38.46913480758667 seconds.
+- Overall skill 0.359479; relative MASE 0.757948; relative WQL 0.541288.
+- term skill: long=0.3823, medium=0.4004, short=0.3340.
+- domain skill: Econ/Fin=0.1248, Energy=0.2178, Healthcare=0.3326, Nature=0.4163, Sales=0.3310, Transport=0.4532, Web/CloudOps=0.4981.
+- frequency skill: A=0.3646, D=0.3506, H=0.3770, M=-0.0134, Q=0.1766, S=0.4267, T=0.4159, W=0.2229.
+- variates skill: multivariate=0.3798, univariate=0.3428.
+- Outcome: Seed307/B48/LR2e-4 original96000schedule. Main0.359478668640 versus matched E79at16000 0.349499285513. Early main-only learning diagnostic; declared48000/96000four-cutoff decisions remain unchanged. No selection, larger model or schedule extension.
+
+2026-10-23T21:56:55.148492+00:00: E150 weighted accumulation CPU/GPU audits passed. CPU seed967: full losssee receipt; correct per-microbatch weight sums are required, not equal averaging. GPU seed971/effectiveB48/micro16/own E79wide96: full loss0.2681879699230194, accumulated0.26818394660949707; global/encoder/decoder gradient cosines{'all': 0.9999871036270944, 'encoder': 0.9999907584392977, 'decoder': 0.9999854996294113}; max C4096/H1024 reserved5538.0MiB including Adam/EMA placeholders, all finite and parameters unchanged. GPU145.3277768800035s,0optimizer updates,no validation. CPU/GPU receipts accumulation_cpu_checks.json and accumulation_gpu_checks.json. E150 recovery fixture seed977 now running,18optimizerupdates across repeated and3+resume3 trajectories; fixture weights never selection candidates. No production command passes --micro-batch.
+
+E141 patch8 continuation1000 diagnostic: {"utc": "2026-10-23T21:56:55.148492+00:00", "step": 1000, "checkpoint_sha256": "11c0aa64a3d92906eb4b750d203c82e27cc9a35692bd676c1f66f7ae38066bc8", "eval_seconds": 53.31513428688049, "skill": 0.36408268984898107, "control_skill": 0.37224099748335526, "delta": -0.008158307634374196, "group_deltas": {"term": {"long": 0.0015769188382022126, "medium": -0.005049938478779548, "short": -0.013338610695910691}, "domain": {"Econ/Fin": 0.003359155094616928, "Energy": -0.005545860630910893, "Healthcare": -0.0828415685051983, "Nature": -0.012468037006636679, "Sales": 0.020352525143488398, "Transport": -0.0025035399701855976, "Web/CloudOps": -0.0001342269294116738}, "frequency": {"A": -0.015021968530428143, "D": 0.004696449223085097, "H": 0.0005775712670897937, "M": -0.11969237722516013, "Q": -0.009789822443574292, "S": 0.0012602974282879265, "T": -0.004439279939718155, "W": -0.047662379459129434}, "variates": {"multivariate": -0.005478302821669168, "univariate": -0.01035034207121499}}, "decision": "Early main-cutoff learning diagnostic only; primary6000/final12000 all four cutoffs remain fixed."}
+
+## E141 patch8 continuation1000 learning diagnostic
+- Checkpoint SHA-256: `11c0aa64a3d92906eb4b750d203c82e27cc9a35692bd676c1f66f7ae38066bc8`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 53.31513428688049 seconds.
+- Overall skill 0.364083; relative MASE 0.759120; relative WQL 0.532710.
+- term skill: long=0.3860, medium=0.3904, short=0.3450.
+- domain skill: Econ/Fin=0.1322, Energy=0.2220, Healthcare=0.4251, Nature=0.4062, Sales=0.3540, Transport=0.4564, Web/CloudOps=0.4921.
+- frequency skill: A=0.3841, D=0.3617, H=0.3891, M=0.0184, Q=0.2024, S=0.4167, T=0.4157, W=0.1956.
+- variates skill: multivariate=0.3750, univariate=0.3553.
+- Outcome: Seed809/B32/LR2e-5/12k. Below matched patch16 control at1000; main cutoff only. Primary6000 and final12000 four-cutoff comparisons remain fixed. No publication or schedule change.
+
+2026-10-23T22:02:06.558370+00:00: E150 recovery fixture seed977 complete, 254.292871383s wall,18optimizerupdates/54backwardpasses across6continuous+6repeat+3interrupted+3resumed. EffectiveB48/micro16 throughout; every optimizer state step6 and prefetched batch48 verified. All sampler/batch/Torch/CUDA RNGs, prefetched arrays and parent ancestry bitwise exact after restart. Learned model/EMA/optimizer tensors show small differences in uninterrupted repeats as well as restarted trajectory: {"uninterrupted_repeat": {"model": {"exact": false, "max_abs_difference": 1.1039432138204575e-05}, "ema_state": {"exact": false, "max_abs_difference": 3.388151526451111e-06}, "optimizer": {"exact": false, "max_abs_difference": 8.117008292174432e-06}, "batch_rng_state": {"exact": true, "max_abs_difference": 0.0}, "sampler_rng_state": {"exact": true, "max_abs_difference": 0.0}, "sampler_rng_states": {"exact": true, "max_abs_difference": 0.0}, "torch_rng_state": {"exact": true, "max_abs_difference": 0.0}, "cuda_rng_state": {"exact": true, "max_abs_difference": 0.0}, "prefetched_batch": {"exact": true, "max_abs_difference": 0.0}, "parent_checkpoints": {"exact": true, "max_abs_difference": 0.0}}, "uninterrupted_vs_restart": {"model": {"exact": false, "max_abs_difference": 1.1313706636428833e-05}, "ema_state": {"exact": false, "max_abs_difference": 3.4477561712265015e-06}, "optimizer": {"exact": false, "max_abs_difference": 1.6264966689050198e-05}, "batch_rng_state": {"exact": true, "max_abs_difference": 0.0}, "sampler_rng_state": {"exact": true, "max_abs_difference": 0.0}, "sampler_rng_states": {"exact": true, "max_abs_difference": 0.0}, "torch_rng_state": {"exact": true, "max_abs_difference": 0.0}, "cuda_rng_state": {"exact": true, "max_abs_difference": 0.0}, "prefetched_batch": {"exact": true, "max_abs_difference": 0.0}, "parent_checkpoints": {"exact": true, "max_abs_difference": 0.0}}}. CPU/GPU/recovery audits now complete; this establishes engineering readiness, not a validation gain or exact training replay. All fixture checkpoints excluded from selection. No production recipe uses accumulation; no larger model or schedule extension selected. Receipts accumulation_{cpu,gpu,recovery}_checks.json.
+
+## E141 patch8 continuation3000 learning diagnostic
+- Checkpoint SHA-256: `f0dadac4af25385ad5ae8b0d2c8e1a6bf3636fb02182caa29d6c2c35e77a4574`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 52.50292778015137 seconds.
+- Overall skill 0.366795; relative MASE 0.755991; relative WQL 0.530362.
+- term skill: long=0.3877, medium=0.3932, short=0.3481.
+- domain skill: Econ/Fin=0.1320, Energy=0.2265, Healthcare=0.4419, Nature=0.4089, Sales=0.3490, Transport=0.4593, Web/CloudOps=0.4912.
+- frequency skill: A=0.3844, D=0.3626, H=0.3911, M=0.0192, Q=0.1984, S=0.4191, T=0.4180, W=0.2105.
+- variates skill: multivariate=0.3776, univariate=0.3580.
+- Outcome: Seed809/B32/LR2e-5/12k; patch8 main0.366794892800 versus matched patch16 main0.371632199914, delta−0.004837307114. The deficit narrowed from1000; primary6000/final12000 four-cutoff criteria remain unchanged. No selection or schedule change.
+
+2026-10-23T22:07:08.490075+00:00: E138primary6k gate-weight diagnostic from cached standalone expert predictions and validation contexts, CPU35.238193693s,0optimizerupdates,no labels/no new expert inference. GateSHA2115a57946abe740b5b8e9a107d0ceca1697b3c71168fe9b565b7a404ee2ea80. [{"cutoff": "valid_screen", "equal_configuration_mean_weights": [0.4477327587349087, 0.1321961277137222, 0.29503999229904787, 0.12503112195225397], "term_fourth_near_floor": {"long": 0.9345565409689959, "medium": 0.959266802507595, "short": 0.9201280392464679}}, {"cutoff": "valid_backcast", "equal_configuration_mean_weights": [0.4488138450395888, 0.13236758770694956, 0.2937819854013298, 0.1250365820506267], "term_fourth_near_floor": {"long": 0.9158424197544438, "medium": 0.9315063689308696, "short": 0.9287019582670833}}, {"cutoff": "valid_backcast2", "equal_configuration_mean_weights": [0.451471667383515, 0.13235528408861266, 0.29114296074737367, 0.12503008852841951], "term_fourth_near_floor": {"long": 0.9022544394356053, "medium": 0.9171937735774898, "short": 0.9188199474693681}}, {"cutoff": "valid_backcast3", "equal_configuration_mean_weights": [0.45337251745549445, 0.13184961902059156, 0.2897504963536573, 0.12502736765581324], "term_fourth_near_floor": {"long": 0.9024491911412599, "medium": 0.9441610879103347, "short": 0.9224622437920529}}]. The fourth expert is near the fixed.125floor for about90–96percent of leads across terms and cutoffs, while mean weights roughly[.45,.132,.293,.125]. CPU gate arithmetic/cached neural batches are descriptive, not a claim of bitwise production equivalence. No gate weights, forecasts or bounds changed. Possible followups are refreshed frozen experts or broader allowed training coverage, but neither is selected pending final E138/E110 review. Receipt ensemble_gate6k_weight_structure.json; code ensemble_gate/describe_weights.py.
+
+E143 low-frequency synthetic origin seed197 storage recovery training started at 2026-10-23T22:07:24Z, after the declared dependency processes exited; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+2026-10-23T22:10:05.166328+00:00: E138final12000 descriptive weight audit complete CPU35.054704830s, no labels,0updates,no new expert inference, gateSHA5ae58d104309db60a53cca11cbbceb7ff8f587b6e32b60888d22a37e9b021dfe. [{"cutoff": "valid_screen", "mean_weights": [0.41123423209068644, 0.1344671819471669, 0.32929237834186065, 0.1250062086092428], "term_fourth_near_floor": {"long": 0.9963695620558646, "medium": 0.9991641274565382, "short": 0.9806240077062377}}, {"cutoff": "valid_backcast", "mean_weights": [0.4087619457331814, 0.1348688711925048, 0.3313622136544217, 0.12500697004668263], "term_fourth_near_floor": {"long": 0.9944008688493359, "medium": 0.9898249268034701, "short": 0.9817737188003557}}, {"cutoff": "valid_backcast2", "mean_weights": [0.41067205599679535, 0.13512396609527852, 0.32919672197071637, 0.12500725692980005], "term_fourth_near_floor": {"long": 0.9969832542320484, "medium": 0.993087838279978, "short": 0.9811441896603474}}, {"cutoff": "valid_backcast3", "mean_weights": [0.4164441191577059, 0.13421093038051773, 0.32433864349603453, 0.12500630758574677], "term_fourth_near_floor": {"long": 0.9891584126929469, "medium": 0.9956309269177843, "short": 0.9842759744169436}}]. Finalmain.388765994298 (103.897483219s inference) is below primary6k.389153895253; earliercutoffs still pending. No bounds/expert change or selection.
+
+E134 fresh146M visible-window curriculum seed307 storage recovery training started at 2026-10-23T22:12:48Z, after the declared dependency processes exited; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+## E143 low-frequency generation-age1000 learning diagnostic
+- Checkpoint SHA-256: `585da194ce1355c1597536c981910daf50c3dabd3b1f854f1a7566defa1d8ecd`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 22.67449688911438 seconds.
+- Overall skill 0.375485; relative MASE 0.746204; relative WQL 0.522671.
+- term skill: long=0.3989, medium=0.4062, short=0.3540.
+- domain skill: Econ/Fin=0.1351, Energy=0.2360, Healthcare=0.4436, Nature=0.4287, Sales=0.3340, Transport=0.4667, Web/CloudOps=0.4983.
+- frequency skill: A=0.3660, D=0.3537, H=0.3945, M=0.0900, Q=0.1975, S=0.4232, T=0.4350, W=0.2201.
+- variates skill: multivariate=0.3924, univariate=0.3617.
+- Outcome: Seed197/B64/LR2e-5/12k, same E113 settings except selected low-frequency synthetic paths start at generation age zero. Main cutoff only; primary6000/final12000 all-four comparisons versus E113 and secondary E80 remain fixed. No selection from this checkpoint.
+
+## E138 learned gate12000 valid_screen
+- Checkpoint SHA-256: `5ae58d104309db60a53cca11cbbceb7ff8f587b6e32b60888d22a37e9b021dfe`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 103.89748321892694 seconds.
+- Overall skill 0.388766; relative MASE 0.732105; relative WQL 0.510319.
+- term skill: long=0.4080, medium=0.4117, short=0.3722.
+- domain skill: Econ/Fin=0.1511, Energy=0.2474, Healthcare=0.4823, Nature=0.4449, Sales=0.3429, Transport=0.4757, Web/CloudOps=0.5081.
+- frequency skill: A=0.3804, D=0.3762, H=0.4035, M=0.1114, Q=0.2023, S=0.4246, T=0.4477, W=0.2435.
+- variates skill: multivariate=0.4010, univariate=0.3788.
+- Outcome: All four own E130 experts frozen; only6340gateparameters trained on allowed corpus. Fixed bounded per-lead quantile weights. Compare immutableE130 then current publication before any full package selection.
+
+## E138 learned gate12000 valid_backcast
+- Checkpoint SHA-256: `5ae58d104309db60a53cca11cbbceb7ff8f587b6e32b60888d22a37e9b021dfe`.
+- Evaluation: 97 configurations, 9330 instances, 0 failed configurations; 103.42768940003589 seconds.
+- Overall skill 0.344360; relative MASE 0.788587; relative WQL 0.545106.
+- term skill: long=0.4038, medium=0.3626, short=0.3128.
+- domain skill: Econ/Fin=0.1825, Energy=0.2352, Healthcare=0.3968, Nature=0.3832, Sales=0.4077, Transport=0.3786, Web/CloudOps=0.4553.
+- frequency skill: A=0.3279, D=0.3621, H=0.3779, M=0.1966, Q=0.2357, S=0.3452, T=0.3397, W=0.2899.
+- variates skill: multivariate=0.3615, univariate=0.3304.
+- Outcome: All four own E130 experts frozen; only6340gateparameters trained on allowed corpus. Fixed bounded per-lead quantile weights. Compare immutableE130 then current publication before any full package selection.
+
+## E138 learned gate12000 valid_backcast2
+- Checkpoint SHA-256: `5ae58d104309db60a53cca11cbbceb7ff8f587b6e32b60888d22a37e9b021dfe`.
+- Evaluation: 94 configurations, 8934 instances, 0 failed configurations; 103.96735997707583 seconds.
+- Overall skill 0.369094; relative MASE 0.759652; relative WQL 0.523980.
+- term skill: long=0.3726, medium=0.3965, short=0.3568.
+- domain skill: Econ/Fin=0.1325, Energy=0.2424, Healthcare=0.3573, Nature=0.4847, Sales=0.5582, Transport=0.4280, Web/CloudOps=0.4435.
+- frequency skill: A=0.3414, D=0.4340, H=0.3995, M=0.1787, Q=0.1565, S=0.2880, T=0.3688, W=0.2870.
+- variates skill: multivariate=0.3701, univariate=0.3683.
+- Outcome: All four own E130 experts frozen; only6340gateparameters trained on allowed corpus. Fixed bounded per-lead quantile weights. Compare immutableE130 then current publication before any full package selection.
+
+## E138 learned gate12000 valid_backcast3
+- Checkpoint SHA-256: `5ae58d104309db60a53cca11cbbceb7ff8f587b6e32b60888d22a37e9b021dfe`.
+- Evaluation: 93 configurations, 8812 instances, 0 failed configurations; 90.11145384493284 seconds.
+- Overall skill 0.391130; relative MASE 0.737562; relative WQL 0.502632.
+- term skill: long=0.3973, medium=0.3700, short=0.3966.
+- domain skill: Econ/Fin=0.1930, Energy=0.3192, Healthcare=0.2364, Nature=0.5047, Sales=0.4031, Transport=0.5126, Web/CloudOps=0.3953.
+- frequency skill: A=0.3598, D=0.4439, H=0.4077, M=0.1476, Q=0.2609, S=0.2854, T=0.4330, W=0.2491.
+- variates skill: multivariate=0.3736, univariate=0.4046.
+- Outcome: All four own E130 experts frozen; only6340gateparameters trained on allowed corpus. Fixed bounded per-lead quantile weights. Compare immutableE130 then current publication before any full package selection.
+
+2026-10-23T22:14:31.271870+00:00: E138 bounded ensemble gate selection: {"step": 12000, "rows": [{"cutoff": "valid_screen", "skill": 0.38876599429759673, "control_skill": 0.3878507268780589, "delta": 0.0009152674195378463, "groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7365977614717659, "rel_wql_gmean": 0.47582331053701155}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7061071446542071, "rel_wql_gmean": 0.49009387634194207}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7405503887929451, "rel_wql_gmean": 0.532295572407821}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8619712033731046, "rel_wql_gmean": 0.8360652826681829}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8917990786476028, "rel_wql_gmean": 0.6350603224400466}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5434876190719932, "rel_wql_gmean": 0.49308959634552224}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7960112811139666, "rel_wql_gmean": 0.38716249511301304}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8424299418268225, "rel_wql_gmean": 0.5125742701697718}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6346223105732777, "rel_wql_gmean": 0.4332312457024731}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5567062412657278, "rel_wql_gmean": 0.4347233367578228}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6101039280438315, "rel_wql_gmean": 0.6292910688977681}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7503809430821773, "rel_wql_gmean": 0.5185216007691995}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7258026917232355, "rel_wql_gmean": 0.4902627677729598}, "M": {"n_configs": 5, "rel_mase_gmean": 0.9685689529422883, "rel_wql_gmean": 0.8151865289550453}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7689190063510615, "rel_wql_gmean": 0.8275202114903546}, "S": {"n_configs": 6, "rel_mase_gmean": 0.6567563259947116, "rel_wql_gmean": 0.5041683422067675}, "T": {"n_configs": 30, "rel_mase_gmean": 0.6811976372671397, "rel_wql_gmean": 0.4477430022460635}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8771182893674765, "rel_wql_gmean": 0.6524916887581507}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7061203660151012, "rel_wql_gmean": 0.5080890958691652}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7534788981966656, "rel_wql_gmean": 0.512101293838852}}}, "checkpoint_sha256": "5ae58d104309db60a53cca11cbbceb7ff8f587b6e32b60888d22a37e9b021dfe", "eval_seconds": 103.89748321892694}, {"cutoff": "valid_backcast", "skill": 0.3443602645537439, "control_skill": 0.34441110518308204, "delta": -5.08406293381336e-05, "groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7688054523761951, "rel_wql_gmean": 0.4623496840596185}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7680721037346657, "rel_wql_gmean": 0.5289916523454897}, "short": {"n_configs": 55, "rel_mase_gmean": 0.8043273414604368, "rel_wql_gmean": 0.587166294915086}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8511022246024794, "rel_wql_gmean": 0.785128735422025}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8832051411168804, "rel_wql_gmean": 0.6622071578357404}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6505975104644636, "rel_wql_gmean": 0.5593079225242267}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.9049801612151203, "rel_wql_gmean": 0.42044177344443956}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.7199768643833949, "rel_wql_gmean": 0.4871936934826317}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.7320038020790084, "rel_wql_gmean": 0.5274235196996094}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.6551948774214172, "rel_wql_gmean": 0.45290684445581997}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6367776857138926, "rel_wql_gmean": 0.7093042266491602}, "D": {"n_configs": 15, "rel_mase_gmean": 0.8289094279572292, "rel_wql_gmean": 0.4908829008827018}, "H": {"n_configs": 31, "rel_mase_gmean": 0.765737372210673, "rel_wql_gmean": 0.5054793167986653}, "M": {"n_configs": 5, "rel_mase_gmean": 0.8393585108325342, "rel_wql_gmean": 0.7689247373261452}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7554614998266875, "rel_wql_gmean": 0.7732403821233665}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8492769495848378, "rel_wql_gmean": 0.5048283597063994}, "T": {"n_configs": 30, "rel_mase_gmean": 0.7615827211816337, "rel_wql_gmean": 0.5725010493702674}, "W": {"n_configs": 8, "rel_mase_gmean": 0.861629788311694, "rel_wql_gmean": 0.5852072490098833}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7789725827874164, "rel_wql_gmean": 0.5234037463113075}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7963270649080393, "rel_wql_gmean": 0.5630295689459095}}}, "checkpoint_sha256": "5ae58d104309db60a53cca11cbbceb7ff8f587b6e32b60888d22a37e9b021dfe", "eval_seconds": 103.42768940003589}, {"cutoff": "valid_backcast2", "skill": 0.3690940997454023, "control_skill": 0.3685168155758046, "delta": 0.0005772841695976805, "groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7905843117969271, "rel_wql_gmean": 0.49786500904722053}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7465989159970967, "rel_wql_gmean": 0.4878439932977569}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7541243641882015, "rel_wql_gmean": 0.5485237002837723}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8600470933942402, "rel_wql_gmean": 0.8750749999999478}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8953120273068197, "rel_wql_gmean": 0.6410637699465395}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.693123924324533, "rel_wql_gmean": 0.5959717650385814}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7416972081272143, "rel_wql_gmean": 0.3580121136767604}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5556945109020246, "rel_wql_gmean": 0.3512784662014784}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6615770033022241, "rel_wql_gmean": 0.4946057221073404}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6725989706828684, "rel_wql_gmean": 0.46049560252990274}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5789122192013664, "rel_wql_gmean": 0.7493174850062028}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7227737164316042, "rel_wql_gmean": 0.4433096525869345}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7484865982331981, "rel_wql_gmean": 0.4817994144590065}, "M": {"n_configs": 4, "rel_mase_gmean": 0.7972026402584701, "rel_wql_gmean": 0.8460833161801249}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.823758485578255, "rel_wql_gmean": 0.8637555975720498}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8998648063552365, "rel_wql_gmean": 0.5633403623010557}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7475997077631884, "rel_wql_gmean": 0.53299962927887}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8224932588467433, "rel_wql_gmean": 0.6181162859705895}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7763676434664911, "rel_wql_gmean": 0.5111393153827358}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7464136787719601, "rel_wql_gmean": 0.5345861860037492}}}, "checkpoint_sha256": "5ae58d104309db60a53cca11cbbceb7ff8f587b6e32b60888d22a37e9b021dfe", "eval_seconds": 103.96735997707583}, {"cutoff": "valid_backcast3", "skill": 0.3911302942468521, "control_skill": 0.3912834616582097, "delta": -0.00015316741135762424, "groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7644994787796191, "rel_wql_gmean": 0.475168686856866}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7594270684693935, "rel_wql_gmean": 0.5226076392330724}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7204741885659599, "rel_wql_gmean": 0.5053207566108459}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8170472820054627, "rel_wql_gmean": 0.7971698577475663}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8362562312433086, "rel_wql_gmean": 0.5543116383351785}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.852141759919277, "rel_wql_gmean": 0.6842143635111066}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.725380080479915, "rel_wql_gmean": 0.33816795534011324}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7503432955053887, "rel_wql_gmean": 0.47484644524369024}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5376446600324237, "rel_wql_gmean": 0.44179964576520625}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7082340035397756, "rel_wql_gmean": 0.5162217563946113}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5302864581478796, "rel_wql_gmean": 0.7728833563449358}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7346096434487994, "rel_wql_gmean": 0.4209853730035232}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7257501768675807, "rel_wql_gmean": 0.483349548126662}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8625534579404655, "rel_wql_gmean": 0.8424007416326724}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7159931398050025, "rel_wql_gmean": 0.7629550268906894}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8415207088514876, "rel_wql_gmean": 0.6068503311273111}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7007962968429426, "rel_wql_gmean": 0.45871627936942655}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8309243657333543, "rel_wql_gmean": 0.678560347612008}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7635684097891197, "rel_wql_gmean": 0.5139514128751824}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7176835988111958, "rel_wql_gmean": 0.49388283158343527}}}, "checkpoint_sha256": "5ae58d104309db60a53cca11cbbceb7ff8f587b6e32b60888d22a37e9b021dfe", "eval_seconds": 90.11145384493284}], "positive_cutoffs": 2, "mean_delta": 0.00032213588710994223, "selection_gate_passed": false}. No automatic full replay or promotion; current-publication comparison also required.
+
+2026-10-23T22:15:00.325086+00:00: E138 final review {"utc": "2026-10-23T22:15:00.325086+00:00", "positive_frozen": 2, "mean_delta_frozen": 0.00032213588710994223, "positive_current": 1, "mean_delta_current": -0.0005839540619213546, "current_descriptor_sha256": "a1b4ab426901be8e1ee299eeeb3f041bac7cea3d4e59c4608824fc3d05c83e03", "decision": "Reject final12k: only2of4 versus immutableE130 and1of4 versus currentE148. No full replay/publication; original schedule complete. Primary6k also failed current-publication gate.", "cutoffs": [{"cutoff": "valid_screen", "skill": 0.38876599429759673, "frozen_skill": 0.3878507268780589, "current_skill": 0.3882279509777624, "delta_frozen": 0.0009152674195378463, "delta_current": 0.0005380433198343137, "checkpoint_sha256": "5ae58d104309db60a53cca11cbbceb7ff8f587b6e32b60888d22a37e9b021dfe", "eval_seconds": 103.89748321892694}, {"cutoff": "valid_backcast", "skill": 0.3443602645537439, "frozen_skill": 0.34441110518308204, "current_skill": 0.3456661156587746, "delta_frozen": -5.08406293381336e-05, "delta_current": -0.0013058511050306754, "checkpoint_sha256": "5ae58d104309db60a53cca11cbbceb7ff8f587b6e32b60888d22a37e9b021dfe", "eval_seconds": 103.42768940003589}, {"cutoff": "valid_backcast2", "skill": 0.3690940997454023, "frozen_skill": 0.3685168155758046, "current_skill": 0.36966272417010304, "delta_frozen": 0.0005772841695976805, "delta_current": -0.0005686244247007366, "checkpoint_sha256": "5ae58d104309db60a53cca11cbbceb7ff8f587b6e32b60888d22a37e9b021dfe", "eval_seconds": 103.96735997707583}, {"cutoff": "valid_backcast3", "skill": 0.3911302942468521, "frozen_skill": 0.3912834616582097, "current_skill": 0.3921296782846404, "delta_frozen": -0.00015316741135762424, "delta_current": -0.00099938403778832, "checkpoint_sha256": "5ae58d104309db60a53cca11cbbceb7ff8f587b6e32b60888d22a37e9b021dfe", "eval_seconds": 90.11145384493284}]}
+
+## E151 full-corpus final fourth member — declared 2026-10-23T22:15:41.337198+00:00
+E110 finalmain0.368644990823 is nearly tied with matched E94final0.368620401816, and below E79originalLRfinal0.371180826606. Hypothesis: broader available-series coverage can provide useful forecast diversity in a fixed ensemble even without a stronger standalone main score. Test exactly one replacement in the current E148quartet: width12 first/E71patch8-6k/E93split-12k/E110full96k, ordered equal quarters, every expert on every instance. Fix final96k source now; no48k/72k alternatives, coefficients or checkpoint sweep. Wait for all four declared source cutoffs, verify every cached member hash, then compare to frozenE148 on the same panels. At least3positive and meanpositive required before package construction/full replay; recheck then-current publication. No new training or new source data. No full replay/promotion without all97/151170/0fail and deterministic relocation/independent composition checks.
+{"status": "declared; waiting all four final E110 reports for one fixed quartet probe", "declared_utc": "2026-10-23T22:15:41.337198+00:00", "formula": "E148 equal ordered quartet with only fourth member replaced by E110full96000; all weights0.25", "members": ["seasonal_width_transfer12k", "patch8_ema6000", "split_patch_ema12000", "random_capacity_full_ema96000"], "member_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "76d63bae25a069c699a5025715ac3f5f89f18b27f2dc61340f14474e305616ba"], "control_descriptor": "a1b4ab426901be8e1ee299eeeb3f041bac7cea3d4e59c4608824fc3d05c83e03", "source_training_experiment": "E110 same seed307/B48/LR1e-4/96k asE94, full allowlisted corpus, slowAR20percent own synthetics", "gate": "At least3positive cutoffs and positive mean against frozen E148; current-publication recheck before full replay; full97/151170/zero failures and all runtime/composition checks before atomic publication", "no_weight_sweep": true, "no_new_training": true}
+
+## E131 fresh seasonal-feature146M24000 learning diagnostic
+- Checkpoint SHA-256: `ee958fba015de5c80d179675b31770cae282ec3daa05d2fe5e386859109abbf3`.
+- Evaluation: 97 configurations, 9473 instances, 0 failed configurations; 32.69905376434326 seconds.
+- Overall skill 0.363919; relative MASE 0.753754; relative WQL 0.536779.
+- term skill: long=0.3819, medium=0.3965, short=0.3438.
+- domain skill: Econ/Fin=0.1297, Energy=0.2227, Healthcare=0.4094, Nature=0.4158, Sales=0.3308, Transport=0.4552, Web/CloudOps=0.4928.
+- frequency skill: A=0.3802, D=0.3543, H=0.3821, M=0.0439, Q=0.1836, S=0.4234, T=0.4138, W=0.2374.
+- variates skill: multivariate=0.3820, univariate=0.3491.
+- Outcome: Same E79seed307/B48/LR2e-4/96k; only zero-initialized long-seasonal query/direct head added at initialization. Main0.363918505994 versus matched E79main0.354493848082, delta+0.009424657911. Early diagnostic only; primary48000/final96000 all-four comparisons remain fixed.
+
+## E152 bounded-gate expert-version × corpus-coverage factorial — declared 2026-10-23T22:18:32.418864+00:00
+E138primary6k improved4of4 versus frozenE130 but failed currentE148; final12k improved only2of4 versusE130 and1of4 versusE148. Cached weight diagnostics show the fourth expert near its lower bound across every term/cutoff. Two distinct explanations remain: the frozen experts are older than the current publication, and gate training on the repeatedly used small pack may not represent generalization to unseen series. Neither is established as a cause. Complete a2×2 factorial with the existing E138 old-experts/old-pack cell and three new cells: E148-experts/old-pack, E130-experts/full-pack, E148-experts/full-pack. All other recipe details fixed, exact same own gate initial SHA, seed751/B64/LR.001/12k, primary6k/final12k, old source probabilities and original synthetic generator. This tests data and expert-version effects separately and their interaction; no bound/architecture/loss change. Preserve E101teacher and E138expert snapshots untouched. Freeze E148 into a new immutable snapshot after verifying all hashes. Queue the three audited arms sequentially after E149final12k, replacing its allocation. No automatic full replay or publication; require own equal-quarter and current-publication cutoffs then complete full/runtime checks.
+{"status": "declared; implementation and audits pending; not yet queued", "declared_utc": "2026-10-23T22:18:32.418864+00:00", "control": "E138ensemble_gate: immutable E130 experts plus packed_sample", "arms": [{"name": "ensemble_gate_refresh", "experts": "gating_experts_E148", "pack": "packed_sample"}, {"name": "ensemble_gate_full", "experts": "gating_experts_E130", "pack": "packed_stratified_full"}, {"name": "ensemble_gate_refresh_full", "experts": "gating_experts_E148", "pack": "packed_stratified_full"}], "initial": "/workspace/research/ensemble_gate_initial.pt", "initial_sha256": "9d1401cd4d5b5f1c3ddd25a4a74e15e5db7ef708116cdf5c4fa7e90580b58da1", "seed": 751, "batch": 64, "lr": 0.001, "steps": 12000, "primary": 6000, "expert_descriptors": {"E130": "fa97880dd132b1cdff459be4798f8f360d7df59b13e78026a9d74a95d63df0db", "E148": "a1b4ab426901be8e1ee299eeeb3f041bac7cea3d4e59c4608824fc3d05c83e03"}, "fixed": "Same6340parameter FP32 gate, originalAR generator20percent, oldpack source probabilities, horizon300754, TF32False, frozen BF16 experts, bounds.125+.5softmax, original12koptimizer/schedule; freshoptimizer/EMA each arm", "planned_after": "E149 integrated_level final12000, then all three arms sequentially, no extra concurrent trainer", "selection": "Primary6k/final12k four cutoffs; main1k/3k diagnostics. Compare each gate to its frozen equal-quarter experts and current publication; at least3positive and meanpositive for both before full replay. Matched factorial contrasts and logerror interaction descriptive; no coefficient or checkpoint sweep.", "new_sources": false}
+
+2026-10-23T22:20:08.405564+00:00: E151 fixed full-corpus final fourth expert {"pid": 3989, "started_utc": "2026-10-23T22:16:00.313799+00:00", "status": "complete; review before package/full replay", "declaration": {"status": "declared; waiting all four final E110 reports for one fixed quartet probe", "declared_utc": "2026-10-23T22:15:41.337198+00:00", "formula": "E148 equal ordered quartet with only fourth member replaced by E110full96000; all weights0.25", "members": ["seasonal_width_transfer12k", "patch8_ema6000", "split_patch_ema12000", "random_capacity_full_ema96000"], "member_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "76d63bae25a069c699a5025715ac3f5f89f18b27f2dc61340f14474e305616ba"], "control_descriptor": "a1b4ab426901be8e1ee299eeeb3f041bac7cea3d4e59c4608824fc3d05c83e03", "source_training_experiment": "E110 same seed307/B48/LR1e-4/96k asE94, full allowlisted corpus, slowAR20percent own synthetics", "gate": "At least3positive cutoffs and positive mean against frozen E148; current-publication recheck before full replay; full97/151170/zero failures and all runtime/composition checks before atomic publication", "no_weight_sweep": true, "no_new_training": true}, "steps": [{"cutoff": "valid_screen", "returncode": 0, "seconds": 2.4755120710469782}, {"cutoff": "valid_backcast", "returncode": 0, "seconds": 2.538295058067888}, {"cutoff": "valid_backcast2", "returncode": 0, "seconds": 2.0572608441580087}, {"cutoff": "valid_backcast3", "returncode": 0, "seconds": 2.6903403277974576}], "results": [{"cutoff": "valid_screen", "skill": 0.3870534098754751, "control_skill": 0.3882279509777624, "delta": -0.0011745411022873187, "groups": {"term": {"short": 0.3707426558283248, "medium": 0.41017679345819946, "long": 0.40536366197438034}, "domain": {"Econ/Fin": 0.15095407653418313, "Energy": 0.24816421965236368, "Healthcare": 0.4772858643533203, "Nature": 0.44137523715398197, "Sales": 0.34706576481686247, "Transport": 0.4725258977492761, "Web/CloudOps": 0.5056976329716455}, "freq": {"A-DEC": 0.38120323819576596, "Q-DEC": 0.20416624850936294, "M": 0.1042096768568237, "W-SUN": 0.10679839127669, "D": 0.37358321612708534, "H": 0.4029807991534249, "15T": 0.21900578345239463, "W-FRI": 0.01916736433581534, "10T": 0.6274141717410623, "W-TUE": 0.6216938931507114, "W-THU": 0.21208547686270263, "W-WED": 0.3283802430085182, "5T": 0.5188349223487096, "10S": 0.4239412354670272}}, "group_deltas": {"term": {"short": -0.0015591144249640365, "medium": -0.0007538825422591655, "long": -0.000643855286405981}, "domain": {"Econ/Fin": 0.0019407917735767555, "Energy": 0.0007638664136130346, "Healthcare": -0.01778341495726332, "Nature": 9.757092481077478e-06, "Sales": 0.002455593057704042, "Transport": -8.374049930293115e-05, "Web/CloudOps": -0.0017781343372195169}, "freq": {"A-DEC": 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"domain": {"Econ/Fin": 0.18241823924105727, "Energy": 0.23489364666131607, "Healthcare": 0.4029339059292407, "Nature": 0.38163145392435727, "Sales": 0.40669129360853107, "Transport": 0.3775684991635614, "Web/CloudOps": 0.46119462042457104}, "freq": {"A-DEC": 0.3293846295553108, "Q-DEC": 0.24250648951012244, "M": 0.20013347616630883, "W-SUN": 0.06042382281956005, "D": 0.35939296168983026, "H": 0.3766257334790948, "15T": 0.2209290431528097, "W-FRI": 0.2439317334081258, "10T": 0.33408352931084706, "W-TUE": 0.5433021304642284, "W-THU": 0.2746025429193527, "W-WED": 0.3569697281368308, "5T": 0.4504723451499756, "10S": 0.3493081270023273}}, "group_deltas": {"term": {"short": 0.0002326208892233783, "medium": -0.0005161266504656314, "long": -0.0002854622232759674}, "domain": {"Econ/Fin": 0.0019829644556076387, "Energy": 7.824271680234585e-05, "Healthcare": -0.0045013808768678, "Nature": 0.0003282091802294662, "Sales": -0.0008329309093433324, "Transport": -9.636788248468253e-05, "Web/CloudOps": 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0.3986138188044297, "long": 0.37361489975791085}, "domain": {"Econ/Fin": 0.1284425604485926, "Energy": 0.24004416335172285, "Healthcare": 0.34920747955014364, "Nature": 0.4835379400306602, "Sales": 0.5589316953346404, "Transport": 0.4287316029424887, "Web/CloudOps": 0.44882648872465813}, "freq": {"A-DEC": 0.338587043261639, "Q-DEC": 0.1586026044386245, "M": 0.16717160730025027, "W-SUN": 0.07928426774412878, "D": 0.43389798770404175, "H": 0.40019531932150887, "15T": 0.10658915252440115, "W-FRI": 0.1354576474962318, "10T": 0.5103691572335352, "W-TUE": 0.12071132302117704, "W-THU": 0.3456655401023526, "W-WED": 0.5620742496075346, "5T": 0.4817676940884632, "10S": 0.2930207291581943}}, "group_deltas": {"term": {"short": -0.0008682403650371873, "medium": -0.00033886691964368776, "long": -0.0005265688995489404}, "domain": {"Econ/Fin": 0.0009346608875278051, "Energy": -0.0007206689098112617, "Healthcare": -0.012962168745787372, "Nature": 0.0012688111927480206, "Sales": -0.00031100185162391547, 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{"short": 0.39649060547071235, "medium": 0.37252657016996227, "long": 0.39794795560415297}, "domain": {"Econ/Fin": 0.19329315586441664, "Energy": 0.31908077176563976, "Healthcare": 0.21969245839228846, "Nature": 0.5081638877458665, "Sales": 0.4027256304144581, "Transport": 0.5117925779225818, "Web/CloudOps": 0.3993705168582439}, "freq": {"A-DEC": 0.37271022614221716, "Q-DEC": 0.26489419710707696, "M": 0.12317954371994755, "W-SUN": 0.17087191329814366, "D": 0.4394822690922854, "H": 0.4084720666866227, "15T": 0.257602259974241, "W-FRI": 0.05179457696317158, "10T": 0.6103735105176933, "W-TUE": 0.2784328753426413, "W-THU": 0.34982525924287045, "W-WED": 0.37740509158067315, "5T": 0.47073780086360684, "10S": 0.2916834589628614}}, "group_deltas": {"term": {"short": 1.9159402230073397e-05, "medium": -0.0006535416929527305, "long": -0.0013968365328854704}, "domain": {"Econ/Fin": 0.0017783207894136455, "Energy": 0.0019309954043392752, "Healthcare": -0.014376662127412398, "Nature": 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"optimizer_updates": 0, "new_neural_inference": false, "selection_gate_passed": false}. No automatic full replay or publication.
+
+2026-10-23T22:20:45.759188+00:00: E151 rejected: fixed full-corpus96k fourth-member replacement loses all4cutoffs to E148; scores[0.3870534098754751, 0.34560864389622914, 0.36898776524780275, 0.391716048086576], deltas[-0.0011745411022873187, -5.7471762545446836e-05, -0.000674958922300295, -0.0004136301980643964], mean-0.000580150496. Cached probe CPU8.950054834s; controller248.089803698s includes waiting for source forecasts.0new optimizerupdates,0new neural inference. Long-term group negative on all4; no package construction, full replay, coefficient sweep or checkpoint substitution. Current E148 unchanged. Receipt full_capacity_ensemble_receipt.json.
+
+2026-10-23T22:23:47.178995+00:00: E152 preparation/audits: immutable gating_experts_E148 SHAa1b4ab42... verified allruntime/member hashes, copied5.390598934s; model/data/normalization/frequency/predict/infer/ts_io byte-identical toE138. CPUseed983 passed512oldstreamrows, all95sourceprobabilities exactlysame betweenpacks, fullsampler RNG recoveryexact, seed751recreatesinitial exactly; CPU40.272790067s. GPUseed1009: twoexisting synthetic runtime cases match every actualstandalone E148expert and equal-quarter ensemble bitwise; B64/C4096/H1024 full-corpus fixture finite, allgate layers nonzero gradients, frozenexperts no gradients, reserved2478.0MiB,loss0.199521839619, GPU119.039306419s includinglockwait62.070755758s,0updates. Full+updatedexpert recoveryfixture6vs3+3 now running; not yet queued. No new training shapes/precision/weightbound changes.
+
+E110 96000 four-cutoff comparison: {"label": "E110 96000 four-cutoff comparison", "utc": "2026-10-23T22:25:19.052676+00:00", "candidate": "random_capacity_full_ema96000", "control": "random_capacity_1024_halflr_ema96000", "rows": [{"cutoff": "evals", "candidate_skill": 0.36864499082324387, "control_skill": 0.36862040181646727, "delta": 2.4589006776598588e-05, "candidate_checkpoint": "76d63bae25a069c699a5025715ac3f5f89f18b27f2dc61340f14474e305616ba", "control_checkpoint": "3ecb2892396bc62c203e0d72ce266d2c6df978c24e68cf87cd6dc0ec34db5d30", "candidate_eval_seconds": 31.973838567733765, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7641208242522688, "rel_wql_gmean": 0.49606041547421686}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7248504917648915, "rel_wql_gmean": 0.5014826044297881}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7653817964529085, "rel_wql_gmean": 0.5499221784437706}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 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{"n_configs": 15, "rel_mase_gmean": 0.7866427703773003, "rel_wql_gmean": 0.44372187032754057}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7384900710957844, "rel_wql_gmean": 0.49860168280518735}, "M": {"n_configs": 4, "rel_mase_gmean": 0.9012974939000788, "rel_wql_gmean": 0.8973923604504697}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7137284262620204, "rel_wql_gmean": 0.7500042118226561}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8556907579410804, "rel_wql_gmean": 0.6196113389572953}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7117294142474627, "rel_wql_gmean": 0.46926136140771346}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8158892617521237, "rel_wql_gmean": 0.6496487875787558}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.781887160889063, "rel_wql_gmean": 0.526989374562575}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7331039879686364, "rel_wql_gmean": 0.5062814703386}}}, "group_skill_delta": {"term": {"long": -0.0027279522601905137, "medium": 0.0070856685969709066, "short": 0.00219798374809943}, "domain": {"Econ/Fin": -0.0009238738749061115, "Energy": 0.005764684844835277, "Healthcare": -0.02799697637283627, "Nature": 0.0067367250138019585, "Sales": 2.7960949640082333e-06, "Transport": 0.0012057028389609314, "Web/CloudOps": 0.00013562778073428206}, "frequency": {"A": 0.02010041328206258, "D": -0.0010447583055208653, "H": 0.004775955576226121, "M": -0.06195988262953489, "Q": 0.004435167774262894, "S": -0.006338849469198493, "T": 0.01001053943417729, "W": -0.00921549745775474}, "variates": {"multivariate": 0.004736631963415894, "univariate": 0.00030216368525315396}}}], "mean_delta": 0.0006860282725105749, "positive_cutoffs": 3, "outcome": "Declared comparison checkpoints 48000,96000. No transfer formula or automatic publication is selected by this controller."}
+
+2026-10-23T22:25:53.634112+00:00: E110 final96k review: matched E94 atsameLR1e-4 now3of4positive,mean+0.000686028272511; primary48k was1of4/negative. Main0.368644990823 versus0.368620401816 nearlytied. Fullcoverage is a modest duration-dependent improvement in this controlled setting, not evidence of a large standalone or ensemble gain. E151fixedquartetlostall4 and remainsrejected. E147higherLR fullpack remainsqueued; no unplanned scheduleextension or modelcapacityrun. Complete recovery and allcheckpointsretained.
+
+E141 6000 four-cutoff comparison: {"label": "E141 6000 four-cutoff comparison", "utc": "2026-10-23T22:28:51.170292+00:00", "candidate": "capacity_patch8_candidate_ema6000", "control": "capacity_patch8_control_ema6000", "rows": [{"cutoff": "evals", "candidate_skill": 0.36978469930277436, "control_skill": 0.37054654226823525, "delta": -0.0007618429654608905, "candidate_checkpoint": "2877a38b94a7d77a53a9df410349cecc2f6ab134c83a76744924543bbe73eb17", "control_checkpoint": "6e40fa235d296be4a3d3b4a5a1503e1ba3435bdee0cc72b7243ab5a0f4e8e1ff", "candidate_eval_seconds": 48.25301194190979, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7574390194245608, "rel_wql_gmean": 0.49515987996994076}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7318341128774869, "rel_wql_gmean": 0.5008060335014628}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7580959269420394, "rel_wql_gmean": 0.5521778537504981}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 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0.5792472078751053}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7734128938469124, "rel_wql_gmean": 0.5304385392303502}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.8109556621867035, "rel_wql_gmean": 0.5779443810907645}}}, "group_skill_delta": {"term": {"long": 0.00045056976061330634, "medium": -0.0006303797647274623, "short": 0.005493820429575358}, "domain": {"Econ/Fin": 0.010095566026877889, "Energy": -0.0009579952770162325, "Healthcare": 0.006891565195451088, "Nature": 0.0049954474357981615, "Sales": 0.017850443432284258, "Transport": 5.991504113123103e-05, "Web/CloudOps": 0.0027125809556808322}, "frequency": {"A": 0.004434368389614529, "D": -0.010870375885140504, "H": 0.002587333224448485, "M": 0.06322057926853908, "Q": 0.004987143992373544, "S": 0.001627201761496666, "T": 0.0034016028177875635, "W": -0.0007098965948213465}, "variates": {"multivariate": 0.003104806884362654, "univariate": 0.0027978484562715122}}}, {"cutoff": "backcast2_evals", "candidate_skill": 0.36105190344557336, "control_skill": 0.35764592194596245, "delta": 0.0034059814996109106, "candidate_checkpoint": "2877a38b94a7d77a53a9df410349cecc2f6ab134c83a76744924543bbe73eb17", "control_checkpoint": "6e40fa235d296be4a3d3b4a5a1503e1ba3435bdee0cc72b7243ab5a0f4e8e1ff", "candidate_eval_seconds": 46.10557222366333, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7938914676962915, "rel_wql_gmean": 0.4997987659435392}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7479010239161168, "rel_wql_gmean": 0.5003155170838207}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7609255559698398, "rel_wql_gmean": 0.5606087217682991}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8799701118196165, "rel_wql_gmean": 0.9122237463187874}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9126101711758015, "rel_wql_gmean": 0.6584155779396941}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6451541873129701, "rel_wql_gmean": 0.5743142288063876}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7693885351800274, "rel_wql_gmean": 0.37629643254467365}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5639827878813234, "rel_wql_gmean": 0.350068781483157}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6803145821810495, "rel_wql_gmean": 0.5111872339630943}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6456926038426319, "rel_wql_gmean": 0.45240238536116284}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5979204759398598, "rel_wql_gmean": 0.8027848591495264}, "D": {"n_configs": 15, "rel_mase_gmean": 0.728730345307442, "rel_wql_gmean": 0.455511782486644}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7482416040189465, "rel_wql_gmean": 0.4877099397835897}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8005120915083439, "rel_wql_gmean": 0.8454317149733872}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8348690847756616, "rel_wql_gmean": 0.8797493502482598}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8928287107309677, "rel_wql_gmean": 0.5523203389775555}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7464318146300277, "rel_wql_gmean": 0.5479341585960555}, "W": {"n_configs": 8, "rel_mase_gmean": 0.877468091078322, "rel_wql_gmean": 0.6369751521426078}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7616377294454229, "rel_wql_gmean": 0.5095984347010085}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7668598001681564, "rel_wql_gmean": 0.5545590182611448}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.795762351065454, "rel_wql_gmean": 0.5048000326518496}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7461726318978812, "rel_wql_gmean": 0.5008300650332542}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7653582854027554, "rel_wql_gmean": 0.5656029122753404}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8844826760826884, "rel_wql_gmean": 0.9038313847396334}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9164866895767413, "rel_wql_gmean": 0.663496204788093}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6692500117284449, "rel_wql_gmean": 0.5885070087969348}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7699516249220331, "rel_wql_gmean": 0.37933953568375045}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.5648558665119814, "rel_wql_gmean": 0.3569667475686651}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.6787659619889969, "rel_wql_gmean": 0.5111557516564603}, "Web/CloudOps": {"n_configs": 19, "rel_mase_gmean": 0.6448916363828848, "rel_wql_gmean": 0.45715640779484273}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5787699821271256, "rel_wql_gmean": 0.7573026212897106}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7315814026600997, "rel_wql_gmean": 0.453348762642364}, "H": {"n_configs": 30, "rel_mase_gmean": 0.7483110836431279, "rel_wql_gmean": 0.49254512193895256}, "M": {"n_configs": 4, "rel_mase_gmean": 0.814008834766287, "rel_wql_gmean": 0.8668648112482316}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.8374023760339016, "rel_wql_gmean": 0.8834045827159994}, "S": {"n_configs": 6, "rel_mase_gmean": 0.901548208978998, "rel_wql_gmean": 0.56269665693173}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7474657467586522, "rel_wql_gmean": 0.5513079435383147}, "W": {"n_configs": 8, "rel_mase_gmean": 0.889909158733574, "rel_wql_gmean": 0.6473687190538074}}, "variates": {"multivariate": {"n_configs": 42, "rel_mase_gmean": 0.7650817704324117, "rel_wql_gmean": 0.5155543582402501}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7686360400870299, "rel_wql_gmean": 0.556713898468019}}}, "group_skill_delta": {"term": {"long": 0.0038892560400821674, "medium": -0.0003931241844432609, "short": 0.004810839916473686}, "domain": {"Econ/Fin": -0.0018471093842234998, "Energy": 0.00463592861902129, "Healthcare": 0.018876777137277867, "Nature": 0.0023689520491817495, "Sales": 0.004703523001896304, "Transport": -0.0006897184372798293, "Web/CloudOps": 0.0024952542172785863}, "frequency": {"A": -0.030776074970406353, "D": -0.0002462874737407805, "H": 0.0030152904604858755, "M": 0.017355829454799077, "Q": 0.003080499902602396, "S": 0.010018322638286725, "T": 0.0024099879404980706, "W": 0.01139897387080091}, "variates": {"multivariate": 0.005045255714340002, "univariate": 0.002022046061624594}}}, {"cutoff": "backcast3_evals", "candidate_skill": 0.3791072202284068, "control_skill": 0.37883891538276704, "delta": 0.0002683048456397774, "candidate_checkpoint": "2877a38b94a7d77a53a9df410349cecc2f6ab134c83a76744924543bbe73eb17", "control_checkpoint": "6e40fa235d296be4a3d3b4a5a1503e1ba3435bdee0cc72b7243ab5a0f4e8e1ff", "candidate_eval_seconds": 48.116257667541504, "candidate_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7700430237734708, "rel_wql_gmean": 0.47820012115872046}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7566729630877351, "rel_wql_gmean": 0.5212625148311835}, "short": {"n_configs": 54, "rel_mase_gmean": 0.743562296889515, "rel_wql_gmean": 0.522447177631314}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8384908114780523, "rel_wql_gmean": 0.8259874785586008}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8533594437002346, "rel_wql_gmean": 0.570433155614779}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.9355966819043433, "rel_wql_gmean": 0.7447592487539578}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.726272409367948, "rel_wql_gmean": 0.34580508538281657}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7645972695514087, "rel_wql_gmean": 0.48376113135894505}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5562216672114354, "rel_wql_gmean": 0.460346317036627}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7061519904001345, "rel_wql_gmean": 0.4978632439753042}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5926728414450325, "rel_wql_gmean": 0.8350213742977284}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7717643606285386, "rel_wql_gmean": 0.43603542856183236}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7361553505567909, "rel_wql_gmean": 0.49604172586139594}, "M": {"n_configs": 4, "rel_mase_gmean": 0.9149964427971231, "rel_wql_gmean": 0.9046096643355541}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7246737629732861, "rel_wql_gmean": 0.7820574068443148}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8551220579630898, "rel_wql_gmean": 0.6006805354195802}, "T": {"n_configs": 29, "rel_mase_gmean": 0.6947625756223746, "rel_wql_gmean": 0.45915444373651726}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8746416354594816, "rel_wql_gmean": 0.6989764967731741}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7712118553212413, "rel_wql_gmean": 0.5165843423740577}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7366951131406715, "rel_wql_gmean": 0.5098998050607239}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7710093862560393, "rel_wql_gmean": 0.48145504558378305}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7556631249580565, "rel_wql_gmean": 0.526891723054916}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7405427790241224, "rel_wql_gmean": 0.5220531634759572}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8369279740219194, "rel_wql_gmean": 0.8162520648058149}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8555156341650978, "rel_wql_gmean": 0.5728948490401817}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.8849050070180935, "rel_wql_gmean": 0.7092177758963885}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7214615809982889, "rel_wql_gmean": 0.351077393491105}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7661080934651817, "rel_wql_gmean": 0.4871542965879641}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5614540308309911, "rel_wql_gmean": 0.46390765128414563}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7041553851660223, "rel_wql_gmean": 0.5013319490268304}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5269109788139515, "rel_wql_gmean": 0.7358353623166535}, "D": {"n_configs": 15, "rel_mase_gmean": 0.786835922299246, "rel_wql_gmean": 0.4461893275560026}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7334761570101263, "rel_wql_gmean": 0.49746378039868394}, "M": {"n_configs": 4, "rel_mase_gmean": 0.888468781818334, "rel_wql_gmean": 0.8746269527619119}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7186577154873245, "rel_wql_gmean": 0.7559252667851271}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8520735137455233, "rel_wql_gmean": 0.610595094303494}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7005291316198885, "rel_wql_gmean": 0.46432834015136293}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8338970447008786, "rel_wql_gmean": 0.677195128798985}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7738533110452481, "rel_wql_gmean": 0.5222455704762278}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7315731035639104, "rel_wql_gmean": 0.5084907007458334}}}, "group_skill_delta": {"term": {"long": 0.0024436437602908256, "medium": 0.0029605374741091817, "short": -0.0015014050169452897}, "domain": {"Econ/Fin": -0.005690294076517222, "Energy": 0.002386617766467092, "Healthcare": -0.042536035991448395, "Nature": 0.0021307293250875103, "Sales": 0.002731878875762317, "Transport": 0.004337207537003618, "Web/CloudOps": 0.0012201944335826775}, "frequency": {"A": -0.08081593061591241, "D": 0.012417666084862944, "H": -0.00023664608583251479, "M": -0.028267531562737203, "Q": -0.015763039460945705, "S": 0.00460135567573372, "T": 0.0055255237700803095, "W": -0.03041869701521771}, "variates": {"multivariate": 0.004535055118099263, "univariate": -0.002978851227565915}}}], "mean_delta": 0.0014632718399651412, "positive_cutoffs": 3, "outcome": "Declared comparison checkpoints 6000,12000. No transfer formula or automatic publication is selected by this controller."}
+
+2026-10-23T22:29:46.370972+00:00: E141primary6k matchedpatch8 effect3of4positive,mean+0.001463271840, longtermgroup positiveall4; final12k originalschedule continues. E152full-corpus/refreshed-expert recovery6vs3+3 passed bitwise all model/optimizer/EMA/NumPyTorchCUDA RNG/prefetch/ancestry keys;12fixtureoptimizerupdates, 269.476998004s incl90.908342783slockwait. Privateinitialpackage393516c89f812393e2a63756d5e794da1c62360539f437ee62dc069e20fe8afb totals256650268parameters, isengineeringfixtureonly; relocationpending. No E152trainingqueued yet.
+
+2026-10-23T22:30:59.769571+00:00: E152 queued after all CPU/GPU/full-recovery/relocation checks. Privateinitialpackage three realconfigurations exact after relocation,42.286248246s, finiteordered. Queues [{"name": "ensemble_gate_refresh", "queue_pid": 4800, "watch_pid": 4801, "selection_pid": 4802, "wait_pid": 1280, "required_checkpoint": "/workspace/research/runs/integrated_level/ema012000.pt"}, {"name": "ensemble_gate_full", "queue_pid": 4803, "watch_pid": 4804, "selection_pid": 4805, "wait_pid": 4800, "required_checkpoint": "/workspace/research/runs/ensemble_gate_refresh/ema012000.pt"}, {"name": "ensemble_gate_refresh_full", "queue_pid": 4806, "watch_pid": 4807, "selection_pid": 4808, "wait_pid": 4803, "required_checkpoint": "/workspace/research/runs/ensemble_gate_full/ema012000.pt"}]. Persistentlauncher85531; sequential E149queue1280final12k -> refresh/oldpack -> E130/fullpack -> refresh/fullpack. All same owninitial9d1401cd...,seed751/B64/LR.001/12k, originalAR/horizon300754, old source probabilities, frozen snapshots; no extra concurrent trainer. First publication gate remains source equalquarters plus currentpublication >=3positive/positive mean on fourcutoffs then full97/runtimechecks. No expert/bound/data-law changes beyond declared2×2factors.
+
+## E153 matched146M patch-resolution fourth-expert replacement — declared 2026-10-23T22:31:33.548246+00:00
+E141primary6k patch8 continuation beats matched patch16 continuation3of4,mean+0.001463271840, with positive long-termgroup inall4. Hypothesis: this long-horizon improvement can help the fourth member of the current equal-quarter ensemble. Fix the firstthree E148members and compare two quartets with only the fourth changing: matched E141patch16control versus E141patch8candidate at6000. Also compare to frozenE148originalwide96fourth. All ordered weights0.25; every expert forecasts every instance; no mixture coefficients/checkpoint sweep. Sourcefinal12000 is a predefined durability repetition only if the source paired result againpasses>=3positive/positive mean. Ensemble selection requires>=3positive/positive mean versus both matchedquartet and frozenE148, followed by then-currentpublication checks/full97replay/runtime/composition. No new training or weightsmerged.
+{"status": "declared; primary fixed quartet probes pending", "declared_utc": "2026-10-23T22:31:33.548246+00:00", "formula": "Keep E148first3 experts and ordered equal quarters; compare E141patch8 candidate fourth against its matched patch16 continuation fourth and frozen E148 original fourth", "fixed_first_three": ["seasonal_width_transfer12k", "patch8_ema6000", "split_patch_ema12000"], "fixed_first_three_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "source_primary": 6000, "source_final": 12000, "source_controls": "capacity_patch8_candidate versus capacity_patch8_control at the same step", "frozen_parent_descriptor": "a1b4ab426901be8e1ee299eeeb3f041bac7cea3d4e59c4608824fc3d05c83e03", "gate": "Source matched >=3positive/positive mean required; quartets then >=3positive/positive mean against both matched quartet and frozen E148; current-publication recheck before full replay. Final12k follows same predeclared source gate.", "no_weight_sweep": true, "new_training": false}
+
+2026-10-23T22:32:12.856802+00:00: E153 fixed matched patch8 quartet {"step": 6000, "status": "probes complete; review required", "rows": [{"cutoff": "valid_screen", "skill": 0.38789438191103254, "matched_control_skill": 0.3879075778166178, "frozen_parent_skill": 0.3882279509777624, "delta_matched": -1.319590558523931e-05, "delta_frozen_parent": -0.0003335690667298774, "groups": {"term": {"short": 0.3716826509138309, "medium": 0.41039569970032963, "long": 0.4065845305446606}, "domain": {"Econ/Fin": 0.152452622133746, "Energy": 0.24758213300465193, "Healthcare": 0.48553074773991434, "Nature": 0.4404309113583975, "Sales": 0.3487392730237323, "Transport": 0.473151650954438, "Web/CloudOps": 0.5073042908980989}, "freq": {"A-DEC": 0.38272711896866796, "Q-DEC": 0.20432279691054978, "M": 0.11688352014517778, "W-SUN": 0.11141699186572074, "D": 0.3739451249684639, "H": 0.4037027086832654, "15T": 0.21730718302115903, "W-FRI": 0.012464757182048536, "10T": 0.6258016915487774, "W-TUE": 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Then-current publication, full replay and runtime checks still required; no automatic publication.
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Ensemble complementarity evaluated separately, unchanged training."}
+
+E135 fixed distilled fourth-member diagnostic: {"step": 48000, "rows": [{"cutoff": "valid_screen", "skill": 0.3862430162504997, "control_skill": 0.3878507268780589, "delta": -0.0016077106275591557, "groups": {"term": {"short": 0.36841724642192164, "medium": 0.41052483675946017, "long": 0.4071256965149448}, "domain": {"Econ/Fin": 0.1503298332977695, "Energy": 0.2475288827346288, "Healthcare": 0.4683602740632855, "Nature": 0.44163662874012, "Sales": 0.344098745410368, "Transport": 0.47037351890782064, "Web/CloudOps": 0.5071816016231339}, "freq": {"A-DEC": 0.38346436683924756, "Q-DEC": 0.20425455434833162, "M": 0.10837540899768172, "W-SUN": 0.1092024214705174, "D": 0.3678067279459968, "H": 0.40260957818843757, "15T": 0.2189861530856474, "W-FRI": 0.011644114431122565, "10T": 0.6285413439602561, "W-TUE": 0.606541810862018, "W-THU": 0.20540963002730894, "W-WED": 0.3258002094201732, "5T": 0.5209776991868179, "10S": 0.42502372724867077}}, "member_checkpoint_hashes": 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"mean_delta": 5.552577861386854e-05, "selection_gate_passed": true}. No automatic publication; any candidate also needs comparison to the then-current package, full replay and all checks.
+
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"group_skill_delta": {"term": {"long": -9.918739388803388e-05, "medium": -0.0005480489735650851, "short": -0.0012658448910782827}, "domain": {"Econ/Fin": 0.0007987057781615459, "Energy": -0.00027294392359555886, "Healthcare": -0.0068662792034112075, "Nature": -0.0016651499128672032, "Sales": -0.0006053142819720803, "Transport": -0.0005515781738771319, "Web/CloudOps": -0.0003271303138128179}, "frequency": {"A": -0.0020116995037390284, "D": -0.001734783094214154, "H": -2.7846182917512508e-05, "M": -0.006514194420064734, "Q": -5.7471102129613705e-05, "S": 0.0009265513986651541, "T": -0.0003718778180832327, "W": -0.003745856152986282}, "variates": {"multivariate": -0.00012714998662788357, "univariate": -0.0014419023942335851}}}], "mean_delta": -0.00011847483236823719, "positive_cutoffs": 3, "outcome": "Declared comparison checkpoints 6000,12000. No transfer formula or automatic publication is selected by this controller."}
+
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"domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.834529721483013, "rel_wql_gmean": 0.8161598941965836}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8349859255498696, "rel_wql_gmean": 0.5565285975619227}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7823310668745433, "rel_wql_gmean": 0.6596457484453172}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7174718369750586, "rel_wql_gmean": 0.3426177834265536}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7572858039499756, "rel_wql_gmean": 0.4810526938286365}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5441367107228744, "rel_wql_gmean": 0.4503318478008759}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7167615825135144, "rel_wql_gmean": 0.5204237491828342}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5269095103460054, "rel_wql_gmean": 0.7367243319442995}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7275250086113206, "rel_wql_gmean": 0.42521784358196796}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7267709667416996, "rel_wql_gmean": 0.4893867261866016}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8620261347208122, "rel_wql_gmean": 0.8548717233187205}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7139945526949583, "rel_wql_gmean": 0.7706200499608589}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8385326549351071, "rel_wql_gmean": 0.5984822446937036}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7072432367122784, "rel_wql_gmean": 0.4644996219125975}, "W": {"n_configs": 8, "rel_mase_gmean": 0.807344260016394, "rel_wql_gmean": 0.6721440013561132}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7634223165781352, "rel_wql_gmean": 0.515267751478812}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7163808854857141, "rel_wql_gmean": 0.49990314250058615}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7650747158835133, "rel_wql_gmean": 0.4758888221311058}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.7703157746376595, "rel_wql_gmean": 0.5298503011269557}, "short": {"n_configs": 54, "rel_mase_gmean": 0.713915671760059, "rel_wql_gmean": 0.5084844315057424}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8452403065316382, "rel_wql_gmean": 0.8277399249864468}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8346950467324626, "rel_wql_gmean": 0.5567599695672222}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7876375654704325, "rel_wql_gmean": 0.6565412569519505}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.710238739553539, "rel_wql_gmean": 0.34191077293082645}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7563686587019305, "rel_wql_gmean": 0.4784563222634317}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5433317817040721, "rel_wql_gmean": 0.4493168613990946}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7161526533494015, "rel_wql_gmean": 0.5178489398382691}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5311143622209655, "rel_wql_gmean": 0.7781586196577971}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7234708846594775, "rel_wql_gmean": 0.42486757337507386}, "H": {"n_configs": 29, "rel_mase_gmean": 0.725973846929309, "rel_wql_gmean": 0.48863382838793346}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8623268790180476, "rel_wql_gmean": 0.8523275939235515}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7182632014266673, "rel_wql_gmean": 0.7707135583961491}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8408993113146807, "rel_wql_gmean": 0.5989715147638536}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7064723895859075, "rel_wql_gmean": 0.463139705670842}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8096257056910313, "rel_wql_gmean": 0.6721758032092567}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7638007775200529, "rel_wql_gmean": 0.5152023145873739}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7148285566222029, "rel_wql_gmean": 0.49905178580637044}}}, "group_skill_delta": {"term": {"long": -0.000259654574695789, "medium": -0.0019850340271861944, "short": -0.0002560158565543258}, "domain": {"Econ/Fin": 0.01115064962656065, "Energy": 2.2915416482693907e-05, "Healthcare": 0.0007340578260338626, "Nature": -0.003014740630669255, "Sales": -0.0019956257144767564, "Transport": -0.0009240216729882733, "Web/CloudOps": -0.0017715792788255413}, "frequency": {"A": 0.019830704026307933, "D": -0.0017803592778993238, "H": -0.0007858224485016718, "M": -0.001128817542738303, "Q": 0.0022591766487027876, "S": 0.0012889170244652082, "T": -0.0011516191154861133, "W": 0.0010575521164962742}, "variates": {"multivariate": 0.00011560665669652082, "univariate": -0.0011579667022737494}}}], "mean_delta": -0.0005163937769804006, "positive_cutoffs": 1, "outcome": "Secondary original-window reference; primary matched control remains E113 lowfreq_windows. No automatic transfer or publication."}
+
+2026-10-23T22:34:09.731589+00:00: E153primary6k quartetpassed3of4versusmatchedcontinuation(mean+.000639453454) and3of4versusE148(mean+.000576218693). Scores[.387894381911,.346581109255,.370472768861,.393043083835], currentE148deltas[-.000333569067,+.000914993596,+.000810044691,+.000913405550]. Full validation started session80670 after currentpublication recheck. Portable member built from own E1416k SHA2877a38b94a7d77a53a9df410349cecc2f6ab134c83a76744924543bbe73eb17,146206864params; fullquartet256373448params. All training/conversion provenance included. No promotion until full97/151170/0fails plus runtime/relocation/actualstandalonecomposition and currentbest guards.
+
+2026-10-23T22:34:31.485169+00:00: E101/E13548k review {"utc": "2026-10-23T22:34:31.485169+00:00", "step": 48000, "current_descriptor_sha256": "a1b4ab426901be8e1ee299eeeb3f041bac7cea3d4e59c4608824fc3d05c83e03", "rows": [{"cutoff": "valid_screen", "skill": 0.3862430162504997, "current_skill": 0.3882279509777624, "delta": -0.0019849347272626883}, {"cutoff": "valid_backcast", "skill": 0.34449643137991137, "current_skill": 0.3456661156587746, "delta": -0.0011696842788632145}, {"cutoff": "valid_backcast2", "skill": 0.3697302062020633, "current_skill": 0.36966272417010304, "delta": 6.748203196027536e-05}, {"cutoff": "valid_backcast3", "skill": 0.3918145585771363, "current_skill": 0.3921296782846404, "delta": -0.0003151197075040857}], "positive_current": 1, "mean_delta_current": -0.0008505641704174283, "standalone_positive_matched": 4, "standalone_mean_delta_matched": 0.01643729063708163, "decision": "No full replay/publication: fixed E135quartet fails current-publication cutoff gate. Original96k distillation schedule and predeclared final comparison continue; no teacher refresh, coefficient or alternative ensemble selected."}
+
+2026-10-23T22:36:29.830419+00:00: E143primary6k not selected: versus matchedE113,3of4positive butmean-0.000118474832; versusoriginalE80,1of4positive andmean-0.000516393777. The earliestcutoffloses.000878051871 againstE113. No transfer/ensemble constructed; original12kschedule/finalcomparison remains. E153primaryprivatequartet descriptorbd228e3d12d89bc9a79acfd7238f4c70ff0a0cfe83d6f34668525458562a8aa9 passed2synthetic actualstandalonecompositioncases(21.022639501s) and3realconfig relocation(25.766290231s), fullpublicreplayrunning.
+
+E147 fresh146M full-corpus originalLR seed307 storage recovery training started at 2026-10-23T22:40:44Z, after the declared dependency processes exited; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+2026-10-23T22:43:27.123813+00:00: E13548k median-error-dependence diagnostic CPU9.300756899872795s,0updates/0newneuralinference/no fittedcoefficients. [{"cutoff": "valid_screen", "equal_configuration_mean_error_cosine_delta": 0.017930837934878506, "equal_configuration_mean_disagreement_delta": -0.09806292251048022}, {"cutoff": "valid_backcast", "equal_configuration_mean_error_cosine_delta": 0.01766023129217782, "equal_configuration_mean_disagreement_delta": -0.1036566867286526}, {"cutoff": "valid_backcast2", "equal_configuration_mean_error_cosine_delta": 0.021640768319654072, "equal_configuration_mean_disagreement_delta": -0.11454244307911492}, {"cutoff": "valid_backcast3", "equal_configuration_mean_error_cosine_delta": 0.02260943588127983, "equal_configuration_mean_disagreement_delta": -0.1164776420047907}]. Relative to fresh48, the distilled48 median errors are more aligned with the fixed first-three median reference at every cutoff and in every term; disagreement falls. This supports a diversity concern, not causal proof or a complete explanation of WQL. Models also differ in learning rate and supervision; use only as descriptive analysis of actual E135/E130 alternatives. Receipt distillation48_error_dependence.json.
+
+2026-10-23T22:45:28.291646+00:00: E153primary6k fullquartet REJECTED. Fullskill0.384152613414 versus E148.384535531421, delta−.000382918007; relMASE.736562470341 and relWQL.514916275043 bothworse. All97configs151170instances,0fails. Longgroupskill+.000559198903, medium−.000525597297,short−.000703467707; monthly−.010424353065,weekly−.006756727310. Inference511.974687707s, suite642.720436965s; all2synthetic/3realcomposition/3realrelocation checks bitwisepassed. Runtimecorrectness does not establishmodelimprovement; no promotion. Preserve privatecandidate bd228e3d12d89bc9a79acfd7238f4c70ff0a0cfe83d6f34668525458562a8aa9, sourceweightsandreceipts. Conditional final12k source/ensemble contrast remains asdeclared.
+
+## E153 patch8-6k quartet full validation — rejected
+- Checkpoint SHA-256: `bd228e3d12d89bc9a79acfd7238f4c70ff0a0cfe83d6f34668525458562a8aa9`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.384153; relative MASE 0.736562; relative WQL 0.514916.
+- term skill: long=0.4008, medium=0.4091, short=0.3678.
+- domain skill: Econ/Fin=0.1343, Energy=0.2411, Healthcare=0.4906, Nature=0.4405, Sales=0.3611, Transport=0.4733, Web/CloudOps=0.4993.
+- frequency skill: A=0.3048, D=0.3778, H=0.3986, M=0.1411, Q=0.1922, S=0.4245, T=0.4385, W=0.2377.
+- variates skill: multivariate=0.3962, univariate=0.3744.
+- Outcome: Correct and replayable package, but full skill0.384152613414 loses to current E1480.384535531421. Both relative MASE and WQL worsen. All97/151170/0fail; real/synthetic independent composition and relocation bitwise passed. No publication. Original conditional final12k comparison remains.
+
+E146 direct-trend independent replication seed523 storage recovery training started at 2026-10-23T22:45:49Z, after the declared dependency processes exited; required checkpoint files are present. Settings are unchanged from the declared comparison.
+
+E141 12000 four-cutoff comparison: {"label": "E141 12000 four-cutoff comparison", "utc": "2026-10-23T22:49:35.850083+00:00", "candidate": "capacity_patch8_candidate_ema12000", "control": "capacity_patch8_control_ema12000", "rows": [{"cutoff": "evals", "candidate_skill": 0.3692643266863477, "control_skill": 0.3697081080636234, "delta": -0.00044378137727574263, "candidate_checkpoint": "8bdc6aec5ac81386cace42f2ef23202df02cc54a97261eae61ececffaae962e2", "control_checkpoint": "78e34c37073a8114d02d99f3d191343386337430495420337e8bf81f518fba12", "candidate_eval_seconds": 52.1950409412384, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7592656160195234, "rel_wql_gmean": 0.4966968333865811}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.730126320951248, "rel_wql_gmean": 0.49944625071300897}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7601553525144238, "rel_wql_gmean": 0.5521919812260615}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8823286134139959, "rel_wql_gmean": 0.8405465544580609}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9082680435980823, "rel_wql_gmean": 0.6565180348958861}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5794121142017361, "rel_wql_gmean": 0.5049110337605541}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8326656550415755, "rel_wql_gmean": 0.41624881953795634}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8234940861461448, "rel_wql_gmean": 0.5075626179137492}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.649163913855379, "rel_wql_gmean": 0.44784933617346223}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5796068228208399, "rel_wql_gmean": 0.447021463968036}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.6017438042458082, "rel_wql_gmean": 0.6304889599515308}, "D": {"n_configs": 15, "rel_mase_gmean": 0.759749537808371, "rel_wql_gmean": 0.5292420586690706}, "H": {"n_configs": 31, "rel_mase_gmean": 0.7390296578936822, "rel_wql_gmean": 0.5056450470869174}, "M": {"n_configs": 5, "rel_mase_gmean": 1.0625822117428756, "rel_wql_gmean": 0.8796429724017721}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7720509044863395, "rel_wql_gmean": 0.8237294969384296}, "S": {"n_configs": 6, "rel_mase_gmean": 0.670662529970206, "rel_wql_gmean": 0.5084784169140807}, "T": {"n_configs": 30, "rel_mase_gmean": 0.711741737454722, "rel_wql_gmean": 0.47046487856550157}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8920501257787306, "rel_wql_gmean": 0.6638689739702408}}, "variates": {"multivariate": {"n_configs": 43, "rel_mase_gmean": 0.7316070488902383, "rel_wql_gmean": 0.5273569809375125}, "univariate": {"n_configs": 54, "rel_mase_gmean": 0.7711437093592233, "rel_wql_gmean": 0.5286398907801284}}}, "control_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7623302007628598, "rel_wql_gmean": 0.49885876408278057}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7312927753075384, "rel_wql_gmean": 0.5012676130561656}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7559248585958686, "rel_wql_gmean": 0.5510349821587733}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8887279104785332, "rel_wql_gmean": 0.8425269970221109}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9105746858650593, "rel_wql_gmean": 0.6600590405892464}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.5419833665761247, "rel_wql_gmean": 0.4713378036640871}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8256753562119806, "rel_wql_gmean": 0.4176381250423008}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8454665614427059, "rel_wql_gmean": 0.5196239566665947}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6511792187049391, "rel_wql_gmean": 0.45046376028245605}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.5794668872329616, "rel_wql_gmean": 0.44654870510396605}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5898086062844351, "rel_wql_gmean": 0.6099831906967607}, "D": {"n_configs": 15, "rel_mase_gmean": 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0.0009701300026396797, "medium": 0.0023611581637045242, "short": -0.0018551340367931601}, "domain": {"Econ/Fin": -0.008478209879048682, "Energy": 0.005126558014600269, "Healthcare": -0.04749079316921834, "Nature": -0.0023811395134898383, "Sales": 0.0012404328843749202, "Transport": 0.004942356147431104, "Web/CloudOps": -0.00023842054860678807}, "frequency": {"A": -0.07884584403483152, "D": 0.0070731954226516125, "H": 0.00029221424355296843, "M": -0.03581608945765935, "Q": -0.018743903084045388, "S": 0.001124135816831795, "T": 0.006425035841104099, "W": -0.026903916285766827}, "variates": {"multivariate": 0.004827942078305059, "univariate": -0.0043259373311788085}}}], "mean_delta": 0.00185064179627209, "positive_cutoffs": 2, "outcome": "Declared comparison checkpoints 6000,12000. No transfer formula or automatic publication is selected by this controller."}
+
+2026-10-23T22:49:43.892589+00:00: E154 declaration {"declared_utc": "2026-10-23T22:49:43.892589+00:00", "status": "declared; cached probes next", "hypothesis": "E141patch8 changes long-horizon forecasting more consistently than short/medium; retain the published original fourth contribution below720 steps.", "formula": "All five models forecast every instance. Ordered FP32 average of members0,1,2 and member4 if requested horizon>=720 else member3.", "threshold": 720, "members": ["candidate-width-transfer12k", "candidate-patch8-6k-complete", "candidate-split-patch-12k-complete", "candidate-fresh146m-96k", "candidate-capacity-patch8-6k"], "member_checkpoint_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33", "2877a38b94a7d77a53a9df410349cecc2f6ab134c83a76744924543bbe73eb17"], "control_descriptor": "a1b4ab426901be8e1ee299eeeb3f041bac7cea3d4e59c4608824fc3d05c83e03", "code": "/workspace/research/horizon_patch8_routing", "seed": "No new training; existing E139/E71/E93/E79(seed307)/E141(seed809) lineages retained", "new_training": false, "selection": "Adaptive validation-selected rule after E153 full failure; no claim of independent validation. One threshold720, fixed6k source, no threshold/coefficient/endpoint sweep. Require>=3 positive cutoffs/positive mean versus E148 and then-current publication, then full97/151170/0fail improvement and independent composition, every-member-call, determinism and relocation checks before atomic publication.", "parameters": 402850792}.
+
+2026-10-23T22:49:53.948095+00:00: E154 fixed horizon routing probes {"declaration": {"declared_utc": "2026-10-23T22:49:43.892589+00:00", "status": "declared; cached probes next", "hypothesis": "E141patch8 changes long-horizon forecasting more consistently than short/medium; retain the published original fourth contribution below720 steps.", "formula": "All five models forecast every instance. Ordered FP32 average of members0,1,2 and member4 if requested horizon>=720 else member3.", "threshold": 720, "members": ["candidate-width-transfer12k", "candidate-patch8-6k-complete", "candidate-split-patch-12k-complete", "candidate-fresh146m-96k", "candidate-capacity-patch8-6k"], "member_checkpoint_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33", "2877a38b94a7d77a53a9df410349cecc2f6ab134c83a76744924543bbe73eb17"], "control_descriptor": "a1b4ab426901be8e1ee299eeeb3f041bac7cea3d4e59c4608824fc3d05c83e03", "code": "/workspace/research/horizon_patch8_routing", "seed": "No new training; existing E139/E71/E93/E79(seed307)/E141(seed809) lineages retained", "new_training": false, "selection": "Adaptive validation-selected rule after E153 full failure; no claim of independent validation. One threshold720, fixed6k source, no threshold/coefficient/endpoint sweep. Require>=3 positive cutoffs/positive mean versus E148 and then-current publication, then full97/151170/0fail improvement and independent composition, every-member-call, determinism and relocation checks before atomic publication.", "parameters": 402850792}, "rows": [{"cutoff": "valid_screen", "skill": 0.38835665952568876, "control_skill": 0.3882279509777624, "delta": 0.00012870854792634834, "groups": {"term": {"short": 0.3723017702532888, "medium": 0.41093067600045863, "long": 0.4065845305446606}, "domain": {"Econ/Fin": 0.14901328476060638, "Energy": 0.24775713213772044, "Healthcare": 0.49506927931058364, "Nature": 0.4412914062724784, "Sales": 0.3446101717591584, "Transport": 0.4732237740360019, "Web/CloudOps": 0.507223222428795}, "freq": {"A-DEC": 0.38365057239921063, "Q-DEC": 0.20606735650303087, "M": 0.12682244603724124, "W-SUN": 0.08968565726055233, "D": 0.37213466457612654, "H": 0.4041811792567682, "15T": 0.21767323376719727, "W-FRI": 0.020360259620060495, "10T": 0.6254426461144121, "W-TUE": 0.6415415405419468, "W-THU": 0.21792947739631574, "W-WED": 0.32708506145318983, "5T": 0.5200991550482101, "10S": 0.4246436987656872}}, "group_deltas": {"term": {"short": 0.0, "medium": 0.0, "long": 0.0005770132838742725}, "domain": {"Econ/Fin": 0.0, "Energy": 0.00035677889896978954, "Healthcare": 0.0, "Nature": -7.407378902246631e-05, "Sales": 0.0, "Transport": 0.0006141357874228648, "Web/CloudOps": -0.00025254488006998255}, "freq": {"A-DEC": 0.0, "Q-DEC": 0.0, "M": 0.0, "W-SUN": 0.0, "D": 0.0, "H": 0.0005933044759885986, "15T": -0.00021419688047119578, "W-FRI": 0.0, "10T": 0.0003744818830176966, "W-TUE": 0.0, "W-THU": 0.0, "W-WED": 0.0, "5T": -0.0007821419089351078, "10S": 0.0006159170539420389}}, "cpu_seconds": 2.1879017560277134}, {"cutoff": "valid_backcast", "skill": 0.34552801983563863, "control_skill": 0.3456661156587746, "delta": -0.00013809582313595303, "groups": {"term": {"short": 0.3130693683031155, "medium": 0.36409584759645663, "long": 0.40661946739644783}, "domain": {"Econ/Fin": 0.18043527478544963, "Energy": 0.2346036566355374, "Healthcare": 0.4074352868061085, "Nature": 0.3813469567478609, "Sales": 0.4075242245178744, "Transport": 0.3773084526081981, "Web/CloudOps": 0.460774674498852}, "freq": {"A-DEC": 0.331781124887934, "Q-DEC": 0.23724173166905804, "M": 0.1874780089767496, "W-SUN": 0.0459263068532203, "D": 0.36418901298748285, "H": 0.3759881746105659, "15T": 0.22342033520859927, "W-FRI": 0.24140491835961553, "10T": 0.3352253995653093, "W-TUE": 0.5514665885251284, "W-THU": 0.2677180644187378, "W-WED": 0.3624726313886212, "5T": 0.4484197836241629, "10S": 0.35023445213774107}}, "group_deltas": {"term": {"short": 0.0, "medium": 0.0, "long": -0.0005781085562804877}, "domain": {"Econ/Fin": 0.0, "Energy": -0.00021174730897632887, "Healthcare": 0.0, "Nature": 4.371200373309847e-05, "Sales": 0.0, "Transport": -0.0003564144378479517, "Web/CloudOps": -0.0001101833905555738}, "freq": {"A-DEC": 0.0, "Q-DEC": 0.0, "M": 0.0, "W-SUN": 0.0, "D": 0.0, "H": -0.0003804917627104887, "15T": 0.00027276206477055975, "W-FRI": 0.0, "10T": -0.00025833136630626363, "W-TUE": 0.0, "W-THU": 0.0, "W-WED": 0.0, "5T": -0.00030964469606897804, "10S": 0.0003569214882658267}}, "cpu_seconds": 2.368547848891467}, {"cutoff": "valid_backcast2", "skill": 0.3697585359683079, "control_skill": 0.36966272417010304, "delta": 9.581179820483676e-05, "groups": {"term": {"short": 0.35627742731482626, "medium": 0.3989526857240734, "long": 0.37461197571352733}, "domain": {"Econ/Fin": 0.12750789956106479, "Energy": 0.24076627732572298, "Healthcare": 0.362169648295931, "Nature": 0.48220133556091316, "Sales": 0.5592426971862643, "Transport": 0.428004648661776, "Web/CloudOps": 0.4501235444860504}, "freq": {"A-DEC": 0.3381861793580494, "Q-DEC": 0.15558369062139554, "M": 0.16912368457803728, "W-SUN": 0.07258683839700397, "D": 0.43534732833058165, "H": 0.4007724618550511, "15T": 0.10618247831915328, "W-FRI": 0.14511791304527222, "10T": 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0.3999017621812754}, "domain": {"Econ/Fin": 0.191514835075003, "Energy": 0.31758990981863744, "Healthcare": 0.23406912051970086, "Nature": 0.5088436803327384, "Sales": 0.40240992387298036, "Transport": 0.510107345503138, "Web/CloudOps": 0.4027043020719411}, "freq": {"A-DEC": 0.3632879895614767, "Q-DEC": 0.2635672907077069, "M": 0.1461601048112755, "W-SUN": 0.17023500340733289, "D": 0.4402406107863014, "H": 0.40842425193398835, "15T": 0.2581725218060308, "W-FRI": 0.044141466152163744, "10T": 0.603768947774882, "W-TUE": 0.28794685929485997, "W-THU": 0.3474718313178913, "W-WED": 0.37413351061197164, "5T": 0.4733562717768982, "10S": 0.29473909143679555}}, "group_deltas": {"term": {"short": 0.0, "medium": 0.0, "long": 0.0005569700442369374}, "domain": {"Econ/Fin": 0.0, "Energy": 0.0004401334573369553, "Healthcare": 0.0, "Nature": 6.636799453929587e-05, "Sales": 0.0, "Transport": -0.00010771903796824578, "Web/CloudOps": -6.483938592760641e-05}, "freq": {"A-DEC": 0.0, "Q-DEC": 0.0, "M": 0.0, "W-SUN": 0.0, "D": 0.0, "H": 0.0003770624356120855, "15T": 5.904467434147964e-05, "W-FRI": 0.0, "10T": 0.0001449326186091504, "W-TUE": 0.0, "W-THU": 0.0, "W-WED": 0.0, "5T": -0.0003717869996854306, "10S": 0.0005353424215627234}}, "cpu_seconds": 2.3115525729954243}], "positive_cutoffs": 3, "mean_delta": 5.040586425572591e-05, "seconds": 9.889360717963427, "optimizer_updates": 0, "new_neural_inference": false, "selection_gate_passed": true}. No automatic publication.
+
+E143 12000 four-cutoff comparison: {"label": "E143 12000 four-cutoff comparison", "utc": "2026-10-23T22:50:00.152342+00:00", "candidate": "lowfreq_age_ema12000", "control": "lowfreq_windows_ema12000", "rows": [{"cutoff": "evals", "candidate_skill": 0.37582629179167915, "control_skill": 0.37612597264381065, "delta": -0.00029968085213150086, "candidate_checkpoint": "8d9f1131f489b02e7183f20ed97597890a23f9e122c040790206f27c6bed115f", "control_checkpoint": "f1deb47b05e0dddb2c882081f6371d4a6c27e5b644446ab2d3848ec6af789fd8", "candidate_eval_seconds": 23.56525444984436, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7430387541291413, "rel_wql_gmean": 0.48059074203433233}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7129065685094401, "rel_wql_gmean": 0.49473451529311596}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7605892163208385, "rel_wql_gmean": 0.5500467199757789}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8751537886005407, "rel_wql_gmean": 0.8489925969168409}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.9033480773349892, "rel_wql_gmean": 0.6466318840186537}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.6067220309390556, "rel_wql_gmean": 0.5463550406044607}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.8148869117975975, "rel_wql_gmean": 0.40230214129477554}, "Sales": {"n_configs": 4, "rel_mase_gmean": 0.8506800907099308, "rel_wql_gmean": 0.5211520439538441}, "Transport": {"n_configs": 15, "rel_mase_gmean": 0.6440073452590136, "rel_wql_gmean": 0.4415893375484053}, "Web/CloudOps": {"n_configs": 20, "rel_mase_gmean": 0.561983941579941, "rel_wql_gmean": 0.43690947135427166}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.620637997328601, "rel_wql_gmean": 0.640384999899985}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7828788301535624, "rel_wql_gmean": 0.5400690656549743}, "H": {"n_configs": 31, "rel_mase_gmean": 0.732086160088439, "rel_wql_gmean": 0.4989357504762419}, "M": {"n_configs": 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No transfer formula or automatic publication is selected by this controller."}
+
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{"n_configs": 29, "rel_mase_gmean": 0.7286034733540714, "rel_wql_gmean": 0.4901422148570005}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8802021855900102, "rel_wql_gmean": 0.868733337230998}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7116971108623457, "rel_wql_gmean": 0.7694009807630384}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8455854089841978, "rel_wql_gmean": 0.6001614189715428}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7095322654888063, "rel_wql_gmean": 0.46338729070416085}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8213483530670851, "rel_wql_gmean": 0.6657289776232989}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7638990373886446, "rel_wql_gmean": 0.5132974511868262}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7242377646968124, "rel_wql_gmean": 0.502370743950782}}}, "control_groups": {"term": {"long": {"n_configs": 19, "rel_mase_gmean": 0.7675699458528298, "rel_wql_gmean": 0.476452132158557}, "medium": {"n_configs": 20, "rel_mase_gmean": 0.773141221801274, "rel_wql_gmean": 0.5311688105422268}, "short": {"n_configs": 54, "rel_mase_gmean": 0.7189800356855841, "rel_wql_gmean": 0.5092012481102669}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 0.8366494448197422, "rel_wql_gmean": 0.8335501433013701}, "Energy": {"n_configs": 32, "rel_mase_gmean": 0.8362673307045482, "rel_wql_gmean": 0.5558565966414833}, "Healthcare": {"n_configs": 5, "rel_mase_gmean": 0.7992789325648234, "rel_wql_gmean": 0.663313586611861}, "Nature": {"n_configs": 15, "rel_mase_gmean": 0.7270958326307262, "rel_wql_gmean": 0.3416485868534862}, "Sales": {"n_configs": 3, "rel_mase_gmean": 0.7558002187371488, "rel_wql_gmean": 0.47806361106286926}, "Transport": {"n_configs": 14, "rel_mase_gmean": 0.5447632408986202, "rel_wql_gmean": 0.45146185813227047}, "Web/CloudOps": {"n_configs": 18, "rel_mase_gmean": 0.7184671561660497, "rel_wql_gmean": 0.5194115372422989}}, "frequency": {"A": {"n_configs": 1, "rel_mase_gmean": 0.5301621193542202, "rel_wql_gmean": 0.8023624756576537}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7320022964105988, "rel_wql_gmean": 0.42747341049868826}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7284419538124913, "rel_wql_gmean": 0.48986156448616525}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8692971155035826, "rel_wql_gmean": 0.8573561489309673}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7145578258684864, "rel_wql_gmean": 0.7671041544075885}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8434376506034783, "rel_wql_gmean": 0.6004240857745707}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7091796225957213, "rel_wql_gmean": 0.4627952794263764}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8186104789687557, "rel_wql_gmean": 0.6672650133055504}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7652944038188929, "rel_wql_gmean": 0.5141740641844628}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7208570784782143, "rel_wql_gmean": 0.5012656144320912}}}, "group_skill_delta": {"term": {"long": -0.0006499023218675681, "medium": -0.00013146939684294257, "short": -0.0009195699214111785}, "domain": {"Econ/Fin": 0.010336768884061431, "Energy": 0.0015517047268557294, "Healthcare": -0.016791163289867517, "Nature": -0.0027046835857964524, "Sales": -0.0002773780983225116, "Transport": -0.0004038920519970346, "Web/CloudOps": -0.0015701142337304352}, "frequency": {"A": 0.031757097259507705, "D": -0.002164382825519673, "H": -0.00023733595737485125, "M": -0.011142972110977256, "Q": 0.0003781680146515143, "S": -0.0007496125391343034, "T": -0.0005088165900317687, "W": -0.00038231704766089614}, "variates": {"multivariate": 0.0011066065728931873, "univariate": -0.00207173387084425}}}], "mean_delta": -0.0007547425854894374, "positive_cutoffs": 1, "outcome": "Secondary original-window reference; primary matched control remains E113 lowfreq_windows. No automatic transfer or publication."}
+
+2026-10-23T22:50:44.006763+00:00: E141 final12000: 2of4 positive, mean delta0.001850641796; declared gate failed. Final source checkpoint8bdc6aec5ac81386cace42f2ef23202df02cc54a97261eae61ececffaae962e2. Training complete; preserve negatives. E141final therefore produces no E153finalquartet. E143final produces no transfer.
+
+2026-10-23T22:50:44.006763+00:00: E143 final12000: 1of4 positive, mean delta-0.000258722884; declared gate failed. Final source checkpoint8d9f1131f489b02e7183f20ed97597890a23f9e122c040790206f27c6bed115f. Training complete; preserve negatives. E141final therefore produces no E153finalquartet. E143final produces no transfer.
+
+2026-10-23T22:52:28.132602+00:00: E155 declaration {"declared_utc": "2026-10-23T22:52:28.132602+00:00", "status": "declared; CPU/GPU memory and accumulation audits next", "hypothesis": "The fresh146M expert still gains from pretraining duration. Increasing width at the same data exposure and objective may improve generalization beyond the146M ceiling.", "factor": "Fresh transformer width1024->1536 and heads16->24 with head dimension64 unchanged; encoder6/decoder4, input/outputpatch16, maxcontext4096/horizon1024 unchanged.", "parameters": 329029920, "seed": 307, "batch": 48, "micro_batch": 16, "lr": 0.0002, "steps": 96000, "primary": 48000, "final": 96000, "diagnostics": [4000, 8000, 16000, 24000, 72000], "control": "E79 random_capacity_1024 fresh146M, same seed/B48/LR2e-4/original96k schedule, already completed; do not retrain.", "packed": "/workspace/research/packed_sample", "source_weights_from": "/workspace/research/packed_sample", "synthetics": "Same20percent own original generator with slowAR time constants[1,10000]; no new source or generator.", "code_dir": "/workspace/research/random_capacity", "study_code": "/workspace/research/random_capacity_1536", "loss": "Identical paired median/quantile objective; E150 context-weight-corrected gradient accumulation micro16 to preserve effectiveB48, one clip/AdamW/EMA update per48 rows.", "numerical_caveat": "Microbatch reduction order differs from E79 fullbatch arithmetic; quantify gradient agreement before training. This is a resource adaptation, not a change in intended weighted objective.", "initialization": "Independent local torch random initialization seed307, no inherited trained tensor or pretrained asset; original zero output head/fixed biases.", "selection": "Matched48k and96k four-cutoff comparisons with>=3positive and positive mean. No automatic ensemble or publication; then-current and full public replay/runtime guards required.", "planned_after": "E101 distill_ensemble original96000 endpoint; replace its allocation, never an additional main trainer.", "optimizer_updates": 0, "training_queued": false}.
+
+E122 48000 four-cutoff comparison: {"label": "E122 48000 four-cutoff comparison", "utc": "2026-10-23T22:53:34.790545+00:00", "candidate": "random_capacity_original_ar_ema48000", "control": "random_capacity_1024_ema48000", "rows": [{"cutoff": "evals", "candidate_skill": 0.3583546216345457, "control_skill": 0.3685434345005941, "delta": -0.010188812866048447, "candidate_checkpoint": "da6f980116bfa7f71037fbe41923d4e8b31a88b08ac6b84c661eaa6fa24661a9", "control_checkpoint": "227ea80abf17fc81ae191f94e91f6dd7adbc0d837235750b5f5e9cef3dd5472c", "candidate_eval_seconds": 37.268303632736206, "candidate_groups": {"term": {"long": {"n_configs": 21, "rel_mase_gmean": 0.7626305716538019, "rel_wql_gmean": 0.5013474487738068}, "medium": {"n_configs": 21, "rel_mase_gmean": 0.7462664970815309, "rel_wql_gmean": 0.5086425008617949}, "short": {"n_configs": 55, "rel_mase_gmean": 0.7737991030438872, "rel_wql_gmean": 0.5645525287434197}}, "domain": {"Econ/Fin": {"n_configs": 6, "rel_mase_gmean": 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"rel_wql_gmean": 0.8446584386780612}, "D": {"n_configs": 15, "rel_mase_gmean": 0.7841644844236885, "rel_wql_gmean": 0.45301801369927336}, "H": {"n_configs": 29, "rel_mase_gmean": 0.7364988058949682, "rel_wql_gmean": 0.5033340588432017}, "M": {"n_configs": 4, "rel_mase_gmean": 0.8691738414068243, "rel_wql_gmean": 0.8678518341048217}, "Q": {"n_configs": 1, "rel_mase_gmean": 0.7280932255410748, "rel_wql_gmean": 0.7727291093550366}, "S": {"n_configs": 6, "rel_mase_gmean": 0.8609187156813392, "rel_wql_gmean": 0.6295058615351511}, "T": {"n_configs": 29, "rel_mase_gmean": 0.7042489928814539, "rel_wql_gmean": 0.4735497097280189}, "W": {"n_configs": 8, "rel_mase_gmean": 0.8282027876303875, "rel_wql_gmean": 0.6880431539037373}}, "variates": {"multivariate": {"n_configs": 41, "rel_mase_gmean": 0.7803099495695505, "rel_wql_gmean": 0.5354633301386752}, "univariate": {"n_configs": 52, "rel_mase_gmean": 0.7294079779755795, "rel_wql_gmean": 0.5139221776313134}}}, "group_skill_delta": {"term": {"long": 0.003049293007639564, "medium": -0.004711584810814018, "short": -0.00481817313538857}, "domain": {"Econ/Fin": -0.009580816437001483, "Energy": 0.006819300666725092, "Healthcare": -0.07278840120626084, "Nature": -0.00553389737315102, "Sales": 0.014524145343071893, "Transport": -0.00017326836298958703, "Web/CloudOps": -0.005965476829611016}, "frequency": {"A": 0.01977248312069435, "D": -0.0023504948050594443, "H": 0.0005945546410990321, "M": -0.10564956066959075, "Q": 0.0086375386299542, "S": 0.0109968371529372, "T": -0.0038695700071561534, "W": 0.007938686673368611}, "variates": {"multivariate": 0.0005552107146263863, "univariate": -0.0059479485015304645}}}], "mean_delta": -0.004799903646381887, "positive_cutoffs": 0, "outcome": "Declared comparison checkpoints 48000,96000. No transfer formula or automatic publication is selected by this controller."}
+
+2026-10-23T22:54:37.290385+00:00: E12248k review {"utc": "2026-10-23T22:54:37.290385+00:00", "experiment": "E122", "step": 48000, "positive_cutoffs": 0, "mean_delta": -0.004799903646381887, "candidate_sha256": "da6f980116bfa7f71037fbe41923d4e8b31a88b08ac6b84c661eaa6fa24661a9", "group_deltas": [{"cutoff": "evals", "skill": 0.3583546216345457, "delta": -0.010188812866048447, "term": {"long": -0.00442748519653946, "medium": -0.007018239319965702, "short": -0.01380744920804422}}, {"cutoff": "backcast_evals", "skill": 0.3270556665005907, "delta": -0.0014015788341475988, "term": {"long": 0.0004221985032669684, "medium": -0.0032256745834825518, "short": -0.0014506505329117125}}, {"cutoff": "backcast2_evals", "skill": 0.34757951018600564, "delta": -0.004449035741193819, "term": {"long": -0.0025955917516057303, "medium": -0.002159244889502143, "short": -0.006083435629386624}}, {"cutoff": "backcast3_evals", "skill": 0.3697603362599087, "delta": -0.0031601871441376828, "term": {"long": 0.003049293007639564, "medium": -0.004711584810814018, "short": -0.00481817313538857}}], "outcome": "Primary48k rejected0of4. No transfer or ensemble candidate selected; original96k durability schedule remains. E155retainsE79slowAR recipe."}.
+
+2026-10-23T22:54:55.998327+00:00: E156 declaration {"declared_utc": "2026-10-23T22:54:55.998327+00:00", "status": "declared; waiting source48k", "hypothesis": "The E131learned seasonal-summary gains from fresh pretraining may survive as the independent fourth expert of the existing quartet.", "formula": "Keep E148first3 experts and equal ordered quarters. Compare E131fresh146Mseasonal fourth to matched E79fresh146M fourth at48k/96k; also compare fixed E148.", "fixed_first_three": ["seasonal_width_transfer12k", "patch8_ema6000", "split_patch_ema12000"], "fixed_first_three_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe"], "source_primary": 48000, "source_final": 96000, "candidate_run": "random_capacity_long_seasonal", "control_run": "random_capacity_1024", "frozen_parent_descriptor": "a1b4ab426901be8e1ee299eeeb3f041bac7cea3d4e59c4608824fc3d05c83e03", "gate": "Source matched>=3positive/positive mean, then candidate quartet>=3positive/positive mean against matched control quartet and frozen E148. Before any full replay, recheck against then-current publication. All full/runtime/relocation/composition guards before atomic publication.", "no_weight_sweep": true, "new_training": false, "code": "/workspace/research/fresh_seasonal_ensemble", "receipt": "/workspace/research/fresh_seasonal_ensemble_receipt.json"}. Primary/final source selection remains predeclared; early learning diagnostics do not select any checkpoint.
+
+2026-10-23T22:58:45.539489+00:00: E156conditional quartet assembler/fullsuite/promoter prepared for48k/96k sourcegates, no weightsselected yet. E152conditional fullsuite/promoter generalized only to its3declared cells and2immutableexpert snapshots; runtime/model/trainer unchanged. Freshcurrentcutoff/full improvement, complete97/151170/0fail, independent actualexpert synthetic+realcomposition and relocation required beforepromotion. InitialE148gatepackage independentcomposition checker queued as engineering-only,0updates/novalidationlabels.
+
+2026-10-23T23:00:57.492290+00:00: Completed-study compute [{"experiment": "E141", "run": "capacity_patch8_candidate", "completed_utc": "2026-10-23T22:40:43Z", "training_process_seconds": 3914.170209452044, "completed_step": 12000, "training_process_loop_seconds": 3897.9371046340093, "scope": "Entire fresh candidate training in recovery4, shared-H100 process wall time; not isolated GPU time"}, {"experiment": "E143", "run": "lowfreq_age", "completed_utc": "2026-10-23T22:45:44Z", "training_process_seconds": 2300.889190593967, "completed_step": 12000, "training_process_loop_seconds": 2290.283922189148, "scope": "Entire fresh candidate training in recovery4, shared-H100 process wall time; not isolated GPU time"}]. Negative endpoints retained as previously recorded.
+
+## E154 fixed horizon rule full replay — selected
+- Checkpoint SHA-256: `85c52464a7bc064e926ff0aa48fd2d94c39af985932e9d752026486a70860742`.
+- Evaluation: 97 configurations, 151170 instances, 0 failed configurations; not separately timed seconds.
+- Overall skill 0.384660; relative MASE 0.736160; relative WQL 0.514350.
+- term skill: long=0.4008, medium=0.4096, short=0.3685.
+- domain skill: Econ/Fin=0.1298, Energy=0.2412, Healthcare=0.5001, Nature=0.4413, Sales=0.3581, Transport=0.4734, Web/CloudOps=0.4995.
+- frequency skill: A=0.3043, D=0.3760, H=0.3990, M=0.1515, Q=0.1921, S=0.4246, T=0.4379, W=0.2444.
+- variates skill: multivariate=0.3963, univariate=0.3752.
+- Outcome: Fixed horizon720 rule improves full skill from 0.384535531421 to 0.384659808175; all97/151170/0fail. All5 models forecast every instance. All97 outputs match the independently validated selected quartet bitwise; real/synthetic composition and relocation pass. Shared-H100 inference613.711s. Adaptive validation selection, not held-out reward.
+
+E154 verified suite {"status": "complete; review required", "package": "/workspace/research/candidate-horizon-patch8-routing", "steps": [{"name": "synthetic_composition_and_every_member_calls", "returncode": 0, "seconds": 71.91766698891297}, {"name": "relocation", "returncode": 0, "seconds": 64.51891429605894}, {"name": "full_public_replay", "returncode": 0, "seconds": 652.2583563679364}, {"name": "real_composition", "returncode": 0, "seconds": 1.3349886659998447}, {"name": "descriptive_comparison", "returncode": 0, "seconds": 0.536687342915684}], "current_parent": "a1b4ab426901be8e1ee299eeeb3f041bac7cea3d4e59c4608824fc3d05c83e03", "current_cutoff_deltas": [0.00012870854792634834, -0.00013809582313595303, 9.581179820483676e-05, 0.00011519893402767156], "current_step": "descriptive_comparison", "seconds": 790.6030867490917, "skill": 0.3846598081752668}. Synthetic {"descriptor_sha256": "85c52464a7bc064e926ff0aa48fd2d94c39af985932e9d752026486a70860742", "checks": [{"config_id": "synthetic_H", "horizon": 384, "selected_members": [0, 1, 2, 3], "bitwise_composition": true, "finite_ordered": true, "every_member_forecasts_every_instance": true, "forward_calls_by_member": [3, 3, 3, 3, 3]}, {"config_id": "synthetic_15T", "horizon": 960, "selected_members": [0, 1, 2, 4], "bitwise_composition": true, "finite_ordered": true, "every_member_forecasts_every_instance": true, "forward_calls_by_member": [3, 3, 3, 3, 3]}], "member_checkpoint_hashes": ["0b11b7351a4a92d1d18c71a4159ec445be3a94ace8c7a9fa0edafad87d14e960", "308389b7217c080af9eb185559d4ee3cafa0f3e0f8c4dd8f5526b68198c89988", "1a2fb20a9fb5cae6526f2e36ea47559626bece78fd9c284899836505030ae9fe", "d7cfcd72c3cb84aa69e447b76c4c3ab98c21b7c600a463f0b805f4b4300baf33", "2877a38b94a7d77a53a9df410349cecc2f6ab134c83a76744924543bbe73eb17"], "seconds": 69.57236026506871}. Current-publication cutoff deltas [0.00012870854792634834, -0.00013809582313595303, 9.581179820483676e-05, 0.00011519893402767156]. Original teacher and gate expert snapshots stay immutable.
diff --git a/shared/package-files/3d/3d63f2fa84ec140ebb94c9d11688dbb2e376f833435953c2547bd4418901cc59/__main__.py b/shared/package-files/3d/3d63f2fa84ec140ebb94c9d11688dbb2e376f833435953c2547bd4418901cc59/__main__.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d6b995305fa34e32cde1297f3664d0098da2239
--- /dev/null
+++ b/shared/package-files/3d/3d63f2fa84ec140ebb94c9d11688dbb2e376f833435953c2547bd4418901cc59/__main__.py
@@ -0,0 +1,5 @@
+import sys
+from fontTools.designspaceLib import main
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/shared/package-files/3d/3d685e2417e91cc59427d6ca4ab58063478a309b57f4ab1d1848e4fb04a810e2/linalg_diagonal_native.h b/shared/package-files/3d/3d685e2417e91cc59427d6ca4ab58063478a309b57f4ab1d1848e4fb04a810e2/linalg_diagonal_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..9e5a55bfa63d472c369d71179589a6a41f07897f
--- /dev/null
+++ b/shared/package-files/3d/3d685e2417e91cc59427d6ca4ab58063478a309b57f4ab1d1848e4fb04a810e2/linalg_diagonal_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor linalg_diagonal(const at::Tensor & A, int64_t offset=0, int64_t dim1=-2, int64_t dim2=-1);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/3d/3d690d066ce7d1480bf978b2c3986971860f3023420a874d82451b6df48ab1c7/__init__.py b/shared/package-files/3d/3d690d066ce7d1480bf978b2c3986971860f3023420a874d82451b6df48ab1c7/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..944319232aec029a4da6db975f005d513636cd61
--- /dev/null
+++ b/shared/package-files/3d/3d690d066ce7d1480bf978b2c3986971860f3023420a874d82451b6df48ab1c7/__init__.py
@@ -0,0 +1,11 @@
+# Copyright (C) 2012 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+"""
+Code in ``conda.common`` is not conda-specific.  Technically, it sits *aside* the application
+stack and not *within* the stack.  It is able to stand independently on its own.
+The *only* allowed imports of conda code in ``conda.common`` modules are imports of other
+``conda.common`` modules and imports from ``conda._vendor``.
+
+If objects are needed from other parts of conda, they should be passed directly as arguments to
+functions and methods.
+"""
diff --git a/shared/package-files/3d/3d694a18ae7822eee485535db7c9e4a859c01ef945d4b91908fb6dc54e634697/formsets.pyi b/shared/package-files/3d/3d694a18ae7822eee485535db7c9e4a859c01ef945d4b91908fb6dc54e634697/formsets.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..dab81a99c08e8990534536177ac0a55dd038ce4a
--- /dev/null
+++ b/shared/package-files/3d/3d694a18ae7822eee485535db7c9e4a859c01ef945d4b91908fb6dc54e634697/formsets.pyi
@@ -0,0 +1,89 @@
+from typing import Any, Dict, Mapping, Optional, Sequence, Sized
+
+from django.forms import Form
+
+TOTAL_FORM_COUNT: str = ...
+INITIAL_FORM_COUNT: str = ...
+MIN_NUM_FORM_COUNT: str = ...
+MAX_NUM_FORM_COUNT: str = ...
+ORDERING_FIELD_NAME: str = ...
+DELETION_FIELD_NAME: str = ...
+
+DEFAULT_MIN_NUM: int = ...
+DEFAULT_MAX_NUM: int = ...
+
+class ManagementForm(Form):
+    cleaned_data: Dict[str, Optional[int]]
+
+class BaseFormSet(Sized, Mapping[str, Any]):
+    is_bound: Any = ...
+    prefix: Any = ...
+    auto_id: Any = ...
+    data: Any = ...
+    files: Any = ...
+    initial: Any = ...
+    form_kwargs: Any = ...
+    error_class: Any = ...
+    def __init__(
+        self,
+        data: Optional[Any] = ...,
+        files: Optional[Any] = ...,
+        auto_id: str = ...,
+        prefix: Optional[Any] = ...,
+        initial: Optional[Any] = ...,
+        error_class: Any = ...,
+        form_kwargs: Optional[Any] = ...,
+    ) -> None: ...
+    def __iter__(self): ...
+    def __getitem__(self, index: Any): ...
+    def __len__(self): ...
+    def __bool__(self): ...
+    def management_form(self): ...
+    def total_form_count(self): ...
+    def initial_form_count(self): ...
+    @property
+    def forms(self): ...
+    def get_form_kwargs(self, index: Any): ...
+    @property
+    def initial_forms(self): ...
+    @property
+    def extra_forms(self): ...
+    @property
+    def empty_form(self): ...
+    @property
+    def cleaned_data(self): ...
+    @property
+    def deleted_forms(self): ...
+    @property
+    def ordered_forms(self): ...
+    @classmethod
+    def get_default_prefix(cls): ...
+    def non_form_errors(self): ...
+    @property
+    def errors(self): ...
+    def total_error_count(self): ...
+    def is_valid(self): ...
+    def full_clean(self): ...
+    def clean(self) -> None: ...
+    def has_changed(self): ...
+    def add_fields(self, form: Any, index: Any) -> None: ...
+    def add_prefix(self, index: Any): ...
+    def is_multipart(self): ...
+    @property
+    def media(self): ...
+    def as_table(self): ...
+    def as_p(self): ...
+    def as_ul(self): ...
+
+def formset_factory(
+    form: Any,
+    formset: Any = ...,
+    extra: int = ...,
+    can_order: bool = ...,
+    can_delete: bool = ...,
+    max_num: Optional[Any] = ...,
+    validate_max: bool = ...,
+    min_num: Optional[Any] = ...,
+    validate_min: bool = ...,
+): ...
+def all_valid(formsets: Sequence[Any]) -> bool: ...
diff --git a/shared/package-files/3d/3d6b80a0d3d9ebe0bc1c26181821bdb4c0a0c2e3338bf79bc88a854890aa93a9/ast_parser.py b/shared/package-files/3d/3d6b80a0d3d9ebe0bc1c26181821bdb4c0a0c2e3338bf79bc88a854890aa93a9/ast_parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..c828ed31a19b02ff4e55fb2de2e6400dfc5289fd
--- /dev/null
+++ b/shared/package-files/3d/3d6b80a0d3d9ebe0bc1c26181821bdb4c0a0c2e3338bf79bc88a854890aa93a9/ast_parser.py
@@ -0,0 +1,79 @@
+"""
+This module implements the functionality to take any Python expression as a
+string and fix all numbers and other things before evaluating it,
+thus
+
+1/2
+
+returns
+
+Integer(1)/Integer(2)
+
+We use the ast module for this. It is well documented at docs.python.org.
+
+Some tips to understand how this works: use dump() to get a nice
+representation of any node. Then write a string of what you want to get,
+e.g. "Integer(1)", parse it, dump it and you'll see that you need to do
+"Call(Name('Integer', Load()), [node], [], None, None)". You do not need
+to bother with lineno and col_offset, just call fix_missing_locations()
+before returning the node.
+"""
+
+from sympy.core.basic import Basic
+from sympy.core.sympify import SympifyError
+
+from ast import parse, NodeTransformer, Call, Name, Load, \
+    fix_missing_locations, Str, Tuple
+
+class Transform(NodeTransformer):
+
+    def __init__(self, local_dict, global_dict):
+        NodeTransformer.__init__(self)
+        self.local_dict = local_dict
+        self.global_dict = global_dict
+
+    def visit_Constant(self, node):
+        if isinstance(node.value, int):
+            return fix_missing_locations(Call(func=Name('Integer', Load()),
+                    args=[node], keywords=[]))
+        elif isinstance(node.value, float):
+            return fix_missing_locations(Call(func=Name('Float', Load()),
+                    args=[node], keywords=[]))
+        return node
+
+    def visit_Name(self, node):
+        if node.id in self.local_dict:
+            return node
+        elif node.id in self.global_dict:
+            name_obj = self.global_dict[node.id]
+
+            if isinstance(name_obj, (Basic, type)) or callable(name_obj):
+                return node
+        elif node.id in ['True', 'False']:
+            return node
+        return fix_missing_locations(Call(func=Name('Symbol', Load()),
+                args=[Str(node.id)], keywords=[]))
+
+    def visit_Lambda(self, node):
+        args = [self.visit(arg) for arg in node.args.args]
+        body = self.visit(node.body)
+        n = Call(func=Name('Lambda', Load()),
+            args=[Tuple(args, Load()), body], keywords=[])
+        return fix_missing_locations(n)
+
+def parse_expr(s, local_dict):
+    """
+    Converts the string "s" to a SymPy expression, in local_dict.
+
+    It converts all numbers to Integers before feeding it to Python and
+    automatically creates Symbols.
+    """
+    global_dict = {}
+    exec('from sympy import *', global_dict)
+    try:
+        a = parse(s.strip(), mode="eval")
+    except SyntaxError:
+        raise SympifyError("Cannot parse %s." % repr(s))
+    a = Transform(local_dict, global_dict).visit(a)
+    e = compile(a, "", "eval")
+    return eval(e, global_dict, local_dict)
diff --git a/shared/package-files/3d/3d6cb82fb65dae743f200284cf82e55f47bff89b232ad642c52c1a2aeb0fb11e/linalg_ldl_solve_cuda_dispatch.h b/shared/package-files/3d/3d6cb82fb65dae743f200284cf82e55f47bff89b232ad642c52c1a2aeb0fb11e/linalg_ldl_solve_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..32fbb5a1a10123e494665d5264630859c464c450
--- /dev/null
+++ b/shared/package-files/3d/3d6cb82fb65dae743f200284cf82e55f47bff89b232ad642c52c1a2aeb0fb11e/linalg_ldl_solve_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor linalg_ldl_solve(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian=false);
+TORCH_API at::Tensor & linalg_ldl_solve_out(at::Tensor & out, const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian=false);
+TORCH_API at::Tensor & linalg_ldl_solve_outf(const at::Tensor & LD, const at::Tensor & pivots, const at::Tensor & B, bool hermitian, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/3d/3d6f20e820f6ca3c20c28260610f4db60d9edafe957eee0060be6488ce2cab44/libarrow_dataset_parquet.pxd b/shared/package-files/3d/3d6f20e820f6ca3c20c28260610f4db60d9edafe957eee0060be6488ce2cab44/libarrow_dataset_parquet.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..8f4917f6c0c80ad969d79a3ce71e035ddf0b0c57
--- /dev/null
+++ b/shared/package-files/3d/3d6f20e820f6ca3c20c28260610f4db60d9edafe957eee0060be6488ce2cab44/libarrow_dataset_parquet.pxd
@@ -0,0 +1,107 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+# distutils: language = c++
+
+from pyarrow.includes.libarrow_dataset cimport *
+from pyarrow.includes.libparquet_encryption cimport *
+
+from pyarrow._parquet cimport *
+
+
+cdef extern from "arrow/dataset/parquet_encryption_config.h" namespace "arrow::dataset" nogil:
+    cdef cppclass CParquetEncryptionConfig "arrow::dataset::ParquetEncryptionConfig":
+        shared_ptr[CCryptoFactory] crypto_factory
+        shared_ptr[CKmsConnectionConfig] kms_connection_config
+        shared_ptr[CEncryptionConfiguration] encryption_config
+
+    cdef cppclass CParquetDecryptionConfig "arrow::dataset::ParquetDecryptionConfig":
+        shared_ptr[CCryptoFactory] crypto_factory
+        shared_ptr[CKmsConnectionConfig] kms_connection_config
+        shared_ptr[CDecryptionConfiguration] decryption_config
+
+
+cdef extern from "arrow/dataset/api.h" namespace "arrow::dataset" nogil:
+
+    cdef cppclass CParquetFileWriter \
+            "arrow::dataset::ParquetFileWriter"(CFileWriter):
+        const shared_ptr[FileWriter]& parquet_writer() const
+
+    cdef cppclass CParquetFileWriteOptions \
+            "arrow::dataset::ParquetFileWriteOptions"(CFileWriteOptions):
+        shared_ptr[WriterProperties] writer_properties
+        shared_ptr[ArrowWriterProperties] arrow_writer_properties
+        shared_ptr[CParquetEncryptionConfig] parquet_encryption_config
+
+    cdef cppclass CParquetFileFragment "arrow::dataset::ParquetFileFragment"(
+            CFileFragment):
+        const vector[int]& row_groups() const
+        shared_ptr[CFileMetaData] metadata() const
+        CResult[vector[shared_ptr[CFragment]]] SplitByRowGroup(
+            CExpression predicate)
+        CResult[shared_ptr[CFragment]] SubsetWithFilter "Subset"(
+            CExpression predicate)
+        CResult[shared_ptr[CFragment]] SubsetWithIds "Subset"(
+            vector[int] row_group_ids)
+        CStatus EnsureCompleteMetadata()
+
+    cdef cppclass CParquetFileFormatReaderOptions \
+            "arrow::dataset::ParquetFileFormat::ReaderOptions":
+        unordered_set[c_string] dict_columns
+        TimeUnit coerce_int96_timestamp_unit
+        Type binary_type
+        Type list_type
+
+    cdef cppclass CParquetFileFormat "arrow::dataset::ParquetFileFormat"(
+            CFileFormat):
+        CParquetFileFormatReaderOptions reader_options
+        CResult[shared_ptr[CFileFragment]] MakeFragment(
+            CFileSource source,
+            CExpression partition_expression,
+            shared_ptr[CSchema] physical_schema,
+            vector[int] row_groups)
+
+    cdef cppclass CParquetFragmentScanOptions \
+            "arrow::dataset::ParquetFragmentScanOptions"(CFragmentScanOptions):
+        shared_ptr[CReaderProperties] reader_properties
+        shared_ptr[ArrowReaderProperties] arrow_reader_properties
+        shared_ptr[CParquetDecryptionConfig] parquet_decryption_config
+
+    cdef cppclass CParquetFactoryOptions \
+            "arrow::dataset::ParquetFactoryOptions":
+        CPartitioningOrFactory partitioning
+        c_string partition_base_dir
+        c_bool validate_column_chunk_paths
+
+    cdef cppclass CParquetDatasetFactory \
+            "arrow::dataset::ParquetDatasetFactory"(CDatasetFactory):
+        @staticmethod
+        CResult[shared_ptr[CDatasetFactory]] MakeFromMetaDataPath "Make"(
+            const c_string& metadata_path,
+            shared_ptr[CFileSystem] filesystem,
+            shared_ptr[CParquetFileFormat] format,
+            CParquetFactoryOptions options
+        )
+
+        @staticmethod
+        CResult[shared_ptr[CDatasetFactory]] MakeFromMetaDataSource "Make"(
+            const CFileSource& metadata_path,
+            const c_string& base_path,
+            shared_ptr[CFileSystem] filesystem,
+            shared_ptr[CParquetFileFormat] format,
+            CParquetFactoryOptions options
+        )
diff --git a/shared/package-files/3d/3d6f91b20fe88c11a71ef46451c2794601aee37f2de1592d662ac9cdee3b561c/dg-generic b/shared/package-files/3d/3d6f91b20fe88c11a71ef46451c2794601aee37f2de1592d662ac9cdee3b561c/dg-generic
new file mode 100644
index 0000000000000000000000000000000000000000..5189f7503555a7804a380ba85eb6a8c145bee555
Binary files /dev/null and b/shared/package-files/3d/3d6f91b20fe88c11a71ef46451c2794601aee37f2de1592d662ac9cdee3b561c/dg-generic differ
diff --git a/shared/package-files/3d/3d71ca14272f43927f1ff9de8631c527982a99c313de27a8e7bfa9bdc7f2b903/environment.yaml b/shared/package-files/3d/3d71ca14272f43927f1ff9de8631c527982a99c313de27a8e7bfa9bdc7f2b903/environment.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f77c062b22f4d1c4bc980c7cc0a6ba02ebfcc580
--- /dev/null
+++ b/shared/package-files/3d/3d71ca14272f43927f1ff9de8631c527982a99c313de27a8e7bfa9bdc7f2b903/environment.yaml
@@ -0,0 +1,3 @@
+name: test
+dependencies:
+  - numpy
diff --git a/shared/package-files/3d/3d71ca14272f43927f1ff9de8631c527982a99c313de27a8e7bfa9bdc7f2b903/environment.yml b/shared/package-files/3d/3d71ca14272f43927f1ff9de8631c527982a99c313de27a8e7bfa9bdc7f2b903/environment.yml
new file mode 100644
index 0000000000000000000000000000000000000000..f77c062b22f4d1c4bc980c7cc0a6ba02ebfcc580
--- /dev/null
+++ b/shared/package-files/3d/3d71ca14272f43927f1ff9de8631c527982a99c313de27a8e7bfa9bdc7f2b903/environment.yml
@@ -0,0 +1,3 @@
+name: test
+dependencies:
+  - numpy
diff --git a/shared/package-files/3d/3d733ec8847d78a62e1e5bb2c10f7ecea4df4280681439805dc73308989c35cd/test_array_analysis.py b/shared/package-files/3d/3d733ec8847d78a62e1e5bb2c10f7ecea4df4280681439805dc73308989c35cd/test_array_analysis.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7afadaa71d1954e0ad5f015cb4cd5f7b6052fe1
--- /dev/null
+++ b/shared/package-files/3d/3d733ec8847d78a62e1e5bb2c10f7ecea4df4280681439805dc73308989c35cd/test_array_analysis.py
@@ -0,0 +1,1146 @@
+import itertools
+
+import numpy as np
+import sys
+from collections import namedtuple
+from io import StringIO
+
+from numba import njit, typeof, prange
+from numba.core import (
+    types,
+    typing,
+    ir,
+    bytecode,
+    postproc,
+    cpu,
+    registry,
+    utils,
+)
+from numba.tests.support import (TestCase, tag, skip_parfors_unsupported,
+                                 skip_unless_scipy)
+from numba.parfors.array_analysis import EquivSet, ArrayAnalysis
+from numba.core.compiler import Compiler, Flags, PassManager
+from numba.core.ir_utils import remove_dead
+from numba.core.untyped_passes import (ExtractByteCode, TranslateByteCode, FixupArgs,
+                             IRProcessing, DeadBranchPrune,
+                             RewriteSemanticConstants, GenericRewrites,
+                             WithLifting, PreserveIR, InlineClosureLikes)
+
+from numba.core.typed_passes import (NopythonTypeInference, AnnotateTypes,
+                                NopythonRewrites, IRLegalization)
+
+from numba.core.compiler_machinery import FunctionPass, PassManager, register_pass
+from numba.experimental import jitclass
+import unittest
+
+
+skip_unsupported = skip_parfors_unsupported
+
+
+# test class for #3700
+@jitclass([('L', types.int32), ('T', types.int32)])
+class ExampleClass3700(object):
+    def __init__(self, n):
+        self.L = n
+        self.T = n + 1
+
+
+# test value for test_global_tuple
+GVAL = (1.2,)
+GVAL2 = (3, 4)
+
+
+class TestEquivSet(TestCase):
+
+    """
+    Test array_analysis.EquivSet.
+    """
+    def test_insert_equiv(self):
+        s1 = EquivSet()
+        s1.insert_equiv('a', 'b')
+        self.assertTrue(s1.is_equiv('a', 'b'))
+        self.assertTrue(s1.is_equiv('b', 'a'))
+        s1.insert_equiv('c', 'd')
+        self.assertTrue(s1.is_equiv('c', 'd'))
+        self.assertFalse(s1.is_equiv('c', 'a'))
+        s1.insert_equiv('a', 'c')
+        self.assertTrue(s1.is_equiv('a', 'b', 'c', 'd'))
+        self.assertFalse(s1.is_equiv('a', 'e'))
+
+    def test_intersect(self):
+        s1 = EquivSet()
+        s2 = EquivSet()
+        r = s1.intersect(s2)
+        self.assertTrue(r.is_empty())
+        s1.insert_equiv('a', 'b')
+        r = s1.intersect(s2)
+        self.assertTrue(r.is_empty())
+        s2.insert_equiv('b', 'c')
+        r = s1.intersect(s2)
+        self.assertTrue(r.is_empty())
+        s2.insert_equiv('d', 'a')
+        r = s1.intersect(s2)
+        self.assertTrue(r.is_empty())
+        s1.insert_equiv('a', 'e')
+        s2.insert_equiv('c', 'd')
+        r = s1.intersect(s2)
+        self.assertTrue(r.is_equiv('a', 'b'))
+        self.assertFalse(r.is_equiv('a', 'e'))
+        self.assertFalse(r.is_equiv('c', 'd'))
+
+
+@register_pass(analysis_only=False, mutates_CFG=True)
+class ArrayAnalysisPass(FunctionPass):
+    _name = "array_analysis_pass"
+
+    def __init__(self):
+        FunctionPass.__init__(self)
+
+    def run_pass(self, state):
+        state.array_analysis = ArrayAnalysis(state.typingctx, state.func_ir,
+                                             state.typemap, state.calltypes)
+        state.array_analysis.run(state.func_ir.blocks)
+        post_proc = postproc.PostProcessor(state.func_ir)
+        post_proc.run()
+        state.func_ir_copies.append(state.func_ir.copy())
+        if state.test_idempotence and len(state.func_ir_copies) > 1:
+            state.test_idempotence(state.func_ir_copies)
+        return False
+
+
+class ArrayAnalysisTester(Compiler):
+
+    @classmethod
+    def mk_pipeline(cls, args, return_type=None, flags=None, locals=None,
+                    library=None, typing_context=None, target_context=None):
+        if locals is None:
+            locals = {}
+        if not flags:
+            flags = Flags()
+        flags.nrt = True
+        if typing_context is None:
+            typing_context = registry.cpu_target.typing_context
+        if target_context is None:
+            target_context =  registry.cpu_target.target_context
+        return cls(typing_context, target_context, library, args, return_type,
+                   flags, locals)
+
+    def compile_to_ir(self, func, test_idempotence=None):
+        """
+        Populate and run compiler pipeline
+        """
+        self.state.func_id = bytecode.FunctionIdentity.from_function(func)
+        ExtractByteCode().run_pass(self.state)
+
+        self.state.lifted = ()
+        self.state.lifted_from = None
+        state = self.state
+        state.func_ir_copies = []
+        state.test_idempotence = test_idempotence
+
+        name = 'array_analysis_testing'
+        pm = PassManager(name)
+        pm.add_pass(TranslateByteCode, "analyzing bytecode")
+        pm.add_pass(FixupArgs, "fix up args")
+        pm.add_pass(IRProcessing, "processing IR")
+        # pre typing
+        if not state.flags.no_rewrites:
+            pm.add_pass(GenericRewrites, "nopython rewrites")
+            pm.add_pass(RewriteSemanticConstants, "rewrite semantic constants")
+            pm.add_pass(DeadBranchPrune, "dead branch pruning")
+        pm.add_pass(InlineClosureLikes,
+                    "inline calls to locally defined closures")
+        # typing
+        pm.add_pass(NopythonTypeInference, "nopython frontend")
+
+        if not state.flags.no_rewrites:
+            pm.add_pass(NopythonRewrites, "nopython rewrites")
+
+        # Array Analysis pass
+        pm.add_pass(ArrayAnalysisPass, "array analysis")
+        if test_idempotence:
+            # Do another pass of array analysis to test idempotence
+            pm.add_pass(ArrayAnalysisPass, "idempotence array analysis")
+        # legalise
+        pm.add_pass(IRLegalization, "ensure IR is legal prior to lowering")
+        pm.add_pass(AnnotateTypes, "annotate types")
+
+        # partial compile
+        pm.finalize()
+        pm.run(state)
+        return state.array_analysis
+
+
+class TestArrayAnalysis(TestCase):
+
+    def compare_ir(self, ir_list):
+        outputs = []
+        for func_ir in ir_list:
+            remove_dead(func_ir.blocks, func_ir.arg_names, func_ir)
+            output = StringIO()
+            func_ir.dump(file=output)
+            outputs.append(output.getvalue())
+        self.assertTrue(len(set(outputs)) == 1)  # assert all outputs are equal
+
+    def _compile_and_test(self, fn, arg_tys, asserts=None, equivs=None, idempotent=True):
+        """
+        Compile the given function and get its IR.
+        """
+        if asserts is None:
+            asserts = []
+        if equivs is None:
+            equivs = []
+        test_pipeline = ArrayAnalysisTester.mk_pipeline(arg_tys)
+        test_idempotence = self.compare_ir if idempotent else lambda x:()
+        analysis = test_pipeline.compile_to_ir(fn, test_idempotence)
+        if equivs:
+            for func in equivs:
+                # only test the equiv_set of the first block
+                func(analysis.equiv_sets[0])
+        if asserts is None:
+            self.assertTrue(self._has_no_assertcall(analysis.func_ir))
+        else:
+            for func in asserts:
+                func(analysis.func_ir, analysis.typemap)
+
+    def _has_assertcall(self, func_ir, typemap, args):
+        msg = "Sizes of {} do not match".format(', '.join(args))
+        for label, block in func_ir.blocks.items():
+            for expr in block.find_exprs(op='call'):
+                fn = func_ir.get_definition(expr.func.name)
+                if isinstance(fn, ir.Global) and fn.name == 'assert_equiv':
+                    typ = typemap[expr.args[0].name]
+                    if typ.literal_value.startswith(msg):
+                        return True
+        return False
+
+    def _has_shapecall(self, func_ir, x):
+        for label, block in func_ir.blocks.items():
+            for expr in block.find_exprs(op='getattr'):
+                if expr.attr == 'shape':
+                    y = func_ir.get_definition(expr.value, lhs_only=True)
+                    z = func_ir.get_definition(x, lhs_only=True)
+                    y = y.name if isinstance(y, ir.Var) else y
+                    z = z.name if isinstance(z, ir.Var) else z
+                    if y == z:
+                        return True
+        return False
+
+    def _has_no_assertcall(self, func_ir):
+        for label, block in func_ir.blocks.items():
+            for expr in block.find_exprs(op='call'):
+                fn = func_ir.get_definition(expr.func.name)
+                if isinstance(fn, ir.Global) and fn.name == 'assert_equiv':
+                    return False
+        return True
+
+    def with_assert(self, *args):
+        return lambda func_ir, typemap: self.assertTrue(
+            self._has_assertcall(func_ir, typemap, args))
+
+    def without_assert(self, *args):
+        return lambda func_ir, typemap: self.assertFalse(
+            self._has_assertcall(func_ir, typemap, args))
+
+    def with_equiv(self, *args):
+        def check(equiv_set):
+            n = len(args)
+            for i in range(n - 1):
+                if not equiv_set.is_equiv(args[i], args[n - 1]):
+                    return False
+            return True
+        return lambda equiv_set: self.assertTrue(check(equiv_set))
+
+    def without_equiv(self, *args):
+        def check(equiv_set):
+            n = len(args)
+            for i in range(n - 1):
+                if equiv_set.is_equiv(args[i], args[n - 1]):
+                    return False
+            return True
+        return lambda equiv_set: self.assertTrue(check(equiv_set))
+
+    def with_shapecall(self, x):
+        return lambda func_ir, s: self.assertTrue(self._has_shapecall(func_ir, x))
+
+    def without_shapecall(self, x):
+        return lambda func_ir, s: self.assertFalse(self._has_shapecall(func_ir, x))
+
+    def test_base_cases(self):
+        def test_0():
+            a = np.zeros(0)
+            b = np.zeros(1)
+            m = 0
+            n = 1
+            c = np.zeros((m, n))
+            return
+        self._compile_and_test(test_0, (),
+                               equivs=[self.with_equiv('a', (0,)),
+                                       self.with_equiv('b', (1,)),
+                                       self.with_equiv('c', (0, 1))])
+
+        def test_1(n):
+            a = np.zeros(n)
+            b = np.zeros(n)
+            return a + b
+        self._compile_and_test(test_1, (types.intp,), asserts=None)
+
+        def test_2(m, n):
+            a = np.zeros(n)
+            b = np.zeros(m)
+            return a + b
+        self._compile_and_test(test_2, (types.intp, types.intp),
+                               asserts=[self.with_assert('a', 'b')])
+
+        def test_3(n):
+            a = np.zeros(n)
+            return a + n
+        self._compile_and_test(test_3, (types.intp,), asserts=None)
+
+        def test_4(n):
+            a = np.zeros(n)
+            b = a + 1
+            c = a + 2
+            return a + c
+        self._compile_and_test(test_4, (types.intp,), asserts=None)
+
+        def test_5(n):
+            a = np.zeros((n, n))
+            m = n
+            b = np.zeros((m, n))
+            return a + b
+        self._compile_and_test(test_5, (types.intp,), asserts=None)
+
+        def test_6(m, n):
+            a = np.zeros(n)
+            b = np.zeros(m)
+            d = a + b
+            e = a - b
+            return d + e
+        self._compile_and_test(test_6, (types.intp, types.intp),
+                               asserts=[self.with_assert('a', 'b'),
+                                        self.without_assert('d', 'e')])
+
+        def test_7(m, n):
+            a = np.zeros(n)
+            b = np.zeros(m)
+            if m == 10:
+                d = a + b
+            else:
+                d = a - b
+            return d + a
+        self._compile_and_test(test_7, (types.intp, types.intp),
+                               asserts=[self.with_assert('a', 'b'),
+                                        self.without_assert('d', 'a')])
+
+        def test_8(m, n):
+            a = np.zeros(n)
+            b = np.zeros(m)
+            if m == 10:
+                d = b + a
+            else:
+                d = a + a
+            return b + d
+        self._compile_and_test(test_8, (types.intp, types.intp),
+                               asserts=[self.with_assert('b', 'a'),
+                                        self.with_assert('b', 'd')])
+
+        def test_9(m):
+            A = np.ones(m)
+            s = 0
+            while m < 2:
+                m += 1
+                B = np.ones(m)
+                s += np.sum(A + B)
+            return s
+        self._compile_and_test(test_9, (types.intp,),
+                               asserts=[self.with_assert('A', 'B')])
+
+        def test_10(m, n):
+            p = m - 1
+            q = n + 1
+            r = q + 1
+            A = np.zeros(p)
+            B = np.zeros(q)
+            C = np.zeros(r)
+            D = np.zeros(m)
+            s = np.sum(A + B)
+            t = np.sum(C + D)
+            return s + t
+        self._compile_and_test(test_10, (types.intp,types.intp,),
+                               asserts=[self.with_assert('A', 'B'),
+                                        self.without_assert('C', 'D')])
+
+        def test_11():
+            a = np.ones(5)
+            b = np.ones(5)
+            c = a[1:]
+            d = b[:-1]
+            e = len(c)
+            f = len(d)
+            return e == f
+        self._compile_and_test(test_11, (),
+                               equivs=[self.with_equiv('e', 'f')])
+
+        def test_12():
+            a = np.ones(25).reshape((5,5))
+            b = np.ones(25).reshape((5,5))
+            c = a[1:,:]
+            d = b[:-1,:]
+            e = c.shape[0]
+            f = d.shape[0]
+            g = len(d)
+            return e == f
+        self._compile_and_test(test_12, (),
+                               equivs=[self.with_equiv('e', 'f', 'g')])
+
+        def test_tup_arg(T):
+            T2 = T
+            return T2[0]
+
+        int_arr_typ = types.Array(types.intp, 1, 'C')
+        self._compile_and_test(test_tup_arg,
+            (types.Tuple((int_arr_typ, int_arr_typ)),), asserts=None)
+
+        def test_arr_in_tup(m):
+            A = np.ones(m)
+            S = (A,)
+            B = np.ones(len(S[0]))
+            return B
+
+        self._compile_and_test(test_arr_in_tup, (types.intp,),
+                               equivs=[self.with_equiv("A", "B")])
+
+        T = namedtuple("T", ['a','b'])
+        def test_namedtuple(n):
+            r = T(n, n)
+            return r[0]
+        self._compile_and_test(test_namedtuple, (types.intp,),
+                                equivs=[self.with_equiv('r', ('n', 'n'))],)
+
+        # np.where is tricky since it returns tuple of arrays
+        def test_np_where_tup_return(A):
+            c = np.where(A)
+            return len(c[0])
+
+        self._compile_and_test(test_np_where_tup_return,
+            (types.Array(types.intp, 1, 'C'),), asserts=None)
+
+        def test_shape(A):
+            (m, n) = A.shape
+            B = np.ones((m, n))
+            return A + B
+        self._compile_and_test(test_shape, (types.Array(types.intp, 2, 'C'),),
+                               asserts=None)
+
+        def test_cond(l, m, n):
+            A = np.ones(l)
+            B = np.ones(m)
+            C = np.ones(n)
+            if l == m:
+                r = np.sum(A + B)
+            else:
+                r = 0
+            if m != n:
+                s = 0
+            else:
+                s = np.sum(B + C)
+            t = 0
+            if l == m:
+                if m == n:
+                    t = np.sum(A + B + C)
+            return r + s + t
+        self._compile_and_test(test_cond, (types.intp, types.intp, types.intp),
+                               asserts=None)
+
+        def test_assert_1(m, n):
+            assert(m == n)
+            A = np.ones(m)
+            B = np.ones(n)
+            return np.sum(A + B)
+        self._compile_and_test(test_assert_1, (types.intp, types.intp),
+                               asserts=None)
+
+        def test_assert_2(A, B):
+            assert(A.shape == B.shape)
+            return np.sum(A + B)
+
+        self._compile_and_test(test_assert_2, (types.Array(types.intp, 1, 'C'),
+                                               types.Array(types.intp, 1, 'C'),),
+                               asserts=None)
+        self._compile_and_test(test_assert_2, (types.Array(types.intp, 2, 'C'),
+                                               types.Array(types.intp, 2, 'C'),),
+                               asserts=None)
+        # expected failure
+        with self.assertRaises(AssertionError) as raises:
+            self._compile_and_test(test_assert_2, (types.Array(types.intp, 1, 'C'),
+                                                   types.Array(types.intp, 2, 'C'),),
+                                   asserts=None)
+        msg = "Dimension mismatch"
+        self.assertIn(msg, str(raises.exception))
+
+
+    def test_stencilcall(self):
+        from numba.stencils.stencil import stencil
+        @stencil
+        def kernel_1(a):
+            return 0.25 * (a[0,1] + a[1,0] + a[0,-1] + a[-1,0])
+
+        def test_1(n):
+            a = np.ones((n,n))
+            b = kernel_1(a)
+            return a + b
+
+        self._compile_and_test(test_1, (types.intp,),
+                               equivs=[self.with_equiv('a', 'b')],
+                               asserts=[self.without_assert('a', 'b')])
+
+        def test_2(n):
+            a = np.ones((n,n))
+            b = np.ones((n+1,n+1))
+            kernel_1(a, out=b)
+            return a
+
+        self._compile_and_test(test_2, (types.intp,),
+                               equivs=[self.without_equiv('a', 'b')])
+
+        @stencil(standard_indexing=('c',))
+        def kernel_2(a, b, c):
+            return a[0,1,0] + b[0,-1,0] + c[0]
+
+        def test_3(n):
+            a = np.arange(64).reshape(4,8,2)
+            b = np.arange(64).reshape(n,8,2)
+            u = np.zeros(1)
+            v = kernel_2(a, b, u)
+            return v
+
+        # standard indexed arrays are not considered in size equivalence
+        self._compile_and_test(test_3, (types.intp,),
+                               equivs=[self.with_equiv('a', 'b', 'v'),
+                                       self.without_equiv('a', 'u')],
+                               asserts=[self.with_assert('a', 'b')])
+
+    def test_slice(self):
+        def test_1(m, n):
+            A = np.zeros(m)
+            B = np.zeros(n)
+            s = np.sum(A + B)
+            C = A[1:m-1]
+            D = B[1:n-1]
+            t = np.sum(C + D)
+            return s + t
+        self._compile_and_test(test_1, (types.intp,types.intp,),
+                               asserts=[self.with_assert('A', 'B'),
+                                        self.without_assert('C', 'D')],
+                               idempotent=False)
+
+        def test_2(m):
+            A = np.zeros(m)
+            B = A[0:m-3]
+            C = A[1:m-2]
+            D = A[2:m-1]
+            E = B + C
+            return D + E
+        self._compile_and_test(test_2, (types.intp,),
+                               asserts=[self.without_assert('B', 'C'),
+                                        self.without_assert('D', 'E')],
+                               idempotent=False)
+
+        def test_3(m):
+            A = np.zeros((m,m))
+            B = A[0:m-2,0:m-2]
+            C = A[1:m-1,1:m-1]
+            E = B + C
+            return E
+        self._compile_and_test(test_3, (types.intp,),
+                               asserts=[self.without_assert('B', 'C')],
+                               idempotent=False)
+
+        def test_4(m):
+            A = np.zeros((m,m))
+            B = A[0:m-2,:]
+            C = A[1:m-1,:]
+            E = B + C
+            return E
+        self._compile_and_test(test_4, (types.intp,),
+                               asserts=[self.without_assert('B', 'C')],
+                               idempotent=False)
+
+        def test_5(m,n):
+            A = np.zeros(m)
+            B = np.zeros(m)
+            B[0:m-2] = A[1:m-1]
+            C = np.zeros(n)
+            D = A[1:m-1]
+            C[0:n-2] = D
+            # B and C are not necessarily of the same size because we can't
+            # derive m == n from (m-2) % m == (n-2) % n
+            return B + C
+        self._compile_and_test(test_5, (types.intp,types.intp),
+                               asserts=[self.without_assert('B', 'A'),
+                                        self.with_assert('C', 'D'),
+                                        self.with_assert('B', 'C')],
+                               idempotent=False)
+
+        def test_6(m):
+            A = np.zeros((m,m))
+            B = A[0:m-2,:-1]
+            C = A[1:m-1,:-1]
+            E = B + C
+            return E
+        self._compile_and_test(test_6, (types.intp,),
+                               asserts=[self.without_assert('B', 'C')],
+                               idempotent=False)
+
+        def test_7(m):
+            A = np.zeros((m,m))
+            B = A[0:m-2,-3:-1]
+            C = A[1:m-1,-4:-2]
+            E = B + C
+            return E
+        self._compile_and_test(test_7, (types.intp,),
+                               asserts=[self.with_assert('B', 'C')],
+                               idempotent=False)
+
+        def test_8(m):
+            A = np.zeros((m,m))
+            B = A[:m-2,0:]
+            C = A[1:-1,:]
+            E = B + C
+            return E
+        self._compile_and_test(test_8, (types.intp,),
+                               asserts=[self.without_assert('B', 'C')],
+                               idempotent=False)
+
+        def test_9(m):
+            # issues #3461 and #3554, checks equivalence on empty slices
+            # and across binop
+            A = np.zeros((m))
+            B = A[:0] # B = array([], dtype=int64)
+            C = A[1:]
+            D = A[:-1:-1] # D = array([], dtype=int64)
+            E = B + D
+            F = E
+            F += 1 # F = array([], dtype=int64)
+            return A, C, F
+        self._compile_and_test(test_9, (types.intp,),
+                               equivs=[self.without_equiv('B', 'C'),
+                                       self.with_equiv('A', 'm'),
+                                       self.with_equiv('B', 'D'),
+                                       self.with_equiv('F', 'D'),],
+                               idempotent=False)
+
+    @skip_unless_scipy
+    def test_numpy_calls(self):
+        def test_zeros(n):
+            a = np.zeros(n)
+            b = np.zeros((n, n))
+            c = np.zeros(shape=(n, n))
+        self._compile_and_test(test_zeros, (types.intp,),
+                               equivs=[self.with_equiv('a', 'n'),
+                                       self.with_equiv('b', ('n', 'n')),
+                                       self.with_equiv('b', 'c')])
+
+        def test_0d_array(n):
+            a = np.array(1)
+            b = np.ones(2)
+            return a + b
+        self._compile_and_test(test_0d_array, (types.intp,),
+                               equivs=[self.without_equiv('a', 'b')],
+                               asserts=[self.without_shapecall('a')])
+
+        def test_ones(n):
+            a = np.ones(n)
+            b = np.ones((n, n))
+            c = np.ones(shape=(n, n))
+        self._compile_and_test(test_ones, (types.intp,),
+                               equivs=[self.with_equiv('a', 'n'),
+                                       self.with_equiv('b', ('n', 'n')),
+                                       self.with_equiv('b', 'c')])
+
+        def test_empty(n):
+            a = np.empty(n)
+            b = np.empty((n, n))
+            c = np.empty(shape=(n, n))
+        self._compile_and_test(test_empty, (types.intp,),
+                               equivs=[self.with_equiv('a', 'n'),
+                                       self.with_equiv('b', ('n', 'n')),
+                                       self.with_equiv('b', 'c')])
+
+        def test_eye(n):
+            a = np.eye(n)
+            b = np.eye(N=n)
+            c = np.eye(N=n, M=n)
+            d = np.eye(N=n, M=n + 1)
+        self._compile_and_test(test_eye, (types.intp,),
+                               equivs=[self.with_equiv('a', ('n', 'n')),
+                                       self.with_equiv('b', ('n', 'n')),
+                                       self.with_equiv('b', 'c'),
+                                       self.without_equiv('b', 'd')])
+
+        def test_identity(n):
+            a = np.identity(n)
+        self._compile_and_test(test_identity, (types.intp,),
+                               equivs=[self.with_equiv('a', ('n', 'n'))])
+
+        def test_diag(n):
+            a = np.identity(n)
+            b = np.diag(a)
+            c = np.diag(b)
+            d = np.diag(a, k=1)
+        self._compile_and_test(test_diag, (types.intp,),
+                               equivs=[self.with_equiv('b', ('n',)),
+                                       self.with_equiv('c', ('n', 'n'))],
+                               asserts=[self.with_shapecall('d'),
+                                        self.without_shapecall('c')])
+
+        def test_array_like(a):
+            b = np.empty_like(a)
+            c = np.zeros_like(a)
+            d = np.ones_like(a)
+            e = np.full_like(a, 1)
+            f = np.asfortranarray(a)
+
+        self._compile_and_test(test_array_like, (types.Array(types.intp, 2, 'C'),),
+                               equivs=[
+                                   self.with_equiv('a', 'b', 'd', 'e', 'f')],
+                               asserts=[self.with_shapecall('a'),
+                                        self.without_shapecall('b')])
+
+        def test_reshape(n):
+            a = np.ones(n * n)
+            b = a.reshape((n, n))
+            return a.sum() + b.sum()
+        self._compile_and_test(test_reshape, (types.intp,),
+                               equivs=[self.with_equiv('b', ('n', 'n'))],
+                               asserts=[self.without_shapecall('b')])
+
+
+        def test_transpose(m, n):
+            a = np.ones((m, n))
+            b = a.T
+            c = a.transpose()
+            # Numba njit cannot compile explicit transpose call!
+            # c = np.transpose(b)
+        self._compile_and_test(test_transpose, (types.intp, types.intp),
+                               equivs=[self.with_equiv('a', ('m', 'n')),
+                                       self.with_equiv('b', ('n', 'm')),
+                                       self.with_equiv('c', ('n', 'm'))])
+
+
+        def test_transpose_3d(m, n, k):
+            a = np.ones((m, n, k))
+            b = a.T
+            c = a.transpose()
+            d = a.transpose(2,0,1)
+            dt = a.transpose((2,0,1))
+            e = a.transpose(0,2,1)
+            et = a.transpose((0,2,1))
+            # Numba njit cannot compile explicit transpose call!
+            # c = np.transpose(b)
+        self._compile_and_test(test_transpose_3d, (types.intp, types.intp, types.intp),
+                               equivs=[self.with_equiv('a', ('m', 'n', 'k')),
+                                       self.with_equiv('b', ('k', 'n', 'm')),
+                                       self.with_equiv('c', ('k', 'n', 'm')),
+                                       self.with_equiv('d', ('k', 'm', 'n')),
+                                       self.with_equiv('dt', ('k', 'm', 'n')),
+                                       self.with_equiv('e', ('m', 'k', 'n')),
+                                       self.with_equiv('et', ('m', 'k', 'n'))])
+
+        def test_real_imag_attr(m, n):
+            a = np.ones((m, n))
+            b = a.real
+            c = a.imag
+
+        self._compile_and_test(test_real_imag_attr, (types.intp, types.intp),
+                               equivs=[self.with_equiv('a', ('m', 'n')),
+                                       self.with_equiv('b', ('m', 'n')),
+                                       self.with_equiv('c', ('m', 'n')),
+                                       ])
+
+        def test_random(n):
+            a0 = np.random.rand(n)
+            a1 = np.random.rand(n, n)
+            b0 = np.random.randn(n)
+            b1 = np.random.randn(n, n)
+            c0 = np.random.ranf(n)
+            c1 = np.random.ranf((n, n))
+            c2 = np.random.ranf(size=(n, n))
+            d0 = np.random.random_sample(n)
+            d1 = np.random.random_sample((n, n))
+            d2 = np.random.random_sample(size=(n, n))
+            e0 = np.random.sample(n)
+            e1 = np.random.sample((n, n))
+            e2 = np.random.sample(size=(n, n))
+            f0 = np.random.random(n)
+            f1 = np.random.random((n, n))
+            f2 = np.random.random(size=(n, n))
+            g0 = np.random.standard_normal(n)
+            g1 = np.random.standard_normal((n, n))
+            g2 = np.random.standard_normal(size=(n, n))
+            h0 = np.random.chisquare(10, n)
+            h1 = np.random.chisquare(10, (n, n))
+            h2 = np.random.chisquare(10, size=(n, n))
+            i0 = np.random.weibull(10, n)
+            i1 = np.random.weibull(10, (n, n))
+            i2 = np.random.weibull(10, size=(n, n))
+            j0 = np.random.power(10, n)
+            j1 = np.random.power(10, (n, n))
+            j2 = np.random.power(10, size=(n, n))
+            k0 = np.random.geometric(0.1, n)
+            k1 = np.random.geometric(0.1, (n, n))
+            k2 = np.random.geometric(0.1, size=(n, n))
+            l0 = np.random.exponential(10, n)
+            l1 = np.random.exponential(10, (n, n))
+            l2 = np.random.exponential(10, size=(n, n))
+            m0 = np.random.poisson(10, n)
+            m1 = np.random.poisson(10, (n, n))
+            m2 = np.random.poisson(10, size=(n, n))
+            n0 = np.random.rayleigh(10, n)
+            n1 = np.random.rayleigh(10, (n, n))
+            n2 = np.random.rayleigh(10, size=(n, n))
+            o0 = np.random.normal(0, 1, n)
+            o1 = np.random.normal(0, 1, (n, n))
+            o2 = np.random.normal(0, 1, size=(n, n))
+            p0 = np.random.uniform(0, 1, n)
+            p1 = np.random.uniform(0, 1, (n, n))
+            p2 = np.random.uniform(0, 1, size=(n, n))
+            q0 = np.random.beta(0.1, 1, n)
+            q1 = np.random.beta(0.1, 1, (n, n))
+            q2 = np.random.beta(0.1, 1, size=(n, n))
+            r0 = np.random.binomial(0, 1, n)
+            r1 = np.random.binomial(0, 1, (n, n))
+            r2 = np.random.binomial(0, 1, size=(n, n))
+            s0 = np.random.f(0.1, 1, n)
+            s1 = np.random.f(0.1, 1, (n, n))
+            s2 = np.random.f(0.1, 1, size=(n, n))
+            t0 = np.random.gamma(0.1, 1, n)
+            t1 = np.random.gamma(0.1, 1, (n, n))
+            t2 = np.random.gamma(0.1, 1, size=(n, n))
+            u0 = np.random.lognormal(0, 1, n)
+            u1 = np.random.lognormal(0, 1, (n, n))
+            u2 = np.random.lognormal(0, 1, size=(n, n))
+            v0 = np.random.laplace(0, 1, n)
+            v1 = np.random.laplace(0, 1, (n, n))
+            v2 = np.random.laplace(0, 1, size=(n, n))
+            w0 = np.random.randint(0, 10, n)
+            w1 = np.random.randint(0, 10, (n, n))
+            w2 = np.random.randint(0, 10, size=(n, n))
+            x0 = np.random.triangular(-3, 0, 10, n)
+            x1 = np.random.triangular(-3, 0, 10, (n, n))
+            x2 = np.random.triangular(-3, 0, 10, size=(n, n))
+
+        last = ord('x') + 1
+        vars1d = [('n',)] + [chr(x) + '0' for x in range(ord('a'), last)]
+        vars2d = [('n', 'n')] + [chr(x) + '1' for x in range(ord('a'), last)]
+        vars2d += [chr(x) + '1' for x in range(ord('c'), last)]
+        self._compile_and_test(test_random, (types.intp,),
+                               equivs=[self.with_equiv(*vars1d),
+                                       self.with_equiv(*vars2d)])
+
+        def test_concatenate(m, n):
+            a = np.ones(m)
+            b = np.ones(n)
+            c = np.concatenate((a, b))
+            d = np.ones((2, n))
+            e = np.ones((3, n))
+            f = np.concatenate((d, e))
+            # Numba njit cannot compile concatenate with single array!
+            # g = np.ones((3,4,5))
+            # h = np.concatenate(g)
+            i = np.ones((m, 2))
+            j = np.ones((m, 3))
+            k = np.concatenate((i, j), axis=1)
+            l = np.ones((m, n))
+            o = np.ones((m, n))
+            p = np.concatenate((l, o))
+            # Numba njit cannot support list argument!
+            # q = np.concatenate([d, e])
+        self._compile_and_test(test_concatenate, (types.intp, types.intp),
+                               equivs=[self.with_equiv('f', (5, 'n')),
+                                       #self.with_equiv('h', (3 + 4 + 5, )),
+                                       self.with_equiv('k', ('m', 5))],
+                               asserts=[self.with_shapecall('c'),
+                                        self.without_shapecall('f'),
+                                        self.without_shapecall('k'),
+                                        self.with_shapecall('p')])
+
+        def test_vsd_stack():
+            k = np.ones((2,))
+            l = np.ones((2, 3))
+            o = np.ones((2, 3, 4))
+            p = np.vstack((k, k))
+            q = np.vstack((l, l))
+            r = np.hstack((k, k))
+            s = np.hstack((l, l))
+            t = np.dstack((k, k))
+            u = np.dstack((l, l))
+            v = np.dstack((o, o))
+
+        self._compile_and_test(test_vsd_stack, (),
+                               equivs=[self.with_equiv('p', (2, 2)),
+                                       self.with_equiv('q', (4, 3)),
+                                       self.with_equiv('r', (4,)),
+                                       self.with_equiv('s', (2, 6)),
+                                       self.with_equiv('t', (1, 2, 2)),
+                                       self.with_equiv('u', (2, 3, 2)),
+                                       self.with_equiv('v', (2, 3, 8)),
+                                       ])
+
+        def test_stack(m, n):
+            a = np.ones(m)
+            b = np.ones(n)
+            c = np.stack((a, b))
+            d = np.ones((m, n))
+            e = np.ones((m, n))
+            f = np.stack((d, e))
+            g = np.stack((d, e), axis=0)
+            h = np.stack((d, e), axis=1)
+            i = np.stack((d, e), axis=2)
+            j = np.stack((d, e), axis=-1)
+
+        self._compile_and_test(test_stack, (types.intp, types.intp),
+                                equivs=[self.with_equiv('m', 'n'),
+                                        self.with_equiv('c', (2, 'm')),
+                                        self.with_equiv(
+                                    'f', 'g', (2, 'm', 'n')),
+            self.with_equiv(
+                                    'h', ('m', 2, 'n')),
+            self.with_equiv(
+                                    'i', 'j', ('m', 'n', 2)),
+        ])
+
+        def test_linspace(m, n):
+            a = np.linspace(m, n)
+            b = np.linspace(m, n, 10)
+            # Numba njit does not support num keyword to linspace call!
+            # c = np.linspace(m,n,num=10)
+        self._compile_and_test(test_linspace, (types.float64, types.float64),
+                               equivs=[self.with_equiv('a', (50,)),
+                                       self.with_equiv('b', (10,))])
+
+        def test_dot(l, m, n):
+            a = np.dot(np.ones(1), np.ones(1))
+            b = np.dot(np.ones(2), np.ones((2, 3)))
+            # Numba njit does not support higher dimensional inputs
+            #c = np.dot(np.ones(2),np.ones((3,2,4)))
+            #d = np.dot(np.ones(2),np.ones((3,5,2,4)))
+            e = np.dot(np.ones((1, 2)), np.ones(2,))
+            #f = np.dot(np.ones((1,2,3)),np.ones(3,))
+            #g = np.dot(np.ones((1,2,3,4)),np.ones(4,))
+            h = np.dot(np.ones((2, 3)), np.ones((3, 4)))
+            i = np.dot(np.ones((m, n)), np.ones((n, m)))
+            j = np.dot(np.ones((m, m)), np.ones((l, l)))
+
+        self._compile_and_test(test_dot, (types.intp, types.intp, types.intp),
+                               equivs=[self.without_equiv('a', (1,)),  # not array
+                                       self.with_equiv('b', (3,)),
+                                       self.with_equiv('e', (1,)),
+                                       self.with_equiv('h', (2, 4)),
+                                       self.with_equiv('i', ('m', 'm')),
+                                       self.with_equiv('j', ('m', 'm')),
+                                       ],
+                               asserts=[self.with_assert('m', 'l')])
+
+        def test_broadcast(m, n):
+            a = np.ones((m, n))
+            b = np.ones(n)
+            c = a + b
+            d = np.ones((1, n))
+            e = a + c - d
+        self._compile_and_test(test_broadcast, (types.intp, types.intp),
+                               equivs=[self.with_equiv('a', 'c', 'e')],
+                               asserts=None)
+
+        # make sure shape of a global tuple of ints is handled properly
+        def test_global_tuple():
+            a = np.ones(GVAL2)
+            b = np.ones(GVAL2)
+
+        self._compile_and_test(test_global_tuple, (),
+                               equivs=[self.with_equiv('a', 'b')],
+                               asserts=None)
+
+
+class TestArrayAnalysisParallelRequired(TestCase):
+    """This is to just split out tests that need the parallel backend and
+    therefore serialised execution.
+    """
+
+    _numba_parallel_test_ = False
+
+    @skip_unsupported
+    def test_misc(self):
+
+        @njit
+        def swap(x, y):
+            return(y, x)
+
+        def test_bug2537(m):
+            a = np.ones(m)
+            b = np.ones(m)
+            for i in range(m):
+                a[i], b[i] = swap(a[i], b[i])
+
+        try:
+            njit(test_bug2537, parallel=True)(10)
+        except IndexError:
+            self.fail("test_bug2537 raised IndexError!")
+
+    @skip_unsupported
+    def test_global_namedtuple(self):
+        Row = namedtuple('Row', ['A'])
+        row = Row(3)
+
+        def test_impl():
+            rr = row
+            res = rr.A
+            if res == 2:
+                res = 3
+            return res
+
+        self.assertEqual(njit(test_impl, parallel=True)(), test_impl())
+
+    @skip_unsupported
+    def test_array_T_issue_3700(self):
+
+        def test_impl(t_obj, X):
+            for i in prange(t_obj.T):
+                X[i] = i
+            return X.sum()
+
+        n = 5
+        t_obj = ExampleClass3700(n)
+        X1 = np.zeros(t_obj.T)
+        X2 = np.zeros(t_obj.T)
+        self.assertEqual(
+            njit(test_impl, parallel=True)(t_obj, X1), test_impl(t_obj, X2))
+
+    @skip_unsupported
+    def test_slice_shape_issue_3380(self):
+        # these tests shouldn't throw error in array analysis
+        def test_impl1():
+            a = slice(None, None)
+            return True
+
+        self.assertEqual(njit(test_impl1, parallel=True)(), test_impl1())
+
+        def test_impl2(A, a):
+            b = a
+            return A[b]
+
+        A = np.arange(10)
+        a = slice(None)
+        np.testing.assert_array_equal(
+            njit(test_impl2, parallel=True)(A, a), test_impl2(A, a))
+
+    @skip_unsupported
+    def test_slice_dtype_issue_5056(self):
+        # see issue 5056
+
+        @njit(parallel=True)
+        def test_impl(data):
+            N = data.shape[0]
+            sums = np.zeros(N)
+            for i in prange(N):
+                sums[i] = np.sum(data[np.int32(0):np.int32(1)])
+            return sums
+
+        data = np.arange(10.)
+        np.testing.assert_array_equal(test_impl(data), test_impl.py_func(data))
+
+    @skip_unsupported
+    def test_global_tuple(self):
+        """make sure a global tuple with non-integer values does not cause errors
+        (test for #6726).
+        """
+
+        def test_impl():
+            d = GVAL[0]
+            return d
+
+        self.assertEqual(njit(test_impl, parallel=True)(), test_impl())
+
+
+class TestArrayAnalysisInterface(TestCase):
+    def test_analyze_op_call_interface(self):
+        # gather _analyze_op_call_*
+        aoc = {}
+        for fname in dir(ArrayAnalysis):
+            if fname.startswith('_analyze_op_call_'):
+                aoc[fname] = getattr(ArrayAnalysis, fname)
+        # check interface
+        def iface_stub(self, scope, equiv_set, loc, args, kws):
+            pass
+        expected = utils.pysignature(iface_stub)
+        for k, v in aoc.items():
+            got = utils.pysignature(v)
+            with self.subTest(fname=k, sig=got):
+                self.assertEqual(got, expected)
+
+    @skip_unsupported
+    def test_array_analysis_extensions(self):
+        # Test that the `array_analysis` object in `array_analysis_extensions`
+        # can perform analysis on the scope using `equiv_sets`.
+        from numba.parfors.parfor import Parfor
+        from numba.parfors import array_analysis
+
+        orig_parfor = array_analysis.array_analysis_extensions[Parfor]
+
+        shared = {'counter': 0}
+
+        def testcode(array_analysis):
+            # Find call node corresponding to the ``A = empty(n)``
+            func_ir = array_analysis.func_ir
+            for call in func_ir.blocks[0].find_exprs('call'):
+                callee = func_ir.get_definition(call.func)
+                if getattr(callee, "value", None) is empty:
+                    if getattr(call.args[0], 'name', None) == 'n':
+                        break
+            else:
+                return
+
+            variable_A = func_ir.get_assignee(call)
+            # n must be equiv to
+            es = array_analysis.equiv_sets[0]
+            self.assertTrue(es.is_equiv('n', variable_A.name))
+            shared['counter'] += 1
+
+        def new_parfor(parfor, equiv_set, typemap, array_analysis):
+            """Recursive array analysis for parfor nodes.
+            """
+            testcode(array_analysis)
+            # Call original
+            return orig_parfor(
+                parfor, equiv_set, typemap, array_analysis,
+            )
+
+        try:
+            # Replace the array-analysis extension for Parfor node
+            array_analysis.array_analysis_extensions[Parfor] = new_parfor
+
+            empty = np.empty   # avoid scanning a getattr in the IR
+            def f(n):
+                A = empty(n)
+                for i in prange(n):
+                    S = np.arange(i)
+                    A[i] = S.sum()
+                return A + 1
+
+            got = njit(parallel=True)(f)(10)
+            executed_count = shared['counter']
+            self.assertGreater(executed_count, 0)
+        finally:
+            # Re-install the original handler
+            array_analysis.array_analysis_extensions[Parfor] = orig_parfor
+
+        # Check normal execution
+        expected = njit(parallel=True)(f)(10)
+        self.assertPreciseEqual(got, expected)
+        # Make sure we have uninstalled the handler
+        self.assertEqual(executed_count, shared['counter'])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/shared/package-files/3d/3d79f8c57968d3d7d02557ea1eb840dcc74bb809f89aaaae8e1093b8cbadce42/utils.py b/shared/package-files/3d/3d79f8c57968d3d7d02557ea1eb840dcc74bb809f89aaaae8e1093b8cbadce42/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..af3703c7aacc699e95015678acdc72865abde602
--- /dev/null
+++ b/shared/package-files/3d/3d79f8c57968d3d7d02557ea1eb840dcc74bb809f89aaaae8e1093b8cbadce42/utils.py
@@ -0,0 +1,35 @@
+from pathlib import Path
+
+from jedi.inference.gradual.typeshed import TYPESHED_PATH, create_stub_module
+
+
+def load_proper_stub_module(inference_state, grammar, file_io, import_names, module_node):
+    """
+    This function is given a random .pyi file and should return the proper
+    module.
+    """
+    path = file_io.path
+    path = Path(path)
+    assert path.suffix == '.pyi'
+    try:
+        relative_path = path.relative_to(TYPESHED_PATH)
+    except ValueError:
+        pass
+    else:
+        # /[...]/stdlib/3/os/__init__.pyi -> stdlib/3/os/__init__
+        rest = relative_path.with_suffix('')
+        # Remove the stdlib/3 or third_party/3.6 part
+        import_names = rest.parts[2:]
+        if rest.name == '__init__':
+            import_names = import_names[:-1]
+
+    if import_names is not None:
+        actual_value_set = inference_state.import_module(import_names, prefer_stubs=False)
+
+        stub = create_stub_module(
+            inference_state, grammar, actual_value_set,
+            module_node, file_io, import_names
+        )
+        inference_state.stub_module_cache[import_names] = stub
+        return stub
+    return None
diff --git a/shared/package-files/3d/3d7aecc7a73816ab79a6415a070f3f0ccaa2aa8b1fc4706b8ca8f0804d2f0fd8/log10.h b/shared/package-files/3d/3d7aecc7a73816ab79a6415a070f3f0ccaa2aa8b1fc4706b8ca8f0804d2f0fd8/log10.h
new file mode 100644
index 0000000000000000000000000000000000000000..8f22fb31bc988716e4cedb8e5e2fa186aea1e073
--- /dev/null
+++ b/shared/package-files/3d/3d7aecc7a73816ab79a6415a070f3f0ccaa2aa8b1fc4706b8ca8f0804d2f0fd8/log10.h
@@ -0,0 +1,44 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::log10(Tensor self) -> Tensor
+inline at::Tensor log10(const at::Tensor & self) {
+    return at::_ops::log10::call(self);
+}
+
+// aten::log10_(Tensor(a!) self) -> Tensor(a!)
+inline at::Tensor & log10_(at::Tensor & self) {
+    return at::_ops::log10_::call(self);
+}
+
+// aten::log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & log10_out(at::Tensor & out, const at::Tensor & self) {
+    return at::_ops::log10_out::call(self, out);
+}
+// aten::log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & log10_outf(const at::Tensor & self, at::Tensor & out) {
+    return at::_ops::log10_out::call(self, out);
+}
+
+}
diff --git a/shared/package-files/3d/3d7c9d1d40a4501b71c2aaa28f2e388c3b7738de281cd5a7e3a36662bb68f157/promote_types_compositeimplicitautograd_dispatch.h b/shared/package-files/3d/3d7c9d1d40a4501b71c2aaa28f2e388c3b7738de281cd5a7e3a36662bb68f157/promote_types_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..619382018766ebc69ea59f1425a92971c9483a15
--- /dev/null
+++ b/shared/package-files/3d/3d7c9d1d40a4501b71c2aaa28f2e388c3b7738de281cd5a7e3a36662bb68f157/promote_types_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::ScalarType promote_types(at::ScalarType type1, at::ScalarType type2);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/3d/3d7f1cf1ede66be20e6a3f85d11ac11785d34d59e049f90634ab76e00332e2aa/dateparse.pyi b/shared/package-files/3d/3d7f1cf1ede66be20e6a3f85d11ac11785d34d59e049f90634ab76e00332e2aa/dateparse.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3645a67ef17081d361f6955078b6e87aabbb887b
--- /dev/null
+++ b/shared/package-files/3d/3d7f1cf1ede66be20e6a3f85d11ac11785d34d59e049f90634ab76e00332e2aa/dateparse.pyi
@@ -0,0 +1,14 @@
+from datetime import date, datetime, time, timedelta
+from typing import Any, Optional
+
+date_re: Any
+time_re: Any
+datetime_re: Any
+standard_duration_re: Any
+iso8601_duration_re: Any
+postgres_interval_re: Any
+
+def parse_date(value: str) -> Optional[date]: ...
+def parse_time(value: str) -> Optional[time]: ...
+def parse_datetime(value: str) -> Optional[datetime]: ...
+def parse_duration(value: str) -> Optional[timedelta]: ...
diff --git a/shared/package-files/3d/3d7f61fa866bf88780e7e482c9de740aa7073acd110848cf7716e0954216361d/triu_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/3d/3d7f61fa866bf88780e7e482c9de740aa7073acd110848cf7716e0954216361d/triu_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..dac4e757fbccaf75d7094e5e88e8faefffa0b689
--- /dev/null
+++ b/shared/package-files/3d/3d7f61fa866bf88780e7e482c9de740aa7073acd110848cf7716e0954216361d/triu_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor triu(const at::Tensor & self, int64_t diagonal=0);
+TORCH_API at::Tensor & triu_(at::Tensor & self, int64_t diagonal=0);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/3d/3d80c2303cfa47cec723b88e54055ba8ae12afb6938a35d52bd901b03c3f2811/rfc822.pyi b/shared/package-files/3d/3d80c2303cfa47cec723b88e54055ba8ae12afb6938a35d52bd901b03c3f2811/rfc822.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..f536568b807dabf42eac642c5ad177d58b9ce295
--- /dev/null
+++ b/shared/package-files/3d/3d80c2303cfa47cec723b88e54055ba8ae12afb6938a35d52bd901b03c3f2811/rfc822.pyi
@@ -0,0 +1,75 @@
+from typing import Any, Optional
+
+class Message:
+    fp: Any
+    seekable: Any
+    startofheaders: Any
+    startofbody: Any
+    def __init__(self, fp, seekable: int = ...): ...
+    def rewindbody(self): ...
+    dict: Any
+    unixfrom: Any
+    headers: Any
+    status: Any
+    def readheaders(self): ...
+    def isheader(self, line): ...
+    def islast(self, line): ...
+    def iscomment(self, line): ...
+    def getallmatchingheaders(self, name): ...
+    def getfirstmatchingheader(self, name): ...
+    def getrawheader(self, name): ...
+    def getheader(self, name, default: Optional[Any] = ...): ...
+    get: Any
+    def getheaders(self, name): ...
+    def getaddr(self, name): ...
+    def getaddrlist(self, name): ...
+    def getdate(self, name): ...
+    def getdate_tz(self, name): ...
+    def __len__(self): ...
+    def __getitem__(self, name): ...
+    def __setitem__(self, name, value): ...
+    def __delitem__(self, name): ...
+    def setdefault(self, name, default=...): ...
+    def has_key(self, name): ...
+    def __contains__(self, name): ...
+    def __iter__(self): ...
+    def keys(self): ...
+    def values(self): ...
+    def items(self): ...
+
+class AddrlistClass:
+    specials: Any
+    pos: Any
+    LWS: Any
+    CR: Any
+    atomends: Any
+    phraseends: Any
+    field: Any
+    commentlist: Any
+    def __init__(self, field): ...
+    def gotonext(self): ...
+    def getaddrlist(self): ...
+    def getaddress(self): ...
+    def getrouteaddr(self): ...
+    def getaddrspec(self): ...
+    def getdomain(self): ...
+    def getdelimited(self, beginchar, endchars, allowcomments: int = ...): ...
+    def getquote(self): ...
+    def getcomment(self): ...
+    def getdomainliteral(self): ...
+    def getatom(self, atomends: Optional[Any] = ...): ...
+    def getphraselist(self): ...
+
+class AddressList(AddrlistClass):
+    addresslist: Any
+    def __init__(self, field): ...
+    def __len__(self): ...
+    def __add__(self, other): ...
+    def __iadd__(self, other): ...
+    def __sub__(self, other): ...
+    def __isub__(self, other): ...
+    def __getitem__(self, index): ...
+
+def parsedate_tz(data): ...
+def parsedate(data): ...
+def mktime_tz(data): ...
diff --git a/shared/package-files/3d/3d836efe52aa039ede1ce51008dbeb2c7934fa81bbf5b4acaed9db40ebc9866b/macrodata_jmulti_cs_granger_causality_realcons_realgdp.txt b/shared/package-files/3d/3d836efe52aa039ede1ce51008dbeb2c7934fa81bbf5b4acaed9db40ebc9866b/macrodata_jmulti_cs_granger_causality_realcons_realgdp.txt
new file mode 100644
index 0000000000000000000000000000000000000000..b539461656eb69a842b183e7f7d3c5303878512d
--- /dev/null
+++ b/shared/package-files/3d/3d836efe52aa039ede1ce51008dbeb2c7934fa81bbf5b4acaed9db40ebc9866b/macrodata_jmulti_cs_granger_causality_realcons_realgdp.txt
@@ -0,0 +1,13 @@
+*** Sat, 3 Sep 2016 22:28:25 ***
+TEST FOR GRANGER-CAUSALITY:
+H0: "realcons, realgdp" do not Granger-cause "realinv"
+
+Test statistic l = 16.5166
+pval-F( l; 8, 549) = 0.0000 
+
+TEST FOR INSTANTANEOUS CAUSALITY:
+H0: No instantaneous causality between "realcons, realgdp" and "realinv"
+
+Test statistic: c = 79.8044
+pval-Chi( c; 2) = 0.0000 
+
diff --git a/shared/package-files/3d/3d894da915104fc2ccddc4f91661c63f48a2b1c1654d6103f763002ef06e9e0a/jpcntx.py b/shared/package-files/3d/3d894da915104fc2ccddc4f91661c63f48a2b1c1654d6103f763002ef06e9e0a/jpcntx.py
new file mode 100644
index 0000000000000000000000000000000000000000..20044e4bc8f5a9775d82be0ecf387ce752732507
--- /dev/null
+++ b/shared/package-files/3d/3d894da915104fc2ccddc4f91661c63f48a2b1c1654d6103f763002ef06e9e0a/jpcntx.py
@@ -0,0 +1,233 @@
+######################## BEGIN LICENSE BLOCK ########################
+# The Original Code is Mozilla Communicator client code.
+#
+# The Initial Developer of the Original Code is
+# Netscape Communications Corporation.
+# Portions created by the Initial Developer are Copyright (C) 1998
+# the Initial Developer. All Rights Reserved.
+#
+# Contributor(s):
+#   Mark Pilgrim - port to Python
+#
+# This library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License, or (at your option) any later version.
+#
+# This library is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with this library; if not, write to the Free Software
+# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
+# 02110-1301  USA
+######################### END LICENSE BLOCK #########################
+
+
+# This is hiragana 2-char sequence table, the number in each cell represents its frequency category
+jp2CharContext = (
+(0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1),
+(2,4,0,4,0,3,0,4,0,3,4,4,4,2,4,3,3,4,3,2,3,3,4,2,3,3,3,2,4,1,4,3,3,1,5,4,3,4,3,4,3,5,3,0,3,5,4,2,0,3,1,0,3,3,0,3,3,0,1,1,0,4,3,0,3,3,0,4,0,2,0,3,5,5,5,5,4,0,4,1,0,3,4),
+(0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2),
+(0,4,0,5,0,5,0,4,0,4,5,4,4,3,5,3,5,1,5,3,4,3,4,4,3,4,3,3,4,3,5,4,4,3,5,5,3,5,5,5,3,5,5,3,4,5,5,3,1,3,2,0,3,4,0,4,2,0,4,2,1,5,3,2,3,5,0,4,0,2,0,5,4,4,5,4,5,0,4,0,0,4,4),
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+(0,3,0,3,0,1,0,3,0,2,3,3,3,0,3,1,2,0,3,3,2,3,3,2,3,2,3,1,3,0,4,3,2,0,3,3,1,4,3,3,2,3,4,3,1,3,3,1,1,0,1,1,0,1,0,1,0,1,0,0,0,4,1,1,0,3,0,3,1,0,2,3,3,3,3,3,1,0,0,2,0,3,3),
+(0,0,0,0,0,0,0,0,0,0,3,0,2,0,3,0,0,0,0,0,0,0,3,0,0,0,0,0,0,0,3,0,3,0,3,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,2,0,2,3,0,0,0,0,0,0,0,0,3),
+(0,2,0,3,1,3,0,3,0,2,3,3,3,1,3,1,3,1,3,1,3,3,3,1,3,0,2,3,1,1,4,3,3,2,3,3,1,2,2,4,1,3,3,0,1,4,2,3,0,1,3,0,3,0,0,1,3,0,2,0,0,3,3,2,1,3,0,3,0,2,0,3,4,4,4,3,1,0,3,0,0,3,3),
+(0,2,0,1,0,2,0,0,0,1,3,2,2,1,3,0,1,1,3,0,3,2,3,1,2,0,2,0,1,1,3,3,3,0,3,3,1,1,2,3,2,3,3,1,2,3,2,0,0,1,0,0,0,0,0,0,3,0,1,0,0,2,1,2,1,3,0,3,0,0,0,3,4,4,4,3,2,0,2,0,0,2,4),
+(0,0,0,1,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,2,2,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,3,1,0,0,0,0,0,0,0,3),
+(0,3,0,3,0,2,0,3,0,3,3,3,2,3,2,2,2,0,3,1,3,3,3,2,3,3,0,0,3,0,3,2,2,0,2,3,1,4,3,4,3,3,2,3,1,5,4,4,0,3,1,2,1,3,0,3,1,1,2,0,2,3,1,3,1,3,0,3,0,1,0,3,3,4,4,2,1,0,2,1,0,2,4),
+(0,1,0,3,0,1,0,2,0,1,4,2,5,1,4,0,2,0,2,1,3,1,4,0,2,1,0,0,2,1,4,1,1,0,3,3,0,5,1,3,2,3,3,1,0,3,2,3,0,1,0,0,0,0,0,0,1,0,0,0,0,4,0,1,0,3,0,2,0,1,0,3,3,3,4,3,3,0,0,0,0,2,3),
+(0,0,0,1,0,0,0,0,0,0,2,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,0,0,1,0,0,0,0,0,3),
+(0,1,0,3,0,4,0,3,0,2,4,3,1,0,3,2,2,1,3,1,2,2,3,1,1,1,2,1,3,0,1,2,0,1,3,2,1,3,0,5,5,1,0,0,1,3,2,1,0,3,0,0,1,0,0,0,0,0,3,4,0,1,1,1,3,2,0,2,0,1,0,2,3,3,1,2,3,0,1,0,1,0,4),
+(0,0,0,1,0,3,0,3,0,2,2,1,0,0,4,0,3,0,3,1,3,0,3,0,3,0,1,0,3,0,3,1,3,0,3,3,0,0,1,2,1,1,1,0,1,2,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,2,2,1,2,0,0,2,0,0,0,0,2,3,3,3,3,0,0,0,0,1,4),
+(0,0,0,3,0,3,0,0,0,0,3,1,1,0,3,0,1,0,2,0,1,0,0,0,0,0,0,0,1,0,3,0,2,0,2,3,0,0,2,2,3,1,2,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,2,0,0,0,0,2,3),
+(2,4,0,5,0,5,0,4,0,3,4,3,3,3,4,3,3,3,4,3,4,4,5,4,5,5,5,2,3,0,5,5,4,1,5,4,3,1,5,4,3,4,4,3,3,4,3,3,0,3,2,0,2,3,0,3,0,0,3,3,0,5,3,2,3,3,0,3,0,3,0,3,4,5,4,5,3,0,4,3,0,3,4),
+(0,3,0,3,0,3,0,3,0,3,3,4,3,2,3,2,3,0,4,3,3,3,3,3,3,3,3,0,3,2,4,3,3,1,3,4,3,4,4,4,3,4,4,3,2,4,4,1,0,2,0,0,1,1,0,2,0,0,3,1,0,5,3,2,1,3,0,3,0,1,2,4,3,2,4,3,3,0,3,2,0,4,4),
+(0,3,0,3,0,1,0,0,0,1,4,3,3,2,3,1,3,1,4,2,3,2,4,2,3,4,3,0,2,2,3,3,3,0,3,3,3,0,3,4,1,3,3,0,3,4,3,3,0,1,1,0,1,0,0,0,4,0,3,0,0,3,1,2,1,3,0,4,0,1,0,4,3,3,4,3,3,0,2,0,0,3,3),
+(0,3,0,4,0,1,0,3,0,3,4,3,3,0,3,3,3,1,3,1,3,3,4,3,3,3,0,0,3,1,5,3,3,1,3,3,2,5,4,3,3,4,5,3,2,5,3,4,0,1,0,0,0,0,0,2,0,0,1,1,0,4,2,2,1,3,0,3,0,2,0,4,4,3,5,3,2,0,1,1,0,3,4),
+(0,5,0,4,0,5,0,2,0,4,4,3,3,2,3,3,3,1,4,3,4,1,5,3,4,3,4,0,4,2,4,3,4,1,5,4,0,4,4,4,4,5,4,1,3,5,4,2,1,4,1,1,3,2,0,3,1,0,3,2,1,4,3,3,3,4,0,4,0,3,0,4,4,4,3,3,3,0,4,2,0,3,4),
+(1,4,0,4,0,3,0,1,0,3,3,3,1,1,3,3,2,2,3,3,1,0,3,2,2,1,2,0,3,1,2,1,2,0,3,2,0,2,2,3,3,4,3,0,3,3,1,2,0,1,1,3,1,2,0,0,3,0,1,1,0,3,2,2,3,3,0,3,0,0,0,2,3,3,4,3,3,0,1,0,0,1,4),
+(0,4,0,4,0,4,0,0,0,3,4,4,3,1,4,2,3,2,3,3,3,1,4,3,4,0,3,0,4,2,3,3,2,2,5,4,2,1,3,4,3,4,3,1,3,3,4,2,0,2,1,0,3,3,0,0,2,0,3,1,0,4,4,3,4,3,0,4,0,1,0,2,4,4,4,4,4,0,3,2,0,3,3),
+(0,0,0,1,0,4,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,3,2,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,2),
+(0,2,0,3,0,4,0,4,0,1,3,3,3,0,4,0,2,1,2,1,1,1,2,0,3,1,1,0,1,0,3,1,0,0,3,3,2,0,1,1,0,0,0,0,0,1,0,2,0,2,2,0,3,1,0,0,1,0,1,1,0,1,2,0,3,0,0,0,0,1,0,0,3,3,4,3,1,0,1,0,3,0,2),
+(0,0,0,3,0,5,0,0,0,0,1,0,2,0,3,1,0,1,3,0,0,0,2,0,0,0,1,0,0,0,1,1,0,0,4,0,0,0,2,3,0,1,4,1,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,0,0,1,0,0,0,0,0,0,0,2,0,0,3,0,0,0,0,0,3),
+(0,2,0,5,0,5,0,1,0,2,4,3,3,2,5,1,3,2,3,3,3,0,4,1,2,0,3,0,4,0,2,2,1,1,5,3,0,0,1,4,2,3,2,0,3,3,3,2,0,2,4,1,1,2,0,1,1,0,3,1,0,1,3,1,2,3,0,2,0,0,0,1,3,5,4,4,4,0,3,0,0,1,3),
+(0,4,0,5,0,4,0,4,0,4,5,4,3,3,4,3,3,3,4,3,4,4,5,3,4,5,4,2,4,2,3,4,3,1,4,4,1,3,5,4,4,5,5,4,4,5,5,5,2,3,3,1,4,3,1,3,3,0,3,3,1,4,3,4,4,4,0,3,0,4,0,3,3,4,4,5,0,0,4,3,0,4,5),
+(0,4,0,4,0,3,0,3,0,3,4,4,4,3,3,2,4,3,4,3,4,3,5,3,4,3,2,1,4,2,4,4,3,1,3,4,2,4,5,5,3,4,5,4,1,5,4,3,0,3,2,2,3,2,1,3,1,0,3,3,3,5,3,3,3,5,4,4,2,3,3,4,3,3,3,2,1,0,3,2,1,4,3),
+(0,4,0,5,0,4,0,3,0,3,5,5,3,2,4,3,4,0,5,4,4,1,4,4,4,3,3,3,4,3,5,5,2,3,3,4,1,2,5,5,3,5,5,2,3,5,5,4,0,3,2,0,3,3,1,1,5,1,4,1,0,4,3,2,3,5,0,4,0,3,0,5,4,3,4,3,0,0,4,1,0,4,4),
+(1,3,0,4,0,2,0,2,0,2,5,5,3,3,3,3,3,0,4,2,3,4,4,4,3,4,0,0,3,4,5,4,3,3,3,3,2,5,5,4,5,5,5,4,3,5,5,5,1,3,1,0,1,0,0,3,2,0,4,2,0,5,2,3,2,4,1,3,0,3,0,4,5,4,5,4,3,0,4,2,0,5,4),
+(0,3,0,4,0,5,0,3,0,3,4,4,3,2,3,2,3,3,3,3,3,2,4,3,3,2,2,0,3,3,3,3,3,1,3,3,3,0,4,4,3,4,4,1,1,4,4,2,0,3,1,0,1,1,0,4,1,0,2,3,1,3,3,1,3,4,0,3,0,1,0,3,1,3,0,0,1,0,2,0,0,4,4),
+(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),
+(0,3,0,3,0,2,0,3,0,1,5,4,3,3,3,1,4,2,1,2,3,4,4,2,4,4,5,0,3,1,4,3,4,0,4,3,3,3,2,3,2,5,3,4,3,2,2,3,0,0,3,0,2,1,0,1,2,0,0,0,0,2,1,1,3,1,0,2,0,4,0,3,4,4,4,5,2,0,2,0,0,1,3),
+(0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,0,0,1,1,0,0,0,4,2,1,1,0,1,0,3,2,0,0,3,1,1,1,2,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,2,0,0,0,1,4,0,4,2,1,0,0,0,0,0,1),
+(0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,3,1,0,0,0,2,0,2,1,0,0,1,2,1,0,1,1,0,0,3,0,0,0,0,0,0,0,0,0,0,0,1,3,1,0,0,0,0,0,1,0,0,2,1,0,0,0,0,0,0,0,0,2),
+(0,4,0,4,0,4,0,3,0,4,4,3,4,2,4,3,2,0,4,4,4,3,5,3,5,3,3,2,4,2,4,3,4,3,1,4,0,2,3,4,4,4,3,3,3,4,4,4,3,4,1,3,4,3,2,1,2,1,3,3,3,4,4,3,3,5,0,4,0,3,0,4,3,3,3,2,1,0,3,0,0,3,3),
+(0,4,0,3,0,3,0,3,0,3,5,5,3,3,3,3,4,3,4,3,3,3,4,4,4,3,3,3,3,4,3,5,3,3,1,3,2,4,5,5,5,5,4,3,4,5,5,3,2,2,3,3,3,3,2,3,3,1,2,3,2,4,3,3,3,4,0,4,0,2,0,4,3,2,2,1,2,0,3,0,0,4,1),
+)
+
+class JapaneseContextAnalysis(object):
+    NUM_OF_CATEGORY = 6
+    DONT_KNOW = -1
+    ENOUGH_REL_THRESHOLD = 100
+    MAX_REL_THRESHOLD = 1000
+    MINIMUM_DATA_THRESHOLD = 4
+
+    def __init__(self):
+        self._total_rel = None
+        self._rel_sample = None
+        self._need_to_skip_char_num = None
+        self._last_char_order = None
+        self._done = None
+        self.reset()
+
+    def reset(self):
+        self._total_rel = 0  # total sequence received
+        # category counters, each integer counts sequence in its category
+        self._rel_sample = [0] * self.NUM_OF_CATEGORY
+        # if last byte in current buffer is not the last byte of a character,
+        # we need to know how many bytes to skip in next buffer
+        self._need_to_skip_char_num = 0
+        self._last_char_order = -1  # The order of previous char
+        # If this flag is set to True, detection is done and conclusion has
+        # been made
+        self._done = False
+
+    def feed(self, byte_str, num_bytes):
+        if self._done:
+            return
+
+        # The buffer we got is byte oriented, and a character may span in more than one
+        # buffers. In case the last one or two byte in last buffer is not
+        # complete, we record how many byte needed to complete that character
+        # and skip these bytes here.  We can choose to record those bytes as
+        # well and analyse the character once it is complete, but since a
+        # character will not make much difference, by simply skipping
+        # this character will simply our logic and improve performance.
+        i = self._need_to_skip_char_num
+        while i < num_bytes:
+            order, char_len = self.get_order(byte_str[i:i + 2])
+            i += char_len
+            if i > num_bytes:
+                self._need_to_skip_char_num = i - num_bytes
+                self._last_char_order = -1
+            else:
+                if (order != -1) and (self._last_char_order != -1):
+                    self._total_rel += 1
+                    if self._total_rel > self.MAX_REL_THRESHOLD:
+                        self._done = True
+                        break
+                    self._rel_sample[jp2CharContext[self._last_char_order][order]] += 1
+                self._last_char_order = order
+
+    def got_enough_data(self):
+        return self._total_rel > self.ENOUGH_REL_THRESHOLD
+
+    def get_confidence(self):
+        # This is just one way to calculate confidence. It works well for me.
+        if self._total_rel > self.MINIMUM_DATA_THRESHOLD:
+            return (self._total_rel - self._rel_sample[0]) / self._total_rel
+        else:
+            return self.DONT_KNOW
+
+    def get_order(self, byte_str):
+        return -1, 1
+
+class SJISContextAnalysis(JapaneseContextAnalysis):
+    def __init__(self):
+        super(SJISContextAnalysis, self).__init__()
+        self._charset_name = "SHIFT_JIS"
+
+    @property
+    def charset_name(self):
+        return self._charset_name
+
+    def get_order(self, byte_str):
+        if not byte_str:
+            return -1, 1
+        # find out current char's byte length
+        first_char = byte_str[0]
+        if (0x81 <= first_char <= 0x9F) or (0xE0 <= first_char <= 0xFC):
+            char_len = 2
+            if (first_char == 0x87) or (0xFA <= first_char <= 0xFC):
+                self._charset_name = "CP932"
+        else:
+            char_len = 1
+
+        # return its order if it is hiragana
+        if len(byte_str) > 1:
+            second_char = byte_str[1]
+            if (first_char == 202) and (0x9F <= second_char <= 0xF1):
+                return second_char - 0x9F, char_len
+
+        return -1, char_len
+
+class EUCJPContextAnalysis(JapaneseContextAnalysis):
+    def get_order(self, byte_str):
+        if not byte_str:
+            return -1, 1
+        # find out current char's byte length
+        first_char = byte_str[0]
+        if (first_char == 0x8E) or (0xA1 <= first_char <= 0xFE):
+            char_len = 2
+        elif first_char == 0x8F:
+            char_len = 3
+        else:
+            char_len = 1
+
+        # return its order if it is hiragana
+        if len(byte_str) > 1:
+            second_char = byte_str[1]
+            if (first_char == 0xA4) and (0xA1 <= second_char <= 0xF3):
+                return second_char - 0xA1, char_len
+
+        return -1, char_len
+
+
diff --git a/shared/package-files/3d/3d8c7d59105feea62d42ce9fdffd953fa52ad6220496617d011bf6d6e59d26bc/qscheme_native.h b/shared/package-files/3d/3d8c7d59105feea62d42ce9fdffd953fa52ad6220496617d011bf6d6e59d26bc/qscheme_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..dd3531f9f8db845f2dc37c5b70b1de17c6ecf929
--- /dev/null
+++ b/shared/package-files/3d/3d8c7d59105feea62d42ce9fdffd953fa52ad6220496617d011bf6d6e59d26bc/qscheme_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::QScheme qscheme_quant(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/3d/3d8cff8d284f89fb08b8ebdf9d7c9316e98bd85441a561853a347244882719ea/sqrt_ops.h b/shared/package-files/3d/3d8cff8d284f89fb08b8ebdf9d7c9316e98bd85441a561853a347244882719ea/sqrt_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..7be1ba026f21fcd83b33a73e364ec3692408636b
--- /dev/null
+++ b/shared/package-files/3d/3d8cff8d284f89fb08b8ebdf9d7c9316e98bd85441a561853a347244882719ea/sqrt_ops.h
@@ -0,0 +1,50 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API sqrt {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::sqrt")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "sqrt(Tensor self) -> Tensor")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+struct TORCH_API sqrt_ {
+  using schema = at::Tensor & (at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::sqrt_")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "sqrt_(Tensor(a!) self) -> Tensor(a!)")
+  static at::Tensor & call(at::Tensor & self);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, at::Tensor & self);
+};
+
+struct TORCH_API sqrt_out {
+  using schema = at::Tensor & (const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::sqrt")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/3d/3d93682de3027c925d78756f527cb450773902d4b485cb540a2ef3533b49a5e1/rows_to_batches.h b/shared/package-files/3d/3d93682de3027c925d78756f527cb450773902d4b485cb540a2ef3533b49a5e1/rows_to_batches.h
new file mode 100644
index 0000000000000000000000000000000000000000..8ad254df200efc08c5c9a4956e0e781b496b2b07
--- /dev/null
+++ b/shared/package-files/3d/3d93682de3027c925d78756f527cb450773902d4b485cb540a2ef3533b49a5e1/rows_to_batches.h
@@ -0,0 +1,163 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include "arrow/record_batch.h"
+#include "arrow/result.h"
+#include "arrow/status.h"
+#include "arrow/table_builder.h"
+#include "arrow/util/iterator.h"
+
+#include 
+
+namespace arrow::util {
+
+namespace detail {
+
+// Default identity function row accessor. Used to for the common case where the value
+// of each row iterated over is it's self also directly iterable.
+[[nodiscard]] constexpr inline auto MakeDefaultRowAccessor() {
+  return [](auto& x) -> Result { return std::ref(x); };
+}
+
+// Meta-function to check if a type `T` is a range (iterable using `std::begin()` /
+// `std::end()`). `is_range::value` will be false if `T` is not a valid range.
+template 
+struct is_range : std::false_type {};
+
+template 
+struct is_range())),
+                               decltype(std::end(std::declval()))>> : std::true_type {
+};
+
+}  // namespace detail
+
+/// Delete overload for `const Range&& rows` because the data's lifetime must exceed
+/// the lifetime of the function call. `data` will be read when client uses the
+/// `RecordBatchReader`
+template 
+[[nodiscard]] typename std::enable_if_t::value,
+                                        Result>>
+/* Result>> */ RowsToBatches(
+    const std::shared_ptr& schema, const Range&& rows,
+    DataPointConvertor&& data_point_convertor,
+    RowAccessor&& row_accessor = detail::MakeDefaultRowAccessor(),
+    MemoryPool* pool = default_memory_pool(),
+    const std::size_t batch_size = 1024) = delete;
+
+/// \brief Utility function for converting any row-based structure into an
+/// `arrow::RecordBatchReader` (this can be easily converted to an `arrow::Table` using
+/// `arrow::RecordBatchReader::ToTable()`).
+///
+/// Examples of supported types:
+/// - `std::vector>>`
+/// - `std::vector`
+
+/// If `rows` (client’s row-based structure) is not a valid C++ range, the client will
+/// need to either make it iterable, or make an adapter/wrapper that is a valid C++
+/// range.
+
+/// The client must provide a `DataPointConvertor` callable type that will convert the
+/// structure’s data points into the corresponding arrow types.
+
+/// Complex nested rows can be supported by providing a custom `row_accessor` instead
+/// of the default.
+
+/// Example usage:
+/// \code{.cpp}
+/// auto IntConvertor = [](ArrayBuilder& array_builder, int value) {
+///  return static_cast(array_builder).Append(value);
+/// };
+/// std::vector> data = {{1, 2, 4}, {5, 6, 7}};
+/// auto batches = RowsToBatches(kTestSchema, data, IntConvertor);
+/// \endcode
+
+/// \param[in] schema - The schema to be used in the `RecordBatchReader`
+
+/// \param[in] rows - Iterable row-based structure that will be converted to arrow
+/// batches
+
+/// \param[in] data_point_convertor - Client provided callable type that will convert
+/// the structure’s data points into the corresponding arrow types. The convertor must
+/// return an error `Status` if an error happens during conversion.
+
+/// \param[in] row_accessor - In the common case where the value of each row iterated
+/// over is it's self also directly iterable, the client can just use the default.
+/// The provided callable must take the values of the `rows` range and return a
+/// `std::reference_wrapper` to the data points in a given row. The data points
+/// must be in order of their corresponding fields in the schema.
+/// see: /ref `MakeDefaultRowAccessor`
+
+/// \param[in] pool - The MemoryPool to use for allocations.
+
+/// \param[in] batch_size - Number of rows to insert into each RecordBatch.
+
+/// \return `Result>>` result will be a
+/// `std::shared_ptr>` if not errors occurred, else an error status.
+template 
+[[nodiscard]] typename std::enable_if_t::value,
+                                        Result>>
+/* Result>> */ RowsToBatches(
+    const std::shared_ptr& schema, const Range& rows,
+    DataPointConvertor&& data_point_convertor,
+    RowAccessor&& row_accessor = detail::MakeDefaultRowAccessor(),
+    MemoryPool* pool = default_memory_pool(), const std::size_t batch_size = 1024) {
+  auto make_next_batch =
+      [pool = pool, batch_size = batch_size, rows_ittr = std::begin(rows),
+       rows_ittr_end = std::end(rows), schema = schema,
+       row_accessor = std::forward(row_accessor),
+       data_point_convertor = std::forward(
+           data_point_convertor)]() mutable -> Result> {
+    if (rows_ittr == rows_ittr_end) return NULLPTR;
+
+    ARROW_ASSIGN_OR_RAISE(auto record_batch_builder,
+                          RecordBatchBuilder::Make(schema, pool, batch_size));
+
+    for (size_t i = 0; i < batch_size && (rows_ittr != rows_ittr_end);
+         i++, std::advance(rows_ittr, 1)) {
+      int col_index = 0;
+      ARROW_ASSIGN_OR_RAISE(const auto row, row_accessor(*rows_ittr));
+
+      // If the accessor returns a `std::reference_wrapper` unwrap if
+      const auto& row_unwrapped = [&]() {
+        if constexpr (detail::is_range::value)
+          return row;
+        else
+          return row.get();
+      }();
+
+      for (auto& data_point : row_unwrapped) {
+        ArrayBuilder* array_builder = record_batch_builder->GetField(col_index);
+        ARROW_RETURN_IF(array_builder == NULLPTR,
+                        Status::Invalid("array_builder == NULLPTR"));
+
+        ARROW_RETURN_NOT_OK(data_point_convertor(*array_builder, data_point));
+        col_index++;
+      }
+    }
+
+    ARROW_ASSIGN_OR_RAISE(auto result, record_batch_builder->Flush());
+    return result;
+  };
+  return RecordBatchReader::MakeFromIterator(MakeFunctionIterator(make_next_batch),
+                                             schema);
+}
+
+}  // namespace arrow::util
diff --git a/shared/package-files/3d/3d94baeb5d7ffe1637e8d663f27f50fff24c5cd2d29bf68c58ba42e756b173e3/cddl.py b/shared/package-files/3d/3d94baeb5d7ffe1637e8d663f27f50fff24c5cd2d29bf68c58ba42e756b173e3/cddl.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd7f54aefd0fa1ee1043bfcf8bd4ec23f2ede5fc
--- /dev/null
+++ b/shared/package-files/3d/3d94baeb5d7ffe1637e8d663f27f50fff24c5cd2d29bf68c58ba42e756b173e3/cddl.py
@@ -0,0 +1,173 @@
+"""
+    pygments.lexers.cddl
+    ~~~~~~~~~~~~~~~~~~~~
+
+    Lexer for the Concise data definition language (CDDL), a notational
+    convention to express CBOR and JSON data structures.
+
+    More information:
+    https://datatracker.ietf.org/doc/rfc8610/
+
+    :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+from pygments.lexer import RegexLexer, bygroups, include, words
+from pygments.token import Comment, Error, Keyword, Name, Number, Operator, \
+    Punctuation, String, Whitespace
+
+__all__ = ['CddlLexer']
+
+
+class CddlLexer(RegexLexer):
+    """
+    Lexer for CDDL definitions.
+
+    .. versionadded:: 2.8
+    """
+    name = "CDDL"
+    url = 'https://datatracker.ietf.org/doc/rfc8610/'
+    aliases = ["cddl"]
+    filenames = ["*.cddl"]
+    mimetypes = ["text/x-cddl"]
+
+    _prelude_types = [
+        "any",
+        "b64legacy",
+        "b64url",
+        "bigfloat",
+        "bigint",
+        "bignint",
+        "biguint",
+        "bool",
+        "bstr",
+        "bytes",
+        "cbor-any",
+        "decfrac",
+        "eb16",
+        "eb64legacy",
+        "eb64url",
+        "encoded-cbor",
+        "false",
+        "float",
+        "float16",
+        "float16-32",
+        "float32",
+        "float32-64",
+        "float64",
+        "int",
+        "integer",
+        "mime-message",
+        "nil",
+        "nint",
+        "null",
+        "number",
+        "regexp",
+        "tdate",
+        "text",
+        "time",
+        "true",
+        "tstr",
+        "uint",
+        "undefined",
+        "unsigned",
+        "uri",
+    ]
+
+    _controls = [
+        ".and",
+        ".bits",
+        ".cbor",
+        ".cborseq",
+        ".default",
+        ".eq",
+        ".ge",
+        ".gt",
+        ".le",
+        ".lt",
+        ".ne",
+        ".regexp",
+        ".size",
+        ".within",
+    ]
+
+    _re_id = (
+        r"[$@A-Z_a-z]"
+        r"(?:[\-\.]+(?=[$@0-9A-Z_a-z])|[$@0-9A-Z_a-z])*"
+
+    )
+
+    # While the spec reads more like "an int must not start with 0" we use a
+    # lookahead here that says "after a 0 there must be no digit". This makes the
+    # '0' the invalid character in '01', which looks nicer when highlighted.
+    _re_uint = r"(?:0b[01]+|0x[0-9a-fA-F]+|[1-9]\d*|0(?!\d))"
+    _re_int = r"-?" + _re_uint
+
+    tokens = {
+        "commentsandwhitespace": [(r"\s+", Whitespace), (r";.+$", Comment.Single)],
+        "root": [
+            include("commentsandwhitespace"),
+            # tag types
+            (r"#(\d\.{uint})?".format(uint=_re_uint), Keyword.Type),  # type or any
+            # occurrence
+            (
+                r"({uint})?(\*)({uint})?".format(uint=_re_uint),
+                bygroups(Number, Operator, Number),
+            ),
+            (r"\?|\+", Operator),  # occurrence
+            (r"\^", Operator),  # cuts
+            (r"(\.\.\.|\.\.)", Operator),  # rangeop
+            (words(_controls, suffix=r"\b"), Operator.Word),  # ctlops
+            # into choice op
+            (r"&(?=\s*({groupname}|\())".format(groupname=_re_id), Operator),
+            (r"~(?=\s*{})".format(_re_id), Operator),  # unwrap op
+            (r"//|/(?!/)", Operator),  # double und single slash
+            (r"=>|/==|/=|=", Operator),
+            (r"[\[\]{}\(\),<>:]", Punctuation),
+            # Bytestrings
+            (r"(b64)(')", bygroups(String.Affix, String.Single), "bstrb64url"),
+            (r"(h)(')", bygroups(String.Affix, String.Single), "bstrh"),
+            (r"'", String.Single, "bstr"),
+            # Barewords as member keys (must be matched before values, types, typenames,
+            # groupnames).
+            # Token type is String as barewords are always interpreted as such.
+            (r"({bareword})(\s*)(:)".format(bareword=_re_id),
+             bygroups(String, Whitespace, Punctuation)),
+            # predefined types
+            (words(_prelude_types, prefix=r"(?![\-_$@])\b", suffix=r"\b(?![\-_$@])"),
+             Name.Builtin),
+            # user-defined groupnames, typenames
+            (_re_id, Name.Class),
+            # values
+            (r"0b[01]+", Number.Bin),
+            (r"0o[0-7]+", Number.Oct),
+            (r"0x[0-9a-fA-F]+(\.[0-9a-fA-F]+)?p[+-]?\d+", Number.Hex),  # hexfloat
+            (r"0x[0-9a-fA-F]+", Number.Hex),  # hex
+            # Float
+            (r"{int}(?=(\.\d|e[+-]?\d))(?:\.\d+)?(?:e[+-]?\d+)?".format(int=_re_int),
+             Number.Float),
+            # Int
+            (_re_int, Number.Integer),
+            (r'"(\\\\|\\"|[^"])*"', String.Double),
+        ],
+        "bstrb64url": [
+            (r"'", String.Single, "#pop"),
+            include("commentsandwhitespace"),
+            (r"\\.", String.Escape),
+            (r"[0-9a-zA-Z\-_=]+", String.Single),
+            (r".", Error),
+            # (r";.+$", Token.Other),
+        ],
+        "bstrh": [
+            (r"'", String.Single, "#pop"),
+            include("commentsandwhitespace"),
+            (r"\\.", String.Escape),
+            (r"[0-9a-fA-F]+", String.Single),
+            (r".", Error),
+        ],
+        "bstr": [
+            (r"'", String.Single, "#pop"),
+            (r"\\.", String.Escape),
+            (r"[^'\\]+", String.Single),
+        ],
+    }
diff --git a/shared/package-files/3d/3d9617330785e6b1dd355e7a2cab58c88847cd0f9dd76dc3394315bc2819d22f/special_i0e_cpu_dispatch.h b/shared/package-files/3d/3d9617330785e6b1dd355e7a2cab58c88847cd0f9dd76dc3394315bc2819d22f/special_i0e_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a59b536a52deff0fd31418afe5f3b647bd2b10cd
--- /dev/null
+++ b/shared/package-files/3d/3d9617330785e6b1dd355e7a2cab58c88847cd0f9dd76dc3394315bc2819d22f/special_i0e_cpu_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor special_i0e(const at::Tensor & self);
+TORCH_API at::Tensor & special_i0e_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_i0e_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/3d/3d96b705679fdb49245ceb9910bdcd2b8599b22f0eae9d69b2d977de0ae09fb5/vt100+pfkeys b/shared/package-files/3d/3d96b705679fdb49245ceb9910bdcd2b8599b22f0eae9d69b2d977de0ae09fb5/vt100+pfkeys
new file mode 100644
index 0000000000000000000000000000000000000000..6eaea921c50bf355eeb5c3bd563409981d0a4711
Binary files /dev/null and b/shared/package-files/3d/3d96b705679fdb49245ceb9910bdcd2b8599b22f0eae9d69b2d977de0ae09fb5/vt100+pfkeys differ
diff --git a/shared/package-files/3d/3d9b22e6a69caf6427dca1f0e2ac371a6d4cceb05b94f1e84dd8ea8c7ec4296c/readers.py b/shared/package-files/3d/3d9b22e6a69caf6427dca1f0e2ac371a6d4cceb05b94f1e84dd8ea8c7ec4296c/readers.py
new file mode 100644
index 0000000000000000000000000000000000000000..ab34db74091c8a04ee9004ce9a786de3146ec917
--- /dev/null
+++ b/shared/package-files/3d/3d9b22e6a69caf6427dca1f0e2ac371a6d4cceb05b94f1e84dd8ea8c7ec4296c/readers.py
@@ -0,0 +1,120 @@
+import collections
+import pathlib
+import operator
+
+from . import abc
+
+from ._itertools import unique_everseen
+from ._compat import ZipPath
+
+
+def remove_duplicates(items):
+    return iter(collections.OrderedDict.fromkeys(items))
+
+
+class FileReader(abc.TraversableResources):
+    def __init__(self, loader):
+        self.path = pathlib.Path(loader.path).parent
+
+    def resource_path(self, resource):
+        """
+        Return the file system path to prevent
+        `resources.path()` from creating a temporary
+        copy.
+        """
+        return str(self.path.joinpath(resource))
+
+    def files(self):
+        return self.path
+
+
+class ZipReader(abc.TraversableResources):
+    def __init__(self, loader, module):
+        _, _, name = module.rpartition('.')
+        self.prefix = loader.prefix.replace('\\', '/') + name + '/'
+        self.archive = loader.archive
+
+    def open_resource(self, resource):
+        try:
+            return super().open_resource(resource)
+        except KeyError as exc:
+            raise FileNotFoundError(exc.args[0])
+
+    def is_resource(self, path):
+        # workaround for `zipfile.Path.is_file` returning true
+        # for non-existent paths.
+        target = self.files().joinpath(path)
+        return target.is_file() and target.exists()
+
+    def files(self):
+        return ZipPath(self.archive, self.prefix)
+
+
+class MultiplexedPath(abc.Traversable):
+    """
+    Given a series of Traversable objects, implement a merged
+    version of the interface across all objects. Useful for
+    namespace packages which may be multihomed at a single
+    name.
+    """
+
+    def __init__(self, *paths):
+        self._paths = list(map(pathlib.Path, remove_duplicates(paths)))
+        if not self._paths:
+            message = 'MultiplexedPath must contain at least one path'
+            raise FileNotFoundError(message)
+        if not all(path.is_dir() for path in self._paths):
+            raise NotADirectoryError('MultiplexedPath only supports directories')
+
+    def iterdir(self):
+        files = (file for path in self._paths for file in path.iterdir())
+        return unique_everseen(files, key=operator.attrgetter('name'))
+
+    def read_bytes(self):
+        raise FileNotFoundError(f'{self} is not a file')
+
+    def read_text(self, *args, **kwargs):
+        raise FileNotFoundError(f'{self} is not a file')
+
+    def is_dir(self):
+        return True
+
+    def is_file(self):
+        return False
+
+    def joinpath(self, *descendants):
+        try:
+            return super().joinpath(*descendants)
+        except abc.TraversalError:
+            # One of the paths did not resolve (a directory does not exist).
+            # Just return something that will not exist.
+            return self._paths[0].joinpath(*descendants)
+
+    def open(self, *args, **kwargs):
+        raise FileNotFoundError(f'{self} is not a file')
+
+    @property
+    def name(self):
+        return self._paths[0].name
+
+    def __repr__(self):
+        paths = ', '.join(f"'{path}'" for path in self._paths)
+        return f'MultiplexedPath({paths})'
+
+
+class NamespaceReader(abc.TraversableResources):
+    def __init__(self, namespace_path):
+        if 'NamespacePath' not in str(namespace_path):
+            raise ValueError('Invalid path')
+        self.path = MultiplexedPath(*list(namespace_path))
+
+    def resource_path(self, resource):
+        """
+        Return the file system path to prevent
+        `resources.path()` from creating a temporary
+        copy.
+        """
+        return str(self.path.joinpath(resource))
+
+    def files(self):
+        return self.path
diff --git a/shared/package-files/3d/3dac594975cdd3c07e33e04b425c47e4ef59f08dbd4b0a0fc8b131e9e2af0747/istft_native.h b/shared/package-files/3d/3dac594975cdd3c07e33e04b425c47e4ef59f08dbd4b0a0fc8b131e9e2af0747/istft_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..4931168ea609ed8437ae1d6b3b1b72802c32ae37
--- /dev/null
+++ b/shared/package-files/3d/3dac594975cdd3c07e33e04b425c47e4ef59f08dbd4b0a0fc8b131e9e2af0747/istft_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor istft(const at::Tensor & self, int64_t n_fft, c10::optional hop_length=c10::nullopt, c10::optional win_length=c10::nullopt, const c10::optional & window={}, bool center=true, bool normalized=false, c10::optional onesided=c10::nullopt, c10::optional length=c10::nullopt, bool return_complex=false);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/3d/3dacde830177ae386238809967bd8fd145d04b075bcf756fd9e6d5fd60b8dfab/BucketizationUtils.h b/shared/package-files/3d/3dacde830177ae386238809967bd8fd145d04b075bcf756fd9e6d5fd60b8dfab/BucketizationUtils.h
new file mode 100644
index 0000000000000000000000000000000000000000..59d459bd9c29edf165bc396d80c0ad7b5847c4aa
--- /dev/null
+++ b/shared/package-files/3d/3dacde830177ae386238809967bd8fd145d04b075bcf756fd9e6d5fd60b8dfab/BucketizationUtils.h
@@ -0,0 +1,173 @@
+#pragma once
+
+#include 
+#include 
+#include 
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include 
+#else
+#include 
+#endif
+
+namespace at::native {
+
+// original values given by raw_*. If an original value is not contiguous, will make a contiguous copy to
+// the corresponding trimmed_* value. Additionally, if the dtypes of the boundary and input tensor do not
+// match, will change them to be a common super type so comparisons are done between the same types.
+// For any trimmed_* tensor, if its outgoing value matches what it was incoming (typically null), then the
+// corresponding raw_* version should be used since it was already contiguous of the right type.
+inline void searchsorted_maybe_trim_input_tensors(
+    Tensor& trimmed_input,
+    Tensor& trimmed_boundaries,
+    Tensor& trimmed_sorter,
+    const Tensor& raw_input,
+    const Tensor& raw_boundaries,
+    const Tensor& raw_sorter) {
+  bool in_is_contiguous = raw_input.is_contiguous();
+  bool bd_is_contiguous = raw_boundaries.is_contiguous();
+  bool sort_is_contiguous = raw_sorter.is_contiguous();
+
+  if (!in_is_contiguous) {
+    TORCH_WARN_ONCE("torch.searchsorted(): input value tensor is non-contiguous, this will lower the performance due "
+      "to extra data copy when converting non-contiguous tensor to contiguous, please use contiguous input value "
+      "tensor if possible. This message will only appear once per program.");
+    trimmed_input = raw_input.contiguous();
+  }
+  if (!bd_is_contiguous) {
+    TORCH_WARN_ONCE("torch.searchsorted(): boundary tensor is non-contiguous, this will lower the performance due "
+      "to extra data copy when converting non-contiguous tensor to contiguous, please use contiguous boundary "
+      "tensor if possible. This message will only appear once per program.");
+    trimmed_boundaries = raw_boundaries.contiguous();
+  }
+  if (!sort_is_contiguous) {
+    TORCH_WARN_ONCE("torch.searchsorted(): sorter tensor is non-contiguous, this will lower the performance due "
+      "to extra data copy when converting non-contiguous tensor to contiguous, please use contiguous sorter "
+      "tensor if possible. This message will only appear once per program.");
+    trimmed_sorter = raw_sorter.contiguous();
+  }
+  if (raw_input.dtype() != raw_boundaries.dtype()) {
+    at::native::ResultTypeState state = {};
+    state = at::native::update_result_type_state(raw_boundaries, state);
+    state = at::native::update_result_type_state(raw_input, state);
+    ScalarType common_stype = at::native::result_type(state);
+
+    TORCH_INTERNAL_ASSERT(common_stype != ScalarType::Undefined);
+    if (common_stype != raw_input.scalar_type()) {
+      trimmed_input = in_is_contiguous ? raw_input.to(common_stype) : trimmed_input.to(common_stype);
+    }
+    if (common_stype != raw_boundaries.scalar_type()) {
+      trimmed_boundaries = bd_is_contiguous ? raw_boundaries.to(common_stype) : trimmed_boundaries.to(common_stype);
+    }
+  }
+}
+
+/* unused but needed for internal jagged tensor class */
+inline void searchsorted_maybe_trim_input_tensors(
+    Tensor& trimmed_input,
+    Tensor& trimmed_boundaries,
+    const Tensor& raw_input,
+    const Tensor& raw_boundaries) {
+  Tensor trimmed_sorter;
+  Tensor raw_sorter;
+  return searchsorted_maybe_trim_input_tensors(
+      trimmed_input,
+      trimmed_boundaries,
+      trimmed_sorter,
+      raw_input,
+      raw_boundaries,
+      raw_sorter);
+}
+
+inline bool searchsorted_dims_matched_before_last_dim(const Tensor& boundaries, const Tensor& input) {
+  if (boundaries.dim() != input.dim()) {
+    return false;
+  }
+  const auto& dims_bd = boundaries.sizes();
+  const auto& dims_in = input.sizes();
+  for (int64_t dim = 0; dim + 1 < boundaries.dim(); ++dim) {
+    if (dims_bd[dim] != dims_in[dim]) {
+      return false;
+    }
+  }
+  return true;
+}
+
+inline Tensor searchsorted_scalar_tensor(const Scalar& scalar, const c10::Device& device) {
+  auto tensor = c10::scalar_to_tensor(scalar, device);
+  // This is to adopt the scalar promotion rules defined in native/TypeProperties.h
+  // So we have the same type promotion rules as binary operations.
+  tensor.unsafeGetTensorImpl()->set_wrapped_number(true);
+  return tensor;
+}
+
+inline void searchsorted_pre_check(
+    const Tensor& boundaries,
+    const Tensor& input,
+    const Tensor& output,
+    const bool out_int32,
+    const bool right,
+    const c10::optional side_opt,
+    const Tensor& sorter) {
+  if (side_opt) {
+    const c10::string_view side = *side_opt;
+    TORCH_CHECK(side == "left" || side == "right", "torch.searchsorted(): side can only be 'left' or 'right' but ",
+      "got ", side);
+
+    // assume the user has not explicitly set (right=False, side="right")
+    TORCH_CHECK(!right || side == "right", "torch.searchsorted(): side and right can't be set to opposites, got side "
+    "of ", side, " while right was True");
+  }
+
+  TORCH_CHECK(boundaries.device() == input.device(), "torch.searchsorted(): boundaries and input value tensors ",
+    "should have same device type, but got boundaries tensor device type ", boundaries.device(), " and input value ",
+    "tensor device type ", input.device());
+
+  if (sorter.defined()) {
+    TORCH_CHECK(sorter.device() == boundaries.device(), "torch.searchsorted(): sorter and boundary tensors should ",
+      "have same device type, but got sorter tensor device type ", sorter.device(), " and input value tensor ",
+      "device type ", boundaries.device());
+
+    TORCH_CHECK(sorter.sizes() == boundaries.sizes(), "torch.searchsorted(): boundary and sorter must have the same "
+      "size, but got boundary tensor ", boundaries.sizes(), "and got sorter tensor ", sorter.sizes());
+
+    TORCH_CHECK(sorter.scalar_type() == ScalarType::Long, "torch.searchsorted(): sorter must be a tensor of long ",
+      "dtype but got dtype ", sorter.scalar_type());
+
+    if (sorter.numel() > 0) {
+      auto minmax = sorter.aminmax();
+      int64_t vmin = std::get<0>(minmax).item().toLong();
+      int64_t vmax = std::get<1>(minmax).item().toLong();
+      TORCH_CHECK(vmin >= 0 && vmax < sorter.sizes().back(), "torch.searchsorted(): sorter index out of range");
+    }
+  }
+
+  TORCH_CHECK(input.dim() > 0 || (input.dim() == 0 && input.numel() == 1 && boundaries.dim() == 1),
+    "torch.searchsorted(): input value can be a scalar only when boundaries tensor dimension is 1, but we got ",
+    "boundaries tensor dim(", boundaries.dim(), ") and input value's dim(", input.dim(), ") numel(",
+    input.numel(), ")");
+
+  TORCH_CHECK(boundaries.dim() != 0, "torch.searchsorted(): boundaries tensor should have positive dimension, but ",
+    "got 0 dimension");
+
+  TORCH_CHECK(boundaries.dim() == 1 || searchsorted_dims_matched_before_last_dim(boundaries, input),
+    "torch.searchsorted(): boundaries tensor should be 1 dimension or the first N-1 dimensions of boundaries tensor ",
+    "and input value tensor must match, but we got boundaries tensor ", boundaries.sizes(), " and input value tensor ",
+    input.sizes());
+
+  ScalarType output_dtype = output.scalar_type();
+  TORCH_CHECK(
+      (output_dtype == ScalarType::Long && !out_int32) ||
+          (output_dtype == ScalarType::Int && out_int32),
+      "torch.searchsorted(): output tensor's dtype is wrong, it can only be Int(int32) or Long(int64) depending on ",
+      "whether out_int32 flag is True, but we got output tensor's dtype ", output_dtype,
+      " and out_int32 flag is ", (out_int32 ? "True" : "False"));
+
+  if (out_int32) {
+    TORCH_CHECK(boundaries.sizes().back() < INT_MAX,
+      "torch.searchsorted(): the size of boundaries' last dimension should be less than ", INT_MAX, ", but we got ",
+      boundaries.sizes().back());
+  }
+}
+
+} // namespace at::native
diff --git a/shared/package-files/3d/3dad1561149e4114857a943a44e819acdb466394f305721d756b0c1ed2b43a43/__init__.pxd b/shared/package-files/3d/3dad1561149e4114857a943a44e819acdb466394f305721d756b0c1ed2b43a43/__init__.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..ca0a3a6c5288ac5ee384636fff3574ed257e49e4
--- /dev/null
+++ b/shared/package-files/3d/3dad1561149e4114857a943a44e819acdb466394f305721d756b0c1ed2b43a43/__init__.pxd
@@ -0,0 +1,1015 @@
+# NumPy static imports for Cython < 3.0
+#
+# If any of the PyArray_* functions are called, import_array must be
+# called first.
+#
+# Author: Dag Sverre Seljebotn
+#
+
+DEF _buffer_format_string_len = 255
+
+cimport cpython.buffer as pybuf
+from cpython.ref cimport Py_INCREF
+from cpython.mem cimport PyObject_Malloc, PyObject_Free
+from cpython.object cimport PyObject, PyTypeObject
+from cpython.buffer cimport PyObject_GetBuffer
+from cpython.type cimport type
+cimport libc.stdio as stdio
+
+cdef extern from "Python.h":
+    ctypedef int Py_intptr_t
+    bint PyObject_TypeCheck(object obj, PyTypeObject* type)
+
+cdef extern from "numpy/arrayobject.h":
+    ctypedef Py_intptr_t npy_intp
+    ctypedef size_t npy_uintp
+
+    cdef enum NPY_TYPES:
+        NPY_BOOL
+        NPY_BYTE
+        NPY_UBYTE
+        NPY_SHORT
+        NPY_USHORT
+        NPY_INT
+        NPY_UINT
+        NPY_LONG
+        NPY_ULONG
+        NPY_LONGLONG
+        NPY_ULONGLONG
+        NPY_FLOAT
+        NPY_DOUBLE
+        NPY_LONGDOUBLE
+        NPY_CFLOAT
+        NPY_CDOUBLE
+        NPY_CLONGDOUBLE
+        NPY_OBJECT
+        NPY_STRING
+        NPY_UNICODE
+        NPY_VOID
+        NPY_DATETIME
+        NPY_TIMEDELTA
+        NPY_NTYPES
+        NPY_NOTYPE
+
+        NPY_INT8
+        NPY_INT16
+        NPY_INT32
+        NPY_INT64
+        NPY_INT128
+        NPY_INT256
+        NPY_UINT8
+        NPY_UINT16
+        NPY_UINT32
+        NPY_UINT64
+        NPY_UINT128
+        NPY_UINT256
+        NPY_FLOAT16
+        NPY_FLOAT32
+        NPY_FLOAT64
+        NPY_FLOAT80
+        NPY_FLOAT96
+        NPY_FLOAT128
+        NPY_FLOAT256
+        NPY_COMPLEX32
+        NPY_COMPLEX64
+        NPY_COMPLEX128
+        NPY_COMPLEX160
+        NPY_COMPLEX192
+        NPY_COMPLEX256
+        NPY_COMPLEX512
+
+        NPY_INTP
+
+    ctypedef enum NPY_ORDER:
+        NPY_ANYORDER
+        NPY_CORDER
+        NPY_FORTRANORDER
+        NPY_KEEPORDER
+
+    ctypedef enum NPY_CASTING:
+        NPY_NO_CASTING
+        NPY_EQUIV_CASTING
+        NPY_SAFE_CASTING
+        NPY_SAME_KIND_CASTING
+        NPY_UNSAFE_CASTING
+
+    ctypedef enum NPY_CLIPMODE:
+        NPY_CLIP
+        NPY_WRAP
+        NPY_RAISE
+
+    ctypedef enum NPY_SCALARKIND:
+        NPY_NOSCALAR,
+        NPY_BOOL_SCALAR,
+        NPY_INTPOS_SCALAR,
+        NPY_INTNEG_SCALAR,
+        NPY_FLOAT_SCALAR,
+        NPY_COMPLEX_SCALAR,
+        NPY_OBJECT_SCALAR
+
+    ctypedef enum NPY_SORTKIND:
+        NPY_QUICKSORT
+        NPY_HEAPSORT
+        NPY_MERGESORT
+
+    ctypedef enum NPY_SEARCHSIDE:
+        NPY_SEARCHLEFT
+        NPY_SEARCHRIGHT
+
+    enum:
+        # DEPRECATED since NumPy 1.7 ! Do not use in new code!
+        NPY_C_CONTIGUOUS
+        NPY_F_CONTIGUOUS
+        NPY_CONTIGUOUS
+        NPY_FORTRAN
+        NPY_OWNDATA
+        NPY_FORCECAST
+        NPY_ENSURECOPY
+        NPY_ENSUREARRAY
+        NPY_ELEMENTSTRIDES
+        NPY_ALIGNED
+        NPY_NOTSWAPPED
+        NPY_WRITEABLE
+        NPY_ARR_HAS_DESCR
+
+        NPY_BEHAVED
+        NPY_BEHAVED_NS
+        NPY_CARRAY
+        NPY_CARRAY_RO
+        NPY_FARRAY
+        NPY_FARRAY_RO
+        NPY_DEFAULT
+
+        NPY_IN_ARRAY
+        NPY_OUT_ARRAY
+        NPY_INOUT_ARRAY
+        NPY_IN_FARRAY
+        NPY_OUT_FARRAY
+        NPY_INOUT_FARRAY
+
+        NPY_UPDATE_ALL
+
+    enum:
+        # Added in NumPy 1.7 to replace the deprecated enums above.
+        NPY_ARRAY_C_CONTIGUOUS
+        NPY_ARRAY_F_CONTIGUOUS
+        NPY_ARRAY_OWNDATA
+        NPY_ARRAY_FORCECAST
+        NPY_ARRAY_ENSURECOPY
+        NPY_ARRAY_ENSUREARRAY
+        NPY_ARRAY_ELEMENTSTRIDES
+        NPY_ARRAY_ALIGNED
+        NPY_ARRAY_NOTSWAPPED
+        NPY_ARRAY_WRITEABLE
+        NPY_ARRAY_WRITEBACKIFCOPY
+
+        NPY_ARRAY_BEHAVED
+        NPY_ARRAY_BEHAVED_NS
+        NPY_ARRAY_CARRAY
+        NPY_ARRAY_CARRAY_RO
+        NPY_ARRAY_FARRAY
+        NPY_ARRAY_FARRAY_RO
+        NPY_ARRAY_DEFAULT
+
+        NPY_ARRAY_IN_ARRAY
+        NPY_ARRAY_OUT_ARRAY
+        NPY_ARRAY_INOUT_ARRAY
+        NPY_ARRAY_IN_FARRAY
+        NPY_ARRAY_OUT_FARRAY
+        NPY_ARRAY_INOUT_FARRAY
+
+        NPY_ARRAY_UPDATE_ALL
+
+    cdef enum:
+        NPY_MAXDIMS
+
+    npy_intp NPY_MAX_ELSIZE
+
+    ctypedef void (*PyArray_VectorUnaryFunc)(void *, void *, npy_intp, void *,  void *)
+
+    ctypedef struct PyArray_ArrayDescr:
+        # shape is a tuple, but Cython doesn't support "tuple shape"
+        # inside a non-PyObject declaration, so we have to declare it
+        # as just a PyObject*.
+        PyObject* shape
+
+    ctypedef struct PyArray_Descr:
+        pass
+
+    ctypedef class numpy.dtype [object PyArray_Descr, check_size ignore]:
+        # Use PyDataType_* macros when possible, however there are no macros
+        # for accessing some of the fields, so some are defined.
+        cdef PyTypeObject* typeobj
+        cdef char kind
+        cdef char type
+        # Numpy sometimes mutates this without warning (e.g. it'll
+        # sometimes change "|" to "<" in shared dtype objects on
+        # little-endian machines). If this matters to you, use
+        # PyArray_IsNativeByteOrder(dtype.byteorder) instead of
+        # directly accessing this field.
+        cdef char byteorder
+        cdef char flags
+        cdef int type_num
+        cdef int itemsize "elsize"
+        cdef int alignment
+        cdef object fields
+        cdef tuple names
+        # Use PyDataType_HASSUBARRAY to test whether this field is
+        # valid (the pointer can be NULL). Most users should access
+        # this field via the inline helper method PyDataType_SHAPE.
+        cdef PyArray_ArrayDescr* subarray
+
+    ctypedef class numpy.flatiter [object PyArrayIterObject, check_size ignore]:
+        # Use through macros
+        pass
+
+    ctypedef class numpy.broadcast [object PyArrayMultiIterObject, check_size ignore]:
+        cdef int numiter
+        cdef npy_intp size, index
+        cdef int nd
+        cdef npy_intp *dimensions
+        cdef void **iters
+
+    ctypedef struct PyArrayObject:
+        # For use in situations where ndarray can't replace PyArrayObject*,
+        # like PyArrayObject**.
+        pass
+
+    ctypedef class numpy.ndarray [object PyArrayObject, check_size ignore]:
+        cdef __cythonbufferdefaults__ = {"mode": "strided"}
+
+        cdef:
+            # Only taking a few of the most commonly used and stable fields.
+            # One should use PyArray_* macros instead to access the C fields.
+            char *data
+            int ndim "nd"
+            npy_intp *shape "dimensions"
+            npy_intp *strides
+            dtype descr  # deprecated since NumPy 1.7 !
+            PyObject* base #  NOT PUBLIC, DO NOT USE !
+
+
+
+    ctypedef unsigned char      npy_bool
+
+    ctypedef signed char      npy_byte
+    ctypedef signed short     npy_short
+    ctypedef signed int       npy_int
+    ctypedef signed long      npy_long
+    ctypedef signed long long npy_longlong
+
+    ctypedef unsigned char      npy_ubyte
+    ctypedef unsigned short     npy_ushort
+    ctypedef unsigned int       npy_uint
+    ctypedef unsigned long      npy_ulong
+    ctypedef unsigned long long npy_ulonglong
+
+    ctypedef float        npy_float
+    ctypedef double       npy_double
+    ctypedef long double  npy_longdouble
+
+    ctypedef signed char        npy_int8
+    ctypedef signed short       npy_int16
+    ctypedef signed int         npy_int32
+    ctypedef signed long long   npy_int64
+    ctypedef signed long long   npy_int96
+    ctypedef signed long long   npy_int128
+
+    ctypedef unsigned char      npy_uint8
+    ctypedef unsigned short     npy_uint16
+    ctypedef unsigned int       npy_uint32
+    ctypedef unsigned long long npy_uint64
+    ctypedef unsigned long long npy_uint96
+    ctypedef unsigned long long npy_uint128
+
+    ctypedef float        npy_float32
+    ctypedef double       npy_float64
+    ctypedef long double  npy_float80
+    ctypedef long double  npy_float96
+    ctypedef long double  npy_float128
+
+    ctypedef struct npy_cfloat:
+        float real
+        float imag
+
+    ctypedef struct npy_cdouble:
+        double real
+        double imag
+
+    ctypedef struct npy_clongdouble:
+        long double real
+        long double imag
+
+    ctypedef struct npy_complex64:
+        float real
+        float imag
+
+    ctypedef struct npy_complex128:
+        double real
+        double imag
+
+    ctypedef struct npy_complex160:
+        long double real
+        long double imag
+
+    ctypedef struct npy_complex192:
+        long double real
+        long double imag
+
+    ctypedef struct npy_complex256:
+        long double real
+        long double imag
+
+    ctypedef struct PyArray_Dims:
+        npy_intp *ptr
+        int len
+
+    int _import_array() except -1
+    # A second definition so _import_array isn't marked as used when we use it here.
+    # Do not use - subject to change any time.
+    int __pyx_import_array "_import_array"() except -1
+
+    #
+    # Macros from ndarrayobject.h
+    #
+    bint PyArray_CHKFLAGS(ndarray m, int flags) nogil
+    bint PyArray_IS_C_CONTIGUOUS(ndarray arr) nogil
+    bint PyArray_IS_F_CONTIGUOUS(ndarray arr) nogil
+    bint PyArray_ISCONTIGUOUS(ndarray m) nogil
+    bint PyArray_ISWRITEABLE(ndarray m) nogil
+    bint PyArray_ISALIGNED(ndarray m) nogil
+
+    int PyArray_NDIM(ndarray) nogil
+    bint PyArray_ISONESEGMENT(ndarray) nogil
+    bint PyArray_ISFORTRAN(ndarray) nogil
+    int PyArray_FORTRANIF(ndarray) nogil
+
+    void* PyArray_DATA(ndarray) nogil
+    char* PyArray_BYTES(ndarray) nogil
+
+    npy_intp* PyArray_DIMS(ndarray) nogil
+    npy_intp* PyArray_STRIDES(ndarray) nogil
+    npy_intp PyArray_DIM(ndarray, size_t) nogil
+    npy_intp PyArray_STRIDE(ndarray, size_t) nogil
+
+    PyObject *PyArray_BASE(ndarray) nogil  # returns borrowed reference!
+    PyArray_Descr *PyArray_DESCR(ndarray) nogil  # returns borrowed reference to dtype!
+    int PyArray_FLAGS(ndarray) nogil
+    npy_intp PyArray_ITEMSIZE(ndarray) nogil
+    int PyArray_TYPE(ndarray arr) nogil
+
+    object PyArray_GETITEM(ndarray arr, void *itemptr)
+    int PyArray_SETITEM(ndarray arr, void *itemptr, object obj) except -1
+
+    bint PyTypeNum_ISBOOL(int) nogil
+    bint PyTypeNum_ISUNSIGNED(int) nogil
+    bint PyTypeNum_ISSIGNED(int) nogil
+    bint PyTypeNum_ISINTEGER(int) nogil
+    bint PyTypeNum_ISFLOAT(int) nogil
+    bint PyTypeNum_ISNUMBER(int) nogil
+    bint PyTypeNum_ISSTRING(int) nogil
+    bint PyTypeNum_ISCOMPLEX(int) nogil
+    bint PyTypeNum_ISPYTHON(int) nogil
+    bint PyTypeNum_ISFLEXIBLE(int) nogil
+    bint PyTypeNum_ISUSERDEF(int) nogil
+    bint PyTypeNum_ISEXTENDED(int) nogil
+    bint PyTypeNum_ISOBJECT(int) nogil
+
+    bint PyDataType_ISBOOL(dtype) nogil
+    bint PyDataType_ISUNSIGNED(dtype) nogil
+    bint PyDataType_ISSIGNED(dtype) nogil
+    bint PyDataType_ISINTEGER(dtype) nogil
+    bint PyDataType_ISFLOAT(dtype) nogil
+    bint PyDataType_ISNUMBER(dtype) nogil
+    bint PyDataType_ISSTRING(dtype) nogil
+    bint PyDataType_ISCOMPLEX(dtype) nogil
+    bint PyDataType_ISPYTHON(dtype) nogil
+    bint PyDataType_ISFLEXIBLE(dtype) nogil
+    bint PyDataType_ISUSERDEF(dtype) nogil
+    bint PyDataType_ISEXTENDED(dtype) nogil
+    bint PyDataType_ISOBJECT(dtype) nogil
+    bint PyDataType_HASFIELDS(dtype) nogil
+    bint PyDataType_HASSUBARRAY(dtype) nogil
+
+    bint PyArray_ISBOOL(ndarray) nogil
+    bint PyArray_ISUNSIGNED(ndarray) nogil
+    bint PyArray_ISSIGNED(ndarray) nogil
+    bint PyArray_ISINTEGER(ndarray) nogil
+    bint PyArray_ISFLOAT(ndarray) nogil
+    bint PyArray_ISNUMBER(ndarray) nogil
+    bint PyArray_ISSTRING(ndarray) nogil
+    bint PyArray_ISCOMPLEX(ndarray) nogil
+    bint PyArray_ISPYTHON(ndarray) nogil
+    bint PyArray_ISFLEXIBLE(ndarray) nogil
+    bint PyArray_ISUSERDEF(ndarray) nogil
+    bint PyArray_ISEXTENDED(ndarray) nogil
+    bint PyArray_ISOBJECT(ndarray) nogil
+    bint PyArray_HASFIELDS(ndarray) nogil
+
+    bint PyArray_ISVARIABLE(ndarray) nogil
+
+    bint PyArray_SAFEALIGNEDCOPY(ndarray) nogil
+    bint PyArray_ISNBO(char) nogil              # works on ndarray.byteorder
+    bint PyArray_IsNativeByteOrder(char) nogil # works on ndarray.byteorder
+    bint PyArray_ISNOTSWAPPED(ndarray) nogil
+    bint PyArray_ISBYTESWAPPED(ndarray) nogil
+
+    bint PyArray_FLAGSWAP(ndarray, int) nogil
+
+    bint PyArray_ISCARRAY(ndarray) nogil
+    bint PyArray_ISCARRAY_RO(ndarray) nogil
+    bint PyArray_ISFARRAY(ndarray) nogil
+    bint PyArray_ISFARRAY_RO(ndarray) nogil
+    bint PyArray_ISBEHAVED(ndarray) nogil
+    bint PyArray_ISBEHAVED_RO(ndarray) nogil
+
+
+    bint PyDataType_ISNOTSWAPPED(dtype) nogil
+    bint PyDataType_ISBYTESWAPPED(dtype) nogil
+
+    bint PyArray_DescrCheck(object)
+
+    bint PyArray_Check(object)
+    bint PyArray_CheckExact(object)
+
+    # Cannot be supported due to out arg:
+    # bint PyArray_HasArrayInterfaceType(object, dtype, object, object&)
+    # bint PyArray_HasArrayInterface(op, out)
+
+
+    bint PyArray_IsZeroDim(object)
+    # Cannot be supported due to ## ## in macro:
+    # bint PyArray_IsScalar(object, verbatim work)
+    bint PyArray_CheckScalar(object)
+    bint PyArray_IsPythonNumber(object)
+    bint PyArray_IsPythonScalar(object)
+    bint PyArray_IsAnyScalar(object)
+    bint PyArray_CheckAnyScalar(object)
+
+    ndarray PyArray_GETCONTIGUOUS(ndarray)
+    bint PyArray_SAMESHAPE(ndarray, ndarray) nogil
+    npy_intp PyArray_SIZE(ndarray) nogil
+    npy_intp PyArray_NBYTES(ndarray) nogil
+
+    object PyArray_FROM_O(object)
+    object PyArray_FROM_OF(object m, int flags)
+    object PyArray_FROM_OT(object m, int type)
+    object PyArray_FROM_OTF(object m, int type, int flags)
+    object PyArray_FROMANY(object m, int type, int min, int max, int flags)
+    object PyArray_ZEROS(int nd, npy_intp* dims, int type, int fortran)
+    object PyArray_EMPTY(int nd, npy_intp* dims, int type, int fortran)
+    void PyArray_FILLWBYTE(object, int val)
+    npy_intp PyArray_REFCOUNT(object)
+    object PyArray_ContiguousFromAny(op, int, int min_depth, int max_depth)
+    unsigned char PyArray_EquivArrTypes(ndarray a1, ndarray a2)
+    bint PyArray_EquivByteorders(int b1, int b2) nogil
+    object PyArray_SimpleNew(int nd, npy_intp* dims, int typenum)
+    object PyArray_SimpleNewFromData(int nd, npy_intp* dims, int typenum, void* data)
+    #object PyArray_SimpleNewFromDescr(int nd, npy_intp* dims, dtype descr)
+    object PyArray_ToScalar(void* data, ndarray arr)
+
+    void* PyArray_GETPTR1(ndarray m, npy_intp i) nogil
+    void* PyArray_GETPTR2(ndarray m, npy_intp i, npy_intp j) nogil
+    void* PyArray_GETPTR3(ndarray m, npy_intp i, npy_intp j, npy_intp k) nogil
+    void* PyArray_GETPTR4(ndarray m, npy_intp i, npy_intp j, npy_intp k, npy_intp l) nogil
+
+    # Cannot be supported due to out arg
+    # void PyArray_DESCR_REPLACE(descr)
+
+
+    object PyArray_Copy(ndarray)
+    object PyArray_FromObject(object op, int type, int min_depth, int max_depth)
+    object PyArray_ContiguousFromObject(object op, int type, int min_depth, int max_depth)
+    object PyArray_CopyFromObject(object op, int type, int min_depth, int max_depth)
+
+    object PyArray_Cast(ndarray mp, int type_num)
+    object PyArray_Take(ndarray ap, object items, int axis)
+    object PyArray_Put(ndarray ap, object items, object values)
+
+    void PyArray_ITER_RESET(flatiter it) nogil
+    void PyArray_ITER_NEXT(flatiter it) nogil
+    void PyArray_ITER_GOTO(flatiter it, npy_intp* destination) nogil
+    void PyArray_ITER_GOTO1D(flatiter it, npy_intp ind) nogil
+    void* PyArray_ITER_DATA(flatiter it) nogil
+    bint PyArray_ITER_NOTDONE(flatiter it) nogil
+
+    void PyArray_MultiIter_RESET(broadcast multi) nogil
+    void PyArray_MultiIter_NEXT(broadcast multi) nogil
+    void PyArray_MultiIter_GOTO(broadcast multi, npy_intp dest) nogil
+    void PyArray_MultiIter_GOTO1D(broadcast multi, npy_intp ind) nogil
+    void* PyArray_MultiIter_DATA(broadcast multi, npy_intp i) nogil
+    void PyArray_MultiIter_NEXTi(broadcast multi, npy_intp i) nogil
+    bint PyArray_MultiIter_NOTDONE(broadcast multi) nogil
+
+    # Functions from __multiarray_api.h
+
+    # Functions taking dtype and returning object/ndarray are disabled
+    # for now as they steal dtype references. I'm conservative and disable
+    # more than is probably needed until it can be checked further.
+    int PyArray_SetNumericOps (object) except -1
+    object PyArray_GetNumericOps ()
+    int PyArray_INCREF (ndarray) except *  # uses PyArray_Item_INCREF...
+    int PyArray_XDECREF (ndarray) except *  # uses PyArray_Item_DECREF...
+    void PyArray_SetStringFunction (object, int)
+    dtype PyArray_DescrFromType (int)
+    object PyArray_TypeObjectFromType (int)
+    char * PyArray_Zero (ndarray)
+    char * PyArray_One (ndarray)
+    #object PyArray_CastToType (ndarray, dtype, int)
+    int PyArray_CastTo (ndarray, ndarray) except -1
+    int PyArray_CastAnyTo (ndarray, ndarray) except -1
+    int PyArray_CanCastSafely (int, int)  # writes errors
+    npy_bool PyArray_CanCastTo (dtype, dtype)  # writes errors
+    int PyArray_ObjectType (object, int) except 0
+    dtype PyArray_DescrFromObject (object, dtype)
+    #ndarray* PyArray_ConvertToCommonType (object, int *)
+    dtype PyArray_DescrFromScalar (object)
+    dtype PyArray_DescrFromTypeObject (object)
+    npy_intp PyArray_Size (object)
+    #object PyArray_Scalar (void *, dtype, object)
+    #object PyArray_FromScalar (object, dtype)
+    void PyArray_ScalarAsCtype (object, void *)
+    #int PyArray_CastScalarToCtype (object, void *, dtype)
+    #int PyArray_CastScalarDirect (object, dtype, void *, int)
+    object PyArray_ScalarFromObject (object)
+    #PyArray_VectorUnaryFunc * PyArray_GetCastFunc (dtype, int)
+    object PyArray_FromDims (int, int *, int)
+    #object PyArray_FromDimsAndDataAndDescr (int, int *, dtype, char *)
+    #object PyArray_FromAny (object, dtype, int, int, int, object)
+    object PyArray_EnsureArray (object)
+    object PyArray_EnsureAnyArray (object)
+    #object PyArray_FromFile (stdio.FILE *, dtype, npy_intp, char *)
+    #object PyArray_FromString (char *, npy_intp, dtype, npy_intp, char *)
+    #object PyArray_FromBuffer (object, dtype, npy_intp, npy_intp)
+    #object PyArray_FromIter (object, dtype, npy_intp)
+    object PyArray_Return (ndarray)
+    #object PyArray_GetField (ndarray, dtype, int)
+    #int PyArray_SetField (ndarray, dtype, int, object) except -1
+    object PyArray_Byteswap (ndarray, npy_bool)
+    object PyArray_Resize (ndarray, PyArray_Dims *, int, NPY_ORDER)
+    int PyArray_MoveInto (ndarray, ndarray) except -1
+    int PyArray_CopyInto (ndarray, ndarray) except -1
+    int PyArray_CopyAnyInto (ndarray, ndarray) except -1
+    int PyArray_CopyObject (ndarray, object) except -1
+    object PyArray_NewCopy (ndarray, NPY_ORDER)
+    object PyArray_ToList (ndarray)
+    object PyArray_ToString (ndarray, NPY_ORDER)
+    int PyArray_ToFile (ndarray, stdio.FILE *, char *, char *) except -1
+    int PyArray_Dump (object, object, int) except -1
+    object PyArray_Dumps (object, int)
+    int PyArray_ValidType (int)  # Cannot error
+    void PyArray_UpdateFlags (ndarray, int)
+    object PyArray_New (type, int, npy_intp *, int, npy_intp *, void *, int, int, object)
+    #object PyArray_NewFromDescr (type, dtype, int, npy_intp *, npy_intp *, void *, int, object)
+    #dtype PyArray_DescrNew (dtype)
+    dtype PyArray_DescrNewFromType (int)
+    double PyArray_GetPriority (object, double)  # clears errors as of 1.25
+    object PyArray_IterNew (object)
+    object PyArray_MultiIterNew (int, ...)
+
+    int PyArray_PyIntAsInt (object) except? -1
+    npy_intp PyArray_PyIntAsIntp (object)
+    int PyArray_Broadcast (broadcast) except -1
+    void PyArray_FillObjectArray (ndarray, object) except *
+    int PyArray_FillWithScalar (ndarray, object) except -1
+    npy_bool PyArray_CheckStrides (int, int, npy_intp, npy_intp, npy_intp *, npy_intp *)
+    dtype PyArray_DescrNewByteorder (dtype, char)
+    object PyArray_IterAllButAxis (object, int *)
+    #object PyArray_CheckFromAny (object, dtype, int, int, int, object)
+    #object PyArray_FromArray (ndarray, dtype, int)
+    object PyArray_FromInterface (object)
+    object PyArray_FromStructInterface (object)
+    #object PyArray_FromArrayAttr (object, dtype, object)
+    #NPY_SCALARKIND PyArray_ScalarKind (int, ndarray*)
+    int PyArray_CanCoerceScalar (int, int, NPY_SCALARKIND)
+    object PyArray_NewFlagsObject (object)
+    npy_bool PyArray_CanCastScalar (type, type)
+    #int PyArray_CompareUCS4 (npy_ucs4 *, npy_ucs4 *, register size_t)
+    int PyArray_RemoveSmallest (broadcast) except -1
+    int PyArray_ElementStrides (object)
+    void PyArray_Item_INCREF (char *, dtype) except *
+    void PyArray_Item_XDECREF (char *, dtype) except *
+    object PyArray_FieldNames (object)
+    object PyArray_Transpose (ndarray, PyArray_Dims *)
+    object PyArray_TakeFrom (ndarray, object, int, ndarray, NPY_CLIPMODE)
+    object PyArray_PutTo (ndarray, object, object, NPY_CLIPMODE)
+    object PyArray_PutMask (ndarray, object, object)
+    object PyArray_Repeat (ndarray, object, int)
+    object PyArray_Choose (ndarray, object, ndarray, NPY_CLIPMODE)
+    int PyArray_Sort (ndarray, int, NPY_SORTKIND) except -1
+    object PyArray_ArgSort (ndarray, int, NPY_SORTKIND)
+    object PyArray_SearchSorted (ndarray, object, NPY_SEARCHSIDE, PyObject *)
+    object PyArray_ArgMax (ndarray, int, ndarray)
+    object PyArray_ArgMin (ndarray, int, ndarray)
+    object PyArray_Reshape (ndarray, object)
+    object PyArray_Newshape (ndarray, PyArray_Dims *, NPY_ORDER)
+    object PyArray_Squeeze (ndarray)
+    #object PyArray_View (ndarray, dtype, type)
+    object PyArray_SwapAxes (ndarray, int, int)
+    object PyArray_Max (ndarray, int, ndarray)
+    object PyArray_Min (ndarray, int, ndarray)
+    object PyArray_Ptp (ndarray, int, ndarray)
+    object PyArray_Mean (ndarray, int, int, ndarray)
+    object PyArray_Trace (ndarray, int, int, int, int, ndarray)
+    object PyArray_Diagonal (ndarray, int, int, int)
+    object PyArray_Clip (ndarray, object, object, ndarray)
+    object PyArray_Conjugate (ndarray, ndarray)
+    object PyArray_Nonzero (ndarray)
+    object PyArray_Std (ndarray, int, int, ndarray, int)
+    object PyArray_Sum (ndarray, int, int, ndarray)
+    object PyArray_CumSum (ndarray, int, int, ndarray)
+    object PyArray_Prod (ndarray, int, int, ndarray)
+    object PyArray_CumProd (ndarray, int, int, ndarray)
+    object PyArray_All (ndarray, int, ndarray)
+    object PyArray_Any (ndarray, int, ndarray)
+    object PyArray_Compress (ndarray, object, int, ndarray)
+    object PyArray_Flatten (ndarray, NPY_ORDER)
+    object PyArray_Ravel (ndarray, NPY_ORDER)
+    npy_intp PyArray_MultiplyList (npy_intp *, int)
+    int PyArray_MultiplyIntList (int *, int)
+    void * PyArray_GetPtr (ndarray, npy_intp*)
+    int PyArray_CompareLists (npy_intp *, npy_intp *, int)
+    #int PyArray_AsCArray (object*, void *, npy_intp *, int, dtype)
+    #int PyArray_As1D (object*, char **, int *, int)
+    #int PyArray_As2D (object*, char ***, int *, int *, int)
+    int PyArray_Free (object, void *)
+    #int PyArray_Converter (object, object*)
+    int PyArray_IntpFromSequence (object, npy_intp *, int) except -1
+    object PyArray_Concatenate (object, int)
+    object PyArray_InnerProduct (object, object)
+    object PyArray_MatrixProduct (object, object)
+    object PyArray_CopyAndTranspose (object)
+    object PyArray_Correlate (object, object, int)
+    int PyArray_TypestrConvert (int, int)
+    #int PyArray_DescrConverter (object, dtype*) except 0
+    #int PyArray_DescrConverter2 (object, dtype*) except 0
+    int PyArray_IntpConverter (object, PyArray_Dims *) except 0
+    #int PyArray_BufferConverter (object, chunk) except 0
+    int PyArray_AxisConverter (object, int *) except 0
+    int PyArray_BoolConverter (object, npy_bool *) except 0
+    int PyArray_ByteorderConverter (object, char *) except 0
+    int PyArray_OrderConverter (object, NPY_ORDER *) except 0
+    unsigned char PyArray_EquivTypes (dtype, dtype)  # clears errors
+    #object PyArray_Zeros (int, npy_intp *, dtype, int)
+    #object PyArray_Empty (int, npy_intp *, dtype, int)
+    object PyArray_Where (object, object, object)
+    object PyArray_Arange (double, double, double, int)
+    #object PyArray_ArangeObj (object, object, object, dtype)
+    int PyArray_SortkindConverter (object, NPY_SORTKIND *) except 0
+    object PyArray_LexSort (object, int)
+    object PyArray_Round (ndarray, int, ndarray)
+    unsigned char PyArray_EquivTypenums (int, int)
+    int PyArray_RegisterDataType (dtype) except -1
+    int PyArray_RegisterCastFunc (dtype, int, PyArray_VectorUnaryFunc *) except -1
+    int PyArray_RegisterCanCast (dtype, int, NPY_SCALARKIND) except -1
+    #void PyArray_InitArrFuncs (PyArray_ArrFuncs *)
+    object PyArray_IntTupleFromIntp (int, npy_intp *)
+    int PyArray_TypeNumFromName (char *)
+    int PyArray_ClipmodeConverter (object, NPY_CLIPMODE *) except 0
+    #int PyArray_OutputConverter (object, ndarray*) except 0
+    object PyArray_BroadcastToShape (object, npy_intp *, int)
+    void _PyArray_SigintHandler (int)
+    void* _PyArray_GetSigintBuf ()
+    #int PyArray_DescrAlignConverter (object, dtype*) except 0
+    #int PyArray_DescrAlignConverter2 (object, dtype*) except 0
+    int PyArray_SearchsideConverter (object, void *) except 0
+    object PyArray_CheckAxis (ndarray, int *, int)
+    npy_intp PyArray_OverflowMultiplyList (npy_intp *, int)
+    int PyArray_CompareString (char *, char *, size_t)
+    int PyArray_SetBaseObject(ndarray, base) except -1 # NOTE: steals a reference to base! Use "set_array_base()" instead.
+
+
+# Typedefs that matches the runtime dtype objects in
+# the numpy module.
+
+# The ones that are commented out needs an IFDEF function
+# in Cython to enable them only on the right systems.
+
+ctypedef npy_int8       int8_t
+ctypedef npy_int16      int16_t
+ctypedef npy_int32      int32_t
+ctypedef npy_int64      int64_t
+#ctypedef npy_int96      int96_t
+#ctypedef npy_int128     int128_t
+
+ctypedef npy_uint8      uint8_t
+ctypedef npy_uint16     uint16_t
+ctypedef npy_uint32     uint32_t
+ctypedef npy_uint64     uint64_t
+#ctypedef npy_uint96     uint96_t
+#ctypedef npy_uint128    uint128_t
+
+ctypedef npy_float32    float32_t
+ctypedef npy_float64    float64_t
+#ctypedef npy_float80    float80_t
+#ctypedef npy_float128   float128_t
+
+ctypedef float complex  complex64_t
+ctypedef double complex complex128_t
+
+# The int types are mapped a bit surprising --
+# numpy.int corresponds to 'l' and numpy.long to 'q'
+ctypedef npy_long       int_t
+ctypedef npy_longlong   longlong_t
+
+ctypedef npy_ulong      uint_t
+ctypedef npy_ulonglong  ulonglong_t
+
+ctypedef npy_intp       intp_t
+ctypedef npy_uintp      uintp_t
+
+ctypedef npy_double     float_t
+ctypedef npy_double     double_t
+ctypedef npy_longdouble longdouble_t
+
+ctypedef npy_cfloat      cfloat_t
+ctypedef npy_cdouble     cdouble_t
+ctypedef npy_clongdouble clongdouble_t
+
+ctypedef npy_cdouble     complex_t
+
+cdef inline object PyArray_MultiIterNew1(a):
+    return PyArray_MultiIterNew(1, a)
+
+cdef inline object PyArray_MultiIterNew2(a, b):
+    return PyArray_MultiIterNew(2, a, b)
+
+cdef inline object PyArray_MultiIterNew3(a, b, c):
+    return PyArray_MultiIterNew(3, a, b,  c)
+
+cdef inline object PyArray_MultiIterNew4(a, b, c, d):
+    return PyArray_MultiIterNew(4, a, b, c,  d)
+
+cdef inline object PyArray_MultiIterNew5(a, b, c, d, e):
+    return PyArray_MultiIterNew(5, a, b, c,  d,  e)
+
+cdef inline tuple PyDataType_SHAPE(dtype d):
+    if PyDataType_HASSUBARRAY(d):
+        return d.subarray.shape
+    else:
+        return ()
+
+
+cdef extern from "numpy/ndarrayobject.h":
+    PyTypeObject PyTimedeltaArrType_Type
+    PyTypeObject PyDatetimeArrType_Type
+    ctypedef int64_t npy_timedelta
+    ctypedef int64_t npy_datetime
+
+cdef extern from "numpy/ndarraytypes.h":
+    ctypedef struct PyArray_DatetimeMetaData:
+        NPY_DATETIMEUNIT base
+        int64_t num
+
+cdef extern from "numpy/arrayscalars.h":
+
+    # abstract types
+    ctypedef class numpy.generic [object PyObject]:
+        pass
+    ctypedef class numpy.number [object PyObject]:
+        pass
+    ctypedef class numpy.integer [object PyObject]:
+        pass
+    ctypedef class numpy.signedinteger [object PyObject]:
+        pass
+    ctypedef class numpy.unsignedinteger [object PyObject]:
+        pass
+    ctypedef class numpy.inexact [object PyObject]:
+        pass
+    ctypedef class numpy.floating [object PyObject]:
+        pass
+    ctypedef class numpy.complexfloating [object PyObject]:
+        pass
+    ctypedef class numpy.flexible [object PyObject]:
+        pass
+    ctypedef class numpy.character [object PyObject]:
+        pass
+
+    ctypedef struct PyDatetimeScalarObject:
+        # PyObject_HEAD
+        npy_datetime obval
+        PyArray_DatetimeMetaData obmeta
+
+    ctypedef struct PyTimedeltaScalarObject:
+        # PyObject_HEAD
+        npy_timedelta obval
+        PyArray_DatetimeMetaData obmeta
+
+    ctypedef enum NPY_DATETIMEUNIT:
+        NPY_FR_Y
+        NPY_FR_M
+        NPY_FR_W
+        NPY_FR_D
+        NPY_FR_B
+        NPY_FR_h
+        NPY_FR_m
+        NPY_FR_s
+        NPY_FR_ms
+        NPY_FR_us
+        NPY_FR_ns
+        NPY_FR_ps
+        NPY_FR_fs
+        NPY_FR_as
+        NPY_FR_GENERIC
+
+
+#
+# ufunc API
+#
+
+cdef extern from "numpy/ufuncobject.h":
+
+    ctypedef void (*PyUFuncGenericFunction) (char **, npy_intp *, npy_intp *, void *)
+
+    ctypedef class numpy.ufunc [object PyUFuncObject, check_size ignore]:
+        cdef:
+            int nin, nout, nargs
+            int identity
+            PyUFuncGenericFunction *functions
+            void **data
+            int ntypes
+            int check_return
+            char *name
+            char *types
+            char *doc
+            void *ptr
+            PyObject *obj
+            PyObject *userloops
+
+    cdef enum:
+        PyUFunc_Zero
+        PyUFunc_One
+        PyUFunc_None
+        UFUNC_ERR_IGNORE
+        UFUNC_ERR_WARN
+        UFUNC_ERR_RAISE
+        UFUNC_ERR_CALL
+        UFUNC_ERR_PRINT
+        UFUNC_ERR_LOG
+        UFUNC_MASK_DIVIDEBYZERO
+        UFUNC_MASK_OVERFLOW
+        UFUNC_MASK_UNDERFLOW
+        UFUNC_MASK_INVALID
+        UFUNC_SHIFT_DIVIDEBYZERO
+        UFUNC_SHIFT_OVERFLOW
+        UFUNC_SHIFT_UNDERFLOW
+        UFUNC_SHIFT_INVALID
+        UFUNC_FPE_DIVIDEBYZERO
+        UFUNC_FPE_OVERFLOW
+        UFUNC_FPE_UNDERFLOW
+        UFUNC_FPE_INVALID
+        UFUNC_ERR_DEFAULT
+        UFUNC_ERR_DEFAULT2
+
+    object PyUFunc_FromFuncAndData(PyUFuncGenericFunction *,
+          void **, char *, int, int, int, int, char *, char *, int)
+    int PyUFunc_RegisterLoopForType(ufunc, int,
+                                    PyUFuncGenericFunction, int *, void *) except -1
+    void PyUFunc_f_f_As_d_d \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_d_d \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_f_f \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_g_g \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_F_F_As_D_D \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_F_F \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_D_D \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_G_G \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_O_O \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_ff_f_As_dd_d \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_ff_f \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_dd_d \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_gg_g \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_FF_F_As_DD_D \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_DD_D \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_FF_F \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_GG_G \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_OO_O \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_O_O_method \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_OO_O_method \
+         (char **, npy_intp *, npy_intp *, void *)
+    void PyUFunc_On_Om \
+         (char **, npy_intp *, npy_intp *, void *)
+    int PyUFunc_GetPyValues \
+        (char *, int *, int *, PyObject **)
+    int PyUFunc_checkfperr \
+           (int, PyObject *, int *)
+    void PyUFunc_clearfperr()
+    int PyUFunc_getfperr()
+    int PyUFunc_handlefperr \
+        (int, PyObject *, int, int *) except -1
+    int PyUFunc_ReplaceLoopBySignature \
+        (ufunc, PyUFuncGenericFunction, int *, PyUFuncGenericFunction *)
+    object PyUFunc_FromFuncAndDataAndSignature \
+             (PyUFuncGenericFunction *, void **, char *, int, int, int,
+              int, char *, char *, int, char *)
+
+    int _import_umath() except -1
+
+cdef inline void set_array_base(ndarray arr, object base):
+    Py_INCREF(base) # important to do this before stealing the reference below!
+    PyArray_SetBaseObject(arr, base)
+
+cdef inline object get_array_base(ndarray arr):
+    base = PyArray_BASE(arr)
+    if base is NULL:
+        return None
+    return base
+
+# Versions of the import_* functions which are more suitable for
+# Cython code.
+cdef inline int import_array() except -1:
+    try:
+        __pyx_import_array()
+    except Exception:
+        raise ImportError("numpy.core.multiarray failed to import")
+
+cdef inline int import_umath() except -1:
+    try:
+        _import_umath()
+    except Exception:
+        raise ImportError("numpy.core.umath failed to import")
+
+cdef inline int import_ufunc() except -1:
+    try:
+        _import_umath()
+    except Exception:
+        raise ImportError("numpy.core.umath failed to import")
+
+cdef extern from *:
+    # Leave a marker that the NumPy declarations came from this file
+    # See https://github.com/cython/cython/issues/3573
+    """
+    /* NumPy API declarations from "numpy/__init__.pxd" */
+    """
+
+
+cdef inline bint is_timedelta64_object(object obj):
+    """
+    Cython equivalent of `isinstance(obj, np.timedelta64)`
+
+    Parameters
+    ----------
+    obj : object
+
+    Returns
+    -------
+    bool
+    """
+    return PyObject_TypeCheck(obj, &PyTimedeltaArrType_Type)
+
+
+cdef inline bint is_datetime64_object(object obj):
+    """
+    Cython equivalent of `isinstance(obj, np.datetime64)`
+
+    Parameters
+    ----------
+    obj : object
+
+    Returns
+    -------
+    bool
+    """
+    return PyObject_TypeCheck(obj, &PyDatetimeArrType_Type)
+
+
+cdef inline npy_datetime get_datetime64_value(object obj) nogil:
+    """
+    returns the int64 value underlying scalar numpy datetime64 object
+
+    Note that to interpret this as a datetime, the corresponding unit is
+    also needed.  That can be found using `get_datetime64_unit`.
+    """
+    return (obj).obval
+
+
+cdef inline npy_timedelta get_timedelta64_value(object obj) nogil:
+    """
+    returns the int64 value underlying scalar numpy timedelta64 object
+    """
+    return (obj).obval
+
+
+cdef inline NPY_DATETIMEUNIT get_datetime64_unit(object obj) nogil:
+    """
+    returns the unit part of the dtype for a numpy datetime64 object.
+    """
+    return (obj).obmeta.base
diff --git a/shared/package-files/3d/3db57b99578533eeeb22ef4f5d5d989011d18814f8fe265c2f52a14059370578/linalg_matrix_power_native.h b/shared/package-files/3d/3db57b99578533eeeb22ef4f5d5d989011d18814f8fe265c2f52a14059370578/linalg_matrix_power_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..89e6e55ae4bfffdf0c70c4766ff9da4b8ec8d683
--- /dev/null
+++ b/shared/package-files/3d/3db57b99578533eeeb22ef4f5d5d989011d18814f8fe265c2f52a14059370578/linalg_matrix_power_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor linalg_matrix_power(const at::Tensor & self, int64_t n);
+TORCH_API at::Tensor & linalg_matrix_power_out(const at::Tensor & self, int64_t n, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/3d/3db6721bf3ab1bd71b630cc1e98dd72848fa960b9dac006f51e7172bdfeb1c84/RECORD b/shared/package-files/3d/3db6721bf3ab1bd71b630cc1e98dd72848fa960b9dac006f51e7172bdfeb1c84/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..27882dd61965f2dbb04b5e2103d4f8bd8b750e8c
--- /dev/null
+++ b/shared/package-files/3d/3db6721bf3ab1bd71b630cc1e98dd72848fa960b9dac006f51e7172bdfeb1c84/RECORD
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diff --git a/shared/package-files/3d/3db930ba432dcc41fde2e5633bd204a92838c668b43d5f941d783d88cd79ad11/_decomp_qr.py b/shared/package-files/3d/3db930ba432dcc41fde2e5633bd204a92838c668b43d5f941d783d88cd79ad11/_decomp_qr.py
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--- /dev/null
+++ b/shared/package-files/3d/3db930ba432dcc41fde2e5633bd204a92838c668b43d5f941d783d88cd79ad11/_decomp_qr.py
@@ -0,0 +1,490 @@
+"""QR decomposition functions."""
+import numpy as np
+
+# Local imports
+from .lapack import get_lapack_funcs
+from ._misc import _datacopied
+
+__all__ = ['qr', 'qr_multiply', 'rq']
+
+
+def safecall(f, name, *args, **kwargs):
+    """Call a LAPACK routine, determining lwork automatically and handling
+    error return values"""
+    lwork = kwargs.get("lwork", None)
+    if lwork in (None, -1):
+        kwargs['lwork'] = -1
+        ret = f(*args, **kwargs)
+        kwargs['lwork'] = ret[-2][0].real.astype(np.int_)
+    ret = f(*args, **kwargs)
+    if ret[-1] < 0:
+        raise ValueError("illegal value in %dth argument of internal %s"
+                         % (-ret[-1], name))
+    return ret[:-2]
+
+
+def qr(a, overwrite_a=False, lwork=None, mode='full', pivoting=False,
+       check_finite=True):
+    """
+    Compute QR decomposition of a matrix.
+
+    Calculate the decomposition ``A = Q R`` where Q is unitary/orthogonal
+    and R upper triangular.
+
+    Parameters
+    ----------
+    a : (M, N) array_like
+        Matrix to be decomposed
+    overwrite_a : bool, optional
+        Whether data in `a` is overwritten (may improve performance if
+        `overwrite_a` is set to True by reusing the existing input data
+        structure rather than creating a new one.)
+    lwork : int, optional
+        Work array size, lwork >= a.shape[1]. If None or -1, an optimal size
+        is computed.
+    mode : {'full', 'r', 'economic', 'raw'}, optional
+        Determines what information is to be returned: either both Q and R
+        ('full', default), only R ('r') or both Q and R but computed in
+        economy-size ('economic', see Notes). The final option 'raw'
+        (added in SciPy 0.11) makes the function return two matrices
+        (Q, TAU) in the internal format used by LAPACK.
+    pivoting : bool, optional
+        Whether or not factorization should include pivoting for rank-revealing
+        qr decomposition. If pivoting, compute the decomposition
+        ``A[:, P] = Q @ R`` as above, but where P is chosen such that the
+        diagonal of R is non-increasing. Equivalently, albeit less efficiently,
+        an explicit P matrix may be formed explicitly by permuting the rows or columns
+        (depending on the side of the equation on which it is to be used) of
+        an identity matrix. See Examples.
+    check_finite : bool, optional
+        Whether to check that the input matrix contains only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (crashes, non-termination) if the inputs do contain infinities or NaNs.
+
+    Returns
+    -------
+    Q : float or complex ndarray
+        Of shape (M, M), or (M, K) for ``mode='economic'``. Not returned
+        if ``mode='r'``. Replaced by tuple ``(Q, TAU)`` if ``mode='raw'``.
+    R : float or complex ndarray
+        Of shape (M, N), or (K, N) for ``mode in ['economic', 'raw']``.
+        ``K = min(M, N)``.
+    P : int ndarray
+        Of shape (N,) for ``pivoting=True``. Not returned if
+        ``pivoting=False``.
+
+    Raises
+    ------
+    LinAlgError
+        Raised if decomposition fails
+
+    Notes
+    -----
+    This is an interface to the LAPACK routines dgeqrf, zgeqrf,
+    dorgqr, zungqr, dgeqp3, and zgeqp3.
+
+    If ``mode=economic``, the shapes of Q and R are (M, K) and (K, N) instead
+    of (M,M) and (M,N), with ``K=min(M,N)``.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import linalg
+    >>> rng = np.random.default_rng()
+    >>> a = rng.standard_normal((9, 6))
+
+    >>> q, r = linalg.qr(a)
+    >>> np.allclose(a, np.dot(q, r))
+    True
+    >>> q.shape, r.shape
+    ((9, 9), (9, 6))
+
+    >>> r2 = linalg.qr(a, mode='r')
+    >>> np.allclose(r, r2)
+    True
+
+    >>> q3, r3 = linalg.qr(a, mode='economic')
+    >>> q3.shape, r3.shape
+    ((9, 6), (6, 6))
+
+    >>> q4, r4, p4 = linalg.qr(a, pivoting=True)
+    >>> d = np.abs(np.diag(r4))
+    >>> np.all(d[1:] <= d[:-1])
+    True
+    >>> np.allclose(a[:, p4], np.dot(q4, r4))
+    True
+    >>> P = np.eye(p4.size)[p4]
+    >>> np.allclose(a, np.dot(q4, r4) @ P)
+    True
+    >>> np.allclose(a @ P.T, np.dot(q4, r4))
+    True
+    >>> q4.shape, r4.shape, p4.shape
+    ((9, 9), (9, 6), (6,))
+
+    >>> q5, r5, p5 = linalg.qr(a, mode='economic', pivoting=True)
+    >>> q5.shape, r5.shape, p5.shape
+    ((9, 6), (6, 6), (6,))
+    >>> P = np.eye(6)[:, p5]
+    >>> np.allclose(a @ P, np.dot(q5, r5))
+    True
+
+    """
+    # 'qr' was the old default, equivalent to 'full'. Neither 'full' nor
+    # 'qr' are used below.
+    # 'raw' is used internally by qr_multiply
+    if mode not in ['full', 'qr', 'r', 'economic', 'raw']:
+        raise ValueError("Mode argument should be one of ['full', 'r', "
+                         "'economic', 'raw']")
+
+    if check_finite:
+        a1 = np.asarray_chkfinite(a)
+    else:
+        a1 = np.asarray(a)
+    if len(a1.shape) != 2:
+        raise ValueError("expected a 2-D array")
+
+    M, N = a1.shape
+
+    # accommodate empty arrays
+    if a1.size == 0:
+        K = min(M, N)
+
+        if mode not in ['economic', 'raw']:
+            Q = np.empty_like(a1, shape=(M, M))
+            Q[...] = np.identity(M)
+            R = np.empty_like(a1)
+        else:
+            Q = np.empty_like(a1, shape=(M, K))
+            R = np.empty_like(a1, shape=(K, N))
+
+        if pivoting:
+            Rj = R, np.arange(N, dtype=np.int32)
+        else:
+            Rj = R,
+
+        if mode == 'r':
+            return Rj
+        elif mode == 'raw':
+            qr = np.empty_like(a1, shape=(M, N))
+            tau = np.zeros_like(a1, shape=(K,))
+            return ((qr, tau),) + Rj
+        return (Q,) + Rj
+
+    overwrite_a = overwrite_a or (_datacopied(a1, a))
+
+    if pivoting:
+        geqp3, = get_lapack_funcs(('geqp3',), (a1,))
+        qr, jpvt, tau = safecall(geqp3, "geqp3", a1, overwrite_a=overwrite_a)
+        jpvt -= 1  # geqp3 returns a 1-based index array, so subtract 1
+    else:
+        geqrf, = get_lapack_funcs(('geqrf',), (a1,))
+        qr, tau = safecall(geqrf, "geqrf", a1, lwork=lwork,
+                           overwrite_a=overwrite_a)
+
+    if mode not in ['economic', 'raw'] or M < N:
+        R = np.triu(qr)
+    else:
+        R = np.triu(qr[:N, :])
+
+    if pivoting:
+        Rj = R, jpvt
+    else:
+        Rj = R,
+
+    if mode == 'r':
+        return Rj
+    elif mode == 'raw':
+        return ((qr, tau),) + Rj
+
+    gor_un_gqr, = get_lapack_funcs(('orgqr',), (qr,))
+
+    if M < N:
+        Q, = safecall(gor_un_gqr, "gorgqr/gungqr", qr[:, :M], tau,
+                      lwork=lwork, overwrite_a=1)
+    elif mode == 'economic':
+        Q, = safecall(gor_un_gqr, "gorgqr/gungqr", qr, tau, lwork=lwork,
+                      overwrite_a=1)
+    else:
+        t = qr.dtype.char
+        qqr = np.empty((M, M), dtype=t)
+        qqr[:, :N] = qr
+        Q, = safecall(gor_un_gqr, "gorgqr/gungqr", qqr, tau, lwork=lwork,
+                      overwrite_a=1)
+
+    return (Q,) + Rj
+
+
+def qr_multiply(a, c, mode='right', pivoting=False, conjugate=False,
+                overwrite_a=False, overwrite_c=False):
+    """
+    Calculate the QR decomposition and multiply Q with a matrix.
+
+    Calculate the decomposition ``A = Q R`` where Q is unitary/orthogonal
+    and R upper triangular. Multiply Q with a vector or a matrix c.
+
+    Parameters
+    ----------
+    a : (M, N), array_like
+        Input array
+    c : array_like
+        Input array to be multiplied by ``q``.
+    mode : {'left', 'right'}, optional
+        ``Q @ c`` is returned if mode is 'left', ``c @ Q`` is returned if
+        mode is 'right'.
+        The shape of c must be appropriate for the matrix multiplications,
+        if mode is 'left', ``min(a.shape) == c.shape[0]``,
+        if mode is 'right', ``a.shape[0] == c.shape[1]``.
+    pivoting : bool, optional
+        Whether or not factorization should include pivoting for rank-revealing
+        qr decomposition, see the documentation of qr.
+    conjugate : bool, optional
+        Whether Q should be complex-conjugated. This might be faster
+        than explicit conjugation.
+    overwrite_a : bool, optional
+        Whether data in a is overwritten (may improve performance)
+    overwrite_c : bool, optional
+        Whether data in c is overwritten (may improve performance).
+        If this is used, c must be big enough to keep the result,
+        i.e. ``c.shape[0]`` = ``a.shape[0]`` if mode is 'left'.
+
+    Returns
+    -------
+    CQ : ndarray
+        The product of ``Q`` and ``c``.
+    R : (K, N), ndarray
+        R array of the resulting QR factorization where ``K = min(M, N)``.
+    P : (N,) ndarray
+        Integer pivot array. Only returned when ``pivoting=True``.
+
+    Raises
+    ------
+    LinAlgError
+        Raised if QR decomposition fails.
+
+    Notes
+    -----
+    This is an interface to the LAPACK routines ``?GEQRF``, ``?ORMQR``,
+    ``?UNMQR``, and ``?GEQP3``.
+
+    .. versionadded:: 0.11.0
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy.linalg import qr_multiply, qr
+    >>> A = np.array([[1, 3, 3], [2, 3, 2], [2, 3, 3], [1, 3, 2]])
+    >>> qc, r1, piv1 = qr_multiply(A, 2*np.eye(4), pivoting=1)
+    >>> qc
+    array([[-1.,  1., -1.],
+           [-1., -1.,  1.],
+           [-1., -1., -1.],
+           [-1.,  1.,  1.]])
+    >>> r1
+    array([[-6., -3., -5.            ],
+           [ 0., -1., -1.11022302e-16],
+           [ 0.,  0., -1.            ]])
+    >>> piv1
+    array([1, 0, 2], dtype=int32)
+    >>> q2, r2, piv2 = qr(A, mode='economic', pivoting=1)
+    >>> np.allclose(2*q2 - qc, np.zeros((4, 3)))
+    True
+
+    """
+    if mode not in ['left', 'right']:
+        raise ValueError("Mode argument can only be 'left' or 'right' but "
+                         f"not '{mode}'")
+    c = np.asarray_chkfinite(c)
+    if c.ndim < 2:
+        onedim = True
+        c = np.atleast_2d(c)
+        if mode == "left":
+            c = c.T
+    else:
+        onedim = False
+
+    a = np.atleast_2d(np.asarray(a))  # chkfinite done in qr
+    M, N = a.shape
+
+    if mode == 'left':
+        if c.shape[0] != min(M, N + overwrite_c*(M-N)):
+            raise ValueError('Array shapes are not compatible for Q @ c'
+                             f' operation: {a.shape} vs {c.shape}')
+    else:
+        if M != c.shape[1]:
+            raise ValueError('Array shapes are not compatible for c @ Q'
+                             f' operation: {c.shape} vs {a.shape}')
+
+    raw = qr(a, overwrite_a, None, "raw", pivoting)
+    Q, tau = raw[0]
+
+    # accommodate empty arrays
+    if c.size == 0:
+        return (np.empty_like(c),) + raw[1:]
+
+    gor_un_mqr, = get_lapack_funcs(('ormqr',), (Q,))
+    if gor_un_mqr.typecode in ('s', 'd'):
+        trans = "T"
+    else:
+        trans = "C"
+
+    Q = Q[:, :min(M, N)]
+    if M > N and mode == "left" and not overwrite_c:
+        if conjugate:
+            cc = np.zeros((c.shape[1], M), dtype=c.dtype, order="F")
+            cc[:, :N] = c.T
+        else:
+            cc = np.zeros((M, c.shape[1]), dtype=c.dtype, order="F")
+            cc[:N, :] = c
+            trans = "N"
+        if conjugate:
+            lr = "R"
+        else:
+            lr = "L"
+        overwrite_c = True
+    elif c.flags["C_CONTIGUOUS"] and trans == "T" or conjugate:
+        cc = c.T
+        if mode == "left":
+            lr = "R"
+        else:
+            lr = "L"
+    else:
+        trans = "N"
+        cc = c
+        if mode == "left":
+            lr = "L"
+        else:
+            lr = "R"
+    cQ, = safecall(gor_un_mqr, "gormqr/gunmqr", lr, trans, Q, tau, cc,
+                   overwrite_c=overwrite_c)
+    if trans != "N":
+        cQ = cQ.T
+    if mode == "right":
+        cQ = cQ[:, :min(M, N)]
+    if onedim:
+        cQ = cQ.ravel()
+
+    return (cQ,) + raw[1:]
+
+
+def rq(a, overwrite_a=False, lwork=None, mode='full', check_finite=True):
+    """
+    Compute RQ decomposition of a matrix.
+
+    Calculate the decomposition ``A = R Q`` where Q is unitary/orthogonal
+    and R upper triangular.
+
+    Parameters
+    ----------
+    a : (M, N) array_like
+        Matrix to be decomposed
+    overwrite_a : bool, optional
+        Whether data in a is overwritten (may improve performance)
+    lwork : int, optional
+        Work array size, lwork >= a.shape[1]. If None or -1, an optimal size
+        is computed.
+    mode : {'full', 'r', 'economic'}, optional
+        Determines what information is to be returned: either both Q and R
+        ('full', default), only R ('r') or both Q and R but computed in
+        economy-size ('economic', see Notes).
+    check_finite : bool, optional
+        Whether to check that the input matrix contains only finite numbers.
+        Disabling may give a performance gain, but may result in problems
+        (crashes, non-termination) if the inputs do contain infinities or NaNs.
+
+    Returns
+    -------
+    R : float or complex ndarray
+        Of shape (M, N) or (M, K) for ``mode='economic'``. ``K = min(M, N)``.
+    Q : float or complex ndarray
+        Of shape (N, N) or (K, N) for ``mode='economic'``. Not returned
+        if ``mode='r'``.
+
+    Raises
+    ------
+    LinAlgError
+        If decomposition fails.
+
+    Notes
+    -----
+    This is an interface to the LAPACK routines sgerqf, dgerqf, cgerqf, zgerqf,
+    sorgrq, dorgrq, cungrq and zungrq.
+
+    If ``mode=economic``, the shapes of Q and R are (K, N) and (M, K) instead
+    of (N,N) and (M,N), with ``K=min(M,N)``.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from scipy import linalg
+    >>> rng = np.random.default_rng()
+    >>> a = rng.standard_normal((6, 9))
+    >>> r, q = linalg.rq(a)
+    >>> np.allclose(a, r @ q)
+    True
+    >>> r.shape, q.shape
+    ((6, 9), (9, 9))
+    >>> r2 = linalg.rq(a, mode='r')
+    >>> np.allclose(r, r2)
+    True
+    >>> r3, q3 = linalg.rq(a, mode='economic')
+    >>> r3.shape, q3.shape
+    ((6, 6), (6, 9))
+
+    """
+    if mode not in ['full', 'r', 'economic']:
+        raise ValueError(
+                 "Mode argument should be one of ['full', 'r', 'economic']")
+
+    if check_finite:
+        a1 = np.asarray_chkfinite(a)
+    else:
+        a1 = np.asarray(a)
+    if len(a1.shape) != 2:
+        raise ValueError('expected matrix')
+
+    M, N = a1.shape
+
+    # accommodate empty arrays
+    if a1.size == 0:
+        K = min(M, N)
+
+        if not mode == 'economic':
+            R = np.empty_like(a1)
+            Q = np.empty_like(a1, shape=(N, N))
+            Q[...] = np.identity(N)
+        else:
+            R = np.empty_like(a1, shape=(M, K))
+            Q = np.empty_like(a1, shape=(K, N))
+
+        if mode == 'r':
+            return R
+        return R, Q
+
+    overwrite_a = overwrite_a or (_datacopied(a1, a))
+
+    gerqf, = get_lapack_funcs(('gerqf',), (a1,))
+    rq, tau = safecall(gerqf, 'gerqf', a1, lwork=lwork,
+                       overwrite_a=overwrite_a)
+    if not mode == 'economic' or N < M:
+        R = np.triu(rq, N-M)
+    else:
+        R = np.triu(rq[-M:, -M:])
+
+    if mode == 'r':
+        return R
+
+    gor_un_grq, = get_lapack_funcs(('orgrq',), (rq,))
+
+    if N < M:
+        Q, = safecall(gor_un_grq, "gorgrq/gungrq", rq[-N:], tau, lwork=lwork,
+                      overwrite_a=1)
+    elif mode == 'economic':
+        Q, = safecall(gor_un_grq, "gorgrq/gungrq", rq, tau, lwork=lwork,
+                      overwrite_a=1)
+    else:
+        rq1 = np.empty((N, N), dtype=rq.dtype)
+        rq1[-M:] = rq
+        Q, = safecall(gor_un_grq, "gorgrq/gungrq", rq1, tau, lwork=lwork,
+                      overwrite_a=1)
+
+    return R, Q
diff --git a/shared/package-files/3d/3dba29cbd94e0eb77f3f8ebfc71956c4bc2a3df1baff8277096582e08a9f0cbe/test_interactive.py b/shared/package-files/3d/3dba29cbd94e0eb77f3f8ebfc71956c4bc2a3df1baff8277096582e08a9f0cbe/test_interactive.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e088c42fd872c13849e593b04734158f5d1e5bc
--- /dev/null
+++ b/shared/package-files/3d/3dba29cbd94e0eb77f3f8ebfc71956c4bc2a3df1baff8277096582e08a9f0cbe/test_interactive.py
@@ -0,0 +1,10 @@
+from sympy.interactive.session import int_to_Integer
+
+
+def test_int_to_Integer():
+    assert int_to_Integer("1 + 2.2 + 0x3 + 40") == \
+        'Integer (1 )+2.2 +Integer (0x3 )+Integer (40 )'
+    assert int_to_Integer("0b101") == 'Integer (0b101 )'
+    assert int_to_Integer("ab1 + 1 + '1 + 2'") == "ab1 +Integer (1 )+'1 + 2'"
+    assert int_to_Integer("(2 + \n3)") == '(Integer (2 )+\nInteger (3 ))'
+    assert int_to_Integer("2 + 2.0 + 2j + 2e-10") == 'Integer (2 )+2.0 +2j +2e-10 '
diff --git a/shared/package-files/3d/3dba3df1ae8065087ad907d4ddabaf1477c2bc60ba3e310fd4e7b3077cce09c2/anova.R b/shared/package-files/3d/3dba3df1ae8065087ad907d4ddabaf1477c2bc60ba3e310fd4e7b3077cce09c2/anova.R
new file mode 100644
index 0000000000000000000000000000000000000000..30947fd32df0712251f6339c36449979620bd603
--- /dev/null
+++ b/shared/package-files/3d/3dba3df1ae8065087ad907d4ddabaf1477c2bc60ba3e310fd4e7b3077cce09c2/anova.R
@@ -0,0 +1,92 @@
+dta <- read.table('data.dat', header=TRUE)
+dta$Duration <- factor(dta$Duration)
+dta$Weight <- factor(dta$Weight)
+dta$logDays <- log(dta$Days + 1) # Use log days to "stabilize" variance
+
+attach(dta)
+library(car)
+source('/home/skipper/statsmodels/statsmodels/tools/topy.R')
+
+sum.lm = lm(logDays ~ Duration * Weight, contrasts=list(Duration=contr.sum,
+                                                        Weight=contr.sum))
+
+anova.lm.sum <- anova(sum.lm)
+
+for(name in names(anova.lm.sum)) {
+    mkarray2(anova.lm.sum[[name]], name, TRUE)
+        }; cat("\n")
+
+anova.lm.interaction <- anova(lm(logDays ~ Duration + Weight), sum.lm)
+
+for(name in names(anova.lm.interaction)) {
+    mkarray2(anova.lm.interaction[[name]], name, TRUE)
+        }; cat("\n")
+
+anova.lm.variable <- anova(lm(logDays ~ Duration), lm(logDays ~ Duration + Weight))
+
+anova.lm.variable2 <- anova(lm(logDays ~ Weight), lm(logDays ~ Duration + Weight))
+
+anova.i <- anova(sum.lm)
+anova.ii <- Anova(sum.lm, type='II')
+anova.iii <- Anova(sum.lm, type='III')
+
+nosum.lm = lm(logDays ~ Duration * Weight, contrasts=list(Duration=contr.treatment, Weight=contr.treatment))
+
+anova.i.nosum <- anova(nosum.lm)
+anova.ii.nosum <- Anova(nosum.lm, type='II')
+anova.iii.nosum <- Anova(nosum.lm, type='III')
+
+dta.dropped <- dta[4:60, ]
+sum.lm.dropped <- lm(logDays ~ Duration * Weight,  dta.dropped,
+                    contrasts=list(Duration=contr.sum, Weight=contr.sum))
+
+anova.i.dropped <- anova(sum.lm.dropped)
+anova.ii.dropped <- Anova(sum.lm.dropped, type='II')
+anova.iii.dropped <- Anova(sum.lm.dropped, type='III')
+
+for(name in names(anova.ii.dropped)) {
+    mkarray2(anova.ii.dropped[[name]], name, TRUE)
+        }; cat("\n")
+
+for(name in names(anova.iii.dropped)) {
+    mkarray2(anova.iii.dropped[[name]], name, TRUE)
+        }; cat("\n")
+
+anova.iii.dropped <- Anova(sum.lm.dropped, white="hc0", type='III')
+for(name in names(anova.iii.dropped)) {
+    mkarray2(anova.iii.dropped[[name]], name, TRUE)
+        }; cat("\n")
+anova.iii.dropped <- Anova(sum.lm.dropped, white="hc1", type='III')
+for(name in names(anova.iii.dropped)) {
+    mkarray2(anova.iii.dropped[[name]], name, TRUE)
+        }; cat("\n")
+anova.iii.dropped <- Anova(sum.lm.dropped, white="hc2", type='III')
+for(name in names(anova.iii.dropped)) {
+    mkarray2(anova.iii.dropped[[name]], name, TRUE)
+        }; cat("\n")
+anova.iii.dropped <- Anova(sum.lm.dropped, white="hc3", type='III')
+for(name in names(anova.iii.dropped)) {
+    mkarray2(anova.iii.dropped[[name]], name, TRUE)
+        }; cat("\n")
+
+
+anova.ii.dropped <- Anova(sum.lm.dropped, type='II', white="hc0")
+for(name in names(anova.ii.dropped)) {
+    mkarray2(anova.ii.dropped[[name]], name, TRUE)
+        }; cat("\n")
+
+anova.ii.dropped <- Anova(sum.lm.dropped, type='II', white="hc1")
+for(name in names(anova.ii.dropped)) {
+    mkarray2(anova.ii.dropped[[name]], name, TRUE)
+        }; cat("\n")
+
+anova.ii.dropped <- Anova(sum.lm.dropped, type='II', white="hc2")
+for(name in names(anova.ii.dropped)) {
+    mkarray2(anova.ii.dropped[[name]], name, TRUE)
+        }; cat("\n")
+
+anova.ii.dropped <- Anova(sum.lm.dropped, type='II', white="hc3")
+for(name in names(anova.ii.dropped)) {
+    mkarray2(anova.ii.dropped[[name]], name, TRUE)
+        }; cat("\n")
+
diff --git a/shared/package-files/3d/3dbbd2c50fbe4522208bcdcbb44383c9c516fa37778c8cc0b849b2eae5024197/tree.py b/shared/package-files/3d/3dbbd2c50fbe4522208bcdcbb44383c9c516fa37778c8cc0b849b2eae5024197/tree.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e9a4c9518559aa9ec5babd7a9ce89fee59ea3cc
--- /dev/null
+++ b/shared/package-files/3d/3dbbd2c50fbe4522208bcdcbb44383c9c516fa37778c8cc0b849b2eae5024197/tree.py
@@ -0,0 +1,138 @@
+from itertools import chain
+
+import networkx as nx
+
+__all__ = ["tree_data", "tree_graph"]
+
+
+def tree_data(G, root, ident="id", children="children"):
+    """Returns data in tree format that is suitable for JSON serialization
+    and use in Javascript documents.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+       G must be an oriented tree
+
+    root : node
+       The root of the tree
+
+    ident : string
+        Attribute name for storing NetworkX-internal graph data. `ident` must
+        have a different value than `children`. The default is 'id'.
+
+    children : string
+        Attribute name for storing NetworkX-internal graph data. `children`
+        must have a different value than `ident`. The default is 'children'.
+
+    Returns
+    -------
+    data : dict
+       A dictionary with node-link formatted data.
+
+    Raises
+    ------
+    NetworkXError
+        If `children` and `ident` attributes are identical.
+
+    Examples
+    --------
+    >>> from networkx.readwrite import json_graph
+    >>> G = nx.DiGraph([(1, 2)])
+    >>> data = json_graph.tree_data(G, root=1)
+
+    To serialize with json
+
+    >>> import json
+    >>> s = json.dumps(data)
+
+    Notes
+    -----
+    Node attributes are stored in this format but keys
+    for attributes must be strings if you want to serialize with JSON.
+
+    Graph and edge attributes are not stored.
+
+    See Also
+    --------
+    tree_graph, node_link_data, adjacency_data
+    """
+    if G.number_of_nodes() != G.number_of_edges() + 1:
+        raise TypeError("G is not a tree.")
+    if not G.is_directed():
+        raise TypeError("G is not directed.")
+    if not nx.is_weakly_connected(G):
+        raise TypeError("G is not weakly connected.")
+
+    if ident == children:
+        raise nx.NetworkXError("The values for `id` and `children` must be different.")
+
+    def add_children(n, G):
+        nbrs = G[n]
+        if len(nbrs) == 0:
+            return []
+        children_ = []
+        for child in nbrs:
+            d = dict(chain(G.nodes[child].items(), [(ident, child)]))
+            c = add_children(child, G)
+            if c:
+                d[children] = c
+            children_.append(d)
+        return children_
+
+    data = dict(chain(G.nodes[root].items(), [(ident, root)]))
+    data[children] = add_children(root, G)
+    return data
+
+
+def tree_graph(data, ident="id", children="children"):
+    """Returns graph from tree data format.
+
+    Parameters
+    ----------
+    data : dict
+        Tree formatted graph data
+
+    ident : string
+        Attribute name for storing NetworkX-internal graph data. `ident` must
+        have a different value than `children`. The default is 'id'.
+
+    children : string
+        Attribute name for storing NetworkX-internal graph data. `children`
+        must have a different value than `ident`. The default is 'children'.
+
+    Returns
+    -------
+    G : NetworkX DiGraph
+
+    Examples
+    --------
+    >>> from networkx.readwrite import json_graph
+    >>> G = nx.DiGraph([(1, 2)])
+    >>> data = json_graph.tree_data(G, root=1)
+    >>> H = json_graph.tree_graph(data)
+
+    See Also
+    --------
+    tree_data, node_link_data, adjacency_data
+    """
+    graph = nx.DiGraph()
+
+    def add_children(parent, children_):
+        for data in children_:
+            child = data[ident]
+            graph.add_edge(parent, child)
+            grandchildren = data.get(children, [])
+            if grandchildren:
+                add_children(child, grandchildren)
+            nodedata = {
+                str(k): v for k, v in data.items() if k != ident and k != children
+            }
+            graph.add_node(child, **nodedata)
+
+    root = data[ident]
+    children_ = data.get(children, [])
+    nodedata = {str(k): v for k, v in data.items() if k != ident and k != children}
+    graph.add_node(root, **nodedata)
+    add_children(root, children_)
+    return graph
diff --git a/shared/package-files/3d/3dbeccd0e635b5dce52e502fc9aadc0609a4323925d83589bda6d700b54d7053/inputtransformer2.py b/shared/package-files/3d/3dbeccd0e635b5dce52e502fc9aadc0609a4323925d83589bda6d700b54d7053/inputtransformer2.py
new file mode 100644
index 0000000000000000000000000000000000000000..949cf383e2748687fc1017ea6c7f4f6e83ccf5cc
--- /dev/null
+++ b/shared/package-files/3d/3dbeccd0e635b5dce52e502fc9aadc0609a4323925d83589bda6d700b54d7053/inputtransformer2.py
@@ -0,0 +1,827 @@
+"""Input transformer machinery to support IPython special syntax.
+
+This includes the machinery to recognise and transform ``%magic`` commands,
+``!system`` commands, ``help?`` querying, prompt stripping, and so forth.
+
+Added: IPython 7.0. Replaces inputsplitter and inputtransformer which were
+deprecated in 7.0.
+"""
+
+# Copyright (c) IPython Development Team.
+# Distributed under the terms of the Modified BSD License.
+
+import ast
+from codeop import CommandCompiler, Compile
+import re
+import sys
+import tokenize
+from typing import List, Tuple, Optional, Any
+import warnings
+
+from IPython.utils import tokenutil
+
+_indent_re = re.compile(r'^[ \t]+')
+
+def leading_empty_lines(lines):
+    """Remove leading empty lines
+
+    If the leading lines are empty or contain only whitespace, they will be
+    removed.
+    """
+    if not lines:
+        return lines
+    for i, line in enumerate(lines):
+        if line and not line.isspace():
+            return lines[i:]
+    return lines
+
+def leading_indent(lines):
+    """Remove leading indentation.
+
+    If the first line starts with a spaces or tabs, the same whitespace will be
+    removed from each following line in the cell.
+    """
+    if not lines:
+        return lines
+    m = _indent_re.match(lines[0])
+    if not m:
+        return lines
+    space = m.group(0)
+    n = len(space)
+    return [l[n:] if l.startswith(space) else l
+            for l in lines]
+
+class PromptStripper:
+    """Remove matching input prompts from a block of input.
+
+    Parameters
+    ----------
+    prompt_re : regular expression
+        A regular expression matching any input prompt (including continuation,
+        e.g. ``...``)
+    initial_re : regular expression, optional
+        A regular expression matching only the initial prompt, but not continuation.
+        If no initial expression is given, prompt_re will be used everywhere.
+        Used mainly for plain Python prompts (``>>>``), where the continuation prompt
+        ``...`` is a valid Python expression in Python 3, so shouldn't be stripped.
+
+    Notes
+    -----
+
+    If initial_re and prompt_re differ,
+    only initial_re will be tested against the first line.
+    If any prompt is found on the first two lines,
+    prompts will be stripped from the rest of the block.
+    """
+    def __init__(self, prompt_re, initial_re=None):
+        self.prompt_re = prompt_re
+        self.initial_re = initial_re or prompt_re
+
+    def _strip(self, lines):
+        return [self.prompt_re.sub('', l, count=1) for l in lines]
+
+    def __call__(self, lines):
+        if not lines:
+            return lines
+        if self.initial_re.match(lines[0]) or \
+                (len(lines) > 1 and self.prompt_re.match(lines[1])):
+            return self._strip(lines)
+        return lines
+
+classic_prompt = PromptStripper(
+    prompt_re=re.compile(r'^(>>>|\.\.\.)( |$)'),
+    initial_re=re.compile(r'^>>>( |$)')
+)
+
+ipython_prompt = PromptStripper(
+    re.compile(
+        r"""
+        ^(                         # Match from the beginning of a line, either:
+
+                                   # 1. First-line prompt:
+        ((\[nav\]|\[ins\])?\ )?    # Vi editing mode prompt, if it's there
+        In\                        # The 'In' of the prompt, with a space
+        \[\d+\]:                   # Command index, as displayed in the prompt
+        \                          # With a mandatory trailing space
+
+        |                          # ... or ...
+
+                                   # 2. The three dots of the multiline prompt
+        \s*                        # All leading whitespace characters
+        \.{3,}:                    # The three (or more) dots
+        \ ?                        # With an optional trailing space
+
+        )
+        """,
+        re.VERBOSE,
+    )
+)
+
+
+def cell_magic(lines):
+    if not lines or not lines[0].startswith('%%'):
+        return lines
+    if re.match(r'%%\w+\?', lines[0]):
+        # This case will be handled by help_end
+        return lines
+    magic_name, _, first_line = lines[0][2:].rstrip().partition(' ')
+    body = ''.join(lines[1:])
+    return ['get_ipython().run_cell_magic(%r, %r, %r)\n'
+            % (magic_name, first_line, body)]
+
+
+def _find_assign_op(token_line) -> Optional[int]:
+    """Get the index of the first assignment in the line ('=' not inside brackets)
+
+    Note: We don't try to support multiple special assignment (a = b = %foo)
+    """
+    paren_level = 0
+    for i, ti in enumerate(token_line):
+        s = ti.string
+        if s == '=' and paren_level == 0:
+            return i
+        if s in {'(','[','{'}:
+            paren_level += 1
+        elif s in {')', ']', '}'}:
+            if paren_level > 0:
+                paren_level -= 1
+    return None
+
+def find_end_of_continued_line(lines, start_line: int):
+    """Find the last line of a line explicitly extended using backslashes.
+
+    Uses 0-indexed line numbers.
+    """
+    end_line = start_line
+    while lines[end_line].endswith('\\\n'):
+        end_line += 1
+        if end_line >= len(lines):
+            break
+    return end_line
+
+def assemble_continued_line(lines, start: Tuple[int, int], end_line: int):
+    r"""Assemble a single line from multiple continued line pieces
+
+    Continued lines are lines ending in ``\``, and the line following the last
+    ``\`` in the block.
+
+    For example, this code continues over multiple lines::
+
+        if (assign_ix is not None) \
+             and (len(line) >= assign_ix + 2) \
+             and (line[assign_ix+1].string == '%') \
+             and (line[assign_ix+2].type == tokenize.NAME):
+
+    This statement contains four continued line pieces.
+    Assembling these pieces into a single line would give::
+
+        if (assign_ix is not None) and (len(line) >= assign_ix + 2) and (line[...
+
+    This uses 0-indexed line numbers. *start* is (lineno, colno).
+
+    Used to allow ``%magic`` and ``!system`` commands to be continued over
+    multiple lines.
+    """
+    parts = [lines[start[0]][start[1]:]] + lines[start[0]+1:end_line+1]
+    return ' '.join([p.rstrip()[:-1] for p in parts[:-1]]  # Strip backslash+newline
+                    + [parts[-1].rstrip()])         # Strip newline from last line
+
+class TokenTransformBase:
+    """Base class for transformations which examine tokens.
+
+    Special syntax should not be transformed when it occurs inside strings or
+    comments. This is hard to reliably avoid with regexes. The solution is to
+    tokenise the code as Python, and recognise the special syntax in the tokens.
+
+    IPython's special syntax is not valid Python syntax, so tokenising may go
+    wrong after the special syntax starts. These classes therefore find and
+    transform *one* instance of special syntax at a time into regular Python
+    syntax. After each transformation, tokens are regenerated to find the next
+    piece of special syntax.
+
+    Subclasses need to implement one class method (find)
+    and one regular method (transform).
+
+    The priority attribute can select which transformation to apply if multiple
+    transformers match in the same place. Lower numbers have higher priority.
+    This allows "%magic?" to be turned into a help call rather than a magic call.
+    """
+    # Lower numbers -> higher priority (for matches in the same location)
+    priority = 10
+
+    def sortby(self):
+        return self.start_line, self.start_col, self.priority
+
+    def __init__(self, start):
+        self.start_line = start[0] - 1   # Shift from 1-index to 0-index
+        self.start_col = start[1]
+
+    @classmethod
+    def find(cls, tokens_by_line):
+        """Find one instance of special syntax in the provided tokens.
+
+        Tokens are grouped into logical lines for convenience,
+        so it is easy to e.g. look at the first token of each line.
+        *tokens_by_line* is a list of lists of tokenize.TokenInfo objects.
+
+        This should return an instance of its class, pointing to the start
+        position it has found, or None if it found no match.
+        """
+        raise NotImplementedError
+
+    def transform(self, lines: List[str]):
+        """Transform one instance of special syntax found by ``find()``
+
+        Takes a list of strings representing physical lines,
+        returns a similar list of transformed lines.
+        """
+        raise NotImplementedError
+
+class MagicAssign(TokenTransformBase):
+    """Transformer for assignments from magics (a = %foo)"""
+    @classmethod
+    def find(cls, tokens_by_line):
+        """Find the first magic assignment (a = %foo) in the cell.
+        """
+        for line in tokens_by_line:
+            assign_ix = _find_assign_op(line)
+            if (assign_ix is not None) \
+                    and (len(line) >= assign_ix + 2) \
+                    and (line[assign_ix+1].string == '%') \
+                    and (line[assign_ix+2].type == tokenize.NAME):
+                return cls(line[assign_ix+1].start)
+
+    def transform(self, lines: List[str]):
+        """Transform a magic assignment found by the ``find()`` classmethod.
+        """
+        start_line, start_col = self.start_line, self.start_col
+        lhs = lines[start_line][:start_col]
+        end_line = find_end_of_continued_line(lines, start_line)
+        rhs = assemble_continued_line(lines, (start_line, start_col), end_line)
+        assert rhs.startswith('%'), rhs
+        magic_name, _, args = rhs[1:].partition(' ')
+
+        lines_before = lines[:start_line]
+        call = "get_ipython().run_line_magic({!r}, {!r})".format(magic_name, args)
+        new_line = lhs + call + '\n'
+        lines_after = lines[end_line+1:]
+
+        return lines_before + [new_line] + lines_after
+
+
+class SystemAssign(TokenTransformBase):
+    """Transformer for assignments from system commands (a = !foo)"""
+    @classmethod
+    def find_pre_312(cls, tokens_by_line):
+        for line in tokens_by_line:
+            assign_ix = _find_assign_op(line)
+            if (assign_ix is not None) \
+                    and not line[assign_ix].line.strip().startswith('=') \
+                    and (len(line) >= assign_ix + 2) \
+                    and (line[assign_ix + 1].type == tokenize.ERRORTOKEN):
+                ix = assign_ix + 1
+
+                while ix < len(line) and line[ix].type == tokenize.ERRORTOKEN:
+                    if line[ix].string == '!':
+                        return cls(line[ix].start)
+                    elif not line[ix].string.isspace():
+                        break
+                    ix += 1
+
+    @classmethod
+    def find_post_312(cls, tokens_by_line):
+        for line in tokens_by_line:
+            assign_ix = _find_assign_op(line)
+            if (
+                (assign_ix is not None)
+                and not line[assign_ix].line.strip().startswith("=")
+                and (len(line) >= assign_ix + 2)
+                and (line[assign_ix + 1].type == tokenize.OP)
+                and (line[assign_ix + 1].string == "!")
+            ):
+                return cls(line[assign_ix + 1].start)
+
+    @classmethod
+    def find(cls, tokens_by_line):
+        """Find the first system assignment (a = !foo) in the cell."""
+        if sys.version_info < (3, 12):
+            return cls.find_pre_312(tokens_by_line)
+        return cls.find_post_312(tokens_by_line)
+
+    def transform(self, lines: List[str]):
+        """Transform a system assignment found by the ``find()`` classmethod.
+        """
+        start_line, start_col = self.start_line, self.start_col
+
+        lhs = lines[start_line][:start_col]
+        end_line = find_end_of_continued_line(lines, start_line)
+        rhs = assemble_continued_line(lines, (start_line, start_col), end_line)
+        assert rhs.startswith('!'), rhs
+        cmd = rhs[1:]
+
+        lines_before = lines[:start_line]
+        call = "get_ipython().getoutput({!r})".format(cmd)
+        new_line = lhs + call + '\n'
+        lines_after = lines[end_line + 1:]
+
+        return lines_before + [new_line] + lines_after
+
+# The escape sequences that define the syntax transformations IPython will
+# apply to user input.  These can NOT be just changed here: many regular
+# expressions and other parts of the code may use their hardcoded values, and
+# for all intents and purposes they constitute the 'IPython syntax', so they
+# should be considered fixed.
+
+ESC_SHELL  = '!'     # Send line to underlying system shell
+ESC_SH_CAP = '!!'    # Send line to system shell and capture output
+ESC_HELP   = '?'     # Find information about object
+ESC_HELP2  = '??'    # Find extra-detailed information about object
+ESC_MAGIC  = '%'     # Call magic function
+ESC_MAGIC2 = '%%'    # Call cell-magic function
+ESC_QUOTE  = ','     # Split args on whitespace, quote each as string and call
+ESC_QUOTE2 = ';'     # Quote all args as a single string, call
+ESC_PAREN  = '/'     # Call first argument with rest of line as arguments
+
+ESCAPE_SINGLES = {'!', '?', '%', ',', ';', '/'}
+ESCAPE_DOUBLES = {'!!', '??'}  # %% (cell magic) is handled separately
+
+def _make_help_call(target, esc):
+    """Prepares a pinfo(2)/psearch call from a target name and the escape
+    (i.e. ? or ??)"""
+    method  = 'pinfo2' if esc == '??' \
+                else 'psearch' if '*' in target \
+                else 'pinfo'
+    arg = " ".join([method, target])
+    #Prepare arguments for get_ipython().run_line_magic(magic_name, magic_args)
+    t_magic_name, _, t_magic_arg_s = arg.partition(' ')
+    t_magic_name = t_magic_name.lstrip(ESC_MAGIC)
+    return "get_ipython().run_line_magic(%r, %r)" % (t_magic_name, t_magic_arg_s)
+
+
+def _tr_help(content):
+    """Translate lines escaped with: ?
+
+    A naked help line should fire the intro help screen (shell.show_usage())
+    """
+    if not content:
+        return 'get_ipython().show_usage()'
+
+    return _make_help_call(content, '?')
+
+def _tr_help2(content):
+    """Translate lines escaped with: ??
+
+    A naked help line should fire the intro help screen (shell.show_usage())
+    """
+    if not content:
+        return 'get_ipython().show_usage()'
+
+    return _make_help_call(content, '??')
+
+def _tr_magic(content):
+    "Translate lines escaped with a percent sign: %"
+    name, _, args = content.partition(' ')
+    return 'get_ipython().run_line_magic(%r, %r)' % (name, args)
+
+def _tr_quote(content):
+    "Translate lines escaped with a comma: ,"
+    name, _, args = content.partition(' ')
+    return '%s("%s")' % (name, '", "'.join(args.split()) )
+
+def _tr_quote2(content):
+    "Translate lines escaped with a semicolon: ;"
+    name, _, args = content.partition(' ')
+    return '%s("%s")' % (name, args)
+
+def _tr_paren(content):
+    "Translate lines escaped with a slash: /"
+    name, _, args = content.partition(' ')
+    return '%s(%s)' % (name, ", ".join(args.split()))
+
+tr = { ESC_SHELL  : 'get_ipython().system({!r})'.format,
+       ESC_SH_CAP : 'get_ipython().getoutput({!r})'.format,
+       ESC_HELP   : _tr_help,
+       ESC_HELP2  : _tr_help2,
+       ESC_MAGIC  : _tr_magic,
+       ESC_QUOTE  : _tr_quote,
+       ESC_QUOTE2 : _tr_quote2,
+       ESC_PAREN  : _tr_paren }
+
+class EscapedCommand(TokenTransformBase):
+    """Transformer for escaped commands like %foo, !foo, or /foo"""
+    @classmethod
+    def find(cls, tokens_by_line):
+        """Find the first escaped command (%foo, !foo, etc.) in the cell.
+        """
+        for line in tokens_by_line:
+            if not line:
+                continue
+            ix = 0
+            ll = len(line)
+            while ll > ix and line[ix].type in {tokenize.INDENT, tokenize.DEDENT}:
+                ix += 1
+            if ix >= ll:
+                continue
+            if line[ix].string in ESCAPE_SINGLES:
+                return cls(line[ix].start)
+
+    def transform(self, lines):
+        """Transform an escaped line found by the ``find()`` classmethod.
+        """
+        start_line, start_col = self.start_line, self.start_col
+
+        indent = lines[start_line][:start_col]
+        end_line = find_end_of_continued_line(lines, start_line)
+        line = assemble_continued_line(lines, (start_line, start_col), end_line)
+
+        if len(line) > 1 and line[:2] in ESCAPE_DOUBLES:
+            escape, content = line[:2], line[2:]
+        else:
+            escape, content = line[:1], line[1:]
+
+        if escape in tr:
+            call = tr[escape](content)
+        else:
+            call = ''
+
+        lines_before = lines[:start_line]
+        new_line = indent + call + '\n'
+        lines_after = lines[end_line + 1:]
+
+        return lines_before + [new_line] + lines_after
+
+
+_help_end_re = re.compile(
+    r"""(%{0,2}
+    (?!\d)[\w*]+            # Variable name
+    (\.(?!\d)[\w*]+|\[-?[0-9]+\])*       # .etc.etc or [0], we only support literal integers.
+    )
+    (\?\??)$                # ? or ??
+    """,
+    re.VERBOSE,
+)
+
+
+class HelpEnd(TokenTransformBase):
+    """Transformer for help syntax: obj? and obj??"""
+    # This needs to be higher priority (lower number) than EscapedCommand so
+    # that inspecting magics (%foo?) works.
+    priority = 5
+
+    def __init__(self, start, q_locn):
+        super().__init__(start)
+        self.q_line = q_locn[0] - 1  # Shift from 1-indexed to 0-indexed
+        self.q_col = q_locn[1]
+
+    @classmethod
+    def find(cls, tokens_by_line):
+        """Find the first help command (foo?) in the cell.
+        """
+        for line in tokens_by_line:
+            # Last token is NEWLINE; look at last but one
+            if len(line) > 2 and line[-2].string == '?':
+                # Find the first token that's not INDENT/DEDENT
+                ix = 0
+                while line[ix].type in {tokenize.INDENT, tokenize.DEDENT}:
+                    ix += 1
+                return cls(line[ix].start, line[-2].start)
+
+    def transform(self, lines):
+        """Transform a help command found by the ``find()`` classmethod.
+        """
+
+        piece = "".join(lines[self.start_line : self.q_line + 1])
+        indent, content = piece[: self.start_col], piece[self.start_col :]
+        lines_before = lines[: self.start_line]
+        lines_after = lines[self.q_line + 1 :]
+
+        m = _help_end_re.search(content)
+        if not m:
+            raise SyntaxError(content)
+        assert m is not None, content
+        target = m.group(1)
+        esc = m.group(3)
+
+
+        call = _make_help_call(target, esc)
+        new_line = indent + call + '\n'
+
+        return lines_before + [new_line] + lines_after
+
+def make_tokens_by_line(lines:List[str]):
+    """Tokenize a series of lines and group tokens by line.
+
+    The tokens for a multiline Python string or expression are grouped as one
+    line. All lines except the last lines should keep their line ending ('\\n',
+    '\\r\\n') for this to properly work. Use `.splitlines(keeplineending=True)`
+    for example when passing block of text to this function.
+
+    """
+    # NL tokens are used inside multiline expressions, but also after blank
+    # lines or comments. This is intentional - see https://bugs.python.org/issue17061
+    # We want to group the former case together but split the latter, so we
+    # track parentheses level, similar to the internals of tokenize.
+
+    #   reexported from token on 3.7+
+    NEWLINE, NL = tokenize.NEWLINE, tokenize.NL  # type: ignore
+    tokens_by_line: List[List[Any]] = [[]]
+    if len(lines) > 1 and not lines[0].endswith(("\n", "\r", "\r\n", "\x0b", "\x0c")):
+        warnings.warn(
+            "`make_tokens_by_line` received a list of lines which do not have lineending markers ('\\n', '\\r', '\\r\\n', '\\x0b', '\\x0c'), behavior will be unspecified",
+            stacklevel=2,
+        )
+    parenlev = 0
+    try:
+        for token in tokenutil.generate_tokens_catch_errors(
+            iter(lines).__next__, extra_errors_to_catch=["expected EOF"]
+        ):
+            tokens_by_line[-1].append(token)
+            if (token.type == NEWLINE) \
+                    or ((token.type == NL) and (parenlev <= 0)):
+                tokens_by_line.append([])
+            elif token.string in {'(', '[', '{'}:
+                parenlev += 1
+            elif token.string in {')', ']', '}'}:
+                if parenlev > 0:
+                    parenlev -= 1
+    except tokenize.TokenError:
+        # Input ended in a multiline string or expression. That's OK for us.
+        pass
+
+
+    if not tokens_by_line[-1]:
+        tokens_by_line.pop()
+
+
+    return tokens_by_line
+
+
+def has_sunken_brackets(tokens: List[tokenize.TokenInfo]):
+    """Check if the depth of brackets in the list of tokens drops below 0"""
+    parenlev = 0
+    for token in tokens:
+        if token.string in {"(", "[", "{"}:
+            parenlev += 1
+        elif token.string in {")", "]", "}"}:
+            parenlev -= 1
+            if parenlev < 0:
+                return True
+    return False
+
+
+def show_linewise_tokens(s: str):
+    """For investigation and debugging"""
+    warnings.warn(
+        "show_linewise_tokens is deprecated since IPython 8.6",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    if not s.endswith("\n"):
+        s += "\n"
+    lines = s.splitlines(keepends=True)
+    for line in make_tokens_by_line(lines):
+        print("Line -------")
+        for tokinfo in line:
+            print(" ", tokinfo)
+
+# Arbitrary limit to prevent getting stuck in infinite loops
+TRANSFORM_LOOP_LIMIT = 500
+
+class TransformerManager:
+    """Applies various transformations to a cell or code block.
+
+    The key methods for external use are ``transform_cell()``
+    and ``check_complete()``.
+    """
+    def __init__(self):
+        self.cleanup_transforms = [
+            leading_empty_lines,
+            leading_indent,
+            classic_prompt,
+            ipython_prompt,
+        ]
+        self.line_transforms = [
+            cell_magic,
+        ]
+        self.token_transformers = [
+            MagicAssign,
+            SystemAssign,
+            EscapedCommand,
+            HelpEnd,
+        ]
+
+    def do_one_token_transform(self, lines):
+        """Find and run the transform earliest in the code.
+
+        Returns (changed, lines).
+
+        This method is called repeatedly until changed is False, indicating
+        that all available transformations are complete.
+
+        The tokens following IPython special syntax might not be valid, so
+        the transformed code is retokenised every time to identify the next
+        piece of special syntax. Hopefully long code cells are mostly valid
+        Python, not using lots of IPython special syntax, so this shouldn't be
+        a performance issue.
+        """
+        tokens_by_line = make_tokens_by_line(lines)
+        candidates = []
+        for transformer_cls in self.token_transformers:
+            transformer = transformer_cls.find(tokens_by_line)
+            if transformer:
+                candidates.append(transformer)
+
+        if not candidates:
+            # Nothing to transform
+            return False, lines
+        ordered_transformers = sorted(candidates, key=TokenTransformBase.sortby)
+        for transformer in ordered_transformers:
+            try:
+                return True, transformer.transform(lines)
+            except SyntaxError:
+                pass
+        return False, lines
+
+    def do_token_transforms(self, lines):
+        for _ in range(TRANSFORM_LOOP_LIMIT):
+            changed, lines = self.do_one_token_transform(lines)
+            if not changed:
+                return lines
+
+        raise RuntimeError("Input transformation still changing after "
+                           "%d iterations. Aborting." % TRANSFORM_LOOP_LIMIT)
+
+    def transform_cell(self, cell: str) -> str:
+        """Transforms a cell of input code"""
+        if not cell.endswith('\n'):
+            cell += '\n'  # Ensure the cell has a trailing newline
+        lines = cell.splitlines(keepends=True)
+        for transform in self.cleanup_transforms + self.line_transforms:
+            lines = transform(lines)
+
+        lines = self.do_token_transforms(lines)
+        return ''.join(lines)
+
+    def check_complete(self, cell: str):
+        """Return whether a block of code is ready to execute, or should be continued
+
+        Parameters
+        ----------
+        cell : string
+            Python input code, which can be multiline.
+
+        Returns
+        -------
+        status : str
+            One of 'complete', 'incomplete', or 'invalid' if source is not a
+            prefix of valid code.
+        indent_spaces : int or None
+            The number of spaces by which to indent the next line of code. If
+            status is not 'incomplete', this is None.
+        """
+        # Remember if the lines ends in a new line.
+        ends_with_newline = False
+        for character in reversed(cell):
+            if character == '\n':
+                ends_with_newline = True
+                break
+            elif character.strip():
+                break
+            else:
+                continue
+
+        if not ends_with_newline:
+            # Append an newline for consistent tokenization
+            # See https://bugs.python.org/issue33899
+            cell += '\n'
+
+        lines = cell.splitlines(keepends=True)
+
+        if not lines:
+            return 'complete', None
+
+        for line in reversed(lines):
+            if not line.strip():
+                continue
+            elif line.strip("\n").endswith("\\"):
+                return "incomplete", find_last_indent(lines)
+            else:
+                break
+
+        try:
+            for transform in self.cleanup_transforms:
+                if not getattr(transform, 'has_side_effects', False):
+                    lines = transform(lines)
+        except SyntaxError:
+            return 'invalid', None
+
+        if lines[0].startswith('%%'):
+            # Special case for cell magics - completion marked by blank line
+            if lines[-1].strip():
+                return 'incomplete', find_last_indent(lines)
+            else:
+                return 'complete', None
+
+        try:
+            for transform in self.line_transforms:
+                if not getattr(transform, 'has_side_effects', False):
+                    lines = transform(lines)
+            lines = self.do_token_transforms(lines)
+        except SyntaxError:
+            return 'invalid', None
+
+        tokens_by_line = make_tokens_by_line(lines)
+
+        # Bail if we got one line and there are more closing parentheses than
+        # the opening ones
+        if (
+            len(lines) == 1
+            and tokens_by_line
+            and has_sunken_brackets(tokens_by_line[0])
+        ):
+            return "invalid", None
+
+        if not tokens_by_line:
+            return 'incomplete', find_last_indent(lines)
+
+        if (
+            tokens_by_line[-1][-1].type != tokenize.ENDMARKER
+            and tokens_by_line[-1][-1].type != tokenize.ERRORTOKEN
+        ):
+            # We're in a multiline string or expression
+            return 'incomplete', find_last_indent(lines)
+
+        newline_types = {tokenize.NEWLINE, tokenize.COMMENT, tokenize.ENDMARKER} # type: ignore
+
+        # Pop the last line which only contains DEDENTs and ENDMARKER
+        last_token_line = None
+        if {t.type for t in tokens_by_line[-1]} in [
+            {tokenize.DEDENT, tokenize.ENDMARKER},
+            {tokenize.ENDMARKER}
+        ] and len(tokens_by_line) > 1:
+            last_token_line = tokens_by_line.pop()
+
+        while tokens_by_line[-1] and tokens_by_line[-1][-1].type in newline_types:
+            tokens_by_line[-1].pop()
+
+        if not tokens_by_line[-1]:
+            return 'incomplete', find_last_indent(lines)
+
+        if tokens_by_line[-1][-1].string == ':':
+            # The last line starts a block (e.g. 'if foo:')
+            ix = 0
+            while tokens_by_line[-1][ix].type in {tokenize.INDENT, tokenize.DEDENT}:
+                ix += 1
+
+            indent = tokens_by_line[-1][ix].start[1]
+            return 'incomplete', indent + 4
+
+        if tokens_by_line[-1][0].line.endswith('\\'):
+            return 'incomplete', None
+
+        # At this point, our checks think the code is complete (or invalid).
+        # We'll use codeop.compile_command to check this with the real parser
+        try:
+            with warnings.catch_warnings():
+                warnings.simplefilter('error', SyntaxWarning)
+                res = compile_command(''.join(lines), symbol='exec')
+        except (SyntaxError, OverflowError, ValueError, TypeError,
+                MemoryError, SyntaxWarning):
+            return 'invalid', None
+        else:
+            if res is None:
+                return 'incomplete', find_last_indent(lines)
+
+        if last_token_line and last_token_line[0].type == tokenize.DEDENT:
+            if ends_with_newline:
+                return 'complete', None
+            return 'incomplete', find_last_indent(lines)
+
+        # If there's a blank line at the end, assume we're ready to execute
+        if not lines[-1].strip():
+            return 'complete', None
+
+        return 'complete', None
+
+
+def find_last_indent(lines):
+    m = _indent_re.match(lines[-1])
+    if not m:
+        return 0
+    return len(m.group(0).replace('\t', ' '*4))
+
+
+class MaybeAsyncCompile(Compile):
+    def __init__(self, extra_flags=0):
+        super().__init__()
+        self.flags |= extra_flags
+
+
+class MaybeAsyncCommandCompiler(CommandCompiler):
+    def __init__(self, extra_flags=0):
+        self.compiler = MaybeAsyncCompile(extra_flags=extra_flags)
+
+
+_extra_flags = ast.PyCF_ALLOW_TOP_LEVEL_AWAIT
+
+compile_command = MaybeAsyncCommandCompiler(extra_flags=_extra_flags)
diff --git a/shared/package-files/3d/3dc03f6337b8743aa6c6c0c9a8260a9bfe599e67dac56e153727c68c7aa46af2/models.pyi b/shared/package-files/3d/3dc03f6337b8743aa6c6c0c9a8260a9bfe599e67dac56e153727c68c7aa46af2/models.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..e9f5e7cbf6fe59fca5013e03e927e99cb4fc75c4
--- /dev/null
+++ b/shared/package-files/3d/3dc03f6337b8743aa6c6c0c9a8260a9bfe599e67dac56e153727c68c7aa46af2/models.pyi
@@ -0,0 +1,24 @@
+from typing import Any, Dict, Optional, Tuple, Type, Union
+
+from django.db import models
+from django.db.models.base import Model
+from django.db.models.query import QuerySet
+
+class ContentTypeManager(models.Manager["ContentType"]):
+    def get_by_natural_key(self, app_label: str, model: str) -> ContentType: ...
+    def get_for_model(self, model: Union[Type[Model], Model], for_concrete_model: bool = ...) -> ContentType: ...
+    def get_for_models(self, *models: Any, for_concrete_models: bool = ...) -> Dict[Type[Model], ContentType]: ...
+    def get_for_id(self, id: int) -> ContentType: ...
+    def clear_cache(self) -> None: ...
+
+class ContentType(models.Model):
+    id: int
+    app_label: models.CharField = ...
+    model: models.CharField = ...
+    objects: ContentTypeManager = ...
+    @property
+    def name(self) -> str: ...
+    def model_class(self) -> Optional[Type[Model]]: ...
+    def get_object_for_this_type(self, **kwargs: Any) -> Model: ...
+    def get_all_objects_for_this_type(self, **kwargs: Any) -> QuerySet: ...
+    def natural_key(self) -> Tuple[str, str]: ...
diff --git a/shared/package-files/3d/3dc308dfdc991b938f797dc27419897f778fb6ebee6941911988d769f9ea75ff/writer.py b/shared/package-files/3d/3dc308dfdc991b938f797dc27419897f778fb6ebee6941911988d769f9ea75ff/writer.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9a1e039040f3e1c50c8709632052206db87565b
--- /dev/null
+++ b/shared/package-files/3d/3dc308dfdc991b938f797dc27419897f778fb6ebee6941911988d769f9ea75ff/writer.py
@@ -0,0 +1,1270 @@
+"""Provide an API for writing protocol buffers to event files to be consumed by TensorBoard for visualization."""
+
+import os
+import time
+from typing import List, Optional, Union, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from matplotlib.figure import Figure
+from tensorboard.compat import tf
+from tensorboard.compat.proto import event_pb2
+from tensorboard.compat.proto.event_pb2 import Event, SessionLog
+from tensorboard.plugins.projector.projector_config_pb2 import ProjectorConfig
+from tensorboard.summary.writer.event_file_writer import EventFileWriter
+
+from ._convert_np import make_np
+from ._embedding import get_embedding_info, make_mat, make_sprite, make_tsv, write_pbtxt
+from ._onnx_graph import load_onnx_graph
+from ._pytorch_graph import graph
+from ._utils import figure_to_image
+from .summary import (
+    audio,
+    custom_scalars,
+    histogram,
+    histogram_raw,
+    hparams,
+    image,
+    image_boxes,
+    mesh,
+    pr_curve,
+    pr_curve_raw,
+    scalar,
+    tensor_proto,
+    text,
+    video,
+)
+
+__all__ = ["FileWriter", "SummaryWriter"]
+
+
+class FileWriter:
+    """Writes protocol buffers to event files to be consumed by TensorBoard.
+
+    The `FileWriter` class provides a mechanism to create an event file in a
+    given directory and add summaries and events to it. The class updates the
+    file contents asynchronously. This allows a training program to call methods
+    to add data to the file directly from the training loop, without slowing down
+    training.
+    """
+
+    def __init__(self, log_dir, max_queue=10, flush_secs=120, filename_suffix=""):
+        """Create a `FileWriter` and an event file.
+
+        On construction the writer creates a new event file in `log_dir`.
+        The other arguments to the constructor control the asynchronous writes to
+        the event file.
+
+        Args:
+          log_dir: A string. Directory where event file will be written.
+          max_queue: Integer. Size of the queue for pending events and
+            summaries before one of the 'add' calls forces a flush to disk.
+            Default is ten items.
+          flush_secs: Number. How often, in seconds, to flush the
+            pending events and summaries to disk. Default is every two minutes.
+          filename_suffix: A string. Suffix added to all event filenames
+            in the log_dir directory. More details on filename construction in
+            tensorboard.summary.writer.event_file_writer.EventFileWriter.
+        """
+        # Sometimes PosixPath is passed in and we need to coerce it to
+        # a string in all cases
+        # TODO: See if we can remove this in the future if we are
+        # actually the ones passing in a PosixPath
+        log_dir = str(log_dir)
+        self.event_writer = EventFileWriter(
+            log_dir, max_queue, flush_secs, filename_suffix
+        )
+
+    def get_logdir(self):
+        """Return the directory where event file will be written."""
+        return self.event_writer.get_logdir()
+
+    def add_event(self, event, step=None, walltime=None):
+        """Add an event to the event file.
+
+        Args:
+          event: An `Event` protocol buffer.
+          step: Number. Optional global step value for training process
+            to record with the event.
+          walltime: float. Optional walltime to override the default (current)
+            walltime (from time.time()) seconds after epoch
+        """
+        event.wall_time = time.time() if walltime is None else walltime
+        if step is not None:
+            # Make sure step is converted from numpy or other formats
+            # since protobuf might not convert depending on version
+            event.step = int(step)
+        self.event_writer.add_event(event)
+
+    def add_summary(self, summary, global_step=None, walltime=None):
+        """Add a `Summary` protocol buffer to the event file.
+
+        This method wraps the provided summary in an `Event` protocol buffer
+        and adds it to the event file.
+
+        Args:
+          summary: A `Summary` protocol buffer.
+          global_step: Number. Optional global step value for training process
+            to record with the summary.
+          walltime: float. Optional walltime to override the default (current)
+            walltime (from time.time()) seconds after epoch
+        """
+        event = event_pb2.Event(summary=summary)
+        self.add_event(event, global_step, walltime)
+
+    def add_graph(self, graph_profile, walltime=None):
+        """Add a `Graph` and step stats protocol buffer to the event file.
+
+        Args:
+          graph_profile: A `Graph` and step stats protocol buffer.
+          walltime: float. Optional walltime to override the default (current)
+            walltime (from time.time()) seconds after epoch
+        """
+        graph = graph_profile[0]
+        stepstats = graph_profile[1]
+        event = event_pb2.Event(graph_def=graph.SerializeToString())
+        self.add_event(event, None, walltime)
+
+        trm = event_pb2.TaggedRunMetadata(
+            tag="step1", run_metadata=stepstats.SerializeToString()
+        )
+        event = event_pb2.Event(tagged_run_metadata=trm)
+        self.add_event(event, None, walltime)
+
+    def add_onnx_graph(self, graph, walltime=None):
+        """Add a `Graph` protocol buffer to the event file.
+
+        Args:
+          graph: A `Graph` protocol buffer.
+          walltime: float. Optional walltime to override the default (current)
+            _get_file_writerfrom time.time())
+        """
+        event = event_pb2.Event(graph_def=graph.SerializeToString())
+        self.add_event(event, None, walltime)
+
+    def flush(self):
+        """Flushes the event file to disk.
+
+        Call this method to make sure that all pending events have been written to
+        disk.
+        """
+        self.event_writer.flush()
+
+    def close(self):
+        """Flushes the event file to disk and close the file.
+
+        Call this method when you do not need the summary writer anymore.
+        """
+        self.event_writer.close()
+
+    def reopen(self):
+        """Reopens the EventFileWriter.
+
+        Can be called after `close()` to add more events in the same directory.
+        The events will go into a new events file.
+        Does nothing if the EventFileWriter was not closed.
+        """
+        self.event_writer.reopen()
+
+
+class SummaryWriter:
+    """Writes entries directly to event files in the log_dir to be consumed by TensorBoard.
+
+    The `SummaryWriter` class provides a high-level API to create an event file
+    in a given directory and add summaries and events to it. The class updates the
+    file contents asynchronously. This allows a training program to call methods
+    to add data to the file directly from the training loop, without slowing down
+    training.
+    """
+
+    def __init__(
+        self,
+        log_dir=None,
+        comment="",
+        purge_step=None,
+        max_queue=10,
+        flush_secs=120,
+        filename_suffix="",
+    ):
+        """Create a `SummaryWriter` that will write out events and summaries to the event file.
+
+        Args:
+            log_dir (str): Save directory location. Default is
+              runs/**CURRENT_DATETIME_HOSTNAME**, which changes after each run.
+              Use hierarchical folder structure to compare
+              between runs easily. e.g. pass in 'runs/exp1', 'runs/exp2', etc.
+              for each new experiment to compare across them.
+            comment (str): Comment log_dir suffix appended to the default
+              ``log_dir``. If ``log_dir`` is assigned, this argument has no effect.
+            purge_step (int):
+              When logging crashes at step :math:`T+X` and restarts at step :math:`T`,
+              any events whose global_step larger or equal to :math:`T` will be
+              purged and hidden from TensorBoard.
+              Note that crashed and resumed experiments should have the same ``log_dir``.
+            max_queue (int): Size of the queue for pending events and
+              summaries before one of the 'add' calls forces a flush to disk.
+              Default is ten items.
+            flush_secs (int): How often, in seconds, to flush the
+              pending events and summaries to disk. Default is every two minutes.
+            filename_suffix (str): Suffix added to all event filenames in
+              the log_dir directory. More details on filename construction in
+              tensorboard.summary.writer.event_file_writer.EventFileWriter.
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+
+            # create a summary writer with automatically generated folder name.
+            writer = SummaryWriter()
+            # folder location: runs/May04_22-14-54_s-MacBook-Pro.local/
+
+            # create a summary writer using the specified folder name.
+            writer = SummaryWriter("my_experiment")
+            # folder location: my_experiment
+
+            # create a summary writer with comment appended.
+            writer = SummaryWriter(comment="LR_0.1_BATCH_16")
+            # folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/
+
+        """
+        torch._C._log_api_usage_once("tensorboard.create.summarywriter")
+        if not log_dir:
+            import socket
+            from datetime import datetime
+
+            current_time = datetime.now().strftime("%b%d_%H-%M-%S")
+            log_dir = os.path.join(
+                "runs", current_time + "_" + socket.gethostname() + comment
+            )
+        self.log_dir = log_dir
+        self.purge_step = purge_step
+        self.max_queue = max_queue
+        self.flush_secs = flush_secs
+        self.filename_suffix = filename_suffix
+
+        # Initialize the file writers, but they can be cleared out on close
+        # and recreated later as needed.
+        self.file_writer = self.all_writers = None
+        self._get_file_writer()
+
+        # Create default bins for histograms, see generate_testdata.py in tensorflow/tensorboard
+        v = 1e-12
+        buckets = []
+        neg_buckets = []
+        while v < 1e20:
+            buckets.append(v)
+            neg_buckets.append(-v)
+            v *= 1.1
+        self.default_bins = neg_buckets[::-1] + [0] + buckets
+
+    def _check_caffe2_blob(self, item):
+        """
+        Check if the input is a string representing a Caffe2 blob name.
+
+        Caffe2 users have the option of passing a string representing the name of a blob
+        in the workspace instead of passing the actual Tensor/array containing the numeric values.
+        Thus, we need to check if we received a string as input
+        instead of an actual Tensor/array, and if so, we need to fetch the Blob
+        from the workspace corresponding to that name. Fetching can be done with the
+        following:
+
+        from caffe2.python import workspace (if not already imported)
+        workspace.FetchBlob(blob_name)
+        workspace.FetchBlobs([blob_name1, blob_name2, ...])
+        """
+        return isinstance(item, str)
+
+    def _get_file_writer(self):
+        """Return the default FileWriter instance. Recreates it if closed."""
+        if self.all_writers is None or self.file_writer is None:
+            self.file_writer = FileWriter(
+                self.log_dir, self.max_queue, self.flush_secs, self.filename_suffix
+            )
+            self.all_writers = {self.file_writer.get_logdir(): self.file_writer}
+            if self.purge_step is not None:
+                most_recent_step = self.purge_step
+                self.file_writer.add_event(
+                    Event(step=most_recent_step, file_version="brain.Event:2")
+                )
+                self.file_writer.add_event(
+                    Event(
+                        step=most_recent_step,
+                        session_log=SessionLog(status=SessionLog.START),
+                    )
+                )
+                self.purge_step = None
+        return self.file_writer
+
+    def get_logdir(self):
+        """Return the directory where event files will be written."""
+        return self.log_dir
+
+    def add_hparams(
+        self, hparam_dict, metric_dict, hparam_domain_discrete=None, run_name=None, global_step=None
+    ):
+        """Add a set of hyperparameters to be compared in TensorBoard.
+
+        Args:
+            hparam_dict (dict): Each key-value pair in the dictionary is the
+              name of the hyper parameter and it's corresponding value.
+              The type of the value can be one of `bool`, `string`, `float`,
+              `int`, or `None`.
+            metric_dict (dict): Each key-value pair in the dictionary is the
+              name of the metric and it's corresponding value. Note that the key used
+              here should be unique in the tensorboard record. Otherwise the value
+              you added by ``add_scalar`` will be displayed in hparam plugin. In most
+              cases, this is unwanted.
+            hparam_domain_discrete: (Optional[Dict[str, List[Any]]]) A dictionary that
+              contains names of the hyperparameters and all discrete values they can hold
+            run_name (str): Name of the run, to be included as part of the logdir.
+              If unspecified, will use current timestamp.
+            global_step (int): Global step value to record
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            with SummaryWriter() as w:
+                for i in range(5):
+                    w.add_hparams({'lr': 0.1*i, 'bsize': i},
+                                  {'hparam/accuracy': 10*i, 'hparam/loss': 10*i})
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_hparam.png
+           :scale: 50 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_hparams")
+        if type(hparam_dict) is not dict or type(metric_dict) is not dict:
+            raise TypeError("hparam_dict and metric_dict should be dictionary.")
+        exp, ssi, sei = hparams(hparam_dict, metric_dict, hparam_domain_discrete)
+
+        if not run_name:
+            run_name = str(time.time())
+        logdir = os.path.join(self._get_file_writer().get_logdir(), run_name)
+        with SummaryWriter(log_dir=logdir) as w_hp:
+            w_hp.file_writer.add_summary(exp, global_step)
+            w_hp.file_writer.add_summary(ssi, global_step)
+            w_hp.file_writer.add_summary(sei, global_step)
+            for k, v in metric_dict.items():
+                w_hp.add_scalar(k, v, global_step)
+
+    def add_scalar(
+        self,
+        tag,
+        scalar_value,
+        global_step=None,
+        walltime=None,
+        new_style=False,
+        double_precision=False,
+    ):
+        """Add scalar data to summary.
+
+        Args:
+            tag (str): Data identifier
+            scalar_value (float or string/blobname): Value to save
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              with seconds after epoch of event
+            new_style (boolean): Whether to use new style (tensor field) or old
+              style (simple_value field). New style could lead to faster data loading.
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            writer = SummaryWriter()
+            x = range(100)
+            for i in x:
+                writer.add_scalar('y=2x', i * 2, i)
+            writer.close()
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_scalar.png
+           :scale: 50 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_scalar")
+        if self._check_caffe2_blob(scalar_value):
+            from caffe2.python import workspace
+
+            scalar_value = workspace.FetchBlob(scalar_value)
+
+        summary = scalar(
+            tag, scalar_value, new_style=new_style, double_precision=double_precision
+        )
+        self._get_file_writer().add_summary(summary, global_step, walltime)
+
+    def add_scalars(self, main_tag, tag_scalar_dict, global_step=None, walltime=None):
+        """Add many scalar data to summary.
+
+        Args:
+            main_tag (str): The parent name for the tags
+            tag_scalar_dict (dict): Key-value pair storing the tag and corresponding values
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            writer = SummaryWriter()
+            r = 5
+            for i in range(100):
+                writer.add_scalars('run_14h', {'xsinx':i*np.sin(i/r),
+                                                'xcosx':i*np.cos(i/r),
+                                                'tanx': np.tan(i/r)}, i)
+            writer.close()
+            # This call adds three values to the same scalar plot with the tag
+            # 'run_14h' in TensorBoard's scalar section.
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_scalars.png
+           :scale: 50 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_scalars")
+        walltime = time.time() if walltime is None else walltime
+        fw_logdir = self._get_file_writer().get_logdir()
+        for tag, scalar_value in tag_scalar_dict.items():
+            fw_tag = fw_logdir + "/" + main_tag.replace("/", "_") + "_" + tag
+            assert self.all_writers is not None
+            if fw_tag in self.all_writers.keys():
+                fw = self.all_writers[fw_tag]
+            else:
+                fw = FileWriter(
+                    fw_tag, self.max_queue, self.flush_secs, self.filename_suffix
+                )
+                self.all_writers[fw_tag] = fw
+            if self._check_caffe2_blob(scalar_value):
+                from caffe2.python import workspace
+
+                scalar_value = workspace.FetchBlob(scalar_value)
+            fw.add_summary(scalar(main_tag, scalar_value), global_step, walltime)
+
+    def add_tensor(
+        self,
+        tag,
+        tensor,
+        global_step=None,
+        walltime=None,
+    ):
+        """Add tensor data to summary.
+
+        Args:
+            tag (str): Data identifier
+            tensor (torch.Tensor): tensor to save
+            global_step (int): Global step value to record
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            writer = SummaryWriter()
+            x = torch.tensor([1,2,3])
+            writer.add_scalar('x', x)
+            writer.close()
+
+        Expected result:
+            Summary::tensor::float_val [1,2,3]
+                   ::tensor::shape [3]
+                   ::tag 'x'
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_tensor")
+        if self._check_caffe2_blob(tensor):
+            from caffe2.python import workspace
+
+            tensor = torch.tensor(workspace.FetchBlob(tensor))
+
+        summary = tensor_proto(tag, tensor)
+        self._get_file_writer().add_summary(summary, global_step, walltime)
+
+    def add_histogram(
+        self,
+        tag,
+        values,
+        global_step=None,
+        bins="tensorflow",
+        walltime=None,
+        max_bins=None,
+    ):
+        """Add histogram to summary.
+
+        Args:
+            tag (str): Data identifier
+            values (torch.Tensor, numpy.ndarray, or string/blobname): Values to build histogram
+            global_step (int): Global step value to record
+            bins (str): One of {'tensorflow','auto', 'fd', ...}. This determines how the bins are made. You can find
+              other options in: https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            import numpy as np
+            writer = SummaryWriter()
+            for i in range(10):
+                x = np.random.random(1000)
+                writer.add_histogram('distribution centers', x + i, i)
+            writer.close()
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_histogram.png
+           :scale: 50 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_histogram")
+        if self._check_caffe2_blob(values):
+            from caffe2.python import workspace
+
+            values = workspace.FetchBlob(values)
+        if isinstance(bins, str) and bins == "tensorflow":
+            bins = self.default_bins
+        self._get_file_writer().add_summary(
+            histogram(tag, values, bins, max_bins=max_bins), global_step, walltime
+        )
+
+    def add_histogram_raw(
+        self,
+        tag,
+        min,
+        max,
+        num,
+        sum,
+        sum_squares,
+        bucket_limits,
+        bucket_counts,
+        global_step=None,
+        walltime=None,
+    ):
+        """Add histogram with raw data.
+
+        Args:
+            tag (str): Data identifier
+            min (float or int): Min value
+            max (float or int): Max value
+            num (int): Number of values
+            sum (float or int): Sum of all values
+            sum_squares (float or int): Sum of squares for all values
+            bucket_limits (torch.Tensor, numpy.ndarray): Upper value per bucket.
+              The number of elements of it should be the same as `bucket_counts`.
+            bucket_counts (torch.Tensor, numpy.ndarray): Number of values per bucket
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+            see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/histogram/README.md
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            import numpy as np
+            writer = SummaryWriter()
+            dummy_data = []
+            for idx, value in enumerate(range(50)):
+                dummy_data += [idx + 0.001] * value
+
+            bins = list(range(50+2))
+            bins = np.array(bins)
+            values = np.array(dummy_data).astype(float).reshape(-1)
+            counts, limits = np.histogram(values, bins=bins)
+            sum_sq = values.dot(values)
+            writer.add_histogram_raw(
+                tag='histogram_with_raw_data',
+                min=values.min(),
+                max=values.max(),
+                num=len(values),
+                sum=values.sum(),
+                sum_squares=sum_sq,
+                bucket_limits=limits[1:].tolist(),
+                bucket_counts=counts.tolist(),
+                global_step=0)
+            writer.close()
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_histogram_raw.png
+           :scale: 50 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_histogram_raw")
+        if len(bucket_limits) != len(bucket_counts):
+            raise ValueError(
+                "len(bucket_limits) != len(bucket_counts), see the document."
+            )
+        self._get_file_writer().add_summary(
+            histogram_raw(
+                tag, min, max, num, sum, sum_squares, bucket_limits, bucket_counts
+            ),
+            global_step,
+            walltime,
+        )
+
+    def add_image(
+        self, tag, img_tensor, global_step=None, walltime=None, dataformats="CHW"
+    ):
+        """Add image data to summary.
+
+        Note that this requires the ``pillow`` package.
+
+        Args:
+            tag (str): Data identifier
+            img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+            dataformats (str): Image data format specification of the form
+              CHW, HWC, HW, WH, etc.
+        Shape:
+            img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` to
+            convert a batch of tensor into 3xHxW format or call ``add_images`` and let us do the job.
+            Tensor with :math:`(1, H, W)`, :math:`(H, W)`, :math:`(H, W, 3)` is also suitable as long as
+            corresponding ``dataformats`` argument is passed, e.g. ``CHW``, ``HWC``, ``HW``.
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            import numpy as np
+            img = np.zeros((3, 100, 100))
+            img[0] = np.arange(0, 10000).reshape(100, 100) / 10000
+            img[1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
+
+            img_HWC = np.zeros((100, 100, 3))
+            img_HWC[:, :, 0] = np.arange(0, 10000).reshape(100, 100) / 10000
+            img_HWC[:, :, 1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000
+
+            writer = SummaryWriter()
+            writer.add_image('my_image', img, 0)
+
+            # If you have non-default dimension setting, set the dataformats argument.
+            writer.add_image('my_image_HWC', img_HWC, 0, dataformats='HWC')
+            writer.close()
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_image.png
+           :scale: 50 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_image")
+        if self._check_caffe2_blob(img_tensor):
+            from caffe2.python import workspace
+
+            img_tensor = workspace.FetchBlob(img_tensor)
+        self._get_file_writer().add_summary(
+            image(tag, img_tensor, dataformats=dataformats), global_step, walltime
+        )
+
+    def add_images(
+        self, tag, img_tensor, global_step=None, walltime=None, dataformats="NCHW"
+    ):
+        """Add batched image data to summary.
+
+        Note that this requires the ``pillow`` package.
+
+        Args:
+            tag (str): Data identifier
+            img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+            dataformats (str): Image data format specification of the form
+              NCHW, NHWC, CHW, HWC, HW, WH, etc.
+        Shape:
+            img_tensor: Default is :math:`(N, 3, H, W)`. If ``dataformats`` is specified, other shape will be
+            accepted. e.g. NCHW or NHWC.
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            import numpy as np
+
+            img_batch = np.zeros((16, 3, 100, 100))
+            for i in range(16):
+                img_batch[i, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 / 16 * i
+                img_batch[i, 1] = (1 - np.arange(0, 10000).reshape(100, 100) / 10000) / 16 * i
+
+            writer = SummaryWriter()
+            writer.add_images('my_image_batch', img_batch, 0)
+            writer.close()
+
+        Expected result:
+
+        .. image:: _static/img/tensorboard/add_images.png
+           :scale: 30 %
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_images")
+        if self._check_caffe2_blob(img_tensor):
+            from caffe2.python import workspace
+
+            img_tensor = workspace.FetchBlob(img_tensor)
+        self._get_file_writer().add_summary(
+            image(tag, img_tensor, dataformats=dataformats), global_step, walltime
+        )
+
+    def add_image_with_boxes(
+        self,
+        tag,
+        img_tensor,
+        box_tensor,
+        global_step=None,
+        walltime=None,
+        rescale=1,
+        dataformats="CHW",
+        labels=None,
+    ):
+        """Add image and draw bounding boxes on the image.
+
+        Args:
+            tag (str): Data identifier
+            img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data
+            box_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Box data (for detected objects)
+              box should be represented as [x1, y1, x2, y2].
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+            rescale (float): Optional scale override
+            dataformats (str): Image data format specification of the form
+              NCHW, NHWC, CHW, HWC, HW, WH, etc.
+            labels (list of string): The label to be shown for each bounding box.
+        Shape:
+            img_tensor: Default is :math:`(3, H, W)`. It can be specified with ``dataformats`` argument.
+            e.g. CHW or HWC
+
+            box_tensor: (torch.Tensor, numpy.ndarray, or string/blobname): NX4,  where N is the number of
+            boxes and each 4 elements in a row represents (xmin, ymin, xmax, ymax).
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_image_with_boxes")
+        if self._check_caffe2_blob(img_tensor):
+            from caffe2.python import workspace
+
+            img_tensor = workspace.FetchBlob(img_tensor)
+        if self._check_caffe2_blob(box_tensor):
+            from caffe2.python import workspace
+
+            box_tensor = workspace.FetchBlob(box_tensor)
+        if labels is not None:
+            if isinstance(labels, str):
+                labels = [labels]
+            if len(labels) != box_tensor.shape[0]:
+                labels = None
+        self._get_file_writer().add_summary(
+            image_boxes(
+                tag,
+                img_tensor,
+                box_tensor,
+                rescale=rescale,
+                dataformats=dataformats,
+                labels=labels,
+            ),
+            global_step,
+            walltime,
+        )
+
+    def add_figure(
+        self,
+        tag: str,
+        figure: Union["Figure", List["Figure"]],
+        global_step: Optional[int] = None,
+        close: bool = True,
+        walltime: Optional[float] = None
+    ) -> None:
+        """Render matplotlib figure into an image and add it to summary.
+
+        Note that this requires the ``matplotlib`` package.
+
+        Args:
+            tag: Data identifier
+            figure: Figure or a list of figures
+            global_step: Global step value to record
+            close: Flag to automatically close the figure
+            walltime: Optional override default walltime (time.time())
+              seconds after epoch of event
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_figure")
+        if isinstance(figure, list):
+            self.add_image(
+                tag,
+                figure_to_image(figure, close),
+                global_step,
+                walltime,
+                dataformats="NCHW",
+            )
+        else:
+            self.add_image(
+                tag,
+                figure_to_image(figure, close),
+                global_step,
+                walltime,
+                dataformats="CHW",
+            )
+
+    def add_video(self, tag, vid_tensor, global_step=None, fps=4, walltime=None):
+        """Add video data to summary.
+
+        Note that this requires the ``moviepy`` package.
+
+        Args:
+            tag (str): Data identifier
+            vid_tensor (torch.Tensor): Video data
+            global_step (int): Global step value to record
+            fps (float or int): Frames per second
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+        Shape:
+            vid_tensor: :math:`(N, T, C, H, W)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`.
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_video")
+        self._get_file_writer().add_summary(
+            video(tag, vid_tensor, fps), global_step, walltime
+        )
+
+    def add_audio(
+        self, tag, snd_tensor, global_step=None, sample_rate=44100, walltime=None
+    ):
+        """Add audio data to summary.
+
+        Args:
+            tag (str): Data identifier
+            snd_tensor (torch.Tensor): Sound data
+            global_step (int): Global step value to record
+            sample_rate (int): sample rate in Hz
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+        Shape:
+            snd_tensor: :math:`(1, L)`. The values should lie between [-1, 1].
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_audio")
+        if self._check_caffe2_blob(snd_tensor):
+            from caffe2.python import workspace
+
+            snd_tensor = workspace.FetchBlob(snd_tensor)
+        self._get_file_writer().add_summary(
+            audio(tag, snd_tensor, sample_rate=sample_rate), global_step, walltime
+        )
+
+    def add_text(self, tag, text_string, global_step=None, walltime=None):
+        """Add text data to summary.
+
+        Args:
+            tag (str): Data identifier
+            text_string (str): String to save
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+        Examples::
+
+            writer.add_text('lstm', 'This is an lstm', 0)
+            writer.add_text('rnn', 'This is an rnn', 10)
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_text")
+        self._get_file_writer().add_summary(
+            text(tag, text_string), global_step, walltime
+        )
+
+    def add_onnx_graph(self, prototxt):
+        torch._C._log_api_usage_once("tensorboard.logging.add_onnx_graph")
+        self._get_file_writer().add_onnx_graph(load_onnx_graph(prototxt))
+
+    def add_graph(
+        self, model, input_to_model=None, verbose=False, use_strict_trace=True
+    ):
+        """Add graph data to summary.
+
+        Args:
+            model (torch.nn.Module): Model to draw.
+            input_to_model (torch.Tensor or list of torch.Tensor): A variable or a tuple of
+                variables to be fed.
+            verbose (bool): Whether to print graph structure in console.
+            use_strict_trace (bool): Whether to pass keyword argument `strict` to
+                `torch.jit.trace`. Pass False when you want the tracer to
+                record your mutable container types (list, dict)
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_graph")
+        if hasattr(model, "forward"):
+            # A valid PyTorch model should have a 'forward' method
+            self._get_file_writer().add_graph(
+                graph(model, input_to_model, verbose, use_strict_trace)
+            )
+        else:
+            # Caffe2 models do not have the 'forward' method
+            from caffe2.proto import caffe2_pb2
+            from caffe2.python import core
+
+            from ._caffe2_graph import (
+                model_to_graph_def,
+                nets_to_graph_def,
+                protos_to_graph_def,
+            )
+
+            if isinstance(model, list):
+                if isinstance(model[0], core.Net):
+                    current_graph = nets_to_graph_def(model)
+                elif isinstance(model[0], caffe2_pb2.NetDef):
+                    current_graph = protos_to_graph_def(model)
+            else:
+                # Handles cnn.CNNModelHelper, model_helper.ModelHelper
+                current_graph = model_to_graph_def(model)
+            event = event_pb2.Event(graph_def=current_graph.SerializeToString())  # type: ignore[possibly-undefined]
+            self._get_file_writer().add_event(event)
+
+    @staticmethod
+    def _encode(rawstr):
+        # I'd use urllib but, I'm unsure about the differences from python3 to python2, etc.
+        retval = rawstr
+        retval = retval.replace("%", f"%{ord('%'):02x}")
+        retval = retval.replace("/", f"%{ord('/'):02x}")
+        retval = retval.replace("\\", "%%%02x" % (ord("\\")))
+        return retval
+
+    def add_embedding(
+        self,
+        mat,
+        metadata=None,
+        label_img=None,
+        global_step=None,
+        tag="default",
+        metadata_header=None,
+    ):
+        """Add embedding projector data to summary.
+
+        Args:
+            mat (torch.Tensor or numpy.ndarray): A matrix which each row is the feature vector of the data point
+            metadata (list): A list of labels, each element will be convert to string
+            label_img (torch.Tensor): Images correspond to each data point
+            global_step (int): Global step value to record
+            tag (str): Name for the embedding
+        Shape:
+            mat: :math:`(N, D)`, where N is number of data and D is feature dimension
+
+            label_img: :math:`(N, C, H, W)`
+
+        Examples::
+
+            import keyword
+            import torch
+            meta = []
+            while len(meta)<100:
+                meta = meta+keyword.kwlist # get some strings
+            meta = meta[:100]
+
+            for i, v in enumerate(meta):
+                meta[i] = v+str(i)
+
+            label_img = torch.rand(100, 3, 10, 32)
+            for i in range(100):
+                label_img[i]*=i/100.0
+
+            writer.add_embedding(torch.randn(100, 5), metadata=meta, label_img=label_img)
+            writer.add_embedding(torch.randn(100, 5), label_img=label_img)
+            writer.add_embedding(torch.randn(100, 5), metadata=meta)
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_embedding")
+        mat = make_np(mat)
+        if global_step is None:
+            global_step = 0
+            # clear pbtxt?
+
+        # Maybe we should encode the tag so slashes don't trip us up?
+        # I don't think this will mess us up, but better safe than sorry.
+        subdir = f"{str(global_step).zfill(5)}/{self._encode(tag)}"
+        save_path = os.path.join(self._get_file_writer().get_logdir(), subdir)
+
+        fs = tf.io.gfile
+        if fs.exists(save_path):
+            if fs.isdir(save_path):
+                print(
+                    "warning: Embedding dir exists, did you set global_step for add_embedding()?"
+                )
+            else:
+                raise Exception(
+                    f"Path: `{save_path}` exists, but is a file. Cannot proceed."
+                )
+        else:
+            fs.makedirs(save_path)
+
+        if metadata is not None:
+            assert mat.shape[0] == len(
+                metadata
+            ), "#labels should equal with #data points"
+            make_tsv(metadata, save_path, metadata_header=metadata_header)
+
+        if label_img is not None:
+            assert (
+                mat.shape[0] == label_img.shape[0]
+            ), "#images should equal with #data points"
+            make_sprite(label_img, save_path)
+
+        assert (
+            mat.ndim == 2
+        ), "mat should be 2D, where mat.size(0) is the number of data points"
+        make_mat(mat, save_path)
+
+        # Filesystem doesn't necessarily have append semantics, so we store an
+        # internal buffer to append to and re-write whole file after each
+        # embedding is added
+        if not hasattr(self, "_projector_config"):
+            self._projector_config = ProjectorConfig()
+        embedding_info = get_embedding_info(
+            metadata, label_img, subdir, global_step, tag
+        )
+        self._projector_config.embeddings.extend([embedding_info])
+
+        from google.protobuf import text_format
+
+        config_pbtxt = text_format.MessageToString(self._projector_config)
+        write_pbtxt(self._get_file_writer().get_logdir(), config_pbtxt)
+
+    def add_pr_curve(
+        self,
+        tag,
+        labels,
+        predictions,
+        global_step=None,
+        num_thresholds=127,
+        weights=None,
+        walltime=None,
+    ):
+        """Add precision recall curve.
+
+        Plotting a precision-recall curve lets you understand your model's
+        performance under different threshold settings. With this function,
+        you provide the ground truth labeling (T/F) and prediction confidence
+        (usually the output of your model) for each target. The TensorBoard UI
+        will let you choose the threshold interactively.
+
+        Args:
+            tag (str): Data identifier
+            labels (torch.Tensor, numpy.ndarray, or string/blobname):
+              Ground truth data. Binary label for each element.
+            predictions (torch.Tensor, numpy.ndarray, or string/blobname):
+              The probability that an element be classified as true.
+              Value should be in [0, 1]
+            global_step (int): Global step value to record
+            num_thresholds (int): Number of thresholds used to draw the curve.
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            import numpy as np
+            labels = np.random.randint(2, size=100)  # binary label
+            predictions = np.random.rand(100)
+            writer = SummaryWriter()
+            writer.add_pr_curve('pr_curve', labels, predictions, 0)
+            writer.close()
+
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_pr_curve")
+        labels, predictions = make_np(labels), make_np(predictions)
+        self._get_file_writer().add_summary(
+            pr_curve(tag, labels, predictions, num_thresholds, weights),
+            global_step,
+            walltime,
+        )
+
+    def add_pr_curve_raw(
+        self,
+        tag,
+        true_positive_counts,
+        false_positive_counts,
+        true_negative_counts,
+        false_negative_counts,
+        precision,
+        recall,
+        global_step=None,
+        num_thresholds=127,
+        weights=None,
+        walltime=None,
+    ):
+        """Add precision recall curve with raw data.
+
+        Args:
+            tag (str): Data identifier
+            true_positive_counts (torch.Tensor, numpy.ndarray, or string/blobname): true positive counts
+            false_positive_counts (torch.Tensor, numpy.ndarray, or string/blobname): false positive counts
+            true_negative_counts (torch.Tensor, numpy.ndarray, or string/blobname): true negative counts
+            false_negative_counts (torch.Tensor, numpy.ndarray, or string/blobname): false negative counts
+            precision (torch.Tensor, numpy.ndarray, or string/blobname): precision
+            recall (torch.Tensor, numpy.ndarray, or string/blobname): recall
+            global_step (int): Global step value to record
+            num_thresholds (int): Number of thresholds used to draw the curve.
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+            see: https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/pr_curve/README.md
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_pr_curve_raw")
+        self._get_file_writer().add_summary(
+            pr_curve_raw(
+                tag,
+                true_positive_counts,
+                false_positive_counts,
+                true_negative_counts,
+                false_negative_counts,
+                precision,
+                recall,
+                num_thresholds,
+                weights,
+            ),
+            global_step,
+            walltime,
+        )
+
+    def add_custom_scalars_multilinechart(
+        self, tags, category="default", title="untitled"
+    ):
+        """Shorthand for creating multilinechart. Similar to ``add_custom_scalars()``, but the only necessary argument is *tags*.
+
+        Args:
+            tags (list): list of tags that have been used in ``add_scalar()``
+
+        Examples::
+
+            writer.add_custom_scalars_multilinechart(['twse/0050', 'twse/2330'])
+        """
+        torch._C._log_api_usage_once(
+            "tensorboard.logging.add_custom_scalars_multilinechart"
+        )
+        layout = {category: {title: ["Multiline", tags]}}
+        self._get_file_writer().add_summary(custom_scalars(layout))
+
+    def add_custom_scalars_marginchart(
+        self, tags, category="default", title="untitled"
+    ):
+        """Shorthand for creating marginchart.
+
+        Similar to ``add_custom_scalars()``, but the only necessary argument is *tags*,
+        which should have exactly 3 elements.
+
+        Args:
+            tags (list): list of tags that have been used in ``add_scalar()``
+
+        Examples::
+
+            writer.add_custom_scalars_marginchart(['twse/0050', 'twse/2330', 'twse/2006'])
+        """
+        torch._C._log_api_usage_once(
+            "tensorboard.logging.add_custom_scalars_marginchart"
+        )
+        assert len(tags) == 3
+        layout = {category: {title: ["Margin", tags]}}
+        self._get_file_writer().add_summary(custom_scalars(layout))
+
+    def add_custom_scalars(self, layout):
+        """Create special chart by collecting charts tags in 'scalars'.
+
+        NOTE: This function can only be called once for each SummaryWriter() object.
+
+        Because it only provides metadata to tensorboard, the function can be called before or after the training loop.
+
+        Args:
+            layout (dict): {categoryName: *charts*}, where *charts* is also a dictionary
+              {chartName: *ListOfProperties*}. The first element in *ListOfProperties* is the chart's type
+              (one of **Multiline** or **Margin**) and the second element should be a list containing the tags
+              you have used in add_scalar function, which will be collected into the new chart.
+
+        Examples::
+
+            layout = {'Taiwan':{'twse':['Multiline',['twse/0050', 'twse/2330']]},
+                         'USA':{ 'dow':['Margin',   ['dow/aaa', 'dow/bbb', 'dow/ccc']],
+                              'nasdaq':['Margin',   ['nasdaq/aaa', 'nasdaq/bbb', 'nasdaq/ccc']]}}
+
+            writer.add_custom_scalars(layout)
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_custom_scalars")
+        self._get_file_writer().add_summary(custom_scalars(layout))
+
+    def add_mesh(
+        self,
+        tag,
+        vertices,
+        colors=None,
+        faces=None,
+        config_dict=None,
+        global_step=None,
+        walltime=None,
+    ):
+        """Add meshes or 3D point clouds to TensorBoard.
+
+        The visualization is based on Three.js,
+        so it allows users to interact with the rendered object. Besides the basic definitions
+        such as vertices, faces, users can further provide camera parameter, lighting condition, etc.
+        Please check https://threejs.org/docs/index.html#manual/en/introduction/Creating-a-scene for
+        advanced usage.
+
+        Args:
+            tag (str): Data identifier
+            vertices (torch.Tensor): List of the 3D coordinates of vertices.
+            colors (torch.Tensor): Colors for each vertex
+            faces (torch.Tensor): Indices of vertices within each triangle. (Optional)
+            config_dict: Dictionary with ThreeJS classes names and configuration.
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+              seconds after epoch of event
+
+        Shape:
+            vertices: :math:`(B, N, 3)`. (batch, number_of_vertices, channels)
+
+            colors: :math:`(B, N, 3)`. The values should lie in [0, 255] for type `uint8` or [0, 1] for type `float`.
+
+            faces: :math:`(B, N, 3)`. The values should lie in [0, number_of_vertices] for type `uint8`.
+
+        Examples::
+
+            from torch.utils.tensorboard import SummaryWriter
+            vertices_tensor = torch.as_tensor([
+                [1, 1, 1],
+                [-1, -1, 1],
+                [1, -1, -1],
+                [-1, 1, -1],
+            ], dtype=torch.float).unsqueeze(0)
+            colors_tensor = torch.as_tensor([
+                [255, 0, 0],
+                [0, 255, 0],
+                [0, 0, 255],
+                [255, 0, 255],
+            ], dtype=torch.int).unsqueeze(0)
+            faces_tensor = torch.as_tensor([
+                [0, 2, 3],
+                [0, 3, 1],
+                [0, 1, 2],
+                [1, 3, 2],
+            ], dtype=torch.int).unsqueeze(0)
+
+            writer = SummaryWriter()
+            writer.add_mesh('my_mesh', vertices=vertices_tensor, colors=colors_tensor, faces=faces_tensor)
+
+            writer.close()
+        """
+        torch._C._log_api_usage_once("tensorboard.logging.add_mesh")
+        self._get_file_writer().add_summary(
+            mesh(tag, vertices, colors, faces, config_dict), global_step, walltime
+        )
+
+    def flush(self):
+        """Flushes the event file to disk.
+
+        Call this method to make sure that all pending events have been written to
+        disk.
+        """
+        if self.all_writers is None:
+            return
+        for writer in self.all_writers.values():
+            writer.flush()
+
+    def close(self):
+        if self.all_writers is None:
+            return  # ignore double close
+        for writer in self.all_writers.values():
+            writer.flush()
+            writer.close()
+        self.file_writer = self.all_writers = None
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.close()
diff --git a/shared/package-files/3d/3dc83e4167d8e0f0214cac9e85a7bcaba2e1b5b5000716b661eeb7fb78757f8c/warnings_and_errors.py b/shared/package-files/3d/3dc83e4167d8e0f0214cac9e85a7bcaba2e1b5b5000716b661eeb7fb78757f8c/warnings_and_errors.py
new file mode 100644
index 0000000000000000000000000000000000000000..a556bf6bcb35b4b3451b49997bef4c8a10d9995b
--- /dev/null
+++ b/shared/package-files/3d/3dc83e4167d8e0f0214cac9e85a7bcaba2e1b5b5000716b661eeb7fb78757f8c/warnings_and_errors.py
@@ -0,0 +1,6 @@
+import numpy as np
+
+np.AxisError("test")
+np.AxisError(1, ndim=2)
+np.AxisError(1, ndim=2, msg_prefix="error")
+np.AxisError(1, ndim=2, msg_prefix=None)
diff --git a/shared/package-files/3d/3dc8dc66ccee9e5070d05448088afadcb00073c954428aa26bd1912de3f3a9cd/cmd.py b/shared/package-files/3d/3dc8dc66ccee9e5070d05448088afadcb00073c954428aa26bd1912de3f3a9cd/cmd.py
new file mode 100644
index 0000000000000000000000000000000000000000..3860c3ff1e0095c27fcfa36501c8c3fcbb1f4674
--- /dev/null
+++ b/shared/package-files/3d/3dc8dc66ccee9e5070d05448088afadcb00073c954428aa26bd1912de3f3a9cd/cmd.py
@@ -0,0 +1,435 @@
+"""distutils.cmd
+
+Provides the Command class, the base class for the command classes
+in the distutils.command package.
+"""
+
+import sys
+import os
+import re
+import logging
+
+from .errors import DistutilsOptionError
+from . import util, dir_util, file_util, archive_util, dep_util
+from ._log import log
+
+
+class Command:
+    """Abstract base class for defining command classes, the "worker bees"
+    of the Distutils.  A useful analogy for command classes is to think of
+    them as subroutines with local variables called "options".  The options
+    are "declared" in 'initialize_options()' and "defined" (given their
+    final values, aka "finalized") in 'finalize_options()', both of which
+    must be defined by every command class.  The distinction between the
+    two is necessary because option values might come from the outside
+    world (command line, config file, ...), and any options dependent on
+    other options must be computed *after* these outside influences have
+    been processed -- hence 'finalize_options()'.  The "body" of the
+    subroutine, where it does all its work based on the values of its
+    options, is the 'run()' method, which must also be implemented by every
+    command class.
+    """
+
+    # 'sub_commands' formalizes the notion of a "family" of commands,
+    # eg. "install" as the parent with sub-commands "install_lib",
+    # "install_headers", etc.  The parent of a family of commands
+    # defines 'sub_commands' as a class attribute; it's a list of
+    #    (command_name : string, predicate : unbound_method | string | None)
+    # tuples, where 'predicate' is a method of the parent command that
+    # determines whether the corresponding command is applicable in the
+    # current situation.  (Eg. we "install_headers" is only applicable if
+    # we have any C header files to install.)  If 'predicate' is None,
+    # that command is always applicable.
+    #
+    # 'sub_commands' is usually defined at the *end* of a class, because
+    # predicates can be unbound methods, so they must already have been
+    # defined.  The canonical example is the "install" command.
+    sub_commands = []
+
+    # -- Creation/initialization methods -------------------------------
+
+    def __init__(self, dist):
+        """Create and initialize a new Command object.  Most importantly,
+        invokes the 'initialize_options()' method, which is the real
+        initializer and depends on the actual command being
+        instantiated.
+        """
+        # late import because of mutual dependence between these classes
+        from distutils.dist import Distribution
+
+        if not isinstance(dist, Distribution):
+            raise TypeError("dist must be a Distribution instance")
+        if self.__class__ is Command:
+            raise RuntimeError("Command is an abstract class")
+
+        self.distribution = dist
+        self.initialize_options()
+
+        # Per-command versions of the global flags, so that the user can
+        # customize Distutils' behaviour command-by-command and let some
+        # commands fall back on the Distribution's behaviour.  None means
+        # "not defined, check self.distribution's copy", while 0 or 1 mean
+        # false and true (duh).  Note that this means figuring out the real
+        # value of each flag is a touch complicated -- hence "self._dry_run"
+        # will be handled by __getattr__, below.
+        # XXX This needs to be fixed.
+        self._dry_run = None
+
+        # verbose is largely ignored, but needs to be set for
+        # backwards compatibility (I think)?
+        self.verbose = dist.verbose
+
+        # Some commands define a 'self.force' option to ignore file
+        # timestamps, but methods defined *here* assume that
+        # 'self.force' exists for all commands.  So define it here
+        # just to be safe.
+        self.force = None
+
+        # The 'help' flag is just used for command-line parsing, so
+        # none of that complicated bureaucracy is needed.
+        self.help = 0
+
+        # 'finalized' records whether or not 'finalize_options()' has been
+        # called.  'finalize_options()' itself should not pay attention to
+        # this flag: it is the business of 'ensure_finalized()', which
+        # always calls 'finalize_options()', to respect/update it.
+        self.finalized = 0
+
+    # XXX A more explicit way to customize dry_run would be better.
+    def __getattr__(self, attr):
+        if attr == 'dry_run':
+            myval = getattr(self, "_" + attr)
+            if myval is None:
+                return getattr(self.distribution, attr)
+            else:
+                return myval
+        else:
+            raise AttributeError(attr)
+
+    def ensure_finalized(self):
+        if not self.finalized:
+            self.finalize_options()
+        self.finalized = 1
+
+    # Subclasses must define:
+    #   initialize_options()
+    #     provide default values for all options; may be customized by
+    #     setup script, by options from config file(s), or by command-line
+    #     options
+    #   finalize_options()
+    #     decide on the final values for all options; this is called
+    #     after all possible intervention from the outside world
+    #     (command-line, option file, etc.) has been processed
+    #   run()
+    #     run the command: do whatever it is we're here to do,
+    #     controlled by the command's various option values
+
+    def initialize_options(self):
+        """Set default values for all the options that this command
+        supports.  Note that these defaults may be overridden by other
+        commands, by the setup script, by config files, or by the
+        command-line.  Thus, this is not the place to code dependencies
+        between options; generally, 'initialize_options()' implementations
+        are just a bunch of "self.foo = None" assignments.
+
+        This method must be implemented by all command classes.
+        """
+        raise RuntimeError(
+            "abstract method -- subclass %s must override" % self.__class__
+        )
+
+    def finalize_options(self):
+        """Set final values for all the options that this command supports.
+        This is always called as late as possible, ie.  after any option
+        assignments from the command-line or from other commands have been
+        done.  Thus, this is the place to code option dependencies: if
+        'foo' depends on 'bar', then it is safe to set 'foo' from 'bar' as
+        long as 'foo' still has the same value it was assigned in
+        'initialize_options()'.
+
+        This method must be implemented by all command classes.
+        """
+        raise RuntimeError(
+            "abstract method -- subclass %s must override" % self.__class__
+        )
+
+    def dump_options(self, header=None, indent=""):
+        from distutils.fancy_getopt import longopt_xlate
+
+        if header is None:
+            header = "command options for '%s':" % self.get_command_name()
+        self.announce(indent + header, level=logging.INFO)
+        indent = indent + "  "
+        for option, _, _ in self.user_options:
+            option = option.translate(longopt_xlate)
+            if option[-1] == "=":
+                option = option[:-1]
+            value = getattr(self, option)
+            self.announce(indent + "{} = {}".format(option, value), level=logging.INFO)
+
+    def run(self):
+        """A command's raison d'etre: carry out the action it exists to
+        perform, controlled by the options initialized in
+        'initialize_options()', customized by other commands, the setup
+        script, the command-line, and config files, and finalized in
+        'finalize_options()'.  All terminal output and filesystem
+        interaction should be done by 'run()'.
+
+        This method must be implemented by all command classes.
+        """
+        raise RuntimeError(
+            "abstract method -- subclass %s must override" % self.__class__
+        )
+
+    def announce(self, msg, level=logging.DEBUG):
+        log.log(level, msg)
+
+    def debug_print(self, msg):
+        """Print 'msg' to stdout if the global DEBUG (taken from the
+        DISTUTILS_DEBUG environment variable) flag is true.
+        """
+        from distutils.debug import DEBUG
+
+        if DEBUG:
+            print(msg)
+            sys.stdout.flush()
+
+    # -- Option validation methods -------------------------------------
+    # (these are very handy in writing the 'finalize_options()' method)
+    #
+    # NB. the general philosophy here is to ensure that a particular option
+    # value meets certain type and value constraints.  If not, we try to
+    # force it into conformance (eg. if we expect a list but have a string,
+    # split the string on comma and/or whitespace).  If we can't force the
+    # option into conformance, raise DistutilsOptionError.  Thus, command
+    # classes need do nothing more than (eg.)
+    #   self.ensure_string_list('foo')
+    # and they can be guaranteed that thereafter, self.foo will be
+    # a list of strings.
+
+    def _ensure_stringlike(self, option, what, default=None):
+        val = getattr(self, option)
+        if val is None:
+            setattr(self, option, default)
+            return default
+        elif not isinstance(val, str):
+            raise DistutilsOptionError(
+                "'{}' must be a {} (got `{}`)".format(option, what, val)
+            )
+        return val
+
+    def ensure_string(self, option, default=None):
+        """Ensure that 'option' is a string; if not defined, set it to
+        'default'.
+        """
+        self._ensure_stringlike(option, "string", default)
+
+    def ensure_string_list(self, option):
+        r"""Ensure that 'option' is a list of strings.  If 'option' is
+        currently a string, we split it either on /,\s*/ or /\s+/, so
+        "foo bar baz", "foo,bar,baz", and "foo,   bar baz" all become
+        ["foo", "bar", "baz"].
+        """
+        val = getattr(self, option)
+        if val is None:
+            return
+        elif isinstance(val, str):
+            setattr(self, option, re.split(r',\s*|\s+', val))
+        else:
+            if isinstance(val, list):
+                ok = all(isinstance(v, str) for v in val)
+            else:
+                ok = False
+            if not ok:
+                raise DistutilsOptionError(
+                    "'{}' must be a list of strings (got {!r})".format(option, val)
+                )
+
+    def _ensure_tested_string(self, option, tester, what, error_fmt, default=None):
+        val = self._ensure_stringlike(option, what, default)
+        if val is not None and not tester(val):
+            raise DistutilsOptionError(
+                ("error in '%s' option: " + error_fmt) % (option, val)
+            )
+
+    def ensure_filename(self, option):
+        """Ensure that 'option' is the name of an existing file."""
+        self._ensure_tested_string(
+            option, os.path.isfile, "filename", "'%s' does not exist or is not a file"
+        )
+
+    def ensure_dirname(self, option):
+        self._ensure_tested_string(
+            option,
+            os.path.isdir,
+            "directory name",
+            "'%s' does not exist or is not a directory",
+        )
+
+    # -- Convenience methods for commands ------------------------------
+
+    def get_command_name(self):
+        if hasattr(self, 'command_name'):
+            return self.command_name
+        else:
+            return self.__class__.__name__
+
+    def set_undefined_options(self, src_cmd, *option_pairs):
+        """Set the values of any "undefined" options from corresponding
+        option values in some other command object.  "Undefined" here means
+        "is None", which is the convention used to indicate that an option
+        has not been changed between 'initialize_options()' and
+        'finalize_options()'.  Usually called from 'finalize_options()' for
+        options that depend on some other command rather than another
+        option of the same command.  'src_cmd' is the other command from
+        which option values will be taken (a command object will be created
+        for it if necessary); the remaining arguments are
+        '(src_option,dst_option)' tuples which mean "take the value of
+        'src_option' in the 'src_cmd' command object, and copy it to
+        'dst_option' in the current command object".
+        """
+        # Option_pairs: list of (src_option, dst_option) tuples
+        src_cmd_obj = self.distribution.get_command_obj(src_cmd)
+        src_cmd_obj.ensure_finalized()
+        for src_option, dst_option in option_pairs:
+            if getattr(self, dst_option) is None:
+                setattr(self, dst_option, getattr(src_cmd_obj, src_option))
+
+    def get_finalized_command(self, command, create=1):
+        """Wrapper around Distribution's 'get_command_obj()' method: find
+        (create if necessary and 'create' is true) the command object for
+        'command', call its 'ensure_finalized()' method, and return the
+        finalized command object.
+        """
+        cmd_obj = self.distribution.get_command_obj(command, create)
+        cmd_obj.ensure_finalized()
+        return cmd_obj
+
+    # XXX rename to 'get_reinitialized_command()'? (should do the
+    # same in dist.py, if so)
+    def reinitialize_command(self, command, reinit_subcommands=0):
+        return self.distribution.reinitialize_command(command, reinit_subcommands)
+
+    def run_command(self, command):
+        """Run some other command: uses the 'run_command()' method of
+        Distribution, which creates and finalizes the command object if
+        necessary and then invokes its 'run()' method.
+        """
+        self.distribution.run_command(command)
+
+    def get_sub_commands(self):
+        """Determine the sub-commands that are relevant in the current
+        distribution (ie., that need to be run).  This is based on the
+        'sub_commands' class attribute: each tuple in that list may include
+        a method that we call to determine if the subcommand needs to be
+        run for the current distribution.  Return a list of command names.
+        """
+        commands = []
+        for cmd_name, method in self.sub_commands:
+            if method is None or method(self):
+                commands.append(cmd_name)
+        return commands
+
+    # -- External world manipulation -----------------------------------
+
+    def warn(self, msg):
+        log.warning("warning: %s: %s\n", self.get_command_name(), msg)
+
+    def execute(self, func, args, msg=None, level=1):
+        util.execute(func, args, msg, dry_run=self.dry_run)
+
+    def mkpath(self, name, mode=0o777):
+        dir_util.mkpath(name, mode, dry_run=self.dry_run)
+
+    def copy_file(
+        self, infile, outfile, preserve_mode=1, preserve_times=1, link=None, level=1
+    ):
+        """Copy a file respecting verbose, dry-run and force flags.  (The
+        former two default to whatever is in the Distribution object, and
+        the latter defaults to false for commands that don't define it.)"""
+        return file_util.copy_file(
+            infile,
+            outfile,
+            preserve_mode,
+            preserve_times,
+            not self.force,
+            link,
+            dry_run=self.dry_run,
+        )
+
+    def copy_tree(
+        self,
+        infile,
+        outfile,
+        preserve_mode=1,
+        preserve_times=1,
+        preserve_symlinks=0,
+        level=1,
+    ):
+        """Copy an entire directory tree respecting verbose, dry-run,
+        and force flags.
+        """
+        return dir_util.copy_tree(
+            infile,
+            outfile,
+            preserve_mode,
+            preserve_times,
+            preserve_symlinks,
+            not self.force,
+            dry_run=self.dry_run,
+        )
+
+    def move_file(self, src, dst, level=1):
+        """Move a file respecting dry-run flag."""
+        return file_util.move_file(src, dst, dry_run=self.dry_run)
+
+    def spawn(self, cmd, search_path=1, level=1):
+        """Spawn an external command respecting dry-run flag."""
+        from distutils.spawn import spawn
+
+        spawn(cmd, search_path, dry_run=self.dry_run)
+
+    def make_archive(
+        self, base_name, format, root_dir=None, base_dir=None, owner=None, group=None
+    ):
+        return archive_util.make_archive(
+            base_name,
+            format,
+            root_dir,
+            base_dir,
+            dry_run=self.dry_run,
+            owner=owner,
+            group=group,
+        )
+
+    def make_file(
+        self, infiles, outfile, func, args, exec_msg=None, skip_msg=None, level=1
+    ):
+        """Special case of 'execute()' for operations that process one or
+        more input files and generate one output file.  Works just like
+        'execute()', except the operation is skipped and a different
+        message printed if 'outfile' already exists and is newer than all
+        files listed in 'infiles'.  If the command defined 'self.force',
+        and it is true, then the command is unconditionally run -- does no
+        timestamp checks.
+        """
+        if skip_msg is None:
+            skip_msg = "skipping %s (inputs unchanged)" % outfile
+
+        # Allow 'infiles' to be a single string
+        if isinstance(infiles, str):
+            infiles = (infiles,)
+        elif not isinstance(infiles, (list, tuple)):
+            raise TypeError("'infiles' must be a string, or a list or tuple of strings")
+
+        if exec_msg is None:
+            exec_msg = "generating {} from {}".format(outfile, ', '.join(infiles))
+
+        # If 'outfile' must be regenerated (either because it doesn't
+        # exist, is out-of-date, or the 'force' flag is true) then
+        # perform the action that presumably regenerates it
+        if self.force or dep_util.newer_group(infiles, outfile):
+            self.execute(func, args, exec_msg, level)
+        # Otherwise, print the "skip" message
+        else:
+            log.debug(skip_msg)
diff --git a/shared/package-files/3d/3dcae1d1d28c8307e91d3761a3cb9771861f2ad6c7cb7e95572cf8a36ba695dd/_meta.py b/shared/package-files/3d/3dcae1d1d28c8307e91d3761a3cb9771861f2ad6c7cb7e95572cf8a36ba695dd/_meta.py
new file mode 100644
index 0000000000000000000000000000000000000000..01a356f46f5301a37cde800a8c0d1b9568a19704
--- /dev/null
+++ b/shared/package-files/3d/3dcae1d1d28c8307e91d3761a3cb9771861f2ad6c7cb7e95572cf8a36ba695dd/_meta.py
@@ -0,0 +1,36 @@
+from typing import Any, Dict, Union
+
+from torchvision import tv_tensors
+from torchvision.transforms.v2 import functional as F, Transform
+
+
+class ConvertBoundingBoxFormat(Transform):
+    """Convert bounding box coordinates to the given ``format``, eg from "CXCYWH" to "XYXY".
+
+    Args:
+        format (str or tv_tensors.BoundingBoxFormat): output bounding box format.
+            Possible values are defined by :class:`~torchvision.tv_tensors.BoundingBoxFormat` and
+            string values match the enums, e.g. "XYXY" or "XYWH" etc.
+    """
+
+    _transformed_types = (tv_tensors.BoundingBoxes,)
+
+    def __init__(self, format: Union[str, tv_tensors.BoundingBoxFormat]) -> None:
+        super().__init__()
+        self.format = format
+
+    def _transform(self, inpt: tv_tensors.BoundingBoxes, params: Dict[str, Any]) -> tv_tensors.BoundingBoxes:
+        return F.convert_bounding_box_format(inpt, new_format=self.format)  # type: ignore[return-value, arg-type]
+
+
+class ClampBoundingBoxes(Transform):
+    """Clamp bounding boxes to their corresponding image dimensions.
+
+    The clamping is done according to the bounding boxes' ``canvas_size`` meta-data.
+
+    """
+
+    _transformed_types = (tv_tensors.BoundingBoxes,)
+
+    def _transform(self, inpt: tv_tensors.BoundingBoxes, params: Dict[str, Any]) -> tv_tensors.BoundingBoxes:
+        return F.clamp_bounding_boxes(inpt)  # type: ignore[return-value]
diff --git a/shared/package-files/3d/3dcb9f53ffb2f462d2e233c0496b8856a7df1558b64c817414f7f92f6ac5f4b5/tvi920b-vb b/shared/package-files/3d/3dcb9f53ffb2f462d2e233c0496b8856a7df1558b64c817414f7f92f6ac5f4b5/tvi920b-vb
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diff --git a/shared/package-files/3d/3dcbd941b62b0dd76e72b3656af04f990d60da68a78a8ffd1a4514151760b683/seaborn-v0_8-notebook.mplstyle b/shared/package-files/3d/3dcbd941b62b0dd76e72b3656af04f990d60da68a78a8ffd1a4514151760b683/seaborn-v0_8-notebook.mplstyle
new file mode 100644
index 0000000000000000000000000000000000000000..18bcf3e1204208f50834f6438d67d26e20b90482
--- /dev/null
+++ b/shared/package-files/3d/3dcbd941b62b0dd76e72b3656af04f990d60da68a78a8ffd1a4514151760b683/seaborn-v0_8-notebook.mplstyle
@@ -0,0 +1,21 @@
+# Seaborn notebook context
+figure.figsize: 8.0, 5.5
+axes.labelsize: 11
+axes.titlesize: 12
+xtick.labelsize: 10
+ytick.labelsize: 10
+legend.fontsize: 10
+
+grid.linewidth: 1
+lines.linewidth: 1.75
+patch.linewidth: .3
+lines.markersize: 7
+lines.markeredgewidth: 0
+
+xtick.major.width: 1
+ytick.major.width: 1
+xtick.minor.width: .5
+ytick.minor.width: .5
+
+xtick.major.pad: 7
+ytick.major.pad: 7
diff --git a/shared/package-files/3d/3dccce2af2b2748ffd39ad922e661f9724bbec9b6a20b86ea42ad7656f01dc8b/RECORD b/shared/package-files/3d/3dccce2af2b2748ffd39ad922e661f9724bbec9b6a20b86ea42ad7656f01dc8b/RECORD
new file mode 100644
index 0000000000000000000000000000000000000000..203bae73bff6ffeff676cfb81d3366840d1a882e
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+++ b/shared/package-files/3d/3dccce2af2b2748ffd39ad922e661f9724bbec9b6a20b86ea42ad7656f01dc8b/RECORD
@@ -0,0 +1,58 @@
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+parso-0.8.3.dist-info/LICENSE.txt,sha256=-meXMHN1PRdiTK-GhNXugW1wyJ2RLFvKfKDwjnsVDts,4176
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+parso-0.8.3.dist-info/top_level.txt,sha256=GOOKQCPcnr0_7IRArxyI0CX5LLu4WLlzIRAVWS-vJ4s,6
+parso/__init__.py,sha256=eatQ-us9dJoFPpMfoSsmGXzoZSnMDxuOTEIY1PC3LSc,1607
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+parso/python/errors.py,sha256=0nTFd692jKvxHpwklkbz1lRByPFTj_f1xmtVsk0ji2E,47955
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diff --git a/shared/package-files/3d/3dcd2703d2d6dc573d34b30cc73ee8b1bc3cb22da34a23954c44be27ef51d32d/key_metadata.h b/shared/package-files/3d/3dcd2703d2d6dc573d34b30cc73ee8b1bc3cb22da34a23954c44be27ef51d32d/key_metadata.h
new file mode 100644
index 0000000000000000000000000000000000000000..6fe8ac7ccb9db3fb92da42064f9fe2aeabdbfb52
--- /dev/null
+++ b/shared/package-files/3d/3dcd2703d2d6dc573d34b30cc73ee8b1bc3cb22da34a23954c44be27ef51d32d/key_metadata.h
@@ -0,0 +1,91 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include 
+#include 
+
+#include "parquet/encryption/key_material.h"
+#include "parquet/exception.h"
+#include "parquet/platform.h"
+
+namespace parquet::encryption {
+
+// Parquet encryption specification defines "key metadata" as an arbitrary byte array,
+// generated by file writers for each encryption key, and passed to the low level API for
+// storage in the file footer. The "key metadata" field is made available to file readers
+// to enable recovery of the key. This interface can be utilized for implementation
+// of any key management scheme.
+//
+// The keytools package (PARQUET-1373) implements one approach, of many possible, to key
+// management and to generation of the "key metadata" fields. This approach, based on the
+// "envelope encryption" pattern, allows integration with KMS servers. It keeps the actual
+// material, required to recover a key, in a "key material" object (see the KeyMaterial
+// class for details). This class is implemented to support version 1 of the parquet key
+// management tools specification.
+//
+// KeyMetadata writes (and reads) the "key metadata" field as a flat json object,
+// with the following fields:
+// 1. "keyMaterialType" - a String, with the type of  key material.
+// 2. "internalStorage" - a boolean. If true, means that "key material" is kept inside the
+// "key metadata" field. If false, "key material" is kept externally (outside Parquet
+// files) - in this case, "key metadata" keeps a reference to the external "key material".
+// 3. "keyReference" - a String, with the reference to the external "key material".
+// Written only if internalStorage is false.
+//
+// If internalStorage is true, "key material" is a part of "key metadata", and the json
+// keeps additional fields, described in the KeyMaterial class.
+class PARQUET_EXPORT KeyMetadata {
+ public:
+  static constexpr const char kKeyMaterialInternalStorageField[] = "internalStorage";
+  static constexpr const char kKeyReferenceField[] = "keyReference";
+
+  /// key_metadata_bytes is the key metadata field stored in the parquet file,
+  /// in the serialized json object format.
+  static KeyMetadata Parse(const std::string& key_metadata_bytes);
+
+  static std::string CreateSerializedForExternalMaterial(
+      const std::string& key_reference);
+
+  bool key_material_stored_internally() const { return is_internal_storage_; }
+
+  const KeyMaterial& key_material() const {
+    if (!is_internal_storage_) {
+      throw ParquetException("key material is stored externally.");
+    }
+    return ::std::get(key_material_or_reference_);
+  }
+
+  const std::string& key_reference() const {
+    if (is_internal_storage_) {
+      throw ParquetException("key material is stored internally.");
+    }
+    return ::std::get(key_material_or_reference_);
+  }
+
+ private:
+  explicit KeyMetadata(const KeyMaterial& key_material);
+  explicit KeyMetadata(const std::string& key_reference);
+
+  bool is_internal_storage_;
+  /// If is_internal_storage_ is true, KeyMaterial is set,
+  /// else a string referencing to an outside "key material" is set.
+  ::std::variant key_material_or_reference_;
+};
+
+}  // namespace parquet::encryption
diff --git a/shared/package-files/3d/3dcd47086529bf178cf7d10f35a8c188a1e9710391acecceef700898a4cf099b/COPYING.md b/shared/package-files/3d/3dcd47086529bf178cf7d10f35a8c188a1e9710391acecceef700898a4cf099b/COPYING.md
new file mode 100644
index 0000000000000000000000000000000000000000..b4325343eb5173c7b5b85e7cc59f000450bfc671
--- /dev/null
+++ b/shared/package-files/3d/3dcd47086529bf178cf7d10f35a8c188a1e9710391acecceef700898a4cf099b/COPYING.md
@@ -0,0 +1,64 @@
+# Licensing terms
+
+Traitlets is adapted from enthought.traits, Copyright (c) Enthought, Inc.,
+under the terms of the Modified BSD License.
+
+This project is licensed under the terms of the Modified BSD License
+(also known as New or Revised or 3-Clause BSD), as follows:
+
+- Copyright (c) 2001-, IPython Development Team
+
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimer.
+
+Redistributions in binary form must reproduce the above copyright notice, this
+list of conditions and the following disclaimer in the documentation and/or
+other materials provided with the distribution.
+
+Neither the name of the IPython Development Team nor the names of its
+contributors may be used to endorse or promote products derived from this
+software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+## About the IPython Development Team
+
+The IPython Development Team is the set of all contributors to the IPython project.
+This includes all of the IPython subprojects.
+
+The core team that coordinates development on GitHub can be found here:
+https://github.com/jupyter/.
+
+## Our Copyright Policy
+
+IPython uses a shared copyright model. Each contributor maintains copyright
+over their contributions to IPython. But, it is important to note that these
+contributions are typically only changes to the repositories. Thus, the IPython
+source code, in its entirety is not the copyright of any single person or
+institution. Instead, it is the collective copyright of the entire IPython
+Development Team. If individual contributors want to maintain a record of what
+changes/contributions they have specific copyright on, they should indicate
+their copyright in the commit message of the change, when they commit the
+change to one of the IPython repositories.
+
+With this in mind, the following banner should be used in any source code file
+to indicate the copyright and license terms:
+
+```
+# Copyright (c) IPython Development Team.
+# Distributed under the terms of the Modified BSD License.
+```
diff --git a/shared/package-files/3d/3dd6d73155eb0330c90860f104bd67da94bf457f7f24b624b29cc0ebea86224b/throughput_benchmark.py b/shared/package-files/3d/3dd6d73155eb0330c90860f104bd67da94bf457f7f24b624b29cc0ebea86224b/throughput_benchmark.py
new file mode 100644
index 0000000000000000000000000000000000000000..5607fadee9e9c6d854491a9517fea4256ebe34f6
--- /dev/null
+++ b/shared/package-files/3d/3dd6d73155eb0330c90860f104bd67da94bf457f7f24b624b29cc0ebea86224b/throughput_benchmark.py
@@ -0,0 +1,159 @@
+
+import torch._C
+
+
+def format_time(time_us=None, time_ms=None, time_s=None):
+    """Define time formatting."""
+    assert sum([time_us is not None, time_ms is not None, time_s is not None]) == 1
+
+    US_IN_SECOND = 1e6
+    US_IN_MS = 1e3
+
+    if time_us is None:
+        if time_ms is not None:
+            time_us = time_ms * US_IN_MS
+        elif time_s is not None:
+            time_us = time_s * US_IN_SECOND
+        else:
+            raise AssertionError("Shouldn't reach here :)")
+
+    if time_us >= US_IN_SECOND:
+        return f'{time_us / US_IN_SECOND:.3f}s'
+    if time_us >= US_IN_MS:
+        return f'{time_us / US_IN_MS:.3f}ms'
+    return f'{time_us:.3f}us'
+
+
+class ExecutionStats:
+    def __init__(self, c_stats, benchmark_config):
+        self._c_stats = c_stats
+        self.benchmark_config = benchmark_config
+
+    @property
+    def latency_avg_ms(self):
+        return self._c_stats.latency_avg_ms
+
+    @property
+    def num_iters(self):
+        return self._c_stats.num_iters
+
+    @property
+    def iters_per_second(self):
+        """Return total number of iterations per second across all calling threads."""
+        return self.num_iters / self.total_time_seconds
+
+    @property
+    def total_time_seconds(self):
+        return self.num_iters * (
+            self.latency_avg_ms / 1000.0) / self.benchmark_config.num_calling_threads
+
+    def __str__(self):
+        return '\n'.join([
+            "Average latency per example: " + format_time(time_ms=self.latency_avg_ms),
+            f"Total number of iterations: {self.num_iters}",
+            f"Total number of iterations per second (across all threads): {self.iters_per_second:.2f}",
+            "Total time: " + format_time(time_s=self.total_time_seconds)
+        ])
+
+
+class ThroughputBenchmark:
+    """
+    This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark.
+
+    This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible
+    for executing a PyTorch module (nn.Module or ScriptModule) under an inference
+    server like load. It can emulate multiple calling threads to a single module
+    provided. In the future we plan to enhance this component to support inter and
+    intra-op parallelism as well as multiple models running in a single process.
+
+    Please note that even though nn.Module is supported, it might incur an overhead
+    from the need to hold GIL every time we execute Python code or pass around
+    inputs as Python objects. As soon as you have a ScriptModule version of your
+    model for inference deployment it is better to switch to using it in this
+    benchmark.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("undefined vars")
+        >>> from torch.utils import ThroughputBenchmark
+        >>> bench = ThroughputBenchmark(my_module)
+        >>> # Pre-populate benchmark's data set with the inputs
+        >>> for input in inputs:
+        ...     # Both args and kwargs work, same as any PyTorch Module / ScriptModule
+        ...     bench.add_input(input[0], x2=input[1])
+        >>> # Inputs supplied above are randomly used during the execution
+        >>> stats = bench.benchmark(
+        ...     num_calling_threads=4,
+        ...     num_warmup_iters = 100,
+        ...     num_iters = 1000,
+        ... )
+        >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms))
+        >>> print("Number of iterations: {}".format(stats.num_iters))
+    """
+
+    def __init__(self, module):
+        if isinstance(module, torch.jit.ScriptModule):
+            self._benchmark = torch._C.ThroughputBenchmark(module._c)
+        else:
+            self._benchmark = torch._C.ThroughputBenchmark(module)
+
+    def run_once(self, *args, **kwargs):
+        """
+        Given input id (input_idx) run benchmark once and return prediction.
+
+        This is useful for testing that benchmark actually runs the module you
+        want it to run. input_idx here is an index into inputs array populated
+        by calling add_input() method.
+        """
+        return self._benchmark.run_once(*args, **kwargs)
+
+    def add_input(self, *args, **kwargs):
+        """
+        Store a single input to a module into the benchmark memory and keep it there.
+
+        During the benchmark execution every thread is going to pick up a
+        random input from the all the inputs ever supplied to the benchmark via
+        this function.
+        """
+        self._benchmark.add_input(*args, **kwargs)
+
+    def benchmark(
+            self,
+            num_calling_threads=1,
+            num_warmup_iters=10,
+            num_iters=100,
+            profiler_output_path=""):
+        """
+        Run a benchmark on the module.
+
+        Args:
+            num_warmup_iters (int): Warmup iters are used to make sure we run a module
+                a few times before actually measuring things. This way we avoid cold
+                caches and any other similar problems. This is the number of warmup
+                iterations for each of the thread in separate
+
+            num_iters (int): Number of iterations the benchmark should run with.
+                This number is separate from the warmup iterations. Also the number is
+                shared across all the threads. Once the num_iters iterations across all
+                the threads is reached, we will stop execution. Though total number of
+                iterations might be slightly larger. Which is reported as
+                stats.num_iters where stats is the result of this function
+
+            profiler_output_path (str): Location to save Autograd Profiler trace.
+                If not empty, Autograd Profiler will be enabled for the main benchmark
+                execution (but not the warmup phase). The full trace will be saved
+                into the file path provided by this argument
+
+
+        This function returns BenchmarkExecutionStats object which is defined via pybind11.
+        It currently has two fields:
+            - num_iters - number of actual iterations the benchmark have made
+            - avg_latency_ms - average time it took to infer on one input example in milliseconds
+        """
+        config = torch._C.BenchmarkConfig()
+        config.num_calling_threads = num_calling_threads
+        config.num_warmup_iters = num_warmup_iters
+        config.num_iters = num_iters
+        config.profiler_output_path = profiler_output_path
+        c_stats = self._benchmark.benchmark(config)
+        return ExecutionStats(c_stats, config)
diff --git a/shared/package-files/3d/3ddb410292618d466b221e76a653c827ed721655d40d6a3c84da2297caa7c77d/_linalg_eigvals_ops.h b/shared/package-files/3d/3ddb410292618d466b221e76a653c827ed721655d40d6a3c84da2297caa7c77d/_linalg_eigvals_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..a606d520c0f6f6b442eba284268e394e9918b65d
--- /dev/null
+++ b/shared/package-files/3d/3ddb410292618d466b221e76a653c827ed721655d40d6a3c84da2297caa7c77d/_linalg_eigvals_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API _linalg_eigvals {
+  using schema = at::Tensor (const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::_linalg_eigvals")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "_linalg_eigvals(Tensor self) -> Tensor")
+  static at::Tensor call(const at::Tensor & self);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/3d/3ddd9cff76b421e48e51ebe13c89573640e6828074ea0e1cf580acb91c3048a3/byte_size.h b/shared/package-files/3d/3ddd9cff76b421e48e51ebe13c89573640e6828074ea0e1cf580acb91c3048a3/byte_size.h
new file mode 100644
index 0000000000000000000000000000000000000000..214c7551b6c76bc95a7d71eb8b8c31bd96d4b838
--- /dev/null
+++ b/shared/package-files/3d/3ddd9cff76b421e48e51ebe13c89573640e6828074ea0e1cf580acb91c3048a3/byte_size.h
@@ -0,0 +1,88 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#pragma once
+
+#include 
+
+#include "arrow/type_fwd.h"
+
+namespace arrow {
+
+namespace util {
+
+/// \brief The sum of bytes in each buffer referenced by the array
+///
+/// Note: An array may only reference a portion of a buffer.
+///       This method will overestimate in this case and return the
+///       byte size of the entire buffer.
+/// Note: If a buffer is referenced multiple times then it will
+///       only be counted once.
+ARROW_EXPORT int64_t TotalBufferSize(const ArrayData& array_data);
+/// \brief The sum of bytes in each buffer referenced by the array
+/// \see TotalBufferSize(const ArrayData& array_data) for details
+ARROW_EXPORT int64_t TotalBufferSize(const Array& array);
+/// \brief The sum of bytes in each buffer referenced by the array
+/// \see TotalBufferSize(const ArrayData& array_data) for details
+ARROW_EXPORT int64_t TotalBufferSize(const ChunkedArray& chunked_array);
+/// \brief The sum of bytes in each buffer referenced by the batch
+/// \see TotalBufferSize(const ArrayData& array_data) for details
+ARROW_EXPORT int64_t TotalBufferSize(const RecordBatch& record_batch);
+/// \brief The sum of bytes in each buffer referenced by the table
+/// \see TotalBufferSize(const ArrayData& array_data) for details
+ARROW_EXPORT int64_t TotalBufferSize(const Table& table);
+
+/// \brief Calculate the buffer ranges referenced by the array
+///
+/// These ranges will take into account array offsets
+///
+/// The ranges may contain duplicates
+///
+/// Dictionary arrays will ignore the offset of their containing array
+///
+/// The return value will be a struct array corresponding to the schema:
+/// schema({field("start", uint64()), field("offset", uint64()), field("length",
+/// uint64()))
+ARROW_EXPORT Result> ReferencedRanges(const ArrayData& array_data);
+
+/// \brief Returns the sum of bytes from all buffer ranges referenced
+///
+/// Unlike TotalBufferSize this method will account for array
+/// offsets.
+///
+/// If buffers are shared between arrays then the shared
+/// portion will be counted multiple times.
+///
+/// Dictionary arrays will always be counted in their entirety
+/// even if the array only references a portion of the dictionary.
+ARROW_EXPORT Result ReferencedBufferSize(const ArrayData& array_data);
+/// \brief Returns the sum of bytes from all buffer ranges referenced
+/// \see ReferencedBufferSize(const ArrayData& array_data) for details
+ARROW_EXPORT Result ReferencedBufferSize(const Array& array_data);
+/// \brief Returns the sum of bytes from all buffer ranges referenced
+/// \see ReferencedBufferSize(const ArrayData& array_data) for details
+ARROW_EXPORT Result ReferencedBufferSize(const ChunkedArray& array_data);
+/// \brief Returns the sum of bytes from all buffer ranges referenced
+/// \see ReferencedBufferSize(const ArrayData& array_data) for details
+ARROW_EXPORT Result ReferencedBufferSize(const RecordBatch& array_data);
+/// \brief Returns the sum of bytes from all buffer ranges referenced
+/// \see ReferencedBufferSize(const ArrayData& array_data) for details
+ARROW_EXPORT Result ReferencedBufferSize(const Table& array_data);
+
+}  // namespace util
+
+}  // namespace arrow
diff --git a/shared/package-files/3d/3ddf351257f8e9a358bd53e45a3f556a3e9bbe8fc2b892d15115d5d27e303ae4/mappings.py b/shared/package-files/3d/3ddf351257f8e9a358bd53e45a3f556a3e9bbe8fc2b892d15115d5d27e303ae4/mappings.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1a872056d16cf502e0e0c6943dec56a0c6bc4df
--- /dev/null
+++ b/shared/package-files/3d/3ddf351257f8e9a358bd53e45a3f556a3e9bbe8fc2b892d15115d5d27e303ae4/mappings.py
@@ -0,0 +1,761 @@
+import operator
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+toq = torch.ops.quantized
+
+import torch.ao.nn.quantized as nnq
+import torch.ao.nn.quantized.dynamic as nnqd
+import torch.ao.nn.intrinsic.quantized as nniq
+import torch.ao.nn.intrinsic.quantized.dynamic as nniqd
+import torch.ao.nn.intrinsic.qat as nniqat
+import torch.ao.nn.intrinsic as nni
+import torch.ao.nn.qat as nnqat
+import torch.ao.nn.qat.dynamic as nnqatd
+from torch.ao.quantization.backend_config import get_native_backend_config
+import torch.ao.quantization.fx._lower_to_native_backend as \
+    _lower_to_native_backend
+import torch.ao.quantization.quantization_mappings as quantization_mappings
+
+from .ns_types import NSNodeTargetType
+
+from typing import Callable, Dict, List, Optional, Set, Tuple
+
+
+def get_base_name_to_sets_of_related_ops() -> Dict[str, Set[NSNodeTargetType]]:
+    # note: this set is modified below by items from backend_config
+    sets_of_related_ops: List[Set[NSNodeTargetType]] = [
+        # conv modules
+        {
+            nn.Conv1d,
+        },
+        {
+            nn.Conv2d,
+        },
+        {
+            nn.Conv3d,
+        },
+        # conv functionals
+        {
+            F.conv1d,
+        },
+        {
+            F.conv2d,
+        },
+        {
+            F.conv3d,
+        },
+        # linear modules
+        {
+            nn.Linear,
+        },
+        # linear functionals
+        {
+            F.linear,
+        },
+        # average pool
+        {
+            nn.AvgPool1d,
+            torch.avg_pool1d,
+        },
+        {
+            nn.AvgPool2d,
+            torch._C._nn.avg_pool2d,
+        },
+        {
+            nn.AvgPool3d,
+            torch._C._nn.avg_pool3d,
+        },
+        # adaptive average pool
+        {
+            nn.AdaptiveAvgPool1d,
+            F.adaptive_avg_pool1d,
+        },
+        {
+            nn.AdaptiveAvgPool2d,
+            F.adaptive_avg_pool2d,
+        },
+        {
+            nn.AdaptiveAvgPool3d,
+            F.adaptive_avg_pool3d,
+        },
+        # LSTM
+        {
+            nn.LSTM,
+        },
+        # add
+        {
+            torch.add,
+            operator.add,  # x + y
+        },
+        # cat
+        {
+            torch.cat,
+        },
+        # mul
+        {
+            torch.mul,
+            operator.mul,
+        },
+        # relu
+        {
+            F.relu,
+            nn.ReLU,
+            'relu',
+            'relu_',
+            torch.relu,
+        },
+        # maxpool
+        {
+            nn.MaxPool1d,
+            F.max_pool1d,
+        },
+        {
+            nn.MaxPool2d,
+            F.max_pool2d,
+        },
+        {
+            nn.MaxPool3d,
+            F.max_pool3d,
+        },
+        # sigmoid
+        {
+            torch.sigmoid,
+            'sigmoid',
+            'sigmoid_',
+            nn.Sigmoid,
+            F.sigmoid,
+        },
+        # BatchNorm
+        {
+            nn.BatchNorm2d,
+        },
+        {
+            nn.BatchNorm3d,
+        },
+        # ConvTranspose
+        {
+            nn.ConvTranspose1d,
+        },
+        {
+            nn.ConvTranspose2d,
+        },
+        {
+            nn.ConvTranspose3d,
+        },
+        # functional transposed conv
+        {
+            F.conv_transpose1d,
+        },
+        {
+            F.conv_transpose2d,
+        },
+        {
+            F.conv_transpose3d,
+        },
+        # ELU
+        {
+            nn.ELU,
+        },
+        # Embedding
+        {
+            nn.Embedding,
+        },
+        # EmbeddingBag
+        {
+            nn.EmbeddingBag,
+        },
+        # GroupNorm
+        {
+            nn.GroupNorm,
+        },
+        # Hardswish
+        {
+            nn.Hardswish,
+        },
+        # InstanceNorm
+        {
+            nn.InstanceNorm1d,
+        },
+        {
+            nn.InstanceNorm2d,
+        },
+        {
+            nn.InstanceNorm3d,
+        },
+        # LayerNorm
+        {
+            nn.LayerNorm,
+        },
+        # LeakyReLU
+        {
+            nn.LeakyReLU,
+        },
+        # ReLU6
+        {
+            nn.ReLU6,
+            F.relu6,
+        },
+        # F.elu
+        {
+            F.elu,
+        },
+        # F.hardswish
+        {
+            F.hardswish,
+        },
+        # F.group_norm
+        {
+            F.group_norm,
+        },
+        # F.instance_norm
+        {
+            F.instance_norm,
+        },
+        # F.layer_norm
+        {
+            F.layer_norm,
+        },
+        # F.leaky_relu
+        {
+            F.leaky_relu,
+        },
+        # F.silu
+        {
+            nn.SiLU,
+            F.silu,
+        },
+        # F.mish
+        {
+            nn.Mish,
+            F.mish,
+        },
+        # F.tanh
+        {
+            nn.Tanh,
+            F.tanh,
+            torch.tanh,
+            'tanh_',
+            'tanh',
+        },
+        # F.hardsigmoid
+        {
+            'hardsigmoid_',
+            'hardsigmoid',
+            F.hardsigmoid,
+            nn.Hardsigmoid,
+        },
+        # F.hardtanh
+        {
+            nn.Hardtanh,
+            F.hardtanh,
+            F.hardtanh_,
+        },
+        # floordiv
+        {
+            operator.floordiv,
+        },
+        # unsqueeze
+        {
+            torch.unsqueeze,
+        },
+        # stack
+        {
+            torch.stack,
+        },
+        # squeeze
+        {
+            torch.squeeze,
+        },
+        # sort
+        {
+            torch.sort,
+        },
+        # repeat_interleave
+        {
+            torch.repeat_interleave,
+        },
+        # min
+        {
+            torch.min,
+        },
+        # mean
+        {
+            torch.mean,
+        },
+        # max
+        {
+            torch.max,
+        },
+        # transpose
+        {
+            torch.transpose,
+        },
+        # flatten
+        {
+            torch.flatten,
+        },
+        # clamp
+        {
+            torch.clamp,
+        },
+        # chunk
+        {
+            torch.chunk,
+        },
+        # interpolate
+        {
+            torch.nn.functional.interpolate,
+        },
+        # dropout
+        {
+            nn.Dropout,
+        },
+        # F.dropout
+        {
+            F.dropout,
+        },
+        # matmul
+        {
+            torch.matmul,
+        },
+        # Softmax
+        {
+            nn.Softmax,
+        },
+        # PReLU
+        {
+            nn.PReLU,
+            nnq.PReLU,
+        },
+        # F.prelu
+        {
+            F.prelu,
+            toq.prelu,
+        },
+        # pixel shuffle
+        {
+            nn.PixelShuffle,
+        },
+        {
+            F.pixel_shuffle,
+        },
+        # pixel unshuffle
+        {
+            nn.PixelUnshuffle,
+        },
+        {
+            F.pixel_unshuffle,
+        },
+        # narrow
+        {
+            torch.narrow,
+        },
+    ]
+
+    # for each floating point op, add versions of the op added by
+    # backend_config
+    backend_config = get_native_backend_config()
+
+    new_connections: List[Tuple[Callable, Callable]] = [
+        # technical debt edge case
+        (nn.Linear, nn.modules.linear.NonDynamicallyQuantizableLinear),
+    ]
+
+    for pattern, config in backend_config._pattern_complex_format_to_config.items():
+
+        # pattern format: (c, (b, a))
+        first_element = pattern
+        # look from the end, because pattern is in reverse order
+        while isinstance(first_element, (list, tuple)):
+            first_element = first_element[-1]
+
+        if config.fused_module is not None:
+            # case 1: pattern fuses a pattern of ops into an op
+            # example: nn.Conv1d, nn.ReLU fused into nni.ConvReLU1d
+            new_connections.append((first_element, config.fused_module))
+
+        if config.qat_module is not None:
+            # case 2: pattern swaps a module into a QAT module
+            # example: nni.ConvReLU1d swapped into nniqat.ConvReLU1d
+            new_connections.append((first_element, config.qat_module))
+
+        if config.reference_quantized_module is not None:
+            # case 3: reference version of floating point module, such as
+            # nn.Conv2d and nnqr.Conv2d
+            new_connections.append((first_element, config.reference_quantized_module))
+
+    #
+    # Add reference module swaps from default lowering path
+    #
+
+    for source_to_target in (
+        _lower_to_native_backend.STATIC_LOWER_MODULE_MAP,
+        _lower_to_native_backend.DYNAMIC_LOWER_MODULE_MAP,
+        _lower_to_native_backend.WEIGHT_ONLY_LOWER_MODULE_MAP,
+        _lower_to_native_backend.SPECIAL_PATTERN_LOWER_MODULE_MAP,
+    ):
+        for source, target in source_to_target.items():  # type: ignore[attr-defined]
+            new_connections.append((source, target))
+
+    for source_to_double_target in (
+        _lower_to_native_backend.STATIC_LOWER_FUSED_MODULE_MAP,
+        _lower_to_native_backend.STATIC_LOWER_FUSED_MODULE_TWO_INPUTS_MAP,
+        _lower_to_native_backend.DYNAMIC_LOWER_FUSED_MODULE_MAP,
+    ):
+        for source, (target1, target2) in source_to_double_target.items():  # type: ignore[attr-defined]
+            new_connections.append((source, target1))
+            new_connections.append((source, target2))
+
+    #
+    # Add function swaps from default lowering path
+    #
+
+    for source, (target1, target2) in \
+            _lower_to_native_backend.STATIC_LOWER_FUNCTIONAL_MAP.items():
+        new_connections.append((source, target1))
+        new_connections.append((source, target2))
+
+    for source_to_target in (
+        _lower_to_native_backend.QBIN_OP_MAPPING,
+        _lower_to_native_backend.QBIN_RELU_OP_MAPPING,
+        quantization_mappings.DEFAULT_FLOAT_TO_QUANTIZED_OPERATOR_MAPPINGS,
+    ):
+        for source, target in source_to_target.items():
+            new_connections.append((source, target))
+
+    #
+    # Add other swaps, ideally in the future this could be removed
+    # after the lowering code stops using these.
+    #
+    for source_to_target in (
+        quantization_mappings.DEFAULT_DYNAMIC_QUANT_MODULE_MAPPINGS,
+    ):
+        for source, target in source_to_target.items():
+            new_connections.append((source, target))
+
+
+    # add the new connections from backend_config
+    for item1, item2 in new_connections:
+        for set_of_related_ops in sets_of_related_ops:
+            if item1 in set_of_related_ops or item2 in set_of_related_ops:
+                set_of_related_ops.add(item1)
+                set_of_related_ops.add(item2)
+                break
+
+    base_name_to_sets_of_related_ops: Dict[str, Set[NSNodeTargetType]] = {}
+
+    counter = 0
+    for set_of_related_ops in sets_of_related_ops:
+        base_name = str(counter)
+        counter += 1
+        base_name_to_sets_of_related_ops[base_name] = set_of_related_ops
+
+    return base_name_to_sets_of_related_ops
+
+
+def get_base_name_for_op(
+    base_name_to_sets_of_related_ops: Dict[str, Set[NSNodeTargetType]],
+    op: NSNodeTargetType,
+) -> Optional[str]:
+    for base_name, set_of_related_ops in base_name_to_sets_of_related_ops.items():
+        if op in set_of_related_ops:
+            return base_name
+    return None
+
+
+def add_op_to_sets_of_related_ops(
+    base_name_to_sets_of_related_ops: Dict[str, Set[NSNodeTargetType]],
+    op: NSNodeTargetType,
+    related_op: Optional[NSNodeTargetType],
+) -> None:
+    if related_op is not None:
+        for set_of_related_ops in base_name_to_sets_of_related_ops.values():
+            if related_op in set_of_related_ops:
+                set_of_related_ops.add(op)
+                return
+        # if we got here, related_op was not found
+        raise AssertionError(f"{related_op} was not found")
+    else:
+        counter = 0
+        while str(counter) in base_name_to_sets_of_related_ops:
+            counter += 1
+        base_name_to_sets_of_related_ops[str(counter)] = {op}
+
+
+# TODO(future PR): clean this up
+def get_node_type_to_io_type_map() -> Dict[str, Set[NSNodeTargetType]]:
+    FUNS_IO_TYPE_FP32: Set[NSNodeTargetType] = {
+        F.linear,
+        F.conv1d,
+        F.conv2d,
+        F.conv3d,
+        torch.cat,
+        F.elu,
+        F.hardswish,
+        F.instance_norm,
+        F.layer_norm,
+        F.leaky_relu,
+        F.dropout,
+        F.silu,
+        F.mish,
+        operator.add,
+        torch.add,
+        operator.mul,
+        torch.mul,
+        torch.sum,
+        F.prelu,
+    }
+
+    FUNS_IO_TYPE_FP16: Set[NSNodeTargetType] = set()
+
+    FUNS_IO_TYPE_INT8: Set[NSNodeTargetType] = {
+        toq.linear,
+        toq.linear_relu,
+        toq.conv1d,
+        toq.conv1d_relu,
+        toq.conv2d,
+        toq.conv2d_relu,
+        toq.conv3d,
+        toq.conv3d_relu,
+        toq.cat,
+        toq.elu,
+        toq.hardswish,
+        toq.instance_norm,
+        toq.layer_norm,
+        toq.leaky_relu,
+        toq.dropout,
+        toq.prelu,
+        # TODO(future PR): implement shadowing for binary ops and
+        # uncomment below
+        # toq.add,
+        # toq.mul,
+    }
+
+    FUNS_IO_TYPE_FP32_OR_INT8: Set[NSNodeTargetType] = {
+        F.relu,
+        F.tanh,
+        torch.tanh,
+        F.sigmoid,
+        torch.sigmoid,
+        F.hardsigmoid,
+        operator.floordiv,
+        torch.adaptive_avg_pool1d,
+        F.adaptive_avg_pool2d,
+        F.adaptive_avg_pool3d,
+        F.dropout,
+        F.hardtanh,
+        F.hardtanh_,
+        F.interpolate,
+        F.max_pool1d,
+        F.max_pool2d,
+        F.max_pool3d,
+        F.relu6,
+        F.pixel_shuffle,
+        F.pixel_unshuffle,
+        torch.avg_pool1d,
+        torch._C._nn.avg_pool2d,
+        torch._C._nn.avg_pool3d,
+        torch.cat,
+        torch.chunk,
+        torch.clamp,
+        torch.flatten,
+        torch.transpose,
+        torch.max,
+        torch.mean,
+        torch.min,
+        torch.narrow,
+        torch.repeat_interleave,
+        torch.sort,
+        torch.squeeze,
+        torch.stack,
+        torch.unsqueeze,
+        operator.add,
+    }
+
+    MODS_IO_TYPE_FP32: Set[NSNodeTargetType] = {
+        nn.Linear,
+        nnqat.Linear,
+        nnqatd.Linear,
+        nnqd.Linear,
+        torch.nn.modules.linear.NonDynamicallyQuantizableLinear,
+        nn.Conv1d,
+        nn.Conv2d,
+        nn.Conv3d,
+        nnqat.Conv1d,
+        nnqat.Conv2d,
+        nnqat.Conv3d,
+        nnqat.Embedding,
+        nnqat.EmbeddingBag,
+        nn.LSTM,
+        # note: nnqd.Linear is an instance of nnq.Linear, so this
+        # check has to happen before the int8 module check
+        nnqd.LSTM,
+        nn.BatchNorm2d,
+        nn.BatchNorm3d,
+        nn.Dropout,
+        nn.ConvTranspose1d,
+        nn.ConvTranspose2d,
+        nn.ConvTranspose3d,
+        nn.ELU,
+        nn.GroupNorm,
+        nn.InstanceNorm1d,
+        nn.InstanceNorm2d,
+        nn.InstanceNorm3d,
+        nn.LayerNorm,
+        nn.Hardswish,
+        nn.LeakyReLU,
+        nn.ReLU6,
+        nn.SiLU,
+        nn.Mish,
+        nn.Softmax,
+        nn.PReLU,
+        nni.BNReLU2d,
+        nni.BNReLU3d,
+        nni.ConvReLU1d,
+        nni.ConvReLU2d,
+        nni.ConvReLU3d,
+        nni.LinearReLU,
+        nni.LinearBn1d,
+        nni.ConvBn1d,
+        nni.ConvBn2d,
+        nni.ConvBn3d,
+        nniqat.ConvBn1d,
+        nniqat.ConvBn2d,
+        nniqat.ConvBn3d,
+        nniqat.ConvBnReLU1d,
+        nniqat.ConvBnReLU2d,
+        nniqat.ConvBnReLU3d,
+        nniqat.ConvReLU1d,
+        nniqat.ConvReLU2d,
+        nniqat.ConvReLU3d,
+        nniqat.LinearReLU,
+        nniqat.LinearBn1d,
+        nniqd.LinearReLU,
+        nni.LinearLeakyReLU,
+        nni.LinearTanh,
+        nni.ConvAdd2d,
+        nni.ConvAddReLU2d,
+    }
+
+    MODS_IO_TYPE_INT8: Set[NSNodeTargetType] = {
+        nnq.Linear,
+        nnq.Conv1d,
+        nnq.Conv2d,
+        nnq.Conv3d,
+        nnq.BatchNorm2d,
+        nnq.BatchNorm3d,
+        nnq.Dropout,
+        nnq.ConvTranspose1d,
+        nnq.ConvTranspose2d,
+        nnq.ELU,
+        nnq.InstanceNorm1d,
+        nnq.InstanceNorm2d,
+        nnq.InstanceNorm3d,
+        nnq.LayerNorm,
+        nnq.Hardswish,
+        nnq.LeakyReLU,
+        nnq.Embedding,
+        nnq.EmbeddingBag,
+        nnq.Dropout,
+        nnq.Softmax,
+        nnq.PReLU,
+        nniq.BNReLU2d,
+        nniq.BNReLU3d,
+        nniq.ConvReLU1d,
+        nniq.ConvReLU2d,
+        nniq.ConvReLU3d,
+        nniq.LinearReLU,
+        nniq.LinearLeakyReLU,
+        nniq.LinearTanh,
+        nniq.ConvAdd2d,
+        nniq.ConvAddReLU2d,
+    }
+
+    MODS_IO_TYPE_FP32_OR_INT8: Set[NSNodeTargetType] = {
+        nn.ReLU,
+        nn.Tanh,
+        nn.Sigmoid,
+        nn.Hardsigmoid,
+        nn.AdaptiveAvgPool1d,
+        nn.AdaptiveAvgPool2d,
+        nn.AdaptiveAvgPool3d,
+        nn.AvgPool1d,
+        nn.AvgPool2d,
+        nn.AvgPool3d,
+        nn.Dropout,
+        nn.Hardtanh,
+        nn.Identity,
+        nn.MaxPool1d,
+        nn.MaxPool2d,
+        nn.MaxPool3d,
+        nn.PixelShuffle,
+        nn.PixelUnshuffle,
+        nn.ReLU6,
+    }
+
+    METHS_IO_TYPE_FP32_OR_INT8: Set[NSNodeTargetType] = {
+        'sigmoid_',
+        'sigmoid',
+        'tanh_',
+        'tanh',
+        'hardsigmoid_',
+        'hardsigmoid',
+        'relu_',
+        'relu',
+    }
+
+    return {
+        'funs_io_type_fp32': FUNS_IO_TYPE_FP32,
+        'funs_io_type_fp16': FUNS_IO_TYPE_FP16,
+        'funs_io_type_int8': FUNS_IO_TYPE_INT8,
+        'funs_io_type_fp32_or_int8': FUNS_IO_TYPE_FP32_OR_INT8,
+        'mods_io_type_fp32': MODS_IO_TYPE_FP32,
+        'mods_io_type_int8': MODS_IO_TYPE_INT8,
+        'mods_io_type_fp32_or_int8': MODS_IO_TYPE_FP32_OR_INT8,
+        'meths_io_type_fp32_or_int8': METHS_IO_TYPE_FP32_OR_INT8,
+    }
+
+
+def get_unmatchable_types_map() -> Dict[str, Set[NSNodeTargetType]]:
+
+    FUNS_UNMATCHABLE: Set[NSNodeTargetType] = {
+        torch.quantize_per_tensor,
+        operator.getitem,
+    }
+
+    MODS_UNMATCHABLE: Set[NSNodeTargetType] = {
+        nn.Identity,
+    }
+
+    METHS_UNMATCHABLE: Set[NSNodeTargetType] = {
+        'to',
+        'dequantize',
+        'reshape',
+        'view',
+        'unsqueeze_',
+        'unsqueeze',
+        'transpose',
+        'squeeze_',
+        'squeeze',
+        'size',
+        'shape',
+        'resize_',
+        'repeat_interleave',
+        'repeat',
+        'permute',
+        'numel',
+        'mean',
+        'detach_',
+        'detach',
+        'contiguous',
+        'clamp',
+        'chunk',
+    }
+
+    return {
+        'funs_unmatchable': FUNS_UNMATCHABLE,
+        'mods_unmatchable': MODS_UNMATCHABLE,
+        'meths_unmatchable': METHS_UNMATCHABLE,
+    }
diff --git a/shared/package-files/3d/3de15fae5ecc0645811dd73bf7ab030d80ee8467a96addc3424ecf9b24d7567c/test_waves.py b/shared/package-files/3d/3de15fae5ecc0645811dd73bf7ab030d80ee8467a96addc3424ecf9b24d7567c/test_waves.py
new file mode 100644
index 0000000000000000000000000000000000000000..3cb8f804fb5be86d6174cb7c7b15fd8979c85ff8
--- /dev/null
+++ b/shared/package-files/3d/3de15fae5ecc0645811dd73bf7ab030d80ee8467a96addc3424ecf9b24d7567c/test_waves.py
@@ -0,0 +1,82 @@
+from sympy.core.function import (Derivative, Function)
+from sympy.core.numbers import (I, pi)
+from sympy.core.symbol import (Symbol, symbols)
+from sympy.functions.elementary.miscellaneous import sqrt
+from sympy.functions.elementary.trigonometric import (atan2, cos, sin)
+from sympy.simplify.simplify import simplify
+from sympy.abc import epsilon, mu
+from sympy.functions.elementary.exponential import exp
+from sympy.physics.units import speed_of_light, m, s
+from sympy.physics.optics import TWave
+
+from sympy.testing.pytest import raises
+
+c = speed_of_light.convert_to(m/s)
+
+def test_twave():
+    A1, phi1, A2, phi2, f = symbols('A1, phi1, A2, phi2, f')
+    n = Symbol('n')  # Refractive index
+    t = Symbol('t')  # Time
+    x = Symbol('x')  # Spatial variable
+    E = Function('E')
+    w1 = TWave(A1, f, phi1)
+    w2 = TWave(A2, f, phi2)
+    assert w1.amplitude == A1
+    assert w1.frequency == f
+    assert w1.phase == phi1
+    assert w1.wavelength == c/(f*n)
+    assert w1.time_period == 1/f
+    assert w1.angular_velocity == 2*pi*f
+    assert w1.wavenumber == 2*pi*f*n/c
+    assert w1.speed == c/n
+
+    w3 = w1 + w2
+    assert w3.amplitude == sqrt(A1**2 + 2*A1*A2*cos(phi1 - phi2) + A2**2)
+    assert w3.frequency == f
+    assert w3.phase == atan2(A1*sin(phi1) + A2*sin(phi2), A1*cos(phi1) + A2*cos(phi2))
+    assert w3.wavelength == c/(f*n)
+    assert w3.time_period == 1/f
+    assert w3.angular_velocity == 2*pi*f
+    assert w3.wavenumber == 2*pi*f*n/c
+    assert w3.speed == c/n
+    assert simplify(w3.rewrite(sin) - w2.rewrite(sin) - w1.rewrite(sin)) == 0
+    assert w3.rewrite('pde') == epsilon*mu*Derivative(E(x, t), t, t) + Derivative(E(x, t), x, x)
+    assert w3.rewrite(cos) == sqrt(A1**2 + 2*A1*A2*cos(phi1 - phi2)
+        + A2**2)*cos(pi*f*n*x*s/(149896229*m) - 2*pi*f*t + atan2(A1*sin(phi1)
+        + A2*sin(phi2), A1*cos(phi1) + A2*cos(phi2)))
+    assert w3.rewrite(exp) == sqrt(A1**2 + 2*A1*A2*cos(phi1 - phi2)
+        + A2**2)*exp(I*(-2*pi*f*t + atan2(A1*sin(phi1) + A2*sin(phi2), A1*cos(phi1)
+        + A2*cos(phi2)) + pi*s*f*n*x/(149896229*m)))
+
+    w4 = TWave(A1, None, 0, 1/f)
+    assert w4.frequency == f
+
+    w5 = w1 - w2
+    assert w5.amplitude == sqrt(A1**2 - 2*A1*A2*cos(phi1 - phi2) + A2**2)
+    assert w5.frequency == f
+    assert w5.phase == atan2(A1*sin(phi1) - A2*sin(phi2), A1*cos(phi1) - A2*cos(phi2))
+    assert w5.wavelength == c/(f*n)
+    assert w5.time_period == 1/f
+    assert w5.angular_velocity == 2*pi*f
+    assert w5.wavenumber == 2*pi*f*n/c
+    assert w5.speed == c/n
+    assert simplify(w5.rewrite(sin) - w1.rewrite(sin) + w2.rewrite(sin)) == 0
+    assert w5.rewrite('pde') == epsilon*mu*Derivative(E(x, t), t, t) + Derivative(E(x, t), x, x)
+    assert w5.rewrite(cos) == sqrt(A1**2 - 2*A1*A2*cos(phi1 - phi2)
+        + A2**2)*cos(-2*pi*f*t + atan2(A1*sin(phi1) - A2*sin(phi2), A1*cos(phi1)
+        - A2*cos(phi2)) + pi*s*f*n*x/(149896229*m))
+    assert w5.rewrite(exp) == sqrt(A1**2 - 2*A1*A2*cos(phi1 - phi2)
+        + A2**2)*exp(I*(-2*pi*f*t + atan2(A1*sin(phi1) - A2*sin(phi2), A1*cos(phi1)
+        - A2*cos(phi2)) + pi*s*f*n*x/(149896229*m)))
+
+    w6 = 2*w1
+    assert w6.amplitude == 2*A1
+    assert w6.frequency == f
+    assert w6.phase == phi1
+    w7 = -w6
+    assert w7.amplitude == -2*A1
+    assert w7.frequency == f
+    assert w7.phase == phi1
+
+    raises(ValueError, lambda:TWave(A1))
+    raises(ValueError, lambda:TWave(A1, f, phi1, t))
diff --git a/shared/package-files/3d/3de5087831daf6b1a082b6846d5251b4b8b619c9cd71792b5dbd28b65b61382e/test_add_prefix_suffix.py b/shared/package-files/3d/3de5087831daf6b1a082b6846d5251b4b8b619c9cd71792b5dbd28b65b61382e/test_add_prefix_suffix.py
new file mode 100644
index 0000000000000000000000000000000000000000..289a56b98b7e123fb1c1edf5cc9ff41369d51122
--- /dev/null
+++ b/shared/package-files/3d/3de5087831daf6b1a082b6846d5251b4b8b619c9cd71792b5dbd28b65b61382e/test_add_prefix_suffix.py
@@ -0,0 +1,41 @@
+import pytest
+
+from pandas import Index
+import pandas._testing as tm
+
+
+def test_add_prefix_suffix(string_series):
+    with_prefix = string_series.add_prefix("foo#")
+    expected = Index([f"foo#{c}" for c in string_series.index])
+    tm.assert_index_equal(with_prefix.index, expected)
+
+    with_suffix = string_series.add_suffix("#foo")
+    expected = Index([f"{c}#foo" for c in string_series.index])
+    tm.assert_index_equal(with_suffix.index, expected)
+
+    with_pct_prefix = string_series.add_prefix("%")
+    expected = Index([f"%{c}" for c in string_series.index])
+    tm.assert_index_equal(with_pct_prefix.index, expected)
+
+    with_pct_suffix = string_series.add_suffix("%")
+    expected = Index([f"{c}%" for c in string_series.index])
+    tm.assert_index_equal(with_pct_suffix.index, expected)
+
+
+def test_add_prefix_suffix_axis(string_series):
+    # GH 47819
+    with_prefix = string_series.add_prefix("foo#", axis=0)
+    expected = Index([f"foo#{c}" for c in string_series.index])
+    tm.assert_index_equal(with_prefix.index, expected)
+
+    with_pct_suffix = string_series.add_suffix("#foo", axis=0)
+    expected = Index([f"{c}#foo" for c in string_series.index])
+    tm.assert_index_equal(with_pct_suffix.index, expected)
+
+
+def test_add_prefix_suffix_invalid_axis(string_series):
+    with pytest.raises(ValueError, match="No axis named 1 for object type Series"):
+        string_series.add_prefix("foo#", axis=1)
+
+    with pytest.raises(ValueError, match="No axis named 1 for object type Series"):
+        string_series.add_suffix("foo#", axis=1)
diff --git a/shared/package-files/3d/3dea0fb4e872d21b9bdc7ead1f4651acb15151951ea309745024c6c95d396bfc/windows.py b/shared/package-files/3d/3dea0fb4e872d21b9bdc7ead1f4651acb15151951ea309745024c6c95d396bfc/windows.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ba3d276a1abe64fff8fd2c3fe9b59817d77661a
--- /dev/null
+++ b/shared/package-files/3d/3dea0fb4e872d21b9bdc7ead1f4651acb15151951ea309745024c6c95d396bfc/windows.py
@@ -0,0 +1,266 @@
+"""Windows."""
+from __future__ import annotations
+
+import ctypes
+import os
+import sys
+from functools import lru_cache
+from typing import TYPE_CHECKING
+
+from .api import PlatformDirsABC
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+
+class Windows(PlatformDirsABC):
+    """
+    `MSDN on where to store app data files
+    `_.
+    Makes use of the
+    `appname `,
+    `appauthor `,
+    `version `,
+    `roaming `,
+    `opinion `,
+    `ensure_exists `.
+    """
+
+    @property
+    def user_data_dir(self) -> str:
+        """
+        :return: data directory tied to the user, e.g.
+         ``%USERPROFILE%\\AppData\\Local\\$appauthor\\$appname`` (not roaming) or
+         ``%USERPROFILE%\\AppData\\Roaming\\$appauthor\\$appname`` (roaming)
+        """
+        const = "CSIDL_APPDATA" if self.roaming else "CSIDL_LOCAL_APPDATA"
+        path = os.path.normpath(get_win_folder(const))
+        return self._append_parts(path)
+
+    def _append_parts(self, path: str, *, opinion_value: str | None = None) -> str:
+        params = []
+        if self.appname:
+            if self.appauthor is not False:
+                author = self.appauthor or self.appname
+                params.append(author)
+            params.append(self.appname)
+            if opinion_value is not None and self.opinion:
+                params.append(opinion_value)
+            if self.version:
+                params.append(self.version)
+        path = os.path.join(path, *params)  # noqa: PTH118
+        self._optionally_create_directory(path)
+        return path
+
+    @property
+    def site_data_dir(self) -> str:
+        """:return: data directory shared by users, e.g. ``C:\\ProgramData\\$appauthor\\$appname``"""
+        path = os.path.normpath(get_win_folder("CSIDL_COMMON_APPDATA"))
+        return self._append_parts(path)
+
+    @property
+    def user_config_dir(self) -> str:
+        """:return: config directory tied to the user, same as `user_data_dir`"""
+        return self.user_data_dir
+
+    @property
+    def site_config_dir(self) -> str:
+        """:return: config directory shared by the users, same as `site_data_dir`"""
+        return self.site_data_dir
+
+    @property
+    def user_cache_dir(self) -> str:
+        """
+        :return: cache directory tied to the user (if opinionated with ``Cache`` folder within ``$appname``) e.g.
+         ``%USERPROFILE%\\AppData\\Local\\$appauthor\\$appname\\Cache\\$version``
+        """
+        path = os.path.normpath(get_win_folder("CSIDL_LOCAL_APPDATA"))
+        return self._append_parts(path, opinion_value="Cache")
+
+    @property
+    def site_cache_dir(self) -> str:
+        """:return: cache directory shared by users, e.g. ``C:\\ProgramData\\$appauthor\\$appname\\Cache\\$version``"""
+        path = os.path.normpath(get_win_folder("CSIDL_COMMON_APPDATA"))
+        return self._append_parts(path, opinion_value="Cache")
+
+    @property
+    def user_state_dir(self) -> str:
+        """:return: state directory tied to the user, same as `user_data_dir`"""
+        return self.user_data_dir
+
+    @property
+    def user_log_dir(self) -> str:
+        """:return: log directory tied to the user, same as `user_data_dir` if not opinionated else ``Logs`` in it"""
+        path = self.user_data_dir
+        if self.opinion:
+            path = os.path.join(path, "Logs")  # noqa: PTH118
+            self._optionally_create_directory(path)
+        return path
+
+    @property
+    def user_documents_dir(self) -> str:
+        """:return: documents directory tied to the user e.g. ``%USERPROFILE%\\Documents``"""
+        return os.path.normpath(get_win_folder("CSIDL_PERSONAL"))
+
+    @property
+    def user_downloads_dir(self) -> str:
+        """:return: downloads directory tied to the user e.g. ``%USERPROFILE%\\Downloads``"""
+        return os.path.normpath(get_win_folder("CSIDL_DOWNLOADS"))
+
+    @property
+    def user_pictures_dir(self) -> str:
+        """:return: pictures directory tied to the user e.g. ``%USERPROFILE%\\Pictures``"""
+        return os.path.normpath(get_win_folder("CSIDL_MYPICTURES"))
+
+    @property
+    def user_videos_dir(self) -> str:
+        """:return: videos directory tied to the user e.g. ``%USERPROFILE%\\Videos``"""
+        return os.path.normpath(get_win_folder("CSIDL_MYVIDEO"))
+
+    @property
+    def user_music_dir(self) -> str:
+        """:return: music directory tied to the user e.g. ``%USERPROFILE%\\Music``"""
+        return os.path.normpath(get_win_folder("CSIDL_MYMUSIC"))
+
+    @property
+    def user_desktop_dir(self) -> str:
+        """:return: desktop directory tied to the user, e.g. ``%USERPROFILE%\\Desktop``"""
+        return os.path.normpath(get_win_folder("CSIDL_DESKTOPDIRECTORY"))
+
+    @property
+    def user_runtime_dir(self) -> str:
+        """
+        :return: runtime directory tied to the user, e.g.
+         ``%USERPROFILE%\\AppData\\Local\\Temp\\$appauthor\\$appname``
+        """
+        path = os.path.normpath(os.path.join(get_win_folder("CSIDL_LOCAL_APPDATA"), "Temp"))  # noqa: PTH118
+        return self._append_parts(path)
+
+    @property
+    def site_runtime_dir(self) -> str:
+        """:return: runtime directory shared by users, same as `user_runtime_dir`"""
+        return self.user_runtime_dir
+
+
+def get_win_folder_from_env_vars(csidl_name: str) -> str:
+    """Get folder from environment variables."""
+    result = get_win_folder_if_csidl_name_not_env_var(csidl_name)
+    if result is not None:
+        return result
+
+    env_var_name = {
+        "CSIDL_APPDATA": "APPDATA",
+        "CSIDL_COMMON_APPDATA": "ALLUSERSPROFILE",
+        "CSIDL_LOCAL_APPDATA": "LOCALAPPDATA",
+    }.get(csidl_name)
+    if env_var_name is None:
+        msg = f"Unknown CSIDL name: {csidl_name}"
+        raise ValueError(msg)
+    result = os.environ.get(env_var_name)
+    if result is None:
+        msg = f"Unset environment variable: {env_var_name}"
+        raise ValueError(msg)
+    return result
+
+
+def get_win_folder_if_csidl_name_not_env_var(csidl_name: str) -> str | None:
+    """Get folder for a CSIDL name that does not exist as an environment variable."""
+    if csidl_name == "CSIDL_PERSONAL":
+        return os.path.join(os.path.normpath(os.environ["USERPROFILE"]), "Documents")  # noqa: PTH118
+
+    if csidl_name == "CSIDL_DOWNLOADS":
+        return os.path.join(os.path.normpath(os.environ["USERPROFILE"]), "Downloads")  # noqa: PTH118
+
+    if csidl_name == "CSIDL_MYPICTURES":
+        return os.path.join(os.path.normpath(os.environ["USERPROFILE"]), "Pictures")  # noqa: PTH118
+
+    if csidl_name == "CSIDL_MYVIDEO":
+        return os.path.join(os.path.normpath(os.environ["USERPROFILE"]), "Videos")  # noqa: PTH118
+
+    if csidl_name == "CSIDL_MYMUSIC":
+        return os.path.join(os.path.normpath(os.environ["USERPROFILE"]), "Music")  # noqa: PTH118
+    return None
+
+
+def get_win_folder_from_registry(csidl_name: str) -> str:
+    """
+    Get folder from the registry.
+
+    This is a fallback technique at best. I'm not sure if using the registry for these guarantees us the correct answer
+    for all CSIDL_* names.
+    """
+    shell_folder_name = {
+        "CSIDL_APPDATA": "AppData",
+        "CSIDL_COMMON_APPDATA": "Common AppData",
+        "CSIDL_LOCAL_APPDATA": "Local AppData",
+        "CSIDL_PERSONAL": "Personal",
+        "CSIDL_DOWNLOADS": "{374DE290-123F-4565-9164-39C4925E467B}",
+        "CSIDL_MYPICTURES": "My Pictures",
+        "CSIDL_MYVIDEO": "My Video",
+        "CSIDL_MYMUSIC": "My Music",
+    }.get(csidl_name)
+    if shell_folder_name is None:
+        msg = f"Unknown CSIDL name: {csidl_name}"
+        raise ValueError(msg)
+    if sys.platform != "win32":  # only needed for mypy type checker to know that this code runs only on Windows
+        raise NotImplementedError
+    import winreg
+
+    key = winreg.OpenKey(winreg.HKEY_CURRENT_USER, r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders")
+    directory, _ = winreg.QueryValueEx(key, shell_folder_name)
+    return str(directory)
+
+
+def get_win_folder_via_ctypes(csidl_name: str) -> str:
+    """Get folder with ctypes."""
+    # There is no 'CSIDL_DOWNLOADS'.
+    # Use 'CSIDL_PROFILE' (40) and append the default folder 'Downloads' instead.
+    # https://learn.microsoft.com/en-us/windows/win32/shell/knownfolderid
+
+    csidl_const = {
+        "CSIDL_APPDATA": 26,
+        "CSIDL_COMMON_APPDATA": 35,
+        "CSIDL_LOCAL_APPDATA": 28,
+        "CSIDL_PERSONAL": 5,
+        "CSIDL_MYPICTURES": 39,
+        "CSIDL_MYVIDEO": 14,
+        "CSIDL_MYMUSIC": 13,
+        "CSIDL_DOWNLOADS": 40,
+        "CSIDL_DESKTOPDIRECTORY": 16,
+    }.get(csidl_name)
+    if csidl_const is None:
+        msg = f"Unknown CSIDL name: {csidl_name}"
+        raise ValueError(msg)
+
+    buf = ctypes.create_unicode_buffer(1024)
+    windll = getattr(ctypes, "windll")  # noqa: B009 # using getattr to avoid false positive with mypy type checker
+    windll.shell32.SHGetFolderPathW(None, csidl_const, None, 0, buf)
+
+    # Downgrade to short path name if it has highbit chars.
+    if any(ord(c) > 255 for c in buf):  # noqa: PLR2004
+        buf2 = ctypes.create_unicode_buffer(1024)
+        if windll.kernel32.GetShortPathNameW(buf.value, buf2, 1024):
+            buf = buf2
+
+    if csidl_name == "CSIDL_DOWNLOADS":
+        return os.path.join(buf.value, "Downloads")  # noqa: PTH118
+
+    return buf.value
+
+
+def _pick_get_win_folder() -> Callable[[str], str]:
+    if hasattr(ctypes, "windll"):
+        return get_win_folder_via_ctypes
+    try:
+        import winreg  # noqa: F401
+    except ImportError:
+        return get_win_folder_from_env_vars
+    else:
+        return get_win_folder_from_registry
+
+
+get_win_folder = lru_cache(maxsize=None)(_pick_get_win_folder())
+
+__all__ = [
+    "Windows",
+]
diff --git a/shared/package-files/3d/3deacb08b63fbe2e70c9235401f0f73f863a7ac113f9606ac78bd20047a27d89/test_reshape.py b/shared/package-files/3d/3deacb08b63fbe2e70c9235401f0f73f863a7ac113f9606ac78bd20047a27d89/test_reshape.py
new file mode 100644
index 0000000000000000000000000000000000000000..548f32fd533232c8a930f2a0763394e76f196a43
--- /dev/null
+++ b/shared/package-files/3d/3deacb08b63fbe2e70c9235401f0f73f863a7ac113f9606ac78bd20047a27d89/test_reshape.py
@@ -0,0 +1,97 @@
+"""
+Tests for ndarray-like method on the base Index class
+"""
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Index
+import pandas._testing as tm
+
+
+class TestReshape:
+    def test_repeat(self):
+        repeats = 2
+        index = Index([1, 2, 3])
+        expected = Index([1, 1, 2, 2, 3, 3])
+
+        result = index.repeat(repeats)
+        tm.assert_index_equal(result, expected)
+
+    def test_insert(self):
+        # GH 7256
+        # validate neg/pos inserts
+        result = Index(["b", "c", "d"])
+
+        # test 0th element
+        tm.assert_index_equal(Index(["a", "b", "c", "d"]), result.insert(0, "a"))
+
+        # test Nth element that follows Python list behavior
+        tm.assert_index_equal(Index(["b", "c", "e", "d"]), result.insert(-1, "e"))
+
+        # test loc +/- neq (0, -1)
+        tm.assert_index_equal(result.insert(1, "z"), result.insert(-2, "z"))
+
+        # test empty
+        null_index = Index([])
+        tm.assert_index_equal(Index(["a"]), null_index.insert(0, "a"))
+
+    def test_insert_missing(self, request, nulls_fixture, using_infer_string):
+        if using_infer_string and nulls_fixture is pd.NA:
+            request.applymarker(pytest.mark.xfail(reason="TODO(infer_string)"))
+        # GH#22295
+        # test there is no mangling of NA values
+        expected = Index(["a", nulls_fixture, "b", "c"], dtype=object)
+        result = Index(list("abc"), dtype=object).insert(
+            1, Index([nulls_fixture], dtype=object)
+        )
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "val", [(1, 2), np.datetime64("2019-12-31"), np.timedelta64(1, "D")]
+    )
+    @pytest.mark.parametrize("loc", [-1, 2])
+    def test_insert_datetime_into_object(self, loc, val):
+        # GH#44509
+        idx = Index(["1", "2", "3"])
+        result = idx.insert(loc, val)
+        expected = Index(["1", "2", val, "3"])
+        tm.assert_index_equal(result, expected)
+        assert type(expected[2]) is type(val)
+
+    def test_insert_none_into_string_numpy(self, string_dtype_no_object):
+        # GH#55365
+        index = Index(["a", "b", "c"], dtype=string_dtype_no_object)
+        result = index.insert(-1, None)
+        expected = Index(["a", "b", None, "c"], dtype=string_dtype_no_object)
+        tm.assert_index_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "pos,expected",
+        [
+            (0, Index(["b", "c", "d"], name="index")),
+            (-1, Index(["a", "b", "c"], name="index")),
+        ],
+    )
+    def test_delete(self, pos, expected):
+        index = Index(["a", "b", "c", "d"], name="index")
+        result = index.delete(pos)
+        tm.assert_index_equal(result, expected)
+        assert result.name == expected.name
+
+    def test_delete_raises(self):
+        index = Index(["a", "b", "c", "d"], name="index")
+        msg = "index 5 is out of bounds for axis 0 with size 4"
+        with pytest.raises(IndexError, match=msg):
+            index.delete(5)
+
+    def test_append_multiple(self):
+        index = Index(["a", "b", "c", "d", "e", "f"])
+
+        foos = [index[:2], index[2:4], index[4:]]
+        result = foos[0].append(foos[1:])
+        tm.assert_index_equal(result, index)
+
+        # empty
+        result = index.append([])
+        tm.assert_index_equal(result, index)
diff --git a/shared/package-files/3d/3dedcfbed84326b92d8d328ee979f4a5f6d093b21f1f22cfeeab1462026ff246/adaptive_max_pool2d_cuda_dispatch.h b/shared/package-files/3d/3dedcfbed84326b92d8d328ee979f4a5f6d093b21f1f22cfeeab1462026ff246/adaptive_max_pool2d_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..c18a5e143f144dad091e90230e93668e662681c3
--- /dev/null
+++ b/shared/package-files/3d/3dedcfbed84326b92d8d328ee979f4a5f6d093b21f1f22cfeeab1462026ff246/adaptive_max_pool2d_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API ::std::tuple adaptive_max_pool2d(const at::Tensor & self, at::IntArrayRef output_size);
+TORCH_API ::std::tuple adaptive_max_pool2d_out(at::Tensor & out, at::Tensor & indices, const at::Tensor & self, at::IntArrayRef output_size);
+TORCH_API ::std::tuple adaptive_max_pool2d_outf(const at::Tensor & self, at::IntArrayRef output_size, at::Tensor & out, at::Tensor & indices);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/3d/3deff5fb67a0d650d43d0b956cc6ef3e371225bc6e8d5859d328e497548d0e84/items.tcl b/shared/package-files/3d/3deff5fb67a0d650d43d0b956cc6ef3e371225bc6e8d5859d328e497548d0e84/items.tcl
new file mode 100644
index 0000000000000000000000000000000000000000..1297046113f1650e4da7df798745dfc6d62a156c
--- /dev/null
+++ b/shared/package-files/3d/3deff5fb67a0d650d43d0b956cc6ef3e371225bc6e8d5859d328e497548d0e84/items.tcl
@@ -0,0 +1,302 @@
+# items.tcl --
+#
+# This demonstration script creates a canvas that displays the
+# canvas item types.
+
+if {![info exists widgetDemo]} {
+    error "This script should be run from the \"widget\" demo."
+}
+
+package require Tk
+
+set w .items
+catch {destroy $w}
+toplevel $w
+wm title $w "Canvas Item Demonstration"
+wm iconname $w "Items"
+positionWindow $w
+set c $w.frame.c
+
+label $w.msg -font $font -wraplength 5i -justify left -text "This window contains a canvas widget with examples of the various kinds of items supported by canvases.  The following operations are supported:\n  Left-Button drag:\tmoves item under pointer.\n  Middle-Button drag:\trepositions view.\n  Right-Button drag:\tstrokes out area.\n  Ctrl+f:\t\tprints items under area."
+pack $w.msg -side top
+
+## See Code / Dismiss buttons
+set btns [addSeeDismiss $w.buttons $w]
+pack $btns -side bottom -fill x
+
+frame $w.frame
+pack $w.frame -side top -fill both -expand yes
+
+canvas $c -scrollregion {0c 0c 30c 24c} -width 15c -height 10c \
+	-relief sunken -borderwidth 2 \
+	-xscrollcommand "$w.frame.hscroll set" \
+	-yscrollcommand "$w.frame.vscroll set"
+ttk::scrollbar $w.frame.vscroll -command "$c yview"
+ttk::scrollbar $w.frame.hscroll -orient horiz -command "$c xview"
+
+grid $c -in $w.frame \
+    -row 0 -column 0 -rowspan 1 -columnspan 1 -sticky news
+grid $w.frame.vscroll \
+    -row 0 -column 1 -rowspan 1 -columnspan 1 -sticky news
+grid $w.frame.hscroll \
+    -row 1 -column 0 -rowspan 1 -columnspan 1 -sticky news
+grid rowconfig    $w.frame 0 -weight 1 -minsize 0
+grid columnconfig $w.frame 0 -weight 1 -minsize 0
+
+# Display a 3x3 rectangular grid.
+
+$c create rect 0c 0c 30c 24c -width 2
+$c create line 0c 8c 30c 8c -width 2
+$c create line 0c 16c 30c 16c -width 2
+$c create line 10c 0c 10c 24c -width 2
+$c create line 20c 0c 20c 24c -width 2
+
+set font1 {Helvetica 12}
+set font2 {Helvetica 24 bold}
+if {[winfo depth $c] > 1} {
+    set blue DeepSkyBlue3
+    set red red
+    set bisque bisque3
+    set green SeaGreen3
+} else {
+    set blue black
+    set red black
+    set bisque black
+    set green black
+}
+
+# Set up demos within each of the areas of the grid.
+
+$c create text 5c .2c -text Lines -anchor n
+$c create line 1c 1c 3c 1c 1c 4c 3c 4c -width 2m -fill $blue \
+	-cap butt -join miter -tags item
+$c create line 4.67c 1c 4.67c 4c -arrow last -tags item
+$c create line 6.33c 1c 6.33c 4c -arrow both -tags item
+$c create line 5c 6c 9c 6c 9c 1c 8c 1c 8c 4.8c 8.8c 4.8c 8.8c 1.2c \
+	8.2c 1.2c 8.2c 4.6c 8.6c 4.6c 8.6c 1.4c 8.4c 1.4c 8.4c 4.4c \
+	-width 3 -fill $red -tags item
+# Main widget program sets variable tk_demoDirectory
+$c create line 1c 5c 7c 5c 7c 7c 9c 7c -width .5c \
+	-stipple @[file join $tk_demoDirectory images gray25.xbm] \
+	-arrow both -arrowshape {15 15 7} -tags item
+$c create line 1c 7c 1.75c 5.8c 2.5c 7c 3.25c 5.8c 4c 7c -width .5c \
+	-cap round -join round -tags item
+
+$c create text 15c .2c -text "Curves (smoothed lines)" -anchor n
+$c create line 11c 4c 11.5c 1c 13.5c 1c 14c 4c -smooth on \
+	-fill $blue -tags item
+$c create line 15.5c 1c 19.5c 1.5c 15.5c 4.5c 19.5c 4c -smooth on \
+	-arrow both -width 3 -tags item
+$c create line 12c 6c 13.5c 4.5c 16.5c 7.5c 18c 6c \
+	16.5c 4.5c 13.5c 7.5c 12c 6c -smooth on -width 3m -cap round \
+	-stipple @[file join $tk_demoDirectory images gray25.xbm] \
+	-fill $red -tags item
+
+$c create text 25c .2c -text Polygons -anchor n
+$c create polygon 21c 1.0c 22.5c 1.75c 24c 1.0c 23.25c 2.5c \
+	24c 4.0c 22.5c 3.25c 21c 4.0c 21.75c 2.5c -fill $green \
+	-outline {} -width 4 -tags item
+$c create polygon 25c 4c 25c 4c 25c 1c 26c 1c 27c 4c 28c 1c \
+	29c 1c 29c 4c 29c 4c -fill $red -outline {} -smooth on -tags item
+$c create polygon 22c 4.5c 25c 4.5c 25c 6.75c 28c 6.75c \
+	28c 5.25c 24c 5.25c 24c 6.0c 26c 6c 26c 7.5c 22c 7.5c \
+	-stipple @[file join $tk_demoDirectory images gray25.xbm] \
+	-fill $blue -outline {} -tags item
+
+$c create text 5c 8.2c -text Rectangles -anchor n
+$c create rectangle 1c 9.5c 4c 12.5c -outline $red -width 3m -tags item
+$c create rectangle 0.5c 13.5c 4.5c 15.5c -fill $green -tags item
+$c create rectangle 6c 10c 9c 15c -outline {} \
+	-stipple @[file join $tk_demoDirectory images gray25.xbm] \
+	-fill $blue -tags item
+
+$c create text 15c 8.2c -text Ovals -anchor n
+$c create oval 11c 9.5c 14c 12.5c -outline $red -width 3m -tags item
+$c create oval 10.5c 13.5c 14.5c 15.5c -fill $green -tags item
+$c create oval 16c 10c 19c 15c -outline {} \
+	-stipple @[file join $tk_demoDirectory images gray25.xbm] \
+	-fill $blue -tags item
+
+$c create text 25c 8.2c -text Text -anchor n
+$c create rectangle 22.4c 8.9c 22.6c 9.1c
+$c create text 22.5c 9c -anchor n -font $font1 -width 4c \
+	-text "A short string of text, word-wrapped, justified left, and anchored north (at the top).  The rectangles show the anchor points for each piece of text." -tags item
+$c create rectangle 25.4c 10.9c 25.6c 11.1c
+$c create text 25.5c 11c -anchor w -font $font1 -fill $blue \
+	-text "Several lines,\n each centered\nindividually,\nand all anchored\nat the left edge." \
+	-justify center -tags item
+$c create rectangle 24.9c 13.9c 25.1c 14.1c
+catch {
+$c create text 25c 14c -font $font2 -anchor c -fill $red -angle 15 \
+	-text "Angled characters" -tags item
+}
+
+$c create text 5c 16.2c -text Arcs -anchor n
+$c create arc 0.5c 17c 7c 20c -fill $green -outline black \
+	-start 45 -extent 270 -style pieslice -tags item
+$c create arc 6.5c 17c 9.5c 20c -width 4m -style arc \
+	-outline $blue -start -135 -extent 270 -tags item \
+	-outlinestipple @[file join $tk_demoDirectory images gray25.xbm]
+$c create arc 0.5c 20c 9.5c 24c -width 4m -style pieslice \
+	-fill {} -outline $red -start 225 -extent -90 -tags item
+$c create arc 5.5c 20.5c 9.5c 23.5c -width 4m -style chord \
+	-fill $blue -outline {} -start 45 -extent 270  -tags item
+
+$c create text 15c 16.2c -text "Bitmaps and Images" -anchor n
+catch {
+image create photo items.ousterhout \
+    -file [file join $tk_demoDirectory images ouster.png]
+image create photo items.ousterhout.active -format "png -alpha 0.5" \
+    -file [file join $tk_demoDirectory images ouster.png]
+$c create image 13c 20c -tags item -image items.ousterhout \
+    -activeimage items.ousterhout.active
+}
+$c create bitmap 17c 18.5c -tags item \
+	-bitmap @[file join $tk_demoDirectory images noletter.xbm]
+$c create bitmap 17c 21.5c -tags item \
+	-bitmap @[file join $tk_demoDirectory images letters.xbm]
+
+$c create text 25c 16.2c -text Windows -anchor n
+button $c.button -text "Press Me" -command "butPress $c $red"
+$c create window 21c 18c -window $c.button -anchor nw -tags item
+entry $c.entry -width 20 -relief sunken
+$c.entry insert end "Edit this text"
+$c create window 21c 21c -window $c.entry -anchor nw -tags item
+scale $c.scale -from 0 -to 100 -length 6c -sliderlength .4c \
+	-width .5c -tickinterval 0
+$c create window 28.5c 17.5c -window $c.scale -anchor n -tags item
+$c create text 21c 17.9c -text Button: -anchor sw
+$c create text 21c 20.9c -text Entry: -anchor sw
+$c create text 28.5c 17.4c -text Scale: -anchor s
+
+# Set up event bindings for canvas:
+
+$c bind item  "itemEnter $c"
+$c bind item  "itemLeave $c"
+if {[tk windowingsystem] eq "aqua" && ![package vsatisfies [package provide Tk] 8.7-]} {
+    bind $c  "itemMark $c %x %y"
+    bind $c  "itemStroke $c %x %y"
+    bind $c  "$c scan mark %x %y"
+    bind $c  "$c scan dragto %x %y"
+} else {
+    bind $c  "$c scan mark %x %y"
+    bind $c  "$c scan dragto %x %y"
+    bind $c  "itemMark $c %x %y"
+    bind $c  "itemStroke $c %x %y"
+}
+bind $c <> "itemsUnderArea $c"
+bind $c  "itemStartDrag $c %x %y"
+bind $c  "itemDrag $c %x %y"
+
+# Utility procedures for highlighting the item under the pointer:
+
+proc itemEnter {c} {
+    global restoreCmd
+
+    if {[winfo depth $c] == 1} {
+	set restoreCmd {}
+	return
+    }
+    set type [$c type current]
+    if {$type == "window" || $type == "image"} {
+	set restoreCmd {}
+	return
+    } elseif {$type == "bitmap"} {
+	set bg [lindex [$c itemconf current -background] 4]
+	set restoreCmd [list $c itemconfig current -background $bg]
+	$c itemconfig current -background SteelBlue2
+	return
+    } elseif {$type == "image"} {
+	set restoreCmd [list $c itemconfig current -state normal]
+	$c itemconfig current -state active
+	return
+    }
+    set fill [lindex [$c itemconfig current -fill] 4]
+    if {(($type == "rectangle") || ($type == "oval") || ($type == "arc"))
+	    && ($fill == "")} {
+	set outline [lindex [$c itemconfig current -outline] 4]
+	set restoreCmd "$c itemconfig current -outline $outline"
+	$c itemconfig current -outline SteelBlue2
+    } else {
+	set restoreCmd "$c itemconfig current -fill $fill"
+	$c itemconfig current -fill SteelBlue2
+    }
+}
+
+proc itemLeave {c} {
+    global restoreCmd
+
+    eval $restoreCmd
+}
+
+# Utility procedures for stroking out a rectangle and printing what's
+# underneath the rectangle's area.
+
+proc itemMark {c x y} {
+    global areaX1 areaY1
+    set areaX1 [$c canvasx $x]
+    set areaY1 [$c canvasy $y]
+    $c delete area
+}
+
+proc itemStroke {c x y} {
+    global areaX1 areaY1 areaX2 areaY2
+    set x [$c canvasx $x]
+    set y [$c canvasy $y]
+    if {($areaX1 != $x) && ($areaY1 != $y)} {
+	$c delete area
+	$c addtag area withtag [$c create rect $areaX1 $areaY1 $x $y \
+		-outline black]
+	set areaX2 $x
+	set areaY2 $y
+    }
+}
+
+proc itemsUnderArea {c} {
+    global areaX1 areaY1 areaX2 areaY2
+    set area [$c find withtag area]
+    set items ""
+    foreach i [$c find enclosed $areaX1 $areaY1 $areaX2 $areaY2] {
+	if {[lsearch [$c gettags $i] item] >= 0} {
+	    lappend items $i
+	}
+    }
+    puts stdout "Items enclosed by area: $items"
+    set items ""
+    foreach i [$c find overlapping $areaX1 $areaY1 $areaX2 $areaY2] {
+	if {[lsearch [$c gettags $i] item] >= 0} {
+	    lappend items $i
+	}
+    }
+    puts stdout "Items overlapping area: $items"
+}
+
+set areaX1 0
+set areaY1 0
+set areaX2 0
+set areaY2 0
+
+# Utility procedures to support dragging of items.
+
+proc itemStartDrag {c x y} {
+    global lastX lastY
+    set lastX [$c canvasx $x]
+    set lastY [$c canvasy $y]
+}
+
+proc itemDrag {c x y} {
+    global lastX lastY
+    set x [$c canvasx $x]
+    set y [$c canvasy $y]
+    $c move current [expr {$x-$lastX}] [expr {$y-$lastY}]
+    set lastX $x
+    set lastY $y
+}
+
+# Procedure that's invoked when the button embedded in the canvas
+# is invoked.
+
+proc butPress {w color} {
+    set i [$w create text 25c 18.1c -text "Oooohhh!!" -fill $color -anchor n]
+    after 500 "$w delete $i"
+}
diff --git a/shared/package-files/3d/3df07eb603ce7a5c95ff479bfe2a492b82f33c7c01e59fb07c95de13f3b700ae/__init__.py b/shared/package-files/3d/3df07eb603ce7a5c95ff479bfe2a492b82f33c7c01e59fb07c95de13f3b700ae/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0fda5a58f2984ee05b0d167297b458f62c37fc59
--- /dev/null
+++ b/shared/package-files/3d/3df07eb603ce7a5c95ff479bfe2a492b82f33c7c01e59fb07c95de13f3b700ae/__init__.py
@@ -0,0 +1 @@
+from . import quantized
diff --git a/shared/package-files/3d/3df8aeb5a930e41e71af5392835b85bd3d06c02ea354eaaac67c7af46109bb9d/Sao_Tome b/shared/package-files/3d/3df8aeb5a930e41e71af5392835b85bd3d06c02ea354eaaac67c7af46109bb9d/Sao_Tome
new file mode 100644
index 0000000000000000000000000000000000000000..425ad3fda7c517742fe01db74e38c0130be4a7ab
Binary files /dev/null and b/shared/package-files/3d/3df8aeb5a930e41e71af5392835b85bd3d06c02ea354eaaac67c7af46109bb9d/Sao_Tome differ
diff --git a/shared/package-files/3d/3df96547801b9b3f4d1083ea82e9b98fcc191166c9850836f24976c950b78dc0/base.py b/shared/package-files/3d/3df96547801b9b3f4d1083ea82e9b98fcc191166c9850836f24976c950b78dc0/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ce3c684c3e09f7a4c9cc4d97ffc2f35558760e8
--- /dev/null
+++ b/shared/package-files/3d/3df96547801b9b3f4d1083ea82e9b98fcc191166c9850836f24976c950b78dc0/base.py
@@ -0,0 +1,959 @@
+from __future__ import annotations
+
+import ast
+import functools
+import inspect
+import itertools
+import operator
+from abc import abstractmethod
+from collections import defaultdict, namedtuple
+from collections.abc import Generator, Hashable, Iterable, Mapping, Sequence
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Optional,
+    Union,
+    cast,
+)
+
+from interface_meta import InterfaceMeta, inherit_docs
+
+from formulaic.errors import (
+    FactorEncodingError,
+    FactorEvaluationError,
+    FormulaMaterializationError,
+    FormulaMaterializerInvalidError,
+    FormulaMaterializerNotFoundError,
+)
+from formulaic.materializers.types.enums import ClusterBy, NAAction
+from formulaic.materializers.types.factor_values import FactorValuesMetadata
+from formulaic.model_matrix import ModelMatrices, ModelMatrix
+from formulaic.parser.types import Factor, Term
+from formulaic.parser.types.ordered_set import OrderedSet
+from formulaic.transforms import TRANSFORMS
+from formulaic.utils.cast import as_columns
+from formulaic.utils.layered_mapping import LayeredMapping
+from formulaic.utils.null_handling import find_nulls
+from formulaic.utils.stateful_transforms import stateful_eval
+from formulaic.utils.variables import Variable
+
+from .types import EvaluatedFactor, FactorValues, ScopedFactor, ScopedTerm
+
+if TYPE_CHECKING:  # pragma: no cover
+    from formulaic import FormulaSpec, ModelSpec, ModelSpecs
+
+EncodedTermStructure = namedtuple(
+    "EncodedTermStructure", ("term", "scoped_terms", "columns")
+)
+
+
+class FormulaMaterializerMeta(InterfaceMeta):
+    INTERFACE_RAISE_ON_VIOLATION = True
+
+    REGISTERED_NAMES: dict[str, type[FormulaMaterializer]] = {}
+    REGISTERED_INPUTS: dict[str, list[type[FormulaMaterializer]]] = defaultdict(list)
+
+    def __register_implementation__(cls: type[FormulaMaterializer]) -> None:  # type: ignore[misc]
+        if "REGISTER_NAME" in cls.__dict__ and cls.REGISTER_NAME:
+            cls.REGISTERED_NAMES[cls.REGISTER_NAME] = cls
+
+            if "REGISTER_INPUTS" in cls.__dict__:
+                for input_type in cls.REGISTER_INPUTS:
+                    cls.REGISTERED_INPUTS[input_type] = sorted(
+                        cls.REGISTERED_INPUTS[input_type] + [cls],
+                        key=lambda x: x.REGISTER_PRECEDENCE,
+                        reverse=True,
+                    )
+
+    def for_materializer(
+        cls, materializer: Union[str, FormulaMaterializer, type[FormulaMaterializer]]
+    ) -> type[FormulaMaterializer]:
+        if isinstance(materializer, str):
+            if materializer not in cls.REGISTERED_NAMES:
+                raise FormulaMaterializerNotFoundError(materializer)
+            return cls.REGISTERED_NAMES[materializer]
+        if isinstance(materializer, FormulaMaterializer):
+            return type(materializer)
+        if not inspect.isclass(materializer) or not issubclass(
+            materializer, FormulaMaterializer
+        ):
+            raise FormulaMaterializerInvalidError(
+                "Materializers must be subclasses of `formulaic.materializers.FormulaMaterializer`."
+            )
+        return materializer
+
+    def for_data(cls, data: Any, output: Hashable = None) -> type[FormulaMaterializer]:
+        datacls = data.__class__
+        input_type = f"{datacls.__module__}.{datacls.__qualname__}"
+
+        materializers_supporting_input = []
+
+        if input_type in cls.REGISTERED_INPUTS:
+            materializers_supporting_input.extend(cls.REGISTERED_INPUTS[input_type])
+
+        if output is None and materializers_supporting_input:
+            return materializers_supporting_input[0]
+
+        for materializer in sorted(
+            set(cls.REGISTERED_NAMES.values()),
+            key=lambda x: x.REGISTER_PRECEDENCE,
+            reverse=True,
+        ):
+            if materializer.SUPPORTS_INPUT(data):
+                materializers_supporting_input.append(materializer)
+
+        if not materializers_supporting_input:
+            raise FormulaMaterializerNotFoundError(
+                f"No materializer is available for input type {repr(input_type)}. Explicitly registered input types are: {tuple(sorted(cls.REGISTERED_INPUTS))}."
+            )
+
+        if output is None:
+            return materializers_supporting_input[0]
+
+        for materializer in materializers_supporting_input:
+            if output in materializer.REGISTER_OUTPUTS:
+                return materializer
+
+        output_types: set[Hashable] = set(
+            itertools.chain(
+                *[
+                    materializer.REGISTER_OUTPUTS
+                    for materializer in materializers_supporting_input
+                ]
+            )
+        )
+        raise FormulaMaterializerNotFoundError(
+            f"No materializer is available for input type {repr(input_type)} that also supports output type {repr(output)}. Available output types for {repr(input_type)} are: {tuple(sorted(output_types, key=lambda x: str(x)))}."
+        )
+
+
+class FormulaMaterializer(metaclass=FormulaMaterializerMeta):
+    REGISTER_NAME: Optional[str] = None
+    REGISTER_INPUTS: Sequence[str] = ()
+    REGISTER_OUTPUTS: Sequence[Hashable] = ()
+    REGISTER_PRECEDENCE: float = 100
+
+    @classmethod
+    def SUPPORTS_INPUT(cls, data: Any) -> bool:
+        """
+        Check whether this materializer materializer supports the given data.
+        This allows for non-explicit input registration where additional
+        dynamism is required, or where this materializer should act as a
+        fallback.
+
+        Note: meterializers with explicitly registered inputs will always take
+        priority.
+        """
+        input_type = f"{data.__class__.__module__}.{data.__class__.__qualname__}"
+        if input_type in cls.REGISTER_OUTPUTS:
+            return True
+        return False
+
+    # Public API
+
+    @inherit_docs(method="_init")
+    def __init__(
+        self, data: Any, context: Optional[Mapping[str, Any]] = None, **params: Any
+    ):
+        self.data = data
+        self.context = context or {}
+        self.params = params
+        self._init()
+
+        self.layered_context = LayeredMapping(
+            LayeredMapping(self.data_context, name="data"),
+            LayeredMapping(self.context, name="context"),
+            LayeredMapping(TRANSFORMS, name="transforms"),
+        )
+
+        self.factor_cache: dict[str, EvaluatedFactor] = {}
+        self.encoded_cache: dict[Union[str, tuple[str, bool]], Any] = {}
+
+    def _init(self) -> None:
+        pass  # pragma: no cover
+
+    @property
+    def data_context(self) -> Mapping[str, Any]:
+        return self.data
+
+    @property
+    def nrows(self) -> int:
+        return len(self.data)
+
+    def get_model_matrix(
+        self,
+        spec: Union[FormulaSpec, ModelMatrix, ModelMatrices, ModelSpec, ModelSpecs],
+        drop_rows: Optional[set[int]] = None,
+        **spec_overrides: Any,
+    ) -> Union[ModelMatrix, ModelMatrices]:
+        from formulaic import ModelSpec
+
+        # Prepare ModelSpec(s)
+        prepared_spec: Union[ModelSpec, ModelSpecs] = ModelSpec.from_spec(
+            spec, context=self.layered_context, **spec_overrides
+        )
+        should_simplify = isinstance(prepared_spec, ModelSpec)
+        model_specs: ModelSpecs = self._prepare_model_specs(prepared_spec)
+
+        # Step 0: Pool all factors and transform state, ensuring consistency
+        # during factor evaluation (esp. which rows get dropped).
+        (
+            factors,
+            factor_evaluation_model_spec,
+        ) = self._prepare_factor_evaluation_model_spec(model_specs)
+
+        # Step 1: Evaluate all factors and cache the results, keeping track of
+        # which rows need dropping (if `self.config.na_action == 'drop'`).
+        drop_rows_set: set[int] = drop_rows if drop_rows is not None else set()
+        for factor in factors:
+            self._evaluate_factor(factor, factor_evaluation_model_spec, drop_rows_set)
+        drop_rows_sorted: Sequence[int] = sorted(drop_rows_set)
+
+        # Step 2: Update the structured model specs with the information from
+        # the shared transform state pool.
+        model_specs._map(
+            lambda ms: ms.transform_state.update(
+                factor_evaluation_model_spec.transform_state
+            )
+        )
+
+        # Step 3: Build the model matrices using the shared factor cache, and
+        # by recursing over the structured model matrices.
+        model_matrices = cast(
+            ModelMatrices,
+            model_specs._map(
+                lambda model_spec: self._build_model_matrix(
+                    model_spec, drop_rows=drop_rows_sorted
+                ),
+                as_type=ModelMatrices,
+            ),
+        )
+
+        if should_simplify:
+            return cast(Union[ModelMatrix, ModelMatrices], model_matrices._simplify())
+        return model_matrices
+
+    def _build_model_matrix(
+        self, spec: ModelSpec, drop_rows: Sequence[int]
+    ) -> ModelMatrix:
+        # Step 0: Apply any requested column/term clustering
+        # This must happen before Step 1 otherwise the greedy rank reduction
+        # below would result in a different outcome than if the columns had
+        # always been in the generated order.
+        terms = self._cluster_terms(spec.formula, cluster_by=spec.cluster_by)
+
+        # Step 1: Determine strategy to maintain structural full-rankness of output matrix
+        # (reusing pre-generated structure if it is available)
+        if spec.structure:
+            scoped_terms_for_terms: Generator[
+                tuple[Term, Iterable[ScopedTerm]], None, None
+            ] = (
+                (s.term, [st.rehydrate(self.factor_cache) for st in s.scoped_terms])
+                for s in spec.structure
+            )
+        else:
+            scoped_terms_for_terms = self._get_scoped_terms(
+                terms,
+                ensure_full_rank=spec.ensure_full_rank,
+            )
+
+        # Step 2: Generate the columns which will be collated into the full matrix
+        cols = []
+        for term, scoped_terms in scoped_terms_for_terms:
+            scoped_cols = {}
+            for scoped_term in scoped_terms:
+                if not scoped_term.factors:
+                    scoped_cols["Intercept"] = (
+                        scoped_term.scale
+                        * self._encode_constant(1, None, {}, spec, drop_rows)
+                    )
+                else:
+                    scoped_cols.update(
+                        self._get_columns_for_term(
+                            [
+                                self._encode_evaled_factor(
+                                    scoped_factor.factor,
+                                    spec,
+                                    drop_rows,
+                                    reduced_rank=scoped_factor.reduced,
+                                )
+                                for scoped_factor in scoped_term.factors
+                            ],
+                            spec=spec,
+                            scale=scoped_term.scale,
+                        )
+                    )
+            cols.append((term, scoped_terms, scoped_cols))
+
+        # Step 3: Populate remaining model spec fields
+        if spec.structure:
+            cols = list(self._enforce_structure(cols, spec, drop_rows))
+        else:
+            spec = spec.update(
+                structure=[
+                    EncodedTermStructure(
+                        term,
+                        list(st.copy(without_values=True) for st in scoped_terms),
+                        list(scoped_cols),
+                    )
+                    for term, scoped_terms, scoped_cols in cols
+                ],
+            )
+
+        # Step 4: Collate factors into one ModelMatrix
+        return ModelMatrix(
+            self._combine_columns(
+                [
+                    (name, values)
+                    for term, scoped_terms, scoped_cols in cols
+                    for name, values in scoped_cols.items()
+                ],
+                spec=spec,
+                drop_rows=drop_rows,
+            ),
+            spec=spec,
+        )
+
+    # Methods related to input preparation
+
+    def _prepare_model_specs(self, spec: Union[ModelSpec, ModelSpecs]) -> ModelSpecs:
+        from formulaic.model_spec import ModelSpecs
+
+        if not isinstance(spec, ModelSpecs):
+            spec = ModelSpecs(spec)
+
+        def prepare_model_spec(model_spec: ModelSpec) -> ModelSpec:
+            overrides: dict[str, Any] = {
+                "materializer": self.REGISTER_NAME,
+                "materializer_params": self.params,
+            }
+
+            if model_spec.output is None:
+                overrides["output"] = self.REGISTER_OUTPUTS[0]
+            elif model_spec.output not in self.REGISTER_OUTPUTS:
+                raise FormulaMaterializationError(
+                    f"Nominated output {repr(model_spec.output)} is invalid. Available output types are: {set(self.REGISTER_OUTPUTS)}."
+                )
+
+            return model_spec.update(**overrides)
+
+        return cast(ModelSpecs, spec._map(prepare_model_spec, as_type=ModelSpecs))
+
+    def _prepare_factor_evaluation_model_spec(
+        self, model_specs: ModelSpecs
+    ) -> tuple[set[Factor], ModelSpec]:
+        from formulaic.model_spec import ModelSpec
+
+        output = set()
+        na_action = set()
+        ensure_full_rank = set()
+        factors: set[Factor] = set()
+        transform_state = {}
+
+        def update_pooled_spec(model_spec: ModelSpec) -> None:
+            output.add(model_spec.output)
+            na_action.add(model_spec.na_action)
+            ensure_full_rank.add(model_spec.ensure_full_rank)
+            factors.update(
+                itertools.chain(*(term.factors for term in model_spec.formula))
+            )
+            transform_state.update(
+                model_spec.transform_state
+            )  # TODO: Check for consistency?
+
+        model_specs._map(update_pooled_spec)
+
+        if len(output) != 1 or len(na_action) != 1 or len(ensure_full_rank) != 1:
+            raise RuntimeError(
+                "Provided `ModelSpec` instances are not consistent."
+            )  # pragma: no cover; will only occur if users manually construct a structured model spec.
+
+        return factors, cast(
+            ModelSpec,
+            ModelSpec.from_spec(
+                [],
+                ensure_full_rank=next(iter(ensure_full_rank)),
+                na_action=next(iter(na_action)),
+                output=next(iter(output)),
+                transform_state=transform_state,
+            ),
+        )
+
+    def _cluster_terms(
+        self, terms: Sequence[Term], cluster_by: ClusterBy = ClusterBy.NONE
+    ) -> Sequence[Term]:
+        if cluster_by is not ClusterBy.NUMERICAL_FACTORS:
+            return terms
+
+        term_clusters = defaultdict(list)
+        for term in terms:
+            numerical_factors = tuple(
+                factor
+                for factor in term.factors
+                if self.factor_cache[factor.expr].metadata.kind is Factor.Kind.NUMERICAL
+            )
+            term_clusters[numerical_factors].append(term)
+
+        return [
+            term for term_cluster in term_clusters.values() for term in term_cluster
+        ]
+
+    # Methods related to ensuring out matrices are structurally full-rank
+
+    def _get_scoped_terms(
+        self, terms: Iterable[Term], ensure_full_rank: bool = True
+    ) -> Generator[tuple[Term, Iterable[ScopedTerm]], None, None]:
+        """
+        Generate the terms to be used in the model matrix.
+
+        This method first evaluates each factor in the context of the data
+        (and environment), and then determines the correct "scope" (full vs.
+        reduced rank) for each term. If `ensure_full_rank` is `True`, then the
+        resulting terms when combined is guaranteed to be structurally full-rank.
+
+        Args:
+            terms (list): A list of term arguments (usually from a formula)
+                object.
+            ensure_full_rank (bool): Whether evaluated terms should be scoped
+                to ensure that their combination will result in a full-rank
+                matrix.
+
+        Returns:
+            list: A list of appropriately scoped terms.
+        """
+        spanned: set[ScopedTerm] = set()
+
+        for term in terms:
+            evaled_factors = [
+                self.factor_cache[factor.expr]
+                for factor in term.factors
+                if self.factor_cache[factor.expr].values.__wrapped__ is not None
+            ]
+            if not evaled_factors:
+                yield term, []
+                continue
+
+            if ensure_full_rank:
+                term_span = (
+                    self._get_scoped_terms_spanned_by_evaled_factors(evaled_factors)
+                    - spanned
+                )
+                scoped_terms: Iterable[ScopedTerm] = self._simplify_scoped_terms(
+                    term_span
+                )
+                spanned.update(term_span)
+            else:
+                scoped_terms = [
+                    ScopedTerm(
+                        factors=(
+                            ScopedFactor(evaled_factor, reduced=False)
+                            for evaled_factor in evaled_factors
+                            if evaled_factor.metadata.kind is not Factor.Kind.CONSTANT
+                        ),
+                        scale=functools.reduce(
+                            operator.mul,
+                            [
+                                evaled_factor.values
+                                for evaled_factor in evaled_factors
+                                if evaled_factor.metadata.kind.value
+                                is Factor.Kind.CONSTANT
+                            ],
+                            1,
+                        ),
+                    )
+                ]
+            yield term, scoped_terms
+
+    @classmethod
+    def _get_scoped_terms_spanned_by_evaled_factors(
+        cls, evaled_factors: Iterable[EvaluatedFactor]
+    ) -> OrderedSet[ScopedTerm]:
+        """
+        Return the set of ScopedTerm instances which span the set of
+        evaluated factors.
+
+        Args:
+            evaled_factors: The evaluated factors for which to generated scoped
+                terms.
+
+        Returns:
+            The scoped terms for the nominated `evaled_factors`.
+        """
+        scale = 1
+        factors: list[tuple[Union[ScopedFactor, int], ...]] = []
+        for factor in evaled_factors:
+            if factor.metadata.kind is Factor.Kind.CONSTANT:
+                scale *= factor.values  # type: ignore[operator]
+            elif factor.metadata.spans_intercept:
+                factors.append((ScopedFactor(factor, reduced=True), 1))
+            else:
+                factors.append((ScopedFactor(factor),))
+        return OrderedSet(
+            ScopedTerm(
+                factors=(cast(ScopedFactor, p) for p in prod if p != 1), scale=scale
+            )
+            for prod in itertools.product(*factors)
+        )
+
+    @classmethod
+    def _simplify_scoped_terms(
+        cls, scoped_terms: Iterable[ScopedTerm]
+    ) -> OrderedSet[ScopedTerm]:
+        """
+        Return the minimal set of ScopedTerm instances that spans the same
+        vectorspace, matching as closely as possible the intended order of the
+        terms.
+
+        This is an iterative algorithm that applies the rule:
+            (anything):(reduced rank) + (anything) |-> (anything):(full rank)
+        To be safe, we recurse whenever we apply the rule to make sure that
+        we have fully simplified the set of terms before adding new ones.
+
+        This is guaranteed to minimially span the vector space, keeping
+        everything full-rank by avoiding overlaps.
+        """
+        terms: OrderedSet[ScopedTerm] = OrderedSet()
+        for scoped_term in sorted(scoped_terms, key=lambda x: len(x.factors)):
+            factors = set(scoped_term.factors)
+            combined = False
+            for existing_term in terms:
+                # Check whether existing term only differs by one factor
+                cofactors = set(existing_term.factors)
+                factors_diff = factors - cofactors
+                if len(factors) - 1 != len(cofactors) or len(factors_diff) != 1:
+                    continue
+                # If the different factor is a reduced factor, we can apply the
+                # rule and recurse to see if there is anything else to pick up.
+                factor_new = next(iter(factors_diff))
+                if factor_new.reduced:
+                    terms = cls._simplify_scoped_terms(
+                        terms - (existing_term,)  # type: ignore
+                        | (  # type: ignore
+                            ScopedTerm(
+                                (
+                                    (
+                                        ScopedFactor(factor_new.factor, reduced=False)
+                                        if factor == factor_new
+                                        else factor
+                                    )
+                                    for factor in scoped_term.factors
+                                ),
+                                scale=existing_term.scale * scoped_term.scale,
+                            ),
+                        )
+                    )
+                    combined = True
+                    break
+            if not combined:
+                terms = terms | (scoped_term,)  # type: ignore
+        return terms
+
+    # Methods related to looking-up, evaluating and encoding terms and factors
+
+    def _evaluate_factor(
+        self, factor: Factor, spec: ModelSpec, drop_rows: set[int]
+    ) -> EvaluatedFactor:
+        if factor.expr not in self.factor_cache:
+            try:
+                if factor.eval_method.value == "lookup":
+                    value, variables = self._lookup(factor.expr)
+                elif factor.eval_method.value == "python":
+                    value, variables = self._evaluate(
+                        factor.expr, factor.metadata, spec
+                    )
+                elif factor.eval_method.value == "literal":
+                    value = FactorValues(
+                        ast.literal_eval(factor.expr),
+                        # self._evaluate(factor.expr, factor.metadata, spec),
+                        kind=Factor.Kind.CONSTANT,
+                    )
+                    variables = None
+                else:  # pragma: no cover; future proofing against new eval methods
+                    raise FactorEvaluationError(
+                        f"The evaluation method `{factor.eval_method.value}` for factor `{factor}` is not understood."
+                    )
+            except (
+                FactorEvaluationError
+            ):  # pragma: no cover; future proofing against new eval methods
+                raise
+            except Exception as e:
+                raise FactorEvaluationError(
+                    f"Unable to evaluate factor `{factor}`. [{type(e).__name__}: {e}]"
+                ) from e
+
+            if not isinstance(value, FactorValues):
+                value = FactorValues(value)
+
+            if value.__formulaic_metadata__.kind is Factor.Kind.UNKNOWN:
+                if self._is_categorical(value):
+                    kind = Factor.Kind.CATEGORICAL
+                    spans_intercept = True
+                else:
+                    kind = Factor.Kind.NUMERICAL
+                    spans_intercept = False
+
+                value = FactorValues(value, kind=kind, spans_intercept=spans_intercept)
+
+            if (
+                factor.kind is not Factor.Kind.UNKNOWN
+                and factor.kind is not value.__formulaic_metadata__.kind
+            ):
+                if factor.kind is Factor.Kind.CATEGORICAL:
+                    value.__formulaic_metadata__.kind = factor.kind
+                else:
+                    raise FactorEncodingError(
+                        f"Factor `{factor}` is expecting values of kind '{factor.kind.value}', "
+                        f"but they are actually of kind '{value.__formulaic_metadata__.kind.value}'."
+                    )
+            if (
+                factor.expr in spec.encoder_state
+                and value.__formulaic_metadata__.kind
+                is not spec.encoder_state[factor.expr][0]
+            ):
+                raise FactorEncodingError(
+                    f"The model specification expects factor `{factor}` to have values of kind "
+                    f"`{spec.encoder_state[factor.expr][0]}`, but they are actually of kind "
+                    f"`{value.__formulaic_metadata__.kind.value}`."
+                )
+            self._check_for_nulls(factor.expr, value, spec.na_action, drop_rows)
+            self.factor_cache[factor.expr] = EvaluatedFactor(
+                factor=factor, values=value, variables=variables
+            )
+        return self.factor_cache[factor.expr]
+
+    def _lookup(self, name: str) -> tuple[Any, set[Variable]]:
+        sentinel = object()
+        values, layer = self.layered_context.get_with_layer_name(name, default=sentinel)
+        if values is sentinel:
+            raise NameError(
+                f"`{name}` is not present in the dataset or evaluation context."
+            )
+        return values, {Variable(name, roles=("value",), source=layer)}
+
+    def _evaluate(
+        self, expr: str, metadata: Any, spec: ModelSpec
+    ) -> tuple[Any, set[Variable]]:
+        variables: set[Variable] = set()
+        return (
+            stateful_eval(
+                expr,
+                self.layered_context,
+                {expr: metadata},
+                spec.transform_state,
+                spec,
+                variables=variables,
+            ),
+            variables,
+        )
+
+    def _is_categorical(self, values: Any) -> bool:
+        if hasattr(values, "__formulaic_metadata__"):
+            return values.__formulaic_metadata__.kind is Factor.Kind.CATEGORICAL
+        return False
+
+    def _check_for_nulls(
+        self, name: str, values: Any, na_action: NAAction, drop_rows: set[int]
+    ) -> None:
+        if na_action is NAAction.IGNORE:
+            return
+
+        try:
+            null_indices = find_nulls(values)
+
+            if na_action is NAAction.RAISE:
+                if null_indices:
+                    raise ValueError(f"`{name}` contains null values after evaluation.")
+
+            elif na_action is NAAction.DROP:
+                drop_rows.update(null_indices)
+
+            else:
+                raise ValueError(
+                    f"Do not know how to interpret `na_action` = {repr(na_action)}."
+                )  # pragma: no cover; this is currently impossible to reach
+        except ValueError as e:
+            raise ValueError(
+                f"Error encountered while checking for nulls in `{name}`: {e}"
+            ) from e
+
+    def _encode_evaled_factor(
+        self,
+        factor: EvaluatedFactor,
+        spec: ModelSpec,
+        drop_rows: Sequence[int],
+        reduced_rank: bool = False,
+    ) -> dict[str, Any]:
+        if not factor.metadata.encoded:
+            if factor.expr in self.encoded_cache:
+                encoded = self.encoded_cache[factor.expr]
+            elif (factor.expr, reduced_rank) in self.encoded_cache:
+                encoded = self.encoded_cache[(factor.expr, reduced_rank)]
+            else:
+
+                def map_dict(f: Any) -> Any:
+                    """
+                    This decorator allows an encoding function to operator on
+                    dictionaries (which should be mapped over). This allows
+                    transforms to output multiple non-encoded columns and still
+                    have everything work as expected.
+                    """
+
+                    @functools.wraps(f)
+                    def wrapped(
+                        values: Any,
+                        metadata: Any,
+                        state: dict[str, Any],
+                        *args: Any,
+                        **kwargs: Any,
+                    ) -> Any:
+                        if isinstance(values, dict):
+                            encoded = {}
+                            for k, v in values.items():
+                                if isinstance(k, str) and k.startswith("__"):
+                                    encoded[k] = v
+                                else:
+                                    nested_state = state.get(k, {})
+                                    encoded[k] = wrapped(
+                                        v, metadata, nested_state, *args, **kwargs
+                                    )
+                                    if nested_state:
+                                        state[k] = nested_state
+                            if isinstance(values, FactorValues):
+                                return FactorValues(
+                                    encoded, metadata=values.__formulaic_metadata__
+                                )
+                            return encoded  # pragma: no cover; nothing in formulaic uses this, but is here for generality.
+                        return f(values, metadata, state, *args, **kwargs)
+
+                    return wrapped
+
+                encoder_state: dict[str, Any] = spec.encoder_state.get(
+                    factor.expr, [None, {}]
+                )[1]
+
+                if factor.metadata.encoder is not None:
+                    encoded = as_columns(
+                        factor.metadata.encoder(  # type: ignore
+                            factor.values,
+                            reduced_rank=reduced_rank,
+                            drop_rows=drop_rows,
+                            encoder_state=encoder_state,
+                            model_spec=spec,
+                        )
+                    )
+                else:
+                    # If we need to unpack values into columns, we do this here.
+                    # Otherwise, we pass through the original values.
+                    factor_values: FactorValues = FactorValues(
+                        self._extract_columns_for_encoding(factor),
+                        metadata=factor.metadata,
+                    )
+
+                    if factor.metadata.kind is Factor.Kind.CATEGORICAL:
+                        encoded = map_dict(self._encode_categorical)(
+                            factor_values,
+                            factor.metadata,
+                            encoder_state,
+                            spec,
+                            drop_rows,
+                            reduced_rank=reduced_rank,
+                        )
+                    elif factor.metadata.kind is Factor.Kind.NUMERICAL:
+                        encoded = map_dict(self._encode_numerical)(
+                            factor_values,
+                            factor.metadata,
+                            encoder_state,
+                            spec,
+                            drop_rows,
+                        )
+                    elif factor.metadata.kind is Factor.Kind.CONSTANT:
+                        encoded = map_dict(self._encode_constant)(
+                            factor_values,
+                            factor.metadata,
+                            encoder_state,
+                            spec,
+                            drop_rows,
+                        )
+                    else:
+                        raise FactorEncodingError(
+                            factor
+                        )  # pragma: no cover; it is not currently possible to reach this sentinel
+                spec.encoder_state[factor.expr] = (factor.metadata.kind, encoder_state)
+
+                # Only encode once for encodings where we can just drop a field
+                # later on below.
+                cache_key: Union[str, tuple[str, bool]] = (
+                    factor.expr
+                    if isinstance(encoded, dict) and factor.metadata.drop_field
+                    else (factor.expr, reduced_rank)
+                )
+                self.encoded_cache[cache_key] = encoded
+        else:
+            encoded = as_columns(
+                factor.values
+            )  # pragma: no cover; we don't use this in formulaic yet.
+
+        encoded = FactorValues(
+            encoded,
+            metadata=getattr(encoded, "__formulaic_metadata__", factor.metadata),
+            encoded=True,
+        )
+
+        # Encoded factors will now all be dicts
+        if (
+            isinstance(encoded, dict)
+            and encoded.__formulaic_metadata__.spans_intercept  # type: ignore
+            and reduced_rank
+        ):
+            encoded = FactorValues(
+                encoded.copy(),
+                metadata=encoded.__formulaic_metadata__,  # type: ignore
+                reduced=True,
+            )
+            del encoded[encoded.__formulaic_metadata__.drop_field]
+
+        return self._flatten_encoded_evaled_factor(factor.expr, encoded)
+
+    def _extract_columns_for_encoding(
+        self, factor: EvaluatedFactor
+    ) -> Union[Any, dict[str, Any]]:
+        """
+        If incoming factor has values that need to be unpacked into columns
+        (e.g. a two-dimensions numpy array), do that expansion here. Otherwise,
+        return the current factor values.
+        """
+        return as_columns(factor.values)
+
+    def _flatten_encoded_evaled_factor(
+        self, name: str, values: FactorValues[dict]
+    ) -> dict[str, Any]:
+        if not isinstance(values, dict):
+            return {name: values}
+
+        # Some nested dictionaries may not be a `FactorValues[dict]` instance,
+        # in which case we impute the default formatter in `FactorValues.format`.
+        if hasattr(values, "__formulaic_metadata__"):
+            name_format = values.__formulaic_metadata__.get_format()
+        else:
+            name_format = FactorValuesMetadata.format
+
+        flattened = {}
+        for subfield, value in values.items():
+            if isinstance(subfield, str) and subfield.startswith("__"):
+                continue
+            subname = name_format.format(name=name, field=subfield)
+            if isinstance(value, dict):
+                flattened.update(self._flatten_encoded_evaled_factor(subname, value))  # type: ignore
+            else:
+                flattened[subname] = value
+
+        return flattened
+
+    @abstractmethod
+    def _encode_constant(
+        self,
+        value: Any,
+        metadata: Any,
+        encoder_state: dict[str, Any],
+        spec: ModelSpec,
+        drop_rows: Sequence[int],
+    ) -> Any:
+        pass  # pragma: no cover
+
+    @abstractmethod
+    def _encode_categorical(
+        self,
+        values: Any,
+        metadata: Any,
+        encoder_state: dict[str, Any],
+        spec: ModelSpec,
+        drop_rows: Sequence[int],
+        reduced_rank: bool = False,
+    ) -> None:
+        pass  # pragma: no cover
+
+    @abstractmethod
+    def _encode_numerical(
+        self,
+        values: Any,
+        metadata: Any,
+        encoder_state: dict[str, Any],
+        spec: ModelSpec,
+        drop_rows: Sequence[int],
+    ) -> Any:
+        pass  # pragma: no cover
+
+    # Methods related to ModelMatrix output
+
+    def _enforce_structure(
+        self,
+        cols: list[tuple[Term, Iterable[ScopedTerm], dict[str, Any]]],
+        spec: ModelSpec,
+        drop_rows: Sequence[int],
+    ) -> Generator[tuple[Term, Iterable[ScopedTerm], dict[str, Any]], None, None]:
+        # TODO: Verify that imputation strategies are intuitive and make sense.
+        structure = cast(list[EncodedTermStructure], spec.structure)
+        if not len(cols) == len(structure):  # pragma: no cover
+            raise RuntimeError(
+                "Specification structure and columns are mismatched. Please report this error with examples!"
+            )
+        for i, col_spec in enumerate(cols):
+            scoped_cols = col_spec[2]
+            target_cols = structure[i][2]
+            if len(scoped_cols) > len(target_cols):
+                raise FactorEncodingError(
+                    f"Term `{col_spec[0]}` has generated too many columns compared to specification: generated {list(scoped_cols)}, expecting {target_cols}."
+                )
+            if len(scoped_cols) < len(target_cols):
+                if len(scoped_cols) == 0:
+                    col = self._encode_constant(0, None, {}, spec, drop_rows)
+                elif len(scoped_cols) == 1:
+                    col = tuple(scoped_cols.values())[0]
+                else:
+                    raise FactorEncodingError(
+                        f"Term `{col_spec[0]}` has generated insufficient columns compared to specification: generated {list(scoped_cols)}, expecting {target_cols}."
+                    )
+                scoped_cols = {name: col for name in target_cols}
+            elif set(scoped_cols) != set(target_cols):
+                raise FactorEncodingError(
+                    f"Term `{col_spec[0]}` has generated columns that are inconsistent with specification: generated {list(scoped_cols)}, expecting {target_cols}."
+                )
+
+            yield (
+                col_spec[0],
+                col_spec[1],
+                {col: scoped_cols[col] for col in target_cols},
+            )
+
+    def _get_columns_for_term(
+        self, factors: list[dict[str, Any]], spec: ModelSpec, scale: float = 1
+    ) -> dict[str, Any]:
+        """
+        Assemble the columns for a model matrix given factors and a scale.
+
+        This performs the row-wise Kronecker product of the factors. For greater
+        compatibility with R and patsy, we reverse this product so that we
+        iterate first over the latter terms.
+
+        Args:
+            factors
+            scale
+
+        Returns:
+            dict
+        """
+        out = {}
+        for reverse_product in itertools.product(
+            *(factor.items() for factor in reversed(factors))
+        ):
+            product = reverse_product[::-1]
+            out[":".join(p[0] for p in product)] = scale * functools.reduce(
+                operator.mul, (p[1] for p in product)
+            )
+        return out
+
+    @abstractmethod
+    def _combine_columns(
+        self, cols: Sequence[tuple[str, Any]], spec: ModelSpec, drop_rows: Sequence[int]
+    ) -> Any:
+        pass  # pragma: no cover
diff --git a/shared/package-files/3d/3dfa5e38ca65c72376bdb7c509d92448a182836d52151c9dbea05d78984156a4/arrays.pyi b/shared/package-files/3d/3dfa5e38ca65c72376bdb7c509d92448a182836d52151c9dbea05d78984156a4/arrays.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..86f69c3cdfc75f3101a8e0afe59f9b102d90ad79
--- /dev/null
+++ b/shared/package-files/3d/3dfa5e38ca65c72376bdb7c509d92448a182836d52151c9dbea05d78984156a4/arrays.pyi
@@ -0,0 +1,40 @@
+from typing import Sequence
+
+import numpy as np
+
+from pandas._typing import (
+    AxisInt,
+    DtypeObj,
+    Self,
+    Shape,
+)
+
+class NDArrayBacked:
+    _dtype: DtypeObj
+    _ndarray: np.ndarray
+    def __init__(self, values: np.ndarray, dtype: DtypeObj) -> None: ...
+    @classmethod
+    def _simple_new(cls, values: np.ndarray, dtype: DtypeObj): ...
+    def _from_backing_data(self, values: np.ndarray): ...
+    def __setstate__(self, state): ...
+    def __len__(self) -> int: ...
+    @property
+    def shape(self) -> Shape: ...
+    @property
+    def ndim(self) -> int: ...
+    @property
+    def size(self) -> int: ...
+    @property
+    def nbytes(self) -> int: ...
+    def copy(self, order=...): ...
+    def delete(self, loc, axis=...): ...
+    def swapaxes(self, axis1, axis2): ...
+    def repeat(self, repeats: int | Sequence[int], axis: int | None = ...): ...
+    def reshape(self, *args, **kwargs): ...
+    def ravel(self, order=...): ...
+    @property
+    def T(self): ...
+    @classmethod
+    def _concat_same_type(
+        cls, to_concat: Sequence[Self], axis: AxisInt = ...
+    ) -> Self: ...
diff --git a/shared/package-files/3d/3dfde0dc535fef39630ab7f2d2ae2caa286f36450f0264d162f9249b636530e8/_foreach_frac_cpu_dispatch.h b/shared/package-files/3d/3dfde0dc535fef39630ab7f2d2ae2caa286f36450f0264d162f9249b636530e8/_foreach_frac_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7d4d93896e1483122dc673ef0e37f4b9e750fc02
--- /dev/null
+++ b/shared/package-files/3d/3dfde0dc535fef39630ab7f2d2ae2caa286f36450f0264d162f9249b636530e8/_foreach_frac_cpu_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::vector _foreach_frac(at::TensorList self);
+TORCH_API void _foreach_frac_(at::TensorList self);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/3d/3dff99533cdc05fd33a114b36d9d443ca537000a4427be5db6db74a6d5759d6f/outer_ops.h b/shared/package-files/3d/3dff99533cdc05fd33a114b36d9d443ca537000a4427be5db6db74a6d5759d6f/outer_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..b1ca274d9d70c879ebc8a9109f67b41edd21e90a
--- /dev/null
+++ b/shared/package-files/3d/3dff99533cdc05fd33a114b36d9d443ca537000a4427be5db6db74a6d5759d6f/outer_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API outer {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::outer")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "outer(Tensor self, Tensor vec2) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, const at::Tensor & vec2);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec2);
+};
+
+struct TORCH_API outer_out {
+  using schema = at::Tensor & (const at::Tensor &, const at::Tensor &, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::outer")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "outer.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, const at::Tensor & vec2, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/44/445be65ebca8d4c0deb70230c6fce159d0e25ca6dd0102a2110e9918acd8a8ab/iter.py b/shared/package-files/44/445be65ebca8d4c0deb70230c6fce159d0e25ca6dd0102a2110e9918acd8a8ab/iter.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e2d3167f9780c160391fc7e88c1d725e67a01b9
--- /dev/null
+++ b/shared/package-files/44/445be65ebca8d4c0deb70230c6fce159d0e25ca6dd0102a2110e9918acd8a8ab/iter.py
@@ -0,0 +1,260 @@
+# mypy: ignore-errors
+
+MAX_CYCLE = 3000
+
+import itertools
+import operator
+
+from typing import Dict, List, Optional
+
+from .. import polyfill, variables
+from ..exc import unimplemented
+
+from .base import MutableLocal, VariableTracker
+from .constant import ConstantVariable
+
+
+class ItertoolsVariable(VariableTracker):
+    def __init__(self, value, **kwargs):
+        super().__init__(**kwargs)
+        self.value = value
+
+    def __repr__(self):
+        return f"ItertoolsVariable({self.value})"
+
+    def python_type(self):
+        return type(self.value)
+
+    def as_python_constant(self):
+        return self.value
+
+    def call_function(
+        self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]"
+    ) -> "VariableTracker":
+        if (
+            self.value is itertools.product
+            and not kwargs
+            and all(arg.has_unpack_var_sequence(tx) for arg in args)
+        ):
+            seqs = [arg.unpack_var_sequence(tx) for arg in args]
+            items = []
+            for item in itertools.product(*seqs):
+                items.append(variables.TupleVariable(list(item)))
+            return variables.ListIteratorVariable(items, mutable_local=MutableLocal())
+        elif (
+            self.value is itertools.chain
+            and not kwargs
+            and all(arg.has_unpack_var_sequence(tx) for arg in args)
+        ):
+            seqs = [arg.unpack_var_sequence(tx) for arg in args]
+            items = list(itertools.chain.from_iterable(seqs))
+            return variables.ListIteratorVariable(items, mutable_local=MutableLocal())
+        elif self.value is itertools.accumulate:
+            from .builtin import BuiltinVariable
+
+            if any(key not in ["initial", "func"] for key in kwargs.keys()):
+                unimplemented(
+                    "Unsupported kwargs for itertools.accumulate: "
+                    f"{','.join(set(kwargs.keys()) - {'initial', 'func'})}"
+                )
+
+            acc = kwargs.get("initial")
+
+            if len(args) in [1, 2] and args[0].has_unpack_var_sequence(tx):
+                seq = args[0].unpack_var_sequence(tx)
+
+                if "func" in kwargs and len(args) == 1:
+                    func = kwargs["func"].call_function
+                elif len(args) == 2:
+                    func = args[1].call_function
+                elif len(args) == 1:
+                    # Default to operator.add
+                    func = BuiltinVariable(operator.add).call_function
+                else:
+                    unimplemented(
+                        "itertools.accumulate can only accept one of: `func` kwarg, pos 2 arg"
+                    )
+            else:
+                unimplemented("Unsupported arguments for itertools.accumulate")
+
+            items = []
+            if acc is not None:
+                items.append(acc)
+            for item in seq:
+                if acc is None:
+                    acc = item
+                else:
+                    try:
+                        acc = func(tx, [acc, item], {})
+                    except Exception:
+                        raise unimplemented(  # noqa: TRY200
+                            f"Unexpected failure in invoking function during accumulate. Failed running func {func}({item}{acc})"
+                        )
+                items.append(acc)
+
+            return variables.ListIteratorVariable(items, mutable_local=MutableLocal())
+        elif (
+            self.value is itertools.combinations
+            and not kwargs
+            and len(args) == 2
+            and args[0].has_unpack_var_sequence(tx)
+            and args[1].is_python_constant()
+        ):
+            iterable = args[0].unpack_var_sequence(tx)
+            r = args[1].as_python_constant()
+
+            items = []
+            for item in itertools.combinations(iterable, r):
+                items.append(variables.TupleVariable(list(item)))
+            return variables.ListIteratorVariable(items, mutable_local=MutableLocal())
+        elif self.value is itertools.groupby:
+            if any(kw != "key" for kw in kwargs.keys()):
+                unimplemented(
+                    "Unsupported kwargs for itertools.groupby: "
+                    f"{','.join(set(kwargs.keys()) - {'key'})}"
+                )
+
+            def retrieve_const_key(key):
+                if isinstance(key, variables.SymNodeVariable):
+                    return key.evaluate_expr()
+                elif isinstance(key, variables.ConstantVariable):
+                    return key.as_python_constant()
+                else:
+                    raise unimplemented(
+                        "Unsupported key type for itertools.groupby: " + str(type(key))
+                    )
+
+            if len(args) == 1 and args[0].has_unpack_var_sequence(tx):
+                seq = args[0].unpack_var_sequence(tx)
+                keyfunc = (
+                    (
+                        lambda x: (
+                            retrieve_const_key(
+                                kwargs.get("key").call_function(tx, [x], {})
+                            )
+                        )
+                    )
+                    if "key" in kwargs
+                    else None
+                )
+            else:
+                unimplemented("Unsupported arguments for itertools.groupby")
+
+            result = []
+            try:
+                for k, v in itertools.groupby(seq, key=keyfunc):
+                    result.append(
+                        variables.TupleVariable(
+                            [
+                                variables.ConstantVariable.create(k)
+                                if variables.ConstantVariable.is_literal(k)
+                                else k,
+                                variables.ListIteratorVariable(
+                                    list(v), mutable_local=MutableLocal()
+                                ),
+                            ],
+                            mutable_local=MutableLocal(),
+                        )
+                    )
+            except Exception:
+                raise unimplemented(  # noqa: TRY200
+                    "Unexpected failure when calling itertools.groupby"
+                )
+            return variables.ListIteratorVariable(result, mutable_local=MutableLocal())
+        elif self.value is itertools.repeat:
+            if len(args) < 2:
+                return variables.RepeatIteratorVariable(
+                    *args, mutable_local=MutableLocal()
+                )
+
+            from .builder import SourcelessBuilder
+
+            return tx.inline_user_function_return(
+                SourcelessBuilder()(tx, polyfill.repeat), args, kwargs
+            )
+        elif self.value is itertools.count:
+            return variables.CountIteratorVariable(*args, mutable_local=MutableLocal())
+        elif self.value is itertools.cycle:
+            return variables.CycleIteratorVariable(*args, mutable_local=MutableLocal())
+        else:
+            return super().call_function(tx, args, kwargs)
+
+
+class IteratorVariable(VariableTracker):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def next_variables(self, tx):
+        unimplemented("abstract method, must implement")
+
+
+class RepeatIteratorVariable(IteratorVariable):
+    def __init__(self, item: VariableTracker, **kwargs):
+        super().__init__(**kwargs)
+        self.item = item
+
+    # Repeat needs no mutation, clone self
+    def next_variables(self, tx):
+        return self.item, self
+
+
+class CountIteratorVariable(IteratorVariable):
+    def __init__(self, item: int = 0, step: int = 1, **kwargs):
+        super().__init__(**kwargs)
+        if not isinstance(item, VariableTracker):
+            item = ConstantVariable.create(item)
+        if not isinstance(step, VariableTracker):
+            step = ConstantVariable.create(step)
+        self.item = item
+        self.step = step
+
+    def next_variables(self, tx):
+        assert self.mutable_local
+        tx.output.side_effects.mutation(self)
+        next_item = self.item.call_method(tx, "__add__", [self.step], {})
+        self.item = next_item
+        return self.item, self
+
+
+class CycleIteratorVariable(IteratorVariable):
+    def __init__(
+        self,
+        iterator: IteratorVariable,
+        saved: List[VariableTracker] = None,
+        saved_index: int = 0,
+        item: Optional[VariableTracker] = None,
+        **kwargs,
+    ):
+        if saved is None:
+            saved = []
+        super().__init__(**kwargs)
+        self.iterator = iterator
+        self.saved = saved
+        self.saved_index = saved_index
+        self.item = item
+
+    def next_variables(self, tx):
+        assert self.mutable_local
+
+        if self.iterator is not None:
+            try:
+                new_item, _ = self.iterator.next_variables(tx)
+                if len(self.saved) > MAX_CYCLE:
+                    unimplemented(
+                        "input iterator to itertools.cycle has too many items"
+                    )
+                tx.output.side_effects.mutation(self)
+                self.saved.append(new_item)
+                self.item = new_item
+                if self.item is None:
+                    return self.next_variables(tx)
+                return self.item, self
+            except StopIteration:
+                self.iterator = None
+                return self.next_variables(tx)
+        elif len(self.saved) > 0:
+            tx.output.side_effects.mutation(self)
+            self.saved_index = (self.saved_index + 1) % len(self.saved)
+            return self.item, self
+        else:
+            raise StopIteration
diff --git a/shared/package-files/51/518c506ae94720d1296cfe3335f3349ddcf734228c4af48e784895e5a046260d/vision.h b/shared/package-files/51/518c506ae94720d1296cfe3335f3349ddcf734228c4af48e784895e5a046260d/vision.h
new file mode 100644
index 0000000000000000000000000000000000000000..814f4b6684d96190e88b72d53a24eceac6eb5b0e
--- /dev/null
+++ b/shared/package-files/51/518c506ae94720d1296cfe3335f3349ddcf734228c4af48e784895e5a046260d/vision.h
@@ -0,0 +1,36 @@
+#pragma once
+
+#include 
+#include 
+#include 
+#include 
+
+namespace torch {
+namespace nn {
+namespace functional {
+
+/// Options for `torch::nn::functional::grid_sample`.
+///
+/// Example:
+/// ```
+/// namespace F = torch::nn::functional;
+/// F::grid_sample(input, grid,
+/// F::GridSampleFuncOptions().mode(torch::kBilinear).padding_mode(torch::kZeros).align_corners(true));
+/// ```
+struct TORCH_API GridSampleFuncOptions {
+  typedef std::variant mode_t;
+  typedef std::
+      variant
+          padding_mode_t;
+
+  /// interpolation mode to calculate output values. Default: Bilinear
+  TORCH_ARG(mode_t, mode) = torch::kBilinear;
+  /// padding mode for outside grid values. Default: Zeros
+  TORCH_ARG(padding_mode_t, padding_mode) = torch::kZeros;
+  /// Specifies perspective to pixel as point. Default: false
+  TORCH_ARG(c10::optional, align_corners) = c10::nullopt;
+};
+
+} // namespace functional
+} // namespace nn
+} // namespace torch
diff --git a/shared/package-files/51/51f0718789e32cb4814a8af759d349b75725707ca515cab09224a2be12c72b7b/utils.pyi b/shared/package-files/51/51f0718789e32cb4814a8af759d349b75725707ca515cab09224a2be12c72b7b/utils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..23b5696f5ac4cdfa14b2eb29f3ee03d4c9e8ee44
--- /dev/null
+++ b/shared/package-files/51/51f0718789e32cb4814a8af759d349b75725707ca515cab09224a2be12c72b7b/utils.pyi
@@ -0,0 +1,10 @@
+from typing import List, Optional, Set, Tuple, Type
+
+from django.apps.config import AppConfig
+from django.db.models.base import Model
+
+def popen_wrapper(args: List[str], stdout_encoding: str = ...) -> Tuple[str, str, int]: ...
+def handle_extensions(extensions: List[str]) -> Set[str]: ...
+def find_command(cmd: str, path: Optional[str] = ..., pathext: Optional[str] = ...) -> Optional[str]: ...
+def get_random_secret_key(): ...
+def parse_apps_and_model_labels(labels: List[str]) -> Tuple[Set[Type[Model]], Set[AppConfig]]: ...
diff --git a/shared/package-files/c9/c900ca52e90e9d70701b1cb8158e36f59df0d7e5435cea0813877bfb93cef488/margin_ranking_loss_ops.h b/shared/package-files/c9/c900ca52e90e9d70701b1cb8158e36f59df0d7e5435cea0813877bfb93cef488/margin_ranking_loss_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..1fa15f464916f595e51672dae099617e8501b511
--- /dev/null
+++ b/shared/package-files/c9/c900ca52e90e9d70701b1cb8158e36f59df0d7e5435cea0813877bfb93cef488/margin_ranking_loss_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API margin_ranking_loss {
+  using schema = at::Tensor (const at::Tensor &, const at::Tensor &, const at::Tensor &, double, int64_t);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::margin_ranking_loss")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "margin_ranking_loss(Tensor input1, Tensor input2, Tensor target, float margin=0.0, int reduction=Mean) -> Tensor")
+  static at::Tensor call(const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & input1, const at::Tensor & input2, const at::Tensor & target, double margin, int64_t reduction);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/c9/c903798389a0e00c9b4639208bef72cb889010589b1909a5cfbf0f8a4e4eafe0/WHEEL b/shared/package-files/c9/c903798389a0e00c9b4639208bef72cb889010589b1909a5cfbf0f8a4e4eafe0/WHEEL
new file mode 100644
index 0000000000000000000000000000000000000000..7e688737d490be3643d705bc16b5a77f7bd567b7
--- /dev/null
+++ b/shared/package-files/c9/c903798389a0e00c9b4639208bef72cb889010589b1909a5cfbf0f8a4e4eafe0/WHEEL
@@ -0,0 +1,5 @@
+Wheel-Version: 1.0
+Generator: bdist_wheel (0.41.2)
+Root-Is-Purelib: true
+Tag: py3-none-any
+
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diff --git a/shared/package-files/c9/c903f32178ae8205b73caca478c6505318dcc84b006acd6e9ca7da6479a1e05f/graphlib.py b/shared/package-files/c9/c903f32178ae8205b73caca478c6505318dcc84b006acd6e9ca7da6479a1e05f/graphlib.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c5d9a413c944d376f9a9b29f8a86fa90e5a31d5
--- /dev/null
+++ b/shared/package-files/c9/c903f32178ae8205b73caca478c6505318dcc84b006acd6e9ca7da6479a1e05f/graphlib.py
@@ -0,0 +1,246 @@
+__all__ = ["TopologicalSorter", "CycleError"]
+
+_NODE_OUT = -1
+_NODE_DONE = -2
+
+
+class _NodeInfo:
+    __slots__ = "node", "npredecessors", "successors"
+
+    def __init__(self, node):
+        # The node this class is augmenting.
+        self.node = node
+
+        # Number of predecessors, generally >= 0. When this value falls to 0,
+        # and is returned by get_ready(), this is set to _NODE_OUT and when the
+        # node is marked done by a call to done(), set to _NODE_DONE.
+        self.npredecessors = 0
+
+        # List of successor nodes. The list can contain duplicated elements as
+        # long as they're all reflected in the successor's npredecessors attribute.
+        self.successors = []
+
+
+class CycleError(ValueError):
+    """Subclass of ValueError raised by TopologicalSorter.prepare if cycles
+    exist in the working graph.
+
+    If multiple cycles exist, only one undefined choice among them will be reported
+    and included in the exception. The detected cycle can be accessed via the second
+    element in the *args* attribute of the exception instance and consists in a list
+    of nodes, such that each node is, in the graph, an immediate predecessor of the
+    next node in the list. In the reported list, the first and the last node will be
+    the same, to make it clear that it is cyclic.
+    """
+
+    pass
+
+
+class TopologicalSorter:
+    """Provides functionality to topologically sort a graph of hashable nodes"""
+
+    def __init__(self, graph=None):
+        self._node2info = {}
+        self._ready_nodes = None
+        self._npassedout = 0
+        self._nfinished = 0
+
+        if graph is not None:
+            for node, predecessors in graph.items():
+                self.add(node, *predecessors)
+
+    def _get_nodeinfo(self, node):
+        if (result := self._node2info.get(node)) is None:
+            self._node2info[node] = result = _NodeInfo(node)
+        return result
+
+    def add(self, node, *predecessors):
+        """Add a new node and its predecessors to the graph.
+
+        Both the *node* and all elements in *predecessors* must be hashable.
+
+        If called multiple times with the same node argument, the set of dependencies
+        will be the union of all dependencies passed in.
+
+        It is possible to add a node with no dependencies (*predecessors* is not provided)
+        as well as provide a dependency twice. If a node that has not been provided before
+        is included among *predecessors* it will be automatically added to the graph with
+        no predecessors of its own.
+
+        Raises ValueError if called after "prepare".
+        """
+        if self._ready_nodes is not None:
+            raise ValueError("Nodes cannot be added after a call to prepare()")
+
+        # Create the node -> predecessor edges
+        nodeinfo = self._get_nodeinfo(node)
+        nodeinfo.npredecessors += len(predecessors)
+
+        # Create the predecessor -> node edges
+        for pred in predecessors:
+            pred_info = self._get_nodeinfo(pred)
+            pred_info.successors.append(node)
+
+    def prepare(self):
+        """Mark the graph as finished and check for cycles in the graph.
+
+        If any cycle is detected, "CycleError" will be raised, but "get_ready" can
+        still be used to obtain as many nodes as possible until cycles block more
+        progress. After a call to this function, the graph cannot be modified and
+        therefore no more nodes can be added using "add".
+        """
+        if self._ready_nodes is not None:
+            raise ValueError("cannot prepare() more than once")
+
+        self._ready_nodes = [
+            i.node for i in self._node2info.values() if i.npredecessors == 0
+        ]
+        # ready_nodes is set before we look for cycles on purpose:
+        # if the user wants to catch the CycleError, that's fine,
+        # they can continue using the instance to grab as many
+        # nodes as possible before cycles block more progress
+        cycle = self._find_cycle()
+        if cycle:
+            raise CycleError(f"nodes are in a cycle", cycle)
+
+    def get_ready(self):
+        """Return a tuple of all the nodes that are ready.
+
+        Initially it returns all nodes with no predecessors; once those are marked
+        as processed by calling "done", further calls will return all new nodes that
+        have all their predecessors already processed. Once no more progress can be made,
+        empty tuples are returned.
+
+        Raises ValueError if called without calling "prepare" previously.
+        """
+        if self._ready_nodes is None:
+            raise ValueError("prepare() must be called first")
+
+        # Get the nodes that are ready and mark them
+        result = tuple(self._ready_nodes)
+        n2i = self._node2info
+        for node in result:
+            n2i[node].npredecessors = _NODE_OUT
+
+        # Clean the list of nodes that are ready and update
+        # the counter of nodes that we have returned.
+        self._ready_nodes.clear()
+        self._npassedout += len(result)
+
+        return result
+
+    def is_active(self):
+        """Return ``True`` if more progress can be made and ``False`` otherwise.
+
+        Progress can be made if cycles do not block the resolution and either there
+        are still nodes ready that haven't yet been returned by "get_ready" or the
+        number of nodes marked "done" is less than the number that have been returned
+        by "get_ready".
+
+        Raises ValueError if called without calling "prepare" previously.
+        """
+        if self._ready_nodes is None:
+            raise ValueError("prepare() must be called first")
+        return self._nfinished < self._npassedout or bool(self._ready_nodes)
+
+    def __bool__(self):
+        return self.is_active()
+
+    def done(self, *nodes):
+        """Marks a set of nodes returned by "get_ready" as processed.
+
+        This method unblocks any successor of each node in *nodes* for being returned
+        in the future by a call to "get_ready".
+
+        Raises :exec:`ValueError` if any node in *nodes* has already been marked as
+        processed by a previous call to this method, if a node was not added to the
+        graph by using "add" or if called without calling "prepare" previously or if
+        node has not yet been returned by "get_ready".
+        """
+
+        if self._ready_nodes is None:
+            raise ValueError("prepare() must be called first")
+
+        n2i = self._node2info
+
+        for node in nodes:
+
+            # Check if we know about this node (it was added previously using add()
+            if (nodeinfo := n2i.get(node)) is None:
+                raise ValueError(f"node {node!r} was not added using add()")
+
+            # If the node has not being returned (marked as ready) previously, inform the user.
+            stat = nodeinfo.npredecessors
+            if stat != _NODE_OUT:
+                if stat >= 0:
+                    raise ValueError(
+                        f"node {node!r} was not passed out (still not ready)"
+                    )
+                elif stat == _NODE_DONE:
+                    raise ValueError(f"node {node!r} was already marked done")
+                else:
+                    assert False, f"node {node!r}: unknown status {stat}"
+
+            # Mark the node as processed
+            nodeinfo.npredecessors = _NODE_DONE
+
+            # Go to all the successors and reduce the number of predecessors, collecting all the ones
+            # that are ready to be returned in the next get_ready() call.
+            for successor in nodeinfo.successors:
+                successor_info = n2i[successor]
+                successor_info.npredecessors -= 1
+                if successor_info.npredecessors == 0:
+                    self._ready_nodes.append(successor)
+            self._nfinished += 1
+
+    def _find_cycle(self):
+        n2i = self._node2info
+        stack = []
+        itstack = []
+        seen = set()
+        node2stacki = {}
+
+        for node in n2i:
+            if node in seen:
+                continue
+
+            while True:
+                if node in seen:
+                    # If we have seen already the node and is in the
+                    # current stack we have found a cycle.
+                    if node in node2stacki:
+                        return stack[node2stacki[node] :] + [node]
+                    # else go on to get next successor
+                else:
+                    seen.add(node)
+                    itstack.append(iter(n2i[node].successors).__next__)
+                    node2stacki[node] = len(stack)
+                    stack.append(node)
+
+                # Backtrack to the topmost stack entry with
+                # at least another successor.
+                while stack:
+                    try:
+                        node = itstack[-1]()
+                        break
+                    except StopIteration:
+                        del node2stacki[stack.pop()]
+                        itstack.pop()
+                else:
+                    break
+        return None
+
+    def static_order(self):
+        """Returns an iterable of nodes in a topological order.
+
+        The particular order that is returned may depend on the specific
+        order in which the items were inserted in the graph.
+
+        Using this method does not require to call "prepare" or "done". If any
+        cycle is detected, :exc:`CycleError` will be raised.
+        """
+        self.prepare()
+        while self.is_active():
+            node_group = self.get_ready()
+            yield from node_group
+            self.done(*node_group)
diff --git a/shared/package-files/c9/c906129405f26ff868b08edf5e780147968ceb7602df6af91813fc29f91e3f3f/main_list.py b/shared/package-files/c9/c906129405f26ff868b08edf5e780147968ceb7602df6af91813fc29f91e3f3f/main_list.py
new file mode 100644
index 0000000000000000000000000000000000000000..06682f56e2bc5b27bd718142d6f04339e01698fb
--- /dev/null
+++ b/shared/package-files/c9/c906129405f26ff868b08edf5e780147968ceb7602df6af91813fc29f91e3f3f/main_list.py
@@ -0,0 +1,39 @@
+# Copyright (C) 2012 Anaconda, Inc
+# SPDX-License-Identifier: BSD-3-Clause
+from argparse import RawDescriptionHelpFormatter
+
+from conda.cli import common
+from conda.cli.conda_argparse import add_parser_json
+from conda.core.envs_manager import list_all_known_prefixes
+
+description = """
+List the Conda environments
+"""
+
+example = """
+examples:
+    conda env list
+    conda env list --json
+"""
+
+
+def configure_parser(sub_parsers):
+    list_parser = sub_parsers.add_parser(
+        "list",
+        formatter_class=RawDescriptionHelpFormatter,
+        description=description,
+        help=description,
+        epilog=example,
+    )
+
+    add_parser_json(list_parser)
+
+    list_parser.set_defaults(func=".main_list.execute")
+
+
+def execute(args, parser):
+    info_dict = {"envs": list_all_known_prefixes()}
+    common.print_envs_list(info_dict["envs"], not args.json)
+
+    if args.json:
+        common.stdout_json(info_dict)
diff --git a/shared/package-files/c9/c9063a5f5833b0751209f4a73e09831625962ff725c058430d473b7966868d6d/std_compositeimplicitautograd_dispatch.h b/shared/package-files/c9/c9063a5f5833b0751209f4a73e09831625962ff725c058430d473b7966868d6d/std_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..7002bfbee317ea3b3bdc6d83f5337ca21821a6bc
--- /dev/null
+++ b/shared/package-files/c9/c9063a5f5833b0751209f4a73e09831625962ff725c058430d473b7966868d6d/std_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,32 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API at::Tensor std(const at::Tensor & self, bool unbiased);
+TORCH_API at::Tensor std(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false);
+TORCH_API at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim=false);
+TORCH_API at::Tensor & std_outf(const at::Tensor & self, at::OptionalIntArrayRef dim, bool unbiased, bool keepdim, at::Tensor & out);
+TORCH_API at::Tensor std(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false);
+TORCH_API at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim=false);
+TORCH_API at::Tensor & std_outf(const at::Tensor & self, at::DimnameList dim, bool unbiased, bool keepdim, at::Tensor & out);
+TORCH_API at::Tensor std(const at::Tensor & self, at::DimnameList dim, const c10::optional & correction=c10::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & std_out(at::Tensor & out, const at::Tensor & self, at::DimnameList dim, const c10::optional & correction=c10::nullopt, bool keepdim=false);
+TORCH_API at::Tensor & std_outf(const at::Tensor & self, at::DimnameList dim, const c10::optional & correction, bool keepdim, at::Tensor & out);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/c9/c906794eec564f31ab310704d9d6504bc7374245c84efc0d7229f4de8e0bac70/pccon-m b/shared/package-files/c9/c906794eec564f31ab310704d9d6504bc7374245c84efc0d7229f4de8e0bac70/pccon-m
new file mode 100644
index 0000000000000000000000000000000000000000..4a76bd3447cff606c76293a009a4bb46b2789c2c
Binary files /dev/null and b/shared/package-files/c9/c906794eec564f31ab310704d9d6504bc7374245c84efc0d7229f4de8e0bac70/pccon-m differ
diff --git a/shared/package-files/c9/c90d568e6dcf7047db2d056a1ed5d26d12b4bb0f8866189745919beded72c1a8/_embedding_bag_dense_backward_compositeexplicitautograd_dispatch.h b/shared/package-files/c9/c90d568e6dcf7047db2d056a1ed5d26d12b4bb0f8866189745919beded72c1a8/_embedding_bag_dense_backward_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..6219b9550ccd485533469469ff4524bd6cd45f7e
--- /dev/null
+++ b/shared/package-files/c9/c90d568e6dcf7047db2d056a1ed5d26d12b4bb0f8866189745919beded72c1a8/_embedding_bag_dense_backward_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor & _embedding_bag_dense_backward_out(at::Tensor & out, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx=-1);
+TORCH_API at::Tensor & _embedding_bag_dense_backward_outf(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, int64_t num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx, at::Tensor & out);
+TORCH_API at::Tensor & _embedding_bag_dense_backward_symint_out(at::Tensor & out, const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx=-1);
+TORCH_API at::Tensor & _embedding_bag_dense_backward_symint_outf(const at::Tensor & grad, const at::Tensor & indices, const at::Tensor & offset2bag, const at::Tensor & bag_size, const at::Tensor & maximum_indices, c10::SymInt num_weights, bool scale_grad_by_freq, int64_t mode, const c10::optional & per_sample_weights, int64_t padding_idx, at::Tensor & out);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/c9/c91060e9ccb539e3ac855cfe4058a63d03f99e896c9ec94cd0a5a0a2d1558ff2/argmax.h b/shared/package-files/c9/c91060e9ccb539e3ac855cfe4058a63d03f99e896c9ec94cd0a5a0a2d1558ff2/argmax.h
new file mode 100644
index 0000000000000000000000000000000000000000..35cce6eb1605f95bccbf942325d92ca7c8eb6c74
--- /dev/null
+++ b/shared/package-files/c9/c91060e9ccb539e3ac855cfe4058a63d03f99e896c9ec94cd0a5a0a2d1558ff2/argmax.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::argmax(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
+inline at::Tensor argmax(const at::Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false) {
+    return at::_ops::argmax::call(self, dim, keepdim);
+}
+
+// aten::argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & argmax_out(at::Tensor & out, const at::Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false) {
+    return at::_ops::argmax_out::call(self, dim, keepdim, out);
+}
+// aten::argmax.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & argmax_outf(const at::Tensor & self, c10::optional dim, bool keepdim, at::Tensor & out) {
+    return at::_ops::argmax_out::call(self, dim, keepdim, out);
+}
+
+}
diff --git a/shared/package-files/c9/c91319dbff34fc4f91aa4a3a73e2a23142553113d004f46cbe6bf8f0e44f0cb4/_nested_view_from_jagged_copy_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/c9/c91319dbff34fc4f91aa4a3a73e2a23142553113d004f46cbe6bf8f0e44f0cb4/_nested_view_from_jagged_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..fc4e2cd907750596f5bc0199fc5cd85078d0e6c9
--- /dev/null
+++ b/shared/package-files/c9/c91319dbff34fc4f91aa4a3a73e2a23142553113d004f46cbe6bf8f0e44f0cb4/_nested_view_from_jagged_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor _nested_view_from_jagged_copy(const at::Tensor & self, const at::Tensor & offsets, const at::Tensor & dummy, const c10::optional & lengths={}, int64_t ragged_idx=1);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/c9/c91418a978a88adf4de2e015c8bac16dbef406da3bf7087a640c2fc9e5fd1c1b/config.cc b/shared/package-files/c9/c91418a978a88adf4de2e015c8bac16dbef406da3bf7087a640c2fc9e5fd1c1b/config.cc
new file mode 100644
index 0000000000000000000000000000000000000000..d66476398724ade81d23bd45e87dce2295543640
--- /dev/null
+++ b/shared/package-files/c9/c91418a978a88adf4de2e015c8bac16dbef406da3bf7087a640c2fc9e5fd1c1b/config.cc
@@ -0,0 +1,35 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#include "arrow/python/config.h"
+#include "arrow/python/config_internal.h"
+
+namespace arrow {
+namespace py {
+
+namespace {
+
+const BuildInfo kBuildInfo = {
+    PYARROW_BUILD_TYPE,
+};
+
+}  // namespace
+
+const BuildInfo& GetBuildInfo() { return kBuildInfo; }
+
+}  // namespace py
+}  // namespace arrow
\ No newline at end of file
diff --git a/shared/package-files/c9/c91578b56302e8ab081c966de2ce17e505c4558db8921829f551fff170400938/negative_native.h b/shared/package-files/c9/c91578b56302e8ab081c966de2ce17e505c4558db8921829f551fff170400938/negative_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..116d853c013af0f418f9c200e7fbd60120306ee8
--- /dev/null
+++ b/shared/package-files/c9/c91578b56302e8ab081c966de2ce17e505c4558db8921829f551fff170400938/negative_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor negative(const at::Tensor & self);
+TORCH_API at::Tensor & negative_out(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & negative_(at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/c9/c9160042c796156c416421e4f7e408ec81e3e63677e32c4a9071c37f6ae02f41/burg.py b/shared/package-files/c9/c9160042c796156c416421e4f7e408ec81e3e63677e32c4a9071c37f6ae02f41/burg.py
new file mode 100644
index 0000000000000000000000000000000000000000..e458034c95e530191d78a28c45d05f8cc221d3f1
--- /dev/null
+++ b/shared/package-files/c9/c9160042c796156c416421e4f7e408ec81e3e63677e32c4a9071c37f6ae02f41/burg.py
@@ -0,0 +1,81 @@
+"""
+Burg's method for estimating AR(p) model parameters
+
+Author: Chad Fulton
+License: BSD-3
+"""
+import numpy as np
+
+from statsmodels.regression import linear_model
+from statsmodels.tools.tools import Bunch
+from statsmodels.tsa.arima.estimators._base import ARMAEstimationResult
+from statsmodels.tsa.arima.params import SARIMAXParams
+from statsmodels.tsa.arima.specification import SARIMAXSpecification
+
+
+def burg(endog, ar_order=0, demean=True):
+    """
+    Estimate AR parameters using Burg technique
+
+    Parameters
+    ----------
+    endog : array_like or SARIMAXSpecification
+        Input time series array, assumed to be stationary.
+    ar_order : int, optional
+        Autoregressive order. Default is 0.
+    demean : bool, optional
+        Whether to estimate and remove the mean from the process prior to
+        fitting the autoregressive coefficients.
+
+    Returns
+    -------
+    ARMAEstimationResult
+        A result object with fields:
+
+        parameters : SARIMAXParams object
+            Contains the parameter estimates from the final iteration.
+        other_results : Bunch
+            Includes one component, `spec`, which is the
+            `SARIMAXSpecification` instance corresponding to the input
+            arguments.
+
+    Notes
+    -----
+    The primary reference is [1]_, section 5.1.2.
+
+    This procedure assumes that the series is stationary.
+
+    This function is a light wrapper around `statsmodels.linear_model.burg`.
+
+    References
+    ----------
+    .. [1] Brockwell, Peter J., and Richard A. Davis. 2016.
+       Introduction to Time Series and Forecasting. Springer.
+    """
+    spec = SARIMAXSpecification(endog, ar_order=ar_order)
+    endog = spec.endog
+
+    # Workaround for statsmodels.tsa.stattools.pacf_burg which does not work
+    # on integer input
+    # TODO: remove when possible
+    if np.issubdtype(endog.dtype, np.dtype(int)):
+        endog = endog * 1.0
+
+    if not spec.is_ar_consecutive:
+        raise ValueError("Burg estimation unavailable for models with"
+                         " seasonal or otherwise non-consecutive AR orders.")
+
+    p = SARIMAXParams(spec=spec)
+
+    if ar_order == 0:
+        p.sigma2 = np.var(endog)
+    else:
+        p.ar_params, p.sigma2 = linear_model.burg(endog, order=ar_order,
+                                                  demean=demean)
+
+        # Construct other results
+    other_results = Bunch({
+        "spec": spec,
+    })
+
+    return ARMAEstimationResult(p, other_results)
diff --git a/shared/package-files/c9/c91628961b4718d60a2b3a3f9fdc07a81aaf6a76e7d5e5fc9307102dafa7db66/hann_window.h b/shared/package-files/c9/c91628961b4718d60a2b3a3f9fdc07a81aaf6a76e7d5e5fc9307102dafa7db66/hann_window.h
new file mode 100644
index 0000000000000000000000000000000000000000..f95483d7ea485c176b450a13d3727846de5b714f
--- /dev/null
+++ b/shared/package-files/c9/c91628961b4718d60a2b3a3f9fdc07a81aaf6a76e7d5e5fc9307102dafa7db66/hann_window.h
@@ -0,0 +1,61 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor hann_window(int64_t window_length, at::TensorOptions options={}) {
+    return at::_ops::hann_window::call(window_length, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+// aten::hann_window(int window_length, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor hann_window(int64_t window_length, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) {
+    return at::_ops::hann_window::call(window_length, dtype, layout, device, pin_memory);
+}
+
+// aten::hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor hann_window(int64_t window_length, bool periodic, at::TensorOptions options={}) {
+    return at::_ops::hann_window_periodic::call(window_length, periodic, c10::optTypeMetaToScalarType(options.dtype_opt()), options.layout_opt(), options.device_opt(), options.pinned_memory_opt());
+}
+// aten::hann_window.periodic(int window_length, bool periodic, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor
+inline at::Tensor hann_window(int64_t window_length, bool periodic, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory) {
+    return at::_ops::hann_window_periodic::call(window_length, periodic, dtype, layout, device, pin_memory);
+}
+
+// aten::hann_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hann_window_out(at::Tensor & out, int64_t window_length) {
+    return at::_ops::hann_window_out::call(window_length, out);
+}
+// aten::hann_window.out(int window_length, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hann_window_outf(int64_t window_length, at::Tensor & out) {
+    return at::_ops::hann_window_out::call(window_length, out);
+}
+
+// aten::hann_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hann_window_out(at::Tensor & out, int64_t window_length, bool periodic) {
+    return at::_ops::hann_window_periodic_out::call(window_length, periodic, out);
+}
+// aten::hann_window.periodic_out(int window_length, bool periodic, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & hann_window_outf(int64_t window_length, bool periodic, at::Tensor & out) {
+    return at::_ops::hann_window_periodic_out::call(window_length, periodic, out);
+}
+
+}
diff --git a/shared/package-files/c9/c917274c64b4339ef8f63519184640f3f531499f8278e782b166016d34b49d36/test_reshape.py b/shared/package-files/c9/c917274c64b4339ef8f63519184640f3f531499f8278e782b166016d34b49d36/test_reshape.py
new file mode 100644
index 0000000000000000000000000000000000000000..06dbb33aadf97a54e4bb283d3aed8fe1169164b3
--- /dev/null
+++ b/shared/package-files/c9/c917274c64b4339ef8f63519184640f3f531499f8278e782b166016d34b49d36/test_reshape.py
@@ -0,0 +1,224 @@
+from datetime import datetime
+
+import numpy as np
+import pytest
+import pytz
+
+import pandas as pd
+from pandas import (
+    Index,
+    MultiIndex,
+)
+import pandas._testing as tm
+
+
+def test_insert(idx):
+    # key contained in all levels
+    new_index = idx.insert(0, ("bar", "two"))
+    assert new_index.equal_levels(idx)
+    assert new_index[0] == ("bar", "two")
+
+    # key not contained in all levels
+    new_index = idx.insert(0, ("abc", "three"))
+
+    exp0 = Index(list(idx.levels[0]) + ["abc"], name="first")
+    tm.assert_index_equal(new_index.levels[0], exp0)
+    assert new_index.names == ["first", "second"]
+
+    exp1 = Index(list(idx.levels[1]) + ["three"], name="second")
+    tm.assert_index_equal(new_index.levels[1], exp1)
+    assert new_index[0] == ("abc", "three")
+
+    # key wrong length
+    msg = "Item must have length equal to number of levels"
+    with pytest.raises(ValueError, match=msg):
+        idx.insert(0, ("foo2",))
+
+    left = pd.DataFrame([["a", "b", 0], ["b", "d", 1]], columns=["1st", "2nd", "3rd"])
+    left.set_index(["1st", "2nd"], inplace=True)
+    ts = left["3rd"].copy(deep=True)
+
+    left.loc[("b", "x"), "3rd"] = 2
+    left.loc[("b", "a"), "3rd"] = -1
+    left.loc[("b", "b"), "3rd"] = 3
+    left.loc[("a", "x"), "3rd"] = 4
+    left.loc[("a", "w"), "3rd"] = 5
+    left.loc[("a", "a"), "3rd"] = 6
+
+    ts.loc[("b", "x")] = 2
+    ts.loc["b", "a"] = -1
+    ts.loc[("b", "b")] = 3
+    ts.loc["a", "x"] = 4
+    ts.loc[("a", "w")] = 5
+    ts.loc["a", "a"] = 6
+
+    right = pd.DataFrame(
+        [
+            ["a", "b", 0],
+            ["b", "d", 1],
+            ["b", "x", 2],
+            ["b", "a", -1],
+            ["b", "b", 3],
+            ["a", "x", 4],
+            ["a", "w", 5],
+            ["a", "a", 6],
+        ],
+        columns=["1st", "2nd", "3rd"],
+    )
+    right.set_index(["1st", "2nd"], inplace=True)
+    # FIXME data types changes to float because
+    # of intermediate nan insertion;
+    tm.assert_frame_equal(left, right, check_dtype=False)
+    tm.assert_series_equal(ts, right["3rd"])
+
+
+def test_insert2():
+    # GH9250
+    idx = (
+        [("test1", i) for i in range(5)]
+        + [("test2", i) for i in range(6)]
+        + [("test", 17), ("test", 18)]
+    )
+
+    left = pd.Series(np.linspace(0, 10, 11), MultiIndex.from_tuples(idx[:-2]))
+
+    left.loc[("test", 17)] = 11
+    left.loc[("test", 18)] = 12
+
+    right = pd.Series(np.linspace(0, 12, 13), MultiIndex.from_tuples(idx))
+
+    tm.assert_series_equal(left, right)
+
+
+def test_append(idx):
+    result = idx[:3].append(idx[3:])
+    assert result.equals(idx)
+
+    foos = [idx[:1], idx[1:3], idx[3:]]
+    result = foos[0].append(foos[1:])
+    assert result.equals(idx)
+
+    # empty
+    result = idx.append([])
+    assert result.equals(idx)
+
+
+def test_append_index():
+    idx1 = Index([1.1, 1.2, 1.3])
+    idx2 = pd.date_range("2011-01-01", freq="D", periods=3, tz="Asia/Tokyo")
+    idx3 = Index(["A", "B", "C"])
+
+    midx_lv2 = MultiIndex.from_arrays([idx1, idx2])
+    midx_lv3 = MultiIndex.from_arrays([idx1, idx2, idx3])
+
+    result = idx1.append(midx_lv2)
+
+    # see gh-7112
+    tz = pytz.timezone("Asia/Tokyo")
+    expected_tuples = [
+        (1.1, tz.localize(datetime(2011, 1, 1))),
+        (1.2, tz.localize(datetime(2011, 1, 2))),
+        (1.3, tz.localize(datetime(2011, 1, 3))),
+    ]
+    expected = Index([1.1, 1.2, 1.3] + expected_tuples)
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv2.append(idx1)
+    expected = Index(expected_tuples + [1.1, 1.2, 1.3])
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv2.append(midx_lv2)
+    expected = MultiIndex.from_arrays([idx1.append(idx1), idx2.append(idx2)])
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv2.append(midx_lv3)
+    tm.assert_index_equal(result, expected)
+
+    result = midx_lv3.append(midx_lv2)
+    expected = Index._simple_new(
+        np.array(
+            [
+                (1.1, tz.localize(datetime(2011, 1, 1)), "A"),
+                (1.2, tz.localize(datetime(2011, 1, 2)), "B"),
+                (1.3, tz.localize(datetime(2011, 1, 3)), "C"),
+            ]
+            + expected_tuples,
+            dtype=object,
+        ),
+        None,
+    )
+    tm.assert_index_equal(result, expected)
+
+
+@pytest.mark.parametrize("name, exp", [("b", "b"), ("c", None)])
+def test_append_names_match(name, exp):
+    # GH#48288
+    midx = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"])
+    midx2 = MultiIndex.from_arrays([[3], [5]], names=["a", name])
+    result = midx.append(midx2)
+    expected = MultiIndex.from_arrays([[1, 2, 3], [3, 4, 5]], names=["a", exp])
+    tm.assert_index_equal(result, expected)
+
+
+def test_append_names_dont_match():
+    # GH#48288
+    midx = MultiIndex.from_arrays([[1, 2], [3, 4]], names=["a", "b"])
+    midx2 = MultiIndex.from_arrays([[3], [5]], names=["x", "y"])
+    result = midx.append(midx2)
+    expected = MultiIndex.from_arrays([[1, 2, 3], [3, 4, 5]], names=None)
+    tm.assert_index_equal(result, expected)
+
+
+def test_append_overlapping_interval_levels():
+    # GH 54934
+    ivl1 = pd.IntervalIndex.from_breaks([0.0, 1.0, 2.0])
+    ivl2 = pd.IntervalIndex.from_breaks([0.5, 1.5, 2.5])
+    mi1 = MultiIndex.from_product([ivl1, ivl1])
+    mi2 = MultiIndex.from_product([ivl2, ivl2])
+    result = mi1.append(mi2)
+    expected = MultiIndex.from_tuples(
+        [
+            (pd.Interval(0.0, 1.0), pd.Interval(0.0, 1.0)),
+            (pd.Interval(0.0, 1.0), pd.Interval(1.0, 2.0)),
+            (pd.Interval(1.0, 2.0), pd.Interval(0.0, 1.0)),
+            (pd.Interval(1.0, 2.0), pd.Interval(1.0, 2.0)),
+            (pd.Interval(0.5, 1.5), pd.Interval(0.5, 1.5)),
+            (pd.Interval(0.5, 1.5), pd.Interval(1.5, 2.5)),
+            (pd.Interval(1.5, 2.5), pd.Interval(0.5, 1.5)),
+            (pd.Interval(1.5, 2.5), pd.Interval(1.5, 2.5)),
+        ]
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_repeat():
+    reps = 2
+    numbers = [1, 2, 3]
+    names = np.array(["foo", "bar"])
+
+    m = MultiIndex.from_product([numbers, names], names=names)
+    expected = MultiIndex.from_product([numbers, names.repeat(reps)], names=names)
+    tm.assert_index_equal(m.repeat(reps), expected)
+
+
+def test_insert_base(idx):
+    result = idx[1:4]
+
+    # test 0th element
+    assert idx[0:4].equals(result.insert(0, idx[0]))
+
+
+def test_delete_base(idx):
+    expected = idx[1:]
+    result = idx.delete(0)
+    assert result.equals(expected)
+    assert result.name == expected.name
+
+    expected = idx[:-1]
+    result = idx.delete(-1)
+    assert result.equals(expected)
+    assert result.name == expected.name
+
+    msg = "index 6 is out of bounds for axis 0 with size 6"
+    with pytest.raises(IndexError, match=msg):
+        idx.delete(len(idx))
diff --git a/shared/package-files/c9/c91944f3fffcaae84cae500c535176f368507edaf1c624906e36b6d28f216267/utils.pyi b/shared/package-files/c9/c91944f3fffcaae84cae500c535176f368507edaf1c624906e36b6d28f216267/utils.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..fb98a1e3dc39eec1dd4ad18ccb3b7ea6be151441
--- /dev/null
+++ b/shared/package-files/c9/c91944f3fffcaae84cae500c535176f368507edaf1c624906e36b6d28f216267/utils.pyi
@@ -0,0 +1,10 @@
+from typing import Any
+
+COMPILED_REGEX_TYPE: Any
+
+class RegexObject:
+    pattern: str = ...
+    flags: int = ...
+    def __init__(self, obj: Any) -> None: ...
+
+def get_migration_name_timestamp() -> str: ...
diff --git a/shared/package-files/c9/c919a639ec6c683785932dfb22b0f67bdb2fcfc09679b07b2f67d2532e0bbee1/debugging.py b/shared/package-files/c9/c919a639ec6c683785932dfb22b0f67bdb2fcfc09679b07b2f67d2532e0bbee1/debugging.py
new file mode 100644
index 0000000000000000000000000000000000000000..2dfe321e9686154c989d6a4a7b4c7db8c303759a
--- /dev/null
+++ b/shared/package-files/c9/c919a639ec6c683785932dfb22b0f67bdb2fcfc09679b07b2f67d2532e0bbee1/debugging.py
@@ -0,0 +1,385 @@
+# mypy: allow-untyped-defs
+# ruff: noqa: T100
+"""Interactive debugging with PDB, the Python Debugger."""
+
+from __future__ import annotations
+
+import argparse
+import functools
+import sys
+import types
+from typing import Any
+from typing import Callable
+from typing import Generator
+import unittest
+
+from _pytest import outcomes
+from _pytest._code import ExceptionInfo
+from _pytest.capture import CaptureManager
+from _pytest.config import Config
+from _pytest.config import ConftestImportFailure
+from _pytest.config import hookimpl
+from _pytest.config import PytestPluginManager
+from _pytest.config.argparsing import Parser
+from _pytest.config.exceptions import UsageError
+from _pytest.nodes import Node
+from _pytest.reports import BaseReport
+from _pytest.runner import CallInfo
+
+
+def _validate_usepdb_cls(value: str) -> tuple[str, str]:
+    """Validate syntax of --pdbcls option."""
+    try:
+        modname, classname = value.split(":")
+    except ValueError as e:
+        raise argparse.ArgumentTypeError(
+            f"{value!r} is not in the format 'modname:classname'"
+        ) from e
+    return (modname, classname)
+
+
+def pytest_addoption(parser: Parser) -> None:
+    group = parser.getgroup("general")
+    group._addoption(
+        "--pdb",
+        dest="usepdb",
+        action="store_true",
+        help="Start the interactive Python debugger on errors or KeyboardInterrupt",
+    )
+    group._addoption(
+        "--pdbcls",
+        dest="usepdb_cls",
+        metavar="modulename:classname",
+        type=_validate_usepdb_cls,
+        help="Specify a custom interactive Python debugger for use with --pdb."
+        "For example: --pdbcls=IPython.terminal.debugger:TerminalPdb",
+    )
+    group._addoption(
+        "--trace",
+        dest="trace",
+        action="store_true",
+        help="Immediately break when running each test",
+    )
+
+
+def pytest_configure(config: Config) -> None:
+    import pdb
+
+    if config.getvalue("trace"):
+        config.pluginmanager.register(PdbTrace(), "pdbtrace")
+    if config.getvalue("usepdb"):
+        config.pluginmanager.register(PdbInvoke(), "pdbinvoke")
+
+    pytestPDB._saved.append(
+        (pdb.set_trace, pytestPDB._pluginmanager, pytestPDB._config)
+    )
+    pdb.set_trace = pytestPDB.set_trace
+    pytestPDB._pluginmanager = config.pluginmanager
+    pytestPDB._config = config
+
+    # NOTE: not using pytest_unconfigure, since it might get called although
+    #       pytest_configure was not (if another plugin raises UsageError).
+    def fin() -> None:
+        (
+            pdb.set_trace,
+            pytestPDB._pluginmanager,
+            pytestPDB._config,
+        ) = pytestPDB._saved.pop()
+
+    config.add_cleanup(fin)
+
+
+class pytestPDB:
+    """Pseudo PDB that defers to the real pdb."""
+
+    _pluginmanager: PytestPluginManager | None = None
+    _config: Config | None = None
+    _saved: list[
+        tuple[Callable[..., None], PytestPluginManager | None, Config | None]
+    ] = []
+    _recursive_debug = 0
+    _wrapped_pdb_cls: tuple[type[Any], type[Any]] | None = None
+
+    @classmethod
+    def _is_capturing(cls, capman: CaptureManager | None) -> str | bool:
+        if capman:
+            return capman.is_capturing()
+        return False
+
+    @classmethod
+    def _import_pdb_cls(cls, capman: CaptureManager | None):
+        if not cls._config:
+            import pdb
+
+            # Happens when using pytest.set_trace outside of a test.
+            return pdb.Pdb
+
+        usepdb_cls = cls._config.getvalue("usepdb_cls")
+
+        if cls._wrapped_pdb_cls and cls._wrapped_pdb_cls[0] == usepdb_cls:
+            return cls._wrapped_pdb_cls[1]
+
+        if usepdb_cls:
+            modname, classname = usepdb_cls
+
+            try:
+                __import__(modname)
+                mod = sys.modules[modname]
+
+                # Handle --pdbcls=pdb:pdb.Pdb (useful e.g. with pdbpp).
+                parts = classname.split(".")
+                pdb_cls = getattr(mod, parts[0])
+                for part in parts[1:]:
+                    pdb_cls = getattr(pdb_cls, part)
+            except Exception as exc:
+                value = ":".join((modname, classname))
+                raise UsageError(
+                    f"--pdbcls: could not import {value!r}: {exc}"
+                ) from exc
+        else:
+            import pdb
+
+            pdb_cls = pdb.Pdb
+
+        wrapped_cls = cls._get_pdb_wrapper_class(pdb_cls, capman)
+        cls._wrapped_pdb_cls = (usepdb_cls, wrapped_cls)
+        return wrapped_cls
+
+    @classmethod
+    def _get_pdb_wrapper_class(cls, pdb_cls, capman: CaptureManager | None):
+        import _pytest.config
+
+        class PytestPdbWrapper(pdb_cls):
+            _pytest_capman = capman
+            _continued = False
+
+            def do_debug(self, arg):
+                cls._recursive_debug += 1
+                ret = super().do_debug(arg)
+                cls._recursive_debug -= 1
+                return ret
+
+            def do_continue(self, arg):
+                ret = super().do_continue(arg)
+                if cls._recursive_debug == 0:
+                    assert cls._config is not None
+                    tw = _pytest.config.create_terminal_writer(cls._config)
+                    tw.line()
+
+                    capman = self._pytest_capman
+                    capturing = pytestPDB._is_capturing(capman)
+                    if capturing:
+                        if capturing == "global":
+                            tw.sep(">", "PDB continue (IO-capturing resumed)")
+                        else:
+                            tw.sep(
+                                ">",
+                                f"PDB continue (IO-capturing resumed for {capturing})",
+                            )
+                        assert capman is not None
+                        capman.resume()
+                    else:
+                        tw.sep(">", "PDB continue")
+                assert cls._pluginmanager is not None
+                cls._pluginmanager.hook.pytest_leave_pdb(config=cls._config, pdb=self)
+                self._continued = True
+                return ret
+
+            do_c = do_cont = do_continue
+
+            def do_quit(self, arg):
+                """Raise Exit outcome when quit command is used in pdb.
+
+                This is a bit of a hack - it would be better if BdbQuit
+                could be handled, but this would require to wrap the
+                whole pytest run, and adjust the report etc.
+                """
+                ret = super().do_quit(arg)
+
+                if cls._recursive_debug == 0:
+                    outcomes.exit("Quitting debugger")
+
+                return ret
+
+            do_q = do_quit
+            do_exit = do_quit
+
+            def setup(self, f, tb):
+                """Suspend on setup().
+
+                Needed after do_continue resumed, and entering another
+                breakpoint again.
+                """
+                ret = super().setup(f, tb)
+                if not ret and self._continued:
+                    # pdb.setup() returns True if the command wants to exit
+                    # from the interaction: do not suspend capturing then.
+                    if self._pytest_capman:
+                        self._pytest_capman.suspend_global_capture(in_=True)
+                return ret
+
+            def get_stack(self, f, t):
+                stack, i = super().get_stack(f, t)
+                if f is None:
+                    # Find last non-hidden frame.
+                    i = max(0, len(stack) - 1)
+                    while i and stack[i][0].f_locals.get("__tracebackhide__", False):
+                        i -= 1
+                return stack, i
+
+        return PytestPdbWrapper
+
+    @classmethod
+    def _init_pdb(cls, method, *args, **kwargs):
+        """Initialize PDB debugging, dropping any IO capturing."""
+        import _pytest.config
+
+        if cls._pluginmanager is None:
+            capman: CaptureManager | None = None
+        else:
+            capman = cls._pluginmanager.getplugin("capturemanager")
+        if capman:
+            capman.suspend(in_=True)
+
+        if cls._config:
+            tw = _pytest.config.create_terminal_writer(cls._config)
+            tw.line()
+
+            if cls._recursive_debug == 0:
+                # Handle header similar to pdb.set_trace in py37+.
+                header = kwargs.pop("header", None)
+                if header is not None:
+                    tw.sep(">", header)
+                else:
+                    capturing = cls._is_capturing(capman)
+                    if capturing == "global":
+                        tw.sep(">", f"PDB {method} (IO-capturing turned off)")
+                    elif capturing:
+                        tw.sep(
+                            ">",
+                            f"PDB {method} (IO-capturing turned off for {capturing})",
+                        )
+                    else:
+                        tw.sep(">", f"PDB {method}")
+
+        _pdb = cls._import_pdb_cls(capman)(**kwargs)
+
+        if cls._pluginmanager:
+            cls._pluginmanager.hook.pytest_enter_pdb(config=cls._config, pdb=_pdb)
+        return _pdb
+
+    @classmethod
+    def set_trace(cls, *args, **kwargs) -> None:
+        """Invoke debugging via ``Pdb.set_trace``, dropping any IO capturing."""
+        frame = sys._getframe().f_back
+        _pdb = cls._init_pdb("set_trace", *args, **kwargs)
+        _pdb.set_trace(frame)
+
+
+class PdbInvoke:
+    def pytest_exception_interact(
+        self, node: Node, call: CallInfo[Any], report: BaseReport
+    ) -> None:
+        capman = node.config.pluginmanager.getplugin("capturemanager")
+        if capman:
+            capman.suspend_global_capture(in_=True)
+            out, err = capman.read_global_capture()
+            sys.stdout.write(out)
+            sys.stdout.write(err)
+        assert call.excinfo is not None
+
+        if not isinstance(call.excinfo.value, unittest.SkipTest):
+            _enter_pdb(node, call.excinfo, report)
+
+    def pytest_internalerror(self, excinfo: ExceptionInfo[BaseException]) -> None:
+        tb = _postmortem_traceback(excinfo)
+        post_mortem(tb)
+
+
+class PdbTrace:
+    @hookimpl(wrapper=True)
+    def pytest_pyfunc_call(self, pyfuncitem) -> Generator[None, object, object]:
+        wrap_pytest_function_for_tracing(pyfuncitem)
+        return (yield)
+
+
+def wrap_pytest_function_for_tracing(pyfuncitem) -> None:
+    """Change the Python function object of the given Function item by a
+    wrapper which actually enters pdb before calling the python function
+    itself, effectively leaving the user in the pdb prompt in the first
+    statement of the function."""
+    _pdb = pytestPDB._init_pdb("runcall")
+    testfunction = pyfuncitem.obj
+
+    # we can't just return `partial(pdb.runcall, testfunction)` because (on
+    # python < 3.7.4) runcall's first param is `func`, which means we'd get
+    # an exception if one of the kwargs to testfunction was called `func`.
+    @functools.wraps(testfunction)
+    def wrapper(*args, **kwargs) -> None:
+        func = functools.partial(testfunction, *args, **kwargs)
+        _pdb.runcall(func)
+
+    pyfuncitem.obj = wrapper
+
+
+def maybe_wrap_pytest_function_for_tracing(pyfuncitem) -> None:
+    """Wrap the given pytestfunct item for tracing support if --trace was given in
+    the command line."""
+    if pyfuncitem.config.getvalue("trace"):
+        wrap_pytest_function_for_tracing(pyfuncitem)
+
+
+def _enter_pdb(
+    node: Node, excinfo: ExceptionInfo[BaseException], rep: BaseReport
+) -> BaseReport:
+    # XXX we reuse the TerminalReporter's terminalwriter
+    # because this seems to avoid some encoding related troubles
+    # for not completely clear reasons.
+    tw = node.config.pluginmanager.getplugin("terminalreporter")._tw
+    tw.line()
+
+    showcapture = node.config.option.showcapture
+
+    for sectionname, content in (
+        ("stdout", rep.capstdout),
+        ("stderr", rep.capstderr),
+        ("log", rep.caplog),
+    ):
+        if showcapture in (sectionname, "all") and content:
+            tw.sep(">", "captured " + sectionname)
+            if content[-1:] == "\n":
+                content = content[:-1]
+            tw.line(content)
+
+    tw.sep(">", "traceback")
+    rep.toterminal(tw)
+    tw.sep(">", "entering PDB")
+    tb = _postmortem_traceback(excinfo)
+    rep._pdbshown = True  # type: ignore[attr-defined]
+    post_mortem(tb)
+    return rep
+
+
+def _postmortem_traceback(excinfo: ExceptionInfo[BaseException]) -> types.TracebackType:
+    from doctest import UnexpectedException
+
+    if isinstance(excinfo.value, UnexpectedException):
+        # A doctest.UnexpectedException is not useful for post_mortem.
+        # Use the underlying exception instead:
+        return excinfo.value.exc_info[2]
+    elif isinstance(excinfo.value, ConftestImportFailure):
+        # A config.ConftestImportFailure is not useful for post_mortem.
+        # Use the underlying exception instead:
+        assert excinfo.value.cause.__traceback__ is not None
+        return excinfo.value.cause.__traceback__
+    else:
+        assert excinfo._excinfo is not None
+        return excinfo._excinfo[2]
+
+
+def post_mortem(t: types.TracebackType) -> None:
+    p = pytestPDB._init_pdb("post_mortem")
+    p.reset()
+    p.interaction(None, t)
+    if p.quitting:
+        outcomes.exit("Quitting debugger")
diff --git a/shared/package-files/c9/c92663e1364515509ae7284c989285610621b904da6d94d105bd58a22083f9a7/arrayprint.py b/shared/package-files/c9/c92663e1364515509ae7284c989285610621b904da6d94d105bd58a22083f9a7/arrayprint.py
new file mode 100644
index 0000000000000000000000000000000000000000..62cd527073a615458b12619545f4da76664c4bc0
--- /dev/null
+++ b/shared/package-files/c9/c92663e1364515509ae7284c989285610621b904da6d94d105bd58a22083f9a7/arrayprint.py
@@ -0,0 +1,1725 @@
+"""Array printing function
+
+$Id: arrayprint.py,v 1.9 2005/09/13 13:58:44 teoliphant Exp $
+
+"""
+__all__ = ["array2string", "array_str", "array_repr", "set_string_function",
+           "set_printoptions", "get_printoptions", "printoptions",
+           "format_float_positional", "format_float_scientific"]
+__docformat__ = 'restructuredtext'
+
+#
+# Written by Konrad Hinsen 
+# last revision: 1996-3-13
+# modified by Jim Hugunin 1997-3-3 for repr's and str's (and other details)
+# and by Perry Greenfield 2000-4-1 for numarray
+# and by Travis Oliphant  2005-8-22 for numpy
+
+
+# Note: Both scalartypes.c.src and arrayprint.py implement strs for numpy
+# scalars but for different purposes. scalartypes.c.src has str/reprs for when
+# the scalar is printed on its own, while arrayprint.py has strs for when
+# scalars are printed inside an ndarray. Only the latter strs are currently
+# user-customizable.
+
+import functools
+import numbers
+import sys
+try:
+    from _thread import get_ident
+except ImportError:
+    from _dummy_thread import get_ident
+
+import numpy as np
+from . import numerictypes as _nt
+from .umath import absolute, isinf, isfinite, isnat
+from . import multiarray
+from .multiarray import (array, dragon4_positional, dragon4_scientific,
+                         datetime_as_string, datetime_data, ndarray,
+                         set_legacy_print_mode)
+from .fromnumeric import any
+from .numeric import concatenate, asarray, errstate
+from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
+                           flexible)
+from .overrides import array_function_dispatch, set_module
+import operator
+import warnings
+import contextlib
+
+_format_options = {
+    'edgeitems': 3,  # repr N leading and trailing items of each dimension
+    'threshold': 1000,  # total items > triggers array summarization
+    'floatmode': 'maxprec',
+    'precision': 8,  # precision of floating point representations
+    'suppress': False,  # suppress printing small floating values in exp format
+    'linewidth': 75,
+    'nanstr': 'nan',
+    'infstr': 'inf',
+    'sign': '-',
+    'formatter': None,
+    # Internally stored as an int to simplify comparisons; converted from/to
+    # str/False on the way in/out.
+    'legacy': sys.maxsize}
+
+def _make_options_dict(precision=None, threshold=None, edgeitems=None,
+                       linewidth=None, suppress=None, nanstr=None, infstr=None,
+                       sign=None, formatter=None, floatmode=None, legacy=None):
+    """
+    Make a dictionary out of the non-None arguments, plus conversion of
+    *legacy* and sanity checks.
+    """
+
+    options = {k: v for k, v in locals().items() if v is not None}
+
+    if suppress is not None:
+        options['suppress'] = bool(suppress)
+
+    modes = ['fixed', 'unique', 'maxprec', 'maxprec_equal']
+    if floatmode not in modes + [None]:
+        raise ValueError("floatmode option must be one of " +
+                         ", ".join('"{}"'.format(m) for m in modes))
+
+    if sign not in [None, '-', '+', ' ']:
+        raise ValueError("sign option must be one of ' ', '+', or '-'")
+
+    if legacy == False:
+        options['legacy'] = sys.maxsize
+    elif legacy == '1.13':
+        options['legacy'] = 113
+    elif legacy == '1.21':
+        options['legacy'] = 121
+    elif legacy is None:
+        pass  # OK, do nothing.
+    else:
+        warnings.warn(
+            "legacy printing option can currently only be '1.13', '1.21', or "
+            "`False`", stacklevel=3)
+
+    if threshold is not None:
+        # forbid the bad threshold arg suggested by stack overflow, gh-12351
+        if not isinstance(threshold, numbers.Number):
+            raise TypeError("threshold must be numeric")
+        if np.isnan(threshold):
+            raise ValueError("threshold must be non-NAN, try "
+                             "sys.maxsize for untruncated representation")
+
+    if precision is not None:
+        # forbid the bad precision arg as suggested by issue #18254
+        try:
+            options['precision'] = operator.index(precision)
+        except TypeError as e:
+            raise TypeError('precision must be an integer') from e
+
+    return options
+
+
+@set_module('numpy')
+def set_printoptions(precision=None, threshold=None, edgeitems=None,
+                     linewidth=None, suppress=None, nanstr=None, infstr=None,
+                     formatter=None, sign=None, floatmode=None, *, legacy=None):
+    """
+    Set printing options.
+
+    These options determine the way floating point numbers, arrays and
+    other NumPy objects are displayed.
+
+    Parameters
+    ----------
+    precision : int or None, optional
+        Number of digits of precision for floating point output (default 8).
+        May be None if `floatmode` is not `fixed`, to print as many digits as
+        necessary to uniquely specify the value.
+    threshold : int, optional
+        Total number of array elements which trigger summarization
+        rather than full repr (default 1000).
+        To always use the full repr without summarization, pass `sys.maxsize`.
+    edgeitems : int, optional
+        Number of array items in summary at beginning and end of
+        each dimension (default 3).
+    linewidth : int, optional
+        The number of characters per line for the purpose of inserting
+        line breaks (default 75).
+    suppress : bool, optional
+        If True, always print floating point numbers using fixed point
+        notation, in which case numbers equal to zero in the current precision
+        will print as zero.  If False, then scientific notation is used when
+        absolute value of the smallest number is < 1e-4 or the ratio of the
+        maximum absolute value to the minimum is > 1e3. The default is False.
+    nanstr : str, optional
+        String representation of floating point not-a-number (default nan).
+    infstr : str, optional
+        String representation of floating point infinity (default inf).
+    sign : string, either '-', '+', or ' ', optional
+        Controls printing of the sign of floating-point types. If '+', always
+        print the sign of positive values. If ' ', always prints a space
+        (whitespace character) in the sign position of positive values.  If
+        '-', omit the sign character of positive values. (default '-')
+    formatter : dict of callables, optional
+        If not None, the keys should indicate the type(s) that the respective
+        formatting function applies to.  Callables should return a string.
+        Types that are not specified (by their corresponding keys) are handled
+        by the default formatters.  Individual types for which a formatter
+        can be set are:
+
+        - 'bool'
+        - 'int'
+        - 'timedelta' : a `numpy.timedelta64`
+        - 'datetime' : a `numpy.datetime64`
+        - 'float'
+        - 'longfloat' : 128-bit floats
+        - 'complexfloat'
+        - 'longcomplexfloat' : composed of two 128-bit floats
+        - 'numpystr' : types `numpy.bytes_` and `numpy.str_`
+        - 'object' : `np.object_` arrays
+
+        Other keys that can be used to set a group of types at once are:
+
+        - 'all' : sets all types
+        - 'int_kind' : sets 'int'
+        - 'float_kind' : sets 'float' and 'longfloat'
+        - 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
+        - 'str_kind' : sets 'numpystr'
+    floatmode : str, optional
+        Controls the interpretation of the `precision` option for
+        floating-point types. Can take the following values
+        (default maxprec_equal):
+
+        * 'fixed': Always print exactly `precision` fractional digits,
+                even if this would print more or fewer digits than
+                necessary to specify the value uniquely.
+        * 'unique': Print the minimum number of fractional digits necessary
+                to represent each value uniquely. Different elements may
+                have a different number of digits. The value of the
+                `precision` option is ignored.
+        * 'maxprec': Print at most `precision` fractional digits, but if
+                an element can be uniquely represented with fewer digits
+                only print it with that many.
+        * 'maxprec_equal': Print at most `precision` fractional digits,
+                but if every element in the array can be uniquely
+                represented with an equal number of fewer digits, use that
+                many digits for all elements.
+    legacy : string or `False`, optional
+        If set to the string `'1.13'` enables 1.13 legacy printing mode. This
+        approximates numpy 1.13 print output by including a space in the sign
+        position of floats and different behavior for 0d arrays. This also
+        enables 1.21 legacy printing mode (described below).
+
+        If set to the string `'1.21'` enables 1.21 legacy printing mode. This
+        approximates numpy 1.21 print output of complex structured dtypes
+        by not inserting spaces after commas that separate fields and after
+        colons.
+
+        If set to `False`, disables legacy mode.
+
+        Unrecognized strings will be ignored with a warning for forward
+        compatibility.
+
+        .. versionadded:: 1.14.0
+        .. versionchanged:: 1.22.0
+
+    See Also
+    --------
+    get_printoptions, printoptions, set_string_function, array2string
+
+    Notes
+    -----
+    `formatter` is always reset with a call to `set_printoptions`.
+
+    Use `printoptions` as a context manager to set the values temporarily.
+
+    Examples
+    --------
+    Floating point precision can be set:
+
+    >>> np.set_printoptions(precision=4)
+    >>> np.array([1.123456789])
+    [1.1235]
+
+    Long arrays can be summarised:
+
+    >>> np.set_printoptions(threshold=5)
+    >>> np.arange(10)
+    array([0, 1, 2, ..., 7, 8, 9])
+
+    Small results can be suppressed:
+
+    >>> eps = np.finfo(float).eps
+    >>> x = np.arange(4.)
+    >>> x**2 - (x + eps)**2
+    array([-4.9304e-32, -4.4409e-16,  0.0000e+00,  0.0000e+00])
+    >>> np.set_printoptions(suppress=True)
+    >>> x**2 - (x + eps)**2
+    array([-0., -0.,  0.,  0.])
+
+    A custom formatter can be used to display array elements as desired:
+
+    >>> np.set_printoptions(formatter={'all':lambda x: 'int: '+str(-x)})
+    >>> x = np.arange(3)
+    >>> x
+    array([int: 0, int: -1, int: -2])
+    >>> np.set_printoptions()  # formatter gets reset
+    >>> x
+    array([0, 1, 2])
+
+    To put back the default options, you can use:
+
+    >>> np.set_printoptions(edgeitems=3, infstr='inf',
+    ... linewidth=75, nanstr='nan', precision=8,
+    ... suppress=False, threshold=1000, formatter=None)
+
+    Also to temporarily override options, use `printoptions` as a context manager:
+
+    >>> with np.printoptions(precision=2, suppress=True, threshold=5):
+    ...     np.linspace(0, 10, 10)
+    array([ 0.  ,  1.11,  2.22, ...,  7.78,  8.89, 10.  ])
+
+    """
+    opt = _make_options_dict(precision, threshold, edgeitems, linewidth,
+                             suppress, nanstr, infstr, sign, formatter,
+                             floatmode, legacy)
+    # formatter is always reset
+    opt['formatter'] = formatter
+    _format_options.update(opt)
+
+    # set the C variable for legacy mode
+    if _format_options['legacy'] == 113:
+        set_legacy_print_mode(113)
+        # reset the sign option in legacy mode to avoid confusion
+        _format_options['sign'] = '-'
+    elif _format_options['legacy'] == 121:
+        set_legacy_print_mode(121)
+    elif _format_options['legacy'] == sys.maxsize:
+        set_legacy_print_mode(0)
+
+
+@set_module('numpy')
+def get_printoptions():
+    """
+    Return the current print options.
+
+    Returns
+    -------
+    print_opts : dict
+        Dictionary of current print options with keys
+
+          - precision : int
+          - threshold : int
+          - edgeitems : int
+          - linewidth : int
+          - suppress : bool
+          - nanstr : str
+          - infstr : str
+          - formatter : dict of callables
+          - sign : str
+
+        For a full description of these options, see `set_printoptions`.
+
+    See Also
+    --------
+    set_printoptions, printoptions, set_string_function
+
+    """
+    opts = _format_options.copy()
+    opts['legacy'] = {
+        113: '1.13', 121: '1.21', sys.maxsize: False,
+    }[opts['legacy']]
+    return opts
+
+
+def _get_legacy_print_mode():
+    """Return the legacy print mode as an int."""
+    return _format_options['legacy']
+
+
+@set_module('numpy')
+@contextlib.contextmanager
+def printoptions(*args, **kwargs):
+    """Context manager for setting print options.
+
+    Set print options for the scope of the `with` block, and restore the old
+    options at the end. See `set_printoptions` for the full description of
+    available options.
+
+    Examples
+    --------
+
+    >>> from numpy.testing import assert_equal
+    >>> with np.printoptions(precision=2):
+    ...     np.array([2.0]) / 3
+    array([0.67])
+
+    The `as`-clause of the `with`-statement gives the current print options:
+
+    >>> with np.printoptions(precision=2) as opts:
+    ...      assert_equal(opts, np.get_printoptions())
+
+    See Also
+    --------
+    set_printoptions, get_printoptions
+
+    """
+    opts = np.get_printoptions()
+    try:
+        np.set_printoptions(*args, **kwargs)
+        yield np.get_printoptions()
+    finally:
+        np.set_printoptions(**opts)
+
+
+def _leading_trailing(a, edgeitems, index=()):
+    """
+    Keep only the N-D corners (leading and trailing edges) of an array.
+
+    Should be passed a base-class ndarray, since it makes no guarantees about
+    preserving subclasses.
+    """
+    axis = len(index)
+    if axis == a.ndim:
+        return a[index]
+
+    if a.shape[axis] > 2*edgeitems:
+        return concatenate((
+            _leading_trailing(a, edgeitems, index + np.index_exp[ :edgeitems]),
+            _leading_trailing(a, edgeitems, index + np.index_exp[-edgeitems:])
+        ), axis=axis)
+    else:
+        return _leading_trailing(a, edgeitems, index + np.index_exp[:])
+
+
+def _object_format(o):
+    """ Object arrays containing lists should be printed unambiguously """
+    if type(o) is list:
+        fmt = 'list({!r})'
+    else:
+        fmt = '{!r}'
+    return fmt.format(o)
+
+def repr_format(x):
+    return repr(x)
+
+def str_format(x):
+    return str(x)
+
+def _get_formatdict(data, *, precision, floatmode, suppress, sign, legacy,
+                    formatter, **kwargs):
+    # note: extra arguments in kwargs are ignored
+
+    # wrapped in lambdas to avoid taking a code path with the wrong type of data
+    formatdict = {
+        'bool': lambda: BoolFormat(data),
+        'int': lambda: IntegerFormat(data),
+        'float': lambda: FloatingFormat(
+            data, precision, floatmode, suppress, sign, legacy=legacy),
+        'longfloat': lambda: FloatingFormat(
+            data, precision, floatmode, suppress, sign, legacy=legacy),
+        'complexfloat': lambda: ComplexFloatingFormat(
+            data, precision, floatmode, suppress, sign, legacy=legacy),
+        'longcomplexfloat': lambda: ComplexFloatingFormat(
+            data, precision, floatmode, suppress, sign, legacy=legacy),
+        'datetime': lambda: DatetimeFormat(data, legacy=legacy),
+        'timedelta': lambda: TimedeltaFormat(data),
+        'object': lambda: _object_format,
+        'void': lambda: str_format,
+        'numpystr': lambda: repr_format}
+
+    # we need to wrap values in `formatter` in a lambda, so that the interface
+    # is the same as the above values.
+    def indirect(x):
+        return lambda: x
+
+    if formatter is not None:
+        fkeys = [k for k in formatter.keys() if formatter[k] is not None]
+        if 'all' in fkeys:
+            for key in formatdict.keys():
+                formatdict[key] = indirect(formatter['all'])
+        if 'int_kind' in fkeys:
+            for key in ['int']:
+                formatdict[key] = indirect(formatter['int_kind'])
+        if 'float_kind' in fkeys:
+            for key in ['float', 'longfloat']:
+                formatdict[key] = indirect(formatter['float_kind'])
+        if 'complex_kind' in fkeys:
+            for key in ['complexfloat', 'longcomplexfloat']:
+                formatdict[key] = indirect(formatter['complex_kind'])
+        if 'str_kind' in fkeys:
+            formatdict['numpystr'] = indirect(formatter['str_kind'])
+        for key in formatdict.keys():
+            if key in fkeys:
+                formatdict[key] = indirect(formatter[key])
+
+    return formatdict
+
+def _get_format_function(data, **options):
+    """
+    find the right formatting function for the dtype_
+    """
+    dtype_ = data.dtype
+    dtypeobj = dtype_.type
+    formatdict = _get_formatdict(data, **options)
+    if dtypeobj is None:
+        return formatdict["numpystr"]()
+    elif issubclass(dtypeobj, _nt.bool_):
+        return formatdict['bool']()
+    elif issubclass(dtypeobj, _nt.integer):
+        if issubclass(dtypeobj, _nt.timedelta64):
+            return formatdict['timedelta']()
+        else:
+            return formatdict['int']()
+    elif issubclass(dtypeobj, _nt.floating):
+        if issubclass(dtypeobj, _nt.longfloat):
+            return formatdict['longfloat']()
+        else:
+            return formatdict['float']()
+    elif issubclass(dtypeobj, _nt.complexfloating):
+        if issubclass(dtypeobj, _nt.clongfloat):
+            return formatdict['longcomplexfloat']()
+        else:
+            return formatdict['complexfloat']()
+    elif issubclass(dtypeobj, (_nt.str_, _nt.bytes_)):
+        return formatdict['numpystr']()
+    elif issubclass(dtypeobj, _nt.datetime64):
+        return formatdict['datetime']()
+    elif issubclass(dtypeobj, _nt.object_):
+        return formatdict['object']()
+    elif issubclass(dtypeobj, _nt.void):
+        if dtype_.names is not None:
+            return StructuredVoidFormat.from_data(data, **options)
+        else:
+            return formatdict['void']()
+    else:
+        return formatdict['numpystr']()
+
+
+def _recursive_guard(fillvalue='...'):
+    """
+    Like the python 3.2 reprlib.recursive_repr, but forwards *args and **kwargs
+
+    Decorates a function such that if it calls itself with the same first
+    argument, it returns `fillvalue` instead of recursing.
+
+    Largely copied from reprlib.recursive_repr
+    """
+
+    def decorating_function(f):
+        repr_running = set()
+
+        @functools.wraps(f)
+        def wrapper(self, *args, **kwargs):
+            key = id(self), get_ident()
+            if key in repr_running:
+                return fillvalue
+            repr_running.add(key)
+            try:
+                return f(self, *args, **kwargs)
+            finally:
+                repr_running.discard(key)
+
+        return wrapper
+
+    return decorating_function
+
+
+# gracefully handle recursive calls, when object arrays contain themselves
+@_recursive_guard()
+def _array2string(a, options, separator=' ', prefix=""):
+    # The formatter __init__s in _get_format_function cannot deal with
+    # subclasses yet, and we also need to avoid recursion issues in
+    # _formatArray with subclasses which return 0d arrays in place of scalars
+    data = asarray(a)
+    if a.shape == ():
+        a = data
+
+    if a.size > options['threshold']:
+        summary_insert = "..."
+        data = _leading_trailing(data, options['edgeitems'])
+    else:
+        summary_insert = ""
+
+    # find the right formatting function for the array
+    format_function = _get_format_function(data, **options)
+
+    # skip over "["
+    next_line_prefix = " "
+    # skip over array(
+    next_line_prefix += " "*len(prefix)
+
+    lst = _formatArray(a, format_function, options['linewidth'],
+                       next_line_prefix, separator, options['edgeitems'],
+                       summary_insert, options['legacy'])
+    return lst
+
+
+def _array2string_dispatcher(
+        a, max_line_width=None, precision=None,
+        suppress_small=None, separator=None, prefix=None,
+        style=None, formatter=None, threshold=None,
+        edgeitems=None, sign=None, floatmode=None, suffix=None,
+        *, legacy=None):
+    return (a,)
+
+
+@array_function_dispatch(_array2string_dispatcher, module='numpy')
+def array2string(a, max_line_width=None, precision=None,
+                 suppress_small=None, separator=' ', prefix="",
+                 style=np._NoValue, formatter=None, threshold=None,
+                 edgeitems=None, sign=None, floatmode=None, suffix="",
+                 *, legacy=None):
+    """
+    Return a string representation of an array.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array.
+    max_line_width : int, optional
+        Inserts newlines if text is longer than `max_line_width`.
+        Defaults to ``numpy.get_printoptions()['linewidth']``.
+    precision : int or None, optional
+        Floating point precision.
+        Defaults to ``numpy.get_printoptions()['precision']``.
+    suppress_small : bool, optional
+        Represent numbers "very close" to zero as zero; default is False.
+        Very close is defined by precision: if the precision is 8, e.g.,
+        numbers smaller (in absolute value) than 5e-9 are represented as
+        zero.
+        Defaults to ``numpy.get_printoptions()['suppress']``.
+    separator : str, optional
+        Inserted between elements.
+    prefix : str, optional
+    suffix : str, optional
+        The length of the prefix and suffix strings are used to respectively
+        align and wrap the output. An array is typically printed as::
+
+          prefix + array2string(a) + suffix
+
+        The output is left-padded by the length of the prefix string, and
+        wrapping is forced at the column ``max_line_width - len(suffix)``.
+        It should be noted that the content of prefix and suffix strings are
+        not included in the output.
+    style : _NoValue, optional
+        Has no effect, do not use.
+
+        .. deprecated:: 1.14.0
+    formatter : dict of callables, optional
+        If not None, the keys should indicate the type(s) that the respective
+        formatting function applies to.  Callables should return a string.
+        Types that are not specified (by their corresponding keys) are handled
+        by the default formatters.  Individual types for which a formatter
+        can be set are:
+
+        - 'bool'
+        - 'int'
+        - 'timedelta' : a `numpy.timedelta64`
+        - 'datetime' : a `numpy.datetime64`
+        - 'float'
+        - 'longfloat' : 128-bit floats
+        - 'complexfloat'
+        - 'longcomplexfloat' : composed of two 128-bit floats
+        - 'void' : type `numpy.void`
+        - 'numpystr' : types `numpy.bytes_` and `numpy.str_`
+
+        Other keys that can be used to set a group of types at once are:
+
+        - 'all' : sets all types
+        - 'int_kind' : sets 'int'
+        - 'float_kind' : sets 'float' and 'longfloat'
+        - 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
+        - 'str_kind' : sets 'numpystr'
+    threshold : int, optional
+        Total number of array elements which trigger summarization
+        rather than full repr.
+        Defaults to ``numpy.get_printoptions()['threshold']``.
+    edgeitems : int, optional
+        Number of array items in summary at beginning and end of
+        each dimension.
+        Defaults to ``numpy.get_printoptions()['edgeitems']``.
+    sign : string, either '-', '+', or ' ', optional
+        Controls printing of the sign of floating-point types. If '+', always
+        print the sign of positive values. If ' ', always prints a space
+        (whitespace character) in the sign position of positive values.  If
+        '-', omit the sign character of positive values.
+        Defaults to ``numpy.get_printoptions()['sign']``.
+    floatmode : str, optional
+        Controls the interpretation of the `precision` option for
+        floating-point types.
+        Defaults to ``numpy.get_printoptions()['floatmode']``.
+        Can take the following values:
+
+        - 'fixed': Always print exactly `precision` fractional digits,
+          even if this would print more or fewer digits than
+          necessary to specify the value uniquely.
+        - 'unique': Print the minimum number of fractional digits necessary
+          to represent each value uniquely. Different elements may
+          have a different number of digits.  The value of the
+          `precision` option is ignored.
+        - 'maxprec': Print at most `precision` fractional digits, but if
+          an element can be uniquely represented with fewer digits
+          only print it with that many.
+        - 'maxprec_equal': Print at most `precision` fractional digits,
+          but if every element in the array can be uniquely
+          represented with an equal number of fewer digits, use that
+          many digits for all elements.
+    legacy : string or `False`, optional
+        If set to the string `'1.13'` enables 1.13 legacy printing mode. This
+        approximates numpy 1.13 print output by including a space in the sign
+        position of floats and different behavior for 0d arrays. If set to
+        `False`, disables legacy mode. Unrecognized strings will be ignored
+        with a warning for forward compatibility.
+
+        .. versionadded:: 1.14.0
+
+    Returns
+    -------
+    array_str : str
+        String representation of the array.
+
+    Raises
+    ------
+    TypeError
+        if a callable in `formatter` does not return a string.
+
+    See Also
+    --------
+    array_str, array_repr, set_printoptions, get_printoptions
+
+    Notes
+    -----
+    If a formatter is specified for a certain type, the `precision` keyword is
+    ignored for that type.
+
+    This is a very flexible function; `array_repr` and `array_str` are using
+    `array2string` internally so keywords with the same name should work
+    identically in all three functions.
+
+    Examples
+    --------
+    >>> x = np.array([1e-16,1,2,3])
+    >>> np.array2string(x, precision=2, separator=',',
+    ...                       suppress_small=True)
+    '[0.,1.,2.,3.]'
+
+    >>> x  = np.arange(3.)
+    >>> np.array2string(x, formatter={'float_kind':lambda x: "%.2f" % x})
+    '[0.00 1.00 2.00]'
+
+    >>> x  = np.arange(3)
+    >>> np.array2string(x, formatter={'int':lambda x: hex(x)})
+    '[0x0 0x1 0x2]'
+
+    """
+
+    overrides = _make_options_dict(precision, threshold, edgeitems,
+                                   max_line_width, suppress_small, None, None,
+                                   sign, formatter, floatmode, legacy)
+    options = _format_options.copy()
+    options.update(overrides)
+
+    if options['legacy'] <= 113:
+        if style is np._NoValue:
+            style = repr
+
+        if a.shape == () and a.dtype.names is None:
+            return style(a.item())
+    elif style is not np._NoValue:
+        # Deprecation 11-9-2017  v1.14
+        warnings.warn("'style' argument is deprecated and no longer functional"
+                      " except in 1.13 'legacy' mode",
+                      DeprecationWarning, stacklevel=2)
+
+    if options['legacy'] > 113:
+        options['linewidth'] -= len(suffix)
+
+    # treat as a null array if any of shape elements == 0
+    if a.size == 0:
+        return "[]"
+
+    return _array2string(a, options, separator, prefix)
+
+
+def _extendLine(s, line, word, line_width, next_line_prefix, legacy):
+    needs_wrap = len(line) + len(word) > line_width
+    if legacy > 113:
+        # don't wrap lines if it won't help
+        if len(line) <= len(next_line_prefix):
+            needs_wrap = False
+
+    if needs_wrap:
+        s += line.rstrip() + "\n"
+        line = next_line_prefix
+    line += word
+    return s, line
+
+
+def _extendLine_pretty(s, line, word, line_width, next_line_prefix, legacy):
+    """
+    Extends line with nicely formatted (possibly multi-line) string ``word``.
+    """
+    words = word.splitlines()
+    if len(words) == 1 or legacy <= 113:
+        return _extendLine(s, line, word, line_width, next_line_prefix, legacy)
+
+    max_word_length = max(len(word) for word in words)
+    if (len(line) + max_word_length > line_width and
+            len(line) > len(next_line_prefix)):
+        s += line.rstrip() + '\n'
+        line = next_line_prefix + words[0]
+        indent = next_line_prefix
+    else:
+        indent = len(line)*' '
+        line += words[0]
+
+    for word in words[1::]:
+        s += line.rstrip() + '\n'
+        line = indent + word
+
+    suffix_length = max_word_length - len(words[-1])
+    line += suffix_length*' '
+
+    return s, line
+
+def _formatArray(a, format_function, line_width, next_line_prefix,
+                 separator, edge_items, summary_insert, legacy):
+    """formatArray is designed for two modes of operation:
+
+    1. Full output
+
+    2. Summarized output
+
+    """
+    def recurser(index, hanging_indent, curr_width):
+        """
+        By using this local function, we don't need to recurse with all the
+        arguments. Since this function is not created recursively, the cost is
+        not significant
+        """
+        axis = len(index)
+        axes_left = a.ndim - axis
+
+        if axes_left == 0:
+            return format_function(a[index])
+
+        # when recursing, add a space to align with the [ added, and reduce the
+        # length of the line by 1
+        next_hanging_indent = hanging_indent + ' '
+        if legacy <= 113:
+            next_width = curr_width
+        else:
+            next_width = curr_width - len(']')
+
+        a_len = a.shape[axis]
+        show_summary = summary_insert and 2*edge_items < a_len
+        if show_summary:
+            leading_items = edge_items
+            trailing_items = edge_items
+        else:
+            leading_items = 0
+            trailing_items = a_len
+
+        # stringify the array with the hanging indent on the first line too
+        s = ''
+
+        # last axis (rows) - wrap elements if they would not fit on one line
+        if axes_left == 1:
+            # the length up until the beginning of the separator / bracket
+            if legacy <= 113:
+                elem_width = curr_width - len(separator.rstrip())
+            else:
+                elem_width = curr_width - max(len(separator.rstrip()), len(']'))
+
+            line = hanging_indent
+            for i in range(leading_items):
+                word = recurser(index + (i,), next_hanging_indent, next_width)
+                s, line = _extendLine_pretty(
+                    s, line, word, elem_width, hanging_indent, legacy)
+                line += separator
+
+            if show_summary:
+                s, line = _extendLine(
+                    s, line, summary_insert, elem_width, hanging_indent, legacy)
+                if legacy <= 113:
+                    line += ", "
+                else:
+                    line += separator
+
+            for i in range(trailing_items, 1, -1):
+                word = recurser(index + (-i,), next_hanging_indent, next_width)
+                s, line = _extendLine_pretty(
+                    s, line, word, elem_width, hanging_indent, legacy)
+                line += separator
+
+            if legacy <= 113:
+                # width of the separator is not considered on 1.13
+                elem_width = curr_width
+            word = recurser(index + (-1,), next_hanging_indent, next_width)
+            s, line = _extendLine_pretty(
+                s, line, word, elem_width, hanging_indent, legacy)
+
+            s += line
+
+        # other axes - insert newlines between rows
+        else:
+            s = ''
+            line_sep = separator.rstrip() + '\n'*(axes_left - 1)
+
+            for i in range(leading_items):
+                nested = recurser(index + (i,), next_hanging_indent, next_width)
+                s += hanging_indent + nested + line_sep
+
+            if show_summary:
+                if legacy <= 113:
+                    # trailing space, fixed nbr of newlines, and fixed separator
+                    s += hanging_indent + summary_insert + ", \n"
+                else:
+                    s += hanging_indent + summary_insert + line_sep
+
+            for i in range(trailing_items, 1, -1):
+                nested = recurser(index + (-i,), next_hanging_indent,
+                                  next_width)
+                s += hanging_indent + nested + line_sep
+
+            nested = recurser(index + (-1,), next_hanging_indent, next_width)
+            s += hanging_indent + nested
+
+        # remove the hanging indent, and wrap in []
+        s = '[' + s[len(hanging_indent):] + ']'
+        return s
+
+    try:
+        # invoke the recursive part with an initial index and prefix
+        return recurser(index=(),
+                        hanging_indent=next_line_prefix,
+                        curr_width=line_width)
+    finally:
+        # recursive closures have a cyclic reference to themselves, which
+        # requires gc to collect (gh-10620). To avoid this problem, for
+        # performance and PyPy friendliness, we break the cycle:
+        recurser = None
+
+def _none_or_positive_arg(x, name):
+    if x is None:
+        return -1
+    if x < 0:
+        raise ValueError("{} must be >= 0".format(name))
+    return x
+
+class FloatingFormat:
+    """ Formatter for subtypes of np.floating """
+    def __init__(self, data, precision, floatmode, suppress_small, sign=False,
+                 *, legacy=None):
+        # for backcompatibility, accept bools
+        if isinstance(sign, bool):
+            sign = '+' if sign else '-'
+
+        self._legacy = legacy
+        if self._legacy <= 113:
+            # when not 0d, legacy does not support '-'
+            if data.shape != () and sign == '-':
+                sign = ' '
+
+        self.floatmode = floatmode
+        if floatmode == 'unique':
+            self.precision = None
+        else:
+            self.precision = precision
+
+        self.precision = _none_or_positive_arg(self.precision, 'precision')
+
+        self.suppress_small = suppress_small
+        self.sign = sign
+        self.exp_format = False
+        self.large_exponent = False
+
+        self.fillFormat(data)
+
+    def fillFormat(self, data):
+        # only the finite values are used to compute the number of digits
+        finite_vals = data[isfinite(data)]
+
+        # choose exponential mode based on the non-zero finite values:
+        abs_non_zero = absolute(finite_vals[finite_vals != 0])
+        if len(abs_non_zero) != 0:
+            max_val = np.max(abs_non_zero)
+            min_val = np.min(abs_non_zero)
+            with errstate(over='ignore'):  # division can overflow
+                if max_val >= 1.e8 or (not self.suppress_small and
+                        (min_val < 0.0001 or max_val/min_val > 1000.)):
+                    self.exp_format = True
+
+        # do a first pass of printing all the numbers, to determine sizes
+        if len(finite_vals) == 0:
+            self.pad_left = 0
+            self.pad_right = 0
+            self.trim = '.'
+            self.exp_size = -1
+            self.unique = True
+            self.min_digits = None
+        elif self.exp_format:
+            trim, unique = '.', True
+            if self.floatmode == 'fixed' or self._legacy <= 113:
+                trim, unique = 'k', False
+            strs = (dragon4_scientific(x, precision=self.precision,
+                               unique=unique, trim=trim, sign=self.sign == '+')
+                    for x in finite_vals)
+            frac_strs, _, exp_strs = zip(*(s.partition('e') for s in strs))
+            int_part, frac_part = zip(*(s.split('.') for s in frac_strs))
+            self.exp_size = max(len(s) for s in exp_strs) - 1
+
+            self.trim = 'k'
+            self.precision = max(len(s) for s in frac_part)
+            self.min_digits = self.precision
+            self.unique = unique
+
+            # for back-compat with np 1.13, use 2 spaces & sign and full prec
+            if self._legacy <= 113:
+                self.pad_left = 3
+            else:
+                # this should be only 1 or 2. Can be calculated from sign.
+                self.pad_left = max(len(s) for s in int_part)
+            # pad_right is only needed for nan length calculation
+            self.pad_right = self.exp_size + 2 + self.precision
+        else:
+            trim, unique = '.', True
+            if self.floatmode == 'fixed':
+                trim, unique = 'k', False
+            strs = (dragon4_positional(x, precision=self.precision,
+                                       fractional=True,
+                                       unique=unique, trim=trim,
+                                       sign=self.sign == '+')
+                    for x in finite_vals)
+            int_part, frac_part = zip(*(s.split('.') for s in strs))
+            if self._legacy <= 113:
+                self.pad_left = 1 + max(len(s.lstrip('-+')) for s in int_part)
+            else:
+                self.pad_left = max(len(s) for s in int_part)
+            self.pad_right = max(len(s) for s in frac_part)
+            self.exp_size = -1
+            self.unique = unique
+
+            if self.floatmode in ['fixed', 'maxprec_equal']:
+                self.precision = self.min_digits = self.pad_right
+                self.trim = 'k'
+            else:
+                self.trim = '.'
+                self.min_digits = 0
+
+        if self._legacy > 113:
+            # account for sign = ' ' by adding one to pad_left
+            if self.sign == ' ' and not any(np.signbit(finite_vals)):
+                self.pad_left += 1
+
+        # if there are non-finite values, may need to increase pad_left
+        if data.size != finite_vals.size:
+            neginf = self.sign != '-' or any(data[isinf(data)] < 0)
+            nanlen = len(_format_options['nanstr'])
+            inflen = len(_format_options['infstr']) + neginf
+            offset = self.pad_right + 1  # +1 for decimal pt
+            self.pad_left = max(self.pad_left, nanlen - offset, inflen - offset)
+
+    def __call__(self, x):
+        if not np.isfinite(x):
+            with errstate(invalid='ignore'):
+                if np.isnan(x):
+                    sign = '+' if self.sign == '+' else ''
+                    ret = sign + _format_options['nanstr']
+                else:  # isinf
+                    sign = '-' if x < 0 else '+' if self.sign == '+' else ''
+                    ret = sign + _format_options['infstr']
+                return ' '*(self.pad_left + self.pad_right + 1 - len(ret)) + ret
+
+        if self.exp_format:
+            return dragon4_scientific(x,
+                                      precision=self.precision,
+                                      min_digits=self.min_digits,
+                                      unique=self.unique,
+                                      trim=self.trim,
+                                      sign=self.sign == '+',
+                                      pad_left=self.pad_left,
+                                      exp_digits=self.exp_size)
+        else:
+            return dragon4_positional(x,
+                                      precision=self.precision,
+                                      min_digits=self.min_digits,
+                                      unique=self.unique,
+                                      fractional=True,
+                                      trim=self.trim,
+                                      sign=self.sign == '+',
+                                      pad_left=self.pad_left,
+                                      pad_right=self.pad_right)
+
+
+@set_module('numpy')
+def format_float_scientific(x, precision=None, unique=True, trim='k',
+                            sign=False, pad_left=None, exp_digits=None,
+                            min_digits=None):
+    """
+    Format a floating-point scalar as a decimal string in scientific notation.
+
+    Provides control over rounding, trimming and padding. Uses and assumes
+    IEEE unbiased rounding. Uses the "Dragon4" algorithm.
+
+    Parameters
+    ----------
+    x : python float or numpy floating scalar
+        Value to format.
+    precision : non-negative integer or None, optional
+        Maximum number of digits to print. May be None if `unique` is
+        `True`, but must be an integer if unique is `False`.
+    unique : boolean, optional
+        If `True`, use a digit-generation strategy which gives the shortest
+        representation which uniquely identifies the floating-point number from
+        other values of the same type, by judicious rounding. If `precision`
+        is given fewer digits than necessary can be printed. If `min_digits`
+        is given more can be printed, in which cases the last digit is rounded
+        with unbiased rounding.
+        If `False`, digits are generated as if printing an infinite-precision
+        value and stopping after `precision` digits, rounding the remaining
+        value with unbiased rounding
+    trim : one of 'k', '.', '0', '-', optional
+        Controls post-processing trimming of trailing digits, as follows:
+
+        * 'k' : keep trailing zeros, keep decimal point (no trimming)
+        * '.' : trim all trailing zeros, leave decimal point
+        * '0' : trim all but the zero before the decimal point. Insert the
+          zero if it is missing.
+        * '-' : trim trailing zeros and any trailing decimal point
+    sign : boolean, optional
+        Whether to show the sign for positive values.
+    pad_left : non-negative integer, optional
+        Pad the left side of the string with whitespace until at least that
+        many characters are to the left of the decimal point.
+    exp_digits : non-negative integer, optional
+        Pad the exponent with zeros until it contains at least this many digits.
+        If omitted, the exponent will be at least 2 digits.
+    min_digits : non-negative integer or None, optional
+        Minimum number of digits to print. This only has an effect for
+        `unique=True`. In that case more digits than necessary to uniquely
+        identify the value may be printed and rounded unbiased.
+
+        -- versionadded:: 1.21.0
+
+    Returns
+    -------
+    rep : string
+        The string representation of the floating point value
+
+    See Also
+    --------
+    format_float_positional
+
+    Examples
+    --------
+    >>> np.format_float_scientific(np.float32(np.pi))
+    '3.1415927e+00'
+    >>> s = np.float32(1.23e24)
+    >>> np.format_float_scientific(s, unique=False, precision=15)
+    '1.230000071797338e+24'
+    >>> np.format_float_scientific(s, exp_digits=4)
+    '1.23e+0024'
+    """
+    precision = _none_or_positive_arg(precision, 'precision')
+    pad_left = _none_or_positive_arg(pad_left, 'pad_left')
+    exp_digits = _none_or_positive_arg(exp_digits, 'exp_digits')
+    min_digits = _none_or_positive_arg(min_digits, 'min_digits')
+    if min_digits > 0 and precision > 0 and min_digits > precision:
+        raise ValueError("min_digits must be less than or equal to precision")
+    return dragon4_scientific(x, precision=precision, unique=unique,
+                              trim=trim, sign=sign, pad_left=pad_left,
+                              exp_digits=exp_digits, min_digits=min_digits)
+
+
+@set_module('numpy')
+def format_float_positional(x, precision=None, unique=True,
+                            fractional=True, trim='k', sign=False,
+                            pad_left=None, pad_right=None, min_digits=None):
+    """
+    Format a floating-point scalar as a decimal string in positional notation.
+
+    Provides control over rounding, trimming and padding. Uses and assumes
+    IEEE unbiased rounding. Uses the "Dragon4" algorithm.
+
+    Parameters
+    ----------
+    x : python float or numpy floating scalar
+        Value to format.
+    precision : non-negative integer or None, optional
+        Maximum number of digits to print. May be None if `unique` is
+        `True`, but must be an integer if unique is `False`.
+    unique : boolean, optional
+        If `True`, use a digit-generation strategy which gives the shortest
+        representation which uniquely identifies the floating-point number from
+        other values of the same type, by judicious rounding. If `precision`
+        is given fewer digits than necessary can be printed, or if `min_digits`
+        is given more can be printed, in which cases the last digit is rounded
+        with unbiased rounding.
+        If `False`, digits are generated as if printing an infinite-precision
+        value and stopping after `precision` digits, rounding the remaining
+        value with unbiased rounding
+    fractional : boolean, optional
+        If `True`, the cutoffs of `precision` and `min_digits` refer to the
+        total number of digits after the decimal point, including leading
+        zeros.
+        If `False`, `precision` and `min_digits` refer to the total number of
+        significant digits, before or after the decimal point, ignoring leading
+        zeros.
+    trim : one of 'k', '.', '0', '-', optional
+        Controls post-processing trimming of trailing digits, as follows:
+
+        * 'k' : keep trailing zeros, keep decimal point (no trimming)
+        * '.' : trim all trailing zeros, leave decimal point
+        * '0' : trim all but the zero before the decimal point. Insert the
+          zero if it is missing.
+        * '-' : trim trailing zeros and any trailing decimal point
+    sign : boolean, optional
+        Whether to show the sign for positive values.
+    pad_left : non-negative integer, optional
+        Pad the left side of the string with whitespace until at least that
+        many characters are to the left of the decimal point.
+    pad_right : non-negative integer, optional
+        Pad the right side of the string with whitespace until at least that
+        many characters are to the right of the decimal point.
+    min_digits : non-negative integer or None, optional
+        Minimum number of digits to print. Only has an effect if `unique=True`
+        in which case additional digits past those necessary to uniquely
+        identify the value may be printed, rounding the last additional digit.
+
+        -- versionadded:: 1.21.0
+
+    Returns
+    -------
+    rep : string
+        The string representation of the floating point value
+
+    See Also
+    --------
+    format_float_scientific
+
+    Examples
+    --------
+    >>> np.format_float_positional(np.float32(np.pi))
+    '3.1415927'
+    >>> np.format_float_positional(np.float16(np.pi))
+    '3.14'
+    >>> np.format_float_positional(np.float16(0.3))
+    '0.3'
+    >>> np.format_float_positional(np.float16(0.3), unique=False, precision=10)
+    '0.3000488281'
+    """
+    precision = _none_or_positive_arg(precision, 'precision')
+    pad_left = _none_or_positive_arg(pad_left, 'pad_left')
+    pad_right = _none_or_positive_arg(pad_right, 'pad_right')
+    min_digits = _none_or_positive_arg(min_digits, 'min_digits')
+    if not fractional and precision == 0:
+        raise ValueError("precision must be greater than 0 if "
+                         "fractional=False")
+    if min_digits > 0 and precision > 0 and min_digits > precision:
+        raise ValueError("min_digits must be less than or equal to precision")
+    return dragon4_positional(x, precision=precision, unique=unique,
+                              fractional=fractional, trim=trim,
+                              sign=sign, pad_left=pad_left,
+                              pad_right=pad_right, min_digits=min_digits)
+
+
+class IntegerFormat:
+    def __init__(self, data):
+        if data.size > 0:
+            max_str_len = max(len(str(np.max(data))),
+                              len(str(np.min(data))))
+        else:
+            max_str_len = 0
+        self.format = '%{}d'.format(max_str_len)
+
+    def __call__(self, x):
+        return self.format % x
+
+
+class BoolFormat:
+    def __init__(self, data, **kwargs):
+        # add an extra space so " True" and "False" have the same length and
+        # array elements align nicely when printed, except in 0d arrays
+        self.truestr = ' True' if data.shape != () else 'True'
+
+    def __call__(self, x):
+        return self.truestr if x else "False"
+
+
+class ComplexFloatingFormat:
+    """ Formatter for subtypes of np.complexfloating """
+    def __init__(self, x, precision, floatmode, suppress_small,
+                 sign=False, *, legacy=None):
+        # for backcompatibility, accept bools
+        if isinstance(sign, bool):
+            sign = '+' if sign else '-'
+
+        floatmode_real = floatmode_imag = floatmode
+        if legacy <= 113:
+            floatmode_real = 'maxprec_equal'
+            floatmode_imag = 'maxprec'
+
+        self.real_format = FloatingFormat(
+            x.real, precision, floatmode_real, suppress_small,
+            sign=sign, legacy=legacy
+        )
+        self.imag_format = FloatingFormat(
+            x.imag, precision, floatmode_imag, suppress_small,
+            sign='+', legacy=legacy
+        )
+
+    def __call__(self, x):
+        r = self.real_format(x.real)
+        i = self.imag_format(x.imag)
+
+        # add the 'j' before the terminal whitespace in i
+        sp = len(i.rstrip())
+        i = i[:sp] + 'j' + i[sp:]
+
+        return r + i
+
+
+class _TimelikeFormat:
+    def __init__(self, data):
+        non_nat = data[~isnat(data)]
+        if len(non_nat) > 0:
+            # Max str length of non-NaT elements
+            max_str_len = max(len(self._format_non_nat(np.max(non_nat))),
+                              len(self._format_non_nat(np.min(non_nat))))
+        else:
+            max_str_len = 0
+        if len(non_nat) < data.size:
+            # data contains a NaT
+            max_str_len = max(max_str_len, 5)
+        self._format = '%{}s'.format(max_str_len)
+        self._nat = "'NaT'".rjust(max_str_len)
+
+    def _format_non_nat(self, x):
+        # override in subclass
+        raise NotImplementedError
+
+    def __call__(self, x):
+        if isnat(x):
+            return self._nat
+        else:
+            return self._format % self._format_non_nat(x)
+
+
+class DatetimeFormat(_TimelikeFormat):
+    def __init__(self, x, unit=None, timezone=None, casting='same_kind',
+                 legacy=False):
+        # Get the unit from the dtype
+        if unit is None:
+            if x.dtype.kind == 'M':
+                unit = datetime_data(x.dtype)[0]
+            else:
+                unit = 's'
+
+        if timezone is None:
+            timezone = 'naive'
+        self.timezone = timezone
+        self.unit = unit
+        self.casting = casting
+        self.legacy = legacy
+
+        # must be called after the above are configured
+        super().__init__(x)
+
+    def __call__(self, x):
+        if self.legacy <= 113:
+            return self._format_non_nat(x)
+        return super().__call__(x)
+
+    def _format_non_nat(self, x):
+        return "'%s'" % datetime_as_string(x,
+                                    unit=self.unit,
+                                    timezone=self.timezone,
+                                    casting=self.casting)
+
+
+class TimedeltaFormat(_TimelikeFormat):
+    def _format_non_nat(self, x):
+        return str(x.astype('i8'))
+
+
+class SubArrayFormat:
+    def __init__(self, format_function, **options):
+        self.format_function = format_function
+        self.threshold = options['threshold']
+        self.edge_items = options['edgeitems']
+
+    def __call__(self, a):
+        self.summary_insert = "..." if a.size > self.threshold else ""
+        return self.format_array(a)
+
+    def format_array(self, a):
+        if np.ndim(a) == 0:
+            return self.format_function(a)
+
+        if self.summary_insert and a.shape[0] > 2*self.edge_items:
+            formatted = (
+                [self.format_array(a_) for a_ in a[:self.edge_items]]
+                + [self.summary_insert]
+                + [self.format_array(a_) for a_ in a[-self.edge_items:]]
+            )
+        else:
+            formatted = [self.format_array(a_) for a_ in a]
+
+        return "[" + ", ".join(formatted) + "]"
+
+
+class StructuredVoidFormat:
+    """
+    Formatter for structured np.void objects.
+
+    This does not work on structured alias types like np.dtype(('i4', 'i2,i2')),
+    as alias scalars lose their field information, and the implementation
+    relies upon np.void.__getitem__.
+    """
+    def __init__(self, format_functions):
+        self.format_functions = format_functions
+
+    @classmethod
+    def from_data(cls, data, **options):
+        """
+        This is a second way to initialize StructuredVoidFormat, using the raw data
+        as input. Added to avoid changing the signature of __init__.
+        """
+        format_functions = []
+        for field_name in data.dtype.names:
+            format_function = _get_format_function(data[field_name], **options)
+            if data.dtype[field_name].shape != ():
+                format_function = SubArrayFormat(format_function, **options)
+            format_functions.append(format_function)
+        return cls(format_functions)
+
+    def __call__(self, x):
+        str_fields = [
+            format_function(field)
+            for field, format_function in zip(x, self.format_functions)
+        ]
+        if len(str_fields) == 1:
+            return "({},)".format(str_fields[0])
+        else:
+            return "({})".format(", ".join(str_fields))
+
+
+def _void_scalar_repr(x):
+    """
+    Implements the repr for structured-void scalars. It is called from the
+    scalartypes.c.src code, and is placed here because it uses the elementwise
+    formatters defined above.
+    """
+    return StructuredVoidFormat.from_data(array(x), **_format_options)(x)
+
+
+_typelessdata = [int_, float_, complex_, bool_]
+
+
+def dtype_is_implied(dtype):
+    """
+    Determine if the given dtype is implied by the representation of its values.
+
+    Parameters
+    ----------
+    dtype : dtype
+        Data type
+
+    Returns
+    -------
+    implied : bool
+        True if the dtype is implied by the representation of its values.
+
+    Examples
+    --------
+    >>> np.core.arrayprint.dtype_is_implied(int)
+    True
+    >>> np.array([1, 2, 3], int)
+    array([1, 2, 3])
+    >>> np.core.arrayprint.dtype_is_implied(np.int8)
+    False
+    >>> np.array([1, 2, 3], np.int8)
+    array([1, 2, 3], dtype=int8)
+    """
+    dtype = np.dtype(dtype)
+    if _format_options['legacy'] <= 113 and dtype.type == bool_:
+        return False
+
+    # not just void types can be structured, and names are not part of the repr
+    if dtype.names is not None:
+        return False
+
+    # should care about endianness *unless size is 1* (e.g., int8, bool)
+    if not dtype.isnative:
+        return False
+
+    return dtype.type in _typelessdata
+
+
+def dtype_short_repr(dtype):
+    """
+    Convert a dtype to a short form which evaluates to the same dtype.
+
+    The intent is roughly that the following holds
+
+    >>> from numpy import *
+    >>> dt = np.int64([1, 2]).dtype
+    >>> assert eval(dtype_short_repr(dt)) == dt
+    """
+    if type(dtype).__repr__ != np.dtype.__repr__:
+        # TODO: Custom repr for user DTypes, logic should likely move.
+        return repr(dtype)
+    if dtype.names is not None:
+        # structured dtypes give a list or tuple repr
+        return str(dtype)
+    elif issubclass(dtype.type, flexible):
+        # handle these separately so they don't give garbage like str256
+        return "'%s'" % str(dtype)
+
+    typename = dtype.name
+    if not dtype.isnative:
+        # deal with cases like dtype(' 0
+
+    prefix = class_name + "("
+    suffix = ")" if skipdtype else ","
+
+    if (_format_options['legacy'] <= 113 and
+            arr.shape == () and not arr.dtype.names):
+        lst = repr(arr.item())
+    elif arr.size > 0 or arr.shape == (0,):
+        lst = array2string(arr, max_line_width, precision, suppress_small,
+                           ', ', prefix, suffix=suffix)
+    else:  # show zero-length shape unless it is (0,)
+        lst = "[], shape=%s" % (repr(arr.shape),)
+
+    arr_str = prefix + lst + suffix
+
+    if skipdtype:
+        return arr_str
+
+    dtype_str = "dtype={})".format(dtype_short_repr(arr.dtype))
+
+    # compute whether we should put dtype on a new line: Do so if adding the
+    # dtype would extend the last line past max_line_width.
+    # Note: This line gives the correct result even when rfind returns -1.
+    last_line_len = len(arr_str) - (arr_str.rfind('\n') + 1)
+    spacer = " "
+    if _format_options['legacy'] <= 113:
+        if issubclass(arr.dtype.type, flexible):
+            spacer = '\n' + ' '*len(class_name + "(")
+    elif last_line_len + len(dtype_str) + 1 > max_line_width:
+        spacer = '\n' + ' '*len(class_name + "(")
+
+    return arr_str + spacer + dtype_str
+
+
+def _array_repr_dispatcher(
+        arr, max_line_width=None, precision=None, suppress_small=None):
+    return (arr,)
+
+
+@array_function_dispatch(_array_repr_dispatcher, module='numpy')
+def array_repr(arr, max_line_width=None, precision=None, suppress_small=None):
+    """
+    Return the string representation of an array.
+
+    Parameters
+    ----------
+    arr : ndarray
+        Input array.
+    max_line_width : int, optional
+        Inserts newlines if text is longer than `max_line_width`.
+        Defaults to ``numpy.get_printoptions()['linewidth']``.
+    precision : int, optional
+        Floating point precision.
+        Defaults to ``numpy.get_printoptions()['precision']``.
+    suppress_small : bool, optional
+        Represent numbers "very close" to zero as zero; default is False.
+        Very close is defined by precision: if the precision is 8, e.g.,
+        numbers smaller (in absolute value) than 5e-9 are represented as
+        zero.
+        Defaults to ``numpy.get_printoptions()['suppress']``.
+
+    Returns
+    -------
+    string : str
+      The string representation of an array.
+
+    See Also
+    --------
+    array_str, array2string, set_printoptions
+
+    Examples
+    --------
+    >>> np.array_repr(np.array([1,2]))
+    'array([1, 2])'
+    >>> np.array_repr(np.ma.array([0.]))
+    'MaskedArray([0.])'
+    >>> np.array_repr(np.array([], np.int32))
+    'array([], dtype=int32)'
+
+    >>> x = np.array([1e-6, 4e-7, 2, 3])
+    >>> np.array_repr(x, precision=6, suppress_small=True)
+    'array([0.000001,  0.      ,  2.      ,  3.      ])'
+
+    """
+    return _array_repr_implementation(
+        arr, max_line_width, precision, suppress_small)
+
+
+@_recursive_guard()
+def _guarded_repr_or_str(v):
+    if isinstance(v, bytes):
+        return repr(v)
+    return str(v)
+
+
+def _array_str_implementation(
+        a, max_line_width=None, precision=None, suppress_small=None,
+        array2string=array2string):
+    """Internal version of array_str() that allows overriding array2string."""
+    if (_format_options['legacy'] <= 113 and
+            a.shape == () and not a.dtype.names):
+        return str(a.item())
+
+    # the str of 0d arrays is a special case: It should appear like a scalar,
+    # so floats are not truncated by `precision`, and strings are not wrapped
+    # in quotes. So we return the str of the scalar value.
+    if a.shape == ():
+        # obtain a scalar and call str on it, avoiding problems for subclasses
+        # for which indexing with () returns a 0d instead of a scalar by using
+        # ndarray's getindex. Also guard against recursive 0d object arrays.
+        return _guarded_repr_or_str(np.ndarray.__getitem__(a, ()))
+
+    return array2string(a, max_line_width, precision, suppress_small, ' ', "")
+
+
+def _array_str_dispatcher(
+        a, max_line_width=None, precision=None, suppress_small=None):
+    return (a,)
+
+
+@array_function_dispatch(_array_str_dispatcher, module='numpy')
+def array_str(a, max_line_width=None, precision=None, suppress_small=None):
+    """
+    Return a string representation of the data in an array.
+
+    The data in the array is returned as a single string.  This function is
+    similar to `array_repr`, the difference being that `array_repr` also
+    returns information on the kind of array and its data type.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array.
+    max_line_width : int, optional
+        Inserts newlines if text is longer than `max_line_width`.
+        Defaults to ``numpy.get_printoptions()['linewidth']``.
+    precision : int, optional
+        Floating point precision.
+        Defaults to ``numpy.get_printoptions()['precision']``.
+    suppress_small : bool, optional
+        Represent numbers "very close" to zero as zero; default is False.
+        Very close is defined by precision: if the precision is 8, e.g.,
+        numbers smaller (in absolute value) than 5e-9 are represented as
+        zero.
+        Defaults to ``numpy.get_printoptions()['suppress']``.
+
+    See Also
+    --------
+    array2string, array_repr, set_printoptions
+
+    Examples
+    --------
+    >>> np.array_str(np.arange(3))
+    '[0 1 2]'
+
+    """
+    return _array_str_implementation(
+        a, max_line_width, precision, suppress_small)
+
+
+# needed if __array_function__ is disabled
+_array2string_impl = getattr(array2string, '__wrapped__', array2string)
+_default_array_str = functools.partial(_array_str_implementation,
+                                       array2string=_array2string_impl)
+_default_array_repr = functools.partial(_array_repr_implementation,
+                                        array2string=_array2string_impl)
+
+
+def set_string_function(f, repr=True):
+    """
+    Set a Python function to be used when pretty printing arrays.
+
+    Parameters
+    ----------
+    f : function or None
+        Function to be used to pretty print arrays. The function should expect
+        a single array argument and return a string of the representation of
+        the array. If None, the function is reset to the default NumPy function
+        to print arrays.
+    repr : bool, optional
+        If True (default), the function for pretty printing (``__repr__``)
+        is set, if False the function that returns the default string
+        representation (``__str__``) is set.
+
+    See Also
+    --------
+    set_printoptions, get_printoptions
+
+    Examples
+    --------
+    >>> def pprint(arr):
+    ...     return 'HA! - What are you going to do now?'
+    ...
+    >>> np.set_string_function(pprint)
+    >>> a = np.arange(10)
+    >>> a
+    HA! - What are you going to do now?
+    >>> _ = a
+    >>> # [0 1 2 3 4 5 6 7 8 9]
+
+    We can reset the function to the default:
+
+    >>> np.set_string_function(None)
+    >>> a
+    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
+
+    `repr` affects either pretty printing or normal string representation.
+    Note that ``__repr__`` is still affected by setting ``__str__``
+    because the width of each array element in the returned string becomes
+    equal to the length of the result of ``__str__()``.
+
+    >>> x = np.arange(4)
+    >>> np.set_string_function(lambda x:'random', repr=False)
+    >>> x.__str__()
+    'random'
+    >>> x.__repr__()
+    'array([0, 1, 2, 3])'
+
+    """
+    if f is None:
+        if repr:
+            return multiarray.set_string_function(_default_array_repr, 1)
+        else:
+            return multiarray.set_string_function(_default_array_str, 0)
+    else:
+        return multiarray.set_string_function(f, repr)
diff --git a/shared/package-files/c9/c926841a7b7f2301b99f3c5ca482e01170bad266eb2006c2dd967a92e13dcc2c/posix_utils.py b/shared/package-files/c9/c926841a7b7f2301b99f3c5ca482e01170bad266eb2006c2dd967a92e13dcc2c/posix_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a78dc421b75620c1ed8ae9f0aba254555535a8c
--- /dev/null
+++ b/shared/package-files/c9/c926841a7b7f2301b99f3c5ca482e01170bad266eb2006c2dd967a92e13dcc2c/posix_utils.py
@@ -0,0 +1,97 @@
+from __future__ import annotations
+
+import os
+import select
+from codecs import getincrementaldecoder
+
+__all__ = [
+    "PosixStdinReader",
+]
+
+
+class PosixStdinReader:
+    """
+    Wrapper around stdin which reads (nonblocking) the next available 1024
+    bytes and decodes it.
+
+    Note that you can't be sure that the input file is closed if the ``read``
+    function returns an empty string. When ``errors=ignore`` is passed,
+    ``read`` can return an empty string if all malformed input was replaced by
+    an empty string. (We can't block here and wait for more input.) So, because
+    of that, check the ``closed`` attribute, to be sure that the file has been
+    closed.
+
+    :param stdin_fd: File descriptor from which we read.
+    :param errors:  Can be 'ignore', 'strict' or 'replace'.
+        On Python3, this can be 'surrogateescape', which is the default.
+
+        'surrogateescape' is preferred, because this allows us to transfer
+        unrecognized bytes to the key bindings. Some terminals, like lxterminal
+        and Guake, use the 'Mxx' notation to send mouse events, where each 'x'
+        can be any possible byte.
+    """
+
+    # By default, we want to 'ignore' errors here. The input stream can be full
+    # of junk.  One occurrence of this that I had was when using iTerm2 on OS X,
+    # with "Option as Meta" checked (You should choose "Option as +Esc".)
+
+    def __init__(
+        self, stdin_fd: int, errors: str = "surrogateescape", encoding: str = "utf-8"
+    ) -> None:
+        self.stdin_fd = stdin_fd
+        self.errors = errors
+
+        # Create incremental decoder for decoding stdin.
+        # We can not just do `os.read(stdin.fileno(), 1024).decode('utf-8')`, because
+        # it could be that we are in the middle of a utf-8 byte sequence.
+        self._stdin_decoder_cls = getincrementaldecoder(encoding)
+        self._stdin_decoder = self._stdin_decoder_cls(errors=errors)
+
+        #: True when there is nothing anymore to read.
+        self.closed = False
+
+    def read(self, count: int = 1024) -> str:
+        # By default we choose a rather small chunk size, because reading
+        # big amounts of input at once, causes the event loop to process
+        # all these key bindings also at once without going back to the
+        # loop. This will make the application feel unresponsive.
+        """
+        Read the input and return it as a string.
+
+        Return the text. Note that this can return an empty string, even when
+        the input stream was not yet closed. This means that something went
+        wrong during the decoding.
+        """
+        if self.closed:
+            return ""
+
+        # Check whether there is some input to read. `os.read` would block
+        # otherwise.
+        # (Actually, the event loop is responsible to make sure that this
+        # function is only called when there is something to read, but for some
+        # reason this happens in certain situations.)
+        try:
+            if not select.select([self.stdin_fd], [], [], 0)[0]:
+                return ""
+        except OSError:
+            # Happens for instance when the file descriptor was closed.
+            # (We had this in ptterm, where the FD became ready, a callback was
+            # scheduled, but in the meantime another callback closed it already.)
+            self.closed = True
+
+        # Note: the following works better than wrapping `self.stdin` like
+        #       `codecs.getreader('utf-8')(stdin)` and doing `read(1)`.
+        #       Somehow that causes some latency when the escape
+        #       character is pressed. (Especially on combination with the `select`.)
+        try:
+            data = os.read(self.stdin_fd, count)
+
+            # Nothing more to read, stream is closed.
+            if data == b"":
+                self.closed = True
+                return ""
+        except OSError:
+            # In case of SIGWINCH
+            data = b""
+
+        return self._stdin_decoder.decode(data)
diff --git a/shared/package-files/c9/c92776a987ddec26029f36c6e4d247c84bed6424c217248ce9f8b1ad0adb591d/overload_usecases.py b/shared/package-files/c9/c92776a987ddec26029f36c6e4d247c84bed6424c217248ce9f8b1ad0adb591d/overload_usecases.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe1ca5799e847dbb37c63b2706e6477cd3ba8947
--- /dev/null
+++ b/shared/package-files/c9/c92776a987ddec26029f36c6e4d247c84bed6424c217248ce9f8b1ad0adb591d/overload_usecases.py
@@ -0,0 +1,28 @@
+# Python 3 syntax only use cases, used in test_extending.py
+
+# arg name is different, and there's no arg name to match before *args
+
+
+def impl4(z, *args, kw=None):
+    if z > 10:
+        return 1
+    else:
+        return -1
+
+# arg name is different but at a detectable location, with *args
+
+
+def impl5(z, b, *args, kw=None):
+    if z > 10:
+        return 1
+    else:
+        return -1
+
+
+def var_positional_impl(a, *star_args_token, kw=None, kw1=12):
+    def impl(a, b, f, kw=None, kw1=12):
+        if a > 10:
+            return 1
+        else:
+            return -1
+    return impl
diff --git a/shared/package-files/c9/c9286f0c631289b36e73073225a325d19e39a68585943cfbeed867f86d62dd01/split_compositeimplicitautograd_dispatch.h b/shared/package-files/c9/c9286f0c631289b36e73073225a325d19e39a68585943cfbeed867f86d62dd01/split_compositeimplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..bbda90516c9ffafaeffbee9d01be274a1f045dab
--- /dev/null
+++ b/shared/package-files/c9/c9286f0c631289b36e73073225a325d19e39a68585943cfbeed867f86d62dd01/split_compositeimplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeimplicitautograd {
+
+TORCH_API ::std::vector split(const at::Tensor & self, at::IntArrayRef split_size, int64_t dim=0);
+TORCH_API ::std::vector split_symint(const at::Tensor & self, c10::SymIntArrayRef split_size, int64_t dim=0);
+
+} // namespace compositeimplicitautograd
+} // namespace at
diff --git a/shared/package-files/c9/c92ecc3d1acf279a71b557a63e41a8ef03733557c85738af9e63ff7a57cd7773/meson.build b/shared/package-files/c9/c92ecc3d1acf279a71b557a63e41a8ef03733557c85738af9e63ff7a57cd7773/meson.build
new file mode 100644
index 0000000000000000000000000000000000000000..6232d747d1feb220eb4656396314d7caddac9c52
--- /dev/null
+++ b/shared/package-files/c9/c92ecc3d1acf279a71b557a63e41a8ef03733557c85738af9e63ff7a57cd7773/meson.build
@@ -0,0 +1,48 @@
+newrand_include = include_directories('src/newrand')
+libsvm_include = include_directories('src/libsvm')
+liblinear_include = include_directories('src/liblinear')
+
+_newrand = py.extension_module(
+  '_newrand',
+  cython_gen_cpp.process('_newrand.pyx'),
+  include_directories: [newrand_include],
+  subdir: 'sklearn/svm',
+  install: true
+)
+
+libsvm_skl = static_library(
+  'libsvm-skl',
+  ['src/libsvm/libsvm_template.cpp'],
+)
+
+py.extension_module(
+  '_libsvm',
+  [cython_gen.process('_libsvm.pyx'), utils_cython_tree],
+  include_directories: [newrand_include, libsvm_include],
+  link_with: libsvm_skl,
+  subdir: 'sklearn/svm',
+  install: true
+)
+
+py.extension_module(
+  '_libsvm_sparse',
+  [cython_gen.process('_libsvm_sparse.pyx'), utils_cython_tree],
+  include_directories: [newrand_include, libsvm_include],
+  link_with: libsvm_skl,
+  subdir: 'sklearn/svm',
+  install: true
+)
+
+liblinear_skl = static_library(
+  'liblinear-skl',
+  ['src/liblinear/linear.cpp', 'src/liblinear/tron.cpp'],
+)
+
+py.extension_module(
+  '_liblinear',
+  [cython_gen.process('_liblinear.pyx'), utils_cython_tree],
+  include_directories: [newrand_include, liblinear_include],
+  link_with: [liblinear_skl],
+  subdir: 'sklearn/svm',
+  install: true
+)
diff --git a/shared/package-files/c9/c92f7a345eedc11cb5900582d256f79836a94903f061423fc015f4afd83dc91b/installed.py b/shared/package-files/c9/c92f7a345eedc11cb5900582d256f79836a94903f061423fc015f4afd83dc91b/installed.py
new file mode 100644
index 0000000000000000000000000000000000000000..de6e613be19e716db215ba3b33ffb392d58673cc
--- /dev/null
+++ b/shared/package-files/c9/c92f7a345eedc11cb5900582d256f79836a94903f061423fc015f4afd83dc91b/installed.py
@@ -0,0 +1,62 @@
+import glob
+import io
+import os
+import sys
+import warnings
+
+from .distribution import Distribution
+
+class Installed(Distribution):
+
+    def __init__(self, package, metadata_version=None):
+        if isinstance(package, str):
+            self.package_name = package
+            try:
+                __import__(package)
+            except ImportError:
+                package = None
+            else:
+                package = sys.modules[package]
+        else:
+            self.package_name = package.__name__
+        self.package = package
+        self.metadata_version = metadata_version
+        self.extractMetadata()
+
+    def read(self):
+        opj = os.path.join
+        if self.package is not None:
+            package = self.package.__package__
+            if package in ('', None):
+                package = self.package.__name__
+            egg_pattern = '%s*.egg-info' % package
+            dist_pattern = '%s*.dist-info' % package
+            pkg_file = getattr(self.package, '__file__', None)
+            if pkg_file is not None:
+                candidates = []
+                def _add_candidate(where):
+                    candidates.extend(glob.glob(where))
+                for entry in sys.path:
+                    if pkg_file.startswith(entry):
+                        _add_candidate(opj(entry, 'EGG-INFO')) # egg?
+                        _add_candidate(opj(entry, egg_pattern))
+                        _add_candidate(opj(entry, dist_pattern))
+                dir, name = os.path.split(self.package.__file__)
+                _add_candidate(opj(dir, egg_pattern))
+                _add_candidate(opj(dir, '..', egg_pattern))
+                _add_candidate(opj(dir, dist_pattern))
+                _add_candidate(opj(dir, '..', dist_pattern))
+                for candidate in candidates:
+                    if os.path.isdir(candidate):
+                        if candidate.lower().endswith("egg-info"):
+                            path = opj(candidate, 'PKG-INFO')
+                        elif candidate.endswith("dist-info"):
+                            path = opj(candidate, 'METADATA')
+                        else:  # pragma: NO COVER
+                            continue
+                    else:
+                        path = candidate
+                    if os.path.exists(path):
+                        with io.open(path, errors='ignore') as f:
+                            return f.read()
+        warnings.warn('No PKG-INFO found for package: %s' % self.package_name)
diff --git a/shared/package-files/c9/c92fe52c34676a36f22d7cf37e38df039a4c5173cd9255e09464f622d0ef0403/biconnected.py b/shared/package-files/c9/c92fe52c34676a36f22d7cf37e38df039a4c5173cd9255e09464f622d0ef0403/biconnected.py
new file mode 100644
index 0000000000000000000000000000000000000000..f638453bd123229243bca8d409d2e8aec48bbd76
--- /dev/null
+++ b/shared/package-files/c9/c92fe52c34676a36f22d7cf37e38df039a4c5173cd9255e09464f622d0ef0403/biconnected.py
@@ -0,0 +1,388 @@
+"""Biconnected components and articulation points."""
+from itertools import chain
+
+from networkx.utils.decorators import not_implemented_for
+
+__all__ = [
+    "biconnected_components",
+    "biconnected_component_edges",
+    "is_biconnected",
+    "articulation_points",
+]
+
+
+@not_implemented_for("directed")
+def is_biconnected(G):
+    """Returns True if the graph is biconnected, False otherwise.
+
+    A graph is biconnected if, and only if, it cannot be disconnected by
+    removing only one node (and all edges incident on that node).  If
+    removing a node increases the number of disconnected components
+    in the graph, that node is called an articulation point, or cut
+    vertex.  A biconnected graph has no articulation points.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        An undirected graph.
+
+    Returns
+    -------
+    biconnected : bool
+        True if the graph is biconnected, False otherwise.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the input graph is not undirected.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> print(nx.is_biconnected(G))
+    False
+    >>> G.add_edge(0, 3)
+    >>> print(nx.is_biconnected(G))
+    True
+
+    See Also
+    --------
+    biconnected_components
+    articulation_points
+    biconnected_component_edges
+    is_strongly_connected
+    is_weakly_connected
+    is_connected
+    is_semiconnected
+
+    Notes
+    -----
+    The algorithm to find articulation points and biconnected
+    components is implemented using a non-recursive depth-first-search
+    (DFS) that keeps track of the highest level that back edges reach
+    in the DFS tree.  A node `n` is an articulation point if, and only
+    if, there exists a subtree rooted at `n` such that there is no
+    back edge from any successor of `n` that links to a predecessor of
+    `n` in the DFS tree.  By keeping track of all the edges traversed
+    by the DFS we can obtain the biconnected components because all
+    edges of a bicomponent will be traversed consecutively between
+    articulation points.
+
+    References
+    ----------
+    .. [1] Hopcroft, J.; Tarjan, R. (1973).
+       "Efficient algorithms for graph manipulation".
+       Communications of the ACM 16: 372–378. doi:10.1145/362248.362272
+
+    """
+    bccs = biconnected_components(G)
+    try:
+        bcc = next(bccs)
+    except StopIteration:
+        # No bicomponents (empty graph?)
+        return False
+    try:
+        next(bccs)
+    except StopIteration:
+        # Only one bicomponent
+        return len(bcc) == len(G)
+    else:
+        # Multiple bicomponents
+        return False
+
+
+@not_implemented_for("directed")
+def biconnected_component_edges(G):
+    """Returns a generator of lists of edges, one list for each biconnected
+    component of the input graph.
+
+    Biconnected components are maximal subgraphs such that the removal of a
+    node (and all edges incident on that node) will not disconnect the
+    subgraph.  Note that nodes may be part of more than one biconnected
+    component.  Those nodes are articulation points, or cut vertices.
+    However, each edge belongs to one, and only one, biconnected component.
+
+    Notice that by convention a dyad is considered a biconnected component.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        An undirected graph.
+
+    Returns
+    -------
+    edges : generator of lists
+        Generator of lists of edges, one list for each bicomponent.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the input graph is not undirected.
+
+    Examples
+    --------
+    >>> G = nx.barbell_graph(4, 2)
+    >>> print(nx.is_biconnected(G))
+    False
+    >>> bicomponents_edges = list(nx.biconnected_component_edges(G))
+    >>> len(bicomponents_edges)
+    5
+    >>> G.add_edge(2, 8)
+    >>> print(nx.is_biconnected(G))
+    True
+    >>> bicomponents_edges = list(nx.biconnected_component_edges(G))
+    >>> len(bicomponents_edges)
+    1
+
+    See Also
+    --------
+    is_biconnected,
+    biconnected_components,
+    articulation_points,
+
+    Notes
+    -----
+    The algorithm to find articulation points and biconnected
+    components is implemented using a non-recursive depth-first-search
+    (DFS) that keeps track of the highest level that back edges reach
+    in the DFS tree.  A node `n` is an articulation point if, and only
+    if, there exists a subtree rooted at `n` such that there is no
+    back edge from any successor of `n` that links to a predecessor of
+    `n` in the DFS tree.  By keeping track of all the edges traversed
+    by the DFS we can obtain the biconnected components because all
+    edges of a bicomponent will be traversed consecutively between
+    articulation points.
+
+    References
+    ----------
+    .. [1] Hopcroft, J.; Tarjan, R. (1973).
+           "Efficient algorithms for graph manipulation".
+           Communications of the ACM 16: 372–378. doi:10.1145/362248.362272
+
+    """
+    yield from _biconnected_dfs(G, components=True)
+
+
+@not_implemented_for("directed")
+def biconnected_components(G):
+    """Returns a generator of sets of nodes, one set for each biconnected
+    component of the graph
+
+    Biconnected components are maximal subgraphs such that the removal of a
+    node (and all edges incident on that node) will not disconnect the
+    subgraph. Note that nodes may be part of more than one biconnected
+    component.  Those nodes are articulation points, or cut vertices.  The
+    removal of articulation points will increase the number of connected
+    components of the graph.
+
+    Notice that by convention a dyad is considered a biconnected component.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        An undirected graph.
+
+    Returns
+    -------
+    nodes : generator
+        Generator of sets of nodes, one set for each biconnected component.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the input graph is not undirected.
+
+    Examples
+    --------
+    >>> G = nx.lollipop_graph(5, 1)
+    >>> print(nx.is_biconnected(G))
+    False
+    >>> bicomponents = list(nx.biconnected_components(G))
+    >>> len(bicomponents)
+    2
+    >>> G.add_edge(0, 5)
+    >>> print(nx.is_biconnected(G))
+    True
+    >>> bicomponents = list(nx.biconnected_components(G))
+    >>> len(bicomponents)
+    1
+
+    You can generate a sorted list of biconnected components, largest
+    first, using sort.
+
+    >>> G.remove_edge(0, 5)
+    >>> [len(c) for c in sorted(nx.biconnected_components(G), key=len, reverse=True)]
+    [5, 2]
+
+    If you only want the largest connected component, it's more
+    efficient to use max instead of sort.
+
+    >>> Gc = max(nx.biconnected_components(G), key=len)
+
+    To create the components as subgraphs use:
+    ``(G.subgraph(c).copy() for c in biconnected_components(G))``
+
+    See Also
+    --------
+    is_biconnected
+    articulation_points
+    biconnected_component_edges
+    k_components : this function is a special case where k=2
+    bridge_components : similar to this function, but is defined using
+        2-edge-connectivity instead of 2-node-connectivity.
+
+    Notes
+    -----
+    The algorithm to find articulation points and biconnected
+    components is implemented using a non-recursive depth-first-search
+    (DFS) that keeps track of the highest level that back edges reach
+    in the DFS tree.  A node `n` is an articulation point if, and only
+    if, there exists a subtree rooted at `n` such that there is no
+    back edge from any successor of `n` that links to a predecessor of
+    `n` in the DFS tree.  By keeping track of all the edges traversed
+    by the DFS we can obtain the biconnected components because all
+    edges of a bicomponent will be traversed consecutively between
+    articulation points.
+
+    References
+    ----------
+    .. [1] Hopcroft, J.; Tarjan, R. (1973).
+           "Efficient algorithms for graph manipulation".
+           Communications of the ACM 16: 372–378. doi:10.1145/362248.362272
+
+    """
+    for comp in _biconnected_dfs(G, components=True):
+        yield set(chain.from_iterable(comp))
+
+
+@not_implemented_for("directed")
+def articulation_points(G):
+    """Yield the articulation points, or cut vertices, of a graph.
+
+    An articulation point or cut vertex is any node whose removal (along with
+    all its incident edges) increases the number of connected components of
+    a graph.  An undirected connected graph without articulation points is
+    biconnected. Articulation points belong to more than one biconnected
+    component of a graph.
+
+    Notice that by convention a dyad is considered a biconnected component.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        An undirected graph.
+
+    Yields
+    ------
+    node
+        An articulation point in the graph.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If the input graph is not undirected.
+
+    Examples
+    --------
+
+    >>> G = nx.barbell_graph(4, 2)
+    >>> print(nx.is_biconnected(G))
+    False
+    >>> len(list(nx.articulation_points(G)))
+    4
+    >>> G.add_edge(2, 8)
+    >>> print(nx.is_biconnected(G))
+    True
+    >>> len(list(nx.articulation_points(G)))
+    0
+
+    See Also
+    --------
+    is_biconnected
+    biconnected_components
+    biconnected_component_edges
+
+    Notes
+    -----
+    The algorithm to find articulation points and biconnected
+    components is implemented using a non-recursive depth-first-search
+    (DFS) that keeps track of the highest level that back edges reach
+    in the DFS tree.  A node `n` is an articulation point if, and only
+    if, there exists a subtree rooted at `n` such that there is no
+    back edge from any successor of `n` that links to a predecessor of
+    `n` in the DFS tree.  By keeping track of all the edges traversed
+    by the DFS we can obtain the biconnected components because all
+    edges of a bicomponent will be traversed consecutively between
+    articulation points.
+
+    References
+    ----------
+    .. [1] Hopcroft, J.; Tarjan, R. (1973).
+           "Efficient algorithms for graph manipulation".
+           Communications of the ACM 16: 372–378. doi:10.1145/362248.362272
+
+    """
+    seen = set()
+    for articulation in _biconnected_dfs(G, components=False):
+        if articulation not in seen:
+            seen.add(articulation)
+            yield articulation
+
+
+@not_implemented_for("directed")
+def _biconnected_dfs(G, components=True):
+    # depth-first search algorithm to generate articulation points
+    # and biconnected components
+    visited = set()
+    for start in G:
+        if start in visited:
+            continue
+        discovery = {start: 0}  # time of first discovery of node during search
+        low = {start: 0}
+        root_children = 0
+        visited.add(start)
+        edge_stack = []
+        stack = [(start, start, iter(G[start]))]
+        edge_index = {}
+        while stack:
+            grandparent, parent, children = stack[-1]
+            try:
+                child = next(children)
+                if grandparent == child:
+                    continue
+                if child in visited:
+                    if discovery[child] <= discovery[parent]:  # back edge
+                        low[parent] = min(low[parent], discovery[child])
+                        if components:
+                            edge_index[parent, child] = len(edge_stack)
+                            edge_stack.append((parent, child))
+                else:
+                    low[child] = discovery[child] = len(discovery)
+                    visited.add(child)
+                    stack.append((parent, child, iter(G[child])))
+                    if components:
+                        edge_index[parent, child] = len(edge_stack)
+                        edge_stack.append((parent, child))
+
+            except StopIteration:
+                stack.pop()
+                if len(stack) > 1:
+                    if low[parent] >= discovery[grandparent]:
+                        if components:
+                            ind = edge_index[grandparent, parent]
+                            yield edge_stack[ind:]
+                            del edge_stack[ind:]
+
+                        else:
+                            yield grandparent
+                    low[grandparent] = min(low[parent], low[grandparent])
+                elif stack:  # length 1 so grandparent is root
+                    root_children += 1
+                    if components:
+                        ind = edge_index[grandparent, parent]
+                        yield edge_stack[ind:]
+                        del edge_stack[ind:]
+        if not components:
+            # root node is articulation point if it has more than 1 child
+            if root_children > 1:
+                yield start
diff --git a/shared/package-files/c9/c93137747773a16a5c5ce2b2b16a5b5998f5710cb7f17530e862fe2972c0b162/glu_backward_jvp_native.h b/shared/package-files/c9/c93137747773a16a5c5ce2b2b16a5b5998f5710cb7f17530e862fe2972c0b162/glu_backward_jvp_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..e4ae9f769d798d328fbc28bbcc8ca0fe1bcc4168
--- /dev/null
+++ b/shared/package-files/c9/c93137747773a16a5c5ce2b2b16a5b5998f5710cb7f17530e862fe2972c0b162/glu_backward_jvp_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & glu_backward_jvp_out(const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim, at::Tensor & out);
+TORCH_API at::Tensor glu_backward_jvp(const at::Tensor & grad_x, const at::Tensor & grad_glu, const at::Tensor & x, const at::Tensor & dgrad_glu, const at::Tensor & dx, int64_t dim);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/c9/c93273ef69e520d0dc3f2811a008d04d267c32fefd0e802152aec28aa3389579/test_gpr.py b/shared/package-files/c9/c93273ef69e520d0dc3f2811a008d04d267c32fefd0e802152aec28aa3389579/test_gpr.py
new file mode 100644
index 0000000000000000000000000000000000000000..f43cc3613b3ff7669aba9b73526fd774bfd8452e
--- /dev/null
+++ b/shared/package-files/c9/c93273ef69e520d0dc3f2811a008d04d267c32fefd0e802152aec28aa3389579/test_gpr.py
@@ -0,0 +1,849 @@
+"""Testing for Gaussian process regression"""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import re
+import sys
+import warnings
+
+import numpy as np
+import pytest
+from scipy.optimize import approx_fprime
+
+from sklearn.exceptions import ConvergenceWarning
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.gaussian_process.kernels import (
+    RBF,
+    DotProduct,
+    ExpSineSquared,
+    WhiteKernel,
+)
+from sklearn.gaussian_process.kernels import (
+    ConstantKernel as C,
+)
+from sklearn.gaussian_process.tests._mini_sequence_kernel import MiniSeqKernel
+from sklearn.utils._testing import (
+    assert_allclose,
+    assert_almost_equal,
+    assert_array_almost_equal,
+    assert_array_less,
+)
+
+
+def f(x):
+    return x * np.sin(x)
+
+
+X = np.atleast_2d([1.0, 3.0, 5.0, 6.0, 7.0, 8.0]).T
+X2 = np.atleast_2d([2.0, 4.0, 5.5, 6.5, 7.5]).T
+y = f(X).ravel()
+
+fixed_kernel = RBF(length_scale=1.0, length_scale_bounds="fixed")
+kernels = [
+    RBF(length_scale=1.0),
+    fixed_kernel,
+    RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3)),
+    C(1.0, (1e-2, 1e2)) * RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3)),
+    C(1.0, (1e-2, 1e2)) * RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3))
+    + C(1e-5, (1e-5, 1e2)),
+    C(0.1, (1e-2, 1e2)) * RBF(length_scale=1.0, length_scale_bounds=(1e-3, 1e3))
+    + C(1e-5, (1e-5, 1e2)),
+]
+non_fixed_kernels = [kernel for kernel in kernels if kernel != fixed_kernel]
+
+
+@pytest.mark.parametrize("kernel", kernels)
+def test_gpr_interpolation(kernel):
+    if sys.maxsize <= 2**32:
+        pytest.xfail("This test may fail on 32 bit Python")
+
+    # Test the interpolating property for different kernels.
+    gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
+    y_pred, y_cov = gpr.predict(X, return_cov=True)
+
+    assert_almost_equal(y_pred, y)
+    assert_almost_equal(np.diag(y_cov), 0.0)
+
+
+def test_gpr_interpolation_structured():
+    # Test the interpolating property for different kernels.
+    kernel = MiniSeqKernel(baseline_similarity_bounds="fixed")
+    X = ["A", "B", "C"]
+    y = np.array([1, 2, 3])
+    gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
+    y_pred, y_cov = gpr.predict(X, return_cov=True)
+
+    assert_almost_equal(
+        kernel(X, eval_gradient=True)[1].ravel(), (1 - np.eye(len(X))).ravel()
+    )
+    assert_almost_equal(y_pred, y)
+    assert_almost_equal(np.diag(y_cov), 0.0)
+
+
+@pytest.mark.parametrize("kernel", non_fixed_kernels)
+def test_lml_improving(kernel):
+    if sys.maxsize <= 2**32:
+        pytest.xfail("This test may fail on 32 bit Python")
+
+    # Test that hyperparameter-tuning improves log-marginal likelihood.
+    gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
+    assert gpr.log_marginal_likelihood(gpr.kernel_.theta) > gpr.log_marginal_likelihood(
+        kernel.theta
+    )
+
+
+@pytest.mark.parametrize("kernel", kernels)
+def test_lml_precomputed(kernel):
+    # Test that lml of optimized kernel is stored correctly.
+    gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
+    assert gpr.log_marginal_likelihood(gpr.kernel_.theta) == pytest.approx(
+        gpr.log_marginal_likelihood()
+    )
+
+
+@pytest.mark.parametrize("kernel", kernels)
+def test_lml_without_cloning_kernel(kernel):
+    # Test that lml of optimized kernel is stored correctly.
+    gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
+    input_theta = np.ones(gpr.kernel_.theta.shape, dtype=np.float64)
+
+    gpr.log_marginal_likelihood(input_theta, clone_kernel=False)
+    assert_almost_equal(gpr.kernel_.theta, input_theta, 7)
+
+
+@pytest.mark.parametrize("kernel", non_fixed_kernels)
+def test_converged_to_local_maximum(kernel):
+    # Test that we are in local maximum after hyperparameter-optimization.
+    gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
+
+    lml, lml_gradient = gpr.log_marginal_likelihood(gpr.kernel_.theta, True)
+
+    assert np.all(
+        (np.abs(lml_gradient) < 1e-4)
+        | (gpr.kernel_.theta == gpr.kernel_.bounds[:, 0])
+        | (gpr.kernel_.theta == gpr.kernel_.bounds[:, 1])
+    )
+
+
+@pytest.mark.parametrize("kernel", non_fixed_kernels)
+def test_solution_inside_bounds(kernel):
+    # Test that hyperparameter-optimization remains in bounds#
+    gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
+
+    bounds = gpr.kernel_.bounds
+    max_ = np.finfo(gpr.kernel_.theta.dtype).max
+    tiny = 1e-10
+    bounds[~np.isfinite(bounds[:, 1]), 1] = max_
+
+    assert_array_less(bounds[:, 0], gpr.kernel_.theta + tiny)
+    assert_array_less(gpr.kernel_.theta, bounds[:, 1] + tiny)
+
+
+@pytest.mark.parametrize("kernel", kernels)
+def test_lml_gradient(kernel):
+    # Compare analytic and numeric gradient of log marginal likelihood.
+    gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
+
+    lml, lml_gradient = gpr.log_marginal_likelihood(kernel.theta, True)
+    lml_gradient_approx = approx_fprime(
+        kernel.theta, lambda theta: gpr.log_marginal_likelihood(theta, False), 1e-10
+    )
+
+    assert_almost_equal(lml_gradient, lml_gradient_approx, 3)
+
+
+@pytest.mark.parametrize("kernel", kernels)
+def test_prior(kernel):
+    # Test that GP prior has mean 0 and identical variances.
+    gpr = GaussianProcessRegressor(kernel=kernel)
+
+    y_mean, y_cov = gpr.predict(X, return_cov=True)
+
+    assert_almost_equal(y_mean, 0, 5)
+    if len(gpr.kernel.theta) > 1:
+        # XXX: quite hacky, works only for current kernels
+        assert_almost_equal(np.diag(y_cov), np.exp(kernel.theta[0]), 5)
+    else:
+        assert_almost_equal(np.diag(y_cov), 1, 5)
+
+
+@pytest.mark.parametrize("kernel", kernels)
+def test_sample_statistics(kernel):
+    # Test that statistics of samples drawn from GP are correct.
+    gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
+
+    y_mean, y_cov = gpr.predict(X2, return_cov=True)
+
+    samples = gpr.sample_y(X2, 300000)
+
+    # More digits accuracy would require many more samples
+    assert_almost_equal(y_mean, np.mean(samples, 1), 1)
+    assert_almost_equal(
+        np.diag(y_cov) / np.diag(y_cov).max(),
+        np.var(samples, 1) / np.diag(y_cov).max(),
+        1,
+    )
+
+
+def test_no_optimizer():
+    # Test that kernel parameters are unmodified when optimizer is None.
+    kernel = RBF(1.0)
+    gpr = GaussianProcessRegressor(kernel=kernel, optimizer=None).fit(X, y)
+    assert np.exp(gpr.kernel_.theta) == 1.0
+
+
+@pytest.mark.parametrize("kernel", kernels)
+@pytest.mark.parametrize("target", [y, np.ones(X.shape[0], dtype=np.float64)])
+def test_predict_cov_vs_std(kernel, target):
+    if sys.maxsize <= 2**32:
+        pytest.xfail("This test may fail on 32 bit Python")
+
+    # Test that predicted std.-dev. is consistent with cov's diagonal.
+    gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
+    y_mean, y_cov = gpr.predict(X2, return_cov=True)
+    y_mean, y_std = gpr.predict(X2, return_std=True)
+    assert_almost_equal(np.sqrt(np.diag(y_cov)), y_std)
+
+
+def test_anisotropic_kernel():
+    # Test that GPR can identify meaningful anisotropic length-scales.
+    # We learn a function which varies in one dimension ten-times slower
+    # than in the other. The corresponding length-scales should differ by at
+    # least a factor 5
+    rng = np.random.RandomState(0)
+    X = rng.uniform(-1, 1, (50, 2))
+    y = X[:, 0] + 0.1 * X[:, 1]
+
+    kernel = RBF([1.0, 1.0])
+    gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)
+    assert np.exp(gpr.kernel_.theta[1]) > np.exp(gpr.kernel_.theta[0]) * 5
+
+
+def test_random_starts():
+    # Test that an increasing number of random-starts of GP fitting only
+    # increases the log marginal likelihood of the chosen theta.
+    n_samples, n_features = 25, 2
+    rng = np.random.RandomState(0)
+    X = rng.randn(n_samples, n_features) * 2 - 1
+    y = (
+        np.sin(X).sum(axis=1)
+        + np.sin(3 * X).sum(axis=1)
+        + rng.normal(scale=0.1, size=n_samples)
+    )
+
+    kernel = C(1.0, (1e-2, 1e2)) * RBF(
+        length_scale=[1.0] * n_features, length_scale_bounds=[(1e-4, 1e2)] * n_features
+    ) + WhiteKernel(noise_level=1e-5, noise_level_bounds=(1e-5, 1e1))
+    last_lml = -np.inf
+    for n_restarts_optimizer in range(5):
+        gp = GaussianProcessRegressor(
+            kernel=kernel,
+            n_restarts_optimizer=n_restarts_optimizer,
+            random_state=0,
+        ).fit(X, y)
+        lml = gp.log_marginal_likelihood(gp.kernel_.theta)
+        assert lml > last_lml - np.finfo(np.float32).eps
+        last_lml = lml
+
+
+@pytest.mark.parametrize("kernel", kernels)
+def test_y_normalization(kernel):
+    """
+    Test normalization of the target values in GP
+
+    Fitting non-normalizing GP on normalized y and fitting normalizing GP
+    on unnormalized y should yield identical results. Note that, here,
+    'normalized y' refers to y that has been made zero mean and unit
+    variance.
+
+    """
+
+    y_mean = np.mean(y)
+    y_std = np.std(y)
+    y_norm = (y - y_mean) / y_std
+
+    # Fit non-normalizing GP on normalized y
+    gpr = GaussianProcessRegressor(kernel=kernel)
+    gpr.fit(X, y_norm)
+
+    # Fit normalizing GP on unnormalized y
+    gpr_norm = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
+    gpr_norm.fit(X, y)
+
+    # Compare predicted mean, std-devs and covariances
+    y_pred, y_pred_std = gpr.predict(X2, return_std=True)
+    y_pred = y_pred * y_std + y_mean
+    y_pred_std = y_pred_std * y_std
+    y_pred_norm, y_pred_std_norm = gpr_norm.predict(X2, return_std=True)
+
+    assert_almost_equal(y_pred, y_pred_norm)
+    assert_almost_equal(y_pred_std, y_pred_std_norm)
+
+    _, y_cov = gpr.predict(X2, return_cov=True)
+    y_cov = y_cov * y_std**2
+    _, y_cov_norm = gpr_norm.predict(X2, return_cov=True)
+
+    assert_almost_equal(y_cov, y_cov_norm)
+
+
+def test_large_variance_y():
+    """
+    Here we test that, when noramlize_y=True, our GP can produce a
+    sensible fit to training data whose variance is significantly
+    larger than unity. This test was made in response to issue #15612.
+
+    GP predictions are verified against predictions that were made
+    using GPy which, here, is treated as the 'gold standard'. Note that we
+    only investigate the RBF kernel here, as that is what was used in the
+    GPy implementation.
+
+    The following code can be used to recreate the GPy data:
+
+    --------------------------------------------------------------------------
+    import GPy
+
+    kernel_gpy = GPy.kern.RBF(input_dim=1, lengthscale=1.)
+    gpy = GPy.models.GPRegression(X, np.vstack(y_large), kernel_gpy)
+    gpy.optimize()
+    y_pred_gpy, y_var_gpy = gpy.predict(X2)
+    y_pred_std_gpy = np.sqrt(y_var_gpy)
+    --------------------------------------------------------------------------
+    """
+
+    # Here we utilise a larger variance version of the training data
+    y_large = 10 * y
+
+    # Standard GP with normalize_y=True
+    RBF_params = {"length_scale": 1.0}
+    kernel = RBF(**RBF_params)
+    gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
+    gpr.fit(X, y_large)
+    y_pred, y_pred_std = gpr.predict(X2, return_std=True)
+
+    # 'Gold standard' mean predictions from GPy
+    y_pred_gpy = np.array(
+        [15.16918303, -27.98707845, -39.31636019, 14.52605515, 69.18503589]
+    )
+
+    # 'Gold standard' std predictions from GPy
+    y_pred_std_gpy = np.array(
+        [7.78860962, 3.83179178, 0.63149951, 0.52745188, 0.86170042]
+    )
+
+    # Based on numerical experiments, it's reasonable to expect our
+    # GP's mean predictions to get within 7% of predictions of those
+    # made by GPy.
+    assert_allclose(y_pred, y_pred_gpy, rtol=0.07, atol=0)
+
+    # Based on numerical experiments, it's reasonable to expect our
+    # GP's std predictions to get within 15% of predictions of those
+    # made by GPy.
+    assert_allclose(y_pred_std, y_pred_std_gpy, rtol=0.15, atol=0)
+
+
+def test_y_multioutput():
+    # Test that GPR can deal with multi-dimensional target values
+    y_2d = np.vstack((y, y * 2)).T
+
+    # Test for fixed kernel that first dimension of 2d GP equals the output
+    # of 1d GP and that second dimension is twice as large
+    kernel = RBF(length_scale=1.0)
+
+    gpr = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=False)
+    gpr.fit(X, y)
+
+    gpr_2d = GaussianProcessRegressor(kernel=kernel, optimizer=None, normalize_y=False)
+    gpr_2d.fit(X, y_2d)
+
+    y_pred_1d, y_std_1d = gpr.predict(X2, return_std=True)
+    y_pred_2d, y_std_2d = gpr_2d.predict(X2, return_std=True)
+    _, y_cov_1d = gpr.predict(X2, return_cov=True)
+    _, y_cov_2d = gpr_2d.predict(X2, return_cov=True)
+
+    assert_almost_equal(y_pred_1d, y_pred_2d[:, 0])
+    assert_almost_equal(y_pred_1d, y_pred_2d[:, 1] / 2)
+
+    # Standard deviation and covariance do not depend on output
+    for target in range(y_2d.shape[1]):
+        assert_almost_equal(y_std_1d, y_std_2d[..., target])
+        assert_almost_equal(y_cov_1d, y_cov_2d[..., target])
+
+    y_sample_1d = gpr.sample_y(X2, n_samples=10)
+    y_sample_2d = gpr_2d.sample_y(X2, n_samples=10)
+
+    assert y_sample_1d.shape == (5, 10)
+    assert y_sample_2d.shape == (5, 2, 10)
+    # Only the first target will be equal
+    assert_almost_equal(y_sample_1d, y_sample_2d[:, 0, :])
+
+    # Test hyperparameter optimization
+    for kernel in kernels:
+        gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
+        gpr.fit(X, y)
+
+        gpr_2d = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
+        gpr_2d.fit(X, np.vstack((y, y)).T)
+
+        assert_almost_equal(gpr.kernel_.theta, gpr_2d.kernel_.theta, 4)
+
+
+@pytest.mark.parametrize("kernel", non_fixed_kernels)
+def test_custom_optimizer(kernel):
+    # Test that GPR can use externally defined optimizers.
+    # Define a dummy optimizer that simply tests 50 random hyperparameters
+    def optimizer(obj_func, initial_theta, bounds):
+        rng = np.random.RandomState(0)
+        theta_opt, func_min = (
+            initial_theta,
+            obj_func(initial_theta, eval_gradient=False),
+        )
+        for _ in range(50):
+            theta = np.atleast_1d(
+                rng.uniform(np.maximum(-2, bounds[:, 0]), np.minimum(1, bounds[:, 1]))
+            )
+            f = obj_func(theta, eval_gradient=False)
+            if f < func_min:
+                theta_opt, func_min = theta, f
+        return theta_opt, func_min
+
+    gpr = GaussianProcessRegressor(kernel=kernel, optimizer=optimizer)
+    gpr.fit(X, y)
+    # Checks that optimizer improved marginal likelihood
+    assert gpr.log_marginal_likelihood(gpr.kernel_.theta) > gpr.log_marginal_likelihood(
+        gpr.kernel.theta
+    )
+
+
+def test_gpr_correct_error_message():
+    X = np.arange(12).reshape(6, -1)
+    y = np.ones(6)
+    kernel = DotProduct()
+    gpr = GaussianProcessRegressor(kernel=kernel, alpha=0.0)
+    message = (
+        "The kernel, %s, is not returning a "
+        "positive definite matrix. Try gradually increasing "
+        "the 'alpha' parameter of your "
+        "GaussianProcessRegressor estimator." % kernel
+    )
+    with pytest.raises(np.linalg.LinAlgError, match=re.escape(message)):
+        gpr.fit(X, y)
+
+
+@pytest.mark.parametrize("kernel", kernels)
+def test_duplicate_input(kernel):
+    # Test GPR can handle two different output-values for the same input.
+    gpr_equal_inputs = GaussianProcessRegressor(kernel=kernel, alpha=1e-2)
+    gpr_similar_inputs = GaussianProcessRegressor(kernel=kernel, alpha=1e-2)
+
+    X_ = np.vstack((X, X[0]))
+    y_ = np.hstack((y, y[0] + 1))
+    gpr_equal_inputs.fit(X_, y_)
+
+    X_ = np.vstack((X, X[0] + 1e-15))
+    y_ = np.hstack((y, y[0] + 1))
+    gpr_similar_inputs.fit(X_, y_)
+
+    X_test = np.linspace(0, 10, 100)[:, None]
+    y_pred_equal, y_std_equal = gpr_equal_inputs.predict(X_test, return_std=True)
+    y_pred_similar, y_std_similar = gpr_similar_inputs.predict(X_test, return_std=True)
+
+    assert_almost_equal(y_pred_equal, y_pred_similar)
+    assert_almost_equal(y_std_equal, y_std_similar)
+
+
+def test_no_fit_default_predict():
+    # Test that GPR predictions without fit does not break by default.
+    default_kernel = C(1.0, constant_value_bounds="fixed") * RBF(
+        1.0, length_scale_bounds="fixed"
+    )
+    gpr1 = GaussianProcessRegressor()
+    _, y_std1 = gpr1.predict(X, return_std=True)
+    _, y_cov1 = gpr1.predict(X, return_cov=True)
+
+    gpr2 = GaussianProcessRegressor(kernel=default_kernel)
+    _, y_std2 = gpr2.predict(X, return_std=True)
+    _, y_cov2 = gpr2.predict(X, return_cov=True)
+
+    assert_array_almost_equal(y_std1, y_std2)
+    assert_array_almost_equal(y_cov1, y_cov2)
+
+
+def test_warning_bounds():
+    kernel = RBF(length_scale_bounds=[1e-5, 1e-3])
+    gpr = GaussianProcessRegressor(kernel=kernel)
+    warning_message = (
+        "The optimal value found for dimension 0 of parameter "
+        "length_scale is close to the specified upper bound "
+        "0.001. Increasing the bound and calling fit again may "
+        "find a better value."
+    )
+    with pytest.warns(ConvergenceWarning, match=warning_message):
+        gpr.fit(X, y)
+
+    kernel_sum = WhiteKernel(noise_level_bounds=[1e-5, 1e-3]) + RBF(
+        length_scale_bounds=[1e3, 1e5]
+    )
+    gpr_sum = GaussianProcessRegressor(kernel=kernel_sum)
+    with warnings.catch_warnings(record=True) as record:
+        warnings.simplefilter("always")
+        gpr_sum.fit(X, y)
+
+        assert len(record) == 2
+
+        assert issubclass(record[0].category, ConvergenceWarning)
+        assert (
+            record[0].message.args[0] == "The optimal value found for "
+            "dimension 0 of parameter "
+            "k1__noise_level is close to the "
+            "specified upper bound 0.001. "
+            "Increasing the bound and calling "
+            "fit again may find a better value."
+        )
+
+        assert issubclass(record[1].category, ConvergenceWarning)
+        assert (
+            record[1].message.args[0] == "The optimal value found for "
+            "dimension 0 of parameter "
+            "k2__length_scale is close to the "
+            "specified lower bound 1000.0. "
+            "Decreasing the bound and calling "
+            "fit again may find a better value."
+        )
+
+    X_tile = np.tile(X, 2)
+    kernel_dims = RBF(length_scale=[1.0, 2.0], length_scale_bounds=[1e1, 1e2])
+    gpr_dims = GaussianProcessRegressor(kernel=kernel_dims)
+
+    with warnings.catch_warnings(record=True) as record:
+        warnings.simplefilter("always")
+        gpr_dims.fit(X_tile, y)
+
+        assert len(record) == 2
+
+        assert issubclass(record[0].category, ConvergenceWarning)
+        assert (
+            record[0].message.args[0] == "The optimal value found for "
+            "dimension 0 of parameter "
+            "length_scale is close to the "
+            "specified lower bound 10.0. "
+            "Decreasing the bound and calling "
+            "fit again may find a better value."
+        )
+
+        assert issubclass(record[1].category, ConvergenceWarning)
+        assert (
+            record[1].message.args[0] == "The optimal value found for "
+            "dimension 1 of parameter "
+            "length_scale is close to the "
+            "specified lower bound 10.0. "
+            "Decreasing the bound and calling "
+            "fit again may find a better value."
+        )
+
+
+def test_bound_check_fixed_hyperparameter():
+    # Regression test for issue #17943
+    # Check that having a hyperparameter with fixed bounds doesn't cause an
+    # error
+    k1 = 50.0**2 * RBF(length_scale=50.0)  # long term smooth rising trend
+    k2 = ExpSineSquared(
+        length_scale=1.0, periodicity=1.0, periodicity_bounds="fixed"
+    )  # seasonal component
+    kernel = k1 + k2
+    GaussianProcessRegressor(kernel=kernel).fit(X, y)
+
+
+@pytest.mark.parametrize("kernel", kernels)
+def test_constant_target(kernel):
+    """Check that the std. dev. is affected to 1 when normalizing a constant
+    feature.
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/18318
+    NaN where affected to the target when scaling due to null std. dev. with
+    constant target.
+    """
+    y_constant = np.ones(X.shape[0], dtype=np.float64)
+
+    gpr = GaussianProcessRegressor(kernel=kernel, normalize_y=True)
+    gpr.fit(X, y_constant)
+    assert gpr._y_train_std == pytest.approx(1.0)
+
+    y_pred, y_cov = gpr.predict(X, return_cov=True)
+    assert_allclose(y_pred, y_constant)
+    # set atol because we compare to zero
+    assert_allclose(np.diag(y_cov), 0.0, atol=1e-9)
+
+    # Test multi-target data
+    n_samples, n_targets = X.shape[0], 2
+    rng = np.random.RandomState(0)
+    y = np.concatenate(
+        [
+            rng.normal(size=(n_samples, 1)),  # non-constant target
+            np.full(shape=(n_samples, 1), fill_value=2),  # constant target
+        ],
+        axis=1,
+    )
+
+    gpr.fit(X, y)
+    Y_pred, Y_cov = gpr.predict(X, return_cov=True)
+
+    assert_allclose(Y_pred[:, 1], 2)
+    assert_allclose(np.diag(Y_cov[..., 1]), 0.0, atol=1e-9)
+
+    assert Y_pred.shape == (n_samples, n_targets)
+    assert Y_cov.shape == (n_samples, n_samples, n_targets)
+
+
+def test_gpr_consistency_std_cov_non_invertible_kernel():
+    """Check the consistency between the returned std. dev. and the covariance.
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/19936
+    Inconsistencies were observed when the kernel cannot be inverted (or
+    numerically stable).
+    """
+    kernel = C(8.98576054e05, (1e-12, 1e12)) * RBF(
+        [5.91326520e02, 1.32584051e03], (1e-12, 1e12)
+    ) + WhiteKernel(noise_level=1e-5)
+    gpr = GaussianProcessRegressor(kernel=kernel, alpha=0, optimizer=None)
+    X_train = np.array(
+        [
+            [0.0, 0.0],
+            [1.54919334, -0.77459667],
+            [-1.54919334, 0.0],
+            [0.0, -1.54919334],
+            [0.77459667, 0.77459667],
+            [-0.77459667, 1.54919334],
+        ]
+    )
+    y_train = np.array(
+        [
+            [-2.14882017e-10],
+            [-4.66975823e00],
+            [4.01823986e00],
+            [-1.30303674e00],
+            [-1.35760156e00],
+            [3.31215668e00],
+        ]
+    )
+    gpr.fit(X_train, y_train)
+    X_test = np.array(
+        [
+            [-1.93649167, -1.93649167],
+            [1.93649167, -1.93649167],
+            [-1.93649167, 1.93649167],
+            [1.93649167, 1.93649167],
+        ]
+    )
+    pred1, std = gpr.predict(X_test, return_std=True)
+    pred2, cov = gpr.predict(X_test, return_cov=True)
+    assert_allclose(std, np.sqrt(np.diagonal(cov)), rtol=1e-5)
+
+
+@pytest.mark.parametrize(
+    "params, TypeError, err_msg",
+    [
+        (
+            {"alpha": np.zeros(100)},
+            ValueError,
+            "alpha must be a scalar or an array with same number of entries as y",
+        ),
+        (
+            {
+                "kernel": WhiteKernel(noise_level_bounds=(-np.inf, np.inf)),
+                "n_restarts_optimizer": 2,
+            },
+            ValueError,
+            "requires that all bounds are finite",
+        ),
+    ],
+)
+def test_gpr_fit_error(params, TypeError, err_msg):
+    """Check that expected error are raised during fit."""
+    gpr = GaussianProcessRegressor(**params)
+    with pytest.raises(TypeError, match=err_msg):
+        gpr.fit(X, y)
+
+
+def test_gpr_lml_error():
+    """Check that we raise the proper error in the LML method."""
+    gpr = GaussianProcessRegressor(kernel=RBF()).fit(X, y)
+
+    err_msg = "Gradient can only be evaluated for theta!=None"
+    with pytest.raises(ValueError, match=err_msg):
+        gpr.log_marginal_likelihood(eval_gradient=True)
+
+
+def test_gpr_predict_error():
+    """Check that we raise the proper error during predict."""
+    gpr = GaussianProcessRegressor(kernel=RBF()).fit(X, y)
+
+    err_msg = "At most one of return_std or return_cov can be requested."
+    with pytest.raises(RuntimeError, match=err_msg):
+        gpr.predict(X, return_cov=True, return_std=True)
+
+
+@pytest.mark.parametrize("normalize_y", [True, False])
+@pytest.mark.parametrize("n_targets", [None, 1, 10])
+def test_predict_shapes(normalize_y, n_targets):
+    """Check the shapes of y_mean, y_std, and y_cov in single-output
+    (n_targets=None) and multi-output settings, including the edge case when
+    n_targets=1, where the sklearn convention is to squeeze the predictions.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/17394
+    https://github.com/scikit-learn/scikit-learn/issues/18065
+    https://github.com/scikit-learn/scikit-learn/issues/22174
+    """
+    rng = np.random.RandomState(1234)
+
+    n_features, n_samples_train, n_samples_test = 6, 9, 7
+
+    y_train_shape = (n_samples_train,)
+    if n_targets is not None:
+        y_train_shape = y_train_shape + (n_targets,)
+
+    # By convention single-output data is squeezed upon prediction
+    y_test_shape = (n_samples_test,)
+    if n_targets is not None and n_targets > 1:
+        y_test_shape = y_test_shape + (n_targets,)
+
+    X_train = rng.randn(n_samples_train, n_features)
+    X_test = rng.randn(n_samples_test, n_features)
+    y_train = rng.randn(*y_train_shape)
+
+    model = GaussianProcessRegressor(normalize_y=normalize_y)
+    model.fit(X_train, y_train)
+
+    y_pred, y_std = model.predict(X_test, return_std=True)
+    _, y_cov = model.predict(X_test, return_cov=True)
+
+    assert y_pred.shape == y_test_shape
+    assert y_std.shape == y_test_shape
+    assert y_cov.shape == (n_samples_test,) + y_test_shape
+
+
+@pytest.mark.parametrize("normalize_y", [True, False])
+@pytest.mark.parametrize("n_targets", [None, 1, 10])
+def test_sample_y_shapes(normalize_y, n_targets):
+    """Check the shapes of y_samples in single-output (n_targets=0) and
+    multi-output settings, including the edge case when n_targets=1, where the
+    sklearn convention is to squeeze the predictions.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/22175
+    """
+    rng = np.random.RandomState(1234)
+
+    n_features, n_samples_train = 6, 9
+    # Number of spatial locations to predict at
+    n_samples_X_test = 7
+    # Number of sample predictions per test point
+    n_samples_y_test = 5
+
+    y_train_shape = (n_samples_train,)
+    if n_targets is not None:
+        y_train_shape = y_train_shape + (n_targets,)
+
+    # By convention single-output data is squeezed upon prediction
+    if n_targets is not None and n_targets > 1:
+        y_test_shape = (n_samples_X_test, n_targets, n_samples_y_test)
+    else:
+        y_test_shape = (n_samples_X_test, n_samples_y_test)
+
+    X_train = rng.randn(n_samples_train, n_features)
+    X_test = rng.randn(n_samples_X_test, n_features)
+    y_train = rng.randn(*y_train_shape)
+
+    model = GaussianProcessRegressor(normalize_y=normalize_y)
+
+    # FIXME: before fitting, the estimator does not have information regarding
+    # the number of targets and default to 1. This is inconsistent with the shape
+    # provided after `fit`. This assert should be made once the following issue
+    # is fixed:
+    # https://github.com/scikit-learn/scikit-learn/issues/22430
+    # y_samples = model.sample_y(X_test, n_samples=n_samples_y_test)
+    # assert y_samples.shape == y_test_shape
+
+    model.fit(X_train, y_train)
+
+    y_samples = model.sample_y(X_test, n_samples=n_samples_y_test)
+    assert y_samples.shape == y_test_shape
+
+
+@pytest.mark.parametrize("n_targets", [None, 1, 2, 3])
+@pytest.mark.parametrize("n_samples", [1, 5])
+def test_sample_y_shape_with_prior(n_targets, n_samples):
+    """Check the output shape of `sample_y` is consistent before and after `fit`."""
+    rng = np.random.RandomState(1024)
+
+    X = rng.randn(10, 3)
+    y = rng.randn(10, n_targets if n_targets is not None else 1)
+
+    model = GaussianProcessRegressor(n_targets=n_targets)
+    shape_before_fit = model.sample_y(X, n_samples=n_samples).shape
+    model.fit(X, y)
+    shape_after_fit = model.sample_y(X, n_samples=n_samples).shape
+    assert shape_before_fit == shape_after_fit
+
+
+@pytest.mark.parametrize("n_targets", [None, 1, 2, 3])
+def test_predict_shape_with_prior(n_targets):
+    """Check the output shape of `predict` with prior distribution."""
+    rng = np.random.RandomState(1024)
+
+    n_sample = 10
+    X = rng.randn(n_sample, 3)
+    y = rng.randn(n_sample, n_targets if n_targets is not None else 1)
+
+    model = GaussianProcessRegressor(n_targets=n_targets)
+    mean_prior, cov_prior = model.predict(X, return_cov=True)
+    _, std_prior = model.predict(X, return_std=True)
+
+    model.fit(X, y)
+    mean_post, cov_post = model.predict(X, return_cov=True)
+    _, std_post = model.predict(X, return_std=True)
+
+    assert mean_prior.shape == mean_post.shape
+    assert cov_prior.shape == cov_post.shape
+    assert std_prior.shape == std_post.shape
+
+
+def test_n_targets_error():
+    """Check that an error is raised when the number of targets seen at fit is
+    inconsistent with n_targets.
+    """
+    rng = np.random.RandomState(0)
+    X = rng.randn(10, 3)
+    y = rng.randn(10, 2)
+
+    model = GaussianProcessRegressor(n_targets=1)
+    with pytest.raises(ValueError, match="The number of targets seen in `y`"):
+        model.fit(X, y)
+
+
+class CustomKernel(C):
+    """
+    A custom kernel that has a diag method that returns the first column of the
+    input matrix X. This is a helper for the test to check that the input
+    matrix X is not mutated.
+    """
+
+    def diag(self, X):
+        return X[:, 0]
+
+
+def test_gpr_predict_input_not_modified():
+    """
+    Check that the input X is not modified by the predict method of the
+    GaussianProcessRegressor when setting return_std=True.
+
+    Non-regression test for:
+    https://github.com/scikit-learn/scikit-learn/issues/24340
+    """
+    gpr = GaussianProcessRegressor(kernel=CustomKernel()).fit(X, y)
+
+    X2_copy = np.copy(X2)
+    _, _ = gpr.predict(X2, return_std=True)
+
+    assert_allclose(X2, X2_copy)
diff --git a/shared/package-files/c9/c933a04036a51a62cccdd1f614c1bfda5491cf1a03ba907f8a4ea43f1a1716d6/argmin.h b/shared/package-files/c9/c933a04036a51a62cccdd1f614c1bfda5491cf1a03ba907f8a4ea43f1a1716d6/argmin.h
new file mode 100644
index 0000000000000000000000000000000000000000..048a67bfc7fb805e588c70507c8833198a929483
--- /dev/null
+++ b/shared/package-files/c9/c933a04036a51a62cccdd1f614c1bfda5491cf1a03ba907f8a4ea43f1a1716d6/argmin.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::argmin(Tensor self, int? dim=None, bool keepdim=False) -> Tensor
+inline at::Tensor argmin(const at::Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false) {
+    return at::_ops::argmin::call(self, dim, keepdim);
+}
+
+// aten::argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & argmin_out(at::Tensor & out, const at::Tensor & self, c10::optional dim=c10::nullopt, bool keepdim=false) {
+    return at::_ops::argmin_out::call(self, dim, keepdim, out);
+}
+// aten::argmin.out(Tensor self, int? dim=None, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & argmin_outf(const at::Tensor & self, c10::optional dim, bool keepdim, at::Tensor & out) {
+    return at::_ops::argmin_out::call(self, dim, keepdim, out);
+}
+
+}
diff --git a/shared/package-files/c9/c9346a902715e6acdfed409698892c930f569438d178547e110668d596422a37/randint_ops.h b/shared/package-files/c9/c9346a902715e6acdfed409698892c930f569438d178547e110668d596422a37/randint_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..6d2804dc14a5077ab907c230cbd0990622cba268
--- /dev/null
+++ b/shared/package-files/c9/c9346a902715e6acdfed409698892c930f569438d178547e110668d596422a37/randint_ops.h
@@ -0,0 +1,105 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API randint {
+  using schema = at::Tensor (c10::SymInt, c10::SymIntArrayRef, c10::optional, c10::optional, c10::optional, c10::optional);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::randint")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "randint(SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor")
+  static at::Tensor call(c10::SymInt high, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory);
+};
+
+struct TORCH_API randint_generator {
+  using schema = at::Tensor (c10::SymInt, c10::SymIntArrayRef, c10::optional, c10::optional, c10::optional, c10::optional, c10::optional);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::randint")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "generator")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "randint.generator(SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor")
+  static at::Tensor call(c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory);
+};
+
+struct TORCH_API randint_low {
+  using schema = at::Tensor (c10::SymInt, c10::SymInt, c10::SymIntArrayRef, c10::optional, c10::optional, c10::optional, c10::optional);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::randint")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "low")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "randint.low(SymInt low, SymInt high, SymInt[] size, *, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor")
+  static at::Tensor call(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory);
+};
+
+struct TORCH_API randint_low_generator {
+  using schema = at::Tensor (c10::SymInt, c10::SymInt, c10::SymIntArrayRef, c10::optional, c10::optional, c10::optional, c10::optional, c10::optional);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::randint")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "low_generator")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "randint.low_generator(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, ScalarType? dtype=long, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensor")
+  static at::Tensor call(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory);
+};
+
+struct TORCH_API randint_out {
+  using schema = at::Tensor & (c10::SymInt, c10::SymIntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::randint")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "randint.out(SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out);
+};
+
+struct TORCH_API randint_generator_out {
+  using schema = at::Tensor & (c10::SymInt, c10::SymIntArrayRef, c10::optional, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::randint")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "generator_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "randint.generator_out(SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, at::Tensor & out);
+};
+
+struct TORCH_API randint_low_out {
+  using schema = at::Tensor & (c10::SymInt, c10::SymInt, c10::SymIntArrayRef, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::randint")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "low_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "randint.low_out(SymInt low, SymInt high, SymInt[] size, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, at::Tensor & out);
+};
+
+struct TORCH_API randint_low_generator_out {
+  using schema = at::Tensor & (c10::SymInt, c10::SymInt, c10::SymIntArrayRef, c10::optional, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::randint")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "low_generator_out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "randint.low_generator_out(SymInt low, SymInt high, SymInt[] size, *, Generator? generator, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, c10::SymInt low, c10::SymInt high, c10::SymIntArrayRef size, c10::optional generator, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/c9/c9366e3a3b4074622af935e271d6e5ae8f95af91abd3481897345a41aecc8db4/test_ir_utils.py b/shared/package-files/c9/c9366e3a3b4074622af935e271d6e5ae8f95af91abd3481897345a41aecc8db4/test_ir_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e7362de516fab1c33d50bcd1d9c934e0f50d550
--- /dev/null
+++ b/shared/package-files/c9/c9366e3a3b4074622af935e271d6e5ae8f95af91abd3481897345a41aecc8db4/test_ir_utils.py
@@ -0,0 +1,282 @@
+import numba
+from numba.tests.support import TestCase, unittest
+from numba.core.registry import cpu_target
+from numba.core.compiler import CompilerBase, Flags
+from numba.core.compiler_machinery import PassManager
+from numba.core import types, ir, bytecode, compiler, ir_utils, registry
+from numba.core.untyped_passes import (ExtractByteCode, TranslateByteCode,
+                                       FixupArgs, IRProcessing,)
+
+from numba.core.typed_passes import (NopythonTypeInference,
+                                     type_inference_stage, DeadCodeElimination)
+from numba.experimental import jitclass
+
+# global constant for testing find_const
+GLOBAL_B = 11
+
+
+@jitclass([('val', numba.core.types.List(numba.intp))])
+class Dummy(object):
+    def __init__(self, val):
+        self.val = val
+
+
+class TestIrUtils(TestCase):
+    """
+    Tests ir handling utility functions like find_callname.
+    """
+
+    def test_obj_func_match(self):
+        """Test matching of an object method (other than Array see #3449)
+        """
+
+        def test_func():
+            d = Dummy([1])
+            d.val.append(2)
+
+        test_ir = compiler.run_frontend(test_func)
+        typingctx = cpu_target.typing_context
+        targetctx = cpu_target.target_context
+        typing_res = type_inference_stage(
+            typingctx, targetctx, test_ir, (), None)
+        matched_call = ir_utils.find_callname(
+            test_ir, test_ir.blocks[0].body[7].value, typing_res.typemap)
+        self.assertTrue(isinstance(matched_call, tuple) and
+                        len(matched_call) == 2 and
+                        matched_call[0] == 'append')
+
+    def test_dead_code_elimination(self):
+
+        class Tester(CompilerBase):
+
+            @classmethod
+            def mk_pipeline(
+                cls,
+                args,
+                return_type=None,
+                flags=None,
+                locals=None,
+                library=None,
+                typing_context=None,
+                target_context=None,
+            ):
+                if locals is None:
+                    locals = {}
+                if not flags:
+                    flags = Flags()
+                flags.nrt = True
+                if typing_context is None:
+                    typing_context = registry.cpu_target.typing_context
+                if target_context is None:
+                    target_context = registry.cpu_target.target_context
+                return cls(typing_context, target_context, library, args,
+                           return_type, flags, locals)
+
+            def compile_to_ir(self, func, DCE=False):
+                """
+                Compile and return IR
+                """
+                func_id = bytecode.FunctionIdentity.from_function(func)
+                self.state.func_id = func_id
+                ExtractByteCode().run_pass(self.state)
+                state = self.state
+
+                name = "DCE_testing"
+                pm = PassManager(name)
+                pm.add_pass(TranslateByteCode, "analyzing bytecode")
+                pm.add_pass(FixupArgs, "fix up args")
+                pm.add_pass(IRProcessing, "processing IR")
+                pm.add_pass(NopythonTypeInference, "nopython frontend")
+                if DCE is True:
+                    pm.add_pass(DeadCodeElimination, "DCE after typing")
+                pm.finalize()
+                pm.run(state)
+                return state.func_ir
+
+        def check_initial_ir(the_ir):
+            # dead stuff:
+            # a const int value 0xdead
+            # an assign of above into to variable `dead`
+            # a const int above 0xdeaddead
+            # an assign of said int to variable `deaddead`
+            # this is 2 statements to remove
+
+            self.assertEqual(len(the_ir.blocks), 1)
+            block = the_ir.blocks[0]
+            deads = []
+            for x in block.find_insts(ir.Assign):
+                if isinstance(getattr(x, 'target', None), ir.Var):
+                    if 'dead' in getattr(x.target, 'name', ''):
+                        deads.append(x)
+
+            self.assertEqual(len(deads), 2)
+            for d in deads:
+                # check the ir.Const is the definition and the value is expected
+                const_val = the_ir.get_definition(d.value)
+                self.assertTrue(int('0x%s' % d.target.name, 16),
+                                const_val.value)
+
+            return deads
+
+        def check_dce_ir(the_ir):
+            self.assertEqual(len(the_ir.blocks), 1)
+            block = the_ir.blocks[0]
+            deads = []
+            consts = []
+            for x in block.find_insts(ir.Assign):
+                if isinstance(getattr(x, 'target', None), ir.Var):
+                    if 'dead' in getattr(x.target, 'name', ''):
+                        deads.append(x)
+                if isinstance(getattr(x, 'value', None), ir.Const):
+                    consts.append(x)
+            self.assertEqual(len(deads), 0)
+
+            # check the consts to make sure there's no reference to 0xdead or
+            # 0xdeaddead
+            for x in consts:
+                self.assertTrue(x.value.value not in [0xdead, 0xdeaddead])
+
+        def foo(x):
+            y = x + 1
+            dead = 0xdead  # noqa
+            z = y + 2
+            deaddead = 0xdeaddead  # noqa
+            ret = z * z
+            return ret
+
+        test_pipeline = Tester.mk_pipeline((types.intp,))
+        no_dce = test_pipeline.compile_to_ir(foo)
+        removed = check_initial_ir(no_dce)
+
+        test_pipeline = Tester.mk_pipeline((types.intp,))
+        w_dce = test_pipeline.compile_to_ir(foo, DCE=True)
+        check_dce_ir(w_dce)
+
+        # check that the count of initial - removed = dce
+        self.assertEqual(len(no_dce.blocks[0].body) - len(removed),
+                         len(w_dce.blocks[0].body))
+
+    def test_find_const_global(self):
+        """
+        Test find_const() for values in globals (ir.Global) and freevars
+        (ir.FreeVar) that are considered constants for compilation.
+        """
+        FREEVAR_C = 12
+
+        def foo(a):
+            b = GLOBAL_B
+            c = FREEVAR_C
+            return a + b + c
+
+        f_ir = compiler.run_frontend(foo)
+        block = f_ir.blocks[0]
+        const_b = None
+        const_c = None
+
+        for inst in block.body:
+            if isinstance(inst, ir.Assign) and inst.target.name == 'b':
+                const_b = ir_utils.guard(
+                    ir_utils.find_const, f_ir, inst.target)
+            if isinstance(inst, ir.Assign) and inst.target.name == 'c':
+                const_c = ir_utils.guard(
+                    ir_utils.find_const, f_ir, inst.target)
+
+        self.assertEqual(const_b, GLOBAL_B)
+        self.assertEqual(const_c, FREEVAR_C)
+
+    def test_flatten_labels(self):
+        """ tests flatten_labels """
+        def foo(a):
+            acc = 0
+            if a > 3:
+                acc += 1
+                if a > 19:
+                    return 53
+            elif a < 1000:
+                if a >= 12:
+                    acc += 1
+                for x in range(10):
+                    acc -= 1
+                    if acc < 2:
+                        break
+                else:
+                    acc += 7
+            else:
+                raise ValueError("some string")
+            # prevents inline of return on py310
+            py310_defeat1 = 1  # noqa
+            py310_defeat2 = 2  # noqa
+            py310_defeat3 = 3  # noqa
+            py310_defeat4 = 4  # noqa
+            return acc
+
+        def bar(a):
+            acc = 0
+            z = 12
+            if a > 3:
+                acc += 1
+                z += 12
+                if a > 19:
+                    z += 12
+                    return 53
+            elif a < 1000:
+                if a >= 12:
+                    z += 12
+                    acc += 1
+                for x in range(10):
+                    z += 12
+                    acc -= 1
+                    if acc < 2:
+                        break
+                else:
+                    z += 12
+                    acc += 7
+            else:
+                raise ValueError("some string")
+            py310_defeat1 = 1  # noqa
+            py310_defeat2 = 2  # noqa
+            py310_defeat3 = 3  # noqa
+            py310_defeat4 = 4  # noqa
+            return acc
+
+        def baz(a):
+            acc = 0
+            if a > 3:
+                acc += 1
+                if a > 19:
+                    return 53
+                else: # extra control flow in comparison to foo
+                    return 55
+            elif a < 1000:
+                if a >= 12:
+                    acc += 1
+                for x in range(10):
+                    acc -= 1
+                    if acc < 2:
+                        break
+                else:
+                    acc += 7
+            else:
+                raise ValueError("some string")
+            py310_defeat1 = 1  # noqa
+            py310_defeat2 = 2  # noqa
+            py310_defeat3 = 3  # noqa
+            py310_defeat4 = 4  # noqa
+            return acc
+
+        def get_flat_cfg(func):
+            func_ir = ir_utils.compile_to_numba_ir(func, dict())
+            flat_blocks = ir_utils.flatten_labels(func_ir.blocks)
+            self.assertEqual(max(flat_blocks.keys()) + 1, len(func_ir.blocks))
+            return ir_utils.compute_cfg_from_blocks(flat_blocks)
+
+        foo_cfg = get_flat_cfg(foo)
+        bar_cfg = get_flat_cfg(bar)
+        baz_cfg = get_flat_cfg(baz)
+
+        self.assertEqual(foo_cfg, bar_cfg)
+        self.assertNotEqual(foo_cfg, baz_cfg)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/shared/package-files/c9/c936b66042083fdc62e55fc6d29daf4a5c6086e84dc23462f603c408cc724623/fast.mplstyle b/shared/package-files/c9/c936b66042083fdc62e55fc6d29daf4a5c6086e84dc23462f603c408cc724623/fast.mplstyle
new file mode 100644
index 0000000000000000000000000000000000000000..1f7be7d4632a352f5c96959c8ecdba06c9a06b86
--- /dev/null
+++ b/shared/package-files/c9/c936b66042083fdc62e55fc6d29daf4a5c6086e84dc23462f603c408cc724623/fast.mplstyle
@@ -0,0 +1,11 @@
+# a small set of changes that will make your plotting FAST (1).
+#
+# (1) in some cases
+
+# Maximally simplify lines.
+path.simplify: True
+path.simplify_threshold: 1.0
+
+# chunk up large lines into smaller lines!
+# simple trick to avoid those pesky O(>n) algorithms!
+agg.path.chunksize: 10000
diff --git a/shared/package-files/c9/c9396951f9cac73ac597b5cd8f2b82fb1301067c54b9f53a195c10ca1400236a/mathtext.py b/shared/package-files/c9/c9396951f9cac73ac597b5cd8f2b82fb1301067c54b9f53a195c10ca1400236a/mathtext.py
new file mode 100644
index 0000000000000000000000000000000000000000..a88c35c15676b027c773eead1e9d0a8b13270768
--- /dev/null
+++ b/shared/package-files/c9/c9396951f9cac73ac597b5cd8f2b82fb1301067c54b9f53a195c10ca1400236a/mathtext.py
@@ -0,0 +1,140 @@
+r"""
+A module for parsing a subset of the TeX math syntax and rendering it to a
+Matplotlib backend.
+
+For a tutorial of its usage, see :ref:`mathtext`.  This
+document is primarily concerned with implementation details.
+
+The module uses pyparsing_ to parse the TeX expression.
+
+.. _pyparsing: https://pypi.org/project/pyparsing/
+
+The Bakoma distribution of the TeX Computer Modern fonts, and STIX
+fonts are supported.  There is experimental support for using
+arbitrary fonts, but results may vary without proper tweaking and
+metrics for those fonts.
+"""
+
+import functools
+import logging
+
+import matplotlib as mpl
+from matplotlib import _api, _mathtext
+from matplotlib.ft2font import LoadFlags
+from matplotlib.font_manager import FontProperties
+from ._mathtext import (  # noqa: F401, reexported API
+    RasterParse, VectorParse, get_unicode_index)
+
+_log = logging.getLogger(__name__)
+
+
+get_unicode_index.__module__ = __name__
+
+##############################################################################
+# MAIN
+
+
+class MathTextParser:
+    _parser = None
+    _font_type_mapping = {
+        'cm':          _mathtext.BakomaFonts,
+        'dejavuserif': _mathtext.DejaVuSerifFonts,
+        'dejavusans':  _mathtext.DejaVuSansFonts,
+        'stix':        _mathtext.StixFonts,
+        'stixsans':    _mathtext.StixSansFonts,
+        'custom':      _mathtext.UnicodeFonts,
+    }
+
+    def __init__(self, output):
+        """
+        Create a MathTextParser for the given backend *output*.
+
+        Parameters
+        ----------
+        output : {"path", "agg"}
+            Whether to return a `VectorParse` ("path") or a
+            `RasterParse` ("agg", or its synonym "macosx").
+        """
+        self._output_type = _api.check_getitem(
+            {"path": "vector", "agg": "raster", "macosx": "raster"},
+            output=output.lower())
+
+    def parse(self, s, dpi=72, prop=None, *, antialiased=None):
+        """
+        Parse the given math expression *s* at the given *dpi*.  If *prop* is
+        provided, it is a `.FontProperties` object specifying the "default"
+        font to use in the math expression, used for all non-math text.
+
+        The results are cached, so multiple calls to `parse`
+        with the same expression should be fast.
+
+        Depending on the *output* type, this returns either a `VectorParse` or
+        a `RasterParse`.
+        """
+        # lru_cache can't decorate parse() directly because prop is
+        # mutable, so we key the cache using an internal copy (see
+        # Text._get_text_metrics_with_cache for a similar case); likewise,
+        # we need to check the mutable state of the text.antialiased and
+        # text.hinting rcParams.
+        prop = prop.copy() if prop is not None else None
+        antialiased = mpl._val_or_rc(antialiased, 'text.antialiased')
+        from matplotlib.backends import backend_agg
+        load_glyph_flags = {
+            "vector": LoadFlags.NO_HINTING,
+            "raster": backend_agg.get_hinting_flag(),
+        }[self._output_type]
+        return self._parse_cached(s, dpi, prop, antialiased, load_glyph_flags)
+
+    @functools.lru_cache(50)
+    def _parse_cached(self, s, dpi, prop, antialiased, load_glyph_flags):
+        if prop is None:
+            prop = FontProperties()
+        fontset_class = _api.check_getitem(
+            self._font_type_mapping, fontset=prop.get_math_fontfamily())
+        fontset = fontset_class(prop, load_glyph_flags)
+        fontsize = prop.get_size_in_points()
+
+        if self._parser is None:  # Cache the parser globally.
+            self.__class__._parser = _mathtext.Parser()
+
+        box = self._parser.parse(s, fontset, fontsize, dpi)
+        output = _mathtext.ship(box)
+        if self._output_type == "vector":
+            return output.to_vector()
+        elif self._output_type == "raster":
+            return output.to_raster(antialiased=antialiased)
+
+
+def math_to_image(s, filename_or_obj, prop=None, dpi=None, format=None,
+                  *, color=None):
+    """
+    Given a math expression, renders it in a closely-clipped bounding
+    box to an image file.
+
+    Parameters
+    ----------
+    s : str
+        A math expression.  The math portion must be enclosed in dollar signs.
+    filename_or_obj : str or path-like or file-like
+        Where to write the image data.
+    prop : `.FontProperties`, optional
+        The size and style of the text.
+    dpi : float, optional
+        The output dpi.  If not set, the dpi is determined as for
+        `.Figure.savefig`.
+    format : str, optional
+        The output format, e.g., 'svg', 'pdf', 'ps' or 'png'.  If not set, the
+        format is determined as for `.Figure.savefig`.
+    color : str, optional
+        Foreground color, defaults to :rc:`text.color`.
+    """
+    from matplotlib import figure
+
+    parser = MathTextParser('path')
+    width, height, depth, _, _ = parser.parse(s, dpi=72, prop=prop)
+
+    fig = figure.Figure(figsize=(width / 72.0, height / 72.0))
+    fig.text(0, depth/height, s, fontproperties=prop, color=color)
+    fig.savefig(filename_or_obj, dpi=dpi, format=format)
+
+    return depth
diff --git a/shared/package-files/c9/c93e802729a080a8d9743f3be9f8c11e5a6fa83c4dcd616be0d0597b4170e968/to_dense_ops.h b/shared/package-files/c9/c93e802729a080a8d9743f3be9f8c11e5a6fa83c4dcd616be0d0597b4170e968/to_dense_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..d1960732c734823ac8fe6115993fb25d783b3711
--- /dev/null
+++ b/shared/package-files/c9/c93e802729a080a8d9743f3be9f8c11e5a6fa83c4dcd616be0d0597b4170e968/to_dense_ops.h
@@ -0,0 +1,28 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API to_dense {
+  using schema = at::Tensor (const at::Tensor &, c10::optional, c10::optional);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::to_dense")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "to_dense(Tensor self, ScalarType? dtype=None, *, bool? masked_grad=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, c10::optional dtype, c10::optional masked_grad);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional dtype, c10::optional masked_grad);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/c9/c941699cd0c87a4c55d00cad8dbafa0b968bed1c7d593e96825dad322a737708/series_list.py b/shared/package-files/c9/c941699cd0c87a4c55d00cad8dbafa0b968bed1c7d593e96825dad322a737708/series_list.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f69ac08f7f480bad949c834f6ec591edcb8414f
--- /dev/null
+++ b/shared/package-files/c9/c941699cd0c87a4c55d00cad8dbafa0b968bed1c7d593e96825dad322a737708/series_list.py
@@ -0,0 +1,99 @@
+from __future__ import annotations
+
+from functools import partial
+from typing import TYPE_CHECKING
+
+from narwhals._compliant.any_namespace import ListNamespace
+from narwhals._pandas_like.utils import (
+    PandasLikeSeriesNamespace,
+    get_dtype_backend,
+    narwhals_to_native_dtype,
+)
+from narwhals._utils import not_implemented
+
+if TYPE_CHECKING:
+    from typing import Literal
+
+    from narwhals._pandas_like.series import PandasLikeSeries
+    from narwhals.typing import NonNestedLiteral
+
+
+class PandasLikeSeriesListNamespace(
+    PandasLikeSeriesNamespace, ListNamespace["PandasLikeSeries"]
+):
+    def len(self) -> PandasLikeSeries:
+        result = self.native.list.len()
+        implementation = self.implementation
+        backend_version = self.backend_version
+        if implementation.is_pandas() and backend_version < (3, 0):  # pragma: no cover
+            # `result` is a new object so it's safe to do this inplace.
+            result.index = self.native.index
+        dtype = narwhals_to_native_dtype(
+            self.version.dtypes.UInt32(),
+            get_dtype_backend(result.dtype, implementation),
+            implementation,
+            self.version,
+        )
+        return self.with_native(result.astype(dtype)).alias(self.native.name)
+
+    def contains(self, item: NonNestedLiteral) -> PandasLikeSeries:
+        self._raise_if_not_pyarrow_backend()
+
+        from narwhals._arrow.utils import list_contains
+
+        result_native = self.compliant._apply_pyarrow_compute_func(
+            self.native, partial(list_contains, item=item)
+        )
+        return self.with_native(result_native)
+
+    def get(self, index: int) -> PandasLikeSeries:
+        result = self.native.list[index]
+        result.name = self.native.name
+        return self.with_native(result)
+
+    def _raise_if_not_pyarrow_backend(self) -> None:
+        dtype_backend = get_dtype_backend(
+            self.native.dtype, self.compliant._implementation
+        )
+        if dtype_backend != "pyarrow":  # pragma: no cover
+            msg = "Only pyarrow backend is currently supported."
+            raise NotImplementedError(msg)
+
+    def _agg(
+        self, func: Literal["min", "max", "mean", "approximate_median", "sum"]
+    ) -> PandasLikeSeries:
+        self._raise_if_not_pyarrow_backend()
+
+        from narwhals._arrow.utils import list_agg
+
+        result_native = self.compliant._apply_pyarrow_compute_func(
+            self.native, partial(list_agg, func=func)
+        )
+        return self.with_native(result_native)
+
+    def min(self) -> PandasLikeSeries:
+        return self._agg("min")
+
+    def max(self) -> PandasLikeSeries:
+        return self._agg("max")
+
+    def mean(self) -> PandasLikeSeries:
+        return self._agg("mean")
+
+    def median(self) -> PandasLikeSeries:
+        return self._agg("approximate_median")
+
+    def sum(self) -> PandasLikeSeries:
+        return self._agg("sum")
+
+    def sort(self, *, descending: bool, nulls_last: bool) -> PandasLikeSeries:
+        self._raise_if_not_pyarrow_backend()
+
+        from narwhals._arrow.utils import list_sort
+
+        result_native = self.compliant._apply_pyarrow_compute_func(
+            self.native, partial(list_sort, descending=descending, nulls_last=nulls_last)
+        )
+        return self.with_native(result_native)
+
+    unique = not_implemented()
diff --git a/shared/package-files/c9/c94409ecbcd37a6bf809dd575bf76bdf1eca108d3b05a86c580c897d72d2d5e0/test_type_F.py b/shared/package-files/c9/c94409ecbcd37a6bf809dd575bf76bdf1eca108d3b05a86c580c897d72d2d5e0/test_type_F.py
new file mode 100644
index 0000000000000000000000000000000000000000..fbb58223d0b5886e6044108c9c5cc3bbf371dd14
--- /dev/null
+++ b/shared/package-files/c9/c94409ecbcd37a6bf809dd575bf76bdf1eca108d3b05a86c580c897d72d2d5e0/test_type_F.py
@@ -0,0 +1,24 @@
+from sympy.liealgebras.cartan_type import CartanType
+from sympy.matrices import Matrix
+from sympy.core.backend import S
+
+def test_type_F():
+    c = CartanType("F4")
+    m = Matrix(4, 4, [2, -1, 0, 0, -1, 2, -2, 0, 0, -1, 2, -1, 0, 0, -1, 2])
+    assert c.cartan_matrix() == m
+    assert c.dimension() == 4
+    assert c.simple_root(1) == [1, -1, 0, 0]
+    assert c.simple_root(2) == [0, 1, -1, 0]
+    assert c.simple_root(3) == [0, 0, 0, 1]
+    assert c.simple_root(4) == [-S.Half, -S.Half, -S.Half, -S.Half]
+    assert c.roots() == 48
+    assert c.basis() == 52
+    diag = "0---0=>=0---0\n" + "   ".join(str(i) for i in range(1, 5))
+    assert c.dynkin_diagram() == diag
+    assert c.positive_roots() == {1: [1, -1, 0, 0], 2: [1, 1, 0, 0], 3: [1, 0, -1, 0],
+            4: [1, 0, 1, 0], 5: [1, 0, 0, -1], 6: [1, 0, 0, 1], 7: [0, 1, -1, 0],
+            8: [0, 1, 1, 0], 9: [0, 1, 0, -1], 10: [0, 1, 0, 1], 11: [0, 0, 1, -1],
+            12: [0, 0, 1, 1], 13: [1, 0, 0, 0], 14: [0, 1, 0, 0], 15: [0, 0, 1, 0],
+            16: [0, 0, 0, 1], 17: [S.Half, S.Half, S.Half, S.Half], 18: [S.Half, -S.Half, S.Half, S.Half],
+            19: [S.Half, S.Half, -S.Half, S.Half], 20: [S.Half, S.Half, S.Half, -S.Half], 21: [S.Half, S.Half, -S.Half, -S.Half],
+            22: [S.Half, -S.Half, S.Half, -S.Half], 23: [S.Half, -S.Half, -S.Half, S.Half], 24: [S.Half, -S.Half, -S.Half, -S.Half]}
diff --git a/shared/package-files/c9/c946737630ed052faf62585f3fea44ce3de6d143e6819b3e840f7d4d90040025/cpu_sse2.c b/shared/package-files/c9/c946737630ed052faf62585f3fea44ce3de6d143e6819b3e840f7d4d90040025/cpu_sse2.c
new file mode 100644
index 0000000000000000000000000000000000000000..33826a9ed1a53ef27e9c686d870d31d4b12f1736
--- /dev/null
+++ b/shared/package-files/c9/c946737630ed052faf62585f3fea44ce3de6d143e6819b3e840f7d4d90040025/cpu_sse2.c
@@ -0,0 +1,20 @@
+#if defined(DETECT_FEATURES) && defined(__INTEL_COMPILER)
+    /*
+     * Unlike GCC and CLANG, Intel Compiler exposes all supported intrinsics,
+     * whether or not the build options for those features are specified.
+     * Therefore, we must test #definitions of CPU features when option native/host
+     * is enabled via `--cpu-baseline` or through env var `CFLAGS` otherwise
+     * the test will be broken and leads to enable all possible features.
+     */
+    #ifndef __SSE2__
+        #error "HOST/ARCH doesn't support SSE2"
+    #endif
+#endif
+
+#include 
+
+int main(void)
+{
+    __m128i a = _mm_add_epi16(_mm_setzero_si128(), _mm_setzero_si128());
+    return _mm_cvtsi128_si32(a);
+}
diff --git a/shared/package-files/c9/c946b8b376bd062300bd9238b11a39b60e4fc0ae8d6f656445b343e69b0c09ad/cond_closed_over_variable.py b/shared/package-files/c9/c946b8b376bd062300bd9238b11a39b60e4fc0ae8d6f656445b343e69b0c09ad/cond_closed_over_variable.py
new file mode 100644
index 0000000000000000000000000000000000000000..b201c5d679b8eab6e9a3a74705772acf3a9a5af8
--- /dev/null
+++ b/shared/package-files/c9/c946b8b376bd062300bd9238b11a39b60e4fc0ae8d6f656445b343e69b0c09ad/cond_closed_over_variable.py
@@ -0,0 +1,23 @@
+import torch
+
+from torch._export.db.case import export_case
+from functorch.experimental.control_flow import cond
+
+
+@export_case(
+    example_inputs=(torch.tensor(True), torch.ones(3, 2)),
+    tags={"torch.cond", "python.closure"},
+)
+class CondClosedOverVariable(torch.nn.Module):
+    """
+    torch.cond() supports branches closed over arbitrary variables.
+    """
+
+    def forward(self, pred, x):
+        def true_fn(val):
+            return x * 2
+
+        def false_fn(val):
+            return x - 2
+
+        return cond(pred, true_fn, false_fn, [x + 1])
diff --git a/shared/package-files/c9/c94b399445e4be9bfeb3eafb10304d68fa2b139eb129c801c0a817b5b1280124/test__differential_evolution.py b/shared/package-files/c9/c94b399445e4be9bfeb3eafb10304d68fa2b139eb129c801c0a817b5b1280124/test__differential_evolution.py
new file mode 100644
index 0000000000000000000000000000000000000000..3f81877f08abe436211210d5bd15d876c9a7177c
--- /dev/null
+++ b/shared/package-files/c9/c94b399445e4be9bfeb3eafb10304d68fa2b139eb129c801c0a817b5b1280124/test__differential_evolution.py
@@ -0,0 +1,1703 @@
+"""
+Unit tests for the differential global minimization algorithm.
+"""
+import multiprocessing
+from multiprocessing.dummy import Pool as ThreadPool
+import platform
+
+from scipy.optimize._differentialevolution import (DifferentialEvolutionSolver,
+                                                   _ConstraintWrapper)
+from scipy.optimize import differential_evolution, OptimizeResult
+from scipy.optimize._constraints import (Bounds, NonlinearConstraint,
+                                         LinearConstraint)
+from scipy.optimize import rosen, minimize
+from scipy.sparse import csr_matrix
+from scipy import stats
+
+import numpy as np
+from numpy.testing import (assert_equal, assert_allclose, assert_almost_equal,
+                           assert_string_equal, assert_, suppress_warnings)
+from pytest import raises as assert_raises, warns
+import pytest
+
+
+class TestDifferentialEvolutionSolver:
+
+    def setup_method(self):
+        self.old_seterr = np.seterr(invalid='raise')
+        self.limits = np.array([[0., 0.],
+                                [2., 2.]])
+        self.bounds = [(0., 2.), (0., 2.)]
+
+        self.dummy_solver = DifferentialEvolutionSolver(self.quadratic,
+                                                        [(0, 100)])
+
+        # dummy_solver2 will be used to test mutation strategies
+        self.dummy_solver2 = DifferentialEvolutionSolver(self.quadratic,
+                                                         [(0, 1)],
+                                                         popsize=7,
+                                                         mutation=0.5)
+        # create a population that's only 7 members long
+        # [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
+        population = np.atleast_2d(np.arange(0.1, 0.8, 0.1)).T
+        self.dummy_solver2.population = population
+
+    def teardown_method(self):
+        np.seterr(**self.old_seterr)
+
+    def quadratic(self, x):
+        return x[0]**2
+
+    def test__strategy_resolves(self):
+        # test that the correct mutation function is resolved by
+        # different requested strategy arguments
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             strategy='best1exp')
+        assert_equal(solver.strategy, 'best1exp')
+        assert_equal(solver.mutation_func.__name__, '_best1')
+
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             strategy='best1bin')
+        assert_equal(solver.strategy, 'best1bin')
+        assert_equal(solver.mutation_func.__name__, '_best1')
+
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             strategy='rand1bin')
+        assert_equal(solver.strategy, 'rand1bin')
+        assert_equal(solver.mutation_func.__name__, '_rand1')
+
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             strategy='rand1exp')
+        assert_equal(solver.strategy, 'rand1exp')
+        assert_equal(solver.mutation_func.__name__, '_rand1')
+
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             strategy='rand2exp')
+        assert_equal(solver.strategy, 'rand2exp')
+        assert_equal(solver.mutation_func.__name__, '_rand2')
+
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             strategy='best2bin')
+        assert_equal(solver.strategy, 'best2bin')
+        assert_equal(solver.mutation_func.__name__, '_best2')
+
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             strategy='rand2bin')
+        assert_equal(solver.strategy, 'rand2bin')
+        assert_equal(solver.mutation_func.__name__, '_rand2')
+
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             strategy='rand2exp')
+        assert_equal(solver.strategy, 'rand2exp')
+        assert_equal(solver.mutation_func.__name__, '_rand2')
+
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             strategy='randtobest1bin')
+        assert_equal(solver.strategy, 'randtobest1bin')
+        assert_equal(solver.mutation_func.__name__, '_randtobest1')
+
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             strategy='randtobest1exp')
+        assert_equal(solver.strategy, 'randtobest1exp')
+        assert_equal(solver.mutation_func.__name__, '_randtobest1')
+
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             strategy='currenttobest1bin')
+        assert_equal(solver.strategy, 'currenttobest1bin')
+        assert_equal(solver.mutation_func.__name__, '_currenttobest1')
+
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             strategy='currenttobest1exp')
+        assert_equal(solver.strategy, 'currenttobest1exp')
+        assert_equal(solver.mutation_func.__name__, '_currenttobest1')
+
+    def test__mutate1(self):
+        # strategies */1/*, i.e. rand/1/bin, best/1/exp, etc.
+        result = np.array([0.05])
+        trial = self.dummy_solver2._best1(np.array([2, 3, 4, 5, 6]))
+        assert_allclose(trial, result)
+
+        result = np.array([0.25])
+        trial = self.dummy_solver2._rand1(np.array([2, 3, 4, 5, 6]))
+        assert_allclose(trial, result)
+
+    def test__mutate2(self):
+        # strategies */2/*, i.e. rand/2/bin, best/2/exp, etc.
+        # [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
+
+        result = np.array([-0.1])
+        trial = self.dummy_solver2._best2(np.array([2, 3, 4, 5, 6]))
+        assert_allclose(trial, result)
+
+        result = np.array([0.1])
+        trial = self.dummy_solver2._rand2(np.array([2, 3, 4, 5, 6]))
+        assert_allclose(trial, result)
+
+    def test__randtobest1(self):
+        # strategies randtobest/1/*
+        result = np.array([0.15])
+        trial = self.dummy_solver2._randtobest1(np.array([2, 3, 4, 5, 6]))
+        assert_allclose(trial, result)
+
+    def test__currenttobest1(self):
+        # strategies currenttobest/1/*
+        result = np.array([0.1])
+        trial = self.dummy_solver2._currenttobest1(
+            1,
+            np.array([2, 3, 4, 5, 6])
+        )
+        assert_allclose(trial, result)
+
+    def test_can_init_with_dithering(self):
+        mutation = (0.5, 1)
+        solver = DifferentialEvolutionSolver(self.quadratic,
+                                             self.bounds,
+                                             mutation=mutation)
+
+        assert_equal(solver.dither, list(mutation))
+
+    def test_invalid_mutation_values_arent_accepted(self):
+        func = rosen
+        mutation = (0.5, 3)
+        assert_raises(ValueError,
+                          DifferentialEvolutionSolver,
+                          func,
+                          self.bounds,
+                          mutation=mutation)
+
+        mutation = (-1, 1)
+        assert_raises(ValueError,
+                          DifferentialEvolutionSolver,
+                          func,
+                          self.bounds,
+                          mutation=mutation)
+
+        mutation = (0.1, np.nan)
+        assert_raises(ValueError,
+                          DifferentialEvolutionSolver,
+                          func,
+                          self.bounds,
+                          mutation=mutation)
+
+        mutation = 0.5
+        solver = DifferentialEvolutionSolver(func,
+                                             self.bounds,
+                                             mutation=mutation)
+        assert_equal(0.5, solver.scale)
+        assert_equal(None, solver.dither)
+
+    def test_invalid_functional(self):
+        def func(x):
+            return np.array([np.sum(x ** 2), np.sum(x)])
+
+        with assert_raises(
+                RuntimeError,
+                match=r"func\(x, \*args\) must return a scalar value"):
+            differential_evolution(func, [(-2, 2), (-2, 2)])
+
+    def test__scale_parameters(self):
+        trial = np.array([0.3])
+        assert_equal(30, self.dummy_solver._scale_parameters(trial))
+
+        # it should also work with the limits reversed
+        self.dummy_solver.limits = np.array([[100], [0.]])
+        assert_equal(30, self.dummy_solver._scale_parameters(trial))
+
+    def test__unscale_parameters(self):
+        trial = np.array([30])
+        assert_equal(0.3, self.dummy_solver._unscale_parameters(trial))
+
+        # it should also work with the limits reversed
+        self.dummy_solver.limits = np.array([[100], [0.]])
+        assert_equal(0.3, self.dummy_solver._unscale_parameters(trial))
+
+    def test_equal_bounds(self):
+        with np.errstate(invalid='raise'):
+            solver = DifferentialEvolutionSolver(
+                self.quadratic,
+                bounds=[(2.0, 2.0), (1.0, 3.0)]
+            )
+            v = solver._unscale_parameters([2.0, 2.0])
+            assert_allclose(v, 0.5)
+
+        res = differential_evolution(self.quadratic, [(2.0, 2.0), (3.0, 3.0)])
+        assert_equal(res.x, [2.0, 3.0])
+
+    def test__ensure_constraint(self):
+        trial = np.array([1.1, -100, 0.9, 2., 300., -0.00001])
+        self.dummy_solver._ensure_constraint(trial)
+
+        assert_equal(trial[2], 0.9)
+        assert_(np.logical_and(trial >= 0, trial <= 1).all())
+
+    def test_differential_evolution(self):
+        # test that the Jmin of DifferentialEvolutionSolver
+        # is the same as the function evaluation
+        solver = DifferentialEvolutionSolver(
+            self.quadratic, [(-2, 2)], maxiter=1, polish=False
+        )
+        result = solver.solve()
+        assert_equal(result.fun, self.quadratic(result.x))
+
+        solver = DifferentialEvolutionSolver(
+            self.quadratic, [(-2, 2)], maxiter=1, polish=True
+        )
+        result = solver.solve()
+        assert_equal(result.fun, self.quadratic(result.x))
+
+    def test_best_solution_retrieval(self):
+        # test that the getter property method for the best solution works.
+        solver = DifferentialEvolutionSolver(self.quadratic, [(-2, 2)])
+        result = solver.solve()
+        assert_equal(result.x, solver.x)
+
+    def test_intermediate_result(self):
+        # Check that intermediate result object passed into the callback
+        # function contains the expected information and that raising
+        # `StopIteration` causes the expected behavior.
+        maxiter = 10
+
+        def func(x):
+            val = rosen(x)
+            if val < func.val:
+                func.x = x
+                func.val = val
+            return val
+        func.x = None
+        func.val = np.inf
+
+        def callback(intermediate_result):
+            callback.nit += 1
+            callback.intermediate_result = intermediate_result
+            assert intermediate_result.population.ndim == 2
+            assert intermediate_result.population.shape[1] == 2
+            assert intermediate_result.nit == callback.nit
+
+            # Check that `x` and `fun` attributes are the best found so far
+            assert_equal(intermediate_result.x, callback.func.x)
+            assert_equal(intermediate_result.fun, callback.func.val)
+
+            # Check for consistency between `fun`, `population_energies`,
+            # `x`, and `population`
+            assert_equal(intermediate_result.fun, rosen(intermediate_result.x))
+            for i in range(len(intermediate_result.population_energies)):
+                res = intermediate_result.population_energies[i]
+                ref = rosen(intermediate_result.population[i])
+                assert_equal(res, ref)
+            assert_equal(intermediate_result.x,
+                         intermediate_result.population[0])
+            assert_equal(intermediate_result.fun,
+                         intermediate_result.population_energies[0])
+
+            assert intermediate_result.message == 'in progress'
+            assert intermediate_result.success is True
+            assert isinstance(intermediate_result, OptimizeResult)
+            if callback.nit == maxiter:
+                raise StopIteration
+        callback.nit = 0
+        callback.intermediate_result = None
+        callback.func = func
+
+        bounds = [(0, 2), (0, 2)]
+        kwargs = dict(func=func, bounds=bounds, rng=838245, polish=False)
+        res = differential_evolution(**kwargs, callback=callback)
+        ref = differential_evolution(**kwargs, maxiter=maxiter)
+
+        # Check that final `intermediate_result` is equivalent to returned
+        # result object and that terminating with callback `StopIteration`
+        # after `maxiter` iterations is equivalent to terminating with
+        # `maxiter` parameter.
+        assert res.success is ref.success is False
+        assert callback.nit == res.nit == maxiter
+        assert res.message == 'callback function requested stop early'
+        assert ref.message == 'Maximum number of iterations has been exceeded.'
+        for field, val in ref.items():
+            if field in {'message', 'success'}:  # checked separately
+                continue
+            assert_equal(callback.intermediate_result[field], val)
+            assert_equal(res[field], val)
+
+        # Check that polish occurs after `StopIteration` as advertised
+        callback.nit = 0
+        func.val = np.inf
+        kwargs['polish'] = True
+        res = differential_evolution(**kwargs, callback=callback)
+        assert res.fun < ref.fun
+
+    def test_callback_terminates(self):
+        # test that if the callback returns true, then the minimization halts
+        bounds = [(0, 2), (0, 2)]
+        expected_msg = 'callback function requested stop early'
+        def callback_python_true(param, convergence=0.):
+            return True
+
+        result = differential_evolution(
+            rosen, bounds, callback=callback_python_true
+        )
+        assert_string_equal(result.message, expected_msg)
+
+        # if callback raises StopIteration then solve should be interrupted
+        def callback_stop(intermediate_result):
+            raise StopIteration
+
+        result = differential_evolution(rosen, bounds, callback=callback_stop)
+        assert not result.success
+
+        def callback_evaluates_true(param, convergence=0.):
+            # DE should stop if bool(self.callback) is True
+            return [10]
+
+        result = differential_evolution(rosen, bounds, callback=callback_evaluates_true)
+        assert_string_equal(result.message, expected_msg)
+        assert not result.success
+
+        def callback_evaluates_false(param, convergence=0.):
+            return []
+
+        result = differential_evolution(rosen, bounds,
+                                        callback=callback_evaluates_false)
+        assert result.success
+
+    def test_args_tuple_is_passed(self):
+        # test that the args tuple is passed to the cost function properly.
+        bounds = [(-10, 10)]
+        args = (1., 2., 3.)
+
+        def quadratic(x, *args):
+            if not isinstance(args, tuple):
+                raise ValueError('args should be a tuple')
+            return args[0] + args[1] * x + args[2] * x**2.
+
+        result = differential_evolution(quadratic,
+                                        bounds,
+                                        args=args,
+                                        polish=True)
+        assert_almost_equal(result.fun, 2 / 3.)
+
+    def test_init_with_invalid_strategy(self):
+        # test that passing an invalid strategy raises ValueError
+        func = rosen
+        bounds = [(-3, 3)]
+        assert_raises(ValueError,
+                          differential_evolution,
+                          func,
+                          bounds,
+                          strategy='abc')
+
+    def test_bounds_checking(self):
+        # test that the bounds checking works
+        func = rosen
+        bounds = [(-3)]
+        assert_raises(ValueError,
+                          differential_evolution,
+                          func,
+                          bounds)
+        bounds = [(-3, 3), (3, 4, 5)]
+        assert_raises(ValueError,
+                          differential_evolution,
+                          func,
+                          bounds)
+
+        # test that we can use a new-type Bounds object
+        result = differential_evolution(rosen, Bounds([0, 0], [2, 2]))
+        assert_almost_equal(result.x, (1., 1.))
+
+    def test_select_samples(self):
+        # select_samples should return 5 separate random numbers.
+        limits = np.arange(12., dtype='float64').reshape(2, 6)
+        bounds = list(zip(limits[0, :], limits[1, :]))
+        solver = DifferentialEvolutionSolver(None, bounds, popsize=1)
+        candidate = 0
+        r1, r2, r3, r4, r5 = solver._select_samples(candidate, 5)
+        assert_equal(
+            len(np.unique(np.array([candidate, r1, r2, r3, r4, r5]))), 6)
+
+    def test_maxiter_stops_solve(self):
+        # test that if the maximum number of iterations is exceeded
+        # the solver stops.
+        solver = DifferentialEvolutionSolver(rosen, self.bounds, maxiter=1)
+        result = solver.solve()
+        assert_equal(result.success, False)
+        assert_equal(result.message,
+                        'Maximum number of iterations has been exceeded.')
+
+    def test_maxfun_stops_solve(self):
+        # test that if the maximum number of function evaluations is exceeded
+        # during initialisation the solver stops
+        solver = DifferentialEvolutionSolver(rosen, self.bounds, maxfun=1,
+                                             polish=False)
+        result = solver.solve()
+
+        assert_equal(result.nfev, 2)
+        assert_equal(result.success, False)
+        assert_equal(result.message,
+                     'Maximum number of function evaluations has '
+                     'been exceeded.')
+
+        # test that if the maximum number of function evaluations is exceeded
+        # during the actual minimisation, then the solver stops.
+        # Have to turn polishing off, as this will still occur even if maxfun
+        # is reached. For popsize=5 and len(bounds)=2, then there are only 10
+        # function evaluations during initialisation.
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             popsize=5,
+                                             polish=False,
+                                             maxfun=40)
+        result = solver.solve()
+
+        assert_equal(result.nfev, 41)
+        assert_equal(result.success, False)
+        assert_equal(result.message,
+                     'Maximum number of function evaluations has '
+                     'been exceeded.')
+
+        # now repeat for updating='deferred version
+        # 47 function evaluations is not a multiple of the population size,
+        # so maxfun is reached partway through a population evaluation.
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             popsize=5,
+                                             polish=False,
+                                             maxfun=47,
+                                             updating='deferred')
+        result = solver.solve()
+
+        assert_equal(result.nfev, 47)
+        assert_equal(result.success, False)
+        assert_equal(result.message,
+                     'Maximum number of function evaluations has '
+                     'been reached.')
+
+    def test_quadratic(self):
+        # test the quadratic function from object
+        solver = DifferentialEvolutionSolver(self.quadratic,
+                                             [(-100, 100)],
+                                             tol=0.02)
+        solver.solve()
+        assert_equal(np.argmin(solver.population_energies), 0)
+
+    def test_quadratic_from_diff_ev(self):
+        # test the quadratic function from differential_evolution function
+        differential_evolution(self.quadratic,
+                               [(-100, 100)],
+                               tol=0.02,
+                               seed=1)
+
+    def test_rng_gives_repeatability(self):
+        result = differential_evolution(self.quadratic,
+                                        [(-100, 100)],
+                                        polish=False,
+                                        rng=1,
+                                        tol=0.5)
+        result2 = differential_evolution(self.quadratic,
+                                        [(-100, 100)],
+                                        polish=False,
+                                        rng=1,
+                                        tol=0.5)
+        assert_equal(result.x, result2.x)
+        assert_equal(result.nfev, result2.nfev)
+
+    def test_random_generator(self):
+        # check that np.random.Generator can be used (numpy >= 1.17)
+        # obtain a np.random.Generator object
+        rng = np.random.default_rng()
+
+        inits = ['random', 'latinhypercube', 'sobol', 'halton']
+        for init in inits:
+            differential_evolution(self.quadratic,
+                                   [(-100, 100)],
+                                   polish=False,
+                                   rng=rng,
+                                   tol=0.5,
+                                   init=init)
+
+    def test_exp_runs(self):
+        # test whether exponential mutation loop runs
+        solver = DifferentialEvolutionSolver(rosen,
+                                             self.bounds,
+                                             strategy='best1exp',
+                                             maxiter=1)
+
+        solver.solve()
+
+    def test_gh_4511_regression(self):
+        # This modification of the differential evolution docstring example
+        # uses a custom popsize that had triggered an off-by-one error.
+        # Because we do not care about solving the optimization problem in
+        # this test, we use maxiter=1 to reduce the testing time.
+        bounds = [(-5, 5), (-5, 5)]
+        # result = differential_evolution(rosen, bounds, popsize=1815,
+        #                                 maxiter=1)
+
+        # the original issue arose because of rounding error in arange, with
+        # linspace being a much better solution. 1815 is quite a large popsize
+        # to use and results in a long test time (~13s). I used the original
+        # issue to figure out the lowest number of samples that would cause
+        # this rounding error to occur, 49.
+        differential_evolution(rosen, bounds, popsize=49, maxiter=1)
+
+    def test_calculate_population_energies(self):
+        # if popsize is 3, then the overall generation has size (6,)
+        solver = DifferentialEvolutionSolver(rosen, self.bounds, popsize=3)
+        solver._calculate_population_energies(solver.population)
+        solver._promote_lowest_energy()
+        assert_equal(np.argmin(solver.population_energies), 0)
+
+        # initial calculation of the energies should require 6 nfev.
+        assert_equal(solver._nfev, 6)
+
+    def test_iteration(self):
+        # test that DifferentialEvolutionSolver is iterable
+        # if popsize is 3, then the overall generation has size (6,)
+        solver = DifferentialEvolutionSolver(rosen, self.bounds, popsize=3,
+                                             maxfun=12)
+        x, fun = next(solver)
+        assert_equal(np.size(x, 0), 2)
+
+        # 6 nfev are required for initial calculation of energies, 6 nfev are
+        # required for the evolution of the 6 population members.
+        assert_equal(solver._nfev, 12)
+
+        # the next generation should halt because it exceeds maxfun
+        assert_raises(StopIteration, next, solver)
+
+        # check a proper minimisation can be done by an iterable solver
+        solver = DifferentialEvolutionSolver(rosen, self.bounds)
+        _, fun_prev = next(solver)
+        for i, soln in enumerate(solver):
+            x_current, fun_current = soln
+            assert fun_prev >= fun_current
+            _, fun_prev = x_current, fun_current
+            # need to have this otherwise the solver would never stop.
+            if i == 50:
+                break
+
+    def test_convergence(self):
+        solver = DifferentialEvolutionSolver(rosen, self.bounds, tol=0.2,
+                                             polish=False)
+        solver.solve()
+        assert_(solver.convergence < 0.2)
+
+    def test_maxiter_none_GH5731(self):
+        # Pre 0.17 the previous default for maxiter and maxfun was None.
+        # the numerical defaults are now 1000 and np.inf. However, some scripts
+        # will still supply None for both of those, this will raise a TypeError
+        # in the solve method.
+        solver = DifferentialEvolutionSolver(rosen, self.bounds, maxiter=None,
+                                             maxfun=None)
+        solver.solve()
+
+    def test_population_initiation(self):
+        # test the different modes of population initiation
+
+        # init must be either 'latinhypercube' or 'random'
+        # raising ValueError is something else is passed in
+        assert_raises(ValueError,
+                      DifferentialEvolutionSolver,
+                      *(rosen, self.bounds),
+                      **{'init': 'rubbish'})
+
+        solver = DifferentialEvolutionSolver(rosen, self.bounds)
+
+        # check that population initiation:
+        # 1) resets _nfev to 0
+        # 2) all population energies are np.inf
+        solver.init_population_random()
+        assert_equal(solver._nfev, 0)
+        assert_(np.all(np.isinf(solver.population_energies)))
+
+        solver.init_population_lhs()
+        assert_equal(solver._nfev, 0)
+        assert_(np.all(np.isinf(solver.population_energies)))
+
+        solver.init_population_qmc(qmc_engine='halton')
+        assert_equal(solver._nfev, 0)
+        assert_(np.all(np.isinf(solver.population_energies)))
+
+        solver = DifferentialEvolutionSolver(rosen, self.bounds, init='sobol')
+        solver.init_population_qmc(qmc_engine='sobol')
+        assert_equal(solver._nfev, 0)
+        assert_(np.all(np.isinf(solver.population_energies)))
+
+        # we should be able to initialize with our own array
+        population = np.linspace(-1, 3, 10).reshape(5, 2)
+        solver = DifferentialEvolutionSolver(rosen, self.bounds,
+                                             init=population,
+                                             strategy='best2bin',
+                                             atol=0.01, rng=1, popsize=5)
+
+        assert_equal(solver._nfev, 0)
+        assert_(np.all(np.isinf(solver.population_energies)))
+        assert_(solver.num_population_members == 5)
+        assert_(solver.population_shape == (5, 2))
+
+        # check that the population was initialized correctly
+        unscaled_population = np.clip(solver._unscale_parameters(population),
+                                      0, 1)
+        assert_almost_equal(solver.population[:5], unscaled_population)
+
+        # population values need to be clipped to bounds
+        assert_almost_equal(np.min(solver.population[:5]), 0)
+        assert_almost_equal(np.max(solver.population[:5]), 1)
+
+        # shouldn't be able to initialize with an array if it's the wrong shape
+        # this would have too many parameters
+        population = np.linspace(-1, 3, 15).reshape(5, 3)
+        assert_raises(ValueError,
+                      DifferentialEvolutionSolver,
+                      *(rosen, self.bounds),
+                      **{'init': population})
+
+        # provide an initial solution
+        # bounds are [(0, 2), (0, 2)]
+        x0 = np.random.uniform(low=0.0, high=2.0, size=2)
+        solver = DifferentialEvolutionSolver(
+            rosen, self.bounds, x0=x0
+        )
+        # parameters are scaled to unit interval
+        assert_allclose(solver.population[0], x0 / 2.0)
+
+    def test_x0(self):
+        # smoke test that checks that x0 is usable.
+        res = differential_evolution(rosen, self.bounds, x0=[0.2, 0.8])
+        assert res.success
+
+        # check what happens if some of the x0 lay outside the bounds
+        with assert_raises(ValueError):
+            differential_evolution(rosen, self.bounds, x0=[0.2, 2.1])
+
+    def test_infinite_objective_function(self):
+        # Test that there are no problems if the objective function
+        # returns inf on some runs
+        def sometimes_inf(x):
+            if x[0] < .5:
+                return np.inf
+            return x[1]
+        bounds = [(0, 1), (0, 1)]
+        differential_evolution(sometimes_inf, bounds=bounds, disp=False)
+
+    def test_deferred_updating(self):
+        # check setting of deferred updating, with default workers
+        bounds = [(0., 2.), (0., 2.)]
+        solver = DifferentialEvolutionSolver(rosen, bounds, updating='deferred')
+        assert_(solver._updating == 'deferred')
+        assert_(solver._mapwrapper._mapfunc is map)
+        res = solver.solve()
+        assert res.success
+
+        # check that deferred updating works with an exponential crossover
+        res = differential_evolution(
+            rosen, bounds, updating='deferred', strategy='best1exp'
+        )
+        assert res.success
+
+    @pytest.mark.thread_unsafe
+    def test_immediate_updating(self):
+        # check setting of immediate updating, with default workers
+        bounds = [(0., 2.), (0., 2.)]
+        solver = DifferentialEvolutionSolver(rosen, bounds)
+        assert_(solver._updating == 'immediate')
+
+        # Safely forking from a multithreaded process is
+        # problematic, and deprecated in Python 3.12, so
+        # we use a slower but portable alternative
+        # see gh-19848
+        ctx = multiprocessing.get_context("spawn")
+        with ctx.Pool(2) as p:
+            # should raise a UserWarning because the updating='immediate'
+            # is being overridden by the workers keyword
+            with warns(UserWarning):
+                with DifferentialEvolutionSolver(rosen, bounds, workers=p.map) as s:
+                    solver.solve()
+            assert s._updating == 'deferred'
+
+    @pytest.mark.fail_slow(10)
+    def test_parallel(self):
+        # smoke test for parallelization with deferred updating
+        bounds = [(0., 2.), (0., 2.)]
+        # use threads instead of Process to speed things up for this simple example
+        with ThreadPool(2) as p, DifferentialEvolutionSolver(
+            rosen, bounds, updating='deferred', workers=p.map, tol=0.1, popsize=3
+        ) as solver:
+            assert solver._mapwrapper.pool is not None
+            assert solver._updating == 'deferred'
+            solver.solve()
+
+        with DifferentialEvolutionSolver(
+            rosen, bounds, updating='deferred', workers=2, popsize=3, tol=0.1
+        ) as solver:
+            assert solver._mapwrapper.pool is not None
+            assert solver._updating == 'deferred'
+            solver.solve()
+
+    def test_converged(self):
+        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)])
+        solver.solve()
+        assert_(solver.converged())
+
+    def test_constraint_violation_fn(self):
+        def constr_f(x):
+            return [x[0] + x[1]]
+
+        def constr_f2(x):
+            return np.array([x[0]**2 + x[1], x[0] - x[1]])
+
+        nlc = NonlinearConstraint(constr_f, -np.inf, 1.9)
+
+        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
+                                             constraints=(nlc,))
+
+        cv = solver._constraint_violation_fn(np.array([1.0, 1.0]))
+        assert_almost_equal(cv, 0.1)
+
+        nlc2 = NonlinearConstraint(constr_f2, -np.inf, 1.8)
+        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
+                                             constraints=(nlc, nlc2))
+
+        # for multiple constraints the constraint violations should
+        # be concatenated.
+        xs = [(1.2, 1), (2.0, 2.0), (0.5, 0.5)]
+        vs = [(0.3, 0.64, 0.0), (2.1, 4.2, 0.0), (0, 0, 0)]
+
+        for x, v in zip(xs, vs):
+            cv = solver._constraint_violation_fn(np.array(x))
+            assert_allclose(cv, np.atleast_2d(v))
+
+        # vectorized calculation of a series of solutions
+        assert_allclose(
+            solver._constraint_violation_fn(np.array(xs)), np.array(vs)
+        )
+
+        # the following line is used in _calculate_population_feasibilities.
+        # _constraint_violation_fn returns an (1, M) array when
+        # x.shape == (N,), i.e. a single solution. Therefore this list
+        # comprehension should generate (S, 1, M) array.
+        constraint_violation = np.array([solver._constraint_violation_fn(x)
+                                         for x in np.array(xs)])
+        assert constraint_violation.shape == (3, 1, 3)
+
+        # we need reasonable error messages if the constraint function doesn't
+        # return the right thing
+        def constr_f3(x):
+            # returns (S, M), rather than (M, S)
+            return constr_f2(x).T
+
+        nlc2 = NonlinearConstraint(constr_f3, -np.inf, 1.8)
+        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
+                                             constraints=(nlc, nlc2),
+                                             vectorized=False)
+        solver.vectorized = True
+        with pytest.raises(
+                RuntimeError, match="An array returned from a Constraint"
+        ):
+            solver._constraint_violation_fn(np.array(xs))
+
+    def test_constraint_population_feasibilities(self):
+        def constr_f(x):
+            return [x[0] + x[1]]
+
+        def constr_f2(x):
+            return [x[0]**2 + x[1], x[0] - x[1]]
+
+        nlc = NonlinearConstraint(constr_f, -np.inf, 1.9)
+
+        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
+                                             constraints=(nlc,))
+
+        # are population feasibilities correct
+        # [0.5, 0.5] corresponds to scaled values of [1., 1.]
+        feas, cv = solver._calculate_population_feasibilities(
+            np.array([[0.5, 0.5], [1., 1.]]))
+        assert_equal(feas, [False, False])
+        assert_almost_equal(cv, np.array([[0.1], [2.1]]))
+        assert cv.shape == (2, 1)
+
+        nlc2 = NonlinearConstraint(constr_f2, -np.inf, 1.8)
+
+        for vectorize in [False, True]:
+            solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
+                                                 constraints=(nlc, nlc2),
+                                                 vectorized=vectorize,
+                                                 updating='deferred')
+
+            feas, cv = solver._calculate_population_feasibilities(
+                np.array([[0.5, 0.5], [0.6, 0.5]]))
+            assert_equal(feas, [False, False])
+            assert_almost_equal(cv, np.array([[0.1, 0.2, 0], [0.3, 0.64, 0]]))
+
+            feas, cv = solver._calculate_population_feasibilities(
+                np.array([[0.5, 0.5], [1., 1.]]))
+            assert_equal(feas, [False, False])
+            assert_almost_equal(cv, np.array([[0.1, 0.2, 0], [2.1, 4.2, 0]]))
+            assert cv.shape == (2, 3)
+
+            feas, cv = solver._calculate_population_feasibilities(
+                np.array([[0.25, 0.25], [1., 1.]]))
+            assert_equal(feas, [True, False])
+            assert_almost_equal(cv, np.array([[0.0, 0.0, 0.], [2.1, 4.2, 0]]))
+            assert cv.shape == (2, 3)
+
+    @pytest.mark.thread_unsafe
+    def test_constraint_solve(self):
+        def constr_f(x):
+            return np.array([x[0] + x[1]])
+
+        nlc = NonlinearConstraint(constr_f, -np.inf, 1.9)
+
+        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
+                                             constraints=(nlc,))
+
+        # trust-constr warns if the constraint function is linear
+        with warns(UserWarning):
+            res = solver.solve()
+
+        assert constr_f(res.x) <= 1.9
+        assert res.success
+
+    @pytest.mark.fail_slow(10)
+    @pytest.mark.thread_unsafe
+    def test_impossible_constraint(self):
+        def constr_f(x):
+            return np.array([x[0] + x[1]])
+
+        nlc = NonlinearConstraint(constr_f, -np.inf, -1)
+
+        solver = DifferentialEvolutionSolver(
+            rosen, [(0, 2), (0, 2)], constraints=(nlc,), popsize=1, rng=1, maxiter=100
+        )
+
+        # a UserWarning is issued because the 'trust-constr' polishing is
+        # attempted on the least infeasible solution found.
+        with warns(UserWarning):
+            res = solver.solve()
+
+        assert res.maxcv > 0
+        assert not res.success
+
+        # test _promote_lowest_energy works when none of the population is
+        # feasible. In this case, the solution with the lowest constraint
+        # violation should be promoted.
+        solver = DifferentialEvolutionSolver(
+            rosen, [(0, 2), (0, 2)], constraints=(nlc,), polish=False)
+        next(solver)
+        assert not solver.feasible.all()
+        assert not np.isfinite(solver.population_energies).all()
+
+        # now swap two of the entries in the population
+        l = 20
+        cv = solver.constraint_violation[0]
+
+        solver.population_energies[[0, l]] = solver.population_energies[[l, 0]]
+        solver.population[[0, l], :] = solver.population[[l, 0], :]
+        solver.constraint_violation[[0, l], :] = (
+            solver.constraint_violation[[l, 0], :])
+
+        solver._promote_lowest_energy()
+        assert_equal(solver.constraint_violation[0], cv)
+
+    def test_accept_trial(self):
+        # _accept_trial(self, energy_trial, feasible_trial, cv_trial,
+        #               energy_orig, feasible_orig, cv_orig)
+        def constr_f(x):
+            return [x[0] + x[1]]
+        nlc = NonlinearConstraint(constr_f, -np.inf, 1.9)
+        solver = DifferentialEvolutionSolver(rosen, [(0, 2), (0, 2)],
+                                             constraints=(nlc,))
+        fn = solver._accept_trial
+        # both solutions are feasible, select lower energy
+        assert fn(0.1, True, np.array([0.]), 1.0, True, np.array([0.]))
+        assert (fn(1.0, True, np.array([0.0]), 0.1, True, np.array([0.0])) is False)
+        assert fn(0.1, True, np.array([0.]), 0.1, True, np.array([0.]))
+
+        # trial is feasible, original is not
+        assert fn(9.9, True, np.array([0.]), 1.0, False, np.array([1.]))
+
+        # trial and original are infeasible
+        # cv_trial have to be <= cv_original to be better
+        assert (fn(0.1, False, np.array([0.5, 0.5]),
+                   1.0, False, np.array([1., 1.0])))
+        assert (fn(0.1, False, np.array([0.5, 0.5]),
+                   1.0, False, np.array([1., 0.50])))
+        assert not (fn(1.0, False, np.array([0.5, 0.5]),
+                       1.0, False, np.array([1.0, 0.4])))
+
+    def test_constraint_wrapper(self):
+        lb = np.array([0, 20, 30])
+        ub = np.array([0.5, np.inf, 70])
+        x0 = np.array([1, 2, 3])
+        pc = _ConstraintWrapper(Bounds(lb, ub), x0)
+        assert (pc.violation(x0) > 0).any()
+        assert (pc.violation([0.25, 21, 31]) == 0).all()
+
+        # check vectorized Bounds constraint
+        xs = np.arange(1, 16).reshape(5, 3)
+        violations = []
+        for x in xs:
+            violations.append(pc.violation(x))
+        np.testing.assert_allclose(pc.violation(xs.T), np.array(violations).T)
+
+        x0 = np.array([1, 2, 3, 4])
+        A = np.array([[1, 2, 3, 4], [5, 0, 0, 6], [7, 0, 8, 0]])
+        pc = _ConstraintWrapper(LinearConstraint(A, -np.inf, 0), x0)
+        assert (pc.violation(x0) > 0).any()
+        assert (pc.violation([-10, 2, -10, 4]) == 0).all()
+
+        # check vectorized LinearConstraint, for 7 lots of parameter vectors
+        # with each parameter vector being 4 long, with 3 constraints
+        # xs is the same shape as stored in the differential evolution
+        # population, but it's sent to the violation function as (len(x), M)
+        xs = np.arange(1, 29).reshape(7, 4)
+        violations = []
+        for x in xs:
+            violations.append(pc.violation(x))
+        np.testing.assert_allclose(pc.violation(xs.T), np.array(violations).T)
+
+        pc = _ConstraintWrapper(LinearConstraint(csr_matrix(A), -np.inf, 0),
+                                x0)
+        assert (pc.violation(x0) > 0).any()
+        assert (pc.violation([-10, 2, -10, 4]) == 0).all()
+
+        def fun(x):
+            return A.dot(x)
+
+        nonlinear = NonlinearConstraint(fun, -np.inf, 0)
+        pc = _ConstraintWrapper(nonlinear, [-10, 2, -10, 4])
+        assert (pc.violation(x0) > 0).any()
+        assert (pc.violation([-10, 2, -10, 4]) == 0).all()
+
+    def test_constraint_wrapper_violation(self):
+        def cons_f(x):
+            # written in vectorised form to accept an array of (N, S)
+            # returning (M, S)
+            # where N is the number of parameters,
+            # S is the number of solution vectors to be examined,
+            # and M is the number of constraint components
+            return np.array([x[0] ** 2 + x[1],
+                             x[0] ** 2 - x[1]])
+
+        nlc = NonlinearConstraint(cons_f, [-1, -0.8500], [2, 2])
+        pc = _ConstraintWrapper(nlc, [0.5, 1])
+        assert np.size(pc.bounds[0]) == 2
+
+        xs = [(0.5, 1), (0.5, 1.2), (1.2, 1.2), (0.1, -1.2), (0.1, 2.0)]
+        vs = [(0, 0), (0, 0.1), (0.64, 0), (0.19, 0), (0.01, 1.14)]
+
+        for x, v in zip(xs, vs):
+            assert_allclose(pc.violation(x), v)
+
+        # now check that we can vectorize the constraint wrapper
+        assert_allclose(pc.violation(np.array(xs).T),
+                        np.array(vs).T)
+        assert pc.fun(np.array(xs).T).shape == (2, len(xs))
+        assert pc.violation(np.array(xs).T).shape == (2, len(xs))
+        assert pc.num_constr == 2
+        assert pc.parameter_count == 2
+
+    def test_matrix_linear_constraint(self):
+        # gh20041 supplying an np.matrix to construct a LinearConstraint caused
+        # _ConstraintWrapper to start returning constraint violations of the
+        # wrong shape.
+        with suppress_warnings() as sup:
+            sup.filter(PendingDeprecationWarning)
+            matrix = np.matrix([[1, 1, 1, 1.],
+                                [2, 2, 2, 2.]])
+        lc = LinearConstraint(matrix, 0, 1)
+        x0 = np.ones(4)
+        cw = _ConstraintWrapper(lc, x0)
+        # the shape of the constraint violation should be the same as the number
+        # of constraints applied.
+        assert cw.violation(x0).shape == (2,)
+
+        # let's try a vectorised violation call.
+        xtrial = np.arange(4 * 5).reshape(4, 5)
+        assert cw.violation(xtrial).shape == (2, 5)
+
+    @pytest.mark.fail_slow(20)
+    def test_L1(self):
+        # Lampinen ([5]) test problem 1
+
+        def f(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            fun = np.sum(5*x[1:5]) - 5*x[1:5]@x[1:5] - np.sum(x[5:])
+            return fun
+
+        A = np.zeros((10, 14))  # 1-indexed to match reference
+        A[1, [1, 2, 10, 11]] = 2, 2, 1, 1
+        A[2, [1, 10]] = -8, 1
+        A[3, [4, 5, 10]] = -2, -1, 1
+        A[4, [1, 3, 10, 11]] = 2, 2, 1, 1
+        A[5, [2, 11]] = -8, 1
+        A[6, [6, 7, 11]] = -2, -1, 1
+        A[7, [2, 3, 11, 12]] = 2, 2, 1, 1
+        A[8, [3, 12]] = -8, 1
+        A[9, [8, 9, 12]] = -2, -1, 1
+        A = A[1:, 1:]
+
+        b = np.array([10, 0, 0, 10, 0, 0, 10, 0, 0])
+
+        L = LinearConstraint(A, -np.inf, b)
+
+        bounds = [(0, 1)]*9 + [(0, 100)]*3 + [(0, 1)]
+
+        # using a lower popsize to speed the test up
+        res = differential_evolution(
+            f, bounds, strategy='best1bin', rng=12345, constraints=(L,),
+            popsize=5, tol=0.01
+        )
+
+        x_opt = (1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 1)
+        f_opt = -15
+
+        assert_allclose(f(x_opt), f_opt, atol=6e-4)
+        assert res.success
+        assert_allclose(res.x, x_opt, atol=6e-4)
+        assert_allclose(res.fun, f_opt, atol=5e-3)
+        assert_(np.all(A@res.x <= b))
+        assert_(np.all(res.x >= np.array(bounds)[:, 0]))
+        assert_(np.all(res.x <= np.array(bounds)[:, 1]))
+
+        # now repeat the same solve, using the same overall constraints,
+        # but using a sparse matrix for the LinearConstraint instead of an
+        # array
+
+        L = LinearConstraint(csr_matrix(A), -np.inf, b)
+
+        # using a lower popsize to speed the test up
+        res = differential_evolution(
+            f, bounds, strategy='best1bin', rng=1211134, constraints=(L,),
+            popsize=2, tol=0.05
+        )
+
+        assert_allclose(f(x_opt), f_opt)
+        assert res.success
+        assert_allclose(res.x, x_opt, atol=5e-4)
+        assert_allclose(res.fun, f_opt, atol=5e-3)
+        assert_(np.all(A@res.x <= b))
+        assert_(np.all(res.x >= np.array(bounds)[:, 0]))
+        assert_(np.all(res.x <= np.array(bounds)[:, 1]))
+
+        # now repeat the same solve, using the same overall constraints,
+        # but specify half the constraints in terms of LinearConstraint,
+        # and the other half by NonlinearConstraint
+        def c1(x):
+            x = np.hstack(([0], x))
+            return [2*x[2] + 2*x[3] + x[11] + x[12],
+                    -8*x[3] + x[12]]
+
+        def c2(x):
+            x = np.hstack(([0], x))
+            return -2*x[8] - x[9] + x[12]
+
+        L = LinearConstraint(A[:5, :], -np.inf, b[:5])
+        L2 = LinearConstraint(A[5:6, :], -np.inf, b[5:6])
+        N = NonlinearConstraint(c1, -np.inf, b[6:8])
+        N2 = NonlinearConstraint(c2, -np.inf, b[8:9])
+        constraints = (L, N, L2, N2)
+
+        with suppress_warnings() as sup:
+            sup.filter(UserWarning)
+            res = differential_evolution(
+                f, bounds, strategy='best1bin', rng=1211134,
+                constraints=constraints, popsize=2, tol=0.05
+            )
+
+        assert_allclose(res.x, x_opt, atol=6e-4)
+        assert_allclose(res.fun, f_opt, atol=5e-3)
+        assert_(np.all(A@res.x <= b))
+        assert_(np.all(res.x >= np.array(bounds)[:, 0]))
+        assert_(np.all(res.x <= np.array(bounds)[:, 1]))
+
+    @pytest.mark.fail_slow(10)
+    def test_L2(self):
+        # Lampinen ([5]) test problem 2
+
+        def f(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            fun = ((x[1]-10)**2 + 5*(x[2]-12)**2 + x[3]**4 + 3*(x[4]-11)**2 +
+                   10*x[5]**6 + 7*x[6]**2 + x[7]**4 - 4*x[6]*x[7] - 10*x[6] -
+                   8*x[7])
+            return fun
+
+        def c1(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            return [127 - 2*x[1]**2 - 3*x[2]**4 - x[3] - 4*x[4]**2 - 5*x[5],
+                    196 - 23*x[1] - x[2]**2 - 6*x[6]**2 + 8*x[7],
+                    282 - 7*x[1] - 3*x[2] - 10*x[3]**2 - x[4] + x[5],
+                    -4*x[1]**2 - x[2]**2 + 3*x[1]*x[2] - 2*x[3]**2 -
+                    5*x[6] + 11*x[7]]
+
+        N = NonlinearConstraint(c1, 0, np.inf)
+        bounds = [(-10, 10)]*7
+        constraints = (N)
+
+        with suppress_warnings() as sup:
+            sup.filter(UserWarning)
+            res = differential_evolution(f, bounds, strategy='best1bin',
+                                         rng=1234, constraints=constraints)
+
+        f_opt = 680.6300599487869
+        x_opt = (2.330499, 1.951372, -0.4775414, 4.365726,
+                 -0.6244870, 1.038131, 1.594227)
+
+        assert_allclose(f(x_opt), f_opt)
+        assert_allclose(res.fun, f_opt)
+        assert_allclose(res.x, x_opt, atol=1e-5)
+        assert res.success
+        assert_(np.all(np.array(c1(res.x)) >= 0))
+        assert_(np.all(res.x >= np.array(bounds)[:, 0]))
+        assert_(np.all(res.x <= np.array(bounds)[:, 1]))
+
+    @pytest.mark.fail_slow(10)
+    def test_L3(self):
+        # Lampinen ([5]) test problem 3
+
+        def f(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            fun = (x[1]**2 + x[2]**2 + x[1]*x[2] - 14*x[1] - 16*x[2] +
+                   (x[3]-10)**2 + 4*(x[4]-5)**2 + (x[5]-3)**2 + 2*(x[6]-1)**2 +
+                   5*x[7]**2 + 7*(x[8]-11)**2 + 2*(x[9]-10)**2 +
+                   (x[10] - 7)**2 + 45
+                   )
+            return fun  # maximize
+
+        A = np.zeros((4, 11))
+        A[1, [1, 2, 7, 8]] = -4, -5, 3, -9
+        A[2, [1, 2, 7, 8]] = -10, 8, 17, -2
+        A[3, [1, 2, 9, 10]] = 8, -2, -5, 2
+        A = A[1:, 1:]
+        b = np.array([-105, 0, -12])
+
+        def c1(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            return [3*x[1] - 6*x[2] - 12*(x[9]-8)**2 + 7*x[10],
+                    -3*(x[1]-2)**2 - 4*(x[2]-3)**2 - 2*x[3]**2 + 7*x[4] + 120,
+                    -x[1]**2 - 2*(x[2]-2)**2 + 2*x[1]*x[2] - 14*x[5] + 6*x[6],
+                    -5*x[1]**2 - 8*x[2] - (x[3]-6)**2 + 2*x[4] + 40,
+                    -0.5*(x[1]-8)**2 - 2*(x[2]-4)**2 - 3*x[5]**2 + x[6] + 30]
+
+        L = LinearConstraint(A, b, np.inf)
+        N = NonlinearConstraint(c1, 0, np.inf)
+        bounds = [(-10, 10)]*10
+        constraints = (L, N)
+
+        with suppress_warnings() as sup:
+            sup.filter(UserWarning)
+            res = differential_evolution(f, bounds, rng=1234,
+                                         constraints=constraints, popsize=3)
+
+        x_opt = (2.171996, 2.363683, 8.773926, 5.095984, 0.9906548,
+                 1.430574, 1.321644, 9.828726, 8.280092, 8.375927)
+        f_opt = 24.3062091
+
+        assert_allclose(f(x_opt), f_opt, atol=1e-5)
+        assert_allclose(res.x, x_opt, atol=1e-6)
+        assert_allclose(res.fun, f_opt, atol=1e-5)
+        assert res.success
+        assert_(np.all(A @ res.x >= b))
+        assert_(np.all(np.array(c1(res.x)) >= 0))
+        assert_(np.all(res.x >= np.array(bounds)[:, 0]))
+        assert_(np.all(res.x <= np.array(bounds)[:, 1]))
+
+    @pytest.mark.fail_slow(10)
+    def test_L4(self):
+        # Lampinen ([5]) test problem 4
+        def f(x):
+            return np.sum(x[:3])
+
+        A = np.zeros((4, 9))
+        A[1, [4, 6]] = 0.0025, 0.0025
+        A[2, [5, 7, 4]] = 0.0025, 0.0025, -0.0025
+        A[3, [8, 5]] = 0.01, -0.01
+        A = A[1:, 1:]
+        b = np.array([1, 1, 1])
+
+        def c1(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            return [x[1]*x[6] - 833.33252*x[4] - 100*x[1] + 83333.333,
+                    x[2]*x[7] - 1250*x[5] - x[2]*x[4] + 1250*x[4],
+                    x[3]*x[8] - 1250000 - x[3]*x[5] + 2500*x[5]]
+
+        L = LinearConstraint(A, -np.inf, 1)
+        N = NonlinearConstraint(c1, 0, np.inf)
+
+        bounds = [(100, 10000)] + [(1000, 10000)]*2 + [(10, 1000)]*5
+        constraints = (L, N)
+
+        with suppress_warnings() as sup:
+            sup.filter(UserWarning)
+            res = differential_evolution(
+                f, bounds, strategy='best1bin', rng=1234,
+                constraints=constraints, popsize=3, tol=0.05
+            )
+
+        f_opt = 7049.248
+
+        x_opt = [579.306692, 1359.97063, 5109.9707, 182.0177, 295.601172,
+                217.9823, 286.416528, 395.601172]
+
+        assert_allclose(f(x_opt), f_opt, atol=0.001)
+        assert_allclose(res.fun, f_opt, atol=0.001)
+
+        # use higher tol here for 32-bit Windows, see gh-11693
+        if (platform.system() == 'Windows' and np.dtype(np.intp).itemsize < 8):
+            assert_allclose(res.x, x_opt, rtol=2.4e-6, atol=0.0035)
+        else:
+            # tolerance determined from macOS + MKL failure, see gh-12701
+            assert_allclose(res.x, x_opt, rtol=5e-6, atol=0.0024)
+
+        assert res.success
+        assert_(np.all(A @ res.x <= b))
+        assert_(np.all(np.array(c1(res.x)) >= 0))
+        assert_(np.all(res.x >= np.array(bounds)[:, 0]))
+        assert_(np.all(res.x <= np.array(bounds)[:, 1]))
+
+    @pytest.mark.fail_slow(10)
+    def test_L5(self):
+        # Lampinen ([5]) test problem 5
+
+        def f(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            fun = (np.sin(2*np.pi*x[1])**3*np.sin(2*np.pi*x[2]) /
+                   (x[1]**3*(x[1]+x[2])))
+            return -fun  # maximize
+
+        def c1(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            return [x[1]**2 - x[2] + 1,
+                    1 - x[1] + (x[2]-4)**2]
+
+        N = NonlinearConstraint(c1, -np.inf, 0)
+        bounds = [(0, 10)]*2
+        constraints = (N)
+
+        res = differential_evolution(f, bounds, strategy='rand1bin', rng=1234,
+                                     constraints=constraints)
+
+        x_opt = (1.22797135, 4.24537337)
+        f_opt = -0.095825
+        assert_allclose(f(x_opt), f_opt, atol=2e-5)
+        assert_allclose(res.fun, f_opt, atol=1e-4)
+        assert res.success
+        assert_(np.all(np.array(c1(res.x)) <= 0))
+        assert_(np.all(res.x >= np.array(bounds)[:, 0]))
+        assert_(np.all(res.x <= np.array(bounds)[:, 1]))
+
+    @pytest.mark.fail_slow(10)
+    def test_L6(self):
+        # Lampinen ([5]) test problem 6
+        def f(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            fun = (x[1]-10)**3 + (x[2] - 20)**3
+            return fun
+
+        def c1(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            return [(x[1]-5)**2 + (x[2] - 5)**2 - 100,
+                    -(x[1]-6)**2 - (x[2] - 5)**2 + 82.81]
+
+        N = NonlinearConstraint(c1, 0, np.inf)
+        bounds = [(13, 100), (0, 100)]
+        constraints = (N)
+        res = differential_evolution(f, bounds, strategy='rand1bin', rng=1234,
+                                     constraints=constraints, tol=1e-7)
+        x_opt = (14.095, 0.84296)
+        f_opt = -6961.814744
+
+        assert_allclose(f(x_opt), f_opt, atol=1e-6)
+        assert_allclose(res.fun, f_opt, atol=0.001)
+        assert_allclose(res.x, x_opt, atol=1e-4)
+        assert res.success
+        assert_(np.all(np.array(c1(res.x)) >= 0))
+        assert_(np.all(res.x >= np.array(bounds)[:, 0]))
+        assert_(np.all(res.x <= np.array(bounds)[:, 1]))
+
+    def test_L7(self):
+        # Lampinen ([5]) test problem 7
+        def f(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            fun = (5.3578547*x[3]**2 + 0.8356891*x[1]*x[5] +
+                   37.293239*x[1] - 40792.141)
+            return fun
+
+        def c1(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            return [
+                    85.334407 + 0.0056858*x[2]*x[5] + 0.0006262*x[1]*x[4] -
+                    0.0022053*x[3]*x[5],
+
+                    80.51249 + 0.0071317*x[2]*x[5] + 0.0029955*x[1]*x[2] +
+                    0.0021813*x[3]**2,
+
+                    9.300961 + 0.0047026*x[3]*x[5] + 0.0012547*x[1]*x[3] +
+                    0.0019085*x[3]*x[4]
+                    ]
+
+        N = NonlinearConstraint(c1, [0, 90, 20], [92, 110, 25])
+
+        bounds = [(78, 102), (33, 45)] + [(27, 45)]*3
+        constraints = (N)
+
+        res = differential_evolution(f, bounds, strategy='rand1bin', rng=1234,
+                                     constraints=constraints)
+
+        # using our best solution, rather than Lampinen/Koziel. Koziel solution
+        # doesn't satisfy constraints, Lampinen f_opt just plain wrong.
+        x_opt = [78.00000686, 33.00000362, 29.99526064, 44.99999971,
+                 36.77579979]
+
+        f_opt = -30665.537578
+
+        assert_allclose(f(x_opt), f_opt)
+        assert_allclose(res.x, x_opt, atol=1e-3)
+        assert_allclose(res.fun, f_opt, atol=1e-3)
+
+        assert res.success
+        assert_(np.all(np.array(c1(res.x)) >= np.array([0, 90, 20])))
+        assert_(np.all(np.array(c1(res.x)) <= np.array([92, 110, 25])))
+        assert_(np.all(res.x >= np.array(bounds)[:, 0]))
+        assert_(np.all(res.x <= np.array(bounds)[:, 1]))
+
+    @pytest.mark.xslow
+    @pytest.mark.xfail(platform.machine() == 'ppc64le',
+                       reason="fails on ppc64le")
+    def test_L8(self):
+        def f(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            fun = 3*x[1] + 0.000001*x[1]**3 + 2*x[2] + 0.000002/3*x[2]**3
+            return fun
+
+        A = np.zeros((3, 5))
+        A[1, [4, 3]] = 1, -1
+        A[2, [3, 4]] = 1, -1
+        A = A[1:, 1:]
+        b = np.array([-.55, -.55])
+
+        def c1(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            return [
+                    1000*np.sin(-x[3]-0.25) + 1000*np.sin(-x[4]-0.25) +
+                    894.8 - x[1],
+                    1000*np.sin(x[3]-0.25) + 1000*np.sin(x[3]-x[4]-0.25) +
+                    894.8 - x[2],
+                    1000*np.sin(x[4]-0.25) + 1000*np.sin(x[4]-x[3]-0.25) +
+                    1294.8
+                    ]
+        L = LinearConstraint(A, b, np.inf)
+        N = NonlinearConstraint(c1, np.full(3, -0.001), np.full(3, 0.001))
+
+        bounds = [(0, 1200)]*2+[(-.55, .55)]*2
+        constraints = (L, N)
+
+        with suppress_warnings() as sup:
+            sup.filter(UserWarning)
+            # original Lampinen test was with rand1bin, but that takes a
+            # huge amount of CPU time. Changing strategy to best1bin speeds
+            # things up a lot
+            res = differential_evolution(f, bounds, strategy='best1bin',
+                                         rng=1234, constraints=constraints,
+                                         maxiter=5000)
+
+        x_opt = (679.9453, 1026.067, 0.1188764, -0.3962336)
+        f_opt = 5126.4981
+
+        assert_allclose(f(x_opt), f_opt, atol=1e-3)
+        assert_allclose(res.x[:2], x_opt[:2], atol=2e-3)
+        assert_allclose(res.x[2:], x_opt[2:], atol=2e-3)
+        assert_allclose(res.fun, f_opt, atol=2e-2)
+        assert res.success
+        assert_(np.all(A@res.x >= b))
+        assert_(np.all(np.array(c1(res.x)) >= -0.001))
+        assert_(np.all(np.array(c1(res.x)) <= 0.001))
+        assert_(np.all(res.x >= np.array(bounds)[:, 0]))
+        assert_(np.all(res.x <= np.array(bounds)[:, 1]))
+
+    @pytest.mark.fail_slow(5)
+    def test_L9(self):
+        # Lampinen ([5]) test problem 9
+
+        def f(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            return x[1]**2 + (x[2]-1)**2
+
+        def c1(x):
+            x = np.hstack(([0], x))  # 1-indexed to match reference
+            return [x[2] - x[1]**2]
+
+        N = NonlinearConstraint(c1, [-.001], [0.001])
+
+        bounds = [(-1, 1)]*2
+        constraints = (N)
+        res = differential_evolution(f, bounds, strategy='rand1bin', rng=1234,
+                                     constraints=constraints)
+
+        x_opt = [np.sqrt(2)/2, 0.5]
+        f_opt = 0.75
+
+        assert_allclose(f(x_opt), f_opt)
+        assert_allclose(np.abs(res.x), x_opt, atol=1e-3)
+        assert_allclose(res.fun, f_opt, atol=1e-3)
+        assert res.success
+        assert_(np.all(np.array(c1(res.x)) >= -0.001))
+        assert_(np.all(np.array(c1(res.x)) <= 0.001))
+        assert_(np.all(res.x >= np.array(bounds)[:, 0]))
+        assert_(np.all(res.x <= np.array(bounds)[:, 1]))
+
+    @pytest.mark.fail_slow(10)
+    def test_integrality(self):
+        # test fitting discrete distribution to data
+        rng = np.random.default_rng(6519843218105)
+        dist = stats.nbinom
+        shapes = (5, 0.5)
+        x = dist.rvs(*shapes, size=10000, random_state=rng)
+
+        def func(p, *args):
+            dist, x = args
+            # negative log-likelihood function
+            ll = -np.log(dist.pmf(x, *p)).sum(axis=-1)
+            if np.isnan(ll):  # occurs when x is outside of support
+                ll = np.inf  # we don't want that
+            return ll
+
+        integrality = [True, False]
+        bounds = [(1, 18), (0, 0.95)]
+
+        res = differential_evolution(func, bounds, args=(dist, x),
+                                     integrality=integrality, polish=False,
+                                     rng=rng)
+        # tolerance has to be fairly relaxed for the second parameter
+        # because we're fitting a distribution to random variates.
+        assert res.x[0] == 5
+        assert_allclose(res.x, shapes, rtol=0.025)
+
+        # check that we can still use integrality constraints with polishing
+        res2 = differential_evolution(func, bounds, args=(dist, x),
+                                      integrality=integrality, polish=True,
+                                      rng=rng)
+
+        def func2(p, *args):
+            n, dist, x = args
+            return func(np.array([n, p[0]]), dist, x)
+
+        # compare the DE derived solution to an LBFGSB solution (that doesn't
+        # have to find the integral values). Note we're setting x0 to be the
+        # output from the first DE result, thereby making the polishing step
+        # and this minimisation pretty much equivalent.
+        LBFGSB = minimize(func2, res2.x[1], args=(5, dist, x),
+                          bounds=[(0, 0.95)])
+        assert_allclose(res2.x[1], LBFGSB.x)
+        assert res2.fun <= res.fun
+
+    def test_integrality_limits(self):
+        def f(x):
+            return x
+
+        integrality = [True, False, True]
+        bounds = [(0.2, 1.1), (0.9, 2.2), (3.3, 4.9)]
+
+        # no integrality constraints
+        solver = DifferentialEvolutionSolver(f, bounds=bounds, polish=False,
+                                             integrality=False)
+        assert_allclose(solver.limits[0], [0.2, 0.9, 3.3])
+        assert_allclose(solver.limits[1], [1.1, 2.2, 4.9])
+
+        # with integrality constraints
+        solver = DifferentialEvolutionSolver(f, bounds=bounds, polish=False,
+                                             integrality=integrality)
+        assert_allclose(solver.limits[0], [0.5, 0.9, 3.5])
+        assert_allclose(solver.limits[1], [1.5, 2.2, 4.5])
+        assert_equal(solver.integrality, [True, False, True])
+        assert solver.polish is False
+
+        bounds = [(-1.2, -0.9), (0.9, 2.2), (-10.3, 4.1)]
+        solver = DifferentialEvolutionSolver(f, bounds=bounds, polish=False,
+                                             integrality=integrality)
+        assert_allclose(solver.limits[0], [-1.5, 0.9, -10.5])
+        assert_allclose(solver.limits[1], [-0.5, 2.2, 4.5])
+
+        # A lower bound of -1.2 is converted to
+        # np.nextafter(np.ceil(-1.2) - 0.5, np.inf)
+        # with a similar process to the upper bound. Check that the
+        # conversions work
+        assert_allclose(np.round(solver.limits[0]), [-1.0, 1.0, -10.0])
+        assert_allclose(np.round(solver.limits[1]), [-1.0, 2.0, 4.0])
+
+        bounds = [(-10.2, -8.1), (0.9, 2.2), (-10.9, -9.9999)]
+        solver = DifferentialEvolutionSolver(f, bounds=bounds, polish=False,
+                                             integrality=integrality)
+        assert_allclose(solver.limits[0], [-10.5, 0.9, -10.5])
+        assert_allclose(solver.limits[1], [-8.5, 2.2, -9.5])
+
+        bounds = [(-10.2, -10.1), (0.9, 2.2), (-10.9, -9.9999)]
+        with pytest.raises(ValueError, match='One of the integrality'):
+            DifferentialEvolutionSolver(f, bounds=bounds, polish=False,
+                                        integrality=integrality)
+
+    @pytest.mark.thread_unsafe
+    @pytest.mark.fail_slow(10)
+    def test_vectorized(self):
+        def quadratic(x):
+            return np.sum(x**2)
+
+        def quadratic_vec(x):
+            return np.sum(x**2, axis=0)
+
+        # A vectorized function needs to accept (len(x), S) and return (S,)
+        with pytest.raises(RuntimeError, match='The vectorized function'):
+            differential_evolution(quadratic, self.bounds,
+                                   vectorized=True, updating='deferred')
+
+        # vectorized overrides the updating keyword, check for warning
+        with warns(UserWarning, match="differential_evolution: the 'vector"):
+            differential_evolution(quadratic_vec, self.bounds,
+                                   vectorized=True)
+
+        # vectorized defers to the workers keyword, check for warning
+        with warns(UserWarning, match="differential_evolution: the 'workers"):
+            differential_evolution(quadratic_vec, self.bounds,
+                                   vectorized=True, workers=map,
+                                   updating='deferred')
+
+        ncalls = [0]
+
+        def rosen_vec(x):
+            ncalls[0] += 1
+            return rosen(x)
+
+        bounds = [(0, 10), (0, 10)]
+        res1 = differential_evolution(rosen, bounds, updating='deferred',
+                                      rng=1)
+        res2 = differential_evolution(rosen_vec, bounds, vectorized=True,
+                                      updating='deferred', rng=1)
+
+        # the two minimisation runs should be functionally equivalent
+        assert_allclose(res1.x, res2.x)
+        assert ncalls[0] == res2.nfev
+        assert res1.nit == res2.nit
+
+    def test_vectorized_constraints(self):
+        def constr_f(x):
+            return np.array([x[0] + x[1]])
+
+        def constr_f2(x):
+            return np.array([x[0]**2 + x[1], x[0] - x[1]])
+
+        nlc1 = NonlinearConstraint(constr_f, -np.inf, 1.9)
+        nlc2 = NonlinearConstraint(constr_f2, (0.9, 0.5), (2.0, 2.0))
+
+        def rosen_vec(x):
+            # accept an (len(x0), S) array, returning a (S,) array
+            v = 100 * (x[1:] - x[:-1]**2.0)**2.0
+            v += (1 - x[:-1])**2.0
+            return np.squeeze(v)
+
+        bounds = [(0, 10), (0, 10)]
+
+        res1 = differential_evolution(rosen, bounds, updating='deferred',
+                                      rng=1, constraints=[nlc1, nlc2],
+                                      polish=False)
+        res2 = differential_evolution(rosen_vec, bounds, vectorized=True,
+                                      updating='deferred', rng=1,
+                                      constraints=[nlc1, nlc2],
+                                      polish=False)
+        # the two minimisation runs should be functionally equivalent
+        assert_allclose(res1.x, res2.x)
+
+    def test_constraint_violation_error_message(self):
+
+        def func(x):
+            return np.cos(x[0]) + np.sin(x[1])
+
+        # Intentionally infeasible constraints.
+        c0 = NonlinearConstraint(lambda x: x[1] - (x[0]-1)**2, 0, np.inf)
+        c1 = NonlinearConstraint(lambda x: x[1] + x[0]**2, -np.inf, 0)
+
+        result = differential_evolution(func,
+                                        bounds=[(-1, 2), (-1, 1)],
+                                        constraints=[c0, c1],
+                                        maxiter=10,
+                                        polish=False,
+                                        rng=864197532)
+        assert result.success is False
+        # The numerical value in the error message might be sensitive to
+        # changes in the implementation.  It can be updated if the code is
+        # changed.  The essential part of the test is that there is a number
+        # after the '=', so if necessary, the text could be reduced to, say,
+        # "MAXCV = 0.".
+        assert "MAXCV = 0." in result.message
+
+    @pytest.mark.fail_slow(20)  # fail-slow exception by request - see gh-20806
+    def test_strategy_fn(self):
+        # examines ability to customize strategy by mimicking one of the
+        # in-built strategies
+        parameter_count = 4
+        popsize = 10
+        bounds = [(0, 10.)] * parameter_count
+        total_popsize = parameter_count * popsize
+        mutation = 0.8
+        recombination = 0.7
+
+        calls = [0]
+        def custom_strategy_fn(candidate, population, rng=None):
+            calls[0] += 1
+            trial = np.copy(population[candidate])
+            fill_point = rng.choice(parameter_count)
+
+            pool = np.arange(total_popsize)
+            rng.shuffle(pool)
+            idxs = pool[:2 + 1]
+            idxs = idxs[idxs != candidate][:2]
+
+            r0, r1 = idxs[:2]
+
+            bprime = (population[0] + mutation *
+                    (population[r0] - population[r1]))
+
+            crossovers = rng.uniform(size=parameter_count)
+            crossovers = crossovers < recombination
+            crossovers[fill_point] = True
+            trial = np.where(crossovers, bprime, trial)
+            return trial
+
+        solver = DifferentialEvolutionSolver(
+            rosen,
+            bounds,
+            popsize=popsize,
+            recombination=recombination,
+            mutation=mutation,
+            maxiter=2,
+            strategy=custom_strategy_fn,
+            rng=10,
+            polish=False
+        )
+        assert solver.strategy is custom_strategy_fn
+        solver.solve()
+        assert calls[0] > 0
+
+        # check custom strategy works with updating='deferred'
+        res = differential_evolution(
+            rosen, bounds, strategy=custom_strategy_fn, updating='deferred'
+        )
+        assert res.success
+
+        def custom_strategy_fn(candidate, population, rng=None):
+            return np.array([1.0, 2.0])
+
+        with pytest.raises(RuntimeError, match="strategy*"):
+            differential_evolution(
+                rosen,
+                bounds,
+                strategy=custom_strategy_fn
+            )
diff --git a/shared/package-files/c9/c950be42e570095a29b2b1a604c9afa2ea8523d95b06c63bcf533e47262b2dcb/earley_forest.py b/shared/package-files/c9/c950be42e570095a29b2b1a604c9afa2ea8523d95b06c63bcf533e47262b2dcb/earley_forest.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d1241f902368f3123525e2d54f5f182df3443e6
--- /dev/null
+++ b/shared/package-files/c9/c950be42e570095a29b2b1a604c9afa2ea8523d95b06c63bcf533e47262b2dcb/earley_forest.py
@@ -0,0 +1,810 @@
+""""This module implements an SPPF implementation
+
+This is used as the primary output mechanism for the Earley parser
+in order to store complex ambiguities.
+
+Full reference and more details is here:
+https://web.archive.org/web/20190616123959/http://www.bramvandersanden.com/post/2014/06/shared-packed-parse-forest/
+"""
+
+from typing import Type, AbstractSet
+from random import randint
+from collections import deque
+from operator import attrgetter
+from importlib import import_module
+from functools import partial
+
+from ..parse_tree_builder import AmbiguousIntermediateExpander
+from ..visitors import Discard
+from ..utils import logger, OrderedSet
+from ..tree import Tree
+
+class ForestNode:
+    pass
+
+class SymbolNode(ForestNode):
+    """
+    A Symbol Node represents a symbol (or Intermediate LR0).
+
+    Symbol nodes are keyed by the symbol (s). For intermediate nodes
+    s will be an LR0, stored as a tuple of (rule, ptr). For completed symbol
+    nodes, s will be a string representing the non-terminal origin (i.e.
+    the left hand side of the rule).
+
+    The children of a Symbol or Intermediate Node will always be Packed Nodes;
+    with each Packed Node child representing a single derivation of a production.
+
+    Hence a Symbol Node with a single child is unambiguous.
+
+    Parameters:
+        s: A Symbol, or a tuple of (rule, ptr) for an intermediate node.
+        start: The index of the start of the substring matched by this symbol (inclusive).
+        end: The index of the end of the substring matched by this symbol (exclusive).
+
+    Properties:
+        is_intermediate: True if this node is an intermediate node.
+        priority: The priority of the node's symbol.
+    """
+    Set: Type[AbstractSet] = set   # Overridden by StableSymbolNode
+    __slots__ = ('s', 'start', 'end', '_children', 'paths', 'paths_loaded', 'priority', 'is_intermediate', '_hash')
+    def __init__(self, s, start, end):
+        self.s = s
+        self.start = start
+        self.end = end
+        self._children = self.Set()
+        self.paths = self.Set()
+        self.paths_loaded = False
+
+        ### We use inf here as it can be safely negated without resorting to conditionals,
+        #   unlike None or float('NaN'), and sorts appropriately.
+        self.priority = float('-inf')
+        self.is_intermediate = isinstance(s, tuple)
+        self._hash = hash((self.s, self.start, self.end))
+
+    def add_family(self, lr0, rule, start, left, right):
+        self._children.add(PackedNode(self, lr0, rule, start, left, right))
+
+    def add_path(self, transitive, node):
+        self.paths.add((transitive, node))
+
+    def load_paths(self):
+        for transitive, node in self.paths:
+            if transitive.next_titem is not None:
+                vn = type(self)(transitive.next_titem.s, transitive.next_titem.start, self.end)
+                vn.add_path(transitive.next_titem, node)
+                self.add_family(transitive.reduction.rule.origin, transitive.reduction.rule, transitive.reduction.start, transitive.reduction.node, vn)
+            else:
+                self.add_family(transitive.reduction.rule.origin, transitive.reduction.rule, transitive.reduction.start, transitive.reduction.node, node)
+        self.paths_loaded = True
+
+    @property
+    def is_ambiguous(self):
+        """Returns True if this node is ambiguous."""
+        return len(self.children) > 1
+
+    @property
+    def children(self):
+        """Returns a list of this node's children sorted from greatest to
+        least priority."""
+        if not self.paths_loaded:
+            self.load_paths()
+        return sorted(self._children, key=attrgetter('sort_key'))
+
+    def __iter__(self):
+        return iter(self._children)
+
+    def __eq__(self, other):
+        if not isinstance(other, SymbolNode):
+            return False
+        return self is other or (type(self.s) == type(other.s) and self.s == other.s and self.start == other.start and self.end is other.end)
+
+    def __hash__(self):
+        return self._hash
+
+    def __repr__(self):
+        if self.is_intermediate:
+            rule = self.s[0]
+            ptr = self.s[1]
+            before = ( expansion.name for expansion in rule.expansion[:ptr] )
+            after = ( expansion.name for expansion in rule.expansion[ptr:] )
+            symbol = "{} ::= {}* {}".format(rule.origin.name, ' '.join(before), ' '.join(after))
+        else:
+            symbol = self.s.name
+        return "({}, {}, {}, {})".format(symbol, self.start, self.end, self.priority)
+
+class StableSymbolNode(SymbolNode):
+    "A version of SymbolNode that uses OrderedSet for output stability"
+    Set = OrderedSet
+
+class PackedNode(ForestNode):
+    """
+    A Packed Node represents a single derivation in a symbol node.
+
+    Parameters:
+        rule: The rule associated with this node.
+        parent: The parent of this node.
+        left: The left child of this node. ``None`` if one does not exist.
+        right: The right child of this node. ``None`` if one does not exist.
+        priority: The priority of this node.
+    """
+    __slots__ = ('parent', 's', 'rule', 'start', 'left', 'right', 'priority', '_hash')
+    def __init__(self, parent, s, rule, start, left, right):
+        self.parent = parent
+        self.s = s
+        self.start = start
+        self.rule = rule
+        self.left = left
+        self.right = right
+        self.priority = float('-inf')
+        self._hash = hash((self.left, self.right))
+
+    @property
+    def is_empty(self):
+        return self.left is None and self.right is None
+
+    @property
+    def sort_key(self):
+        """
+        Used to sort PackedNode children of SymbolNodes.
+        A SymbolNode has multiple PackedNodes if it matched
+        ambiguously. Hence, we use the sort order to identify
+        the order in which ambiguous children should be considered.
+        """
+        return self.is_empty, -self.priority, self.rule.order
+
+    @property
+    def children(self):
+        """Returns a list of this node's children."""
+        return [x for x in [self.left, self.right] if x is not None]
+
+    def __iter__(self):
+        yield self.left
+        yield self.right
+
+    def __eq__(self, other):
+        if not isinstance(other, PackedNode):
+            return False
+        return self is other or (self.left == other.left and self.right == other.right)
+
+    def __hash__(self):
+        return self._hash
+
+    def __repr__(self):
+        if isinstance(self.s, tuple):
+            rule = self.s[0]
+            ptr = self.s[1]
+            before = ( expansion.name for expansion in rule.expansion[:ptr] )
+            after = ( expansion.name for expansion in rule.expansion[ptr:] )
+            symbol = "{} ::= {}* {}".format(rule.origin.name, ' '.join(before), ' '.join(after))
+        else:
+            symbol = self.s.name
+        return "({}, {}, {}, {})".format(symbol, self.start, self.priority, self.rule.order)
+
+class TokenNode(ForestNode):
+    """
+    A Token Node represents a matched terminal and is always a leaf node.
+
+    Parameters:
+        token: The Token associated with this node.
+        term: The TerminalDef matched by the token.
+        priority: The priority of this node.
+    """
+    __slots__ = ('token', 'term', 'priority', '_hash')
+    def __init__(self, token, term, priority=None):
+        self.token = token
+        self.term = term
+        if priority is not None:
+            self.priority = priority
+        else:
+            self.priority = term.priority if term is not None else 0
+        self._hash = hash(token)
+
+    def __eq__(self, other):
+        if not isinstance(other, TokenNode):
+            return False
+        return self is other or (self.token == other.token)
+
+    def __hash__(self):
+        return self._hash
+
+    def __repr__(self):
+        return repr(self.token)
+
+class ForestVisitor:
+    """
+    An abstract base class for building forest visitors.
+
+    This class performs a controllable depth-first walk of an SPPF.
+    The visitor will not enter cycles and will backtrack if one is encountered.
+    Subclasses are notified of cycles through the ``on_cycle`` method.
+
+    Behavior for visit events is defined by overriding the
+    ``visit*node*`` functions.
+
+    The walk is controlled by the return values of the ``visit*node_in``
+    methods. Returning a node(s) will schedule them to be visited. The visitor
+    will begin to backtrack if no nodes are returned.
+
+    Parameters:
+        single_visit: If ``True``, non-Token nodes will only be visited once.
+    """
+
+    def __init__(self, single_visit=False):
+        self.single_visit = single_visit
+
+    def visit_token_node(self, node):
+        """Called when a ``Token`` is visited. ``Token`` nodes are always leaves."""
+        pass
+
+    def visit_symbol_node_in(self, node):
+        """Called when a symbol node is visited. Nodes that are returned
+        will be scheduled to be visited. If ``visit_intermediate_node_in``
+        is not implemented, this function will be called for intermediate
+        nodes as well."""
+        pass
+
+    def visit_symbol_node_out(self, node):
+        """Called after all nodes returned from a corresponding ``visit_symbol_node_in``
+        call have been visited. If ``visit_intermediate_node_out``
+        is not implemented, this function will be called for intermediate
+        nodes as well."""
+        pass
+
+    def visit_packed_node_in(self, node):
+        """Called when a packed node is visited. Nodes that are returned
+        will be scheduled to be visited. """
+        pass
+
+    def visit_packed_node_out(self, node):
+        """Called after all nodes returned from a corresponding ``visit_packed_node_in``
+        call have been visited."""
+        pass
+
+    def on_cycle(self, node, path):
+        """Called when a cycle is encountered.
+
+        Parameters:
+            node: The node that causes a cycle.
+            path: The list of nodes being visited: nodes that have been
+                entered but not exited. The first element is the root in a forest
+                visit, and the last element is the node visited most recently.
+                ``path`` should be treated as read-only.
+        """
+        pass
+
+    def get_cycle_in_path(self, node, path):
+        """A utility function for use in ``on_cycle`` to obtain a slice of
+        ``path`` that only contains the nodes that make up the cycle."""
+        index = len(path) - 1
+        while id(path[index]) != id(node):
+            index -= 1
+        return path[index:]
+
+    def visit(self, root):
+        # Visiting is a list of IDs of all symbol/intermediate nodes currently in
+        # the stack. It serves two purposes: to detect when we 'recurse' in and out
+        # of a symbol/intermediate so that we can process both up and down. Also,
+        # since the SPPF can have cycles it allows us to detect if we're trying
+        # to recurse into a node that's already on the stack (infinite recursion).
+        visiting = set()
+
+        # set of all nodes that have been visited
+        visited = set()
+
+        # a list of nodes that are currently being visited
+        # used for the `on_cycle` callback
+        path = []
+
+        # We do not use recursion here to walk the Forest due to the limited
+        # stack size in python. Therefore input_stack is essentially our stack.
+        input_stack = deque([root])
+
+        # It is much faster to cache these as locals since they are called
+        # many times in large parses.
+        vpno = getattr(self, 'visit_packed_node_out')
+        vpni = getattr(self, 'visit_packed_node_in')
+        vsno = getattr(self, 'visit_symbol_node_out')
+        vsni = getattr(self, 'visit_symbol_node_in')
+        vino = getattr(self, 'visit_intermediate_node_out', vsno)
+        vini = getattr(self, 'visit_intermediate_node_in', vsni)
+        vtn = getattr(self, 'visit_token_node')
+        oc = getattr(self, 'on_cycle')
+
+        while input_stack:
+            current = next(reversed(input_stack))
+            try:
+                next_node = next(current)
+            except StopIteration:
+                input_stack.pop()
+                continue
+            except TypeError:
+                ### If the current object is not an iterator, pass through to Token/SymbolNode
+                pass
+            else:
+                if next_node is None:
+                    continue
+
+                if id(next_node) in visiting:
+                    oc(next_node, path)
+                    continue
+
+                input_stack.append(next_node)
+                continue
+
+            if isinstance(current, TokenNode):
+                vtn(current.token)
+                input_stack.pop()
+                continue
+
+            current_id = id(current)
+            if current_id in visiting:
+                if isinstance(current, PackedNode):
+                    vpno(current)
+                elif current.is_intermediate:
+                    vino(current)
+                else:
+                    vsno(current)
+                input_stack.pop()
+                path.pop()
+                visiting.remove(current_id)
+                visited.add(current_id)
+            elif self.single_visit and current_id in visited:
+                input_stack.pop()
+            else:
+                visiting.add(current_id)
+                path.append(current)
+                if isinstance(current, PackedNode):
+                    next_node = vpni(current)
+                elif current.is_intermediate:
+                    next_node = vini(current)
+                else:
+                    next_node = vsni(current)
+                if next_node is None:
+                    continue
+
+                if not isinstance(next_node, ForestNode):
+                    next_node = iter(next_node)
+                elif id(next_node) in visiting:
+                    oc(next_node, path)
+                    continue
+
+                input_stack.append(next_node)
+
+class ForestTransformer(ForestVisitor):
+    """The base class for a bottom-up forest transformation. Most users will
+    want to use ``TreeForestTransformer`` instead as it has a friendlier
+    interface and covers most use cases.
+
+    Transformations are applied via inheritance and overriding of the
+    ``transform*node`` methods.
+
+    ``transform_token_node`` receives a ``Token`` as an argument.
+    All other methods receive the node that is being transformed and
+    a list of the results of the transformations of that node's children.
+    The return value of these methods are the resulting transformations.
+
+    If ``Discard`` is raised in a node's transformation, no data from that node
+    will be passed to its parent's transformation.
+    """
+
+    def __init__(self):
+        super(ForestTransformer, self).__init__()
+        # results of transformations
+        self.data = dict()
+        # used to track parent nodes
+        self.node_stack = deque()
+
+    def transform(self, root):
+        """Perform a transformation on an SPPF."""
+        self.node_stack.append('result')
+        self.data['result'] = []
+        self.visit(root)
+        assert len(self.data['result']) <= 1
+        if self.data['result']:
+            return self.data['result'][0]
+
+    def transform_symbol_node(self, node, data):
+        """Transform a symbol node."""
+        return node
+
+    def transform_intermediate_node(self, node, data):
+        """Transform an intermediate node."""
+        return node
+
+    def transform_packed_node(self, node, data):
+        """Transform a packed node."""
+        return node
+
+    def transform_token_node(self, node):
+        """Transform a ``Token``."""
+        return node
+
+    def visit_symbol_node_in(self, node):
+        self.node_stack.append(id(node))
+        self.data[id(node)] = []
+        return node.children
+
+    def visit_packed_node_in(self, node):
+        self.node_stack.append(id(node))
+        self.data[id(node)] = []
+        return node.children
+
+    def visit_token_node(self, node):
+        transformed = self.transform_token_node(node)
+        if transformed is not Discard:
+            self.data[self.node_stack[-1]].append(transformed)
+
+    def _visit_node_out_helper(self, node, method):
+        self.node_stack.pop()
+        transformed = method(node, self.data[id(node)])
+        if transformed is not Discard:
+            self.data[self.node_stack[-1]].append(transformed)
+        del self.data[id(node)]
+
+    def visit_symbol_node_out(self, node):
+        self._visit_node_out_helper(node, self.transform_symbol_node)
+
+    def visit_intermediate_node_out(self, node):
+        self._visit_node_out_helper(node, self.transform_intermediate_node)
+
+    def visit_packed_node_out(self, node):
+        self._visit_node_out_helper(node, self.transform_packed_node)
+
+
+class ForestSumVisitor(ForestVisitor):
+    """
+    A visitor for prioritizing ambiguous parts of the Forest.
+
+    This visitor is used when support for explicit priorities on
+    rules is requested (whether normal, or invert). It walks the
+    forest (or subsets thereof) and cascades properties upwards
+    from the leaves.
+
+    It would be ideal to do this during parsing, however this would
+    require processing each Earley item multiple times. That's
+    a big performance drawback; so running a forest walk is the
+    lesser of two evils: there can be significantly more Earley
+    items created during parsing than there are SPPF nodes in the
+    final tree.
+    """
+    def __init__(self):
+        super(ForestSumVisitor, self).__init__(single_visit=True)
+
+    def visit_packed_node_in(self, node):
+        yield node.left
+        yield node.right
+
+    def visit_symbol_node_in(self, node):
+        return iter(node.children)
+
+    def visit_packed_node_out(self, node):
+        priority = node.rule.options.priority if not node.parent.is_intermediate and node.rule.options.priority else 0
+        priority += getattr(node.right, 'priority', 0)
+        priority += getattr(node.left, 'priority', 0)
+        node.priority = priority
+
+    def visit_symbol_node_out(self, node):
+        node.priority = max(child.priority for child in node.children)
+
+class PackedData():
+    """Used in transformationss of packed nodes to distinguish the data
+    that comes from the left child and the right child.
+    """
+
+    class _NoData():
+        pass
+
+    NO_DATA = _NoData()
+
+    def __init__(self, node, data):
+        self.left = self.NO_DATA
+        self.right = self.NO_DATA
+        if data:
+            if node.left is not None:
+                self.left = data[0]
+                if len(data) > 1:
+                    self.right = data[1]
+            else:
+                self.right = data[0]
+
+class ForestToParseTree(ForestTransformer):
+    """Used by the earley parser when ambiguity equals 'resolve' or
+    'explicit'. Transforms an SPPF into an (ambiguous) parse tree.
+
+    Parameters:
+        tree_class: The tree class to use for construction
+        callbacks: A dictionary of rules to functions that output a tree
+        prioritizer: A ``ForestVisitor`` that manipulates the priorities of ForestNodes
+        resolve_ambiguity: If True, ambiguities will be resolved based on
+                        priorities. Otherwise, `_ambig` nodes will be in the resulting tree.
+        use_cache: If True, the results of packed node transformations will be cached.
+    """
+
+    def __init__(self, tree_class=Tree, callbacks=dict(), prioritizer=ForestSumVisitor(), resolve_ambiguity=True, use_cache=True):
+        super(ForestToParseTree, self).__init__()
+        self.tree_class = tree_class
+        self.callbacks = callbacks
+        self.prioritizer = prioritizer
+        self.resolve_ambiguity = resolve_ambiguity
+        self._use_cache = use_cache
+        self._cache = {}
+        self._on_cycle_retreat = False
+        self._cycle_node = None
+        self._successful_visits = set()
+
+    def visit(self, root):
+        if self.prioritizer:
+            self.prioritizer.visit(root)
+        super(ForestToParseTree, self).visit(root)
+        self._cache = {}
+
+    def on_cycle(self, node, path):
+        logger.debug("Cycle encountered in the SPPF at node: %s. "
+                "As infinite ambiguities cannot be represented in a tree, "
+                "this family of derivations will be discarded.", node)
+        self._cycle_node = node
+        self._on_cycle_retreat = True
+
+    def _check_cycle(self, node):
+        if self._on_cycle_retreat:
+            if id(node) == id(self._cycle_node) or id(node) in self._successful_visits:
+                self._cycle_node = None
+                self._on_cycle_retreat = False
+            else:
+                return Discard
+
+    def _collapse_ambig(self, children):
+        new_children = []
+        for child in children:
+            if hasattr(child, 'data') and child.data == '_ambig':
+                new_children += child.children
+            else:
+                new_children.append(child)
+        return new_children
+
+    def _call_rule_func(self, node, data):
+        # called when transforming children of symbol nodes
+        # data is a list of trees or tokens that correspond to the
+        # symbol's rule expansion
+        return self.callbacks[node.rule](data)
+
+    def _call_ambig_func(self, node, data):
+        # called when transforming a symbol node
+        # data is a list of trees where each tree's data is
+        # equal to the name of the symbol or one of its aliases.
+        if len(data) > 1:
+            return self.tree_class('_ambig', data)
+        elif data:
+            return data[0]
+        return Discard
+
+    def transform_symbol_node(self, node, data):
+        if id(node) not in self._successful_visits:
+            return Discard
+        r = self._check_cycle(node)
+        if r is Discard:
+            return r
+        self._successful_visits.remove(id(node))
+        data = self._collapse_ambig(data)
+        return self._call_ambig_func(node, data)
+
+    def transform_intermediate_node(self, node, data):
+        if id(node) not in self._successful_visits:
+            return Discard
+        r = self._check_cycle(node)
+        if r is Discard:
+            return r
+        self._successful_visits.remove(id(node))
+        if len(data) > 1:
+            children = [self.tree_class('_inter', c) for c in data]
+            return self.tree_class('_iambig', children)
+        return data[0]
+
+    def transform_packed_node(self, node, data):
+        r = self._check_cycle(node)
+        if r is Discard:
+            return r
+        if self.resolve_ambiguity and id(node.parent) in self._successful_visits:
+            return Discard
+        if self._use_cache and id(node) in self._cache:
+            return self._cache[id(node)]
+        children = []
+        assert len(data) <= 2
+        data = PackedData(node, data)
+        if data.left is not PackedData.NO_DATA:
+            if node.left.is_intermediate and isinstance(data.left, list):
+                children += data.left
+            else:
+                children.append(data.left)
+        if data.right is not PackedData.NO_DATA:
+            children.append(data.right)
+        if node.parent.is_intermediate:
+            return self._cache.setdefault(id(node), children)
+        return self._cache.setdefault(id(node), self._call_rule_func(node, children))
+
+    def visit_symbol_node_in(self, node):
+        super(ForestToParseTree, self).visit_symbol_node_in(node)
+        if self._on_cycle_retreat:
+            return
+        return node.children
+
+    def visit_packed_node_in(self, node):
+        self._on_cycle_retreat = False
+        to_visit = super(ForestToParseTree, self).visit_packed_node_in(node)
+        if not self.resolve_ambiguity or id(node.parent) not in self._successful_visits:
+            if not self._use_cache or id(node) not in self._cache:
+                return to_visit
+
+    def visit_packed_node_out(self, node):
+        super(ForestToParseTree, self).visit_packed_node_out(node)
+        if not self._on_cycle_retreat:
+            self._successful_visits.add(id(node.parent))
+
+def handles_ambiguity(func):
+    """Decorator for methods of subclasses of ``TreeForestTransformer``.
+    Denotes that the method should receive a list of transformed derivations."""
+    func.handles_ambiguity = True
+    return func
+
+class TreeForestTransformer(ForestToParseTree):
+    """A ``ForestTransformer`` with a tree ``Transformer``-like interface.
+    By default, it will construct a tree.
+
+    Methods provided via inheritance are called based on the rule/symbol
+    names of nodes in the forest.
+
+    Methods that act on rules will receive a list of the results of the
+    transformations of the rule's children. By default, trees and tokens.
+
+    Methods that act on tokens will receive a token.
+
+    Alternatively, methods that act on rules may be annotated with
+    ``handles_ambiguity``. In this case, the function will receive a list
+    of all the transformations of all the derivations of the rule.
+    By default, a list of trees where each tree.data is equal to the
+    rule name or one of its aliases.
+
+    Non-tree transformations are made possible by override of
+    ``__default__``, ``__default_token__``, and ``__default_ambig__``.
+
+    Note:
+        Tree shaping features such as inlined rules and token filtering are
+        not built into the transformation. Positions are also not propagated.
+
+    Parameters:
+        tree_class: The tree class to use for construction
+        prioritizer: A ``ForestVisitor`` that manipulates the priorities of nodes in the SPPF.
+        resolve_ambiguity: If True, ambiguities will be resolved based on priorities.
+        use_cache (bool): If True, caches the results of some transformations,
+                          potentially improving performance when ``resolve_ambiguity==False``.
+                          Only use if you know what you are doing: i.e. All transformation
+                          functions are pure and referentially transparent.
+    """
+
+    def __init__(self, tree_class=Tree, prioritizer=ForestSumVisitor(), resolve_ambiguity=True, use_cache=False):
+        super(TreeForestTransformer, self).__init__(tree_class, dict(), prioritizer, resolve_ambiguity, use_cache)
+
+    def __default__(self, name, data):
+        """Default operation on tree (for override).
+
+        Returns a tree with name with data as children.
+        """
+        return self.tree_class(name, data)
+
+    def __default_ambig__(self, name, data):
+        """Default operation on ambiguous rule (for override).
+
+        Wraps data in an '_ambig_' node if it contains more than
+        one element.
+        """
+        if len(data) > 1:
+            return self.tree_class('_ambig', data)
+        elif data:
+            return data[0]
+        return Discard
+
+    def __default_token__(self, node):
+        """Default operation on ``Token`` (for override).
+
+        Returns ``node``.
+        """
+        return node
+
+    def transform_token_node(self, node):
+        return getattr(self, node.type, self.__default_token__)(node)
+
+    def _call_rule_func(self, node, data):
+        name = node.rule.alias or node.rule.options.template_source or node.rule.origin.name
+        user_func = getattr(self, name, self.__default__)
+        if user_func == self.__default__ or hasattr(user_func, 'handles_ambiguity'):
+            user_func = partial(self.__default__, name)
+        if not self.resolve_ambiguity:
+            wrapper = partial(AmbiguousIntermediateExpander, self.tree_class)
+            user_func = wrapper(user_func)
+        return user_func(data)
+
+    def _call_ambig_func(self, node, data):
+        name = node.s.name
+        user_func = getattr(self, name, self.__default_ambig__)
+        if user_func == self.__default_ambig__ or not hasattr(user_func, 'handles_ambiguity'):
+            user_func = partial(self.__default_ambig__, name)
+        return user_func(data)
+
+class ForestToPyDotVisitor(ForestVisitor):
+    """
+    A Forest visitor which writes the SPPF to a PNG.
+
+    The SPPF can get really large, really quickly because
+    of the amount of meta-data it stores, so this is probably
+    only useful for trivial trees and learning how the SPPF
+    is structured.
+    """
+    def __init__(self, rankdir="TB"):
+        super(ForestToPyDotVisitor, self).__init__(single_visit=True)
+        self.pydot = import_module('pydot')
+        self.graph = self.pydot.Dot(graph_type='digraph', rankdir=rankdir)
+
+    def visit(self, root, filename):
+        super(ForestToPyDotVisitor, self).visit(root)
+        try:
+            self.graph.write_png(filename)
+        except FileNotFoundError as e:
+            logger.error("Could not write png: ", e)
+
+    def visit_token_node(self, node):
+        graph_node_id = str(id(node))
+        graph_node_label = "\"{}\"".format(node.value.replace('"', '\\"'))
+        graph_node_color = 0x808080
+        graph_node_style = "\"filled,rounded\""
+        graph_node_shape = "diamond"
+        graph_node = self.pydot.Node(graph_node_id, style=graph_node_style, fillcolor="#{:06x}".format(graph_node_color), shape=graph_node_shape, label=graph_node_label)
+        self.graph.add_node(graph_node)
+
+    def visit_packed_node_in(self, node):
+        graph_node_id = str(id(node))
+        graph_node_label = repr(node)
+        graph_node_color = 0x808080
+        graph_node_style = "filled"
+        graph_node_shape = "diamond"
+        graph_node = self.pydot.Node(graph_node_id, style=graph_node_style, fillcolor="#{:06x}".format(graph_node_color), shape=graph_node_shape, label=graph_node_label)
+        self.graph.add_node(graph_node)
+        yield node.left
+        yield node.right
+
+    def visit_packed_node_out(self, node):
+        graph_node_id = str(id(node))
+        graph_node = self.graph.get_node(graph_node_id)[0]
+        for child in [node.left, node.right]:
+            if child is not None:
+                child_graph_node_id = str(id(child.token if isinstance(child, TokenNode) else child))
+                child_graph_node = self.graph.get_node(child_graph_node_id)[0]
+                self.graph.add_edge(self.pydot.Edge(graph_node, child_graph_node))
+            else:
+                #### Try and be above the Python object ID range; probably impl. specific, but maybe this is okay.
+                child_graph_node_id = str(randint(100000000000000000000000000000,123456789012345678901234567890))
+                child_graph_node_style = "invis"
+                child_graph_node = self.pydot.Node(child_graph_node_id, style=child_graph_node_style, label="None")
+                child_edge_style = "invis"
+                self.graph.add_node(child_graph_node)
+                self.graph.add_edge(self.pydot.Edge(graph_node, child_graph_node, style=child_edge_style))
+
+    def visit_symbol_node_in(self, node):
+        graph_node_id = str(id(node))
+        graph_node_label = repr(node)
+        graph_node_color = 0x808080
+        graph_node_style = "\"filled\""
+        if node.is_intermediate:
+            graph_node_shape = "ellipse"
+        else:
+            graph_node_shape = "rectangle"
+        graph_node = self.pydot.Node(graph_node_id, style=graph_node_style, fillcolor="#{:06x}".format(graph_node_color), shape=graph_node_shape, label=graph_node_label)
+        self.graph.add_node(graph_node)
+        return iter(node.children)
+
+    def visit_symbol_node_out(self, node):
+        graph_node_id = str(id(node))
+        graph_node = self.graph.get_node(graph_node_id)[0]
+        for child in node.children:
+            child_graph_node_id = str(id(child))
+            child_graph_node = self.graph.get_node(child_graph_node_id)[0]
+            self.graph.add_edge(self.pydot.Edge(graph_node, child_graph_node))
diff --git a/shared/package-files/c9/c951a80a012812b1922b6486c7e4c4c82c5fbb5c2f10a6e30ef21e9ba6fb0621/test_iomenu.py b/shared/package-files/c9/c951a80a012812b1922b6486c7e4c4c82c5fbb5c2f10a6e30ef21e9ba6fb0621/test_iomenu.py
new file mode 100644
index 0000000000000000000000000000000000000000..2fb836dba216721052f4493b5417b8b84ce34d11
--- /dev/null
+++ b/shared/package-files/c9/c951a80a012812b1922b6486c7e4c4c82c5fbb5c2f10a6e30ef21e9ba6fb0621/test_iomenu.py
@@ -0,0 +1,78 @@
+"Test , coverage 17%."
+
+from idlelib import iomenu
+import unittest
+from test.support import requires
+from tkinter import Tk
+from idlelib.editor import EditorWindow
+from idlelib import util
+from idlelib.idle_test.mock_idle import Func
+
+
+class IOBindingTest(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        requires('gui')
+        cls.root = Tk()
+        cls.root.withdraw()
+        cls.editwin = EditorWindow(root=cls.root)
+        cls.io = iomenu.IOBinding(cls.editwin)
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.io.close()
+        cls.editwin._close()
+        del cls.editwin
+        cls.root.update_idletasks()
+        for id in cls.root.tk.call('after', 'info'):
+            cls.root.after_cancel(id)  # Need for EditorWindow.
+        cls.root.destroy()
+        del cls.root
+
+    def test_init(self):
+        self.assertIs(self.io.editwin, self.editwin)
+
+    def test_fixnewlines_end(self):
+        eq = self.assertEqual
+        io = self.io
+        fix = io.fixnewlines
+        text = io.editwin.text
+
+        # Make the editor temporarily look like Shell.
+        self.editwin.interp = None
+        shelltext = '>>> if 1'
+        self.editwin.get_prompt_text = Func(result=shelltext)
+        eq(fix(), shelltext)  # Get... call and '\n' not added.
+        del self.editwin.interp, self.editwin.get_prompt_text
+
+        text.insert(1.0, 'a')
+        eq(fix(), 'a'+io.eol_convention)
+        eq(text.get('1.0', 'end-1c'), 'a\n')
+        eq(fix(), 'a'+io.eol_convention)
+
+
+def _extension_in_filetypes(extension):
+    return any(
+        f'*{extension}' in filetype_tuple[1]
+        for filetype_tuple in iomenu.IOBinding.filetypes
+    )
+
+
+class FiletypesTest(unittest.TestCase):
+    def test_python_source_files(self):
+        for extension in util.py_extensions:
+            with self.subTest(extension=extension):
+                self.assertTrue(
+                    _extension_in_filetypes(extension)
+                )
+
+    def test_text_files(self):
+        self.assertTrue(_extension_in_filetypes('.txt'))
+
+    def test_all_files(self):
+        self.assertTrue(_extension_in_filetypes(''))
+
+
+if __name__ == '__main__':
+    unittest.main(verbosity=2)
diff --git a/shared/package-files/c9/c95317fda1039e8713c7a3e9260a6f38b56a0fe702981cfdf839c50e99797242/log.pyi b/shared/package-files/c9/c95317fda1039e8713c7a3e9260a6f38b56a0fe702981cfdf839c50e99797242/log.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..a9cd1274688d8b6ad0a390093e1467788b86a509
--- /dev/null
+++ b/shared/package-files/c9/c95317fda1039e8713c7a3e9260a6f38b56a0fe702981cfdf839c50e99797242/log.pyi
@@ -0,0 +1,45 @@
+import logging.config
+from logging import LogRecord
+from typing import Any, Callable, Dict, Optional, Union
+
+from django.core.management.color import Style
+
+request_logger: Any
+DEFAULT_LOGGING: Any
+
+def configure_logging(logging_config: str, logging_settings: Dict[str, Any]) -> None: ...
+
+class AdminEmailHandler(logging.Handler):
+    include_html: bool = ...
+    email_backend: Optional[str] = ...
+    def __init__(self, include_html: bool = ..., email_backend: Optional[str] = ...) -> None: ...
+    def send_mail(self, subject: str, message: str, *args: Any, **kwargs: Any) -> None: ...
+    def connection(self) -> Any: ...
+    def format_subject(self, subject: str) -> str: ...
+
+class CallbackFilter(logging.Filter):
+    callback: Callable = ...
+    def __init__(self, callback: Callable) -> None: ...
+    def filter(self, record: Union[str, LogRecord]) -> bool: ...
+
+class RequireDebugFalse(logging.Filter):
+    def filter(self, record: Union[str, LogRecord]) -> bool: ...
+
+class RequireDebugTrue(logging.Filter):
+    def filter(self, record: Union[str, LogRecord]) -> bool: ...
+
+class ServerFormatter(logging.Formatter):
+    datefmt: None
+    style: Style = ...
+    def __init__(self, *args: Any, **kwargs: Any) -> None: ...
+    def uses_server_time(self) -> bool: ...
+
+def log_response(
+    message: str,
+    *args: Any,
+    response: Optional[Any] = ...,
+    request: Optional[Any] = ...,
+    logger: Any = ...,
+    level: Optional[Any] = ...,
+    exc_info: Optional[Any] = ...
+) -> None: ...
diff --git a/shared/package-files/c9/c958f0f51048c9e7e6aa6a82878230629562f990df89693556196774ae786e6f/mode_compositeexplicitautograd_dispatch.h b/shared/package-files/c9/c958f0f51048c9e7e6aa6a82878230629562f990df89693556196774ae786e6f/mode_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..53021626912e8a87044af031ffeaec02e6e69037
--- /dev/null
+++ b/shared/package-files/c9/c958f0f51048c9e7e6aa6a82878230629562f990df89693556196774ae786e6f/mode_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API ::std::tuple mode_out(at::Tensor & values, at::Tensor & indices, const at::Tensor & self, int64_t dim=-1, bool keepdim=false);
+TORCH_API ::std::tuple mode_outf(const at::Tensor & self, int64_t dim, bool keepdim, at::Tensor & values, at::Tensor & indices);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/c9/c95e29720b2b2fdde51c05d4ce5fd96a0468db43bafdfa815b83862cd2183886/METADATA b/shared/package-files/c9/c95e29720b2b2fdde51c05d4ce5fd96a0468db43bafdfa815b83862cd2183886/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..28a260e9c9f906ee2d4cf334020c2eab1fb48ae2
--- /dev/null
+++ b/shared/package-files/c9/c95e29720b2b2fdde51c05d4ce5fd96a0468db43bafdfa815b83862cd2183886/METADATA
@@ -0,0 +1,307 @@
+Metadata-Version: 2.1
+Name: sympy
+Version: 1.12
+Summary: Computer algebra system (CAS) in Python
+Home-page: https://sympy.org
+Author: SymPy development team
+Author-email: sympy@googlegroups.com
+License: BSD
+Project-URL: Source, https://github.com/sympy/sympy
+Keywords: Math CAS
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Topic :: Scientific/Engineering
+Classifier: Topic :: Scientific/Engineering :: Mathematics
+Classifier: Topic :: Scientific/Engineering :: Physics
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Programming Language :: Python :: Implementation :: CPython
+Classifier: Programming Language :: Python :: Implementation :: PyPy
+Requires-Python: >=3.8
+Description-Content-Type: text/markdown
+License-File: LICENSE
+License-File: AUTHORS
+Requires-Dist: mpmath >=0.19
+
+# SymPy
+
+[![pypi version](https://img.shields.io/pypi/v/sympy.svg)](https://pypi.python.org/pypi/sympy)
+[![Join the chat at https://gitter.im/sympy/sympy](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/sympy/sympy?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
+[![Zenodo Badge](https://zenodo.org/badge/18918/sympy/sympy.svg)](https://zenodo.org/badge/latestdoi/18918/sympy/sympy)
+[![Downloads](https://pepy.tech/badge/sympy/month)](https://pepy.tech/project/sympy)
+[![GitHub Issues](https://img.shields.io/badge/issue_tracking-github-blue.svg)](https://github.com/sympy/sympy/issues)
+[![Git Tutorial](https://img.shields.io/badge/PR-Welcome-%23FF8300.svg?)](https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project)
+[![Powered by NumFocus](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org)
+[![Commits since last release](https://img.shields.io/github/commits-since/sympy/sympy/latest.svg?longCache=true&style=flat-square&logo=git&logoColor=fff)](https://github.com/sympy/sympy/releases)
+
+[![SymPy Banner](https://github.com/sympy/sympy/raw/master/banner.svg)](https://sympy.org/)
+
+
+See the [AUTHORS](AUTHORS) file for the list of authors.
+
+And many more people helped on the SymPy mailing list, reported bugs,
+helped organize SymPy's participation in the Google Summer of Code, the
+Google Highly Open Participation Contest, Google Code-In, wrote and
+blogged about SymPy...
+
+License: New BSD License (see the [LICENSE](LICENSE) file for details) covers all
+files in the sympy repository unless stated otherwise.
+
+Our mailing list is at
+.
+
+We have a community chat at [Gitter](https://gitter.im/sympy/sympy). Feel
+free to ask us anything there. We have a very welcoming and helpful
+community.
+
+## Download
+
+The recommended installation method is through Anaconda,
+
+
+You can also get the latest version of SymPy from
+
+
+To get the git version do
+
+    $ git clone https://github.com/sympy/sympy.git
+
+For other options (tarballs, debs, etc.), see
+.
+
+## Documentation and Usage
+
+For in-depth instructions on installation and building the
+documentation, see the [SymPy Documentation Style Guide](https://docs.sympy.org/dev/documentation-style-guide.html).
+
+Everything is at:
+
+
+
+You can generate everything at the above site in your local copy of
+SymPy by:
+
+    $ cd doc
+    $ make html
+
+Then the docs will be in \_build/html. If
+you don't want to read that, here is a short usage:
+
+From this directory, start Python and:
+
+``` python
+>>> from sympy import Symbol, cos
+>>> x = Symbol('x')
+>>> e = 1/cos(x)
+>>> print(e.series(x, 0, 10))
+1 + x**2/2 + 5*x**4/24 + 61*x**6/720 + 277*x**8/8064 + O(x**10)
+```
+
+SymPy also comes with a console that is a simple wrapper around the
+classic python console (or IPython when available) that loads the SymPy
+namespace and executes some common commands for you.
+
+To start it, issue:
+
+    $ bin/isympy
+
+from this directory, if SymPy is not installed or simply:
+
+    $ isympy
+
+if SymPy is installed.
+
+## Installation
+
+SymPy has a hard dependency on the [mpmath](http://mpmath.org/) library
+(version \>= 0.19). You should install it first, please refer to the
+mpmath installation guide:
+
+
+
+To install SymPy using PyPI, run the following command:
+
+    $ pip install sympy
+
+To install SymPy using Anaconda, run the following command:
+
+    $ conda install -c anaconda sympy
+
+To install SymPy from GitHub source, first clone SymPy using `git`:
+
+    $ git clone https://github.com/sympy/sympy.git
+
+Then, in the `sympy` repository that you cloned, simply run:
+
+    $ pip install .
+
+See  for more information.
+
+## Contributing
+
+We welcome contributions from anyone, even if you are new to open
+source. Please read our [Introduction to Contributing](https://github.com/sympy/sympy/wiki/Introduction-to-contributing)
+page and the [SymPy Documentation Style Guide](https://docs.sympy.org/dev/documentation-style-guide.html). If you
+are new and looking for some way to contribute, a good place to start is
+to look at the issues tagged [Easy to Fix](https://github.com/sympy/sympy/issues?q=is%3Aopen+is%3Aissue+label%3A%22Easy+to+Fix%22).
+
+Please note that all participants in this project are expected to follow
+our Code of Conduct. By participating in this project you agree to abide
+by its terms. See [CODE\_OF\_CONDUCT.md](CODE_OF_CONDUCT.md).
+
+## Tests
+
+To execute all tests, run:
+
+    $./setup.py test
+
+in the current directory.
+
+For the more fine-grained running of tests or doctests, use `bin/test`
+or respectively `bin/doctest`. The master branch is automatically tested
+by GitHub Actions.
+
+To test pull requests, use
+[sympy-bot](https://github.com/sympy/sympy-bot).
+
+## Regenerate Experimental LaTeX Parser/Lexer
+
+The parser and lexer were generated with the [ANTLR4](http://antlr4.org)
+toolchain in `sympy/parsing/latex/_antlr` and checked into the repo.
+Presently, most users should not need to regenerate these files, but
+if you plan to work on this feature, you will need the `antlr4`
+command-line tool (and you must ensure that it is in your `PATH`).
+One way to get it is:
+
+    $ conda install -c conda-forge antlr=4.11.1
+
+Alternatively, follow the instructions on the ANTLR website and download
+the `antlr-4.11.1-complete.jar`. Then export the `CLASSPATH` as instructed
+and instead of creating `antlr4` as an alias, make it an executable file
+with the following contents:
+``` bash
+#!/bin/bash
+java -jar /usr/local/lib/antlr-4.11.1-complete.jar "$@"
+```
+
+After making changes to `sympy/parsing/latex/LaTeX.g4`, run:
+
+    $ ./setup.py antlr
+
+## Clean
+
+To clean everything (thus getting the same tree as in the repository):
+
+    $ git clean -Xdf
+
+which will clear everything ignored by `.gitignore`, and:
+
+    $ git clean -df
+
+to clear all untracked files. You can revert the most recent changes in
+git with:
+
+    $ git reset --hard
+
+WARNING: The above commands will all clear changes you may have made,
+and you will lose them forever. Be sure to check things with `git
+status`, `git diff`, `git clean -Xn`, and `git clean -n` before doing any
+of those.
+
+## Bugs
+
+Our issue tracker is at . Please
+report any bugs that you find. Or, even better, fork the repository on
+GitHub and create a pull request. We welcome all changes, big or small,
+and we will help you make the pull request if you are new to git (just
+ask on our mailing list or Gitter Channel). If you further have any queries, you can find answers
+on Stack Overflow using the [sympy](https://stackoverflow.com/questions/tagged/sympy) tag.
+
+## Brief History
+
+SymPy was started by Ondřej Čertík in 2005, he wrote some code during
+the summer, then he wrote some more code during summer 2006. In February
+2007, Fabian Pedregosa joined the project and helped fix many things,
+contributed documentation, and made it alive again. 5 students (Mateusz
+Paprocki, Brian Jorgensen, Jason Gedge, Robert Schwarz, and Chris Wu)
+improved SymPy incredibly during summer 2007 as part of the Google
+Summer of Code. Pearu Peterson joined the development during the summer
+2007 and he has made SymPy much more competitive by rewriting the core
+from scratch, which has made it from 10x to 100x faster. Jurjen N.E. Bos
+has contributed pretty-printing and other patches. Fredrik Johansson has
+written mpmath and contributed a lot of patches.
+
+SymPy has participated in every Google Summer of Code since 2007. You
+can see  for
+full details. Each year has improved SymPy by bounds. Most of SymPy's
+development has come from Google Summer of Code students.
+
+In 2011, Ondřej Čertík stepped down as lead developer, with Aaron
+Meurer, who also started as a Google Summer of Code student, taking his
+place. Ondřej Čertík is still active in the community but is too busy
+with work and family to play a lead development role.
+
+Since then, a lot more people have joined the development and some
+people have also left. You can see the full list in doc/src/aboutus.rst,
+or online at:
+
+
+
+The git history goes back to 2007 when development moved from svn to hg.
+To see the history before that point, look at
+.
+
+You can use git to see the biggest developers. The command:
+
+    $ git shortlog -ns
+
+will show each developer, sorted by commits to the project. The command:
+
+    $ git shortlog -ns --since="1 year"
+
+will show the top developers from the last year.
+
+## Citation
+
+To cite SymPy in publications use
+
+> Meurer A, Smith CP, Paprocki M, Čertík O, Kirpichev SB, Rocklin M,
+> Kumar A, Ivanov S, Moore JK, Singh S, Rathnayake T, Vig S, Granger BE,
+> Muller RP, Bonazzi F, Gupta H, Vats S, Johansson F, Pedregosa F, Curry
+> MJ, Terrel AR, RouÄka Å , Saboo A, Fernando I, Kulal S, Cimrman R,
+> Scopatz A. (2017) SymPy: symbolic computing in Python. *PeerJ Computer
+> Science* 3:e103 
+
+A BibTeX entry for LaTeX users is
+
+``` bibtex
+@article{10.7717/peerj-cs.103,
+ title = {SymPy: symbolic computing in Python},
+ author = {Meurer, Aaron and Smith, Christopher P. and Paprocki, Mateusz and \v{C}ert\'{i}k, Ond\v{r}ej and Kirpichev, Sergey B. and Rocklin, Matthew and Kumar, Amit and Ivanov, Sergiu and Moore, Jason K. and Singh, Sartaj and Rathnayake, Thilina and Vig, Sean and Granger, Brian E. and Muller, Richard P. and Bonazzi, Francesco and Gupta, Harsh and Vats, Shivam and Johansson, Fredrik and Pedregosa, Fabian and Curry, Matthew J. and Terrel, Andy R. and Rou\v{c}ka, \v{S}t\v{e}p\'{a}n and Saboo, Ashutosh and Fernando, Isuru and Kulal, Sumith and Cimrman, Robert and Scopatz, Anthony},
+ year = 2017,
+ month = Jan,
+ keywords = {Python, Computer algebra system, Symbolics},
+ abstract = {
+            SymPy is an open-source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provides additional examples and further outlines details of the architecture and features of SymPy.
+         },
+ volume = 3,
+ pages = {e103},
+ journal = {PeerJ Computer Science},
+ issn = {2376-5992},
+ url = {https://doi.org/10.7717/peerj-cs.103},
+ doi = {10.7717/peerj-cs.103}
+}
+```
+
+SymPy is BSD licensed, so you are free to use it whatever you like, be
+it academic, commercial, creating forks or derivatives, as long as you
+copy the BSD statement if you redistribute it (see the LICENSE file for
+details). That said, although not required by the SymPy license, if it
+is convenient for you, please cite SymPy when using it in your work and
+also consider contributing all your changes back, so that we can
+incorporate it and all of us will benefit in the end.
diff --git a/shared/package-files/c9/c95e5d6d997f851206139b1411dabaaa984d2e6874cc463a5e7f345d033f4676/graph_hashing.py b/shared/package-files/c9/c95e5d6d997f851206139b1411dabaaa984d2e6874cc463a5e7f345d033f4676/graph_hashing.py
new file mode 100644
index 0000000000000000000000000000000000000000..1ca3928e0849f8736b2a10289f05a796014d4cc9
--- /dev/null
+++ b/shared/package-files/c9/c95e5d6d997f851206139b1411dabaaa984d2e6874cc463a5e7f345d033f4676/graph_hashing.py
@@ -0,0 +1,309 @@
+"""
+Functions for hashing graphs to strings.
+Isomorphic graphs should be assigned identical hashes.
+For now, only Weisfeiler-Lehman hashing is implemented.
+"""
+
+from collections import Counter, defaultdict
+from hashlib import blake2b
+
+__all__ = ["weisfeiler_lehman_graph_hash", "weisfeiler_lehman_subgraph_hashes"]
+
+
+def _hash_label(label, digest_size):
+    return blake2b(label.encode("ascii"), digest_size=digest_size).hexdigest()
+
+
+def _init_node_labels(G, edge_attr, node_attr):
+    if node_attr:
+        return {u: str(dd[node_attr]) for u, dd in G.nodes(data=True)}
+    elif edge_attr:
+        return {u: "" for u in G}
+    else:
+        return {u: str(deg) for u, deg in G.degree()}
+
+
+def _neighborhood_aggregate(G, node, node_labels, edge_attr=None):
+    """
+    Compute new labels for given node by aggregating
+    the labels of each node's neighbors.
+    """
+    label_list = []
+    for nbr in G.neighbors(node):
+        prefix = "" if edge_attr is None else str(G[node][nbr][edge_attr])
+        label_list.append(prefix + node_labels[nbr])
+    return node_labels[node] + "".join(sorted(label_list))
+
+
+def weisfeiler_lehman_graph_hash(
+    G, edge_attr=None, node_attr=None, iterations=3, digest_size=16
+):
+    """Return Weisfeiler Lehman (WL) graph hash.
+
+    The function iteratively aggregates and hashes neighbourhoods of each node.
+    After each node's neighbors are hashed to obtain updated node labels,
+    a hashed histogram of resulting labels is returned as the final hash.
+
+    Hashes are identical for isomorphic graphs and strong guarantees that
+    non-isomorphic graphs will get different hashes. See [1]_ for details.
+
+    If no node or edge attributes are provided, the degree of each node
+    is used as its initial label.
+    Otherwise, node and/or edge labels are used to compute the hash.
+
+    Parameters
+    ----------
+    G: graph
+        The graph to be hashed.
+        Can have node and/or edge attributes. Can also have no attributes.
+    edge_attr: string, default=None
+        The key in edge attribute dictionary to be used for hashing.
+        If None, edge labels are ignored.
+    node_attr: string, default=None
+        The key in node attribute dictionary to be used for hashing.
+        If None, and no edge_attr given, use the degrees of the nodes as labels.
+    iterations: int, default=3
+        Number of neighbor aggregations to perform.
+        Should be larger for larger graphs.
+    digest_size: int, default=16
+        Size (in bits) of blake2b hash digest to use for hashing node labels.
+
+    Returns
+    -------
+    h : string
+        Hexadecimal string corresponding to hash of the input graph.
+
+    Examples
+    --------
+    Two graphs with edge attributes that are isomorphic, except for
+    differences in the edge labels.
+
+    >>> G1 = nx.Graph()
+    >>> G1.add_edges_from(
+    ...     [
+    ...         (1, 2, {"label": "A"}),
+    ...         (2, 3, {"label": "A"}),
+    ...         (3, 1, {"label": "A"}),
+    ...         (1, 4, {"label": "B"}),
+    ...     ]
+    ... )
+    >>> G2 = nx.Graph()
+    >>> G2.add_edges_from(
+    ...     [
+    ...         (5, 6, {"label": "B"}),
+    ...         (6, 7, {"label": "A"}),
+    ...         (7, 5, {"label": "A"}),
+    ...         (7, 8, {"label": "A"}),
+    ...     ]
+    ... )
+
+    Omitting the `edge_attr` option, results in identical hashes.
+
+    >>> nx.weisfeiler_lehman_graph_hash(G1)
+    '7bc4dde9a09d0b94c5097b219891d81a'
+    >>> nx.weisfeiler_lehman_graph_hash(G2)
+    '7bc4dde9a09d0b94c5097b219891d81a'
+
+    With edge labels, the graphs are no longer assigned
+    the same hash digest.
+
+    >>> nx.weisfeiler_lehman_graph_hash(G1, edge_attr="label")
+    'c653d85538bcf041d88c011f4f905f10'
+    >>> nx.weisfeiler_lehman_graph_hash(G2, edge_attr="label")
+    '3dcd84af1ca855d0eff3c978d88e7ec7'
+
+    Notes
+    -----
+    To return the WL hashes of each subgraph of a graph, use
+    `weisfeiler_lehman_subgraph_hashes`
+
+    Similarity between hashes does not imply similarity between graphs.
+
+    References
+    ----------
+    .. [1] Shervashidze, Nino, Pascal Schweitzer, Erik Jan Van Leeuwen,
+       Kurt Mehlhorn, and Karsten M. Borgwardt. Weisfeiler Lehman
+       Graph Kernels. Journal of Machine Learning Research. 2011.
+       http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf
+
+    See also
+    --------
+    weisfeiler_lehman_subgraph_hashes
+    """
+
+    def weisfeiler_lehman_step(G, labels, edge_attr=None):
+        """
+        Apply neighborhood aggregation to each node
+        in the graph.
+        Computes a dictionary with labels for each node.
+        """
+        new_labels = {}
+        for node in G.nodes():
+            label = _neighborhood_aggregate(G, node, labels, edge_attr=edge_attr)
+            new_labels[node] = _hash_label(label, digest_size)
+        return new_labels
+
+    # set initial node labels
+    node_labels = _init_node_labels(G, edge_attr, node_attr)
+
+    subgraph_hash_counts = []
+    for _ in range(iterations):
+        node_labels = weisfeiler_lehman_step(G, node_labels, edge_attr=edge_attr)
+        counter = Counter(node_labels.values())
+        # sort the counter, extend total counts
+        subgraph_hash_counts.extend(sorted(counter.items(), key=lambda x: x[0]))
+
+    # hash the final counter
+    return _hash_label(str(tuple(subgraph_hash_counts)), digest_size)
+
+
+def weisfeiler_lehman_subgraph_hashes(
+    G, edge_attr=None, node_attr=None, iterations=3, digest_size=16
+):
+    """
+    Return a dictionary of subgraph hashes by node.
+
+    Dictionary keys are nodes in `G`, and values are a list of hashes.
+    Each hash corresponds to a subgraph rooted at a given node u in `G`.
+    Lists of subgraph hashes are sorted in increasing order of depth from
+    their root node, with the hash at index i corresponding to a subgraph
+    of nodes at most i edges distance from u. Thus, each list will contain
+    ``iterations + 1`` elements - a hash for a subgraph at each depth, and
+    additionally a hash of the initial node label (or equivalently a
+    subgraph of depth 0)
+
+    The function iteratively aggregates and hashes neighbourhoods of each node.
+    This is achieved for each step by replacing for each node its label from
+    the previous iteration with its hashed 1-hop neighborhood aggregate.
+    The new node label is then appended to a list of node labels for each
+    node.
+
+    To aggregate neighborhoods at each step for a node $n$, all labels of
+    nodes adjacent to $n$ are concatenated. If the `edge_attr` parameter is set,
+    labels for each neighboring node are prefixed with the value of this attribute
+    along the connecting edge from this neighbor to node $n$. The resulting string
+    is then hashed to compress this information into a fixed digest size.
+
+    Thus, at the $i$-th iteration, nodes within $i$ hops influence any given
+    hashed node label. We can therefore say that at depth $i$ for node $n$
+    we have a hash for a subgraph induced by the $2i$-hop neighborhood of $n$.
+
+    The output can be used to to create general Weisfeiler-Lehman graph kernels,
+    or generate features for graphs or nodes - for example to generate 'words' in
+    a graph as seen in the 'graph2vec' algorithm.
+    See [1]_ & [2]_ respectively for details.
+
+    Hashes are identical for isomorphic subgraphs and there exist strong
+    guarantees that non-isomorphic graphs will get different hashes.
+    See [1]_ for details.
+
+    If no node or edge attributes are provided, the degree of each node
+    is used as its initial label.
+    Otherwise, node and/or edge labels are used to compute the hash.
+
+    Parameters
+    ----------
+    G: graph
+        The graph to be hashed.
+        Can have node and/or edge attributes. Can also have no attributes.
+    edge_attr: string, default=None
+        The key in edge attribute dictionary to be used for hashing.
+        If None, edge labels are ignored.
+    node_attr: string, default=None
+        The key in node attribute dictionary to be used for hashing.
+        If None, and no edge_attr given, use the degrees of the nodes as labels.
+    iterations: int, default=3
+        Number of neighbor aggregations to perform.
+        Should be larger for larger graphs.
+    digest_size: int, default=16
+        Size (in bits) of blake2b hash digest to use for hashing node labels.
+        The default size is 16 bits
+
+    Returns
+    -------
+    node_subgraph_hashes : dict
+        A dictionary with each key given by a node in G, and each value given
+        by the subgraph hashes in order of depth from the key node.
+
+    Examples
+    --------
+    Finding similar nodes in different graphs:
+
+    >>> G1 = nx.Graph()
+    >>> G1.add_edges_from([
+    ...     (1, 2), (2, 3), (2, 4), (3, 5), (4, 6), (5, 7), (6, 7)
+    ... ])
+    >>> G2 = nx.Graph()
+    >>> G2.add_edges_from([
+    ...     (1, 3), (2, 3), (1, 6), (1, 5), (4, 6)
+    ... ])
+    >>> g1_hashes = nx.weisfeiler_lehman_subgraph_hashes(G1, iterations=3, digest_size=8)
+    >>> g2_hashes = nx.weisfeiler_lehman_subgraph_hashes(G2, iterations=3, digest_size=8)
+
+    Even though G1 and G2 are not isomorphic (they have different numbers of edges),
+    the hash sequence of depth 3 for node 1 in G1 and node 5 in G2 are similar:
+
+    >>> g1_hashes[1]
+    ['a93b64973cfc8897', 'db1b43ae35a1878f', '57872a7d2059c1c0']
+    >>> g2_hashes[5]
+    ['a93b64973cfc8897', 'db1b43ae35a1878f', '1716d2a4012fa4bc']
+
+    The first 2 WL subgraph hashes match. From this we can conclude that it's very
+    likely the neighborhood of 4 hops around these nodes are isomorphic: each
+    iteration aggregates 1-hop neighbourhoods meaning hashes at depth $n$ are influenced
+    by every node within $2n$ hops.
+
+    However the neighborhood of 6 hops is no longer isomorphic since their 3rd hash does
+    not match.
+
+    These nodes may be candidates to be classified together since their local topology
+    is similar.
+
+    Notes
+    -----
+    To hash the full graph when subgraph hashes are not needed, use
+    `weisfeiler_lehman_graph_hash` for efficiency.
+
+    Similarity between hashes does not imply similarity between graphs.
+
+    References
+    ----------
+    .. [1] Shervashidze, Nino, Pascal Schweitzer, Erik Jan Van Leeuwen,
+       Kurt Mehlhorn, and Karsten M. Borgwardt. Weisfeiler Lehman
+       Graph Kernels. Journal of Machine Learning Research. 2011.
+       http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf
+    .. [2] Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan,
+       Lihui Chen, Yang Liu and Shantanu Jaiswa. graph2vec: Learning
+       Distributed Representations of Graphs. arXiv. 2017
+       https://arxiv.org/pdf/1707.05005.pdf
+
+    See also
+    --------
+    weisfeiler_lehman_graph_hash
+    """
+
+    def weisfeiler_lehman_step(G, labels, node_subgraph_hashes, edge_attr=None):
+        """
+        Apply neighborhood aggregation to each node
+        in the graph.
+        Computes a dictionary with labels for each node.
+        Appends the new hashed label to the dictionary of subgraph hashes
+        originating from and indexed by each node in G
+        """
+        new_labels = {}
+        for node in G.nodes():
+            label = _neighborhood_aggregate(G, node, labels, edge_attr=edge_attr)
+            hashed_label = _hash_label(label, digest_size)
+            new_labels[node] = hashed_label
+            node_subgraph_hashes[node].append(hashed_label)
+        return new_labels
+
+    node_labels = _init_node_labels(G, edge_attr, node_attr)
+
+    node_subgraph_hashes = defaultdict(list)
+    for _ in range(iterations):
+        node_labels = weisfeiler_lehman_step(
+            G, node_labels, node_subgraph_hashes, edge_attr
+        )
+
+    return dict(node_subgraph_hashes)
diff --git a/shared/package-files/c9/c95fa80f56a6bd7401fa3c59beb4d0e5e7225e42616c46123b067b11bee3860f/special_spherical_bessel_j0_meta.h b/shared/package-files/c9/c95fa80f56a6bd7401fa3c59beb4d0e5e7225e42616c46123b067b11bee3860f/special_spherical_bessel_j0_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..5443d5c9a467cc55ea7c272a07391cd069b1fab5
--- /dev/null
+++ b/shared/package-files/c9/c95fa80f56a6bd7401fa3c59beb4d0e5e7225e42616c46123b067b11bee3860f/special_spherical_bessel_j0_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured_special_spherical_bessel_j0 : public TensorIteratorBase {
+    
+    
+    void meta(const at::Tensor & x);
+};
+
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/c9/c960492de542947b03f0162eb277fd6ddab2a4e30fae3dffce83bf950db354c7/memmap.py b/shared/package-files/c9/c960492de542947b03f0162eb277fd6ddab2a4e30fae3dffce83bf950db354c7/memmap.py
new file mode 100644
index 0000000000000000000000000000000000000000..79c695455d343365d2a299bbe499f7eac1d0906b
--- /dev/null
+++ b/shared/package-files/c9/c960492de542947b03f0162eb277fd6ddab2a4e30fae3dffce83bf950db354c7/memmap.py
@@ -0,0 +1,338 @@
+from contextlib import nullcontext
+
+import numpy as np
+from .._utils import set_module
+from .numeric import uint8, ndarray, dtype
+from numpy.compat import os_fspath, is_pathlib_path
+
+__all__ = ['memmap']
+
+dtypedescr = dtype
+valid_filemodes = ["r", "c", "r+", "w+"]
+writeable_filemodes = ["r+", "w+"]
+
+mode_equivalents = {
+    "readonly":"r",
+    "copyonwrite":"c",
+    "readwrite":"r+",
+    "write":"w+"
+    }
+
+
+@set_module('numpy')
+class memmap(ndarray):
+    """Create a memory-map to an array stored in a *binary* file on disk.
+
+    Memory-mapped files are used for accessing small segments of large files
+    on disk, without reading the entire file into memory.  NumPy's
+    memmap's are array-like objects.  This differs from Python's ``mmap``
+    module, which uses file-like objects.
+
+    This subclass of ndarray has some unpleasant interactions with
+    some operations, because it doesn't quite fit properly as a subclass.
+    An alternative to using this subclass is to create the ``mmap``
+    object yourself, then create an ndarray with ndarray.__new__ directly,
+    passing the object created in its 'buffer=' parameter.
+
+    This class may at some point be turned into a factory function
+    which returns a view into an mmap buffer.
+
+    Flush the memmap instance to write the changes to the file. Currently there
+    is no API to close the underlying ``mmap``. It is tricky to ensure the
+    resource is actually closed, since it may be shared between different
+    memmap instances.
+
+
+    Parameters
+    ----------
+    filename : str, file-like object, or pathlib.Path instance
+        The file name or file object to be used as the array data buffer.
+    dtype : data-type, optional
+        The data-type used to interpret the file contents.
+        Default is `uint8`.
+    mode : {'r+', 'r', 'w+', 'c'}, optional
+        The file is opened in this mode:
+
+        +------+-------------------------------------------------------------+
+        | 'r'  | Open existing file for reading only.                        |
+        +------+-------------------------------------------------------------+
+        | 'r+' | Open existing file for reading and writing.                 |
+        +------+-------------------------------------------------------------+
+        | 'w+' | Create or overwrite existing file for reading and writing.  |
+        |      | If ``mode == 'w+'`` then `shape` must also be specified.    |
+        +------+-------------------------------------------------------------+
+        | 'c'  | Copy-on-write: assignments affect data in memory, but       |
+        |      | changes are not saved to disk.  The file on disk is         |
+        |      | read-only.                                                  |
+        +------+-------------------------------------------------------------+
+
+        Default is 'r+'.
+    offset : int, optional
+        In the file, array data starts at this offset. Since `offset` is
+        measured in bytes, it should normally be a multiple of the byte-size
+        of `dtype`. When ``mode != 'r'``, even positive offsets beyond end of
+        file are valid; The file will be extended to accommodate the
+        additional data. By default, ``memmap`` will start at the beginning of
+        the file, even if ``filename`` is a file pointer ``fp`` and
+        ``fp.tell() != 0``.
+    shape : tuple, optional
+        The desired shape of the array. If ``mode == 'r'`` and the number
+        of remaining bytes after `offset` is not a multiple of the byte-size
+        of `dtype`, you must specify `shape`. By default, the returned array
+        will be 1-D with the number of elements determined by file size
+        and data-type.
+    order : {'C', 'F'}, optional
+        Specify the order of the ndarray memory layout:
+        :term:`row-major`, C-style or :term:`column-major`,
+        Fortran-style.  This only has an effect if the shape is
+        greater than 1-D.  The default order is 'C'.
+
+    Attributes
+    ----------
+    filename : str or pathlib.Path instance
+        Path to the mapped file.
+    offset : int
+        Offset position in the file.
+    mode : str
+        File mode.
+
+    Methods
+    -------
+    flush
+        Flush any changes in memory to file on disk.
+        When you delete a memmap object, flush is called first to write
+        changes to disk.
+
+
+    See also
+    --------
+    lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.
+
+    Notes
+    -----
+    The memmap object can be used anywhere an ndarray is accepted.
+    Given a memmap ``fp``, ``isinstance(fp, numpy.ndarray)`` returns
+    ``True``.
+
+    Memory-mapped files cannot be larger than 2GB on 32-bit systems.
+
+    When a memmap causes a file to be created or extended beyond its
+    current size in the filesystem, the contents of the new part are
+    unspecified. On systems with POSIX filesystem semantics, the extended
+    part will be filled with zero bytes.
+
+    Examples
+    --------
+    >>> data = np.arange(12, dtype='float32')
+    >>> data.resize((3,4))
+
+    This example uses a temporary file so that doctest doesn't write
+    files to your directory. You would use a 'normal' filename.
+
+    >>> from tempfile import mkdtemp
+    >>> import os.path as path
+    >>> filename = path.join(mkdtemp(), 'newfile.dat')
+
+    Create a memmap with dtype and shape that matches our data:
+
+    >>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4))
+    >>> fp
+    memmap([[0., 0., 0., 0.],
+            [0., 0., 0., 0.],
+            [0., 0., 0., 0.]], dtype=float32)
+
+    Write data to memmap array:
+
+    >>> fp[:] = data[:]
+    >>> fp
+    memmap([[  0.,   1.,   2.,   3.],
+            [  4.,   5.,   6.,   7.],
+            [  8.,   9.,  10.,  11.]], dtype=float32)
+
+    >>> fp.filename == path.abspath(filename)
+    True
+
+    Flushes memory changes to disk in order to read them back
+
+    >>> fp.flush()
+
+    Load the memmap and verify data was stored:
+
+    >>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
+    >>> newfp
+    memmap([[  0.,   1.,   2.,   3.],
+            [  4.,   5.,   6.,   7.],
+            [  8.,   9.,  10.,  11.]], dtype=float32)
+
+    Read-only memmap:
+
+    >>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
+    >>> fpr.flags.writeable
+    False
+
+    Copy-on-write memmap:
+
+    >>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4))
+    >>> fpc.flags.writeable
+    True
+
+    It's possible to assign to copy-on-write array, but values are only
+    written into the memory copy of the array, and not written to disk:
+
+    >>> fpc
+    memmap([[  0.,   1.,   2.,   3.],
+            [  4.,   5.,   6.,   7.],
+            [  8.,   9.,  10.,  11.]], dtype=float32)
+    >>> fpc[0,:] = 0
+    >>> fpc
+    memmap([[  0.,   0.,   0.,   0.],
+            [  4.,   5.,   6.,   7.],
+            [  8.,   9.,  10.,  11.]], dtype=float32)
+
+    File on disk is unchanged:
+
+    >>> fpr
+    memmap([[  0.,   1.,   2.,   3.],
+            [  4.,   5.,   6.,   7.],
+            [  8.,   9.,  10.,  11.]], dtype=float32)
+
+    Offset into a memmap:
+
+    >>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16)
+    >>> fpo
+    memmap([  4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.], dtype=float32)
+
+    """
+
+    __array_priority__ = -100.0
+
+    def __new__(subtype, filename, dtype=uint8, mode='r+', offset=0,
+                shape=None, order='C'):
+        # Import here to minimize 'import numpy' overhead
+        import mmap
+        import os.path
+        try:
+            mode = mode_equivalents[mode]
+        except KeyError as e:
+            if mode not in valid_filemodes:
+                raise ValueError(
+                    "mode must be one of {!r} (got {!r})"
+                    .format(valid_filemodes + list(mode_equivalents.keys()), mode)
+                ) from None
+
+        if mode == 'w+' and shape is None:
+            raise ValueError("shape must be given if mode == 'w+'")
+
+        if hasattr(filename, 'read'):
+            f_ctx = nullcontext(filename)
+        else:
+            f_ctx = open(os_fspath(filename), ('r' if mode == 'c' else mode)+'b')
+
+        with f_ctx as fid:
+            fid.seek(0, 2)
+            flen = fid.tell()
+            descr = dtypedescr(dtype)
+            _dbytes = descr.itemsize
+
+            if shape is None:
+                bytes = flen - offset
+                if bytes % _dbytes:
+                    raise ValueError("Size of available data is not a "
+                            "multiple of the data-type size.")
+                size = bytes // _dbytes
+                shape = (size,)
+            else:
+                if not isinstance(shape, tuple):
+                    shape = (shape,)
+                size = np.intp(1)  # avoid default choice of np.int_, which might overflow
+                for k in shape:
+                    size *= k
+
+            bytes = int(offset + size*_dbytes)
+
+            if mode in ('w+', 'r+') and flen < bytes:
+                fid.seek(bytes - 1, 0)
+                fid.write(b'\0')
+                fid.flush()
+
+            if mode == 'c':
+                acc = mmap.ACCESS_COPY
+            elif mode == 'r':
+                acc = mmap.ACCESS_READ
+            else:
+                acc = mmap.ACCESS_WRITE
+
+            start = offset - offset % mmap.ALLOCATIONGRANULARITY
+            bytes -= start
+            array_offset = offset - start
+            mm = mmap.mmap(fid.fileno(), bytes, access=acc, offset=start)
+
+            self = ndarray.__new__(subtype, shape, dtype=descr, buffer=mm,
+                                   offset=array_offset, order=order)
+            self._mmap = mm
+            self.offset = offset
+            self.mode = mode
+
+            if is_pathlib_path(filename):
+                # special case - if we were constructed with a pathlib.path,
+                # then filename is a path object, not a string
+                self.filename = filename.resolve()
+            elif hasattr(fid, "name") and isinstance(fid.name, str):
+                # py3 returns int for TemporaryFile().name
+                self.filename = os.path.abspath(fid.name)
+            # same as memmap copies (e.g. memmap + 1)
+            else:
+                self.filename = None
+
+        return self
+
+    def __array_finalize__(self, obj):
+        if hasattr(obj, '_mmap') and np.may_share_memory(self, obj):
+            self._mmap = obj._mmap
+            self.filename = obj.filename
+            self.offset = obj.offset
+            self.mode = obj.mode
+        else:
+            self._mmap = None
+            self.filename = None
+            self.offset = None
+            self.mode = None
+
+    def flush(self):
+        """
+        Write any changes in the array to the file on disk.
+
+        For further information, see `memmap`.
+
+        Parameters
+        ----------
+        None
+
+        See Also
+        --------
+        memmap
+
+        """
+        if self.base is not None and hasattr(self.base, 'flush'):
+            self.base.flush()
+
+    def __array_wrap__(self, arr, context=None):
+        arr = super().__array_wrap__(arr, context)
+
+        # Return a memmap if a memmap was given as the output of the
+        # ufunc. Leave the arr class unchanged if self is not a memmap
+        # to keep original memmap subclasses behavior
+        if self is arr or type(self) is not memmap:
+            return arr
+        # Return scalar instead of 0d memmap, e.g. for np.sum with
+        # axis=None
+        if arr.shape == ():
+            return arr[()]
+        # Return ndarray otherwise
+        return arr.view(np.ndarray)
+
+    def __getitem__(self, index):
+        res = super().__getitem__(index)
+        if type(res) is memmap and res._mmap is None:
+            return res.view(type=ndarray)
+        return res
diff --git a/shared/package-files/c9/c96af71947ed917f7f159d74a619ff579496cb581bce96699d2a95420aa5870a/random.py b/shared/package-files/c9/c96af71947ed917f7f159d74a619ff579496cb581bce96699d2a95420aa5870a/random.py
new file mode 100644
index 0000000000000000000000000000000000000000..b10a4c667c8c6376dfef7663c2b18d91e6582d99
--- /dev/null
+++ b/shared/package-files/c9/c96af71947ed917f7f159d74a619ff579496cb581bce96699d2a95420aa5870a/random.py
@@ -0,0 +1,191 @@
+# mypy: ignore-errors
+
+"""Wrapper to mimic (parts of) np.random API surface.
+
+NumPy has strict guarantees on reproducibility etc; here we don't give any.
+
+Q: default dtype is float64 in numpy
+
+"""
+from __future__ import annotations
+
+import functools
+from math import sqrt
+from typing import Optional
+
+import torch
+
+from . import _dtypes_impl, _util
+from ._normalizations import array_or_scalar, ArrayLike, normalizer
+
+
+__all__ = [
+    "seed",
+    "random_sample",
+    "sample",
+    "random",
+    "rand",
+    "randn",
+    "normal",
+    "choice",
+    "randint",
+    "shuffle",
+    "uniform",
+]
+
+
+def use_numpy_random():
+    # local import to avoid ref cycles
+    import torch._dynamo.config as config
+
+    return config.use_numpy_random_stream
+
+
+def deco_stream(func):
+    @functools.wraps(func)
+    def inner(*args, **kwds):
+        if not use_numpy_random():
+            return func(*args, **kwds)
+        else:
+            import numpy
+
+            from ._ndarray import ndarray
+
+            f = getattr(numpy.random, func.__name__)
+
+            # numpy funcs accept numpy ndarrays, unwrap
+            args = tuple(
+                arg.tensor.numpy() if isinstance(arg, ndarray) else arg for arg in args
+            )
+            kwds = {
+                key: val.tensor.numpy() if isinstance(val, ndarray) else val
+                for key, val in kwds.items()
+            }
+
+            value = f(*args, **kwds)
+
+            # `value` can be either numpy.ndarray or python scalar (or None)
+            if isinstance(value, numpy.ndarray):
+                value = ndarray(torch.as_tensor(value))
+
+            return value
+
+    return inner
+
+
+@deco_stream
+def seed(seed=None):
+    if seed is not None:
+        torch.random.manual_seed(seed)
+
+
+@deco_stream
+def random_sample(size=None):
+    if size is None:
+        size = ()
+    dtype = _dtypes_impl.default_dtypes().float_dtype
+    values = torch.empty(size, dtype=dtype).uniform_()
+    return array_or_scalar(values, return_scalar=size == ())
+
+
+def rand(*size):
+    if size == ():
+        size = None
+    return random_sample(size)
+
+
+sample = random_sample
+random = random_sample
+
+
+@deco_stream
+def uniform(low=0.0, high=1.0, size=None):
+    if size is None:
+        size = ()
+    dtype = _dtypes_impl.default_dtypes().float_dtype
+    values = torch.empty(size, dtype=dtype).uniform_(low, high)
+    return array_or_scalar(values, return_scalar=size == ())
+
+
+@deco_stream
+def randn(*size):
+    dtype = _dtypes_impl.default_dtypes().float_dtype
+    values = torch.randn(size, dtype=dtype)
+    return array_or_scalar(values, return_scalar=size == ())
+
+
+@deco_stream
+def normal(loc=0.0, scale=1.0, size=None):
+    if size is None:
+        size = ()
+    dtype = _dtypes_impl.default_dtypes().float_dtype
+    values = torch.empty(size, dtype=dtype).normal_(loc, scale)
+    return array_or_scalar(values, return_scalar=size == ())
+
+
+@deco_stream
+def shuffle(x):
+    # no @normalizer because we do not cast e.g. lists to tensors
+    from ._ndarray import ndarray
+
+    if isinstance(x, torch.Tensor):
+        tensor = x
+    elif isinstance(x, ndarray):
+        tensor = x.tensor
+    else:
+        raise NotImplementedError("We do not random.shuffle lists in-place")
+
+    perm = torch.randperm(tensor.shape[0])
+    xp = tensor[perm]
+    tensor.copy_(xp)
+
+
+@deco_stream
+def randint(low, high=None, size=None):
+    if size is None:
+        size = ()
+    if not isinstance(size, (tuple, list)):
+        size = (size,)
+    if high is None:
+        low, high = 0, low
+    values = torch.randint(low, high, size=size)
+    return array_or_scalar(values, int, return_scalar=size == ())
+
+
+@deco_stream
+@normalizer
+def choice(a: ArrayLike, size=None, replace=True, p: Optional[ArrayLike] = None):
+    # https://stackoverflow.com/questions/59461811/random-choice-with-pytorch
+    if a.numel() == 1:
+        a = torch.arange(a)
+
+    # TODO: check a.dtype is integer -- cf np.random.choice(3.4) which raises
+
+    # number of draws
+    if size is None:
+        num_el = 1
+    elif _util.is_sequence(size):
+        num_el = 1
+        for el in size:
+            num_el *= el
+    else:
+        num_el = size
+
+    # prepare the probabilities
+    if p is None:
+        p = torch.ones_like(a) / a.shape[0]
+
+    # cf https://github.com/numpy/numpy/blob/main/numpy/random/mtrand.pyx#L973
+    atol = sqrt(torch.finfo(p.dtype).eps)
+    if abs(p.sum() - 1.0) > atol:
+        raise ValueError("probabilities do not sum to 1.")
+
+    # actually sample
+    indices = torch.multinomial(p, num_el, replacement=replace)
+
+    if _util.is_sequence(size):
+        indices = indices.reshape(size)
+
+    samples = a[indices]
+
+    return samples
diff --git a/shared/package-files/c9/c96d3ac0cacf947ccb3282e8e8ac8be780516bbae78f35e20f9f744a4f7adff6/match_utils.py b/shared/package-files/c9/c96d3ac0cacf947ccb3282e8e8ac8be780516bbae78f35e20f9f744a4f7adff6/match_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0f7a9f6293d9aa935eaf117377630e32386f5c1
--- /dev/null
+++ b/shared/package-files/c9/c96d3ac0cacf947ccb3282e8e8ac8be780516bbae78f35e20f9f744a4f7adff6/match_utils.py
@@ -0,0 +1,59 @@
+"""
+Contains utility functions to check if a pattern is in the graph and return the matching nodes
+"""
+import torch
+from torch import nn
+from torch.ao.quantization.utils import (
+    MatchAllNode,
+)
+from torch.fx import Node
+from torch.nn.utils import parametrize
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+def _match(modules: Dict[str, nn.ModuleDict], node: Node, current: Union[nn.Module, Any]) -> bool:
+    r"""
+    checks to see if a single node of a pattern matches
+    """
+    if isinstance(current, type) and issubclass(current, MatchAllNode):
+        return True
+    if not isinstance(node, Node):
+        return False
+    if isinstance(current, type) and issubclass(current, torch.nn.Module):
+        return (
+            node.op == "call_module"
+            and parametrize.type_before_parametrizations(modules[node.target])
+            == current
+        )
+    elif callable(current):
+        return node.op == "call_function" and node.target is current
+    elif isinstance(current, str):
+        return node.target == current
+    return False
+
+def apply_match(
+    modules: Dict[str, nn.ModuleDict],
+    pattern: Union[Tuple[Any], Any],
+    node: Node,
+    matched_node_pattern: List[Node],
+) -> Optional[List[Node]]:
+    r"""
+    This function will return the matched nodes if the pattern matches the node given
+    If there is no match, it will return None
+    """
+    if isinstance(pattern, tuple):
+        if len(pattern) == 1:
+            if _match(modules, node, pattern[0]):
+                return matched_node_pattern + [node]
+
+        first, *rest = pattern
+        if _match(modules, node, first):
+            if rest is None:
+                return matched_node_pattern + [node]
+
+            for user in node.users:
+                return apply_match(
+                    modules, tuple(rest), user, matched_node_pattern + [node]
+                )
+    elif _match(modules, node, pattern):
+        return [node]
+    return None
diff --git a/shared/package-files/c9/c972be66b50ec59717759f6bb48360465ee5e36ead768721f06c99212fe708c2/c_parser_wrapper.py b/shared/package-files/c9/c972be66b50ec59717759f6bb48360465ee5e36ead768721f06c99212fe708c2/c_parser_wrapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cd788c5e57399597e3fe4ee1b1bf2af4bffd74b
--- /dev/null
+++ b/shared/package-files/c9/c972be66b50ec59717759f6bb48360465ee5e36ead768721f06c99212fe708c2/c_parser_wrapper.py
@@ -0,0 +1,410 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from typing import TYPE_CHECKING
+import warnings
+
+import numpy as np
+
+from pandas._libs import (
+    lib,
+    parsers,
+)
+from pandas.compat._optional import import_optional_dependency
+from pandas.errors import DtypeWarning
+from pandas.util._exceptions import find_stack_level
+
+from pandas.core.dtypes.common import pandas_dtype
+from pandas.core.dtypes.concat import (
+    concat_compat,
+    union_categoricals,
+)
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+from pandas.core.indexes.api import ensure_index_from_sequences
+
+from pandas.io.common import (
+    dedup_names,
+    is_potential_multi_index,
+)
+from pandas.io.parsers.base_parser import (
+    ParserBase,
+    ParserError,
+    is_index_col,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import (
+        Hashable,
+        Mapping,
+        Sequence,
+    )
+
+    from pandas._typing import (
+        ArrayLike,
+        DtypeArg,
+        DtypeObj,
+        ReadCsvBuffer,
+    )
+
+    from pandas import (
+        Index,
+        MultiIndex,
+    )
+
+
+class CParserWrapper(ParserBase):
+    low_memory: bool
+    _reader: parsers.TextReader
+
+    def __init__(self, src: ReadCsvBuffer[str], **kwds) -> None:
+        super().__init__(kwds)
+        self.kwds = kwds
+        kwds = kwds.copy()
+
+        self.low_memory = kwds.pop("low_memory", False)
+
+        # #2442
+        # error: Cannot determine type of 'index_col'
+        kwds["allow_leading_cols"] = (
+            self.index_col is not False  # type: ignore[has-type]
+        )
+
+        # GH20529, validate usecol arg before TextReader
+        kwds["usecols"] = self.usecols
+
+        # Have to pass int, would break tests using TextReader directly otherwise :(
+        kwds["on_bad_lines"] = self.on_bad_lines.value
+
+        for key in (
+            "storage_options",
+            "encoding",
+            "memory_map",
+            "compression",
+        ):
+            kwds.pop(key, None)
+
+        kwds["dtype"] = ensure_dtype_objs(kwds.get("dtype", None))
+        if "dtype_backend" not in kwds or kwds["dtype_backend"] is lib.no_default:
+            kwds["dtype_backend"] = "numpy"
+        if kwds["dtype_backend"] == "pyarrow":
+            # Fail here loudly instead of in cython after reading
+            import_optional_dependency("pyarrow")
+        self._reader = parsers.TextReader(src, **kwds)
+
+        self.unnamed_cols = self._reader.unnamed_cols
+
+        # error: Cannot determine type of 'names'
+        passed_names = self.names is None  # type: ignore[has-type]
+
+        if self._reader.header is None:
+            self.names = None
+        else:
+            # error: Cannot determine type of 'names'
+            # error: Cannot determine type of 'index_names'
+            (
+                self.names,  # type: ignore[has-type]
+                self.index_names,
+                self.col_names,
+                passed_names,
+            ) = self._extract_multi_indexer_columns(
+                self._reader.header,
+                self.index_names,  # type: ignore[has-type]
+                passed_names,
+            )
+
+        # error: Cannot determine type of 'names'
+        if self.names is None:  # type: ignore[has-type]
+            self.names = list(range(self._reader.table_width))
+
+        # gh-9755
+        #
+        # need to set orig_names here first
+        # so that proper indexing can be done
+        # with _set_noconvert_columns
+        #
+        # once names has been filtered, we will
+        # then set orig_names again to names
+        # error: Cannot determine type of 'names'
+        self.orig_names = self.names[:]  # type: ignore[has-type]
+
+        if self.usecols:
+            usecols = self._evaluate_usecols(self.usecols, self.orig_names)
+
+            # GH 14671
+            # assert for mypy, orig_names is List or None, None would error in issubset
+            assert self.orig_names is not None
+            if self.usecols_dtype == "string" and not set(usecols).issubset(
+                self.orig_names
+            ):
+                self._validate_usecols_names(usecols, self.orig_names)
+
+            # error: Cannot determine type of 'names'
+            if len(self.names) > len(usecols):  # type: ignore[has-type]
+                # error: Cannot determine type of 'names'
+                self.names = [  # type: ignore[has-type]
+                    n
+                    # error: Cannot determine type of 'names'
+                    for i, n in enumerate(self.names)  # type: ignore[has-type]
+                    if (i in usecols or n in usecols)
+                ]
+
+            # error: Cannot determine type of 'names'
+            if len(self.names) < len(usecols):  # type: ignore[has-type]
+                # error: Cannot determine type of 'names'
+                self._validate_usecols_names(
+                    usecols,
+                    self.names,  # type: ignore[has-type]
+                )
+
+        # error: Cannot determine type of 'names'
+        self._validate_parse_dates_presence(self.names)  # type: ignore[has-type]
+        self._set_noconvert_columns()
+
+        # error: Cannot determine type of 'names'
+        self.orig_names = self.names  # type: ignore[has-type]
+
+        if not self._has_complex_date_col:
+            # error: Cannot determine type of 'index_col'
+            if self._reader.leading_cols == 0 and is_index_col(
+                self.index_col  # type: ignore[has-type]
+            ):
+                self._name_processed = True
+                (
+                    index_names,
+                    # error: Cannot determine type of 'names'
+                    self.names,  # type: ignore[has-type]
+                    self.index_col,
+                ) = self._clean_index_names(
+                    # error: Cannot determine type of 'names'
+                    self.names,  # type: ignore[has-type]
+                    # error: Cannot determine type of 'index_col'
+                    self.index_col,  # type: ignore[has-type]
+                )
+
+                if self.index_names is None:
+                    self.index_names = index_names
+
+            if self._reader.header is None and not passed_names:
+                assert self.index_names is not None
+                self.index_names = [None] * len(self.index_names)
+
+        self._implicit_index = self._reader.leading_cols > 0
+
+    def close(self) -> None:
+        # close handles opened by C parser
+        try:
+            self._reader.close()
+        except ValueError:
+            pass
+
+    def _set_noconvert_columns(self) -> None:
+        """
+        Set the columns that should not undergo dtype conversions.
+
+        Currently, any column that is involved with date parsing will not
+        undergo such conversions.
+        """
+        assert self.orig_names is not None
+        # error: Cannot determine type of 'names'
+
+        # much faster than using orig_names.index(x) xref GH#44106
+        names_dict = {x: i for i, x in enumerate(self.orig_names)}
+        col_indices = [names_dict[x] for x in self.names]  # type: ignore[has-type]
+        # error: Cannot determine type of 'names'
+        noconvert_columns = self._set_noconvert_dtype_columns(
+            col_indices,
+            self.names,  # type: ignore[has-type]
+        )
+        for col in noconvert_columns:
+            self._reader.set_noconvert(col)
+
+    def read(
+        self,
+        nrows: int | None = None,
+    ) -> tuple[
+        Index | MultiIndex | None,
+        Sequence[Hashable] | MultiIndex,
+        Mapping[Hashable, ArrayLike],
+    ]:
+        index: Index | MultiIndex | None
+        column_names: Sequence[Hashable] | MultiIndex
+        try:
+            if self.low_memory:
+                chunks = self._reader.read_low_memory(nrows)
+                # destructive to chunks
+                data = _concatenate_chunks(chunks)
+
+            else:
+                data = self._reader.read(nrows)
+        except StopIteration:
+            if self._first_chunk:
+                self._first_chunk = False
+                names = dedup_names(
+                    self.orig_names,
+                    is_potential_multi_index(self.orig_names, self.index_col),
+                )
+                index, columns, col_dict = self._get_empty_meta(
+                    names,
+                    dtype=self.dtype,
+                )
+                columns = self._maybe_make_multi_index_columns(columns, self.col_names)
+
+                if self.usecols is not None:
+                    columns = self._filter_usecols(columns)
+
+                col_dict = {k: v for k, v in col_dict.items() if k in columns}
+
+                return index, columns, col_dict
+
+            else:
+                self.close()
+                raise
+
+        # Done with first read, next time raise StopIteration
+        self._first_chunk = False
+
+        # error: Cannot determine type of 'names'
+        names = self.names  # type: ignore[has-type]
+
+        if self._reader.leading_cols:
+            if self._has_complex_date_col:
+                raise NotImplementedError("file structure not yet supported")
+
+            # implicit index, no index names
+            arrays = []
+
+            if self.index_col and self._reader.leading_cols != len(self.index_col):
+                raise ParserError(
+                    "Could not construct index. Requested to use "
+                    f"{len(self.index_col)} number of columns, but "
+                    f"{self._reader.leading_cols} left to parse."
+                )
+
+            for i in range(self._reader.leading_cols):
+                if self.index_col is None:
+                    values = data.pop(i)
+                else:
+                    values = data.pop(self.index_col[i])
+
+                values = self._maybe_parse_dates(values, i, try_parse_dates=True)
+                arrays.append(values)
+
+            index = ensure_index_from_sequences(arrays)
+
+            if self.usecols is not None:
+                names = self._filter_usecols(names)
+
+            names = dedup_names(names, is_potential_multi_index(names, self.index_col))
+
+            # rename dict keys
+            data_tups = sorted(data.items())
+            data = {k: v for k, (i, v) in zip(names, data_tups)}
+
+            column_names, date_data = self._do_date_conversions(names, data)
+
+            # maybe create a mi on the columns
+            column_names = self._maybe_make_multi_index_columns(
+                column_names, self.col_names
+            )
+
+        else:
+            # rename dict keys
+            data_tups = sorted(data.items())
+
+            # ugh, mutation
+
+            # assert for mypy, orig_names is List or None, None would error in list(...)
+            assert self.orig_names is not None
+            names = list(self.orig_names)
+            names = dedup_names(names, is_potential_multi_index(names, self.index_col))
+
+            if self.usecols is not None:
+                names = self._filter_usecols(names)
+
+            # columns as list
+            alldata = [x[1] for x in data_tups]
+            if self.usecols is None:
+                self._check_data_length(names, alldata)
+
+            data = {k: v for k, (i, v) in zip(names, data_tups)}
+
+            names, date_data = self._do_date_conversions(names, data)
+            index, column_names = self._make_index(date_data, alldata, names)
+
+        return index, column_names, date_data
+
+    def _filter_usecols(self, names: Sequence[Hashable]) -> Sequence[Hashable]:
+        # hackish
+        usecols = self._evaluate_usecols(self.usecols, names)
+        if usecols is not None and len(names) != len(usecols):
+            names = [
+                name for i, name in enumerate(names) if i in usecols or name in usecols
+            ]
+        return names
+
+    def _maybe_parse_dates(self, values, index: int, try_parse_dates: bool = True):
+        if try_parse_dates and self._should_parse_dates(index):
+            values = self._date_conv(
+                values,
+                col=self.index_names[index] if self.index_names is not None else None,
+            )
+        return values
+
+
+def _concatenate_chunks(chunks: list[dict[int, ArrayLike]]) -> dict:
+    """
+    Concatenate chunks of data read with low_memory=True.
+
+    The tricky part is handling Categoricals, where different chunks
+    may have different inferred categories.
+    """
+    names = list(chunks[0].keys())
+    warning_columns = []
+
+    result: dict = {}
+    for name in names:
+        arrs = [chunk.pop(name) for chunk in chunks]
+        # Check each arr for consistent types.
+        dtypes = {a.dtype for a in arrs}
+        non_cat_dtypes = {x for x in dtypes if not isinstance(x, CategoricalDtype)}
+
+        dtype = dtypes.pop()
+        if isinstance(dtype, CategoricalDtype):
+            result[name] = union_categoricals(arrs, sort_categories=False)
+        else:
+            result[name] = concat_compat(arrs)
+            if len(non_cat_dtypes) > 1 and result[name].dtype == np.dtype(object):
+                warning_columns.append(str(name))
+
+    if warning_columns:
+        warning_names = ",".join(warning_columns)
+        warning_message = " ".join(
+            [
+                f"Columns ({warning_names}) have mixed types. "
+                f"Specify dtype option on import or set low_memory=False."
+            ]
+        )
+        warnings.warn(warning_message, DtypeWarning, stacklevel=find_stack_level())
+    return result
+
+
+def ensure_dtype_objs(
+    dtype: DtypeArg | dict[Hashable, DtypeArg] | None
+) -> DtypeObj | dict[Hashable, DtypeObj] | None:
+    """
+    Ensure we have either None, a dtype object, or a dictionary mapping to
+    dtype objects.
+    """
+    if isinstance(dtype, defaultdict):
+        # "None" not callable  [misc]
+        default_dtype = pandas_dtype(dtype.default_factory())  # type: ignore[misc]
+        dtype_converted: defaultdict = defaultdict(lambda: default_dtype)
+        for key in dtype.keys():
+            dtype_converted[key] = pandas_dtype(dtype[key])
+        return dtype_converted
+    elif isinstance(dtype, dict):
+        return {k: pandas_dtype(dtype[k]) for k in dtype}
+    elif dtype is not None:
+        return pandas_dtype(dtype)
+    return dtype
diff --git a/shared/package-files/c9/c975a5594b302e8fe90db2a6858f0e90148f7c2ad15e5536a26d906f04c01c85/test_matfuncs.py b/shared/package-files/c9/c975a5594b302e8fe90db2a6858f0e90148f7c2ad15e5536a26d906f04c01c85/test_matfuncs.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e87333c40b64d3f54024779dfb959be7a599178
--- /dev/null
+++ b/shared/package-files/c9/c975a5594b302e8fe90db2a6858f0e90148f7c2ad15e5536a26d906f04c01c85/test_matfuncs.py
@@ -0,0 +1,1063 @@
+#
+# Created by: Pearu Peterson, March 2002
+#
+""" Test functions for linalg.matfuncs module
+
+"""
+import functools
+
+import numpy as np
+from numpy import array, identity, dot, sqrt
+from numpy.testing import (assert_array_almost_equal, assert_allclose, assert_,
+                           assert_array_less, assert_array_equal, assert_warns)
+import pytest
+
+import scipy.linalg
+from scipy.linalg import (funm, signm, logm, sqrtm, fractional_matrix_power,
+                          expm, expm_frechet, expm_cond, norm, khatri_rao,
+                          cosm, sinm, tanm, coshm, sinhm, tanhm)
+from scipy.linalg import _matfuncs_inv_ssq
+from scipy.linalg._matfuncs import pick_pade_structure
+from scipy.linalg._matfuncs_inv_ssq import LogmExactlySingularWarning
+import scipy.linalg._expm_frechet
+
+from scipy.optimize import minimize
+
+
+def _get_al_mohy_higham_2012_experiment_1():
+    """
+    Return the test matrix from Experiment (1) of [1]_.
+
+    References
+    ----------
+    .. [1] Awad H. Al-Mohy and Nicholas J. Higham (2012)
+           "Improved Inverse Scaling and Squaring Algorithms
+           for the Matrix Logarithm."
+           SIAM Journal on Scientific Computing, 34 (4). C152-C169.
+           ISSN 1095-7197
+
+    """
+    A = np.array([
+        [3.2346e-1, 3e4, 3e4, 3e4],
+        [0, 3.0089e-1, 3e4, 3e4],
+        [0, 0, 3.2210e-1, 3e4],
+        [0, 0, 0, 3.0744e-1]], dtype=float)
+    return A
+
+
+class TestSignM:
+
+    def test_nils(self):
+        a = array([[29.2, -24.2, 69.5, 49.8, 7.],
+                   [-9.2, 5.2, -18., -16.8, -2.],
+                   [-10., 6., -20., -18., -2.],
+                   [-9.6, 9.6, -25.5, -15.4, -2.],
+                   [9.8, -4.8, 18., 18.2, 2.]])
+        cr = array([[11.94933333,-2.24533333,15.31733333,21.65333333,-2.24533333],
+                    [-3.84266667,0.49866667,-4.59066667,-7.18666667,0.49866667],
+                    [-4.08,0.56,-4.92,-7.6,0.56],
+                    [-4.03466667,1.04266667,-5.59866667,-7.02666667,1.04266667],
+                    [4.15733333,-0.50133333,4.90933333,7.81333333,-0.50133333]])
+        r = signm(a)
+        assert_array_almost_equal(r,cr)
+
+    def test_defective1(self):
+        a = array([[0.0,1,0,0],[1,0,1,0],[0,0,0,1],[0,0,1,0]])
+        signm(a, disp=False)
+        #XXX: what would be the correct result?
+
+    def test_defective2(self):
+        a = array((
+            [29.2,-24.2,69.5,49.8,7.0],
+            [-9.2,5.2,-18.0,-16.8,-2.0],
+            [-10.0,6.0,-20.0,-18.0,-2.0],
+            [-9.6,9.6,-25.5,-15.4,-2.0],
+            [9.8,-4.8,18.0,18.2,2.0]))
+        signm(a, disp=False)
+        #XXX: what would be the correct result?
+
+    def test_defective3(self):
+        a = array([[-2., 25., 0., 0., 0., 0., 0.],
+                   [0., -3., 10., 3., 3., 3., 0.],
+                   [0., 0., 2., 15., 3., 3., 0.],
+                   [0., 0., 0., 0., 15., 3., 0.],
+                   [0., 0., 0., 0., 3., 10., 0.],
+                   [0., 0., 0., 0., 0., -2., 25.],
+                   [0., 0., 0., 0., 0., 0., -3.]])
+        signm(a, disp=False)
+        #XXX: what would be the correct result?
+
+
+class TestLogM:
+
+    def test_nils(self):
+        a = array([[-2., 25., 0., 0., 0., 0., 0.],
+                   [0., -3., 10., 3., 3., 3., 0.],
+                   [0., 0., 2., 15., 3., 3., 0.],
+                   [0., 0., 0., 0., 15., 3., 0.],
+                   [0., 0., 0., 0., 3., 10., 0.],
+                   [0., 0., 0., 0., 0., -2., 25.],
+                   [0., 0., 0., 0., 0., 0., -3.]])
+        m = (identity(7)*3.1+0j)-a
+        logm(m, disp=False)
+        #XXX: what would be the correct result?
+
+    def test_al_mohy_higham_2012_experiment_1_logm(self):
+        # The logm completes the round trip successfully.
+        # Note that the expm leg of the round trip is badly conditioned.
+        A = _get_al_mohy_higham_2012_experiment_1()
+        A_logm, info = logm(A, disp=False)
+        A_round_trip = expm(A_logm)
+        assert_allclose(A_round_trip, A, rtol=5e-5, atol=1e-14)
+
+    def test_al_mohy_higham_2012_experiment_1_funm_log(self):
+        # The raw funm with np.log does not complete the round trip.
+        # Note that the expm leg of the round trip is badly conditioned.
+        A = _get_al_mohy_higham_2012_experiment_1()
+        A_funm_log, info = funm(A, np.log, disp=False)
+        A_round_trip = expm(A_funm_log)
+        assert_(not np.allclose(A_round_trip, A, rtol=1e-5, atol=1e-14))
+
+    def test_round_trip_random_float(self):
+        np.random.seed(1234)
+        for n in range(1, 6):
+            M_unscaled = np.random.randn(n, n)
+            for scale in np.logspace(-4, 4, 9):
+                M = M_unscaled * scale
+
+                # Eigenvalues are related to the branch cut.
+                W = np.linalg.eigvals(M)
+                err_msg = f'M:{M} eivals:{W}'
+
+                # Check sqrtm round trip because it is used within logm.
+                M_sqrtm, info = sqrtm(M, disp=False)
+                M_sqrtm_round_trip = M_sqrtm.dot(M_sqrtm)
+                assert_allclose(M_sqrtm_round_trip, M)
+
+                # Check logm round trip.
+                M_logm, info = logm(M, disp=False)
+                M_logm_round_trip = expm(M_logm)
+                assert_allclose(M_logm_round_trip, M, err_msg=err_msg)
+
+    def test_round_trip_random_complex(self):
+        np.random.seed(1234)
+        for n in range(1, 6):
+            M_unscaled = np.random.randn(n, n) + 1j * np.random.randn(n, n)
+            for scale in np.logspace(-4, 4, 9):
+                M = M_unscaled * scale
+                M_logm, info = logm(M, disp=False)
+                M_round_trip = expm(M_logm)
+                assert_allclose(M_round_trip, M)
+
+    def test_logm_type_preservation_and_conversion(self):
+        # The logm matrix function should preserve the type of a matrix
+        # whose eigenvalues are positive with zero imaginary part.
+        # Test this preservation for variously structured matrices.
+        complex_dtype_chars = ('F', 'D', 'G')
+        for matrix_as_list in (
+                [[1, 0], [0, 1]],
+                [[1, 0], [1, 1]],
+                [[2, 1], [1, 1]],
+                [[2, 3], [1, 2]]):
+
+            # check that the spectrum has the expected properties
+            W = scipy.linalg.eigvals(matrix_as_list)
+            assert_(not any(w.imag or w.real < 0 for w in W))
+
+            # check float type preservation
+            A = np.array(matrix_as_list, dtype=float)
+            A_logm, info = logm(A, disp=False)
+            assert_(A_logm.dtype.char not in complex_dtype_chars)
+
+            # check complex type preservation
+            A = np.array(matrix_as_list, dtype=complex)
+            A_logm, info = logm(A, disp=False)
+            assert_(A_logm.dtype.char in complex_dtype_chars)
+
+            # check float->complex type conversion for the matrix negation
+            A = -np.array(matrix_as_list, dtype=float)
+            A_logm, info = logm(A, disp=False)
+            assert_(A_logm.dtype.char in complex_dtype_chars)
+
+    def test_complex_spectrum_real_logm(self):
+        # This matrix has complex eigenvalues and real logm.
+        # Its output dtype depends on its input dtype.
+        M = [[1, 1, 2], [2, 1, 1], [1, 2, 1]]
+        for dt in float, complex:
+            X = np.array(M, dtype=dt)
+            w = scipy.linalg.eigvals(X)
+            assert_(1e-2 < np.absolute(w.imag).sum())
+            Y, info = logm(X, disp=False)
+            assert_(np.issubdtype(Y.dtype, np.inexact))
+            assert_allclose(expm(Y), X)
+
+    def test_real_mixed_sign_spectrum(self):
+        # These matrices have real eigenvalues with mixed signs.
+        # The output logm dtype is complex, regardless of input dtype.
+        for M in (
+                [[1, 0], [0, -1]],
+                [[0, 1], [1, 0]]):
+            for dt in float, complex:
+                A = np.array(M, dtype=dt)
+                A_logm, info = logm(A, disp=False)
+                assert_(np.issubdtype(A_logm.dtype, np.complexfloating))
+
+    @pytest.mark.thread_unsafe
+    def test_exactly_singular(self):
+        A = np.array([[0, 0], [1j, 1j]])
+        B = np.asarray([[1, 1], [0, 0]])
+        for M in A, A.T, B, B.T:
+            expected_warning = _matfuncs_inv_ssq.LogmExactlySingularWarning
+            L, info = assert_warns(expected_warning, logm, M, disp=False)
+            E = expm(L)
+            assert_allclose(E, M, atol=1e-14)
+
+    @pytest.mark.thread_unsafe
+    def test_nearly_singular(self):
+        M = np.array([[1e-100]])
+        expected_warning = _matfuncs_inv_ssq.LogmNearlySingularWarning
+        L, info = assert_warns(expected_warning, logm, M, disp=False)
+        E = expm(L)
+        assert_allclose(E, M, atol=1e-14)
+
+    def test_opposite_sign_complex_eigenvalues(self):
+        # See gh-6113
+        E = [[0, 1], [-1, 0]]
+        L = [[0, np.pi*0.5], [-np.pi*0.5, 0]]
+        assert_allclose(expm(L), E, atol=1e-14)
+        assert_allclose(logm(E), L, atol=1e-14)
+        E = [[1j, 4], [0, -1j]]
+        L = [[1j*np.pi*0.5, 2*np.pi], [0, -1j*np.pi*0.5]]
+        assert_allclose(expm(L), E, atol=1e-14)
+        assert_allclose(logm(E), L, atol=1e-14)
+        E = [[1j, 0], [0, -1j]]
+        L = [[1j*np.pi*0.5, 0], [0, -1j*np.pi*0.5]]
+        assert_allclose(expm(L), E, atol=1e-14)
+        assert_allclose(logm(E), L, atol=1e-14)
+
+    def test_readonly(self):
+        n = 5
+        a = np.ones((n, n)) + np.identity(n)
+        a.flags.writeable = False
+        logm(a)
+
+    @pytest.mark.xfail(reason="ValueError: attempt to get argmax of an empty sequence")
+    @pytest.mark.parametrize('dt', [int, float, np.float32, complex, np.complex64])
+    def test_empty(self, dt):
+        a = np.empty((0, 0), dtype=dt)
+        log_a = logm(a)
+        a0 = np.eye(2, dtype=dt)
+        log_a0 = logm(a0)
+
+        assert log_a.shape == (0, 0)
+        assert log_a.dtype == log_a0.dtype
+
+    @pytest.mark.thread_unsafe
+    @pytest.mark.parametrize('dtype', [int, float, np.float32, complex, np.complex64])
+    def test_no_ZeroDivisionError(self, dtype):
+        # gh-17136 reported inconsistent behavior in `logm` depending on input dtype:
+        # sometimes it raised an error, and sometimes it printed a warning message.
+        # check that this is resolved and that the warning is emitted properly.
+        with (pytest.warns(RuntimeWarning, match="logm result may be inaccurate"),
+              pytest.warns(LogmExactlySingularWarning)):
+            logm(np.zeros((2, 2), dtype=dtype))
+
+
+class TestSqrtM:
+    def test_round_trip_random_float(self):
+        rng = np.random.RandomState(1234)
+        for n in range(1, 6):
+            M_unscaled = rng.randn(n, n)
+            for scale in np.logspace(-4, 4, 9):
+                M = M_unscaled * scale
+                M_sqrtm, info = sqrtm(M, disp=False)
+                M_sqrtm_round_trip = M_sqrtm.dot(M_sqrtm)
+                assert_allclose(M_sqrtm_round_trip, M)
+
+    def test_round_trip_random_complex(self):
+        rng = np.random.RandomState(1234)
+        for n in range(1, 6):
+            M_unscaled = rng.randn(n, n) + 1j * rng.randn(n, n)
+            for scale in np.logspace(-4, 4, 9):
+                M = M_unscaled * scale
+                M_sqrtm, info = sqrtm(M, disp=False)
+                M_sqrtm_round_trip = M_sqrtm.dot(M_sqrtm)
+                assert_allclose(M_sqrtm_round_trip, M)
+
+    def test_bad(self):
+        # See https://web.archive.org/web/20051220232650/http://www.maths.man.ac.uk/~nareports/narep336.ps.gz
+        e = 2**-5
+        se = sqrt(e)
+        a = array([[1.0,0,0,1],
+                   [0,e,0,0],
+                   [0,0,e,0],
+                   [0,0,0,1]])
+        sa = array([[1,0,0,0.5],
+                    [0,se,0,0],
+                    [0,0,se,0],
+                    [0,0,0,1]])
+        n = a.shape[0]
+        assert_array_almost_equal(dot(sa,sa),a)
+        # Check default sqrtm.
+        esa = sqrtm(a, disp=False, blocksize=n)[0]
+        assert_array_almost_equal(dot(esa,esa),a)
+        # Check sqrtm with 2x2 blocks.
+        esa = sqrtm(a, disp=False, blocksize=2)[0]
+        assert_array_almost_equal(dot(esa,esa),a)
+
+    def test_sqrtm_type_preservation_and_conversion(self):
+        # The sqrtm matrix function should preserve the type of a matrix
+        # whose eigenvalues are nonnegative with zero imaginary part.
+        # Test this preservation for variously structured matrices.
+        complex_dtype_chars = ('F', 'D', 'G')
+        for matrix_as_list in (
+                [[1, 0], [0, 1]],
+                [[1, 0], [1, 1]],
+                [[2, 1], [1, 1]],
+                [[2, 3], [1, 2]],
+                [[1, 1], [1, 1]]):
+
+            # check that the spectrum has the expected properties
+            W = scipy.linalg.eigvals(matrix_as_list)
+            assert_(not any(w.imag or w.real < 0 for w in W))
+
+            # check float type preservation
+            A = np.array(matrix_as_list, dtype=float)
+            A_sqrtm, info = sqrtm(A, disp=False)
+            assert_(A_sqrtm.dtype.char not in complex_dtype_chars)
+
+            # check complex type preservation
+            A = np.array(matrix_as_list, dtype=complex)
+            A_sqrtm, info = sqrtm(A, disp=False)
+            assert_(A_sqrtm.dtype.char in complex_dtype_chars)
+
+            # check float->complex type conversion for the matrix negation
+            A = -np.array(matrix_as_list, dtype=float)
+            A_sqrtm, info = sqrtm(A, disp=False)
+            assert_(A_sqrtm.dtype.char in complex_dtype_chars)
+
+    def test_sqrtm_type_conversion_mixed_sign_or_complex_spectrum(self):
+        complex_dtype_chars = ('F', 'D', 'G')
+        for matrix_as_list in (
+                [[1, 0], [0, -1]],
+                [[0, 1], [1, 0]],
+                [[0, 1, 0], [0, 0, 1], [1, 0, 0]]):
+
+            # check that the spectrum has the expected properties
+            W = scipy.linalg.eigvals(matrix_as_list)
+            assert_(any(w.imag or w.real < 0 for w in W))
+
+            # check complex->complex
+            A = np.array(matrix_as_list, dtype=complex)
+            A_sqrtm, info = sqrtm(A, disp=False)
+            assert_(A_sqrtm.dtype.char in complex_dtype_chars)
+
+            # check float->complex
+            A = np.array(matrix_as_list, dtype=float)
+            A_sqrtm, info = sqrtm(A, disp=False)
+            assert_(A_sqrtm.dtype.char in complex_dtype_chars)
+
+    def test_blocksizes(self):
+        # Make sure I do not goof up the blocksizes when they do not divide n.
+        np.random.seed(1234)
+        for n in range(1, 8):
+            A = np.random.rand(n, n) + 1j*np.random.randn(n, n)
+            A_sqrtm_default, info = sqrtm(A, disp=False, blocksize=n)
+            assert_allclose(A, np.linalg.matrix_power(A_sqrtm_default, 2))
+            for blocksize in range(1, 10):
+                A_sqrtm_new, info = sqrtm(A, disp=False, blocksize=blocksize)
+                assert_allclose(A_sqrtm_default, A_sqrtm_new)
+
+    def test_al_mohy_higham_2012_experiment_1(self):
+        # Matrix square root of a tricky upper triangular matrix.
+        A = _get_al_mohy_higham_2012_experiment_1()
+        A_sqrtm, info = sqrtm(A, disp=False)
+        A_round_trip = A_sqrtm.dot(A_sqrtm)
+        assert_allclose(A_round_trip, A, rtol=1e-5)
+        assert_allclose(np.tril(A_round_trip), np.tril(A))
+
+    def test_strict_upper_triangular(self):
+        # This matrix has no square root.
+        for dt in int, float:
+            A = np.array([
+                [0, 3, 0, 0],
+                [0, 0, 3, 0],
+                [0, 0, 0, 3],
+                [0, 0, 0, 0]], dtype=dt)
+            A_sqrtm, info = sqrtm(A, disp=False)
+            assert_(np.isnan(A_sqrtm).all())
+
+    def test_weird_matrix(self):
+        # The square root of matrix B exists.
+        for dt in int, float:
+            A = np.array([
+                [0, 0, 1],
+                [0, 0, 0],
+                [0, 1, 0]], dtype=dt)
+            B = np.array([
+                [0, 1, 0],
+                [0, 0, 0],
+                [0, 0, 0]], dtype=dt)
+            assert_array_equal(B, A.dot(A))
+
+            # But scipy sqrtm is not clever enough to find it.
+            B_sqrtm, info = sqrtm(B, disp=False)
+            assert_(np.isnan(B_sqrtm).all())
+
+    def test_disp(self):
+        np.random.seed(1234)
+
+        A = np.random.rand(3, 3)
+        B = sqrtm(A, disp=True)
+        assert_allclose(B.dot(B), A)
+
+    def test_opposite_sign_complex_eigenvalues(self):
+        M = [[2j, 4], [0, -2j]]
+        R = [[1+1j, 2], [0, 1-1j]]
+        assert_allclose(np.dot(R, R), M, atol=1e-14)
+        assert_allclose(sqrtm(M), R, atol=1e-14)
+
+    def test_gh4866(self):
+        M = np.array([[1, 0, 0, 1],
+                      [0, 0, 0, 0],
+                      [0, 0, 0, 0],
+                      [1, 0, 0, 1]])
+        R = np.array([[sqrt(0.5), 0, 0, sqrt(0.5)],
+                      [0, 0, 0, 0],
+                      [0, 0, 0, 0],
+                      [sqrt(0.5), 0, 0, sqrt(0.5)]])
+        assert_allclose(np.dot(R, R), M, atol=1e-14)
+        assert_allclose(sqrtm(M), R, atol=1e-14)
+
+    def test_gh5336(self):
+        M = np.diag([2, 1, 0])
+        R = np.diag([sqrt(2), 1, 0])
+        assert_allclose(np.dot(R, R), M, atol=1e-14)
+        assert_allclose(sqrtm(M), R, atol=1e-14)
+
+    def test_gh7839(self):
+        M = np.zeros((2, 2))
+        R = np.zeros((2, 2))
+        assert_allclose(np.dot(R, R), M, atol=1e-14)
+        assert_allclose(sqrtm(M), R, atol=1e-14)
+
+    @pytest.mark.xfail(reason="failing on macOS after gh-20212")
+    def test_gh17918(self):
+        M = np.empty((19, 19))
+        M.fill(0.94)
+        np.fill_diagonal(M, 1)
+        assert np.isrealobj(sqrtm(M))
+
+    def test_data_size_preservation_uint_in_float_out(self):
+        M = np.zeros((10, 10), dtype=np.uint8)
+        assert sqrtm(M).dtype == np.float64
+        M = np.zeros((10, 10), dtype=np.uint16)
+        assert sqrtm(M).dtype == np.float64
+        M = np.zeros((10, 10), dtype=np.uint32)
+        assert sqrtm(M).dtype == np.float64
+        M = np.zeros((10, 10), dtype=np.uint64)
+        assert sqrtm(M).dtype == np.float64
+
+    def test_data_size_preservation_int_in_float_out(self):
+        M = np.zeros((10, 10), dtype=np.int8)
+        assert sqrtm(M).dtype == np.float64
+        M = np.zeros((10, 10), dtype=np.int16)
+        assert sqrtm(M).dtype == np.float64
+        M = np.zeros((10, 10), dtype=np.int32)
+        assert sqrtm(M).dtype == np.float64
+        M = np.zeros((10, 10), dtype=np.int64)
+        assert sqrtm(M).dtype == np.float64
+
+    def test_data_size_preservation_int_in_comp_out(self):
+        M = np.array([[2, 4], [0, -2]], dtype=np.int8)
+        assert sqrtm(M).dtype == np.complex128
+        M = np.array([[2, 4], [0, -2]], dtype=np.int16)
+        assert sqrtm(M).dtype == np.complex128
+        M = np.array([[2, 4], [0, -2]], dtype=np.int32)
+        assert sqrtm(M).dtype == np.complex128
+        M = np.array([[2, 4], [0, -2]], dtype=np.int64)
+        assert sqrtm(M).dtype == np.complex128
+
+    def test_data_size_preservation_float_in_float_out(self):
+        M = np.zeros((10, 10), dtype=np.float16)
+        assert sqrtm(M).dtype == np.float32
+        M = np.zeros((10, 10), dtype=np.float32)
+        assert sqrtm(M).dtype == np.float32
+        M = np.zeros((10, 10), dtype=np.float64)
+        assert sqrtm(M).dtype == np.float64
+        if hasattr(np, 'float128'):
+            M = np.zeros((10, 10), dtype=np.float128)
+            assert sqrtm(M).dtype == np.float64
+
+    def test_data_size_preservation_float_in_comp_out(self):
+        M = np.array([[2, 4], [0, -2]], dtype=np.float16)
+        assert sqrtm(M).dtype == np.complex64
+        M = np.array([[2, 4], [0, -2]], dtype=np.float32)
+        assert sqrtm(M).dtype == np.complex64
+        M = np.array([[2, 4], [0, -2]], dtype=np.float64)
+        assert sqrtm(M).dtype == np.complex128
+        if hasattr(np, 'float128') and hasattr(np, 'complex256'):
+            M = np.array([[2, 4], [0, -2]], dtype=np.float128)
+            assert sqrtm(M).dtype == np.complex128
+
+    def test_data_size_preservation_comp_in_comp_out(self):
+        M = np.array([[2j, 4], [0, -2j]], dtype=np.complex64)
+        assert sqrtm(M).dtype == np.complex64
+        M = np.array([[2j, 4], [0, -2j]], dtype=np.complex128)
+        assert sqrtm(M).dtype == np.complex128
+        if hasattr(np, 'complex256'):
+            M = np.array([[2j, 4], [0, -2j]], dtype=np.complex256)
+            assert sqrtm(M).dtype == np.complex128
+
+    @pytest.mark.parametrize('dt', [int, float, np.float32, complex, np.complex64])
+    def test_empty(self, dt):
+        a = np.empty((0, 0), dtype=dt)
+        s = sqrtm(a)
+        a0 = np.eye(2, dtype=dt)
+        s0 = sqrtm(a0)
+
+        assert s.shape == (0, 0)
+        assert s.dtype == s0.dtype
+
+
+class TestFractionalMatrixPower:
+    def test_round_trip_random_complex(self):
+        np.random.seed(1234)
+        for p in range(1, 5):
+            for n in range(1, 5):
+                M_unscaled = np.random.randn(n, n) + 1j * np.random.randn(n, n)
+                for scale in np.logspace(-4, 4, 9):
+                    M = M_unscaled * scale
+                    M_root = fractional_matrix_power(M, 1/p)
+                    M_round_trip = np.linalg.matrix_power(M_root, p)
+                    assert_allclose(M_round_trip, M)
+
+    def test_round_trip_random_float(self):
+        # This test is more annoying because it can hit the branch cut;
+        # this happens when the matrix has an eigenvalue
+        # with no imaginary component and with a real negative component,
+        # and it means that the principal branch does not exist.
+        np.random.seed(1234)
+        for p in range(1, 5):
+            for n in range(1, 5):
+                M_unscaled = np.random.randn(n, n)
+                for scale in np.logspace(-4, 4, 9):
+                    M = M_unscaled * scale
+                    M_root = fractional_matrix_power(M, 1/p)
+                    M_round_trip = np.linalg.matrix_power(M_root, p)
+                    assert_allclose(M_round_trip, M)
+
+    def test_larger_abs_fractional_matrix_powers(self):
+        np.random.seed(1234)
+        for n in (2, 3, 5):
+            for i in range(10):
+                M = np.random.randn(n, n) + 1j * np.random.randn(n, n)
+                M_one_fifth = fractional_matrix_power(M, 0.2)
+                # Test the round trip.
+                M_round_trip = np.linalg.matrix_power(M_one_fifth, 5)
+                assert_allclose(M, M_round_trip)
+                # Test a large abs fractional power.
+                X = fractional_matrix_power(M, -5.4)
+                Y = np.linalg.matrix_power(M_one_fifth, -27)
+                assert_allclose(X, Y)
+                # Test another large abs fractional power.
+                X = fractional_matrix_power(M, 3.8)
+                Y = np.linalg.matrix_power(M_one_fifth, 19)
+                assert_allclose(X, Y)
+
+    def test_random_matrices_and_powers(self):
+        # Each independent iteration of this fuzz test picks random parameters.
+        # It tries to hit some edge cases.
+        rng = np.random.default_rng(1726500458620605)
+        nsamples = 20
+        for i in range(nsamples):
+            # Sample a matrix size and a random real power.
+            n = rng.integers(1, 5)
+            p = rng.random()
+
+            # Sample a random real or complex matrix.
+            matrix_scale = np.exp(rng.integers(-4, 5))
+            A = rng.random(size=[n, n])
+            if [True, False][rng.choice(2)]:
+                A = A + 1j * rng.random(size=[n, n])
+            A = A * matrix_scale
+
+            # Check a couple of analytically equivalent ways
+            # to compute the fractional matrix power.
+            # These can be compared because they both use the principal branch.
+            A_power = fractional_matrix_power(A, p)
+            A_logm, info = logm(A, disp=False)
+            A_power_expm_logm = expm(A_logm * p)
+            assert_allclose(A_power, A_power_expm_logm)
+
+    def test_al_mohy_higham_2012_experiment_1(self):
+        # Fractional powers of a tricky upper triangular matrix.
+        A = _get_al_mohy_higham_2012_experiment_1()
+
+        # Test remainder matrix power.
+        A_funm_sqrt, info = funm(A, np.sqrt, disp=False)
+        A_sqrtm, info = sqrtm(A, disp=False)
+        A_rem_power = _matfuncs_inv_ssq._remainder_matrix_power(A, 0.5)
+        A_power = fractional_matrix_power(A, 0.5)
+        assert_allclose(A_rem_power, A_power, rtol=1e-11)
+        assert_allclose(A_sqrtm, A_power)
+        assert_allclose(A_sqrtm, A_funm_sqrt)
+
+        # Test more fractional powers.
+        for p in (1/2, 5/3):
+            A_power = fractional_matrix_power(A, p)
+            A_round_trip = fractional_matrix_power(A_power, 1/p)
+            assert_allclose(A_round_trip, A, rtol=1e-2)
+            assert_allclose(np.tril(A_round_trip, 1), np.tril(A, 1))
+
+    def test_briggs_helper_function(self):
+        np.random.seed(1234)
+        for a in np.random.randn(10) + 1j * np.random.randn(10):
+            for k in range(5):
+                x_observed = _matfuncs_inv_ssq._briggs_helper_function(a, k)
+                x_expected = a ** np.exp2(-k) - 1
+                assert_allclose(x_observed, x_expected)
+
+    def test_type_preservation_and_conversion(self):
+        # The fractional_matrix_power matrix function should preserve
+        # the type of a matrix whose eigenvalues
+        # are positive with zero imaginary part.
+        # Test this preservation for variously structured matrices.
+        complex_dtype_chars = ('F', 'D', 'G')
+        for matrix_as_list in (
+                [[1, 0], [0, 1]],
+                [[1, 0], [1, 1]],
+                [[2, 1], [1, 1]],
+                [[2, 3], [1, 2]]):
+
+            # check that the spectrum has the expected properties
+            W = scipy.linalg.eigvals(matrix_as_list)
+            assert_(not any(w.imag or w.real < 0 for w in W))
+
+            # Check various positive and negative powers
+            # with absolute values bigger and smaller than 1.
+            for p in (-2.4, -0.9, 0.2, 3.3):
+
+                # check float type preservation
+                A = np.array(matrix_as_list, dtype=float)
+                A_power = fractional_matrix_power(A, p)
+                assert_(A_power.dtype.char not in complex_dtype_chars)
+
+                # check complex type preservation
+                A = np.array(matrix_as_list, dtype=complex)
+                A_power = fractional_matrix_power(A, p)
+                assert_(A_power.dtype.char in complex_dtype_chars)
+
+                # check float->complex for the matrix negation
+                A = -np.array(matrix_as_list, dtype=float)
+                A_power = fractional_matrix_power(A, p)
+                assert_(A_power.dtype.char in complex_dtype_chars)
+
+    def test_type_conversion_mixed_sign_or_complex_spectrum(self):
+        complex_dtype_chars = ('F', 'D', 'G')
+        for matrix_as_list in (
+                [[1, 0], [0, -1]],
+                [[0, 1], [1, 0]],
+                [[0, 1, 0], [0, 0, 1], [1, 0, 0]]):
+
+            # check that the spectrum has the expected properties
+            W = scipy.linalg.eigvals(matrix_as_list)
+            assert_(any(w.imag or w.real < 0 for w in W))
+
+            # Check various positive and negative powers
+            # with absolute values bigger and smaller than 1.
+            for p in (-2.4, -0.9, 0.2, 3.3):
+
+                # check complex->complex
+                A = np.array(matrix_as_list, dtype=complex)
+                A_power = fractional_matrix_power(A, p)
+                assert_(A_power.dtype.char in complex_dtype_chars)
+
+                # check float->complex
+                A = np.array(matrix_as_list, dtype=float)
+                A_power = fractional_matrix_power(A, p)
+                assert_(A_power.dtype.char in complex_dtype_chars)
+
+    @pytest.mark.xfail(reason='Too unstable across LAPACKs.')
+    def test_singular(self):
+        # Negative fractional powers do not work with singular matrices.
+        for matrix_as_list in (
+                [[0, 0], [0, 0]],
+                [[1, 1], [1, 1]],
+                [[1, 2], [3, 6]],
+                [[0, 0, 0], [0, 1, 1], [0, -1, 1]]):
+
+            # Check fractional powers both for float and for complex types.
+            for newtype in (float, complex):
+                A = np.array(matrix_as_list, dtype=newtype)
+                for p in (-0.7, -0.9, -2.4, -1.3):
+                    A_power = fractional_matrix_power(A, p)
+                    assert_(np.isnan(A_power).all())
+                for p in (0.2, 1.43):
+                    A_power = fractional_matrix_power(A, p)
+                    A_round_trip = fractional_matrix_power(A_power, 1/p)
+                    assert_allclose(A_round_trip, A)
+
+    def test_opposite_sign_complex_eigenvalues(self):
+        M = [[2j, 4], [0, -2j]]
+        R = [[1+1j, 2], [0, 1-1j]]
+        assert_allclose(np.dot(R, R), M, atol=1e-14)
+        assert_allclose(fractional_matrix_power(M, 0.5), R, atol=1e-14)
+
+
+class TestExpM:
+    def test_zero(self):
+        a = array([[0.,0],[0,0]])
+        assert_array_almost_equal(expm(a),[[1,0],[0,1]])
+
+    def test_single_elt(self):
+        elt = expm(1)
+        assert_allclose(elt, np.array([[np.e]]))
+
+    @pytest.mark.parametrize('func', [expm, cosm, sinm, tanm, coshm, sinhm, tanhm])
+    @pytest.mark.parametrize('dt',[int, float, np.float32, complex, np.complex64])
+    @pytest.mark.parametrize('shape', [(0, 0), (1, 1)])
+    def test_small_empty_matrix_input(self, func, dt, shape):
+        # regression test for gh-11082 / gh-20372 - test behavior of expm
+        # and related functions for small and zero-sized arrays.
+        A = np.zeros(shape, dtype=dt)
+        A0 = np.zeros((10, 10), dtype=dt)
+        result = func(A)
+        result0 = func(A0)
+        assert result.shape == shape
+        assert result.dtype == result0.dtype
+
+    def test_2x2_input(self):
+        E = np.e
+        a = array([[1, 4], [1, 1]])
+        aa = (E**4 + 1)/(2*E)
+        bb = (E**4 - 1)/E
+        assert_allclose(expm(a), array([[aa, bb], [bb/4, aa]]))
+        assert expm(a.astype(np.complex64)).dtype.char == 'F'
+        assert expm(a.astype(np.float32)).dtype.char == 'f'
+
+    def test_nx2x2_input(self):
+        E = np.e
+        # These are integer matrices with integer eigenvalues
+        a = np.array([[[1, 4], [1, 1]],
+                      [[1, 3], [1, -1]],
+                      [[1, 3], [4, 5]],
+                      [[1, 3], [5, 3]],
+                      [[4, 5], [-3, -4]]], order='F')
+        # Exact results are computed symbolically
+        a_res = np.array([
+                          [[(E**4+1)/(2*E), (E**4-1)/E],
+                           [(E**4-1)/4/E, (E**4+1)/(2*E)]],
+                          [[1/(4*E**2)+(3*E**2)/4, (3*E**2)/4-3/(4*E**2)],
+                           [E**2/4-1/(4*E**2), 3/(4*E**2)+E**2/4]],
+                          [[3/(4*E)+E**7/4, -3/(8*E)+(3*E**7)/8],
+                           [-1/(2*E)+E**7/2, 1/(4*E)+(3*E**7)/4]],
+                          [[5/(8*E**2)+(3*E**6)/8, -3/(8*E**2)+(3*E**6)/8],
+                           [-5/(8*E**2)+(5*E**6)/8, 3/(8*E**2)+(5*E**6)/8]],
+                          [[-3/(2*E)+(5*E)/2, -5/(2*E)+(5*E)/2],
+                           [3/(2*E)-(3*E)/2, 5/(2*E)-(3*E)/2]]
+                         ])
+        assert_allclose(expm(a), a_res)
+
+    def test_readonly(self):
+        n = 7
+        a = np.ones((n, n))
+        a.flags.writeable = False
+        expm(a)
+
+    @pytest.mark.thread_unsafe
+    @pytest.mark.fail_slow(5)
+    def test_gh18086(self):
+        A = np.zeros((400, 400), dtype=float)
+        rng = np.random.default_rng(100)
+        i = rng.integers(0, 399, 500)
+        j = rng.integers(0, 399, 500)
+        A[i, j] = rng.random(500)
+        # Problem appears when m = 9
+        Am = np.empty((5, 400, 400), dtype=float)
+        Am[0] = A.copy()
+        m, s = pick_pade_structure(Am)
+        assert m == 9
+        # Check that result is accurate
+        first_res = expm(A)
+        np.testing.assert_array_almost_equal(logm(first_res), A)
+        # Check that result is consistent
+        for i in range(5):
+            next_res = expm(A)
+            np.testing.assert_array_almost_equal(first_res, next_res)
+
+
+class TestExpmFrechet:
+
+    def test_expm_frechet(self):
+        # a test of the basic functionality
+        M = np.array([
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [0, 0, 1, 2],
+            [0, 0, 5, 6],
+            ], dtype=float)
+        A = np.array([
+            [1, 2],
+            [5, 6],
+            ], dtype=float)
+        E = np.array([
+            [3, 4],
+            [7, 8],
+            ], dtype=float)
+        expected_expm = scipy.linalg.expm(A)
+        expected_frechet = scipy.linalg.expm(M)[:2, 2:]
+        for kwargs in ({}, {'method':'SPS'}, {'method':'blockEnlarge'}):
+            observed_expm, observed_frechet = expm_frechet(A, E, **kwargs)
+            assert_allclose(expected_expm, observed_expm)
+            assert_allclose(expected_frechet, observed_frechet)
+
+    def test_small_norm_expm_frechet(self):
+        # methodically test matrices with a range of norms, for better coverage
+        M_original = np.array([
+            [1, 2, 3, 4],
+            [5, 6, 7, 8],
+            [0, 0, 1, 2],
+            [0, 0, 5, 6],
+            ], dtype=float)
+        A_original = np.array([
+            [1, 2],
+            [5, 6],
+            ], dtype=float)
+        E_original = np.array([
+            [3, 4],
+            [7, 8],
+            ], dtype=float)
+        A_original_norm_1 = scipy.linalg.norm(A_original, 1)
+        selected_m_list = [1, 3, 5, 7, 9, 11, 13, 15]
+        m_neighbor_pairs = zip(selected_m_list[:-1], selected_m_list[1:])
+        for ma, mb in m_neighbor_pairs:
+            ell_a = scipy.linalg._expm_frechet.ell_table_61[ma]
+            ell_b = scipy.linalg._expm_frechet.ell_table_61[mb]
+            target_norm_1 = 0.5 * (ell_a + ell_b)
+            scale = target_norm_1 / A_original_norm_1
+            M = scale * M_original
+            A = scale * A_original
+            E = scale * E_original
+            expected_expm = scipy.linalg.expm(A)
+            expected_frechet = scipy.linalg.expm(M)[:2, 2:]
+            observed_expm, observed_frechet = expm_frechet(A, E)
+            assert_allclose(expected_expm, observed_expm)
+            assert_allclose(expected_frechet, observed_frechet)
+
+    def test_fuzz(self):
+        rng = np.random.default_rng(1726500908359153)
+        # try a bunch of crazy inputs
+        rfuncs = (
+                np.random.uniform,
+                np.random.normal,
+                np.random.standard_cauchy,
+                np.random.exponential)
+        ntests = 100
+        for i in range(ntests):
+            rfunc = rfuncs[rng.choice(4)]
+            target_norm_1 = rng.exponential()
+            n = rng.integers(2, 16)
+            A_original = rfunc(size=(n,n))
+            E_original = rfunc(size=(n,n))
+            A_original_norm_1 = scipy.linalg.norm(A_original, 1)
+            scale = target_norm_1 / A_original_norm_1
+            A = scale * A_original
+            E = scale * E_original
+            M = np.vstack([
+                np.hstack([A, E]),
+                np.hstack([np.zeros_like(A), A])])
+            expected_expm = scipy.linalg.expm(A)
+            expected_frechet = scipy.linalg.expm(M)[:n, n:]
+            observed_expm, observed_frechet = expm_frechet(A, E)
+            assert_allclose(expected_expm, observed_expm, atol=5e-8)
+            assert_allclose(expected_frechet, observed_frechet, atol=1e-7)
+
+    def test_problematic_matrix(self):
+        # this test case uncovered a bug which has since been fixed
+        A = np.array([
+                [1.50591997, 1.93537998],
+                [0.41203263, 0.23443516],
+                ], dtype=float)
+        E = np.array([
+                [1.87864034, 2.07055038],
+                [1.34102727, 0.67341123],
+                ], dtype=float)
+        scipy.linalg.norm(A, 1)
+        sps_expm, sps_frechet = expm_frechet(
+                A, E, method='SPS')
+        blockEnlarge_expm, blockEnlarge_frechet = expm_frechet(
+                A, E, method='blockEnlarge')
+        assert_allclose(sps_expm, blockEnlarge_expm)
+        assert_allclose(sps_frechet, blockEnlarge_frechet)
+
+    @pytest.mark.slow
+    @pytest.mark.skip(reason='this test is deliberately slow')
+    def test_medium_matrix(self):
+        # profile this to see the speed difference
+        n = 1000
+        A = np.random.exponential(size=(n, n))
+        E = np.random.exponential(size=(n, n))
+        sps_expm, sps_frechet = expm_frechet(
+                A, E, method='SPS')
+        blockEnlarge_expm, blockEnlarge_frechet = expm_frechet(
+                A, E, method='blockEnlarge')
+        assert_allclose(sps_expm, blockEnlarge_expm)
+        assert_allclose(sps_frechet, blockEnlarge_frechet)
+
+
+def _help_expm_cond_search(A, A_norm, X, X_norm, eps, p):
+    p = np.reshape(p, A.shape)
+    p_norm = norm(p)
+    perturbation = eps * p * (A_norm / p_norm)
+    X_prime = expm(A + perturbation)
+    scaled_relative_error = norm(X_prime - X) / (X_norm * eps)
+    return -scaled_relative_error
+
+
+def _normalized_like(A, B):
+    return A * (scipy.linalg.norm(B) / scipy.linalg.norm(A))
+
+
+def _relative_error(f, A, perturbation):
+    X = f(A)
+    X_prime = f(A + perturbation)
+    return norm(X_prime - X) / norm(X)
+
+
+class TestExpmConditionNumber:
+    def test_expm_cond_smoke(self):
+        np.random.seed(1234)
+        for n in range(1, 4):
+            A = np.random.randn(n, n)
+            kappa = expm_cond(A)
+            assert_array_less(0, kappa)
+
+    def test_expm_bad_condition_number(self):
+        A = np.array([
+            [-1.128679820, 9.614183771e4, -4.524855739e9, 2.924969411e14],
+            [0, -1.201010529, 9.634696872e4, -4.681048289e9],
+            [0, 0, -1.132893222, 9.532491830e4],
+            [0, 0, 0, -1.179475332],
+            ])
+        kappa = expm_cond(A)
+        assert_array_less(1e36, kappa)
+
+    def test_univariate(self):
+        np.random.seed(12345)
+        for x in np.linspace(-5, 5, num=11):
+            A = np.array([[x]])
+            assert_allclose(expm_cond(A), abs(x))
+        for x in np.logspace(-2, 2, num=11):
+            A = np.array([[x]])
+            assert_allclose(expm_cond(A), abs(x))
+        for i in range(10):
+            A = np.random.randn(1, 1)
+            assert_allclose(expm_cond(A), np.absolute(A)[0, 0])
+
+    @pytest.mark.slow
+    def test_expm_cond_fuzz(self):
+        rng = np.random.RandomState(12345)
+        eps = 1e-5
+        nsamples = 10
+        for i in range(nsamples):
+            n = rng.randint(2, 5)
+            A = rng.randn(n, n)
+            A_norm = scipy.linalg.norm(A)
+            X = expm(A)
+            X_norm = scipy.linalg.norm(X)
+            kappa = expm_cond(A)
+
+            # Look for the small perturbation that gives the greatest
+            # relative error.
+            f = functools.partial(_help_expm_cond_search,
+                    A, A_norm, X, X_norm, eps)
+            guess = np.ones(n*n)
+            out = minimize(f, guess, method='L-BFGS-B')
+            xopt = out.x
+            yopt = f(xopt)
+            p_best = eps * _normalized_like(np.reshape(xopt, A.shape), A)
+            p_best_relerr = _relative_error(expm, A, p_best)
+            assert_allclose(p_best_relerr, -yopt * eps)
+
+            # Check that the identified perturbation indeed gives greater
+            # relative error than random perturbations with similar norms.
+            for j in range(5):
+                p_rand = eps * _normalized_like(rng.randn(*A.shape), A)
+                assert_allclose(norm(p_best), norm(p_rand))
+                p_rand_relerr = _relative_error(expm, A, p_rand)
+                assert_array_less(p_rand_relerr, p_best_relerr)
+
+            # The greatest relative error should not be much greater than
+            # eps times the condition number kappa.
+            # In the limit as eps approaches zero it should never be greater.
+            assert_array_less(p_best_relerr, (1 + 2*eps) * eps * kappa)
+
+
+class TestKhatriRao:
+
+    def test_basic(self):
+        a = khatri_rao(array([[1, 2], [3, 4]]),
+                       array([[5, 6], [7, 8]]))
+
+        assert_array_equal(a, array([[5, 12],
+                                     [7, 16],
+                                     [15, 24],
+                                     [21, 32]]))
+
+        b = khatri_rao(np.empty([2, 2]), np.empty([2, 2]))
+        assert_array_equal(b.shape, (4, 2))
+
+    def test_number_of_columns_equality(self):
+        with pytest.raises(ValueError):
+            a = array([[1, 2, 3],
+                       [4, 5, 6]])
+            b = array([[1, 2],
+                       [3, 4]])
+            khatri_rao(a, b)
+
+    def test_to_assure_2d_array(self):
+        with pytest.raises(ValueError):
+            # both arrays are 1-D
+            a = array([1, 2, 3])
+            b = array([4, 5, 6])
+            khatri_rao(a, b)
+
+        with pytest.raises(ValueError):
+            # first array is 1-D
+            a = array([1, 2, 3])
+            b = array([
+                [1, 2, 3],
+                [4, 5, 6]
+            ])
+            khatri_rao(a, b)
+
+        with pytest.raises(ValueError):
+            # second array is 1-D
+            a = array([
+                [1, 2, 3],
+                [7, 8, 9]
+            ])
+            b = array([4, 5, 6])
+            khatri_rao(a, b)
+
+    def test_equality_of_two_equations(self):
+        a = array([[1, 2], [3, 4]])
+        b = array([[5, 6], [7, 8]])
+
+        res1 = khatri_rao(a, b)
+        res2 = np.vstack([np.kron(a[:, k], b[:, k])
+                          for k in range(b.shape[1])]).T
+
+        assert_array_equal(res1, res2)
+
+    def test_empty(self):
+        a = np.empty((0, 2))
+        b = np.empty((3, 2))
+        res = khatri_rao(a, b)
+        assert_allclose(res, np.empty((0, 2)))
+
+        a = np.empty((3, 0))
+        b = np.empty((5, 0))
+        res = khatri_rao(a, b)
+        assert_allclose(res, np.empty((15, 0)))
diff --git a/shared/package-files/c9/c976059add1f40ce4455e65edb6fa2e067672c604c85f33cf1ef266f038f7ab6/avt-rv-s b/shared/package-files/c9/c976059add1f40ce4455e65edb6fa2e067672c604c85f33cf1ef266f038f7ab6/avt-rv-s
new file mode 100644
index 0000000000000000000000000000000000000000..cddc50fa2f88f9f70e85c804f06956b930060446
Binary files /dev/null and b/shared/package-files/c9/c976059add1f40ce4455e65edb6fa2e067672c604c85f33cf1ef266f038f7ab6/avt-rv-s differ
diff --git a/shared/package-files/c9/c97614194f8300e18121d9507d0b21545205c0c2102fcb82fb4026ad6590a729/hinge_embedding_loss.h b/shared/package-files/c9/c97614194f8300e18121d9507d0b21545205c0c2102fcb82fb4026ad6590a729/hinge_embedding_loss.h
new file mode 100644
index 0000000000000000000000000000000000000000..5a918d46d70cb55c88763ff20e3eb89db57f02cc
--- /dev/null
+++ b/shared/package-files/c9/c97614194f8300e18121d9507d0b21545205c0c2102fcb82fb4026ad6590a729/hinge_embedding_loss.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::hinge_embedding_loss(Tensor self, Tensor target, float margin=1.0, int reduction=Mean) -> Tensor
+inline at::Tensor hinge_embedding_loss(const at::Tensor & self, const at::Tensor & target, double margin=1.0, int64_t reduction=at::Reduction::Mean) {
+    return at::_ops::hinge_embedding_loss::call(self, target, margin, reduction);
+}
+
+}
diff --git a/shared/package-files/c9/c9822b8bac6a3898dc58d559dd271003667a2eeafd88c73ad9fcd409f5cde4a1/logit_cpu_dispatch.h b/shared/package-files/c9/c9822b8bac6a3898dc58d559dd271003667a2eeafd88c73ad9fcd409f5cde4a1/logit_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..4d2dd1db3c1f095871172d7cfb77c10fb1899c76
--- /dev/null
+++ b/shared/package-files/c9/c9822b8bac6a3898dc58d559dd271003667a2eeafd88c73ad9fcd409f5cde4a1/logit_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor logit(const at::Tensor & self, c10::optional eps=c10::nullopt);
+TORCH_API at::Tensor & logit_out(at::Tensor & out, const at::Tensor & self, c10::optional eps=c10::nullopt);
+TORCH_API at::Tensor & logit_outf(const at::Tensor & self, c10::optional eps, at::Tensor & out);
+TORCH_API at::Tensor & logit_(at::Tensor & self, c10::optional eps=c10::nullopt);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/c9/c9828d5ec5d5457c465d30baed112dca4fb861499b08f22b60a22ce70f53b6d9/faulty_rpc_agent_test_fixture.py b/shared/package-files/c9/c9828d5ec5d5457c465d30baed112dca4fb861499b08f22b60a22ce70f53b6d9/faulty_rpc_agent_test_fixture.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa7287a0c984f007ed757961df7473c3f27940dd
--- /dev/null
+++ b/shared/package-files/c9/c9828d5ec5d5457c465d30baed112dca4fb861499b08f22b60a22ce70f53b6d9/faulty_rpc_agent_test_fixture.py
@@ -0,0 +1,62 @@
+# mypy: ignore-errors
+
+import torch.distributed.rpc as rpc
+import torch.distributed.rpc._testing  # noqa: F401
+from torch.testing._internal.distributed.rpc.rpc_agent_test_fixture import (
+    RpcAgentTestFixture,
+)
+
+# The following message types are currently retried in the RREF protocol and
+# distributed autograd. Thus only these messages should be tested with the
+# Faulty RPC Agent.
+retryable_message_types = ["RREF_FORK_REQUEST",
+                           "RREF_CHILD_ACCEPT",
+                           "RREF_USER_DELETE",
+                           "CLEANUP_AUTOGRAD_CONTEXT_REQ"]
+
+# The following messages incur the corresponding delay in seconds while being
+# processed in FaultyTensorPipeAgent's enqueueSend() function.
+default_messages_to_delay = {
+    "PYTHON_CALL": 1.5,  # Python UDF
+    "SCRIPT_CALL": 1.5,  # Script/Builtin
+}
+
+class FaultyRpcAgentTestFixture(RpcAgentTestFixture):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.messages_to_fail = retryable_message_types
+        self.messages_to_delay = default_messages_to_delay
+
+    @property
+    def rpc_backend(self):
+        return rpc.backend_registry.BackendType[
+            "FAULTY_TENSORPIPE"
+        ]
+
+    @property
+    def rpc_backend_options(self):
+        return rpc.backend_registry.construct_rpc_backend_options(
+            self.rpc_backend,
+            init_method=self.init_method,
+            num_worker_threads=8,
+            num_fail_sends=3,
+            messages_to_fail=self.messages_to_fail,
+            messages_to_delay=self.messages_to_delay,
+        )
+
+    def setup_fault_injection(self, faulty_messages, messages_to_delay):
+        if faulty_messages is not None:
+            self.messages_to_fail = faulty_messages
+        if messages_to_delay is not None:
+            self.messages_to_delay = messages_to_delay
+
+    def get_shutdown_error_regex(self):
+        error_regexes = [
+            "Exception in thread pool task",
+            "Connection reset by peer",
+            "Connection closed by peer"
+        ]
+        return "|".join([f"({error_str})" for error_str in error_regexes])
+
+    def get_timeout_error_regex(self):
+        return "RPC ran for more than"
diff --git a/shared/package-files/c9/c982f517baf2815b924899a775f7f1b3fc4b145d7d0d002f8bfdce5f0b8c81a2/atan2_native.h b/shared/package-files/c9/c982f517baf2815b924899a775f7f1b3fc4b145d7d0d002f8bfdce5f0b8c81a2/atan2_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..9d6ce68706eb2ba60a38e2d768609e93dfaedf30
--- /dev/null
+++ b/shared/package-files/c9/c982f517baf2815b924899a775f7f1b3fc4b145d7d0d002f8bfdce5f0b8c81a2/atan2_native.h
@@ -0,0 +1,23 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+namespace at {
+namespace native {
+struct TORCH_API structured_atan2_out : public at::meta::structured_atan2 {
+void impl(const at::Tensor & self, const at::Tensor & other, const at::Tensor & out);
+};
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/c9/c984e116a60b3d4253a33e05cfd2d8c48f9927cf64f78c2aa6f854a0ad4af900/inspect.pyi b/shared/package-files/c9/c984e116a60b3d4253a33e05cfd2d8c48f9927cf64f78c2aa6f854a0ad4af900/inspect.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..d0627d54981b0222717b1681e4ec3eefd7c38110
--- /dev/null
+++ b/shared/package-files/c9/c984e116a60b3d4253a33e05cfd2d8c48f9927cf64f78c2aa6f854a0ad4af900/inspect.pyi
@@ -0,0 +1,309 @@
+import enum
+import sys
+from collections import OrderedDict
+from types import CodeType, FrameType, FunctionType, MethodType, ModuleType, TracebackType
+from typing import (
+    AbstractSet,
+    Any,
+    Callable,
+    ClassVar,
+    Dict,
+    Generator,
+    List,
+    Mapping,
+    NamedTuple,
+    Optional,
+    Sequence,
+    Tuple,
+    Type,
+    Union,
+)
+from typing_extensions import Literal
+
+#
+# Types and members
+#
+class EndOfBlock(Exception): ...
+
+class BlockFinder:
+    indent: int
+    islambda: bool
+    started: bool
+    passline: bool
+    indecorator: bool
+    decoratorhasargs: bool
+    last: int
+    def tokeneater(self, type: int, token: str, srowcol: Tuple[int, int], erowcol: Tuple[int, int], line: str) -> None: ...
+
+CO_OPTIMIZED: int
+CO_NEWLOCALS: int
+CO_VARARGS: int
+CO_VARKEYWORDS: int
+CO_NESTED: int
+CO_GENERATOR: int
+CO_NOFREE: int
+CO_COROUTINE: int
+CO_ITERABLE_COROUTINE: int
+CO_ASYNC_GENERATOR: int
+TPFLAGS_IS_ABSTRACT: int
+
+def getmembers(object: object, predicate: Optional[Callable[[Any], bool]] = ...) -> List[Tuple[str, Any]]: ...
+def getmodulename(path: str) -> Optional[str]: ...
+def ismodule(object: object) -> bool: ...
+def isclass(object: object) -> bool: ...
+def ismethod(object: object) -> bool: ...
+def isfunction(object: object) -> bool: ...
+
+if sys.version_info >= (3, 8):
+    def isgeneratorfunction(obj: object) -> bool: ...
+    def iscoroutinefunction(obj: object) -> bool: ...
+
+else:
+    def isgeneratorfunction(object: object) -> bool: ...
+    def iscoroutinefunction(object: object) -> bool: ...
+
+def isgenerator(object: object) -> bool: ...
+def iscoroutine(object: object) -> bool: ...
+def isawaitable(object: object) -> bool: ...
+
+if sys.version_info >= (3, 8):
+    def isasyncgenfunction(obj: object) -> bool: ...
+
+else:
+    def isasyncgenfunction(object: object) -> bool: ...
+
+def isasyncgen(object: object) -> bool: ...
+def istraceback(object: object) -> bool: ...
+def isframe(object: object) -> bool: ...
+def iscode(object: object) -> bool: ...
+def isbuiltin(object: object) -> bool: ...
+def isroutine(object: object) -> bool: ...
+def isabstract(object: object) -> bool: ...
+def ismethoddescriptor(object: object) -> bool: ...
+def isdatadescriptor(object: object) -> bool: ...
+def isgetsetdescriptor(object: object) -> bool: ...
+def ismemberdescriptor(object: object) -> bool: ...
+
+#
+# Retrieving source code
+#
+_SourceObjectType = Union[ModuleType, Type[Any], MethodType, FunctionType, TracebackType, FrameType, CodeType, Callable[..., Any]]
+
+def findsource(object: _SourceObjectType) -> Tuple[List[str], int]: ...
+def getabsfile(object: _SourceObjectType, _filename: Optional[str] = ...) -> str: ...
+def getblock(lines: Sequence[str]) -> Sequence[str]: ...
+def getdoc(object: object) -> Optional[str]: ...
+def getcomments(object: object) -> Optional[str]: ...
+def getfile(object: _SourceObjectType) -> str: ...
+def getmodule(object: object, _filename: Optional[str] = ...) -> Optional[ModuleType]: ...
+def getsourcefile(object: _SourceObjectType) -> Optional[str]: ...
+def getsourcelines(object: _SourceObjectType) -> Tuple[List[str], int]: ...
+def getsource(object: _SourceObjectType) -> str: ...
+def cleandoc(doc: str) -> str: ...
+def indentsize(line: str) -> int: ...
+
+#
+# Introspecting callables with the Signature object
+#
+def signature(obj: Callable[..., Any], *, follow_wrapped: bool = ...) -> Signature: ...
+
+class Signature:
+    def __init__(self, parameters: Optional[Sequence[Parameter]] = ..., *, return_annotation: Any = ...) -> None: ...
+    # TODO: can we be more specific here?
+    empty: object = ...
+
+    parameters: Mapping[str, Parameter]
+
+    # TODO: can we be more specific here?
+    return_annotation: Any
+    def bind(self, *args: Any, **kwargs: Any) -> BoundArguments: ...
+    def bind_partial(self, *args: Any, **kwargs: Any) -> BoundArguments: ...
+    def replace(self, *, parameters: Optional[Sequence[Parameter]] = ..., return_annotation: Any = ...) -> Signature: ...
+    @classmethod
+    def from_callable(cls, obj: Callable[..., Any], *, follow_wrapped: bool = ...) -> Signature: ...
+
+# The name is the same as the enum's name in CPython
+class _ParameterKind(enum.IntEnum):
+    POSITIONAL_ONLY: int
+    POSITIONAL_OR_KEYWORD: int
+    VAR_POSITIONAL: int
+    KEYWORD_ONLY: int
+    VAR_KEYWORD: int
+
+    if sys.version_info >= (3, 8):
+        description: str
+
+class Parameter:
+    def __init__(self, name: str, kind: _ParameterKind, *, default: Any = ..., annotation: Any = ...) -> None: ...
+    empty: Any = ...
+    name: str
+    default: Any
+    annotation: Any
+
+    kind: _ParameterKind
+    POSITIONAL_ONLY: ClassVar[Literal[_ParameterKind.POSITIONAL_ONLY]]
+    POSITIONAL_OR_KEYWORD: ClassVar[Literal[_ParameterKind.POSITIONAL_OR_KEYWORD]]
+    VAR_POSITIONAL: ClassVar[Literal[_ParameterKind.VAR_POSITIONAL]]
+    KEYWORD_ONLY: ClassVar[Literal[_ParameterKind.KEYWORD_ONLY]]
+    VAR_KEYWORD: ClassVar[Literal[_ParameterKind.VAR_KEYWORD]]
+    def replace(
+        self, *, name: Optional[str] = ..., kind: Optional[_ParameterKind] = ..., default: Any = ..., annotation: Any = ...
+    ) -> Parameter: ...
+
+class BoundArguments:
+    arguments: OrderedDict[str, Any]
+    args: Tuple[Any, ...]
+    kwargs: Dict[str, Any]
+    signature: Signature
+    def __init__(self, signature: Signature, arguments: OrderedDict[str, Any]) -> None: ...
+    def apply_defaults(self) -> None: ...
+
+#
+# Classes and functions
+#
+
+# TODO: The actual return type should be List[_ClassTreeItem] but mypy doesn't
+# seem to be supporting this at the moment:
+# _ClassTreeItem = Union[List[_ClassTreeItem], Tuple[type, Tuple[type, ...]]]
+def getclasstree(classes: List[type], unique: bool = ...) -> Any: ...
+
+class ArgSpec(NamedTuple):
+    args: List[str]
+    varargs: Optional[str]
+    keywords: Optional[str]
+    defaults: Tuple[Any, ...]
+
+class Arguments(NamedTuple):
+    args: List[str]
+    varargs: Optional[str]
+    varkw: Optional[str]
+
+def getargs(co: CodeType) -> Arguments: ...
+def getargspec(func: object) -> ArgSpec: ...
+
+class FullArgSpec(NamedTuple):
+    args: List[str]
+    varargs: Optional[str]
+    varkw: Optional[str]
+    defaults: Optional[Tuple[Any, ...]]
+    kwonlyargs: List[str]
+    kwonlydefaults: Optional[Dict[str, Any]]
+    annotations: Dict[str, Any]
+
+def getfullargspec(func: object) -> FullArgSpec: ...
+
+class ArgInfo(NamedTuple):
+    args: List[str]
+    varargs: Optional[str]
+    keywords: Optional[str]
+    locals: Dict[str, Any]
+
+def getargvalues(frame: FrameType) -> ArgInfo: ...
+def formatannotation(annotation: object, base_module: Optional[str] = ...) -> str: ...
+def formatannotationrelativeto(object: object) -> Callable[[object], str]: ...
+def formatargspec(
+    args: List[str],
+    varargs: Optional[str] = ...,
+    varkw: Optional[str] = ...,
+    defaults: Optional[Tuple[Any, ...]] = ...,
+    kwonlyargs: Optional[Sequence[str]] = ...,
+    kwonlydefaults: Optional[Dict[str, Any]] = ...,
+    annotations: Dict[str, Any] = ...,
+    formatarg: Callable[[str], str] = ...,
+    formatvarargs: Callable[[str], str] = ...,
+    formatvarkw: Callable[[str], str] = ...,
+    formatvalue: Callable[[Any], str] = ...,
+    formatreturns: Callable[[Any], str] = ...,
+    formatannotation: Callable[[Any], str] = ...,
+) -> str: ...
+def formatargvalues(
+    args: List[str],
+    varargs: Optional[str],
+    varkw: Optional[str],
+    locals: Optional[Dict[str, Any]],
+    formatarg: Optional[Callable[[str], str]] = ...,
+    formatvarargs: Optional[Callable[[str], str]] = ...,
+    formatvarkw: Optional[Callable[[str], str]] = ...,
+    formatvalue: Optional[Callable[[Any], str]] = ...,
+) -> str: ...
+def getmro(cls: type) -> Tuple[type, ...]: ...
+def getcallargs(__func: Callable[..., Any], *args: Any, **kwds: Any) -> Dict[str, Any]: ...
+
+class ClosureVars(NamedTuple):
+    nonlocals: Mapping[str, Any]
+    globals: Mapping[str, Any]
+    builtins: Mapping[str, Any]
+    unbound: AbstractSet[str]
+
+def getclosurevars(func: Callable[..., Any]) -> ClosureVars: ...
+def unwrap(func: Callable[..., Any], *, stop: Optional[Callable[[Any], Any]] = ...) -> Any: ...
+
+#
+# The interpreter stack
+#
+
+class Traceback(NamedTuple):
+    filename: str
+    lineno: int
+    function: str
+    code_context: Optional[List[str]]
+    index: Optional[int]  # type: ignore
+
+class FrameInfo(NamedTuple):
+    frame: FrameType
+    filename: str
+    lineno: int
+    function: str
+    code_context: Optional[List[str]]
+    index: Optional[int]  # type: ignore
+
+def getframeinfo(frame: Union[FrameType, TracebackType], context: int = ...) -> Traceback: ...
+def getouterframes(frame: Any, context: int = ...) -> List[FrameInfo]: ...
+def getinnerframes(tb: TracebackType, context: int = ...) -> List[FrameInfo]: ...
+def getlineno(frame: FrameType) -> int: ...
+def currentframe() -> Optional[FrameType]: ...
+def stack(context: int = ...) -> List[FrameInfo]: ...
+def trace(context: int = ...) -> List[FrameInfo]: ...
+
+#
+# Fetching attributes statically
+#
+
+def getattr_static(obj: object, attr: str, default: Optional[Any] = ...) -> Any: ...
+
+#
+# Current State of Generators and Coroutines
+#
+
+# TODO In the next two blocks of code, can we be more specific regarding the
+# type of the "enums"?
+
+GEN_CREATED: str
+GEN_RUNNING: str
+GEN_SUSPENDED: str
+GEN_CLOSED: str
+
+def getgeneratorstate(generator: Generator[Any, Any, Any]) -> str: ...
+
+CORO_CREATED: str
+CORO_RUNNING: str
+CORO_SUSPENDED: str
+CORO_CLOSED: str
+# TODO can we be more specific than "object"?
+def getcoroutinestate(coroutine: object) -> str: ...
+def getgeneratorlocals(generator: Generator[Any, Any, Any]) -> Dict[str, Any]: ...
+
+# TODO can we be more specific than "object"?
+def getcoroutinelocals(coroutine: object) -> Dict[str, Any]: ...
+
+# Create private type alias to avoid conflict with symbol of same
+# name created in Attribute class.
+_Object = object
+
+class Attribute(NamedTuple):
+    name: str
+    kind: str
+    defining_class: type
+    object: _Object
+
+def classify_class_attrs(cls: type) -> List[Attribute]: ...
diff --git a/shared/package-files/c9/c9877ffc1720bc9e2efc9bf9d23f293dbad2deb05616141d7286ef4a9876823f/addrinfo b/shared/package-files/c9/c9877ffc1720bc9e2efc9bf9d23f293dbad2deb05616141d7286ef4a9876823f/addrinfo
new file mode 100644
index 0000000000000000000000000000000000000000..33ab39cfa9ddf2d459bce3dfc7123941594c354c
Binary files /dev/null and b/shared/package-files/c9/c9877ffc1720bc9e2efc9bf9d23f293dbad2deb05616141d7286ef4a9876823f/addrinfo differ
diff --git a/shared/package-files/c9/c987b7fbffd9c3f5534c812631d11f8f50ab02e9abf1854ae54c63e3020f4a21/native_dropout_cpu_dispatch.h b/shared/package-files/c9/c987b7fbffd9c3f5534c812631d11f8f50ab02e9abf1854ae54c63e3020f4a21/native_dropout_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..732b2f77a60e5c401902ae54eafbd12541a7ce57
--- /dev/null
+++ b/shared/package-files/c9/c987b7fbffd9c3f5534c812631d11f8f50ab02e9abf1854ae54c63e3020f4a21/native_dropout_cpu_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API ::std::tuple native_dropout(const at::Tensor & input, double p, c10::optional train);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/c9/c9882dadfd1a111de2e9568facfa7323fb836bcc6312674208349201cc7dd72d/test_overload.py b/shared/package-files/c9/c9882dadfd1a111de2e9568facfa7323fb836bcc6312674208349201cc7dd72d/test_overload.py
new file mode 100644
index 0000000000000000000000000000000000000000..412fe2434ca9f1358c3a0d23e395d786ccd64d5f
--- /dev/null
+++ b/shared/package-files/c9/c9882dadfd1a111de2e9568facfa7323fb836bcc6312674208349201cc7dd72d/test_overload.py
@@ -0,0 +1,327 @@
+from numba import cuda, njit, types
+from numba.core.errors import TypingError
+from numba.core.extending import overload, overload_attribute
+from numba.core.typing.typeof import typeof
+from numba.cuda.testing import CUDATestCase, skip_on_cudasim, unittest
+import numpy as np
+
+
+# Dummy function definitions to overload
+
+def generic_func_1():
+    pass
+
+
+def cuda_func_1():
+    pass
+
+
+def generic_func_2():
+    pass
+
+
+def cuda_func_2():
+    pass
+
+
+def generic_calls_generic():
+    pass
+
+
+def generic_calls_cuda():
+    pass
+
+
+def cuda_calls_generic():
+    pass
+
+
+def cuda_calls_cuda():
+    pass
+
+
+def target_overloaded():
+    pass
+
+
+def generic_calls_target_overloaded():
+    pass
+
+
+def cuda_calls_target_overloaded():
+    pass
+
+
+def target_overloaded_calls_target_overloaded():
+    pass
+
+
+# To recognise which functions are resolved for a call, we identify each with a
+# prime number. Each function called multiplies a value by its prime (starting
+# with the value 1), and we can check that the result is as expected based on
+# the final value after all multiplications.
+
+GENERIC_FUNCTION_1 = 2
+CUDA_FUNCTION_1 = 3
+GENERIC_FUNCTION_2 = 5
+CUDA_FUNCTION_2 = 7
+GENERIC_CALLS_GENERIC = 11
+GENERIC_CALLS_CUDA = 13
+CUDA_CALLS_GENERIC = 17
+CUDA_CALLS_CUDA = 19
+GENERIC_TARGET_OL = 23
+CUDA_TARGET_OL = 29
+GENERIC_CALLS_TARGET_OL = 31
+CUDA_CALLS_TARGET_OL = 37
+GENERIC_TARGET_OL_CALLS_TARGET_OL = 41
+CUDA_TARGET_OL_CALLS_TARGET_OL = 43
+
+
+# Overload implementations
+
+@overload(generic_func_1, target='generic')
+def ol_generic_func_1(x):
+    def impl(x):
+        x[0] *= GENERIC_FUNCTION_1
+    return impl
+
+
+@overload(cuda_func_1, target='cuda')
+def ol_cuda_func_1(x):
+    def impl(x):
+        x[0] *= CUDA_FUNCTION_1
+    return impl
+
+
+@overload(generic_func_2, target='generic')
+def ol_generic_func_2(x):
+    def impl(x):
+        x[0] *= GENERIC_FUNCTION_2
+    return impl
+
+
+@overload(cuda_func_2, target='cuda')
+def ol_cuda_func(x):
+    def impl(x):
+        x[0] *= CUDA_FUNCTION_2
+    return impl
+
+
+@overload(generic_calls_generic, target='generic')
+def ol_generic_calls_generic(x):
+    def impl(x):
+        x[0] *= GENERIC_CALLS_GENERIC
+        generic_func_1(x)
+    return impl
+
+
+@overload(generic_calls_cuda, target='generic')
+def ol_generic_calls_cuda(x):
+    def impl(x):
+        x[0] *= GENERIC_CALLS_CUDA
+        cuda_func_1(x)
+    return impl
+
+
+@overload(cuda_calls_generic, target='cuda')
+def ol_cuda_calls_generic(x):
+    def impl(x):
+        x[0] *= CUDA_CALLS_GENERIC
+        generic_func_1(x)
+    return impl
+
+
+@overload(cuda_calls_cuda, target='cuda')
+def ol_cuda_calls_cuda(x):
+    def impl(x):
+        x[0] *= CUDA_CALLS_CUDA
+        cuda_func_1(x)
+    return impl
+
+
+@overload(target_overloaded, target='generic')
+def ol_target_overloaded_generic(x):
+    def impl(x):
+        x[0] *= GENERIC_TARGET_OL
+    return impl
+
+
+@overload(target_overloaded, target='cuda')
+def ol_target_overloaded_cuda(x):
+    def impl(x):
+        x[0] *= CUDA_TARGET_OL
+    return impl
+
+
+@overload(generic_calls_target_overloaded, target='generic')
+def ol_generic_calls_target_overloaded(x):
+    def impl(x):
+        x[0] *= GENERIC_CALLS_TARGET_OL
+        target_overloaded(x)
+    return impl
+
+
+@overload(cuda_calls_target_overloaded, target='cuda')
+def ol_cuda_calls_target_overloaded(x):
+    def impl(x):
+        x[0] *= CUDA_CALLS_TARGET_OL
+        target_overloaded(x)
+    return impl
+
+
+@overload(target_overloaded_calls_target_overloaded, target='generic')
+def ol_generic_calls_target_overloaded_generic(x):
+    def impl(x):
+        x[0] *= GENERIC_TARGET_OL_CALLS_TARGET_OL
+        target_overloaded(x)
+    return impl
+
+
+@overload(target_overloaded_calls_target_overloaded, target='cuda')
+def ol_generic_calls_target_overloaded_cuda(x):
+    def impl(x):
+        x[0] *= CUDA_TARGET_OL_CALLS_TARGET_OL
+        target_overloaded(x)
+    return impl
+
+
+@skip_on_cudasim('Overloading not supported in cudasim')
+class TestOverload(CUDATestCase):
+    def check_overload(self, kernel, expected):
+        x = np.ones(1, dtype=np.int32)
+        cuda.jit(kernel)[1, 1](x)
+        self.assertEqual(x[0], expected)
+
+    def check_overload_cpu(self, kernel, expected):
+        x = np.ones(1, dtype=np.int32)
+        njit(kernel)(x)
+        self.assertEqual(x[0], expected)
+
+    def test_generic(self):
+        def kernel(x):
+            generic_func_1(x)
+
+        expected = GENERIC_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_cuda(self):
+        def kernel(x):
+            cuda_func_1(x)
+
+        expected = CUDA_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_generic_and_cuda(self):
+        def kernel(x):
+            generic_func_1(x)
+            cuda_func_1(x)
+
+        expected = GENERIC_FUNCTION_1 * CUDA_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_call_two_generic_calls(self):
+        def kernel(x):
+            generic_func_1(x)
+            generic_func_2(x)
+
+        expected = GENERIC_FUNCTION_1 * GENERIC_FUNCTION_2
+        self.check_overload(kernel, expected)
+
+    def test_call_two_cuda_calls(self):
+        def kernel(x):
+            cuda_func_1(x)
+            cuda_func_2(x)
+
+        expected = CUDA_FUNCTION_1 * CUDA_FUNCTION_2
+        self.check_overload(kernel, expected)
+
+    def test_generic_calls_generic(self):
+        def kernel(x):
+            generic_calls_generic(x)
+
+        expected = GENERIC_CALLS_GENERIC * GENERIC_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_generic_calls_cuda(self):
+        def kernel(x):
+            generic_calls_cuda(x)
+
+        expected = GENERIC_CALLS_CUDA * CUDA_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_cuda_calls_generic(self):
+        def kernel(x):
+            cuda_calls_generic(x)
+
+        expected = CUDA_CALLS_GENERIC * GENERIC_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_cuda_calls_cuda(self):
+        def kernel(x):
+            cuda_calls_cuda(x)
+
+        expected = CUDA_CALLS_CUDA * CUDA_FUNCTION_1
+        self.check_overload(kernel, expected)
+
+    def test_call_target_overloaded(self):
+        def kernel(x):
+            target_overloaded(x)
+
+        expected = CUDA_TARGET_OL
+        self.check_overload(kernel, expected)
+
+    def test_generic_calls_target_overloaded(self):
+        def kernel(x):
+            generic_calls_target_overloaded(x)
+
+        expected = GENERIC_CALLS_TARGET_OL * CUDA_TARGET_OL
+        self.check_overload(kernel, expected)
+
+    def test_cuda_calls_target_overloaded(self):
+        def kernel(x):
+            cuda_calls_target_overloaded(x)
+
+        expected = CUDA_CALLS_TARGET_OL * CUDA_TARGET_OL
+        self.check_overload(kernel, expected)
+
+    def test_target_overloaded_calls_target_overloaded(self):
+        def kernel(x):
+            target_overloaded_calls_target_overloaded(x)
+
+        # Check the CUDA overloads are used on CUDA
+        expected = CUDA_TARGET_OL_CALLS_TARGET_OL * CUDA_TARGET_OL
+        self.check_overload(kernel, expected)
+
+        # Also check that the CPU overloads are used on the CPU
+        expected = GENERIC_TARGET_OL_CALLS_TARGET_OL * GENERIC_TARGET_OL
+        self.check_overload_cpu(kernel, expected)
+
+    def test_overload_attribute_target(self):
+        MyDummy, MyDummyType = self.make_dummy_type()
+        mydummy_type = typeof(MyDummy())
+
+        @overload_attribute(MyDummyType, 'cuda_only', target='cuda')
+        def ov_dummy_cuda_attr(obj):
+            def imp(obj):
+                return 42
+
+            return imp
+
+        # Ensure that we cannot use the CUDA target-specific attribute on the
+        # CPU, and that an appropriate typing error is raised
+        with self.assertRaisesRegex(TypingError,
+                                    "Unknown attribute 'cuda_only'"):
+            @njit(types.int64(mydummy_type))
+            def illegal_target_attr_use(x):
+                return x.cuda_only
+
+        # Ensure that the CUDA target-specific attribute is usable and works
+        # correctly when the target is CUDA - note eager compilation via
+        # signature
+        @cuda.jit(types.void(types.int64[::1], mydummy_type))
+        def cuda_target_attr_use(res, dummy):
+            res[0] = dummy.cuda_only
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/shared/package-files/c9/c989e463f9d49c1ee9490023597172092ffc20ddc1b8395798f22e65b9253e85/file_io.py b/shared/package-files/c9/c989e463f9d49c1ee9490023597172092ffc20ddc1b8395798f22e65b9253e85/file_io.py
new file mode 100644
index 0000000000000000000000000000000000000000..ead17335cfd3011ad3a4a3100aee63002a559901
--- /dev/null
+++ b/shared/package-files/c9/c989e463f9d49c1ee9490023597172092ffc20ddc1b8395798f22e65b9253e85/file_io.py
@@ -0,0 +1,83 @@
+import os
+
+from parso import file_io
+
+
+class AbstractFolderIO:
+    def __init__(self, path):
+        self.path = path
+
+    def get_base_name(self):
+        raise NotImplementedError
+
+    def list(self):
+        raise NotImplementedError
+
+    def get_file_io(self, name):
+        raise NotImplementedError
+
+    def get_parent_folder(self):
+        raise NotImplementedError
+
+    def __repr__(self):
+        return '<%s: %s>' % (self.__class__.__name__, self.path)
+
+
+class FolderIO(AbstractFolderIO):
+    def get_base_name(self):
+        return os.path.basename(self.path)
+
+    def list(self):
+        return os.listdir(self.path)
+
+    def get_file_io(self, name):
+        return FileIO(os.path.join(self.path, name))
+
+    def get_parent_folder(self):
+        return FolderIO(os.path.dirname(self.path))
+
+    def walk(self):
+        for root, dirs, files in os.walk(self.path):
+            root_folder_io = FolderIO(root)
+            original_folder_ios = [FolderIO(os.path.join(root, d)) for d in dirs]
+            modified_folder_ios = list(original_folder_ios)
+            yield (
+                root_folder_io,
+                modified_folder_ios,
+                [FileIO(os.path.join(root, f)) for f in files],
+            )
+            modified_iterator = iter(reversed(modified_folder_ios))
+            current = next(modified_iterator, None)
+            i = len(original_folder_ios)
+            for folder_io in reversed(original_folder_ios):
+                i -= 1   # Basically enumerate but reversed
+                if current is folder_io:
+                    current = next(modified_iterator, None)
+                else:
+                    del dirs[i]
+
+
+class FileIOFolderMixin:
+    def get_parent_folder(self):
+        return FolderIO(os.path.dirname(self.path))
+
+
+class ZipFileIO(file_io.KnownContentFileIO, FileIOFolderMixin):
+    """For .zip and .egg archives"""
+    def __init__(self, path, code, zip_path):
+        super().__init__(path, code)
+        self._zip_path = zip_path
+
+    def get_last_modified(self):
+        try:
+            return os.path.getmtime(self._zip_path)
+        except (FileNotFoundError, PermissionError, NotADirectoryError):
+            return None
+
+
+class FileIO(file_io.FileIO, FileIOFolderMixin):
+    pass
+
+
+class KnownContentFileIO(file_io.KnownContentFileIO, FileIOFolderMixin):
+    pass
diff --git a/shared/package-files/c9/c98b837e35c0ed2dd71d1d3551490553e5490ce954233f48e63e756c5f15938e/fractions.py b/shared/package-files/c9/c98b837e35c0ed2dd71d1d3551490553e5490ce954233f48e63e756c5f15938e/fractions.py
new file mode 100644
index 0000000000000000000000000000000000000000..96047beb4546a5384e23e2963f45abe5186dd9cc
--- /dev/null
+++ b/shared/package-files/c9/c98b837e35c0ed2dd71d1d3551490553e5490ce954233f48e63e756c5f15938e/fractions.py
@@ -0,0 +1,748 @@
+# Originally contributed by Sjoerd Mullender.
+# Significantly modified by Jeffrey Yasskin .
+
+"""Fraction, infinite-precision, real numbers."""
+
+from decimal import Decimal
+import math
+import numbers
+import operator
+import re
+import sys
+
+__all__ = ['Fraction']
+
+
+# Constants related to the hash implementation;  hash(x) is based
+# on the reduction of x modulo the prime _PyHASH_MODULUS.
+_PyHASH_MODULUS = sys.hash_info.modulus
+# Value to be used for rationals that reduce to infinity modulo
+# _PyHASH_MODULUS.
+_PyHASH_INF = sys.hash_info.inf
+
+_RATIONAL_FORMAT = re.compile(r"""
+    \A\s*                      # optional whitespace at the start, then
+    (?P[-+]?)            # an optional sign, then
+    (?=\d|\.\d)                # lookahead for digit or .digit
+    (?P\d*)               # numerator (possibly empty)
+    (?:                        # followed by
+       (?:/(?P\d+))?    # an optional denominator
+    |                          # or
+       (?:\.(?P\d*))? # an optional fractional part
+       (?:E(?P[-+]?\d+))? # and optional exponent
+    )
+    \s*\Z                      # and optional whitespace to finish
+""", re.VERBOSE | re.IGNORECASE)
+
+
+class Fraction(numbers.Rational):
+    """This class implements rational numbers.
+
+    In the two-argument form of the constructor, Fraction(8, 6) will
+    produce a rational number equivalent to 4/3. Both arguments must
+    be Rational. The numerator defaults to 0 and the denominator
+    defaults to 1 so that Fraction(3) == 3 and Fraction() == 0.
+
+    Fractions can also be constructed from:
+
+      - numeric strings similar to those accepted by the
+        float constructor (for example, '-2.3' or '1e10')
+
+      - strings of the form '123/456'
+
+      - float and Decimal instances
+
+      - other Rational instances (including integers)
+
+    """
+
+    __slots__ = ('_numerator', '_denominator')
+
+    # We're immutable, so use __new__ not __init__
+    def __new__(cls, numerator=0, denominator=None, *, _normalize=True):
+        """Constructs a Rational.
+
+        Takes a string like '3/2' or '1.5', another Rational instance, a
+        numerator/denominator pair, or a float.
+
+        Examples
+        --------
+
+        >>> Fraction(10, -8)
+        Fraction(-5, 4)
+        >>> Fraction(Fraction(1, 7), 5)
+        Fraction(1, 35)
+        >>> Fraction(Fraction(1, 7), Fraction(2, 3))
+        Fraction(3, 14)
+        >>> Fraction('314')
+        Fraction(314, 1)
+        >>> Fraction('-35/4')
+        Fraction(-35, 4)
+        >>> Fraction('3.1415') # conversion from numeric string
+        Fraction(6283, 2000)
+        >>> Fraction('-47e-2') # string may include a decimal exponent
+        Fraction(-47, 100)
+        >>> Fraction(1.47)  # direct construction from float (exact conversion)
+        Fraction(6620291452234629, 4503599627370496)
+        >>> Fraction(2.25)
+        Fraction(9, 4)
+        >>> Fraction(Decimal('1.47'))
+        Fraction(147, 100)
+
+        """
+        self = super(Fraction, cls).__new__(cls)
+
+        if denominator is None:
+            if type(numerator) is int:
+                self._numerator = numerator
+                self._denominator = 1
+                return self
+
+            elif isinstance(numerator, numbers.Rational):
+                self._numerator = numerator.numerator
+                self._denominator = numerator.denominator
+                return self
+
+            elif isinstance(numerator, (float, Decimal)):
+                # Exact conversion
+                self._numerator, self._denominator = numerator.as_integer_ratio()
+                return self
+
+            elif isinstance(numerator, str):
+                # Handle construction from strings.
+                m = _RATIONAL_FORMAT.match(numerator)
+                if m is None:
+                    raise ValueError('Invalid literal for Fraction: %r' %
+                                     numerator)
+                numerator = int(m.group('num') or '0')
+                denom = m.group('denom')
+                if denom:
+                    denominator = int(denom)
+                else:
+                    denominator = 1
+                    decimal = m.group('decimal')
+                    if decimal:
+                        scale = 10**len(decimal)
+                        numerator = numerator * scale + int(decimal)
+                        denominator *= scale
+                    exp = m.group('exp')
+                    if exp:
+                        exp = int(exp)
+                        if exp >= 0:
+                            numerator *= 10**exp
+                        else:
+                            denominator *= 10**-exp
+                if m.group('sign') == '-':
+                    numerator = -numerator
+
+            else:
+                raise TypeError("argument should be a string "
+                                "or a Rational instance")
+
+        elif type(numerator) is int is type(denominator):
+            pass # *very* normal case
+
+        elif (isinstance(numerator, numbers.Rational) and
+            isinstance(denominator, numbers.Rational)):
+            numerator, denominator = (
+                numerator.numerator * denominator.denominator,
+                denominator.numerator * numerator.denominator
+                )
+        else:
+            raise TypeError("both arguments should be "
+                            "Rational instances")
+
+        if denominator == 0:
+            raise ZeroDivisionError('Fraction(%s, 0)' % numerator)
+        if _normalize:
+            g = math.gcd(numerator, denominator)
+            if denominator < 0:
+                g = -g
+            numerator //= g
+            denominator //= g
+        self._numerator = numerator
+        self._denominator = denominator
+        return self
+
+    @classmethod
+    def from_float(cls, f):
+        """Converts a finite float to a rational number, exactly.
+
+        Beware that Fraction.from_float(0.3) != Fraction(3, 10).
+
+        """
+        if isinstance(f, numbers.Integral):
+            return cls(f)
+        elif not isinstance(f, float):
+            raise TypeError("%s.from_float() only takes floats, not %r (%s)" %
+                            (cls.__name__, f, type(f).__name__))
+        return cls(*f.as_integer_ratio())
+
+    @classmethod
+    def from_decimal(cls, dec):
+        """Converts a finite Decimal instance to a rational number, exactly."""
+        from decimal import Decimal
+        if isinstance(dec, numbers.Integral):
+            dec = Decimal(int(dec))
+        elif not isinstance(dec, Decimal):
+            raise TypeError(
+                "%s.from_decimal() only takes Decimals, not %r (%s)" %
+                (cls.__name__, dec, type(dec).__name__))
+        return cls(*dec.as_integer_ratio())
+
+    def as_integer_ratio(self):
+        """Return the integer ratio as a tuple.
+
+        Return a tuple of two integers, whose ratio is equal to the
+        Fraction and with a positive denominator.
+        """
+        return (self._numerator, self._denominator)
+
+    def limit_denominator(self, max_denominator=1000000):
+        """Closest Fraction to self with denominator at most max_denominator.
+
+        >>> Fraction('3.141592653589793').limit_denominator(10)
+        Fraction(22, 7)
+        >>> Fraction('3.141592653589793').limit_denominator(100)
+        Fraction(311, 99)
+        >>> Fraction(4321, 8765).limit_denominator(10000)
+        Fraction(4321, 8765)
+
+        """
+        # Algorithm notes: For any real number x, define a *best upper
+        # approximation* to x to be a rational number p/q such that:
+        #
+        #   (1) p/q >= x, and
+        #   (2) if p/q > r/s >= x then s > q, for any rational r/s.
+        #
+        # Define *best lower approximation* similarly.  Then it can be
+        # proved that a rational number is a best upper or lower
+        # approximation to x if, and only if, it is a convergent or
+        # semiconvergent of the (unique shortest) continued fraction
+        # associated to x.
+        #
+        # To find a best rational approximation with denominator <= M,
+        # we find the best upper and lower approximations with
+        # denominator <= M and take whichever of these is closer to x.
+        # In the event of a tie, the bound with smaller denominator is
+        # chosen.  If both denominators are equal (which can happen
+        # only when max_denominator == 1 and self is midway between
+        # two integers) the lower bound---i.e., the floor of self, is
+        # taken.
+
+        if max_denominator < 1:
+            raise ValueError("max_denominator should be at least 1")
+        if self._denominator <= max_denominator:
+            return Fraction(self)
+
+        p0, q0, p1, q1 = 0, 1, 1, 0
+        n, d = self._numerator, self._denominator
+        while True:
+            a = n//d
+            q2 = q0+a*q1
+            if q2 > max_denominator:
+                break
+            p0, q0, p1, q1 = p1, q1, p0+a*p1, q2
+            n, d = d, n-a*d
+
+        k = (max_denominator-q0)//q1
+        bound1 = Fraction(p0+k*p1, q0+k*q1)
+        bound2 = Fraction(p1, q1)
+        if abs(bound2 - self) <= abs(bound1-self):
+            return bound2
+        else:
+            return bound1
+
+    @property
+    def numerator(a):
+        return a._numerator
+
+    @property
+    def denominator(a):
+        return a._denominator
+
+    def __repr__(self):
+        """repr(self)"""
+        return '%s(%s, %s)' % (self.__class__.__name__,
+                               self._numerator, self._denominator)
+
+    def __str__(self):
+        """str(self)"""
+        if self._denominator == 1:
+            return str(self._numerator)
+        else:
+            return '%s/%s' % (self._numerator, self._denominator)
+
+    def _operator_fallbacks(monomorphic_operator, fallback_operator):
+        """Generates forward and reverse operators given a purely-rational
+        operator and a function from the operator module.
+
+        Use this like:
+        __op__, __rop__ = _operator_fallbacks(just_rational_op, operator.op)
+
+        In general, we want to implement the arithmetic operations so
+        that mixed-mode operations either call an implementation whose
+        author knew about the types of both arguments, or convert both
+        to the nearest built in type and do the operation there. In
+        Fraction, that means that we define __add__ and __radd__ as:
+
+            def __add__(self, other):
+                # Both types have numerators/denominator attributes,
+                # so do the operation directly
+                if isinstance(other, (int, Fraction)):
+                    return Fraction(self.numerator * other.denominator +
+                                    other.numerator * self.denominator,
+                                    self.denominator * other.denominator)
+                # float and complex don't have those operations, but we
+                # know about those types, so special case them.
+                elif isinstance(other, float):
+                    return float(self) + other
+                elif isinstance(other, complex):
+                    return complex(self) + other
+                # Let the other type take over.
+                return NotImplemented
+
+            def __radd__(self, other):
+                # radd handles more types than add because there's
+                # nothing left to fall back to.
+                if isinstance(other, numbers.Rational):
+                    return Fraction(self.numerator * other.denominator +
+                                    other.numerator * self.denominator,
+                                    self.denominator * other.denominator)
+                elif isinstance(other, Real):
+                    return float(other) + float(self)
+                elif isinstance(other, Complex):
+                    return complex(other) + complex(self)
+                return NotImplemented
+
+
+        There are 5 different cases for a mixed-type addition on
+        Fraction. I'll refer to all of the above code that doesn't
+        refer to Fraction, float, or complex as "boilerplate". 'r'
+        will be an instance of Fraction, which is a subtype of
+        Rational (r : Fraction <: Rational), and b : B <:
+        Complex. The first three involve 'r + b':
+
+            1. If B <: Fraction, int, float, or complex, we handle
+               that specially, and all is well.
+            2. If Fraction falls back to the boilerplate code, and it
+               were to return a value from __add__, we'd miss the
+               possibility that B defines a more intelligent __radd__,
+               so the boilerplate should return NotImplemented from
+               __add__. In particular, we don't handle Rational
+               here, even though we could get an exact answer, in case
+               the other type wants to do something special.
+            3. If B <: Fraction, Python tries B.__radd__ before
+               Fraction.__add__. This is ok, because it was
+               implemented with knowledge of Fraction, so it can
+               handle those instances before delegating to Real or
+               Complex.
+
+        The next two situations describe 'b + r'. We assume that b
+        didn't know about Fraction in its implementation, and that it
+        uses similar boilerplate code:
+
+            4. If B <: Rational, then __radd_ converts both to the
+               builtin rational type (hey look, that's us) and
+               proceeds.
+            5. Otherwise, __radd__ tries to find the nearest common
+               base ABC, and fall back to its builtin type. Since this
+               class doesn't subclass a concrete type, there's no
+               implementation to fall back to, so we need to try as
+               hard as possible to return an actual value, or the user
+               will get a TypeError.
+
+        """
+        def forward(a, b):
+            if isinstance(b, (int, Fraction)):
+                return monomorphic_operator(a, b)
+            elif isinstance(b, float):
+                return fallback_operator(float(a), b)
+            elif isinstance(b, complex):
+                return fallback_operator(complex(a), b)
+            else:
+                return NotImplemented
+        forward.__name__ = '__' + fallback_operator.__name__ + '__'
+        forward.__doc__ = monomorphic_operator.__doc__
+
+        def reverse(b, a):
+            if isinstance(a, numbers.Rational):
+                # Includes ints.
+                return monomorphic_operator(a, b)
+            elif isinstance(a, numbers.Real):
+                return fallback_operator(float(a), float(b))
+            elif isinstance(a, numbers.Complex):
+                return fallback_operator(complex(a), complex(b))
+            else:
+                return NotImplemented
+        reverse.__name__ = '__r' + fallback_operator.__name__ + '__'
+        reverse.__doc__ = monomorphic_operator.__doc__
+
+        return forward, reverse
+
+    # Rational arithmetic algorithms: Knuth, TAOCP, Volume 2, 4.5.1.
+    #
+    # Assume input fractions a and b are normalized.
+    #
+    # 1) Consider addition/subtraction.
+    #
+    # Let g = gcd(da, db). Then
+    #
+    #              na   nb    na*db ± nb*da
+    #     a ± b == -- ± -- == ------------- ==
+    #              da   db        da*db
+    #
+    #              na*(db//g) ± nb*(da//g)    t
+    #           == ----------------------- == -
+    #                      (da*db)//g         d
+    #
+    # Now, if g > 1, we're working with smaller integers.
+    #
+    # Note, that t, (da//g) and (db//g) are pairwise coprime.
+    #
+    # Indeed, (da//g) and (db//g) share no common factors (they were
+    # removed) and da is coprime with na (since input fractions are
+    # normalized), hence (da//g) and na are coprime.  By symmetry,
+    # (db//g) and nb are coprime too.  Then,
+    #
+    #     gcd(t, da//g) == gcd(na*(db//g), da//g) == 1
+    #     gcd(t, db//g) == gcd(nb*(da//g), db//g) == 1
+    #
+    # Above allows us optimize reduction of the result to lowest
+    # terms.  Indeed,
+    #
+    #     g2 = gcd(t, d) == gcd(t, (da//g)*(db//g)*g) == gcd(t, g)
+    #
+    #                       t//g2                   t//g2
+    #     a ± b == ----------------------- == ----------------
+    #              (da//g)*(db//g)*(g//g2)    (da//g)*(db//g2)
+    #
+    # is a normalized fraction.  This is useful because the unnormalized
+    # denominator d could be much larger than g.
+    #
+    # We should special-case g == 1 (and g2 == 1), since 60.8% of
+    # randomly-chosen integers are coprime:
+    # https://en.wikipedia.org/wiki/Coprime_integers#Probability_of_coprimality
+    # Note, that g2 == 1 always for fractions, obtained from floats: here
+    # g is a power of 2 and the unnormalized numerator t is an odd integer.
+    #
+    # 2) Consider multiplication
+    #
+    # Let g1 = gcd(na, db) and g2 = gcd(nb, da), then
+    #
+    #            na*nb    na*nb    (na//g1)*(nb//g2)
+    #     a*b == ----- == ----- == -----------------
+    #            da*db    db*da    (db//g1)*(da//g2)
+    #
+    # Note, that after divisions we're multiplying smaller integers.
+    #
+    # Also, the resulting fraction is normalized, because each of
+    # two factors in the numerator is coprime to each of the two factors
+    # in the denominator.
+    #
+    # Indeed, pick (na//g1).  It's coprime with (da//g2), because input
+    # fractions are normalized.  It's also coprime with (db//g1), because
+    # common factors are removed by g1 == gcd(na, db).
+    #
+    # As for addition/subtraction, we should special-case g1 == 1
+    # and g2 == 1 for same reason.  That happens also for multiplying
+    # rationals, obtained from floats.
+
+    def _add(a, b):
+        """a + b"""
+        na, da = a.numerator, a.denominator
+        nb, db = b.numerator, b.denominator
+        g = math.gcd(da, db)
+        if g == 1:
+            return Fraction(na * db + da * nb, da * db, _normalize=False)
+        s = da // g
+        t = na * (db // g) + nb * s
+        g2 = math.gcd(t, g)
+        if g2 == 1:
+            return Fraction(t, s * db, _normalize=False)
+        return Fraction(t // g2, s * (db // g2), _normalize=False)
+
+    __add__, __radd__ = _operator_fallbacks(_add, operator.add)
+
+    def _sub(a, b):
+        """a - b"""
+        na, da = a.numerator, a.denominator
+        nb, db = b.numerator, b.denominator
+        g = math.gcd(da, db)
+        if g == 1:
+            return Fraction(na * db - da * nb, da * db, _normalize=False)
+        s = da // g
+        t = na * (db // g) - nb * s
+        g2 = math.gcd(t, g)
+        if g2 == 1:
+            return Fraction(t, s * db, _normalize=False)
+        return Fraction(t // g2, s * (db // g2), _normalize=False)
+
+    __sub__, __rsub__ = _operator_fallbacks(_sub, operator.sub)
+
+    def _mul(a, b):
+        """a * b"""
+        na, da = a.numerator, a.denominator
+        nb, db = b.numerator, b.denominator
+        g1 = math.gcd(na, db)
+        if g1 > 1:
+            na //= g1
+            db //= g1
+        g2 = math.gcd(nb, da)
+        if g2 > 1:
+            nb //= g2
+            da //= g2
+        return Fraction(na * nb, db * da, _normalize=False)
+
+    __mul__, __rmul__ = _operator_fallbacks(_mul, operator.mul)
+
+    def _div(a, b):
+        """a / b"""
+        # Same as _mul(), with inversed b.
+        na, da = a.numerator, a.denominator
+        nb, db = b.numerator, b.denominator
+        g1 = math.gcd(na, nb)
+        if g1 > 1:
+            na //= g1
+            nb //= g1
+        g2 = math.gcd(db, da)
+        if g2 > 1:
+            da //= g2
+            db //= g2
+        n, d = na * db, nb * da
+        if d < 0:
+            n, d = -n, -d
+        return Fraction(n, d, _normalize=False)
+
+    __truediv__, __rtruediv__ = _operator_fallbacks(_div, operator.truediv)
+
+    def _floordiv(a, b):
+        """a // b"""
+        return (a.numerator * b.denominator) // (a.denominator * b.numerator)
+
+    __floordiv__, __rfloordiv__ = _operator_fallbacks(_floordiv, operator.floordiv)
+
+    def _divmod(a, b):
+        """(a // b, a % b)"""
+        da, db = a.denominator, b.denominator
+        div, n_mod = divmod(a.numerator * db, da * b.numerator)
+        return div, Fraction(n_mod, da * db)
+
+    __divmod__, __rdivmod__ = _operator_fallbacks(_divmod, divmod)
+
+    def _mod(a, b):
+        """a % b"""
+        da, db = a.denominator, b.denominator
+        return Fraction((a.numerator * db) % (b.numerator * da), da * db)
+
+    __mod__, __rmod__ = _operator_fallbacks(_mod, operator.mod)
+
+    def __pow__(a, b):
+        """a ** b
+
+        If b is not an integer, the result will be a float or complex
+        since roots are generally irrational. If b is an integer, the
+        result will be rational.
+
+        """
+        if isinstance(b, numbers.Rational):
+            if b.denominator == 1:
+                power = b.numerator
+                if power >= 0:
+                    return Fraction(a._numerator ** power,
+                                    a._denominator ** power,
+                                    _normalize=False)
+                elif a._numerator >= 0:
+                    return Fraction(a._denominator ** -power,
+                                    a._numerator ** -power,
+                                    _normalize=False)
+                else:
+                    return Fraction((-a._denominator) ** -power,
+                                    (-a._numerator) ** -power,
+                                    _normalize=False)
+            else:
+                # A fractional power will generally produce an
+                # irrational number.
+                return float(a) ** float(b)
+        else:
+            return float(a) ** b
+
+    def __rpow__(b, a):
+        """a ** b"""
+        if b._denominator == 1 and b._numerator >= 0:
+            # If a is an int, keep it that way if possible.
+            return a ** b._numerator
+
+        if isinstance(a, numbers.Rational):
+            return Fraction(a.numerator, a.denominator) ** b
+
+        if b._denominator == 1:
+            return a ** b._numerator
+
+        return a ** float(b)
+
+    def __pos__(a):
+        """+a: Coerces a subclass instance to Fraction"""
+        return Fraction(a._numerator, a._denominator, _normalize=False)
+
+    def __neg__(a):
+        """-a"""
+        return Fraction(-a._numerator, a._denominator, _normalize=False)
+
+    def __abs__(a):
+        """abs(a)"""
+        return Fraction(abs(a._numerator), a._denominator, _normalize=False)
+
+    def __trunc__(a):
+        """trunc(a)"""
+        if a._numerator < 0:
+            return -(-a._numerator // a._denominator)
+        else:
+            return a._numerator // a._denominator
+
+    def __floor__(a):
+        """math.floor(a)"""
+        return a.numerator // a.denominator
+
+    def __ceil__(a):
+        """math.ceil(a)"""
+        # The negations cleverly convince floordiv to return the ceiling.
+        return -(-a.numerator // a.denominator)
+
+    def __round__(self, ndigits=None):
+        """round(self, ndigits)
+
+        Rounds half toward even.
+        """
+        if ndigits is None:
+            floor, remainder = divmod(self.numerator, self.denominator)
+            if remainder * 2 < self.denominator:
+                return floor
+            elif remainder * 2 > self.denominator:
+                return floor + 1
+            # Deal with the half case:
+            elif floor % 2 == 0:
+                return floor
+            else:
+                return floor + 1
+        shift = 10**abs(ndigits)
+        # See _operator_fallbacks.forward to check that the results of
+        # these operations will always be Fraction and therefore have
+        # round().
+        if ndigits > 0:
+            return Fraction(round(self * shift), shift)
+        else:
+            return Fraction(round(self / shift) * shift)
+
+    def __hash__(self):
+        """hash(self)"""
+
+        # To make sure that the hash of a Fraction agrees with the hash
+        # of a numerically equal integer, float or Decimal instance, we
+        # follow the rules for numeric hashes outlined in the
+        # documentation.  (See library docs, 'Built-in Types').
+
+        try:
+            dinv = pow(self._denominator, -1, _PyHASH_MODULUS)
+        except ValueError:
+            # ValueError means there is no modular inverse.
+            hash_ = _PyHASH_INF
+        else:
+            # The general algorithm now specifies that the absolute value of
+            # the hash is
+            #    (|N| * dinv) % P
+            # where N is self._numerator and P is _PyHASH_MODULUS.  That's
+            # optimized here in two ways:  first, for a non-negative int i,
+            # hash(i) == i % P, but the int hash implementation doesn't need
+            # to divide, and is faster than doing % P explicitly.  So we do
+            #    hash(|N| * dinv)
+            # instead.  Second, N is unbounded, so its product with dinv may
+            # be arbitrarily expensive to compute.  The final answer is the
+            # same if we use the bounded |N| % P instead, which can again
+            # be done with an int hash() call.  If 0 <= i < P, hash(i) == i,
+            # so this nested hash() call wastes a bit of time making a
+            # redundant copy when |N| < P, but can save an arbitrarily large
+            # amount of computation for large |N|.
+            hash_ = hash(hash(abs(self._numerator)) * dinv)
+        result = hash_ if self._numerator >= 0 else -hash_
+        return -2 if result == -1 else result
+
+    def __eq__(a, b):
+        """a == b"""
+        if type(b) is int:
+            return a._numerator == b and a._denominator == 1
+        if isinstance(b, numbers.Rational):
+            return (a._numerator == b.numerator and
+                    a._denominator == b.denominator)
+        if isinstance(b, numbers.Complex) and b.imag == 0:
+            b = b.real
+        if isinstance(b, float):
+            if math.isnan(b) or math.isinf(b):
+                # comparisons with an infinity or nan should behave in
+                # the same way for any finite a, so treat a as zero.
+                return 0.0 == b
+            else:
+                return a == a.from_float(b)
+        else:
+            # Since a doesn't know how to compare with b, let's give b
+            # a chance to compare itself with a.
+            return NotImplemented
+
+    def _richcmp(self, other, op):
+        """Helper for comparison operators, for internal use only.
+
+        Implement comparison between a Rational instance `self`, and
+        either another Rational instance or a float `other`.  If
+        `other` is not a Rational instance or a float, return
+        NotImplemented. `op` should be one of the six standard
+        comparison operators.
+
+        """
+        # convert other to a Rational instance where reasonable.
+        if isinstance(other, numbers.Rational):
+            return op(self._numerator * other.denominator,
+                      self._denominator * other.numerator)
+        if isinstance(other, float):
+            if math.isnan(other) or math.isinf(other):
+                return op(0.0, other)
+            else:
+                return op(self, self.from_float(other))
+        else:
+            return NotImplemented
+
+    def __lt__(a, b):
+        """a < b"""
+        return a._richcmp(b, operator.lt)
+
+    def __gt__(a, b):
+        """a > b"""
+        return a._richcmp(b, operator.gt)
+
+    def __le__(a, b):
+        """a <= b"""
+        return a._richcmp(b, operator.le)
+
+    def __ge__(a, b):
+        """a >= b"""
+        return a._richcmp(b, operator.ge)
+
+    def __bool__(a):
+        """a != 0"""
+        # bpo-39274: Use bool() because (a._numerator != 0) can return an
+        # object which is not a bool.
+        return bool(a._numerator)
+
+    # support for pickling, copy, and deepcopy
+
+    def __reduce__(self):
+        return (self.__class__, (str(self),))
+
+    def __copy__(self):
+        if type(self) == Fraction:
+            return self     # I'm immutable; therefore I am my own clone
+        return self.__class__(self._numerator, self._denominator)
+
+    def __deepcopy__(self, memo):
+        if type(self) == Fraction:
+            return self     # My components are also immutable
+        return self.__class__(self._numerator, self._denominator)
diff --git a/shared/package-files/c9/c994a9e744edbcfaaffcf37bce655c34e5544d45cc358146720b8c363db913d6/type_check.pyi b/shared/package-files/c9/c994a9e744edbcfaaffcf37bce655c34e5544d45cc358146720b8c363db913d6/type_check.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..12af9a66d9dd9eab04ede2baf9e8471044cfbe74
--- /dev/null
+++ b/shared/package-files/c9/c994a9e744edbcfaaffcf37bce655c34e5544d45cc358146720b8c363db913d6/type_check.pyi
@@ -0,0 +1,87 @@
+import sys
+from typing import Any, Literal
+
+import numpy as np
+import numpy.typing as npt
+from numpy._typing import _16Bit, _32Bit, _64Bit, _128Bit
+
+if sys.version_info >= (3, 11):
+    from typing import assert_type
+else:
+    from typing_extensions import assert_type
+
+f8: np.float64
+f: float
+
+# NOTE: Avoid importing the platform specific `np.float128` type
+AR_i8: npt.NDArray[np.int64]
+AR_i4: npt.NDArray[np.int32]
+AR_f2: npt.NDArray[np.float16]
+AR_f8: npt.NDArray[np.float64]
+AR_f16: npt.NDArray[np.floating[_128Bit]]
+AR_c8: npt.NDArray[np.complex64]
+AR_c16: npt.NDArray[np.complex128]
+
+AR_LIKE_f: list[float]
+
+class RealObj:
+    real: slice
+
+class ImagObj:
+    imag: slice
+
+assert_type(np.mintypecode(["f8"], typeset="qfQF"), str)
+
+assert_type(np.asfarray(AR_f8), npt.NDArray[np.float64])
+assert_type(np.asfarray(AR_LIKE_f), npt.NDArray[np.float64])
+assert_type(np.asfarray(AR_f8, dtype="c16"), npt.NDArray[np.complexfloating[Any, Any]])
+assert_type(np.asfarray(AR_f8, dtype="i8"), npt.NDArray[np.floating[Any]])
+
+assert_type(np.real(RealObj()), slice)
+assert_type(np.real(AR_f8), npt.NDArray[np.float64])
+assert_type(np.real(AR_c16), npt.NDArray[np.float64])
+assert_type(np.real(AR_LIKE_f), npt.NDArray[Any])
+
+assert_type(np.imag(ImagObj()), slice)
+assert_type(np.imag(AR_f8), npt.NDArray[np.float64])
+assert_type(np.imag(AR_c16), npt.NDArray[np.float64])
+assert_type(np.imag(AR_LIKE_f), npt.NDArray[Any])
+
+assert_type(np.iscomplex(f8), np.bool_)
+assert_type(np.iscomplex(AR_f8), npt.NDArray[np.bool_])
+assert_type(np.iscomplex(AR_LIKE_f), npt.NDArray[np.bool_])
+
+assert_type(np.isreal(f8), np.bool_)
+assert_type(np.isreal(AR_f8), npt.NDArray[np.bool_])
+assert_type(np.isreal(AR_LIKE_f), npt.NDArray[np.bool_])
+
+assert_type(np.iscomplexobj(f8), bool)
+assert_type(np.isrealobj(f8), bool)
+
+assert_type(np.nan_to_num(f8), np.float64)
+assert_type(np.nan_to_num(f, copy=True), Any)
+assert_type(np.nan_to_num(AR_f8, nan=1.5), npt.NDArray[np.float64])
+assert_type(np.nan_to_num(AR_LIKE_f, posinf=9999), npt.NDArray[Any])
+
+assert_type(np.real_if_close(AR_f8), npt.NDArray[np.float64])
+assert_type(np.real_if_close(AR_c16), npt.NDArray[np.float64] | npt.NDArray[np.complex128])
+assert_type(np.real_if_close(AR_c8), npt.NDArray[np.float32] | npt.NDArray[np.complex64])
+assert_type(np.real_if_close(AR_LIKE_f), npt.NDArray[Any])
+
+assert_type(np.typename("h"), Literal["short"])
+assert_type(np.typename("B"), Literal["unsigned char"])
+assert_type(np.typename("V"), Literal["void"])
+assert_type(np.typename("S1"), Literal["character"])
+
+assert_type(np.common_type(AR_i4), type[np.float64])
+assert_type(np.common_type(AR_f2), type[np.float16])
+assert_type(np.common_type(AR_f2, AR_i4), type[np.floating[_16Bit | _64Bit]])
+assert_type(np.common_type(AR_f16, AR_i4), type[np.floating[_64Bit | _128Bit]])
+assert_type(
+    np.common_type(AR_c8, AR_f2),
+    type[np.complexfloating[_16Bit | _32Bit, _16Bit | _32Bit]],
+)
+assert_type(
+    np.common_type(AR_f2, AR_c8, AR_i4),
+    type[np.complexfloating[_16Bit | _32Bit | _64Bit, _16Bit | _32Bit | _64Bit]],
+)
diff --git a/shared/package-files/c9/c997a24c5e83b1e8c2b91332f7433b244285e802eb2d5729d98d42870150c9e4/min_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/c9/c997a24c5e83b1e8c2b91332f7433b244285e802eb2d5729d98d42870150c9e4/min_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..a7d972ba66aab4338481312c15d58c47860c6a28
--- /dev/null
+++ b/shared/package-files/c9/c997a24c5e83b1e8c2b91332f7433b244285e802eb2d5729d98d42870150c9e4/min_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API ::std::tuple min(const at::Tensor & self, int64_t dim, bool keepdim=false);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/c9/c9a9fc95d7dfaa1d1a0191c87ea366cc2c6be0f94ec09d1ab580f65dd3bdcaca/decorators.py b/shared/package-files/c9/c9a9fc95d7dfaa1d1a0191c87ea366cc2c6be0f94ec09d1ab580f65dd3bdcaca/decorators.py
new file mode 100644
index 0000000000000000000000000000000000000000..854a63fea670362d8bf0cec9bdd06678fe20c2c6
--- /dev/null
+++ b/shared/package-files/c9/c9a9fc95d7dfaa1d1a0191c87ea366cc2c6be0f94ec09d1ab580f65dd3bdcaca/decorators.py
@@ -0,0 +1,305 @@
+"""
+Define @jit and related decorators.
+"""
+
+
+import sys
+import warnings
+import inspect
+import logging
+from types import MappingProxyType
+
+from numba.core.errors import DeprecationError, NumbaDeprecationWarning
+from numba.stencils.stencil import stencil
+from numba.core import config, extending, sigutils, registry
+
+_logger = logging.getLogger(__name__)
+
+
+# -----------------------------------------------------------------------------
+# Decorators
+
+_msg_deprecated_signature_arg = ("Deprecated keyword argument `{0}`. "
+                                 "Signatures should be passed as the first "
+                                 "positional argument.")
+
+
+def jit(signature_or_function=None, locals=MappingProxyType({}), cache=False,
+        pipeline_class=None, boundscheck=None, **options):
+    """
+    This decorator is used to compile a Python function into native code.
+
+    Args
+    -----
+    signature_or_function:
+        The (optional) signature or list of signatures to be compiled.
+        If not passed, required signatures will be compiled when the
+        decorated function is called, depending on the argument values.
+        As a convenience, you can directly pass the function to be compiled
+        instead.
+
+    locals: dict
+        Mapping of local variable names to Numba types. Used to override the
+        types deduced by Numba's type inference engine.
+
+    pipeline_class: type numba.compiler.CompilerBase
+            The compiler pipeline type for customizing the compilation stages.
+
+    options:
+        For a cpu target, valid options are:
+            nopython: bool
+                Set to True to disable the use of PyObjects and Python API
+                calls. The default behavior is to allow the use of PyObjects
+                and Python API. Default value is True.
+
+            forceobj: bool
+                Set to True to force the use of PyObjects for every value.
+                Default value is False.
+
+            looplift: bool
+                Set to True to enable jitting loops in nopython mode while
+                leaving surrounding code in object mode. This allows functions
+                to allocate NumPy arrays and use Python objects, while the
+                tight loops in the function can still be compiled in nopython
+                mode. Any arrays that the tight loop uses should be created
+                before the loop is entered. Default value is True.
+
+            error_model: str
+                The error-model affects divide-by-zero behavior.
+                Valid values are 'python' and 'numpy'. The 'python' model
+                raises exception.  The 'numpy' model sets the result to
+                *+/-inf* or *nan*. Default value is 'python'.
+
+            inline: str or callable
+                The inline option will determine whether a function is inlined
+                at into its caller if called. String options are 'never'
+                (default) which will never inline, and 'always', which will
+                always inline. If a callable is provided it will be called with
+                the call expression node that is requesting inlining, the
+                caller's IR and callee's IR as arguments, it is expected to
+                return Truthy as to whether to inline.
+                NOTE: This inlining is performed at the Numba IR level and is in
+                no way related to LLVM inlining.
+
+            boundscheck: bool or None
+                Set to True to enable bounds checking for array indices. Out
+                of bounds accesses will raise IndexError. The default is to
+                not do bounds checking. If False, bounds checking is disabled,
+                out of bounds accesses can produce garbage results or segfaults.
+                However, enabling bounds checking will slow down typical
+                functions, so it is recommended to only use this flag for
+                debugging. You can also set the NUMBA_BOUNDSCHECK environment
+                variable to 0 or 1 to globally override this flag. The default
+                value is None, which under normal execution equates to False,
+                but if debug is set to True then bounds checking will be
+                enabled.
+
+    Returns
+    -------
+    A callable usable as a compiled function.  Actual compiling will be
+    done lazily if no explicit signatures are passed.
+
+    Examples
+    --------
+    The function can be used in the following ways:
+
+    1) jit(signatures, **targetoptions) -> jit(function)
+
+        Equivalent to:
+
+            d = dispatcher(function, targetoptions)
+            for signature in signatures:
+                d.compile(signature)
+
+        Create a dispatcher object for a python function.  Then, compile
+        the function with the given signature(s).
+
+        Example:
+
+            @jit("int32(int32, int32)")
+            def foo(x, y):
+                return x + y
+
+            @jit(["int32(int32, int32)", "float32(float32, float32)"])
+            def bar(x, y):
+                return x + y
+
+    2) jit(function, **targetoptions) -> dispatcher
+
+        Create a dispatcher function object that specializes at call site.
+
+        Examples:
+
+            @jit
+            def foo(x, y):
+                return x + y
+
+            @jit(nopython=True)
+            def bar(x, y):
+                return x + y
+
+    """
+    locals = dict(locals)
+    forceobj = options.get('forceobj', False)
+    if 'argtypes' in options:
+        raise DeprecationError(_msg_deprecated_signature_arg.format('argtypes'))
+    if 'restype' in options:
+        raise DeprecationError(_msg_deprecated_signature_arg.format('restype'))
+    nopython = options.get('nopython', None)
+    if nopython is not None:
+        assert type(nopython) is bool, "nopython option must be a bool"
+    if nopython is True and forceobj:
+        raise ValueError("Only one of 'nopython' or 'forceobj' can be True.")
+    target = options.pop('_target', 'cpu')
+
+    if nopython is False:
+        msg = ("The keyword argument 'nopython=False' was supplied. From "
+               "Numba 0.59.0 the default is True and supplying this argument "
+               "has no effect.")
+        warnings.warn(msg, NumbaDeprecationWarning)
+    # nopython is True by default since 0.59.0, but if `forceobj` is set
+    # `nopython` needs to set to False so that things like typing of args in the
+    # dispatcher layer continues to work.
+    if forceobj:
+        options['nopython'] = False
+    else:
+        options['nopython'] = True
+
+    options['boundscheck'] = boundscheck
+
+    # Handle signature
+    if signature_or_function is None:
+        # No signature, no function
+        pyfunc = None
+        sigs = None
+    elif isinstance(signature_or_function, list):
+        # A list of signatures is passed
+        pyfunc = None
+        sigs = signature_or_function
+    elif sigutils.is_signature(signature_or_function):
+        # A single signature is passed
+        pyfunc = None
+        sigs = [signature_or_function]
+    else:
+        # A function is passed
+        pyfunc = signature_or_function
+        sigs = None
+
+    dispatcher_args = {}
+    if pipeline_class is not None:
+        dispatcher_args['pipeline_class'] = pipeline_class
+    wrapper = _jit(sigs, locals=locals, target=target, cache=cache,
+                   targetoptions=options, **dispatcher_args)
+    if pyfunc is not None:
+        return wrapper(pyfunc)
+    else:
+        return wrapper
+
+
+def _jit(sigs, locals, target, cache, targetoptions, **dispatcher_args):
+
+    from numba.core.target_extension import resolve_dispatcher_from_str
+    dispatcher = resolve_dispatcher_from_str(target)
+
+    def wrapper(func):
+        if extending.is_jitted(func):
+            raise TypeError(
+                "A jit decorator was called on an already jitted function "
+                f"{func}.  If trying to access the original python "
+                f"function, use the {func}.py_func attribute."
+            )
+
+        if not inspect.isfunction(func):
+            raise TypeError(
+                "The decorated object is not a function (got type "
+                f"{type(func)})."
+            )
+
+        if config.ENABLE_CUDASIM and target == 'cuda':
+            from numba import cuda
+            return cuda.jit(func)
+        if config.DISABLE_JIT and not target == 'npyufunc':
+            return func
+        disp = dispatcher(py_func=func, locals=locals,
+                          targetoptions=targetoptions,
+                          **dispatcher_args)
+        if cache:
+            disp.enable_caching()
+        if sigs is not None:
+            # Register the Dispatcher to the type inference mechanism,
+            # even though the decorator hasn't returned yet.
+            from numba.core import typeinfer
+            with typeinfer.register_dispatcher(disp):
+                for sig in sigs:
+                    disp.compile(sig)
+                disp.disable_compile()
+        return disp
+
+    return wrapper
+
+
+def njit(*args, **kws):
+    """
+    Legacy decorator that is equivalent to the preferred API: jit().
+
+    See documentation for jit function/decorator for full description.
+    """
+    if 'nopython' in kws:
+        warnings.warn('nopython is set for njit and is ignored', RuntimeWarning)
+    if 'forceobj' in kws:
+        warnings.warn('forceobj is set for njit and is ignored', RuntimeWarning)
+        del kws['forceobj']
+    kws.update({'nopython': True})
+    return jit(*args, **kws)
+
+
+def cfunc(sig, locals=MappingProxyType({}), cache=False, pipeline_class=None, **options):
+    """
+    This decorator is used to compile a Python function into a C callback
+    usable with foreign C libraries.
+
+    Usage::
+        @cfunc("float64(float64, float64)", nopython=True, cache=True)
+        def add(a, b):
+            return a + b
+
+    """
+    locals = dict(locals)
+    sig = sigutils.normalize_signature(sig)
+
+    def wrapper(func):
+        from numba.core.ccallback import CFunc
+        additional_args = {}
+        if pipeline_class is not None:
+            additional_args['pipeline_class'] = pipeline_class
+        res = CFunc(func, sig, locals=locals, options=options, **additional_args)
+        if cache:
+            res.enable_caching()
+        res.compile()
+        return res
+
+    return wrapper
+
+
+def jit_module(**kwargs):
+    """ Automatically ``jit``-wraps functions defined in a Python module
+
+    Note that ``jit_module`` should only be called at the end of the module to
+    be jitted. In addition, only functions which are defined in the module
+    ``jit_module`` is called from are considered for automatic jit-wrapping.
+    See the Numba documentation for more information about what can/cannot be
+    jitted.
+
+    :param kwargs: Keyword arguments to pass to ``jit`` such as ``nopython``
+                   or ``error_model``.
+
+    """
+    # Get the module jit_module is being called from
+    frame = inspect.stack()[1]
+    module = inspect.getmodule(frame[0])
+    # Replace functions in module with jit-wrapped versions
+    for name, obj in module.__dict__.items():
+        if inspect.isfunction(obj) and inspect.getmodule(obj) == module:
+            _logger.debug("Auto decorating function {} from module {} with jit "
+                          "and options: {}".format(obj, module.__name__, kwargs))
+            module.__dict__[name] = jit(obj, **kwargs)
diff --git a/shared/package-files/c9/c9acee7890b226f7a3523bb7a2ef0180db7e1311caf3cc50ff71768fb4e69c2a/test_survfunc.py b/shared/package-files/c9/c9acee7890b226f7a3523bb7a2ef0180db7e1311caf3cc50ff71768fb4e69c2a/test_survfunc.py
new file mode 100644
index 0000000000000000000000000000000000000000..15d016115ea18f17d446bfc0d35f2b796ee4ce3b
--- /dev/null
+++ b/shared/package-files/c9/c9acee7890b226f7a3523bb7a2ef0180db7e1311caf3cc50ff71768fb4e69c2a/test_survfunc.py
@@ -0,0 +1,624 @@
+from pathlib import Path
+
+import numpy as np
+from numpy.testing import assert_allclose
+import pandas as pd
+import pytest
+
+from statsmodels.duration.survfunc import (
+    CumIncidenceRight,
+    SurvfuncRight,
+    plot_survfunc,
+    survdiff,
+)
+
+try:
+    import matplotlib.pyplot as plt
+except ImportError:
+    pass
+
+
+"""
+library(survival)
+ti1 = c(3, 1, 2, 3, 2, 1, 5, 3)
+st1 = c(0, 1, 1, 1, 0, 0, 1, 0)
+ti2 = c(1, 1, 2, 3, 7, 1, 5, 3, 9)
+st2 = c(0, 1, 0, 0, 1, 0, 1, 0, 1)
+
+ti = c(ti1, ti2)
+st = c(st1, st2)
+ix = c(rep(1, length(ti1)), rep(2, length(ti2)))
+sd = survdiff(Surv(ti, st) ~ ix)
+"""
+
+ti1 = np.r_[3, 1, 2, 3, 2, 1, 5, 3]
+st1 = np.r_[0, 1, 1, 1, 0, 0, 1, 0]
+times1 = np.r_[1, 2, 3, 5]
+surv_prob1 = np.r_[0.8750000, 0.7291667, 0.5468750, 0.0000000]
+surv_prob_se1 = np.r_[0.1169268, 0.1649762, 0.2005800, np.nan]
+n_risk1 = np.r_[8, 6, 4, 1]
+n_events1 = np.r_[1.0, 1.0, 1.0, 1.0]
+
+ti2 = np.r_[1, 1, 2, 3, 7, 1, 5, 3, 9]
+st2 = np.r_[0, 1, 0, 0, 1, 0, 1, 0, 1]
+times2 = np.r_[1, 5, 7, 9]
+surv_prob2 = np.r_[0.8888889, 0.5925926, 0.2962963, 0.0000000]
+surv_prob_se2 = np.r_[0.1047566, 0.2518034, 0.2444320, np.nan]
+n_risk2 = np.r_[9, 3, 2, 1]
+n_events2 = np.r_[1.0, 1.0, 1.0, 1.0]
+
+cur_dir = Path(__file__).resolve().parent
+fp = Path(cur_dir).joinpath("results", "bmt.csv")
+bmt = pd.read_csv(fp)
+
+
+def test_survfunc1():
+    # Test where all times have at least 1 event.
+
+    sr = SurvfuncRight(ti1, st1)
+    assert_allclose(sr.surv_prob, surv_prob1, atol=1e-5, rtol=1e-5)
+    assert_allclose(sr.surv_prob_se, surv_prob_se1, atol=1e-5, rtol=1e-5)
+    assert_allclose(sr.surv_times, times1)
+    assert_allclose(sr.n_risk, n_risk1)
+    assert_allclose(sr.n_events, n_events1)
+
+    df = sr.summary()
+    assert list(df.index) == list(sr.surv_times)
+    assert_allclose(df["Surv prob"].values, sr.surv_prob)
+    assert_allclose(df["Surv prob SE"].values, sr.surv_prob_se)
+    assert_allclose(df["num at risk"].values, sr.n_risk)
+    assert_allclose(df["num events"].values, sr.n_events)
+
+    # quantile(p): first time where the survival curve drops below 1-p,
+    # i.e. the smallest t with P(T > t) < 1-p
+    for p in (0.1, 0.5, 0.9):
+        q = sr.quantile(p)
+        ii = np.flatnonzero(sr.surv_prob < 1 - p)
+        expected = np.nan if len(ii) == 0 else sr.surv_times[ii[0]]
+        if np.isnan(expected):
+            assert np.isnan(q)
+        else:
+            assert q == expected
+            # the survival probability at (just before) the quantile has
+            # indeed just dropped below 1-p
+            assert sr.surv_prob[sr.surv_times == q][0] < 1 - p
+
+    # p beyond the lowest observed survival probability: no such time
+    p_beyond = 1 - sr.surv_prob.min() + 0.01
+    assert np.isnan(sr.quantile(p_beyond))
+
+
+def test_survfunc2():
+    # Test where some times have no events.
+
+    sr = SurvfuncRight(ti2, st2)
+    assert_allclose(sr.surv_prob, surv_prob2, atol=1e-5, rtol=1e-5)
+    assert_allclose(sr.surv_prob_se, surv_prob_se2, atol=1e-5, rtol=1e-5)
+    assert_allclose(sr.surv_times, times2)
+    assert_allclose(sr.n_risk, n_risk2)
+    assert_allclose(sr.n_events, n_events2)
+
+
+def test_survdiff_basic():
+
+    # Constants taken from R, code above
+    ti = np.concatenate((ti1, ti2))
+    st = np.concatenate((st1, st2))
+    groups = np.ones(len(ti))
+    groups[0 : len(ti1)] = 0
+    z, p = survdiff(ti, st, groups)
+    assert_allclose(z, 2.14673, atol=1e-4, rtol=1e-4)
+    assert_allclose(p, 0.14287, atol=1e-4, rtol=1e-4)
+
+
+def test_simultaneous_cb():
+
+    # The exact numbers here are regression tests, but they are close
+    # to page 103 of Klein and Moeschberger.
+
+    df = bmt.loc[bmt["Group"] == "ALL", :]
+    sf = SurvfuncRight(df["T"], df["Status"])
+    lcb1, ucb1 = sf.simultaneous_cb(transform="log")
+    lcb2, ucb2 = sf.simultaneous_cb(transform="arcsin")
+
+    ti = sf.surv_times.tolist()
+    ix = [ti.index(x) for x in (110, 122, 129, 172)]
+    assert_allclose(lcb1[ix], np.r_[0.43590582, 0.42115592, 0.4035897, 0.38785927])
+    assert_allclose(ucb1[ix], np.r_[0.93491636, 0.89776803, 0.87922239, 0.85894181])
+
+    assert_allclose(lcb2[ix], np.r_[0.52115708, 0.48079378, 0.45595321, 0.43341115])
+    assert_allclose(ucb2[ix], np.r_[0.96465636, 0.92745068, 0.90885428, 0.88796708])
+
+    with pytest.raises(ValueError, match="method"):
+        sf.simultaneous_cb(method="not-a-method")
+    with pytest.raises(ValueError, match="transform"):
+        sf.simultaneous_cb(transform="not-a-transform")
+
+
+def test_bmt():
+    # All tests against SAS
+    # Results taken from here:
+    # http://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_lifetest_details03.htm
+
+    # Confidence intervals for 25% percentile of the survival
+    # distribution (for "ALL" subjects), taken from the SAS web site
+    cb = {
+        "linear": [107, 276],
+        "cloglog": [86, 230],
+        "log": [107, 332],
+        "asinsqrt": [104, 276],
+        "logit": [104, 230],
+    }
+
+    dfa = bmt[bmt.Group == "ALL"]
+
+    cur_dir = Path(__file__).resolve().parent
+    fp = Path(cur_dir).joinpath("results", "bmt_results.csv")
+    rslt = pd.read_csv(fp)
+
+    sf = SurvfuncRight(dfa["T"].values, dfa.Status.values)
+
+    assert_allclose(sf.surv_times, rslt.t)
+    assert_allclose(sf.surv_prob, rslt.s, atol=1e-4, rtol=1e-4)
+    assert_allclose(sf.surv_prob_se, rslt.se, atol=1e-4, rtol=1e-4)
+
+    for method in "linear", "cloglog", "log", "logit", "asinsqrt":
+        lcb, ucb = sf.quantile_ci(0.25, method=method)
+        assert_allclose(cb[method], np.r_[lcb, ucb])
+
+    with pytest.raises(ValueError, match="method"):
+        sf.quantile_ci(0.25, method="not-a-method")
+
+
+def test_survdiff():
+    # Results come from R survival and survMisc packages (survMisc is
+    # used for non G-rho family tests but does not seem to support
+    # stratification)
+
+    full_df = bmt.copy()
+    df = bmt[bmt.Group != "ALL"].copy()
+
+    # Not stratified
+    stat, p = survdiff(df["T"], df.Status, df.Group)
+    assert_allclose(stat, 13.44556, atol=1e-4, rtol=1e-4)
+    stat, p = survdiff(df["T"], df.Status, df.Group, weight_type="gb")
+    assert_allclose(stat, 15.38787, atol=1e-4, rtol=1e-4)
+    stat, p = survdiff(df["T"], df.Status, df.Group, weight_type="tw")
+    assert_allclose(stat, 14.98382, atol=1e-4, rtol=1e-4)
+    stat, p = survdiff(df["T"], df.Status, df.Group, weight_type="fh", fh_p=0.5)
+    assert_allclose(stat, 14.46866, atol=1e-4, rtol=1e-4)
+    stat, p = survdiff(df["T"], df.Status, df.Group, weight_type="fh", fh_p=1)
+    assert_allclose(stat, 14.84500, atol=1e-4, rtol=1e-4)
+
+    # Not stratified, >2 groups
+    stat, p = survdiff(
+        full_df["T"], full_df.Status, full_df.Group, weight_type="fh", fh_p=1
+    )
+    assert_allclose(stat, 15.67247, atol=1e-4, rtol=1e-4)
+
+    # 5 strata
+    strata = np.arange(df.shape[0]) % 5
+    df["strata"] = strata
+    stat, p = survdiff(df["T"], df.Status, df.Group, strata=df.strata)
+    assert_allclose(stat, 11.97799, atol=1e-4, rtol=1e-4)
+    stat, p = survdiff(
+        df["T"], df.Status, df.Group, strata=df.strata, weight_type="fh", fh_p=0.5
+    )
+    assert_allclose(stat, 12.6257, atol=1e-4, rtol=1e-4)
+    stat, p = survdiff(
+        df["T"], df.Status, df.Group, strata=df.strata, weight_type="fh", fh_p=1
+    )
+    assert_allclose(stat, 12.73565, atol=1e-4, rtol=1e-4)
+
+    # 5 strata, >2 groups
+    full_strata = np.arange(full_df.shape[0]) % 5
+    full_df["strata"] = full_strata
+    stat, p = survdiff(
+        full_df["T"],
+        full_df.Status,
+        full_df.Group,
+        strata=full_df.strata,
+        weight_type="fh",
+        fh_p=0.5,
+    )
+    assert_allclose(stat, 13.56793, atol=1e-4, rtol=1e-4)
+
+    # 8 strata
+    df["strata"] = np.arange(df.shape[0]) % 8
+    stat, p = survdiff(df["T"], df.Status, df.Group, strata=df.strata)
+    assert_allclose(stat, 12.12631, atol=1e-4, rtol=1e-4)
+    stat, p = survdiff(
+        df["T"], df.Status, df.Group, strata=df.strata, weight_type="fh", fh_p=0.5
+    )
+    assert_allclose(stat, 12.9633, atol=1e-4, rtol=1e-4)
+    stat, p = survdiff(
+        df["T"], df.Status, df.Group, strata=df.strata, weight_type="fh", fh_p=1
+    )
+    assert_allclose(stat, 13.35259, atol=1e-4, rtol=1e-4)
+
+
+@pytest.mark.thread_unsafe(reason="Uses matplotlib")
+@pytest.mark.matplotlib
+def test_plot_km(close_figures):
+
+    sr1 = SurvfuncRight(ti1, st1)
+    sr2 = SurvfuncRight(ti2, st2)
+
+    fig = plot_survfunc(sr1)
+
+    fig = plot_survfunc(sr2)
+
+    fig = plot_survfunc([sr1, sr2])
+
+    # Plot the SAS BMT data
+    gb = bmt.groupby("Group")
+    sv = []
+    for g in gb:
+        s0 = SurvfuncRight(g[1]["T"], g[1]["Status"], title=g[0])
+        sv.append(s0)
+    fig = plot_survfunc(sv)
+    ax = fig.get_axes()[0]
+    ax.set_position([0.1, 0.1, 0.64, 0.8])
+    ha, lb = ax.get_legend_handles_labels()
+    fig.legend(
+        [ha[k] for k in (0, 2, 4)], [lb[k] for k in (0, 2, 4)], loc="center right"
+    )
+
+    # Simultaneous CB for BMT data
+    ii = bmt.Group == "ALL"
+    sf = SurvfuncRight(bmt.loc[ii, "T"], bmt.loc[ii, "Status"])
+    fig = sf.plot()
+    ax = fig.get_axes()[0]
+    ax.set_position([0.1, 0.1, 0.64, 0.8])
+    ha, lb = ax.get_legend_handles_labels()
+    lcb, ucb = sf.simultaneous_cb(transform="log")
+    plt.fill_between(sf.surv_times, lcb, ucb, color="lightgrey")
+    lcb, ucb = sf.simultaneous_cb(transform="arcsin")
+    plt.plot(sf.surv_times, lcb, color="darkgrey")
+    plt.plot(sf.surv_times, ucb, color="darkgrey")
+    plt.plot(sf.surv_times, sf.surv_prob - 2 * sf.surv_prob_se, color="red")
+    plt.plot(sf.surv_times, sf.surv_prob + 2 * sf.surv_prob_se, color="red")
+    plt.xlim(100, 600)
+
+
+def test_weights1():
+    # tm = c(1, 3, 5, 6, 7, 8, 8, 9, 3, 4, 1, 3, 2)
+    # st = c(1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0)
+    # wt = c(1, 2, 3, 2, 3, 1, 2, 1, 1, 2, 2, 3, 1)
+    # library(survival)
+    # sf = survfit(Surv(tm, st) ~ 1, weights=wt, err='tsiatis')
+
+    tm = np.r_[1, 3, 5, 6, 7, 8, 8, 9, 3, 4, 1, 3, 2]
+    st = np.r_[1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0]
+    wt = np.r_[1, 2, 3, 2, 3, 1, 2, 1, 1, 2, 2, 3, 1]
+
+    sf = SurvfuncRight(tm, st, freq_weights=wt)
+    assert_allclose(sf.surv_times, np.r_[1, 3, 6, 7, 9])
+    assert_allclose(sf.surv_prob, np.r_[0.875, 0.65625, 0.51041667, 0.29166667, 0.0])
+    assert_allclose(
+        sf.surv_prob_se,
+        np.r_[0.07216878, 0.13307266, 0.20591185, 0.3219071, 1.05053519],
+    )
+
+
+def test_weights2():
+    # tm = c(1, 3, 5, 6, 7, 2, 4, 6, 8, 10)
+    # st = c(1, 1, 0, 1, 1, 1, 1, 0, 1, 1)
+    # wt = c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2)
+    # library(survival)
+    # sf =s urvfit(Surv(tm, st) ~ 1, weights=wt, err='tsiatis')
+
+    tm = np.r_[1, 3, 5, 6, 7, 2, 4, 6, 8, 10]
+    st = np.r_[1, 1, 0, 1, 1, 1, 1, 0, 1, 1]
+    wt = np.r_[1, 1, 1, 1, 1, 2, 2, 2, 2, 2]
+    tm0 = np.r_[1, 3, 5, 6, 7, 2, 4, 6, 8, 10, 2, 4, 6, 8, 10]
+    st0 = np.r_[1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1]
+
+    sf0 = SurvfuncRight(tm, st, freq_weights=wt)
+    sf1 = SurvfuncRight(tm0, st0)
+
+    assert_allclose(sf0.surv_times, sf1.surv_times)
+    assert_allclose(sf0.surv_prob, sf1.surv_prob)
+
+    assert_allclose(
+        sf0.surv_prob_se,
+        np.r_[
+            0.06666667,
+            0.1210311,
+            0.14694547,
+            0.19524829,
+            0.23183377,
+            0.30618115,
+            0.46770386,
+            0.84778942,
+        ],
+    )
+
+
+def test_incidence():
+    # Check estimates in R:
+    # ftime = c(1, 1, 2, 4, 4, 4, 6, 6, 7, 8, 9, 9, 9, 1, 2, 2, 4, 4)
+    # fstat = c(1, 1, 1, 2, 2, 2, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0)
+    # cuminc(ftime, fstat)
+    #
+    # The standard errors agree with Stata, not with R (cmprisk
+    # package), which uses a different SE formula from Aalen (1978)
+    #
+    # To check with Stata:
+    # stset ftime failure(fstat==1)
+    # stcompet ci=ci, compet1(2)
+
+    ftime = np.r_[1, 1, 2, 4, 4, 4, 6, 6, 7, 8, 9, 9, 9, 1, 2, 2, 4, 4]
+    fstat = np.r_[1, 1, 1, 2, 2, 2, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+
+    ci = CumIncidenceRight(ftime, fstat)
+
+    cinc = [
+        np.array(
+            [
+                0.11111111,
+                0.17037037,
+                0.17037037,
+                0.17037037,
+                0.17037037,
+                0.17037037,
+                0.17037037,
+            ]
+        ),
+        np.array(
+            [0.0, 0.0, 0.20740741, 0.20740741, 0.20740741, 0.20740741, 0.20740741]
+        ),
+        np.array([0.0, 0.0, 0.0, 0.17777778, 0.26666667, 0.26666667, 0.26666667]),
+    ]
+    assert_allclose(cinc[0], ci.cinc[0])
+    assert_allclose(cinc[1], ci.cinc[1])
+    assert_allclose(cinc[2], ci.cinc[2])
+
+    cinc_se = [
+        np.array(
+            [
+                0.07407407,
+                0.08976251,
+                0.08976251,
+                0.08976251,
+                0.08976251,
+                0.08976251,
+                0.08976251,
+            ]
+        ),
+        np.array(
+            [0.0, 0.0, 0.10610391, 0.10610391, 0.10610391, 0.10610391, 0.10610391]
+        ),
+        np.array([0.0, 0.0, 0.0, 0.11196147, 0.12787781, 0.12787781, 0.12787781]),
+    ]
+    assert_allclose(cinc_se[0], ci.cinc_se[0])
+    assert_allclose(cinc_se[1], ci.cinc_se[1])
+    assert_allclose(cinc_se[2], ci.cinc_se[2])
+
+    # Simple check for frequency weights
+    weights = np.ones(len(ftime))
+    ciw = CumIncidenceRight(ftime, fstat, freq_weights=weights)
+    assert_allclose(ci.cinc[0], ciw.cinc[0])
+    assert_allclose(ci.cinc[1], ciw.cinc[1])
+    assert_allclose(ci.cinc[2], ciw.cinc[2])
+
+
+def test_survfunc_entry_1():
+    # times = c(1, 3, 3, 5, 5, 7, 7, 8, 8, 9, 10, 10)
+    # status = c(1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1)
+    # entry = c(0, 1, 1, 2, 2, 2, 3, 4, 4, 4, 4, 0)
+    # sv = Surv(entry, times, event=status)
+    # sdf = survfit(coxph(sv ~ 1), type='kaplan-meier')
+
+    times = np.r_[1, 3, 3, 5, 5, 7, 7, 8, 8, 9, 10, 10]
+    status = np.r_[1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1]
+    entry = np.r_[0, 1, 1, 2, 2, 2, 3, 4, 4, 4, 4, 0]
+
+    sf = SurvfuncRight(times, status, entry=entry)
+
+    assert_allclose(sf.n_risk, np.r_[2, 6, 9, 7, 5, 3, 2])
+    assert_allclose(sf.surv_times, np.r_[1, 3, 5, 7, 8, 9, 10])
+    assert_allclose(
+        sf.surv_prob,
+        np.r_[0.5000, 0.4167, 0.3241, 0.2778, 0.2222, 0.1481, 0.0741],
+        atol=1e-4,
+    )
+    assert_allclose(
+        sf.surv_prob_se,
+        np.r_[0.3536, 0.3043, 0.2436, 0.2132, 0.1776, 0.1330, 0.0846],
+        atol=1e-4,
+    )
+
+
+def test_survfunc_entry_2():
+    # entry = 0 is equivalent to no entry time
+
+    times = np.r_[1, 3, 3, 5, 5, 7, 7, 8, 8, 9, 10, 10]
+    status = np.r_[1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1]
+    entry = np.r_[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+
+    sf = SurvfuncRight(times, status, entry=entry)
+    sf0 = SurvfuncRight(times, status)
+
+    assert_allclose(sf.n_risk, sf0.n_risk)
+    assert_allclose(sf.surv_times, sf0.surv_times)
+    assert_allclose(sf.surv_prob, sf0.surv_prob)
+    assert_allclose(sf.surv_prob_se, sf0.surv_prob_se)
+
+
+def test_survfunc_entry_3():
+    # times = c(1, 2, 5, 6, 6, 6, 6, 6, 9)
+    # status = c(0, 0, 1, 1, 1, 0, 1, 1, 0)
+    # entry = c(0, 1, 1, 2, 2, 2, 3, 4, 4)
+    # sv = Surv(entry, times, event=status)
+    # sdf = survfit(coxph(sv ~ 1), type='kaplan-meier')
+
+    times = np.r_[1, 2, 5, 6, 6, 6, 6, 6, 9]
+    status = np.r_[0, 0, 1, 1, 1, 0, 1, 1, 0]
+    entry = np.r_[0, 1, 1, 2, 2, 2, 3, 4, 4]
+
+    sf = SurvfuncRight(times, status, entry=entry)
+
+    assert_allclose(sf.n_risk, np.r_[7, 6])
+    assert_allclose(sf.surv_times, np.r_[5, 6])
+    assert_allclose(sf.surv_prob, np.r_[0.857143, 0.285714], atol=1e-5)
+    assert_allclose(sf.surv_prob_se, np.r_[0.13226, 0.170747], atol=1e-5)
+
+
+def test_survdiff_entry_1():
+    # entry times = 0 is equivalent to no entry times
+    ti = np.r_[1, 3, 4, 2, 5, 4, 6, 7, 5, 9]
+    st = np.r_[1, 1, 0, 1, 1, 0, 1, 1, 0, 0]
+    gr = np.r_[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
+    entry = np.r_[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+    z1, p1 = survdiff(ti, st, gr, entry=entry)
+    z2, p2 = survdiff(ti, st, gr)
+    assert_allclose(z1, z2)
+    assert_allclose(p1, p2)
+
+
+def test_survdiff_entry_2():
+    # Tests against Stata:
+    #
+    # stset time, failure(status) entry(entry)
+    # sts test group, logrank
+
+    ti = np.r_[5, 3, 4, 2, 5, 4, 6, 7, 5, 9]
+    st = np.r_[1, 1, 0, 1, 1, 0, 1, 1, 0, 0]
+    gr = np.r_[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
+    entry = np.r_[1, 2, 2, 1, 3, 3, 5, 4, 2, 5]
+
+    # Check with no entry times
+    z, p = survdiff(ti, st, gr)
+    assert_allclose(z, 6.694424)
+    assert_allclose(p, 0.00967149)
+
+    # Check with entry times
+    z, p = survdiff(ti, st, gr, entry=entry)
+    assert_allclose(z, 3.0)
+    assert_allclose(p, 0.083264516)
+
+
+def test_survdiff_entry_3():
+    # Tests against Stata:
+    #
+    # stset time, failure(status) entry(entry)
+    # sts test group, logrank
+
+    ti = np.r_[2, 1, 5, 8, 7, 8, 8, 9, 4, 9]
+    st = np.r_[1, 1, 1, 1, 1, 0, 1, 0, 0, 0]
+    gr = np.r_[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
+    entry = np.r_[1, 1, 2, 2, 3, 3, 2, 1, 2, 0]
+
+    # Check with no entry times
+    z, p = survdiff(ti, st, gr)
+    assert_allclose(z, 6.9543024)
+    assert_allclose(p, 0.008361789)
+
+    # Check with entry times
+    z, p = survdiff(ti, st, gr, entry=entry)
+    assert_allclose(z, 6.75082959)
+    assert_allclose(p, 0.00937041)
+
+
+def test_incidence2():
+    # Check that the cumulative incidence functions for all competing
+    # risks sum to the complementary survival function.
+
+    rs = np.random.RandomState(2423)
+    n = 200
+    time = -np.log(rs.uniform(size=n))
+    status = rs.randint(0, 3, size=n)
+    ii = np.argsort(time)
+    time = time[ii]
+    status = status[ii]
+    ci = CumIncidenceRight(time, status)
+    statusa = 1 * (status >= 1)
+    sf = SurvfuncRight(time, statusa)
+    x = 1 - sf.surv_prob
+    y = (ci.cinc[0] + ci.cinc[1])[np.flatnonzero(statusa)]
+    assert_allclose(x, y)
+
+
+def test_kernel_survfunc1():
+    # Regression test
+    n = 100
+    rs = np.random.RandomState(3434)
+    x = rs.normal(size=(n, 3))
+    time = rs.uniform(size=n)
+    status = rs.randint(0, 2, size=n)
+
+    result = SurvfuncRight(time, status, exog=x)
+
+    timex = np.r_[0.30721103, 0.0515439, 0.69246897, 0.16446079, 0.31308528]
+    sprob = np.r_[0.98948277, 0.98162275, 0.97129237, 0.96044668, 0.95030368]
+
+    assert_allclose(result.time[0:5], timex)
+    assert_allclose(result.surv_prob[0:5], sprob)
+
+
+def test_kernel_survfunc2():
+    # Check that when bandwidth is very large, the kernel procedure
+    # agrees with standard KM. (Note: the results do not agree
+    # perfectly when there are tied times).
+
+    n = 100
+    rs = np.random.RandomState(3434)
+    x = rs.normal(size=(n, 3))
+    time = rs.uniform(0, 10, size=n)
+    status = rs.randint(0, 2, size=n)
+
+    resultkm = SurvfuncRight(time, status)
+    result = SurvfuncRight(time, status, exog=x, bw_factor=10000)
+
+    assert_allclose(resultkm.surv_times, result.surv_times)
+    assert_allclose(resultkm.surv_prob, result.surv_prob, rtol=1e-6, atol=1e-6)
+
+
+@pytest.mark.smoke
+def test_kernel_survfunc3():
+    # cases with tied times
+
+    n = 100
+    rs = np.random.RandomState(3434)
+    x = rs.normal(size=(n, 3))
+    time = rs.randint(0, 10, size=n)
+    status = rs.randint(0, 2, size=n)
+    SurvfuncRight(time, status, exog=x, bw_factor=10000)
+    SurvfuncRight(time, status, exog=x, bw_factor=np.r_[10000, 10000])
+
+
+def test_kernel_cumincidence1():
+    # Check that when the bandwidth is very large, the kernel
+    # procedure agrees with standard cumulative incidence
+    # calculations. (Note: the results do not agree perfectly when
+    # there are tied times).
+
+    n = 100
+    rs = np.random.RandomState(3434)
+    x = rs.normal(size=(n, 3))
+    time = rs.uniform(0, 10, size=n)
+    status = rs.randint(0, 3, size=n)
+
+    result1 = CumIncidenceRight(time, status)
+
+    for dimred in False, True:
+        result2 = CumIncidenceRight(
+            time, status, exog=x, bw_factor=10000, dimred=dimred
+        )
+
+        assert_allclose(result1.times, result2.times)
+        for k in 0, 1:
+            assert_allclose(result1.cinc[k], result2.cinc[k], rtol=1e-5)
+
+
+@pytest.mark.smoke
+def test_kernel_cumincidence2():
+    # cases with tied times
+
+    n = 100
+    rs = np.random.RandomState(3434)
+    x = rs.normal(size=(n, 3))
+    time = rs.randint(0, 10, size=n)
+    status = rs.randint(0, 3, size=n)
+    CumIncidenceRight(time, status, exog=x, bw_factor=10000)
diff --git a/shared/package-files/c9/c9acfeaaa22fac96a6910363c4bf3f32f34c52a33af9cd930002ffbe32af1bf1/rshift_meta_dispatch.h b/shared/package-files/c9/c9acfeaaa22fac96a6910363c4bf3f32f34c52a33af9cd930002ffbe32af1bf1/rshift_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..8b76812276ce34b971bae51bef7a475dee8fd1b9
--- /dev/null
+++ b/shared/package-files/c9/c9acfeaaa22fac96a6910363c4bf3f32f34c52a33af9cd930002ffbe32af1bf1/rshift_meta_dispatch.h
@@ -0,0 +1,24 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor & __irshift__(at::Tensor & self, const at::Scalar & other);
+TORCH_API at::Tensor & __irshift__(at::Tensor & self, const at::Tensor & other);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/c9/c9ae7e1a6a9bf4d08793a9a427e1aa426b9b846a6b8189350df9dddd526a1e94/_addmm_activation_meta.h b/shared/package-files/c9/c9ae7e1a6a9bf4d08793a9a427e1aa426b9b846a6b8189350df9dddd526a1e94/_addmm_activation_meta.h
new file mode 100644
index 0000000000000000000000000000000000000000..849ca5fec0e90f591d84717958ac70ed8b2eb508
--- /dev/null
+++ b/shared/package-files/c9/c9ae7e1a6a9bf4d08793a9a427e1aa426b9b846a6b8189350df9dddd526a1e94/_addmm_activation_meta.h
@@ -0,0 +1,27 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeMetaFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+namespace at {
+namespace meta {
+
+struct TORCH_API structured__addmm_activation : public at::impl::MetaBase {
+    
+    
+    void meta(const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha, bool use_gelu);
+};
+
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/c9/c9b11c1e8b1ecc655ea068e29e62bd00b3efec4e86ae4af795126ad16f588a5e/to_dense_native.h b/shared/package-files/c9/c9b11c1e8b1ecc655ea068e29e62bd00b3efec4e86ae4af795126ad16f588a5e/to_dense_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..644abdaed5d93aba30c8e947fb41851753024473
--- /dev/null
+++ b/shared/package-files/c9/c9b11c1e8b1ecc655ea068e29e62bd00b3efec4e86ae4af795126ad16f588a5e/to_dense_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor to_dense(const at::Tensor & self, c10::optional dtype=c10::nullopt, c10::optional masked_grad=c10::nullopt);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/c9/c9b75132f5bb8a1bade9138caa73fc0e2ff58da9c5607f3688dd8100ee11ade8/table.pyi b/shared/package-files/c9/c9b75132f5bb8a1bade9138caa73fc0e2ff58da9c5607f3688dd8100ee11ade8/table.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..2d67c9d4dbc4c8423cb28e7624a10476af5bb39e
--- /dev/null
+++ b/shared/package-files/c9/c9b75132f5bb8a1bade9138caa73fc0e2ff58da9c5607f3688dd8100ee11ade8/table.pyi
@@ -0,0 +1,108 @@
+from typing import Any, Optional
+
+class TableConnection:
+    table_name: Any
+    connection: Any
+    def __init__(
+        self,
+        table_name,
+        region: Optional[Any] = ...,
+        host: Optional[Any] = ...,
+        session_cls: Optional[Any] = ...,
+        request_timeout_seconds: Optional[Any] = ...,
+        max_retry_attempts: Optional[Any] = ...,
+        base_backoff_ms: Optional[Any] = ...,
+    ) -> None: ...
+    def delete_item(
+        self,
+        hash_key,
+        range_key: Optional[Any] = ...,
+        expected: Optional[Any] = ...,
+        conditional_operator: Optional[Any] = ...,
+        return_values: Optional[Any] = ...,
+        return_consumed_capacity: Optional[Any] = ...,
+        return_item_collection_metrics: Optional[Any] = ...,
+    ): ...
+    def update_item(
+        self,
+        hash_key,
+        range_key: Optional[Any] = ...,
+        attribute_updates: Optional[Any] = ...,
+        expected: Optional[Any] = ...,
+        conditional_operator: Optional[Any] = ...,
+        return_consumed_capacity: Optional[Any] = ...,
+        return_item_collection_metrics: Optional[Any] = ...,
+        return_values: Optional[Any] = ...,
+    ): ...
+    def put_item(
+        self,
+        hash_key,
+        range_key: Optional[Any] = ...,
+        attributes: Optional[Any] = ...,
+        expected: Optional[Any] = ...,
+        conditional_operator: Optional[Any] = ...,
+        return_values: Optional[Any] = ...,
+        return_consumed_capacity: Optional[Any] = ...,
+        return_item_collection_metrics: Optional[Any] = ...,
+    ): ...
+    def batch_write_item(
+        self,
+        put_items: Optional[Any] = ...,
+        delete_items: Optional[Any] = ...,
+        return_consumed_capacity: Optional[Any] = ...,
+        return_item_collection_metrics: Optional[Any] = ...,
+    ): ...
+    def batch_get_item(
+        self,
+        keys,
+        consistent_read: Optional[Any] = ...,
+        return_consumed_capacity: Optional[Any] = ...,
+        attributes_to_get: Optional[Any] = ...,
+    ): ...
+    def get_item(
+        self, hash_key, range_key: Optional[Any] = ..., consistent_read: bool = ..., attributes_to_get: Optional[Any] = ...
+    ): ...
+    def scan(
+        self,
+        attributes_to_get: Optional[Any] = ...,
+        limit: Optional[Any] = ...,
+        conditional_operator: Optional[Any] = ...,
+        scan_filter: Optional[Any] = ...,
+        return_consumed_capacity: Optional[Any] = ...,
+        segment: Optional[Any] = ...,
+        total_segments: Optional[Any] = ...,
+        exclusive_start_key: Optional[Any] = ...,
+    ): ...
+    def query(
+        self,
+        hash_key,
+        attributes_to_get: Optional[Any] = ...,
+        consistent_read: bool = ...,
+        exclusive_start_key: Optional[Any] = ...,
+        index_name: Optional[Any] = ...,
+        key_conditions: Optional[Any] = ...,
+        query_filters: Optional[Any] = ...,
+        limit: Optional[Any] = ...,
+        return_consumed_capacity: Optional[Any] = ...,
+        scan_index_forward: Optional[Any] = ...,
+        conditional_operator: Optional[Any] = ...,
+        select: Optional[Any] = ...,
+    ): ...
+    def describe_table(self): ...
+    def delete_table(self): ...
+    def update_table(
+        self,
+        read_capacity_units: Optional[Any] = ...,
+        write_capacity_units: Optional[Any] = ...,
+        global_secondary_index_updates: Optional[Any] = ...,
+    ): ...
+    def create_table(
+        self,
+        attribute_definitions: Optional[Any] = ...,
+        key_schema: Optional[Any] = ...,
+        read_capacity_units: Optional[Any] = ...,
+        write_capacity_units: Optional[Any] = ...,
+        global_secondary_indexes: Optional[Any] = ...,
+        local_secondary_indexes: Optional[Any] = ...,
+        stream_specification: Optional[Any] = ...,
+    ): ...
diff --git a/shared/package-files/c9/c9b78d4f34993d502bfbe725caffe69ff99c38b4307901b28cb61fe3ef2d8692/frobenius_norm_native.h b/shared/package-files/c9/c9b78d4f34993d502bfbe725caffe69ff99c38b4307901b28cb61fe3ef2d8692/frobenius_norm_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..3eebb53610f50830348c22f242ce0cd8b39db0b8
--- /dev/null
+++ b/shared/package-files/c9/c9b78d4f34993d502bfbe725caffe69ff99c38b4307901b28cb61fe3ef2d8692/frobenius_norm_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor frobenius_norm(const at::Tensor & self, at::IntArrayRef dim, bool keepdim=false);
+TORCH_API at::Tensor & frobenius_norm_out(const at::Tensor & self, at::IntArrayRef dim, bool keepdim, at::Tensor & out);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/c9/c9bb8fb6c3eae269343649be9a7fcf991decfd509f892c34320032f5a6741a35/efficiency_measures.py b/shared/package-files/c9/c9bb8fb6c3eae269343649be9a7fcf991decfd509f892c34320032f5a6741a35/efficiency_measures.py
new file mode 100644
index 0000000000000000000000000000000000000000..32343990c871fa0e604e9f608051a9b88a7fc402
--- /dev/null
+++ b/shared/package-files/c9/c9bb8fb6c3eae269343649be9a7fcf991decfd509f892c34320032f5a6741a35/efficiency_measures.py
@@ -0,0 +1,165 @@
+"""Provides functions for computing the efficiency of nodes and graphs."""
+
+import networkx as nx
+from networkx.exception import NetworkXNoPath
+
+from ..utils import not_implemented_for
+
+__all__ = ["efficiency", "local_efficiency", "global_efficiency"]
+
+
+@not_implemented_for("directed")
+def efficiency(G, u, v):
+    """Returns the efficiency of a pair of nodes in a graph.
+
+    The *efficiency* of a pair of nodes is the multiplicative inverse of the
+    shortest path distance between the nodes [1]_. Returns 0 if no path
+    between nodes.
+
+    Parameters
+    ----------
+    G : :class:`networkx.Graph`
+        An undirected graph for which to compute the average local efficiency.
+    u, v : node
+        Nodes in the graph ``G``.
+
+    Returns
+    -------
+    float
+        Multiplicative inverse of the shortest path distance between the nodes.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
+    >>> nx.efficiency(G, 2, 3)  # this gives efficiency for node 2 and 3
+    0.5
+
+    Notes
+    -----
+    Edge weights are ignored when computing the shortest path distances.
+
+    See also
+    --------
+    local_efficiency
+    global_efficiency
+
+    References
+    ----------
+    .. [1] Latora, Vito, and Massimo Marchiori.
+           "Efficient behavior of small-world networks."
+           *Physical Review Letters* 87.19 (2001): 198701.
+           
+
+    """
+    try:
+        eff = 1 / nx.shortest_path_length(G, u, v)
+    except NetworkXNoPath:
+        eff = 0
+    return eff
+
+
+@not_implemented_for("directed")
+def global_efficiency(G):
+    """Returns the average global efficiency of the graph.
+
+    The *efficiency* of a pair of nodes in a graph is the multiplicative
+    inverse of the shortest path distance between the nodes. The *average
+    global efficiency* of a graph is the average efficiency of all pairs of
+    nodes [1]_.
+
+    Parameters
+    ----------
+    G : :class:`networkx.Graph`
+        An undirected graph for which to compute the average global efficiency.
+
+    Returns
+    -------
+    float
+        The average global efficiency of the graph.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
+    >>> round(nx.global_efficiency(G), 12)
+    0.916666666667
+
+    Notes
+    -----
+    Edge weights are ignored when computing the shortest path distances.
+
+    See also
+    --------
+    local_efficiency
+
+    References
+    ----------
+    .. [1] Latora, Vito, and Massimo Marchiori.
+           "Efficient behavior of small-world networks."
+           *Physical Review Letters* 87.19 (2001): 198701.
+           
+
+    """
+    n = len(G)
+    denom = n * (n - 1)
+    if denom != 0:
+        lengths = nx.all_pairs_shortest_path_length(G)
+        g_eff = 0
+        for source, targets in lengths:
+            for target, distance in targets.items():
+                if distance > 0:
+                    g_eff += 1 / distance
+        g_eff /= denom
+        # g_eff = sum(1 / d for s, tgts in lengths
+        #                   for t, d in tgts.items() if d > 0) / denom
+    else:
+        g_eff = 0
+    # TODO This can be made more efficient by computing all pairs shortest
+    # path lengths in parallel.
+    return g_eff
+
+
+@not_implemented_for("directed")
+def local_efficiency(G):
+    """Returns the average local efficiency of the graph.
+
+    The *efficiency* of a pair of nodes in a graph is the multiplicative
+    inverse of the shortest path distance between the nodes. The *local
+    efficiency* of a node in the graph is the average global efficiency of the
+    subgraph induced by the neighbors of the node. The *average local
+    efficiency* is the average of the local efficiencies of each node [1]_.
+
+    Parameters
+    ----------
+    G : :class:`networkx.Graph`
+        An undirected graph for which to compute the average local efficiency.
+
+    Returns
+    -------
+    float
+        The average local efficiency of the graph.
+
+    Examples
+    --------
+    >>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])
+    >>> nx.local_efficiency(G)
+    0.9166666666666667
+
+    Notes
+    -----
+    Edge weights are ignored when computing the shortest path distances.
+
+    See also
+    --------
+    global_efficiency
+
+    References
+    ----------
+    .. [1] Latora, Vito, and Massimo Marchiori.
+           "Efficient behavior of small-world networks."
+           *Physical Review Letters* 87.19 (2001): 198701.
+           
+
+    """
+    # TODO This summation can be trivially parallelized.
+    efficiency_list = (global_efficiency(G.subgraph(G[v])) for v in G)
+    return sum(efficiency_list) / len(G)
diff --git a/shared/package-files/c9/c9bba31af2fe1c7698529f32bbc957b715badbb0111b761e959e27fd1dfcdb7c/__init__.py b/shared/package-files/c9/c9bba31af2fe1c7698529f32bbc957b715badbb0111b761e959e27fd1dfcdb7c/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..51fd6d4f26c7cda5e5a8bf3d130443c04c12b6f0
--- /dev/null
+++ b/shared/package-files/c9/c9bba31af2fe1c7698529f32bbc957b715badbb0111b761e959e27fd1dfcdb7c/__init__.py
@@ -0,0 +1,67 @@
+from collections.abc import Mapping
+
+try:
+    from collections import OrderedDict
+except ImportError:  # python < 2.7
+    OrderedDict = NotImplemented
+
+
+iteritems = getattr(dict, 'iteritems', dict.items) # py2-3 compatibility
+
+
+class frozendict(Mapping):
+    """
+    An immutable wrapper around dictionaries that implements the complete :py:class:`collections.Mapping`
+    interface. It can be used as a drop-in replacement for dictionaries where immutability is desired.
+    """
+
+    dict_cls = dict
+
+    def __init__(self, *args, **kwargs):
+        self._dict = self.dict_cls(*args, **kwargs)
+        self._hash = None
+
+    def __getitem__(self, key):
+        return self._dict[key]
+
+    def __contains__(self, key):
+        return key in self._dict
+
+    def copy(self, **add_or_replace):
+        return self.__class__(self, **add_or_replace)
+
+    def __iter__(self):
+        return iter(self._dict)
+
+    def __len__(self):
+        return len(self._dict)
+
+    def __repr__(self):
+        return '<%s %r>' % (self.__class__.__name__, self._dict)
+
+    def __hash__(self):
+        if self._hash is None:
+            h = 0
+            for key, value in iteritems(self._dict):
+                h ^= hash((key, value))
+            self._hash = h
+        return self._hash
+
+    def __json__(self):
+        # Works with auxlib's EntityEncoder.
+        return self._dict
+
+    def to_json(self):
+        return self.__json__()
+
+
+class FrozenOrderedDict(frozendict):
+    """
+    A frozendict subclass that maintains key order
+    """
+
+    dict_cls = OrderedDict
+
+
+if OrderedDict is NotImplemented:
+    del FrozenOrderedDict
diff --git a/shared/package-files/c9/c9bc69de66e81f9efbd891d358a04dd43f9cb3a43101342268be33255578444d/availability.py b/shared/package-files/c9/c9bc69de66e81f9efbd891d358a04dd43f9cb3a43101342268be33255578444d/availability.py
new file mode 100644
index 0000000000000000000000000000000000000000..dc97b3e7b8c7e7307c6c21352ed4035d977aabb3
--- /dev/null
+++ b/shared/package-files/c9/c9bc69de66e81f9efbd891d358a04dd43f9cb3a43101342268be33255578444d/availability.py
@@ -0,0 +1,77 @@
+import os
+from .compilation import compile_run_strings
+from .util import CompilerNotFoundError
+
+def has_fortran():
+    if not hasattr(has_fortran, 'result'):
+        try:
+            (stdout, stderr), info = compile_run_strings(
+                [('main.f90', (
+                    'program foo\n'
+                    'print *, "hello world"\n'
+                    'end program'
+                ))], clean=True
+            )
+        except CompilerNotFoundError:
+            has_fortran.result = False
+            if os.environ.get('SYMPY_STRICT_COMPILER_CHECKS', '0') == '1':
+                raise
+        else:
+            if info['exit_status'] != os.EX_OK or 'hello world' not in stdout:
+                if os.environ.get('SYMPY_STRICT_COMPILER_CHECKS', '0') == '1':
+                    raise ValueError("Failed to compile test program:\n%s\n%s\n" % (stdout, stderr))
+                has_fortran.result = False
+            else:
+                has_fortran.result = True
+    return has_fortran.result
+
+
+def has_c():
+    if not hasattr(has_c, 'result'):
+        try:
+            (stdout, stderr), info = compile_run_strings(
+                [('main.c', (
+                    '#include \n'
+                    'int main(){\n'
+                    'printf("hello world\\n");\n'
+                    'return 0;\n'
+                    '}'
+                ))], clean=True
+            )
+        except CompilerNotFoundError:
+            has_c.result = False
+            if os.environ.get('SYMPY_STRICT_COMPILER_CHECKS', '0') == '1':
+                raise
+        else:
+            if info['exit_status'] != os.EX_OK or 'hello world' not in stdout:
+                if os.environ.get('SYMPY_STRICT_COMPILER_CHECKS', '0') == '1':
+                    raise ValueError("Failed to compile test program:\n%s\n%s\n" % (stdout, stderr))
+                has_c.result = False
+            else:
+                has_c.result = True
+    return has_c.result
+
+
+def has_cxx():
+    if not hasattr(has_cxx, 'result'):
+        try:
+            (stdout, stderr), info = compile_run_strings(
+                [('main.cxx', (
+                    '#include \n'
+                    'int main(){\n'
+                    'std::cout << "hello world" << std::endl;\n'
+                    '}'
+                ))], clean=True
+            )
+        except CompilerNotFoundError:
+            has_cxx.result = False
+            if os.environ.get('SYMPY_STRICT_COMPILER_CHECKS', '0') == '1':
+                raise
+        else:
+            if info['exit_status'] != os.EX_OK or 'hello world' not in stdout:
+                if os.environ.get('SYMPY_STRICT_COMPILER_CHECKS', '0') == '1':
+                    raise ValueError("Failed to compile test program:\n%s\n%s\n" % (stdout, stderr))
+                has_cxx.result = False
+            else:
+                has_cxx.result = True
+    return has_cxx.result
diff --git a/shared/package-files/c9/c9bcbebede2c631735bb39670fe8844d111ba2380d9294066cb92b3c447f4acb/test_vf2userfunc.py b/shared/package-files/c9/c9bcbebede2c631735bb39670fe8844d111ba2380d9294066cb92b3c447f4acb/test_vf2userfunc.py
new file mode 100644
index 0000000000000000000000000000000000000000..66a434e05ceb7b42bb4e4893d26298001046386f
--- /dev/null
+++ b/shared/package-files/c9/c9bcbebede2c631735bb39670fe8844d111ba2380d9294066cb92b3c447f4acb/test_vf2userfunc.py
@@ -0,0 +1,200 @@
+"""
+    Tests for VF2 isomorphism algorithm for weighted graphs.
+"""
+
+import math
+from operator import eq
+
+import networkx as nx
+import networkx.algorithms.isomorphism as iso
+
+
+def test_simple():
+    # 16 simple tests
+    w = "weight"
+    edges = [(0, 0, 1), (0, 0, 1.5), (0, 1, 2), (1, 0, 3)]
+    for g1 in [nx.Graph(), nx.DiGraph(), nx.MultiGraph(), nx.MultiDiGraph()]:
+        g1.add_weighted_edges_from(edges)
+        g2 = g1.subgraph(g1.nodes())
+        if g1.is_multigraph():
+            em = iso.numerical_multiedge_match("weight", 1)
+        else:
+            em = iso.numerical_edge_match("weight", 1)
+        assert nx.is_isomorphic(g1, g2, edge_match=em)
+
+        for mod1, mod2 in [(False, True), (True, False), (True, True)]:
+            # mod1 tests a regular edge
+            # mod2 tests a selfloop
+            if g2.is_multigraph():
+                if mod1:
+                    data1 = {0: {"weight": 10}}
+                if mod2:
+                    data2 = {0: {"weight": 1}, 1: {"weight": 2.5}}
+            else:
+                if mod1:
+                    data1 = {"weight": 10}
+                if mod2:
+                    data2 = {"weight": 2.5}
+
+            g2 = g1.subgraph(g1.nodes()).copy()
+            if mod1:
+                if not g1.is_directed():
+                    g2._adj[1][0] = data1
+                    g2._adj[0][1] = data1
+                else:
+                    g2._succ[1][0] = data1
+                    g2._pred[0][1] = data1
+            if mod2:
+                if not g1.is_directed():
+                    g2._adj[0][0] = data2
+                else:
+                    g2._succ[0][0] = data2
+                    g2._pred[0][0] = data2
+
+            assert not nx.is_isomorphic(g1, g2, edge_match=em)
+
+
+def test_weightkey():
+    g1 = nx.DiGraph()
+    g2 = nx.DiGraph()
+
+    g1.add_edge("A", "B", weight=1)
+    g2.add_edge("C", "D", weight=0)
+
+    assert nx.is_isomorphic(g1, g2)
+    em = iso.numerical_edge_match("nonexistent attribute", 1)
+    assert nx.is_isomorphic(g1, g2, edge_match=em)
+    em = iso.numerical_edge_match("weight", 1)
+    assert not nx.is_isomorphic(g1, g2, edge_match=em)
+
+    g2 = nx.DiGraph()
+    g2.add_edge("C", "D")
+    assert nx.is_isomorphic(g1, g2, edge_match=em)
+
+
+class TestNodeMatch_Graph:
+    def setup_method(self):
+        self.g1 = nx.Graph()
+        self.g2 = nx.Graph()
+        self.build()
+
+    def build(self):
+        self.nm = iso.categorical_node_match("color", "")
+        self.em = iso.numerical_edge_match("weight", 1)
+
+        self.g1.add_node("A", color="red")
+        self.g2.add_node("C", color="blue")
+
+        self.g1.add_edge("A", "B", weight=1)
+        self.g2.add_edge("C", "D", weight=1)
+
+    def test_noweight_nocolor(self):
+        assert nx.is_isomorphic(self.g1, self.g2)
+
+    def test_color1(self):
+        assert not nx.is_isomorphic(self.g1, self.g2, node_match=self.nm)
+
+    def test_color2(self):
+        self.g1.nodes["A"]["color"] = "blue"
+        assert nx.is_isomorphic(self.g1, self.g2, node_match=self.nm)
+
+    def test_weight1(self):
+        assert nx.is_isomorphic(self.g1, self.g2, edge_match=self.em)
+
+    def test_weight2(self):
+        self.g1.add_edge("A", "B", weight=2)
+        assert not nx.is_isomorphic(self.g1, self.g2, edge_match=self.em)
+
+    def test_colorsandweights1(self):
+        iso = nx.is_isomorphic(self.g1, self.g2, node_match=self.nm, edge_match=self.em)
+        assert not iso
+
+    def test_colorsandweights2(self):
+        self.g1.nodes["A"]["color"] = "blue"
+        iso = nx.is_isomorphic(self.g1, self.g2, node_match=self.nm, edge_match=self.em)
+        assert iso
+
+    def test_colorsandweights3(self):
+        # make the weights disagree
+        self.g1.add_edge("A", "B", weight=2)
+        assert not nx.is_isomorphic(
+            self.g1, self.g2, node_match=self.nm, edge_match=self.em
+        )
+
+
+class TestEdgeMatch_MultiGraph:
+    def setup_method(self):
+        self.g1 = nx.MultiGraph()
+        self.g2 = nx.MultiGraph()
+        self.GM = iso.MultiGraphMatcher
+        self.build()
+
+    def build(self):
+        g1 = self.g1
+        g2 = self.g2
+
+        # We will assume integer weights only.
+        g1.add_edge("A", "B", color="green", weight=0, size=0.5)
+        g1.add_edge("A", "B", color="red", weight=1, size=0.35)
+        g1.add_edge("A", "B", color="red", weight=2, size=0.65)
+
+        g2.add_edge("C", "D", color="green", weight=1, size=0.5)
+        g2.add_edge("C", "D", color="red", weight=0, size=0.45)
+        g2.add_edge("C", "D", color="red", weight=2, size=0.65)
+
+        if g1.is_multigraph():
+            self.em = iso.numerical_multiedge_match("weight", 1)
+            self.emc = iso.categorical_multiedge_match("color", "")
+            self.emcm = iso.categorical_multiedge_match(["color", "weight"], ["", 1])
+            self.emg1 = iso.generic_multiedge_match("color", "red", eq)
+            self.emg2 = iso.generic_multiedge_match(
+                ["color", "weight", "size"],
+                ["red", 1, 0.5],
+                [eq, eq, math.isclose],
+            )
+        else:
+            self.em = iso.numerical_edge_match("weight", 1)
+            self.emc = iso.categorical_edge_match("color", "")
+            self.emcm = iso.categorical_edge_match(["color", "weight"], ["", 1])
+            self.emg1 = iso.generic_multiedge_match("color", "red", eq)
+            self.emg2 = iso.generic_edge_match(
+                ["color", "weight", "size"],
+                ["red", 1, 0.5],
+                [eq, eq, math.isclose],
+            )
+
+    def test_weights_only(self):
+        assert nx.is_isomorphic(self.g1, self.g2, edge_match=self.em)
+
+    def test_colors_only(self):
+        gm = self.GM(self.g1, self.g2, edge_match=self.emc)
+        assert gm.is_isomorphic()
+
+    def test_colorsandweights(self):
+        gm = self.GM(self.g1, self.g2, edge_match=self.emcm)
+        assert not gm.is_isomorphic()
+
+    def test_generic1(self):
+        gm = self.GM(self.g1, self.g2, edge_match=self.emg1)
+        assert gm.is_isomorphic()
+
+    def test_generic2(self):
+        gm = self.GM(self.g1, self.g2, edge_match=self.emg2)
+        assert not gm.is_isomorphic()
+
+
+class TestEdgeMatch_DiGraph(TestNodeMatch_Graph):
+    def setup_method(self):
+        TestNodeMatch_Graph.setup_method(self)
+        self.g1 = nx.DiGraph()
+        self.g2 = nx.DiGraph()
+        self.build()
+
+
+class TestEdgeMatch_MultiDiGraph(TestEdgeMatch_MultiGraph):
+    def setup_method(self):
+        TestEdgeMatch_MultiGraph.setup_method(self)
+        self.g1 = nx.MultiDiGraph()
+        self.g2 = nx.MultiDiGraph()
+        self.GM = iso.MultiDiGraphMatcher
+        self.build()
diff --git a/shared/package-files/c9/c9be2c9ad31d516b508d01e85bcca375aaf807d6d8cd7c658085d5007069fffd/en_hk.msg b/shared/package-files/c9/c9be2c9ad31d516b508d01e85bcca375aaf807d6d8cd7c658085d5007069fffd/en_hk.msg
new file mode 100644
index 0000000000000000000000000000000000000000..8b33bc067ad99e738745ba9d9bf29e25d94205bd
--- /dev/null
+++ b/shared/package-files/c9/c9be2c9ad31d516b508d01e85bcca375aaf807d6d8cd7c658085d5007069fffd/en_hk.msg
@@ -0,0 +1,8 @@
+# created by tools/loadICU.tcl -- do not edit
+namespace eval ::tcl::clock {
+    ::msgcat::mcset en_HK AM "AM"
+    ::msgcat::mcset en_HK PM "PM"
+    ::msgcat::mcset en_HK DATE_FORMAT "%B %e, %Y"
+    ::msgcat::mcset en_HK TIME_FORMAT_12 "%l:%M:%S %P"
+    ::msgcat::mcset en_HK DATE_TIME_FORMAT "%B %e, %Y %l:%M:%S %P %z"
+}
diff --git a/shared/package-files/c9/c9c038ced05c44d41e9e2369004348bb8047075440c8781b6694bef2bf26c2f2/validators.pyi b/shared/package-files/c9/c9c038ced05c44d41e9e2369004348bb8047075440c8781b6694bef2bf26c2f2/validators.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..49a7cbad5758beac5af3b5a86933ec128397c035
--- /dev/null
+++ b/shared/package-files/c9/c9c038ced05c44d41e9e2369004348bb8047075440c8781b6694bef2bf26c2f2/validators.pyi
@@ -0,0 +1,54 @@
+from typing import (
+    Any,
+    AnyStr,
+    Callable,
+    Container,
+    Iterable,
+    List,
+    Mapping,
+    Match,
+    Optional,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    overload,
+)
+
+from . import _ValidatorType
+
+_T = TypeVar("_T")
+_T1 = TypeVar("_T1")
+_T2 = TypeVar("_T2")
+_T3 = TypeVar("_T3")
+_I = TypeVar("_I", bound=Iterable)
+_K = TypeVar("_K")
+_V = TypeVar("_V")
+_M = TypeVar("_M", bound=Mapping)
+
+# To be more precise on instance_of use some overloads.
+# If there are more than 3 items in the tuple then we fall back to Any
+@overload
+def instance_of(type: Type[_T]) -> _ValidatorType[_T]: ...
+@overload
+def instance_of(type: Tuple[Type[_T]]) -> _ValidatorType[_T]: ...
+@overload
+def instance_of(type: Tuple[Type[_T1], Type[_T2]]) -> _ValidatorType[Union[_T1, _T2]]: ...
+@overload
+def instance_of(type: Tuple[Type[_T1], Type[_T2], Type[_T3]]) -> _ValidatorType[Union[_T1, _T2, _T3]]: ...
+@overload
+def instance_of(type: Tuple[type, ...]) -> _ValidatorType[Any]: ...
+def provides(interface: Any) -> _ValidatorType[Any]: ...
+def optional(validator: Union[_ValidatorType[_T], List[_ValidatorType[_T]]]) -> _ValidatorType[Optional[_T]]: ...
+def in_(options: Container[_T]) -> _ValidatorType[_T]: ...
+def and_(*validators: _ValidatorType[_T]) -> _ValidatorType[_T]: ...
+def matches_re(
+    regex: AnyStr, flags: int = ..., func: Optional[Callable[[AnyStr, AnyStr, int], Optional[Match[AnyStr]]]] = ...
+) -> _ValidatorType[AnyStr]: ...
+def deep_iterable(
+    member_validator: _ValidatorType[_T], iterable_validator: Optional[_ValidatorType[_I]] = ...
+) -> _ValidatorType[_I]: ...
+def deep_mapping(
+    key_validator: _ValidatorType[_K], value_validator: _ValidatorType[_V], mapping_validator: Optional[_ValidatorType[_M]] = ...
+) -> _ValidatorType[_M]: ...
+def is_callable() -> _ValidatorType[_T]: ...
diff --git a/shared/package-files/c9/c9c21b1db0dc35d98c0e8db6ca44cf85b093a13b1474aa5fcbe0637bf40692b2/macrodata_jmulti_cst_fc5.txt b/shared/package-files/c9/c9c21b1db0dc35d98c0e8db6ca44cf85b093a13b1474aa5fcbe0637bf40692b2/macrodata_jmulti_cst_fc5.txt
new file mode 100644
index 0000000000000000000000000000000000000000..21a842e7afe93dee7f6efc00042df8c1e7e67eda
--- /dev/null
+++ b/shared/package-files/c9/c9c21b1db0dc35d98c0e8db6ca44cf85b093a13b1474aa5fcbe0637bf40692b2/macrodata_jmulti_cst_fc5.txt
@@ -0,0 +1,31 @@
+*** Sat, 3 Sep 2016 22:36:29 ***
+FORECASTING
+Reference: Lütkepohl (1993), IMTSA, 2ed, ch. 5.2.6, ch. 10.5
+CI coverage:              0.95     
+forecast horizon:         5 periods 
+using standard confidence intervals
+
+            realcons                  
+time        forecast     lower CI     upper CI     +/-          
+2009 Q4     9300.6496    9248.1947    9353.1045    52.4549     
+2010 Q1     9321.5224    9239.7531    9403.2917    81.7693     
+2010 Q2     9352.1985    9241.4242    9462.9729    110.7743    
+2010 Q3     9380.6350    9235.4295    9525.8404    145.2055    
+2010 Q4     9399.9545    9223.8058    9576.1033    176.1488    
+
+            realgdp                   
+time        forecast     lower CI     upper CI     +/-          
+2009 Q4     13110.1070   13020.1406   13200.0734   89.9664     
+2010 Q1     13209.0014   13065.3335   13352.6692   143.6678    
+2010 Q2     13303.1382   13103.1689   13503.1075   199.9693    
+2010 Q3     13374.8516   13123.4596   13626.2435   251.3920    
+2010 Q4     13430.1995   13129.9346   13730.4644   300.2649    
+
+            realinv                   
+time        forecast     lower CI     upper CI     +/-          
+2009 Q4     1533.3529    1468.1252    1598.5806    65.2277     
+2010 Q1     1587.7720    1485.5726    1689.9714    102.1994    
+2010 Q2     1636.0621    1497.1493    1774.9749    138.9128    
+2010 Q3     1673.6534    1501.6651    1845.6417    171.9883    
+2010 Q4     1703.1185    1497.8072    1908.4298    205.3113    
+
diff --git a/shared/package-files/c9/c9c6192c3c03c9deb5d9a3db4ca215de0aa10912cc745d3731010b72de0b786b/_deprecated.py b/shared/package-files/c9/c9c6192c3c03c9deb5d9a3db4ca215de0aa10912cc745d3731010b72de0b786b/_deprecated.py
new file mode 100644
index 0000000000000000000000000000000000000000..116ea31587a1d7f7172267898152e0167531f303
--- /dev/null
+++ b/shared/package-files/c9/c9c6192c3c03c9deb5d9a3db4ca215de0aa10912cc745d3731010b72de0b786b/_deprecated.py
@@ -0,0 +1,24 @@
+import warnings
+from typing import Any, List
+
+import torch
+
+from torchvision.transforms import functional as _F
+
+
+@torch.jit.unused
+def to_tensor(inpt: Any) -> torch.Tensor:
+    """[DEPREACTED] Use to_image() and to_dtype() instead."""
+    warnings.warn(
+        "The function `to_tensor(...)` is deprecated and will be removed in a future release. "
+        "Instead, please use `to_image(...)` followed by `to_dtype(..., dtype=torch.float32, scale=True)`."
+    )
+    return _F.to_tensor(inpt)
+
+
+def get_image_size(inpt: torch.Tensor) -> List[int]:
+    warnings.warn(
+        "The function `get_image_size(...)` is deprecated and will be removed in a future release. "
+        "Instead, please use `get_size(...)` which returns `[h, w]` instead of `[w, h]`."
+    )
+    return _F.get_image_size(inpt)
diff --git a/shared/package-files/c9/c9c7ec8448b56bc40eacd26538a670296295ccda3b178dbea597a266d825c6e6/message.h b/shared/package-files/c9/c9c7ec8448b56bc40eacd26538a670296295ccda3b178dbea597a266d825c6e6/message.h
new file mode 100644
index 0000000000000000000000000000000000000000..364962ebcae3ed521978f438d11d00459b49f8d4
--- /dev/null
+++ b/shared/package-files/c9/c9c7ec8448b56bc40eacd26538a670296295ccda3b178dbea597a266d825c6e6/message.h
@@ -0,0 +1,104 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include 
+#include 
+#include 
+
+#include 
+#include 
+
+namespace tensorpipe {
+
+// Messages consist of a primary buffer and zero or more separate
+// buffers. The primary buffer is always a host-side memory region that
+// contains a serialized version of the message we're dealing with. This
+// serialized message, in turn, may have references to the separate
+// buffers that accompany the primary buffer. These separate buffers may
+// point to any type of memory, host-side or device-side.
+//
+class Message final {
+ public:
+  std::string metadata;
+
+  struct Payload {
+    void* data{nullptr};
+    size_t length{0};
+
+    // Users may include arbitrary metadata in the following fields.
+    // This may contain allocation hints for the receiver, for example.
+    std::string metadata;
+  };
+
+  // Holds the payloads that are transferred over the primary connection.
+  std::vector payloads;
+
+  struct Tensor {
+    tensorpipe::Buffer buffer;
+    size_t length{0};
+
+    // Users may optionally specify the target device, on which the receiver
+    // should allocate memory for this tensor. If left unset, the receiver will
+    // choose one at their convenience.
+    optional targetDevice;
+
+    // Users may include arbitrary metadata in the following field.
+    // This may contain allocation hints for the receiver, for example.
+    std::string metadata;
+  };
+
+  // Holds the tensors that are offered to the side channels.
+  std::vector tensors;
+};
+
+// Descriptors consist of metadata required by the receiver to allocate memory
+// for an incoming message.
+class Descriptor final {
+ public:
+  std::string metadata;
+
+  struct Payload {
+    size_t length{0};
+    std::string metadata;
+  };
+  std::vector payloads;
+
+  struct Tensor {
+    size_t length{0};
+
+    // This is the sender-side device from which this tensor is being sent.
+    Device sourceDevice;
+
+    // The sender may optionally specify a target device, in which case the
+    // receiver must allocate memory for this tensor on the specified device.
+    optional targetDevice;
+
+    std::string metadata;
+  };
+  std::vector tensors;
+};
+
+// Allocations consist of actual memory allocations provided by the receiver for
+// an incoming message. They must match the length and target devices specified
+// in the corresponding Descriptor.
+class Allocation final {
+ public:
+  struct Payload {
+    void* data{nullptr};
+  };
+  std::vector payloads;
+
+  struct Tensor {
+    tensorpipe::Buffer buffer;
+  };
+  std::vector tensors;
+};
+
+} // namespace tensorpipe
diff --git a/shared/package-files/c9/c9c95d4ccc519ba8b6a9d4b36bc5caa7e673bc253b0b6e94e2d7408ed0c7abd4/eye_compositeexplicitautograd_dispatch.h b/shared/package-files/c9/c9c95d4ccc519ba8b6a9d4b36bc5caa7e673bc253b0b6e94e2d7408ed0c7abd4/eye_compositeexplicitautograd_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..03a5a1c1693e6a8835ad9eaa5af77ed1203c7eb2
--- /dev/null
+++ b/shared/package-files/c9/c9c95d4ccc519ba8b6a9d4b36bc5caa7e673bc253b0b6e94e2d7408ed0c7abd4/eye_compositeexplicitautograd_dispatch.h
@@ -0,0 +1,30 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautograd {
+
+TORCH_API at::Tensor eye(int64_t n, at::TensorOptions options={});
+TORCH_API at::Tensor eye(int64_t n, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory);
+TORCH_API at::Tensor eye_symint(c10::SymInt n, at::TensorOptions options={});
+TORCH_API at::Tensor eye_symint(c10::SymInt n, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory);
+TORCH_API at::Tensor eye(int64_t n, int64_t m, at::TensorOptions options={});
+TORCH_API at::Tensor eye(int64_t n, int64_t m, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory);
+TORCH_API at::Tensor eye_symint(c10::SymInt n, c10::SymInt m, at::TensorOptions options={});
+TORCH_API at::Tensor eye_symint(c10::SymInt n, c10::SymInt m, c10::optional dtype, c10::optional layout, c10::optional device, c10::optional pin_memory);
+
+} // namespace compositeexplicitautograd
+} // namespace at
diff --git a/shared/package-files/c9/c9cafc1023ff60f598b689d93aacf59a45b9d162ae49822513ec1042c40ca050/stateful_transforms.py b/shared/package-files/c9/c9cafc1023ff60f598b689d93aacf59a45b9d162ae49822513ec1042c40ca050/stateful_transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf12777e3f5b0e21ea1ee0460a6d595f4c372708
--- /dev/null
+++ b/shared/package-files/c9/c9cafc1023ff60f598b689d93aacf59a45b9d162ae49822513ec1042c40ca050/stateful_transforms.py
@@ -0,0 +1,248 @@
+import ast
+import functools
+import inspect
+from collections.abc import Iterable, Mapping, MutableMapping
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Optional,
+    cast,
+)
+
+from .code import format_expr, sanitize_variable_names
+from .layered_mapping import LayeredMapping
+from .variables import Variable, get_expression_variables
+
+if TYPE_CHECKING:
+    from formulaic.model_spec import ModelSpec  # pragma: no cover
+
+
+def stateful_transform(
+    func: Optional[Callable] = None,
+    *,
+    get_required_variables: Optional[Callable[..., Iterable[Variable]]] = None,
+) -> Callable:
+    """
+    Transform a callable object into a stateful transform.
+
+    This is done by adding special arguments to the callable's signature:
+    - _state: The existing state or an empty dictionary.
+    - _metadata: Any extra metadata passed about the factor being evaluated.
+    - _spec: The `ModelSpec` instance being evaluated (or an empty `ModelSpec`).
+    - _context: A mapping of the name to value for all the variables available
+        in the formula evaluation context (including data column names).
+    If the callable has any of these in its signature, these will be passed onto
+    it; otherwise, they will be swallowed by the stateful transform wrapper.
+
+    Stateful transforms are also transformed into single dispatches, allowing
+    different implementations for incoming data types.
+
+    This can be used either as a bare decorator (`@stateful_transform`) or with
+    optional arguments (`@stateful_transform(...)`).
+
+    Args:
+        func: The function (or other callable) to be made into a stateful
+            transform.
+        get_required_variables: An optional callable that reports the variables
+            required to evaluate the transform. If provided, it is attached to
+            the returned wrapper as `.get_required_variables` so that variable
+            extraction can be delegated to it. It should accept the same
+            arguments as the wrapped function, and return the appropriate
+            `Variable` instances as a sequence.
+
+    Returns:
+        The stateful transform callable (or, when called with only keyword
+        arguments, a decorator that produces one).
+    """
+    if func is None:
+        return functools.partial(
+            stateful_transform, get_required_variables=get_required_variables
+        )
+
+    func = functools.singledispatch(func)
+    params = set(inspect.signature(func).parameters.keys())
+
+    @functools.wraps(func)
+    def wrapper(  # type: ignore[no-untyped-def]
+        data, *args, _metadata=None, _state=None, _spec=None, _context=None, **kwargs
+    ):
+        from formulaic.model_spec import ModelSpec
+
+        _state = {} if _state is None else _state
+        extra_params = {}
+        if "_metadata" in params:
+            extra_params["_metadata"] = _metadata
+        if "_spec" in params:
+            extra_params["_spec"] = _spec or ModelSpec(formula=[])
+        if "_context" in params:
+            extra_params["_context"] = _context
+
+        if isinstance(data, dict):
+            results = {}
+            for key, datum in data.items():
+                if isinstance(key, str) and key.startswith("__"):
+                    results[key] = datum
+                else:
+                    statum = _state.get(key, {})
+                    results[key] = wrapper(
+                        datum, *args, _state=statum, **extra_params, **kwargs
+                    )
+                    if statum:
+                        _state[key] = statum
+            return results
+
+        return func(
+            data,
+            *args,
+            **({"_state": _state} if "_state" in params else {}),
+            **extra_params,
+            **kwargs,
+        )
+
+    wrapper.__is_stateful_transform__ = True  # type: ignore[attr-defined]
+    wrapper.get_required_variables = get_required_variables or (  # type: ignore[attr-defined]
+        lambda *args, **kwargs: ()
+    )
+    return wrapper
+
+
+def stateful_eval(
+    expr: str,
+    env: Optional[Mapping],
+    metadata: Optional[Mapping],
+    state: Optional[MutableMapping],
+    spec: Optional["ModelSpec"],
+    variables: Optional[set[Variable]] = None,
+) -> Any:
+    """
+    Evaluate an expression in a nominated environment and with a nominated state.
+
+    Under the hood this calls out to `eval`, and so if incoming expressions are
+    not safe, you should make sure that your `env` is properly isolated from
+    potentially unsafe methods and/or sys-calls.
+
+    Args:
+        expr: The expression to be evaluated.
+        env: The environment in which the expression is to be evaluated. This
+            environment is the only environment from which variables can be
+            looked up during the evaluation.
+        metadata: Additional metadata about the expression (passed through to
+            stateful transforms).
+        state: The current state of any stateful transforms (passed through to
+            stateful transforms).
+        spec: The current `ModelSpec` instance being evaluated (passed through
+            to stateful transforms).
+        variables: A (optional) set of variables to update with the variables
+            used in this stateful evaluation.
+
+    Returns:
+        The result of the evaluation.
+
+    Notes:
+        - The state mapping is likely to be mutated in-place when using stateful
+            transforms. If you need to retain the original state, copy it
+            *before* calling this method.
+    """
+    metadata = {} if metadata is None else metadata
+    state = {} if state is None else state
+    env = LayeredMapping(
+        env
+    )  # We sometimes mutate env, so we make sure we do so in a local mutable layer.
+
+    # Ensure that variable names in code are valid for Python's interpreter
+    # If not, create new variable in mutable env layer, and update code.
+    aliases: dict[str, str] = {}
+    expr = sanitize_variable_names(expr, env, aliases)
+
+    # Parse Python code
+    code = ast.parse(expr, mode="eval")
+
+    if variables is not None:
+        variables.update(get_expression_variables(code, env, aliases))
+
+    # Extract the nodes of the graph that correspond to stateful transforms
+    stateful_nodes: dict[str, ast.Call] = {}
+    for node in ast.walk(code):
+        if _is_stateful_transform(node, env):
+            stateful_nodes[format_expr(node)] = cast(ast.Call, node)
+
+    # Mutate stateful nodes to pass in state from a shared dictionary.
+    for name, node in stateful_nodes.items():
+        name = name.replace('"', r'\\\\"')
+        if name not in state:
+            state[name] = {}
+        node.keywords.append(
+            ast.keyword(
+                "_context",
+                ast.parse("__FORMULAIC_CONTEXT__", mode="eval").body,
+            )
+        )
+        node.keywords.append(
+            ast.keyword(
+                "_metadata",
+                ast.parse(f'__FORMULAIC_METADATA__.get("{name}")', mode="eval").body,
+            )
+        )
+        node.keywords.append(
+            ast.keyword(
+                "_state", ast.parse(f'__FORMULAIC_STATE__["{name}"]', mode="eval").body
+            )
+        )
+        node.keywords.append(
+            ast.keyword("_spec", ast.parse("__FORMULAIC_SPEC__", mode="eval").body)
+        )
+
+    # Compile mutated AST
+    compiled = compile(ast.fix_missing_locations(code), "", "eval")
+
+    used_reserved = {
+        "__FORMULAIC_CONTEXT__",
+        "__FORMULAIC_METADATA__",
+        "__FORMULAIC_STATE__",
+        "__FORMULAIC_SPEC__",
+    }.intersection(env)
+    if used_reserved:
+        raise RuntimeError(
+            f"Reserved names {repr(used_reserved)} are already in use in the "
+            "evaluation environment."
+        )
+
+    # Evaluate and return
+    return eval(
+        compiled,
+        {},
+        LayeredMapping(
+            {
+                "__FORMULAIC_CONTEXT__": env,
+                "__FORMULAIC_METADATA__": metadata,
+                "__FORMULAIC_SPEC__": spec,
+                "__FORMULAIC_STATE__": state,
+            },
+            env,
+        ),
+    )  # nosec
+
+
+def _is_stateful_transform(node: ast.AST, env: Mapping) -> bool:
+    """
+    Check whether a given ast.Call node enacts a stateful transform given
+    the available symbols in `env`.
+
+    Args:
+        node: The AST node in question.
+        env: The current environment in which the node is evaluated. This is
+            used to look up the function handle so it can be inspected.
+
+    Return:
+        `True` if the node is a call node and the callable associated with the
+        node is a stateful transform. `False` otherwise.
+    """
+    if not isinstance(node, ast.Call):
+        return False
+
+    try:
+        func = eval(compile(format_expr(node.func), "", "eval"), {}, env)  # nosec; Get function handle (assuming it exists in env)
+        return getattr(func, "__is_stateful_transform__", False)
+    except NameError:
+        return False
diff --git a/shared/package-files/c9/c9cc2aef63a773ea77dbd3b015e677db46f956393f68fa528c2823ffb66c88b4/tensor.py b/shared/package-files/c9/c9cc2aef63a773ea77dbd3b015e677db46f956393f68fa528c2823ffb66c88b4/tensor.py
new file mode 100644
index 0000000000000000000000000000000000000000..f091d38777fcd578904b6bc1b9f22f2063877066
--- /dev/null
+++ b/shared/package-files/c9/c9cc2aef63a773ea77dbd3b015e677db46f956393f68fa528c2823ffb66c88b4/tensor.py
@@ -0,0 +1,63 @@
+import operator
+import warnings
+from functools import reduce
+
+import torch
+import torch._utils
+from ..function import Function
+
+
+class Type(Function):
+    @staticmethod
+    def forward(ctx, i, dest_type):
+        warnings.warn(
+            "torch.autograd._functions.Type is deprecated as of PyTorch 2.1, please use "
+            "torch.tensor.to(dtype=dtype) instead."
+        )
+        ctx.input_type = type(i)
+        ctx.input_device = -1 if not i.is_cuda else i.get_device()
+        return i.type(dest_type)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        if ctx.input_device == -1:
+            return grad_output.type(ctx.input_type), None
+        else:
+            with torch.cuda.device(ctx.input_device):
+                return grad_output.type(ctx.input_type), None
+
+
+# TODO: deprecate this
+class Resize(Function):
+    @staticmethod
+    def forward(ctx, tensor, sizes):
+        ctx.sizes = sizes
+        ctx.numel = reduce(operator.mul, sizes, 1)
+        if tensor.numel() != ctx.numel:
+            raise RuntimeError(
+                (
+                    "requested resize to {} ({} elements in total), "
+                    "but the given tensor has a size of {} ({} elements). "
+                    "autograd's resize can only change the shape of a given "
+                    "tensor, while preserving the number of elements. "
+                ).format(
+                    "x".join(map(str, sizes)),
+                    ctx.numel,
+                    "x".join(map(str, tensor.size())),
+                    tensor.numel(),
+                )
+            )
+        ctx.input_sizes = tensor.size()
+        if tensor.is_quantized:
+            tensor.copy_(tensor)
+            return tensor.contiguous().view(*sizes)
+        if tensor.is_contiguous():
+            result = tensor.new(tensor).contiguous().view(*sizes)
+            return result
+        else:
+            return tensor.contiguous().view(*sizes)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        assert grad_output.numel() == ctx.numel
+        return grad_output.contiguous().view(ctx.input_sizes), None
diff --git a/shared/package-files/c9/c9cf7ce5e62308eef865ee3d14a16f1e3359688d261b3a35d84a6240f34bc090/quantized_rnn_relu_cell.h b/shared/package-files/c9/c9cf7ce5e62308eef865ee3d14a16f1e3359688d261b3a35d84a6240f34bc090/quantized_rnn_relu_cell.h
new file mode 100644
index 0000000000000000000000000000000000000000..c8dbdca0580c665ad91920cefd67bad6dc62de59
--- /dev/null
+++ b/shared/package-files/c9/c9cf7ce5e62308eef865ee3d14a16f1e3359688d261b3a35d84a6240f34bc090/quantized_rnn_relu_cell.h
@@ -0,0 +1,30 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::quantized_rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor b_ih, Tensor b_hh, Tensor packed_ih, Tensor packed_hh, Tensor col_offsets_ih, Tensor col_offsets_hh, Scalar scale_ih, Scalar scale_hh, Scalar zero_point_ih, Scalar zero_point_hh) -> Tensor
+inline at::Tensor quantized_rnn_relu_cell(const at::Tensor & input, const at::Tensor & hx, const at::Tensor & w_ih, const at::Tensor & w_hh, const at::Tensor & b_ih, const at::Tensor & b_hh, const at::Tensor & packed_ih, const at::Tensor & packed_hh, const at::Tensor & col_offsets_ih, const at::Tensor & col_offsets_hh, const at::Scalar & scale_ih, const at::Scalar & scale_hh, const at::Scalar & zero_point_ih, const at::Scalar & zero_point_hh) {
+    return at::_ops::quantized_rnn_relu_cell::call(input, hx, w_ih, w_hh, b_ih, b_hh, packed_ih, packed_hh, col_offsets_ih, col_offsets_hh, scale_ih, scale_hh, zero_point_ih, zero_point_hh);
+}
+
+}
diff --git a/shared/package-files/c9/c9d0102a19b2d8c86ad0668fafadb04d0b2d94f40d69f4af321dc7608fd542b5/_replacement.py b/shared/package-files/c9/c9d0102a19b2d8c86ad0668fafadb04d0b2d94f40d69f4af321dc7608fd542b5/_replacement.py
new file mode 100644
index 0000000000000000000000000000000000000000..9acbc9d8133a118c3aa215ee42f7767178f6d19b
--- /dev/null
+++ b/shared/package-files/c9/c9d0102a19b2d8c86ad0668fafadb04d0b2d94f40d69f4af321dc7608fd542b5/_replacement.py
@@ -0,0 +1,31 @@
+# DO NOT EDIT! This file was generated by jschema_to_python version 0.0.1.dev29,
+# with extension for dataclasses and type annotation.
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Optional
+
+from torch.onnx._internal.diagnostics.infra.sarif import (
+    _artifact_content,
+    _property_bag,
+    _region,
+)
+
+
+@dataclasses.dataclass
+class Replacement(object):
+    """The replacement of a single region of an artifact."""
+
+    deleted_region: _region.Region = dataclasses.field(
+        metadata={"schema_property_name": "deletedRegion"}
+    )
+    inserted_content: Optional[_artifact_content.ArtifactContent] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "insertedContent"}
+    )
+    properties: Optional[_property_bag.PropertyBag] = dataclasses.field(
+        default=None, metadata={"schema_property_name": "properties"}
+    )
+
+
+# flake8: noqa
diff --git a/shared/package-files/c9/c9d8a7f101bc047a9846c3d8e0e2fa7266f8e026ea5e5d53d31c52f7b5611e49/compatibility_tags.py b/shared/package-files/c9/c9d8a7f101bc047a9846c3d8e0e2fa7266f8e026ea5e5d53d31c52f7b5611e49/compatibility_tags.py
new file mode 100644
index 0000000000000000000000000000000000000000..b6ed9a78e552806cb23d8ac48ada6d41db5b4de5
--- /dev/null
+++ b/shared/package-files/c9/c9d8a7f101bc047a9846c3d8e0e2fa7266f8e026ea5e5d53d31c52f7b5611e49/compatibility_tags.py
@@ -0,0 +1,165 @@
+"""Generate and work with PEP 425 Compatibility Tags.
+"""
+
+import re
+from typing import List, Optional, Tuple
+
+from pip._vendor.packaging.tags import (
+    PythonVersion,
+    Tag,
+    compatible_tags,
+    cpython_tags,
+    generic_tags,
+    interpreter_name,
+    interpreter_version,
+    mac_platforms,
+)
+
+_osx_arch_pat = re.compile(r"(.+)_(\d+)_(\d+)_(.+)")
+
+
+def version_info_to_nodot(version_info: Tuple[int, ...]) -> str:
+    # Only use up to the first two numbers.
+    return "".join(map(str, version_info[:2]))
+
+
+def _mac_platforms(arch: str) -> List[str]:
+    match = _osx_arch_pat.match(arch)
+    if match:
+        name, major, minor, actual_arch = match.groups()
+        mac_version = (int(major), int(minor))
+        arches = [
+            # Since we have always only checked that the platform starts
+            # with "macosx", for backwards-compatibility we extract the
+            # actual prefix provided by the user in case they provided
+            # something like "macosxcustom_". It may be good to remove
+            # this as undocumented or deprecate it in the future.
+            "{}_{}".format(name, arch[len("macosx_") :])
+            for arch in mac_platforms(mac_version, actual_arch)
+        ]
+    else:
+        # arch pattern didn't match (?!)
+        arches = [arch]
+    return arches
+
+
+def _custom_manylinux_platforms(arch: str) -> List[str]:
+    arches = [arch]
+    arch_prefix, arch_sep, arch_suffix = arch.partition("_")
+    if arch_prefix == "manylinux2014":
+        # manylinux1/manylinux2010 wheels run on most manylinux2014 systems
+        # with the exception of wheels depending on ncurses. PEP 599 states
+        # manylinux1/manylinux2010 wheels should be considered
+        # manylinux2014 wheels:
+        # https://www.python.org/dev/peps/pep-0599/#backwards-compatibility-with-manylinux2010-wheels
+        if arch_suffix in {"i686", "x86_64"}:
+            arches.append("manylinux2010" + arch_sep + arch_suffix)
+            arches.append("manylinux1" + arch_sep + arch_suffix)
+    elif arch_prefix == "manylinux2010":
+        # manylinux1 wheels run on most manylinux2010 systems with the
+        # exception of wheels depending on ncurses. PEP 571 states
+        # manylinux1 wheels should be considered manylinux2010 wheels:
+        # https://www.python.org/dev/peps/pep-0571/#backwards-compatibility-with-manylinux1-wheels
+        arches.append("manylinux1" + arch_sep + arch_suffix)
+    return arches
+
+
+def _get_custom_platforms(arch: str) -> List[str]:
+    arch_prefix, arch_sep, arch_suffix = arch.partition("_")
+    if arch.startswith("macosx"):
+        arches = _mac_platforms(arch)
+    elif arch_prefix in ["manylinux2014", "manylinux2010"]:
+        arches = _custom_manylinux_platforms(arch)
+    else:
+        arches = [arch]
+    return arches
+
+
+def _expand_allowed_platforms(platforms: Optional[List[str]]) -> Optional[List[str]]:
+    if not platforms:
+        return None
+
+    seen = set()
+    result = []
+
+    for p in platforms:
+        if p in seen:
+            continue
+        additions = [c for c in _get_custom_platforms(p) if c not in seen]
+        seen.update(additions)
+        result.extend(additions)
+
+    return result
+
+
+def _get_python_version(version: str) -> PythonVersion:
+    if len(version) > 1:
+        return int(version[0]), int(version[1:])
+    else:
+        return (int(version[0]),)
+
+
+def _get_custom_interpreter(
+    implementation: Optional[str] = None, version: Optional[str] = None
+) -> str:
+    if implementation is None:
+        implementation = interpreter_name()
+    if version is None:
+        version = interpreter_version()
+    return f"{implementation}{version}"
+
+
+def get_supported(
+    version: Optional[str] = None,
+    platforms: Optional[List[str]] = None,
+    impl: Optional[str] = None,
+    abis: Optional[List[str]] = None,
+) -> List[Tag]:
+    """Return a list of supported tags for each version specified in
+    `versions`.
+
+    :param version: a string version, of the form "33" or "32",
+        or None. The version will be assumed to support our ABI.
+    :param platform: specify a list of platforms you want valid
+        tags for, or None. If None, use the local system platform.
+    :param impl: specify the exact implementation you want valid
+        tags for, or None. If None, use the local interpreter impl.
+    :param abis: specify a list of abis you want valid
+        tags for, or None. If None, use the local interpreter abi.
+    """
+    supported: List[Tag] = []
+
+    python_version: Optional[PythonVersion] = None
+    if version is not None:
+        python_version = _get_python_version(version)
+
+    interpreter = _get_custom_interpreter(impl, version)
+
+    platforms = _expand_allowed_platforms(platforms)
+
+    is_cpython = (impl or interpreter_name()) == "cp"
+    if is_cpython:
+        supported.extend(
+            cpython_tags(
+                python_version=python_version,
+                abis=abis,
+                platforms=platforms,
+            )
+        )
+    else:
+        supported.extend(
+            generic_tags(
+                interpreter=interpreter,
+                abis=abis,
+                platforms=platforms,
+            )
+        )
+    supported.extend(
+        compatible_tags(
+            python_version=python_version,
+            interpreter=interpreter,
+            platforms=platforms,
+        )
+    )
+
+    return supported
diff --git a/shared/package-files/c9/c9d8d1a6ebfdadd02c21b9f4d15b1c26716f181c4bb6bcccb1006c9d892da31f/METADATA b/shared/package-files/c9/c9d8d1a6ebfdadd02c21b9f4d15b1c26716f181c4bb6bcccb1006c9d892da31f/METADATA
new file mode 100644
index 0000000000000000000000000000000000000000..36e9d3099a711efb20a7c3d7c6aac27c4208d3f5
--- /dev/null
+++ b/shared/package-files/c9/c9d8d1a6ebfdadd02c21b9f4d15b1c26716f181c4bb6bcccb1006c9d892da31f/METADATA
@@ -0,0 +1,91 @@
+Metadata-Version: 2.4
+Name: patsy
+Version: 1.0.3
+Summary: A Python package for describing statistical models and for building design matrices.
+Home-page: https://github.com/pydata/patsy
+Author: Nathaniel J. Smith
+Author-email: njs@pobox.com
+License: 2-clause BSD
+Classifier: Development Status :: 6 - Mature
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Science/Research
+Classifier: Intended Audience :: Financial and Insurance Industry
+Classifier: License :: OSI Approved :: BSD License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3.14
+Classifier: Topic :: Scientific/Engineering
+Requires-Python: >=3.6
+Description-Content-Type: text/markdown
+License-File: LICENSE.txt
+Requires-Dist: numpy>=1.4
+Requires-Dist: packaging
+Provides-Extra: test
+Requires-Dist: pytest; extra == "test"
+Requires-Dist: pytest-cov; extra == "test"
+Requires-Dist: scipy; extra == "test"
+Dynamic: author
+Dynamic: author-email
+Dynamic: classifier
+Dynamic: description
+Dynamic: description-content-type
+Dynamic: home-page
+Dynamic: license
+Dynamic: license-file
+Dynamic: provides-extra
+Dynamic: requires-dist
+Dynamic: requires-python
+Dynamic: summary
+
+# Patsy
+
+**Notice:** `patsy` is no longer under active development. As of August 2021,
+Matthew Wardrop (@matthewwardrop) and Tomás Capretto (@tomicapretto) have taken
+on responsibility from Nathaniel Smith (@njsmith) for keeping the lights on, but
+no new feature development is planned. The spiritual successor of this project
+is [Formulaic](https://github.com/matthewwardrop/formulaic), and we
+recommend that users [migrate](https://matthewwardrop.github.io/formulaic/migration/)
+when possible. For the time being, until major software packages have successfully
+transitioned, we will attempt to keep `patsy` working in its current state with
+current releases in the Python ecosystem.
+
+---
+
+Patsy is a Python library for describing statistical models
+(especially linear models, or models that have a linear component) and
+building design matrices. Patsy brings the convenience of [R](http://www.r-project.org/) "formulas" to Python.
+
+[![PyPI - Version](https://img.shields.io/pypi/v/patsy.svg)](https://pypi.org/project/spec-classes/)
+![PyPI - Python Version](https://img.shields.io/pypi/pyversions/patsy.svg)
+![https://patsy.readthedocs.io/](https://img.shields.io/badge/docs-read%20now-blue.svg)
+![PyPI - Status](https://img.shields.io/pypi/status/patsy.svg)
+[![https://coveralls.io/r/pydata/patsy?branch=master](https://coveralls.io/repos/pydata/patsy/badge.png?branch=master)](https://coveralls.io/github/pydata/patsy)
+![https://doi.org/10.5281/zenodo.592075](https://zenodo.org/badge/DOI/10.5281/zenodo.592075.svg)
+
+- **Documentation:** 
+- **Downloads:** 
+- **Code and issues:** 
+- **Mailing list:**  ()
+
+
+## Dependencies
+
+  * Python (3.6+)
+  * numpy
+  * Optional:
+    * pytest/pytest-cov: needed to run tests
+    * scipy: needed for spline-related functions like ``bs``
+
+## Installation
+  ``pip install patsy`` (or, for traditionalists: ``python setup.py install``)
+
+## License
+
+2-clause BSD, see LICENSE.txt for details.
diff --git a/shared/package-files/c9/c9da1fc9e99f56e81eaf688b7d5954036a6c7a8943a27a31ca9740010493b417/_array_api.py b/shared/package-files/c9/c9da1fc9e99f56e81eaf688b7d5954036a6c7a8943a27a31ca9740010493b417/_array_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b4094d88c75d93367c481f5c3f603424cd6c13c
--- /dev/null
+++ b/shared/package-files/c9/c9da1fc9e99f56e81eaf688b7d5954036a6c7a8943a27a31ca9740010493b417/_array_api.py
@@ -0,0 +1,606 @@
+"""Utility functions to use Python Array API compatible libraries.
+
+For the context about the Array API see:
+https://data-apis.org/array-api/latest/purpose_and_scope.html
+
+The SciPy use case of the Array API is described on the following page:
+https://data-apis.org/array-api/latest/use_cases.html#use-case-scipy
+"""
+import os
+
+from types import ModuleType
+from typing import Any, Literal, TypeAlias
+
+import numpy as np
+import numpy.typing as npt
+
+from scipy._lib import array_api_compat
+from scipy._lib.array_api_compat import (
+    is_array_api_obj,
+    size as xp_size,
+    numpy as np_compat,
+    device as xp_device,
+    is_numpy_namespace as is_numpy,
+    is_cupy_namespace as is_cupy,
+    is_torch_namespace as is_torch,
+    is_jax_namespace as is_jax,
+    is_array_api_strict_namespace as is_array_api_strict
+)
+
+__all__ = [
+    '_asarray', 'array_namespace', 'assert_almost_equal', 'assert_array_almost_equal',
+    'get_xp_devices',
+    'is_array_api_strict', 'is_complex', 'is_cupy', 'is_jax', 'is_numpy', 'is_torch', 
+    'SCIPY_ARRAY_API', 'SCIPY_DEVICE', 'scipy_namespace_for',
+    'xp_assert_close', 'xp_assert_equal', 'xp_assert_less',
+    'xp_copy', 'xp_copysign', 'xp_device',
+    'xp_moveaxis_to_end', 'xp_ravel', 'xp_real', 'xp_sign', 'xp_size',
+    'xp_take_along_axis', 'xp_unsupported_param_msg', 'xp_vector_norm',
+]
+
+
+# To enable array API and strict array-like input validation
+SCIPY_ARRAY_API: str | bool = os.environ.get("SCIPY_ARRAY_API", False)
+# To control the default device - for use in the test suite only
+SCIPY_DEVICE = os.environ.get("SCIPY_DEVICE", "cpu")
+
+_GLOBAL_CONFIG = {
+    "SCIPY_ARRAY_API": SCIPY_ARRAY_API,
+    "SCIPY_DEVICE": SCIPY_DEVICE,
+}
+
+
+Array: TypeAlias = Any  # To be changed to a Protocol later (see array-api#589)
+ArrayLike: TypeAlias = Array | npt.ArrayLike
+
+
+def _compliance_scipy(arrays):
+    """Raise exceptions on known-bad subclasses.
+
+    The following subclasses are not supported and raise and error:
+    - `numpy.ma.MaskedArray`
+    - `numpy.matrix`
+    - NumPy arrays which do not have a boolean or numerical dtype
+    - Any array-like which is neither array API compatible nor coercible by NumPy
+    - Any array-like which is coerced by NumPy to an unsupported dtype
+    """
+    for i in range(len(arrays)):
+        array = arrays[i]
+
+        from scipy.sparse import issparse
+        # this comes from `_util._asarray_validated`
+        if issparse(array):
+            msg = ('Sparse arrays/matrices are not supported by this function. '
+                   'Perhaps one of the `scipy.sparse.linalg` functions '
+                   'would work instead.')
+            raise ValueError(msg)
+
+        if isinstance(array, np.ma.MaskedArray):
+            raise TypeError("Inputs of type `numpy.ma.MaskedArray` are not supported.")
+        elif isinstance(array, np.matrix):
+            raise TypeError("Inputs of type `numpy.matrix` are not supported.")
+        if isinstance(array, np.ndarray | np.generic):
+            dtype = array.dtype
+            if not (np.issubdtype(dtype, np.number) or np.issubdtype(dtype, np.bool_)):
+                raise TypeError(f"An argument has dtype `{dtype!r}`; "
+                                f"only boolean and numerical dtypes are supported.")
+        elif not is_array_api_obj(array):
+            try:
+                array = np.asanyarray(array)
+            except TypeError:
+                raise TypeError("An argument is neither array API compatible nor "
+                                "coercible by NumPy.")
+            dtype = array.dtype
+            if not (np.issubdtype(dtype, np.number) or np.issubdtype(dtype, np.bool_)):
+                message = (
+                    f"An argument was coerced to an unsupported dtype `{dtype!r}`; "
+                    f"only boolean and numerical dtypes are supported."
+                )
+                raise TypeError(message)
+            arrays[i] = array
+    return arrays
+
+
+def _check_finite(array: Array, xp: ModuleType) -> None:
+    """Check for NaNs or Infs."""
+    msg = "array must not contain infs or NaNs"
+    try:
+        if not xp.all(xp.isfinite(array)):
+            raise ValueError(msg)
+    except TypeError:
+        raise ValueError(msg)
+
+
+def array_namespace(*arrays: Array) -> ModuleType:
+    """Get the array API compatible namespace for the arrays xs.
+
+    Parameters
+    ----------
+    *arrays : sequence of array_like
+        Arrays used to infer the common namespace.
+
+    Returns
+    -------
+    namespace : module
+        Common namespace.
+
+    Notes
+    -----
+    Thin wrapper around `array_api_compat.array_namespace`.
+
+    1. Check for the global switch: SCIPY_ARRAY_API. This can also be accessed
+       dynamically through ``_GLOBAL_CONFIG['SCIPY_ARRAY_API']``.
+    2. `_compliance_scipy` raise exceptions on known-bad subclasses. See
+       its definition for more details.
+
+    When the global switch is False, it defaults to the `numpy` namespace.
+    In that case, there is no compliance check. This is a convenience to
+    ease the adoption. Otherwise, arrays must comply with the new rules.
+    """
+    if not _GLOBAL_CONFIG["SCIPY_ARRAY_API"]:
+        # here we could wrap the namespace if needed
+        return np_compat
+
+    _arrays = [array for array in arrays if array is not None]
+
+    _arrays = _compliance_scipy(_arrays)
+
+    return array_api_compat.array_namespace(*_arrays)
+
+
+def _asarray(
+        array: ArrayLike,
+        dtype: Any = None,
+        order: Literal['K', 'A', 'C', 'F'] | None = None,
+        copy: bool | None = None,
+        *,
+        xp: ModuleType | None = None,
+        check_finite: bool = False,
+        subok: bool = False,
+    ) -> Array:
+    """SciPy-specific replacement for `np.asarray` with `order`, `check_finite`, and
+    `subok`.
+
+    Memory layout parameter `order` is not exposed in the Array API standard.
+    `order` is only enforced if the input array implementation
+    is NumPy based, otherwise `order` is just silently ignored.
+
+    `check_finite` is also not a keyword in the array API standard; included
+    here for convenience rather than that having to be a separate function
+    call inside SciPy functions.
+
+    `subok` is included to allow this function to preserve the behaviour of
+    `np.asanyarray` for NumPy based inputs.
+    """
+    if xp is None:
+        xp = array_namespace(array)
+    if is_numpy(xp):
+        # Use NumPy API to support order
+        if copy is True:
+            array = np.array(array, order=order, dtype=dtype, subok=subok)
+        elif subok:
+            array = np.asanyarray(array, order=order, dtype=dtype)
+        else:
+            array = np.asarray(array, order=order, dtype=dtype)
+    else:
+        try:
+            array = xp.asarray(array, dtype=dtype, copy=copy)
+        except TypeError:
+            coerced_xp = array_namespace(xp.asarray(3))
+            array = coerced_xp.asarray(array, dtype=dtype, copy=copy)
+
+    if check_finite:
+        _check_finite(array, xp)
+
+    return array
+
+
+def xp_copy(x: Array, *, xp: ModuleType | None = None) -> Array:
+    """
+    Copies an array.
+
+    Parameters
+    ----------
+    x : array
+
+    xp : array_namespace
+
+    Returns
+    -------
+    copy : array
+        Copied array
+
+    Notes
+    -----
+    This copy function does not offer all the semantics of `np.copy`, i.e. the
+    `subok` and `order` keywords are not used.
+    """
+    # Note: for older NumPy versions, `np.asarray` did not support the `copy` kwarg,
+    # so this uses our other helper `_asarray`.
+    if xp is None:
+        xp = array_namespace(x)
+
+    return _asarray(x, copy=True, xp=xp)
+
+
+def _strict_check(actual, desired, xp, *,
+                  check_namespace=True, check_dtype=True, check_shape=True,
+                  check_0d=True):
+    __tracebackhide__ = True  # Hide traceback for py.test
+    if check_namespace:
+        _assert_matching_namespace(actual, desired)
+
+    # only NumPy distinguishes between scalars and arrays; we do if check_0d=True.
+    # do this first so we can then cast to array (and thus use the array API) below.
+    if is_numpy(xp) and check_0d:
+        _msg = ("Array-ness does not match:\n Actual: "
+                f"{type(actual)}\n Desired: {type(desired)}")
+        assert ((xp.isscalar(actual) and xp.isscalar(desired))
+                or (not xp.isscalar(actual) and not xp.isscalar(desired))), _msg
+
+    actual = xp.asarray(actual)
+    desired = xp.asarray(desired)
+
+    if check_dtype:
+        _msg = f"dtypes do not match.\nActual: {actual.dtype}\nDesired: {desired.dtype}"
+        assert actual.dtype == desired.dtype, _msg
+
+    if check_shape:
+        _msg = f"Shapes do not match.\nActual: {actual.shape}\nDesired: {desired.shape}"
+        assert actual.shape == desired.shape, _msg
+
+    desired = xp.broadcast_to(desired, actual.shape)
+    return actual, desired
+
+
+def _assert_matching_namespace(actual, desired):
+    __tracebackhide__ = True  # Hide traceback for py.test
+    actual = actual if isinstance(actual, tuple) else (actual,)
+    desired_space = array_namespace(desired)
+    for arr in actual:
+        arr_space = array_namespace(arr)
+        _msg = (f"Namespaces do not match.\n"
+                f"Actual: {arr_space.__name__}\n"
+                f"Desired: {desired_space.__name__}")
+        assert arr_space == desired_space, _msg
+
+
+def xp_assert_equal(actual, desired, *, check_namespace=True, check_dtype=True,
+                    check_shape=True, check_0d=True, err_msg='', xp=None):
+    __tracebackhide__ = True  # Hide traceback for py.test
+    if xp is None:
+        xp = array_namespace(actual)
+
+    actual, desired = _strict_check(
+        actual, desired, xp, check_namespace=check_namespace,
+        check_dtype=check_dtype, check_shape=check_shape,
+        check_0d=check_0d
+    )
+
+    if is_cupy(xp):
+        return xp.testing.assert_array_equal(actual, desired, err_msg=err_msg)
+    elif is_torch(xp):
+        # PyTorch recommends using `rtol=0, atol=0` like this
+        # to test for exact equality
+        err_msg = None if err_msg == '' else err_msg
+        return xp.testing.assert_close(actual, desired, rtol=0, atol=0, equal_nan=True,
+                                       check_dtype=False, msg=err_msg)
+    # JAX uses `np.testing`
+    return np.testing.assert_array_equal(actual, desired, err_msg=err_msg)
+
+
+def xp_assert_close(actual, desired, *, rtol=None, atol=0, check_namespace=True,
+                    check_dtype=True, check_shape=True, check_0d=True,
+                    err_msg='', xp=None):
+    __tracebackhide__ = True  # Hide traceback for py.test
+    if xp is None:
+        xp = array_namespace(actual)
+
+    actual, desired = _strict_check(
+        actual, desired, xp,
+        check_namespace=check_namespace, check_dtype=check_dtype,
+        check_shape=check_shape, check_0d=check_0d
+    )
+
+    floating = xp.isdtype(actual.dtype, ('real floating', 'complex floating'))
+    if rtol is None and floating:
+        # multiplier of 4 is used as for `np.float64` this puts the default `rtol`
+        # roughly half way between sqrt(eps) and the default for
+        # `numpy.testing.assert_allclose`, 1e-7
+        rtol = xp.finfo(actual.dtype).eps**0.5 * 4
+    elif rtol is None:
+        rtol = 1e-7
+
+    if is_cupy(xp):
+        return xp.testing.assert_allclose(actual, desired, rtol=rtol,
+                                          atol=atol, err_msg=err_msg)
+    elif is_torch(xp):
+        err_msg = None if err_msg == '' else err_msg
+        return xp.testing.assert_close(actual, desired, rtol=rtol, atol=atol,
+                                       equal_nan=True, check_dtype=False, msg=err_msg)
+    # JAX uses `np.testing`
+    return np.testing.assert_allclose(actual, desired, rtol=rtol,
+                                      atol=atol, err_msg=err_msg)
+
+
+def xp_assert_less(actual, desired, *, check_namespace=True, check_dtype=True,
+                   check_shape=True, check_0d=True, err_msg='', verbose=True, xp=None):
+    __tracebackhide__ = True  # Hide traceback for py.test
+    if xp is None:
+        xp = array_namespace(actual)
+
+    actual, desired = _strict_check(
+        actual, desired, xp, check_namespace=check_namespace,
+        check_dtype=check_dtype, check_shape=check_shape,
+        check_0d=check_0d
+    )
+
+    if is_cupy(xp):
+        return xp.testing.assert_array_less(actual, desired,
+                                            err_msg=err_msg, verbose=verbose)
+    elif is_torch(xp):
+        if actual.device.type != 'cpu':
+            actual = actual.cpu()
+        if desired.device.type != 'cpu':
+            desired = desired.cpu()
+    # JAX uses `np.testing`
+    return np.testing.assert_array_less(actual, desired,
+                                        err_msg=err_msg, verbose=verbose)
+
+
+def assert_array_almost_equal(actual, desired, decimal=6, *args, **kwds):
+    """Backwards compatible replacement. In new code, use xp_assert_close instead.
+    """
+    rtol, atol = 0, 1.5*10**(-decimal)
+    return xp_assert_close(actual, desired,
+                           atol=atol, rtol=rtol, check_dtype=False, check_shape=False,
+                           *args, **kwds)
+
+
+def assert_almost_equal(actual, desired, decimal=7, *args, **kwds):
+    """Backwards compatible replacement. In new code, use xp_assert_close instead.
+    """
+    rtol, atol = 0, 1.5*10**(-decimal)
+    return xp_assert_close(actual, desired,
+                           atol=atol, rtol=rtol, check_dtype=False, check_shape=False,
+                           *args, **kwds)
+
+
+def xp_unsupported_param_msg(param: Any) -> str:
+    return f'Providing {param!r} is only supported for numpy arrays.'
+
+
+def is_complex(x: Array, xp: ModuleType) -> bool:
+    return xp.isdtype(x.dtype, 'complex floating')
+
+
+def get_xp_devices(xp: ModuleType) -> list[str] | list[None]:
+    """Returns a list of available devices for the given namespace."""
+    devices: list[str] = []
+    if is_torch(xp):
+        devices += ['cpu']
+        import torch # type: ignore[import]
+        num_cuda = torch.cuda.device_count()
+        for i in range(0, num_cuda):
+            devices += [f'cuda:{i}']
+        if torch.backends.mps.is_available():
+            devices += ['mps']
+        return devices
+    elif is_cupy(xp):
+        import cupy # type: ignore[import]
+        num_cuda = cupy.cuda.runtime.getDeviceCount()
+        for i in range(0, num_cuda):
+            devices += [f'cuda:{i}']
+        return devices
+    elif is_jax(xp):
+        import jax # type: ignore[import]
+        num_cpu = jax.device_count(backend='cpu')
+        for i in range(0, num_cpu):
+            devices += [f'cpu:{i}']
+        num_gpu = jax.device_count(backend='gpu')
+        for i in range(0, num_gpu):
+            devices += [f'gpu:{i}']
+        num_tpu = jax.device_count(backend='tpu')
+        for i in range(0, num_tpu):
+            devices += [f'tpu:{i}']
+        return devices
+
+    # given namespace is not known to have a list of available devices;
+    # return `[None]` so that one can use this in tests for `device=None`.
+    return [None]
+
+
+def scipy_namespace_for(xp: ModuleType) -> ModuleType | None:
+    """Return the `scipy`-like namespace of a non-NumPy backend
+
+    That is, return the namespace corresponding with backend `xp` that contains
+    `scipy` sub-namespaces like `linalg` and `special`. If no such namespace
+    exists, return ``None``. Useful for dispatching.
+    """
+
+    if is_cupy(xp):
+        import cupyx  # type: ignore[import-not-found,import-untyped]
+        return cupyx.scipy
+
+    if is_jax(xp):
+        import jax  # type: ignore[import-not-found]
+        return jax.scipy
+
+    if is_torch(xp):
+        return xp
+
+    return None
+
+
+# temporary substitute for xp.moveaxis, which is not yet in all backends
+# or covered by array_api_compat.
+def xp_moveaxis_to_end(
+        x: Array,
+        source: int,
+        /, *,
+        xp: ModuleType | None = None) -> Array:
+    xp = array_namespace(xp) if xp is None else xp
+    axes = list(range(x.ndim))
+    temp = axes.pop(source)
+    axes = axes + [temp]
+    return xp.permute_dims(x, axes)
+
+
+# temporary substitute for xp.copysign, which is not yet in all backends
+# or covered by array_api_compat.
+def xp_copysign(x1: Array, x2: Array, /, *, xp: ModuleType | None = None) -> Array:
+    # no attempt to account for special cases
+    xp = array_namespace(x1, x2) if xp is None else xp
+    abs_x1 = xp.abs(x1)
+    return xp.where(x2 >= 0, abs_x1, -abs_x1)
+
+
+# partial substitute for xp.sign, which does not cover the NaN special case
+# that I need. (https://github.com/data-apis/array-api-compat/issues/136)
+def xp_sign(x: Array, /, *, xp: ModuleType | None = None) -> Array:
+    xp = array_namespace(x) if xp is None else xp
+    if is_numpy(xp):  # only NumPy implements the special cases correctly
+        return xp.sign(x)
+    sign = xp.zeros_like(x)
+    one = xp.asarray(1, dtype=x.dtype)
+    sign = xp.where(x > 0, one, sign)
+    sign = xp.where(x < 0, -one, sign)
+    sign = xp.where(xp.isnan(x), xp.nan*one, sign)
+    return sign
+
+# maybe use `scipy.linalg` if/when array API support is added
+def xp_vector_norm(x: Array, /, *,
+                   axis: int | tuple[int] | None = None,
+                   keepdims: bool = False,
+                   ord: int | float = 2,
+                   xp: ModuleType | None = None) -> Array:
+    xp = array_namespace(x) if xp is None else xp
+
+    if SCIPY_ARRAY_API:
+        # check for optional `linalg` extension
+        if hasattr(xp, 'linalg'):
+            return xp.linalg.vector_norm(x, axis=axis, keepdims=keepdims, ord=ord)
+        else:
+            if ord != 2:
+                raise ValueError(
+                    "only the Euclidean norm (`ord=2`) is currently supported in "
+                    "`xp_vector_norm` for backends not implementing the `linalg` "
+                    "extension."
+                )
+            # return (x @ x)**0.5
+            # or to get the right behavior with nd, complex arrays
+            return xp.sum(xp.conj(x) * x, axis=axis, keepdims=keepdims)**0.5
+    else:
+        # to maintain backwards compatibility
+        return np.linalg.norm(x, ord=ord, axis=axis, keepdims=keepdims)
+
+
+def xp_ravel(x: Array, /, *, xp: ModuleType | None = None) -> Array:
+    # Equivalent of np.ravel written in terms of array API
+    # Even though it's one line, it comes up so often that it's worth having
+    # this function for readability
+    xp = array_namespace(x) if xp is None else xp
+    return xp.reshape(x, (-1,))
+
+
+def xp_real(x: Array, /, *, xp: ModuleType | None = None) -> Array:
+    # Convenience wrapper of xp.real that allows non-complex input;
+    # see data-apis/array-api#824
+    xp = array_namespace(x) if xp is None else xp
+    return xp.real(x) if xp.isdtype(x.dtype, 'complex floating') else x
+
+
+def xp_take_along_axis(arr: Array,
+                       indices: Array, /, *,
+                       axis: int = -1,
+                       xp: ModuleType | None = None) -> Array:
+    # Dispatcher for np.take_along_axis for backends that support it;
+    # see data-apis/array-api/pull#816
+    xp = array_namespace(arr) if xp is None else xp
+    if is_torch(xp):
+        return xp.take_along_dim(arr, indices, dim=axis)
+    elif is_array_api_strict(xp):
+        raise NotImplementedError("Array API standard does not define take_along_axis")
+    else:
+        return xp.take_along_axis(arr, indices, axis)
+
+
+# utility to broadcast arrays and promote to common dtype
+def xp_broadcast_promote(*args, ensure_writeable=False, force_floating=False, xp=None):
+    xp = array_namespace(*args) if xp is None else xp
+
+    args = [(_asarray(arg, subok=True) if arg is not None else arg) for arg in args]
+    args_not_none = [arg for arg in args if arg is not None]
+
+    # determine minimum dtype
+    default_float = xp.asarray(1.).dtype
+    dtypes = [arg.dtype for arg in args_not_none]
+    try:  # follow library's prefered mixed promotion rules
+        dtype = xp.result_type(*dtypes)
+        if force_floating and xp.isdtype(dtype, 'integral'):
+            # If we were to add `default_float` before checking whether the result
+            # type is otherwise integral, we risk promotion from lower float.
+            dtype = xp.result_type(dtype, default_float)
+    except TypeError:  # mixed type promotion isn't defined
+        float_dtypes = [dtype for dtype in dtypes
+                        if not xp.isdtype(dtype, 'integral')]
+        if float_dtypes:
+            dtype = xp.result_type(*float_dtypes, default_float)
+        elif force_floating:
+            dtype = default_float
+        else:
+            dtype = xp.result_type(*dtypes)
+
+    # determine result shape
+    shapes = {arg.shape for arg in args_not_none}
+    try:
+        shape = (np.broadcast_shapes(*shapes) if len(shapes) != 1
+                 else args_not_none[0].shape)
+    except ValueError as e:
+        message = "Array shapes are incompatible for broadcasting."
+        raise ValueError(message) from e
+
+    out = []
+    for arg in args:
+        if arg is None:
+            out.append(arg)
+            continue
+
+        # broadcast only if needed
+        # Even if two arguments need broadcasting, this is faster than
+        # `broadcast_arrays`, especially since we've already determined `shape`
+        if arg.shape != shape:
+            kwargs = {'subok': True} if is_numpy(xp) else {}
+            arg = xp.broadcast_to(arg, shape, **kwargs)
+
+        # convert dtype/copy only if needed
+        if (arg.dtype != dtype) or ensure_writeable:
+            arg = xp.astype(arg, dtype, copy=True)
+        out.append(arg)
+
+    return out
+
+
+def xp_float_to_complex(arr: Array, xp: ModuleType | None = None) -> Array:
+    xp = array_namespace(arr) if xp is None else xp
+    arr_dtype = arr.dtype
+    # The standard float dtypes are float32 and float64.
+    # Convert float32 to complex64,
+    # and float64 (and non-standard real dtypes) to complex128
+    if xp.isdtype(arr_dtype, xp.float32):
+        arr = xp.astype(arr, xp.complex64)
+    elif xp.isdtype(arr_dtype, 'real floating'):
+        arr = xp.astype(arr, xp.complex128)
+
+    return arr
+
+
+def xp_default_dtype(xp):
+    """Query the namespace-dependent default floating-point dtype.
+    """
+    if is_torch(xp):
+        # historically, we allow pytorch to keep its default of float32
+        return xp.get_default_dtype()
+    else:
+        # we default to float64
+        return xp.float64
diff --git a/shared/package-files/c9/c9dd4c5db52b144c57c0e8268281c99a373347eca070b48d25e292c9a1e73f99/tek4015 b/shared/package-files/c9/c9dd4c5db52b144c57c0e8268281c99a373347eca070b48d25e292c9a1e73f99/tek4015
new file mode 100644
index 0000000000000000000000000000000000000000..4e4c83d68ba56aa71c730ee1bd8ccae944065ad4
Binary files /dev/null and b/shared/package-files/c9/c9dd4c5db52b144c57c0e8268281c99a373347eca070b48d25e292c9a1e73f99/tek4015 differ
diff --git a/shared/package-files/c9/c9e202b225ef8ca3226f06a50e84a77bc79cf44dee3228c9200c8b97c15d64e7/special_bessel_j1_meta_dispatch.h b/shared/package-files/c9/c9e202b225ef8ca3226f06a50e84a77bc79cf44dee3228c9200c8b97c15d64e7/special_bessel_j1_meta_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..300df779b2a6ce36824f30ba946fea0b638b1631
--- /dev/null
+++ b/shared/package-files/c9/c9e202b225ef8ca3226f06a50e84a77bc79cf44dee3228c9200c8b97c15d64e7/special_bessel_j1_meta_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace meta {
+
+TORCH_API at::Tensor special_bessel_j1(const at::Tensor & self);
+TORCH_API at::Tensor & special_bessel_j1_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & special_bessel_j1_outf(const at::Tensor & self, at::Tensor & out);
+
+} // namespace meta
+} // namespace at
diff --git a/shared/package-files/c9/c9e27a837442e5b8ad3f7b46f9bb562ce667f79e04fdef51c4a3ed82c8b07157/visitor.pyi b/shared/package-files/c9/c9e27a837442e5b8ad3f7b46f9bb562ce667f79e04fdef51c4a3ed82c8b07157/visitor.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ef34328dfe16a3ac7d53cffd27b9055d9dd9444c
--- /dev/null
+++ b/shared/package-files/c9/c9e27a837442e5b8ad3f7b46f9bb562ce667f79e04fdef51c4a3ed82c8b07157/visitor.pyi
@@ -0,0 +1,8 @@
+class NodeVisitor:
+    def get_visitor(self, node): ...
+    def visit(self, node, *args, **kwargs): ...
+    def generic_visit(self, node, *args, **kwargs): ...
+
+class NodeTransformer(NodeVisitor):
+    def generic_visit(self, node, *args, **kwargs): ...
+    def visit_list(self, node, *args, **kwargs): ...
diff --git a/shared/package-files/c9/c9e27ce2bd4c7fdbbfe9e07b935371582fbe38799dbf530683dfcb8a2c378df7/isnan_cuda_dispatch.h b/shared/package-files/c9/c9e27ce2bd4c7fdbbfe9e07b935371582fbe38799dbf530683dfcb8a2c378df7/isnan_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..b31ac1b5353a6c1a60cfc6f5ac2e50b5f2266e5b
--- /dev/null
+++ b/shared/package-files/c9/c9e27ce2bd4c7fdbbfe9e07b935371582fbe38799dbf530683dfcb8a2c378df7/isnan_cuda_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor isnan(const at::Tensor & self);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/c9/c9e50d8de15d398063a398ebac2a166da8b8056fa3faa2390e0ccafd81c20f10/quantize_per_tensor_dynamic_native.h b/shared/package-files/c9/c9e50d8de15d398063a398ebac2a166da8b8056fa3faa2390e0ccafd81c20f10/quantize_per_tensor_dynamic_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..ccdc074aa7be93e92496184d2ed68cc25d0b6308
--- /dev/null
+++ b/shared/package-files/c9/c9e50d8de15d398063a398ebac2a166da8b8056fa3faa2390e0ccafd81c20f10/quantize_per_tensor_dynamic_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor & quantize_per_tensor_dynamic_out(const at::Tensor & self, at::ScalarType dtype, bool reduce_range, at::Tensor & out);
+TORCH_API at::Tensor quantize_per_tensor_dynamic(const at::Tensor & self, at::ScalarType dtype, bool reduce_range);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/c9/c9e7545f668852914b91e0f2b214f97ea68e0c9834890cd4ff3bc6392af824b7/dtexample.py b/shared/package-files/c9/c9e7545f668852914b91e0f2b214f97ea68e0c9834890cd4ff3bc6392af824b7/dtexample.py
new file mode 100644
index 0000000000000000000000000000000000000000..68f7016e34dc57b9ae7aadfd028c36889fe36088
--- /dev/null
+++ b/shared/package-files/c9/c9e7545f668852914b91e0f2b214f97ea68e0c9834890cd4ff3bc6392af824b7/dtexample.py
@@ -0,0 +1,167 @@
+"""Simple example using doctests.
+
+This file just contains doctests both using plain python and IPython prompts.
+All tests should be loaded by nose.
+"""
+
+import os
+
+
+def pyfunc():
+    """Some pure python tests...
+
+    >>> pyfunc()
+    'pyfunc'
+
+    >>> import os
+
+    >>> 2+3
+    5
+
+    >>> for i in range(3):
+    ...     print(i, end=' ')
+    ...     print(i+1, end=' ')
+    ...
+    0 1 1 2 2 3 
+    """
+    return 'pyfunc'
+
+def ipfunc():
+    """Some ipython tests...
+
+    In [1]: import os
+
+    In [3]: 2+3
+    Out[3]: 5
+
+    In [26]: for i in range(3):
+       ....:     print(i, end=' ')
+       ....:     print(i+1, end=' ')
+       ....:
+    0 1 1 2 2 3
+
+
+    It's OK to use '_' for the last result, but do NOT try to use IPython's
+    numbered history of _NN outputs, since those won't exist under the
+    doctest environment:
+
+    In [7]: 'hi'
+    Out[7]: 'hi'
+
+    In [8]: print(repr(_))
+    'hi'
+
+    In [7]: 3+4
+    Out[7]: 7
+
+    In [8]: _+3
+    Out[8]: 10
+
+    In [9]: ipfunc()
+    Out[9]: 'ipfunc'
+    """
+    return "ipfunc"
+
+
+def ipos():
+    """Examples that access the operating system work:
+
+    In [1]: !echo hello
+    hello
+
+    In [2]: !echo hello > /tmp/foo_iptest
+
+    In [3]: !cat /tmp/foo_iptest
+    hello
+
+    In [4]: rm -f /tmp/foo_iptest
+    """
+    pass
+
+
+ipos.__skip_doctest__ = os.name == "nt"
+
+
+def ranfunc():
+    """A function with some random output.
+
+       Normal examples are verified as usual:
+       >>> 1+3
+       4
+
+       But if you put '# random' in the output, it is ignored:
+       >>> 1+3
+       junk goes here...  # random
+
+       >>> 1+2
+       again,  anything goes #random
+       if multiline, the random mark is only needed once.
+
+       >>> 1+2
+       You can also put the random marker at the end:
+       # random
+
+       >>> 1+2
+       # random
+       .. or at the beginning.
+
+       More correct input is properly verified:
+       >>> ranfunc()
+       'ranfunc'
+    """
+    return 'ranfunc'
+
+
+def random_all():
+    """A function where we ignore the output of ALL examples.
+
+    Examples:
+
+      # all-random
+
+      This mark tells the testing machinery that all subsequent examples should
+      be treated as random (ignoring their output).  They are still executed,
+      so if a they raise an error, it will be detected as such, but their
+      output is completely ignored.
+
+      >>> 1+3
+      junk goes here...
+
+      >>> 1+3
+      klasdfj;
+
+      >>> 1+2
+      again,  anything goes
+      blah...
+    """
+    pass
+
+def iprand():
+    """Some ipython tests with random output.
+
+    In [7]: 3+4
+    Out[7]: 7
+
+    In [8]: print('hello')
+    world  # random
+
+    In [9]: iprand()
+    Out[9]: 'iprand'
+    """
+    return 'iprand'
+
+def iprand_all():
+    """Some ipython tests with fully random output.
+
+    # all-random
+    
+    In [7]: 1
+    Out[7]: 99
+
+    In [8]: print('hello')
+    world
+
+    In [9]: iprand_all()
+    Out[9]: 'junk'
+    """
+    return 'iprand_all'
diff --git a/shared/package-files/c9/c9e9f9b428e6fcdc1798f41b8b4f97f20c0bf47214bc7127b36f8837f6dbaa7d/_shape_as_tensor_native.h b/shared/package-files/c9/c9e9f9b428e6fcdc1798f41b8b4f97f20c0bf47214bc7127b36f8837f6dbaa7d/_shape_as_tensor_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..98054c765d663575c88a9efd5efb4cb70e06218c
--- /dev/null
+++ b/shared/package-files/c9/c9e9f9b428e6fcdc1798f41b8b4f97f20c0bf47214bc7127b36f8837f6dbaa7d/_shape_as_tensor_native.h
@@ -0,0 +1,21 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API at::Tensor _shape_as_tensor(const at::Tensor & self);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/c9/c9ea98f8b38f1573c7ede2c75c6484cf746bc9acd881723935ea1f1be193df51/special_xlog1py_cuda_dispatch.h b/shared/package-files/c9/c9ea98f8b38f1573c7ede2c75c6484cf746bc9acd881723935ea1f1be193df51/special_xlog1py_cuda_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..112df862ff51facbb20e6925329f59bbc5687d17
--- /dev/null
+++ b/shared/package-files/c9/c9ea98f8b38f1573c7ede2c75c6484cf746bc9acd881723935ea1f1be193df51/special_xlog1py_cuda_dispatch.h
@@ -0,0 +1,25 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cuda {
+
+TORCH_API at::Tensor special_xlog1py(const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlog1py_out(at::Tensor & out, const at::Tensor & self, const at::Tensor & other);
+TORCH_API at::Tensor & special_xlog1py_outf(const at::Tensor & self, const at::Tensor & other, at::Tensor & out);
+
+} // namespace cuda
+} // namespace at
diff --git a/shared/package-files/c9/c9ef29c298e89691bf9142921530bc66845d8f7cdef2ab495ef0d46798296a9e/test_combine.py b/shared/package-files/c9/c9ef29c298e89691bf9142921530bc66845d8f7cdef2ab495ef0d46798296a9e/test_combine.py
new file mode 100644
index 0000000000000000000000000000000000000000..75d47e3daa10339f4c4cc7b35c52f24bbb20277a
--- /dev/null
+++ b/shared/package-files/c9/c9ef29c298e89691bf9142921530bc66845d8f7cdef2ab495ef0d46798296a9e/test_combine.py
@@ -0,0 +1,17 @@
+from pandas import Series
+import pandas._testing as tm
+
+
+class TestCombine:
+    def test_combine_scalar(self):
+        # GH#21248
+        # Note - combine() with another Series is tested elsewhere because
+        # it is used when testing operators
+        ser = Series([i * 10 for i in range(5)])
+        result = ser.combine(3, lambda x, y: x + y)
+        expected = Series([i * 10 + 3 for i in range(5)])
+        tm.assert_series_equal(result, expected)
+
+        result = ser.combine(22, lambda x, y: min(x, y))
+        expected = Series([min(i * 10, 22) for i in range(5)])
+        tm.assert_series_equal(result, expected)
diff --git a/shared/package-files/c9/c9f17b465b152872b3b07d36de5f4e1ebb0331c568e35eb7eb82238128b7a6eb/beacon b/shared/package-files/c9/c9f17b465b152872b3b07d36de5f4e1ebb0331c568e35eb7eb82238128b7a6eb/beacon
new file mode 100644
index 0000000000000000000000000000000000000000..ff0130362ec511a19147b581522d5c2153feeed9
Binary files /dev/null and b/shared/package-files/c9/c9f17b465b152872b3b07d36de5f4e1ebb0331c568e35eb7eb82238128b7a6eb/beacon differ
diff --git a/shared/package-files/c9/c9f3f44815c621ca74699ab83794ea345f170d2988aa010d65e19a738c5df629/jiterator.h b/shared/package-files/c9/c9f3f44815c621ca74699ab83794ea345f170d2988aa010d65e19a738c5df629/jiterator.h
new file mode 100644
index 0000000000000000000000000000000000000000..a7a440cd7b614732d5ad16eb28c56e71bb20b6e1
--- /dev/null
+++ b/shared/package-files/c9/c9f3f44815c621ca74699ab83794ea345f170d2988aa010d65e19a738c5df629/jiterator.h
@@ -0,0 +1,40 @@
+#pragma once
+#include 
+
+#if AT_USE_JITERATOR()
+
+#include 
+#include 
+#include 
+
+#include 
+#include 
+
+namespace at::cuda {
+
+TORCH_CUDA_CPP_API c10::SmallVector CompileAndLaunchKernel(
+  const std::string& code_string,
+  const std::string& kernel_name,
+  const int num_outputs,
+  const c10::SmallVector& tensors,
+  const c10::SmallVector& extra_args,
+  bool return_by_ref);
+
+} // namespace at::cuda
+
+#else
+
+namespace at::cuda {
+
+TORCH_CUDA_CPP_API c10::SmallVector CompileAndLaunchKernel(
+  const std::string& code_string,
+  const std::string& kernel_name,
+  const int num_outputs,
+  const c10::SmallVector& tensors,
+  const c10::SmallVector& extra_args,
+  bool return_by_ref) {
+    TORCH_CHECK(false, "Jiterator is not supported");
+  }
+} // namespace at::cuda
+
+#endif // AT_USE_JITERATOR()
diff --git a/shared/package-files/c9/c9f4ac43ba6c7b9f66a7d2a28314aac53f91928d7fdbf1a58dce97cdffeac38b/tokens.py b/shared/package-files/c9/c9f4ac43ba6c7b9f66a7d2a28314aac53f91928d7fdbf1a58dce97cdffeac38b/tokens.py
new file mode 100644
index 0000000000000000000000000000000000000000..34d8efcccdd516d36ea6764460e0c0eff4a46ff6
--- /dev/null
+++ b/shared/package-files/c9/c9f4ac43ba6c7b9f66a7d2a28314aac53f91928d7fdbf1a58dce97cdffeac38b/tokens.py
@@ -0,0 +1,203 @@
+# This file is part of Patsy
+# Copyright (C) 2011 Nathaniel Smith 
+# See file LICENSE.txt for license information.
+
+# Utilities for dealing with Python code at the token level.
+#
+# Includes:
+#   a "pretty printer" that converts a sequence of tokens back into a
+#       readable, white-space normalized string.
+#   a utility function to replace calls to global functions with calls to
+#       other functions
+
+from io import StringIO
+
+import tokenize
+
+from patsy import PatsyError
+from patsy.origin import Origin
+
+__all__ = ["python_tokenize", "pretty_untokenize", "normalize_token_spacing"]
+
+
+# A convenience wrapper around tokenize.generate_tokens. yields tuples
+#   (tokenize type, token string, origin object)
+def python_tokenize(code):
+    # Since formulas can only contain Python expressions, and Python
+    # expressions cannot meaningfully contain newlines, we'll just remove all
+    # the newlines up front to avoid any complications:
+    code = code.replace("\n", " ").strip()
+    it = tokenize.generate_tokens(StringIO(code).readline)
+    try:
+        for pytype, string, (_, start), (_, end), code in it:
+            if pytype == tokenize.ENDMARKER:
+                break
+            if pytype in (tokenize.NL, tokenize.NEWLINE):
+                assert string == ""
+                continue
+            origin = Origin(code, start, end)
+            if pytype == tokenize.ERRORTOKEN:
+                raise PatsyError(
+                    "error tokenizing input (maybe an unclosed string?)", origin
+                )
+            if pytype == tokenize.COMMENT:
+                raise PatsyError("comments are not allowed", origin)
+            yield (pytype, string, origin)
+        else:  # pragma: no cover
+            raise ValueError("stream ended without ENDMARKER?!?")
+    except tokenize.TokenError as e:
+        # TokenError is raised iff the tokenizer thinks that there is
+        # some sort of multi-line construct in progress (e.g., an
+        # unclosed parentheses, which in Python lets a virtual line
+        # continue past the end of the physical line), and it hits the
+        # end of the source text. We have our own error handling for
+        # such cases, so just treat this as an end-of-stream.
+        #
+        if "unterminated string literal" in e.args[0]:
+            raise PatsyError(
+                "error tokenizing input ({})".format(e.args[0]),
+                Origin(code, 0, len(code)),
+            )
+
+        # Just in case someone adds some other error case:
+        assert "EOF in multi-line" in e.args[0]
+        return
+
+
+def test_python_tokenize():
+    code = "a + (foo * -1)"
+    tokens = list(python_tokenize(code))
+    expected = [
+        (tokenize.NAME, "a", Origin(code, 0, 1)),
+        (tokenize.OP, "+", Origin(code, 2, 3)),
+        (tokenize.OP, "(", Origin(code, 4, 5)),
+        (tokenize.NAME, "foo", Origin(code, 5, 8)),
+        (tokenize.OP, "*", Origin(code, 9, 10)),
+        (tokenize.OP, "-", Origin(code, 11, 12)),
+        (tokenize.NUMBER, "1", Origin(code, 12, 13)),
+        (tokenize.OP, ")", Origin(code, 13, 14)),
+    ]
+    assert tokens == expected
+
+    code2 = "a + (b"
+    tokens2 = list(python_tokenize(code2))
+    expected2 = [
+        (tokenize.NAME, "a", Origin(code2, 0, 1)),
+        (tokenize.OP, "+", Origin(code2, 2, 3)),
+        (tokenize.OP, "(", Origin(code2, 4, 5)),
+        (tokenize.NAME, "b", Origin(code2, 5, 6)),
+    ]
+    assert tokens2 == expected2
+
+    import pytest
+
+    pytest.raises(PatsyError, list, python_tokenize("a b # c"))
+
+    import pytest
+
+    pytest.raises(PatsyError, list, python_tokenize('a b "c'))
+
+
+_python_space_both = list("+-*/%&^|<>") + [
+    "==",
+    "<>",
+    "!=",
+    "<=",
+    ">=",
+    "<<",
+    ">>",
+    "**",
+    "//",
+]
+_python_space_before = _python_space_both + ["!", "~"]
+_python_space_after = _python_space_both + [",", ":"]
+
+
+def pretty_untokenize(typed_tokens):
+    text = []
+    prev_was_space_delim = False
+    prev_wants_space = False
+    prev_was_open_paren_or_comma = False
+    prev_was_object_like = False
+    brackets = []
+    for token_type, token in typed_tokens:
+        assert token_type not in (tokenize.INDENT, tokenize.DEDENT, tokenize.NL)
+        if token_type == tokenize.NEWLINE:
+            continue
+        if token_type == tokenize.ENDMARKER:
+            continue
+        if token_type in (tokenize.NAME, tokenize.NUMBER, tokenize.STRING):
+            if prev_wants_space or prev_was_space_delim:
+                text.append(" ")
+            text.append(token)
+            prev_wants_space = False
+            prev_was_space_delim = True
+        else:
+            if token in ("(", "[", "{"):
+                brackets.append(token)
+            elif brackets and token in (")", "]", "}"):
+                brackets.pop()
+            this_wants_space_before = token in _python_space_before
+            this_wants_space_after = token in _python_space_after
+            # Special case for slice syntax: foo[:10]
+            # Otherwise ":" is spaced after, like: "{1: ...}", "if a: ..."
+            if token == ":" and brackets and brackets[-1] == "[":
+                this_wants_space_after = False
+            # Special case for foo(*args), foo(a, *args):
+            if token in ("*", "**") and prev_was_open_paren_or_comma:
+                this_wants_space_before = False
+                this_wants_space_after = False
+            # Special case for "a = foo(b=1)":
+            if token == "=" and not brackets:
+                this_wants_space_before = True
+                this_wants_space_after = True
+            # Special case for unary -, +. Our heuristic is that if we see the
+            # + or - after something that looks like an object (a NAME,
+            # NUMBER, STRING, or close paren) then it is probably binary,
+            # otherwise it is probably unary.
+            if token in ("+", "-") and not prev_was_object_like:
+                this_wants_space_before = False
+                this_wants_space_after = False
+            if prev_wants_space or this_wants_space_before:
+                text.append(" ")
+            text.append(token)
+            prev_wants_space = this_wants_space_after
+            prev_was_space_delim = False
+        if (
+            token_type in (tokenize.NAME, tokenize.NUMBER, tokenize.STRING)
+            or token == ")"
+        ):
+            prev_was_object_like = True
+        else:
+            prev_was_object_like = False
+        prev_was_open_paren_or_comma = token in ("(", ",")
+    return "".join(text)
+
+
+def normalize_token_spacing(code):
+    tokens = [(t[0], t[1]) for t in tokenize.generate_tokens(StringIO(code).readline)]
+    return pretty_untokenize(tokens)
+
+
+def test_pretty_untokenize_and_normalize_token_spacing():
+    assert normalize_token_spacing("1 + 1") == "1 + 1"
+    assert normalize_token_spacing("1+1") == "1 + 1"
+    assert normalize_token_spacing("1*(2+3**2)") == "1 * (2 + 3 ** 2)"
+    assert normalize_token_spacing("a and b") == "a and b"
+    assert normalize_token_spacing("foo(a=bar.baz[1:])") == "foo(a=bar.baz[1:])"
+    assert normalize_token_spacing("""{"hi":foo[:]}""") == """{"hi": foo[:]}"""
+    assert normalize_token_spacing("""'a' "b" 'c'""") == """'a' "b" 'c'"""
+    assert normalize_token_spacing('"""a""" is 1 or 2==3') == '"""a""" is 1 or 2 == 3'
+    assert normalize_token_spacing("foo ( * args )") == "foo(*args)"
+    assert normalize_token_spacing("foo ( a * args )") == "foo(a * args)"
+    assert normalize_token_spacing("foo ( ** args )") == "foo(**args)"
+    assert normalize_token_spacing("foo ( a ** args )") == "foo(a ** args)"
+    assert normalize_token_spacing("foo (1, * args )") == "foo(1, *args)"
+    assert normalize_token_spacing("foo (1, a * args )") == "foo(1, a * args)"
+    assert normalize_token_spacing("foo (1, ** args )") == "foo(1, **args)"
+    assert normalize_token_spacing("foo (1, a ** args )") == "foo(1, a ** args)"
+
+    assert normalize_token_spacing("a=foo(b = 1)") == "a = foo(b=1)"
+
+    assert normalize_token_spacing("foo(+ 10, bar = - 1)") == "foo(+10, bar=-1)"
+    assert normalize_token_spacing("1 + +10 + -1 - 5") == "1 + +10 + -1 - 5"
diff --git a/shared/package-files/c9/c9f90683b4c5df23e185150386f3cc4f5b898126e84f08003ad28e25d3fb77c1/plot_camera.py b/shared/package-files/c9/c9f90683b4c5df23e185150386f3cc4f5b898126e84f08003ad28e25d3fb77c1/plot_camera.py
new file mode 100644
index 0000000000000000000000000000000000000000..7f4adb9bbe5376dcb8c04e5f7dd006a8e19ab91e
--- /dev/null
+++ b/shared/package-files/c9/c9f90683b4c5df23e185150386f3cc4f5b898126e84f08003ad28e25d3fb77c1/plot_camera.py
@@ -0,0 +1,128 @@
+import pyglet.gl as pgl
+from sympy.plotting.pygletplot.plot_rotation import get_spherical_rotatation
+from sympy.plotting.pygletplot.util import get_model_matrix, model_to_screen, \
+                                            screen_to_model, vec_subs
+
+
+class PlotCamera:
+
+    min_dist = 0.05
+    max_dist = 500.0
+
+    min_ortho_dist = 100.0
+    max_ortho_dist = 10000.0
+
+    _default_dist = 6.0
+    _default_ortho_dist = 600.0
+
+    rot_presets = {
+        'xy': (0, 0, 0),
+        'xz': (-90, 0, 0),
+        'yz': (0, 90, 0),
+        'perspective': (-45, 0, -45)
+    }
+
+    def __init__(self, window, ortho=False):
+        self.window = window
+        self.axes = self.window.plot.axes
+        self.ortho = ortho
+        self.reset()
+
+    def init_rot_matrix(self):
+        pgl.glPushMatrix()
+        pgl.glLoadIdentity()
+        self._rot = get_model_matrix()
+        pgl.glPopMatrix()
+
+    def set_rot_preset(self, preset_name):
+        self.init_rot_matrix()
+        try:
+            r = self.rot_presets[preset_name]
+        except AttributeError:
+            raise ValueError(
+                "%s is not a valid rotation preset." % preset_name)
+        try:
+            self.euler_rotate(r[0], 1, 0, 0)
+            self.euler_rotate(r[1], 0, 1, 0)
+            self.euler_rotate(r[2], 0, 0, 1)
+        except AttributeError:
+            pass
+
+    def reset(self):
+        self._dist = 0.0
+        self._x, self._y = 0.0, 0.0
+        self._rot = None
+        if self.ortho:
+            self._dist = self._default_ortho_dist
+        else:
+            self._dist = self._default_dist
+        self.init_rot_matrix()
+
+    def mult_rot_matrix(self, rot):
+        pgl.glPushMatrix()
+        pgl.glLoadMatrixf(rot)
+        pgl.glMultMatrixf(self._rot)
+        self._rot = get_model_matrix()
+        pgl.glPopMatrix()
+
+    def setup_projection(self):
+        pgl.glMatrixMode(pgl.GL_PROJECTION)
+        pgl.glLoadIdentity()
+        if self.ortho:
+            # yep, this is pseudo ortho (don't tell anyone)
+            pgl.gluPerspective(
+                0.3, float(self.window.width)/float(self.window.height),
+                self.min_ortho_dist - 0.01, self.max_ortho_dist + 0.01)
+        else:
+            pgl.gluPerspective(
+                30.0, float(self.window.width)/float(self.window.height),
+                self.min_dist - 0.01, self.max_dist + 0.01)
+        pgl.glMatrixMode(pgl.GL_MODELVIEW)
+
+    def _get_scale(self):
+        return 1.0, 1.0, 1.0
+
+    def apply_transformation(self):
+        pgl.glLoadIdentity()
+        pgl.glTranslatef(self._x, self._y, -self._dist)
+        if self._rot is not None:
+            pgl.glMultMatrixf(self._rot)
+        pgl.glScalef(*self._get_scale())
+
+    def spherical_rotate(self, p1, p2, sensitivity=1.0):
+        mat = get_spherical_rotatation(p1, p2, self.window.width,
+                                       self.window.height, sensitivity)
+        if mat is not None:
+            self.mult_rot_matrix(mat)
+
+    def euler_rotate(self, angle, x, y, z):
+        pgl.glPushMatrix()
+        pgl.glLoadMatrixf(self._rot)
+        pgl.glRotatef(angle, x, y, z)
+        self._rot = get_model_matrix()
+        pgl.glPopMatrix()
+
+    def zoom_relative(self, clicks, sensitivity):
+
+        if self.ortho:
+            dist_d = clicks * sensitivity * 50.0
+            min_dist = self.min_ortho_dist
+            max_dist = self.max_ortho_dist
+        else:
+            dist_d = clicks * sensitivity
+            min_dist = self.min_dist
+            max_dist = self.max_dist
+
+        new_dist = (self._dist - dist_d)
+        if (clicks < 0 and new_dist < max_dist) or new_dist > min_dist:
+            self._dist = new_dist
+
+    def mouse_translate(self, x, y, dx, dy):
+        pgl.glPushMatrix()
+        pgl.glLoadIdentity()
+        pgl.glTranslatef(0, 0, -self._dist)
+        z = model_to_screen(0, 0, 0)[2]
+        d = vec_subs(screen_to_model(x, y, z), screen_to_model(x - dx, y - dy, z))
+        pgl.glPopMatrix()
+        self._x += d[0]
+        self._y += d[1]
diff --git a/shared/package-files/c9/c9fb2e596ead63b6be27a784de6ee43592f3bccd162c1f6d75c168b30da85c4b/fft_fft_ops.h b/shared/package-files/c9/c9fb2e596ead63b6be27a784de6ee43592f3bccd162c1f6d75c168b30da85c4b/fft_fft_ops.h
new file mode 100644
index 0000000000000000000000000000000000000000..adae1c4b3c26fbae1ff27f81f1d7a4e9530c7604
--- /dev/null
+++ b/shared/package-files/c9/c9fb2e596ead63b6be27a784de6ee43592f3bccd162c1f6d75c168b30da85c4b/fft_fft_ops.h
@@ -0,0 +1,39 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Operator.h
+
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+namespace _ops {
+
+
+struct TORCH_API fft_fft {
+  using schema = at::Tensor (const at::Tensor &, c10::optional, int64_t, c10::optional);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::fft_fft")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "fft_fft(Tensor self, SymInt? n=None, int dim=-1, str? norm=None) -> Tensor")
+  static at::Tensor call(const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm);
+  static at::Tensor redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm);
+};
+
+struct TORCH_API fft_fft_out {
+  using schema = at::Tensor & (const at::Tensor &, c10::optional, int64_t, c10::optional, at::Tensor &);
+  using ptr_schema = schema*;
+  // See Note [static constexpr char* members for windows NVCC]
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(name, "aten::fft_fft")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(overload_name, "out")
+  STATIC_CONSTEXPR_STR_INL_EXCEPT_WIN_CUDA(schema_str, "fft_fft.out(Tensor self, SymInt? n=None, int dim=-1, str? norm=None, *, Tensor(a!) out) -> Tensor(a!)")
+  static at::Tensor & call(const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out);
+  static at::Tensor & redispatch(c10::DispatchKeySet dispatchKeySet, const at::Tensor & self, c10::optional n, int64_t dim, c10::optional norm, at::Tensor & out);
+};
+
+}} // namespace at::_ops
diff --git a/shared/package-files/c9/c9fc3ca63993ef4e63a11252294f827e6ba2c89f9775c7a56c30b3282c6ac559/namespace.py b/shared/package-files/c9/c9fc3ca63993ef4e63a11252294f827e6ba2c89f9775c7a56c30b3282c6ac559/namespace.py
new file mode 100644
index 0000000000000000000000000000000000000000..11737cc97f5ee2a00bc6ddb774cb71be2645ebd3
--- /dev/null
+++ b/shared/package-files/c9/c9fc3ca63993ef4e63a11252294f827e6ba2c89f9775c7a56c30b3282c6ac559/namespace.py
@@ -0,0 +1,74 @@
+from pathlib import Path
+from typing import Optional
+
+from jedi.inference.cache import inference_state_method_cache
+from jedi.inference.filters import DictFilter
+from jedi.inference.names import ValueNameMixin, AbstractNameDefinition
+from jedi.inference.base_value import Value
+from jedi.inference.value.module import SubModuleDictMixin
+from jedi.inference.context import NamespaceContext
+
+
+class ImplicitNSName(ValueNameMixin, AbstractNameDefinition):
+    """
+    Accessing names for implicit namespace packages should infer to nothing.
+    This object will prevent Jedi from raising exceptions
+    """
+    def __init__(self, implicit_ns_value, string_name):
+        self._value = implicit_ns_value
+        self.string_name = string_name
+
+
+class ImplicitNamespaceValue(Value, SubModuleDictMixin):
+    """
+    Provides support for implicit namespace packages
+    """
+    api_type = 'namespace'
+    parent_context = None
+
+    def __init__(self, inference_state, string_names, paths):
+        super().__init__(inference_state, parent_context=None)
+        self.inference_state = inference_state
+        self.string_names = string_names
+        self._paths = paths
+
+    def get_filters(self, origin_scope=None):
+        yield DictFilter(self.sub_modules_dict())
+
+    def get_qualified_names(self):
+        return ()
+
+    @property  # type: ignore[misc]
+    @inference_state_method_cache()
+    def name(self):
+        string_name = self.py__package__()[-1]
+        return ImplicitNSName(self, string_name)
+
+    def py__file__(self) -> Optional[Path]:
+        return None
+
+    def py__package__(self):
+        """Return the fullname
+        """
+        return self.string_names
+
+    def py__path__(self):
+        return self._paths
+
+    def py__name__(self):
+        return '.'.join(self.string_names)
+
+    def is_namespace(self):
+        return True
+
+    def is_stub(self):
+        return False
+
+    def is_package(self):
+        return True
+
+    def as_context(self):
+        return NamespaceContext(self)
+
+    def __repr__(self):
+        return '<%s: %s>' % (self.__class__.__name__, self.py__name__())
diff --git a/shared/package-files/ec/ec01b919fc3088225edd4aa368844fe71b5bc9134a0b2a8eba79dedb8e6e9a5d/argparse.pyi b/shared/package-files/ec/ec01b919fc3088225edd4aa368844fe71b5bc9134a0b2a8eba79dedb8e6e9a5d/argparse.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..3dd6e56175dd2119f6d9f7371e43c331af44523b
--- /dev/null
+++ b/shared/package-files/ec/ec01b919fc3088225edd4aa368844fe71b5bc9134a0b2a8eba79dedb8e6e9a5d/argparse.pyi
@@ -0,0 +1,487 @@
+import sys
+from typing import (
+    IO,
+    Any,
+    Callable,
+    Dict,
+    Generator,
+    Iterable,
+    List,
+    NoReturn,
+    Optional,
+    Pattern,
+    Protocol,
+    Sequence,
+    Text,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    overload,
+)
+
+_T = TypeVar("_T")
+_ActionT = TypeVar("_ActionT", bound=Action)
+_N = TypeVar("_N")
+
+if sys.version_info >= (3,):
+    _Text = str
+else:
+    _Text = Union[str, unicode]
+
+ONE_OR_MORE: str
+OPTIONAL: str
+PARSER: str
+REMAINDER: str
+SUPPRESS: str
+ZERO_OR_MORE: str
+_UNRECOGNIZED_ARGS_ATTR: str  # undocumented
+
+class ArgumentError(Exception):
+    argument_name: Optional[str]
+    message: str
+    def __init__(self, argument: Optional[Action], message: str) -> None: ...
+
+# undocumented
+class _AttributeHolder:
+    def _get_kwargs(self) -> List[Tuple[str, Any]]: ...
+    def _get_args(self) -> List[Any]: ...
+
+# undocumented
+class _ActionsContainer:
+    description: Optional[_Text]
+    prefix_chars: _Text
+    argument_default: Any
+    conflict_handler: _Text
+
+    _registries: Dict[_Text, Dict[Any, Any]]
+    _actions: List[Action]
+    _option_string_actions: Dict[_Text, Action]
+    _action_groups: List[_ArgumentGroup]
+    _mutually_exclusive_groups: List[_MutuallyExclusiveGroup]
+    _defaults: Dict[str, Any]
+    _negative_number_matcher: Pattern[str]
+    _has_negative_number_optionals: List[bool]
+    def __init__(
+        self, description: Optional[Text], prefix_chars: Text, argument_default: Any, conflict_handler: Text
+    ) -> None: ...
+    def register(self, registry_name: Text, value: Any, object: Any) -> None: ...
+    def _registry_get(self, registry_name: Text, value: Any, default: Any = ...) -> Any: ...
+    def set_defaults(self, **kwargs: Any) -> None: ...
+    def get_default(self, dest: Text) -> Any: ...
+    def add_argument(
+        self,
+        *name_or_flags: Text,
+        action: Union[Text, Type[Action]] = ...,
+        nargs: Union[int, Text] = ...,
+        const: Any = ...,
+        default: Any = ...,
+        type: Union[Callable[[Text], _T], Callable[[str], _T], FileType] = ...,
+        choices: Iterable[_T] = ...,
+        required: bool = ...,
+        help: Optional[Text] = ...,
+        metavar: Optional[Union[Text, Tuple[Text, ...]]] = ...,
+        dest: Optional[Text] = ...,
+        version: Text = ...,
+        **kwargs: Any,
+    ) -> Action: ...
+    def add_argument_group(self, *args: Any, **kwargs: Any) -> _ArgumentGroup: ...
+    def add_mutually_exclusive_group(self, **kwargs: Any) -> _MutuallyExclusiveGroup: ...
+    def _add_action(self, action: _ActionT) -> _ActionT: ...
+    def _remove_action(self, action: Action) -> None: ...
+    def _add_container_actions(self, container: _ActionsContainer) -> None: ...
+    def _get_positional_kwargs(self, dest: Text, **kwargs: Any) -> Dict[str, Any]: ...
+    def _get_optional_kwargs(self, *args: Any, **kwargs: Any) -> Dict[str, Any]: ...
+    def _pop_action_class(self, kwargs: Any, default: Optional[Type[Action]] = ...) -> Type[Action]: ...
+    def _get_handler(self) -> Callable[[Action, Iterable[Tuple[Text, Action]]], Any]: ...
+    def _check_conflict(self, action: Action) -> None: ...
+    def _handle_conflict_error(self, action: Action, conflicting_actions: Iterable[Tuple[Text, Action]]) -> NoReturn: ...
+    def _handle_conflict_resolve(self, action: Action, conflicting_actions: Iterable[Tuple[Text, Action]]) -> None: ...
+
+class _FormatterClass(Protocol):
+    def __call__(self, prog: str) -> HelpFormatter: ...
+
+class ArgumentParser(_AttributeHolder, _ActionsContainer):
+    prog: _Text
+    usage: Optional[_Text]
+    epilog: Optional[_Text]
+    formatter_class: _FormatterClass
+    fromfile_prefix_chars: Optional[_Text]
+    add_help: bool
+
+    if sys.version_info >= (3, 5):
+        allow_abbrev: bool
+
+    # undocumented
+    _positionals: _ArgumentGroup
+    _optionals: _ArgumentGroup
+    _subparsers: Optional[_ArgumentGroup]
+
+    if sys.version_info >= (3, 9):
+        def __init__(
+            self,
+            prog: Optional[str] = ...,
+            usage: Optional[str] = ...,
+            description: Optional[str] = ...,
+            epilog: Optional[str] = ...,
+            parents: Sequence[ArgumentParser] = ...,
+            formatter_class: _FormatterClass = ...,
+            prefix_chars: str = ...,
+            fromfile_prefix_chars: Optional[str] = ...,
+            argument_default: Any = ...,
+            conflict_handler: str = ...,
+            add_help: bool = ...,
+            allow_abbrev: bool = ...,
+            exit_on_error: bool = ...,
+        ) -> None: ...
+    elif sys.version_info >= (3, 5):
+        def __init__(
+            self,
+            prog: Optional[str] = ...,
+            usage: Optional[str] = ...,
+            description: Optional[str] = ...,
+            epilog: Optional[str] = ...,
+            parents: Sequence[ArgumentParser] = ...,
+            formatter_class: _FormatterClass = ...,
+            prefix_chars: str = ...,
+            fromfile_prefix_chars: Optional[str] = ...,
+            argument_default: Any = ...,
+            conflict_handler: str = ...,
+            add_help: bool = ...,
+            allow_abbrev: bool = ...,
+        ) -> None: ...
+    else:
+        def __init__(
+            self,
+            prog: Optional[Text] = ...,
+            usage: Optional[Text] = ...,
+            description: Optional[Text] = ...,
+            epilog: Optional[Text] = ...,
+            parents: Sequence[ArgumentParser] = ...,
+            formatter_class: _FormatterClass = ...,
+            prefix_chars: Text = ...,
+            fromfile_prefix_chars: Optional[Text] = ...,
+            argument_default: Any = ...,
+            conflict_handler: Text = ...,
+            add_help: bool = ...,
+        ) -> None: ...
+    # The type-ignores in these overloads should be temporary.  See:
+    # https://github.com/python/typeshed/pull/2643#issuecomment-442280277
+    @overload
+    def parse_args(self, args: Optional[Sequence[Text]] = ...) -> Namespace: ...
+    @overload
+    def parse_args(self, args: Optional[Sequence[Text]], namespace: None) -> Namespace: ...  # type: ignore
+    @overload
+    def parse_args(self, args: Optional[Sequence[Text]], namespace: _N) -> _N: ...
+    @overload
+    def parse_args(self, *, namespace: None) -> Namespace: ...  # type: ignore
+    @overload
+    def parse_args(self, *, namespace: _N) -> _N: ...
+    if sys.version_info >= (3, 7):
+        def add_subparsers(
+            self,
+            *,
+            title: str = ...,
+            description: Optional[str] = ...,
+            prog: str = ...,
+            parser_class: Type[ArgumentParser] = ...,
+            action: Type[Action] = ...,
+            option_string: str = ...,
+            dest: Optional[str] = ...,
+            required: bool = ...,
+            help: Optional[str] = ...,
+            metavar: Optional[str] = ...,
+        ) -> _SubParsersAction: ...
+    else:
+        def add_subparsers(
+            self,
+            *,
+            title: Text = ...,
+            description: Optional[Text] = ...,
+            prog: Text = ...,
+            parser_class: Type[ArgumentParser] = ...,
+            action: Type[Action] = ...,
+            option_string: Text = ...,
+            dest: Optional[Text] = ...,
+            help: Optional[Text] = ...,
+            metavar: Optional[Text] = ...,
+        ) -> _SubParsersAction: ...
+    def print_usage(self, file: Optional[IO[str]] = ...) -> None: ...
+    def print_help(self, file: Optional[IO[str]] = ...) -> None: ...
+    def format_usage(self) -> str: ...
+    def format_help(self) -> str: ...
+    def parse_known_args(
+        self, args: Optional[Sequence[Text]] = ..., namespace: Optional[Namespace] = ...
+    ) -> Tuple[Namespace, List[str]]: ...
+    def convert_arg_line_to_args(self, arg_line: Text) -> List[str]: ...
+    def exit(self, status: int = ..., message: Optional[Text] = ...) -> NoReturn: ...
+    def error(self, message: Text) -> NoReturn: ...
+    if sys.version_info >= (3, 7):
+        def parse_intermixed_args(
+            self, args: Optional[Sequence[str]] = ..., namespace: Optional[Namespace] = ...
+        ) -> Namespace: ...
+        def parse_known_intermixed_args(
+            self, args: Optional[Sequence[str]] = ..., namespace: Optional[Namespace] = ...
+        ) -> Tuple[Namespace, List[str]]: ...
+    # undocumented
+    def _get_optional_actions(self) -> List[Action]: ...
+    def _get_positional_actions(self) -> List[Action]: ...
+    def _parse_known_args(self, arg_strings: List[Text], namespace: Namespace) -> Tuple[Namespace, List[str]]: ...
+    def _read_args_from_files(self, arg_strings: List[Text]) -> List[Text]: ...
+    def _match_argument(self, action: Action, arg_strings_pattern: Text) -> int: ...
+    def _match_arguments_partial(self, actions: Sequence[Action], arg_strings_pattern: Text) -> List[int]: ...
+    def _parse_optional(self, arg_string: Text) -> Optional[Tuple[Optional[Action], Text, Optional[Text]]]: ...
+    def _get_option_tuples(self, option_string: Text) -> List[Tuple[Action, Text, Optional[Text]]]: ...
+    def _get_nargs_pattern(self, action: Action) -> _Text: ...
+    def _get_values(self, action: Action, arg_strings: List[Text]) -> Any: ...
+    def _get_value(self, action: Action, arg_string: Text) -> Any: ...
+    def _check_value(self, action: Action, value: Any) -> None: ...
+    def _get_formatter(self) -> HelpFormatter: ...
+    def _print_message(self, message: str, file: Optional[IO[str]] = ...) -> None: ...
+
+class HelpFormatter:
+    # undocumented
+    _prog: _Text
+    _indent_increment: int
+    _max_help_position: int
+    _width: int
+    _current_indent: int
+    _level: int
+    _action_max_length: int
+    _root_section: Any
+    _current_section: Any
+    _whitespace_matcher: Pattern[str]
+    _long_break_matcher: Pattern[str]
+    _Section: Type[Any]  # Nested class
+    def __init__(
+        self, prog: Text, indent_increment: int = ..., max_help_position: int = ..., width: Optional[int] = ...
+    ) -> None: ...
+    def _indent(self) -> None: ...
+    def _dedent(self) -> None: ...
+    def _add_item(self, func: Callable[..., _Text], args: Iterable[Any]) -> None: ...
+    def start_section(self, heading: Optional[Text]) -> None: ...
+    def end_section(self) -> None: ...
+    def add_text(self, text: Optional[Text]) -> None: ...
+    def add_usage(
+        self, usage: Text, actions: Iterable[Action], groups: Iterable[_ArgumentGroup], prefix: Optional[Text] = ...
+    ) -> None: ...
+    def add_argument(self, action: Action) -> None: ...
+    def add_arguments(self, actions: Iterable[Action]) -> None: ...
+    def format_help(self) -> _Text: ...
+    def _join_parts(self, part_strings: Iterable[Text]) -> _Text: ...
+    def _format_usage(
+        self, usage: Text, actions: Iterable[Action], groups: Iterable[_ArgumentGroup], prefix: Optional[Text]
+    ) -> _Text: ...
+    def _format_actions_usage(self, actions: Iterable[Action], groups: Iterable[_ArgumentGroup]) -> _Text: ...
+    def _format_text(self, text: Text) -> _Text: ...
+    def _format_action(self, action: Action) -> _Text: ...
+    def _format_action_invocation(self, action: Action) -> _Text: ...
+    def _metavar_formatter(self, action: Action, default_metavar: Text) -> Callable[[int], Tuple[_Text, ...]]: ...
+    def _format_args(self, action: Action, default_metavar: Text) -> _Text: ...
+    def _expand_help(self, action: Action) -> _Text: ...
+    def _iter_indented_subactions(self, action: Action) -> Generator[Action, None, None]: ...
+    def _split_lines(self, text: Text, width: int) -> List[_Text]: ...
+    def _fill_text(self, text: Text, width: int, indent: Text) -> _Text: ...
+    def _get_help_string(self, action: Action) -> Optional[_Text]: ...
+    def _get_default_metavar_for_optional(self, action: Action) -> _Text: ...
+    def _get_default_metavar_for_positional(self, action: Action) -> _Text: ...
+
+class RawDescriptionHelpFormatter(HelpFormatter): ...
+class RawTextHelpFormatter(RawDescriptionHelpFormatter): ...
+class ArgumentDefaultsHelpFormatter(HelpFormatter): ...
+
+if sys.version_info >= (3,):
+    class MetavarTypeHelpFormatter(HelpFormatter): ...
+
+class Action(_AttributeHolder):
+    option_strings: Sequence[_Text]
+    dest: _Text
+    nargs: Optional[Union[int, _Text]]
+    const: Any
+    default: Any
+    type: Union[Callable[[str], Any], FileType, None]
+    choices: Optional[Iterable[Any]]
+    required: bool
+    help: Optional[_Text]
+    metavar: Optional[Union[_Text, Tuple[_Text, ...]]]
+    def __init__(
+        self,
+        option_strings: Sequence[Text],
+        dest: Text,
+        nargs: Optional[Union[int, Text]] = ...,
+        const: Optional[_T] = ...,
+        default: Union[_T, str, None] = ...,
+        type: Optional[Union[Callable[[Text], _T], Callable[[str], _T], FileType]] = ...,
+        choices: Optional[Iterable[_T]] = ...,
+        required: bool = ...,
+        help: Optional[Text] = ...,
+        metavar: Optional[Union[Text, Tuple[Text, ...]]] = ...,
+    ) -> None: ...
+    def __call__(
+        self,
+        parser: ArgumentParser,
+        namespace: Namespace,
+        values: Union[Text, Sequence[Any], None],
+        option_string: Optional[Text] = ...,
+    ) -> None: ...
+    if sys.version_info >= (3, 9):
+        def format_usage(self) -> str: ...
+
+if sys.version_info >= (3, 9):
+    class BooleanOptionalAction(Action):
+        def __init__(
+            self,
+            option_strings: Sequence[str],
+            dest: str,
+            default: Union[_T, str, None] = ...,
+            type: Optional[Union[Callable[[Text], _T], Callable[[str], _T], FileType]] = ...,
+            choices: Optional[Iterable[_T]] = ...,
+            required: bool = ...,
+            help: Optional[Text] = ...,
+            metavar: Optional[Union[Text, Tuple[Text, ...]]] = ...,
+        ) -> None: ...
+
+class Namespace(_AttributeHolder):
+    def __init__(self, **kwargs: Any) -> None: ...
+    def __getattr__(self, name: Text) -> Any: ...
+    def __setattr__(self, name: Text, value: Any) -> None: ...
+    def __contains__(self, key: str) -> bool: ...
+
+class FileType:
+    # undocumented
+    _mode: _Text
+    _bufsize: int
+    if sys.version_info >= (3,):
+        _encoding: Optional[str]
+        _errors: Optional[str]
+        def __init__(
+            self, mode: str = ..., bufsize: int = ..., encoding: Optional[str] = ..., errors: Optional[str] = ...
+        ) -> None: ...
+    else:
+        def __init__(self, mode: Text = ..., bufsize: Optional[int] = ...) -> None: ...
+    def __call__(self, string: Text) -> IO[Any]: ...
+
+# undocumented
+class _ArgumentGroup(_ActionsContainer):
+    title: Optional[_Text]
+    _group_actions: List[Action]
+    def __init__(
+        self, container: _ActionsContainer, title: Optional[Text] = ..., description: Optional[Text] = ..., **kwargs: Any
+    ) -> None: ...
+
+# undocumented
+class _MutuallyExclusiveGroup(_ArgumentGroup):
+    required: bool
+    _container: _ActionsContainer
+    def __init__(self, container: _ActionsContainer, required: bool = ...) -> None: ...
+
+# undocumented
+class _StoreAction(Action): ...
+
+# undocumented
+class _StoreConstAction(Action):
+    def __init__(
+        self,
+        option_strings: Sequence[Text],
+        dest: Text,
+        const: Any,
+        default: Any = ...,
+        required: bool = ...,
+        help: Optional[Text] = ...,
+        metavar: Optional[Union[Text, Tuple[Text, ...]]] = ...,
+    ) -> None: ...
+
+# undocumented
+class _StoreTrueAction(_StoreConstAction):
+    def __init__(
+        self, option_strings: Sequence[Text], dest: Text, default: bool = ..., required: bool = ..., help: Optional[Text] = ...
+    ) -> None: ...
+
+# undocumented
+class _StoreFalseAction(_StoreConstAction):
+    def __init__(
+        self, option_strings: Sequence[Text], dest: Text, default: bool = ..., required: bool = ..., help: Optional[Text] = ...
+    ) -> None: ...
+
+# undocumented
+class _AppendAction(Action): ...
+
+# undocumented
+class _AppendConstAction(Action):
+    def __init__(
+        self,
+        option_strings: Sequence[Text],
+        dest: Text,
+        const: Any,
+        default: Any = ...,
+        required: bool = ...,
+        help: Optional[Text] = ...,
+        metavar: Optional[Union[Text, Tuple[Text, ...]]] = ...,
+    ) -> None: ...
+
+# undocumented
+class _CountAction(Action):
+    def __init__(
+        self, option_strings: Sequence[Text], dest: Text, default: Any = ..., required: bool = ..., help: Optional[Text] = ...
+    ) -> None: ...
+
+# undocumented
+class _HelpAction(Action):
+    def __init__(
+        self, option_strings: Sequence[Text], dest: Text = ..., default: Text = ..., help: Optional[Text] = ...
+    ) -> None: ...
+
+# undocumented
+class _VersionAction(Action):
+    version: Optional[_Text]
+    def __init__(
+        self,
+        option_strings: Sequence[Text],
+        version: Optional[Text] = ...,
+        dest: Text = ...,
+        default: Text = ...,
+        help: Text = ...,
+    ) -> None: ...
+
+# undocumented
+class _SubParsersAction(Action):
+    _ChoicesPseudoAction: Type[Any]  # nested class
+    _prog_prefix: _Text
+    _parser_class: Type[ArgumentParser]
+    _name_parser_map: Dict[_Text, ArgumentParser]
+    choices: Dict[_Text, ArgumentParser]
+    _choices_actions: List[Action]
+    if sys.version_info >= (3, 7):
+        def __init__(
+            self,
+            option_strings: Sequence[Text],
+            prog: Text,
+            parser_class: Type[ArgumentParser],
+            dest: Text = ...,
+            required: bool = ...,
+            help: Optional[Text] = ...,
+            metavar: Optional[Union[Text, Tuple[Text, ...]]] = ...,
+        ) -> None: ...
+    else:
+        def __init__(
+            self,
+            option_strings: Sequence[Text],
+            prog: Text,
+            parser_class: Type[ArgumentParser],
+            dest: Text = ...,
+            help: Optional[Text] = ...,
+            metavar: Optional[Union[Text, Tuple[Text, ...]]] = ...,
+        ) -> None: ...
+    # TODO: Type keyword args properly.
+    def add_parser(self, name: Text, **kwargs: Any) -> ArgumentParser: ...
+    def _get_subactions(self) -> List[Action]: ...
+
+# undocumented
+class ArgumentTypeError(Exception): ...
+
+if sys.version_info < (3, 7):
+    # undocumented
+    def _ensure_value(namespace: Namespace, name: Text, value: Any) -> Any: ...
+
+# undocumented
+def _get_action_name(argument: Optional[Action]) -> Optional[str]: ...
diff --git a/shared/package-files/ec/ec01cde068b8038fa73d6f23fc21b933d3d0267deb92c9f332f9df11032add30/linalg_matrix_rank.h b/shared/package-files/ec/ec01cde068b8038fa73d6f23fc21b933d3d0267deb92c9f332f9df11032add30/linalg_matrix_rank.h
new file mode 100644
index 0000000000000000000000000000000000000000..35edad1ae2474459ee3ea9c3402ab68662352957
--- /dev/null
+++ b/shared/package-files/ec/ec01cde068b8038fa73d6f23fc21b933d3d0267deb92c9f332f9df11032add30/linalg_matrix_rank.h
@@ -0,0 +1,81 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::linalg_matrix_rank.atol_rtol_tensor(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False) -> Tensor
+inline at::Tensor linalg_matrix_rank(const at::Tensor & input, const c10::optional & atol={}, const c10::optional & rtol={}, bool hermitian=false) {
+    return at::_ops::linalg_matrix_rank_atol_rtol_tensor::call(input, atol, rtol, hermitian);
+}
+
+// aten::linalg_matrix_rank.atol_rtol_tensor_out(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & linalg_matrix_rank_out(at::Tensor & out, const at::Tensor & input, const c10::optional & atol={}, const c10::optional & rtol={}, bool hermitian=false) {
+    return at::_ops::linalg_matrix_rank_atol_rtol_tensor_out::call(input, atol, rtol, hermitian, out);
+}
+// aten::linalg_matrix_rank.atol_rtol_tensor_out(Tensor input, *, Tensor? atol=None, Tensor? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & linalg_matrix_rank_outf(const at::Tensor & input, const c10::optional & atol, const c10::optional & rtol, bool hermitian, at::Tensor & out) {
+    return at::_ops::linalg_matrix_rank_atol_rtol_tensor_out::call(input, atol, rtol, hermitian, out);
+}
+
+// aten::linalg_matrix_rank.atol_rtol_float(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False) -> Tensor
+inline at::Tensor linalg_matrix_rank(const at::Tensor & self, c10::optional atol, c10::optional rtol, bool hermitian=false) {
+    return at::_ops::linalg_matrix_rank_atol_rtol_float::call(self, atol, rtol, hermitian);
+}
+
+// aten::linalg_matrix_rank.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & linalg_matrix_rank_out(at::Tensor & out, const at::Tensor & self, c10::optional atol, c10::optional rtol, bool hermitian=false) {
+    return at::_ops::linalg_matrix_rank_atol_rtol_float_out::call(self, atol, rtol, hermitian, out);
+}
+// aten::linalg_matrix_rank.atol_rtol_float_out(Tensor self, *, float? atol=None, float? rtol=None, bool hermitian=False, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & linalg_matrix_rank_outf(const at::Tensor & self, c10::optional atol, c10::optional rtol, bool hermitian, at::Tensor & out) {
+    return at::_ops::linalg_matrix_rank_atol_rtol_float_out::call(self, atol, rtol, hermitian, out);
+}
+
+// aten::linalg_matrix_rank(Tensor self, float tol, bool hermitian=False) -> Tensor
+inline at::Tensor linalg_matrix_rank(const at::Tensor & self, double tol, bool hermitian=false) {
+    return at::_ops::linalg_matrix_rank::call(self, tol, hermitian);
+}
+
+// aten::linalg_matrix_rank.out(Tensor self, float tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & linalg_matrix_rank_out(at::Tensor & out, const at::Tensor & self, double tol, bool hermitian=false) {
+    return at::_ops::linalg_matrix_rank_out::call(self, tol, hermitian, out);
+}
+// aten::linalg_matrix_rank.out(Tensor self, float tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & linalg_matrix_rank_outf(const at::Tensor & self, double tol, bool hermitian, at::Tensor & out) {
+    return at::_ops::linalg_matrix_rank_out::call(self, tol, hermitian, out);
+}
+
+// aten::linalg_matrix_rank.tol_tensor(Tensor input, Tensor tol, bool hermitian=False) -> Tensor
+inline at::Tensor linalg_matrix_rank(const at::Tensor & input, const at::Tensor & tol, bool hermitian=false) {
+    return at::_ops::linalg_matrix_rank_tol_tensor::call(input, tol, hermitian);
+}
+
+// aten::linalg_matrix_rank.out_tol_tensor(Tensor input, Tensor tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & linalg_matrix_rank_out(at::Tensor & out, const at::Tensor & input, const at::Tensor & tol, bool hermitian=false) {
+    return at::_ops::linalg_matrix_rank_out_tol_tensor::call(input, tol, hermitian, out);
+}
+// aten::linalg_matrix_rank.out_tol_tensor(Tensor input, Tensor tol, bool hermitian=False, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & linalg_matrix_rank_outf(const at::Tensor & input, const at::Tensor & tol, bool hermitian, at::Tensor & out) {
+    return at::_ops::linalg_matrix_rank_out_tol_tensor::call(input, tol, hermitian, out);
+}
+
+}
diff --git a/shared/package-files/ec/ec02f07654ef810d3799069feb6c5743c0d4c8f3aac20e3941dd05e0ae9de882/context.h b/shared/package-files/ec/ec02f07654ef810d3799069feb6c5743c0d4c8f3aac20e3941dd05e0ae9de882/context.h
new file mode 100644
index 0000000000000000000000000000000000000000..cbcef807f6b4c345252504bdbef518405d4584d4
--- /dev/null
+++ b/shared/package-files/ec/ec02f07654ef810d3799069feb6c5743c0d4c8f3aac20e3941dd05e0ae9de882/context.h
@@ -0,0 +1,108 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#pragma once
+
+#include 
+#include 
+#include 
+#include 
+
+#include 
+#include 
+
+namespace tensorpipe {
+namespace channel {
+
+enum class Endpoint : bool { kConnect, kListen };
+
+class Channel;
+
+// Abstract base class for channel context classes.
+//
+// Instances of these classes are expected to be registered with a
+// context. All registered instances are assumed to be eligible
+// channels for all pairs.
+//
+class Context {
+ public:
+  // Return whether the context is able to operate correctly.
+  //
+  // Some channel types may be unable to perform as intended under some
+  // circumstances (e.g., specialized hardware unavailable, lack of
+  // permissions). They can report it through this method in order for
+  // the core context to avoid registering them in the first place.
+  //
+  virtual bool isViable() const = 0;
+
+  // Return the number of control connections needed to create an instance of
+  // this channel.
+  //
+  // Most channels require only one, but some require more (cuda_basic), and
+  // some might require none.
+  //
+  virtual size_t numConnectionsNeeded() const = 0;
+
+  // Return a map from supported devices to strings describing the device from
+  // the channel's perspective.
+  //
+  // Two processes with a channel context of the same type can leverage this
+  // channel to make two devices communicate if one side's device descriptor is
+  // "accepted" by the other one, using the canCommunicateWithRemote method
+  // below. That method must be symmetric, and unless overridden defaults to
+  // string comparison.
+  //
+  virtual const std::unordered_map& deviceDescriptors()
+      const = 0;
+
+  // Compare local and remote device descriptors for compatibility.
+  //
+  // Determine whether a channel can be opened between a local device and
+  // a remote one that has the given device descriptor. This function
+  // needs to be symmetric: if we called this method on the remote
+  // context with the local descriptor we should get the same answer.
+  // Unless overridden it defaults to string comparison.
+  //
+  virtual bool canCommunicateWithRemote(
+      const std::string& localDeviceDescriptor,
+      const std::string& remoteDeviceDescriptor) const = 0;
+
+  // Return newly created channel using the specified connections.
+  //
+  // It is up to the channel to either use these connections for further
+  // initialization, or use them directly. Either way, the returned
+  // channel should be immediately usable. If the channel isn't fully
+  // initialized yet, take care to queue these operations to execute
+  // as soon as initialization has completed.
+  //
+  virtual std::shared_ptr createChannel(
+      std::vector>,
+      Endpoint) = 0;
+
+  // Tell the context what its identifier is.
+  //
+  // This is only supposed to be called from the high-level context. It will
+  // only used for logging and debugging purposes.
+  virtual void setId(std::string id) = 0;
+
+  // Put the channel context in a terminal state, in turn closing all of its
+  // channels, and release its resources. This may be done asynchronously, in
+  // background.
+  virtual void close() = 0;
+
+  // Wait for all resources to be released and all background activity to stop.
+  virtual void join() = 0;
+
+  virtual ~Context() = default;
+
+ private:
+  std::string name_;
+};
+
+} // namespace channel
+} // namespace tensorpipe
diff --git a/shared/package-files/ec/ec048a15617aef040e78f8b91d1dd1a84b8001bdb1aa47f562b461966352e114/socketserver.pyi b/shared/package-files/ec/ec048a15617aef040e78f8b91d1dd1a84b8001bdb1aa47f562b461966352e114/socketserver.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..01ab760bfcd34734ab5fdf3768d84748465a1428
--- /dev/null
+++ b/shared/package-files/ec/ec048a15617aef040e78f8b91d1dd1a84b8001bdb1aa47f562b461966352e114/socketserver.pyi
@@ -0,0 +1,133 @@
+import sys
+import types
+from socket import SocketType
+from typing import Any, BinaryIO, Callable, ClassVar, List, Optional, Tuple, Type, Union
+
+class BaseServer:
+    address_family: int
+    RequestHandlerClass: Callable[..., BaseRequestHandler]
+    server_address: Tuple[str, int]
+    socket: SocketType
+    allow_reuse_address: bool
+    request_queue_size: int
+    socket_type: int
+    timeout: Optional[float]
+    def __init__(self, server_address: Any, RequestHandlerClass: Callable[..., BaseRequestHandler]) -> None: ...
+    def fileno(self) -> int: ...
+    def handle_request(self) -> None: ...
+    def serve_forever(self, poll_interval: float = ...) -> None: ...
+    def shutdown(self) -> None: ...
+    def server_close(self) -> None: ...
+    def finish_request(self, request: bytes, client_address: Tuple[str, int]) -> None: ...
+    def get_request(self) -> Tuple[SocketType, Tuple[str, int]]: ...
+    def handle_error(self, request: bytes, client_address: Tuple[str, int]) -> None: ...
+    def handle_timeout(self) -> None: ...
+    def process_request(self, request: bytes, client_address: Tuple[str, int]) -> None: ...
+    def server_activate(self) -> None: ...
+    def server_bind(self) -> None: ...
+    def verify_request(self, request: bytes, client_address: Tuple[str, int]) -> bool: ...
+    if sys.version_info >= (3, 6):
+        def __enter__(self) -> BaseServer: ...
+        def __exit__(
+            self, exc_type: Optional[Type[BaseException]], exc_val: Optional[BaseException], exc_tb: Optional[types.TracebackType]
+        ) -> None: ...
+    def service_actions(self) -> None: ...
+
+class TCPServer(BaseServer):
+    def __init__(
+        self,
+        server_address: Tuple[str, int],
+        RequestHandlerClass: Callable[..., BaseRequestHandler],
+        bind_and_activate: bool = ...,
+    ) -> None: ...
+
+class UDPServer(BaseServer):
+    def __init__(
+        self,
+        server_address: Tuple[str, int],
+        RequestHandlerClass: Callable[..., BaseRequestHandler],
+        bind_and_activate: bool = ...,
+    ) -> None: ...
+
+if sys.platform != "win32":
+    class UnixStreamServer(BaseServer):
+        def __init__(
+            self,
+            server_address: Union[str, bytes],
+            RequestHandlerClass: Callable[..., BaseRequestHandler],
+            bind_and_activate: bool = ...,
+        ) -> None: ...
+    class UnixDatagramServer(BaseServer):
+        def __init__(
+            self,
+            server_address: Union[str, bytes],
+            RequestHandlerClass: Callable[..., BaseRequestHandler],
+            bind_and_activate: bool = ...,
+        ) -> None: ...
+
+if sys.platform != "win32":
+    class ForkingMixIn:
+        timeout: Optional[float]  # undocumented
+        active_children: Optional[List[int]]  # undocumented
+        max_children: int  # undocumented
+        if sys.version_info >= (3, 7):
+            block_on_close: bool
+        if sys.version_info >= (3, 6):
+            def collect_children(self, *, blocking: bool = ...) -> None: ...  # undocumented
+        else:
+            def collect_children(self) -> None: ...  # undocumented
+        def handle_timeout(self) -> None: ...  # undocumented
+        def service_actions(self) -> None: ...  # undocumented
+        def process_request(self, request: bytes, client_address: Tuple[str, int]) -> None: ...
+        if sys.version_info >= (3, 6):
+            def server_close(self) -> None: ...
+
+class ThreadingMixIn:
+    daemon_threads: bool
+    if sys.version_info >= (3, 7):
+        block_on_close: bool
+    def process_request_thread(self, request: bytes, client_address: Tuple[str, int]) -> None: ...  # undocumented
+    def process_request(self, request: bytes, client_address: Tuple[str, int]) -> None: ...
+    if sys.version_info >= (3, 6):
+        def server_close(self) -> None: ...
+
+if sys.platform != "win32":
+    class ForkingTCPServer(ForkingMixIn, TCPServer): ...
+    class ForkingUDPServer(ForkingMixIn, UDPServer): ...
+
+class ThreadingTCPServer(ThreadingMixIn, TCPServer): ...
+class ThreadingUDPServer(ThreadingMixIn, UDPServer): ...
+
+if sys.platform != "win32":
+    class ThreadingUnixStreamServer(ThreadingMixIn, UnixStreamServer): ...
+    class ThreadingUnixDatagramServer(ThreadingMixIn, UnixDatagramServer): ...
+
+class BaseRequestHandler:
+    # Those are technically of types, respectively:
+    # * Union[SocketType, Tuple[bytes, SocketType]]
+    # * Union[Tuple[str, int], str]
+    # But there are some concerns that having unions here would cause
+    # too much inconvenience to people using it (see
+    # https://github.com/python/typeshed/pull/384#issuecomment-234649696)
+    request: Any
+    client_address: Any
+    server: BaseServer
+    def __init__(self, request: Any, client_address: Any, server: BaseServer) -> None: ...
+    def setup(self) -> None: ...
+    def handle(self) -> None: ...
+    def finish(self) -> None: ...
+
+class StreamRequestHandler(BaseRequestHandler):
+    rbufsize: ClassVar[int]  # Undocumented
+    wbufsize: ClassVar[int]  # Undocumented
+    timeout: ClassVar[Optional[float]]  # Undocumented
+    disable_nagle_algorithm: ClassVar[bool]  # Undocumented
+    connection: SocketType  # Undocumented
+    rfile: BinaryIO
+    wfile: BinaryIO
+
+class DatagramRequestHandler(BaseRequestHandler):
+    packet: SocketType  # Undocumented
+    socket: SocketType  # Undocumented
+    rfile: BinaryIO
+    wfile: BinaryIO
diff --git a/shared/package-files/ec/ec04b2cde3ebf3fc6e65626c9ea263201b7257cbe1128d30042bf530f4518b74/top_level.txt b/shared/package-files/ec/ec04b2cde3ebf3fc6e65626c9ea263201b7257cbe1128d30042bf530f4518b74/top_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..66958f0a069d7aea7939bed40b9197608e93b243
--- /dev/null
+++ b/shared/package-files/ec/ec04b2cde3ebf3fc6e65626c9ea263201b7257cbe1128d30042bf530f4518b74/top_level.txt
@@ -0,0 +1 @@
+charset_normalizer
diff --git a/shared/package-files/ec/ec04ccc333ef0285a21fb09ec7e0fade79a539adb487bd333b94d61958ac1703/test_fp_groups.py b/shared/package-files/ec/ec04ccc333ef0285a21fb09ec7e0fade79a539adb487bd333b94d61958ac1703/test_fp_groups.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f042fc937ac747108e10bcbaba375a8b443e3a5
--- /dev/null
+++ b/shared/package-files/ec/ec04ccc333ef0285a21fb09ec7e0fade79a539adb487bd333b94d61958ac1703/test_fp_groups.py
@@ -0,0 +1,252 @@
+from sympy.core.singleton import S
+from sympy.combinatorics.fp_groups import (FpGroup, low_index_subgroups,
+                                   reidemeister_presentation, FpSubgroup,
+                                           simplify_presentation)
+from sympy.combinatorics.free_groups import (free_group, FreeGroup)
+
+from sympy.testing.pytest import slow
+
+"""
+References
+==========
+
+[1] Holt, D., Eick, B., O'Brien, E.
+"Handbook of Computational Group Theory"
+
+[2] John J. Cannon; Lucien A. Dimino; George Havas; Jane M. Watson
+Mathematics of Computation, Vol. 27, No. 123. (Jul., 1973), pp. 463-490.
+"Implementation and Analysis of the Todd-Coxeter Algorithm"
+
+[3] PROC. SECOND  INTERNAT. CONF. THEORY OF GROUPS, CANBERRA 1973,
+pp. 347-356. "A Reidemeister-Schreier program" by George Havas.
+http://staff.itee.uq.edu.au/havas/1973cdhw.pdf
+
+"""
+
+def test_low_index_subgroups():
+    F, x, y = free_group("x, y")
+
+    # Example 5.10 from [1] Pg. 194
+    f = FpGroup(F, [x**2, y**3, (x*y)**4])
+    L = low_index_subgroups(f, 4)
+    t1 = [[[0, 0, 0, 0]],
+          [[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 3, 3]],
+          [[0, 0, 1, 2], [2, 2, 2, 0], [1, 1, 0, 1]],
+          [[1, 1, 0, 0], [0, 0, 1, 1]]]
+    for i in range(len(t1)):
+        assert L[i].table == t1[i]
+
+    f = FpGroup(F, [x**2, y**3, (x*y)**7])
+    L = low_index_subgroups(f, 15)
+    t2 = [[[0, 0, 0, 0]],
+           [[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5],
+            [4, 4, 5, 3], [6, 6, 3, 4], [5, 5, 6, 6]],
+           [[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5],
+            [6, 6, 5, 3], [5, 5, 3, 4], [4, 4, 6, 6]],
+           [[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5],
+            [6, 6, 5, 3], [7, 7, 3, 4], [4, 4, 8, 9], [5, 5, 10, 11],
+            [11, 11, 9, 6], [9, 9, 6, 8], [12, 12, 11, 7], [8, 8, 7, 10],
+            [10, 10, 13, 14], [14, 14, 14, 12], [13, 13, 12, 13]],
+           [[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5],
+            [6, 6, 5, 3], [7, 7, 3, 4], [4, 4, 8, 9], [5, 5, 10, 11],
+            [11, 11, 9, 6], [12, 12, 6, 8], [10, 10, 11, 7], [8, 8, 7, 10],
+            [9, 9, 13, 14], [14, 14, 14, 12], [13, 13, 12, 13]],
+           [[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5],
+            [6, 6, 5, 3], [7, 7, 3, 4], [4, 4, 8, 9], [5, 5, 10, 11],
+            [11, 11, 9, 6], [12, 12, 6, 8], [13, 13, 11, 7], [8, 8, 7, 10],
+            [9, 9, 12, 12], [10, 10, 13, 13]],
+           [[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 3, 3], [2, 2, 5, 6]
+            , [7, 7, 6, 4], [8, 8, 4, 5], [5, 5, 8, 9], [6, 6, 9, 7],
+            [10, 10, 7, 8], [9, 9, 11, 12], [11, 11, 12, 10], [13, 13, 10, 11],
+            [12, 12, 13, 13]],
+           [[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 3, 3], [2, 2, 5, 6]
+            , [7, 7, 6, 4], [8, 8, 4, 5], [5, 5, 8, 9], [6, 6, 9, 7],
+            [10, 10, 7, 8], [9, 9, 11, 12], [13, 13, 12, 10], [12, 12, 10, 11],
+            [11, 11, 13, 13]],
+           [[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 4, 4]
+            , [7, 7, 6, 3], [8, 8, 3, 5], [5, 5, 8, 9], [6, 6, 9, 7],
+            [10, 10, 7, 8], [9, 9, 11, 12], [13, 13, 12, 10], [12, 12, 10, 11],
+            [11, 11, 13, 13]],
+           [[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
+            , [5, 5, 6, 3], [9, 9, 3, 5], [10, 10, 8, 4], [8, 8, 4, 7],
+            [6, 6, 10, 11], [7, 7, 11, 9], [12, 12, 9, 10], [11, 11, 13, 14],
+            [14, 14, 14, 12], [13, 13, 12, 13]],
+           [[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
+            , [6, 6, 6, 3], [5, 5, 3, 5], [8, 8, 8, 4], [7, 7, 4, 7]],
+           [[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
+            , [9, 9, 6, 3], [6, 6, 3, 5], [10, 10, 8, 4], [11, 11, 4, 7],
+            [5, 5, 10, 12], [7, 7, 12, 9], [8, 8, 11, 11], [13, 13, 9, 10],
+            [12, 12, 13, 13]],
+           [[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
+            , [9, 9, 6, 3], [6, 6, 3, 5], [10, 10, 8, 4], [11, 11, 4, 7],
+            [5, 5, 12, 11], [7, 7, 10, 10], [8, 8, 9, 12], [13, 13, 11, 9],
+            [12, 12, 13, 13]],
+           [[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
+            , [9, 9, 6, 3], [10, 10, 3, 5], [7, 7, 8, 4], [11, 11, 4, 7],
+            [5, 5, 9, 9], [6, 6, 11, 12], [8, 8, 12, 10], [13, 13, 10, 11],
+            [12, 12, 13, 13]],
+           [[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
+            , [9, 9, 6, 3], [10, 10, 3, 5], [7, 7, 8, 4], [11, 11, 4, 7],
+            [5, 5, 12, 11], [6, 6, 10, 10], [8, 8, 9, 12], [13, 13, 11, 9],
+            [12, 12, 13, 13]],
+           [[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8]
+            , [9, 9, 6, 3], [10, 10, 3, 5], [11, 11, 8, 4], [12, 12, 4, 7],
+            [5, 5, 9, 9], [6, 6, 12, 13], [7, 7, 11, 11], [8, 8, 13, 10],
+            [13, 13, 10, 12]],
+           [[1, 1, 0, 0], [0, 0, 2, 3], [4, 4, 3, 1], [5, 5, 1, 2], [2, 2, 4, 4]
+            , [3, 3, 6, 7], [7, 7, 7, 5], [6, 6, 5, 6]]]
+    for i  in range(len(t2)):
+        assert L[i].table == t2[i]
+
+    f = FpGroup(F, [x**2, y**3, (x*y)**7])
+    L = low_index_subgroups(f, 10, [x])
+    t3 = [[[0, 0, 0, 0]],
+          [[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5], [4, 4, 5, 3],
+           [6, 6, 3, 4], [5, 5, 6, 6]],
+          [[0, 0, 1, 2], [1, 1, 2, 0], [3, 3, 0, 1], [2, 2, 4, 5], [6, 6, 5, 3],
+           [5, 5, 3, 4], [4, 4, 6, 6]],
+          [[0, 0, 1, 2], [3, 3, 2, 0], [4, 4, 0, 1], [1, 1, 5, 6], [2, 2, 7, 8],
+           [6, 6, 6, 3], [5, 5, 3, 5], [8, 8, 8, 4], [7, 7, 4, 7]]]
+    for i in range(len(t3)):
+        assert L[i].table == t3[i]
+
+
+def test_subgroup_presentations():
+    F, x, y = free_group("x, y")
+    f = FpGroup(F, [x**3, y**5, (x*y)**2])
+    H = [x*y, x**-1*y**-1*x*y*x]
+    p1 = reidemeister_presentation(f, H)
+    assert str(p1) == "((y_1, y_2), (y_1**2, y_2**3, y_2*y_1*y_2*y_1*y_2*y_1))"
+
+    H = f.subgroup(H)
+    assert (H.generators, H.relators) == p1
+
+    f = FpGroup(F, [x**3, y**3, (x*y)**3])
+    H = [x*y, x*y**-1]
+    p2 = reidemeister_presentation(f, H)
+    assert str(p2) == "((x_0, y_0), (x_0**3, y_0**3, x_0*y_0*x_0*y_0*x_0*y_0))"
+
+    f = FpGroup(F, [x**2*y**2, y**-1*x*y*x**-3])
+    H = [x]
+    p3 = reidemeister_presentation(f, H)
+    assert str(p3) == "((x_0,), (x_0**4,))"
+
+    f = FpGroup(F, [x**3*y**-3, (x*y)**3, (x*y**-1)**2])
+    H = [x]
+    p4 = reidemeister_presentation(f, H)
+    assert str(p4) == "((x_0,), (x_0**6,))"
+
+    # this presentation can be improved, the most simplified form
+    # of presentation is 
+    # See [2] Pg 474 group PSL_2(11)
+    # This is the group PSL_2(11)
+    F, a, b, c = free_group("a, b, c")
+    f = FpGroup(F, [a**11, b**5, c**4, (b*c**2)**2, (a*b*c)**3, (a**4*c**2)**3, b**2*c**-1*b**-1*c, a**4*b**-1*a**-1*b])
+    H = [a, b, c**2]
+    gens, rels = reidemeister_presentation(f, H)
+    assert str(gens) == "(b_1, c_3)"
+    assert len(rels) == 18
+
+
+@slow
+def test_order():
+    F, x, y = free_group("x, y")
+    f = FpGroup(F, [x**4, y**2, x*y*x**-1*y])
+    assert f.order() == 8
+
+    f = FpGroup(F, [x*y*x**-1*y**-1, y**2])
+    assert f.order() is S.Infinity
+
+    F, a, b, c = free_group("a, b, c")
+    f = FpGroup(F, [a**250, b**2, c*b*c**-1*b, c**4, c**-1*a**-1*c*a, a**-1*b**-1*a*b])
+    assert f.order() == 2000
+
+    F, x = free_group("x")
+    f = FpGroup(F, [])
+    assert f.order() is S.Infinity
+
+    f = FpGroup(free_group('')[0], [])
+    assert f.order() == 1
+
+def test_fp_subgroup():
+    def _test_subgroup(K, T, S):
+        _gens = T(K.generators)
+        assert all(elem in S for elem in _gens)
+        assert T.is_injective()
+        assert T.image().order() == S.order()
+    F, x, y = free_group("x, y")
+    f = FpGroup(F, [x**4, y**2, x*y*x**-1*y])
+    S = FpSubgroup(f, [x*y])
+    assert (x*y)**-3 in S
+    K, T = f.subgroup([x*y], homomorphism=True)
+    assert T(K.generators) == [y*x**-1]
+    _test_subgroup(K, T, S)
+
+    S = FpSubgroup(f, [x**-1*y*x])
+    assert x**-1*y**4*x in S
+    assert x**-1*y**4*x**2 not in S
+    K, T = f.subgroup([x**-1*y*x], homomorphism=True)
+    assert T(K.generators[0]**3) == y**3
+    _test_subgroup(K, T, S)
+
+    f = FpGroup(F, [x**3, y**5, (x*y)**2])
+    H = [x*y, x**-1*y**-1*x*y*x]
+    K, T = f.subgroup(H, homomorphism=True)
+    S = FpSubgroup(f, H)
+    _test_subgroup(K, T, S)
+
+def test_permutation_methods():
+    F, x, y = free_group("x, y")
+    # DihedralGroup(8)
+    G = FpGroup(F, [x**2, y**8, x*y*x**-1*y])
+    T = G._to_perm_group()[1]
+    assert T.is_isomorphism()
+    assert G.center() == [y**4]
+
+    # DiheadralGroup(4)
+    G = FpGroup(F, [x**2, y**4, x*y*x**-1*y])
+    S = FpSubgroup(G, G.normal_closure([x]))
+    assert x in S
+    assert y**-1*x*y in S
+
+    # Z_5xZ_4
+    G = FpGroup(F, [x*y*x**-1*y**-1, y**5, x**4])
+    assert G.is_abelian
+    assert G.is_solvable
+
+    # AlternatingGroup(5)
+    G = FpGroup(F, [x**3, y**2, (x*y)**5])
+    assert not G.is_solvable
+
+    # AlternatingGroup(4)
+    G = FpGroup(F, [x**3, y**2, (x*y)**3])
+    assert len(G.derived_series()) == 3
+    S = FpSubgroup(G, G.derived_subgroup())
+    assert S.order() == 4
+
+
+def test_simplify_presentation():
+    # ref #16083
+    G = simplify_presentation(FpGroup(FreeGroup([]), []))
+    assert not G.generators
+    assert not G.relators
+
+
+def test_cyclic():
+    F, x, y = free_group("x, y")
+    f = FpGroup(F, [x*y, x**-1*y**-1*x*y*x])
+    assert f.is_cyclic
+    f = FpGroup(F, [x*y, x*y**-1])
+    assert f.is_cyclic
+    f = FpGroup(F, [x**4, y**2, x*y*x**-1*y])
+    assert not f.is_cyclic
+
+
+def test_abelian_invariants():
+    F, x, y = free_group("x, y")
+    f = FpGroup(F, [x*y, x**-1*y**-1*x*y*x])
+    assert f.abelian_invariants() == []
+    f = FpGroup(F, [x*y, x*y**-1])
+    assert f.abelian_invariants() == [2]
+    f = FpGroup(F, [x**4, y**2, x*y*x**-1*y])
+    assert f.abelian_invariants() == [2, 4]
diff --git a/shared/package-files/ec/ec04d9f818e3dea2507b78907413152ee9d802e1eb62de66fe6a42e6c9df07cb/scatter.h b/shared/package-files/ec/ec04d9f818e3dea2507b78907413152ee9d802e1eb62de66fe6a42e6c9df07cb/scatter.h
new file mode 100644
index 0000000000000000000000000000000000000000..e745cac7818056eec5ece0de0deed2d5e66a1fca
--- /dev/null
+++ b/shared/package-files/ec/ec04d9f818e3dea2507b78907413152ee9d802e1eb62de66fe6a42e6c9df07cb/scatter.h
@@ -0,0 +1,91 @@
+#pragma once
+
+// @generated by torchgen/gen.py from Function.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+
+#include 
+
+namespace at {
+
+
+// aten::scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> Tensor
+inline at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) {
+    return at::_ops::scatter_src::call(self, dim, index, src);
+}
+
+// aten::scatter.src_out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & scatter_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src) {
+    return at::_ops::scatter_src_out::call(self, dim, index, src, out);
+}
+// aten::scatter.src_out(Tensor self, int dim, Tensor index, Tensor src, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & scatter_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, at::Tensor & out) {
+    return at::_ops::scatter_src_out::call(self, dim, index, src, out);
+}
+
+// aten::scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> Tensor
+inline at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) {
+    return at::_ops::scatter_value::call(self, dim, index, value);
+}
+
+// aten::scatter.value_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & scatter_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value) {
+    return at::_ops::scatter_value_out::call(self, dim, index, value, out);
+}
+// aten::scatter.value_out(Tensor self, int dim, Tensor index, Scalar value, *, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & scatter_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, at::Tensor & out) {
+    return at::_ops::scatter_value_out::call(self, dim, index, value, out);
+}
+
+// aten::scatter.reduce(Tensor self, int dim, Tensor index, Tensor src, *, str reduce) -> Tensor
+inline at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) {
+    return at::_ops::scatter_reduce::call(self, dim, index, src, reduce);
+}
+
+// aten::scatter.reduce_out(Tensor self, int dim, Tensor index, Tensor src, *, str reduce, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & scatter_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce) {
+    return at::_ops::scatter_reduce_out::call(self, dim, index, src, reduce, out);
+}
+// aten::scatter.reduce_out(Tensor self, int dim, Tensor index, Tensor src, *, str reduce, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & scatter_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Tensor & src, c10::string_view reduce, at::Tensor & out) {
+    return at::_ops::scatter_reduce_out::call(self, dim, index, src, reduce, out);
+}
+
+// aten::scatter.value_reduce(Tensor self, int dim, Tensor index, Scalar value, *, str reduce) -> Tensor
+inline at::Tensor scatter(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) {
+    return at::_ops::scatter_value_reduce::call(self, dim, index, value, reduce);
+}
+
+// aten::scatter.value_reduce_out(Tensor self, int dim, Tensor index, Scalar value, *, str reduce, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & scatter_out(at::Tensor & out, const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce) {
+    return at::_ops::scatter_value_reduce_out::call(self, dim, index, value, reduce, out);
+}
+// aten::scatter.value_reduce_out(Tensor self, int dim, Tensor index, Scalar value, *, str reduce, Tensor(a!) out) -> Tensor(a!)
+inline at::Tensor & scatter_outf(const at::Tensor & self, int64_t dim, const at::Tensor & index, const at::Scalar & value, c10::string_view reduce, at::Tensor & out) {
+    return at::_ops::scatter_value_reduce_out::call(self, dim, index, value, reduce, out);
+}
+
+// aten::scatter.dimname_src(Tensor self, Dimname dim, Tensor index, Tensor src) -> Tensor
+inline at::Tensor scatter(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Tensor & src) {
+    return at::_ops::scatter_dimname_src::call(self, dim, index, src);
+}
+
+// aten::scatter.dimname_value(Tensor self, Dimname dim, Tensor index, Scalar value) -> Tensor
+inline at::Tensor scatter(const at::Tensor & self, at::Dimname dim, const at::Tensor & index, const at::Scalar & value) {
+    return at::_ops::scatter_dimname_value::call(self, dim, index, value);
+}
+
+}
diff --git a/shared/package-files/ec/ec050a7ad8e667e5fbd6bf1a70430b4673e018df7ad7c4a352c2d9bf2229f0b4/results_var.py b/shared/package-files/ec/ec050a7ad8e667e5fbd6bf1a70430b4673e018df7ad7c4a352c2d9bf2229f0b4/results_var.py
new file mode 100644
index 0000000000000000000000000000000000000000..009f05a9588db43821d52ed8796379134402729d
--- /dev/null
+++ b/shared/package-files/ec/ec050a7ad8e667e5fbd6bf1a70430b4673e018df7ad7c4a352c2d9bf2229f0b4/results_var.py
@@ -0,0 +1,64 @@
+"""Test results for the VAR model, obtained from Stata using datasets/macrodata/var.do"""
+
+import numpy as np
+
+
+class MacrodataResults:
+    def __init__(self):
+        params = [
+            -0.2794863875, 0.0082427826, 0.6750534746, 0.2904420695,
+            0.0332267098,  -0.0073250059, 0.0015269951,  -0.1004938623,
+            -0.1231841792, 0.2686635768, 0.2325045441, 0.0257430635,
+            0.0235035714, 0.0054596064, -1.97116e+00, 0.3809752365,
+            4.4143364022, 0.8001168377, 0.2255078864,  -0.1241109271,
+            -0.0239026118]
+        params = np.asarray(params).reshape(3, -1)
+        params = np.hstack((params[:, -1][:, None],
+                            params[:, :-1:2],
+                            params[:, 1::2]))
+        self.params = params
+        self.neqs = 3
+        self.nobs = 200
+        self.df_eq = 7
+        self.nobs_1 = 200
+        self.df_model_1 = 6
+        self.rmse_1 = .0075573716985351
+        self.rsquared_1 = .2739094844780006
+        self.llf_1 = 696.8213727557811
+        self.nobs_2 = 200
+        self.rmse_2 = .0065444260782597
+        self.rsquared_2 = .1423626064753714
+        self.llf_2 = 725.6033255319256
+        self.nobs_3 = 200
+        self.rmse_3 = .0395942039671031
+        self.rsquared_3 = .2955406949737428
+        self.llf_3 = 365.5895183036045
+        # These are from Stata.  They use the LL based definition
+        # We return Lutkepohl statistics.  See Stata TS manual page 436
+        #        self.bic = -19.06939794312953
+        #        self.aic = -19.41572126661708
+        #        self.hqic = -19.27556951526737
+        # These are from R.  See var.R in macrodata folder
+        self.bic = -2.758301611618373e+01
+        self.aic = -2.792933943967127e+01
+        self.hqic = -2.778918768832157e+01
+        self.fpe = 7.421287668357018e-13
+        self.detsig = 6.01498432283e-13
+        self.llf = 1962.572126661708
+
+        self.chi2_1 = 75.44775165699033
+        # do not know how they calculate this;  it's not -2 * (ll1 - ll0)
+
+        self.chi2_2 = 33.19878716815366
+        self.chi2_3 = 83.90568280242312
+        bse = [
+            .1666662376, .1704584393, .1289691456, .1433308696, .0257313781,
+            .0253307796, .0010992645, .1443272761, .1476111934, .1116828804,
+            .1241196435, .0222824956, .021935591, .0009519255, .8731894193,
+            .8930573331, .6756886998, .7509319263, .1348105496, .1327117543,
+            .0057592114]
+        bse = np.asarray(bse).reshape(3, -1)
+        bse = np.hstack((bse[:, -1][:, None],
+                         bse[:, :-1:2],
+                         bse[:, 1::2]))
+        self.bse = bse
diff --git a/shared/package-files/ec/ec05369d0c85be7c59f80a35e6532bb3d3950fa58628886ad266e7da40cec8bd/embedding_ops.py b/shared/package-files/ec/ec05369d0c85be7c59f80a35e6532bb3d3950fa58628886ad266e7da40cec8bd/embedding_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..da7f33363742d71069240da06dca8531e8bf7003
--- /dev/null
+++ b/shared/package-files/ec/ec05369d0c85be7c59f80a35e6532bb3d3950fa58628886ad266e7da40cec8bd/embedding_ops.py
@@ -0,0 +1,143 @@
+import torch
+from torch import Tensor
+import torch.nn as nn
+import torch.nn.functional as F
+
+__all__ = ['Embedding', 'EmbeddingBag']
+
+class Embedding(nn.Embedding):
+    r"""
+    An embedding bag module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as `torch.nn.Embedding`, please see
+    https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html#torch.nn.Embedding
+    for documentation.
+
+    Similar to `torch.nn.Embedding`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight: fake quant module for weight
+    """
+    _FLOAT_MODULE = nn.Embedding
+
+    def __init__(self, num_embeddings, embedding_dim, padding_idx=None,
+                 max_norm=None, norm_type=2.0, scale_grad_by_freq=False,
+                 sparse=False, _weight=None, device=None, dtype=None, qconfig=None) -> None:
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        super().__init__(num_embeddings, embedding_dim, padding_idx, max_norm,
+                         norm_type, scale_grad_by_freq, sparse, _weight,
+                         **factory_kwargs)
+        assert qconfig, 'qconfig must be provided for QAT module'
+        assert qconfig.weight().qscheme == torch.per_channel_affine_float_qparams, \
+            'Embedding weights requires a qscheme of torch.per_channel_affine_float_qparams Got ' + \
+            str(qconfig.weight().qscheme)
+        self.qconfig = qconfig
+        self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
+
+    def forward(self, input) -> Tensor:
+        return F.embedding(input, self.weight_fake_quant(self.weight), self.padding_idx,
+                           self.max_norm, self.norm_type, self.scale_grad_by_freq,
+                           self.sparse)
+
+    @classmethod
+    def from_float(cls, mod):
+        r"""Create a qat module from a float module
+
+            Args: `mod` a float module, either produced by torch.ao.quantization utilities
+            or directly from user
+        """
+        assert type(mod) == cls._FLOAT_MODULE, ' qat.' + cls.__name__ + '.from_float only works for ' + \
+            cls._FLOAT_MODULE.__name__
+        assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
+        assert mod.qconfig, 'Input float module must have a valid qconfig'
+        weight_qscheme = mod.qconfig.weight().qscheme  # type: ignore[union-attr, operator]
+        assert weight_qscheme == torch.per_channel_affine_float_qparams, \
+            'Embedding weights requires a qscheme of torch.per_channel_affine_float_qparams Got ' + \
+            str(weight_qscheme)
+
+        qconfig = mod.qconfig
+        qat_embedding_bag = cls(mod.num_embeddings, mod.embedding_dim, mod.padding_idx,
+                                mod.max_norm, mod.norm_type, mod.scale_grad_by_freq,
+                                mod.sparse, mod.weight, qconfig=qconfig)
+
+        return qat_embedding_bag
+
+    def to_float(self):
+        embedding_bag = torch.nn.Embedding(self.num_embeddings, self.embedding_dim, self.padding_idx,
+                                           self.max_norm, self.norm_type, self.scale_grad_by_freq,
+                                           self.sparse, None)
+        embedding_bag.weight = torch.nn.Parameter(self.weight.detach())
+        embedding_bag.train(self.training)
+        return embedding_bag
+
+class EmbeddingBag(nn.EmbeddingBag):
+    r"""
+    An embedding bag module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+
+    We adopt the same interface as `torch.nn.EmbeddingBag`, please see
+    https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag
+    for documentation.
+
+    Similar to `torch.nn.EmbeddingBag`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight: fake quant module for weight
+    """
+    _FLOAT_MODULE = nn.EmbeddingBag
+
+    def __init__(self, num_embeddings, embedding_dim, max_norm=None,
+                 norm_type=2.0, scale_grad_by_freq=False, mode='mean',
+                 sparse=False, _weight=None, include_last_offset=False,
+                 padding_idx=None, qconfig=None, device=None, dtype=None) -> None:
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        super().__init__(num_embeddings, embedding_dim, max_norm, norm_type,
+                         scale_grad_by_freq, mode, sparse, _weight,
+                         include_last_offset, padding_idx, **factory_kwargs)
+        assert qconfig, 'qconfig must be provided for QAT module'
+        assert qconfig.weight().qscheme == torch.per_channel_affine_float_qparams, \
+            'Embedding Bag weights requires a qscheme of torch.per_channel_affine_float_qparams Got ' + \
+            str(qconfig.weight().qscheme)
+        self.qconfig = qconfig
+        self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
+
+    def forward(self, input, offsets=None, per_sample_weights=None) -> Tensor:
+        return F.embedding_bag(input, self.weight_fake_quant(self.weight), offsets,
+                               self.max_norm, self.norm_type,
+                               self.scale_grad_by_freq, self.mode, self.sparse,
+                               per_sample_weights, self.include_last_offset,
+                               self.padding_idx)
+
+    @classmethod
+    def from_float(cls, mod):
+        r"""Create a qat module from a float module
+
+            Args: `mod` a float module, either produced by torch.ao.quantization utilities
+            or directly from user
+        """
+        assert type(mod) == cls._FLOAT_MODULE, ' qat.' + cls.__name__ + '.from_float only works for ' + \
+            cls._FLOAT_MODULE.__name__
+        assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
+        assert mod.qconfig, 'Input float module must have a valid qconfig'
+        weight_qscheme = mod.qconfig.weight().qscheme  # type: ignore[union-attr, operator]
+        assert weight_qscheme == torch.per_channel_affine_float_qparams, \
+            'Embedding Bag weights requires a qscheme of torch.per_channel_affine_float_qparams Got ' + \
+            str(weight_qscheme)
+
+        qconfig = mod.qconfig
+        qat_embedding_bag = cls(mod.num_embeddings, mod.embedding_dim, mod.max_norm, mod.norm_type,
+                                mod.scale_grad_by_freq, mod.mode, mod.sparse, mod.weight,
+                                mod.include_last_offset, mod.padding_idx, qconfig=qconfig)
+
+        return qat_embedding_bag
+
+    def to_float(self):
+        embedding_bag = torch.nn.EmbeddingBag(self.num_embeddings, self.embedding_dim, self.max_norm,
+                                              self.norm_type, self.scale_grad_by_freq, self.mode, self.sparse,
+                                              None, self.include_last_offset, self.padding_idx)
+        embedding_bag.weight = torch.nn.Parameter(self.weight.detach())
+        embedding_bag.train(self.training)
+        return embedding_bag
diff --git a/shared/package-files/ec/ec05ee21c39dcc368bc646a9342d340546d3040170890db5ae8ad72c69497780/_util.py b/shared/package-files/ec/ec05ee21c39dcc368bc646a9342d340546d3040170890db5ae8ad72c69497780/_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..35fdfb1a9ee82b4e09afc2da35bb89cdc5f10739
--- /dev/null
+++ b/shared/package-files/ec/ec05ee21c39dcc368bc646a9342d340546d3040170890db5ae8ad72c69497780/_util.py
@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Literal,
+)
+
+import numpy as np
+
+from pandas._config import using_string_dtype
+
+from pandas._libs import lib
+from pandas.compat import (
+    pa_version_under18p0,
+    pa_version_under19p0,
+)
+from pandas.compat._optional import import_optional_dependency
+
+import pandas as pd
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    import pyarrow
+
+    from pandas._typing import DtypeBackend
+
+
+def _arrow_dtype_mapping() -> dict:
+    pa = import_optional_dependency("pyarrow")
+    return {
+        pa.int8(): pd.Int8Dtype(),
+        pa.int16(): pd.Int16Dtype(),
+        pa.int32(): pd.Int32Dtype(),
+        pa.int64(): pd.Int64Dtype(),
+        pa.uint8(): pd.UInt8Dtype(),
+        pa.uint16(): pd.UInt16Dtype(),
+        pa.uint32(): pd.UInt32Dtype(),
+        pa.uint64(): pd.UInt64Dtype(),
+        pa.bool_(): pd.BooleanDtype(),
+        pa.string(): pd.StringDtype(),
+        pa.float32(): pd.Float32Dtype(),
+        pa.float64(): pd.Float64Dtype(),
+        pa.string(): pd.StringDtype(),
+        pa.large_string(): pd.StringDtype(),
+    }
+
+
+def _arrow_string_types_mapper() -> Callable:
+    pa = import_optional_dependency("pyarrow")
+
+    mapping = {
+        pa.string(): pd.StringDtype(na_value=np.nan),
+        pa.large_string(): pd.StringDtype(na_value=np.nan),
+    }
+    if not pa_version_under18p0:
+        mapping[pa.string_view()] = pd.StringDtype(na_value=np.nan)
+
+    return mapping.get
+
+
+def arrow_table_to_pandas(
+    table: pyarrow.Table,
+    dtype_backend: DtypeBackend | Literal["numpy"] | lib.NoDefault = lib.no_default,
+    null_to_int64: bool = False,
+    to_pandas_kwargs: dict | None = None,
+) -> pd.DataFrame:
+    if to_pandas_kwargs is None:
+        to_pandas_kwargs = {}
+
+    pa = import_optional_dependency("pyarrow")
+
+    types_mapper: type[pd.ArrowDtype] | None | Callable
+    if dtype_backend == "numpy_nullable":
+        mapping = _arrow_dtype_mapping()
+        if null_to_int64:
+            # Modify the default mapping to also map null to Int64
+            # (to match other engines - only for CSV parser)
+            mapping[pa.null()] = pd.Int64Dtype()
+        types_mapper = mapping.get
+    elif dtype_backend == "pyarrow":
+        types_mapper = pd.ArrowDtype
+    elif using_string_dtype():
+        if pa_version_under19p0:
+            types_mapper = _arrow_string_types_mapper()
+        else:
+            types_mapper = None
+    elif dtype_backend is lib.no_default or dtype_backend == "numpy":
+        types_mapper = None
+    else:
+        raise NotImplementedError
+
+    df = table.to_pandas(types_mapper=types_mapper, **to_pandas_kwargs)
+    return df
diff --git a/shared/package-files/ec/ec061a1caabc15a39bba66581959ee186b206befdbfa2752d69c9dcfb376d9a8/meson.build b/shared/package-files/ec/ec061a1caabc15a39bba66581959ee186b206befdbfa2752d69c9dcfb376d9a8/meson.build
new file mode 100644
index 0000000000000000000000000000000000000000..50f91f7f9ef838ae87fc8a0a22bc813c0e802a3f
--- /dev/null
+++ b/shared/package-files/ec/ec061a1caabc15a39bba66581959ee186b206befdbfa2752d69c9dcfb376d9a8/meson.build
@@ -0,0 +1 @@
+_special_tree = [fs.copyfile('__init__.pxd'), fs.copyfile('math.pxd')]
diff --git a/shared/package-files/ec/ec063f7d559db706788afe6e59dc8a01471b1108920edd65378b2ac7dc57c04d/standardGlyphOrder.py b/shared/package-files/ec/ec063f7d559db706788afe6e59dc8a01471b1108920edd65378b2ac7dc57c04d/standardGlyphOrder.py
new file mode 100644
index 0000000000000000000000000000000000000000..4062385240096ac822814aebb8bf7c59cf003a8f
--- /dev/null
+++ b/shared/package-files/ec/ec063f7d559db706788afe6e59dc8a01471b1108920edd65378b2ac7dc57c04d/standardGlyphOrder.py
@@ -0,0 +1,271 @@
+#
+# 'post' table formats 1.0 and 2.0 rely on this list of "standard"
+# glyphs.
+#
+# My list is correct according to the Apple documentation for the 'post'  table:
+# https://developer.apple.com/fonts/TrueType-Reference-Manual/RM06/Chap6post.html
+# (However, it seems that TTFdump (from MS) and FontLab disagree, at
+# least with respect to the last glyph, which they list as 'dslash'
+# instead of 'dcroat'.)
+#
+
+standardGlyphOrder = [
+    ".notdef",  # 0
+    ".null",  # 1
+    "nonmarkingreturn",  # 2
+    "space",  # 3
+    "exclam",  # 4
+    "quotedbl",  # 5
+    "numbersign",  # 6
+    "dollar",  # 7
+    "percent",  # 8
+    "ampersand",  # 9
+    "quotesingle",  # 10
+    "parenleft",  # 11
+    "parenright",  # 12
+    "asterisk",  # 13
+    "plus",  # 14
+    "comma",  # 15
+    "hyphen",  # 16
+    "period",  # 17
+    "slash",  # 18
+    "zero",  # 19
+    "one",  # 20
+    "two",  # 21
+    "three",  # 22
+    "four",  # 23
+    "five",  # 24
+    "six",  # 25
+    "seven",  # 26
+    "eight",  # 27
+    "nine",  # 28
+    "colon",  # 29
+    "semicolon",  # 30
+    "less",  # 31
+    "equal",  # 32
+    "greater",  # 33
+    "question",  # 34
+    "at",  # 35
+    "A",  # 36
+    "B",  # 37
+    "C",  # 38
+    "D",  # 39
+    "E",  # 40
+    "F",  # 41
+    "G",  # 42
+    "H",  # 43
+    "I",  # 44
+    "J",  # 45
+    "K",  # 46
+    "L",  # 47
+    "M",  # 48
+    "N",  # 49
+    "O",  # 50
+    "P",  # 51
+    "Q",  # 52
+    "R",  # 53
+    "S",  # 54
+    "T",  # 55
+    "U",  # 56
+    "V",  # 57
+    "W",  # 58
+    "X",  # 59
+    "Y",  # 60
+    "Z",  # 61
+    "bracketleft",  # 62
+    "backslash",  # 63
+    "bracketright",  # 64
+    "asciicircum",  # 65
+    "underscore",  # 66
+    "grave",  # 67
+    "a",  # 68
+    "b",  # 69
+    "c",  # 70
+    "d",  # 71
+    "e",  # 72
+    "f",  # 73
+    "g",  # 74
+    "h",  # 75
+    "i",  # 76
+    "j",  # 77
+    "k",  # 78
+    "l",  # 79
+    "m",  # 80
+    "n",  # 81
+    "o",  # 82
+    "p",  # 83
+    "q",  # 84
+    "r",  # 85
+    "s",  # 86
+    "t",  # 87
+    "u",  # 88
+    "v",  # 89
+    "w",  # 90
+    "x",  # 91
+    "y",  # 92
+    "z",  # 93
+    "braceleft",  # 94
+    "bar",  # 95
+    "braceright",  # 96
+    "asciitilde",  # 97
+    "Adieresis",  # 98
+    "Aring",  # 99
+    "Ccedilla",  # 100
+    "Eacute",  # 101
+    "Ntilde",  # 102
+    "Odieresis",  # 103
+    "Udieresis",  # 104
+    "aacute",  # 105
+    "agrave",  # 106
+    "acircumflex",  # 107
+    "adieresis",  # 108
+    "atilde",  # 109
+    "aring",  # 110
+    "ccedilla",  # 111
+    "eacute",  # 112
+    "egrave",  # 113
+    "ecircumflex",  # 114
+    "edieresis",  # 115
+    "iacute",  # 116
+    "igrave",  # 117
+    "icircumflex",  # 118
+    "idieresis",  # 119
+    "ntilde",  # 120
+    "oacute",  # 121
+    "ograve",  # 122
+    "ocircumflex",  # 123
+    "odieresis",  # 124
+    "otilde",  # 125
+    "uacute",  # 126
+    "ugrave",  # 127
+    "ucircumflex",  # 128
+    "udieresis",  # 129
+    "dagger",  # 130
+    "degree",  # 131
+    "cent",  # 132
+    "sterling",  # 133
+    "section",  # 134
+    "bullet",  # 135
+    "paragraph",  # 136
+    "germandbls",  # 137
+    "registered",  # 138
+    "copyright",  # 139
+    "trademark",  # 140
+    "acute",  # 141
+    "dieresis",  # 142
+    "notequal",  # 143
+    "AE",  # 144
+    "Oslash",  # 145
+    "infinity",  # 146
+    "plusminus",  # 147
+    "lessequal",  # 148
+    "greaterequal",  # 149
+    "yen",  # 150
+    "mu",  # 151
+    "partialdiff",  # 152
+    "summation",  # 153
+    "product",  # 154
+    "pi",  # 155
+    "integral",  # 156
+    "ordfeminine",  # 157
+    "ordmasculine",  # 158
+    "Omega",  # 159
+    "ae",  # 160
+    "oslash",  # 161
+    "questiondown",  # 162
+    "exclamdown",  # 163
+    "logicalnot",  # 164
+    "radical",  # 165
+    "florin",  # 166
+    "approxequal",  # 167
+    "Delta",  # 168
+    "guillemotleft",  # 169
+    "guillemotright",  # 170
+    "ellipsis",  # 171
+    "nonbreakingspace",  # 172
+    "Agrave",  # 173
+    "Atilde",  # 174
+    "Otilde",  # 175
+    "OE",  # 176
+    "oe",  # 177
+    "endash",  # 178
+    "emdash",  # 179
+    "quotedblleft",  # 180
+    "quotedblright",  # 181
+    "quoteleft",  # 182
+    "quoteright",  # 183
+    "divide",  # 184
+    "lozenge",  # 185
+    "ydieresis",  # 186
+    "Ydieresis",  # 187
+    "fraction",  # 188
+    "currency",  # 189
+    "guilsinglleft",  # 190
+    "guilsinglright",  # 191
+    "fi",  # 192
+    "fl",  # 193
+    "daggerdbl",  # 194
+    "periodcentered",  # 195
+    "quotesinglbase",  # 196
+    "quotedblbase",  # 197
+    "perthousand",  # 198
+    "Acircumflex",  # 199
+    "Ecircumflex",  # 200
+    "Aacute",  # 201
+    "Edieresis",  # 202
+    "Egrave",  # 203
+    "Iacute",  # 204
+    "Icircumflex",  # 205
+    "Idieresis",  # 206
+    "Igrave",  # 207
+    "Oacute",  # 208
+    "Ocircumflex",  # 209
+    "apple",  # 210
+    "Ograve",  # 211
+    "Uacute",  # 212
+    "Ucircumflex",  # 213
+    "Ugrave",  # 214
+    "dotlessi",  # 215
+    "circumflex",  # 216
+    "tilde",  # 217
+    "macron",  # 218
+    "breve",  # 219
+    "dotaccent",  # 220
+    "ring",  # 221
+    "cedilla",  # 222
+    "hungarumlaut",  # 223
+    "ogonek",  # 224
+    "caron",  # 225
+    "Lslash",  # 226
+    "lslash",  # 227
+    "Scaron",  # 228
+    "scaron",  # 229
+    "Zcaron",  # 230
+    "zcaron",  # 231
+    "brokenbar",  # 232
+    "Eth",  # 233
+    "eth",  # 234
+    "Yacute",  # 235
+    "yacute",  # 236
+    "Thorn",  # 237
+    "thorn",  # 238
+    "minus",  # 239
+    "multiply",  # 240
+    "onesuperior",  # 241
+    "twosuperior",  # 242
+    "threesuperior",  # 243
+    "onehalf",  # 244
+    "onequarter",  # 245
+    "threequarters",  # 246
+    "franc",  # 247
+    "Gbreve",  # 248
+    "gbreve",  # 249
+    "Idotaccent",  # 250
+    "Scedilla",  # 251
+    "scedilla",  # 252
+    "Cacute",  # 253
+    "cacute",  # 254
+    "Ccaron",  # 255
+    "ccaron",  # 256
+    "dcroat",  # 257
+]
diff --git a/shared/package-files/ec/ec08244f10eec89cb9d5b4974b2d385867c6c6a2a94fbded785fe6652a267368/cc.py b/shared/package-files/ec/ec08244f10eec89cb9d5b4974b2d385867c6c6a2a94fbded785fe6652a267368/cc.py
new file mode 100644
index 0000000000000000000000000000000000000000..872cd39ec974ef578cde38538533a88530e00216
--- /dev/null
+++ b/shared/package-files/ec/ec08244f10eec89cb9d5b4974b2d385867c6c6a2a94fbded785fe6652a267368/cc.py
@@ -0,0 +1,327 @@
+from setuptools import distutils as dutils
+from setuptools.command import build_ext
+from setuptools.extension import Extension
+
+import os
+import shutil
+import sys
+import tempfile
+
+from numba.core import typing, sigutils
+from numba.core.compiler_lock import global_compiler_lock
+from numba.pycc.compiler import ModuleCompiler, ExportEntry
+from numba.pycc.platform import Toolchain
+from numba import cext
+
+
+dir_util = dutils.dir_util
+log = dutils.log
+extension_libs = cext.get_extension_libs()
+
+
+class CC(object):
+    """
+    An ahead-of-time compiler to create extension modules that don't
+    depend on Numba.
+    """
+
+    # NOTE: using ccache can speed up repetitive builds
+    # (especially for the mixin modules)
+
+    _mixin_sources = ['modulemixin.c',]  + extension_libs
+
+    # -flto strips all unused helper functions, which 1) makes the
+    # produced output much smaller and 2) can make the linking step faster.
+    # (the Windows linker seems to do this by default, judging by the results)
+
+    _extra_cflags = {
+        # Comment out due to odd behavior with GCC 4.9+ with LTO
+        # 'posix': ['-flto'],
+        }
+
+    _extra_ldflags = {
+        # Comment out due to odd behavior with GCC 4.9+ with LTO
+        # 'posix': ['-flto'],
+        # On macOS, if the object files carry debug info (e.g. when the
+        # interpreter's sysconfig compiles extensions with ``-g``), ld
+        # embeds an N_OSO debug-map entry per object holding the object's
+        # absolute path and mtime. Because pycc builds the objects in a
+        # randomly-named temp directory, those paths (and mtimes) make the
+        # emitted binary non-reproducible (see issue #10668). ``-Wl,-S``
+        # strips the debug map while keeping the symbols needed at runtime.
+        'darwin': ['-Wl,-S'],
+        }
+
+    def __init__(self, extension_name, source_module=None):
+        if '.' in extension_name:
+            raise ValueError("basename should be a simple module name, not "
+                             "qualified name")
+
+        self._basename = extension_name
+        self._init_function = 'pycc_init_' + extension_name
+        self._exported_functions = {}
+        # Resolve source module name and directory
+        f = sys._getframe(1)
+        if source_module is None:
+            dct = f.f_globals
+            source_module = dct['__name__']
+        elif hasattr(source_module, '__name__'):
+            dct = source_module.__dict__
+            source_module = source_module.__name__
+        else:
+            dct = sys.modules[source_module].__dict__
+
+        self._source_path = dct.get('__file__', '')
+        self._source_module = source_module
+        self._toolchain = Toolchain()
+        self._verbose = False
+        # By default, output in directory of caller module
+        self._output_dir = os.path.dirname(self._source_path)
+        self._output_file = self._toolchain.get_ext_filename(extension_name)
+        self._use_nrt = True
+        self._target_cpu = ''
+
+    @property
+    def name(self):
+        """
+        The name of the extension module to create.
+        """
+        return self._basename
+
+    @property
+    def output_file(self):
+        """
+        The specific output file (a DLL) that will be generated.
+        """
+        return self._output_file
+
+    @output_file.setter
+    def output_file(self, value):
+        self._output_file = value
+
+    @property
+    def output_dir(self):
+        """
+        The directory the output file will be put in.
+        """
+        return self._output_dir
+
+    @output_dir.setter
+    def output_dir(self, value):
+        self._output_dir = value
+
+    @property
+    def use_nrt(self):
+        return self._use_nrt
+
+    @use_nrt.setter
+    def use_nrt(self, value):
+        self._use_nrt = value
+
+    @property
+    def target_cpu(self):
+        """
+        The target CPU model for code generation.
+        """
+        return self._target_cpu
+
+    @target_cpu.setter
+    def target_cpu(self, value):
+        self._target_cpu = value
+
+    @property
+    def verbose(self):
+        """
+        Whether to display detailed information when compiling.
+        """
+        return self._verbose
+
+    @verbose.setter
+    def verbose(self, value):
+        self._verbose = value
+
+    def export(self, exported_name, sig):
+        """
+        Mark a function for exporting in the extension module.
+        """
+        fn_args, fn_retty = sigutils.normalize_signature(sig)
+        sig = typing.signature(fn_retty, *fn_args)
+        if exported_name in self._exported_functions:
+            raise KeyError("duplicated export symbol %s" % (exported_name))
+
+        def decorator(func):
+            entry = ExportEntry(exported_name, sig, func)
+            self._exported_functions[exported_name] = entry
+            return func
+
+        return decorator
+
+    @property
+    def _export_entries(self):
+        return sorted(self._exported_functions.values(),
+                      key=lambda entry: entry.symbol)
+
+    def _get_mixin_sources(self):
+        here = os.path.dirname(__file__)
+        mixin_sources = self._mixin_sources[:]
+        if self._use_nrt:
+            mixin_sources.append('../core/runtime/nrt.cpp')
+        return [os.path.join(here, f) for f in mixin_sources]
+
+    def _get_mixin_defines(self):
+        # Macro definitions required by modulemixin.c
+        from numba import _helperlib
+        return [
+            ('PYCC_MODULE_NAME', self._basename),
+            ('PYCC_USE_NRT', int(self._use_nrt)),
+            # modulemixin.c #includes _helperlib.c -> _lapack.c, whose Fortran
+            # integer width (LP64 vs ILP64) is fixed at compile time by
+            # NUMBA_LAPACK_BUILD_ILP64.  It must match the numba this AOT module
+            # is built with, otherwise the AOT BLAS/LAPACK wrappers pass
+            # wrong-width integers to scipy at runtime -- silent corruption or a
+            # crash (see e.g. an ILP64 numba: np.dot in an AOT module segfaults
+            # in the BLAS call).  Mirror numba's own build width.
+            ('NUMBA_LAPACK_BUILD_ILP64',
+             int(getattr(_helperlib, "LAPACK_BUILD_ILP64", 0))),
+            ]
+
+    def _get_extra_cflags(self):
+        extra_cflags = self._extra_cflags.get(sys.platform, [])
+        if not extra_cflags:
+            extra_cflags = self._extra_cflags.get(os.name, [])
+        return extra_cflags
+
+    def _get_extra_ldflags(self):
+        extra_ldflags = self._extra_ldflags.get(sys.platform, [])
+        if not extra_ldflags:
+            extra_ldflags = self._extra_ldflags.get(os.name, [])
+        # helperlib uses pthread on linux. make sure we are linking to it.
+        if sys.platform.startswith("linux"):
+            if "-pthread" not in extra_ldflags:
+                extra_ldflags.append('-pthread')
+        return extra_ldflags
+
+    def _compile_mixins(self, build_dir):
+        sources = self._get_mixin_sources()
+        macros = self._get_mixin_defines()
+        include_dirs = self._toolchain.get_python_include_dirs()
+
+        extra_cflags = self._get_extra_cflags()
+        # XXX distutils creates a whole subtree inside build_dir,
+        # e.g. /tmp/test_pycc/home/antoine/numba/numba/pycc/modulemixin.o
+        objects = self._toolchain.compile_objects(sources, build_dir,
+                                                  include_dirs=include_dirs,
+                                                  macros=macros,
+                                                  extra_cflags=extra_cflags)
+        return objects
+
+    @global_compiler_lock
+    def _compile_object_files(self, build_dir):
+        compiler = ModuleCompiler(self._export_entries, self._basename,
+                                self._use_nrt, cpu_name=self._target_cpu)
+        compiler.external_init_function = self._init_function
+        temp_obj = os.path.join(build_dir,
+                                os.path.splitext(self._output_file)[0] + '.o')
+        log.info("generating LLVM code for '%s' into %s",
+                self._basename, temp_obj)
+        compiler.write_native_object(temp_obj, wrap=True)
+        return [temp_obj], compiler.dll_exports
+
+    @global_compiler_lock
+    def compile(self):
+        """
+        Compile the extension module.
+        """
+        self._toolchain.verbose = self.verbose
+        build_dir = tempfile.mkdtemp(prefix='pycc-build-%s-' % self._basename)
+
+        # Compile object file
+        objects, dll_exports = self._compile_object_files(build_dir)
+
+        # Compile mixins
+        objects += self._compile_mixins(build_dir)
+
+        # Then create shared library
+        extra_ldflags = self._get_extra_ldflags()
+        output_dll = os.path.join(self._output_dir, self._output_file)
+        libraries = self._toolchain.get_python_libraries()
+        library_dirs = self._toolchain.get_python_library_dirs()
+        self._toolchain.link_shared(output_dll, objects,
+                                    libraries, library_dirs,
+                                    export_symbols=dll_exports,
+                                    extra_ldflags=extra_ldflags)
+
+        shutil.rmtree(build_dir)
+
+    def distutils_extension(self, **kwargs):
+        """
+        Create a distutils extension object that can be used in your
+        setup.py.
+        """
+        macros = kwargs.pop('macros', []) + self._get_mixin_defines()
+        depends = kwargs.pop('depends', []) + [self._source_path]
+        extra_compile_args = (kwargs.pop('extra_compile_args', [])
+                              + self._get_extra_cflags())
+        extra_link_args = (kwargs.pop('extra_link_args', [])
+                           + self._get_extra_ldflags())
+        include_dirs = (kwargs.pop('include_dirs', [])
+                        + self._toolchain.get_python_include_dirs())
+        libraries = (kwargs.pop('libraries', [])
+                     + self._toolchain.get_python_libraries())
+        library_dirs = (kwargs.pop('library_dirs', [])
+                        + self._toolchain.get_python_library_dirs())
+        python_package_path = self._source_module[:self._source_module.rfind('.')+1]
+
+        ext = _CCExtension(name=python_package_path + self._basename,
+                           sources=self._get_mixin_sources(),
+                           depends=depends,
+                           define_macros=macros,
+                           include_dirs=include_dirs,
+                           libraries=libraries,
+                           library_dirs=library_dirs,
+                           extra_compile_args=extra_compile_args,
+                           extra_link_args=extra_link_args,
+                           **kwargs)
+        ext.monkey_patch_distutils()
+        ext._cc = self
+        return ext
+
+
+class _CCExtension(Extension):
+    """
+    A Numba-specific Extension subclass to LLVM-compile pure Python code
+    to an extension module.
+    """
+
+    _cc = None
+    _distutils_monkey_patched = False
+
+    def _prepare_object_files(self, build_ext):
+        cc = self._cc
+        dir_util.mkpath(os.path.join(build_ext.build_temp, *self.name.split('.')[:-1]))
+        objects, _ = cc._compile_object_files(build_ext.build_temp)
+        # Add generated object files for linking
+        self.extra_objects = objects
+
+    @classmethod
+    def monkey_patch_distutils(cls):
+        """
+        Monkey-patch distutils with our own build_ext class knowing
+        about pycc-compiled extensions modules.
+        """
+        if cls._distutils_monkey_patched:
+            return
+
+        _orig_build_ext = build_ext.build_ext
+
+        class _CC_build_ext(_orig_build_ext):
+
+            def build_extension(self, ext):
+                if isinstance(ext, _CCExtension):
+                    ext._prepare_object_files(self)
+
+                _orig_build_ext.build_extension(self, ext)
+
+        build_ext.build_ext = _CC_build_ext
+
+        cls._distutils_monkey_patched = True
diff --git a/shared/package-files/ec/ec0af8bdd4029e733498a4773148db20ab19ac155f1e0770b0965cfe619845ec/tzwin.py b/shared/package-files/ec/ec0af8bdd4029e733498a4773148db20ab19ac155f1e0770b0965cfe619845ec/tzwin.py
new file mode 100644
index 0000000000000000000000000000000000000000..cebc673e40fc376653ebf037e96f0a6d0b33e906
--- /dev/null
+++ b/shared/package-files/ec/ec0af8bdd4029e733498a4773148db20ab19ac155f1e0770b0965cfe619845ec/tzwin.py
@@ -0,0 +1,2 @@
+# tzwin has moved to dateutil.tz.win
+from .tz.win import *
diff --git a/shared/package-files/ec/ec0b3d798b51628b1bc762c7971a0d9ab6646086e0adcea2529504bad340ed9b/_indices_copy_compositeexplicitautogradnonfunctional_dispatch.h b/shared/package-files/ec/ec0b3d798b51628b1bc762c7971a0d9ab6646086e0adcea2529504bad340ed9b/_indices_copy_compositeexplicitautogradnonfunctional_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..f5e98cc3184bb7d4fa6da515a8513bd71ff6552d
--- /dev/null
+++ b/shared/package-files/ec/ec0b3d798b51628b1bc762c7971a0d9ab6646086e0adcea2529504bad340ed9b/_indices_copy_compositeexplicitautogradnonfunctional_dispatch.h
@@ -0,0 +1,23 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace compositeexplicitautogradnonfunctional {
+
+TORCH_API at::Tensor _indices_copy(const at::Tensor & self);
+
+} // namespace compositeexplicitautogradnonfunctional
+} // namespace at
diff --git a/shared/package-files/ec/ec0f5af26a458d6c449d6057a0e958e81e61dfeb15e0ef8a5a23570275858f07/requires.txt b/shared/package-files/ec/ec0f5af26a458d6c449d6057a0e958e81e61dfeb15e0ef8a5a23570275858f07/requires.txt
new file mode 100644
index 0000000000000000000000000000000000000000..57e94d0479f1c3f7de810b687b632b8f3b294df0
--- /dev/null
+++ b/shared/package-files/ec/ec0f5af26a458d6c449d6057a0e958e81e61dfeb15e0ef8a5a23570275858f07/requires.txt
@@ -0,0 +1,20 @@
+filelock
+
+[build]
+cmake>=3.20
+lit
+
+[tests]
+autopep8
+flake8
+isort
+numpy
+pytest
+scipy>=1.7.1
+torch
+
+[tutorials]
+matplotlib
+pandas
+tabulate
+torch
diff --git a/shared/package-files/ec/ec10111ef6de85a3250b8ecd1d9ec61c639062dfdf3076fd0a9f509c2cfa9432/bytecode_transformation.py b/shared/package-files/ec/ec10111ef6de85a3250b8ecd1d9ec61c639062dfdf3076fd0a9f509c2cfa9432/bytecode_transformation.py
new file mode 100644
index 0000000000000000000000000000000000000000..63edf253062fece499cd276570cef07c4c58e5b7
--- /dev/null
+++ b/shared/package-files/ec/ec10111ef6de85a3250b8ecd1d9ec61c639062dfdf3076fd0a9f509c2cfa9432/bytecode_transformation.py
@@ -0,0 +1,1114 @@
+import copy
+import dataclasses
+import dis
+import itertools
+import sys
+import types
+from typing import Any, Callable, cast, Dict, Iterator, List, Optional, Tuple
+
+from .bytecode_analysis import (
+    get_indexof,
+    propagate_line_nums,
+    remove_extra_line_nums,
+    stacksize_analysis,
+)
+
+
+@dataclasses.dataclass
+class InstructionExnTabEntry:
+    start: "Instruction"
+    end: "Instruction"
+    target: "Instruction"
+    depth: int
+    lasti: bool
+
+    def __repr__(self) -> str:
+        return (
+            f"InstructionExnTabEntry(start={self.start.short_inst_repr()}, "
+            f"end={self.end.short_inst_repr()}, "
+            f"target={self.target.short_inst_repr()}, "
+            f"depth={self.depth}, lasti={self.lasti})"
+        )
+
+    def __eq__(self, o) -> bool:
+        return (
+            self.start is o.start
+            and self.end is o.end
+            and self.target is o.target
+            and self.depth == o.depth
+            and self.lasti == o.lasti
+        )
+
+
+@dataclasses.dataclass
+class Instruction:
+    """A mutable version of dis.Instruction"""
+
+    opcode: int
+    opname: str
+    arg: Optional[int]
+    argval: Any
+    offset: Optional[int] = None
+    starts_line: Optional[int] = None
+    is_jump_target: bool = False
+    positions: Optional["dis.Positions"] = None
+    # extra fields to make modification easier:
+    target: Optional["Instruction"] = None
+    exn_tab_entry: Optional[InstructionExnTabEntry] = None
+
+    def __hash__(self) -> int:
+        return id(self)
+
+    def __eq__(self, other) -> bool:
+        return id(self) == id(other)
+
+    def short_inst_repr(self) -> str:
+        return f"Instruction(opname={self.opname}, offset={self.offset})"
+
+
+def convert_instruction(i: dis.Instruction) -> Instruction:
+    return Instruction(
+        i.opcode,
+        i.opname,
+        i.arg,
+        i.argval,
+        i.offset,
+        i.starts_line,
+        i.is_jump_target,
+        getattr(i, "positions", None),
+    )
+
+
+class _NotProvided:
+    def __repr__(self) -> str:
+        return "_NotProvided"
+
+
+def create_instruction(
+    name, *, arg=None, argval=_NotProvided, target=None
+) -> Instruction:
+    """
+    At most one of `arg`, `argval`, and `target` can be not None/_NotProvided.
+    This is to prevent ambiguity, e.g. does
+        create_instruction("LOAD_CONST", 5)
+    mean load the constant at co_consts[5], or load the constant 5?
+
+    If `arg` is not provided, it will be computed during assembly from
+    `argval` or `target`.
+
+    Do not use for LOAD_GLOBAL - use create_load_global instead.
+    """
+    assert name != "LOAD_GLOBAL"
+    cnt = (arg is not None) + (argval is not _NotProvided) + (target is not None)
+    if cnt > 1:
+        raise RuntimeError(
+            "only one of arg, argval, and target can be not None/_NotProvided"
+        )
+    if arg is not None and not isinstance(arg, int):
+        raise RuntimeError("instruction arg must be int or None")
+    return Instruction(
+        opcode=dis.opmap[name], opname=name, arg=arg, argval=argval, target=target
+    )
+
+
+# Python 3.11 remaps
+def create_jump_absolute(target) -> Instruction:
+    inst = "JUMP_FORWARD" if sys.version_info >= (3, 11) else "JUMP_ABSOLUTE"
+    return create_instruction(inst, target=target)
+
+
+def create_load_global(name, push_null) -> Instruction:
+    """
+    `name` is the name of the global to be loaded.
+    `push_null` specifies whether or not a NULL should be pushed to the stack
+    before the global (Python 3.11+ only).
+
+    Python 3.11 changed the LOAD_GLOBAL instruction in that the first bit of
+    the instruction arg specifies whether a NULL should be pushed to the stack
+    before the global. The remaining bits of the instruction arg contain the
+    name index. See `create_call_function` for why this NULL is needed.
+
+    The instruction's `arg` is actually computed when assembling the bytecode.
+    For Python 3.11, push_null information is propagated through the arg.
+
+    NOTE: we don't use create_instruction since LOAD_GLOBAL is the only instruction
+    where both arg and argval need to be specified.
+    """
+    return Instruction(
+        opcode=dis.opmap["LOAD_GLOBAL"],
+        opname="LOAD_GLOBAL",
+        arg=push_null,
+        argval=name,
+    )
+
+
+def create_dup_top() -> Instruction:
+    if sys.version_info >= (3, 11):
+        return create_instruction("COPY", arg=1)
+    return create_instruction("DUP_TOP")
+
+
+def create_rot_n(n) -> List[Instruction]:
+    """
+    Returns a "simple" sequence of instructions that rotates TOS to the n-th
+    position in the stack. For Python < 3.11, returns a single ROT_*
+    instruction. If no such instruction exists, an error is raised and the
+    caller is expected to generate an equivalent sequence of instructions.
+    For Python >= 3.11, any rotation can be expressed as a simple sequence of
+    swaps.
+    """
+    if n <= 1:
+        # don't rotate
+        return []
+
+    if sys.version_info >= (3, 11):
+        # rotate can be expressed as a sequence of swap operations
+        # e.g. rotate 3 is equivalent to swap 3, swap 2
+        return [create_instruction("SWAP", arg=i) for i in range(n, 1, -1)]
+
+    # ensure desired rotate function exists
+    if sys.version_info < (3, 8) and n >= 4:
+        raise AttributeError(f"rotate {n} not supported for Python < 3.8")
+    if sys.version_info < (3, 10) and n >= 5:
+        raise AttributeError(f"rotate {n} not supported for Python < 3.10")
+
+    if n <= 4:
+        return [create_instruction("ROT_" + ["TWO", "THREE", "FOUR"][n - 2])]
+    return [create_instruction("ROT_N", arg=n)]
+
+
+def create_call_function(nargs, push_null) -> List[Instruction]:
+    """
+    Creates a sequence of instructions that makes a function call.
+
+    `push_null` is used in Python 3.11+ only. It is used in codegen when
+    a function call is intended to be made with the NULL + fn convention,
+    and we know that the NULL has not been pushed yet. We will push a
+    NULL and rotate it to the correct position immediately before making
+    the function call.
+    push_null should default to True unless you know you are calling a function
+    that you codegen'd with a null already pushed, for example
+    (assume `math` is available in the global scope),
+
+    create_load_global("math", True)  # pushes a null
+    create_instruction("LOAD_ATTR", argval="sqrt")
+    create_instruction("LOAD_CONST", argval=25)
+    create_call_function(1, False)
+    """
+    if sys.version_info >= (3, 11):
+        output = []
+        if push_null:
+            output.append(create_instruction("PUSH_NULL"))
+            output.extend(create_rot_n(nargs + 2))
+        output.append(create_instruction("PRECALL", arg=nargs))
+        output.append(create_instruction("CALL", arg=nargs))
+        return output
+    return [create_instruction("CALL_FUNCTION", arg=nargs)]
+
+
+def create_call_method(nargs) -> List[Instruction]:
+    if sys.version_info >= (3, 11):
+        return [
+            create_instruction("PRECALL", arg=nargs),
+            create_instruction("CALL", arg=nargs),
+        ]
+    return [create_instruction("CALL_METHOD", arg=nargs)]
+
+
+def lnotab_writer(
+    lineno: int, byteno: int = 0
+) -> Tuple[List[int], Callable[[int, int], None]]:
+    """
+    Used to create typing.CodeType.co_lnotab
+    See https://github.com/python/cpython/blob/main/Objects/lnotab_notes.txt
+    This is the internal format of the line number table if Python < 3.10
+    """
+    assert sys.version_info < (3, 10)
+    lnotab: List[int] = []
+
+    def update(lineno_new, byteno_new):
+        nonlocal byteno, lineno
+        while byteno_new != byteno or lineno_new != lineno:
+            byte_offset = max(0, min(byteno_new - byteno, 255))
+            line_offset = max(-128, min(lineno_new - lineno, 127))
+            assert byte_offset != 0 or line_offset != 0
+            byteno += byte_offset
+            lineno += line_offset
+            lnotab.extend((byte_offset, line_offset & 0xFF))
+
+    return lnotab, update
+
+
+def linetable_310_writer(first_lineno):
+    """
+    Used to create typing.CodeType.co_linetable
+    See https://github.com/python/cpython/blob/main/Objects/lnotab_notes.txt
+    This is the internal format of the line number table for Python 3.10
+    """
+    assert sys.version_info >= (3, 10) and sys.version_info < (3, 11)
+    linetable: List[int] = []
+    lineno = first_lineno
+    lineno_delta = 0
+    byteno = 0
+
+    def _update(byteno_delta, lineno_delta):
+        while byteno_delta != 0 or lineno_delta != 0:
+            byte_offset = max(0, min(byteno_delta, 254))
+            line_offset = max(-127, min(lineno_delta, 127))
+            assert byte_offset != 0 or line_offset != 0
+            byteno_delta -= byte_offset
+            lineno_delta -= line_offset
+            linetable.extend((byte_offset, line_offset & 0xFF))
+
+    def update(lineno_new, byteno_new):
+        nonlocal lineno, lineno_delta, byteno
+        byteno_delta = byteno_new - byteno
+        byteno = byteno_new
+        _update(byteno_delta, lineno_delta)
+        lineno_delta = lineno_new - lineno
+        lineno = lineno_new
+
+    def end(total_bytes):
+        _update(total_bytes - byteno, lineno_delta)
+
+    return linetable, update, end
+
+
+def encode_varint(n: int) -> List[int]:
+    """
+    6-bit chunk encoding of an unsigned integer
+    See https://github.com/python/cpython/blob/3.11/Objects/locations.md
+    """
+    assert n >= 0
+    b = [n & 63]
+    n >>= 6
+    while n > 0:
+        b[-1] |= 64
+        b.append(n & 63)
+        n >>= 6
+    return b
+
+
+def linetable_311_writer(first_lineno: int):
+    """
+    Used to create typing.CodeType.co_linetable
+    See https://github.com/python/cpython/blob/3.11/Objects/locations.md
+    This is the internal format of the line number table for Python 3.11
+    """
+    assert sys.version_info >= (3, 11)
+    linetable = []
+    lineno = first_lineno
+
+    def update(positions: "dis.Positions", inst_size):
+        nonlocal lineno
+        lineno_new = positions.lineno if positions else None
+
+        def _update(delta, size):
+            assert 0 < size <= 8
+            # first byte - use 13 (no column info) is positions is
+            # malformed, otherwise use 14 (long form)
+            other_varints: Tuple[int, ...] = ()
+            if (
+                positions
+                and positions.lineno is not None
+                and positions.end_lineno is not None
+                and positions.col_offset is not None
+                and positions.end_col_offset is not None
+            ):
+                linetable.append(0b1_1110_000 + size - 1)
+                # for whatever reason, column offset needs `+ 1`
+                # https://github.com/python/cpython/blob/1931c2a438c50e6250725c84dff94fc760b9b951/Python/compile.c#L7603
+                other_varints = (
+                    positions.end_lineno - positions.lineno,
+                    positions.col_offset + 1,
+                    positions.end_col_offset + 1,
+                )
+            else:
+                linetable.append(0b1_1101_000 + size - 1)
+            # encode signed int
+            if delta < 0:
+                delta = ((-delta) << 1) | 1
+            else:
+                delta <<= 1
+            # encode unsigned int
+            linetable.extend(encode_varint(delta))
+            for n in other_varints:
+                linetable.extend(encode_varint(n))
+
+        if lineno_new is None:
+            lineno_delta = 0
+        else:
+            lineno_delta = lineno_new - lineno
+            lineno = lineno_new
+        while inst_size > 8:
+            _update(lineno_delta, 8)
+            inst_size -= 8
+        _update(lineno_delta, inst_size)
+
+    return linetable, update
+
+
+@dataclasses.dataclass
+class ExceptionTableEntry:
+    start: int
+    end: int
+    target: int
+    depth: int
+    lasti: bool
+
+
+def encode_exception_table_varint(n: int) -> List[int]:
+    """
+    Similar to `encode_varint`, but the 6-bit chunks are ordered in reverse.
+    """
+    assert n >= 0
+    b = [n & 63]
+    n >>= 6
+    while n > 0:
+        b.append(n & 63)
+        n >>= 6
+    b.reverse()
+    for i in range(len(b) - 1):
+        b[i] |= 64
+    return b
+
+
+def decode_exception_table_varint(bytes_iter: Iterator[int]) -> int:
+    """
+    Inverse of `encode_exception_table_varint`.
+    """
+    b = next(bytes_iter)
+    val = b & 63
+    while b & 64:
+        val <<= 6
+        b = next(bytes_iter)
+        val |= b & 63
+    return val
+
+
+def check_exception_table(tab: List[ExceptionTableEntry]) -> None:
+    """
+    Verifies that a list of ExceptionTableEntries will make a well-formed
+    jump table: entries are non-empty, sorted, and do not overlap.
+    """
+    for i in range(len(tab) - 1):
+        assert (
+            tab[i].start <= tab[i].end
+            and tab[i].end < tab[i + 1].start
+            and tab[i + 1].start <= tab[i + 1].end
+        )
+
+
+def parse_exception_table(exntab: bytes) -> List[ExceptionTableEntry]:
+    """
+    Parse the exception table according to
+    https://github.com/python/cpython/blob/3.11/Objects/exception_handling_notes.txt
+    """
+    exntab_iter = iter(exntab)
+    tab = []
+    try:
+        while True:
+            start = decode_exception_table_varint(exntab_iter) * 2
+            length = decode_exception_table_varint(exntab_iter) * 2
+            end = start + length - 2
+            target = decode_exception_table_varint(exntab_iter) * 2
+            dl = decode_exception_table_varint(exntab_iter)
+            depth = dl >> 1
+            lasti = bool(dl & 1)
+            tab.append(ExceptionTableEntry(start, end, target, depth, lasti))
+    except StopIteration:
+        check_exception_table(tab)
+        return tab
+
+
+def assemble_exception_table(tab: List[ExceptionTableEntry]) -> bytes:
+    """
+    Inverse of parse_exception_table - encodes list of exception
+    table entries into bytes.
+    """
+    b = []
+    for entry in tab:
+        first_entry = encode_exception_table_varint(entry.start // 2)
+        first_entry[0] |= 1 << 7
+        b.extend(first_entry)
+        length = entry.end - entry.start + 2
+        b.extend(encode_exception_table_varint(length // 2))
+        b.extend(encode_exception_table_varint(entry.target // 2))
+        dl = (entry.depth << 1) + entry.lasti
+        b.extend(encode_exception_table_varint(dl))
+    return bytes(b)
+
+
+def assemble(instructions: List[Instruction], firstlineno: int) -> Tuple[bytes, bytes]:
+    """Do the opposite of dis.get_instructions()"""
+    code: List[int] = []
+    if sys.version_info >= (3, 11):
+        lnotab, update_lineno = linetable_311_writer(firstlineno)
+        num_ext = 0
+        for i, inst in enumerate(instructions):
+            if inst.opname == "EXTENDED_ARG":
+                inst_size = 1
+                num_ext += 1
+                # copy positions from the actual instruction
+                for j in (1, 2, 3):
+                    if instructions[i + j].opname != "EXTENDED_ARG":
+                        inst.positions = instructions[i + j].positions
+                        break
+            else:
+                inst_size = instruction_size(inst) // 2 + num_ext
+                num_ext = 0
+            update_lineno(inst.positions, inst_size)
+            num_ext = 0
+            arg = inst.arg or 0
+            code.extend((inst.opcode, arg & 0xFF))
+            for _ in range(instruction_size(inst) // 2 - 1):
+                code.extend((0, 0))
+    else:
+        if sys.version_info < (3, 10):
+            lnotab, update_lineno = lnotab_writer(firstlineno)
+        else:
+            lnotab, update_lineno, end = linetable_310_writer(firstlineno)
+
+        for inst in instructions:
+            if inst.starts_line is not None:
+                update_lineno(inst.starts_line, len(code))
+            arg = inst.arg or 0
+            code.extend((inst.opcode, arg & 0xFF))
+
+        if sys.version_info >= (3, 10):
+            end(len(code))
+
+    return bytes(code), bytes(lnotab)
+
+
+def _get_instruction_by_offset(offset_to_inst: Dict[int, Instruction], offset: int):
+    """
+    Get the instruction located at a given offset, accounting for EXTENDED_ARGs
+    """
+    for n in (0, 2, 4, 6):
+        if offset_to_inst[offset + n].opcode != dis.EXTENDED_ARG:
+            return offset_to_inst[offset + n]
+    return None
+
+
+def virtualize_jumps(instructions) -> None:
+    """Replace jump targets with pointers to make editing easier"""
+    jump_targets = {inst.offset: inst for inst in instructions}
+
+    for inst in instructions:
+        if inst.opcode in dis.hasjabs or inst.opcode in dis.hasjrel:
+            inst.target = _get_instruction_by_offset(jump_targets, inst.argval)
+
+
+_REL_JUMPS = set(dis.hasjrel)
+
+
+def flip_jump_direction(instruction: Instruction) -> None:
+    if sys.version_info < (3, 11):
+        raise RuntimeError("Cannot flip jump direction in Python < 3.11")
+    if "FORWARD" in instruction.opname:
+        instruction.opname = instruction.opname.replace("FORWARD", "BACKWARD")
+    elif "BACKWARD" in instruction.opname:
+        instruction.opname = instruction.opname.replace("BACKWARD", "FORWARD")
+    else:
+        raise AttributeError("Instruction is not a forward or backward jump")
+    instruction.opcode = dis.opmap[instruction.opname]
+    assert instruction.opcode in _REL_JUMPS
+
+
+def _get_instruction_front(instructions: List[Instruction], idx: int):
+    """
+    i.e. get the first EXTENDED_ARG instruction (if any) when targeting
+    instructions[idx] with a jump.
+    """
+    target = instructions[idx]
+    for offset in (1, 2, 3):
+        if idx >= offset and instructions[idx - offset].opcode == dis.EXTENDED_ARG:
+            target = instructions[idx - offset]
+        else:
+            break
+    return target
+
+
+def devirtualize_jumps(instructions):
+    """Fill in args for virtualized jump target after instructions may have moved"""
+    indexof = get_indexof(instructions)
+    jumps = set(dis.hasjabs).union(set(dis.hasjrel))
+
+    for inst in instructions:
+        if inst.opcode in jumps:
+            target = _get_instruction_front(instructions, indexof[inst.target])
+            if inst.opcode in dis.hasjabs:
+                if sys.version_info < (3, 10):
+                    inst.arg = target.offset
+                elif sys.version_info < (3, 11):
+                    # `arg` is expected to be bytecode offset, whereas `offset` is byte offset.
+                    # Divide since bytecode is 2 bytes large.
+                    inst.arg = int(target.offset / 2)
+                else:
+                    raise RuntimeError("Python 3.11+ should not have absolute jumps")
+            else:  # relative jump
+                # byte offset between target and next instruction
+                inst.arg = int(target.offset - inst.offset - instruction_size(inst))
+                if inst.arg < 0:
+                    if sys.version_info < (3, 11):
+                        raise RuntimeError("Got negative jump offset for Python < 3.11")
+                    inst.arg = -inst.arg
+                    # forward jumps become backward
+                    if "FORWARD" in inst.opname:
+                        flip_jump_direction(inst)
+                elif inst.arg > 0:
+                    # backward jumps become forward
+                    if sys.version_info >= (3, 11) and "BACKWARD" in inst.opname:
+                        flip_jump_direction(inst)
+                if sys.version_info >= (3, 10):
+                    # see bytecode size comment in the absolute jump case above
+                    inst.arg //= 2
+            inst.argval = target.offset
+            inst.argrepr = f"to {target.offset}"
+
+
+def virtualize_exception_table(exn_tab_bytes: bytes, instructions: List[Instruction]):
+    """Replace exception table entries with pointers to make editing easier"""
+    exn_tab = parse_exception_table(exn_tab_bytes)
+    offset_to_inst = {cast(int, inst.offset): inst for inst in instructions}
+    offsets = sorted(offset_to_inst.keys())
+    end_offset_idx = 0
+    exn_tab_iter = iter(exn_tab)
+    try:
+
+        def step():
+            nonlocal end_offset_idx
+            entry = next(exn_tab_iter)
+            # find rightmost offset <= entry.end, since entry.end may not be
+            # an actual instruction, e.g. if the end instruction is LOAD_GLOBAL,
+            # which takes more than 2 bytes, then entry.end points to the end
+            # of the LOAD_GLOBAL instruction, not the beginning.
+            while (
+                end_offset_idx < len(offsets) and offsets[end_offset_idx] <= entry.end
+            ):
+                end_offset_idx += 1
+            assert end_offset_idx > 0
+            end_offset = offsets[end_offset_idx - 1]
+            inst_entry = InstructionExnTabEntry(
+                _get_instruction_by_offset(offset_to_inst, entry.start),
+                _get_instruction_by_offset(offset_to_inst, end_offset),
+                _get_instruction_by_offset(offset_to_inst, entry.target),
+                entry.depth,
+                entry.lasti,
+            )
+            return entry, inst_entry
+
+        entry, inst_entry = step()
+        for inst in instructions:
+            while inst.offset > entry.end:
+                entry, inst_entry = step()
+            if inst.offset >= entry.start:
+                inst.exn_tab_entry = copy.copy(inst_entry)
+    except StopIteration:
+        pass
+
+
+def compute_exception_table(
+    instructions: List[Instruction],
+) -> List[ExceptionTableEntry]:
+    """Compute exception table in list format from instructions with exn_tab_entries"""
+    exn_dict: Dict[Tuple[int, int], Tuple[int, int, bool]] = {}
+    indexof = get_indexof(instructions)
+
+    for inst in instructions:
+        if inst.exn_tab_entry:
+            # account for prefixed EXTENDED_ARGS
+            start = _get_instruction_front(
+                instructions, indexof[inst.exn_tab_entry.start]
+            ).offset
+            # point to the last 2 bytes of the end instruction
+            end = (
+                cast(int, inst.exn_tab_entry.end.offset)
+                + instruction_size(inst.exn_tab_entry.end)
+                - 2
+            )
+            target = _get_instruction_front(
+                instructions, indexof[inst.exn_tab_entry.target]
+            ).offset
+            key = (start, end)
+            val = (target, inst.exn_tab_entry.depth, inst.exn_tab_entry.lasti)
+            if key in exn_dict:
+                assert exn_dict[key] == val
+            exn_dict[key] = val
+
+    # Dynamo may construct nested exception table entries for convenience,
+    # but Python expects exception table entries to not overlap.
+    # NOTE: below, "keys" refer to old instruction entries' starts and ends,
+    # and "entries" refer to the generated exception table entries.
+
+    # Sort keys by increasing start, then decreasing end
+    keys_sorted = sorted(exn_dict.keys(), key=lambda t: (t[0], -t[1]))
+    # smallest byte that the next exception table entry can start at
+    nexti = 0
+    # stack of current nested keys
+    key_stack: List[Tuple[int, int]] = []
+    exn_tab: List[ExceptionTableEntry] = []
+
+    def pop():
+        """
+        Pop the key_stack and append an exception table entry if possible.
+        """
+        nonlocal nexti
+        if key_stack:
+            key = key_stack.pop()
+            if nexti <= key[1]:
+                exn_tab.append(
+                    ExceptionTableEntry(max(key[0], nexti), key[1], *exn_dict[key])
+                )
+                nexti = key[1] + 2
+
+    for key in keys_sorted:
+        # pop keys that are no longer nested over the current key
+        while key_stack and key_stack[-1][1] < key[0]:
+            pop()
+        if key_stack:
+            # create an entry covering to the current key, if possible
+            assert key_stack[-1][0] <= key[0] <= key[1] <= key_stack[-1][1]
+            left = max(nexti, key_stack[-1][0])
+            if left < key[0]:
+                exn_tab.append(
+                    ExceptionTableEntry(left, key[0] - 2, *exn_dict[key_stack[-1]])
+                )
+            nexti = key[0]
+        key_stack.append(key)
+    while key_stack:
+        pop()
+    check_exception_table(exn_tab)
+    return exn_tab
+
+
+def check_inst_exn_tab_entries_nested(
+    tab: List[InstructionExnTabEntry], indexof
+) -> None:
+    """
+    Checks `tab` is a properly sorted list of nested InstructionExnTabEntry's,
+    i.e. no entries partially overlap.
+    "Properly sorted" means entries are sorted by increasing starts, then
+    decreasing ends.
+    """
+    entry_stack: List[Tuple[int, int]] = []
+    for entry in tab:
+        key = (indexof[entry.start], indexof[entry.end])
+        while entry_stack and entry_stack[-1][1] < key[0]:
+            entry_stack.pop()
+        if entry_stack:
+            assert entry_stack[-1][0] <= key[0] <= key[1] <= entry_stack[-1][1]
+        entry_stack.append(key)
+
+
+def propagate_inst_exn_table_entries(instructions: List[Instruction]) -> None:
+    """
+    Copies exception table entries to all instructions in an entry's range.
+    Supports nested exception table entries.
+    """
+    indexof = get_indexof(instructions)
+    entries: Dict[Tuple[int, int], InstructionExnTabEntry] = {}
+    for inst in instructions:
+        if inst.exn_tab_entry:
+            key = (
+                indexof[inst.exn_tab_entry.start],
+                indexof[inst.exn_tab_entry.end],
+            )
+            if key in entries:
+                assert inst.exn_tab_entry == entries[key]
+            entries[key] = inst.exn_tab_entry
+    sorted_entries = [
+        entries[key] for key in sorted(entries.keys(), key=lambda t: (t[0], -t[1]))
+    ]
+    check_inst_exn_tab_entries_nested(sorted_entries, indexof)
+    # Propagation of nested entries works since nested entries come later
+    # in sorted order.
+    for entry in sorted_entries:
+        for i in range(indexof[entry.start], indexof[entry.end] + 1):
+            instructions[i].exn_tab_entry = copy.copy(entry)
+
+
+def check_inst_exn_tab_entries_valid(instructions: List[Instruction]):
+    """
+    Checks that exn_tab_entries of instructions are valid.
+    An entry's start, end, and target must be in instructions.
+    Instructions with an exn_tab_entry are located within
+    the entry's start and end instructions.
+    Instructions do not share exn_tab_entries.
+
+    Implicitly checks for no duplicate instructions.
+    """
+    indexof = get_indexof(instructions)
+    exn_tab_entry_set = set()
+    for i, inst in enumerate(instructions):
+        if inst.exn_tab_entry:
+            assert sys.version_info >= (3, 11)
+            assert id(inst.exn_tab_entry) not in exn_tab_entry_set
+            exn_tab_entry_set.add(id(inst.exn_tab_entry))
+            entry = inst.exn_tab_entry
+            assert entry.start in indexof
+            assert entry.end in indexof
+            assert entry.target in indexof
+            assert indexof[entry.start] <= i <= indexof[entry.end]
+
+
+def strip_extended_args(instructions: List[Instruction]) -> None:
+    instructions[:] = [i for i in instructions if i.opcode != dis.EXTENDED_ARG]
+
+
+def remove_load_call_method(instructions: List[Instruction]) -> List[Instruction]:
+    """LOAD_METHOD puts a NULL on the stack which causes issues, so remove it"""
+    rewrites = {"LOAD_METHOD": "LOAD_ATTR", "CALL_METHOD": "CALL_FUNCTION"}
+    for inst in instructions:
+        if inst.opname in rewrites:
+            inst.opname = rewrites[inst.opname]
+            inst.opcode = dis.opmap[inst.opname]
+    return instructions
+
+
+def remove_jump_if_none(instructions: List[Instruction]) -> None:
+    new_insts = []
+    for inst in instructions:
+        new_insts.append(inst)
+        if "_NONE" in inst.opname:
+            is_op = create_instruction("IS_OP", arg=int("NOT" in inst.opname))
+            is_op.argval = is_op.arg
+            jump_op = create_instruction(
+                "POP_JUMP_FORWARD_IF_TRUE"
+                if "FORWARD" in inst.opname
+                else "POP_JUMP_BACKWARD_IF_TRUE",
+                target=inst.target,
+            )
+            # modify inst in-place to preserve jump target
+            inst.opcode = dis.opmap["LOAD_CONST"]
+            inst.opname = "LOAD_CONST"
+            inst.arg = None
+            inst.argval = None
+            new_insts.extend([is_op, jump_op])
+    instructions[:] = new_insts
+
+
+def explicit_super(code: types.CodeType, instructions: List[Instruction]) -> None:
+    """convert super() with no args into explicit arg form"""
+    cell_and_free = (code.co_cellvars or tuple()) + (code.co_freevars or tuple())
+    if not len(code.co_varnames):
+        # A function with no argument cannot contain a valid "super()" call
+        return
+    output = []
+    for idx, inst in enumerate(instructions):
+        output.append(inst)
+        if inst.opname == "LOAD_GLOBAL" and inst.argval == "super":
+            nexti = instructions[idx + 1]
+            if nexti.opname in ("CALL_FUNCTION", "PRECALL") and nexti.arg == 0:
+                assert "__class__" in cell_and_free
+                output.append(create_instruction("LOAD_DEREF", argval="__class__"))
+                first_var = code.co_varnames[0]
+                if first_var in cell_and_free:
+                    output.append(create_instruction("LOAD_DEREF", argval=first_var))
+                else:
+                    output.append(create_instruction("LOAD_FAST", argval=first_var))
+                nexti.arg = 2
+                nexti.argval = 2
+                if nexti.opname == "PRECALL":
+                    # also update the following CALL instruction
+                    call_inst = instructions[idx + 2]
+                    call_inst.arg = 2
+                    call_inst.argval = 2
+
+    instructions[:] = output
+
+
+def fix_extended_args(instructions: List[Instruction]) -> int:
+    """Fill in correct argvals for EXTENDED_ARG ops"""
+    output: List[Instruction] = []
+
+    def maybe_pop_n(n):
+        for _ in range(n):
+            if output and output[-1].opcode == dis.EXTENDED_ARG:
+                output.pop()
+
+    for inst in instructions:
+        if inst.opcode == dis.EXTENDED_ARG:
+            # Leave this instruction alone for now so we never shrink code
+            inst.arg = 0
+        elif inst.arg and inst.arg > 0xFFFFFF:
+            maybe_pop_n(3)
+            output.append(create_instruction("EXTENDED_ARG", arg=inst.arg >> 24))
+            output.append(create_instruction("EXTENDED_ARG", arg=inst.arg >> 16))
+            output.append(create_instruction("EXTENDED_ARG", arg=inst.arg >> 8))
+        elif inst.arg and inst.arg > 0xFFFF:
+            maybe_pop_n(2)
+            output.append(create_instruction("EXTENDED_ARG", arg=inst.arg >> 16))
+            output.append(create_instruction("EXTENDED_ARG", arg=inst.arg >> 8))
+        elif inst.arg and inst.arg > 0xFF:
+            maybe_pop_n(1)
+            output.append(create_instruction("EXTENDED_ARG", arg=inst.arg >> 8))
+        output.append(inst)
+
+    added = len(output) - len(instructions)
+    assert added >= 0
+    instructions[:] = output
+    return added
+
+
+# from https://github.com/python/cpython/blob/v3.11.1/Include/internal/pycore_opcode.h#L41
+# TODO use the actual object instead, can interface from eval_frame.c
+_PYOPCODE_CACHES = {
+    "BINARY_SUBSCR": 4,
+    "STORE_SUBSCR": 1,
+    "UNPACK_SEQUENCE": 1,
+    "STORE_ATTR": 4,
+    "LOAD_ATTR": 4,
+    "COMPARE_OP": 2,
+    "LOAD_GLOBAL": 5,
+    "BINARY_OP": 1,
+    "LOAD_METHOD": 10,
+    "PRECALL": 1,
+    "CALL": 4,
+}
+
+
+def instruction_size(inst) -> int:
+    if sys.version_info >= (3, 11):
+        return 2 * (_PYOPCODE_CACHES.get(dis.opname[inst.opcode], 0) + 1)
+    return 2
+
+
+def check_offsets(instructions) -> None:
+    offset = 0
+    for inst in instructions:
+        assert inst.offset == offset
+        offset += instruction_size(inst)
+
+
+def update_offsets(instructions) -> None:
+    offset = 0
+    for inst in instructions:
+        inst.offset = offset
+        offset += instruction_size(inst)
+
+
+def debug_bytes(*args) -> str:
+    index = range(max(map(len, args)))
+    result = []
+    for arg in (
+        [index] + list(args) + [[int(a != b) for a, b in zip(args[-1], args[-2])]]
+    ):
+        result.append(" ".join(f"{x:03}" for x in arg))
+
+    return "bytes mismatch\n" + "\n".join(result)
+
+
+def debug_checks(code):
+    """Make sure our assembler produces same bytes as we start with"""
+    dode = transform_code_object(code, lambda x, y: None, safe=True)
+    assert code.co_code == dode.co_code, debug_bytes(code.co_code, dode.co_code)
+    assert code.co_lnotab == dode.co_lnotab, debug_bytes(code.co_lnotab, dode.co_lnotab)
+
+
+HAS_LOCAL = set(dis.haslocal)
+HAS_NAME = set(dis.hasname)
+HAS_FREE = set(dis.hasfree)
+HAS_CONST = set(dis.hasconst)
+
+
+def get_const_index(code_options, val) -> int:
+    for i, v in enumerate(code_options["co_consts"]):
+        # NOTE: stronger comparison is required, since we have
+        # examples where two values compare equal but have
+        # different semantic meaning in some cases, e.g.
+        # 0.0 == -0.0 but have different effects in torch.copysign.
+        if val is v:
+            return i
+    code_options["co_consts"] += (val,)
+    return len(code_options["co_consts"]) - 1
+
+
+def fix_vars(instructions: List[Instruction], code_options, varname_from_oparg=None):
+    # compute instruction arg from argval if arg is not provided
+    names = {name: idx for idx, name in enumerate(code_options["co_names"])}
+    if sys.version_info < (3, 11):
+        assert varname_from_oparg is None
+        varnames = {name: idx for idx, name in enumerate(code_options["co_varnames"])}
+        freenames = {
+            name: idx
+            for idx, name in enumerate(
+                code_options["co_cellvars"] + code_options["co_freevars"]
+            )
+        }
+    else:
+        assert callable(varname_from_oparg)
+        allnames = {}
+        for idx in itertools.count():
+            try:
+                name = varname_from_oparg(idx)
+                allnames[name] = idx
+            except IndexError:
+                break
+        varnames = {name: allnames[name] for name in code_options["co_varnames"]}
+        freenames = {
+            name: allnames[name]
+            for name in code_options["co_cellvars"] + code_options["co_freevars"]
+        }
+    for i in range(len(instructions)):
+
+        def should_compute_arg():
+            # argval is prioritized over arg
+            return instructions[i].argval is not _NotProvided
+
+        if instructions[i].opname == "LOAD_GLOBAL":
+            # 3.11 LOAD_GLOBAL requires both arg and argval - see create_load_global
+            assert instructions[i].arg is not None
+            assert instructions[i].argval is not _NotProvided
+            if sys.version_info >= (3, 11):
+                instructions[i].arg = (names[instructions[i].argval] << 1) + (
+                    cast(int, instructions[i].arg) % 2
+                )
+            else:
+                instructions[i].arg = names[instructions[i].argval]
+        elif instructions[i].opcode in HAS_LOCAL:
+            if should_compute_arg():
+                instructions[i].arg = varnames[instructions[i].argval]
+        elif instructions[i].opcode in HAS_NAME:
+            if should_compute_arg():
+                instructions[i].arg = names[instructions[i].argval]
+        elif instructions[i].opcode in HAS_FREE:
+            if should_compute_arg():
+                instructions[i].arg = freenames[instructions[i].argval]
+        elif instructions[i].opcode in HAS_CONST:
+            # NOTE: only update argval if arg is not provided. This assumes
+            # that any additions to co_consts are appended.
+            if instructions[i].arg is None:
+                # cannot use a dictionary since consts may not be hashable
+                idx = get_const_index(code_options, instructions[i].argval)
+                assert idx >= 0
+                instructions[i].arg = idx
+
+
+def get_code_keys() -> List[str]:
+    # Python 3.11 changes to code keys are not fully documented.
+    # See https://github.com/python/cpython/blob/3.11/Objects/clinic/codeobject.c.h#L24
+    # for new format.
+    keys = ["co_argcount"]
+    keys.append("co_posonlyargcount")
+    keys.extend(
+        [
+            "co_kwonlyargcount",
+            "co_nlocals",
+            "co_stacksize",
+            "co_flags",
+            "co_code",
+            "co_consts",
+            "co_names",
+            "co_varnames",
+            "co_filename",
+            "co_name",
+        ]
+    )
+    if sys.version_info >= (3, 11):
+        keys.append("co_qualname")
+    keys.append("co_firstlineno")
+    if sys.version_info >= (3, 10):
+        keys.append("co_linetable")
+    else:
+        keys.append("co_lnotab")
+    if sys.version_info >= (3, 11):
+        # not documented, but introduced in https://github.com/python/cpython/issues/84403
+        keys.append("co_exceptiontable")
+    keys.extend(
+        [
+            "co_freevars",
+            "co_cellvars",
+        ]
+    )
+    return keys
+
+
+def transform_code_object(code, transformations, safe=False) -> types.CodeType:
+    keys = get_code_keys()
+    code_options = {k: getattr(code, k) for k in keys}
+    assert len(code_options["co_varnames"]) == code_options["co_nlocals"]
+
+    instructions = cleaned_instructions(code, safe)
+    propagate_line_nums(instructions)
+
+    transformations(instructions, code_options)
+    return clean_and_assemble_instructions(instructions, keys, code_options)[1]
+
+
+def clean_and_assemble_instructions(
+    instructions: List[Instruction], keys: List[str], code_options: Dict[str, Any]
+) -> Tuple[List[Instruction], types.CodeType]:
+    # also implicitly checks for no duplicate instructions
+    check_inst_exn_tab_entries_valid(instructions)
+
+    code_options["co_nlocals"] = len(code_options["co_varnames"])
+    varname_from_oparg = None
+    if sys.version_info >= (3, 11):
+        # temporary code object with updated names
+        tmp_code = types.CodeType(*[code_options[k] for k in keys])
+        varname_from_oparg = tmp_code._varname_from_oparg  # type: ignore[attr-defined]
+    fix_vars(instructions, code_options, varname_from_oparg=varname_from_oparg)
+
+    dirty = True
+    while dirty:
+        update_offsets(instructions)
+        devirtualize_jumps(instructions)
+        # this pass might change offsets, if so we need to try again
+        dirty = bool(fix_extended_args(instructions))
+
+    remove_extra_line_nums(instructions)
+    bytecode, lnotab = assemble(instructions, code_options["co_firstlineno"])
+    if sys.version_info < (3, 10):
+        code_options["co_lnotab"] = lnotab
+    else:
+        code_options["co_linetable"] = lnotab
+
+    code_options["co_code"] = bytecode
+    code_options["co_stacksize"] = stacksize_analysis(instructions)
+    assert set(keys) - {"co_posonlyargcount"} == set(code_options.keys()) - {
+        "co_posonlyargcount"
+    }
+    if sys.version_info >= (3, 11):
+        code_options["co_exceptiontable"] = assemble_exception_table(
+            compute_exception_table(instructions)
+        )
+    return instructions, types.CodeType(*[code_options[k] for k in keys])
+
+
+def populate_kw_names_argval(instructions, consts):
+    for inst in instructions:
+        if inst.opname == "KW_NAMES":
+            inst.argval = consts[inst.arg]
+
+
+def cleaned_instructions(code, safe=False) -> List[Instruction]:
+    instructions = list(map(convert_instruction, dis.get_instructions(code)))
+    check_offsets(instructions)
+    if sys.version_info >= (3, 11):
+        populate_kw_names_argval(instructions, code.co_consts)
+        virtualize_exception_table(code.co_exceptiontable, instructions)
+    virtualize_jumps(instructions)
+    strip_extended_args(instructions)
+    if not safe:
+        if sys.version_info < (3, 11):
+            remove_load_call_method(instructions)
+        else:
+            remove_jump_if_none(instructions)
+            update_offsets(instructions)
+            devirtualize_jumps(instructions)
+        explicit_super(code, instructions)
+    return instructions
+
+
+_unique_id_counter = itertools.count()
+
+
+def unique_id(name) -> str:
+    return f"{name}_{next(_unique_id_counter)}"
+
+
+def is_generator(code: types.CodeType) -> bool:
+    co_generator = 0x20
+    return (code.co_flags & co_generator) > 0
diff --git a/shared/package-files/ec/ec11bfd49c715cd89fb9d387a07cf54261e0f4a1ccec1a810e02c7b38ad2f285/gb2312-raw.enc b/shared/package-files/ec/ec11bfd49c715cd89fb9d387a07cf54261e0f4a1ccec1a810e02c7b38ad2f285/gb2312-raw.enc
new file mode 100644
index 0000000000000000000000000000000000000000..813d7a6f3707068b67946aa377a3e409ee1967b1
--- /dev/null
+++ b/shared/package-files/ec/ec11bfd49c715cd89fb9d387a07cf54261e0f4a1ccec1a810e02c7b38ad2f285/gb2312-raw.enc
@@ -0,0 +1,1380 @@
+# Encoding file: gb2312, double-byte
+D
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diff --git a/shared/package-files/ec/ec12f1e249fdcb98a981f654599e32fcc2c450d817e8c4390f20f0b21849c553/__init__.py b/shared/package-files/ec/ec12f1e249fdcb98a981f654599e32fcc2c450d817e8c4390f20f0b21849c553/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff11968db15f0f7c6057a46c252a91daee7b9cd9
--- /dev/null
+++ b/shared/package-files/ec/ec12f1e249fdcb98a981f654599e32fcc2c450d817e8c4390f20f0b21849c553/__init__.py
@@ -0,0 +1,9 @@
+from pandas.io.parsers.readers import (
+    TextFileReader,
+    TextParser,
+    read_csv,
+    read_fwf,
+    read_table,
+)
+
+__all__ = ["TextFileReader", "TextParser", "read_csv", "read_fwf", "read_table"]
diff --git a/shared/package-files/ec/ec14762d48c4d8c10b3f77d5f4e647f6d5da929b30f1042e981162fc28cc5345/_datasets_pair.pxd.tp b/shared/package-files/ec/ec14762d48c4d8c10b3f77d5f4e647f6d5da929b30f1042e981162fc28cc5345/_datasets_pair.pxd.tp
new file mode 100644
index 0000000000000000000000000000000000000000..1e57b3291a8f4be47902a1c4c26c1a41d1f43297
--- /dev/null
+++ b/shared/package-files/ec/ec14762d48c4d8c10b3f77d5f4e647f6d5da929b30f1042e981162fc28cc5345/_datasets_pair.pxd.tp
@@ -0,0 +1,67 @@
+{{py:
+
+implementation_specific_values = [
+    # Values are the following ones:
+    #
+    # name_suffix, INPUT_DTYPE_t, INPUT_DTYPE
+    ('64', 'DistanceMetric64', 'float64_t'),
+    ('32', 'DistanceMetric32', 'float32_t')
+]
+
+}}
+from ...utils._typedefs cimport float64_t, float32_t, int32_t, intp_t
+from ...metrics._dist_metrics cimport DistanceMetric64, DistanceMetric32, DistanceMetric
+
+{{for name_suffix, DistanceMetric, INPUT_DTYPE_t in implementation_specific_values}}
+
+
+cdef class DatasetsPair{{name_suffix}}:
+    cdef:
+        {{DistanceMetric}} distance_metric
+        intp_t n_features
+
+    cdef intp_t n_samples_X(self) noexcept nogil
+
+    cdef intp_t n_samples_Y(self) noexcept nogil
+
+    cdef float64_t dist(self, intp_t i, intp_t j) noexcept nogil
+
+    cdef float64_t surrogate_dist(self, intp_t i, intp_t j) noexcept nogil
+
+
+cdef class DenseDenseDatasetsPair{{name_suffix}}(DatasetsPair{{name_suffix}}):
+    cdef:
+        const {{INPUT_DTYPE_t}}[:, ::1] X
+        const {{INPUT_DTYPE_t}}[:, ::1] Y
+
+
+cdef class SparseSparseDatasetsPair{{name_suffix}}(DatasetsPair{{name_suffix}}):
+    cdef:
+        const {{INPUT_DTYPE_t}}[:] X_data
+        const int32_t[::1] X_indices
+        const int32_t[::1] X_indptr
+
+        const {{INPUT_DTYPE_t}}[:] Y_data
+        const int32_t[::1] Y_indices
+        const int32_t[::1] Y_indptr
+
+
+cdef class SparseDenseDatasetsPair{{name_suffix}}(DatasetsPair{{name_suffix}}):
+    cdef:
+        const {{INPUT_DTYPE_t}}[:] X_data
+        const int32_t[::1] X_indices
+        const int32_t[::1] X_indptr
+
+        const {{INPUT_DTYPE_t}}[:] Y_data
+        const int32_t[::1] Y_indices
+        intp_t n_Y
+
+
+cdef class DenseSparseDatasetsPair{{name_suffix}}(DatasetsPair{{name_suffix}}):
+    cdef:
+        # As distance metrics are commutative, we can simply rely
+        # on the implementation of SparseDenseDatasetsPair and
+        # swap arguments.
+        DatasetsPair{{name_suffix}} datasets_pair
+
+{{endfor}}
diff --git a/shared/package-files/ec/ec155158ef014dede1be26e803c57f996297f888ab6432da3a8ccdfc8bb38618/fully_shard.py b/shared/package-files/ec/ec155158ef014dede1be26e803c57f996297f888ab6432da3a8ccdfc8bb38618/fully_shard.py
new file mode 100644
index 0000000000000000000000000000000000000000..37e3d1544cd176438b1173627b819f58a747e2a9
--- /dev/null
+++ b/shared/package-files/ec/ec155158ef014dede1be26e803c57f996297f888ab6432da3a8ccdfc8bb38618/fully_shard.py
@@ -0,0 +1,133 @@
+import warnings
+from typing import Callable, Iterable, Optional, Union
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed._composable.contract import contract
+from torch.distributed._composable_state import _get_module_state, _insert_module_state
+from torch.distributed.fsdp._common_utils import _FSDPState
+from torch.distributed.fsdp._dynamo_utils import _annotate_modules_for_dynamo
+
+from torch.distributed.fsdp._init_utils import (
+    _init_buffer_state,
+    _init_core_state,
+    _init_device_handle,
+    _init_ignored_module_states,
+    _init_param_handle_from_module,
+    _init_prefetching_state,
+    _init_process_group_state,
+    _init_runtime_state,
+    _init_state_dict_state,
+    HYBRID_SHARDING_STRATEGIES,
+)
+from torch.distributed.fsdp._runtime_utils import (
+    _register_post_forward_hook,
+    _register_pre_forward_hook,
+    _register_root_pre_forward_hook,
+)
+from torch.distributed.fsdp._state_dict_utils import _register_all_state_dict_hooks
+from torch.distributed.fsdp._wrap_utils import _auto_wrap
+from torch.distributed.fsdp.api import (
+    BackwardPrefetch,
+    CPUOffload,
+    MixedPrecision,
+    ShardingStrategy,
+)
+from torch.distributed.fsdp.wrap import _Policy
+
+
+@contract(state_cls=_FSDPState)
+def fully_shard(
+    module: nn.Module,
+    *,
+    process_group: Optional[dist.ProcessGroup] = None,
+    policy: Optional[_Policy] = None,
+    strategy: Optional[ShardingStrategy] = None,
+    mixed_precision: Optional[MixedPrecision] = None,
+    cpu_offload: Optional[CPUOffload] = None,
+    ignored_modules: Optional[Iterable[torch.nn.Module]] = None,
+    device_id: Optional[Union[int, torch.device]] = None,
+    param_init_fn: Optional[Callable[[nn.Module], None]] = None,
+    sync_module_states: bool = False,
+    forward_prefetch: bool = False,
+    ignored_states: Union[
+        Optional[Iterable[torch.nn.Parameter]], Optional[Iterable[torch.nn.Module]]
+    ] = None,
+) -> nn.Module:
+    """
+    Applies ``FullyShardedDataParallel` (FSDP) semantics to ``module``.
+    """
+    warnings.warn(
+        "``torch.distributed._composable.fully_shard`` is being deprecated."
+        "You can contintue to use the wrapper based FSDP."
+        "See usage in: https://github.com/pytorch/pytorch/blob/main/torch/distributed/fsdp/fully_sharded_data_parallel.py."
+        "``torch.distributed._composable.fully_shard`` will be removed after PyTorch 2.5."
+    )
+
+    torch._C._log_api_usage_once("torch.distributed.fully_shard")
+    # Enforce the new auto wrap policy
+    if policy is not None and not isinstance(policy, _Policy):
+        raise ValueError(f"Expects a `_Policy` but got {policy}")
+    state = fully_shard.state(module)
+    state = _init_ignored_module_states(state, module, ignored_modules, ignored_states)
+    state = _init_device_handle(state, module, state._ignored_params, device_id)
+    _annotate_modules_for_dynamo(module, state._ignored_modules, True)
+    state = _init_process_group_state(state, process_group, strategy, policy)
+    if policy is not None:
+        root_kwargs = {
+            "process_group": process_group,
+            "strategy": strategy,
+            "mixed_precision": mixed_precision,
+            "cpu_offload": cpu_offload,
+            "ignored_modules": ignored_modules,
+            "device_id": device_id,
+            "param_init_fn": param_init_fn,
+            "sync_module_states": sync_module_states,
+            "forward_prefetch": forward_prefetch,
+            "ignored_states": ignored_states,
+        }
+        if strategy in HYBRID_SHARDING_STRATEGIES:
+            root_kwargs["process_group"] = (state.process_group, state._inter_node_pg)
+        _auto_wrap(
+            module,
+            policy,
+            state._ignored_modules,
+            state._ignored_params,
+            root_kwargs,
+            fully_shard,
+        )
+    state = _init_core_state(
+        state,
+        strategy or ShardingStrategy.FULL_SHARD,
+        mixed_precision,
+        cpu_offload,
+        limit_all_gathers=True,
+        use_orig_params=True,
+        backward_prefetch_limit=1,
+        forward_prefetch_limit=1,
+    )
+    state = _init_runtime_state(state)
+    state = _init_prefetching_state(
+        state, BackwardPrefetch.BACKWARD_PRE, forward_prefetch=forward_prefetch
+    )
+    state = _init_buffer_state(state, module)
+    state = _init_param_handle_from_module(
+        state, module, device_id, param_init_fn, sync_module_states
+    )
+    state = _init_state_dict_state(state)
+    _register_all_state_dict_hooks(state)
+    _register_pre_forward_hook(state, module)
+    _register_post_forward_hook(state, module)
+    _register_root_pre_forward_hook(state, module)  # prepend last
+    # Always insert the state for the passed-in module even if it has no
+    # managed parameters, in which case it has no handles and does not appear
+    # in `_fully_sharded_module_to_handles`
+    _insert_module_state(module, state)
+    for submodule in module.modules():
+        if (
+            submodule in state._fully_sharded_module_to_handle
+            and _get_module_state(submodule) is None
+        ):
+            _insert_module_state(submodule, state)
+    return module
diff --git a/shared/package-files/ec/ec17ce32921fdd7dbb992b66446052c07a36440d97be079a0d69576eaf69335f/mish_cpu_dispatch.h b/shared/package-files/ec/ec17ce32921fdd7dbb992b66446052c07a36440d97be079a0d69576eaf69335f/mish_cpu_dispatch.h
new file mode 100644
index 0000000000000000000000000000000000000000..806e108437c6b578d888da3c02051172513ce3f7
--- /dev/null
+++ b/shared/package-files/ec/ec17ce32921fdd7dbb992b66446052c07a36440d97be079a0d69576eaf69335f/mish_cpu_dispatch.h
@@ -0,0 +1,26 @@
+#pragma once
+// @generated by torchgen/gen.py from DispatchKeyFunction.h
+
+// NB: The implementing C++ file is RegisterDispatchKey.cpp
+
+// The only #includes we need are for custom classes that have defaults in the C++ API
+#include 
+#include 
+#include 
+
+// Forward declarations of any types needed in the operator signatures.
+// We can't directly include these classes because it will cause circular include dependencies.
+// This file is included by TensorBody.h, which defines the Tensor class.
+#include 
+
+namespace at {
+
+namespace cpu {
+
+TORCH_API at::Tensor mish(const at::Tensor & self);
+TORCH_API at::Tensor & mish_out(at::Tensor & out, const at::Tensor & self);
+TORCH_API at::Tensor & mish_outf(const at::Tensor & self, at::Tensor & out);
+TORCH_API at::Tensor & mish_(at::Tensor & self);
+
+} // namespace cpu
+} // namespace at
diff --git a/shared/package-files/ec/ec185c92770ba6f075bf539588f8dac975be80c2034b22771d673c82110d8018/print_handler.h b/shared/package-files/ec/ec185c92770ba6f075bf539588f8dac975be80c2034b22771d673c82110d8018/print_handler.h
new file mode 100644
index 0000000000000000000000000000000000000000..36feaffb200b655bd452ff822ae7af5149bc2670
--- /dev/null
+++ b/shared/package-files/ec/ec185c92770ba6f075bf539588f8dac975be80c2034b22771d673c82110d8018/print_handler.h
@@ -0,0 +1,15 @@
+#pragma once
+
+#include 
+
+#include 
+
+namespace torch::jit {
+
+using PrintHandler = void (*)(const std::string&);
+
+TORCH_API PrintHandler getDefaultPrintHandler();
+TORCH_API PrintHandler getPrintHandler();
+TORCH_API void setPrintHandler(PrintHandler ph);
+
+} // namespace torch::jit
diff --git a/shared/package-files/ec/ec1bbe2ae56f2ee099b508c18ba64ffb6b039ab378ea2b14b41c8ecddfa91cbc/test_partial_dependence.py b/shared/package-files/ec/ec1bbe2ae56f2ee099b508c18ba64ffb6b039ab378ea2b14b41c8ecddfa91cbc/test_partial_dependence.py
new file mode 100644
index 0000000000000000000000000000000000000000..816fe5512edc4a142380c3d84bc59a030e1168ff
--- /dev/null
+++ b/shared/package-files/ec/ec1bbe2ae56f2ee099b508c18ba64ffb6b039ab378ea2b14b41c8ecddfa91cbc/test_partial_dependence.py
@@ -0,0 +1,1217 @@
+"""
+Testing for the partial dependence module.
+"""
+
+import re
+import warnings
+
+import numpy as np
+import pytest
+
+import sklearn
+from sklearn.base import BaseEstimator, ClassifierMixin, clone, is_regressor
+from sklearn.cluster import KMeans
+from sklearn.compose import make_column_transformer
+from sklearn.datasets import load_iris, make_classification, make_regression
+from sklearn.dummy import DummyClassifier
+from sklearn.ensemble import (
+    GradientBoostingClassifier,
+    GradientBoostingRegressor,
+    HistGradientBoostingClassifier,
+    HistGradientBoostingRegressor,
+    RandomForestRegressor,
+)
+from sklearn.exceptions import NotFittedError
+from sklearn.impute import SimpleImputer
+from sklearn.inspection import partial_dependence
+from sklearn.inspection._partial_dependence import (
+    _grid_from_X,
+    _partial_dependence_brute,
+    _partial_dependence_recursion,
+)
+from sklearn.linear_model import LinearRegression, LogisticRegression, MultiTaskLasso
+from sklearn.metrics import r2_score
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import (
+    OneHotEncoder,
+    PolynomialFeatures,
+    RobustScaler,
+    StandardScaler,
+    scale,
+)
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.tree.tests.test_tree import assert_is_subtree
+from sklearn.utils._testing import assert_allclose, assert_array_equal
+from sklearn.utils.fixes import _IS_32BIT
+from sklearn.utils.validation import check_random_state
+
+# toy sample
+X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
+y = [-1, -1, -1, 1, 1, 1]
+
+
+# (X, y), n_targets  <-- as expected in the output of partial_dep()
+binary_classification_data = (make_classification(n_samples=50, random_state=0), 1)
+multiclass_classification_data = (
+    make_classification(
+        n_samples=50, n_classes=3, n_clusters_per_class=1, random_state=0
+    ),
+    3,
+)
+regression_data = (make_regression(n_samples=50, random_state=0), 1)
+multioutput_regression_data = (
+    make_regression(n_samples=50, n_targets=2, random_state=0),
+    2,
+)
+
+# iris
+iris = load_iris()
+
+
+@pytest.mark.parametrize(
+    "Estimator, method, data",
+    [
+        (GradientBoostingClassifier, "auto", binary_classification_data),
+        (GradientBoostingClassifier, "auto", multiclass_classification_data),
+        (GradientBoostingClassifier, "brute", binary_classification_data),
+        (GradientBoostingClassifier, "brute", multiclass_classification_data),
+        (GradientBoostingRegressor, "auto", regression_data),
+        (GradientBoostingRegressor, "brute", regression_data),
+        (DecisionTreeRegressor, "brute", regression_data),
+        (LinearRegression, "brute", regression_data),
+        (LinearRegression, "brute", multioutput_regression_data),
+        (LogisticRegression, "brute", binary_classification_data),
+        (LogisticRegression, "brute", multiclass_classification_data),
+        (MultiTaskLasso, "brute", multioutput_regression_data),
+    ],
+)
+@pytest.mark.parametrize("grid_resolution", (5, 10))
+@pytest.mark.parametrize("features", ([1], [1, 2]))
+@pytest.mark.parametrize("kind", ("average", "individual", "both"))
+@pytest.mark.parametrize("use_custom_values", [True, False])
+def test_output_shape(
+    Estimator, method, data, grid_resolution, features, kind, use_custom_values
+):
+    # Check that partial_dependence has consistent output shape for different
+    # kinds of estimators:
+    # - classifiers with binary and multiclass settings
+    # - regressors
+    # - multi-task regressors
+
+    est = Estimator()
+    if hasattr(est, "n_estimators"):
+        est.set_params(n_estimators=2)  # speed-up computations
+
+    # n_target corresponds to the number of classes (1 for binary classif) or
+    # the number of tasks / outputs in multi task settings. It's equal to 1 for
+    # classical regression_data.
+    (X, y), n_targets = data
+    n_instances = X.shape[0]
+
+    custom_values = None
+    if use_custom_values:
+        grid_resolution = 5
+        custom_values = {f: X[:grid_resolution, f] for f in features}
+
+    est.fit(X, y)
+    result = partial_dependence(
+        est,
+        X=X,
+        features=features,
+        method=method,
+        kind=kind,
+        grid_resolution=grid_resolution,
+        custom_values=custom_values,
+    )
+    pdp, axes = result, result["grid_values"]
+
+    expected_pdp_shape = (n_targets, *[grid_resolution for _ in range(len(features))])
+    expected_ice_shape = (
+        n_targets,
+        n_instances,
+        *[grid_resolution for _ in range(len(features))],
+    )
+    if kind == "average":
+        assert pdp.average.shape == expected_pdp_shape
+    elif kind == "individual":
+        assert pdp.individual.shape == expected_ice_shape
+    else:  # 'both'
+        assert pdp.average.shape == expected_pdp_shape
+        assert pdp.individual.shape == expected_ice_shape
+
+    expected_axes_shape = (len(features), grid_resolution)
+    assert axes is not None
+    assert np.asarray(axes).shape == expected_axes_shape
+
+
+def test_grid_from_X():
+    # tests for _grid_from_X: sanity check for output, and for shapes.
+
+    # Make sure that the grid is a cartesian product of the input (it will use
+    # the unique values instead of the percentiles)
+    percentiles = (0.05, 0.95)
+    grid_resolution = 100
+    is_categorical = [False, False]
+    X = np.asarray([[1, 2], [3, 4]])
+    grid, axes = _grid_from_X(X, percentiles, is_categorical, grid_resolution, {})
+    assert_array_equal(grid, [[1, 2], [1, 4], [3, 2], [3, 4]])
+    assert_array_equal(axes, X.T)
+
+    # test shapes of returned objects depending on the number of unique values
+    # for a feature.
+    rng = np.random.RandomState(0)
+    grid_resolution = 15
+
+    # n_unique_values > grid_resolution
+    X = rng.normal(size=(20, 2))
+    grid, axes = _grid_from_X(
+        X,
+        percentiles,
+        is_categorical,
+        grid_resolution=grid_resolution,
+        custom_values={},
+    )
+    assert grid.shape == (grid_resolution * grid_resolution, X.shape[1])
+    assert np.asarray(axes).shape == (2, grid_resolution)
+    assert grid.dtype == X.dtype
+
+    # n_unique_values < grid_resolution, will use actual values
+    n_unique_values = 12
+    X[n_unique_values - 1 :, 0] = 12345
+    rng.shuffle(X)  # just to make sure the order is irrelevant
+    grid, axes = _grid_from_X(
+        X,
+        percentiles,
+        is_categorical,
+        grid_resolution=grid_resolution,
+        custom_values={},
+    )
+    assert grid.shape == (n_unique_values * grid_resolution, X.shape[1])
+    # axes is a list of arrays of different shapes
+    assert axes[0].shape == (n_unique_values,)
+    assert axes[1].shape == (grid_resolution,)
+    assert grid.dtype == X.dtype
+
+    # Check that uses custom_range
+    X = rng.normal(size=(20, 2))
+    X[n_unique_values - 1 :, 0] = 12345
+    col_1_range = [0, 2, 3]
+    grid, axes = _grid_from_X(
+        X,
+        percentiles,
+        is_categorical=is_categorical,
+        grid_resolution=grid_resolution,
+        custom_values={1: col_1_range},
+    )
+    assert grid.shape == (n_unique_values * len(col_1_range), X.shape[1])
+    # axes is a list of arrays of different shapes
+    assert axes[0].shape == (n_unique_values,)
+    assert axes[1].shape == (len(col_1_range),)
+    assert grid.dtype == X.dtype
+
+    # Check that grid_resolution does not impact custom_range
+    X = rng.normal(size=(20, 2))
+    col_0_range = [0, 2, 3, 4, 5, 6]
+    grid_resolution = 5
+    grid, axes = _grid_from_X(
+        X,
+        percentiles,
+        is_categorical=is_categorical,
+        grid_resolution=grid_resolution,
+        custom_values={0: col_0_range},
+    )
+    assert grid.shape == (grid_resolution * len(col_0_range), X.shape[1])
+    # axes is a list of arrays of different shapes
+    assert axes[0].shape == (len(col_0_range),)
+    assert axes[1].shape == (grid_resolution,)
+    assert grid.dtype == np.result_type(X, np.asarray(col_0_range).dtype)
+
+    X = np.array([[0, "a"], [1, "b"], [2, "c"]])
+
+    grid, axes = _grid_from_X(
+        X,
+        percentiles,
+        is_categorical=is_categorical,
+        grid_resolution=grid_resolution,
+        custom_values={1: ["a", "b", "c"]},
+    )
+    assert grid.dtype == object
+
+
+@pytest.mark.parametrize(
+    "grid_resolution",
+    [
+        2,  # since n_categories > 2, we should not use quantiles resampling
+        100,
+    ],
+)
+def test_grid_from_X_with_categorical(grid_resolution):
+    """Check that `_grid_from_X` always sample from categories and does not
+    depend from the percentiles.
+    """
+    pd = pytest.importorskip("pandas")
+    percentiles = (0.05, 0.95)
+    is_categorical = [True]
+    X = pd.DataFrame({"cat_feature": ["A", "B", "C", "A", "B", "D", "E"]})
+    grid, axes = _grid_from_X(
+        X,
+        percentiles,
+        is_categorical,
+        grid_resolution=grid_resolution,
+        custom_values={},
+    )
+    assert grid.shape == (5, X.shape[1])
+    assert axes[0].shape == (5,)
+
+
+@pytest.mark.parametrize("grid_resolution", [3, 100])
+def test_grid_from_X_heterogeneous_type(grid_resolution):
+    """Check that `_grid_from_X` always sample from categories and does not
+    depend from the percentiles.
+    """
+    pd = pytest.importorskip("pandas")
+    percentiles = (0.05, 0.95)
+    is_categorical = [True, False]
+    X = pd.DataFrame(
+        {
+            "cat": ["A", "B", "C", "A", "B", "D", "E", "A", "B", "D"],
+            "num": [1, 1, 1, 2, 5, 6, 6, 6, 6, 8],
+        }
+    )
+    nunique = X.nunique()
+
+    grid, axes = _grid_from_X(
+        X,
+        percentiles,
+        is_categorical,
+        grid_resolution=grid_resolution,
+        custom_values={},
+    )
+    if grid_resolution == 3:
+        assert grid.shape == (15, 2)
+        assert axes[0].shape[0] == nunique["num"]
+        assert axes[1].shape[0] == grid_resolution
+    else:
+        assert grid.shape == (25, 2)
+        assert axes[0].shape[0] == nunique["cat"]
+        assert axes[1].shape[0] == nunique["cat"]
+
+
+@pytest.mark.parametrize(
+    "grid_resolution, percentiles, err_msg",
+    [
+        (2, (0, 0.0001), "percentiles are too close"),
+        (100, (1, 2, 3, 4), "'percentiles' must be a sequence of 2 elements"),
+        (100, 12345, "'percentiles' must be a sequence of 2 elements"),
+        (100, (-1, 0.95), r"'percentiles' values must be in \[0, 1\]"),
+        (100, (0.05, 2), r"'percentiles' values must be in \[0, 1\]"),
+        (100, (0.9, 0.1), r"percentiles\[0\] must be strictly less than"),
+        (1, (0.05, 0.95), "'grid_resolution' must be strictly greater than 1"),
+    ],
+)
+def test_grid_from_X_error(grid_resolution, percentiles, err_msg):
+    X = np.asarray([[1, 2], [3, 4]])
+    is_categorical = [False]
+    with pytest.raises(ValueError, match=err_msg):
+        _grid_from_X(X, percentiles, is_categorical, grid_resolution, custom_values={})
+
+
+@pytest.mark.parametrize("target_feature", range(5))
+@pytest.mark.parametrize(
+    "est, method",
+    [
+        (LinearRegression(), "brute"),
+        (GradientBoostingRegressor(random_state=0), "brute"),
+        (GradientBoostingRegressor(random_state=0), "recursion"),
+        (HistGradientBoostingRegressor(random_state=0), "brute"),
+        (HistGradientBoostingRegressor(random_state=0), "recursion"),
+    ],
+)
+def test_partial_dependence_helpers(est, method, target_feature):
+    # Check that what is returned by _partial_dependence_brute or
+    # _partial_dependence_recursion is equivalent to manually setting a target
+    # feature to a given value, and computing the average prediction over all
+    # samples.
+    # This also checks that the brute and recursion methods give the same
+    # output.
+    # Note that even on the trainset, the brute and the recursion methods
+    # aren't always strictly equivalent, in particular when the slow method
+    # generates unrealistic samples that have low mass in the joint
+    # distribution of the input features, and when some of the features are
+    # dependent. Hence the high tolerance on the checks.
+
+    X, y = make_regression(random_state=0, n_features=5, n_informative=5)
+    # The 'init' estimator for GBDT (here the average prediction) isn't taken
+    # into account with the recursion method, for technical reasons. We set
+    # the mean to 0 to that this 'bug' doesn't have any effect.
+    y = y - y.mean()
+
+    # Clone is necessary to make the test thread-safe.
+    est = clone(est).fit(X, y)
+
+    # target feature will be set to .5 and then to 123
+    features = np.array([target_feature], dtype=np.intp)
+    grid = np.array([[0.5], [123]])
+
+    if method == "brute":
+        pdp, predictions = _partial_dependence_brute(
+            est, grid, features, X, response_method="auto"
+        )
+    else:
+        pdp = _partial_dependence_recursion(est, grid, features)
+
+    mean_predictions = []
+    for val in (0.5, 123):
+        X_ = X.copy()
+        X_[:, target_feature] = val
+        mean_predictions.append(est.predict(X_).mean())
+
+    pdp = pdp[0]  # (shape is (1, 2) so make it (2,))
+
+    # allow for greater margin for error with recursion method
+    rtol = 1e-1 if method == "recursion" else 1e-3
+    assert np.allclose(pdp, mean_predictions, rtol=rtol)
+
+
+@pytest.mark.parametrize("seed", range(1))
+def test_recursion_decision_tree_vs_forest_and_gbdt(seed):
+    # Make sure that the recursion method gives the same results on a
+    # DecisionTreeRegressor and a GradientBoostingRegressor or a
+    # RandomForestRegressor with 1 tree and equivalent parameters.
+
+    rng = np.random.RandomState(seed)
+
+    # Purely random dataset to avoid correlated features
+    n_samples = 1000
+    n_features = 5
+    X = rng.randn(n_samples, n_features)
+    y = rng.randn(n_samples) * 10
+
+    # The 'init' estimator for GBDT (here the average prediction) isn't taken
+    # into account with the recursion method, for technical reasons. We set
+    # the mean to 0 to that this 'bug' doesn't have any effect.
+    y = y - y.mean()
+
+    # set max_depth not too high to avoid splits with same gain but different
+    # features
+    max_depth = 5
+
+    tree_seed = 0
+    forest = RandomForestRegressor(
+        n_estimators=1,
+        max_features=None,
+        bootstrap=False,
+        max_depth=max_depth,
+        random_state=tree_seed,
+    )
+    # The forest will use ensemble.base._set_random_states to set the
+    # random_state of the tree sub-estimator. We simulate this here to have
+    # equivalent estimators.
+    equiv_random_state = check_random_state(tree_seed).randint(np.iinfo(np.int32).max)
+    gbdt = GradientBoostingRegressor(
+        n_estimators=1,
+        learning_rate=1,
+        criterion="squared_error",
+        max_depth=max_depth,
+        random_state=equiv_random_state,
+    )
+    tree = DecisionTreeRegressor(max_depth=max_depth, random_state=equiv_random_state)
+
+    forest.fit(X, y)
+    gbdt.fit(X, y)
+    tree.fit(X, y)
+
+    # sanity check: if the trees aren't the same, the PD values won't be equal
+    try:
+        assert_is_subtree(tree.tree_, gbdt[0, 0].tree_)
+        assert_is_subtree(tree.tree_, forest[0].tree_)
+    except AssertionError:
+        # For some reason the trees aren't exactly equal on 32bits, so the PDs
+        # cannot be equal either. See
+        # https://github.com/scikit-learn/scikit-learn/issues/8853
+        assert _IS_32BIT, "this should only fail on 32 bit platforms"
+        return
+
+    grid = rng.randn(50).reshape(-1, 1)
+    for f in range(n_features):
+        features = np.array([f], dtype=np.intp)
+
+        pdp_forest = _partial_dependence_recursion(forest, grid, features)
+        pdp_gbdt = _partial_dependence_recursion(gbdt, grid, features)
+        pdp_tree = _partial_dependence_recursion(tree, grid, features)
+
+        np.testing.assert_allclose(pdp_gbdt, pdp_tree)
+        np.testing.assert_allclose(pdp_forest, pdp_tree)
+
+
+@pytest.mark.parametrize(
+    "est",
+    (
+        GradientBoostingClassifier(random_state=0),
+        HistGradientBoostingClassifier(random_state=0),
+    ),
+)
+@pytest.mark.parametrize("target_feature", (0, 1, 2, 3, 4, 5))
+def test_recursion_decision_function(est, target_feature):
+    # Make sure the recursion method (implicitly uses decision_function) has
+    # the same result as using brute method with
+    # response_method=decision_function
+
+    X, y = make_classification(n_classes=2, n_clusters_per_class=1, random_state=1)
+    assert np.mean(y) == 0.5  # make sure the init estimator predicts 0 anyway
+
+    est = clone(est).fit(X, y)
+
+    preds_1 = partial_dependence(
+        est,
+        X,
+        [target_feature],
+        response_method="decision_function",
+        method="recursion",
+        kind="average",
+    )
+    preds_2 = partial_dependence(
+        est,
+        X,
+        [target_feature],
+        response_method="decision_function",
+        method="brute",
+        kind="average",
+    )
+
+    assert_allclose(preds_1["average"], preds_2["average"], atol=1e-7)
+
+
+@pytest.mark.parametrize(
+    "est",
+    (
+        LinearRegression(),
+        GradientBoostingRegressor(random_state=0),
+        HistGradientBoostingRegressor(
+            random_state=0, min_samples_leaf=1, max_leaf_nodes=None, max_iter=1
+        ),
+        DecisionTreeRegressor(random_state=0),
+    ),
+)
+@pytest.mark.parametrize("power", (1, 2))
+def test_partial_dependence_easy_target(est, power):
+    # If the target y only depends on one feature in an obvious way (linear or
+    # quadratic) then the partial dependence for that feature should reflect
+    # it.
+    # We here fit a linear regression_data model (with polynomial features if
+    # needed) and compute r_squared to check that the partial dependence
+    # correctly reflects the target.
+
+    rng = np.random.RandomState(0)
+    n_samples = 200
+    target_variable = 2
+    X = rng.normal(size=(n_samples, 5))
+    y = X[:, target_variable] ** power
+
+    est = clone(est).fit(X, y)
+
+    pdp = partial_dependence(
+        est, features=[target_variable], X=X, grid_resolution=1000, kind="average"
+    )
+
+    new_X = pdp["grid_values"][0].reshape(-1, 1)
+    new_y = pdp["average"][0]
+    # add polynomial features if needed
+    new_X = PolynomialFeatures(degree=power).fit_transform(new_X)
+
+    lr = LinearRegression().fit(new_X, new_y)
+    r2 = r2_score(new_y, lr.predict(new_X))
+
+    assert r2 > 0.99
+
+
+@pytest.mark.parametrize(
+    "Estimator",
+    (
+        sklearn.tree.DecisionTreeClassifier,
+        sklearn.tree.ExtraTreeClassifier,
+        sklearn.ensemble.ExtraTreesClassifier,
+        sklearn.neighbors.KNeighborsClassifier,
+        sklearn.neighbors.RadiusNeighborsClassifier,
+        sklearn.ensemble.RandomForestClassifier,
+    ),
+)
+def test_multiclass_multioutput(Estimator):
+    # Make sure error is raised for multiclass-multioutput classifiers
+
+    # make multiclass-multioutput dataset
+    X, y = make_classification(n_classes=3, n_clusters_per_class=1, random_state=0)
+    y = np.array([y, y]).T
+
+    est = Estimator()
+    est.fit(X, y)
+
+    with pytest.raises(
+        ValueError, match="Multiclass-multioutput estimators are not supported"
+    ):
+        partial_dependence(est, X, [0])
+
+
+class NoPredictProbaNoDecisionFunction(ClassifierMixin, BaseEstimator):
+    def fit(self, X, y):
+        # simulate that we have some classes
+        self.classes_ = [0, 1]
+        return self
+
+
+@pytest.mark.parametrize(
+    "estimator, params, err_msg",
+    [
+        (
+            KMeans(random_state=0, n_init="auto"),
+            {"features": [0]},
+            "'estimator' must be a fitted regressor or classifier",
+        ),
+        (
+            LinearRegression(),
+            {"features": [0], "response_method": "predict_proba"},
+            "The response_method parameter is ignored for regressors",
+        ),
+        (
+            GradientBoostingClassifier(random_state=0),
+            {
+                "features": [0],
+                "response_method": "predict_proba",
+                "method": "recursion",
+            },
+            "'recursion' method, the response_method must be 'decision_function'",
+        ),
+        (
+            GradientBoostingClassifier(random_state=0),
+            {"features": [0], "response_method": "predict_proba", "method": "auto"},
+            "'recursion' method, the response_method must be 'decision_function'",
+        ),
+        (
+            LinearRegression(),
+            {"features": [0], "method": "recursion", "kind": "individual"},
+            "The 'recursion' method only applies when 'kind' is set to 'average'",
+        ),
+        (
+            LinearRegression(),
+            {"features": [0], "method": "recursion", "kind": "both"},
+            "The 'recursion' method only applies when 'kind' is set to 'average'",
+        ),
+        (
+            LinearRegression(),
+            {"features": [0], "method": "recursion"},
+            "Only the following estimators support the 'recursion' method:",
+        ),
+        (
+            LinearRegression(),
+            {"features": [0, 1], "custom_values": {0: [1, 2, 3], 1: np.ones((3, 3))}},
+            (
+                "The custom grid for some features is not a one-dimensional array. "
+                "Feature 1: 2 dimensions"
+            ),
+        ),
+    ],
+)
+def test_partial_dependence_error(estimator, params, err_msg):
+    X, y = make_classification(random_state=0)
+    estimator = clone(estimator).fit(X, y)
+
+    with pytest.raises(ValueError, match=err_msg):
+        partial_dependence(estimator, X, **params)
+
+
+@pytest.mark.parametrize(
+    "estimator", [LinearRegression(), GradientBoostingClassifier(random_state=0)]
+)
+@pytest.mark.parametrize("features", [-1, 10000])
+def test_partial_dependence_unknown_feature_indices(estimator, features):
+    X, y = make_classification(random_state=0)
+    estimator = clone(estimator).fit(X, y)
+
+    err_msg = "all features must be in"
+    with pytest.raises(ValueError, match=err_msg):
+        partial_dependence(estimator, X, [features])
+
+
+@pytest.mark.parametrize(
+    "estimator", [LinearRegression(), GradientBoostingClassifier(random_state=0)]
+)
+def test_partial_dependence_unknown_feature_string(estimator):
+    pd = pytest.importorskip("pandas")
+    X, y = make_classification(random_state=0)
+    df = pd.DataFrame(X)
+    estimator = clone(estimator).fit(df, y)
+
+    features = ["random"]
+    err_msg = "A given column is not a column of the dataframe"
+    with pytest.raises(ValueError, match=err_msg):
+        partial_dependence(estimator, df, features)
+
+
+@pytest.mark.parametrize(
+    "estimator", [LinearRegression(), GradientBoostingClassifier(random_state=0)]
+)
+def test_partial_dependence_X_list(estimator):
+    # check that array-like objects are accepted
+    X, y = make_classification(random_state=0)
+    estimator = clone(estimator).fit(X, y)
+    partial_dependence(estimator, list(X), [0], kind="average")
+
+
+def test_warning_recursion_non_constant_init():
+    # make sure that passing a non-constant init parameter to a GBDT and using
+    # recursion method yields a warning.
+
+    gbc = GradientBoostingClassifier(init=DummyClassifier(), random_state=0)
+    gbc.fit(X, y)
+
+    with pytest.warns(
+        UserWarning, match="Using recursion method with a non-constant init predictor"
+    ):
+        partial_dependence(gbc, X, [0], method="recursion", kind="average")
+
+    with pytest.warns(
+        UserWarning, match="Using recursion method with a non-constant init predictor"
+    ):
+        partial_dependence(gbc, X, [0], method="recursion", kind="average")
+
+
+def test_partial_dependence_sample_weight_of_fitted_estimator():
+    # Test near perfect correlation between partial dependence and diagonal
+    # when sample weights emphasize y = x predictions
+    # non-regression test for #13193
+    # TODO: extend to HistGradientBoosting once sample_weight is supported
+    N = 1000
+    rng = np.random.RandomState(123456)
+    mask = rng.randint(2, size=N, dtype=bool)
+
+    x = rng.rand(N)
+    # set y = x on mask and y = -x outside
+    y = x.copy()
+    y[~mask] = -y[~mask]
+    X = np.c_[mask, x]
+    # sample weights to emphasize data points where y = x
+    sample_weight = np.ones(N)
+    sample_weight[mask] = 1000.0
+
+    clf = GradientBoostingRegressor(n_estimators=10, random_state=1)
+    clf.fit(X, y, sample_weight=sample_weight)
+
+    pdp = partial_dependence(clf, X, features=[1], kind="average")
+
+    assert np.corrcoef(pdp["average"], pdp["grid_values"])[0, 1] > 0.99
+
+
+def test_hist_gbdt_sw_not_supported():
+    # TODO: remove/fix when PDP supports HGBT with sample weights
+    clf = HistGradientBoostingRegressor(random_state=1)
+    clf.fit(X, y, sample_weight=np.ones(len(X)))
+
+    with pytest.raises(
+        NotImplementedError, match="does not support partial dependence"
+    ):
+        partial_dependence(clf, X, features=[1])
+
+
+def test_partial_dependence_pipeline():
+    # check that the partial dependence support pipeline
+    iris = load_iris()
+
+    scaler = StandardScaler()
+    clf = DummyClassifier(random_state=42)
+    pipe = make_pipeline(scaler, clf)
+
+    clf.fit(scaler.fit_transform(iris.data), iris.target)
+    pipe.fit(iris.data, iris.target)
+
+    features = 0
+    pdp_pipe = partial_dependence(
+        pipe, iris.data, features=[features], grid_resolution=10, kind="average"
+    )
+    pdp_clf = partial_dependence(
+        clf,
+        scaler.transform(iris.data),
+        features=[features],
+        grid_resolution=10,
+        kind="average",
+    )
+    assert_allclose(pdp_pipe["average"], pdp_clf["average"])
+    assert_allclose(
+        pdp_pipe["grid_values"][0],
+        pdp_clf["grid_values"][0] * scaler.scale_[features] + scaler.mean_[features],
+    )
+
+
+@pytest.mark.parametrize(
+    "features, grid_resolution, n_vals_expected",
+    [
+        (["a"], 10, 10),
+        (["a"], 2, 2),
+    ],
+)
+def test_partial_dependence_binary_model_grid_resolution(
+    features, grid_resolution, n_vals_expected
+):
+    pd = pytest.importorskip("pandas")
+    model = DummyClassifier()
+
+    rng = np.random.RandomState(0)
+    X = pd.DataFrame(
+        {
+            "a": rng.randint(0, 10, size=100).astype(np.float64),
+            "b": rng.randint(0, 10, size=100).astype(np.float64),
+        }
+    )
+    y = pd.Series(rng.randint(0, 2, size=100))
+    model.fit(X, y)
+
+    part_dep = partial_dependence(
+        model,
+        X,
+        features=features,
+        grid_resolution=grid_resolution,
+        kind="average",
+    )
+    assert part_dep["average"].size == n_vals_expected
+
+
+@pytest.mark.parametrize(
+    "features, custom_values, n_vals_expected",
+    [
+        (["a"], {"a": [1.0, 2.0, 3.0, 4.0]}, 4),
+        (["a"], {"a": [1.0, 2.0]}, 2),
+        (["a"], {"a": [1.0]}, 1),
+    ],
+)
+def test_partial_dependence_binary_model_custom_values(
+    features, custom_values, n_vals_expected
+):
+    pd = pytest.importorskip("pandas")
+    model = DummyClassifier()
+
+    X = pd.DataFrame({"a": [1.0, 2.0, 3.0, 4.0], "b": [6.0, 7.0, 8.0, 9.0]})
+    y = pd.Series([0, 1, 0, 1])
+    model.fit(X, y)
+
+    part_dep = partial_dependence(
+        model,
+        X,
+        features=features,
+        grid_resolution=3,
+        custom_values=custom_values,
+        kind="average",
+    )
+    assert part_dep["average"].size == n_vals_expected
+
+
+@pytest.mark.parametrize(
+    "features, custom_values, n_vals_expected",
+    [
+        (["b"], {"b": ["a", "b"]}, 2),
+        (["b"], {"b": ["a"]}, 1),
+        (["a", "b"], {"a": [1.0, 2.0], "b": ["a", "b"]}, 4),
+    ],
+)
+def test_partial_dependence_pipeline_custom_values(
+    features, custom_values, n_vals_expected
+):
+    pd = pytest.importorskip("pandas")
+    pl = make_pipeline(
+        SimpleImputer(strategy="most_frequent"), OneHotEncoder(), DummyClassifier()
+    )
+
+    X = pd.DataFrame({"a": [1.0, 2.0, 3.0, 4.0], "b": ["a", "b", "a", "b"]})
+    y = pd.Series([0, 1, 0, 1])
+    pl.fit(X, y)
+
+    X_holdout = pd.DataFrame({"a": [1.0, 2.0, 3.0, 4.0], "b": ["a", "b", "a", None]})
+    part_dep = partial_dependence(
+        pl,
+        X_holdout,
+        features=features,
+        grid_resolution=3,
+        custom_values=custom_values,
+        kind="average",
+    )
+    assert part_dep["average"].size == n_vals_expected
+
+
+@pytest.mark.parametrize(
+    "estimator",
+    [
+        LogisticRegression(max_iter=1000, random_state=0),
+        GradientBoostingClassifier(random_state=0, n_estimators=5),
+    ],
+    ids=["estimator-brute", "estimator-recursion"],
+)
+@pytest.mark.parametrize(
+    "preprocessor",
+    [
+        None,
+        make_column_transformer(
+            (StandardScaler(), [iris.feature_names[i] for i in (0, 2)]),
+            (RobustScaler(), [iris.feature_names[i] for i in (1, 3)]),
+        ),
+        make_column_transformer(
+            (StandardScaler(), [iris.feature_names[i] for i in (0, 2)]),
+            remainder="passthrough",
+        ),
+    ],
+    ids=["None", "column-transformer", "column-transformer-passthrough"],
+)
+@pytest.mark.parametrize(
+    "features",
+    [[0, 2], [iris.feature_names[i] for i in (0, 2)]],
+    ids=["features-integer", "features-string"],
+)
+def test_partial_dependence_dataframe(estimator, preprocessor, features):
+    # check that the partial dependence support dataframe and pipeline
+    # including a column transformer
+    pd = pytest.importorskip("pandas")
+    df = pd.DataFrame(scale(iris.data), columns=iris.feature_names)
+
+    pipe = make_pipeline(preprocessor, clone(estimator))
+    pipe.fit(df, iris.target)
+    pdp_pipe = partial_dependence(
+        pipe, df, features=features, grid_resolution=10, kind="average"
+    )
+
+    # the column transformer will reorder the column when transforming
+    # we mixed the index to be sure that we are computing the partial
+    # dependence of the right columns
+    if preprocessor is not None:
+        X_proc = clone(preprocessor).fit_transform(df)
+        features_clf = [0, 1]
+    else:
+        X_proc = df
+        features_clf = [0, 2]
+
+    clf = clone(estimator).fit(X_proc, iris.target)
+    pdp_clf = partial_dependence(
+        clf,
+        X_proc,
+        features=features_clf,
+        method="brute",
+        grid_resolution=10,
+        kind="average",
+    )
+
+    assert_allclose(pdp_pipe["average"], pdp_clf["average"])
+    if preprocessor is not None:
+        scaler = preprocessor.named_transformers_["standardscaler"]
+        assert_allclose(
+            pdp_pipe["grid_values"][1],
+            pdp_clf["grid_values"][1] * scaler.scale_[1] + scaler.mean_[1],
+        )
+    else:
+        assert_allclose(pdp_pipe["grid_values"][1], pdp_clf["grid_values"][1])
+
+
+@pytest.mark.parametrize(
+    "features, custom_values, expected_pd_shape",
+    [
+        (0, None, (3, 10)),
+        (0, {0: [1.0, 2.0, 3.0]}, (3, 3)),
+        (iris.feature_names[0], None, (3, 10)),
+        (iris.feature_names[0], {iris.feature_names[0]: np.array([1.0, 2.0])}, (3, 2)),
+        ([0, 2], None, (3, 10, 10)),
+        ([0, 2], {2: [7, 8, 9, 10]}, (3, 10, 4)),
+        ([iris.feature_names[i] for i in (0, 2)], None, (3, 10, 10)),
+        (
+            [iris.feature_names[i] for i in (0, 2)],
+            {iris.feature_names[2]: [1, 2, 3, 10]},
+            (3, 10, 4),
+        ),
+        ([iris.feature_names[i] for i in (0, 2)], {2: [1, 2, 3, 10]}, (3, 10, 10)),
+        (
+            [iris.feature_names[i] for i in (0, 2, 3)],
+            {iris.feature_names[2]: [1, 10]},
+            (3, 10, 2, 10),
+        ),
+        ([True, False, True, False], None, (3, 10, 10)),
+    ],
+    ids=[
+        "scalar-int",
+        "scalar-int-custom-values",
+        "scalar-str",
+        "scalar-str-custom-values",
+        "list-int",
+        "list-int-custom-values",
+        "list-str",
+        "list-str-custom-values",
+        "list-str-custom-values-incorrect",
+        "list-str-three-features",
+        "mask",
+    ],
+)
+def test_partial_dependence_feature_type(features, custom_values, expected_pd_shape):
+    # check all possible features type supported in PDP
+    pd = pytest.importorskip("pandas")
+    df = pd.DataFrame(iris.data, columns=iris.feature_names)
+
+    preprocessor = make_column_transformer(
+        (StandardScaler(), [iris.feature_names[i] for i in (0, 2)]),
+        (RobustScaler(), [iris.feature_names[i] for i in (1, 3)]),
+    )
+    pipe = make_pipeline(
+        preprocessor, LogisticRegression(max_iter=1000, random_state=0)
+    )
+    pipe.fit(df, iris.target)
+    pdp_pipe = partial_dependence(
+        pipe,
+        df,
+        features=features,
+        grid_resolution=10,
+        kind="average",
+        custom_values=custom_values,
+    )
+    assert pdp_pipe["average"].shape == expected_pd_shape
+    assert len(pdp_pipe["grid_values"]) == len(pdp_pipe["average"].shape) - 1
+
+
+@pytest.mark.parametrize(
+    "estimator",
+    [
+        LinearRegression(),
+        LogisticRegression(),
+        GradientBoostingRegressor(),
+        GradientBoostingClassifier(),
+    ],
+)
+def test_partial_dependence_unfitted(estimator):
+    X = iris.data
+    preprocessor = make_column_transformer(
+        (StandardScaler(), [0, 2]), (RobustScaler(), [1, 3])
+    )
+    pipe = make_pipeline(preprocessor, estimator)
+    with pytest.raises(NotFittedError, match="is not fitted yet"):
+        partial_dependence(pipe, X, features=[0, 2], grid_resolution=10)
+    with pytest.raises(NotFittedError, match="is not fitted yet"):
+        partial_dependence(estimator, X, features=[0, 2], grid_resolution=10)
+
+
+@pytest.mark.parametrize(
+    "Estimator, data",
+    [
+        (LinearRegression, multioutput_regression_data),
+        (LogisticRegression, binary_classification_data),
+    ],
+)
+def test_kind_average_and_average_of_individual(Estimator, data):
+    est = Estimator()
+    (X, y), n_targets = data
+    est.fit(X, y)
+
+    pdp_avg = partial_dependence(est, X=X, features=[1, 2], kind="average")
+    pdp_ind = partial_dependence(est, X=X, features=[1, 2], kind="individual")
+    avg_ind = np.mean(pdp_ind["individual"], axis=1)
+    assert_allclose(avg_ind, pdp_avg["average"])
+
+
+@pytest.mark.parametrize(
+    "Estimator, data",
+    [
+        (LinearRegression, multioutput_regression_data),
+        (LogisticRegression, binary_classification_data),
+    ],
+)
+def test_partial_dependence_kind_individual_ignores_sample_weight(Estimator, data):
+    """Check that `sample_weight` does not have any effect on reported ICE."""
+    est = Estimator()
+    (X, y), n_targets = data
+    sample_weight = np.arange(X.shape[0])
+    est.fit(X, y)
+
+    pdp_nsw = partial_dependence(est, X=X, features=[1, 2], kind="individual")
+    pdp_sw = partial_dependence(
+        est, X=X, features=[1, 2], kind="individual", sample_weight=sample_weight
+    )
+    assert_allclose(pdp_nsw["individual"], pdp_sw["individual"])
+    assert_allclose(pdp_nsw["grid_values"], pdp_sw["grid_values"])
+
+
+@pytest.mark.parametrize(
+    "estimator",
+    [
+        LinearRegression(),
+        LogisticRegression(),
+        RandomForestRegressor(),
+        GradientBoostingClassifier(),
+    ],
+)
+@pytest.mark.parametrize("non_null_weight_idx", [0, 1, -1])
+def test_partial_dependence_non_null_weight_idx(estimator, non_null_weight_idx):
+    """Check that if we pass a `sample_weight` of zeros with only one index with
+    sample weight equals one, then the average `partial_dependence` with this
+    `sample_weight` is equal to the individual `partial_dependence` of the
+    corresponding index.
+    """
+    X, y = iris.data, iris.target
+    preprocessor = make_column_transformer(
+        (StandardScaler(), [0, 2]), (RobustScaler(), [1, 3])
+    )
+    pipe = make_pipeline(preprocessor, clone(estimator)).fit(X, y)
+
+    sample_weight = np.zeros_like(y)
+    sample_weight[non_null_weight_idx] = 1
+    pdp_sw = partial_dependence(
+        pipe,
+        X,
+        [2, 3],
+        kind="average",
+        sample_weight=sample_weight,
+        grid_resolution=10,
+    )
+    pdp_ind = partial_dependence(pipe, X, [2, 3], kind="individual", grid_resolution=10)
+    output_dim = 1 if is_regressor(pipe) else len(np.unique(y))
+    for i in range(output_dim):
+        assert_allclose(
+            pdp_ind["individual"][i][non_null_weight_idx],
+            pdp_sw["average"][i],
+        )
+
+
+@pytest.mark.parametrize(
+    "Estimator, data",
+    [
+        (LinearRegression, multioutput_regression_data),
+        (LogisticRegression, binary_classification_data),
+    ],
+)
+def test_partial_dependence_equivalence_equal_sample_weight(Estimator, data):
+    """Check that `sample_weight=None` is equivalent to having equal weights."""
+
+    est = Estimator()
+    (X, y), n_targets = data
+    est.fit(X, y)
+
+    sample_weight, params = None, {"X": X, "features": [1, 2], "kind": "average"}
+    pdp_sw_none = partial_dependence(est, **params, sample_weight=sample_weight)
+    sample_weight = np.ones(len(y))
+    pdp_sw_unit = partial_dependence(est, **params, sample_weight=sample_weight)
+    assert_allclose(pdp_sw_none["average"], pdp_sw_unit["average"])
+    sample_weight = 2 * np.ones(len(y))
+    pdp_sw_doubling = partial_dependence(est, **params, sample_weight=sample_weight)
+    assert_allclose(pdp_sw_none["average"], pdp_sw_doubling["average"])
+
+
+def test_partial_dependence_sample_weight_size_error():
+    """Check that we raise an error when the size of `sample_weight` is not
+    consistent with `X` and `y`.
+    """
+    est = LogisticRegression()
+    (X, y), n_targets = binary_classification_data
+    sample_weight = np.ones_like(y)
+    est.fit(X, y)
+
+    with pytest.raises(ValueError, match="sample_weight.shape =="):
+        partial_dependence(
+            est, X, features=[0], sample_weight=sample_weight[1:], grid_resolution=10
+        )
+
+
+def test_partial_dependence_sample_weight_with_recursion():
+    """Check that we raise an error when `sample_weight` is provided with
+    `"recursion"` method.
+    """
+    est = RandomForestRegressor()
+    (X, y), n_targets = regression_data
+    sample_weight = np.ones_like(y)
+    est.fit(X, y, sample_weight=sample_weight)
+
+    with pytest.raises(ValueError, match="'recursion' method can only be applied when"):
+        partial_dependence(
+            est, X, features=[0], method="recursion", sample_weight=sample_weight
+        )
+
+
+def test_mixed_type_categorical():
+    """Check that we raise a proper error when a column has mixed types and
+    the sorting of `np.unique` will fail."""
+    X = np.array(["A", "B", "C", np.nan], dtype=object).reshape(-1, 1)
+    y = np.array([0, 1, 0, 1])
+
+    from sklearn.preprocessing import OrdinalEncoder
+
+    clf = make_pipeline(
+        OrdinalEncoder(encoded_missing_value=-1),
+        LogisticRegression(),
+    ).fit(X, y)
+    with pytest.raises(ValueError, match="The column #0 contains mixed data types"):
+        partial_dependence(clf, X, features=[0])
+
+
+def test_reject_array_with_integer_dtype():
+    X = np.arange(8).reshape(4, 2)
+    y = np.array([0, 1, 0, 1])
+    clf = DummyClassifier()
+    clf.fit(X, y)
+    with pytest.warns(
+        FutureWarning, match=re.escape("The column 0 contains integer data.")
+    ):
+        partial_dependence(clf, X, features=0)
+
+    with pytest.warns(
+        FutureWarning, match=re.escape("The column 1 contains integer data.")
+    ):
+        partial_dependence(clf, X, features=[1], categorical_features=[0])
+
+    with pytest.warns(
+        FutureWarning, match=re.escape("The column 0 contains integer data.")
+    ):
+        partial_dependence(clf, X, features=[0, 1])
+
+    # The following should not raise as we do not compute numerical partial
+    # dependence on integer columns.
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        partial_dependence(clf, X, features=1, categorical_features=[1])
+
+
+def test_reject_pandas_with_integer_dtype():
+    pd = pytest.importorskip("pandas")
+    X = pd.DataFrame(
+        {
+            "a": [1.0, 2.0, 3.0],
+            "b": [1, 2, 3],
+            "c": [1, 2, 3],
+        }
+    )
+    y = np.array([0, 1, 0])
+    clf = DummyClassifier()
+    clf.fit(X, y)
+
+    with pytest.warns(
+        FutureWarning, match=re.escape("The column 'c' contains integer data.")
+    ):
+        partial_dependence(clf, X, features="c")
+
+    with pytest.warns(
+        FutureWarning, match=re.escape("The column 'c' contains integer data.")
+    ):
+        partial_dependence(clf, X, features=["a", "c"])
+
+    # The following should not raise as we do not compute numerical partial
+    # dependence on integer columns.
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        partial_dependence(clf, X, features=["a"])
+        partial_dependence(clf, X, features=["c"], categorical_features=["c"])
+
+
+def test_partial_dependence_empty_categorical_features():
+    """Check that we raise the proper exception when `categorical_features`
+    is an empty list"""
+    clf = make_pipeline(StandardScaler(), LogisticRegression())
+    clf.fit(iris.data, iris.target)
+
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            "Passing an empty list (`[]`) to `categorical_features` is not "
+            "supported. Use `None` instead to indicate that there are no "
+            "categorical features."
+        ),
+    ):
+        partial_dependence(
+            estimator=clf, X=iris.data, features=[0], categorical_features=[]
+        )
diff --git a/shared/package-files/ec/ec24e42273d76f9583a518018c1818892f99b5d1429aa07185bf2657a4b2efa9/_scaled_mm_native.h b/shared/package-files/ec/ec24e42273d76f9583a518018c1818892f99b5d1429aa07185bf2657a4b2efa9/_scaled_mm_native.h
new file mode 100644
index 0000000000000000000000000000000000000000..a42cbdedb9467141d58446f51906e56d28456882
--- /dev/null
+++ b/shared/package-files/ec/ec24e42273d76f9583a518018c1818892f99b5d1429aa07185bf2657a4b2efa9/_scaled_mm_native.h
@@ -0,0 +1,22 @@
+#pragma once
+
+// @generated by torchgen/gen.py from NativeFunction.h
+
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+#include 
+
+
+namespace at {
+namespace native {
+TORCH_API ::std::tuple _scaled_mm_cuda(const at::Tensor & self, const at::Tensor & mat2, const c10::optional & bias={}, c10::optional out_dtype=c10::nullopt, const c10::optional & scale_a={}, const c10::optional & scale_b={}, const c10::optional & scale_result={}, bool use_fast_accum=false);
+TORCH_API ::std::tuple _scaled_mm_out_cuda(const at::Tensor & self, const at::Tensor & mat2, const c10::optional & bias, c10::optional out_dtype, const c10::optional & scale_a, const c10::optional & scale_b, const c10::optional & scale_result, bool use_fast_accum, at::Tensor & out, at::Tensor & out_amax);
+} // namespace native
+} // namespace at
diff --git a/shared/package-files/ec/ec26b44c24ac6acceaef53039c268edbe32ac925a565cd82410aad41904868bd/mixins.pyi b/shared/package-files/ec/ec26b44c24ac6acceaef53039c268edbe32ac925a565cd82410aad41904868bd/mixins.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..50b726369af78cafc3fe4b09cc72399cdb9cc74e
--- /dev/null
+++ b/shared/package-files/ec/ec26b44c24ac6acceaef53039c268edbe32ac925a565cd82410aad41904868bd/mixins.pyi
@@ -0,0 +1,28 @@
+from typing import Any, Callable, List, Optional
+
+from django import http
+from django.http.response import HttpResponse, HttpResponseRedirect
+
+class AccessMixin:
+    login_url: Any = ...
+    permission_denied_message: str = ...
+    raise_exception: bool = ...
+    redirect_field_name: Any = ...
+    def get_login_url(self) -> str: ...
+    def get_permission_denied_message(self) -> str: ...
+    def get_redirect_field_name(self) -> str: ...
+    def handle_no_permission(self) -> HttpResponseRedirect: ...
+
+class LoginRequiredMixin(AccessMixin):
+    def dispatch(self, request: http.HttpRequest, *args: Any, **kwargs: Any) -> HttpResponse: ...
+
+class PermissionRequiredMixin(AccessMixin):
+    permission_required: Any = ...
+    def get_permission_required(self) -> List[str]: ...
+    def has_permission(self) -> bool: ...
+    def dispatch(self, request: http.HttpRequest, *args: Any, **kwargs: Any) -> HttpResponse: ...
+
+class UserPassesTestMixin(AccessMixin):
+    def test_func(self) -> Optional[bool]: ...
+    def get_test_func(self) -> Callable: ...
+    def dispatch(self, request: http.HttpRequest, *args: Any, **kwargs: Any) -> HttpResponse: ...
diff --git a/shared/package-files/ec/ec28fa34877e4be8913d9b4ee95022c890ea73358a9007a4128ef0ab02e04ad3/_huber.py b/shared/package-files/ec/ec28fa34877e4be8913d9b4ee95022c890ea73358a9007a4128ef0ab02e04ad3/_huber.py
new file mode 100644
index 0000000000000000000000000000000000000000..87e735ec998db226235f22f33477f50dc9e4152e
--- /dev/null
+++ b/shared/package-files/ec/ec28fa34877e4be8913d9b4ee95022c890ea73358a9007a4128ef0ab02e04ad3/_huber.py
@@ -0,0 +1,363 @@
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+from numbers import Integral, Real
+
+import numpy as np
+from scipy import optimize
+
+from ..base import BaseEstimator, RegressorMixin, _fit_context
+from ..utils._mask import axis0_safe_slice
+from ..utils._param_validation import Interval
+from ..utils.extmath import safe_sparse_dot
+from ..utils.fixes import _get_additional_lbfgs_options_dict
+from ..utils.optimize import _check_optimize_result
+from ..utils.validation import _check_sample_weight, validate_data
+from ._base import LinearModel
+
+
+def _huber_loss_and_gradient(w, X, y, epsilon, alpha, sample_weight=None):
+    """Returns the Huber loss and the gradient.
+
+    Parameters
+    ----------
+    w : ndarray, shape (n_features + 1,) or (n_features + 2,)
+        Feature vector.
+        w[:n_features] gives the coefficients
+        w[-1] gives the scale factor and if the intercept is fit w[-2]
+        gives the intercept factor.
+
+    X : ndarray of shape (n_samples, n_features)
+        Input data.
+
+    y : ndarray of shape (n_samples,)
+        Target vector.
+
+    epsilon : float
+        Robustness of the Huber estimator.
+
+    alpha : float
+        Regularization parameter.
+
+    sample_weight : ndarray of shape (n_samples,), default=None
+        Weight assigned to each sample.
+
+    Returns
+    -------
+    loss : float
+        Huber loss.
+
+    gradient : ndarray, shape (len(w))
+        Returns the derivative of the Huber loss with respect to each
+        coefficient, intercept and the scale as a vector.
+    """
+    _, n_features = X.shape
+    fit_intercept = n_features + 2 == w.shape[0]
+    if fit_intercept:
+        intercept = w[-2]
+    sigma = w[-1]
+    w = w[:n_features]
+    n_samples = np.sum(sample_weight)
+
+    # Calculate the values where |y - X'w -c / sigma| > epsilon
+    # The values above this threshold are outliers.
+    linear_loss = y - safe_sparse_dot(X, w)
+    if fit_intercept:
+        linear_loss -= intercept
+    abs_linear_loss = np.abs(linear_loss)
+    outliers_mask = abs_linear_loss > epsilon * sigma
+
+    # Calculate the linear loss due to the outliers.
+    # This is equal to (2 * M * |y - X'w -c / sigma| - M**2) * sigma
+    outliers = abs_linear_loss[outliers_mask]
+    num_outliers = np.count_nonzero(outliers_mask)
+    n_non_outliers = X.shape[0] - num_outliers
+
+    # n_sq_outliers includes the weight give to the outliers while
+    # num_outliers is just the number of outliers.
+    outliers_sw = sample_weight[outliers_mask]
+    n_sw_outliers = np.sum(outliers_sw)
+    outlier_loss = (
+        2.0 * epsilon * np.sum(outliers_sw * outliers)
+        - sigma * n_sw_outliers * epsilon**2
+    )
+
+    # Calculate the quadratic loss due to the non-outliers.-
+    # This is equal to |(y - X'w - c)**2 / sigma**2| * sigma
+    non_outliers = linear_loss[~outliers_mask]
+    weighted_non_outliers = sample_weight[~outliers_mask] * non_outliers
+    weighted_loss = np.dot(weighted_non_outliers.T, non_outliers)
+    squared_loss = weighted_loss / sigma
+
+    if fit_intercept:
+        grad = np.zeros(n_features + 2)
+    else:
+        grad = np.zeros(n_features + 1)
+
+    # Gradient due to the squared loss.
+    X_non_outliers = -axis0_safe_slice(X, ~outliers_mask, n_non_outliers)
+    grad[:n_features] = (
+        2.0 / sigma * safe_sparse_dot(weighted_non_outliers, X_non_outliers)
+    )
+
+    # Gradient due to the linear loss.
+    signed_outliers = np.ones_like(outliers)
+    signed_outliers_mask = linear_loss[outliers_mask] < 0
+    signed_outliers[signed_outliers_mask] = -1.0
+    X_outliers = axis0_safe_slice(X, outliers_mask, num_outliers)
+    sw_outliers = sample_weight[outliers_mask] * signed_outliers
+    grad[:n_features] -= 2.0 * epsilon * (safe_sparse_dot(sw_outliers, X_outliers))
+
+    # Gradient due to the penalty.
+    grad[:n_features] += alpha * 2.0 * w
+
+    # Gradient due to sigma.
+    grad[-1] = n_samples
+    grad[-1] -= n_sw_outliers * epsilon**2
+    grad[-1] -= squared_loss / sigma
+
+    # Gradient due to the intercept.
+    if fit_intercept:
+        grad[-2] = -2.0 * np.sum(weighted_non_outliers) / sigma
+        grad[-2] -= 2.0 * epsilon * np.sum(sw_outliers)
+
+    loss = n_samples * sigma + squared_loss + outlier_loss
+    loss += alpha * np.dot(w, w)
+    return loss, grad
+
+
+class HuberRegressor(LinearModel, RegressorMixin, BaseEstimator):
+    """L2-regularized linear regression model that is robust to outliers.
+
+    The Huber Regressor optimizes the squared loss for the samples where
+    ``|(y - Xw - c) / sigma| < epsilon`` and the absolute loss for the samples
+    where ``|(y - Xw - c) / sigma| > epsilon``, where the model coefficients
+    ``w``, the intercept ``c`` and the scale ``sigma`` are parameters
+    to be optimized. The parameter `sigma` makes sure that if `y` is scaled up
+    or down by a certain factor, one does not need to rescale `epsilon` to
+    achieve the same robustness. Note that this does not take into account
+    the fact that the different features of `X` may be of different scales.
+
+    The Huber loss function has the advantage of not being heavily influenced
+    by the outliers while not completely ignoring their effect.
+
+    Read more in the :ref:`User Guide `
+
+    .. versionadded:: 0.18
+
+    Parameters
+    ----------
+    epsilon : float, default=1.35
+        The parameter epsilon controls the number of samples that should be
+        classified as outliers. The smaller the epsilon, the more robust it is
+        to outliers. Epsilon must be in the range `[1, inf)`.
+
+    max_iter : int, default=100
+        Maximum number of iterations that
+        ``scipy.optimize.minimize(method="L-BFGS-B")`` should run for.
+
+    alpha : float, default=0.0001
+        Strength of the squared L2 regularization. Note that the penalty is
+        equal to ``alpha * ||w||^2``.
+        Must be in the range `[0, inf)`.
+
+    warm_start : bool, default=False
+        This is useful if the stored attributes of a previously used model
+        has to be reused. If set to False, then the coefficients will
+        be rewritten for every call to fit.
+        See :term:`the Glossary `.
+
+    fit_intercept : bool, default=True
+        Whether or not to fit the intercept. This can be set to False
+        if the data is already centered around the origin.
+
+    tol : float, default=1e-05
+        The iteration will stop when
+        ``max{|proj g_i | i = 1, ..., n}`` <= ``tol``
+        where pg_i is the i-th component of the projected gradient.
+
+    Attributes
+    ----------
+    coef_ : array, shape (n_features,)
+        Features got by optimizing the L2-regularized Huber loss.
+
+    intercept_ : float
+        Bias.
+
+    scale_ : float
+        The value by which ``|y - Xw - c|`` is scaled down.
+
+    n_features_in_ : int
+        Number of features seen during :term:`fit`.
+
+        .. versionadded:: 0.24
+
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
+    n_iter_ : int
+        Number of iterations that
+        ``scipy.optimize.minimize(method="L-BFGS-B")`` has run for.
+
+        .. versionchanged:: 0.20
+
+            In SciPy <= 1.0.0 the number of lbfgs iterations may exceed
+            ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.
+
+    outliers_ : array, shape (n_samples,)
+        A boolean mask which is set to True where the samples are identified
+        as outliers.
+
+    See Also
+    --------
+    RANSACRegressor : RANSAC (RANdom SAmple Consensus) algorithm.
+    TheilSenRegressor : Theil-Sen Estimator robust multivariate regression model.
+    SGDRegressor : Fitted by minimizing a regularized empirical loss with SGD.
+
+    References
+    ----------
+    .. [1] Peter J. Huber, Elvezio M. Ronchetti, Robust Statistics
+           Concomitant scale estimates, p. 172
+    .. [2] Art B. Owen (2006), `A robust hybrid of lasso and ridge regression.
+           `_
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.linear_model import HuberRegressor, LinearRegression
+    >>> from sklearn.datasets import make_regression
+    >>> rng = np.random.RandomState(0)
+    >>> X, y, coef = make_regression(
+    ...     n_samples=200, n_features=2, noise=4.0, coef=True, random_state=0)
+    >>> X[:4] = rng.uniform(10, 20, (4, 2))
+    >>> y[:4] = rng.uniform(10, 20, 4)
+    >>> huber = HuberRegressor().fit(X, y)
+    >>> huber.score(X, y)
+    -7.284
+    >>> huber.predict(X[:1,])
+    array([806.7200])
+    >>> linear = LinearRegression().fit(X, y)
+    >>> print("True coefficients:", coef)
+    True coefficients: [20.4923...  34.1698...]
+    >>> print("Huber coefficients:", huber.coef_)
+    Huber coefficients: [17.7906... 31.0106...]
+    >>> print("Linear Regression coefficients:", linear.coef_)
+    Linear Regression coefficients: [-1.9221...  7.0226...]
+    """
+
+    _parameter_constraints: dict = {
+        "epsilon": [Interval(Real, 1.0, None, closed="left")],
+        "max_iter": [Interval(Integral, 0, None, closed="left")],
+        "alpha": [Interval(Real, 0, None, closed="left")],
+        "warm_start": ["boolean"],
+        "fit_intercept": ["boolean"],
+        "tol": [Interval(Real, 0.0, None, closed="left")],
+    }
+
+    def __init__(
+        self,
+        *,
+        epsilon=1.35,
+        max_iter=100,
+        alpha=0.0001,
+        warm_start=False,
+        fit_intercept=True,
+        tol=1e-05,
+    ):
+        self.epsilon = epsilon
+        self.max_iter = max_iter
+        self.alpha = alpha
+        self.warm_start = warm_start
+        self.fit_intercept = fit_intercept
+        self.tol = tol
+
+    @_fit_context(prefer_skip_nested_validation=True)
+    def fit(self, X, y, sample_weight=None):
+        """Fit the model according to the given training data.
+
+        Parameters
+        ----------
+        X : array-like, shape (n_samples, n_features)
+            Training vector, where `n_samples` is the number of samples and
+            `n_features` is the number of features.
+
+        y : array-like, shape (n_samples,)
+            Target vector relative to X.
+
+        sample_weight : array-like, shape (n_samples,)
+            Weight given to each sample.
+
+        Returns
+        -------
+        self : object
+            Fitted `HuberRegressor` estimator.
+        """
+        X, y = validate_data(
+            self,
+            X,
+            y,
+            copy=False,
+            accept_sparse=["csr"],
+            y_numeric=True,
+            dtype=[np.float64, np.float32],
+        )
+
+        sample_weight = _check_sample_weight(sample_weight, X)
+
+        if self.warm_start and hasattr(self, "coef_"):
+            parameters = np.concatenate((self.coef_, [self.intercept_, self.scale_]))
+        else:
+            if self.fit_intercept:
+                parameters = np.zeros(X.shape[1] + 2)
+            else:
+                parameters = np.zeros(X.shape[1] + 1)
+            # Make sure to initialize the scale parameter to a strictly
+            # positive value:
+            parameters[-1] = 1
+
+        # Sigma or the scale factor should be non-negative.
+        # Setting it to be zero might cause undefined bounds hence we set it
+        # to a value close to zero.
+        bounds = np.tile([-np.inf, np.inf], (parameters.shape[0], 1))
+        bounds[-1][0] = np.finfo(np.float64).eps * 10
+
+        opt_res = optimize.minimize(
+            _huber_loss_and_gradient,
+            parameters,
+            method="L-BFGS-B",
+            jac=True,
+            args=(X, y, self.epsilon, self.alpha, sample_weight),
+            options={
+                "maxiter": self.max_iter,
+                "gtol": self.tol,
+                **_get_additional_lbfgs_options_dict("iprint", -1),
+            },
+            bounds=bounds,
+        )
+
+        parameters = opt_res.x
+
+        if opt_res.status == 2:
+            raise ValueError(
+                "HuberRegressor convergence failed: l-BFGS-b solver terminated with %s"
+                % opt_res.message
+            )
+        self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)
+        self.scale_ = parameters[-1]
+        if self.fit_intercept:
+            self.intercept_ = parameters[-2]
+        else:
+            self.intercept_ = 0.0
+        self.coef_ = parameters[: X.shape[1]]
+
+        residual = np.abs(y - safe_sparse_dot(X, self.coef_) - self.intercept_)
+        self.outliers_ = residual > self.scale_ * self.epsilon
+        return self
+
+    def __sklearn_tags__(self):
+        tags = super().__sklearn_tags__()
+        tags.input_tags.sparse = True
+        return tags
diff --git a/shared/package-files/ec/ec293df474be16d0a69c10df092c68c1a34568406b4795f9b9342a0b3c0f0b5d/_duration.py b/shared/package-files/ec/ec293df474be16d0a69c10df092c68c1a34568406b4795f9b9342a0b3c0f0b5d/_duration.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9350cf41cc4e21dcc41eb7f6a1d60253875e2eb
--- /dev/null
+++ b/shared/package-files/ec/ec293df474be16d0a69c10df092c68c1a34568406b4795f9b9342a0b3c0f0b5d/_duration.py
@@ -0,0 +1,92 @@
+"""Tools for working with the Polars duration string language."""
+
+from __future__ import annotations
+
+import datetime as dt
+import re
+from typing import TYPE_CHECKING, Literal, cast, get_args
+
+if TYPE_CHECKING:
+    from collections.abc import Container, Mapping
+    from typing import TypeAlias
+
+__all__ = ["IntervalUnit"]
+
+IntervalUnit: TypeAlias = Literal["ns", "us", "ms", "s", "m", "h", "d", "mo", "q", "y"]
+"""A Polars duration string interval unit.
+
+- 'ns': nanosecond.
+- 'us': microsecond.
+- 'ms': millisecond.
+- 's': second.
+- 'm': minute.
+- 'h': hour.
+- 'd': day.
+- 'mo': month.
+- 'q': quarter.
+- 'y': year.
+"""
+TimedeltaKwd: TypeAlias = Literal[
+    "days", "hours", "minutes", "seconds", "milliseconds", "microseconds"
+]
+
+PATTERN_INTERVAL: re.Pattern[str] = re.compile(
+    r"^(?P-?\d+)(?Pns|us|ms|mo|m|s|h|d|q|y)\Z"
+)
+MONTH_MULTIPLES = frozenset([1, 2, 3, 4, 6, 12])
+QUARTER_MULTIPLES = frozenset([1, 2, 4])
+UNIT_TO_TIMEDELTA: Mapping[IntervalUnit, TimedeltaKwd] = {
+    "d": "days",
+    "h": "hours",
+    "m": "minutes",
+    "s": "seconds",
+    "ms": "milliseconds",
+    "us": "microseconds",
+}
+
+
+class Interval:
+    def __init__(self, multiple: int, unit: IntervalUnit, /) -> None:
+        self.multiple: int = multiple
+        self.unit: IntervalUnit = unit
+
+    def to_timedelta(
+        self, *, unsupported: Container[IntervalUnit] = frozenset(("ns", "mo", "q", "y"))
+    ) -> dt.timedelta:
+        if self.unit in unsupported:  # pragma: no cover
+            msg = f"Creating timedelta with {self.unit} unit is not supported."
+            raise NotImplementedError(msg)
+        kwd = UNIT_TO_TIMEDELTA[self.unit]
+        return dt.timedelta(**{kwd: self.multiple})
+
+    @classmethod
+    def parse(cls, every: str) -> Interval:
+        multiple, unit = cls._parse(every)
+        if unit == "mo" and multiple not in MONTH_MULTIPLES:
+            msg = f"Only the following multiples are supported for 'mo' unit: {MONTH_MULTIPLES}.\nGot: {multiple}."
+            raise ValueError(msg)
+        if unit == "q" and multiple not in QUARTER_MULTIPLES:
+            msg = f"Only the following multiples are supported for 'q' unit: {QUARTER_MULTIPLES}.\nGot: {multiple}."
+            raise ValueError(msg)
+        if unit == "y" and multiple != 1:
+            msg = (
+                f"Only multiple 1 is currently supported for 'y' unit.\nGot: {multiple}."
+            )
+            raise ValueError(msg)
+        return cls(multiple, unit)
+
+    @classmethod
+    def parse_no_constraints(cls, every: str) -> Interval:
+        return cls(*cls._parse(every))
+
+    @staticmethod
+    def _parse(every: str) -> tuple[int, IntervalUnit]:
+        if match := PATTERN_INTERVAL.match(every):
+            multiple = int(match["multiple"])
+            unit = cast("IntervalUnit", match["unit"])
+            return multiple, unit
+        msg = (
+            f"Invalid `every` string: {every}. Expected string of kind , "
+            f"where 'unit' is one of: {get_args(IntervalUnit)}."
+        )
+        raise ValueError(msg)
diff --git a/shared/package-files/ec/ec2dc4133d4eb45b8bb65daf5f478555911f2eccaa7ead6c3a1b5920b2bfb5ba/reference-e177-report.json b/shared/package-files/ec/ec2dc4133d4eb45b8bb65daf5f478555911f2eccaa7ead6c3a1b5920b2bfb5ba/reference-e177-report.json
new file mode 100644
index 0000000000000000000000000000000000000000..84a72e82097ab2c2887d75207db6c70ec8efb4a5
--- /dev/null
+++ b/shared/package-files/ec/ec2dc4133d4eb45b8bb65daf5f478555911f2eccaa7ead6c3a1b5920b2bfb5ba/reference-e177-report.json
@@ -0,0 +1,136384 @@
+{
+  "schema": "natural-disease-progression-report/v3",
+  "reward_metric": "panel_recall_at_5pct",
+  "panel": {
+    "panel_recall_at_5pct": 0.31725792706461575,
+    "panel_recall_at_1pct": 0.0871174073099208,
+    "panel_ppv_at_5pct": 0.09956215695459547,
+    "panel_auroc": 0.8074421312783728,
+    "panel_auprc": 0.09928749565308599,
+    "panel_calibration_slope_median": 0.7103088232772341,
+    "panel_prediction_to_observed_median": 0.8796704328381382,
+    "panel_ece_decile": 0.007149670380517231,
+    "endpoints_in_reward": [
+      "I50",
+      "I48",
+      "I21",
+      "I26",
+      "I82",
+      "E11",
+      "N18",
+      "N17",
+      "A41",
+      "J18",
+      "J96",
+      "K72",
+      "J44",
+      "C78"
+    ],
+    "min_positives": 20,
+    "endpoints": {
+      "I50": {
+        "name": "heart failure",
+        "eligible": 2408,
+        "positives": 52,
+        "family_history_excluded": 7,
+        "in_reward": true,
+        "recall_at_5pct": 0.25,
+        "ppv_at_5pct": 0.10743801652892562,
+        "recall_at_1pct": 0.07692307692307693,
+        "ppv_at_1pct": 0.16,
+        "auroc": 0.8218052109181142,
+        "auprc": 0.10165579878590929,
+        "prevalence": 0.02159468438538206,
+        "calibration": {
+          "observed_rate": 0.02159468438538206,
+          "mean_prediction": 0.020140874905445135,
+          "prediction_to_observed": 0.9326774379290748,
+          "slope": 0.6110492440674338,
+          "ece_decile": 0.012756141703772584
+        }
+      },
+      "I48": {
+        "name": "atrial fibrillation/flutter",
+        "eligible": 2385,
+        "positives": 32,
+        "family_history_excluded": 0,
+        "in_reward": true,
+        "recall_at_5pct": 0.21875,
+        "ppv_at_5pct": 0.058333333333333334,
+        "recall_at_1pct": 0.0,
+        "ppv_at_1pct": 0.0,
+        "auroc": 0.7624707819804505,
+        "auprc": 0.03823638438694251,
+        "prevalence": 0.013417190775681341,
+        "calibration": {
+          "observed_rate": 0.013417190775681341,
+          "mean_prediction": 0.012453179725352334,
+          "prediction_to_observed": 0.9281510514051662,
+          "slope": 0.6866993137606869,
+          "ece_decile": 0.004851874887102002
+        }
+      },
+      "I21": {
+        "name": "acute myocardial infarction",
+        "eligible": 2671,
+        "positives": 23,
+        "family_history_excluded": 0,
+        "in_reward": true,
+        "recall_at_5pct": 0.30434782608695654,
+        "ppv_at_5pct": 0.05223880597014925,
+        "recall_at_1pct": 0.043478260869565216,
+        "ppv_at_1pct": 0.037037037037037035,
+        "auroc": 0.8119663733088138,
+        "auprc": 0.03749768983046738,
+        "prevalence": 0.008611007113440658,
+        "calibration": {
+          "observed_rate": 0.008611007113440658,
+          "mean_prediction": 0.012286682836584411,
+          "prediction_to_observed": 1.4268578198485637,
+          "slope": 0.7121203187599373,
+          "ece_decile": 0.006886446521250237
+        }
+      },
+      "I63": {
+        "name": "ischaemic stroke",
+        "eligible": 2662,
+        "positives": 14,
+        "family_history_excluded": 73,
+        "in_reward": false,
+        "recall_at_5pct": 0.14285714285714285,
+        "ppv_at_5pct": 0.014925373134328358,
+        "recall_at_1pct": 0.0,
+        "ppv_at_1pct": 0.0,
+        "auroc": 0.7224859732412603,
+        "auprc": 0.012844067274134636,
+        "prevalence": 0.005259203606311044,
+        "calibration": {
+          "observed_rate": 0.005259203606311044,
+          "mean_prediction": 0.0042564507653129625,
+          "prediction_to_observed": 0.8093337098045076,
+          "slope": 0.5274808861512388,
+          "ece_decile": 0.003975739864118458
+        }
+      },
+      "I61": {
+        "name": "intracerebral haemorrhage",
+        "eligible": 2802,
+        "positives": 9,
+        "family_history_excluded": 0,
+        "in_reward": false,
+        "recall_at_5pct": 0.3333333333333333,
+        "ppv_at_5pct": 0.02127659574468085,
+        "recall_at_1pct": 0.2222222222222222,
+        "ppv_at_1pct": 0.06896551724137931,
+        "auroc": 0.8719019771651351,
+        "auprc": 0.02974225916197339,
+        "prevalence": 0.0032119914346895075,
+        "calibration": {
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+          "mean_prediction": 0.0010951458762401107,
+          "prediction_to_observed": 0.3409554161360878,
+          "slope": 1.2719579218352035,
+          "ece_decile": 0.0025063210940755544
+        }
+      },
+      "I26": {
+        "name": "pulmonary embolism",
+        "eligible": 2760,
+        "positives": 20,
+        "family_history_excluded": 0,
+        "in_reward": true,
+        "recall_at_5pct": 0.4,
+        "ppv_at_5pct": 0.057971014492753624,
+        "recall_at_1pct": 0.15,
+        "ppv_at_1pct": 0.10714285714285714,
+        "auroc": 0.7649817518248175,
+        "auprc": 0.06746637464489078,
+        "prevalence": 0.007246376811594203,
+        "calibration": {
+          "observed_rate": 0.007246376811594203,
+          "mean_prediction": 0.006922103657621925,
+          "prediction_to_observed": 0.9552503047518257,
+          "slope": 0.8937359739327159,
+          "ece_decile": 0.002837595303861999
+        }
+      },
+      "I82": {
+        "name": "venous thrombosis",
+        "eligible": 2739,
+        "positives": 35,
+        "family_history_excluded": 0,
+        "in_reward": true,
+        "recall_at_5pct": 0.2571428571428571,
+        "ppv_at_5pct": 0.06569343065693431,
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+        "prevalence": 0.012778386272362175,
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+          "ece_decile": 0.0067270370261679705
+        }
+      },
+      "I71": {
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+        "in_reward": false,
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+          "slope": 0.5155787756080856,
+          "ece_decile": 0.0025649927238119334
+        }
+      },
+      "E11": {
+        "name": "type 2 diabetes",
+        "eligible": 2161,
+        "positives": 21,
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+          "slope": 0.5094360113910746,
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+        }
+      },
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+        }
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+        }
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+        }
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+        }
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+          "ece_decile": 0.0012061161106274902
+        }
+      },
+      "C18": {
+        "name": "colon cancer",
+        "eligible": 2824,
+        "positives": 2,
+        "family_history_excluded": 0,
+        "in_reward": false,
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+        "prevalence": 0.000708215297450425,
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+        }
+      },
+      "C22": {
+        "name": "liver cancer",
+        "eligible": 2812,
+        "positives": 7,
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+        "recall_at_5pct": 0.5714285714285714,
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diff --git a/shared/package-files/ec/ec2dc4133d4eb45b8bb65daf5f478555911f2eccaa7ead6c3a1b5920b2bfb5ba/report.json b/shared/package-files/ec/ec2dc4133d4eb45b8bb65daf5f478555911f2eccaa7ead6c3a1b5920b2bfb5ba/report.json
new file mode 100644
index 0000000000000000000000000000000000000000..84a72e82097ab2c2887d75207db6c70ec8efb4a5
--- /dev/null
+++ b/shared/package-files/ec/ec2dc4133d4eb45b8bb65daf5f478555911f2eccaa7ead6c3a1b5920b2bfb5ba/report.json
@@ -0,0 +1,136384 @@
+{
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+}
diff --git a/shared/package-files/ec/ec2f648f29b943c716365c68cd01147d3de11fff581e7d8e0de19da8e63f7e23/copulas.py b/shared/package-files/ec/ec2f648f29b943c716365c68cd01147d3de11fff581e7d8e0de19da8e63f7e23/copulas.py
new file mode 100644
index 0000000000000000000000000000000000000000..164b038826376b17c827a8c2e3340ca7dc707be8
--- /dev/null
+++ b/shared/package-files/ec/ec2f648f29b943c716365c68cd01147d3de11fff581e7d8e0de19da8e63f7e23/copulas.py
@@ -0,0 +1,553 @@
+"""
+
+Which Archimedean is Best?
+Extreme Value copulas formulas are based on Genest 2009
+
+References
+----------
+
+Genest, C., 2009. Rank-based inference for bivariate extreme-value
+copulas. The Annals of Statistics, 37(5), pp.2990-3022.
+
+"""
+
+from statsmodels.compat.pandas import deprecate_kwarg
+
+from abc import ABC, abstractmethod
+
+import numpy as np
+from scipy import stats
+
+from statsmodels.graphics import utils
+from statsmodels.graphics.utils import _import_mpl
+
+
+class CopulaDistribution:
+    """Multivariate copula distribution
+
+    Parameters
+    ----------
+    copula : :class:`Copula` instance
+        An instance of :class:`Copula`, e.g., :class:`GaussianCopula`,
+        :class:`FrankCopula`, etc.
+    marginals : list of distribution instances
+        Marginal distributions.
+    cop_args : tuple, optional
+        Parameters for the copula.
+
+    Notes
+    -----
+    Status: experimental, argument handling may still change
+
+    """
+
+    def __init__(self, copula, marginals, cop_args=()):
+
+        self.copula = copula
+
+        # no checking done on marginals
+        self.marginals = marginals
+        self.cop_args = cop_args
+        self.k_vars = len(marginals)
+
+    @deprecate_kwarg("random_state", "rng")
+    def rvs(self, nobs=1, cop_args=None, marg_args=None, rng=None):
+        """Draw random samples from the joint (copula) distribution.
+
+        Parameters
+        ----------
+        nobs : int, optional
+            Number of samples to generate in the parameter space.
+            Default is 1.
+        cop_args : tuple, optional
+            Copula parameters. If None, then the copula parameters will be
+            taken from the ``cop_args`` attribute created when initiializing
+            the instance.
+        marg_args : list of tuples, optional
+            Parameters for the marginal distributions. It can be None if none
+            of the marginal distributions have parameters, otherwise it needs
+            to be a list of tuples with the same length has the number of
+            marginal distributions. The list can contain empty tuples for
+            marginal distributions that do not take parameter arguments.
+        rng : int, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional
+            If `rng` is None, a new ``Generator`` is created using fresh
+            entropy from the operating system. If `rng` is an int or array
+            of ints, a new ``Generator`` is created, seeded with `rng`. If
+            `rng` is already a ``Generator`` or ``RandomState`` instance,
+            that instance is used.
+        rng : int, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional
+            .. deprecated:: 0.15
+
+               random_state has been deprecated. In-line with SPEC-007, use
+               rng for passing a random number generator or seed.
+
+        Returns
+        -------
+        sample : ndarray of shape (nobs, k_vars)
+            Sample from the joint distribution, with marginals transformed
+            to the distributions in ``self.marginals``.
+
+        Notes
+        -----
+        The random samples are generated by creating a sample with uniform
+        margins from the copula, and using ``ppf`` to convert uniform margins
+        to the one specified by the marginal distribution.
+
+        See Also
+        --------
+        statsmodels.tools.rng_qrng.check_random_state
+        """
+        if cop_args is None:
+            cop_args = self.cop_args
+        if marg_args is None:
+            marg_args = [()] * self.k_vars
+
+        sample = self.copula.rvs(nobs=nobs, args=cop_args, rng=rng)
+
+        for i, dist in enumerate(self.marginals):
+            sample[:, i] = dist.ppf(
+                0.5 + (1 - 1e-10) * (sample[:, i] - 0.5), *marg_args[i]
+            )
+        return sample
+
+    def cdf(self, y, cop_args=None, marg_args=None):
+        """CDF of copula distribution.
+
+        Parameters
+        ----------
+        y : array_like
+            Values of random variable at which to evaluate cdf.
+            If 2-dimensional, then components of multivariate random variable
+            need to be in columns
+        cop_args : tuple, optional
+            Copula parameters. If None, then the copula parameters will be
+            taken from the ``cop_args`` attribute created when initiializing
+            the instance.
+        marg_args : list of tuples, optional
+            Parameters for the marginal distributions. It can be None if none
+            of the marginal distributions have parameters, otherwise it needs
+            to be a list of tuples with the same length has the number of
+            marginal distributions. The list can contain empty tuples for
+            marginal distributions that do not take parameter arguments.
+
+        Returns
+        -------
+        ndarray
+            CDF values evaluated at ``y``.
+
+        """
+        y = np.asarray(y)
+        if cop_args is None:
+            cop_args = self.cop_args
+        if marg_args is None:
+            marg_args = [()] * y.shape[-1]
+
+        cdf_marg = []
+        for i in range(self.k_vars):
+            cdf_marg.append(self.marginals[i].cdf(y[..., i], *marg_args[i]))
+
+        u = np.column_stack(cdf_marg)
+        if y.ndim == 1:
+            u = u.squeeze()
+        return self.copula.cdf(u, cop_args)
+
+    def pdf(self, y, cop_args=None, marg_args=None):
+        """PDF of copula distribution.
+
+        Parameters
+        ----------
+        y : array_like
+            Values of random variable at which to evaluate pdf.
+            If 2-dimensional, then components of multivariate random variable
+            need to be in columns
+        cop_args : tuple, optional
+            Copula parameters. If None, then the copula parameters will be
+            taken from the ``cop_args`` attribute created when initiializing
+            the instance.
+        marg_args : list of tuples, optional
+            Parameters for the marginal distributions. It can be None if none
+            of the marginal distributions have parameters, otherwise it needs
+            to be a list of tuples with the same length has the number of
+            marginal distributions. The list can contain empty tuples for
+            marginal distributions that do not take parameter arguments.
+
+        Returns
+        -------
+        ndarray
+            PDF values evaluated at ``y``.
+        """
+        return np.exp(self.logpdf(y, cop_args=cop_args, marg_args=marg_args))
+
+    def logpdf(self, y, cop_args=None, marg_args=None):
+        """Log-pdf of copula distribution.
+
+        Parameters
+        ----------
+        y : array_like
+            Values of random variable at which to evaluate log-pdf.
+            If 2-dimensional, then components of multivariate random variable
+            need to be in columns
+        cop_args : tuple, optional
+            Copula parameters. If None, then the copula parameters will be
+            taken from the ``cop_args`` attribute created when initiializing
+            the instance.
+        marg_args : list of tuples, optional
+            Parameters for the marginal distributions. It can be None if none
+            of the marginal distributions have parameters, otherwise it needs
+            to be a list of tuples with the same length has the number of
+            marginal distributions. The list can contain empty tuples for
+            marginal distributions that do not take parameter arguments.
+
+        Returns
+        -------
+        ndarray
+            Log-pdf values evaluated at ``y``.
+
+        """
+        y = np.asarray(y)
+        if cop_args is None:
+            cop_args = self.cop_args
+        if marg_args is None:
+            marg_args = tuple([()] * y.shape[-1])
+
+        lpdf = 0.0
+        cdf_marg = []
+        for i in range(self.k_vars):
+            lpdf += self.marginals[i].logpdf(y[..., i], *marg_args[i])
+            cdf_marg.append(self.marginals[i].cdf(y[..., i], *marg_args[i]))
+
+        u = np.column_stack(cdf_marg)
+        if y.ndim == 1:
+            u = u.squeeze()
+
+        lpdf += self.copula.logpdf(u, cop_args)
+        return lpdf
+
+
+class Copula(ABC):
+    r"""A generic Copula class meant for subclassing.
+
+    Notes
+    -----
+    A function :math:`\phi` on :math:`[0, \infty]` is the Laplace-Stieltjes
+    transform of a distribution function if and only if :math:`\phi` is
+    completely monotone and :math:`\phi(0) = 1` [2]_.
+
+    The following algorithm for sampling a ``d``-dimensional exchangeable
+    Archimedean copula with generator :math:`\phi` is due to Marshall, Olkin
+    (1988) [1]_, where :math:`LS^{−1}(\phi)` denotes the inverse
+    Laplace-Stieltjes transform of :math:`\phi`.
+
+    From a mixture representation with respect to :math:`F`, the following
+    algorithm may be derived for sampling Archimedean copulas, see [1]_.
+
+    1. Sample :math:`V \sim F = LS^{−1}(\phi)`.
+    2. Sample i.i.d. :math:`X_i \sim U[0,1], i \in \{1,...,d\}`.
+    3. Return :math:`(U_1,..., U_d)`, where
+       :math:`U_i = \phi(−\log(X_i)/V), i \in \{1, ...,d\}`.
+
+    Detailed properties of each copula can be found in [3]_.
+
+    Instances of the class can access the attributes: ``rng`` for the random
+    number generator (used for the ``seed``).
+
+    **Subclassing**
+
+    When subclassing `Copula` to create a new copula, ``__init__`` and
+    ``random`` must be redefined.
+
+    * ``__init__(theta)``: If the copula
+      does not take advantage of a ``theta``, this parameter can be omitted.
+    * ``random(n, random_state)``: draw ``n`` from the copula.
+    * ``pdf(x)``: PDF from the copula.
+    * ``cdf(x)``: CDF from the copula.
+
+    References
+    ----------
+    .. [1] Marshall AW, Olkin I. “Families of Multivariate Distributionsâ€,
+      Journal of the American Statistical Association, 83, 834-841, 1988.
+    .. [2] Marius Hofert. "Sampling Archimedean copulas",
+      Universität Ulm, 2008.
+    .. [3] Harry Joe. "Dependence Modeling with Copulas", Monographs on
+      Statistics and Applied Probability 134, 2015.
+
+    """
+
+    def __init__(self, k_dim=2):
+        self.k_dim = k_dim
+
+    @deprecate_kwarg("random_state", "rng")
+    def rvs(self, nobs=1, args=(), rng=None):
+        """Draw `n` in the half-open interval ``[0, 1)``.
+
+        Marginals are uniformly distributed.
+
+        Parameters
+        ----------
+        nobs : int, optional
+            Number of samples to generate from the copula. Default is 1.
+        args : tuple, optional
+            Arguments for copula parameters. The number of arguments depends
+            on the copula.
+        rng : int, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional
+            If `rng` is None, a new ``Generator`` is created using fresh
+            entropy from the operating system. If `rng` is an int or array
+            of ints, a new ``Generator`` is created, seeded with `rng`. If
+            `rng` is already a ``Generator`` or ``RandomState`` instance,
+            that instance is used.
+        rng : int, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional
+            .. deprecated:: 0.15
+
+               random_state has been deprecated. In-line with SPEC-007, use
+               rng for passing a random number generator or seed.
+
+        Returns
+        -------
+        sample : ndarray of shape (nobs, k_dim)
+            Sample from the copula.
+
+        See Also
+        --------
+        statsmodels.tools.rng_qrng.check_random_state
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def pdf(self, u, args=()):
+        """Probability density function of copula.
+
+        Parameters
+        ----------
+        u : array_like, 2-D
+            Points of random variables in unit hypercube at which method is
+            evaluated.
+            The second (or last) dimension should be the same as the dimension
+            of the random variable, e.g., 2 for bivariate copula.
+        args : tuple, optional
+            Arguments for copula parameters. The number of arguments depends
+            on the copula.
+
+        Returns
+        -------
+        pdf : ndarray, (nobs, k_dim)
+            Copula pdf evaluated at points ``u``.
+        """
+
+    def logpdf(self, u, args=()):
+        """Log of copula pdf, loglikelihood.
+
+        Parameters
+        ----------
+        u : array_like, 2-D
+            Points of random variables in unit hypercube at which method is
+            evaluated.
+            The second (or last) dimension should be the same as the dimension
+            of the random variable, e.g., 2 for bivariate copula.
+        args : tuple, optional
+            Arguments for copula parameters. The number of arguments depends
+            on the copula.
+
+        Returns
+        -------
+        logpdf : ndarray, (nobs, k_dim)
+            Copula log-pdf evaluated at points ``u``.
+        """
+        return np.log(self.pdf(u, *args))
+
+    @abstractmethod
+    def cdf(self, u, args=()):
+        """Cumulative distribution function evaluated at points u.
+
+        Parameters
+        ----------
+        u : array_like, 2-D
+            Points of random variables in unit hypercube at which method is
+            evaluated.
+            The second (or last) dimension should be the same as the dimension
+            of the random variable, e.g., 2 for bivariate copula.
+        args : tuple, optional
+            Arguments for copula parameters. The number of arguments depends
+            on the copula.
+
+        Returns
+        -------
+        cdf : ndarray, (nobs, k_dim)
+            Copula cdf evaluated at points ``u``.
+        """
+
+    def plot_scatter(self, sample=None, nobs=500, rng=None, ax=None):
+        """Sample the copula and plot.
+
+        Parameters
+        ----------
+        sample : array_like, optional
+            The sample to plot.  If not provided (the default), a sample
+            is generated.
+        nobs : int, optional
+            Number of samples to generate from the copula.
+        rng : int, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional
+            If `rng` is None, a new ``Generator`` is created using fresh
+            entropy from the operating system. If `rng` is an int or array
+            of ints, a new ``Generator`` is created, seeded with `rng`. If
+            `rng` is already a ``Generator`` or ``RandomState`` instance,
+            that instance is used.
+        ax : AxesSubplot, optional
+            If given, this subplot is used to plot in instead of a new figure
+            being created.
+
+        Returns
+        -------
+        fig : Figure
+            If `ax` is None, the created figure.  Otherwise the figure to which
+            `ax` is connected.
+        sample : ndarray of shape (nobs, k_dim)
+            Sample from the copula.
+
+        See Also
+        --------
+        statsmodels.tools.rng_qrng.check_random_state
+        """
+        if self.k_dim != 2:
+            raise ValueError("Can only plot 2-dimensional Copula.")
+
+        if sample is None:
+            sample = self.rvs(nobs=nobs, rng=rng)
+
+        fig, ax = utils.create_mpl_ax(ax)
+        ax.scatter(sample[:, 0], sample[:, 1])
+        ax.set_xlabel("u")
+        ax.set_ylabel("v")
+
+        return fig, sample
+
+    def plot_pdf(self, ticks_nbr=10, ax=None):
+        """Plot the PDF.
+
+        Parameters
+        ----------
+        ticks_nbr : int, optional
+            Number of color isolines for the PDF. Default is 10.
+        ax : AxesSubplot, optional
+            If given, this subplot is used to plot in instead of a new figure
+            being created.
+
+        Returns
+        -------
+        fig : Figure
+            If `ax` is None, the created figure.  Otherwise the figure to which
+            `ax` is connected.
+
+        """
+        plt = _import_mpl()
+
+        if self.k_dim != 2:
+            import warnings
+
+            warnings.warn(
+                "Plotting 2-dimensional Copula.", RuntimeWarning, stacklevel=2
+            )
+
+        n_samples = 100
+
+        eps = 1e-4
+        uu, vv = np.meshgrid(
+            np.linspace(eps, 1 - eps, n_samples), np.linspace(eps, 1 - eps, n_samples)
+        )
+        points = np.vstack([uu.ravel(), vv.ravel()]).T
+
+        data = self.pdf(points).T.reshape(uu.shape)
+        min_ = np.nanpercentile(data, 5)
+        max_ = np.nanpercentile(data, 95)
+
+        fig, ax = utils.create_mpl_ax(ax)
+
+        vticks = np.linspace(min_, max_, num=ticks_nbr)
+        range_cbar = [min_, max_]
+        cs = ax.contourf(
+            uu,
+            vv,
+            data,
+            vticks,
+            antialiased=True,
+            vmin=range_cbar[0],
+            vmax=range_cbar[1],
+        )
+
+        ax.set_xlabel("u")
+        ax.set_ylabel("v")
+        ax.set_xlim(0, 1)
+        ax.set_ylim(0, 1)
+        ax.set_aspect("equal")
+        cbar = plt.colorbar(cs, ticks=vticks)
+        cbar.set_label("p")
+        fig.tight_layout()
+
+        return fig
+
+    def tau_simulated(self, nobs=1024, rng=None):
+        """Kendall's tau based on simulated samples.
+
+        Parameters
+        ----------
+        nobs : int, optional
+            Number of samples to generate from the copula.
+        rng : int, array_like of int, numpy.random.Generator, or numpy.random.RandomState, optional
+            If `rng` is None, a new ``Generator`` is created using fresh
+            entropy from the operating system. If `rng` is an int or array
+            of ints, a new ``Generator`` is created, seeded with `rng`. If
+            `rng` is already a ``Generator`` or ``RandomState`` instance,
+            that instance is used.
+
+        Returns
+        -------
+        tau : float
+            Kendall's tau.
+        """
+        x = self.rvs(nobs, rng=rng)
+        return stats.kendalltau(x[:, 0], x[:, 1])[0]
+
+    def fit_corr_param(self, data):
+        """Copula correlation parameter using Kendall's tau of sample data.
+
+        Parameters
+        ----------
+        data : array_like
+            Sample data used to fit `theta` using Kendall's tau.
+
+        Returns
+        -------
+        corr_param : float
+            Correlation parameter of the copula, ``theta`` in Archimedean and
+            pearson correlation in elliptical.
+            If k_dim > 2, then average tau is used.
+        """
+        x = np.asarray(data)
+
+        if x.shape[1] == 2:
+            tau = stats.kendalltau(x[:, 0], x[:, 1])[0]
+        else:
+            k = self.k_dim
+            taus = [
+                stats.kendalltau(x[..., i], x[..., j])[0]
+                for i in range(k)
+                for j in range(i + 1, k)
+            ]
+            tau = np.mean(taus)
+        return self._arg_from_tau(tau)
+
+    def _arg_from_tau(self, tau):
+        """Compute correlation parameter from tau.
+
+        Parameters
+        ----------
+        tau : float
+            Kendall's tau.
+
+        Returns
+        -------
+        corr_param : float
+            Correlation parameter of the copula, ``theta`` in Archimedean and
+            pearson correlation in elliptical.
+
+        """
+        raise NotImplementedError
diff --git a/shared/package-files/ec/ec313e995dc7fc38a193838ae203071e4c83e2d3d7d5c022e39ef4ce2e42237d/c_distributions.pxd b/shared/package-files/ec/ec313e995dc7fc38a193838ae203071e4c83e2d3d7d5c022e39ef4ce2e42237d/c_distributions.pxd
new file mode 100644
index 0000000000000000000000000000000000000000..b978d13503eaff74ec54a2ad7a9813b24dab68d2
--- /dev/null
+++ b/shared/package-files/ec/ec313e995dc7fc38a193838ae203071e4c83e2d3d7d5c022e39ef4ce2e42237d/c_distributions.pxd
@@ -0,0 +1,120 @@
+#!python
+#cython: wraparound=False, nonecheck=False, boundscheck=False, cdivision=True, language_level=3
+from numpy cimport npy_intp
+
+from libc.stdint cimport (uint64_t, int32_t, int64_t)
+from numpy.random cimport bitgen_t
+
+cdef extern from "numpy/random/distributions.h":
+
+    struct s_binomial_t:
+        int has_binomial
+        double psave
+        int64_t nsave
+        double r
+        double q
+        double fm
+        int64_t m
+        double p1
+        double xm
+        double xl
+        double xr
+        double c
+        double laml
+        double lamr
+        double p2
+        double p3
+        double p4
+
+    ctypedef s_binomial_t binomial_t
+
+    float random_standard_uniform_f(bitgen_t *bitgen_state) nogil
+    double random_standard_uniform(bitgen_t *bitgen_state) nogil
+    void random_standard_uniform_fill(bitgen_t* bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_uniform_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    
+    double random_standard_exponential(bitgen_t *bitgen_state) nogil
+    float random_standard_exponential_f(bitgen_t *bitgen_state) nogil
+    void random_standard_exponential_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_exponential_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    void random_standard_exponential_inv_fill(bitgen_t *bitgen_state, npy_intp cnt, double *out) nogil
+    void random_standard_exponential_inv_fill_f(bitgen_t *bitgen_state, npy_intp cnt, float *out) nogil
+    
+    double random_standard_normal(bitgen_t* bitgen_state) nogil
+    float random_standard_normal_f(bitgen_t *bitgen_state) nogil
+    void random_standard_normal_fill(bitgen_t *bitgen_state, npy_intp count, double *out) nogil
+    void random_standard_normal_fill_f(bitgen_t *bitgen_state, npy_intp count, float *out) nogil
+    double random_standard_gamma(bitgen_t *bitgen_state, double shape) nogil
+    float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) nogil
+
+    float random_standard_uniform_f(bitgen_t *bitgen_state) nogil
+    void random_standard_uniform_fill_f(bitgen_t* bitgen_state, npy_intp cnt, float *out) nogil
+    float random_standard_normal_f(bitgen_t* bitgen_state) nogil
+    float random_standard_gamma_f(bitgen_t *bitgen_state, float shape) nogil
+
+    int64_t random_positive_int64(bitgen_t *bitgen_state) nogil
+    int32_t random_positive_int32(bitgen_t *bitgen_state) nogil
+    int64_t random_positive_int(bitgen_t *bitgen_state) nogil
+    uint64_t random_uint(bitgen_t *bitgen_state) nogil
+
+    double random_normal(bitgen_t *bitgen_state, double loc, double scale) nogil
+
+    double random_gamma(bitgen_t *bitgen_state, double shape, double scale) nogil
+    float random_gamma_f(bitgen_t *bitgen_state, float shape, float scale) nogil
+
+    double random_exponential(bitgen_t *bitgen_state, double scale) nogil
+    double random_uniform(bitgen_t *bitgen_state, double lower, double range) nogil
+    double random_beta(bitgen_t *bitgen_state, double a, double b) nogil
+    double random_chisquare(bitgen_t *bitgen_state, double df) nogil
+    double random_f(bitgen_t *bitgen_state, double dfnum, double dfden) nogil
+    double random_standard_cauchy(bitgen_t *bitgen_state) nogil
+    double random_pareto(bitgen_t *bitgen_state, double a) nogil
+    double random_weibull(bitgen_t *bitgen_state, double a) nogil
+    double random_power(bitgen_t *bitgen_state, double a) nogil
+    double random_laplace(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_gumbel(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_logistic(bitgen_t *bitgen_state, double loc, double scale) nogil
+    double random_lognormal(bitgen_t *bitgen_state, double mean, double sigma) nogil
+    double random_rayleigh(bitgen_t *bitgen_state, double mode) nogil
+    double random_standard_t(bitgen_t *bitgen_state, double df) nogil
+    double random_noncentral_chisquare(bitgen_t *bitgen_state, double df,
+                                       double nonc) nogil
+    double random_noncentral_f(bitgen_t *bitgen_state, double dfnum,
+                               double dfden, double nonc) nogil
+    double random_wald(bitgen_t *bitgen_state, double mean, double scale) nogil
+    double random_vonmises(bitgen_t *bitgen_state, double mu, double kappa) nogil
+    double random_triangular(bitgen_t *bitgen_state, double left, double mode,
+                             double right) nogil
+
+    int64_t random_poisson(bitgen_t *bitgen_state, double lam) nogil
+    int64_t random_negative_binomial(bitgen_t *bitgen_state, double n, double p) nogil
+    int64_t random_binomial(bitgen_t *bitgen_state, double p, int64_t n, binomial_t *binomial) nogil
+    int64_t random_logseries(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric_search(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric_inversion(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_geometric(bitgen_t *bitgen_state, double p) nogil
+    int64_t random_zipf(bitgen_t *bitgen_state, double a) nogil
+    int64_t random_hypergeometric(bitgen_t *bitgen_state, int64_t good, int64_t bad,
+                                    int64_t sample) nogil
+
+    uint64_t random_interval(bitgen_t *bitgen_state, uint64_t max) nogil
+
+    # Generate random uint64 numbers in closed interval [off, off + rng].
+    uint64_t random_bounded_uint64(bitgen_t *bitgen_state,
+                                   uint64_t off, uint64_t rng,
+                                   uint64_t mask, bint use_masked) nogil
+
+    void random_multinomial(bitgen_t *bitgen_state, int64_t n, int64_t *mnix,
+                            double *pix, npy_intp d, binomial_t *binomial) nogil
+
+    int random_multivariate_hypergeometric_count(bitgen_t *bitgen_state,
+                          int64_t total,
+                          size_t num_colors, int64_t *colors,
+                          int64_t nsample,
+                          size_t num_variates, int64_t *variates) nogil
+    void random_multivariate_hypergeometric_marginals(bitgen_t *bitgen_state,
+                               int64_t total,
+                               size_t num_colors, int64_t *colors,
+                               int64_t nsample,
+                               size_t num_variates, int64_t *variates) nogil
+
diff --git a/shared/package-files/ec/ec31d2d210e4f9122cdc841cdd26d9541d3ed4c86248227d5e0bc4c849682fb3/__init__.py b/shared/package-files/ec/ec31d2d210e4f9122cdc841cdd26d9541d3ed4c86248227d5e0bc4c849682fb3/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8e36b2e65d11e7f3b2c540c1b292a39a6cc219d
--- /dev/null
+++ b/shared/package-files/ec/ec31d2d210e4f9122cdc841cdd26d9541d3ed4c86248227d5e0bc4c849682fb3/__init__.py
@@ -0,0 +1,46 @@
+__all__ = [
+    "BaseExceptionGroup",
+    "ExceptionGroup",
+    "catch",
+    "format_exception",
+    "format_exception_only",
+    "print_exception",
+    "print_exc",
+    "suppress",
+]
+
+import os
+import sys
+
+from ._catch import catch
+from ._version import version as __version__  # noqa: F401
+
+if sys.version_info < (3, 11):
+    from ._exceptions import BaseExceptionGroup, ExceptionGroup
+    from ._formatting import (
+        format_exception,
+        format_exception_only,
+        print_exc,
+        print_exception,
+    )
+
+    if os.getenv("EXCEPTIONGROUP_NO_PATCH") != "1":
+        from . import _formatting  # noqa: F401
+
+    BaseExceptionGroup.__module__ = __name__
+    ExceptionGroup.__module__ = __name__
+else:
+    from traceback import (
+        format_exception,
+        format_exception_only,
+        print_exc,
+        print_exception,
+    )
+
+    BaseExceptionGroup = BaseExceptionGroup
+    ExceptionGroup = ExceptionGroup
+
+if sys.version_info < (3, 12, 1):
+    from ._suppress import suppress
+else:
+    from contextlib import suppress
diff --git a/shared/package-files/ec/ec3545d38d863fd7444fe263259a18eb8060334b40bf29bf0dd68baf03f9d63f/test_constructors.py b/shared/package-files/ec/ec3545d38d863fd7444fe263259a18eb8060334b40bf29bf0dd68baf03f9d63f/test_constructors.py
new file mode 100644
index 0000000000000000000000000000000000000000..60b2ec7b6912de4f497428026329f241814f4c62
--- /dev/null
+++ b/shared/package-files/ec/ec3545d38d863fd7444fe263259a18eb8060334b40bf29bf0dd68baf03f9d63f/test_constructors.py
@@ -0,0 +1,2296 @@
+from collections import OrderedDict
+from collections.abc import Iterator
+from datetime import (
+    datetime,
+    timedelta,
+)
+
+from dateutil.tz import tzoffset
+import numpy as np
+from numpy import ma
+import pytest
+
+from pandas._libs import (
+    iNaT,
+    lib,
+)
+from pandas.compat import HAS_PYARROW
+from pandas.compat.numpy import np_version_gt2
+from pandas.errors import IntCastingNaNError
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.dtypes import CategoricalDtype
+
+import pandas as pd
+from pandas import (
+    Categorical,
+    DataFrame,
+    DatetimeIndex,
+    DatetimeTZDtype,
+    Index,
+    Interval,
+    IntervalIndex,
+    MultiIndex,
+    NaT,
+    Period,
+    RangeIndex,
+    Series,
+    Timestamp,
+    date_range,
+    isna,
+    period_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.core.arrays import (
+    IntegerArray,
+    IntervalArray,
+    period_array,
+)
+from pandas.core.internals.blocks import NumpyBlock
+
+
+class TestSeriesConstructors:
+    def test_from_ints_with_non_nano_dt64_dtype(self, index_or_series):
+        values = np.arange(10)
+
+        res = index_or_series(values, dtype="M8[s]")
+        expected = index_or_series(values.astype("M8[s]"))
+        tm.assert_equal(res, expected)
+
+        res = index_or_series(list(values), dtype="M8[s]")
+        tm.assert_equal(res, expected)
+
+    def test_from_na_value_and_interval_of_datetime_dtype(self):
+        # GH#41805
+        ser = Series([None], dtype="interval[datetime64[ns]]")
+        assert ser.isna().all()
+        assert ser.dtype == "interval[datetime64[ns], right]"
+
+    def test_infer_with_date_and_datetime(self):
+        # GH#49341 pre-2.0 we inferred datetime-and-date to datetime64, which
+        #  was inconsistent with Index behavior
+        ts = Timestamp(2016, 1, 1)
+        vals = [ts.to_pydatetime(), ts.date()]
+
+        ser = Series(vals)
+        expected = Series(vals, dtype=object)
+        tm.assert_series_equal(ser, expected)
+
+        idx = Index(vals)
+        expected = Index(vals, dtype=object)
+        tm.assert_index_equal(idx, expected)
+
+    def test_unparsable_strings_with_dt64_dtype(self):
+        # pre-2.0 these would be silently ignored and come back with object dtype
+        vals = ["aa"]
+        msg = "^Unknown datetime string format, unable to parse: aa, at position 0$"
+        with pytest.raises(ValueError, match=msg):
+            Series(vals, dtype="datetime64[ns]")
+
+        with pytest.raises(ValueError, match=msg):
+            Series(np.array(vals, dtype=object), dtype="datetime64[ns]")
+
+    @pytest.mark.parametrize(
+        "constructor",
+        [
+            # NOTE: some overlap with test_constructor_empty but that test does not
+            # test for None or an empty generator.
+            # test_constructor_pass_none tests None but only with the index also
+            # passed.
+            (lambda idx: Series(index=idx)),
+            (lambda idx: Series(None, index=idx)),
+            (lambda idx: Series({}, index=idx)),
+            (lambda idx: Series((), index=idx)),
+            (lambda idx: Series([], index=idx)),
+            (lambda idx: Series((_ for _ in []), index=idx)),
+            (lambda idx: Series(data=None, index=idx)),
+            (lambda idx: Series(data={}, index=idx)),
+            (lambda idx: Series(data=(), index=idx)),
+            (lambda idx: Series(data=[], index=idx)),
+            (lambda idx: Series(data=(_ for _ in []), index=idx)),
+        ],
+    )
+    @pytest.mark.parametrize("empty_index", [None, []])
+    def test_empty_constructor(self, constructor, empty_index):
+        # GH 49573 (addition of empty_index parameter)
+        expected = Series(index=empty_index)
+        result = constructor(empty_index)
+
+        assert result.dtype == object
+        assert len(result.index) == 0
+        tm.assert_series_equal(result, expected, check_index_type=True)
+
+    def test_invalid_dtype(self):
+        # GH15520
+        msg = "not understood"
+        invalid_list = [Timestamp, "Timestamp", list]
+        for dtype in invalid_list:
+            with pytest.raises(TypeError, match=msg):
+                Series([], name="time", dtype=dtype)
+
+    def test_invalid_compound_dtype(self):
+        # GH#13296
+        c_dtype = np.dtype([("a", "i8"), ("b", "f4")])
+        cdt_arr = np.array([(1, 0.4), (256, -13)], dtype=c_dtype)
+
+        with pytest.raises(ValueError, match="Use DataFrame instead"):
+            Series(cdt_arr, index=["A", "B"])
+
+    def test_scalar_conversion(self):
+        # Pass in scalar is disabled
+        scalar = Series(0.5)
+        assert not isinstance(scalar, float)
+
+    def test_scalar_extension_dtype(self, ea_scalar_and_dtype):
+        # GH 28401
+
+        ea_scalar, ea_dtype = ea_scalar_and_dtype
+
+        ser = Series(ea_scalar, index=range(3))
+        expected = Series([ea_scalar] * 3, dtype=ea_dtype)
+
+        assert ser.dtype == ea_dtype
+        tm.assert_series_equal(ser, expected)
+
+    def test_constructor(self, datetime_series, using_infer_string):
+        empty_series = Series()
+        assert datetime_series.index._is_all_dates
+
+        # Pass in Series
+        derived = Series(datetime_series)
+        assert derived.index._is_all_dates
+
+        tm.assert_index_equal(derived.index, datetime_series.index)
+        # Ensure new index is not created
+        assert id(datetime_series.index) == id(derived.index)
+
+        # Mixed type Series
+        mixed = Series(["hello", np.nan], index=[0, 1])
+        assert mixed.dtype == np.object_ if not using_infer_string else "str"
+        assert np.isnan(mixed[1])
+
+        assert not empty_series.index._is_all_dates
+        assert not Series().index._is_all_dates
+
+        # exception raised is of type ValueError GH35744
+        with pytest.raises(
+            ValueError,
+            match=r"Data must be 1-dimensional, got ndarray of shape \(3, 3\) instead",
+        ):
+            Series(np.random.default_rng(2).standard_normal((3, 3)), index=np.arange(3))
+
+        mixed.name = "Series"
+        rs = Series(mixed).name
+        xp = "Series"
+        assert rs == xp
+
+        # raise on MultiIndex GH4187
+        m = MultiIndex.from_arrays([[1, 2], [3, 4]])
+        msg = "initializing a Series from a MultiIndex is not supported"
+        with pytest.raises(NotImplementedError, match=msg):
+            Series(m)
+
+    def test_constructor_index_ndim_gt_1_raises(self):
+        # GH#18579
+        df = DataFrame([[1, 2], [3, 4], [5, 6]], index=[3, 6, 9])
+        with pytest.raises(ValueError, match="Index data must be 1-dimensional"):
+            Series([1, 3, 2], index=df)
+
+    @pytest.mark.parametrize("input_class", [list, dict, OrderedDict])
+    def test_constructor_empty(self, input_class, using_infer_string):
+        empty = Series()
+        empty2 = Series(input_class())
+
+        # these are Index() and RangeIndex() which don't compare type equal
+        # but are just .equals
+        tm.assert_series_equal(empty, empty2, check_index_type=False)
+
+        # With explicit dtype:
+        empty = Series(dtype="float64")
+        empty2 = Series(input_class(), dtype="float64")
+        tm.assert_series_equal(empty, empty2, check_index_type=False)
+
+        # GH 18515 : with dtype=category:
+        empty = Series(dtype="category")
+        empty2 = Series(input_class(), dtype="category")
+        tm.assert_series_equal(empty, empty2, check_index_type=False)
+
+        if input_class is not list:
+            # With index:
+            empty = Series(index=range(10))
+            empty2 = Series(input_class(), index=range(10))
+            tm.assert_series_equal(empty, empty2)
+
+            # With index and dtype float64:
+            empty = Series(np.nan, index=range(10))
+            empty2 = Series(input_class(), index=range(10), dtype="float64")
+            tm.assert_series_equal(empty, empty2)
+
+            # GH 19853 : with empty string, index and dtype str
+            empty = Series("", dtype=str, index=range(3))
+            if using_infer_string:
+                empty2 = Series("", index=range(3), dtype="str")
+            else:
+                empty2 = Series("", index=range(3))
+            tm.assert_series_equal(empty, empty2)
+
+    @pytest.mark.parametrize("input_arg", [np.nan, float("nan")])
+    def test_constructor_nan(self, input_arg):
+        empty = Series(dtype="float64", index=range(10))
+        empty2 = Series(input_arg, index=range(10))
+
+        tm.assert_series_equal(empty, empty2, check_index_type=False)
+
+    @pytest.mark.parametrize(
+        "dtype",
+        ["f8", "i8", "M8[ns]", "m8[ns]", "category", "object", "datetime64[ns, UTC]"],
+    )
+    @pytest.mark.parametrize("index", [None, Index([])])
+    def test_constructor_dtype_only(self, dtype, index):
+        # GH-20865
+        result = Series(dtype=dtype, index=index)
+        assert result.dtype == dtype
+        assert len(result) == 0
+
+    def test_constructor_no_data_index_order(self):
+        result = Series(index=["b", "a", "c"])
+        assert result.index.tolist() == ["b", "a", "c"]
+
+    def test_constructor_no_data_string_type(self):
+        # GH 22477
+        result = Series(index=[1], dtype=str)
+        assert np.isnan(result.iloc[0])
+
+    @pytest.mark.parametrize("item", ["entry", "Ñ", 13])
+    def test_constructor_string_element_string_type(self, item):
+        # GH 22477
+        result = Series(item, index=[1], dtype=str)
+        assert result.iloc[0] == str(item)
+
+    def test_constructor_dtype_str_na_values(self, string_dtype):
+        # https://github.com/pandas-dev/pandas/issues/21083
+        ser = Series(["x", None], dtype=string_dtype)
+        result = ser.isna()
+        expected = Series([False, True])
+        tm.assert_series_equal(result, expected)
+        assert ser.iloc[1] is None
+
+        ser = Series(["x", np.nan], dtype=string_dtype)
+        assert np.isnan(ser.iloc[1])
+
+    def test_constructor_series(self):
+        index1 = ["d", "b", "a", "c"]
+        index2 = sorted(index1)
+        s1 = Series([4, 7, -5, 3], index=index1)
+        s2 = Series(s1, index=index2)
+
+        tm.assert_series_equal(s2, s1.sort_index())
+
+    def test_constructor_iterable(self):
+        # GH 21987
+        class Iter:
+            def __iter__(self) -> Iterator:
+                yield from range(10)
+
+        expected = Series(list(range(10)), dtype="int64")
+        result = Series(Iter(), dtype="int64")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_sequence(self):
+        # GH 21987
+        expected = Series(list(range(10)), dtype="int64")
+        result = Series(range(10), dtype="int64")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_single_str(self):
+        # GH 21987
+        expected = Series(["abc"])
+        result = Series("abc")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_list_like(self):
+        # make sure that we are coercing different
+        # list-likes to standard dtypes and not
+        # platform specific
+        expected = Series([1, 2, 3], dtype="int64")
+        for obj in [[1, 2, 3], (1, 2, 3), np.array([1, 2, 3], dtype="int64")]:
+            result = Series(obj, index=[0, 1, 2])
+            tm.assert_series_equal(result, expected)
+
+    def test_constructor_boolean_index(self):
+        # GH#18579
+        s1 = Series([1, 2, 3], index=[4, 5, 6])
+
+        index = s1 == 2
+        result = Series([1, 3, 2], index=index)
+        expected = Series([1, 3, 2], index=[False, True, False])
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("dtype", ["bool", "int32", "int64", "float64"])
+    def test_constructor_index_dtype(self, dtype):
+        # GH 17088
+
+        s = Series(Index([0, 2, 4]), dtype=dtype)
+        assert s.dtype == dtype
+
+    @pytest.mark.parametrize(
+        "input_vals",
+        [
+            ([1, 2]),
+            (["1", "2"]),
+            (list(date_range("1/1/2011", periods=2, freq="h"))),
+            (list(date_range("1/1/2011", periods=2, freq="h", tz="US/Eastern"))),
+            ([Interval(left=0, right=5)]),
+        ],
+    )
+    def test_constructor_list_str(self, input_vals, string_dtype):
+        # GH 16605
+        # Ensure that data elements from a list are converted to strings
+        # when dtype is str, 'str', or 'U'
+        result = Series(input_vals, dtype=string_dtype)
+        expected = Series(input_vals).astype(string_dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_list_str_na(self, string_dtype):
+        result = Series([1.0, 2.0, np.nan], dtype=string_dtype)
+        expected = Series(["1.0", "2.0", np.nan], dtype=object)
+        tm.assert_series_equal(result, expected)
+        assert np.isnan(result[2])
+
+    def test_constructor_generator(self):
+        gen = (i for i in range(10))
+
+        result = Series(gen)
+        exp = Series(range(10))
+        tm.assert_series_equal(result, exp)
+
+        # same but with non-default index
+        gen = (i for i in range(10))
+        result = Series(gen, index=range(10, 20))
+        exp.index = range(10, 20)
+        tm.assert_series_equal(result, exp)
+
+    def test_constructor_map(self):
+        # GH8909
+        m = (x for x in range(10))
+
+        result = Series(m)
+        exp = Series(range(10))
+        tm.assert_series_equal(result, exp)
+
+        # same but with non-default index
+        m = (x for x in range(10))
+        result = Series(m, index=range(10, 20))
+        exp.index = range(10, 20)
+        tm.assert_series_equal(result, exp)
+
+    def test_constructor_categorical(self):
+        cat = Categorical([0, 1, 2, 0, 1, 2], ["a", "b", "c"])
+        res = Series(cat)
+        tm.assert_categorical_equal(res.values, cat)
+
+        # can cast to a new dtype
+        result = Series(Categorical([1, 2, 3]), dtype="int64")
+        expected = Series([1, 2, 3], dtype="int64")
+        tm.assert_series_equal(result, expected)
+
+    def test_construct_from_categorical_with_dtype(self):
+        # GH12574
+        ser = Series(Categorical([1, 2, 3]), dtype="category")
+        assert isinstance(ser.dtype, CategoricalDtype)
+
+    def test_construct_intlist_values_category_dtype(self):
+        ser = Series([1, 2, 3], dtype="category")
+        assert isinstance(ser.dtype, CategoricalDtype)
+
+    def test_constructor_categorical_with_coercion(self):
+        factor = Categorical(["a", "b", "b", "a", "a", "c", "c", "c"])
+        # test basic creation / coercion of categoricals
+        s = Series(factor, name="A")
+        assert s.dtype == "category"
+        assert len(s) == len(factor)
+
+        # in a frame
+        df = DataFrame({"A": factor})
+        result = df["A"]
+        tm.assert_series_equal(result, s)
+        result = df.iloc[:, 0]
+        tm.assert_series_equal(result, s)
+        assert len(df) == len(factor)
+
+        df = DataFrame({"A": s})
+        result = df["A"]
+        tm.assert_series_equal(result, s)
+        assert len(df) == len(factor)
+
+        # multiples
+        df = DataFrame({"A": s, "B": s, "C": 1})
+        result1 = df["A"]
+        result2 = df["B"]
+        tm.assert_series_equal(result1, s)
+        tm.assert_series_equal(result2, s, check_names=False)
+        assert result2.name == "B"
+        assert len(df) == len(factor)
+
+    def test_constructor_categorical_with_coercion2(self):
+        # GH8623
+        x = DataFrame(
+            [[1, "John P. Doe"], [2, "Jane Dove"], [1, "John P. Doe"]],
+            columns=["person_id", "person_name"],
+        )
+        x["person_name"] = Categorical(x.person_name)  # doing this breaks transform
+
+        expected = x.iloc[0].person_name
+        result = x.person_name.iloc[0]
+        assert result == expected
+
+        result = x.person_name[0]
+        assert result == expected
+
+        result = x.person_name.loc[0]
+        assert result == expected
+
+    def test_constructor_series_to_categorical(self):
+        # see GH#16524: test conversion of Series to Categorical
+        series = Series(["a", "b", "c"])
+
+        result = Series(series, dtype="category")
+        expected = Series(["a", "b", "c"], dtype="category")
+
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_categorical_dtype(self):
+        result = Series(
+            ["a", "b"], dtype=CategoricalDtype(["a", "b", "c"], ordered=True)
+        )
+        assert isinstance(result.dtype, CategoricalDtype)
+        tm.assert_index_equal(result.cat.categories, Index(["a", "b", "c"]))
+        assert result.cat.ordered
+
+        result = Series(["a", "b"], dtype=CategoricalDtype(["b", "a"]))
+        assert isinstance(result.dtype, CategoricalDtype)
+        tm.assert_index_equal(result.cat.categories, Index(["b", "a"]))
+        assert result.cat.ordered is False
+
+        # GH 19565 - Check broadcasting of scalar with Categorical dtype
+        result = Series(
+            "a", index=[0, 1], dtype=CategoricalDtype(["a", "b"], ordered=True)
+        )
+        expected = Series(
+            ["a", "a"], index=[0, 1], dtype=CategoricalDtype(["a", "b"], ordered=True)
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_categorical_string(self):
+        # GH 26336: the string 'category' maintains existing CategoricalDtype
+        cdt = CategoricalDtype(categories=list("dabc"), ordered=True)
+        expected = Series(list("abcabc"), dtype=cdt)
+
+        # Series(Categorical, dtype='category') keeps existing dtype
+        cat = Categorical(list("abcabc"), dtype=cdt)
+        result = Series(cat, dtype="category")
+        tm.assert_series_equal(result, expected)
+
+        # Series(Series[Categorical], dtype='category') keeps existing dtype
+        result = Series(result, dtype="category")
+        tm.assert_series_equal(result, expected)
+
+    def test_categorical_sideeffects_free(self):
+        # Passing a categorical to a Series and then changing values in either
+        # the series or the categorical should not change the values in the
+        # other one, IF you specify copy!
+        cat = Categorical(["a", "b", "c", "a"])
+        s = Series(cat, copy=True)
+        assert s.cat is not cat
+        s = s.cat.rename_categories([1, 2, 3])
+        exp_s = np.array([1, 2, 3, 1], dtype=np.int64)
+        exp_cat = np.array(["a", "b", "c", "a"], dtype=np.object_)
+        tm.assert_numpy_array_equal(s.__array__(), exp_s)
+        tm.assert_numpy_array_equal(cat.__array__(), exp_cat)
+
+        # setting
+        s[0] = 2
+        exp_s2 = np.array([2, 2, 3, 1], dtype=np.int64)
+        tm.assert_numpy_array_equal(s.__array__(), exp_s2)
+        tm.assert_numpy_array_equal(cat.__array__(), exp_cat)
+
+        # however, copy is False by default
+        # so this WILL change values
+        cat = Categorical(["a", "b", "c", "a"])
+        s = Series(cat, copy=False)
+        assert s.values is cat
+        s = s.cat.rename_categories([1, 2, 3])
+        assert s.values is not cat
+        exp_s = np.array([1, 2, 3, 1], dtype=np.int64)
+        tm.assert_numpy_array_equal(s.__array__(), exp_s)
+
+        s[0] = 2
+        exp_s2 = np.array([2, 2, 3, 1], dtype=np.int64)
+        tm.assert_numpy_array_equal(s.__array__(), exp_s2)
+
+    def test_unordered_compare_equal(self):
+        left = Series(["a", "b", "c"], dtype=CategoricalDtype(["a", "b"]))
+        right = Series(Categorical(["a", "b", np.nan], categories=["a", "b"]))
+        tm.assert_series_equal(left, right)
+
+    def test_constructor_maskedarray(self):
+        data = ma.masked_all((3,), dtype=float)
+        result = Series(data)
+        expected = Series([np.nan, np.nan, np.nan])
+        tm.assert_series_equal(result, expected)
+
+        data[0] = 0.0
+        data[2] = 2.0
+        index = ["a", "b", "c"]
+        result = Series(data, index=index)
+        expected = Series([0.0, np.nan, 2.0], index=index)
+        tm.assert_series_equal(result, expected)
+
+        data[1] = 1.0
+        result = Series(data, index=index)
+        expected = Series([0.0, 1.0, 2.0], index=index)
+        tm.assert_series_equal(result, expected)
+
+        data = ma.masked_all((3,), dtype=int)
+        result = Series(data)
+        expected = Series([np.nan, np.nan, np.nan], dtype=float)
+        tm.assert_series_equal(result, expected)
+
+        data[0] = 0
+        data[2] = 2
+        index = ["a", "b", "c"]
+        result = Series(data, index=index)
+        expected = Series([0, np.nan, 2], index=index, dtype=float)
+        tm.assert_series_equal(result, expected)
+
+        data[1] = 1
+        result = Series(data, index=index)
+        expected = Series([0, 1, 2], index=index, dtype=int)
+        with pytest.raises(AssertionError, match="Series classes are different"):
+            # TODO should this be raising at all?
+            # https://github.com/pandas-dev/pandas/issues/56131
+            tm.assert_series_equal(result, expected)
+
+        data = ma.masked_all((3,), dtype=bool)
+        result = Series(data)
+        expected = Series([np.nan, np.nan, np.nan], dtype=object)
+        tm.assert_series_equal(result, expected)
+
+        data[0] = True
+        data[2] = False
+        index = ["a", "b", "c"]
+        result = Series(data, index=index)
+        expected = Series([True, np.nan, False], index=index, dtype=object)
+        tm.assert_series_equal(result, expected)
+
+        data[1] = True
+        result = Series(data, index=index)
+        expected = Series([True, True, False], index=index, dtype=bool)
+        with pytest.raises(AssertionError, match="Series classes are different"):
+            # TODO should this be raising at all?
+            # https://github.com/pandas-dev/pandas/issues/56131
+            tm.assert_series_equal(result, expected)
+
+        data = ma.masked_all((3,), dtype="M8[ns]")
+        result = Series(data)
+        expected = Series([iNaT, iNaT, iNaT], dtype="M8[ns]")
+        tm.assert_series_equal(result, expected)
+
+        data[0] = datetime(2001, 1, 1)
+        data[2] = datetime(2001, 1, 3)
+        index = ["a", "b", "c"]
+        result = Series(data, index=index)
+        expected = Series(
+            [datetime(2001, 1, 1), iNaT, datetime(2001, 1, 3)],
+            index=index,
+            dtype="M8[ns]",
+        )
+        tm.assert_series_equal(result, expected)
+
+        data[1] = datetime(2001, 1, 2)
+        result = Series(data, index=index)
+        expected = Series(
+            [datetime(2001, 1, 1), datetime(2001, 1, 2), datetime(2001, 1, 3)],
+            index=index,
+            dtype="M8[ns]",
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_maskedarray_hardened(self):
+        # Check numpy masked arrays with hard masks -- from GH24574
+        data = ma.masked_all((3,), dtype=float).harden_mask()
+        result = Series(data)
+        expected = Series([np.nan, np.nan, np.nan])
+        tm.assert_series_equal(result, expected)
+
+    def test_series_ctor_plus_datetimeindex(self, using_copy_on_write):
+        rng = date_range("20090415", "20090519", freq="B")
+        data = {k: 1 for k in rng}
+
+        result = Series(data, index=rng)
+        if using_copy_on_write:
+            assert result.index.is_(rng)
+        else:
+            assert result.index is rng
+
+    def test_constructor_default_index(self):
+        s = Series([0, 1, 2])
+        tm.assert_index_equal(s.index, Index(range(3)), exact=True)
+
+    @pytest.mark.parametrize(
+        "input",
+        [
+            [1, 2, 3],
+            (1, 2, 3),
+            list(range(3)),
+            Categorical(["a", "b", "a"]),
+            (i for i in range(3)),
+            (x for x in range(3)),
+        ],
+    )
+    def test_constructor_index_mismatch(self, input):
+        # GH 19342
+        # test that construction of a Series with an index of different length
+        # raises an error
+        msg = r"Length of values \(3\) does not match length of index \(4\)"
+        with pytest.raises(ValueError, match=msg):
+            Series(input, index=np.arange(4))
+
+    def test_constructor_numpy_scalar(self):
+        # GH 19342
+        # construction with a numpy scalar
+        # should not raise
+        result = Series(np.array(100), index=np.arange(4), dtype="int64")
+        expected = Series(100, index=np.arange(4), dtype="int64")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_broadcast_list(self):
+        # GH 19342
+        # construction with single-element container and index
+        # should raise
+        msg = r"Length of values \(1\) does not match length of index \(3\)"
+        with pytest.raises(ValueError, match=msg):
+            Series(["foo"], index=["a", "b", "c"])
+
+    def test_constructor_corner(self):
+        df = DataFrame(range(5), index=date_range("2020-01-01", periods=5))
+        objs = [df, df]
+        s = Series(objs, index=[0, 1])
+        assert isinstance(s, Series)
+
+    def test_constructor_sanitize(self):
+        s = Series(np.array([1.0, 1.0, 8.0]), dtype="i8")
+        assert s.dtype == np.dtype("i8")
+
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+        with pytest.raises(IntCastingNaNError, match=msg):
+            Series(np.array([1.0, 1.0, np.nan]), copy=True, dtype="i8")
+
+    def test_constructor_copy(self):
+        # GH15125
+        # test dtype parameter has no side effects on copy=True
+        for data in [[1.0], np.array([1.0])]:
+            x = Series(data)
+            y = Series(x, copy=True, dtype=float)
+
+            # copy=True maintains original data in Series
+            tm.assert_series_equal(x, y)
+
+            # changes to origin of copy does not affect the copy
+            x[0] = 2.0
+            assert not x.equals(y)
+            assert x[0] == 2.0
+            assert y[0] == 1.0
+
+    @td.skip_array_manager_invalid_test  # TODO(ArrayManager) rewrite test
+    @pytest.mark.parametrize(
+        "index",
+        [
+            date_range("20170101", periods=3, tz="US/Eastern"),
+            date_range("20170101", periods=3),
+            timedelta_range("1 day", periods=3),
+            period_range("2012Q1", periods=3, freq="Q"),
+            Index(list("abc")),
+            Index([1, 2, 3]),
+            RangeIndex(0, 3),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_constructor_limit_copies(self, index):
+        # GH 17449
+        # limit copies of input
+        s = Series(index)
+
+        # we make 1 copy; this is just a smoke test here
+        assert s._mgr.blocks[0].values is not index
+
+    def test_constructor_shallow_copy(self):
+        # constructing a Series from Series with copy=False should still
+        # give a "shallow" copy (share data, not attributes)
+        # https://github.com/pandas-dev/pandas/issues/49523
+        s = Series([1, 2, 3])
+        s_orig = s.copy()
+        s2 = Series(s)
+        assert s2._mgr is not s._mgr
+        # Overwriting index of s2 doesn't change s
+        s2.index = ["a", "b", "c"]
+        tm.assert_series_equal(s, s_orig)
+
+    def test_constructor_pass_none(self):
+        s = Series(None, index=range(5))
+        assert s.dtype == np.float64
+
+        s = Series(None, index=range(5), dtype=object)
+        assert s.dtype == np.object_
+
+        # GH 7431
+        # inference on the index
+        s = Series(index=np.array([None]))
+        expected = Series(index=Index([None]))
+        tm.assert_series_equal(s, expected)
+
+    def test_constructor_pass_nan_nat(self):
+        # GH 13467
+        exp = Series([np.nan, np.nan], dtype=np.float64)
+        assert exp.dtype == np.float64
+        tm.assert_series_equal(Series([np.nan, np.nan]), exp)
+        tm.assert_series_equal(Series(np.array([np.nan, np.nan])), exp)
+
+        exp = Series([NaT, NaT])
+        assert exp.dtype == "datetime64[ns]"
+        tm.assert_series_equal(Series([NaT, NaT]), exp)
+        tm.assert_series_equal(Series(np.array([NaT, NaT])), exp)
+
+        tm.assert_series_equal(Series([NaT, np.nan]), exp)
+        tm.assert_series_equal(Series(np.array([NaT, np.nan])), exp)
+
+        tm.assert_series_equal(Series([np.nan, NaT]), exp)
+        tm.assert_series_equal(Series(np.array([np.nan, NaT])), exp)
+
+    def test_constructor_cast(self):
+        msg = "could not convert string to float"
+        with pytest.raises(ValueError, match=msg):
+            Series(["a", "b", "c"], dtype=float)
+
+    def test_constructor_signed_int_overflow_raises(self):
+        # GH#41734 disallow silent overflow, enforced in 2.0
+        if np_version_gt2:
+            msg = "The elements provided in the data cannot all be casted to the dtype"
+            err = OverflowError
+        else:
+            msg = "Values are too large to be losslessly converted"
+            err = ValueError
+        with pytest.raises(err, match=msg):
+            Series([1, 200, 923442], dtype="int8")
+
+        with pytest.raises(err, match=msg):
+            Series([1, 200, 923442], dtype="uint8")
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            np.array([1], dtype=np.uint16),
+            np.array([1], dtype=np.uint32),
+            np.array([1], dtype=np.uint64),
+            [np.uint16(1)],
+            [np.uint32(1)],
+            [np.uint64(1)],
+        ],
+    )
+    def test_constructor_numpy_uints(self, values):
+        # GH#47294
+        value = values[0]
+        result = Series(values)
+
+        assert result[0].dtype == value.dtype
+        assert result[0] == value
+
+    def test_constructor_unsigned_dtype_overflow(self, any_unsigned_int_numpy_dtype):
+        # see gh-15832
+        if np_version_gt2:
+            msg = (
+                f"The elements provided in the data cannot "
+                f"all be casted to the dtype {any_unsigned_int_numpy_dtype}"
+            )
+        else:
+            msg = "Trying to coerce negative values to unsigned integers"
+        with pytest.raises(OverflowError, match=msg):
+            Series([-1], dtype=any_unsigned_int_numpy_dtype)
+
+    def test_constructor_floating_data_int_dtype(self, frame_or_series):
+        # GH#40110
+        arr = np.random.default_rng(2).standard_normal(2)
+
+        # Long-standing behavior (for Series, new in 2.0 for DataFrame)
+        #  has been to ignore the dtype on these;
+        #  not clear if this is what we want long-term
+        # expected = frame_or_series(arr)
+
+        # GH#49599 as of 2.0 we raise instead of silently retaining float dtype
+        msg = "Trying to coerce float values to integer"
+        with pytest.raises(ValueError, match=msg):
+            frame_or_series(arr, dtype="i8")
+
+        with pytest.raises(ValueError, match=msg):
+            frame_or_series(list(arr), dtype="i8")
+
+        # pre-2.0, when we had NaNs, we silently ignored the integer dtype
+        arr[0] = np.nan
+        # expected = frame_or_series(arr)
+
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+        with pytest.raises(IntCastingNaNError, match=msg):
+            frame_or_series(arr, dtype="i8")
+
+        exc = IntCastingNaNError
+        if frame_or_series is Series:
+            # TODO: try to align these
+            exc = ValueError
+            msg = "cannot convert float NaN to integer"
+        with pytest.raises(exc, match=msg):
+            # same behavior if we pass list instead of the ndarray
+            frame_or_series(list(arr), dtype="i8")
+
+        # float array that can be losslessly cast to integers
+        arr = np.array([1.0, 2.0], dtype="float64")
+        expected = frame_or_series(arr.astype("i8"))
+
+        obj = frame_or_series(arr, dtype="i8")
+        tm.assert_equal(obj, expected)
+
+        obj = frame_or_series(list(arr), dtype="i8")
+        tm.assert_equal(obj, expected)
+
+    def test_constructor_coerce_float_fail(self, any_int_numpy_dtype):
+        # see gh-15832
+        # Updated: make sure we treat this list the same as we would treat
+        #  the equivalent ndarray
+        # GH#49599 pre-2.0 we silently retained float dtype, in 2.0 we raise
+        vals = [1, 2, 3.5]
+
+        msg = "Trying to coerce float values to integer"
+        with pytest.raises(ValueError, match=msg):
+            Series(vals, dtype=any_int_numpy_dtype)
+        with pytest.raises(ValueError, match=msg):
+            Series(np.array(vals), dtype=any_int_numpy_dtype)
+
+    def test_constructor_coerce_float_valid(self, float_numpy_dtype):
+        s = Series([1, 2, 3.5], dtype=float_numpy_dtype)
+        expected = Series([1, 2, 3.5]).astype(float_numpy_dtype)
+        tm.assert_series_equal(s, expected)
+
+    def test_constructor_invalid_coerce_ints_with_float_nan(self, any_int_numpy_dtype):
+        # GH 22585
+        # Updated: make sure we treat this list the same as we would treat the
+        # equivalent ndarray
+        vals = [1, 2, np.nan]
+        # pre-2.0 this would return with a float dtype, in 2.0 we raise
+
+        msg = "cannot convert float NaN to integer"
+        with pytest.raises(ValueError, match=msg):
+            Series(vals, dtype=any_int_numpy_dtype)
+        msg = r"Cannot convert non-finite values \(NA or inf\) to integer"
+        with pytest.raises(IntCastingNaNError, match=msg):
+            Series(np.array(vals), dtype=any_int_numpy_dtype)
+
+    def test_constructor_dtype_no_cast(self, using_copy_on_write, warn_copy_on_write):
+        # see gh-1572
+        s = Series([1, 2, 3])
+        s2 = Series(s, dtype=np.int64)
+
+        warn = FutureWarning if warn_copy_on_write else None
+        with tm.assert_produces_warning(warn):
+            s2[1] = 5
+        if using_copy_on_write:
+            assert s[1] == 2
+        else:
+            assert s[1] == 5
+
+    def test_constructor_datelike_coercion(self):
+        # GH 9477
+        # incorrectly inferring on dateimelike looking when object dtype is
+        # specified
+        s = Series([Timestamp("20130101"), "NOV"], dtype=object)
+        assert s.iloc[0] == Timestamp("20130101")
+        assert s.iloc[1] == "NOV"
+        assert s.dtype == object
+
+    def test_constructor_datelike_coercion2(self):
+        # the dtype was being reset on the slicing and re-inferred to datetime
+        # even thought the blocks are mixed
+        belly = "216 3T19".split()
+        wing1 = "2T15 4H19".split()
+        wing2 = "416 4T20".split()
+        mat = pd.to_datetime("2016-01-22 2019-09-07".split())
+        df = DataFrame({"wing1": wing1, "wing2": wing2, "mat": mat}, index=belly)
+
+        result = df.loc["3T19"]
+        assert result.dtype == object
+        result = df.loc["216"]
+        assert result.dtype == object
+
+    def test_constructor_mixed_int_and_timestamp(self, frame_or_series):
+        # specifically Timestamp with nanos, not datetimes
+        objs = [Timestamp(9), 10, NaT._value]
+        result = frame_or_series(objs, dtype="M8[ns]")
+
+        expected = frame_or_series([Timestamp(9), Timestamp(10), NaT])
+        tm.assert_equal(result, expected)
+
+    def test_constructor_datetimes_with_nulls(self):
+        # gh-15869
+        for arr in [
+            np.array([None, None, None, None, datetime.now(), None]),
+            np.array([None, None, datetime.now(), None]),
+        ]:
+            result = Series(arr)
+            assert result.dtype == "M8[ns]"
+
+    def test_constructor_dtype_datetime64(self):
+        s = Series(iNaT, dtype="M8[ns]", index=range(5))
+        assert isna(s).all()
+
+        # in theory this should be all nulls, but since
+        # we are not specifying a dtype is ambiguous
+        s = Series(iNaT, index=range(5))
+        assert not isna(s).all()
+
+        s = Series(np.nan, dtype="M8[ns]", index=range(5))
+        assert isna(s).all()
+
+        s = Series([datetime(2001, 1, 2, 0, 0), iNaT], dtype="M8[ns]")
+        assert isna(s[1])
+        assert s.dtype == "M8[ns]"
+
+        s = Series([datetime(2001, 1, 2, 0, 0), np.nan], dtype="M8[ns]")
+        assert isna(s[1])
+        assert s.dtype == "M8[ns]"
+
+    def test_constructor_dtype_datetime64_10(self):
+        # GH3416
+        pydates = [datetime(2013, 1, 1), datetime(2013, 1, 2), datetime(2013, 1, 3)]
+        dates = [np.datetime64(x) for x in pydates]
+
+        ser = Series(dates)
+        assert ser.dtype == "M8[ns]"
+
+        ser.iloc[0] = np.nan
+        assert ser.dtype == "M8[ns]"
+
+        # GH3414 related
+        expected = Series(pydates, dtype="datetime64[ms]")
+
+        result = Series(Series(dates).astype(np.int64) / 1000000, dtype="M8[ms]")
+        tm.assert_series_equal(result, expected)
+
+        result = Series(dates, dtype="datetime64[ms]")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series(
+            [NaT, datetime(2013, 1, 2), datetime(2013, 1, 3)], dtype="datetime64[ns]"
+        )
+        result = Series([np.nan] + dates[1:], dtype="datetime64[ns]")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dtype_datetime64_11(self):
+        pydates = [datetime(2013, 1, 1), datetime(2013, 1, 2), datetime(2013, 1, 3)]
+        dates = [np.datetime64(x) for x in pydates]
+
+        dts = Series(dates, dtype="datetime64[ns]")
+
+        # valid astype
+        dts.astype("int64")
+
+        # invalid casting
+        msg = r"Converting from datetime64\[ns\] to int32 is not supported"
+        with pytest.raises(TypeError, match=msg):
+            dts.astype("int32")
+
+        # ints are ok
+        # we test with np.int64 to get similar results on
+        # windows / 32-bit platforms
+        result = Series(dts, dtype=np.int64)
+        expected = Series(dts.astype(np.int64))
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dtype_datetime64_9(self):
+        # invalid dates can be help as object
+        result = Series([datetime(2, 1, 1)])
+        assert result[0] == datetime(2, 1, 1, 0, 0)
+
+        result = Series([datetime(3000, 1, 1)])
+        assert result[0] == datetime(3000, 1, 1, 0, 0)
+
+    def test_constructor_dtype_datetime64_8(self):
+        # don't mix types
+        result = Series([Timestamp("20130101"), 1], index=["a", "b"])
+        assert result["a"] == Timestamp("20130101")
+        assert result["b"] == 1
+
+    def test_constructor_dtype_datetime64_7(self):
+        # GH6529
+        # coerce datetime64 non-ns properly
+        dates = date_range("01-Jan-2015", "01-Dec-2015", freq="ME")
+        values2 = dates.view(np.ndarray).astype("datetime64[ns]")
+        expected = Series(values2, index=dates)
+
+        for unit in ["s", "D", "ms", "us", "ns"]:
+            dtype = np.dtype(f"M8[{unit}]")
+            values1 = dates.view(np.ndarray).astype(dtype)
+            result = Series(values1, dates)
+            if unit == "D":
+                # for unit="D" we cast to nearest-supported reso, i.e. "s"
+                dtype = np.dtype("M8[s]")
+            assert result.dtype == dtype
+            tm.assert_series_equal(result, expected.astype(dtype))
+
+        # GH 13876
+        # coerce to non-ns to object properly
+        expected = Series(values2, index=dates, dtype=object)
+        for dtype in ["s", "D", "ms", "us", "ns"]:
+            values1 = dates.view(np.ndarray).astype(f"M8[{dtype}]")
+            result = Series(values1, index=dates, dtype=object)
+            tm.assert_series_equal(result, expected)
+
+        # leave datetime.date alone
+        dates2 = np.array([d.date() for d in dates.to_pydatetime()], dtype=object)
+        series1 = Series(dates2, dates)
+        tm.assert_numpy_array_equal(series1.values, dates2)
+        assert series1.dtype == object
+
+    def test_constructor_dtype_datetime64_6(self):
+        # as of 2.0, these no longer infer datetime64 based on the strings,
+        #  matching the Index behavior
+
+        ser = Series([None, NaT, "2013-08-05 15:30:00.000001"])
+        assert ser.dtype == object
+
+        ser = Series([np.nan, NaT, "2013-08-05 15:30:00.000001"])
+        assert ser.dtype == object
+
+        ser = Series([NaT, None, "2013-08-05 15:30:00.000001"])
+        assert ser.dtype == object
+
+        ser = Series([NaT, np.nan, "2013-08-05 15:30:00.000001"])
+        assert ser.dtype == object
+
+    def test_constructor_dtype_datetime64_5(self):
+        # tz-aware (UTC and other tz's)
+        # GH 8411
+        dr = date_range("20130101", periods=3)
+        assert Series(dr).iloc[0].tz is None
+        dr = date_range("20130101", periods=3, tz="UTC")
+        assert str(Series(dr).iloc[0].tz) == "UTC"
+        dr = date_range("20130101", periods=3, tz="US/Eastern")
+        assert str(Series(dr).iloc[0].tz) == "US/Eastern"
+
+    def test_constructor_dtype_datetime64_4(self):
+        # non-convertible
+        ser = Series([1479596223000, -1479590, NaT])
+        assert ser.dtype == "object"
+        assert ser[2] is NaT
+        assert "NaT" in str(ser)
+
+    def test_constructor_dtype_datetime64_3(self):
+        # if we passed a NaT it remains
+        ser = Series([datetime(2010, 1, 1), datetime(2, 1, 1), NaT])
+        assert ser.dtype == "object"
+        assert ser[2] is NaT
+        assert "NaT" in str(ser)
+
+    def test_constructor_dtype_datetime64_2(self):
+        # if we passed a nan it remains
+        ser = Series([datetime(2010, 1, 1), datetime(2, 1, 1), np.nan])
+        assert ser.dtype == "object"
+        assert ser[2] is np.nan
+        assert "NaN" in str(ser)
+
+    def test_constructor_with_datetime_tz(self):
+        # 8260
+        # support datetime64 with tz
+
+        dr = date_range("20130101", periods=3, tz="US/Eastern")
+        s = Series(dr)
+        assert s.dtype.name == "datetime64[ns, US/Eastern]"
+        assert s.dtype == "datetime64[ns, US/Eastern]"
+        assert isinstance(s.dtype, DatetimeTZDtype)
+        assert "datetime64[ns, US/Eastern]" in str(s)
+
+        # export
+        result = s.values
+        assert isinstance(result, np.ndarray)
+        assert result.dtype == "datetime64[ns]"
+
+        exp = DatetimeIndex(result)
+        exp = exp.tz_localize("UTC").tz_convert(tz=s.dt.tz)
+        tm.assert_index_equal(dr, exp)
+
+        # indexing
+        result = s.iloc[0]
+        assert result == Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern")
+        result = s[0]
+        assert result == Timestamp("2013-01-01 00:00:00-0500", tz="US/Eastern")
+
+        result = s[Series([True, True, False], index=s.index)]
+        tm.assert_series_equal(result, s[0:2])
+
+        result = s.iloc[0:1]
+        tm.assert_series_equal(result, Series(dr[0:1]))
+
+        # concat
+        result = pd.concat([s.iloc[0:1], s.iloc[1:]])
+        tm.assert_series_equal(result, s)
+
+        # short str
+        assert "datetime64[ns, US/Eastern]" in str(s)
+
+        # formatting with NaT
+        result = s.shift()
+        assert "datetime64[ns, US/Eastern]" in str(result)
+        assert "NaT" in str(result)
+
+        result = DatetimeIndex(s, freq="infer")
+        tm.assert_index_equal(result, dr)
+
+    def test_constructor_with_datetime_tz5(self):
+        # long str
+        ser = Series(date_range("20130101", periods=1000, tz="US/Eastern"))
+        assert "datetime64[ns, US/Eastern]" in str(ser)
+
+    def test_constructor_with_datetime_tz4(self):
+        # inference
+        ser = Series(
+            [
+                Timestamp("2013-01-01 13:00:00-0800", tz="US/Pacific"),
+                Timestamp("2013-01-02 14:00:00-0800", tz="US/Pacific"),
+            ]
+        )
+        assert ser.dtype == "datetime64[ns, US/Pacific]"
+        assert lib.infer_dtype(ser, skipna=True) == "datetime64"
+
+    def test_constructor_with_datetime_tz3(self):
+        ser = Series(
+            [
+                Timestamp("2013-01-01 13:00:00-0800", tz="US/Pacific"),
+                Timestamp("2013-01-02 14:00:00-0800", tz="US/Eastern"),
+            ]
+        )
+        assert ser.dtype == "object"
+        assert lib.infer_dtype(ser, skipna=True) == "datetime"
+
+    def test_constructor_with_datetime_tz2(self):
+        # with all NaT
+        ser = Series(NaT, index=[0, 1], dtype="datetime64[ns, US/Eastern]")
+        dti = DatetimeIndex(["NaT", "NaT"], tz="US/Eastern").as_unit("ns")
+        expected = Series(dti)
+        tm.assert_series_equal(ser, expected)
+
+    def test_constructor_no_partial_datetime_casting(self):
+        # GH#40111
+        vals = [
+            "nan",
+            Timestamp("1990-01-01"),
+            "2015-03-14T16:15:14.123-08:00",
+            "2019-03-04T21:56:32.620-07:00",
+            None,
+        ]
+        ser = Series(vals)
+        assert all(ser[i] is vals[i] for i in range(len(vals)))
+
+    @pytest.mark.parametrize("arr_dtype", [np.int64, np.float64])
+    @pytest.mark.parametrize("kind", ["M", "m"])
+    @pytest.mark.parametrize("unit", ["ns", "us", "ms", "s", "h", "m", "D"])
+    def test_construction_to_datetimelike_unit(self, arr_dtype, kind, unit):
+        # tests all units
+        # gh-19223
+        # TODO: GH#19223 was about .astype, doesn't belong here
+        dtype = f"{kind}8[{unit}]"
+        arr = np.array([1, 2, 3], dtype=arr_dtype)
+        ser = Series(arr)
+        result = ser.astype(dtype)
+
+        expected = Series(arr.astype(dtype))
+
+        if unit in ["ns", "us", "ms", "s"]:
+            assert result.dtype == dtype
+            assert expected.dtype == dtype
+        else:
+            # Otherwise we cast to nearest-supported unit, i.e. seconds
+            assert result.dtype == f"{kind}8[s]"
+            assert expected.dtype == f"{kind}8[s]"
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("arg", ["2013-01-01 00:00:00", NaT, np.nan, None])
+    def test_constructor_with_naive_string_and_datetimetz_dtype(self, arg):
+        # GH 17415: With naive string
+        result = Series([arg], dtype="datetime64[ns, CET]")
+        expected = Series(Timestamp(arg)).dt.tz_localize("CET")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_datetime64_bigendian(self):
+        # GH#30976
+        ms = np.datetime64(1, "ms")
+        arr = np.array([np.datetime64(1, "ms")], dtype=">M8[ms]")
+
+        result = Series(arr)
+        expected = Series([Timestamp(ms)]).astype("M8[ms]")
+        assert expected.dtype == "M8[ms]"
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("interval_constructor", [IntervalIndex, IntervalArray])
+    def test_construction_interval(self, interval_constructor):
+        # construction from interval & array of intervals
+        intervals = interval_constructor.from_breaks(np.arange(3), closed="right")
+        result = Series(intervals)
+        assert result.dtype == "interval[int64, right]"
+        tm.assert_index_equal(Index(result.values), Index(intervals))
+
+    @pytest.mark.parametrize(
+        "data_constructor", [list, np.array], ids=["list", "ndarray[object]"]
+    )
+    def test_constructor_infer_interval(self, data_constructor):
+        # GH 23563: consistent closed results in interval dtype
+        data = [Interval(0, 1), Interval(0, 2), None]
+        result = Series(data_constructor(data))
+        expected = Series(IntervalArray(data))
+        assert result.dtype == "interval[float64, right]"
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "data_constructor", [list, np.array], ids=["list", "ndarray[object]"]
+    )
+    def test_constructor_interval_mixed_closed(self, data_constructor):
+        # GH 23563: mixed closed results in object dtype (not interval dtype)
+        data = [Interval(0, 1, closed="both"), Interval(0, 2, closed="neither")]
+        result = Series(data_constructor(data))
+        assert result.dtype == object
+        assert result.tolist() == data
+
+    def test_construction_consistency(self):
+        # make sure that we are not re-localizing upon construction
+        # GH 14928
+        ser = Series(date_range("20130101", periods=3, tz="US/Eastern"))
+
+        result = Series(ser, dtype=ser.dtype)
+        tm.assert_series_equal(result, ser)
+
+        result = Series(ser.dt.tz_convert("UTC"), dtype=ser.dtype)
+        tm.assert_series_equal(result, ser)
+
+        # Pre-2.0 dt64 values were treated as utc, which was inconsistent
+        #  with DatetimeIndex, which treats them as wall times, see GH#33401
+        result = Series(ser.values, dtype=ser.dtype)
+        expected = Series(ser.values).dt.tz_localize(ser.dtype.tz)
+        tm.assert_series_equal(result, expected)
+
+        with tm.assert_produces_warning(None):
+            # one suggested alternative to the deprecated (changed in 2.0) usage
+            middle = Series(ser.values).dt.tz_localize("UTC")
+            result = middle.dt.tz_convert(ser.dtype.tz)
+        tm.assert_series_equal(result, ser)
+
+        with tm.assert_produces_warning(None):
+            # the other suggested alternative to the deprecated usage
+            result = Series(ser.values.view("int64"), dtype=ser.dtype)
+        tm.assert_series_equal(result, ser)
+
+    @pytest.mark.parametrize(
+        "data_constructor", [list, np.array], ids=["list", "ndarray[object]"]
+    )
+    def test_constructor_infer_period(self, data_constructor):
+        data = [Period("2000", "D"), Period("2001", "D"), None]
+        result = Series(data_constructor(data))
+        expected = Series(period_array(data))
+        tm.assert_series_equal(result, expected)
+        assert result.dtype == "Period[D]"
+
+    @pytest.mark.xfail(reason="PeriodDtype Series not supported yet")
+    def test_construct_from_ints_including_iNaT_scalar_period_dtype(self):
+        series = Series([0, 1000, 2000, pd._libs.iNaT], dtype="period[D]")
+
+        val = series[3]
+        assert isna(val)
+
+        series[2] = val
+        assert isna(series[2])
+
+    def test_constructor_period_incompatible_frequency(self):
+        data = [Period("2000", "D"), Period("2001", "Y")]
+        result = Series(data)
+        assert result.dtype == object
+        assert result.tolist() == data
+
+    def test_constructor_periodindex(self):
+        # GH7932
+        # converting a PeriodIndex when put in a Series
+
+        pi = period_range("20130101", periods=5, freq="D")
+        s = Series(pi)
+        assert s.dtype == "Period[D]"
+        with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+            expected = Series(pi.astype(object))
+        tm.assert_series_equal(s, expected)
+
+    def test_constructor_dict(self):
+        d = {"a": 0.0, "b": 1.0, "c": 2.0}
+
+        result = Series(d)
+        expected = Series(d, index=sorted(d.keys()))
+        tm.assert_series_equal(result, expected)
+
+        result = Series(d, index=["b", "c", "d", "a"])
+        expected = Series([1, 2, np.nan, 0], index=["b", "c", "d", "a"])
+        tm.assert_series_equal(result, expected)
+
+        pidx = period_range("2020-01-01", periods=10, freq="D")
+        d = {pidx[0]: 0, pidx[1]: 1}
+        result = Series(d, index=pidx)
+        expected = Series(np.nan, pidx, dtype=np.float64)
+        expected.iloc[0] = 0
+        expected.iloc[1] = 1
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dict_list_value_explicit_dtype(self):
+        # GH 18625
+        d = {"a": [[2], [3], [4]]}
+        result = Series(d, index=["a"], dtype="object")
+        expected = Series(d, index=["a"])
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dict_order(self):
+        # GH19018
+        # initialization ordering: by insertion order
+        d = {"b": 1, "a": 0, "c": 2}
+        result = Series(d)
+        expected = Series([1, 0, 2], index=list("bac"))
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dict_extension(self, ea_scalar_and_dtype, request):
+        ea_scalar, ea_dtype = ea_scalar_and_dtype
+        if isinstance(ea_scalar, Timestamp):
+            mark = pytest.mark.xfail(
+                reason="Construction from dict goes through "
+                "maybe_convert_objects which casts to nano"
+            )
+            request.applymarker(mark)
+        d = {"a": ea_scalar}
+        result = Series(d, index=["a"])
+        expected = Series(ea_scalar, index=["a"], dtype=ea_dtype)
+
+        assert result.dtype == ea_dtype
+
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("value", [2, np.nan, None, float("nan")])
+    def test_constructor_dict_nan_key(self, value):
+        # GH 18480
+        d = {1: "a", value: "b", float("nan"): "c", 4: "d"}
+        result = Series(d).sort_values()
+        expected = Series(["a", "b", "c", "d"], index=[1, value, np.nan, 4])
+        tm.assert_series_equal(result, expected)
+
+        # MultiIndex:
+        d = {(1, 1): "a", (2, np.nan): "b", (3, value): "c"}
+        result = Series(d).sort_values()
+        expected = Series(
+            ["a", "b", "c"], index=Index([(1, 1), (2, np.nan), (3, value)])
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dict_datetime64_index(self):
+        # GH 9456
+
+        dates_as_str = ["1984-02-19", "1988-11-06", "1989-12-03", "1990-03-15"]
+        values = [42544017.198965244, 1234565, 40512335.181958228, -1]
+
+        def create_data(constructor):
+            return dict(zip((constructor(x) for x in dates_as_str), values))
+
+        data_datetime64 = create_data(np.datetime64)
+        data_datetime = create_data(lambda x: datetime.strptime(x, "%Y-%m-%d"))
+        data_Timestamp = create_data(Timestamp)
+
+        expected = Series(values, (Timestamp(x) for x in dates_as_str))
+
+        result_datetime64 = Series(data_datetime64)
+        result_datetime = Series(data_datetime)
+        result_Timestamp = Series(data_Timestamp)
+
+        tm.assert_series_equal(result_datetime64, expected)
+        tm.assert_series_equal(result_datetime, expected)
+        tm.assert_series_equal(result_Timestamp, expected)
+
+    def test_constructor_dict_tuple_indexer(self):
+        # GH 12948
+        data = {(1, 1, None): -1.0}
+        result = Series(data)
+        expected = Series(
+            -1.0, index=MultiIndex(levels=[[1], [1], [np.nan]], codes=[[0], [0], [-1]])
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_mapping(self, non_dict_mapping_subclass):
+        # GH 29788
+        ndm = non_dict_mapping_subclass({3: "three"})
+        result = Series(ndm)
+        expected = Series(["three"], index=[3])
+
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_list_of_tuples(self):
+        data = [(1, 1), (2, 2), (2, 3)]
+        s = Series(data)
+        assert list(s) == data
+
+    def test_constructor_tuple_of_tuples(self):
+        data = ((1, 1), (2, 2), (2, 3))
+        s = Series(data)
+        assert tuple(s) == data
+
+    def test_constructor_dict_of_tuples(self):
+        data = {(1, 2): 3, (None, 5): 6}
+        result = Series(data).sort_values()
+        expected = Series([3, 6], index=MultiIndex.from_tuples([(1, 2), (None, 5)]))
+        tm.assert_series_equal(result, expected)
+
+    # https://github.com/pandas-dev/pandas/issues/22698
+    @pytest.mark.filterwarnings("ignore:elementwise comparison:FutureWarning")
+    def test_fromDict(self, using_infer_string):
+        data = {"a": 0, "b": 1, "c": 2, "d": 3}
+
+        series = Series(data)
+        tm.assert_is_sorted(series.index)
+
+        data = {"a": 0, "b": "1", "c": "2", "d": datetime.now()}
+        series = Series(data)
+        assert series.dtype == np.object_
+
+        data = {"a": 0, "b": "1", "c": "2", "d": "3"}
+        series = Series(data)
+        assert series.dtype == np.object_ if not using_infer_string else "str"
+
+        data = {"a": "0", "b": "1"}
+        series = Series(data, dtype=float)
+        assert series.dtype == np.float64
+
+    def test_fromValue(self, datetime_series, using_infer_string):
+        nans = Series(np.nan, index=datetime_series.index, dtype=np.float64)
+        assert nans.dtype == np.float64
+        assert len(nans) == len(datetime_series)
+
+        strings = Series("foo", index=datetime_series.index)
+        assert strings.dtype == np.object_ if not using_infer_string else "str"
+        assert len(strings) == len(datetime_series)
+
+        d = datetime.now()
+        dates = Series(d, index=datetime_series.index)
+        assert dates.dtype == "M8[us]"
+        assert len(dates) == len(datetime_series)
+
+        # GH12336
+        # Test construction of categorical series from value
+        categorical = Series(0, index=datetime_series.index, dtype="category")
+        expected = Series(0, index=datetime_series.index).astype("category")
+        assert categorical.dtype == "category"
+        assert len(categorical) == len(datetime_series)
+        tm.assert_series_equal(categorical, expected)
+
+    def test_constructor_dtype_timedelta64(self):
+        # basic
+        td = Series([timedelta(days=i) for i in range(3)])
+        assert td.dtype == "timedelta64[ns]"
+
+        td = Series([timedelta(days=1)])
+        assert td.dtype == "timedelta64[ns]"
+
+        td = Series([timedelta(days=1), timedelta(days=2), np.timedelta64(1, "s")])
+
+        assert td.dtype == "timedelta64[ns]"
+
+        # mixed with NaT
+        td = Series([timedelta(days=1), NaT], dtype="m8[ns]")
+        assert td.dtype == "timedelta64[ns]"
+
+        td = Series([timedelta(days=1), np.nan], dtype="m8[ns]")
+        assert td.dtype == "timedelta64[ns]"
+
+        td = Series([np.timedelta64(300000000), NaT], dtype="m8[ns]")
+        assert td.dtype == "timedelta64[ns]"
+
+        # improved inference
+        # GH5689
+        td = Series([np.timedelta64(300000000), NaT])
+        assert td.dtype == "timedelta64[ns]"
+
+        # because iNaT is int, not coerced to timedelta
+        td = Series([np.timedelta64(300000000), iNaT])
+        assert td.dtype == "object"
+
+        td = Series([np.timedelta64(300000000), np.nan])
+        assert td.dtype == "timedelta64[ns]"
+
+        td = Series([NaT, np.timedelta64(300000000)])
+        assert td.dtype == "timedelta64[ns]"
+
+        td = Series([np.timedelta64(1, "s")])
+        assert td.dtype == "timedelta64[ns]"
+
+        # valid astype
+        td.astype("int64")
+
+        # invalid casting
+        msg = r"Converting from timedelta64\[ns\] to int32 is not supported"
+        with pytest.raises(TypeError, match=msg):
+            td.astype("int32")
+
+        # this is an invalid casting
+        msg = "|".join(
+            [
+                "Could not convert object to NumPy timedelta",
+                "Could not convert 'foo' to NumPy timedelta",
+            ]
+        )
+        with pytest.raises(ValueError, match=msg):
+            Series([timedelta(days=1), "foo"], dtype="m8[ns]")
+
+        # leave as object here
+        td = Series([timedelta(days=i) for i in range(3)] + ["foo"])
+        assert td.dtype == "object"
+
+        # as of 2.0, these no longer infer timedelta64 based on the strings,
+        #  matching Index behavior
+        ser = Series([None, NaT, "1 Day"])
+        assert ser.dtype == object
+
+        ser = Series([np.nan, NaT, "1 Day"])
+        assert ser.dtype == object
+
+        ser = Series([NaT, None, "1 Day"])
+        assert ser.dtype == object
+
+        ser = Series([NaT, np.nan, "1 Day"])
+        assert ser.dtype == object
+
+    # GH 16406
+    def test_constructor_mixed_tz(self):
+        s = Series([Timestamp("20130101"), Timestamp("20130101", tz="US/Eastern")])
+        expected = Series(
+            [Timestamp("20130101"), Timestamp("20130101", tz="US/Eastern")],
+            dtype="object",
+        )
+        tm.assert_series_equal(s, expected)
+
+    def test_NaT_scalar(self):
+        series = Series([0, 1000, 2000, iNaT], dtype="M8[ns]")
+
+        val = series[3]
+        assert isna(val)
+
+        series[2] = val
+        assert isna(series[2])
+
+    def test_NaT_cast(self):
+        # GH10747
+        result = Series([np.nan]).astype("M8[ns]")
+        expected = Series([NaT], dtype="M8[ns]")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_name_hashable(self):
+        for n in [777, 777.0, "name", datetime(2001, 11, 11), (1,), "\u05D0"]:
+            for data in [[1, 2, 3], np.ones(3), {"a": 0, "b": 1}]:
+                s = Series(data, name=n)
+                assert s.name == n
+
+    def test_constructor_name_unhashable(self):
+        msg = r"Series\.name must be a hashable type"
+        for n in [["name_list"], np.ones(2), {1: 2}]:
+            for data in [["name_list"], np.ones(2), {1: 2}]:
+                with pytest.raises(TypeError, match=msg):
+                    Series(data, name=n)
+
+    def test_auto_conversion(self):
+        series = Series(list(date_range("1/1/2000", periods=10)))
+        assert series.dtype == "M8[ns]"
+
+    def test_convert_non_ns(self):
+        # convert from a numpy array of non-ns timedelta64
+        arr = np.array([1, 2, 3], dtype="timedelta64[s]")
+        ser = Series(arr)
+        assert ser.dtype == arr.dtype
+
+        tdi = timedelta_range("00:00:01", periods=3, freq="s").as_unit("s")
+        expected = Series(tdi)
+        assert expected.dtype == arr.dtype
+        tm.assert_series_equal(ser, expected)
+
+        # convert from a numpy array of non-ns datetime64
+        arr = np.array(
+            ["2013-01-01", "2013-01-02", "2013-01-03"], dtype="datetime64[D]"
+        )
+        ser = Series(arr)
+        expected = Series(date_range("20130101", periods=3, freq="D"), dtype="M8[s]")
+        assert expected.dtype == "M8[s]"
+        tm.assert_series_equal(ser, expected)
+
+        arr = np.array(
+            ["2013-01-01 00:00:01", "2013-01-01 00:00:02", "2013-01-01 00:00:03"],
+            dtype="datetime64[s]",
+        )
+        ser = Series(arr)
+        expected = Series(
+            date_range("20130101 00:00:01", periods=3, freq="s"), dtype="M8[s]"
+        )
+        assert expected.dtype == "M8[s]"
+        tm.assert_series_equal(ser, expected)
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            date_range("1/1/2000", periods=10),
+            timedelta_range("1 day", periods=10),
+            period_range("2000-Q1", periods=10, freq="Q"),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_constructor_cant_cast_datetimelike(self, index):
+        # floats are not ok
+        # strip Index to convert PeriodIndex -> Period
+        # We don't care whether the error message says
+        # PeriodIndex or PeriodArray
+        msg = f"Cannot cast {type(index).__name__.rstrip('Index')}.*? to "
+
+        with pytest.raises(TypeError, match=msg):
+            Series(index, dtype=float)
+
+        # ints are ok
+        # we test with np.int64 to get similar results on
+        # windows / 32-bit platforms
+        result = Series(index, dtype=np.int64)
+        expected = Series(index.astype(np.int64))
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            date_range("1/1/2000", periods=10),
+            timedelta_range("1 day", periods=10),
+            period_range("2000-Q1", periods=10, freq="Q"),
+        ],
+        ids=lambda x: type(x).__name__,
+    )
+    def test_constructor_cast_object(self, index):
+        s = Series(index, dtype=object)
+        exp = Series(index).astype(object)
+        tm.assert_series_equal(s, exp)
+
+        s = Series(Index(index, dtype=object), dtype=object)
+        exp = Series(index).astype(object)
+        tm.assert_series_equal(s, exp)
+
+        s = Series(index.astype(object), dtype=object)
+        exp = Series(index).astype(object)
+        tm.assert_series_equal(s, exp)
+
+    @pytest.mark.parametrize("dtype", [np.datetime64, np.timedelta64])
+    def test_constructor_generic_timestamp_no_frequency(self, dtype, request):
+        # see gh-15524, gh-15987
+        msg = "dtype has no unit. Please pass in"
+
+        if np.dtype(dtype).name not in ["timedelta64", "datetime64"]:
+            mark = pytest.mark.xfail(reason="GH#33890 Is assigned ns unit")
+            request.applymarker(mark)
+
+        with pytest.raises(ValueError, match=msg):
+            Series([], dtype=dtype)
+
+    @pytest.mark.parametrize("unit", ["ps", "as", "fs", "Y", "M", "W", "D", "h", "m"])
+    @pytest.mark.parametrize("kind", ["m", "M"])
+    def test_constructor_generic_timestamp_bad_frequency(self, kind, unit):
+        # see gh-15524, gh-15987
+        # as of 2.0 we raise on any non-supported unit rather than silently
+        #  cast to nanos; previously we only raised for frequencies higher
+        #  than ns
+        dtype = f"{kind}8[{unit}]"
+
+        msg = "dtype=.* is not supported. Supported resolutions are"
+        with pytest.raises(TypeError, match=msg):
+            Series([], dtype=dtype)
+
+        with pytest.raises(TypeError, match=msg):
+            # pre-2.0 the DataFrame cast raised but the Series case did not
+            DataFrame([[0]], dtype=dtype)
+
+    @pytest.mark.parametrize("dtype", [None, "uint8", "category"])
+    def test_constructor_range_dtype(self, dtype):
+        # GH 16804
+        expected = Series([0, 1, 2, 3, 4], dtype=dtype or "int64")
+        result = Series(range(5), dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_range_overflows(self):
+        # GH#30173 range objects that overflow int64
+        rng = range(2**63, 2**63 + 4)
+        ser = Series(rng)
+        expected = Series(list(rng))
+        tm.assert_series_equal(ser, expected)
+        assert list(ser) == list(rng)
+        assert ser.dtype == np.uint64
+
+        rng2 = range(2**63 + 4, 2**63, -1)
+        ser2 = Series(rng2)
+        expected2 = Series(list(rng2))
+        tm.assert_series_equal(ser2, expected2)
+        assert list(ser2) == list(rng2)
+        assert ser2.dtype == np.uint64
+
+        rng3 = range(-(2**63), -(2**63) - 4, -1)
+        ser3 = Series(rng3)
+        expected3 = Series(list(rng3))
+        tm.assert_series_equal(ser3, expected3)
+        assert list(ser3) == list(rng3)
+        assert ser3.dtype == object
+
+        rng4 = range(2**73, 2**73 + 4)
+        ser4 = Series(rng4)
+        expected4 = Series(list(rng4))
+        tm.assert_series_equal(ser4, expected4)
+        assert list(ser4) == list(rng4)
+        assert ser4.dtype == object
+
+    def test_constructor_tz_mixed_data(self):
+        # GH 13051
+        dt_list = [
+            Timestamp("2016-05-01 02:03:37"),
+            Timestamp("2016-04-30 19:03:37-0700", tz="US/Pacific"),
+        ]
+        result = Series(dt_list)
+        expected = Series(dt_list, dtype=object)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("pydt", [True, False])
+    def test_constructor_data_aware_dtype_naive(self, tz_aware_fixture, pydt):
+        # GH#25843, GH#41555, GH#33401
+        tz = tz_aware_fixture
+        ts = Timestamp("2019", tz=tz)
+        if pydt:
+            ts = ts.to_pydatetime()
+
+        msg = (
+            "Cannot convert timezone-aware data to timezone-naive dtype. "
+            r"Use pd.Series\(values\).dt.tz_localize\(None\) instead."
+        )
+        with pytest.raises(ValueError, match=msg):
+            Series([ts], dtype="datetime64[ns]")
+
+        with pytest.raises(ValueError, match=msg):
+            Series(np.array([ts], dtype=object), dtype="datetime64[ns]")
+
+        with pytest.raises(ValueError, match=msg):
+            Series({0: ts}, dtype="datetime64[ns]")
+
+        msg = "Cannot unbox tzaware Timestamp to tznaive dtype"
+        with pytest.raises(TypeError, match=msg):
+            Series(ts, index=[0], dtype="datetime64[ns]")
+
+    def test_constructor_datetime64(self):
+        rng = date_range("1/1/2000 00:00:00", "1/1/2000 1:59:50", freq="10s")
+        dates = np.asarray(rng)
+
+        series = Series(dates)
+        assert np.issubdtype(series.dtype, np.dtype("M8[ns]"))
+
+    def test_constructor_datetimelike_scalar_to_string_dtype(
+        self, nullable_string_dtype
+    ):
+        # https://github.com/pandas-dev/pandas/pull/33846
+        result = Series("M", index=[1, 2, 3], dtype=nullable_string_dtype)
+        expected = Series(["M", "M", "M"], index=[1, 2, 3], dtype=nullable_string_dtype)
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "values",
+        [
+            [np.datetime64("2012-01-01"), np.datetime64("2013-01-01")],
+            ["2012-01-01", "2013-01-01"],
+        ],
+    )
+    def test_constructor_sparse_datetime64(self, values):
+        # https://github.com/pandas-dev/pandas/issues/35762
+        dtype = pd.SparseDtype("datetime64[ns]")
+        result = Series(values, dtype=dtype)
+        arr = pd.arrays.SparseArray(values, dtype=dtype)
+        expected = Series(arr)
+        tm.assert_series_equal(result, expected)
+
+    def test_construction_from_ordered_collection(self):
+        # https://github.com/pandas-dev/pandas/issues/36044
+        result = Series({"a": 1, "b": 2}.keys())
+        expected = Series(["a", "b"])
+        tm.assert_series_equal(result, expected)
+
+        result = Series({"a": 1, "b": 2}.values())
+        expected = Series([1, 2])
+        tm.assert_series_equal(result, expected)
+
+    def test_construction_from_large_int_scalar_no_overflow(self):
+        # https://github.com/pandas-dev/pandas/issues/36291
+        n = 1_000_000_000_000_000_000_000
+        result = Series(n, index=[0])
+        expected = Series(n)
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_list_of_periods_infers_period_dtype(self):
+        series = Series(list(period_range("2000-01-01", periods=10, freq="D")))
+        assert series.dtype == "Period[D]"
+
+        series = Series(
+            [Period("2011-01-01", freq="D"), Period("2011-02-01", freq="D")]
+        )
+        assert series.dtype == "Period[D]"
+
+    def test_constructor_subclass_dict(self, dict_subclass):
+        data = dict_subclass((x, 10.0 * x) for x in range(10))
+        series = Series(data)
+        expected = Series(dict(data.items()))
+        tm.assert_series_equal(series, expected)
+
+    def test_constructor_ordereddict(self):
+        # GH3283
+        data = OrderedDict(
+            (f"col{i}", np.random.default_rng(2).random()) for i in range(12)
+        )
+
+        series = Series(data)
+        expected = Series(list(data.values()), list(data.keys()))
+        tm.assert_series_equal(series, expected)
+
+        # Test with subclass
+        class A(OrderedDict):
+            pass
+
+        series = Series(A(data))
+        tm.assert_series_equal(series, expected)
+
+    def test_constructor_dict_multiindex(self):
+        d = {("a", "a"): 0.0, ("b", "a"): 1.0, ("b", "c"): 2.0}
+        _d = sorted(d.items())
+        result = Series(d)
+        expected = Series(
+            [x[1] for x in _d], index=MultiIndex.from_tuples([x[0] for x in _d])
+        )
+        tm.assert_series_equal(result, expected)
+
+        d["z"] = 111.0
+        _d.insert(0, ("z", d["z"]))
+        result = Series(d)
+        expected = Series(
+            [x[1] for x in _d], index=Index([x[0] for x in _d], tupleize_cols=False)
+        )
+        result = result.reindex(index=expected.index)
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dict_multiindex_reindex_flat(self):
+        # construction involves reindexing with a MultiIndex corner case
+        data = {("i", "i"): 0, ("i", "j"): 1, ("j", "i"): 2, "j": np.nan}
+        expected = Series(data)
+
+        result = Series(expected[:-1].to_dict(), index=expected.index)
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_dict_timedelta_index(self):
+        # GH #12169 : Resample category data with timedelta index
+        # construct Series from dict as data and TimedeltaIndex as index
+        # will result NaN in result Series data
+        expected = Series(
+            data=["A", "B", "C"], index=pd.to_timedelta([0, 10, 20], unit="s")
+        )
+
+        result = Series(
+            data={
+                pd.to_timedelta(0, unit="s"): "A",
+                pd.to_timedelta(10, unit="s"): "B",
+                pd.to_timedelta(20, unit="s"): "C",
+            },
+            index=pd.to_timedelta([0, 10, 20], unit="s"),
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_infer_index_tz(self):
+        values = [188.5, 328.25]
+        tzinfo = tzoffset(None, 7200)
+        index = [
+            datetime(2012, 5, 11, 11, tzinfo=tzinfo),
+            datetime(2012, 5, 11, 12, tzinfo=tzinfo),
+        ]
+        series = Series(data=values, index=index)
+
+        assert series.index.tz == tzinfo
+
+        # it works! GH#2443
+        repr(series.index[0])
+
+    def test_constructor_with_pandas_dtype(self):
+        # going through 2D->1D path
+        vals = [(1,), (2,), (3,)]
+        ser = Series(vals)
+        dtype = ser.array.dtype  # NumpyEADtype
+        ser2 = Series(vals, dtype=dtype)
+        tm.assert_series_equal(ser, ser2)
+
+    def test_constructor_int_dtype_missing_values(self):
+        # GH#43017
+        result = Series(index=[0], dtype="int64")
+        expected = Series(np.nan, index=[0], dtype="float64")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_bool_dtype_missing_values(self):
+        # GH#43018
+        result = Series(index=[0], dtype="bool")
+        expected = Series(True, index=[0], dtype="bool")
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_int64_dtype(self, any_int_dtype):
+        # GH#44923
+        result = Series(["0", "1", "2"], dtype=any_int_dtype)
+        expected = Series([0, 1, 2], dtype=any_int_dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_constructor_raise_on_lossy_conversion_of_strings(self):
+        # GH#44923
+        if not np_version_gt2:
+            raises = pytest.raises(
+                ValueError, match="string values cannot be losslessly cast to int8"
+            )
+        else:
+            raises = pytest.raises(
+                OverflowError, match="The elements provided in the data"
+            )
+        with raises:
+            Series(["128"], dtype="int8")
+
+    def test_constructor_dtype_timedelta_alternative_construct(self):
+        # GH#35465
+        result = Series([1000000, 200000, 3000000], dtype="timedelta64[ns]")
+        expected = Series(pd.to_timedelta([1000000, 200000, 3000000], unit="ns"))
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.xfail(
+        reason="Not clear what the correct expected behavior should be with "
+        "integers now that we support non-nano. ATM (2022-10-08) we treat ints "
+        "as nanoseconds, then cast to the requested dtype. xref #48312"
+    )
+    def test_constructor_dtype_timedelta_ns_s(self):
+        # GH#35465
+        result = Series([1000000, 200000, 3000000], dtype="timedelta64[ns]")
+        expected = Series([1000000, 200000, 3000000], dtype="timedelta64[s]")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.xfail(
+        reason="Not clear what the correct expected behavior should be with "
+        "integers now that we support non-nano. ATM (2022-10-08) we treat ints "
+        "as nanoseconds, then cast to the requested dtype. xref #48312"
+    )
+    def test_constructor_dtype_timedelta_ns_s_astype_int64(self):
+        # GH#35465
+        result = Series([1000000, 200000, 3000000], dtype="timedelta64[ns]").astype(
+            "int64"
+        )
+        expected = Series([1000000, 200000, 3000000], dtype="timedelta64[s]").astype(
+            "int64"
+        )
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.filterwarnings(
+        "ignore:elementwise comparison failed:DeprecationWarning"
+    )
+    @pytest.mark.parametrize("func", [Series, DataFrame, Index, pd.array])
+    def test_constructor_mismatched_null_nullable_dtype(
+        self, func, any_numeric_ea_dtype
+    ):
+        # GH#44514
+        msg = "|".join(
+            [
+                "cannot safely cast non-equivalent object",
+                r"int\(\) argument must be a string, a bytes-like object "
+                "or a (real )?number",
+                r"Cannot cast array data from dtype\('O'\) to dtype\('float64'\) "
+                "according to the rule 'safe'",
+                "object cannot be converted to a FloatingDtype",
+                "'values' contains non-numeric NA",
+            ]
+        )
+
+        for null in tm.NP_NAT_OBJECTS + [NaT]:
+            with pytest.raises(TypeError, match=msg):
+                func([null, 1.0, 3.0], dtype=any_numeric_ea_dtype)
+
+    def test_series_constructor_ea_int_from_bool(self):
+        # GH#42137
+        result = Series([True, False, True, pd.NA], dtype="Int64")
+        expected = Series([1, 0, 1, pd.NA], dtype="Int64")
+        tm.assert_series_equal(result, expected)
+
+        result = Series([True, False, True], dtype="Int64")
+        expected = Series([1, 0, 1], dtype="Int64")
+        tm.assert_series_equal(result, expected)
+
+    def test_series_constructor_ea_int_from_string_bool(self):
+        # GH#42137
+        with pytest.raises(ValueError, match="invalid literal"):
+            Series(["True", "False", "True", pd.NA], dtype="Int64")
+
+    @pytest.mark.parametrize("val", [1, 1.0])
+    def test_series_constructor_overflow_uint_ea(self, val):
+        # GH#38798
+        max_val = np.iinfo(np.uint64).max - 1
+        result = Series([max_val, val], dtype="UInt64")
+        expected = Series(np.array([max_val, 1], dtype="uint64"), dtype="UInt64")
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("val", [1, 1.0])
+    def test_series_constructor_overflow_uint_ea_with_na(self, val):
+        # GH#38798
+        max_val = np.iinfo(np.uint64).max - 1
+        result = Series([max_val, val, pd.NA], dtype="UInt64")
+        expected = Series(
+            IntegerArray(
+                np.array([max_val, 1, 0], dtype="uint64"),
+                np.array([0, 0, 1], dtype=np.bool_),
+            )
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_series_constructor_overflow_uint_with_nan(self):
+        # GH#38798
+        max_val = np.iinfo(np.uint64).max - 1
+        result = Series([max_val, np.nan], dtype="UInt64")
+        expected = Series(
+            IntegerArray(
+                np.array([max_val, 1], dtype="uint64"),
+                np.array([0, 1], dtype=np.bool_),
+            )
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_series_constructor_ea_all_na(self):
+        # GH#38798
+        result = Series([np.nan, np.nan], dtype="UInt64")
+        expected = Series(
+            IntegerArray(
+                np.array([1, 1], dtype="uint64"),
+                np.array([1, 1], dtype=np.bool_),
+            )
+        )
+        tm.assert_series_equal(result, expected)
+
+    def test_series_from_index_dtype_equal_does_not_copy(self):
+        # GH#52008
+        idx = Index([1, 2, 3])
+        expected = idx.copy(deep=True)
+        ser = Series(idx, dtype="int64")
+        ser.iloc[0] = 100
+        tm.assert_index_equal(idx, expected)
+
+    def test_series_string_inference(self):
+        # GH#54430
+        with pd.option_context("future.infer_string", True):
+            ser = Series(["a", "b"])
+        dtype = pd.StringDtype("pyarrow" if HAS_PYARROW else "python", na_value=np.nan)
+        expected = Series(["a", "b"], dtype=dtype)
+        tm.assert_series_equal(ser, expected)
+
+        expected = Series(["a", 1], dtype="object")
+        with pd.option_context("future.infer_string", True):
+            ser = Series(["a", 1])
+        tm.assert_series_equal(ser, expected)
+
+    @pytest.mark.parametrize("na_value", [None, np.nan, pd.NA])
+    def test_series_string_with_na_inference(self, na_value):
+        # GH#54430
+        with pd.option_context("future.infer_string", True):
+            ser = Series(["a", na_value])
+        dtype = pd.StringDtype("pyarrow" if HAS_PYARROW else "python", na_value=np.nan)
+        expected = Series(["a", None], dtype=dtype)
+        tm.assert_series_equal(ser, expected)
+
+    def test_series_string_inference_scalar(self):
+        # GH#54430
+        with pd.option_context("future.infer_string", True):
+            ser = Series("a", index=[1])
+        dtype = pd.StringDtype("pyarrow" if HAS_PYARROW else "python", na_value=np.nan)
+        expected = Series("a", index=[1], dtype=dtype)
+        tm.assert_series_equal(ser, expected)
+
+    def test_series_string_inference_array_string_dtype(self):
+        # GH#54496
+        with pd.option_context("future.infer_string", True):
+            ser = Series(np.array(["a", "b"]))
+        dtype = pd.StringDtype("pyarrow" if HAS_PYARROW else "python", na_value=np.nan)
+        expected = Series(["a", "b"], dtype=dtype)
+        tm.assert_series_equal(ser, expected)
+
+    def test_series_string_inference_storage_definition(self):
+        # https://github.com/pandas-dev/pandas/issues/54793
+        # but after PDEP-14 (string dtype), it was decided to keep dtype="string"
+        # returning the NA string dtype, so expected is changed from
+        # "string[pyarrow_numpy]" to "string[python]"
+        expected = Series(["a", "b"], dtype="string[python]")
+        with pd.option_context("future.infer_string", True):
+            result = Series(["a", "b"], dtype="string")
+        tm.assert_series_equal(result, expected)
+
+        expected = Series(["a", "b"], dtype=pd.StringDtype(na_value=np.nan))
+        with pd.option_context("future.infer_string", True):
+            result = Series(["a", "b"], dtype="str")
+        tm.assert_series_equal(result, expected)
+
+    def test_series_constructor_infer_string_scalar(self):
+        # GH#55537
+        with pd.option_context("future.infer_string", True):
+            ser = Series("a", index=[1, 2], dtype="string[python]")
+        expected = Series(["a", "a"], index=[1, 2], dtype="string[python]")
+        tm.assert_series_equal(ser, expected)
+        assert ser.dtype.storage == "python"
+
+    def test_series_string_inference_na_first(self):
+        # GH#55655
+        with pd.option_context("future.infer_string", True):
+            result = Series([pd.NA, "b"])
+        dtype = pd.StringDtype("pyarrow" if HAS_PYARROW else "python", na_value=np.nan)
+        expected = Series([None, "b"], dtype=dtype)
+        tm.assert_series_equal(result, expected)
+
+    def test_inference_on_pandas_objects(self):
+        # GH#56012
+        ser = Series([Timestamp("2019-12-31")], dtype=object)
+        with tm.assert_produces_warning(None):
+            # This doesn't do inference
+            result = Series(ser)
+        assert result.dtype == np.object_
+
+        idx = Index([Timestamp("2019-12-31")], dtype=object)
+
+        with tm.assert_produces_warning(FutureWarning, match="Dtype inference"):
+            result = Series(idx)
+        assert result.dtype != np.object_
+
+
+class TestSeriesConstructorIndexCoercion:
+    def test_series_constructor_datetimelike_index_coercion(self):
+        idx = date_range("2020-01-01", periods=5)
+        ser = Series(
+            np.random.default_rng(2).standard_normal(len(idx)), idx.astype(object)
+        )
+        # as of 2.0, we no longer silently cast the object-dtype index
+        #  to DatetimeIndex GH#39307, GH#23598
+        assert not isinstance(ser.index, DatetimeIndex)
+
+    @pytest.mark.parametrize("container", [None, np.array, Series, Index])
+    @pytest.mark.parametrize("data", [1.0, range(4)])
+    def test_series_constructor_infer_multiindex(self, container, data):
+        indexes = [["a", "a", "b", "b"], ["x", "y", "x", "y"]]
+        if container is not None:
+            indexes = [container(ind) for ind in indexes]
+
+        multi = Series(data, index=indexes)
+        assert isinstance(multi.index, MultiIndex)
+
+    # TODO: make this not cast to object in pandas 3.0
+    @pytest.mark.skipif(
+        not np_version_gt2, reason="StringDType only available in numpy 2 and above"
+    )
+    @pytest.mark.parametrize(
+        "data",
+        [
+            ["a", "b", "c"],
+            ["a", "b", np.nan],
+        ],
+    )
+    def test_np_string_array_object_cast(self, data):
+        from numpy.dtypes import StringDType
+
+        arr = np.array(data, dtype=StringDType())
+        res = Series(arr)
+        assert res.dtype == np.object_
+        assert (res == data).all()
+
+
+class TestSeriesConstructorInternals:
+    def test_constructor_no_pandas_array(self, using_array_manager):
+        ser = Series([1, 2, 3])
+        result = Series(ser.array)
+        tm.assert_series_equal(ser, result)
+        if not using_array_manager:
+            assert isinstance(result._mgr.blocks[0], NumpyBlock)
+            assert result._mgr.blocks[0].is_numeric
+
+    @td.skip_array_manager_invalid_test
+    def test_from_array(self):
+        result = Series(pd.array(["1h", "2h"], dtype="timedelta64[ns]"))
+        assert result._mgr.blocks[0].is_extension is False
+
+        result = Series(pd.array(["2015"], dtype="datetime64[ns]"))
+        assert result._mgr.blocks[0].is_extension is False
+
+    @td.skip_array_manager_invalid_test
+    def test_from_list_dtype(self):
+        result = Series(["1h", "2h"], dtype="timedelta64[ns]")
+        assert result._mgr.blocks[0].is_extension is False
+
+        result = Series(["2015"], dtype="datetime64[ns]")
+        assert result._mgr.blocks[0].is_extension is False
+
+
+def test_constructor(rand_series_with_duplicate_datetimeindex):
+    dups = rand_series_with_duplicate_datetimeindex
+    assert isinstance(dups, Series)
+    assert isinstance(dups.index, DatetimeIndex)
+
+
+@pytest.mark.parametrize(
+    "input_dict,expected",
+    [
+        ({0: 0}, np.array([[0]], dtype=np.int64)),
+        ({"a": "a"}, np.array([["a"]], dtype=object)),
+        ({1: 1}, np.array([[1]], dtype=np.int64)),
+    ],
+)
+def test_numpy_array(input_dict, expected):
+    result = np.array([Series(input_dict)])
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_index_ordered_dict_keys():
+    # GH 22077
+
+    param_index = OrderedDict(
+        [
+            ((("a", "b"), ("c", "d")), 1),
+            ((("a", None), ("c", "d")), 2),
+        ]
+    )
+    series = Series([1, 2], index=param_index.keys())
+    expected = Series(
+        [1, 2],
+        index=MultiIndex.from_tuples(
+            [(("a", "b"), ("c", "d")), (("a", None), ("c", "d"))]
+        ),
+    )
+    tm.assert_series_equal(series, expected)
+
+
+@pytest.mark.parametrize(
+    "input_list",
+    [
+        [1, complex("nan"), 2],
+        [1 + 1j, complex("nan"), 2 + 2j],
+    ],
+)
+def test_series_with_complex_nan(input_list):
+    # GH#53627
+    ser = Series(input_list)
+    result = Series(ser.array)
+    assert ser.dtype == "complex128"
+    tm.assert_series_equal(ser, result)
diff --git a/shared/package-files/ec/ec358d58fa0bbe5fd98bda17718418cc1b56223bfbb0b7bb87bdfd8a3b00ac14/common_state_dict.py b/shared/package-files/ec/ec358d58fa0bbe5fd98bda17718418cc1b56223bfbb0b7bb87bdfd8a3b00ac14/common_state_dict.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa05792dc176ff4a8e9f6845669536978bab7a3c
--- /dev/null
+++ b/shared/package-files/ec/ec358d58fa0bbe5fd98bda17718418cc1b56223bfbb0b7bb87bdfd8a3b00ac14/common_state_dict.py
@@ -0,0 +1,113 @@
+# mypy: ignore-errors
+
+# Owner(s): ["oncall: distributed"]
+
+import copy
+from itertools import chain
+from typing import Any, Dict
+
+import torch
+import torch.nn as nn
+
+from torch.distributed._sharded_tensor import ShardedTensor
+from torch.distributed._state_dict_utils import _gather_state_dict
+from torch.distributed._tensor import DTensor
+from torch.distributed.checkpoint.state_dict import (
+    PG,
+    set_state_dict,
+    STATE,
+    StateDictOptions,
+)
+
+
+class VerifyStateDictMixin:
+    def _compare_tensor(self, orig_tensor, dist_tensor):
+        if isinstance(dist_tensor, (DTensor, ShardedTensor)):
+            dist_tensor = _gather_state_dict({"mykey": dist_tensor}).pop("mykey")
+        self.assertTrue(isinstance(dist_tensor, torch.Tensor))
+        self.assertTrue(torch.allclose(orig_tensor, dist_tensor))
+
+    def _verify_msd(
+        self,
+        msd: Dict[str, Any],
+        dist_msd: Dict[str, Any],
+        options: StateDictOptions = StateDictOptions(),
+    ) -> None:
+        if not options.ignore_frozen_params:
+            self.assertEqual(len(msd), len(dist_msd))
+        for fqn, param in msd.items():
+            dist_param = dist_msd.get(fqn, None)
+            if not options.ignore_frozen_params:
+                self.assertIsNotNone(dist_param, f"{fqn=}")
+                self._compare_tensor(param, dist_param)
+            elif dist_param is None:
+                self.assertFalse(param.requires_grad, f"{fqn=}")
+
+    def _verify_osd(
+        self,
+        model: nn.Module,
+        optim: torch.optim.Optimizer,
+        osd: Dict[str, Any],
+        dist_osd: Dict[str, Any],
+    ) -> None:
+        params = list(chain.from_iterable(g["params"] for g in optim.param_groups))
+        param_pid_mapping = dict(zip(params, range(len(params))))
+        fqn_pid_mapping = {}
+        for fqn, param in model.named_parameters():
+            pid = param_pid_mapping[param]
+            fqn_pid_mapping[fqn] = pid
+            fqn_pid_mapping[pid] = fqn
+        # Check optimizer_state_dict state
+
+        self.assertEqual(len(osd[STATE]), len(dist_osd[STATE]))
+        for pid, states in osd[STATE].items():
+            fqn = fqn_pid_mapping[pid]
+            dist_states = dist_osd[STATE].get(fqn, None)
+            self.assertIsNotNone(dist_states, fqn)
+            self.assertEqual(len(states), len(dist_states))
+            for key, state in states.items():
+                dist_state = states.get(key, None)
+                self.assertIsNotNone(dist_state)
+                self._compare_tensor(state, dist_state)
+
+        # Check optimizer_state_dict param_group
+        old_dist_osd_pg = dist_osd[PG]
+        if len(osd[PG]) != len(dist_osd[PG]):
+            self.assertTrue(len(dist_osd[PG]) > len(osd[PG]))
+            new_pg = copy.deepcopy(dist_osd[PG][0])
+            new_pg["params"] = []
+            for dist_group in dist_osd[PG]:
+                new_pg["params"].extend(dist_group["params"])
+            dist_osd[PG] = [new_pg]
+
+        self.assertEqual(len(osd[PG]), len(dist_osd[PG]))
+        for group, dist_group in zip(osd[PG], dist_osd[PG]):
+            self.assertEqual(len(group), len(dist_group))
+            for key, value in group.items():
+                # Below doesn't work because param_groups can have None
+                # values.
+                # dist_value = dist_group.get(key, None)
+                # self.assertIsNotNone(dist_value, (dist_group, group))
+                dist_value = dist_group[key]
+                if key == "params":
+                    fqns = [fqn_pid_mapping[pid] for pid in value]
+                    self.assertEqual(sorted(fqns), sorted(dist_value))
+                else:
+                    self.assertEqual(value, dist_value)
+        dist_osd[PG] = old_dist_osd_pg
+
+    def _verify_osd_by_load(
+        self,
+        model: nn.Module,
+        optim: torch.optim.Optimizer,
+        new_optim: torch.optim.Optimizer,
+        dist_osd: Dict[str, Any],
+    ) -> None:
+        new_dist_osd = _gather_state_dict(dist_osd)
+        set_state_dict(
+            model,
+            optimizers=new_optim,
+            model_state_dict={},
+            optim_state_dict=new_dist_osd,
+        )
+        self.assertEqual(optim.state_dict(), new_optim.state_dict())
diff --git a/shared/package-files/ec/ec374fd69ec1057652394dbfd4fc77fadaaadf59dc8a847d116d4a34301c24ff/solve.py b/shared/package-files/ec/ec374fd69ec1057652394dbfd4fc77fadaaadf59dc8a847d116d4a34301c24ff/solve.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d1113bea891df2be89e7bb324a558a73afc0425
--- /dev/null
+++ b/shared/package-files/ec/ec374fd69ec1057652394dbfd4fc77fadaaadf59dc8a847d116d4a34301c24ff/solve.py
@@ -0,0 +1,175 @@
+import logging
+
+from typing import Dict, Optional, Tuple, Type
+
+import sympy
+
+from torch.utils._sympy.functions import FloorDiv
+
+log = logging.getLogger(__name__)
+
+_MIRROR_REL_OP: Dict[Type[sympy.Basic], Type[sympy.Rel]] = {
+    sympy.Eq: sympy.Eq,
+    sympy.Ne: sympy.Ne,
+    sympy.Ge: sympy.Le,
+    sympy.Gt: sympy.Lt,
+    sympy.Le: sympy.Ge,
+    sympy.Lt: sympy.Gt,
+}
+
+INEQUALITY_TYPES = (sympy.Gt, sympy.Ge, sympy.Lt, sympy.Le)
+
+
+def mirror_rel_op(type: Type) -> Optional[Type[sympy.Rel]]:
+    return _MIRROR_REL_OP.get(type, None)
+
+
+# Tries to simplify 'expr', so as to leave only 'thing' in the left-hand side.
+#
+# Returns a tuple of:
+#   1. The simplified expression
+#   2. The expression on the right-hand side
+#
+# Returns 'None' if it can't reach a state where the only thing in the left
+# hand side is 'thing'.
+#
+# 'trials': number of times 'try_solve' will try to isolate 'thing' to the
+# left-hand side.
+#
+# 'floordiv_inequality': flag to enable conversion of 'FloorDiv' into
+# inequalities.
+def try_solve(
+    expr: sympy.Basic,
+    thing: sympy.Basic,
+    trials: int = 5,
+    floordiv_inequality: bool = True,
+) -> Optional[Tuple[sympy.Rel, sympy.Basic]]:
+    mirror = mirror_rel_op(type(expr))
+
+    # Ignore unsupported expressions:
+    #   - Those that are not relational operations
+    #   - Those that don't have a mirror (just avoiding unexpected classes)
+    if not isinstance(expr, sympy.Rel) or mirror is None:
+        log.debug("expression with unsupported type: %s", type(expr))
+        return None
+
+    lhs_has_thing = expr.lhs.has(thing)
+    rhs_has_thing = expr.rhs.has(thing)
+
+    # Give up when 'thing' appears on both sides of the relational expression.
+    # That is because, as is, we assume the thing we are trying to isolate is
+    # only on the right-hand side.
+    if lhs_has_thing and rhs_has_thing:
+        log.debug("thing (%s) found in both sides of expression: %s", thing, expr)
+        return None
+
+    # Try considering both LHS and RHS by mirroring the original expression:
+    # a < b ==> b > a
+    expressions = []
+
+    # Add each version of 'expr' if 'thing' is in its left-hand side.
+    if lhs_has_thing:
+        expressions.append(expr)
+    if rhs_has_thing:
+        expressions.append(mirror(expr.rhs, expr.lhs))
+
+    for e in expressions:
+        if e is None:
+            continue
+
+        assert isinstance(e, sympy.Rel)
+
+        for _ in range(trials):
+            trial = _try_isolate_lhs(e, thing, floordiv_inequality=floordiv_inequality)
+            # Stop if there was no change in this trial.
+            if trial == e:
+                break
+            e = trial  # type: ignore[assignment]
+
+        # Return if we were able to isolate 'thing' on the left-hand side.
+        if isinstance(e, sympy.Rel) and e.lhs == thing:
+            return e, e.rhs
+
+    return None
+
+
+def _try_isolate_lhs(
+    expr: sympy.Basic, thing: sympy.Basic, floordiv_inequality: bool
+) -> sympy.Basic:
+    e = expr
+    op = type(expr)
+
+    if isinstance(e, sympy.Rel):
+        # Move any constants in the left-hand side to the right-hand side.
+        lhs_not_thing = (
+            sum([a for a in e.lhs.args if not a.has(thing)])
+            if isinstance(e.lhs, sympy.Add)
+            else 0
+        )
+        e = op(expr.lhs - lhs_not_thing, expr.rhs - lhs_not_thing)  # type: ignore[attr-defined]
+
+    # Divide both sides by the factors that don't contain thing.
+    if isinstance(e, sympy.Rel) and isinstance(e.lhs, sympy.Mul):
+        lhs, rhs = e.args
+        other = sympy.Mul(*[a for a in lhs.args if not a.has(thing)])
+
+        # If we can't tell whether 'other' is negative or positive, we do nothing.
+        # That is because we don't know whether we have mirror the operation or not.
+        if not (isinstance(e, INEQUALITY_TYPES) and other.is_negative is None):
+            # Divide both sides by 'other'.
+            lhs = lhs / other
+            rhs = rhs / other
+
+            # If 'e' is an inequality and 'other' is negative, we have to
+            # mirror the expression.
+            if isinstance(e, INEQUALITY_TYPES) and other.is_negative:
+                op = mirror_rel_op(op)  # type: ignore[assignment]
+
+            assert op is not None
+            e = op(lhs, rhs)
+
+    ################################################################################
+    # left-hand side is FloorDiv
+    ################################################################################
+    #
+    # Given the expression: a // b op c
+    # where 'op' is a relational operation, these rules only work if:
+    #   - b > 0
+    #   - c is an integer
+    if (
+        floordiv_inequality
+        and isinstance(e, sympy.Rel)
+        and isinstance(e.lhs, FloorDiv)
+        and e.lhs.divisor.is_positive
+        and e.rhs.is_integer
+    ):
+        # a // b == expr
+        # => a >= (b * expr) and a < (b * (expr + 1))
+        if isinstance(expr, sympy.Eq):
+            numerator, denominator = e.lhs.args
+            return sympy.And(
+                sympy.Ge(numerator, (e.rhs * denominator)),  # type: ignore[arg-type]
+                sympy.Lt(numerator, ((e.rhs + 1) * denominator)),  # type: ignore[arg-type]
+            )
+        # a // b != expr
+        # => a < (b * expr) or a >= (b * (expr + 1))
+        if isinstance(expr, sympy.Ne):
+            numerator, denominator = e.lhs.args
+            return sympy.Or(
+                sympy.Lt(numerator, (e.rhs * denominator)),  # type: ignore[arg-type]
+                sympy.Ge(numerator, ((e.rhs + 1) * denominator)),  # type: ignore[arg-type]
+            )
+        # The transformations below only work if b is positive.
+        # Note: we only have this information for constants.
+        # a // b > expr  => a >= b * (expr + 1)
+        # a // b >= expr => a >= b * expr
+        if isinstance(expr, (sympy.Gt, sympy.Ge)):
+            quotient = e.rhs if isinstance(expr, sympy.Ge) else (e.rhs + 1)  # type: ignore[arg-type]
+            return sympy.Ge(e.lhs.args[0], (quotient * e.lhs.args[1]))  # type: ignore[arg-type]
+        # a // b < expr  => a < b * expr
+        # a // b <= expr => a < b * (expr + 1)
+        if isinstance(expr, (sympy.Lt, sympy.Le)):
+            quotient = e.rhs if isinstance(expr, sympy.Lt) else (e.rhs + 1)  # type: ignore[arg-type]
+            return sympy.Lt(e.lhs.args[0], (quotient * e.lhs.args[1]))  # type: ignore[arg-type]
+
+    return e
diff --git a/shared/package-files/ec/ec3850df132deb79558a7a1642fd2fc08e95986d887cd3e53292d11aefe0abeb/exceptions.pyi b/shared/package-files/ec/ec3850df132deb79558a7a1642fd2fc08e95986d887cd3e53292d11aefe0abeb/exceptions.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..4dd76cca56ae6805c9db5579f607be3ab72723af
--- /dev/null
+++ b/shared/package-files/ec/ec3850df132deb79558a7a1642fd2fc08e95986d887cd3e53292d11aefe0abeb/exceptions.pyi
@@ -0,0 +1,58 @@
+from typing import Any
+
+class KazooException(Exception): ...
+class ZookeeperError(KazooException): ...
+class CancelledError(KazooException): ...
+class ConfigurationError(KazooException): ...
+class ZookeeperStoppedError(KazooException): ...
+class ConnectionDropped(KazooException): ...
+class LockTimeout(KazooException): ...
+class WriterNotClosedException(KazooException): ...
+
+EXCEPTIONS: Any
+
+class RolledBackError(ZookeeperError): ...
+class SystemZookeeperError(ZookeeperError): ...
+class RuntimeInconsistency(ZookeeperError): ...
+class DataInconsistency(ZookeeperError): ...
+class ConnectionLoss(ZookeeperError): ...
+class MarshallingError(ZookeeperError): ...
+class UnimplementedError(ZookeeperError): ...
+class OperationTimeoutError(ZookeeperError): ...
+class BadArgumentsError(ZookeeperError): ...
+class NewConfigNoQuorumError(ZookeeperError): ...
+class ReconfigInProcessError(ZookeeperError): ...
+class APIError(ZookeeperError): ...
+class NoNodeError(ZookeeperError): ...
+class NoAuthError(ZookeeperError): ...
+class BadVersionError(ZookeeperError): ...
+class NoChildrenForEphemeralsError(ZookeeperError): ...
+class NodeExistsError(ZookeeperError): ...
+class NotEmptyError(ZookeeperError): ...
+class SessionExpiredError(ZookeeperError): ...
+class InvalidCallbackError(ZookeeperError): ...
+class InvalidACLError(ZookeeperError): ...
+class AuthFailedError(ZookeeperError): ...
+class SessionMovedError(ZookeeperError): ...
+class NotReadOnlyCallError(ZookeeperError): ...
+class ConnectionClosedError(SessionExpiredError): ...
+
+ConnectionLossException: Any
+MarshallingErrorException: Any
+SystemErrorException: Any
+RuntimeInconsistencyException: Any
+DataInconsistencyException: Any
+UnimplementedException: Any
+OperationTimeoutException: Any
+BadArgumentsException: Any
+ApiErrorException: Any
+NoNodeException: Any
+NoAuthException: Any
+BadVersionException: Any
+NoChildrenForEphemeralsException: Any
+NodeExistsException: Any
+InvalidACLException: Any
+AuthFailedException: Any
+NotEmptyException: Any
+SessionExpiredException: Any
+InvalidCallbackException: Any
diff --git a/shared/package-files/ec/ec38a6025e191ed909521bb52d1c24e1b82482a74e9eec24c4d3e1dd7b69034a/tokenutil.py b/shared/package-files/ec/ec38a6025e191ed909521bb52d1c24e1b82482a74e9eec24c4d3e1dd7b69034a/tokenutil.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b99a58ae23b881af28db4737030ad84cc4455a8
--- /dev/null
+++ b/shared/package-files/ec/ec38a6025e191ed909521bb52d1c24e1b82482a74e9eec24c4d3e1dd7b69034a/tokenutil.py
@@ -0,0 +1,160 @@
+"""Token-related utilities"""
+
+# Copyright (c) IPython Development Team.
+# Distributed under the terms of the Modified BSD License.
+
+from collections import namedtuple
+from io import StringIO
+from keyword import iskeyword
+
+import tokenize
+from tokenize import TokenInfo
+from typing import List, Optional
+
+
+Token = namedtuple('Token', ['token', 'text', 'start', 'end', 'line'])
+
+def generate_tokens(readline):
+    """wrap generate_tkens to catch EOF errors"""
+    try:
+        for token in tokenize.generate_tokens(readline):
+            yield token
+    except tokenize.TokenError:
+        # catch EOF error
+        return
+
+
+def generate_tokens_catch_errors(
+    readline, extra_errors_to_catch: Optional[List[str]] = None
+):
+    default_errors_to_catch = [
+        "unterminated string literal",
+        "invalid non-printable character",
+        "after line continuation character",
+    ]
+    assert extra_errors_to_catch is None or isinstance(extra_errors_to_catch, list)
+    errors_to_catch = default_errors_to_catch + (extra_errors_to_catch or [])
+
+    tokens: List[TokenInfo] = []
+    try:
+        for token in tokenize.generate_tokens(readline):
+            tokens.append(token)
+            yield token
+    except tokenize.TokenError as exc:
+        if any(error in exc.args[0] for error in errors_to_catch):
+            if tokens:
+                start = tokens[-1].start[0], tokens[-1].end[0]
+                end = start
+                line = tokens[-1].line
+            else:
+                start = end = (1, 0)
+                line = ""
+            yield tokenize.TokenInfo(tokenize.ERRORTOKEN, "", start, end, line)
+        else:
+            # Catch EOF
+            raise
+
+
+def line_at_cursor(cell, cursor_pos=0):
+    """Return the line in a cell at a given cursor position
+
+    Used for calling line-based APIs that don't support multi-line input, yet.
+
+    Parameters
+    ----------
+    cell : str
+        multiline block of text
+    cursor_pos : integer
+        the cursor position
+
+    Returns
+    -------
+    (line, offset): (string, integer)
+        The line with the current cursor, and the character offset of the start of the line.
+    """
+    offset = 0
+    lines = cell.splitlines(True)
+    for line in lines:
+        next_offset = offset + len(line)
+        if not line.endswith('\n'):
+            # If the last line doesn't have a trailing newline, treat it as if
+            # it does so that the cursor at the end of the line still counts
+            # as being on that line.
+            next_offset += 1
+        if next_offset > cursor_pos:
+            break
+        offset = next_offset
+    else:
+        line = ""
+    return (line, offset)
+
+
+def token_at_cursor(cell: str, cursor_pos: int = 0):
+    """Get the token at a given cursor
+
+    Used for introspection.
+
+    Function calls are prioritized, so the token for the callable will be returned
+    if the cursor is anywhere inside the call.
+
+    Parameters
+    ----------
+    cell : str
+        A block of Python code
+    cursor_pos : int
+        The location of the cursor in the block where the token should be found
+    """
+    names: List[str] = []
+    tokens: List[Token] = []
+    call_names = []
+    
+    offsets = {1: 0} # lines start at 1
+    for tup in generate_tokens(StringIO(cell).readline):
+        
+        tok = Token(*tup)
+        
+        # token, text, start, end, line = tup
+        start_line, start_col = tok.start
+        end_line, end_col = tok.end
+        if end_line + 1 not in offsets:
+            # keep track of offsets for each line
+            lines = tok.line.splitlines(True)
+            for lineno, line in enumerate(lines, start_line + 1):
+                if lineno not in offsets:
+                    offsets[lineno] = offsets[lineno-1] + len(line)
+        
+        offset = offsets[start_line]
+        # allow '|foo' to find 'foo' at the beginning of a line
+        boundary = cursor_pos + 1 if start_col == 0 else cursor_pos
+        if offset + start_col >= boundary:
+            # current token starts after the cursor,
+            # don't consume it
+            break
+        
+        if tok.token == tokenize.NAME and not iskeyword(tok.text):
+            if names and tokens and tokens[-1].token == tokenize.OP and tokens[-1].text == '.':
+                names[-1] = "%s.%s" % (names[-1], tok.text)
+            else:
+                names.append(tok.text)
+        elif tok.token == tokenize.OP:
+            if tok.text == '=' and names:
+                # don't inspect the lhs of an assignment
+                names.pop(-1)
+            if tok.text == '(' and names:
+                # if we are inside a function call, inspect the function
+                call_names.append(names[-1])
+            elif tok.text == ')' and call_names:
+                call_names.pop(-1)
+        
+        tokens.append(tok)
+        
+        if offsets[end_line] + end_col > cursor_pos:
+            # we found the cursor, stop reading
+            break
+        
+    if call_names:
+        return call_names[-1]
+    elif names:
+        return names[-1]
+    else:
+        return ''
diff --git a/shared/package-files/ec/ec3af6d57ca0c417c35613abf3715f58488936dfd6eae3733a6c8b7deae613fa/structures.pyi b/shared/package-files/ec/ec3af6d57ca0c417c35613abf3715f58488936dfd6eae3733a6c8b7deae613fa/structures.pyi
new file mode 100644
index 0000000000000000000000000000000000000000..ffc459796cb40cb0047621563ce2e604bca9eb61
--- /dev/null
+++ b/shared/package-files/ec/ec3af6d57ca0c417c35613abf3715f58488936dfd6eae3733a6c8b7deae613fa/structures.pyi
@@ -0,0 +1,20 @@
+from typing import Any, Dict, Generic, Iterable, Iterator, Mapping, MutableMapping, Optional, Tuple, TypeVar, Union
+
+_VT = TypeVar("_VT")
+
+class CaseInsensitiveDict(MutableMapping[str, _VT], Generic[_VT]):
+    def __init__(self, data: Optional[Union[Mapping[str, _VT], Iterable[Tuple[str, _VT]]]] = ..., **kwargs: _VT) -> None: ...
+    def lower_items(self) -> Iterator[Tuple[str, _VT]]: ...
+    def __setitem__(self, key: str, value: _VT) -> None: ...
+    def __getitem__(self, key: str) -> _VT: ...
+    def __delitem__(self, key: str) -> None: ...
+    def __iter__(self) -> Iterator[str]: ...
+    def __len__(self) -> int: ...
+    def copy(self) -> CaseInsensitiveDict[_VT]: ...
+
+class LookupDict(Dict[str, _VT]):
+    name: Any
+    def __init__(self, name: Any = ...) -> None: ...
+    def __getitem__(self, key: str) -> Optional[_VT]: ...  # type: ignore
+    def __getattr__(self, attr: str) -> _VT: ...
+    def __setattr__(self, attr: str, value: _VT) -> None: ...
diff --git a/shared/package-files/ec/ec3c3e65f1f6ec86f268b27d56c2453f91c8f46bd18b2cfde80112ed72105f24/core.py b/shared/package-files/ec/ec3c3e65f1f6ec86f268b27d56c2453f91c8f46bd18b2cfde80112ed72105f24/core.py
new file mode 100644
index 0000000000000000000000000000000000000000..83edacbfefda22b6cf94224c60ee8abf1c2432d5
--- /dev/null
+++ b/shared/package-files/ec/ec3c3e65f1f6ec86f268b27d56c2453f91c8f46bd18b2cfde80112ed72105f24/core.py
@@ -0,0 +1,2485 @@
+# This file is part of Hypothesis, which may be found at
+# https://github.com/HypothesisWorks/hypothesis/
+#
+# Copyright the Hypothesis Authors.
+# Individual contributors are listed in AUTHORS.rst and the git log.
+#
+# This Source Code Form is subject to the terms of the Mozilla Public License,
+# v. 2.0. If a copy of the MPL was not distributed with this file, You can
+# obtain one at https://mozilla.org/MPL/2.0/.
+
+import codecs
+import enum
+import math
+import operator
+import random
+import re
+import string
+import sys
+import typing
+import warnings
+from contextvars import ContextVar
+from decimal import Context, Decimal, localcontext
+from fractions import Fraction
+from functools import lru_cache, reduce
+from inspect import Parameter, Signature, isabstract, isclass
+from types import FunctionType
+from typing import (
+    Any,
+    AnyStr,
+    Callable,
+    Collection,
+    Dict,
+    FrozenSet,
+    Hashable,
+    Iterable,
+    List,
+    Literal,
+    Optional,
+    Pattern,
+    Protocol,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    TypeVar,
+    Union,
+    get_args,
+    get_origin,
+    overload,
+)
+from uuid import UUID
+
+import attr
+
+from hypothesis._settings import note_deprecation
+from hypothesis.control import (
+    RandomSeeder,
+    cleanup,
+    current_build_context,
+    deprecate_random_in_strategy,
+    note,
+)
+from hypothesis.errors import (
+    HypothesisSideeffectWarning,
+    HypothesisWarning,
+    InvalidArgument,
+    ResolutionFailed,
+    RewindRecursive,
+    SmallSearchSpaceWarning,
+)
+from hypothesis.internal.cathetus import cathetus
+from hypothesis.internal.charmap import (
+    as_general_categories,
+    categories as all_categories,
+)
+from hypothesis.internal.compat import (
+    Concatenate,
+    ParamSpec,
+    bit_count,
+    ceil,
+    floor,
+    get_type_hints,
+    is_typed_named_tuple,
+)
+from hypothesis.internal.conjecture.utils import calc_label_from_cls, check_sample
+from hypothesis.internal.entropy import get_seeder_and_restorer
+from hypothesis.internal.floats import float_of
+from hypothesis.internal.observability import TESTCASE_CALLBACKS
+from hypothesis.internal.reflection import (
+    define_function_signature,
+    get_pretty_function_description,
+    get_signature,
+    is_first_param_referenced_in_function,
+    nicerepr,
+    repr_call,
+    required_args,
+)
+from hypothesis.internal.validation import (
+    check_type,
+    check_valid_integer,
+    check_valid_interval,
+    check_valid_magnitude,
+    check_valid_size,
+    check_valid_sizes,
+    try_convert,
+)
+from hypothesis.strategies._internal import SearchStrategy, check_strategy
+from hypothesis.strategies._internal.collections import (
+    FixedAndOptionalKeysDictStrategy,
+    FixedKeysDictStrategy,
+    ListStrategy,
+    TupleStrategy,
+    UniqueListStrategy,
+    UniqueSampledListStrategy,
+    tuples,
+)
+from hypothesis.strategies._internal.deferred import DeferredStrategy
+from hypothesis.strategies._internal.functions import FunctionStrategy
+from hypothesis.strategies._internal.lazy import LazyStrategy, unwrap_strategies
+from hypothesis.strategies._internal.misc import BooleansStrategy, just, none, nothing
+from hypothesis.strategies._internal.numbers import (
+    IntegersStrategy,
+    Real,
+    floats,
+    integers,
+)
+from hypothesis.strategies._internal.recursive import RecursiveStrategy
+from hypothesis.strategies._internal.shared import SharedStrategy
+from hypothesis.strategies._internal.strategies import (
+    Ex,
+    Ex_Inv,
+    SampledFromStrategy,
+    T,
+    one_of,
+)
+from hypothesis.strategies._internal.strings import (
+    BytesStrategy,
+    OneCharStringStrategy,
+    TextStrategy,
+)
+from hypothesis.strategies._internal.utils import (
+    cacheable,
+    defines_strategy,
+    to_jsonable,
+)
+from hypothesis.utils.conventions import not_set
+from hypothesis.vendor.pretty import RepresentationPrinter
+
+if sys.version_info >= (3, 10):
+    from types import EllipsisType as EllipsisType
+    from typing import TypeAlias as TypeAlias
+elif typing.TYPE_CHECKING:  # pragma: no cover
+    from builtins import ellipsis as EllipsisType
+
+    from typing_extensions import TypeAlias
+else:
+    EllipsisType = type(Ellipsis)  # pragma: no cover
+
+
+@cacheable
+@defines_strategy(force_reusable_values=True)
+def booleans() -> SearchStrategy[bool]:
+    """Returns a strategy which generates instances of :class:`python:bool`.
+
+    Examples from this strategy will shrink towards ``False`` (i.e.
+    shrinking will replace ``True`` with ``False`` where possible).
+    """
+    return BooleansStrategy()
+
+
+@overload
+def sampled_from(elements: Sequence[T]) -> SearchStrategy[T]:  # pragma: no cover
+    ...
+
+
+@overload
+def sampled_from(elements: Type[enum.Enum]) -> SearchStrategy[Any]:  # pragma: no cover
+    # `SearchStrategy[Enum]` is unreliable due to metaclass issues.
+    ...
+
+
+@overload
+def sampled_from(
+    elements: Union[Type[enum.Enum], Sequence[Any]]
+) -> SearchStrategy[Any]:  # pragma: no cover
+    ...
+
+
+@defines_strategy(try_non_lazy=True)
+def sampled_from(
+    elements: Union[Type[enum.Enum], Sequence[Any]]
+) -> SearchStrategy[Any]:
+    """Returns a strategy which generates any value present in ``elements``.
+
+    Note that as with :func:`~hypothesis.strategies.just`, values will not be
+    copied and thus you should be careful of using mutable data.
+
+    ``sampled_from`` supports ordered collections, as well as
+    :class:`~python:enum.Enum` objects.  :class:`~python:enum.Flag` objects
+    may also generate any combination of their members.
+
+    Examples from this strategy shrink by replacing them with values earlier in
+    the list. So e.g. ``sampled_from([10, 1])`` will shrink by trying to replace
+    1 values with 10, and ``sampled_from([1, 10])`` will shrink by trying to
+    replace 10 values with 1.
+
+    It is an error to sample from an empty sequence, because returning :func:`nothing`
+    makes it too easy to silently drop parts of compound strategies.  If you need
+    that behaviour, use ``sampled_from(seq) if seq else nothing()``.
+    """
+    values = check_sample(elements, "sampled_from")
+    try:
+        if isinstance(elements, type) and issubclass(elements, enum.Enum):
+            repr_ = f"sampled_from({elements.__module__}.{elements.__name__})"
+        else:
+            repr_ = f"sampled_from({elements!r})"
+    except Exception:  # pragma: no cover
+        repr_ = None
+    if isclass(elements) and issubclass(elements, enum.Flag):
+        # Combinations of enum.Flag members (including empty) are also members.  We generate these
+        # dynamically, because static allocation takes O(2^n) memory.  LazyStrategy is used for the
+        # ease of force_repr.
+        # Add all named values, both flag bits (== list(elements)) and aliases. The aliases are
+        # necessary for full coverage for flags that would fail enum.NAMED_FLAGS check, and they
+        # are also nice values to shrink to.
+        flags = sorted(
+            set(elements.__members__.values()),
+            key=lambda v: (bit_count(v.value), v.value),
+        )
+        # Finally, try to construct the empty state if it is not named. It's placed at the
+        # end so that we shrink to named values.
+        flags_with_empty = flags
+        if not flags or flags[0].value != 0:
+            try:
+                flags_with_empty = [*flags, elements(0)]
+            except TypeError:  # pragma: no cover
+                # Happens on some python versions (at least 3.12) when there are no named values
+                pass
+        inner = [
+            # Consider one or no named flags set, with shrink-to-named-flag behaviour.
+            # Special cases (length zero or one) are handled by the inner sampled_from.
+            sampled_from(flags_with_empty),
+        ]
+        if len(flags) > 1:
+            inner += [
+                # Uniform distribution over number of named flags or combinations set. The overlap
+                # at r=1 is intentional, it may lead to oversampling but gives consistent shrinking
+                # behaviour.
+                integers(min_value=1, max_value=len(flags))
+                .flatmap(lambda r: sets(sampled_from(flags), min_size=r, max_size=r))
+                .map(lambda s: elements(reduce(operator.or_, s))),
+            ]
+        return LazyStrategy(one_of, args=inner, kwargs={}, force_repr=repr_)
+    if not values:
+        if (
+            isinstance(elements, type)
+            and issubclass(elements, enum.Enum)
+            and vars(elements).get("__annotations__")
+        ):
+            # See https://github.com/HypothesisWorks/hypothesis/issues/2923
+            raise InvalidArgument(
+                f"Cannot sample from {elements.__module__}.{elements.__name__} "
+                "because it contains no elements.  It does however have annotations, "
+                "so maybe you tried to write an enum as if it was a dataclass?"
+            )
+        raise InvalidArgument("Cannot sample from a length-zero sequence.")
+    if len(values) == 1:
+        return just(values[0])
+    return SampledFromStrategy(values, repr_)
+
+
+def identity(x):
+    return x
+
+
+@cacheable
+@defines_strategy()
+def lists(
+    elements: SearchStrategy[Ex],
+    *,
+    min_size: int = 0,
+    max_size: Optional[int] = None,
+    unique_by: Union[
+        None,
+        Callable[[Ex], Hashable],
+        Tuple[Callable[[Ex], Hashable], ...],
+    ] = None,
+    unique: bool = False,
+) -> SearchStrategy[List[Ex]]:
+    """Returns a list containing values drawn from elements with length in the
+    interval [min_size, max_size] (no bounds in that direction if these are
+    None). If max_size is 0, only the empty list will be drawn.
+
+    If ``unique`` is True (or something that evaluates to True), we compare direct
+    object equality, as if unique_by was ``lambda x: x``. This comparison only
+    works for hashable types.
+
+    If ``unique_by`` is not None it must be a callable or tuple of callables
+    returning a hashable type when given a value drawn from elements. The
+    resulting list will satisfy the condition that for ``i`` != ``j``,
+    ``unique_by(result[i])`` != ``unique_by(result[j])``.
+
+    If ``unique_by`` is a tuple of callables the uniqueness will be respective
+    to each callable.
+
+    For example, the following will produce two columns of integers with both
+    columns being unique respectively.
+
+    .. code-block:: pycon
+
+        >>> twoints = st.tuples(st.integers(), st.integers())
+        >>> st.lists(twoints, unique_by=(lambda x: x[0], lambda x: x[1]))
+
+    Examples from this strategy shrink by trying to remove elements from the
+    list, and by shrinking each individual element of the list.
+    """
+    check_valid_sizes(min_size, max_size)
+    check_strategy(elements, "elements")
+    if unique:
+        if unique_by is not None:
+            raise InvalidArgument(
+                "cannot specify both unique and unique_by "
+                "(you probably only want to set unique_by)"
+            )
+        else:
+            unique_by = identity
+
+    if max_size == 0:
+        return builds(list)
+    if unique_by is not None:
+        if not (callable(unique_by) or isinstance(unique_by, tuple)):
+            raise InvalidArgument(
+                f"{unique_by=} is not a callable or tuple of callables"
+            )
+        if callable(unique_by):
+            unique_by = (unique_by,)
+        if len(unique_by) == 0:
+            raise InvalidArgument("unique_by is empty")
+        for i, f in enumerate(unique_by):
+            if not callable(f):
+                raise InvalidArgument(f"unique_by[{i}]={f!r} is not a callable")
+        # Note that lazy strategies automatically unwrap when passed to a defines_strategy
+        # function.
+        tuple_suffixes = None
+        if (
+            # We're generating a list of tuples unique by the first element, perhaps
+            # via st.dictionaries(), and this will be more efficient if we rearrange
+            # our strategy somewhat to draw the first element then draw add the rest.
+            isinstance(elements, TupleStrategy)
+            and len(elements.element_strategies) >= 1
+            and len(unique_by) == 1
+            and (
+                # Introspection for either `itemgetter(0)`, or `lambda x: x[0]`
+                isinstance(unique_by[0], operator.itemgetter)
+                and repr(unique_by[0]) == "operator.itemgetter(0)"
+                or isinstance(unique_by[0], FunctionType)
+                and re.fullmatch(
+                    get_pretty_function_description(unique_by[0]),
+                    r"lambda ([a-z]+): \1\[0\]",
+                )
+            )
+        ):
+            unique_by = (identity,)
+            tuple_suffixes = TupleStrategy(elements.element_strategies[1:])
+            elements = elements.element_strategies[0]
+
+        # UniqueSampledListStrategy offers a substantial performance improvement for
+        # unique arrays with few possible elements, e.g. of eight-bit integer types.
+        if (
+            isinstance(elements, IntegersStrategy)
+            and None not in (elements.start, elements.end)
+            and (elements.end - elements.start) <= 255
+        ):
+            elements = SampledFromStrategy(
+                sorted(range(elements.start, elements.end + 1), key=abs)
+                if elements.end < 0 or elements.start > 0
+                else list(range(elements.end + 1))
+                + list(range(-1, elements.start - 1, -1))
+            )
+
+        if isinstance(elements, SampledFromStrategy):
+            element_count = len(elements.elements)
+            if min_size > element_count:
+                raise InvalidArgument(
+                    f"Cannot create a collection of {min_size=} unique "
+                    f"elements with values drawn from only {element_count} distinct "
+                    "elements"
+                )
+
+            if max_size is not None:
+                max_size = min(max_size, element_count)
+            else:
+                max_size = element_count
+
+            return UniqueSampledListStrategy(
+                elements=elements,
+                max_size=max_size,
+                min_size=min_size,
+                keys=unique_by,
+                tuple_suffixes=tuple_suffixes,
+            )
+
+        return UniqueListStrategy(
+            elements=elements,
+            max_size=max_size,
+            min_size=min_size,
+            keys=unique_by,
+            tuple_suffixes=tuple_suffixes,
+        )
+    return ListStrategy(elements, min_size=min_size, max_size=max_size)
+
+
+@cacheable
+@defines_strategy()
+def sets(
+    elements: SearchStrategy[Ex],
+    *,
+    min_size: int = 0,
+    max_size: Optional[int] = None,
+) -> SearchStrategy[Set[Ex]]:
+    """This has the same behaviour as lists, but returns sets instead.
+
+    Note that Hypothesis cannot tell if values are drawn from elements
+    are hashable until running the test, so you can define a strategy
+    for sets of an unhashable type but it will fail at test time.
+
+    Examples from this strategy shrink by trying to remove elements from the
+    set, and by shrinking each individual element of the set.
+    """
+    return lists(
+        elements=elements, min_size=min_size, max_size=max_size, unique=True
+    ).map(set)
+
+
+@cacheable
+@defines_strategy()
+def frozensets(
+    elements: SearchStrategy[Ex],
+    *,
+    min_size: int = 0,
+    max_size: Optional[int] = None,
+) -> SearchStrategy[FrozenSet[Ex]]:
+    """This is identical to the sets function but instead returns
+    frozensets."""
+    return lists(
+        elements=elements, min_size=min_size, max_size=max_size, unique=True
+    ).map(frozenset)
+
+
+class PrettyIter:
+    def __init__(self, values):
+        self._values = values
+        self._iter = iter(self._values)
+
+    def __iter__(self):
+        return self._iter
+
+    def __next__(self):
+        return next(self._iter)
+
+    def __repr__(self):
+        return f"iter({self._values!r})"
+
+
+@defines_strategy()
+def iterables(
+    elements: SearchStrategy[Ex],
+    *,
+    min_size: int = 0,
+    max_size: Optional[int] = None,
+    unique_by: Union[
+        None,
+        Callable[[Ex], Hashable],
+        Tuple[Callable[[Ex], Hashable], ...],
+    ] = None,
+    unique: bool = False,
+) -> SearchStrategy[Iterable[Ex]]:
+    """This has the same behaviour as lists, but returns iterables instead.
+
+    Some iterables cannot be indexed (e.g. sets) and some do not have a
+    fixed length (e.g. generators). This strategy produces iterators,
+    which cannot be indexed and do not have a fixed length. This ensures
+    that you do not accidentally depend on sequence behaviour.
+    """
+    return lists(
+        elements=elements,
+        min_size=min_size,
+        max_size=max_size,
+        unique_by=unique_by,
+        unique=unique,
+    ).map(PrettyIter)
+
+
+@defines_strategy()
+def fixed_dictionaries(
+    mapping: Dict[T, SearchStrategy[Ex]],
+    *,
+    optional: Optional[Dict[T, SearchStrategy[Ex]]] = None,
+) -> SearchStrategy[Dict[T, Ex]]:
+    """Generates a dictionary of the same type as mapping with a fixed set of
+    keys mapping to strategies. ``mapping`` must be a dict subclass.
+
+    Generated values have all keys present in mapping, in iteration order,
+    with the corresponding values drawn from mapping[key].
+
+    If ``optional`` is passed, the generated value *may or may not* contain each
+    key from ``optional`` and a value drawn from the corresponding strategy.
+    Generated values may contain optional keys in an arbitrary order.
+
+    Examples from this strategy shrink by shrinking each individual value in
+    the generated dictionary, and omitting optional key-value pairs.
+    """
+    check_type(dict, mapping, "mapping")
+    for k, v in mapping.items():
+        check_strategy(v, f"mapping[{k!r}]")
+    if optional is not None:
+        check_type(dict, optional, "optional")
+        for k, v in optional.items():
+            check_strategy(v, f"optional[{k!r}]")
+        if type(mapping) != type(optional):
+            raise InvalidArgument(
+                "Got arguments of different types: mapping=%s, optional=%s"
+                % (nicerepr(type(mapping)), nicerepr(type(optional)))
+            )
+        if set(mapping) & set(optional):
+            raise InvalidArgument(
+                "The following keys were in both mapping and optional, "
+                f"which is invalid: {set(mapping) & set(optional)!r}"
+            )
+        return FixedAndOptionalKeysDictStrategy(mapping, optional)
+    return FixedKeysDictStrategy(mapping)
+
+
+@cacheable
+@defines_strategy()
+def dictionaries(
+    keys: SearchStrategy[Ex],
+    values: SearchStrategy[T],
+    *,
+    dict_class: type = dict,
+    min_size: int = 0,
+    max_size: Optional[int] = None,
+) -> SearchStrategy[Dict[Ex, T]]:
+    # Describing the exact dict_class to Mypy drops the key and value types,
+    # so we report Dict[K, V] instead of Mapping[Any, Any] for now.  Sorry!
+    """Generates dictionaries of type ``dict_class`` with keys drawn from the ``keys``
+    argument and values drawn from the ``values`` argument.
+
+    The size parameters have the same interpretation as for
+    :func:`~hypothesis.strategies.lists`.
+
+    Examples from this strategy shrink by trying to remove keys from the
+    generated dictionary, and by shrinking each generated key and value.
+    """
+    check_valid_sizes(min_size, max_size)
+    if max_size == 0:
+        return fixed_dictionaries(dict_class())
+    check_strategy(keys, "keys")
+    check_strategy(values, "values")
+
+    return lists(
+        tuples(keys, values),
+        min_size=min_size,
+        max_size=max_size,
+        unique_by=operator.itemgetter(0),
+    ).map(dict_class)
+
+
+# See https://en.wikipedia.org/wiki/Unicode_character_property#General_Category
+CategoryName: "TypeAlias" = Literal[
+    "L",  #  Letter
+    "Lu",  # Letter, uppercase
+    "Ll",  # Letter, lowercase
+    "Lt",  # Letter, titlecase
+    "Lm",  # Letter, modifier
+    "Lo",  # Letter, other
+    "M",  #  Mark
+    "Mn",  # Mark, nonspacing
+    "Mc",  # Mark, spacing combining
+    "Me",  # Mark, enclosing
+    "N",  #  Number
+    "Nd",  # Number, decimal digit
+    "Nl",  # Number, letter
+    "No",  # Number, other
+    "P",  #  Punctuation
+    "Pc",  # Punctuation, connector
+    "Pd",  # Punctuation, dash
+    "Ps",  # Punctuation, open
+    "Pe",  # Punctuation, close
+    "Pi",  # Punctuation, initial quote
+    "Pf",  # Punctuation, final quote
+    "Po",  # Punctuation, other
+    "S",  #  Symbol
+    "Sm",  # Symbol, math
+    "Sc",  # Symbol, currency
+    "Sk",  # Symbol, modifier
+    "So",  # Symbol, other
+    "Z",  #  Separator
+    "Zs",  # Separator, space
+    "Zl",  # Separator, line
+    "Zp",  # Separator, paragraph
+    "C",  #  Other
+    "Cc",  # Other, control
+    "Cf",  # Other, format
+    "Cs",  # Other, surrogate
+    "Co",  # Other, private use
+    "Cn",  # Other, not assigned
+]
+
+
+@cacheable
+@defines_strategy(force_reusable_values=True)
+def characters(
+    *,
+    codec: Optional[str] = None,
+    min_codepoint: Optional[int] = None,
+    max_codepoint: Optional[int] = None,
+    categories: Optional[Collection[CategoryName]] = None,
+    exclude_categories: Optional[Collection[CategoryName]] = None,
+    exclude_characters: Optional[Collection[str]] = None,
+    include_characters: Optional[Collection[str]] = None,
+    # Note: these arguments are deprecated aliases for backwards compatibility
+    blacklist_categories: Optional[Collection[CategoryName]] = None,
+    whitelist_categories: Optional[Collection[CategoryName]] = None,
+    blacklist_characters: Optional[Collection[str]] = None,
+    whitelist_characters: Optional[Collection[str]] = None,
+) -> SearchStrategy[str]:
+    r"""Generates characters, length-one :class:`python:str`\ ings,
+    following specified filtering rules.
+
+    - When no filtering rules are specified, any character can be produced.
+    - If ``min_codepoint`` or ``max_codepoint`` is specified, then only
+      characters having a codepoint in that range will be produced.
+    - If ``categories`` is specified, then only characters from those
+      Unicode categories will be produced. This is a further restriction,
+      characters must also satisfy ``min_codepoint`` and ``max_codepoint``.
+    - If ``exclude_categories`` is specified, then any character from those
+      categories will not be produced.  You must not pass both ``categories``
+      and ``exclude_categories``; these arguments are alternative ways to
+      specify exactly the same thing.
+    - If ``include_characters`` is specified, then any additional characters
+      in that list will also be produced.
+    - If ``exclude_characters`` is specified, then any characters in
+      that list will be not be produced. Any overlap between
+      ``include_characters`` and ``exclude_characters`` will raise an
+      exception.
+    - If ``codec`` is specified, only characters in the specified `codec encodings`_
+      will be produced.
+
+    The ``_codepoint`` arguments must be integers between zero and
+    :obj:`python:sys.maxunicode`.  The ``_characters`` arguments must be
+    collections of length-one unicode strings, such as a unicode string.
+
+    The ``_categories`` arguments must be used to specify either the
+    one-letter Unicode major category or the two-letter Unicode
+    `general category`_.  For example, ``('Nd', 'Lu')`` signifies "Number,
+    decimal digit" and "Letter, uppercase".  A single letter ('major category')
+    can be given to match all corresponding categories, for example ``'P'``
+    for characters in any punctuation category.
+
+    We allow codecs from the :mod:`codecs` module and their aliases, platform
+    specific and user-registered codecs if they are available, and
+    `python-specific text encodings`_ (but not text or binary transforms).
+    ``include_characters`` which cannot be encoded using this codec will
+    raise an exception.  If non-encodable codepoints or categories are
+    explicitly allowed, the ``codec`` argument will exclude them without
+    raising an exception.
+
+    .. _general category: https://wikipedia.org/wiki/Unicode_character_property
+    .. _codec encodings: https://docs.python.org/3/library/codecs.html#encodings-and-unicode
+    .. _python-specific text encodings: https://docs.python.org/3/library/codecs.html#python-specific-encodings
+
+    Examples from this strategy shrink towards the codepoint for ``'0'``,
+    or the first allowable codepoint after it if ``'0'`` is excluded.
+    """
+    check_valid_size(min_codepoint, "min_codepoint")
+    check_valid_size(max_codepoint, "max_codepoint")
+    check_valid_interval(min_codepoint, max_codepoint, "min_codepoint", "max_codepoint")
+
+    if categories is not None and exclude_categories is not None:
+        raise InvalidArgument(
+            f"Pass at most one of {categories=} and {exclude_categories=} - "
+            "these arguments both specify which categories are allowed, so it "
+            "doesn't make sense to use both in a single call."
+        )
+
+    # Handle deprecation of whitelist/blacklist arguments
+    has_old_arg = any(v is not None for k, v in locals().items() if "list" in k)
+    has_new_arg = any(v is not None for k, v in locals().items() if "lude" in k)
+    if has_old_arg and has_new_arg:
+        raise InvalidArgument(
+            "The deprecated blacklist/whitelist arguments cannot be used in "
+            "the same call as their replacement include/exclude arguments."
+        )
+    if blacklist_categories is not None:
+        exclude_categories = blacklist_categories
+    if whitelist_categories is not None:
+        categories = whitelist_categories
+    if blacklist_characters is not None:
+        exclude_characters = blacklist_characters
+    if whitelist_characters is not None:
+        include_characters = whitelist_characters
+
+    if (
+        min_codepoint is None
+        and max_codepoint is None
+        and categories is None
+        and exclude_categories is None
+        and include_characters is not None
+        and codec is None
+    ):
+        raise InvalidArgument(
+            "Nothing is excluded by other arguments, so passing only "
+            f"{include_characters=} would have no effect.  "
+            "Also pass categories=(), or use "
+            f"sampled_from({include_characters!r}) instead."
+        )
+    exclude_characters = exclude_characters or ""
+    include_characters = include_characters or ""
+    overlap = set(exclude_characters).intersection(include_characters)
+    if overlap:
+        raise InvalidArgument(
+            f"Characters {sorted(overlap)!r} are present in both "
+            f"{include_characters=} and {exclude_characters=}"
+        )
+    categories = as_general_categories(categories, "categories")
+    exclude_categories = as_general_categories(exclude_categories, "exclude_categories")
+    if categories is not None and not categories and not include_characters:
+        raise InvalidArgument(
+            "When `categories` is an empty collection and there are "
+            "no characters specified in include_characters, nothing can "
+            "be generated by the characters() strategy."
+        )
+    both_cats = set(exclude_categories or ()).intersection(categories or ())
+    if both_cats:
+        # Note: we check that exactly one of `categories` or `exclude_categories` is
+        # passed above, but retain this older check for the deprecated arguments.
+        raise InvalidArgument(
+            f"Categories {sorted(both_cats)!r} are present in both "
+            f"{categories=} and {exclude_categories=}"
+        )
+    elif exclude_categories is not None:
+        categories = set(all_categories()) - set(exclude_categories)
+    del exclude_categories
+
+    if codec is not None:
+        try:
+            codec = codecs.lookup(codec).name
+            # Check this is not a str-to-str or bytes-to-bytes codec; see
+            # https://docs.python.org/3/library/codecs.html#binary-transforms
+            "".encode(codec)
+        except LookupError:
+            raise InvalidArgument(f"{codec=} is not valid on this system") from None
+        except Exception:
+            raise InvalidArgument(f"{codec=} is not a valid codec") from None
+
+        for char in include_characters:
+            try:
+                char.encode(encoding=codec, errors="strict")
+            except UnicodeEncodeError:
+                raise InvalidArgument(
+                    f"Character {char!r} in {include_characters=} "
+                    f"cannot be encoded with {codec=}"
+                ) from None
+
+        # ascii and utf-8 are sufficient common that we have faster special handling
+        if codec == "ascii":
+            if (max_codepoint is None) or (max_codepoint > 127):
+                max_codepoint = 127
+            codec = None
+        elif codec == "utf-8":
+            if categories is None:
+                categories = all_categories()
+            categories = tuple(c for c in categories if c != "Cs")
+
+    return OneCharStringStrategy.from_characters_args(
+        categories=categories,
+        exclude_characters=exclude_characters,
+        min_codepoint=min_codepoint,
+        max_codepoint=max_codepoint,
+        include_characters=include_characters,
+        codec=codec,
+    )
+
+
+# Hide the deprecated aliases from documentation and casual inspection
+characters.__signature__ = (__sig := get_signature(characters)).replace(  # type: ignore
+    parameters=[p for p in __sig.parameters.values() if "list" not in p.name]
+)
+
+
+# Cache size is limited by sys.maxunicode, but passing None makes it slightly faster.
+@lru_cache(maxsize=None)
+def _check_is_single_character(c):
+    # In order to mitigate the performance cost of this check, we use a shared cache,
+    # even at the cost of showing the culprit strategy in the error message.
+    if not isinstance(c, str):
+        type_ = get_pretty_function_description(type(c))
+        raise InvalidArgument(f"Got non-string {c!r} (type {type_})")
+    if len(c) != 1:
+        raise InvalidArgument(f"Got {c!r} (length {len(c)} != 1)")
+    return c
+
+
+@cacheable
+@defines_strategy(force_reusable_values=True)
+def text(
+    alphabet: Union[Collection[str], SearchStrategy[str]] = characters(codec="utf-8"),
+    *,
+    min_size: int = 0,
+    max_size: Optional[int] = None,
+) -> SearchStrategy[str]:
+    """Generates strings with characters drawn from ``alphabet``, which should
+    be a collection of length one strings or a strategy generating such strings.
+
+    The default alphabet strategy can generate the full unicode range but
+    excludes surrogate characters because they are invalid in the UTF-8
+    encoding.  You can use :func:`~hypothesis.strategies.characters` without
+    arguments to find surrogate-related bugs such as :bpo:`34454`.
+
+    ``min_size`` and ``max_size`` have the usual interpretations.
+    Note that Python measures string length by counting codepoints: U+00C5
+    ``Ã…`` is a single character, while U+0041 U+030A ``AÌŠ`` is two - the ``A``,
+    and a combining ring above.
+
+    Examples from this strategy shrink towards shorter strings, and with the
+    characters in the text shrinking as per the alphabet strategy.
+    This strategy does not :func:`~python:unicodedata.normalize` examples,
+    so generated strings may be in any or none of the 'normal forms'.
+    """
+    check_valid_sizes(min_size, max_size)
+    if isinstance(alphabet, SearchStrategy):
+        char_strategy = unwrap_strategies(alphabet)
+        if isinstance(char_strategy, SampledFromStrategy):
+            # Check this via the up-front validation logic below, and incidentally
+            # convert into a `characters()` strategy for standard text shrinking.
+            return text(char_strategy.elements, min_size=min_size, max_size=max_size)
+        elif not isinstance(char_strategy, OneCharStringStrategy):
+            char_strategy = char_strategy.map(_check_is_single_character)
+    else:
+        non_string = [c for c in alphabet if not isinstance(c, str)]
+        if non_string:
+            raise InvalidArgument(
+                "The following elements in alphabet are not unicode "
+                f"strings:  {non_string!r}"
+            )
+        not_one_char = [c for c in alphabet if len(c) != 1]
+        if not_one_char:
+            raise InvalidArgument(
+                "The following elements in alphabet are not of length one, "
+                f"which leads to violation of size constraints:  {not_one_char!r}"
+            )
+        char_strategy = (
+            characters(categories=(), include_characters=alphabet)
+            if alphabet
+            else nothing()
+        )
+    if (max_size == 0 or char_strategy.is_empty) and not min_size:
+        return just("")
+    return TextStrategy(char_strategy, min_size=min_size, max_size=max_size)
+
+
+@overload
+def from_regex(
+    regex: Union[bytes, Pattern[bytes]],
+    *,
+    fullmatch: bool = False,
+) -> SearchStrategy[bytes]:  # pragma: no cover
+    ...
+
+
+@overload
+def from_regex(
+    regex: Union[str, Pattern[str]],
+    *,
+    fullmatch: bool = False,
+    alphabet: Union[str, SearchStrategy[str]] = characters(codec="utf-8"),
+) -> SearchStrategy[str]:  # pragma: no cover
+    ...
+
+
+@cacheable
+@defines_strategy()
+def from_regex(
+    regex: Union[AnyStr, Pattern[AnyStr]],
+    *,
+    fullmatch: bool = False,
+    alphabet: Union[str, SearchStrategy[str], None] = None,
+) -> SearchStrategy[AnyStr]:
+    r"""Generates strings that contain a match for the given regex (i.e. ones
+    for which :func:`python:re.search` will return a non-None result).
+
+    ``regex`` may be a pattern or :func:`compiled regex `.
+    Both byte-strings and unicode strings are supported, and will generate
+    examples of the same type.
+
+    You can use regex flags such as :obj:`python:re.IGNORECASE` or
+    :obj:`python:re.DOTALL` to control generation. Flags can be passed either
+    in compiled regex or inside the pattern with a ``(?iLmsux)`` group.
+
+    Some regular expressions are only partly supported - the underlying
+    strategy checks local matching and relies on filtering to resolve
+    context-dependent expressions.  Using too many of these constructs may
+    cause health-check errors as too many examples are filtered out. This
+    mainly includes (positive or negative) lookahead and lookbehind groups.
+
+    If you want the generated string to match the whole regex you should use
+    boundary markers. So e.g. ``r"\A.\Z"`` will return a single character
+    string, while ``"."`` will return any string, and ``r"\A.$"`` will return
+    a single character optionally followed by a ``"\n"``.
+    Alternatively, passing ``fullmatch=True`` will ensure that the whole
+    string is a match, as if you had used the ``\A`` and ``\Z`` markers.
+
+    The ``alphabet=`` argument constrains the characters in the generated
+    string, as for :func:`text`, and is only supported for unicode strings.
+
+    Examples from this strategy shrink towards shorter strings and lower
+    character values, with exact behaviour that may depend on the pattern.
+    """
+    check_type((str, bytes, re.Pattern), regex, "regex")
+    check_type(bool, fullmatch, "fullmatch")
+    pattern = regex.pattern if isinstance(regex, re.Pattern) else regex
+    if alphabet is not None:
+        check_type((str, SearchStrategy), alphabet, "alphabet")
+        if not isinstance(pattern, str):
+            raise InvalidArgument("alphabet= is not supported for bytestrings")
+
+        if isinstance(alphabet, str):
+            alphabet = characters(categories=(), include_characters=alphabet)
+        char_strategy = unwrap_strategies(alphabet)
+        if isinstance(char_strategy, SampledFromStrategy):
+            alphabet = characters(
+                categories=(),
+                include_characters=alphabet.elements,  # type: ignore
+            )
+        elif not isinstance(char_strategy, OneCharStringStrategy):
+            raise InvalidArgument(
+                f"{alphabet=} must be a sampled_from() or characters() strategy"
+            )
+    elif isinstance(pattern, str):
+        alphabet = characters(codec="utf-8")
+
+    # TODO: We would like to move this to the top level, but pending some major
+    # refactoring it's hard to do without creating circular imports.
+    from hypothesis.strategies._internal.regex import regex_strategy
+
+    return regex_strategy(regex, fullmatch, alphabet=alphabet)
+
+
+@cacheable
+@defines_strategy(force_reusable_values=True)
+def binary(
+    *,
+    min_size: int = 0,
+    max_size: Optional[int] = None,
+) -> SearchStrategy[bytes]:
+    """Generates :class:`python:bytes`.
+
+    The generated :class:`python:bytes` will have a length of at least ``min_size``
+    and at most ``max_size``.  If ``max_size`` is None there is no upper limit.
+
+    Examples from this strategy shrink towards smaller strings and lower byte
+    values.
+    """
+    check_valid_sizes(min_size, max_size)
+    return BytesStrategy(min_size, max_size)
+
+
+@cacheable
+@defines_strategy()
+def randoms(
+    *,
+    note_method_calls: bool = False,
+    use_true_random: bool = False,
+) -> SearchStrategy[random.Random]:
+    """Generates instances of ``random.Random``. The generated Random instances
+    are of a special HypothesisRandom subclass.
+
+    - If ``note_method_calls`` is set to ``True``, Hypothesis will print the
+      randomly drawn values in any falsifying test case. This can be helpful
+      for debugging the behaviour of randomized algorithms.
+    - If ``use_true_random`` is set to ``True`` then values will be drawn from
+      their usual distribution, otherwise they will actually be Hypothesis
+      generated values (and will be shrunk accordingly for any failing test
+      case). Setting ``use_true_random=False`` will tend to expose bugs that
+      would occur with very low probability when it is set to True, and this
+      flag should only be set to True when your code relies on the distribution
+      of values for correctness.
+
+    For managing global state, see the :func:`~hypothesis.strategies.random_module`
+    strategy and :func:`~hypothesis.register_random` function.
+    """
+    check_type(bool, note_method_calls, "note_method_calls")
+    check_type(bool, use_true_random, "use_true_random")
+
+    from hypothesis.strategies._internal.random import RandomStrategy
+
+    return RandomStrategy(
+        use_true_random=use_true_random, note_method_calls=note_method_calls
+    )
+
+
+class RandomModule(SearchStrategy):
+    def do_draw(self, data):
+        # It would be unsafe to do run this method more than once per test case,
+        # because cleanup() runs tasks in FIFO order (at time of writing!).
+        # Fortunately, the random_module() strategy wraps us in shared(), so
+        # it's cached for all but the first of any number of calls.
+        seed = data.draw(integers(0, 2**32 - 1))
+        seed_all, restore_all = get_seeder_and_restorer(seed)
+        seed_all()
+        cleanup(restore_all)
+        return RandomSeeder(seed)
+
+
+@cacheable
+@defines_strategy()
+def random_module() -> SearchStrategy[RandomSeeder]:
+    """Hypothesis always seeds global PRNGs before running a test, and restores the
+    previous state afterwards.
+
+    If having a fixed seed would unacceptably weaken your tests, and you
+    cannot use a ``random.Random`` instance provided by
+    :func:`~hypothesis.strategies.randoms`, this strategy calls
+    :func:`python:random.seed` with an arbitrary integer and passes you
+    an opaque object whose repr displays the seed value for debugging.
+    If ``numpy.random`` is available, that state is also managed, as is anything
+    managed by :func:`hypothesis.register_random`.
+
+    Examples from these strategy shrink to seeds closer to zero.
+    """
+    return shared(RandomModule(), key="hypothesis.strategies.random_module()")
+
+
+class BuildsStrategy(SearchStrategy):
+    def __init__(self, target, args, kwargs):
+        self.target = target
+        self.args = args
+        self.kwargs = kwargs
+
+    def do_draw(self, data):
+        args = [data.draw(a) for a in self.args]
+        kwargs = {k: data.draw(v) for k, v in self.kwargs.items()}
+        try:
+            obj = self.target(*args, **kwargs)
+        except TypeError as err:
+            if (
+                isinstance(self.target, type)
+                and issubclass(self.target, enum.Enum)
+                and not (self.args or self.kwargs)
+            ):
+                name = self.target.__module__ + "." + self.target.__qualname__
+                raise InvalidArgument(
+                    f"Calling {name} with no arguments raised an error - "
+                    f"try using sampled_from({name}) instead of builds({name})"
+                ) from err
+            if not (self.args or self.kwargs):
+                from .types import is_a_new_type, is_generic_type
+
+                if is_a_new_type(self.target) or is_generic_type(self.target):
+                    raise InvalidArgument(
+                        f"Calling {self.target!r} with no arguments raised an "
+                        f"error - try using from_type({self.target!r}) instead "
+                        f"of builds({self.target!r})"
+                    ) from err
+            if getattr(self.target, "__no_type_check__", None) is True:
+                # Note: could use PEP-678 __notes__ here.  Migrate over once we're
+                # using an `exceptiongroup` backport with support for that.
+                raise TypeError(
+                    "This might be because the @no_type_check decorator prevented "
+                    "Hypothesis from inferring a strategy for some required arguments."
+                ) from err
+            raise
+
+        current_build_context().record_call(obj, self.target, args, kwargs)
+        return obj
+
+    def validate(self):
+        tuples(*self.args).validate()
+        fixed_dictionaries(self.kwargs).validate()
+
+    def __repr__(self):
+        bits = [get_pretty_function_description(self.target)]
+        bits.extend(map(repr, self.args))
+        bits.extend(f"{k}={v!r}" for k, v in self.kwargs.items())
+        return f"builds({', '.join(bits)})"
+
+
+@cacheable
+@defines_strategy()
+def builds(
+    target: Callable[..., Ex],
+    /,
+    *args: SearchStrategy[Any],
+    **kwargs: Union[SearchStrategy[Any], EllipsisType],
+) -> SearchStrategy[Ex]:
+    """Generates values by drawing from ``args`` and ``kwargs`` and passing
+    them to the callable (provided as the first positional argument) in the
+    appropriate argument position.
+
+    e.g. ``builds(target, integers(), flag=booleans())`` would draw an
+    integer ``i`` and a boolean ``b`` and call ``target(i, flag=b)``.
+
+    If the callable has type annotations, they will be used to infer a strategy
+    for required arguments that were not passed to builds.  You can also tell
+    builds to infer a strategy for an optional argument by passing ``...``
+    (:obj:`python:Ellipsis`) as a keyword argument to builds, instead of a strategy for
+    that argument to the callable.
+
+    If the callable is a class defined with :pypi:`attrs`, missing required
+    arguments will be inferred from the attribute on a best-effort basis,
+    e.g. by checking :ref:`attrs standard validators `.
+    Dataclasses are handled natively by the inference from type hints.
+
+    Examples from this strategy shrink by shrinking the argument values to
+    the callable.
+    """
+    if not callable(target):
+        raise InvalidArgument(
+            "The first positional argument to builds() must be a callable "
+            "target to construct."
+        )
+
+    if ... in args:  # type: ignore  # we only annotated the allowed types
+        # Avoid an implementation nightmare juggling tuples and worse things
+        raise InvalidArgument(
+            "... was passed as a positional argument to "
+            "builds(), but is only allowed as a keyword arg"
+        )
+    required = required_args(target, args, kwargs)
+    to_infer = {k for k, v in kwargs.items() if v is ...}
+    if required or to_infer:
+        if isinstance(target, type) and attr.has(target):
+            # Use our custom introspection for attrs classes
+            from hypothesis.strategies._internal.attrs import from_attrs
+
+            return from_attrs(target, args, kwargs, required | to_infer)
+        # Otherwise, try using type hints
+        hints = get_type_hints(target)
+        if to_infer - set(hints):
+            badargs = ", ".join(sorted(to_infer - set(hints)))
+            raise InvalidArgument(
+                f"passed ... for {badargs}, but we cannot infer a strategy "
+                "because these arguments have no type annotation"
+            )
+        infer_for = {k: v for k, v in hints.items() if k in (required | to_infer)}
+        if infer_for:
+            from hypothesis.strategies._internal.types import _global_type_lookup
+
+            for kw, t in infer_for.items():
+                if t in _global_type_lookup:
+                    kwargs[kw] = from_type(t)
+                else:
+                    # We defer resolution of these type annotations so that the obvious
+                    # approach to registering recursive types just works.  I.e.,
+                    # if we're inside `register_type_strategy(cls, builds(cls, ...))`
+                    # and `...` contains recursion on `cls`.  See
+                    # https://github.com/HypothesisWorks/hypothesis/issues/3026
+                    kwargs[kw] = deferred(lambda t=t: from_type(t))  # type: ignore
+    return BuildsStrategy(target, args, kwargs)
+
+
+@cacheable
+@defines_strategy(never_lazy=True)
+def from_type(thing: Type[Ex_Inv]) -> SearchStrategy[Ex_Inv]:
+    """Looks up the appropriate search strategy for the given type.
+
+    ``from_type`` is used internally to fill in missing arguments to
+    :func:`~hypothesis.strategies.builds` and can be used interactively
+    to explore what strategies are available or to debug type resolution.
+
+    You can use :func:`~hypothesis.strategies.register_type_strategy` to
+    handle your custom types, or to globally redefine certain strategies -
+    for example excluding NaN from floats, or use timezone-aware instead of
+    naive time and datetime strategies.
+
+    The resolution logic may be changed in a future version, but currently
+    tries these five options:
+
+    1. If ``thing`` is in the default lookup mapping or user-registered lookup,
+       return the corresponding strategy.  The default lookup covers all types
+       with Hypothesis strategies, including extras where possible.
+    2. If ``thing`` is from the :mod:`python:typing` module, return the
+       corresponding strategy (special logic).
+    3. If ``thing`` has one or more subtypes in the merged lookup, return
+       the union of the strategies for those types that are not subtypes of
+       other elements in the lookup.
+    4. Finally, if ``thing`` has type annotations for all required arguments,
+       and is not an abstract class, it is resolved via
+       :func:`~hypothesis.strategies.builds`.
+    5. Because :mod:`abstract types ` cannot be instantiated,
+       we treat abstract types as the union of their concrete subclasses.
+       Note that this lookup works via inheritance but not via
+       :obj:`~python:abc.ABCMeta.register`, so you may still need to use
+       :func:`~hypothesis.strategies.register_type_strategy`.
+
+    There is a valuable recipe for leveraging ``from_type()`` to generate
+    "everything except" values from a specified type. I.e.
+
+    .. code-block:: python
+
+        def everything_except(excluded_types):
+            return (
+                from_type(type)
+                .flatmap(from_type)
+                .filter(lambda x: not isinstance(x, excluded_types))
+            )
+
+    For example, ``everything_except(int)`` returns a strategy that can
+    generate anything that ``from_type()`` can ever generate, except for
+    instances of :class:`python:int`, and excluding instances of types
+    added via :func:`~hypothesis.strategies.register_type_strategy`.
+
+    This is useful when writing tests which check that invalid input is
+    rejected in a certain way.
+    """
+    try:
+        with warnings.catch_warnings():
+            warnings.simplefilter("error")
+            return _from_type(thing)
+    except Exception:
+        return _from_type_deferred(thing)
+
+
+def _from_type_deferred(thing: Type[Ex]) -> SearchStrategy[Ex]:
+    # This tricky little dance is because we want to show the repr of the actual
+    # underlying strategy wherever possible, as a form of user education, but
+    # would prefer to fall back to the default "from_type(...)" repr instead of
+    # "deferred(...)" for recursive types or invalid arguments.
+    try:
+        thing_repr = nicerepr(thing)
+        if hasattr(thing, "__module__"):
+            module_prefix = f"{thing.__module__}."
+            if not thing_repr.startswith(module_prefix):
+                thing_repr = module_prefix + thing_repr
+        repr_ = f"from_type({thing_repr})"
+    except Exception:  # pragma: no cover
+        repr_ = None
+    return LazyStrategy(
+        lambda thing: deferred(lambda: _from_type(thing)),
+        (thing,),
+        {},
+        force_repr=repr_,
+    )
+
+
+_recurse_guard: ContextVar = ContextVar("recurse_guard")
+
+
+def _from_type(thing: Type[Ex]) -> SearchStrategy[Ex]:
+    # TODO: We would like to move this to the top level, but pending some major
+    # refactoring it's hard to do without creating circular imports.
+    from hypothesis.strategies._internal import types
+
+    def as_strategy(strat_or_callable, thing):
+        # User-provided strategies need some validation, and callables even more
+        # of it.  We do this in three places, hence the helper function
+        if not isinstance(strat_or_callable, SearchStrategy):
+            assert callable(strat_or_callable)  # Validated in register_type_strategy
+            strategy = strat_or_callable(thing)
+        else:
+            strategy = strat_or_callable
+        if strategy is NotImplemented:
+            return NotImplemented
+        if not isinstance(strategy, SearchStrategy):
+            raise ResolutionFailed(
+                f"Error: {thing} was registered for {nicerepr(strat_or_callable)}, "
+                f"but returned non-strategy {strategy!r}"
+            )
+        if strategy.is_empty:
+            raise ResolutionFailed(f"Error: {thing!r} resolved to an empty strategy")
+        return strategy
+
+    def from_type_guarded(thing):
+        """Returns the result of producer, or ... if recursion on thing is encountered"""
+        try:
+            recurse_guard = _recurse_guard.get()
+        except LookupError:
+            # We can't simply define the contextvar with default=[], as the
+            # default object would be shared across contexts
+            _recurse_guard.set(recurse_guard := [])
+        if thing in recurse_guard:
+            raise RewindRecursive(thing)
+        recurse_guard.append(thing)
+        try:
+            return _from_type(thing)
+        except RewindRecursive as rr:
+            if rr.target != thing:
+                raise
+            return ...  # defer resolution
+        finally:
+            recurse_guard.pop()
+
+    # Let registered extra modules handle their own recognized types first, before
+    # e.g. Unions are resolved
+    try:
+        known = thing in types._global_type_lookup
+    except TypeError:
+        # thing is not always hashable!
+        pass
+    else:
+        if not known:
+            for module, resolver in types._global_extra_lookup.items():
+                if module in sys.modules:
+                    strat = resolver(thing)
+                    if strat is not None:
+                        return strat
+    if not isinstance(thing, type):
+        if types.is_a_new_type(thing):
+            # Check if we have an explicitly registered strategy for this thing,
+            # resolve it so, and otherwise resolve as for the base type.
+            if thing in types._global_type_lookup:
+                strategy = as_strategy(types._global_type_lookup[thing], thing)
+                if strategy is not NotImplemented:
+                    return strategy
+            return _from_type(thing.__supertype__)
+        # Unions are not instances of `type` - but we still want to resolve them!
+        if types.is_a_union(thing):
+            args = sorted(thing.__args__, key=types.type_sorting_key)
+            return one_of([_from_type(t) for t in args])
+    # We also have a special case for TypeVars.
+    # They are represented as instances like `~T` when they come here.
+    # We need to work with their type instead.
+    if isinstance(thing, TypeVar) and type(thing) in types._global_type_lookup:
+        strategy = as_strategy(types._global_type_lookup[type(thing)], thing)
+        if strategy is not NotImplemented:
+            return strategy
+    if not types.is_a_type(thing):
+        if isinstance(thing, str):
+            # See https://github.com/HypothesisWorks/hypothesis/issues/3016
+            raise InvalidArgument(
+                f"Got {thing!r} as a type annotation, but the forward-reference "
+                "could not be resolved from a string to a type.  Consider using "
+                "`from __future__ import annotations` instead of forward-reference "
+                "strings."
+            )
+        raise InvalidArgument(f"{thing=} must be a type")  # pragma: no cover
+    if thing in types.NON_RUNTIME_TYPES:
+        # Some code like `st.from_type(TypeAlias)` does not make sense.
+        # Because there are types in python that do not exist in runtime.
+        raise InvalidArgument(
+            f"Could not resolve {thing!r} to a strategy, "
+            f"because there is no such thing as a runtime instance of {thing!r}"
+        )
+    # Now that we know `thing` is a type, the first step is to check for an
+    # explicitly registered strategy. This is the best (and hopefully most
+    # common) way to resolve a type to a strategy.  Note that the value in the
+    # lookup may be a strategy or a function from type -> strategy; and we
+    # convert empty results into an explicit error.
+    try:
+        if thing in types._global_type_lookup:
+            strategy = as_strategy(types._global_type_lookup[thing], thing)
+            if strategy is not NotImplemented:
+                return strategy
+    except TypeError:  # pragma: no cover
+        # This is due to a bizarre divergence in behaviour under Python 3.9.0:
+        # typing.Callable[[], foo] has __args__ = (foo,) but collections.abc.Callable
+        # has __args__ = ([], foo); and as a result is non-hashable.
+        pass
+    if (
+        hasattr(typing, "_TypedDictMeta")
+        and type(thing) is typing._TypedDictMeta
+        or hasattr(types.typing_extensions, "_TypedDictMeta")  # type: ignore
+        and type(thing) is types.typing_extensions._TypedDictMeta  # type: ignore
+    ):  # pragma: no cover
+        # The __optional_keys__ attribute may or may not be present, but if there's no
+        # way to tell and we just have to assume that everything is required.
+        # See https://github.com/python/cpython/pull/17214 for details.
+        optional = set(getattr(thing, "__optional_keys__", ()))
+        anns = {}
+        for k, v in get_type_hints(thing).items():
+            origin = get_origin(v)
+            if origin in types.RequiredTypes + types.NotRequiredTypes:
+                if origin in types.NotRequiredTypes:
+                    optional.add(k)
+                else:
+                    optional.discard(k)
+                try:
+                    v = v.__args__[0]
+                except IndexError:
+                    raise InvalidArgument(
+                        f"`{k}: {v.__name__}` is not a valid type annotation"
+                    ) from None
+            anns[k] = from_type_guarded(v)
+            if anns[k] is ...:
+                anns[k] = _from_type_deferred(v)
+        if (
+            (not anns)
+            and thing.__annotations__
+            and ".." in getattr(thing, "__qualname__", "")
+        ):
+            raise InvalidArgument("Failed to retrieve type annotations for local type")
+        return fixed_dictionaries(  # type: ignore
+            mapping={k: v for k, v in anns.items() if k not in optional},
+            optional={k: v for k, v in anns.items() if k in optional},
+        )
+
+    # If there's no explicitly registered strategy, maybe a subtype of thing
+    # is registered - if so, we can resolve it to the subclass strategy.
+    # We'll start by checking if thing is from from the typing module,
+    # because there are several special cases that don't play well with
+    # subclass and instance checks.
+    if isinstance(thing, types.typing_root_type) or (
+        sys.version_info[:2] >= (3, 9)
+        and isinstance(get_origin(thing), type)
+        and get_args(thing)
+    ):
+        return types.from_typing_type(thing)
+    # If it's not from the typing module, we get all registered types that are
+    # a subclass of `thing` and are not themselves a subtype of any other such
+    # type.  For example, `Number -> integers() | floats()`, but bools() is
+    # not included because bool is a subclass of int as well as Number.
+    strategies = [
+        s
+        for s in (
+            as_strategy(v, thing)
+            for k, v in sorted(types._global_type_lookup.items(), key=repr)
+            if isinstance(k, type)
+            and issubclass(k, thing)
+            and sum(types.try_issubclass(k, typ) for typ in types._global_type_lookup)
+            == 1
+        )
+        if s is not NotImplemented
+    ]
+    if any(not s.is_empty for s in strategies):
+        return one_of(strategies)
+    # If we don't have a strategy registered for this type or any subtype, we
+    # may be able to fall back on type annotations.
+    if issubclass(thing, enum.Enum):
+        return sampled_from(thing)
+    # Finally, try to build an instance by calling the type object.  Unlike builds(),
+    # this block *does* try to infer strategies for arguments with default values.
+    # That's because of the semantic different; builds() -> "call this with ..."
+    # so we only infer when *not* doing so would be an error; from_type() -> "give
+    # me arbitrary instances" so the greater variety is acceptable.
+    # And if it's *too* varied, express your opinions with register_type_strategy()
+    if not isabstract(thing):
+        # If we know that builds(thing) will fail, give a better error message
+        required = required_args(thing)
+        if required and not (
+            required.issubset(get_type_hints(thing))
+            or attr.has(thing)
+            or is_typed_named_tuple(thing)  # weird enough that we have a specific check
+        ):
+            raise ResolutionFailed(
+                f"Could not resolve {thing!r} to a strategy; consider "
+                "using register_type_strategy"
+            )
+        try:
+            hints = get_type_hints(thing)
+            params = get_signature(thing).parameters
+        except Exception:
+            params = {}  # type: ignore
+        kwargs = {}
+        for k, p in params.items():
+            if (
+                k in hints
+                and k != "return"
+                and p.kind in (Parameter.POSITIONAL_OR_KEYWORD, Parameter.KEYWORD_ONLY)
+            ):
+                kwargs[k] = from_type_guarded(hints[k])
+                if p.default is not Parameter.empty and kwargs[k] is not ...:
+                    kwargs[k] = just(p.default) | kwargs[k]
+        if params and not kwargs and not issubclass(thing, BaseException):
+            from_type_repr = repr_call(from_type, (thing,), {})
+            builds_repr = repr_call(builds, (thing,), {})
+            warnings.warn(
+                f"{from_type_repr} resolved to {builds_repr}, because we could not "
+                "find any (non-varargs) arguments. Use st.register_type_strategy() "
+                "to resolve to a strategy which can generate more than one value, "
+                "or silence this warning.",
+                SmallSearchSpaceWarning,
+                stacklevel=2,
+            )
+        return builds(thing, **kwargs)
+    # And if it's an abstract type, we'll resolve to a union of subclasses instead.
+    subclasses = thing.__subclasses__()
+    if not subclasses:
+        raise ResolutionFailed(
+            f"Could not resolve {thing!r} to a strategy, because it is an abstract "
+            "type without any subclasses. Consider using register_type_strategy"
+        )
+    subclass_strategies = nothing()
+    for sc in subclasses:
+        try:
+            subclass_strategies |= _from_type(sc)
+        except Exception:
+            pass
+    if subclass_strategies.is_empty:
+        # We're unable to resolve subclasses now, but we might be able to later -
+        # so we'll just go back to the mixed distribution.
+        return sampled_from(subclasses).flatmap(_from_type)
+    return subclass_strategies
+
+
+@cacheable
+@defines_strategy(force_reusable_values=True)
+def fractions(
+    min_value: Optional[Union[Real, str]] = None,
+    max_value: Optional[Union[Real, str]] = None,
+    *,
+    max_denominator: Optional[int] = None,
+) -> SearchStrategy[Fraction]:
+    """Returns a strategy which generates Fractions.
+
+    If ``min_value`` is not None then all generated values are no less than
+    ``min_value``.  If ``max_value`` is not None then all generated values are no
+    greater than ``max_value``.  ``min_value`` and ``max_value`` may be anything accepted
+    by the :class:`~fractions.Fraction` constructor.
+
+    If ``max_denominator`` is not None then the denominator of any generated
+    values is no greater than ``max_denominator``. Note that ``max_denominator`` must
+    be None or a positive integer.
+
+    Examples from this strategy shrink towards smaller denominators, then
+    closer to zero.
+    """
+    min_value = try_convert(Fraction, min_value, "min_value")
+    max_value = try_convert(Fraction, max_value, "max_value")
+    # These assertions tell Mypy what happened in try_convert
+    assert min_value is None or isinstance(min_value, Fraction)
+    assert max_value is None or isinstance(max_value, Fraction)
+
+    check_valid_interval(min_value, max_value, "min_value", "max_value")
+    check_valid_integer(max_denominator, "max_denominator")
+
+    if max_denominator is not None:
+        if max_denominator < 1:
+            raise InvalidArgument(f"{max_denominator=} must be >= 1")
+        if min_value is not None and min_value.denominator > max_denominator:
+            raise InvalidArgument(
+                f"The {min_value=} has a denominator greater than the "
+                f"{max_denominator=}"
+            )
+        if max_value is not None and max_value.denominator > max_denominator:
+            raise InvalidArgument(
+                f"The {max_value=} has a denominator greater than the "
+                f"{max_denominator=}"
+            )
+
+    if min_value is not None and min_value == max_value:
+        return just(min_value)
+
+    def dm_func(denom):
+        """Take denom, construct numerator strategy, and build fraction."""
+        # Four cases of algebra to get integer bounds and scale factor.
+        min_num, max_num = None, None
+        if max_value is None and min_value is None:
+            pass
+        elif min_value is None:
+            max_num = denom * max_value.numerator
+            denom *= max_value.denominator
+        elif max_value is None:
+            min_num = denom * min_value.numerator
+            denom *= min_value.denominator
+        else:
+            low = min_value.numerator * max_value.denominator
+            high = max_value.numerator * min_value.denominator
+            scale = min_value.denominator * max_value.denominator
+            # After calculating our integer bounds and scale factor, we remove
+            # the gcd to avoid drawing more bytes for the example than needed.
+            # Note that `div` can be at most equal to `scale`.
+            div = math.gcd(scale, math.gcd(low, high))
+            min_num = denom * low // div
+            max_num = denom * high // div
+            denom *= scale // div
+
+        return builds(
+            Fraction, integers(min_value=min_num, max_value=max_num), just(denom)
+        )
+
+    if max_denominator is None:
+        return integers(min_value=1).flatmap(dm_func)
+
+    return (
+        integers(1, max_denominator)
+        .flatmap(dm_func)
+        .map(lambda f: f.limit_denominator(max_denominator))
+    )
+
+
+def _as_finite_decimal(
+    value: Union[Real, str, None], name: str, allow_infinity: Optional[bool]
+) -> Optional[Decimal]:
+    """Convert decimal bounds to decimals, carefully."""
+    assert name in ("min_value", "max_value")
+    if value is None:
+        return None
+    if not isinstance(value, Decimal):
+        with localcontext(Context()):  # ensure that default traps are enabled
+            value = try_convert(Decimal, value, name)
+    assert isinstance(value, Decimal)
+    if value.is_finite():
+        return value
+    if value.is_infinite() and (value < 0 if "min" in name else value > 0):
+        if allow_infinity or allow_infinity is None:
+            return None
+        raise InvalidArgument(f"{allow_infinity=}, but {name}={value!r}")
+    # This could be infinity, quiet NaN, or signalling NaN
+    raise InvalidArgument(f"Invalid {name}={value!r}")
+
+
+@cacheable
+@defines_strategy(force_reusable_values=True)
+def decimals(
+    min_value: Optional[Union[Real, str]] = None,
+    max_value: Optional[Union[Real, str]] = None,
+    *,
+    allow_nan: Optional[bool] = None,
+    allow_infinity: Optional[bool] = None,
+    places: Optional[int] = None,
+) -> SearchStrategy[Decimal]:
+    """Generates instances of :class:`python:decimal.Decimal`, which may be:
+
+    - A finite rational number, between ``min_value`` and ``max_value``.
+    - Not a Number, if ``allow_nan`` is True.  None means "allow NaN, unless
+      ``min_value`` and ``max_value`` are not None".
+    - Positive or negative infinity, if ``max_value`` and ``min_value``
+      respectively are None, and ``allow_infinity`` is not False.  None means
+      "allow infinity, unless excluded by the min and max values".
+
+    Note that where floats have one ``NaN`` value, Decimals have four: signed,
+    and either *quiet* or *signalling*.  See `the decimal module docs
+    `_ for
+    more information on special values.
+
+    If ``places`` is not None, all finite values drawn from the strategy will
+    have that number of digits after the decimal place.
+
+    Examples from this strategy do not have a well defined shrink order but
+    try to maximize human readability when shrinking.
+    """
+    # Convert min_value and max_value to Decimal values, and validate args
+    check_valid_integer(places, "places")
+    if places is not None and places < 0:
+        raise InvalidArgument(f"{places=} may not be negative")
+    min_value = _as_finite_decimal(min_value, "min_value", allow_infinity)
+    max_value = _as_finite_decimal(max_value, "max_value", allow_infinity)
+    check_valid_interval(min_value, max_value, "min_value", "max_value")
+    if allow_infinity and (None not in (min_value, max_value)):
+        raise InvalidArgument("Cannot allow infinity between finite bounds")
+    # Set up a strategy for finite decimals.  Note that both floating and
+    # fixed-point decimals require careful handling to remain isolated from
+    # any external precision context - in short, we always work out the
+    # required precision for lossless operation and use context methods.
+    if places is not None:
+        # Fixed-point decimals are basically integers with a scale factor
+        def ctx(val):
+            """Return a context in which this value is lossless."""
+            precision = ceil(math.log10(abs(val) or 1)) + places + 1
+            return Context(prec=max([precision, 1]))
+
+        def int_to_decimal(val):
+            context = ctx(val)
+            return context.quantize(context.multiply(val, factor), factor)
+
+        factor = Decimal(10) ** -places
+        min_num, max_num = None, None
+        if min_value is not None:
+            min_num = ceil(ctx(min_value).divide(min_value, factor))
+        if max_value is not None:
+            max_num = floor(ctx(max_value).divide(max_value, factor))
+        if min_num is not None and max_num is not None and min_num > max_num:
+            raise InvalidArgument(
+                f"There are no decimals with {places} places between "
+                f"{min_value=} and {max_value=}"
+            )
+        strat = integers(min_num, max_num).map(int_to_decimal)
+    else:
+        # Otherwise, they're like fractions featuring a power of ten
+        def fraction_to_decimal(val):
+            precision = (
+                ceil(math.log10(abs(val.numerator) or 1) + math.log10(val.denominator))
+                + 1
+            )
+            return Context(prec=precision or 1).divide(
+                Decimal(val.numerator), val.denominator
+            )
+
+        strat = fractions(min_value, max_value).map(fraction_to_decimal)
+    # Compose with sampled_from for infinities and NaNs as appropriate
+    special: List[Decimal] = []
+    if allow_nan or (allow_nan is None and (None in (min_value, max_value))):
+        special.extend(map(Decimal, ("NaN", "-NaN", "sNaN", "-sNaN")))
+    if allow_infinity or (allow_infinity is None and max_value is None):
+        special.append(Decimal("Infinity"))
+    if allow_infinity or (allow_infinity is None and min_value is None):
+        special.append(Decimal("-Infinity"))
+    return strat | (sampled_from(special) if special else nothing())
+
+
+@defines_strategy(never_lazy=True)
+def recursive(
+    base: SearchStrategy[Ex],
+    extend: Callable[[SearchStrategy[Any]], SearchStrategy[T]],
+    *,
+    max_leaves: int = 100,
+) -> SearchStrategy[Union[T, Ex]]:
+    """base: A strategy to start from.
+
+    extend: A function which takes a strategy and returns a new strategy.
+
+    max_leaves: The maximum number of elements to be drawn from base on a given
+    run.
+
+    This returns a strategy ``S`` such that ``S = extend(base | S)``. That is,
+    values may be drawn from base, or from any strategy reachable by mixing
+    applications of | and extend.
+
+    An example may clarify: ``recursive(booleans(), lists)`` would return a
+    strategy that may return arbitrarily nested and mixed lists of booleans.
+    So e.g. ``False``, ``[True]``, ``[False, []]``, and ``[[[[True]]]]`` are
+    all valid values to be drawn from that strategy.
+
+    Examples from this strategy shrink by trying to reduce the amount of
+    recursion and by shrinking according to the shrinking behaviour of base
+    and the result of extend.
+
+    """
+
+    return RecursiveStrategy(base, extend, max_leaves)
+
+
+class PermutationStrategy(SearchStrategy):
+    def __init__(self, values):
+        self.values = values
+
+    def do_draw(self, data):
+        # Reversed Fisher-Yates shuffle: swap each element with itself or with
+        # a later element.  This shrinks i==j for each element, i.e. to no
+        # change.  We don't consider the last element as it's always a no-op.
+        result = list(self.values)
+        for i in range(len(result) - 1):
+            j = data.draw_integer(i, len(result) - 1)
+            result[i], result[j] = result[j], result[i]
+        return result
+
+
+@defines_strategy()
+def permutations(values: Sequence[T]) -> SearchStrategy[List[T]]:
+    """Return a strategy which returns permutations of the ordered collection
+    ``values``.
+
+    Examples from this strategy shrink by trying to become closer to the
+    original order of values.
+    """
+    values = check_sample(values, "permutations")
+    if not values:
+        return builds(list)
+
+    return PermutationStrategy(values)
+
+
+class CompositeStrategy(SearchStrategy):
+    def __init__(self, definition, args, kwargs):
+        self.definition = definition
+        self.args = args
+        self.kwargs = kwargs
+
+    def do_draw(self, data):
+        return self.definition(data.draw, *self.args, **self.kwargs)
+
+    def calc_label(self):
+        return calc_label_from_cls(self.definition)
+
+
+class DrawFn(Protocol):
+    """This type only exists so that you can write type hints for functions
+    decorated with :func:`@composite `.
+
+    .. code-block:: python
+
+        @composite
+        def list_and_index(draw: DrawFn) -> Tuple[int, str]:
+            i = draw(integers())  # type inferred as 'int'
+            s = draw(text())  # type inferred as 'str'
+            return i, s
+
+    """
+
+    def __init__(self):
+        raise TypeError("Protocols cannot be instantiated")  # pragma: no cover
+
+    # Protocol overrides our signature for __init__,
+    # so we override it right back to make the docs look nice.
+    __signature__: Signature = Signature(parameters=[])
+
+    # We define this as a callback protocol because a simple typing.Callable is
+    # insufficient to fully represent the interface, due to the optional `label`
+    # parameter.
+    def __call__(self, strategy: SearchStrategy[Ex], label: object = None) -> Ex:
+        raise NotImplementedError
+
+
+def _composite(f):
+    # Wrapped below, using ParamSpec if available
+    if isinstance(f, (classmethod, staticmethod)):
+        special_method = type(f)
+        f = f.__func__
+    else:
+        special_method = None
+
+    sig = get_signature(f)
+    params = tuple(sig.parameters.values())
+
+    if not (params and "POSITIONAL" in params[0].kind.name):
+        raise InvalidArgument(
+            "Functions wrapped with composite must take at least one "
+            "positional argument."
+        )
+    if params[0].default is not sig.empty:
+        raise InvalidArgument("A default value for initial argument will never be used")
+    if not is_first_param_referenced_in_function(f):
+        note_deprecation(
+            "There is no reason to use @st.composite on a function which "
+            "does not call the provided draw() function internally.",
+            since="2022-07-17",
+            has_codemod=False,
+        )
+    if get_origin(sig.return_annotation) is SearchStrategy:
+        ret_repr = repr(sig.return_annotation).replace("hypothesis.strategies.", "st.")
+        warnings.warn(
+            f"Return-type annotation is `{ret_repr}`, but the decorated "
+            "function should return a value (not a strategy)",
+            HypothesisWarning,
+            stacklevel=3 if sys.version_info[:2] > (3, 9) else 5,  # ugh
+        )
+    if params[0].kind.name != "VAR_POSITIONAL":
+        params = params[1:]
+    newsig = sig.replace(
+        parameters=params,
+        return_annotation=(
+            SearchStrategy
+            if sig.return_annotation is sig.empty
+            else SearchStrategy[sig.return_annotation]
+        ),
+    )
+
+    @defines_strategy()
+    @define_function_signature(f.__name__, f.__doc__, newsig)
+    def accept(*args, **kwargs):
+        return CompositeStrategy(f, args, kwargs)
+
+    accept.__module__ = f.__module__
+    accept.__signature__ = newsig
+    if special_method is not None:
+        return special_method(accept)
+    return accept
+
+
+if typing.TYPE_CHECKING or ParamSpec is not None:
+    P = ParamSpec("P")
+
+    def composite(
+        f: Callable[Concatenate[DrawFn, P], Ex]
+    ) -> Callable[P, SearchStrategy[Ex]]:
+        """Defines a strategy that is built out of potentially arbitrarily many
+        other strategies.
+
+        This is intended to be used as a decorator. See
+        :ref:`the full documentation for more details `
+        about how to use this function.
+
+        Examples from this strategy shrink by shrinking the output of each draw
+        call.
+        """
+        return _composite(f)
+
+else:  # pragma: no cover
+
+    @cacheable
+    def composite(f: Callable[..., Ex]) -> Callable[..., SearchStrategy[Ex]]:
+        """Defines a strategy that is built out of potentially arbitrarily many
+        other strategies.
+
+        This is intended to be used as a decorator. See
+        :ref:`the full documentation for more details `
+        about how to use this function.
+
+        Examples from this strategy shrink by shrinking the output of each draw
+        call.
+        """
+        return _composite(f)
+
+
+@defines_strategy(force_reusable_values=True)
+@cacheable
+def complex_numbers(
+    *,
+    min_magnitude: Real = 0,
+    max_magnitude: Optional[Real] = None,
+    allow_infinity: Optional[bool] = None,
+    allow_nan: Optional[bool] = None,
+    allow_subnormal: bool = True,
+    width: Literal[32, 64, 128] = 128,
+) -> SearchStrategy[complex]:
+    """Returns a strategy that generates :class:`~python:complex`
+    numbers.
+
+    This strategy draws complex numbers with constrained magnitudes.
+    The ``min_magnitude`` and ``max_magnitude`` parameters should be
+    non-negative :class:`~python:numbers.Real` numbers; a value
+    of ``None`` corresponds an infinite upper bound.
+
+    If ``min_magnitude`` is nonzero or ``max_magnitude`` is finite, it
+    is an error to enable ``allow_nan``.  If ``max_magnitude`` is finite,
+    it is an error to enable ``allow_infinity``.
+
+    ``allow_infinity``, ``allow_nan``, and ``allow_subnormal`` are
+    applied to each part of the complex number separately, as for
+    :func:`~hypothesis.strategies.floats`.
+
+    The magnitude constraints are respected up to a relative error
+    of (around) floating-point epsilon, due to implementation via
+    the system ``sqrt`` function.
+
+    The ``width`` argument specifies the maximum number of bits of precision
+    required to represent the entire generated complex number.
+    Valid values are 32, 64 or 128, which correspond to the real and imaginary
+    components each having width 16, 32 or 64, respectively.
+    Passing ``width=64`` will still use the builtin 128-bit
+    :class:`~python:complex` class, but always for values which can be
+    exactly represented as two 32-bit floats.
+
+    Examples from this strategy shrink by shrinking their real and
+    imaginary parts, as :func:`~hypothesis.strategies.floats`.
+
+    If you need to generate complex numbers with particular real and
+    imaginary parts or relationships between parts, consider using
+    :func:`builds(complex, ...) ` or
+    :func:`@composite ` respectively.
+    """
+    check_valid_magnitude(min_magnitude, "min_magnitude")
+    check_valid_magnitude(max_magnitude, "max_magnitude")
+    check_valid_interval(min_magnitude, max_magnitude, "min_magnitude", "max_magnitude")
+    if max_magnitude == math.inf:
+        max_magnitude = None
+
+    if allow_infinity is None:
+        allow_infinity = bool(max_magnitude is None)
+    elif allow_infinity and max_magnitude is not None:
+        raise InvalidArgument(f"Cannot have {allow_infinity=} with {max_magnitude=}")
+    if allow_nan is None:
+        allow_nan = bool(min_magnitude == 0 and max_magnitude is None)
+    elif allow_nan and not (min_magnitude == 0 and max_magnitude is None):
+        raise InvalidArgument(
+            f"Cannot have {allow_nan=}, {min_magnitude=}, {max_magnitude=}"
+        )
+    check_type(bool, allow_subnormal, "allow_subnormal")
+    if width not in (32, 64, 128):
+        raise InvalidArgument(
+            f"{width=}, but must be 32, 64 or 128 (other complex dtypes "
+            "such as complex192 or complex256 are not supported)"
+            # For numpy, these types would be supported (but not by CPython):
+            # https://numpy.org/doc/stable/reference/arrays.scalars.html#complex-floating-point-types
+        )
+    component_width = width // 2
+    allow_kw = {
+        "allow_nan": allow_nan,
+        "allow_infinity": allow_infinity,
+        # If we have a nonzero normal min_magnitude and draw a zero imaginary part,
+        # then allow_subnormal=True would be an error with the min_value to the floats()
+        # strategy for the real part.  We therefore replace True with None.
+        "allow_subnormal": None if allow_subnormal else allow_subnormal,
+        "width": component_width,
+    }
+
+    if min_magnitude == 0 and max_magnitude is None:
+        # In this simple but common case, there are no constraints on the
+        # magnitude and therefore no relationship between the real and
+        # imaginary parts.
+        return builds(complex, floats(**allow_kw), floats(**allow_kw))  # type: ignore
+
+    @composite
+    def constrained_complex(draw):
+        # We downcast drawn floats to the desired (component) width so we
+        # guarantee the resulting complex values are representable. Note
+        # truncating the mantissa bits with float_of() cannot increase the
+        # magnitude of a float, so we are guaranteed to stay within the allowed
+        # range. See https://github.com/HypothesisWorks/hypothesis/issues/3573
+
+        # Draw the imaginary part, and determine the maximum real part given
+        # this and the max_magnitude
+        if max_magnitude is None:
+            zi = draw(floats(**allow_kw))
+            rmax = None
+        else:
+            zi = draw(
+                floats(
+                    -float_of(max_magnitude, component_width),
+                    float_of(max_magnitude, component_width),
+                    **allow_kw,
+                )
+            )
+            rmax = float_of(cathetus(max_magnitude, zi), component_width)
+        # Draw the real part from the allowed range given the imaginary part
+        if min_magnitude == 0 or math.fabs(zi) >= min_magnitude:
+            zr = draw(floats(None if rmax is None else -rmax, rmax, **allow_kw))
+        else:
+            rmin = float_of(cathetus(min_magnitude, zi), component_width)
+            zr = draw(floats(rmin, rmax, **allow_kw))
+        # Order of conditions carefully tuned so that for a given pair of
+        # magnitude arguments, we always either draw or do not draw the bool
+        # (crucial for good shrinking behaviour) but only invert when needed.
+        if min_magnitude > 0 and draw(booleans()) and math.fabs(zi) <= min_magnitude:
+            zr = -zr
+        return complex(zr, zi)
+
+    return constrained_complex()
+
+
+@defines_strategy(never_lazy=True)
+def shared(
+    base: SearchStrategy[Ex],
+    *,
+    key: Optional[Hashable] = None,
+) -> SearchStrategy[Ex]:
+    """Returns a strategy that draws a single shared value per run, drawn from
+    base. Any two shared instances with the same key will share the same value,
+    otherwise the identity of this strategy will be used. That is:
+
+    >>> s = integers()  # or any other strategy
+    >>> x = shared(s)
+    >>> y = shared(s)
+
+    In the above x and y may draw different (or potentially the same) values.
+    In the following they will always draw the same:
+
+    >>> x = shared(s, key="hi")
+    >>> y = shared(s, key="hi")
+
+    Examples from this strategy shrink as per their base strategy.
+    """
+    return SharedStrategy(base, key)
+
+
+@composite
+def _maybe_nil_uuids(draw, uuid):
+    # Equivalent to `random_uuids | just(...)`, with a stronger bias to the former.
+    if draw(data()).conjecture_data.draw_boolean(1 / 64):
+        return UUID("00000000-0000-0000-0000-000000000000")
+    return uuid
+
+
+@cacheable
+@defines_strategy(force_reusable_values=True)
+def uuids(
+    *, version: Optional[Literal[1, 2, 3, 4, 5]] = None, allow_nil: bool = False
+) -> SearchStrategy[UUID]:
+    """Returns a strategy that generates :class:`UUIDs `.
+
+    If the optional version argument is given, value is passed through
+    to :class:`~python:uuid.UUID` and only UUIDs of that version will
+    be generated.
+
+    If ``allow_nil`` is True, generate the nil UUID much more often.
+    Otherwise, all returned values from this will be unique, so e.g. if you do
+    ``lists(uuids())`` the resulting list will never contain duplicates.
+
+    Examples from this strategy don't have any meaningful shrink order.
+    """
+    check_type(bool, allow_nil, "allow_nil")
+    if version not in (None, 1, 2, 3, 4, 5):
+        raise InvalidArgument(
+            f"{version=}, but version must be in "
+            "(None, 1, 2, 3, 4, 5) to pass to the uuid.UUID constructor."
+        )
+    random_uuids = shared(
+        randoms(use_true_random=True), key="hypothesis.strategies.uuids.generator"
+    ).map(lambda r: UUID(version=version, int=r.getrandbits(128)))
+
+    if allow_nil:
+        if version is not None:
+            raise InvalidArgument("The nil UUID is not of any version")
+        return random_uuids.flatmap(_maybe_nil_uuids)
+    return random_uuids
+
+
+class RunnerStrategy(SearchStrategy):
+    def __init__(self, default):
+        self.default = default
+
+    def do_draw(self, data):
+        runner = getattr(data, "hypothesis_runner", not_set)
+        if runner is not_set:
+            if self.default is not_set:
+                raise InvalidArgument(
+                    "Cannot use runner() strategy with no "
+                    "associated runner or explicit default."
+                )
+            else:
+                return self.default
+        else:
+            return runner
+
+
+@defines_strategy(force_reusable_values=True)
+def runner(*, default: Any = not_set) -> SearchStrategy[Any]:
+    """A strategy for getting "the current test runner", whatever that may be.
+    The exact meaning depends on the entry point, but it will usually be the
+    associated 'self' value for it.
+
+    If you are using this in a rule for stateful testing, this strategy
+    will return the instance of the :class:`~hypothesis.stateful.RuleBasedStateMachine`
+    that the rule is running for.
+
+    If there is no current test runner and a default is provided, return
+    that default. If no default is provided, raises InvalidArgument.
+
+    Examples from this strategy do not shrink (because there is only one).
+    """
+    return RunnerStrategy(default)
+
+
+class DataObject:
+    """This type only exists so that you can write type hints for tests using
+    the :func:`~hypothesis.strategies.data` strategy.  Do not use it directly!
+    """
+
+    # Note that "only exists" here really means "is only exported to users",
+    # but we want to treat it as "semi-stable", not document it as "public API".
+
+    def __init__(self, data):
+        self.count = 0
+        self.conjecture_data = data
+
+    __signature__ = Signature()  # hide internals from Sphinx introspection
+
+    def __repr__(self):
+        return "data(...)"
+
+    def draw(self, strategy: SearchStrategy[Ex], label: Any = None) -> Ex:
+        check_strategy(strategy, "strategy")
+        self.count += 1
+        printer = RepresentationPrinter(context=current_build_context())
+        desc = f"Draw {self.count}{'' if label is None else f' ({label})'}: "
+        with deprecate_random_in_strategy("{}from {!r}", desc, strategy):
+            result = self.conjecture_data.draw(strategy, observe_as=f"generate:{desc}")
+        if TESTCASE_CALLBACKS:
+            self.conjecture_data._observability_args[desc] = to_jsonable(result)
+
+        printer.text(desc)
+        printer.pretty(result)
+        note(printer.getvalue())
+        return result
+
+
+class DataStrategy(SearchStrategy):
+    supports_find = False
+
+    def do_draw(self, data):
+        if not hasattr(data, "hypothesis_shared_data_strategy"):
+            data.hypothesis_shared_data_strategy = DataObject(data)
+        return data.hypothesis_shared_data_strategy
+
+    def __repr__(self):
+        return "data()"
+
+    def map(self, f):
+        self.__not_a_first_class_strategy("map")
+
+    def filter(self, f):
+        self.__not_a_first_class_strategy("filter")
+
+    def flatmap(self, f):
+        self.__not_a_first_class_strategy("flatmap")
+
+    def example(self):
+        self.__not_a_first_class_strategy("example")
+
+    def __not_a_first_class_strategy(self, name):
+        raise InvalidArgument(
+            f"Cannot call {name} on a DataStrategy. You should probably "
+            "be using @composite for whatever it is you're trying to do."
+        )
+
+
+@cacheable
+@defines_strategy(never_lazy=True)
+def data() -> SearchStrategy[DataObject]:
+    """This isn't really a normal strategy, but instead gives you an object
+    which can be used to draw data interactively from other strategies.
+
+    See :ref:`the rest of the documentation ` for more
+    complete information.
+
+    Examples from this strategy do not shrink (because there is only one),
+    but the result of calls to each ``data.draw()`` call shrink as they normally would.
+    """
+    return DataStrategy()
+
+
+def register_type_strategy(
+    custom_type: Type[Ex],
+    strategy: Union[SearchStrategy[Ex], Callable[[Type[Ex]], SearchStrategy[Ex]]],
+) -> None:
+    """Add an entry to the global type-to-strategy lookup.
+
+    This lookup is used in :func:`~hypothesis.strategies.builds` and
+    :func:`@given `.
+
+    :func:`~hypothesis.strategies.builds` will be used automatically for
+    classes with type annotations on ``__init__`` , so you only need to
+    register a strategy if one or more arguments need to be more tightly
+    defined than their type-based default, or if you want to supply a strategy
+    for an argument with a default value.
+
+    ``strategy`` may be a search strategy, or a function that takes a type and
+    returns a strategy (useful for generic types). The function may return
+    :data:`NotImplemented` to conditionally not provide a strategy for the type
+    (the type will still be resolved by other methods, if possible, as if the
+    function was not registered).
+
+    Note that you may not register a parametrised generic type (such as
+    ``MyCollection[int]``) directly, because the resolution logic does not
+    handle this case correctly.  Instead, you may register a *function* for
+    ``MyCollection`` and `inspect the type parameters within that function
+    `__.
+    """
+    # TODO: We would like to move this to the top level, but pending some major
+    # refactoring it's hard to do without creating circular imports.
+    from hypothesis.strategies._internal import types
+
+    if not types.is_a_type(custom_type):
+        raise InvalidArgument(f"{custom_type=} must be a type")
+    if custom_type in types.NON_RUNTIME_TYPES:
+        raise InvalidArgument(
+            f"{custom_type=} is not allowed to be registered, "
+            f"because there is no such thing as a runtime instance of {custom_type!r}"
+        )
+    if not (isinstance(strategy, SearchStrategy) or callable(strategy)):
+        raise InvalidArgument(
+            f"{strategy=} must be a SearchStrategy, or a function that takes "
+            "a generic type and returns a specific SearchStrategy"
+        )
+    if isinstance(strategy, SearchStrategy):
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", HypothesisSideeffectWarning)
+
+            # Calling is_empty forces materialization of lazy strategies. If this is done at import
+            # time, lazy strategies will warn about it; here, we force that warning to raise to
+            # avoid the materialization. Ideally, we'd just check if the strategy is lazy, but the
+            # lazy strategy may be wrapped underneath another strategy so that's complicated.
+            try:
+                if strategy.is_empty:
+                    raise InvalidArgument(f"{strategy=} must not be empty")
+            except HypothesisSideeffectWarning:  # pragma: no cover
+                pass
+    if types.has_type_arguments(custom_type):
+        raise InvalidArgument(
+            f"Cannot register generic type {custom_type!r}, because it has type "
+            "arguments which would not be handled.  Instead, register a function "
+            f"for {get_origin(custom_type)!r} which can inspect specific type "
+            "objects and return a strategy."
+        )
+    if (
+        "pydantic.generics" in sys.modules
+        and issubclass(custom_type, sys.modules["pydantic.generics"].GenericModel)
+        and not re.search(r"[A-Za-z_]+\[.+\]", repr(custom_type))
+        and callable(strategy)
+    ):  # pragma: no cover
+        # See https://github.com/HypothesisWorks/hypothesis/issues/2940
+        raise InvalidArgument(
+            f"Cannot register a function for {custom_type!r}, because parametrized "
+            "`pydantic.generics.GenericModel` subclasses aren't actually generic "
+            "types at runtime.  In this case, you should register a strategy "
+            "directly for each parametrized form that you anticipate using."
+        )
+
+    types._global_type_lookup[custom_type] = strategy
+    from_type.__clear_cache()  # type: ignore
+
+
+@cacheable
+@defines_strategy(never_lazy=True)
+def deferred(definition: Callable[[], SearchStrategy[Ex]]) -> SearchStrategy[Ex]:
+    """A deferred strategy allows you to write a strategy that references other
+    strategies that have not yet been defined. This allows for the easy
+    definition of recursive and mutually recursive strategies.
+
+    The definition argument should be a zero-argument function that returns a
+    strategy. It will be evaluated the first time the strategy is used to
+    produce an example.
+
+    Example usage:
+
+    >>> import hypothesis.strategies as st
+    >>> x = st.deferred(lambda: st.booleans() | st.tuples(x, x))
+    >>> x.example()
+    (((False, (True, True)), (False, True)), (True, True))
+    >>> x.example()
+    True
+
+    Mutual recursion also works fine:
+
+    >>> a = st.deferred(lambda: st.booleans() | b)
+    >>> b = st.deferred(lambda: st.tuples(a, a))
+    >>> a.example()
+    True
+    >>> b.example()
+    (False, (False, ((False, True), False)))
+
+    Examples from this strategy shrink as they normally would from the strategy
+    returned by the definition.
+    """
+    return DeferredStrategy(definition)
+
+
+def domains():
+    import hypothesis.provisional
+
+    return hypothesis.provisional.domains()
+
+
+@defines_strategy(force_reusable_values=True)
+def emails(
+    *, domains: SearchStrategy[str] = LazyStrategy(domains, (), {})
+) -> SearchStrategy[str]:
+    """A strategy for generating email addresses as unicode strings. The
+    address format is specified in :rfc:`5322#section-3.4.1`. Values shrink
+    towards shorter local-parts and host domains.
+
+    If ``domains`` is given then it must be a strategy that generates domain
+    names for the emails, defaulting to :func:`~hypothesis.provisional.domains`.
+
+    This strategy is useful for generating "user data" for tests, as
+    mishandling of email addresses is a common source of bugs.
+    """
+    local_chars = string.ascii_letters + string.digits + "!#$%&'*+-/=^_`{|}~"
+    local_part = text(local_chars, min_size=1, max_size=64)
+    # TODO: include dot-atoms, quoted strings, escaped chars, etc in local part
+    return builds("{}@{}".format, local_part, domains).filter(
+        lambda addr: len(addr) <= 254
+    )
+
+
+def _functions(*, like, returns, pure):
+    # Wrapped up to use ParamSpec below
+    check_type(bool, pure, "pure")
+    if not callable(like):
+        raise InvalidArgument(
+            "The first argument to functions() must be a callable to imitate, "
+            f"but got non-callable like={nicerepr(like)!r}"
+        )
+    if returns in (None, ...):
+        # Passing `None` has never been *documented* as working, but it still
+        # did from May 2020 to Jan 2022 so we'll avoid breaking it without cause.
+        hints = get_type_hints(like)
+        returns = from_type(hints.get("return", type(None)))
+    check_strategy(returns, "returns")
+    return FunctionStrategy(like, returns, pure)
+
+
+if typing.TYPE_CHECKING or ParamSpec is not None:
+
+    @overload
+    def functions(
+        *, pure: bool = ...
+    ) -> SearchStrategy[Callable[[], None]]:  # pragma: no cover
+        ...
+
+    @overload
+    def functions(
+        *,
+        like: Callable[P, T],
+        pure: bool = ...,
+    ) -> SearchStrategy[Callable[P, T]]:  # pragma: no cover
+        ...
+
+    @overload
+    def functions(
+        *,
+        returns: SearchStrategy[T],
+        pure: bool = ...,
+    ) -> SearchStrategy[Callable[[], T]]:  # pragma: no cover
+        ...
+
+    @overload
+    def functions(
+        *,
+        like: Callable[P, Any],
+        returns: SearchStrategy[T],
+        pure: bool = ...,
+    ) -> SearchStrategy[Callable[P, T]]:  # pragma: no cover
+        ...
+
+    @defines_strategy()
+    def functions(*, like=lambda: None, returns=..., pure=False):
+        # We shouldn't need overloads here, but mypy disallows default args for
+        # generics: https://github.com/python/mypy/issues/3737
+        """functions(*, like=lambda: None, returns=..., pure=False)
+
+        A strategy for functions, which can be used in callbacks.
+
+        The generated functions will mimic the interface of ``like``, which must
+        be a callable (including a class, method, or function).  The return value
+        for the function is drawn from the ``returns`` argument, which must be a
+        strategy.  If ``returns`` is not passed, we attempt to infer a strategy
+        from the return-type annotation if present, falling back to :func:`~none`.
+
+        If ``pure=True``, all arguments passed to the generated function must be
+        hashable, and if passed identical arguments the original return value will
+        be returned again - *not* regenerated, so beware mutable values.
+
+        If ``pure=False``, generated functions do not validate their arguments, and
+        may return a different value if called again with the same arguments.
+
+        Generated functions can only be called within the scope of the ``@given``
+        which created them.  This strategy does not support ``.example()``.
+        """
+        return _functions(like=like, returns=returns, pure=pure)
+
+else:  # pragma: no cover
+
+    @defines_strategy()
+    def functions(
+        *,
+        like: Callable[..., Any] = lambda: None,
+        returns: Union[SearchStrategy[Any], EllipsisType] = ...,
+        pure: bool = False,
+    ) -> SearchStrategy[Callable[..., Any]]:
+        """functions(*, like=lambda: None, returns=..., pure=False)
+
+        A strategy for functions, which can be used in callbacks.
+
+        The generated functions will mimic the interface of ``like``, which must
+        be a callable (including a class, method, or function).  The return value
+        for the function is drawn from the ``returns`` argument, which must be a
+        strategy.  If ``returns`` is not passed, we attempt to infer a strategy
+        from the return-type annotation if present, falling back to :func:`~none`.
+
+        If ``pure=True``, all arguments passed to the generated function must be
+        hashable, and if passed identical arguments the original return value will
+        be returned again - *not* regenerated, so beware mutable values.
+
+        If ``pure=False``, generated functions do not validate their arguments, and
+        may return a different value if called again with the same arguments.
+
+        Generated functions can only be called within the scope of the ``@given``
+        which created them.  This strategy does not support ``.example()``.
+        """
+        return _functions(like=like, returns=returns, pure=pure)
+
+
+@composite
+def slices(draw: Any, size: int) -> slice:
+    """Generates slices that will select indices up to the supplied size
+
+    Generated slices will have start and stop indices that range from -size to size - 1
+    and will step in the appropriate direction. Slices should only produce an empty selection
+    if the start and end are the same.
+
+    Examples from this strategy shrink toward 0 and smaller values
+    """
+    check_valid_size(size, "size")
+    if size == 0:
+        step = draw(none() | integers().filter(bool))
+        return slice(None, None, step)
+    # For slices start is inclusive and stop is exclusive
+    start = draw(integers(0, size - 1) | none())
+    stop = draw(integers(0, size) | none())
+
+    # Limit step size to be reasonable
+    if start is None and stop is None:
+        max_step = size
+    elif start is None:
+        max_step = stop
+    elif stop is None:
+        max_step = start
+    else:
+        max_step = abs(start - stop)
+
+    step = draw(integers(1, max_step or 1))
+
+    if (draw(booleans()) and start == stop) or (stop or 0) < (start or 0):
+        step *= -1
+
+    if draw(booleans()) and start is not None:
+        start -= size
+    if draw(booleans()) and stop is not None:
+        stop -= size
+    if (not draw(booleans())) and step == 1:
+        step = None
+
+    return slice(start, stop, step)
diff --git a/shared/package-files/ec/ec3f284b7e607a72da2dd48f7e08777e859740cb1ad9b52c42707958a7ca3c00/mkldnn.cmake b/shared/package-files/ec/ec3f284b7e607a72da2dd48f7e08777e859740cb1ad9b52c42707958a7ca3c00/mkldnn.cmake
new file mode 100644
index 0000000000000000000000000000000000000000..87935625f9bfb543d1cdc7f2b59f11e8d4a709e7
--- /dev/null
+++ b/shared/package-files/ec/ec3f284b7e607a72da2dd48f7e08777e859740cb1ad9b52c42707958a7ca3c00/mkldnn.cmake
@@ -0,0 +1,18 @@
+set(MKLDNN_USE_NATIVE_ARCH ${USE_NATIVE_ARCH})
+
+if(CPU_AARCH64)
+  include(${CMAKE_CURRENT_LIST_DIR}/ComputeLibrary.cmake)
+endif()
+
+find_package(MKLDNN QUIET)
+
+if(NOT TARGET caffe2::mkldnn)
+  add_library(caffe2::mkldnn INTERFACE IMPORTED)
+endif()
+
+set_property(
+  TARGET caffe2::mkldnn PROPERTY INTERFACE_INCLUDE_DIRECTORIES
+  ${MKLDNN_INCLUDE_DIR})
+set_property(
+  TARGET caffe2::mkldnn PROPERTY INTERFACE_LINK_LIBRARIES
+  ${MKLDNN_LIBRARIES})
diff --git a/shared/package-files/ec/ec43f2a04ff0c99fb5e2c03d4e8ee3d832ad41a09b06f926242d5df8b8ed72f6/_special.py b/shared/package-files/ec/ec43f2a04ff0c99fb5e2c03d4e8ee3d832ad41a09b06f926242d5df8b8ed72f6/_special.py
new file mode 100644
index 0000000000000000000000000000000000000000..3002af318035151b6054d0fc758eaacfb5c67413
--- /dev/null
+++ b/shared/package-files/ec/ec43f2a04ff0c99fb5e2c03d4e8ee3d832ad41a09b06f926242d5df8b8ed72f6/_special.py
@@ -0,0 +1,142 @@
+"""
+
+Special functions for copulas not available in scipy
+
+Created on Jan. 27, 2023
+"""
+
+import numpy as np
+from scipy.special import factorial
+
+
+class Sterling1:
+    """Stirling numbers of the first kind
+    """
+    # based on
+    # https://rosettacode.org/wiki/Stirling_numbers_of_the_first_kind#Python
+
+    def __init__(self):
+        self._cache = {}
+
+    def __call__(self, n, k):
+        key = str(n) + "," + str(k)
+
+        if key in self._cache.keys():
+            return self._cache[key]
+        if n == k == 0:
+            return 1
+        if n > 0 and k == 0:
+            return 0
+        if k > n:
+            return 0
+        result = sterling1(n - 1, k - 1) + (n - 1) * sterling1(n - 1, k)
+        self._cache[key] = result
+        return result
+
+    def clear_cache(self):
+        """clear cache of Sterling numbers
+        """
+        self._cache = {}
+
+
+sterling1 = Sterling1()
+
+
+class Sterling2:
+    """Stirling numbers of the second kind
+    """
+    # based on
+    # https://rosettacode.org/wiki/Stirling_numbers_of_the_second_kind#Python
+
+    def __init__(self):
+        self._cache = {}
+
+    def __call__(self, n, k):
+        key = str(n) + "," + str(k)
+
+        if key in self._cache.keys():
+            return self._cache[key]
+        if n == k == 0:
+            return 1
+        if (n > 0 and k == 0) or (n == 0 and k > 0):
+            return 0
+        if n == k:
+            return 1
+        if k > n:
+            return 0
+        result = k * sterling2(n - 1, k) + sterling2(n - 1, k - 1)
+        self._cache[key] = result
+        return result
+
+    def clear_cache(self):
+        """clear cache of Sterling numbers
+        """
+        self._cache = {}
+
+
+sterling2 = Sterling2()
+
+
+def li3(z):
+    """Polylogarithm for negative integer order -3
+
+    Li(-3, z)
+
+    Parameters
+    ----------
+    z : array_like
+        Value(s) at which the polylogarithm is evaluated.
+
+    Returns
+    -------
+    ndarray or float
+        Value(s) of the polylogarithm Li(-3, z).
+    """
+    return z * (1 + 4 * z + z**2) / (1 - z)**4
+
+
+def li4(z):
+    """Polylogarithm for negative integer order -4
+
+    Li(-4, z)
+
+    Parameters
+    ----------
+    z : array_like
+        Value(s) at which the polylogarithm is evaluated.
+
+    Returns
+    -------
+    ndarray or float
+        Value(s) of the polylogarithm Li(-4, z).
+    """
+    return z * (1 + z) * (1 + 10 * z + z**2) / (1 - z)**5
+
+
+def lin(n, z):
+    """Polylogarithm for negative integer order -n
+
+    Li(-n, z)
+
+    https://en.wikipedia.org/wiki/Polylogarithm#Particular_values
+
+    Parameters
+    ----------
+    n : int
+        Order of the polylogarithm, evaluated at -n.
+    z : array_like
+        Value(s) at which the polylogarithm is evaluated.
+
+    Returns
+    -------
+    ndarray
+        Value(s) of the polylogarithm Li(-n, z).
+    """
+    if np.size(z) > 1:
+        z = np.array(z)[..., None]
+
+    k = np.arange(n+1)
+    st2 = np.array([sterling2(n + 1, ki + 1) for ki in k])
+    res = (-1)**(n+1) * np.sum(factorial(k) * st2 * (-1 / (1 - z))**(k+1),
+                               axis=-1)
+    return res
diff --git a/shared/package-files/ec/ec4dd316cb093fc0d4af1e9bd20e5a1cdda495474f3943e682afa76a11dd5d18/vanilla b/shared/package-files/ec/ec4dd316cb093fc0d4af1e9bd20e5a1cdda495474f3943e682afa76a11dd5d18/vanilla
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Binary files /dev/null and b/shared/package-files/ec/ec4dd316cb093fc0d4af1e9bd20e5a1cdda495474f3943e682afa76a11dd5d18/vanilla differ
diff --git a/shared/package-files/ec/ec585de283837d1f5992f82d632acaad2aa08b2ff19f0bdb19a2e5d3626ee177/combobox.tcl b/shared/package-files/ec/ec585de283837d1f5992f82d632acaad2aa08b2ff19f0bdb19a2e5d3626ee177/combobox.tcl
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--- /dev/null
+++ b/shared/package-files/ec/ec585de283837d1f5992f82d632acaad2aa08b2ff19f0bdb19a2e5d3626ee177/combobox.tcl
@@ -0,0 +1,448 @@
+#
+# Combobox bindings.
+#
+# <>:
+#
+#	Need to set [wm transient] just before mapping the popdown
+#	instead of when it's created, in case a containing frame
+#	has been reparented [#1818441].
+#
+#	On Windows: setting [wm transient] prevents the parent
+#	toplevel from becoming inactive when the popdown is posted
+#	(Tk 8.4.8+)
+#
+#	On X11: WM_TRANSIENT_FOR on override-redirect windows
+#	may be used by compositing managers and by EWMH-aware
+#	window managers (even though the older ICCCM spec says
+#	it's meaningless).
+#
+#	On OSX: [wm transient] does utterly the wrong thing.
+#	Instead, we use [MacWindowStyle "help" "noActivates hideOnSuspend"].
+#	The "noActivates" attribute prevents the parent toplevel
+#	from deactivating when the popdown is posted, and is also
+#	necessary for "help" windows to receive mouse events.
+#	"hideOnSuspend" makes the popdown disappear (resp. reappear)
+#	when the parent toplevel is deactivated (resp. reactivated).
+#	(see [#1814778]).  Also set [wm resizable 0 0], to prevent
+#	TkAqua from shrinking the scrollbar to make room for a grow box
+#	that isn't there.
+#
+#	In order to work around other platform quirks in TkAqua,
+#	[grab] and [focus] are set in  bindings instead of
+#	immediately after deiconifying the window.
+#
+
+namespace eval ttk::combobox {
+    variable Values	;# Values($cb) is -listvariable of listbox widget
+    variable State
+    set State(entryPress) 0
+}
+
+### Combobox bindings.
+#
+# Duplicate the Entry bindings, override if needed:
+#
+
+ttk::copyBindings TEntry TCombobox
+
+bind TCombobox  			{ ttk::combobox::Post %W }
+bind TCombobox  		{ ttk::combobox::Unpost %W }
+
+bind TCombobox  		{ ttk::combobox::Press "" %W %x %y }
+bind TCombobox 		{ ttk::combobox::Press "s" %W %x %y }
+bind TCombobox  	{ ttk::combobox::Press "2" %W %x %y }
+bind TCombobox  	{ ttk::combobox::Press "3" %W %x %y }
+bind TCombobox 		{ ttk::combobox::Drag %W %x }
+bind TCombobox 			{ ttk::combobox::Motion %W %x %y }
+
+ttk::bindMouseWheel TCombobox [list ttk::combobox::Scroll %W]
+
+bind TCombobox <> 		{ ttk::combobox::TraverseIn %W }
+
+### Combobox listbox bindings.
+#
+bind ComboboxListbox 	{ ttk::combobox::LBSelected %W }
+bind ComboboxListbox 		{ ttk::combobox::LBSelected %W }
+bind ComboboxListbox 		{ ttk::combobox::LBCancel %W }
+bind ComboboxListbox 		{ ttk::combobox::LBTab %W next }
+bind ComboboxListbox <>	{ ttk::combobox::LBTab %W prev }
+bind ComboboxListbox 		{ ttk::combobox::LBCleanup %W }
+bind ComboboxListbox 		{ ttk::combobox::LBHover %W %x %y }
+bind ComboboxListbox 		{ focus -force %W }
+
+switch -- [tk windowingsystem] {
+    win32 {
+	# Dismiss listbox when user switches to a different application.
+	# NB: *only* do this on Windows (see #1814778)
+	bind ComboboxListbox 		{ ttk::combobox::LBCancel %W }
+    }
+}
+
+### Combobox popdown window bindings.
+#
+bind ComboboxPopdown			{ ttk::combobox::MapPopdown %W }
+bind ComboboxPopdown			{ ttk::combobox::UnmapPopdown %W }
+bind ComboboxPopdown